Proceedings of the Fourteenth International Conference on Management Science and Engineering Management: Volume 1 [1st ed.] 9783030498283, 9783030498290

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Advances in Intelligent Systems and Computing 1190

Jiuping Xu · Gheorghe Duca · Syed Ejaz Ahmed · Fausto Pedro García Márquez · Asaf Hajiyev   Editors

Proceedings of the Fourteenth International Conference on Management Science and Engineering Management Volume 1

Advances in Intelligent Systems and Computing Volume 1190

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen , Faculty of Computer Science and Management, Wrocław University of Technology, Wrocław, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

The series “Advances in Intelligent Systems and Computing” contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within “Advances in Intelligent Systems and Computing” are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink **

More information about this series at http://www.springer.com/series/11156

Jiuping Xu Gheorghe Duca Syed Ejaz Ahmed Fausto Pedro García Márquez Asaf Hajiyev •

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Editors

Proceedings of the Fourteenth International Conference on Management Science and Engineering Management Volume 1

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Editors Jiuping Xu Business School Sichuan University Chengdu, China Syed Ejaz Ahmed Faculty of Math and Science Brock University Hamilton, ON, Canada

Gheorghe Duca Academy of Sciences of Moldova Chisinau, Moldova Fausto Pedro García Márquez ETSI Industriales de Ciudad Real University of Castile-La Mancha (UCLM) Ciudad Real, Spain

Asaf Hajiyev Institute of Control Systems Azerbaijan Natl Academy of Sciences Baku, Azerbaijan

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-3-030-49828-3 ISBN 978-3-030-49829-0 (eBook) https://doi.org/10.1007/978-3-030-49829-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Welcome to the proceedings of the Fourteenth International Conference on Management Science and Engineering Management (ICMSEM 2020), held from 30 July to 2 August, 2020, at the Academy of Studies of Moldova. This annual conference, organized by the International Society of Management Science and Engineering Management (ISMSEM), aims to foster international research collaborations in Management Science (MS) and Engineering Management (EM) and provides a forum for presenting current research work through technical sessions and round table discussions in a relaxing convivial atmosphere. The ICMSEM has been held thirteen times since 2007 over the world: Asia, Europe, the Americas and Oceania, and has had a great influence on MS and EM research. In the past thirteen years, the ICMSEM has been successfully held in Chengdu, Chongqing, Bangkok, Chungli, Macau, Islamabad, Philadelphia, Lisbon, Karlsruhe, Baku, Kanazawa, Melbourne and Ontario. The accepted papers have been published in the proceedings of each International Conference on Management Science and Engineering Management by high-level publishing houses, being retrieved by EI or ISTP Compendex. This year, 122 papers from 18 countries, including Pakistan, Uzbekistan, Japan, Istanbul, Moldova, Canada, Turkey, Azerbaijan, USA, Spain, UK, Thailand, Russia, Iran, Romania, Malaysia, China and Morocco, have been accepted for presentation or poster display at the conference. Each accepted paper had three reviewers, each one of them providing the constructive comments and insightful suggestions to the authors, contributing to the utmost quality of the conference proceedings. The papers have been classified into six sections: Statistical Analysis, Machine Learning, Decision Analysis, Supply Chain, Strategic Planning and Industry Innovation. The key issues at the fourteenth ICMSEM covered many popular topics in MS and EM. To further encourage state-of-the-art research in these field, the ISMSEM Advancement Prize is awarded for the excellent papers which have focused on innovative practical applications for MS and EM at this conference.

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We would like to take this opportunity to thank our participants, all of whom worked exceptionally hard to ensure the success of this conference. We want to express our sincere gratitude to the following prestigious academies and institutions for their high-quality papers and great support for the ICMSEM: The Azerbaijan Academy of Sciences, Academy of Sciences of the Republic of Uzbekistan, Fuzzy Logic Systems Institute, Tokyo University of Science, Brock University, Academy of Economic Studies of Moldova and Sichuan University. We would also like to acknowledge the assistance received from the ISMSEM, Academy of Economic Studies of Moldova and Sichuan University in organizing this conference. We also appreciate the Advances in Intelligent Systems and Computing (AISC) of Springer for the publication of the proceedings. We are grateful to Professor Gheorghe Duca as the General Chair, Professor Grigore Belostecinic, Dr. Lidia Romanciuc, Dr. Nina Roscovan, Dr. Igor Serotila as the Organizing Committee Chairs. We appreciate the great support received from all the members of the Organizing Committee, the Local Arrangement Committee, and the Program Committee as well as all the participants. Finally, we would like to thank all the authors for their excellent conference papers, which have great value for both educational and research purposes. The conference papers and recommendations can also serve as guiding materials for the administration/managing of institutes, enterprises, as well as the drafting or amending the relevant laws by politicians and managing authorities. As MS and EM research is in continuous development and many new trends have emerged, our work needs to continue to focus on the latest MS and EM development, so that we can encourage greater and more innovative activity. Next year, we plan to continue the innovative and successful ICMSEM and intend to increase our efforts in improving the quality of the proceedings and recommending more excellent papers for the ISMSEM Advancement Prize. The Fifteenth International Conference on Management Science and Engineering Management will be hosted by the University of Castilla-La Mancha (UCLM), Spain, in July 2021. Professor Fausto Pedro Garca Márquez has been nominated as the Organizing Committee Chair for the 2021 ICMSEM. We sincerely hope you can submit your new MSEM findings and share your wisdom in Moldova in 2020 and Spain in 2021. March 2020

Jiuping Xu Gheorghe Duca Syed Ejaz Ahmed Fausto Pedro García Márquez Asaf Hajiyev

Organization

ICMSEM 2020 was organized by the International Society of Management Science and Engineering Management (ISMSEM), Sichuan University, Moldova Research and Development Association and Academy of Economic Studies of Moldova. It was held in cooperation with Lecture Notes on Advances in Intelligent Systems and Computing (AISC) of Springer.

Executive Committee General Chairs Jiuping Xu Gheorghe Duca

Sichuan University, China Academy of Sciences of Moldova, Moldova

Program Committee Chairs Benjamin Lev Asaf Hajiyev V. Cruz Machado Mitsuo Gen Ion Aurel Pop Veceslav Khomici Fang Lee Cooke Syed Ejaz Ahmed

Drexel University, Philadelphia, USA Institute of Systems Control, National Academy of Sciences, Baku, Azerbaijan Universidade Nova de Lisboa, Lisbon, Portugal Tokyo University of Science, Japan Romanian Academy Russian Academy of Science Monash University, Australia Brock University, Canada

Organizing Committee Chairs Grigore Belostecinic Lidia Romanciuc

Academy of Economic Studies, Moldova Moldova Research and Development Association, Moldova

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Nina Roscovan Igor Serotila

Organization

Academy of Economic Studies of Moldova, Moldova State University Dimitrie Cantemir, Moldova

Program Committee Mohammad Z. Abu-Sbeih Joseph G. Aguayo Basem S. Attili Alain Billionnet Borut Buchmeister Daria Bugajewska Saibal Chattopadhyay Edwin Cheng Anthony Shun Fung Chiu Jeong-Whan Choi Kaharudin Dimyati Behloul Djilali Eid Hassan Doha O’Regan Donal Siham El-Kafafi Christodoulos A. Floudas Masao Fukushima Oleg Granichin Bernard Han Rene Henrion Voratas Kachitvichyanukul Arne Løkketangen Andres Medaglia Venkat Murali Shmuel S. Orenş Turgut Öziş Panos M. Pardalos Gianni Di Pillo Nasrudin Abd Rahim Celso Ribeiro Hsin Rau Jan Joachim Ruckmann Martin Skitmore Frits C. R. Spieksma Yong Tan

King Fahd University of Petroleum and Minerals, Saudi Arabia University of Concepcion, Chile United Arab Emirates University, United Arab Emirates Ecole National Superieure Informatics for Industry and Enterprise, France University of Maribor, Slovenia Adam Mickiewicz University, Poland Indian Institute of Management, India Hong Kong Polytechnic University, Hong Kong De La Salle University, Philippines Department of Mathematics, Republic of Korea University of Malaya, Malaysia University of Sciences and Technology Houari Boumediene, Algeria Cairo University, Giza, Egypt National University of Ireland, Ireland Manukau Institute of Technology, New Zealand Princeton University, USA Kyoto University, Japan Sankt-Petersburg State University, Russia Western Michigan University, USA Humboldt University, Germany Asian Institute of Technology, Thailand Molde University College, Norway University of the Andes, Colombia Rhodes University, South Africa University of California Berkeley, USA Ege University, Turkey University of Florida, USA Sapienza University of Rome, Italy University of Malaya, Malaysia Fluminense Federal University, Brazil Chung Yuan Christian University, Taiwan University of Birmingham, UK Queensland University of Technology, Australia Katholieke University Leuven, Belgium University of Washington, USA

Organization

Albert P. M. Wagelmans Desheng Dash Wu Hong Yan

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Erasmus University Rotterdam, Netherlands University of Toronto, Canada Hong Kong Polytechnic University, Hong Kong

Secretary-General Zhineng Hu

Sichuan University, China

Under-Secretary Tingting Liu

Sichuan University, China

General Zongmin Li Secretaries Ruolan Li Yidan Huang Rongwei Sun Mengyuan Zhu Zhiwen Liu

Sichuan University, China

Contents

Advancement of Statistical Analysis, Machine Learning and Decision Analysis Based on the Fourteenth ICMSEM Proceedings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiuping Xu

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Part I: Statistical Analysis Factors Influencing Online Shopping Experience and Customer Satisfaction in Karachi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asmara Haidery, Asif Kamran, Nadeem A. Syed, and S. M. Ahsan Rizvi Factors Affecting Furniture Purchase in Pakistan . . . . . . . . . . . . . . . . . Muhammad Fazal Qureshi, Asif Kamran, M. Ahad Hayat Khan, and Mohammad Ahmed Desai Impact of Ownership Structure and Credit Behavior on Performance of Rural Commercial Banks: Evidence from China . . . Wenli Wang, Xinghua Dang, and Xiaomei Zhang Education Impact on Health Shocks: Evidence from C.H.N.S. Data . . . Issam Khelfaoui, Yuantao Xie, Muhammad Hafeez, and Danish Ahmed Spatio-Temporal Evolution and Regional Difference Analysis of China’s Agricultural Technology Progress Under Two-Way Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanqiu He, Chengyi Liang, and Yunqiang Liu Feature Analysis of Rumor Refuting Message Commentators on Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaolu Liu and Zongmin Li

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Resilience Through Big Data: Natural Disaster Vulnerability Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Md Nazirul Islam Sarker, Min Wu, Bouasone Chanthamith, and Chenwei Ma xi

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The Characteristics of Agricultural Product Quality and Safety Crisis Based on Content Analysis Method . . . . . . . . . . . . . . . . . . . . . . . 119 Xu Zu, Yupei He, Yu Pu, and Lan Yang Financialization, Earnings Management and Investment Efficiency . . . . 130 Zhenlong Lin and Shuanghai Li The Impact of Poverty Alleviation Policy on the Financing Capability of Companies Related to Agriculture . . . . . . . . . . . . . . . . . . 142 Qiang Jiang, Luoyi Jia, and Shidi Liang Determinants of Chinese Outward FDI in Energy Sector . . . . . . . . . . . . 157 Yile Wang, Qin Zhang, Junshan Liu, and Dongmei He Bibliometric and Visualized Analysis of Visual Brand Identity of Sports Team Based on Web of Science and CiteSpace . . . . . . . . . . . . 171 Mute Xie and Shan Li Multi-path Combination Analysis of Economic Development: Based on the Fuzzy Set Comparison of Multinational Data . . . . . . . . . . 182 Hongchang Mei, Yiding Chen, and Yuzhu Wei Nature of Property Right, Free Cash Flow and Goodwill of M&A . . . . 197 Lan Wu, Lingli Yu, and Liqin Mao Influencing Factors of China’s Liquor Enterprises’ Communication Effect in Microblog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 Dongyang Si, Xu Zu, and Weiping Yu Research on the Quality of Agricultural Patents Under the Perspective of Rural Revitalization . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Na Wang, Yuandi Wang, and Ruifeng Hu The Causality Between Liquidity and Volatility: New Evidence from China’s Stock Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 Jing Liu, Yanyan Xu, and Chengzheng Zhu Study on the Transmission Mechanism of Potato Market Price in Main Potato Producing Area – Empirical Analysis Based on the Potato Price Index of Shandong Province and China . . . . . . . . . 259 Zhan Chen, Qianyou Zhang, Xinxin Xu, and Zhiwen Khoo Statistical Methods for Estimating the Pipelines Reliability . . . . . . . . . . 274 Asaf Hajiyev, Yasin Rustamov, and Narmina Abdullayeva Gonorrhea Disease Mapping in Malaysia Using Standardized Morbidity Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286 Nazrina Aziz, Nur Ain Binti Haron, and Nur Atikah Binti Mohamad Azmi

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Management of the Quality of the Air in the Republic of Moldova Based on the Moss Biomonitoring Data . . . . . . . . . . . . . . . . . . . . . . . . . 297 Inga Zinicovscaia Part II: Machine Learning An Empirical Analysis of the Influencing Factors of Farmers’ Income Growth in the Middle and Lower Reaches of the Yangtze River Based on the Grey Correlation Model . . . . . . . . . . . . . . . . . . . . . 309 Ming You, Xiaoyu Shao, and Yunqiang Liu Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study . . . . . 322 Mitsuo Gen, John R. Cheng, Krisanarach Nitisiri, and Hayato Ohwada Parameter and Mixture Component Estimation in Spatial Hidden Markov Models: A Comparative Analysis of Computational Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 Eugene A. Opoku, Syed Ejaz Ahmed, Trisalyn Nelson, and Farouk S. Nathoo Sustainable Water Allocation Under Multi-disciplinary Framework: Dealing with Uncertainties in Decision Making and Optimization . . . . . 356 Liming Yao, Zerui Su, and Xudong Chen A Hybrid Model for Online Merchandise Recommendation Based on Ordination and Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . 373 Siqi Hu, Shihang Wang, and Zhineng Hu Model Selection and Post-estimation via Pretesting: Ridge Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Pannipa Rintara, Supranee Lisawadi, and Syed Ejaz Ahmed Opening Margin Trading Business Probability Forecasts Based on Decision Tree Model–A Case Study of S Securities Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396 Dan Zhang, Zhi Yong, Shuying Deng, and Yue He The Herd Effect on Chinese Firms’ OFDI - A Data Mining Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Jie Jiang, Cangyu Wang, Junshan Liu, and Lei Zhang Analysis of Variant Data Mining Methods for Depiction of Fraud . . . . . 423 Qurat Ul Ain, Muhammad Azam Zia, Naeem Asghar, and Asim Saleem Imputation Method Based on Sliding Window for Right-Censored Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Syed Ejaz Ahmed, Dursun Aydın, and Ersin Yılmaz

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The Behaviour of Solutions to Degenerate Double Nonlinear Parabolic Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Tahir Gadjiev, Yasin Rustamov, and Aybeniz Yangaliyeva Fault Detection and Identification for Maintenance Management . . . . . 460 Isaac Segovia Ramirez and Fausto Pedro Garcia Marquez Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . 470 Isaac Segovia Ramirez and Fausto Pedro Garcia Marquez Assessing the Relative Performance of Penalty and Non-penalty Estimators in a Partially Linear Model . . . . . . . . . . . . . . . . . . . . . . . . . 481 Siwaporn Phukongtong, Supranee Lisawadi, and Syed Ejaz Ahmed Improving the Performance of Least Squares Estimator in a Nonlinear Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492 Janjira Piladaeng, Supranee Lisawadi, and Syed Ejaz Ahmed A Mathematical Model of Soil Fertility . . . . . . . . . . . . . . . . . . . . . . . . . 503 Yasin Rustamov, Tahir Gadjiev, and Sheker Askerova Did Alibaba Fake the Tmall “Double Eleven” Data? Evidence from Benford’s Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 Xinxin Xu and Ziqiang Zeng The Identification of the Company Profile Listed on the Romanian Stock Exchange Involved in CSR Actions . . . . . . . . . . . . . . . . . . . . . . . 525 Nucă Dumitriţa, Grosu Maria, Mihalciuc Camelia, and Apetri Anişoara Department Efficiency Evaluation of Chinese Commercial Bank Based on EBM-DEA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Ying Li, Miao Wu, Jin Liu, Xingling Hu, and Suichuan Zhou Risk Evaluation of Technology Innovation Project on Aspect of Life Cycle Based on Multi-dimensional Extensible Matter-Element Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 Liping Li, Xiaofeng Li, and Qisheng Chen Part III: Decision Analysis Research on Consensus Mechanism of Diagnosis and Treatment Conclusion of Consultation . . . . . . . . . . . . . . . . . . . . . . 577 Yueyu Li, Xiyang Li, Qianjun Bu, and Ling Kuang The Approval Evaluation of Agricultural Project Based on the Integration of 2-Tuples and GR . . . . . . . . . . . . . . . . . . . . . . . . . 588 Guoqiang Xiong, Yue Cao, Ying Yang, and Yang Chai

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The Influence of Referral Cognition on Referral Intention Among Outpatient Patients: An Empirical Research . . . . . . . . . . . . . . . . . . . . . 601 Xinli Zhang, Lingyun Zhang, Zhen Zeng, and Yeli Chen The Influence of Sugar-Free Label Formats and Colors on Consumers’ Acceptance of Sugar-Free Foods . . . . . . . . . . . . . . . . . . 614 Ping Liu, Hong Wang, Wei Li, and Bo Hu Study on the Effect of Exposure to Death Information and Perceived Personal Control on Healthy Food Choices . . . . . . . . . . . 627 Shouwei Li, Ping Liu, and Yan Guo Evaluation of Modern Service Industry Under Economic Transformation Based on Catastrophe Series Method . . . . . . . . . . . . . . 640 Xiaoning Yang, Yingchun Chen, and Lu Gan Research on Evaluation and Influencing Factors of Provincial Technological Innovation Capability in China . . . . . . . . . . . . . . . . . . . . 655 Lihong Wang and Hongchang Mei Digital Trust Mediated by the Platform in the Sharing Economy from a Consumer Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 670 Xiaodan Liu, Chunhui Yuan, Muhammad Hafeez, and Ch. Muhammad Nadeem Faisal Market Feedback, Investor Compensation and Decisions of Subsequent Private Placement: Based on the Analysis of Mediating Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Ying Zhang and Chunming He Research on the Impact of Gamification Application Interaction on B2C Mobile’s Continued Using Intention . . . . . . . . . . . . . . . . . . . . . 701 Jingdong Chen, Anbang Wang, and Mo Chen Research on the Influence of Product Design on Purchase Intention Based on Customer Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716 Mo Chen, Jingdong Chen, and Zhihu Li Effects of the Recommendation Label Prominence on Online Hotel Booking Intention: An Eye-Tracking Study . . . . . . . . . . . . . . . . . . . . . . 731 Luoyi Xiong, Chenzhu Zhao, and Li Huang Research on CEO Power and Charitable Donation: Evidence from China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744 Furong Guo, Shengdao Gan, Chengyan Zhan, and Ziyang Li Influencing Factors of Fresh Food Online Repurchase Intention . . . . . . 757 Weiping Yu, Wenyang Bian, Wenjie Li, and Xiaoyun Han

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Research on the Impact of Taobao Live Broadcasting on College Students’ Online Consumption Behavior Based on TAM Model . . . . . . 771 Fumin Deng, Yaqi Wang, and Xuedong Liang Integrated Reporting – An Influencing Factor on the Solvency and Liquidity of a Company and Its Role in the Managerial Decision-Making Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 783 Mihăilă Svetlana, Tanasă Simona-Maria (Brînzaru), Grosu Veronica, and Timofte Cristina (Coca) Bidding Strategy of Wind Power with Uncertain Supply in the Spot Electricity Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 Tingting Liu, Jingqi Dai, Lurong Fan, and Ruolan Li How Does Environmental Regulation Enhance Firms’ Competitiveness Through Innovation Incentive in China? A Nonlinear Mediating Model for the Porter Hypothesis . . . . . . . . . . . . 807 Die Hu, Maoyan She, and Xue Yang Which Kind of Sponsor Brand Crisis Most Decreases Sport-Event Brand Evaluation?—The Moderating Effects of Brand Relationship Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Bo Hu, Xueling Jiang, and Hong Wang Development Potential of Biomass Energy in Western Ethnic Regions by PSR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831 Yanting Yuan and Han Meng Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843

Advancement of Statistical Analysis, Machine Learning and Decision Analysis Based on the Fourteenth ICMSEM Proceedings Jiuping Xu(B) Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. Over the past few years, there have been significant developments in Management Science and Engineering Management (MSEM), across all fields and industries, which together have contributed to global socio-economic development. In this paper, the basic concepts covered in the fourteenth ICMSEM proceedings Volume I are first described, after which a review of the key areas in management science (MS) research are given: statistical analysis, machine learning and decision analysis. And the related research in Proceedings Volume I are discussed. The research trends from both MSEM journals and the ICMSEM are then summarized using the CiteSpace tool. As always, ICMSEM is committed to providing an international forum for academic exchange and communication and plans to continue these MSEM innovations in the future. Keywords: Statistical analysis · Machine learning · Decision analysis

1 Introduction The Fourteenth International Conference on Management Science and Engineering Management (ICMSEM) in Chisinau, Moldova has given Management Science and Engineering Management (MSEM) academics the opportunity to present their innovative achievements in this increasingly popular research field, which covers computing science, statistics, innovation, as well as public management. Therefore, the papers in this volume exemplify the substantial developments that have taken place in the multidisciplinary MSEM methodologies. MSEM, which is a scientific approach to decision making that involves the operations of organizational systems, has experienced significant growth. Research processes in this field generally begin by carefully observing and formulating the problem and then constructing a scientific and logical model that abstracts the reality [13]. MS uses mathematics, information science, systems science, cybernetics, statistics and other theories as well as methods derived from natural science to develop innovative management and control systems. Therefore, the integration of these research areas has brought significant improvements to the ICMSEM proceedings Volume I, which is focused on statistical analysis, machine learning, and decision analysis this year. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 1–9, 2020. https://doi.org/10.1007/978-3-030-49829-0_1

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J. Xu

This paper is organized as follows. In Sect. 2a brief literature review is given on the three key focus areas, in Sect. 3, the central issues focused on in Proceedings Volume I are given, in Sect. 4, the MS development trends are analyzed, and in Sect. 5, the conclusions and future research trends are given.

2 Literature Review Literature reviews highlight the theoretical and methodological research contributions to a particular topic. Therefore, the literature review for Proceedings Volume I examines the most important innovations that have taken place in three main areas: statistical analysis, machine learning and decision analysis. 2.1

Statistical Analysis

Statistical analysis (SA) is the use of statistical methods or tools to analyze data, extract information and come to conclusions in a wide range of fields. For example, Shesh and Chen et al. analyzed the microbiome data and its application in preclinical studies [9]. Xu and Zhou developed an adaptive tri-variate dimension-reduction method for statistical moments and reliability analysis [14]. To encourage the reuse of waste building materials, based on statistical analyses, I. Taji and S. Ghorbani et al. examined the influence of marble and granite content in concrete on the corrosion resistance of steel reinforcement [12]. Advances in information technology have assisted data collection, and the availability of big data has allowed for large sample sizes and higher dimensions, thereby providing more objective statistical analyses for decision-makers [4]. The development of big data also puts forward higher requirements for statistical analysis, which can provide more objective and true opinions for decision-makers. Therefore, because SA has an increasingly more dominant role in modern society, many competing models have been developed that have contributed conceptually and empirically to technological progress and organizational resource management. 2.2

Machine Learning

Machine learning is a technique that allows a computer to “learn” the data provided, rather than programming every problem thoroughly and explicitly [7]. The result of learning can be used for estimation, prediction, and classification meaning that machine learning enables classification and prediction based on known data and can achieve high accuracy and reliability, which makes it more likely to come to a correct decision. There are three main methods of machine learning: supervised learning, semisupervised learning and unsupervised learning [1]. Supervised learning methods, such as Nave Bayes, support vector machines (SVM), decision trees, k-nearest neighbor (KNN) algorithms, neural networks and logistic analysis, are a set of samples from known classes are used to adjust classifier parameters to achieve a desired performance. Unsupervised learning is when the input data sets are directly modeled, such as in clustering, and semi-supervised learning is when data with and without class indexes are employed to generate appropriate classification functions. Machine learning is being

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applied in a wild range fields for decision making. JKS. Meza and DO. Yepes conducted predictive analyses for urban waste generation in Bogota, Colombia, using decision trees-based machine learning, support vector machines and artificial neural networks [6]. M. Rezapour and AM. Molan applied machine learning to predict motorcycle atfault injury severity, and then compared the machine learning prediction model with a traditional logistic regression model [10]. Therefore, machine learning has a broad application prospect across nearly all fields of modern society, with new data science applications emerging, such as neural networks, genetic algorithms, and social media predictions. 2.3 Decision Analysis Decision analyses assist decision makers to make effective, scientific choices in highly uncertain, complex environments. [3]. As decision-making environments become increasingly complex, there is an increasing need for more flexible decision analysis methods and associated decision criteria [15]. As a result, there has been increased research interest in multi-criteria decision analysis and fuzzy decision-making across many fields. Narula et al. developed an interactive method for solving multi-criteria decision analysis problems, that had a large number of discrete choices and a few criteria, which allowed decision makers to set the criteria based on the expected or acceptable variations in the standard values [8]. Development in database technology have given rise to decision analysis techniques based on database management system. Y. Guo et al. proposed an intelligent decision analysis system based on data mining technology and applied it to intelligent manufacturing to ensure more effective and scientific manufacturing enterprises decision-making [5]. CB. Smith used an adaptive scientific management framework and decision analysis to manage land and water resources and assist in the recovery of four endangered species to address species and habitat loss in the Platte River area of central Nebraska [11]. Decision Analysis is therefore being used in a wide range of contexts such as finance, planning, telecommunications and ecology to assist decision-makers make more scientific and effective decisions based on different criteria.

3 The Central Issues in the Proceedings Volume I Based on these popular research topics, papers were submitted from around the world, with 122 papers being accepted for the two proceedings volumes (61 papers each). The classification and keyword analysis in volume I clearly showed the relationships between these selected papers and modern MS concepts and metrics. As the basis of all management science, statistical analysis based on big data is still a major research focus. Therefore, at the 2020 ICMSEM, many scholars proposed a wide range of the statistical methods and statistical analysis applications. Based on an analysis of the China Health and Nutrition Survey (CHNS) data, Khelfaoui et al., studied the effects of education on health using linear and multi-logit regression models. Li conducted a sentiment analysis of the comments on rumor refuting microblogs to analyze the emotional tendencies and commentators characteristics for five main rumor

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categories: economics, society, military, politics and disaster, finding that except for the military category, there were positive correlation between the emotional tendencies towards the contents, which was of great significance in the management of network public opinion. Sarker explored the potential of big data for disaster management to increase socio-ecological vulnerability and recommended implementing proper infrastructure, technologies, tools and expertise to ensure the proper utilization of big data for disaster resilience. Machine learning has been widely applied in various fields. Using a real word train scheduling case study problem, Gen proposed a parallel hybrid multi-objective genetic algorithm with learning abilities, and conducted numerical experiments using a multiobjective genetic algorithm in GPU, with the primary objective being to obtain the best compromised solution for passenger satisfaction and operational train costs by minimizing total averaged passenger waiting times and the total number of train operating cycles. Opoku et al. compared several machine learning algorithms, iterated conditional modes (ICM), simulated annealing (SA) and hybrid ICM with ant colony system (ACS-ICM) optimization for pixel labelling, parameter estimation and mixture component estimation. Hu proposed a hybrid model that combined unconstrained ordination analysis and cluster analysis, used it to elucidate the relationships between online merchandising and its indexes and then employed cluster analysis to classify the ranking analysis results, the results from which allowed the buyer to directly understand the product from the product index and the store index from the bi-plots. Decision analysis has been another growing MS research field, with new applications ranging from scientific discoveries to business intelligence and analytics. Xiong et al. proposed a suitable method based on the integration of 2-tuples and grey correlation (GR) to resolve problems associated with the lack of index information and fuzzy evaluation criteria in agricultural project approval evaluations, which effectively reduced the decision-making subjectivity and uncertainty and provided a scientific basis for project sorting and selection. Zhang, constructed a market feedback mediating effect model to research investor compensation and the subsequent private placement decisions and to analyze investor return break-through points, for which market feedback was taken as the direct effect variable and investor compensation taken as the mediating effect variable.

4 Evaluation of MS and ICMSEM Development Trends In this section, the MS development trends are evaluated, in which, CiteSpace software was used to transform the research data into a scientific knowledge map. As a free Java application for visualizing and analyzing citations and the content of scientific literature, by integrating an information visualization method, a bibliometric method, and a data mining algorithm, CiteSpace was used as the analysis tool, which elucidated the trends and tendencies within a knowledge domain over a certain period and allows for the identification of the research evolutions occurring at the research frontier through knowledge map visualization [2]. To do this, an advanced search function with management science as the identifier was applied to the Web of Science database with the timespan set from 1990 to 2020. Because of the large number of redundant items in

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the initial retrieval results, to ensure accuracy, paper types, articles and proceedings were then selected to refine the research selection, which reduced the paper numbers to 6,091. After a further careful review, some literature was also eliminated because of its indirect relationships to MS. In this way, the literature was scrutinized until only 3,378 MS documents were finally extracted and input to Citespace. 4.1 The Development of MS The record output statistics from the 3,378 articles were saved and converted into CiteSpace, which transformed the data into a format that could be identified by the software to allow for parameter selection. The time span was set from 1990 to 2020 with the time slice set at one year and the theme selection based on the titles, abstract subject words, identifiers, and keywords to allow for the node selection. Then, each zone with the 30 highest keyword records were clustered and analyzed, from which a map was drawn for the minimum spanning tree. By setting “Threshold = 30”, a total of 414 nodes were obtained, with the overall network density being 0.0169, S = 0.5602. Using the keywords and the label title clustering, 40 categories were identified. As shown in Fig. 1, the system frequency identified machine learning, statistical analysis, decision analysis, system and big data were the highest ranked areas, indicating that these areas were the most popular current management science research fields and the future MS development trends. Table 1, shows

Fig. 1. The results of research fields clustering of MS

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J. Xu Table 1. Summary of the largest 10 clusters. Cluster ID Size Silhouette Label

Year

0

59

0.542

Machine learning

2008

1

43

0.522

Management

2008

2

40

0.568

Model

2015

3

37

0.657

Science

2012

4

34

0.677

Statistical analysis

2017

5

31

0.644

Decision analysis

2014

6

26

0.747

Big data

2004

7

26

0.712

Artificial intelligence 2016

8

26

0.73

Prediction

2012

9

24

0.722

Performance

2010

Fig. 2. The category clustering of MS timeline view

that the network is divided into 37 co-citation clusters, which were labeled by the index terms from their own citers with the largest 10 clusters being summarized based on the size and Sihouette value (a positive relationship between the S value [range from (0,1)] and the overall cluster result reliability). The top terms were then taken as the key research foci in the corresponding cluster, from which it can be seen that machine learning was the most studied areas in 2008 as it had the largest cluster. Other topics such as modeling, statistical analysis, decision analysis and big data were also dominant research areas in different years. In addition, it is worth noting that big data has become a research topic from as early as 2004. With the continuous development of information technology, the computing science and artificial intelligence that accompany the generation of big data are also gradually becoming a key development trend.

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4.2 Future Development Predictions To provide a more active research forum and to further analyze the popular management science research fields, the ICMSEM evaluation development trends were analyzed. Therefore, to determine the MS development trajectory over time, the layout was changed to a time zone layout, as shown in Fig. 2. Most high frequency words appeared in the early years, indicating that MS has had a long history as it has been combined with an ever-widening range of applications. Further more, statistical analysis is more and more dependent on the collection of big data and the application of information technology, while the decision analysis also pays more attention to the uncertainty of the environment. After the 414 keyword reference frequencies were organized from high to low, the top thirty were analyzed, as shown in Table 2, from which it was found that keywords such as machine learning, decision analysis, decision support system and the statistical analysis had relatively high centralities. Volume I includes the main research areas associated with management, economics, engineering, and data and information, all of which have strong links to MSEM and a range of methodologies and models. The latest research areas and research directions are also included, such as resource optimization, decision-making science, and project management. Big data and information technology were found to be the basis for many of the innovations in last year’s proceedings. Table 2. The top thirty hot keywords of MS Frequency Centrality Keywords

Year

306

0.25

Management

1996

295

0.15

Machine learning

2001

170

0.09

Model

1997

127

0.07

System

1992

120

0.12

Decision analysis

2006

105

0.08

Decision support system 2013

88

0.12

Statistic analysis

1998

76

0.07

Big data

2003

71

0.04

Classification

1998

71

0.11

Science

1999

70

0.10

Uncertainty

2016

59

0.06

Algorithm

2013

56

0.09

Deep learning

2018

55

0.08

Information

1993

52

0.14

Framework

1997

50

0.11

Impact

1998

46

0.09

Design

1994

46

0.04

Optimization

2016

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J. Xu Table 2. (continued) Frequency Centrality Keywords

Year

45

0.02

Data science

44

0.10

Artificial neural network 2017

2015

42

0.08

Policy

1999

41

0.05

Artificial intelligence

2016

40

0.04

Performance

1997

40

0.04

Neural network

1998

39

0.02

Conservation

2001

38

0.03

Prediction

1999

36

0.04

Decision making

1998

36

0.07

Regression

2019

35

0.05

Risk

1997

34

0.04

Diagnosis

2017

The computer science and environmental science research highlighted how hightech developments can spur social progress and environmental protection. Therefore, we believe that management science research need to focus on management problems and popularizing MS knowledge, as excellent academic research can influence global developments and also assist in identifying regional issues. To ensure a bright future for study of MS, practical theories and effective methods need to be developed and applied, for which information and economics knowledge needs to be popularized, which is the responsibility of all MSEM scholars.

5 Conclusion MS employs various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms to improve an organization’s ability to enact rational and accurate management decisions by determining at optimal or near optimal solutions to complex decision problems. This review found that MS was widely represented in scientific debates on statistical analysis, machine learning and decision analysis in proceedings Volume I. First, we briefly analyzed the development status of these three MS subdisciplines. Then the main research foci in the ICMSEM proceedings Volume I were itemized using CiteSpace and the prominent topics in these three areas identified to assist readers better understand the content in this year’s papers. The visual literature analysis results showed that while the development trends and the research focus for the ICMSEM proceedings Volume I were found to be basically the same as in mainstream MS research, the topics and content were slightly different. Further analysis is therefore needed to study mainstream MS journals and associated cutting-edge articles to determine the future development directions for the ICMSEM.

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Acknowledgements. The author gratefully acknowledges Tingting Liu and Ruolan Li’s efforts on the paper collection and classification, Zongmin Li and Yidan Huang’s efforts on data collation and analysis, and Rongwei Sun and Zhiwen Liu’s efforts on the chart drawing.

References 1. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2020) 2. Chen, C.: Citespace ii: detecting and visualizing emerging trends and transient patterns in scientific literature. J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006) 3. Clemen, R.T.: Making Hard Decisions: An Introduction to Decision Analysis. Brooks/Cole Publishing Company, Pacific Grove (1996) 4. Giraldo, R., Dabo-Niang, S., Martinez, S.: Statistical modeling of spatial big data: an approach from a functional data analysis perspective. Stat. Probab. Lett. 136, 126–129 (2018) 5. Guo, Y., Wang, N., Xu, Z.Y., Wu, K.: The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mech. Syst. Sig. Process. 142, 106630 (2020) 6. Johanna, S.M., David, O.Y., Javier, R.I., et al.: Predictive analysis of urban waste generation for the city of Bogota, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon 5(11) (2019) 7. Michie, D., Spiegelhalter, D.J., Taylor, C., et al.: Machine learning. Neural Stat. Classif. 13(1994), 1–298 (1994) 8. Narula, S.C., Vassilev, V.S., Genova, K.B., et al.: A reference neighbourhood interactive method for solving a class of multiple criteria decision analysis problem. IFAC Proc. 37(19), 131–137 (2004) 9. Rai, S.N., Qian, C., Pan, J., et al.: Microbiome data analysis with an application to a preclinical study using qiime2: statistical considerations. Genes Dis. (2019) 10. Rezapour, M., Molan, A.M., Ksaibati, K.: Analyzing injury severity of motorcycle at-fault crashes using machine learning techniques, decision tree and logistic regression models. Int. J. Transp. Sci. Technol. (2019) 11. Smith, C.B.: Adaptive management on the central platte river–science, engineering, and decision analysis to assist in the recovery of four species. J. Environ. Manag. 92, 1414–1419 (2010) 12. Taji, I., Ghorbani, S., De Brito, J., et al.: Application of statistical analysis to evaluate the corrosion resistance of steel rebars embedded in concrete with marble and granite waste dust. J. Cleaner Prod. 210, 837–846 (2019) 13. Verma, R.: Management science, theory of constraints/optimized production technology and local optimization. Omega 25(2), 189–200 (1997) 14. Xu, J., Zhou, L.: An adaptive trivariate dimension-reduction method for statistical moments assessment and reliability analysis. Appl. Math. Model. 82, 748–765 (2020) 15. Xu, Z.: Uncertain Multi-attribute Decision Making: Methods and Applications. Springer, Heidelberg (2015)

Part I: Statistical Analysis

Factors Influencing Online Shopping Experience and Customer Satisfaction in Karachi Asmara Haidery1 , Asif Kamran1(B) , Nadeem A. Syed2 , and S. M. Ahsan Rizvi3 1

3

Faculty of Management Science, ILMA University, Karachi, Pakistan [email protected] 2 Management and HR Department College of Business Management, Institute of Business Management, Karachi, Pakistan Management Science Department, Bahria University, Karachi, Pakistan

Abstract. For this research, the objective of the study was to explore the factors affecting the online shopping experience of the customer. To test the hypothesis of this study, a sample of 250 respondents’ data used, which was collected through a structured questionnaire. Furthermore, the questionnaire consisted of four independent variables each containing further questions and one dependent variable. In total 27 scale items used for the study. The pilot test of the study conducted and the reliability test of the Cronbach Alpha test value scored satisfactory. Moreover, the multiple regression tests applied on the data. Most importantly, if website technicalities can be fixed, and further upgraded, then the customers’ purchase intention can convert into sales opportunities, and the customers can be persuaded to switch the online medium as a favorable option. Likewise, the business should develop credibility with the customers through maintaining quality of the products and services, and regularly updating their websites. This in turn, may result in customer to be satisfied with the dealing, and readily re-order online from the company. Lastly, socio-economic and demographic factors, such as qualification, income, and age of the customers also play a vital role in their decision-making and easiness of understanding the website content. Thus, e-retailers may include these variables in their strategies, and subsequently increase their target audience by strong promotion. Keywords: Online shopping · Karachi · Product experience · Payment method · Convenience · Trusted website · Online shopping experience

1

Introduction

Online retailing (otherwise called e-tail) is a web-enabled interface between a retailer and its target consumers for selling items and services on the web with the office of ecommerce. These sorts of retailers called e-tailers. E-commerce has c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 13–31, 2020. https://doi.org/10.1007/978-3-030-49829-0_2

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been around for quite some time now in Pakistan however, it is yet developing. As Pakistan’s internet populace increases, so does e-commerce. An increasingly larger number of businesses are changing to e-commerce by registering a prominent online presence. In Pakistan, numerous new initiatives and businesses launched online, effectively making them a piece of e-commerce immediately. One such e-commerce venture that offers a comprehensive online shopping experience is Azmalo.pk. Symbioses is another relatively more established e-commerce business venture, which focuses on electronics, delivered to you in 24 to 48 hours. Daraz.pk is likewise a unique online business, which delivers latest design items straight to your home. TCS, for quite some time been an established courier service in Pakistan, has expanded its online presence by offering a wide exhibit of items under brand name Yayvo. Though online shopping is developing quickly in Pakistan, with more than 29 million internet users, the trend is changing, however not with the rate it should. Although-shopping saves you time, without leaving the solace of your home and giving customers convenience. Online discounts and rebates, there are numerous elements which make customers reluctant, As indicated by a recent report, 43% online shoppers have failed in their attempt to purchase an item, on account of ineffectively designed websites. Apart from this, there are many other factors like customer perceived risk, online payment methods, product experience etc. This is why majority of the customers still prefer to shop the old fashioned way C by going to markets and malls.

2

Problem Statement

This study is conducted to study that why people in Pakistan are still not converting to online shopping at a fast pace as compared to other countries of the world having similar resources. This study will cover factors like the payment method, product experience, website trustworthiness and convenience, which play a major role in customer satisfaction doing online shopping. 2.1

Objective of the Study

To find the relationship between factors that affect customer satisfaction in online shopping. 2.2

Significance of the Study

The preliminary purpose of this research is to understand the different factors that affect the online shopping experience. This study will be beneficial for all those business entities, which are already associated with online shopping or are seeking to affiliate themselves with this emerging business platform of the future. It is essential for the online retailers to work on their customers the problems while experiencing their service online and find ways to do work more productively. If the factors identified in the study can be resolved or enhanced then the

Factors Influencing Online Shopping

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chances of customer satisfaction in online shopping increased. The resolution to the problems identified is also beneficial for the economy of Pakistan as e commerce can generate a huge amount of revenue and thus plays a major role in GDP and IT sector growth. If this sector developed positively then it will be very much beneficial for the businesses that are already associated with e-commerce platform and thus can help them in providing quality products for the export too. Pakistan can acquire a great name in E commerce in the world. 2.3

Scope of the Study

This study is focused on finding out answer as how to online shopping is being done in Karachi. The study conducted on the people living in the urban area of Karachi who are engaged in online shopping and have gone through this experienced. The data collected in year 2018. 2.4

Delimitations

There were some hindrances in doing research. Results calculated on certain assumptions, which may change in future. These changes may have adverse or favorable impact. Information biased from respondents. The study only covers the urban areas of Karachi in Pakistan.

3

Definition of Terms

Product Experience: it is the overall perceived value of the product or service evaluated by the customer before as well as after he/she consumes it. Payment Method: it is the payment modes like cash, credit card etc. through which the customer buys the product/service. Convenience: the way to proceed with something without difficulty. Trusted Website: it is a website that is authentic in a sense that it retains your privacy as well as can be contacted anytime for complaints etc. E-commerce: otherwise called electronic commerce or internet commerce, refers to the purchasing and selling of products or services utilizing the internet, and the transfer of money and information to execute these exchanges. Quality: the standard of something as measured against other things of a comparative kind; the degree of excellence of something. It tends to be just defined keeping up the standard of your item to compare with other items.

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Literature Review

Development of the Internet has unequivocally impacted the worldwide marketing environment and the Internet has provided companies with the capacity to expand their business reach through e-commerce [2]. The Internet is becoming an increasingly prominent medium to facilitate data search, choice, and purchase. Business-to-consumer (B2C) electronic commerce involves the use of the Internet to market and sell items and services to singular consumers [19]. Time efficiency, avoidance of crowds, and 24 hour shopping availability has been cited as core reasons for shopping online [26]. The Internet makes range of products and services available for consumers all around the world, people can buy or sell almost anything, at any time, from anywhere, through online shopping [43]. Despite the benefits of online commerce over conventional commerce and idealistic predictions for future development of online shopping, negative aspects associated with this shopping method are becoming critical [31]. Online environment as opposed to a physical one greater perceived risk and less trust is expected due to the way that there is real trouble in evaluating an item or service as there are no visual or tangible signs about the nature of the item nor face-to-face interaction with sales staff, and the purchase is affected by security and confidentiality issues [32]. Hence, it assumed that people might feel a certain degree of risk when acquiring an item through the Internet. For instance, consumers are nervous that the Internet still has very little security with respect to utilizing their credit cards and revealing their personal information or concerned about buying a product from sellers without physically inspecting the products [39]. The degree to which shoppers are presently swinging to the Internet as a shopping channel underscores the need to better understand and predict consumers’ online shopping behaviors so as to design and boost effective retail Websites that coordinate the preferences of their target market [52]. According to Perera and Sachitra (2019) [41], identified that customers purchasing online are very much concerned about convenience, website functionality, security and customer service of online shopping and that these factors have significant impact on customer satisfaction in online shopping. There have been intensive studies of online shopping attitudes and behavior in recent years. A large portion of them has attempted to identify factors affecting or adding to online shopping attitudes and behavior. These studies have all made vital promises to our understanding of the elements of online shopping experience. Businesses in Pakistan are beginning to embrace e-commerce business models and sell their offerings online. However, there is an absence of intelligible understanding of the effect of perceived dangers on online shopping in Pakistan. The researcher aimed to in depth focus on perceived risks involved in customer satisfaction, which identified by earlier studies, incorporate these dimensions of perceived risks into a research model, and identify their effect on online shopping experience in Karachi.

Factors Influencing Online Shopping

4.1

17

Customer Perceived Risk

Although consumers perceive the Internet as offering number of benefits, the Internet tends to intensify some of the uncertainties involved with any purchase process. Consumers perceive a higher level of risk when buying on the Internet as compared with conventional retail designs [33]. Perceived risk is defined as the possible loss in pursuing a desired outcome while engaged in online shopping. It is a mixture of uncertainty with the possibility of series of undefined outcome [27]. Perceived risk has been identified using different scales by estimating the impression of uncertain occasions happening [15]. Perceived risk decreases the eagerness of purchasers to purchase products on internet [7]. More prominent view of risk with respect to customers’ goes about as a barrier to their buying intentions. A few researchers have seen that the perceived risk in E-commerce negatively affects shopping behavior on the Internet, demeanor toward use, conduct and aim to embrace E-Commerce [54]. Web based purchasing might be related with contrary outcomes that is not found in conventional trade for example, buyer’s inability to assess the quality of the item right away, the absence of individual contact with a sales representative, the expenses of figuring out how to utilize the web or website, the change from different channels to the electronic one, the age of uneasiness and agitation of customers who don’t feel great utilizing the web, the nonappearance of cooperation and social contact with other individuals, and security of payment and individual privacy [44,55]. The worldwide accepted causes of consumers’ perceived risk that include financial risk, product risk, delivery risk, time risk and privacy risk have shown a significant impact on online shopping behavior of customers and adversely affect their purchase behavior [38]. In online shopping there are numbers of apprehensions that the customers have like trade fraud, product quality, monetary loss, privacy invasion, information quality etc [1]. Shoppers, on the Web, may fear giving credit card details to any business internet provider and those buyers essentially don’t confide in most Web providers enough to take part in exchange process including cash. This apparent risk among purchasers makes difficult for the customers to utilize credit card and provide information and thus causes hindrance to use online retailing thus bringing about their withdrawal from electronic exchanges [21]. In any case, the opinion of risks and expenses isn’t indistinguishable for all purchasers. While a few purchasers see electronic trade as a risky and costly method for purchasing, others appreciate the upsides of online business, for example, the simplicity of data looking and of contrasting items and costs. Regardless, it is very well may be assumed that the perceived risk will lead buyers to consider diverse signs while framing their behavior and feelings towards a website [45]. Past investigations have contended that the accompanying sorts of risks are typically associated with purchase decisions: financial risks, product risk, convenience risk, health risk, quality risk, time risk, delivery risk, after-sale risk, performance, psychological, social, and privacy risk, website design style and attributes, and trust in the website influence altogether online customers’ buying behavior [4,24,45,49,54].

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Product Experience

Product experience assumes an important job in deciding consumer loyalty in web based shopping. As clients cannot contact, feel or experience the quality or working of the product they consider it a significant obstacle in taking any buy choice. Commonly the product that comes is not the same as the one that ordered. Aside from that on multiple occasions the product comes faulty or does not meet the perceived value. According to Atulkar, Sunil & Kesari, Bikrant (2019) [6], the attitude and intention to buy online includes the ease of use, enjoyment but also external factors like consumer traits, situational factors, product characteristics and trust and previous online shopping experience. Product experience or product perceived risk characterized as the likelihood of the thing neglecting to meet the product performance initially expected [42]. Product risk accounted for as the most as often as possible referred to purpose behind not shopping on the web. For instance, product risk found to have noteworthy effect on the recurrence of buying on the web [17]. The Internet, much the same as a non-store shopping, makes it hard to analyze physical products; customers must depend upon to some degree partial information and pictures that appear on the PC screen [23]. Product risk is the discernment that a product obtained may neglect to work as initially expected [29]. Furthermore, it is the misfortune caused when a brand or product does not execute of course, is generally because of the customers’ inability to precisely assess the nature of the product online [8]. Customers may go through many stresses like does not receive the product on time or the product may not perform as expected and the quality also not provided as claimed [3]. 4.3

Convenience

Various shopping researches have distinguished convenience as a discrete drive for online store decision [16,25]. Convenience customers are frequently described as choosing things dependent on time or exertion saving. Purchasers involved in online shopping like it because it lessens the weight of visiting the stores in person [30]. Customers who want to shop specific types of products or services online are motivated by convenience [38]. A moderately abnormal state of product risk is normal while purchasing online especially for some product classes, because of customers’ failure to physically analyze and test product characteristics online [5,18], proposing that risks related with product vulnerability are probably going to adversely influence online buying goals C at any rate for a few products [8]. For example, customers see a larger amount of product risk for clothing when acquiring online instead of when buying in conventional stores [20]. 4.4

Payment Method

The payment methods in online shopping are limited and are considered insecure by the customers. They feel uncomfortable in sharing their financial details to

Factors Influencing Online Shopping

19

the websites where they want to shop. Customers are also reluctant as they do not know what product quality they will receive and have to pay prior unlike traditional shopping. There is another method Cash on delivery method, that is use in Pakistan but then again customers have to pay prior seeing the product. Financial risk characterized as the probability of experiencing a monetary loss from a purchase [22,42,48]. Customers purchasing online usually fear and hesitate in giving their credit card details to any commercial website i.e. they do not trust most of the web retailers enough to engage in money transactions [1]. This phenomenon is very much true for Pakistani customers also. There are diverse reasons why online customers may endure financial loss when shopping online. To begin with, it is hard for online customers to decide if the cost of the thing bought at a specific online retailer is the most minimal accessible contrasted with others. View of such financial risk clarifies why online customers abandon carts [14]. Second, financial misfortunes may happen because of credit card frauds, which is an essential financial worry among online customers. [9] reports customers’ worries with respect to financial losses if products acquired online fail to perform as expected. Besides, customers might be hesitant to buy products online because of different costs, for example, shipping. Largely, financial risk adversely connected with online shopping [8,16]. In addition, financial risk is observed to be a solid indicator of customers’ online shopping expectations [8] and practices, for example, inclination to abandon online shopping carts, purchase recurrence, amount spent online, and recurrence of looking with expectation to purchase [14,17]. 4.5

Trusted Websites

Customer Service, website design and security have major and considerable effect on online customer satisfaction [12]. In spite of the developing online sales volume, concerns with respect to security stay high among numerous online customers e.g., [11,13,37,50] finds that more than 69% of US Internet customers limit their online buys because of concerns identified with the security and privacy of their own data. Nevertheless, [17] find that despite the fact that security concern was an every now and again referred to explanation behind not obtaining online, it does not fundamentally affect the recurrence of buying online and seeking with plan to purchase. Along these lines, the impact of apparent security risk on buy aim remains rather hazy. The numerous reasons of dissatisfaction of online customers include hackers stealing the personal information which is due to the fact that the websites have designing failures and poor performances [3]. Shoppers can find out about the estimation of products through site highlights, for example, product information quality, transaction and delivery capacity, and efficient service quality; nonetheless, if there is no data security instruments set up, purchase intention will be antagonistically influenced. This information security factor can be abuse adequately relying upon Internet retailers’ capacity to live up to clients’ desires in the virtual shopping condition [10].

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[53] referenced that information security and protection are identified with the vulnerability related with how close to personal information is dealt with by online businesses and who has access to it. [27] also emphasized that shoppers maintain a strategic distance from sites that require individual information for enlistment, driving a few people to fabricate or give fragmented information. Numerous researchers underline that website security and privacy ought to envelop confidentiality of information, information trustworthiness, and correspondence of non-repudiation, verification security, IT adequacy, and protection of individual privacy, all of which identify with website qualities [47]. 4.6

Online Shopping

Internet shopping phenomenon is rapidly increasing [35]. Online shopping behavior (additionally called online purchasing conduct and Internet shopping) refers to the way toward obtaining products or services by means of the Internet. The procedure comprises of five stages like those related with traditional shopping behavior [34]. Numerous E-commerce studies have demonstrated that consumer intentions to engage in online exchanges are a huge predictor of consumers’ real support in E-commerce transactions, the relationship between intention and behavior is based on the presumption that individuals attempt to make normal decisions based on the information available to them [40]. The study confirms that variable such as security, availability of information, shipping, quality of product, pricing and time have positive effect on customer satisfaction [51]. The future of retail 2019 study explains that all purchase decisions are mostly because of two factors, convenience and connection. The customers are more comfortable in spending online as online retailers like Amazon are delivering on convenience that consumers want especially the young customers who want to gain advantage of convenience [46]. Given that online shopping is a relatively new type of shopping method, huge changes must happen so as to encourage more consumers to shop online. For this to happen, consumers must recognize that they could get a better deal from online shopping than from traditional shopping channels [28].

5 5.1

Research Methodology Research Design

This research is quantitative and will describe the data collected by survey. 5.2

Data Collection Method

Primary data has been composed through survey questionnaire consisting of the people using online websites as means for shopping. This research is based on an analysis that is purely quantitative. The following data will be organized by evaluating the responses collected through the questionnaire from its respondents. The SPSS Software will be utilized to analyze the questionnaire.

Factors Influencing Online Shopping

5.3

21

Sample Size

A sample size of 250 respondents has been taken. This sample size will be precise, so that the respondents will respond by going through the pre-structural questionnaire. This sample size is selected in order to reduce chances of human errors and inaccuracy. 5.4

Time Frame

As the research is not too simple, so it will take a little bit time in collecting data and, get the questionnaires filled from the respondents. The estimation of period it will take is more than 10 days. 5.5

Sampling Technique

In this study convenience sampling is used, the questionnaire will assist in collecting the data, testing, analyzing and interpreting the facts. This questionnaire checked and analyzed with the help of SPSS software to get the accurate and meaningful result. 5.6

Statistical Technique

The questionnaire will be support in data collection, testing, analyzing and interpretation of facts. The questionnaire checked and analyzed with the help of SPSS software version 16, in order to get accurate, precise and meaningful results. Multiple Linear Regression will be applied as it is used to understand the causal relationship between the independent variables and dependent variables (Fig. 1).

Product Experience Convenience

Customer satisfaction

Payment Method

Trusted websites

Fig. 1. Theoretical framework

Online Purchase Experience

22

A. Haidery et al.

6

Measurement

To test the main hypothesis of this research, a multi item scale constructed to measure relationship between the online shopping experience factors and customer satisfaction from perspective of customers residing in Karachi. This questionnaire adopted and combined by investigating previous researches and experts’ suggestions, the questionnaire number of items and references shown in Table 1. In accordance with the research model, the questionnaires comprised three sections; demographic information, perceived risks, and online shopping. Demographic variables (gender, age, and education) were measured using ordinal scales. The second section includes a list of perceived risk components was used to measure the degree of customer satisfaction when purchasing a product online; product experience, trusted website, payment method, and convenience. The last section includes items measuring online shopping. Responses for the second and third sections obtained in a five-point Likert scale from “strongly disagree” (1) to “strongly agree” (5) (Table 2). Table 1. Adoption of questions details and reliability analysis of online shopping and customer satisfaction Measured variables

Question item Source

Cronbachs alpha

Customer’s perceived risk 19 Product experience

4

Y. Masoud (2013)*

Payment method

3

Y. Masoud (2013)

Convenience

6

Y. Masoud (2013)

Trusted websites

6

Y. Masoud (2013)

8

Y. Masoud (2013)

Online shopping

27

Total ∗ Ref [36]

Table 2. Demographic profile of participants (n = 100) Variable

Frequency Percentage

Gender

Male Female

Age

Less than 25 25–39 40–54 55 and above

Education High school or lower Bachelor’s degree Master’s degree or Higher

Factors Influencing Online Shopping

7

23

Appendix

See Table 3. Table 3. Proposed measurement items for constructs Constructs

Items Measurement items

Product experience

PE

Payment method

Convenience

Trusted website

PE1

I may not get the product I want

PE2

I might not get what I ordered through online shopping

PE3

It is hard to judge the quality of product over Internet

PE4

I cant to touch and examine the actual product

Mean Std. dev

PM PM1

Shopping online can involve a waste of money

PM2

I feel that my credit card number may not be secure

PM3

I might get overcharged if I shop online

C C1

It is not easy to cancel orders when shop online

C2

Buying a product online can involve a waste of time

C3

Too complicated to place order

C4

Finding right product online is difficult

C5

The goods returned may be waiting a long time

C6

I may not get the product I want

TW TW1 I cant trust the online company TW2 Difficult to find appropriate websites TW3 I might not receive the product ordered online TW4 This website will protect my private information TW5 This website provides me with complete information TW6 I trust this website for purchasing products

Online shopping

8

OS OS1

Using Internet for online shopping is easy

OS2

I shop online as I do not have to leave home for shopping

OS3

I shop online as I can get detailed product information online

OS4

I shop online as I get broader selection of products online

OS5

Online shopping gives facility of easy price comparison

OS6

I shop online as I can take as much time as I want to decide

OS7

I find online shopping compatible with my life-style

OS8

I use online shopping for buying products which are otherwise not easily available in the nearby market or are unique (new)

Hypothesis

In this research, the essential hypotheses are: Hypothesis 1. Product Experience has significant impact on Online Shopping Experience

24

A. Haidery et al.

Product experience plays a major role in online shopping experience. Online shopping provides variety of products and customers seek quality products, but due to e-commerce platform, they are unable to touch or feel the product quality, which might cause dissatisfaction. So the hypothesis is given below: Hypothesis 1a. The product experience does not have significant impact on online shopping experience. Hypothesis 1b. The product experience does have significant impact on online shopping experience. Hypothesis 2. Payment Method has significant impact on Online Shopping Experience Payment method is another factor, which is very important in online shopping platform. If the customers do not feel that the payment method is secure or are reluctant to share their financial details like credit card number etc., it can negatively impact the online shopping experience thus eventually customer will not adapt to online shopping resulting in financial loss of the e retailers. So the hypothesis is given below: Hypothesis 2a. Payment method does not have significant impact on online shopping experience. Hypothesis 2b. Payment method does not have significant impact on online shopping experience. Hypothesis 3. Convenience has significant impact on Online Shopping Experience Convenience is something, which people seek in online shopping experience. Customers usually shop online because they want to shop variety of products, but if finding right product becomes difficult or the order cancellation and order placing is difficult, customers tend to get annoyed. Many times the product that is ordered differs from the one received. So the hypothesis is given below: Hypothesis 3a. Convenience has significant impact on Online Shopping Experience. Hypothesis 3b.Convenience does not have significant impact on Online Shopping Experience. Hypothesis 4. Trusted Website has significant impact on Online Shopping Experience Trusted website also plays a vital role in online shopping experience, as customers should trust the website they are purchasing. Apart from that the website should provide complete information about the produce purchased. Trusting the online company providing the product is very important, as it is difficult for the customers to judge the authenticity of the website offering products, which might cause dissatisfaction in online shopping experience. Therefore, the hypothesis given below: Hypothesis 4a. Trusted website has significant impact on Online Shopping Experience. Hypothesis 4b. Trusted website does not have significant impact on Online Shopping Experience.

Factors Influencing Online Shopping

9

25

Analysis of Data

The questionnaire is based on to ask relationship between independent and dependent variables therefore, it is tested in SPSS Software to analyze and interpret the result of questionnaire, the test analysis is given below: Table 4. Descriptive statistics Mean OS

Std. deviation N

3.319500000 0.475274000

250

PE

2.193000000 0.576480000

250

PM

2.524000000 0.744218831

250

C

3.512000000 0.618824150

250

TW

3.106666667 0.666465833

250

Age

1.636000000 0.607400000

250

Qualification

3.196000000 0.834500000

250

Monthly income in Rupees 2.464000000 1.414500000

250

Above Table 4 shows that total 250 respondents data collected for this study. The Online Shopping is dependent variable and shows mean of 3.31950. The independent factors Product Experience has mean of 2.1930, Payment Method has mean of 2.5240, Convenience has a mean of 3.5120 and Trusted Website has mean of 3.1066. Table 5. Reliability statistics Cronbach’s alpha N of items 0.814

39

Table 5 checks reliability of the measures of the research variables good or weak The results show that value of Cronbach alpha is 0.814of all 39 items. Table 6 shows that there was a strong positive and significant at 1% level association between willingness, awareness, economic, social and financial variables. Table 7 showed that the adjusted R square was 0.067 which indicates the goodness of fit of the model. Adjusted R Square .067 = 6.7% shows that independent variables explain the model 6.7% variation in the awareness and remaining 93.3% variations remains unexplained. This means that other factors may effect on awareness but not taken in this study. R value .658 shows that there is a strong positive association between dependent and independent variables.

26

A. Haidery et al. Table 6. Correlations OS

Pearson Correlation

PE

PM

C

TW

Age

Qualification Monthly income in Rupees

OS

1.000 −.011 −.008 .1460 .253

.005

.067

PE

−.011 1.000 −.024 −.138 −.164 .7950 .6970

PM

−.008 −.024 1.000 .3260 .2970 .0330 −.015

−.048

C

.1460 −.138 .3260 1.000 .5290 −.092 −.067

−.173

TW

.2530 −.164 .2970 .5290 1.000 −.117 −.099

−.148

Age

.0050 .7950 .0330 −.092 −.117 1.000 .4270

.6690

.8860

Qualification .0670 .6970 −.015 −.067 −.099 .4270 1.000

.4130

−.018 .8860 −.048 −.173 −.148 .6690 .4130

1.000

Monthly income in Rupees Sig.(1-tailed) OS PE

.4300 .4510 .0110 .0000 .4720 .1460

.3910

.3550 .0150 .0050 .0000 .0000

.0000

.4300

PM

.4510 .3550

C

.0110 .0150 .0000

.0000 .0000 .3030 .4060

.2270

.0000 .0740 .1460

.0030

.0320 .0590

.0100

.0000

.0000

TW

.0000 .0050 .0000 .0000

Age

.4720 .0000 .3030 .0740 .0320

Qualification .1460 .0000 .4060 .1460 .0590 .0000 Monthly income in Rupees N

−.018

.0000

.3910 .0000 .2270 .0030 .0100 .0000 .0000

OS

250

250

250

250

250

250

250

250

PE

250

250

250

250

250

250

250

250

PM

250

250

250

250

250

250

250

250

C

250

250

250

250

250

250

250

250

TW

250

250

250

250

250

250

250

250

Age

250

250

250

250

250

250

250

250

Qualification 250

250

250

250

250

250

250

250

Monthly 250 250 250 250 250 income in Rupees ∗∗ Correlation is significant at the 0.01 level (2-tailed).

250

250

250

Table 7. Model summaryb Model R

R square Adjusted R square Std. error of the estimate

1 .306a .093 .067 .459019 a Predictors: (Constant), Monthly Income in Rupees, PM, TW, Qualification, C, Age, PE b Dependent Variable: OS

Factors Influencing Online Shopping

27

Table 8. ANOVAa Model Sum of squares df

Mean square F

Sig

Regression 5.257 7 .751 3.564 .001b Residual 50.989 242 .211 Total 56.246 249 a Dependent Variable: OS b Predictors: (Constant), Monthly Income in Rupees, PM, TW, Qualification, C, Age, PE 1

Table 8 the analysis of variance provided the statistical test for the overall model fit in terms of the F-ratio. The significance value of dependent variable of ANOVA showed that the model had significant effect at value .001. Anova table shows that there is statistical significant association among dependent and independent variable. The F test value 3.564 with sig. value 0.001 shows that model is good fit. This means that variables taken in this study must have a relationship among them. Table 9. Coefficientsa Model

1 (Constant) PE PM C TW Age Qualification Monthly income in rupees a Dependent Variable: OS

Unstandardized coefficients B Std. error

Standardized coefficients Beta

2.784 −.422 −.065 .0380 .1830 .1250 .1580 .0860

−.512 −.101 .0500 .2570 .1590 .2770 .2570

.2500 .2420 .0420 .0570 .0530 .0910 .0700 .0620

t

Sig.

11.136 −1.747 −1.537 .66500 3.5820 1.3750 2.2420 1.3860

.0000 .0820 .1260 .5060 .0010 .1700 .0260 .1670

The above Table 9 shows that product experience, payment method, convenience and trusted website, has positive and significant relationship. All other variables have positive and non-significant relationship with willingness due to sig value more than 0.05.

10

Conclusion

In conclusion, to ensure the growth of e-commerce, e-retailers should continuously attempt to improve the experience of consumers visiting their websites and

28

A. Haidery et al.

social media. Additionally, pre-existing companies that operate online and aim to convert consumers to e-commerce should be aware of upcoming opportunities and issues that may come at the expense of them losing their credibility. Therefore, if every single online venture decides to make it their mission to deliver on their undertakings and adopted values, then they may before long witness an E-commerce revolution in Pakistan. The study shows that companies of online must focus on product quality, payment method which largely causes bad shopping experience of the customer. Like most researches this study also has some shortcomings. A sample size of 250 respondents was taken and the study only covers the urban areas of Karachi Pakistan, which might not be applicable in other parts of the world. Information may be biased from respondents. Results are calculated on certain assumptions that might change in future. As the online shopping phenomenon has global implications thus the study can also be conducted in future in other countries to further understand the multi-cultural and cross cultural aspect of online shopping behavior and customer satisfaction factors around the globe. The future research may be taken other variables time of the order placed, duration to complete one order, staff behavior, other factors such awareness, legal challenges, and so on. The sample size can also be increased and study may focus on other major cities of Pakistan as well as collecting and comparing the data with other countries to have a more in-depth view of the factors affecting online shopping and customer satisfaction.

11

Recommendations

The online shopping industry is dynamic area for the growth so there are few recommendations for the improvement: • Should work on brand image which is possible through improving their service by delivering what they promise to the customers as customers feel safe to purchase from the websites of branded products as they are more safe and reliable for them. • Online shopping retailers should become flexible to make the customers check the parcel before the payment as many times the customers do not receive the product they ordered. • They should also provide the actual product picture what they are delivering not an image created with the help of software or taken from some other website in order to decrease dissatisfaction and improve trust issues in online shopping. • Online shopping websites shall try to gain more people trust. One option is to pay 50% initially and pay the remaining payment after receiving the item. • Continuous improvement is needed in operations and customer facilitation. • Quality assurance is a must; for the website design as well as product quality and convenience. • Cyber insurance must be in place if credit cards details are leaked from website. Must strengthen system regarding bank account security.

Factors Influencing Online Shopping

29

• Customers at times feel the need to talk to a product representative (this facility is not always available on all web sites), especially when the item received is faulty, or lacks quality or not the same as ordered. The customers want to explain their feelings and give feedback after the product purchase.

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Factors Affecting Furniture Purchase in Pakistan Muhammad Fazal Qureshi, Asif Kamran(B) , M. Ahad Hayat Khan, and Mohammad Ahmed Desai Faculty of Management Sciences, ILMA University, Karachi, Pakistan [email protected]

Abstract. The local furniture market is facing declining sales due to an increased influx of imported Chinese furniture in the Pakistani market. This paper aims to find out the extent to which specific factors affect on furniture purchases in Pakistan. 362 people were a part of this study and their responses were used to generate the results. The questionnaire adopted for this study had been established reliabilities and validities, which re-ascertained for the present set of data. Eight constructs used in this study; each element having items ranging from three to five. These items were based on a five-point Likert scale. It found that price, product quality, brand loyalty, customer reviews, and attitude have a positive effect on the purchase intention of the customer, whereas customer services have seen as insignificant. The findings of this study can help the furniture brands to focus on the exact factors that customers are looking for when purchasing the furniture. Based on the results of this study that are consistent with previous studies, the brands were more focused on customers’ requirements. The limitations of this study are that the responses gathered were only from Karachi so the study is not an exact representation of the entire Pakistani population. Keywords: Theory of Reason Action (TRA) · Attitude on Purchase Intention (API) · Brand Loyalty (BL) · Brand Quality (BQ) · Customers Reviews (CR)

1

Introduction

World trade of furniture estimated to be $23.2 billion. Wood furniture represents 77%; plastic furniture 6% and metal furniture represents 17% of the total demand. In 2010, Pakistan’s share in the global furniture market was insignificant. Despite the fact that Pakistan takes pride in having a past filled with craftsmanship, it doesn’t share a critical position in the worldwide wood furniture market [13]. The local furniture industry is suffering because of the increased influx of furniture from other countries, especially China, that is being imported and being sold at much cheaper prices than the local furniture. The costs of producing furniture in Pakistan have increased manifold due to a hike in the prices c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 32–47, 2020. https://doi.org/10.1007/978-3-030-49829-0_3

Factors Affecting Furniture Purchase

33

of raw materials including timber, chipboard, color paints, polish materials, and foam. Timber production in the country has also suffered due to unchecked deforestation [6]. Some decades back, furniture purchase was just restricted to occasions like weddings, house shifting, etc. but the trend has now changed. The purchase pattern has changed now. People prefer buying furniture with an increased frequency compared to some decades back might be changing in lifestyle. This paper aims to find out the main factors that affect furniture purchase. A number of factors considered when furniture purchase is taken into consideration. These factors can differ from person to person, culture-to-culture, country-tocountry, place-to-place, etc. For the purpose of research of this paper, a few factors that considered most important for furniture purchase taken into consideration and their effects have seen. In the Pakistani context, it can be seen that the various socioeconomic classes have different preferences for furniture. The people who are of high socioeconomic class prefer designer furniture [29]. The factors that affect their choices are very different. The others who are of a lower socioeconomic class prefer more commercial brands. This research can be generalized to other markets where factors affecting consumer purchase intention in the furniture industry are being studied since the factors being taken into consideration are very much generic. Household needs furniture, regardless of which country, place or class they belong to, this study can help study the effects of these in the future. This research can help uncover the most important factor that affects people’s purchase intention towards purchasing of furniture. This research will also provide secondary data for future researchers to work and explore more components that can affect purchase intention. 1.1

Problem Statement

The sales of locally manufactured furniture are decreasing due to the increased influx of Chinese furniture in the market. 1.2

Research Questions

The following research questions tried to be answered in this research paper: • What is the impact of different factors on consumer purchase intention of furniture? • How different factors are important in making the purchase decision? • Which factors are the most important and to what extent? 1.3

Research Objectives

This research aims to address certain objectives that would focus on this research paper. These are: (1) To determine the effect of Customer Attitude on Purchase Intention.

34

M. F. Qureshi et al.

(2) (3) (4) (5) (6)

To To To To To

determine determine determine determine determine

the the the the the

effect effect effect effect effect

of of of of of

2

Literature Review

Customer Service on Customer Attitude. Price on Customer Attitude. Brand loyalty on Customer Attitude. Customer Review on Customer Attitude. Quality on Customer Attitude.

The sales of locally manufactured furniture have substantially decreased due to an increased influx of Chinese furniture which is dominating the Pakistani market. The Chinese furniture has beautiful designs and it is being a sale at a much cheaper price than that of local furniture. The quality of Pakistani furniture is undoubtedly the best as it made from real wood. However, due to the Chinese furniture being a sale at much cheaper prices, the Pakistani industry affected. The most important factors influencing consumers’ attitudes towards purchasing furniture are price, brand loyalty, customer reviews, and product quality and customer service. By assessing the influence these factors have on people’s attitudes towards buying furniture, it can figure out that what exactly the Pakistani market lacks that the Chinese furniture has, which can be the main reason for the Chinese dominating the Pakistani market [28]. The quality of wood available in the country or imported to the country has an impact on the type of furniture being produced; the higher the wood quality the better the overall quality of the furniture [9]. The theory of reasoned action (TRA) is a very well known theory given by Fishbein & Ajzen (1975) [12]. It has its roots in the field of social psychology. Figure 1 gives an overview of what the theory entails. Figure 1 the TRA discusses the relationship between beliefs, norms, attitudes, intentions, and behaviors of individuals. It suggested that a person’s behavioral intentions determine a person’s behavior. The person’s subjective norms and attitudes towards the behavior determine this intention. This model suggests that by adjusting the structure of an individual’s beliefs, external stimuli have an impact on attitudes. This theory also suggests that other factors called ‘external variables’ have an indirect effect on behavior by influencing the subjective norms [12]. A consumer’s purchase decision is a complicated process. There are different factors that affect consumers’ purchase intentions. These characteristics are the consumers’ personal preferences, social standing, monetary standings and some other factors too [25]. Purchase intention may be influenced under the effect of perceived quality, value and price. Moreover, internal and external motivations also affect the buying intention of consumers. Researchers have identified six stages before the decision making stage of consumers, which are: knowledge, awareness, interest, preference, persuasion, and purchase [19]. Purchase intention is a widely studied concept in marketing literature. Studies have been coordinated in different zones of the world with the desire for understanding what truly impacts the purchase intention of consumers [18]. There are some elements

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Fig. 1. TRA theory

of a brand, which have a solid impact on the purchase intention of a buyer i.e. product quality, product involvement, brand image, brand loyalty, product knowledge and product attributes [1]. Hence the hypothesis is: Attitude has a significant positive effect on Purchase Intention. Prices determined by demand and supply that are the market forces in freemarket economies. In planned economies, the government fixes the price. In mixed economies, both the market and government jointly set the prices [3]. Price considered among the topmost concerned factors when it comes to purchasing any goods and services. Different types of pricing strategies are therefore adhered to cater to the different types of customers in the market [14]. There is a relationship between price and the buying behavior of the customers; it is not necessary that high priced items are always considered as not-to-buy items but actually, there may be a chance that due to its high pricing, consumer perceived value for the product increases because the customer might link price with highquality product [5]. Price acts as an imperative factor in consumer purchases in mostly all types of products and services, unless the nature of the product is otherwise, like lifesaving drugs [22]. Hence the hypothesis is: Price has a significant positive effect on customer attitude. Customer service involves making sure that the customer’s needs are fully met according to what he desires. Customer service involves tending to the necessities and needs of any customer. The better the customer service, the better will be the customer attitude towards their purchase [16]. Some qualities of good customer service include, Promptness i.e. ensuring that things are always on time and no customer has to be kept waiting [15]. Other is politeness i.e. being very sociable and sweet in conduct as a way of pleasing the customer so that his experience is enhanced [16]. Professionalism i.e. customers should be dealt with professionally, with to-the-point conversations mostly pertaining to the point in concern [7]. Personalization involves referring to customers by taking their names so that they feel honored and valued [17]. The kind of administration that customer experience will rely upon the item or administration that a business gives, what

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the customers’ needs are, and whether the administration is issue situated or centered toward upgrading the purchaser’s involvement [2]. Hence the hypothesis is: Customer service has a significant positive effect on customer attitude. Product quality defined as the collection of characteristics and features that play a part in its ability to meet the specified requirements. Brands actually play their part in perceived quality when they want the consumers to perceive the quality to be competent in comparison with the competitors. The better the product quality, the stronger an effect it has on customer attitude [8]. In a study, it was found that there is a strong connection between the two. The prerequisite of quality commands that producers deliver items that satisfy the shopper’s desire for sturdiness [4]. Hence the hypothesis is: Product Quality has a significant positive effect on customer attitude. Brand loyalty defined as a profoundly held commitment to re-purchase a preferred product repeatedly over a period of time [5]. The theory says that the first phase of brand loyalty is cognitive loyalty. In this phase, the consumer purchases the product, analyses it and compares it with its competitors to review and give a thorough analysis of its performance [10]. Brand loyalty is thought of as an imperative construct in consumer behavior research for somewhere around four decades and most researchers suggest that brand loyalty can have significant advantages like for lower advertising costs, positive word of mouth, a competitive edge and increased market share [24]. Hence the hypothesis is: Brand Loyalty has a significant positive effect on customer attitude. Most customers read customer reviews before making their purchase to ensure that what they are buying is worth their while. The risks associated with buying that product service go significantly down when that product or service has positive reviews. A significant effect of customer reviews seen on customer attitudes [21]. Reviews act as ‘social proof’ to make informed decisions about what to buy. Given how strongly reviews affect consumers buying decisions, brands make sure that they in cash this opportunity and provide excellent services so that they get better reviews that pull more potential customers towards their brand [27]. Just around 65% of customers who dependably or quite often read online evaluations trust that the reviews they swing to are reliably sound and precise [23]. Hence the hypothesis is: Customer Review has a significant positive effect on customer attitude. 2.1

Conceptual Framework

The dependent variable is the one that is being measured and tested. The dependent variable here is the purchase intention. The independent variables are price, customer service, product quality, brand loyalty, and customer reviews. They have an effect on the attitude that in turn has an effect on purchase intention.

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37

Methodology

This research has been using a deductive approach. The quantitative analysis is applied. By using the mentioned approach, we are able to apply the statistical analysis to get accurate insight so that we can also standby through our hypotheses. A hypothesis has been formed that will be tested through the statistical analysis method. There are 32.21 million households in Pakistan, bringing the average size of households to 6.45 persons [11]. The study has identified a sample size of 385 respondents for our research with a confidence interval of 95% and a 5% margin of error. The population of furniture purchasers in Karachi is above million. According to Mosahab R, Mohamad O, and Ramayah T (2010) [26], a sample of 385 would be adequate to use in the study. Out of 385, a total of 362 responses have been found usable. A type of non-probabilistic sampling is convenience sampling that has been used as a sampling method because of its convenience in the form of time and cost associated with it (Fig. 2).

Fig. 2. Conceptual framework

3.1

Scale and Measure

This study used 5 points Likert scale ranging from 1 to 5, where 1 is considered as “Strongly Disagree” and 5 is considered as “Strongly Agree” on a scale of 1 to 5. Have eight constructs in our research where each construct contains a different number of items ranging from a minimum number of three items to a maximum of five. The first independent variable is price. It has 3 items and was adopted from research with a reliability value of 0.668. Customer service is our second variable that has five items. That adopted from a Cronbach alpha value of 0.758. The third independent variable is product quality that has six items and a Cronbach value of 0.80 has been adopted. The fourth independent variable is brand loyalty that has four items and having a Cronbach alpha value of 0.780. The fifth independent variable is customer review that has five items with a Cronbach alpha value

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of 0.80. The sixth variable is “attitude” with a Cronbach alpha value of 0.80. The dependent variable is the purchase intention that has five items and was adopted from a Cronbach alpha value of 0.86. After conducting a pilot study, the research has dropped some items that are not reliable enough to be a part of the construct. The criteria for reliability were set as 0.70 for the research. 3.2

Demographic Profile of Respondents

64.4% of the respondents were females whereas 35.6% of respondents were males. Furthermore, 77.6% of our respondents were between the age group of 21–30, 9.9% were between the age group of fewer than 21 years old, 6.4% respondents were between the age group of 31–40, 3% respondents were between the age group of 41–50 and the remaining respondents were between 50 and above. 47.5% of the respondents were earning equal to or less than Rs. 19,000, 20.4% of the respondents were earning between Rs. 20,000 to 50,000, 16.3% of the respondents were earning between Rs. 51,000 to 75,000 and the rest are earning above Rs. 75,000. 3.3

Descriptive Statistics

Descriptive statistics used to check the normality of the data; the values should lie between the defined ranges to prove the normality of the data. We have used SPSS 12 to generate the results provided in Table 1. Table 1 shows that the dependent construct is Purchase Intention has the lowest skewness that is 0.26, and the construct Attitude has the highest skewness that is 0.65. The Kurtosis for all constructs is negative and it is between the ranges from (−1.29 to −1.76). The highest absolute value of kurtosis is for Customer Reviews, which is 1.76; and the lowest absolute value of kurtosis is for Attitude, which is 1.29. We assume that the normal tendency since each construct has a value of skewness and kurtosis between ±2.5. Table 1. Descriptive statistics Purchase intention

Attitude Customer Price service

Brand loyalty

Customer Quality reviews

Mean

3.01

2.53

2.75

2.67

2.71

2.87

2.73

Median

2.41

2.00

2.02

1.83

2.00

1.80

2.00

Mode

2.00

2.02

2.05

1.33

1.50

1.60

1.33

Std. Dev 1.13

1.34

1.41

1.46

1.35

1.39

1.37

Skewness 0.26

0.65

0.36

0.39

0.33

0.37

0.35

−1.29

−1.67

−1.67 −1.69

−1.76

−1.60

Kurtosis

−1.71

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39

Reliability

The items for the constructs are adopted from the previous research papers. They are already tested and reliable but to check it on our data we have reapplied reliability analysis into it to make sure the values lie within the desired ranges. The values provided in Table 2 show that the Cronbach Alpha value of Customer Service is the highest (Cronbach = 0.98), and the lowest value observed in Table 2 is of Purchase Intention (Cronbach = 0.88). The overall value for Cronbach alpha is 0.95. Table 2. Reliability of the constructs Constructs

3.5

Cronbach’s Cronbach’s Alpha No of Alpha on standardized items item

Mean S.D

Purchase intention 0.88

0.88

5

3.00

1.13

Attitude

0.97

0.97

3

2.53

1.34

Customer service

0.98

0.98

5

2.75

1.41

Price

0.96

0.96

3

2.67

1.46

Brand loyalty

0.96

0.96

4

2.71

1.35

Customer reviews

0.97

0.97

5

2.87

1.39

Quality

0.95

0.95

3

2.73

1.37

Overall

0.95

0.95

28

2.75

1.35

Correlation

Correlation Analysis used to check the multi co-linearity among variables; it checks the relationship between the variables (Bell, 2005). Correlation analysis is a pre-requisite test of regression analysis. 0.2 shows a weak but positive relationship whereas 0.9 shows a strong positive relationship between the variables. Table 3 shows the Correlation Analysis has been done on SPSS 21, with the level of significance at 5% and a confidence interval of 95%. The correlation between Quality and Price (Mean = 2.67, standard deviation = 1.46, N = 362) = 0.91, p = 0.00 < 0.05, was strongest. While the weakest was with Attitude and Purchase Intention (Mean = 3.00, standard deviation = 1.13, N = 362) = 0.47, all the values of variables for correlation lies between 0.2 and 0.9 which shows that the variable is unique from each other and they also have a noticeable relationship among them.

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M. F. Qureshi et al. Table 3. Summarized correlation results

Purchase intention Attitude Customer service Price Brand loyalty Customer review Quality

3.6

PI

Att CS

P

1.00 0.47 0.68 0.67 0.75 0.80 0.68

1.00 0.52 0.71 0.67 0.55 0.67

1.00 0.86 1.00 0.78 0.85 1.00 0.91 0.83 0.81 1.00

1.00 0.72 0.72 0.79 0.78

BL

CR

Q

External Factor Analysis

Exploratory factor explains the validity of the constructs; furthermore, it shows the relationship among latent variables. Table 4 shows the principal component method, which applied with Varimax rotation. Kaiser Meyer Olkin (KMO) is considered to be acceptable if the value is greater than 0.7; furthermore, Bartlett’s Test of Sphericity should be < 0.05 to be significant, cumulative loading factor should be greater than 0.40. All the values of KMO are greater than 0.70. The highest KMO value is of Customer Review (Mean = 2.87, Standard Deviation = 1.39) is 0.92, whereas the lowest KMO value is of Attitude (Mean = 2.53, Standard Deviation = 1.34) and Quality (Mean = 2.73, Standard Deviation = 1.37), which are both 0.76; however, Bartlett’s Test of Sphericity for all constructs is significant. Table 4. EFA for the constructs

3.7

Constructs

Original Kaiser items Meyer Olkin

Bartlett’s test of sphericity at P = .000

Cumulative Items factor retained loading

Purchase intention Attitude Customer satisfaction Price Brand loyalty Customer reviews Quality

5 3 5 3 4 5 3

2013.63 1413.18 2953.06 1286.42 1688.56 2593.63 1068.28

93.14% 93.65% 91.83% 93.00% 89.55% 90.58% 90.47%

0.83 0.76 0.89 0.77 0.87 0.92 0.76

5 3 5 3 4 5 3

Regression Analysis

The regression analysis has been done in two steps. First, check all the independent variables with attitude and then we check the impact of attitude on

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purchase intention. In order to find out the impact of one variable on another we have first applied a prerequisite of regression analysis that was correlation analysis and after having valid results we have then applied linear regression analysis first with each independent and dependent variable and then on the overall model. Step 1: Customer Service and Attitude Table 5 tests our Hypothesis “Customer Service has a significant positive effect on Customer Attitude”, we have applied regression analysis. It can be interpreted from Table 5 that Customer service will bring 27.4% variation in the dependent variable i.e. customer attitude. Since the value of R2 is 27.4% P < 0.05, B = 0.500, the effect is great therefore we can say that the hypothesis is failed to reject. Table 5. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 1.158 0.133 8.733 0.000 CS 0.500 0.043 0.524 11.665 0.000 Note: Dependent variable: Attitude toward furniture buying

Regression Equation Attitude = 1.158 + 0.500(Customer Service) Price and Attitude Table 6 tests our Hypothesis “Price has a significant positive effect on Customer Attitude”, we have applied regression analysis. It can be interpreted from Table 6 that Price will bring a 50.1% variation in the dependent variable i.e. customer attitude. Since the value of R2 is 50.1% P < 0.05, B = 0.651, the effect is great therefore we can say that the hypothesis is failed to reject. Table 6. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 0.791 0.104 7.574 0.000 Price 0.651 0.034 0.708 19.018 0.000 Note: Dependent variable: Attitude toward furniture buying

Regression Equation Attitude = 0.791 + 0.651(Price)

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Brand Loyalty and Attitude Table 7 test our Hypothesis “Brand Loyalty has a significant positive effect on Customer Attitude”, we have applied regression analysis. It can be interpreted from Table 7 that Brand Loyalty will bring a 44.3% variation in the dependent variable i.e. customer attitude. Since the value of R2 is 44.3% P < 0.05, B = 0.660, the effect is great therefore we can say that the hypothesis is failed to reject. Table 7. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 0.744 0.118 6.290 0.000 BL 0.660 0.039 0.666 16.919 0.000 Note: Dependent variable: Attitude toward furniture buying

Regression Equation Attitude = 0.744 + 0.660(Brand Loyalty) Customer Reviews and Attitude Table 8 test our Hypothesis “Customer Review has a significant positive effect on Customer Attitude”, we have applied regression analysis. It can be interpreted from Table 8 that Customer Review will bring a 30.1% variation in the dependent variable i.e. customer attitude. Since the value of R2 is 30.1% P < 0.05, B = 0.530, the effect is great therefore we can say that the hypothesis is failed to reject. Table 8. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 1.014 0.136 7.476 0.000 CR 0.530 0.043 0.549 12.455 0.000 Note: Dependent variable: Attitude toward furniture buying

Regression Equation Attitude = 1.014 + 0.530(Customer Reviews)

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Quality and Attitude Table 9 tests our Hypothesis “Quality has a significant positive effect on Customer Attitude”, we have applied regression analysis. It can be interpreted from Table 9 that Quality will bring 45.3% variation in the dependent variable i.e. customer attitude. Since the value of R2 is 45.3% P < 0.05, B = 0.659, the effect is great therefore we can say that the hypothesis is failed to reject. Table 9. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 0.733 0.117 6.283 0.000 Q 0.659 0.038 0.673 17.264 0.000 Note: Dependent variable: Attitude toward furniture buying

Regression Equation Attitude = 0.733 + 0.659(Quality) Customer Service, Price, Brand Loyalty, Customer Reviews, Quality, and Attitude Table 10 shown the Dependent variable: Consumer Attitude towards furniture purchase, Independent variables: Customer Service, Price, Brand Loyalty, Customer Reviews and Quality, R2 = 0.527, adjusted R2 = 0.520, P < 0.05, F(5, 356) = 79.30. Table 10. Summarized regression results Model

1 (Constant) CS P BL CR Q

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

0.692 0.016 0.374 0.325 −0.218 0.198

5.882 0.265 4.19 3.753 −2.758 2.052

0.000 0.791 0.000 0.000 0.006 0.041

0.118 0.062 0.089 0.086 0.079 0.096

0.017 0.407 0.327 −0.226 0.202

The value of R is 0.726 which shows a good correlation among variables, furthermore, the value of Adjusted R2 is 0.520 which means that our model is fit. We can conclude that independent variables bring 52% variation, P < 0.05 in the attitude (dependent variable).

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Four variables identified as significant variables from Table 10. Which is Price, Brand Loyalty, Customer Reviews and Quality as their significant value is less than 0.5, Customer Service is found insignificant when multiple regression is applied as its sig value is 0.791, however, we will not exclude it because the results showed that it was significant when it was tested independently with dependent variable [20]. Regression Equation Attitude = 0.692 + 0.374P + 0.325BL − 0.218CR + 0.198Q Step 2: Consumer Attitude and Purchase Intention Table 11 tests our Hypothesis “Attitude has a significant positive effect on Purchase Intention”, we have applied regression analysis. It can be interpreted from Table 11 that Attitude will bring 22.1% variation in the dependent variable i.e Purchase Intention. Since the value of R2 is 22.1%, P < 0.05, B = 0.398, the effect is great therefore we can say that the hypothesis is failed to reject. Table 11. Summarized regression results Model

Unstandardized Standardized coefficients coefficients B Std. error Beta

T

Sig.

1 (Constant) 1.998 0.113 17.714 0.000 ATT 0.398 0.039 0.470 10.114 0.000 Note: Dependent variable: Purchase Intention toward furniture buying

4

Conclusions

Furniture purchases decision that takes up a lot of time and mind of the people, alongside the fact that the frequency of furniture purchase has also increased the time has progressed. The hypothesis on the relationship between attitude and purchase intention was substantiating and failed. There is a strong connection between customer attitude and their intention to purchase. It can be seen that only when a customer has a positive attitude towards something will he be internally motivated and have an intention to purchase that. The hypothesis on the relationship between price and customer attitude was substantiated and failed to reject. This research also showed how much of an effect price has on the purchase of furniture. The hypothesis on the relationship between customer service and customer attitude was substantiated and failed to reject. Customer service has a significant impact on customer satisfaction and customer positive attitudes.

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The hypothesis on the relationship between product quality and customer attitude was substantiated and failed to reject. The research suggests how important the effect of product quality is on customer attitude. Especially in the furniture category, it is highly important that the product is of good quality, only then can it be sturdy enough to last for a longer time. The hypothesis on the relationship between brand loyalty and customer attitude was substantiated and failed to reject. In the research of the highest level of love that a brand can create is when the consumers start resonating with the brand. The study has shown how brand loyalty is a very important factor in shaping customer attitude. The hypothesis on the relationship between customer reviews and customer attitude was substantiated and failed to reject. 4.1

Limitation and Future Research

Limited research work is done on this topic so there is less literature available to take reference from. Demographics like age, gender, and income can be tested individually with the constructs. This research helps to suggest how price, customer service, brand loyalty, quality, customer reviews, and attitude affect the purchase intention of consumers in the furniture category. 4.2

Implications for Managers and Policymakers

The findings have brought some serious implications for furniture brands. The results derived suggested some strategies that include having occasional discounted deals and sales, having no compromise on the quality of the furniture, focusing on giving customers the best experience and having loyalty cards can enhance the experience of customers and can thus result in a fulfilling customer attitude, which will eventually lead to more intention of buying.

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Impact of Ownership Structure and Credit Behavior on Performance of Rural Commercial Banks: Evidence from China Wenli Wang(B) , Xinghua Dang, and Xiaomei Zhang School of Economics and Management, Xi’an University of Technology, Xi’an 710054, Shaanxi, People’s Republic of China [email protected]

Abstract. Based on the sample data of 40 rural commercial banks in China from 2011 to 2016, the paper empirically tested the influence of ownership structure on the performance of rural commercial banks when loan behavior was used as an intermediary variable. First, the direct influence of ownership structure on the performance of rural commercial banks is tested. The regression results show that ownership concentration, ownership balance and the nature of state-owned ownership have a significant negative influence on the performance of rural commercial banks. Secondly, on the basis of the regression model of the relationship between ownership structure and performance, loan behavior is added as an intermediary variable to test whether ownership structure influences the performance of rural commercial banks through loan behavior. The results of the mediation effect show that the loan concentration plays a partial intermediary role in the influence of ownership concentration and ownership balance on the performance of rural commercial banks, and a complete intermediary role in the influence of ownership nature on the performance of rural commercial banks. The loan scale plays a full intermediary role in the influence of equity nature on the performance of rural commercial banks. Keywords: Rural commercial banks · Equity structure · Credit behavior · Performance

1 Introduction Rural credit cooperatives are the main force in the rural financial market and play an important role in alleviating the problems of agriculture, rural areas and farmers. However, due to unclear property rights and imperfect corporate governance structure, rural credit cooperatives are facing serious losses. In order to maintain the stability of the rural financial market and alleviate the shortage of funds in the rural financial market, rural credit cooperatives started the practical exploration of property rights system reform in 2001. In 2010, joint-stock rural commercial banks were identified as the final organizational form of the property rights reform of rural credit cooperatives. In the process of shareholding reform of rural credit cooperatives, corporate governance has c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 48–65, 2020. https://doi.org/10.1007/978-3-030-49829-0_4

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always attracted much attention. In a sense, equity structure is regarded as the basis of corporate governance research. Reasonable equity structure can effectively improve the performance of rural commercial banks. However, the existing literature mainly focuses on the direct impact of ownership structure on the performance of rural commercial banks, and there are few studies on the mechanism of the relationship between ownership structure and bank performance. In recent years, some scholars have found that equity structure can significantly affect the credit behavior of commercial banks [12], and it will affect the business performance by influencing the loan concentration and loan flow of commercial banks. According to the development status of rural commercial banks in China, the main source of financing for county enterprises and county residents is rural commercial banks, whose credit behavior still dominates. Therefore, by analyzing the credit behaviors of rural commercial banks, this paper tries to find out the possible ways that the shareholding structure affect the performance of rural commercial banks, and then continuously improve the corporate governance structure of rural commercial banks to promote their performance. The major innovations of this paper are as follows. First, through analyzing the manually collected data of the research object, rural commercial banks, this paper enriches the studies on the relationship between ownership structure and performance of rural commercial banks in the reform and transformation period. Second, this paper explores the internal mechanism of the impact of ownership structure on bank performance from credit behavior, providing a new perspective and evidence for studying the relationship between ownership structure and rural commercial banks’ performance.

2 Literature Review This paper reviews the literature from three aspects of the relationship between the ownership structure and performance, the relationship between the ownership structure and credit behavior, the relationship between the ownership structure, credit behavior and the performance of rural commercial banks, trying to make a comprehensive understanding of the research status of the ownership structure, credit behavior and performance of rural commercial banks. 2.1 Research on Equity Structure and Performance The equity structure determines the distribution of corporate control, the nature of the principal-agent relationship between shareholders and managers, major shareholders, minority shareholders and creditors. As for the influence of ownership concentration on performance, different scholars get different results due to different samples. However, these studies show that the degree of ownership concentration will have an important impact on managers, thus affecting the performance level. Equity balance degree can well reflect the equilibrium of equity and control level, and some studies have included it into the scope of ownership concentration. The study of Wang Wenli et al. (2015) found that it is necessary to form a balance between shareholders to improve the management ability of rural

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commercial banks, preventing major shareholders and managers of the rural commercial banks from having the intention of the “conspiracy” to infringe the interests of minority shareholders [10]. As for the influence of the nature of equity on performance, there are two kinds of situations: government ownership and foreign ownership. Ferri (2009) studied 20 urban commercial banks in China and found that non-state-owned urban commercial banks have better performance [3]. Li Weian et al. (2004) found that the nature of state-owned shares had no significant impact on the performance of urban commercial banks [7]. In recent years, many commercial banks have introduced foreign capital stock. Liu Yuan (2005) found that foreign capital stock can bring advanced technology and excellent management experience to commercial banks, increasing the innovation of financial products, and improving the customers’ recognition on commercial banks, on the other hand, stronger supervision motivation of foreign capital stock can increase the ability of commercial banks to prevent and resist risks, promoting the steady and continuous improvement of commercial banks’ performance [5]. 2.2

Research on Equity Structure and Credit Behavior

There is no consensus on the effect of equity concentration on credit behavior. The first kind of view is that centralized equity can produce stronger supervision and motivation, increasing the commercial bank’s risk bearing capacity [12]. The second view is that concentrated ownership leads major shareholders to pursue PBC and make more conservative operating decisions [4]. The third view holds that the influence of ownership concentration on credit behavior is not unitized [8, 9]. As for the influence of equity nature on credit behavior, it mainly focuses on the influence of state (local government) equity on credit behavior. Wang Tao et al (2011) study the relationship between the equity structure of commercial banks in China and risk behavior, finding that the credit asset allocation decisions of the first commercial bank whose shareholders are stateowned shares are more conservative and cautious [9]. Jia (2009) studied the prudent lending behaviors of state-owned and private banks in China, finding that private banks are more prudent in credit activities. Compared with state-owned banks, the stakeholders of private banks have a stronger incentive to supervise the management [2]. 2.3

Research on Equity Structure, Credit Behavior and Performance of Rural Commercial Banks

Zhu Jigao et al. (2012) took urban commercial banks as research objects and revealed the mechanism of equity structure affecting the performance of urban commercial banks, that is, equity structure first affects loan concentration and loan flow, and then influences the performance of urban commercial banks [12]. Dong Lili (2013) empirically found that sum of the squares of the proportion of the first largest shareholder and the proportion of the top five shareholders are negatively related to city commercial bank performance, and it is positively correlated with the non-performing loan ratio. It is caused by high loan concentration, further verifying that the absolute holding status of the local government will increase the non-performing loan ratio of the city bank [6]. Li Yezhong (2016) pointed out that local government shareholders of city commercial banks in China usually rely on their own status to control bank credit behavior in order

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to meet local financing standards, making loans flow into the local financing platform. In the short term, the asset portfolio of local city commercial bank increases, improving performance, but in the long run, the operation of city commercial bank have credit risk, which is not conducive to the improvement of performance [1].

3 Mechanism Analysis and Research Hypothesis On the basis of the foregoing, this section carries out an in-depth discussion on the mechanism by which equity structure affects the performance of rural commercial banks through credit behavior. 3.1 The Mechanism and Hypothesis of the Relationship Between Equity Structure and Performance of Rural Commercial Banks The research foundation and core of corporate governance is the equity structure, which determines the distribution of corporate control rights. This paper analyzes the influence of ownership concentration, ownership balance and ownership nature on the performance of rural commercial banks. (1) Relationship Between Ownership Concentration and Performance of Rural Commercial Banks The traditional principal-agent theory believes that decentralized equity can produce conflicts of interest between shareholders and managers. Solving the conflict must carry on effective supervision on management. But for small and medium-sized shareholders, supervision income is far lower than supervision cost, and unprotected minority shareholders are reluctant to pay high costs to supervise managers, hindering the improvement of the bank’s performance. When the ownership is concentrated, the supervision benefit of the major shareholders is higher than the supervision cost, and they are willing to supervise the managers effectively. However, under the situation of increasing concentration, the major shareholders may conspire with the management to damage the interests of minority shareholders by taking advantage of their own advantages, namely the “tunneling” hypothesis [5]. For rural commercial banks in China, minority shareholders are often harmed, but the measures to protect their interests are very limited, and the information asymmetry between shareholders and management, major shareholders is very serious, reducing the hidden cost of “tunneling” behavior of major shareholders. Therefore, Hypothesis 1. is proposed in this paper: Hypothesis 1. Equity concentration has a significant negative impact on the performance of rural commercial banks. (2) The Relationship Between the Degree of Equity Balance and the Performance of Rural Commercial Banks According to the principal-agent theory, dispersed equity makes it difficult for shareholders to form effective supervision over the management, so there are no socalled checks and balances. If the equity is relatively concentrated and there are several major shareholders, then these major shareholders have enough control rights and are motivated to supervise the management. In this case, the principal-agent problem between shareholders and management can be effectively alleviated. In addition, there

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are some checks and balances among major shareholders in the bank, partly avoiding the behavior of “tunneling” by major shareholders and protecting the interests of the bank. Therefore, this paper proposes H2.: Hypothesis 2. The degree of equity balance has a significant positive impact on the performance of rural commercial banks. (3) The Relationship Between the Nature of Equity and the Performance of Rural Commercial Banks After the reform, most rural commercial banks are controlled by state-owned legal entities or local governments, so this paper focuses on the equity nature of the largest shareholder. There are two main viewpoints in existing researches. One is the development view, which holds that state-owned equity can bring benign external supervision and bring benefits to banks in terms of tax and other preferential policies. The second is the political view, which holds that the government shareholders will interfere in the management decisions of the bank through the administrative power to obtain the dual political and economic interests for themselves. In the process of the restructuring of China’s rural commercial banks, the government or state-owned enterprises hold shares for political purposes and use administrative powers to meet their performance or their own interests, leading to unclear property rights of rural commercial banks, thus reducing their performance. Therefore, H3. is proposed in this paper: Hypothesis 3. The largest shareholder with state-owned shares is negatively correlated with the performance of rural commercial banks. 3.2

The Largest Shareholder with State-Owned Shares Is Negatively Correlated with the Performance of Rural Commercial Banks

At present, the main business and profit source of rural commercial banks are credit business, so the credit behavior in credit activities is an important way to study the influence of equity structure on the performance of rural commercial banks. If the equity structure of a bank is set up reasonably, its operation and management will be in an efficient and formal state, and its credit behavior will be in line with its operation objectives and market positioning, thus improving its earnings. If the equity structure of a bank is set irrationally, the normal credit behavior will be interfered with by the implicit behavior and shady operation of shareholders, leading to the deviation of credit behavior from the normal track and reducing the overall performance level of the bank. (1) The Intermediary Effect of the Influence of Credit Behavior on the Relationship between Ownership Concentration and Performance of Rural Commercial Banks When equity is dispersed, the effect of checks and balances among shareholders is better, and the bank’s credit decision is difficult to be interfered by shareholders. If there are controlling shareholders, these shareholders will use their power to intervene in the operation and management of rural commercial banks and invest more loans in related enterprises that bring benefits to them, so as to increase the loan concentration of these enterprises. The more loans they issue, the more profits they can obtain, virtually bringing risks to rural commercial banks. Therefore, based on H1., this paper proposes: Hypothesis 1a. the higher the ownership concentration, the higher the rural commercial bank loan concentration;

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Hypothesis 1a-1. equity concentration will reduce the performance of rural commercial banks by increasing loan concentration. Hypothesis 1b. the higher the ownership concentration, the more inclined rural commercial banks are to expand the loan scale; Hypothesis 1b-1. ownership concentration will reduce the performance of rural commercial banks by expanding loan scale. (2) The Intermediary Effect of the Influence of Credit Behavior on the Relationship between Equity Balance and Performance of Rural Commercial Banks Corporate information and control rights are shared by two or more major shareholders. This equity model can pool wisdom, effectively adopting the opinions and choices of shareholders, and select favorable decisions for the development of rural commercial banks. It can effectively restrain major shareholders from intervening in the issuance of related loans by rural commercial bank, improving the quality of credit assets. Therefore, based on H2., this paper proposes: Hypothesis 2a. the higher the degree of equity balance, the lower the loan concentration of rural commercial banks; Hypothesis 2a-1. equity balance will improve the performance of rural commercial banks by reducing loan concentration. Hypothesis 2b. the higher the degree of equity balance, the tendency of rural commercial banks to expand loan scale will be restrained; Hypothesis 2b-1. the degree of equity balance will improve the performance of rural commercial banks by inhibiting the scale of loans. (3) The Intermediary Effect of the Influence of Credit Behavior on the Relationship between the Nature of Equity and the Performance of Rural Commercial Banks There are two pathways: first, the government property brings more attention and support for the bank, leading to bank lending show less prudence and less motivation of controlling risk, virtually increasing credit expansion impulse, blind expansion of credit scale, lowering the quality of credit assets, and it is not conducive to performance improvement. Secondly, the government, as the shareholder of the rural commercial bank, leads to the administrative color of the credit behavior of the rural commercial banks to some extent, and it has to respond to the macro-control by lending funds to government-related enterprises. Such behavior makes the loan objects of the rural commercial bank limited, which is not conducive to the improvement of its performance. Therefore, based on H3., this paper proposes the following assumptions: Hypothesis 3a. rural commercial banks with state-owned shares as the largest shareholder have higher loan concentration; Hypothesis 3a-1. the nature of equity will reduce the performance of rural commercial banks by increasing loan concentration. Hypothesis 3b. rural commercial banks with state-owned shares as the largest shareholder have larger scale loans

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Hypothesis 3b-1. the equity nature will reduce the performance of rural commercial banks by expanding the loan scale.

4 Research Design 4.1

Source of Samples

Through the manual collection of Annual Reports of Rural Commercial Banks, this paper obtained the valid data of 40 rural commercial banks from 2011 to 2016. Combined with the statistical yearbook of China from 2012 to 2017 [1], 240 valid samples were obtained. 4.2

Selection of Variables

(1) Selection of Explained Variables The explained variable in this paper is the performance of rural commercial banks, which is a comprehensive evaluation of the profitability, asset quality, solvency and operating growth of rural commercial banks in a fiscal year. In this paper, the factor analysis method is adopted to build an evaluation model. According to the Performance Evaluation of Financial Enterprises issued by the Ministry of Finance in 2011, the performance of rural commercial banks is comprehensively evaluated from asset size, profitability, liquidity, safety and growth. The specific indicators selected are shown in Table 1. Table 1. Index design of performance evaluation of rural commercial banks by factor analysis The level indicators

The secondary indicators

The index type

Assets size

The total assets Total liabilities Operating income Operating spending

Positive indicators Appropriate indicators Positive indicators Appropriate indicators

Profitability indicator Return on total assets Return on equity

Positive indicators Positive indicators

Liquidity indicator

Liquidity ratio Loan to deposit ratio Asset-liability ratio

Appropriate indicators Appropriate indicators Appropriate indicators

Safety indicator

Non-performing loan ratio Capital adequacy ratio Provision coverage Cost to income ratio

Reverse indicators Appropriate indicators Positive indicators Reverse indicators

Growth indicator

Deposit growth rate Loan growth rate Net profit growth rate

Positive indicators Positive indicators Positive indicators

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SPSS21.0 was used to process the original data. The results show that the value of KMO test is 0.640 > 0.6, which meets the requirement of factor analysis. The factor contribution rates obtained from orthogonal rotation of the maximum variance of the factor load matrix are listed in Table 2 below. Table 2. Factor contribution rate (%) Components Initial eigenvalues Total Variance contribution rate%

Cumulative variance contribution rate%

Extraction sums of squared loading

Rotation sums of squared loading

Total Variance contribution rate%

Total Variance contribution rate%

Cumulative variance contribution rate%

Cumulative variance contribution rate%

1

4.28

26.753

26.753

4.28

26.753

26.753

4.053 25.333

25.333

2

2.829 17.684

44.436

2.829 17.684

44.436

2.775 17.342

42.675

3

1.654 10.336

54.772

1.654 10.336

54.772

1.75

10.94

53.614

4

1.335 8.346

63.118

1.335 8.346

63.118

1.51

9.436

63.051

5

1.061 6.63

69.747

1.061 6.63

69.747

1.071 6.697

69.747

6

0.96

75.748

7

0.806 5.04

80.789

8

0.726 4.537

85.325

9

0.698 4.364

89.689

10

0.642 4.015

93.704

11

0.451 2.819

96.523

12

0.354 2.211

98.734

13

0.126 0.785

99.519

14

0.063 0.391

99.91

15

0.014 0.089

100

16

0

100

6.001

0

According to Table 2, five common factors can be selected to explain the original variables. Five principal components F1, F2, F3, F4 and F5 are extracted to preliminarily determine the evaluation model, as shown below: F=

0.25333F1 + 0.1742F2 + 0.10940F3 + 0.09436F4 + 0.06697F5 . 0.69747

From this model, the comprehensive performance scores and the factor scores of 40 rural commercial banks from 2011 to 2016 are obtained. The comprehensive performance score is used to quantify the performance of rural commercial banks in this paper. (2) Selection of Explanatory Variables The explanatory variables of this paper are the ownership structure of rural commercial banks, mainly including three dimensions: ownership concentration degree, ownership balance degree and ownership nature. The specific index design is shown in Table 3.

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W. Wang et al. Table 3. Indicator design of explanatory variables

Variables

Symbol Explanation of variables

Ownership concentration degree

TOP1

The shareholding ratio of the largest shareholder

Ownership balance degree

Z

The ratio of the total shareholding of the second to fifth largest shareholders to that of the first largest shareholder

Ownership nature

DG

The nature of the first major shareholder is the state-owned shares for 1, otherwise for 0

(3) Selection of Intermediary Variables The intermediary variable in this paper is the credit behavior of rural commercial banks, namely the bank credit information based on the rural commercial banks itself. Under certain rules and regulations, in order to achieve their business goals or meet certain political goals, the act of conducting credit transactions with county enterprises or residents mainly includes loan concentration and loan size. The specific index design is shown in Table 4. Table 4. Indicator design of mediation variables Variables

Symbol Explanation of variables

Loan concentration M1

Loan concentration of top ten customers

Loan size

The logarithm of the loan balance at the end of the year

M2

(4) The Selection of Control Variables The performance of rural commercial banks is influenced not only by the ownership structure but also by other internal governance characteristics, their own size and external macroeconomic situation. The specific index design is shown in Table 5. Table 5. Index design of control variables Variables

Symbol Explanation of variables

The level of regional economic development

lnGDP

Assets size

lnSIZE The logarithm of the total assets of rural commercial banks at the end of the year

Board size

BS

Ratio of independent directors

Indratio Ratio of independent directors in rural commercial banks

The logarithm of the local gross national product

Board size of rural commercial banks

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4.3 Model Establishment To explore the mechanism of the influence of equity structure on the performance of rural commercial banks, first Model 1 was established: test the direct impact of ownership structure on the performance of rural commercial banks. Then, according to the influence mechanism proposed above, Model 2 and Model 3 were established to test the influence of equity structure on the credit behavior of rural commercial banks (loan concentration degree M1 and loan scale M2). Finally, on the basis of Model 1, the intermediary role of credit behavior was tested, and Model 4 was established: credit behavior was introduced to jointly test the impact of equity structure on the performance of rural commercial banks. The model design is as follows: Model 1: Y = α0 + α1 Ownership + α2Control + ε , Model 2: M1 = β0 + β1 Ownership + β2Control + ε , Model 3: M2 = β0 + β1 Ownership + β2Control + ε , Model 4: Y = γ0 + γ1 Ownership + γ2 M1 + γ3 M2 + γ4Control + ε . During the empirical process in this paper, the test of intermediary effect is reference for Wen Zhonglin’s analysis of mediation effect and the mediation effect [11]. It tested the direct impact of equity structure on the rural commercial bank, the influence of ownership structure on the credit behavior and intermediary role including credit behavior, finding the path of equity structure affecting the performance of rural commercial banks through credit behavior.

5 Empirical Analysis 5.1 Descriptive Statistical Analysis Before carrying out the empirical analysis, descriptive statistical analysis is conducted on the variables selected in the previous section, with the purpose of understanding the current characteristics of performance, ownership structure, credit behavior and other aspects of rural commercial banks based on the descriptive statistical results, so as to lay a foundation for better interpretation of the empirical analysis results in the future. The data statistical characteristics of each variable are shown in Table 6.

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W. Wang et al. Table 6. Descriptive statistical analysis of sample rural commercial banks Variables Sample Mean value value

Standard deviation Minimum value Maximum value

Y

240

−0.0003181 0.3438679

−1.57

1.2

TOP1

240

8.930542

4.90156

1.383

25

Z

240

2.772195

0.8826706

0.8715

4.141667

DG

240

0.4875

0.5008883

0

1

M1

240

41.06509

14.89173

10.5

123.91

M2

240

5.68473

1.095555

2.880489

8.128795

lnGDP

240

8.596351

0.8817541

5.920237

10.24632

lnSIZE

240

6.345807

1.154272

3.421518

8.991137

BS

240

12.98333

2.101935

8

19

Indratio

240

20.48512

9.162289

0

41.66667

From the perspective of the explained variables, the mean value of the performance (Y ) of the sample rural commercial bank is −0.0003181, indicating that the overall performance level is poor. The minimum value is −1.57 and the maximum value is 1.2, indicating that the performance level of rural commercial banks in different regions varies greatly. From the perspective of explanatory variables, the mean shareholding ratio of the largest shareholder (TOP1) is 8.930542, indicating that the largest shareholder of the sample rural commercial bank is generally non-controlling shareholders. The mean value of checks and balances (Z) between the second to fifth largest shareholders to the first largest shareholder is 2.772195, indicating that the checks and balances effect is good. The mean value of loan concentration (M1) is 41.06509, the maximum value is 123.91, and the minimum value is 10.5, which generally conforms to the regulations of the China Banking Regulatory Commission. But the loan concentration of individual rural commercial bank is too high, existing potential credit risk. From the perspective of control variables, the mean value of board size (BS) is 12.98333, the maximum value is 19, and the minimum value is 8, indicating that the board size of sample rural commercial banks is moderate, but there are differences among different rural commercial banks. 5.2

Correlation Analysis

Then, the correlation analysis is made for all variables. On the one hand, the purpose of correlation analysis is to detect whether there is correlation between independent variables and dependent variables. If there is correlation, it is necessary to further verify the accuracy of the correlation through regression analysis. On the other hand, correlation analysis is used to detect the multicollinearity problem caused by high correlation between independent variables, which will affect the accuracy of regression results. The correlation analysis results of each variable are shown in Table 7.

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Table 7. Pearson correlation coefficient matrix among variables Y

TOP1

Y

1

TOP1

−0.1523 1

Z

DG

lnGDP

lnSIZE

BS

Indratio M1

M2

*** Z

0.2352

−0.6292 1

***

***

DG

−0.1091 0.0832

M1

−0.358

0.198

−0.1969 0.2456 1

***

***

***

M2

−0.6173 0.1101

−0.1182 1 ***

−0.1504 0.2748 0.6852

***

***

−0.0202 −0.0454 0.0197 0.2389

lnGDP 0.338 ***

***

lnSIZE 0.301

0.0545

*** BS

−0.2978 0.3004 ***

0.105

0.5301

1

***

−0.1619 0.2118 0.4606

0.6041

0.4059

**

***

***

***

0.0032

0.0923 −0.0656 −0.2936 −0.3976 −0.2247 1

***

***

Indratio 0.5902

1

***

−0.1601 0.2694 0.7047

1

***

***

***

0.9897

0.5337

0.5969

−0.2776 1

*** ** *** *** *** *** *** *** Note: ***, ** and * mean it is significant at the statistical level of 1%, 5% and 10% respectively.

Table 8. Results of model 1–4 F test and test results of Hausman F test results Test results of Hausman d.f F-Statistic P-value Chi2(8) P-value Model 1 −39, 193

6.88

0

25.23

0.0014

Model 2 −39, 193 11.76

0

22.79

0.0037

Model 3 −39, 193 16.67

0

53.32

0

Model 4 −39, 193

0

41.49

0

6.99

As can be seen from the above table, except for the control variable, the correlation coefficient between the asset size (lnSIZE) of rural commercial banks and the loan size (M2) is 0.9897. The correlation coefficient between other explanatory variables is all below 0.7. It can be considered that there is no serious multicollinearity problem between explanatory variables. 5.3 Multiple Regression Analysis (1) Model Selection and Determination of the Estimation Method First, through the F test, it was determined whether the model should be a mixed regression model or a fixed effect regression model. Secondly, the Hausman test is used to determine whether the model should choose an individual fixed effect or individual random effect. The following Table 8 shows the test results of model 1–4:

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As can be seen from the above table, the p-value of F test of each model is 0.0000, strongly rejecting the null hypothesis, so the fixed effect regression model should be chosen. The Hausman test results showed that the P values of all models were less than 0.01, rejecting the null hypothesis, so the individual fixed effect model was selected for model 1–4. (2) Regression Analysis a. Empirical Test of the Impact of Equity Structure on the Performance of Rural Commercial Banks The research premise of the mediating effect is that explanatory variables significantly affect the explained variables. Therefore, the influence of equity structure on the performance of rural commercial bank is tested first. The regression results are shown in Table 9 below: Table 9. The empirical results of the influence of equity structure on the performance of rural commercial banks The explained variable: Y-Performance of rural commercial banks Variables

Model1

TOP1

−0.0925* (−1.75)

Model2

Model3

−0.1207*** (−3.33) −0.0625** (−2.18)

Z

Model4

−0.0824*** (−4.64) −0.637* −0.689*** (−1.85) (−3.05)

DG lnGDP

0.00021 −0.81

0.00039 −1.23

0.0042 −0.79

0.00381 −0.94

lnSIZE

0.0156* −1.69

0.00574* −1.87

0.0217* 0.0175** −1.73 −2.24

BS

0.0181 −1.17

0.0166 −1.07

0.0179 −1.16

Indratio

0.00383* 0.0033 −1.86 −0.86

0.0162** −2.37

0.0034* 0.0027** −1.71 −2.08

R-sq

0.4745

0.4923

0.5027

Value of F

2.891**

2.763**

3.013** 5.178***

0.6482

Observations 240 240 240 240 Note: ***, ** and * mean it is significant at the statistical level of 1%, 5% and 10%, respectively.

As can be seen from the above table, Model 1 shows a significant negative correlation between TOP1 and Y , and H1. is verified. Model 2 shows that Z has a significant negative effect on Y at the 5% level, namely the increase of equity balance degree is not

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conducive to the improvement of rural commercial bank performance, which is inconsistent with H2.. The author thinks that because the increase of the degree of equity balance makes the interest demands of each shareholder inconsistent and it is difficult to form a unified management decision, increasing the cost of communication and coordination between shareholders, reducing the performance of the rural commercial bank; Model 3 shows a significant negative correlation between DG and Y , and H3. was verified. TOP1, Z and DG were introduced into Model 4, and the results showed that the influence direction of the three actings together was the same as that of acting alone, and the influence degree increased significantly all at the level of 1%. In addition, the control variables lnSize, BS and Indratio significantly positively affected the performance of rural commercial banks (Y ), and lnGDP was positively correlated with the performance of rural commercial banks (Y ), but not statistically significant. b. The Empirical Test of the Influence of Equity Structure on the Credit Behavior of Rural Commercial Banks Then the relationship between explanatory variables and mediating variables is further verified. The following Table 10 shows the empirical test results of equity structure and loan concentration of rural commercial banks (M1), while Table 11 shows the empirical test results of equity structure and loan scale of rural commercial banks (M2). Table 10. The empirical results of the influence of equity structure on the loan concentration of rural commercial banks The explained variable: Y-Performance of rural commercial banks Variables

Model5

TOP1

0.437** −2.32

Model6

Model7

0.1014*** −3.26 −0.2953* (−1.85)

Z

Model8

DG

−0.562*** (−2.62) 0.3667** 0.501* −2.41 −1.89

lnGDP

0.2675 −1.21

0.2795* −1.73

0.2869* −1.83

0.0740*** −2.76

lnSIZE

0.0477* −1.82

0.0490* −1.68

0.0456* −1.79

0.0391* −1.75

BS

−1.6264* −1.7465** −1.6700* −1.237** (−1.76) (−2.14) (−1.87) (−2.24)

Indratio

−0.0398* −0.0049* (−1.68) (−1.92)

−0.0712* −0.0592* (−1.71) (−1.86)

R-sq

0.4219

0.4342

0.4259

0.4525

Value of F

5.41***

6.04***

5.62***

4.96***

Observations 240 240 240 240 Note: ***, ** and * mean it is significant at the statistical level of 1%, 5% and 10%, respectively.

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Table 11. The empirical results of the influence of equity structure on loan scale of rural commercial banks The explained variable: M2-loan scale of rural commercial bank Variables

Model9

TOP1

−0.0072 (−0.51)

Model10

Model11

−0.0033 −3.26 −0.0306 (−1.85)

Z

Model12

DG

−0.0265 (−2.62) 0.0326** −2.41

0.0296*** −1.89

lnGDP

0.0993*** 0.0994*** 0.0999*** 0.1008*** −2.99 −2.99 −3.02 −2.76

lnSIZE

0.7079*** 0.7048*** 0.7018*** 0.7030*** −9.3 −9.76 −8.72 −1.75

BS

−0.0008 (−0.21)

−0.0013 (−0.19)

Indratio

−0.0018 (−1.69)

−0.0022* −0.0015** −0.0019* (−2.03) (−1.68) (−1.86)

−0.0057 (−0.24)

−0.0015 (−2.24)

R-sq

0.706

0.7069

0.7058

0.7074

Value of F

12.98***

17.70***

15.15***

10.28***

Observations 240 240 240 240 Note: ***, ** and * mean it is significant at the statistical level of 1%, 5% and 10%, respectively.

As can be seen from Table 10, Model 5 shows that TOP1 has a positive effect on M1, and H1a is verified. Model 6 shows a negative correlation between Z and M1, verifying H2a.; Model 7 showed a significant positive correlation between DG and M1, and H3a. was verified. Model 8 introduced TOP1, Z and DG, and the results showed that when the three acted together, the regression results significantly affected M1 at the 10% level, which was consistent with the results when acting alone. As can be seen from Table 11, Model 9 shows that TOP1 has a negative effect on M2, which is inconsistent with H1b.. The reason may be that the relatively concentrated equity rights encourage the major shareholders to supervise the management more effectively and prevent the opportunistic motives of the management so that the rural commercial banks tend to be more cautious in lending, resulting in the suppression of the loan scale. Model 10 shows that Z has a negative effect on M2, but it is not statistically significant and H2b. is not verified. This may be due to the partial completion of the restructuring of the rural commercial bank’s equity structure and the situation of rural credit cooperatives before the restructuring is not much different, and the balance effect of other shareholders on the largest shareholder is poor. In addition, the increase of the loan scale of rural commercial banks may be related to its current business objectives, and the role of equity balance is not obvious; Model 11 showed a significant

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Table 12. The empirical results of the influence of credit behavior on equity structure and performance of rural commercial banks Model13-The explained variable: Y-Performance of rural commercial banks Variables

Coefficient

TOP1

−0.1412*** −3.39

t-Statistic

Z

−0.0781*** −4.85

DG

−0.5241

−1.18

M1

−0.0041**

−2.33

M2

−0.4943*** −2.98

lnSIZE

0.0181*

1.92

BS

0.0204**

2.43

Indratio

0.00219*

1.75

R-sq

0.5986

Value of F

6.92***

Observations 240 Note: ***, ** and * mean it is significant at the statistical level of 1%, 5% and 10%, respectively.

positive correlation between DG and M2, and H3b. was verified. Model 12 shows that after introducing TOP1, Z and DG at the same time, only DG has a significant impact on M2. Therefore, in the next step of testing the mediation effect, the intermediary effect of loan size on the influence of TOP1 and Z on the performance of rural commercial banks is not analyzed. c. The Intermediary Test of the Credit Behavior on the Relationship Between the Equity Structure and the Performance of Rural Commercial Banks On the basis of the above two tests, the mediation effect of credit behavior on the relationship between ownership structure and the performance of rural commercial bank is tested. The following Table 12 shows the test results. When explanatory variables and mediating variables are introduced into the model, the results of Model 13 show that: TOP1 and Z still have a significant influence on Y , which is consistent with Model 4, but the relationship between DG and Y is no longer significant. At the same time, the intermediary variables M1 and M2 are negatively correlated with Y at the level of 5%. In other words, the higher the number of loans issued by rural commercial banks, the higher the concentration of loans to customers, resulting in more risks in credit activities and ultimately lower the overall performance level of rural commercial banks. According to the analysis method of intermediary effect, according to the test method of regression coefficient in turn [1], it is concluded that: relationship between equity structure and performance of the rural commercial bank is partly caused by loan concentration and size, and M1 plays a partial mediating role in the influence of TOP1 and Z on Y , the influence of the DG on Y is entirely caused by M1 and M2. In addition, M2 did not play a mediating role in the influence of TOP1 and Z on Y .

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6 Research Conclusions Through empirical research, this paper mainly draws the following conclusions: (1) Ownership concentration, ownership balance and the nature of state-owned ownership have a significant negative impact on the performance of rural commercial banks. (2) Ownership concentration is significantly positively correlated with the loan concentration of rural commercial banks, that is, the higher the shareholding ratio of the largest shareholder is, the higher the loan concentration of rural commercial banks is; The degree of equity balance has a significant negative impact on the loan concentration of rural commercial bank, that is, the stronger the ability of checking and balancing of second to fifth largest shareholder is to the largest shareholder, the lower the loan concentration of rural commercial bank is. The nature of state-owned equity has a significant positive effect on the loan concentration of rural commercial banks, that is, the loan concentration of rural commercial banks whose largest shareholder is state-owned shares is higher. (3) The degree of ownership concentration and the degree of ownership balance have a negative impact on the loan scale of rural commercial banks, but it is not significant. The nature of state-owned equity has a significant positive impact on the loan scale of rural commercial banks. (4) The loan concentration plays a partial intermediary role in the influence of ownership concentration and ownership balance on the performance of rural commercial banks, while it plays a complete intermediary role in the influence of ownership nature on the performance of rural commercial banks. The loan scale plays a completely intermediary role in the influence of equity nature on the performance of rural commercial banks. Acknowledgements. This work was supported by the National Social Science Fund of China Project “Research on the service ability promotion strategy of rural credit cooperatives from the perspective of corporate governance” (Item number 14BJY104) and the Shaanxi Social Science Foundation Project “Study on the evaluation and influencing factors of county Inclusive Finance Development in Shaanxi Province” (Item number 2019D008).

References 1. 2012–2017 China statistical yearbook. http://www.stats.gov.cn 2. Jia, C.X.: The effect of ownership on the prudential behavior of banks-the case of China. J. Bank. Finance 33(2), 77–87 (2009) 3. Ferri, G.: Are new tigers supplanting old mammoths in China’s banking system? Evidence from a sample of city commercial banks. J. Bank. Finance 33(1), 131–140 (2009) 4. Iannotta, G., Nocera, G., Sironi, A.: Ownership structure, risk and performance in the European banking industry. J. Bank. Finance 31(7), 2127–2149 (2007) 5. Liu, Y., Li, T., Gan, Y.: Strategic transformation and positioning return of urban commercial banks. China Finance 21, 37–39 (2005). (in Chinese) 6. Li, Y.Z.: Exploring the relationship between equity structure and performance of urban commercial banks and its mechanism of action. Bus. Modern. 168 (2016). (in Chinese) 7. Li, W., Cao, T.: Ownership structure, governance mechanism and performance of urban banks: evidence from Shandong and Henan. Econ. Res. 12, 4–15 (2004). (in Chinese)

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8. Magalhaes, R., Gutiérrez Urtiaga, M., Tribó, J.A.: Banks’ ownership structure, risk and performance. SSRN Electron. J. (2008). https://doi.org/10.2139/ssrn.1102390 9. Wang, T., Jiang, Z.: Empirical analysis on the ownership structure, governance mechanism and risk behavior of China’s commercial banks - from the perspective of asset allocation. Explor. Econ. Probl. 5, 102–107 (2011). (in Chinese) 10. Wang, W., Sun, Q., Hu, P.: Research on the optimal ownership structure of rural credit cooperatives-an empirical analysis based on the double principal agent theory. Macroecon. Res. 11, 93–105 (2015). (in Chinese) 11. Wen, Z., Liu, H., Hou, J.: Analysis of Regulating Effect and Mediating Effect. Education Science Press, Beijing (2012) 12. Zhu, J., Rao, P., Bao, M.: Ownership structure, credit behavior and bank performance - an empirical study based on the data of china’s urban commercial banks. Financ. Res. 7, 48–62 (2012). (in Chinese)

Education Impact on Health Shocks: Evidence from C.H.N.S. Data Issam Khelfaoui1 , Yuantao Xie1(B) , Muhammad Hafeez2 , and Danish Ahmed3 1

School of Insurance and Economics, University of International Business and Economics, Beijing 100876, People’s Republic of China [email protected] 2 The Centre of Industrial Economics and Green Development, BUPT, Beijing 100876, People’s Republic of China 3 Department of Accounting and Finance, Barret Hodgson University, Karachi, Pakistan

Abstract. In the digital era, social and economic development directly related to education and health. The key question is to figure out the link between health and human capital through education. To solve this research question, the present study has investigated the impact of education on health shocks whether these health shocks are subjective or objective. To estimate the causal linkage including education and health, the Chinese Health and Nutrition Survey is examined through linear and multi-logit regression models respectively. The empirical estimates infer that higher education has a significant positive impact on health shocks, irrespective the subjective or an objective one. It also unfolds that it is more apparent in the Subjective than the objective health shock. Moreover, the more years of formal education significantly decreases the Subjective health shock. Keywords: Education · Health Shock Health and Nutrition Survey · China

1

· Human capital · Chinese

Introduction

Better health reflects a great mind. It is widely known that education has a great effect on health, wealth, wellbeing and peace of mind. Consequently, to sustain and achieve a great lifespan, having great health and peace of mind is crucial. The previously mentioned fact gave interest to an important question, the question is: What if a sudden negative change in our health or peace of mind happened, would we be able to cope through it? Reduce its severity? But then, greater panoply arises, what if we could avoid that to happen in the first place? And how much knowledge or education a person needs to avoid it and reduce its impact. Motivated by the previous questions, this research studies education effects on sudden negative health occurrences and changes; those that we identify them as c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 66–80, 2020. https://doi.org/10.1007/978-3-030-49829-0_5

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“Health Shocks” (HS). HS can be one of the major determinants of individuals Health. In general, HS is a sudden change in a persons health status. HS can be both negative and positive. A negative shock, that can be illustrated by the change from a healthy state to an unhealthy one, through injuries, accidents, the first discovery of a chronic illness or a negative change in Social-Health Status (SHS). While, A positive shock is defined by a positive change of any of the previous states mentioned in the negative shock, or simply getting better for individuals, through natural treatment or the development of new medicine. In general, health is usually the state of social well-being or physical, and mental in which there is an absence of illness or infirmity [1]. It can be measured in several ways, and those relevant to our research are the subjective and objective ways. Subjective health is measured by the subjective opinion of the individual, and the objective health is measured through an objective formula that is not affected by an individuals opinion or subjectivity. Therefore, the HS would be partitioned in two, subjective and objective, putting in mind in our research we only measure the negative state-defined earlier. In the purpose of reducing these HS, we have focused on how education would cause this reduction. While there is a wide range of literature about the topic of education and health, very few addressed the link between them, and that would be clarified more in our literature review part. Taking that education is the accumulation of knowledge and experiences individuals have throughout life; the link between education and health can also be seen as: First, education will not only improve health through the increase in income, earning and better access and use of time and resources, but it will also improve it by the peer effect, of spending time with healthy and educated individuals, who have less unhealthy behaviours. Second, education will improve our understanding of health, healthy behaviors outcome and the unhealthy behaviors implication, and therefore may bring a great act towards a beneficial use of this understanding. Third, the general change in habits that education brings, which have a direct or indirect effect on health, this effect is both positive and beneficial. Finally, by staying at school and class, it is less common to have unhealthy results and outcomes. Thus studying the link and the effect it is important to consider education, and in our research we consider education by the amount of schooling and formal years of education, using the same approach used by several other relative pieces of research. Therefore, the present research aims is to estimate the effect of education on the appearances or occurrences of HS, and to our knowledge, there are little (in the US or the UK related studies) to no literature (Asian more specifically ‘Chinese’) done in this matter before, and only two studies on the causality of education on health. The present study is contributing to the research field in many ways: First, as compared to the old related articles, the present study extract the newest up-to-date possible data-set from waves of the China Health and Nutrition Survey (CHNS), while most of the data used for this matter are from United Kingdom (UK) or the United States (US). Second, it also constructs and incorporates the HS variable; the present study will be the first to start a discussion between different HS measures, where the discussion

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will be based on Subjective HS and Objective ones. Third, it also starts a discussion on the focus of HS and how much investment in education that can reduce those shocks; knowing that these HS are mostly the main absorbing factor of health expenditure. The results of the discussion can help policy makers to put more focus on the matter of these occurrences and their link to education.

2

Theoretical Background and Literature Review

Education and health are two pivotal determinants of human capital and, they have both a complex relationship and a strong correlation within each other even under controlling for other demographic and socioeconomic variables and groups respectively [12,13]. In Grossman’s (1972) great work, education linkage to health is proposed in two possible causal ways; 1) people with higher education level use health services more efficiently, and 2) with better education a person has fewer tendencies to follow harmful or unhealthy habits. Other researches proposed different pathways and causal effects of education on health, where those effects are: First, education will not only improve health through the increments in income, earning and better access and use of time and resources, but it will also improve it by the peer effect, of spending time with healthy and educated individuals, who has less unhealthy behaviours. Second, education will improve our understanding of health, healthy behaviors outcome and the unhealthy behaviors implication, and therefore may bring a great act towards a beneficial use of this understanding. Third, the general change in habits that education brings which have a direct or indirect effect on health, thus the effect is both positive and beneficial. Finally, just by staying in classrooms, individuals are less confronted with unhealthy outcomes which are called the “incarceration effect”. To devise effective public policy and investment in education, it is a necessity to figure out the impact of education on health either it is significant or not along with other factors, such as personal ability [12,15,24,25]. Otherwise, all the investments in the education sector to improve health outcomes are with no tangible use [12,15,24]. For instance, using different health measures has led to different causal results; some have validated the causal effect [4,7,16,18,19,21, 22,24], while others find no obvious evidence in their estimation design [9,10,20]. This research work focuses on a particular important health issue, Health Shocks “HS”. The unpredictable occurrences of HS may directly result to poverty traps and loss in wages [11,26,27], also may force the elder employees to an early retirement [5,8,14,23], push the younger ones to quit the labour market [17], and a long term occurrence of such a shock might have a stagnant effect on individual’s economic factors [2]. A HS may be defined in several ways, and variables, some of are the occurrence of road injuries, sudden chronicle illnesses appearance, and some other by being hospitalized or just simply a proxy of getting sick or a weighted sum of proxies. The modelling approach for such a research is mostly derived from the famous Grossman (1972) health production function. We followed the approach of several works of literatures on this matter

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such as the OLS model of [16,24] or the large use of different Regression models in all the previous or the next literature sources mentioned in this paper. To our best knowledge, there are few (in the US or the UK related studies) to no literature (Asian more specifically ‘Chinese’) done in this matter before. The closest literature, as far as we know, is the two studies that started studying the causality of education on health in China are [6]. Compared to old related articles, the present study extracts the newest up-to-date possible data-set from waves of the Chinese Health and Nutrition Survey (CHNS), while most of the data used for this matter are from United Kingdom (UK) or the United States (US) [3]. Interest in other countries did not arise until recently [18,28]. Our paper would be the first to use HS (Objective or Subjective) as a determining variable for bad health, measured it in that sense and the first paper to study the direct effect of education on these Shocks.

3 3.1

Data Collection and Research Methodology Data Collection

This paper uses the data-set of the Chinese Health and Nutrition Survey (CHNS) [18]. This survey has around 10 waves from 1989 to 2015. A wave of the data set has around 26,000 individuals, or grouped to 4400 households. The wave is gathered from urban and rural areas of nine provinces in China; Guizhou, Hunan, Guangxi, Jiangsu, Shandong, Henan, Hubei, Heilongjiang and Liaoning. It considers the random, and multistage cluster process. As for the individuals, the data consisted of their demographic characteristics, labor force activities, health status, economic resources, expenditure and functioning. Regarding the health variables, the CHNS collects numerous objective indicators such as weight, height, systolic blood pressure, and diastolic blood pressure, as well as multiple subjective health indicators, including Self-Reported Health status (SRH). As this study is based on the effects or impacts of education on HS, So, it is only fair to start by defining our dependent variables i.e. HS variables which are the subjective and objective ones concerning the definition of HS. The definition of HS is elaborated in Table 1. The notion of subjective comes from the idea of selfperceived status; that’s how an individual feels about himself and subjectively reports about his health, while the objective is an abstract time-related variable where an individual gives accurate time of how many days, he missed work or couldn’t perform his daily activities regardless to how he felt at that moment. So, it has no subjective intake in the matter. The descriptive statistics of concerned variables are reported in Table 2. The independent variable, Education (Edu) is defined by two terms (1) Highest Level of Education Attained, and (2) Completed Years of Formal Education in Regular School. Table 1 reports the set of demographics and control variables

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Subjective HS (SHS)

Objective HS (OHS)

SHSt,i = min(SRt,i − SRt−1,i , 0)

OHSt,i =

SRt,i : Self-reported assessment [very good = 5 to bad = 0], according to the CHNS data. t = is the year of recording, and t − 1 = is the previous year or the previous wave according to the data. i = number of individuals

SDt,i : Sick days or days where a person was unable to carry on his normal activities in the last four weeks. For t and i, they are for wave year and individual terms

SDt,i 28

that include: sex, marital status, province or county number etc. One note to be mentioned on the data calibration is that it is limited it to the age groups of 20 to 60 years old, which means mostly for working individuals, for they present mostly the working force of any given country while the graduation age is around 20 years old and retirement is 60 for Chinese males. 3.2

Statistical Computational Tools

The statistical analyzing and programing tool (SAS) was used as the analysis platform. An obvious observation on the OHS that is a continuous dependent variable between [0.1], while SHS is discontinued varying values of this set {−4, −3, −2, −1, 0}. This observation may lead to the fact of having two different models for our dependent variables. The first would be a simple linear regression for OHS, while the second will be a Multinomial Logit Regression for our SHS. OHSt,i = intercept + αEdut,i + βXt,i + t,i . M logit (SHSt,i ) = intercept + αEdut,i + βXt,i .

(1) (2)

Based on data nature, linear regression, multi-logit were used to compute the impact of education on OHS, and SHS which is illustrated in Eqs. (1), and (2) respectively. The introduction HS instrument also tackles the econometric issues such as endogeneity, biasness, and robustness. Education precedes HS in almost all our cases. The specification of age calibration and HS definitions makes it clear that most of those who had HS had already graduated. This specification explains the independence from the reverse effect of HS on education, or the possible biasness of the estimates due to endogeneity. In both models, our outcome of interest is the estimate of the parameter related to the independent variable education. We also have β that is the vector of parameters related to the control variables regrouped in Xt,i ; t,i is the error terms of the first model.

Education Impact on Health Shocks

4 4.1

71

Data Analysis and Results Discussion Demographic Attributes

Table 2 elaborates the descriptive statistics of HS and education variables respectively. In addition, Figs. 1 and 2 encapsulate the Education level and HS distributions concerning the different ages across waves. It demonstrates that most of the rural individuals have basic schooling, mostly primary schools while very few have high level of schooling where the numbers from one 1–5 in the X-axis of the graph, are equivalent to the level of schooling under university-level, while 6 and higher (where we grouped it till 9) reflect university level education. One positive thing about the Chinese system, which is also noticed in the graphs and data that most of the population have basic six years of schooling, and that’s due to the free education policy. This free basic education policy in China states that education is free and mandatory until high-school. In Fig. 2, it is clear that the number of those who have it, is relatively small. For Objective HS, not more than a thousand and it is considerable for Subjective HS. It also observes that Objective HS is more in older people as compared to the Subjective one. As for Objective HS, most of the shocks are above, 0.1, which means by the same calculating method used for this type of shock, that most of the shocks come from people who are unable to continue their daily acts for more than three days for the last month of the data collection date of each wave. While for The Subjective one, we can see that most of the shocks are in {−1, −2} which means these individuals changed their view of their health negatively by one or two degrees across waves. As for the other demographics, Table 2 depicts that the number of female and male are almost equal, most of the individuals are rural with the average age of 33 years old, where only 30% have insurance. Originally, there are ten waves which mean ten graphs per variable. The present study has considered limited number of graphs; 4, and 3 for education and shocks to unveil the education and HS levels distributed amongst different age groups per wave. It also infers that more people have achieved higher levels of education in recent years due to direction setting by the government to elevate rural education level. Moreover, the raise can be clearly shown in numbers and especially for young aged groups. Also, the graphs report that the distribution of HS stays stable for different waves and different age groups, especially for the subjective HS. In both shocks, most individuals who receive a shock are more aged group, where the vast majority is over 30 years old. 4.2

Empirical Results and Discussion

The results indicate that there is a positive effect of education on reducing the appearances and severity of HS, for both Objective and Subjective ones. The estimators were more significant for the education variable “completed years of formal education” as compared to “highest level of education attained”, as shown in both Table 3, and Table 4. To our benefits, the occurrences of HS in nature (For both Objective and Subjective) are independent of education and

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the reasons why can be summed in: the duration of the happening of both, where education happened before the HS for most of the data, therefore HS can’t effect Education; the second reason is the nature of calculating the HS, where Objective is the inability to your daily on activities, for people of age more than 20 years old and there is no simultaneous appearance nor precedent one. The subjective HS has both the benefit of the former and the benefit that it is calculated through the difference between years and waves which is the reason why we exclude the possibility of the reverse effect. Deducing from the previous reasons, it’s safe to assume that no endogeneity, collinearity or heterogeneity problems may appear next to the form of our data and R2 of the models, but a future appropriate test to our sort of data, in these matters, can be done to verify these conditions. Table 3 of our set of tables report the linear regression results, it specifically carries the result of regressing education variables to the Objective HS, where the significant effect was mostly captured by the number of years of completed education, as compared to the highest level of education attained variable. And even though the estimate is relatively small, but it still reflects a causal relationship between the two, and one reason for it being small is the number of those having an Objective HS is relatively small as compared to the whole population in the data set. Another reason is that the variable of this type of HS is defined by the number of days you were unable to do your work in the previous last four weeks of conducting the survey. If an educated person faces any sort of illness will cope better to it, which means he will slightly reduce his absence from the work force or will continue his chores faster than a less educated person. While there other attributes that we find also significant, but our focus is Education levels. These attributes or controls that we find significant are insurance types, working status, the money spent on illness, and others; and for a future aspect, they can also be studied for causality. More specifically Table 4 report the whole Multi-logit-regression, the estimates for such a model are presented by the log odds of being in one of the Subjective HS classes starting from 0 to −4 class. And we can see that again only the years of formal education is significant with 2% odds of change from a lower class to higher class for each extra year of education. But it is also noticeable that the only case where the other education’s estimate is significant; and that case is having higher education represented by the number 6, where 6 means having university or college-level education. In this case, the log odds of a change in a class from a lower to a higher one is greater than two, which means having a high level of education will reduce greatly the odds of a person belonging to lower classes of negative Subjective HS. Hence the same findings regarding the insurance, money spent on illness and other variables (next to the control variables) are also significant, and may open another research direction.

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Fig. 1. Education levels according to different ages and waves (Y-axis = population in each group for all the Graphs)

5 5.1

Conclusion and Policy Implications Study Outcomes

The empirical estimates infer that education has a significant positive marginal effect on health shocks. It also reveals that the effect of health shocks is more apparent in the subjective HS. In nutshell, it is measured by taking the negative first-order difference of self-perceived health measure. The purpose of the study is to investigate how education can affect HS, and if it does occur then it can be quantified as an economical measure to count the HS cost. It can be elaborated in its different forms; identified by an occurrence of bad health, a change in health status or by sudden accidents or the appearance of a chronicle disease for individuals. As HS takes a large part of government medical expenditures on healthcare, next to individuals of chronicle diseases. Another pivotal indicator is education, not just might help reduce these occurrences with more educated people choosing a less harmful or unhealthy lifestyle, plus it might direct them to better use of healthy instruments, sports and similar healthy choices. The primal goal of study eventually is to start a discussion and to give a certain substitution cost, or a tangible real rational cost. This cost can be the

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Fig. 2. Objective HS and Subjective HS levels according to different ages and for the last 3 waves

result of governments policy to invest in the education of the under privileged people to middle income people, those that are similar to the majority in CHNS dataset. Similarly, effective health policy must be devised for them to protect them from HS for any urban and rural individuals around the globe. The estimated results recommend that this policy should dictate a minimum level of education must be attained, a level higher than high school for free. And this policy should be both free and mandatory for both the sake of protecting their health (especially from HS) and even achieving all the economic growth related possibilities. Finally, this study is highly limited to multiple factors such as it is only Chinese, rural level short term results. It just initiates a research direction is to build a theory on the substitution cost of education to health through Health Shocks. Using multiple data from all around the globe to measure such cost of an extra year or an extra level of education on these Shocks is desired. Last but not least, the present work only had included two measures of the shocks, more measures should be taken. Finally, it is suggested to devise way and use other models to solve the endogeneity issue, through relative two-steps linear square (2SLS) methods, Difference in difference or any available suitable method.

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Table 2. Demographic attributes Abbreviations Variable name

N

Average

Standard deviation

Min variable

SHS

Subjective Health Shock

215352 −0.20474

OHS

Objective Health Shock

215352

0.43274

0.418999

0

1

A12

Highest Level of Education Attained

154014

1.551658

1.377844

0

9

A11

Completed Years of Formal Ed. in Regular School

151139 17.54836

8.996545

0

36

M1

DO YOU HAVE MEDICAL INSURANCE?

215352

0.33338

0.471422

0

1

M3A

INSURANCE TYPE

215352

0.288402

0.45302

0

1

M1_M3A

Interaction term between insurance and insurance type

215352

0.276013

0.447024

0

1

C5

AVERAGE # OF DAYS/WK WORKED LAST YEAR

215352

1.392437

2.511787

0

9

0.658999 −4

Max variable 0

B2

PRESENTLY WORKING?

215352

0.407988

0.4915

0

2

A8B

SPOUSE’S LINE NUMBER

215352

2.862509

14.60635

0

181

A5C

DOES MOTHER LIVE IN HOUSEHOLD?

215352

0.305393

0.460575

0

1

A5A

DOES FATHER LIVE IN HOUSEHOLD?

215352

0.285876

0.451832

0

1

t1

PROVINCE

215352 38.79073

9.632386 11

55

t2

1 = URBAN SITE(U) 2 = RURAL SITE(R)

215349

1.690628

0.462236

1

2

t3

U:1–2 = CITY NUM(number) /R:1–4 = COUNTY NUM

215349

2.202625

1.080111

1

4

t4

U:1–2, 5–6, 9–10 = URB(Urban) 3–4, 7–8, 11–12 = (Suburbs) /R:1,5,9 = TWN(town) 2–4, 6–8, 10–12 = VIL(Village)

215349

2.759493

1.368114

1

9

t5

HOUSEHOLD NUMBER

215349 21.42405

M39

MONEY SPENT ON ILLNESS OR INJURY

215352 14.10045

Age

Calculated Age in Years to 0 Decimal Points

213405 33.65802

GENDER

SEX

215298

5.2

0.494241

30.62383 650.5129 21.54265 0.499968

1

180

0

88888

0

101

0

1

Policy Implications

The estimated results will help to devise a health policy plan for individuals leaving in Chinese rural areas. It also provides a guideline to policy makers to devise the health plans for rural areas around the globe. The benefits of these results are not to just have these plans but get to the core point of our research which is whether investing in health through the education channels. Taken that education has a significant effect on our HS, then it is only logical to quantify the effect in real cost. It also suggests an insurance policy or fund just aimed for this goal especially if the cost is measured. Lastly, a final measure to educate low tier, rural and urban citizens health education through devised classes especially for those who already gone through any sort of HS.

76

I. Khelfaoui et al. Table 3. Simple regression model’s result

Abbreviation D.F PS Intercept

1

St. Err

t VALUE Pr > |t| Variables name

0.011595 0.002902 4

A12

1

−0.00063 0.00049

−1.28

A11

1

−0.00019 0.000082 −2.3

M1

1

0.012097 0.001396 8.66

P b > Zn > U > F e > As > Cd > Cu > Sb for adults. For children HI values calculated for V were higher than 1.0, indicating that this element can be dangerous for children health. Vanadium (V) in atmosphere originates mainly from man’s activities: ceramic production and decoration,

3.00E-04 3.00E-04 3.00E-04 5.23E-02

3.5E-4

3.00E-3

3.5E-03

1.00E-03 1.00E-03 5.00E-05 0.00519

3.7E-02

Sb

U

Pb

Cd

Cu

5.30E-03

8.00E-06 1.11E-02

–

4.02E-02 1.9E-03

5.14E-03

3.52E-03 5.25E-04 5.00E-03

–

–

3.00E-01 3.00E-01 6.00E-02 1.67E-03

As

Adult

HI Children

–

–

–

5.30E-03

4.35E-03 3.25E-03 1.55E-02

7.08E-04 2.45E-09 9.88E-08 9.26E-04 6.89E-04 6.07E-03

6.97E-04 9.84E-08 1.05E-07 9.34E-04 6.96E-04 6.12E-03

6.69E-03 9.40E-07 1.01E-06 2.99E-03 2.23E-03 7.99E-03

7.13E-04 –

–

7.03E-03 9.93E-07 1.06E-06 8.98E-03 3.50E-04 6.13E-02

1.72E-04 3.16E-08 3.37E-08 7.50E-05 5.60E-05 1.75E-03 1.49E-03 –

Adult

1.40E-03

1.39E-03

8.92E-03

7.13E-04

4.74E-03

7.38E-03

2.28E-04

7.17E-03

1.22E-04 9.11E-05 1.71E+00 2.34E-01

HQderm Children Adult

3.54E-03 8.75E-05 9.37E-05 4.72E-03 3.54E-03 3.08E-02

–

HQinh Children Adult

1.71E+00 2.34E-01 – 2.60E-02

Zn

9.00E-3

7.00E-05 –

5.00E-03 2.86E-05 2.5E-04

Rf Dderm HQing Children

Cr

Rf Dinh

V

Metal Rf Ding

Table 3. Average Hazard Quotient (HQ) and Hazard Index (HI) values for nine heavy metals based on three daily dose models via ingestion pathway, dermal contact pathway and inhalation pathway.

304 I. Zinicovscaia

Management of the Quality of the Air

305

production of pigments, steel, machinery, batteries, petroleum refinery, smelting, etc. [4]. Vanadium causes a variety of toxic effects such as hematological and biochemical changes, neurobehavioral injury, or morphological and functional lesions in liver, kidneys, bones, spleen and leukocytes. Inhalation of vanadium may cause rhinitis, pharyngitis, chronic productive cough, tracheobronchitis, and bronchopneumonia [5].

4

Conclusions

By means of NAA and AAS techniques it was possible to determine the contents of 41 elements in 33 moss samples collected in the Republic of Moldova. The concentration of some elements in collected moss samples (As, Fe, V, Sb) were the highest among European countries participating in “moss surve”. Hazard Index value calculated for V in case of children was higher than 1.0 indicating on possibility of its harmful impact on childrens’ health. High concentrations of some elements in the Republic of Moldova requires further monitoring of the air quality as well as attention from the national authorities in order to prevent their emissions in the atmosphere. Acknowledgements. The author would like to thank members of the staff of the Department of Activation Analysis and Applied Research of FLNP, JINR for handling of radioactive samples.

References 1. Du, Y., Gao, B., et al.: Health risk assessment of heavy metals in road dusts in urban parks of Beijing, China. Procedia Environ. Sci. 18, 299–309 (2013) 2. Duong, T.T., Lee, B.K.: Determining contamination level of heavy metals in road dust from busy traffic areas with different characteristics. J. Environ. Manag. 92(3), 554–562 (2011) 3. Ercilla-Montserrat, M., Mu˜ noz, P., et al.: A study on air quality and heavy metals content of urban food produced in a Mediterranean city (Barcelona). J. Clean. Prod. 195, 385–395 (2018) 4. Fortoul, T., Rojas-Lemus, M., et al.: Overview of environmental and occupational vanadium exposure and associated health outcomes: an article based on a presentation at the 8th international symposium on vanadium chemistry, biological chemistry, and toxicology, Washington DC, August 15–18, 2012. J. Immunotoxicol. 11(1), 13–18 (2014) 5. Ghosh, S.K., Saha, R., Saha, B.: Toxicity of inorganic vanadium compounds. Res. Chem. Intermed. 41(7), 4873–4897 (2015) 6. Harmens, H., Norris, D., et al.: Mosses as biomonitors of atmospheric heavy metal deposition: spatial patterns and temporal trends in Europe. Environ. Pollut. 158(10), 3144–3156 (2010) 7. Harmens, H., Norris, D., et al.: Heavy metals and nitrogen in mosses: spatial patterns in 2010/2011 and long-term temporal trends in Europe. ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Bangor. Center for Ecology and Gidrology, Bangor, UK 63 (2013)

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8. Harmens, H., Norris, D., et al.: Heavy metal and nitrogen concentrations in mosses are declining across Europe whilst some “hotspots” remain in 2010. Environ. Pollut. 200, 93–104 (2015) 9. Harmens, H., et al.: Heavy metals in European mosses: 2010 survey. Monitoring manual (2009) 10. Kabatapendias, A.: Trace Elements in Soils and Plants. CRC Press, Boco Raton (2011) ˇ 11. Spiri´ c, Z., Vuˇckovi´c, I., et al.: Air pollution study in Croatia using moss biomonitoring and ICP-AES and AAS analytical techniques. Arch. Environ. Contam. Toxicol. 65(1), 33–46 (2013) 12. Weerasundara, L., Magana-Arachchi, D., et al.: Health risk assessment of heavy metals in atmospheric deposition in a congested city environment in a developing country: Kandy City, Sri Lanka. J. Environ. Manag. 220, 198–206 (2018) 13. Zinicovscaia, I., Hramco, C., et al.: Air pollution study in the Republic of Moldova using moss biomonitoring technique. Bull. Environ. Contam. Toxicol. 98(2), 262– 269 (2017)

Part II: Machine Learning

An Empirical Analysis of the Influencing Factors of Farmers’ Income Growth in the Middle and Lower Reaches of the Yangtze River Based on the Grey Correlation Model Ming You, Xiaoyu Shao, and Yunqiang Liu(B) College of Management, Sichuan Agricultural University, Chengdu 611130, People’s Republic of China [email protected] Abstract. With the deep implementation of the rural revitalization strategy, increasing farmers’ income has also become a key measure to solve the three rural issues. The transformation of rural production environment and ecological environment has brought opportunities and challenges for farmers to increase their income. Analysis of factors affecting agricultural income is of great significance for improving farmers’ lives and increasing farmers’ income. In this paper, the grey relational theory is used to analyze the correlation between indicators and farmers’ income. The internal relationship between natural disasters and agricultural income is studied, and the effective way to increase farmers’ income is proposed. The assessment results show that, first of all, first of all, the income of farmers’ family business in the study area is affected by the level of agricultural science and technology, land productivity and disaster mechanism. Secondly, the application of agricultural science and technology with machinery and fertilizer as the mains has the greatest impact on farmers’ income. Third, in the middle and lower reaches of the Yangtze River, the income of farmers’ family business is threatened by droughts and floods. Through the analysis of the grey correlation model, the intrinsic link between farmers’ income and indicators is more intuitive, and the difference in the importance of indicators is more obvious. According to the analysis, the government and farmers can better allocate limited resources, be alert to the threat of natural disasters, and increase farmers’ income. Keywords: Farmers’ income · Grey correlation · Droughts and floods · Agricultural science and technology · Land resources

1

Introduction

Solving the problem of increasing farmers’ income is an important part of solving the “three rural issues” and an important way to improve farmers’ lives, c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 309–321, 2020. https://doi.org/10.1007/978-3-030-49829-0_23

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promote social harmony and guarantee China’s food security. With the continuous changes in China’s economic situation and social environment and the rapid development of urbanization, agricultural and rural farmers are facing the transformation of production materials, the transformation of production structure and the changes in the production environment. Increasing the income of farmers under the influence of various factors has also become a long-term and arduous task. Agricultural production is an important source of income for rural families. The improvement of comprehensive agricultural productivity can maximize the benefits of agriculture [16]. Agriculture is an industry that obtains products through artificial planting based on the laws of animal and plant growth. The input and effective allocation of production materials can effectively improve comprehensive productivity and increase farmers’ income. Whether it is the use of chemical fertilizers and pesticides [25], the application of agricultural mechanization [17], the improvement of infrastructure [7] and the input of land resources [22] have become indispensable means to increase production efficiency and increase farmers’ income. Agricultural production relying on natural resources is greatly affected by the external environment [19]. Natural disasters have a significant impact on farmers’ livelihoods as external environmental risks [18]. Oscar and so on. It is believed that natural disasters will have a negative impact on farmers’ income, which may lead to more serious poverty in rural areas in the affected areas [8]. Khondoker mentioned in the article that natural disasters can cause fluctuations in rice income and crop income, which often occurs in rice cultivation in Asia and Africa [15]. The quality of the natural environment of agricultural production affects agricultural productivity, which in turn affects farmers’ income. In areas with harsh climatic conditions, there are many rural poor, especially in areas with serious natural disasters [13]. Since the 1980s, China, a large agricultural country, has often been threatened by drought and flooding [26]. Drought and flood are the most common and most destructive natural disasters in the middle and lower reaches of the Yangtze River and are the main threat to agricultural income in the region. The instability of the monsoon in the middle and lower reaches of the Yangtze River often leads to excessive precipitation or insufficient precipitation, leading to agricultural drought and flooding [27]. Frequent droughts and floods caused by unstable monsoon climates often lead to a decline in agricultural production and a decline in farmers’ income [6]. Exploring the intrinsic link between drought and floods and agricultural income will better protect farmers’ property and increase farmers’ income. Farmers’ income based on farmers’ production is affected by the natural and social environment. Scholars are also actively exploring empirical methods for studying farmers’ incomes. Factors affecting income are studied to help farmers allocate resources reasonably. Regression models have been widely used in farmers’ income research [1,2,20]. In addition to the application of the above regression model, the grey correlation model is also widely used in the field of related factor analysis [5]. Compared with traditional regression analysis, the

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grey correlation model is used to explore the strength of the correlation between factors, determine the dominant factors and non-dominant factors, and is more suitable for analyzing systems with unclear influence factors [4,14,28]. In this paper, based on past research results and availability analysis of disaster data, the indicator framework and grey correlation model of agricultural income impact factors are used to analyze the factors affecting farmers’ family business income, and effective production and income increase proposals are proposed. The grey correlation model is used to calculate and rank the correlation between influencing factors and income. According to the data, it proves that there is an intrinsic relationship between drought and flood disasters and farmers’ income, which clarifies the main factors affecting the income of farmers’ family business, and provides reliable suggestions for farmers to increase their income.

2

Research Areas and Data Sources

The study area is shown in Fig. 1. The middle and lower reaches of the Yangtze River (25◦ –34◦ north latitude and 109◦ –122◦ east longitude) include Hubei, Hunan, Jiangsu, Jiangxi, Anhui and Zhejiang [27]. Flat terrain, fertile soil, dense river networks, and wide waters provide good natural conditions for agricultural production in the area. The middle and lower reaches of the Yangtze River are China’s important agricultural production bases, state-level commodity grain production bases and state-level high-quality cotton production bases. The region’s food production accounts for 31% of the country’s total grain production, and rice production accounts for 53% of the country’s total rice production [10]. The annual average precipitation in the area is about 1311 mm, and the time variation and spatial distribution of precipitation are uneven, which makes the area often threatened by drought and flooding [21]. The indicator data for the middle and lower reaches of the Yangtze River from 2000 to 2015 comes from the statistical yearbooks and statistical bulletins. The indicator data of the drought and flood disaster index is derived from the article entitled Integrated Risk Assessment for Agricultural Drought and Flood Disasters Based on Entropy Information Diffusion Theory in the Middle and Lower Reaches of the Yangtze River, China.

3 3.1

Research Methods Selection of Influencing Factors

In this paper, the availability of data, the composition of agricultural income, and the input of agricultural production are considered when selecting the seven basic indicators that may affect income and building the indicator system for research. From 2000 to 2015, the average net income of rural households in China increased from 1,427 yuan to 1453 yuan, accounting for 62.56% of farmers’ income, down to 39.4%. Although the proportion of family business income has decreased year by

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year, it is still the most important component of farmers’ income. In this paper, family business income is used as a dependent variable Y to explore the degree of correlation between impact factors and farmers’ income. Comprehensive agricultural productivity is the material basis for ensuring national food security and the necessary condition for increasing farmers’ income [3]. The basic indicators were chosen based on the comprehensive agricultural productivity. According to the composition of agricultural family business income and comprehensive agricultural production capacity, the indicators affecting the income of farmers’ family business are selected from three aspects: agricultural science and technology, land productivity and natural environment. The evaluation index system of this paper is shown in Fig. 2. The first level indicators of land productivity mainly include three basic indicators. The income of farmers is mainly derived from the income of crops, and the grain production (B1) has a greater internal relationship with the income of farmers. Total planting area of crops (B2) and effective irrigated area (B3) are material carriers for agricultural production. Land is an indispensable and important agricultural resource that will greatly affect the sustained and steady growth of farmers’ income [24]. By studying the input of production materials affecting agricultural production, three basic indicators of agriculture, including the total power of agricultural machinery (B4), the use of chemical fertilizers (B5) and agricultural fixed assets investment (B6), were determined. As part of advances in agricultural science and technology, the total power of agricultural machinery and the use of fertilizers provide opportunities to increase farmers’ incomes [23]. The advancement of agricultural science and technology has a significant positive impact on the growth of farmers’ agricultural income [12]. Investment in rural fixed assets affects agricultural productivity. Agriculture is a product of the interweaving of natural production and social production, and agricultural production depends on the natural environment. Agricultural drought and flood disasters are one of the biggest threats to agricultural production in the middle and lower reaches

Fig. 1. Research area map

An Empirical Analysis of the Influencing Factors

313

of the Yangtze River, affecting the stability of this farmer’s income [11]. Agricultural drought and flood disasters are quantified through agricultural drought and flood disaster indices [9]. By measuring the correlation between drought and flood disaster index (B7) and household income, the intrinsic link between drought and flood disasters and farmers’ household income helps to protect farmers’ property and stabilize farmers’ income. 3.2

Research Model

Grey relational theory is an interdisciplinary approach that helps people better analyze and understand systems in the context of missing data or unclear data, helping to solve small sample and insufficient information problems. Grey correlation theory is used to measure the consistency of trends between two different factors over time. This model can be used to analyze the factors that influence household income. The specific calculation steps of the grey correlation analysis are as follows: 1. Determine the reference sequence that reflects the behavioral characteristics of the system and the comparison series that affect the behavior of the system. This article sets the system behavior sequence as: ⎡

⎤ x 0 (1) x 1 (1) · · · x n (1) ⎢ x 0 (2) x 1 (2) · · · x n (2) ⎥ ⎢ ⎥ (X  0 , X  1 , · · · X  n ) = ⎢ . ⎥ .. .. . ⎣ . ⎦ . . x 0 (N ) x 1 (N ) · · · x n (N )

Fig. 2. Correlation degree evaluation system chart

(1)

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M. You et al. T

Where X  i =(x i (1), x i (2), · · · , x i (N )) , i = 0, 1, 2, · · · , n is the length of the variable sequence. 2. In the case of gray-scale correlation analysis, dimensionless data processing is usually required. This article uses the mean process to make: X0 = (x 0 (1), x 0 (2), · · · , x 0 (n)) X1 = (x 1 (1), x 1 (2), · · · , x 1 (n)) ··············· Xi = (x i (1), x i (2), · · · , x i (n)) ··············· Xm = (x m (1), x m (2), · · · , x m (n))

(2)

For the sequence of related factors Commonly used dimensionless methods are the means of averaging Eq. (3), initial value Eq. (4), etc.

xi (k) = 1 N

xi (k) =

x i (k) N  x i (k)

(3)

x i (k) x i (1)

(4)

k=1

Where i = 0, 1, · · · , n; k = 1, 2, · · · , N . 3. Difference sequence, maximum difference and minimum difference The absolute difference between the reference sequence and the comparison sequence is calculated to form the following absolute difference matrix: ⎤ Δ01 (1) Δ02 (1) · · · Δ0n (1) ⎢ Δ01 (2) Δ02 (2) · · · Δ0n (2) ⎥ ⎥ ⎢ ⎥ ⎢ .. .. .. ⎦ ⎣ . . . Δ01 (N ) Δ02 (N ) · · · Δ0n (N )

(5)

Δ0i (k) = |Δ0 (k) − Δi (k)|

(6)

⎡

among them i = 0, 1, · · · , n; k = 1, 2, · · · , N . The maximum and minimum numbers in the absolute difference matrix are the maximum difference and the minimum difference: max (Δ0i (k)) = Δ (max) min (Δ0i (k)) = Δ (min)

(7)

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4. Calculate the correlation coefficient The data in the absolute difference matrix is transformed as follows:

ξ0i (k) =

Δ (min) + ρΔ (max) Δ0i (k) + ρΔ (max)

(8)

Get the correlation coefficient matrix: ⎡

⎤ ξ01 (1) ξ02 (1) · · · ξ0n (1) ⎢ ξ01 (2) ξ02 (2) · · · ξ0n (2) ⎥ ⎢ ⎥ ⎢ ⎥ .. .. .. ⎣ ⎦ . . . ξ01 (N ) ξ02 (N ) · · · ξ0n (N )

(9)

5. Calculate the degree of relevance The degree of correlation between the comparison sequence and the reference sequence is reflected by N correlation coefficients, ie, the ith column in 9, and the degree of correlation can be obtained by averaging. r0i

n 1 = ξ0i (k) N

(10)

k=1

4

Empirical Analysis

According to the data of the middle and lower reaches of the Yangtze River from 2000 to 2015, the correlation between basic indicators and farmers’ household income was calculated and ranked, and the results were analyzed. Table 1 shows the correlation between family business income and various indicators. Figure 2 shows the importance score for each indicator. 4.1

Relevance Analysis

Table 1 shows the correlation between the various basic indicators and the income of farmers’ family business. The average correlation values of each indicator are sorted as follows: 0.849>0.820>0.811>0.806>0.803>0.714>0.683. The closer the grey correlation is to 1, the greater the impact of factors of production on the income of farmers. It can be seen from Table 1 that the correlation between each indicator and the household income of farmers is relatively high, and the value is basically around 0.8, indicating that each indicator has an important impact on the income of agricultural family enterprises. The average total power of agricultural machinery is 0.85, which is the highest correlation with the income of agricultural family enterprises. The average correlation of the agricultural drought and flood disaster index is 0.68, which is the lowest of the seven indicators.

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M. You et al. Table 1. Relevance of indicator calculation

Hefei Wuhu Bengbu Maanshan Anqing Chizhou Nanjing Yangzhou Suzhou Wuxi Zhenjiang Changzhou Hangzhou Jiaxing Shaoxing Ningbo Huzhou Jinhua Wuhan Huangshi Xiaogan Ezhou Huanggang Yichang Jingzhou Jingmen Changsha Zhuzhou Xiangtan Yueyang Nanchang Jiujiang Jingdezhen Average value

B1

B2

B3

B4

B5

B6

B7

0.938 0.953 0.858 0.853 0.768 0.823 0.939 0.911 0.644 0.716 0.903 0.791 0.798 0.757 0.805 0.825 0.755 0.854 0.754 0.863 0.899 0.771 0.878 0.707 0.718 0.763 0.943 0.851 0.841 0.958 0.703 0.765 0.772 0.820

0.899 0.932 0.809 0.851 0.772 0.825 0.899 0.868 0.603 0.709 0.901 0.774 0.866 0.737 0.787 0.813 0.739 0.861 0.765 0.870 0.893 0.741 0.865 0.710 0.707 0.729 0.948 0.800 0.830 0.957 0.640 0.736 0.750 0.806

0.896 0.934 0.821 0.848 0.801 0.823 0.925 0.866 0.588 0.682 0.904 0.789 0.885 0.754 0.807 0.865 0.785 0.886 0.762 0.873 0.885 0.726 0.831 0.763 0.717 0.737 0.945 0.800 0.831 0.959 0.613 0.715 0.732 0.811

0.898 0.919 0.874 0.835 0.699 0.854 0.959 0.905 0.594 0.652 0.909 0.761 0.915 0.797 0.840 0.913 0.771 0.942 0.862 0.955 0.963 0.861 0.831 0.795 0.868 0.906 0.971 0.876 0.835 0.974 0.736 0.772 0.763 0.849

0.906 0.933 0.834 0.816 0.791 0.845 0.834 0.867 0.568 0.632 0.845 0.714 0.862 0.830 0.812 0.840 0.756 0.908 0.759 0.905 0.908 0.712 0.834 0.733 0.702 0.774 0.852 0.859 0.845 0.850 0.660 0.736 0.769 0.803

0.740 0.718 0.594 0.653 0.679 0.702 0.799 0.789 0.716 0.652 0.708 0.655 0.760 0.523 0.702 0.712 0.554 0.764 0.879 0.782 0.793 0.636 0.581 0.743 0.763 0.770 0.938 0.765 0.706 0.897 0.511 0.713 0.656 0.714

0.728 0.860 0.700 0.724 0.667 0.709 0.764 0.727 0.582 0.598 0.662 0.609 0.744 0.638 0.617 0.693 0.640 0.722 0.628 0.735 0.740 0.538 0.608 0.542 0.543 0.639 0.923 0.675 0.649 0.944 0.555 0.711 0.742 0.683

There is also a difference in the correlation between indicators and income in each prefecture-level city because of differences in agricultural development, economic conditions, economic structure and social environment in each city.

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Figure 2 shows the scores of the correlation degree of each indicator. The average scores of each indicator are ranked as follows: B4>B1>B5>B3>B2>B7>B8. The higher the value associated with farmers’ income, the higher the score. The highest score of the total power of agricultural machinery indicates that it has the greatest impact on the income of farmers’ family business. In recent years, the speed of the transformation of traditional agriculture to modern agriculture has been accelerating, and the scale of agricultural production and mechanization in the middle and lower reaches of the Yangtze River have been continuously improved. The improvement of the level of agricultural mechanization optimizes production conditions, increases production and quality, and increases farmers’ income. The correlation between grain yield, effective irrigated area and total sown area was ranked second, fourth and fifth respectively. Under intensive production conditions, land resource inputs for crop production have begun to saturate. Therefore, irrigated area and planted area have less impact on increasing income. As can be seen from the chart, the food production correlation of 33 cities is very close to 0.82. Agriculture still occupies an important position in China, so agricultural income accounts for a large part of the income of farmers. According to Table 1, the correlation coefficient between disaster risk index and household enterprise income is about 0.68, which is the lowest compared with other evaluation indicators. The large correlation coefficient between disaster index and agricultural household income indicates that the impact of drought and flood disasters on farmers’ income cannot be ignored. The process of agricultural production is the product of a combination of natural and social attributes. The impact of changes in the natural environment on agricultural production is fundamental and difficult to reverse, especially climate change. When droughts and floods occur, their effects usually occur at a certain stage of production, and their effects are often devastating and difficult to recover, so droughts and floods have a huge impact on agricultural income. Compared with other indicators, the impact of drought and flood disasters on the income of farmers’ family businesses is often interfered with by more factors. With the acceleration of China’s agricultural modernization process and the continuous improvement of agricultural science and technology, breakthroughs in crop research continue to increase the resistance of crops themselves. Whether it is farmers or crops, their ability to prevent and mitigate disasters is increasing, which greatly reduces the impact of drought and floods on farmers’ family business income. However, the suddenness and unpredictability of natural disasters make the link between disasters and income a factor that cannot be ignored.

5

Policy Suggestions and Conclusion

With the rapid development of China’s economy and society, farmers face new challenges in the transformation of rural production structure and the transformation of farmers’ income sources. A correct understanding of the structural characteristics of farmers’ income and its influencing factors are of great significance to increase farmers’ income. With global climate change, the increase in

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the frequency of droughts and floods affects agricultural production activities and thus affects the income of farmers’ family business. Studying the relationship between family income and natural disasters, and analyzing the main factors affecting the income of farmers’ family business, can effectively help farmers optimize resource allocation, increase agricultural income, and eliminate the threat of natural environment. According to empirical analysis, the seven indicators are highly correlated with farmers’ income. As an important feature of agricultural science and technology progress, the total power of agricultural machinery and the use of chemical fertilizers have become the main important factors affecting the growth of farmers’ family business income. Advances in agricultural science and technology have increased the overall productivity of agriculture and increased the income of farmers. The correlation between agricultural drought and flood disaster index and farmers’ family business income is the lowest, but the suddenness and uncertainty of drought and flooding often have an irreversible and serious impact on farmers’ income. The occurrence of droughts and floods and the impact of sudden changes in the natural environment on farmers’ income cannot be ignored. Increasing farmers’ income is of great significance to promoting rural development, peasant happiness, and social harmony. According to the empirical analysis, effective suggestions for increasing farmers’ income are put forward (Fig. 3). 1 2 1 1 2 1 1 1 1 1 1 2 1 1 2 1 2 2 2 2 7 2 2 2 2 6 5 2 1 1 1 3 3 4 4 4 6 6 5 3 3 3 3 3 6 2 4 5 6 6 7 4 3 5 3 3 6 5 3 3 3 6 6 4 6 7 6 5 6 6 5 5 5 5 5 4 2 4 5 5 5 1 5 5 4 4 4 3 7 7 4 4 3 4 3 2 7 4 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 4 4 4 4 4 4 4 3 3

1 3 2 1 1.42 2 2 2 1 2.27 1 3 2 4 4 4.21 4 4 4 3 3 3 4.42 6 7 5 5 6 4.52 1 6 6 5 7 5.23 6 7 7 7 5

5.88 5

Hefei Wuhu Bengbu Maanshan Anqing Chizhou Nanjing Yangzhou Suzhou Wuxi Zhenjiang Changzhou Hangzhou Jiaxing Shaoxing Ningbo Huzhou Jinhua Wuhan Huangshi Xiaogan Ezhou Huanggang Yichang Jingzhou Jingmen Changsha zhuzhou Xiangtan Yueyang Nanchang Jiujiang Jingdezhen Average score

1 2 2 2 1 2 2 1 2 1 1 1 2 2 2 3 2 2 1 1 1 1 1 7 5 4 3 6 5 5 4 5 4 5 6 4 6 5 6 3 7 3 3 6 5 5 5 5 6 6 4 3 5 3 3 3 3 6 6 7 6 2 5 7 7 4 7 1 7 7 6

Drought and flood disaster index Total planting area of crops The amount of agricultural fertilizer used Agricultural machinery total power

Investment in fixed assets Effective irrigated area Grain production

Fig. 3. Relevance of indicator calculation

1. Accelerate the advancement of agricultural science and technology and promote agricultural modernization. The application of agricultural science and technology in the development of agricultural production has effectively stimulated the growth of agricultural productivity. The research and development of agricultural science and technology should be accelerated, and the mechanization and intelligence of production should be applied. Starting from agricul-

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tural equipment, agricultural chemistry, and agricultural talents, the government promotes the transformation, application, and promotion of scientific and technological achievements. Researchers should vigorously develop resource-saving and environment-friendly agricultural science and technology to protect farmers’ incomes on the basis of protecting the rural environment. 2. Strengthen disaster prevention and mitigation capabilities and improve disaster warning systems. In addition to the investment in the agricultural social environment and resources, the natural environment of agricultural production should be improved. For natural disasters in the middle and lower reaches of the Yangtze River, especially droughts and floods, it is necessary to strengthen the management of disaster risks and improve the early warning system for disaster risks. For farmers, it is necessary to raise awareness of risk prevention and attach importance to purchasing agricultural insurance. The government should do a good job in disaster prevention, strengthen the propaganda of disaster prevention and mitigation knowledge, increase the capital investment in the construction of farmland water conservancy facilities, and improve the agricultural production infrastructure. 3. Strengthen land management and promote land transfer. Land is the most basic material information for farmers to carry out agricultural production activities. The rational allocation of land resources can effectively increase farmers’ income. The government should strengthen the protection of agricultural land, focus on protecting cultivated land, comprehensively rectify agricultural land, improve the agricultural production environment, and promote the scale development of agricultural land. Acknowledgements. This research work is supported by the Ministry of education “humanities and social sciences youth project” of China [Grant No. 15YJC630081].

References 1. Alemu, A.E., Maertens, M., et al.: Impact of supply chain coordination on honey farmers’ income in Tigray, Northern Ethiopia. Agric. Food Econ. 4(1), 9 (2016) 2. Chen, S., Pu, C., et al.: Empirical analysis of factors affecting farmers’ income increase in Awat county of Xinjiang. Acta Agriculturae Jiangxi 25(6), 140–146 (2013) 3. Constant, L.A, Winkler, H.: Linkage of agricultural productivity improvement and climate mitigation action in Africa (2015) 4. Dawa, D.H.: Gm (1,1) model and factor analysis of the development trend of per capita net income of rural residents in Tibet autonomous region (2010) 5. Deng, J.L.: Control problems of grey systems. Syst. Control Lett. 1(5), 288–294 (1982) 6. De Silva, M., Kawasaki, A.: Socioeconomic vulnerability to disaster risk: a case study of flood and drought impact in a rural Sri Lankan community. Ecol. Econ. 152, 131–140 (2018) 7. Doppler, W., Bahadur, K.K.: Impact of natural resources and infrastructure on future livelihood in mountain farming in the Himalaya region. In: The Future of Mountain Agriculture, pp. 145–170. Springer (2013)

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8. Ishizawa, O.A., Miranda, J.J.: Weathering storms: understanding the impact of natural disasters in Central America. Environ. Resour. Econ. 73(1), 181–211 (2019) ¨ ¨ Forecasting Turkey’s credit ratings with multivariate 9. Karaaslan, A., Ozden, K.O.: grey model and grey relational analysis. J. Quan. Econ. 15(3), 583–610 (2017) 10. Leipnik, M., Su, Y., et al.: Agriculture and food production in China and the US. In: A Comparative Geography of China and the US, pp. 117–158. Springer (2014) 11. Liu, Y., You, M., et al.: Integrated risk assessment for agricultural drought and flood disasters based on entropy information diffusion theory in the middle and lower reaches of the Yangtze river, China. Int. J. Disaster Risk Reduct. 38(101), 194 (2019) 12. Lu, W.: Agricultural technological progress and farmers’ income growth in China. J. Zhejiang Univ. (Hum. Soci. Sci.) 4 (2013). (in Chinese) 13. Mendelsohn, R., Basist, A., et al.: Climate and rural income. Clim. Change 81(1), 101–118 (2007) 14. Meng, Z., Xu, C., Lan, H.: Fuzzy gm (1, 1) model based per capital income predicted of farmers in the world natural and cultural heritage area: take Leshan city for an example. In: International Conference on Management Science and Engineering Management, pp. 1007–1018. Springer (2017) 15. Mottaleb, K.A., Mohanty, S., et al.: The effects of natural disasters on farm household income and expenditures: a study on rice farmers in Bangladesh. Agric. Syst. 121, 43–52 (2013) 16. Pan, H., Liu, Y., Gao, H.: Impact of agricultural industrial structure adjustment on energy conservation and income growth in Western China: a statistical study. Ann. Oper. Res. 228(1), 23–33 (2015) 17. Paudel, G.P., KC, D.B., et al.: Scale-appropriatemechanization impacts on productivity among smallholders: evidence from rice systems in the mid-hills of Nepal. Land Use Pol. 85, 104–113 (2019) 18. Pishbahar, E., Abedi, S., et al.: Agricultural risk management through weatherbased insurance in Iran. In: Sustainable Agriculture and Agribusiness in Iran, pp. 11–28. Springer (2019) 19. Prokopy, L.S., Arbuckle, J.G., et al.: Farmers and climate change: a cross-national comparison of beliefs and risk perceptions in high-income countries. Environ. Manag. 56(2), 492–504 (2015) 20. Safa, M.S.: Socio-economic factors affecting the income of small-scale agroforestry farms in hill country areas in Yemen: a comparison of OLS and WLS determinants. Small-scale For. Econ. Manag. Pol. 4(1), 117–134 (2005) 21. Shan, L., Zhang, L., et al.: Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze river basin and the relationship with Enso. J. Geogr. Sci. 28(8), 1039–1058 (2018) 22. Singh, A.: Optimal allocation of water and land resources for maximizing the farm income and minimizing the irrigation-induced environmental problems. Stoch. Environ. Res. Risk Assess. 31(5), 1147–1154 (2017) 23. Tabo, R., Bationo, A., et al.: Improving cereal productivity and farmers’ income using a strategic application of fertilizers in West Africa. In: Advances in Integrated Soil Fertility Management in Sub-Saharan Africa: Challenges and opportunities, pp. 201–208. Springer (2007) 24. Verma, P., Singh, D., et al.: Strategies to improve agriculture sustainability, soil fertility and enhancement of farmers income for the economic development. In: Soil Fertility Management for Sustainable Development, pp. 43–70. Springer (2019)

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25. Wang, X., Shao, S., Li, L.: Agricultural inputs, urbanization, and urban-rural income disparity: evidence from china. China Econ. Rev. 55, 67–84 (2019). (in Chinese) 26. Ye, M., Qian, Z.H., Wu, Y.P.: Spatiotemporal evolution of the droughts and floods over China (2013) 27. Zhang, Y., Bo, T., Li, Y.: Assessment on the vulnerability of different ecosystems to extreme rainfalls in the middle and lower reaches of Yangtze river. Theoret. Appl. Climatol. 121(1–2), 157–166 (2015) 28. Zheng, W., Li, X., et al.: Impact of human activities on haze in Beijing based on grey relational analysis. Rendiconti Lincei 26(2), 187–192 (2015)

Advances in Hybrid Genetic Algorithms with Learning and GPU for Scheduling Problems: Brief Survey and Case Study Mitsuo Gen1,2(B) , John R. Cheng3 , Krisanarach Nitisiri4 , and Hayato Ohwada5 1

5

Fuzzy Logic Systems Institute, Kitakyushu, Japan [email protected] 2 Tokyo University of science, Tokyo, Japan 3 Broad Geophysical Technology, Inc., 10630 Haddington Dr, Houston, TX 77043, USA [email protected] 4 Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand [email protected] Graduate School of Science and Engineering, Tokyo University of Science, Tokyo, Japan [email protected]

Abstract. Scheduling is one of the very important tools for treating a complex combinatorial optimization problem (COP) model, where it can have a major impact on the productivity of a manufacturing process. The most well known models of scheduling are confirmed as NP-hard or NP-complete problems. The aim of scheduling is to find a schedule with the best performance through selecting resources for each operation, the sequence for each resource and the beginning time. Genetic algorithm is one of the most efficient methods among metaheuristics for solving the real-world manufacturing problems. In this paper we firstly survey the literature on genetic algorithms (GAs) with GPU acceleration. A parallel multiobjective GA (MoGA) acceleration with CUDA (Compute Unified Device Architecture) will be introduced. A parallel hybrid multiobjective GA with learning is introduced through a real-world case study of the train scheduling problem and numerical experiments on GPU for multiobjective GA approaches are also demonstrated. Keywords: Multiobjective Genetic Algorithm (MoGA) · Machine Learning (ML) · Graphics Processing Units (GPUs) · Train scheduling

1

Introduction

In real-world optimization problems, there are many combinatorial optimization problems (COPs) imposed with many complex issues, such as complex structure, nonlinear constraints, and multiple objectives to be handled simultaneously. Manufacturing scheduling is one of the important and complex COP c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 322–339, 2020. https://doi.org/10.1007/978-3-030-49829-0_24

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models, where it can have a major impact on the productivity of a manufacturing process. Moreover, the COP models make the problem intractable to traditional optimization techniques because most of scheduling problems fall into the class of NP-hard combinatorial problems. Scheduling can be descripted as n operations need to be performed by m resources with the processing time which varies according to resource availabilities. The target of scheduling is to find a schedule with best performance through selecting a proper resource for each operation, the operation sequence for each resource and the beginning time [2,3,35]. The research on scheduling started from 1950s, Giffler and Thompson [16] proposed priority dispatching heuristics. Panwalkar and Iskander [32] surveyed scheduling rules. Blackstone, Phillips and Hogg [1] reviewed dispatching rules. From the 1980s, the research of scheduling was in a prosperous period. The computational complexity of scheduling has attracted a great attention. The researchers have showed that the most models of scheduling were NP-hard problems or NP-complete problem. Lots of scheduling approaches then have proposed, such as: heuristics, simulationbased approaches, artificial intelligence (AI) based approaches [18]. Recently Lin and Gen [24] reported state-of-the-art survey on algorithms and applications for hybrid evolutionary optimization with learning for manufacturing scheduling. Genetic algorithm (GA) as one of metaheuristics is a stochastic search algorithm inspired by the mechanism of population genetics and the principles of natural selection, such as reproduction, recombination, mutation, and selection. Different from conventional search techniques, GAs work on a set of solutions, called a population. Each individual in the population, called a chromosome, represents a solution to the problem at hand. The set of solutions evolves through successive iterations, called generations. During each generation, the chromosomes are evaluated, using some measures of fitness. To create the next generation, new chromosomes, called offspring, are formed by either merging two chromosomes using a crossover operator or modifying a chromosome using a mutation operator. A new generation is formed by a selection, according to each fitness, some of the parents and offspring. Chromosomes with better fitness values have much higher possibilities of being selected. After several generations, the algorithm converges to the best one, which hopefully is close to the optimal solution [9,11,12,17,25]. As one of metaheuristics, GAs possess many advantages over the conventional search methods. It requires neither much mathematical properties about the problem, nor domain knowledge, so it can handle much complex problems with any kind of objective functions and constraints, linear or nonlinear, defined on discrete, continuous or mixed search spaces. The ergodicity of genetic operators makes GAs very effective at performing global search. GAs also provide us a great flexibility to hybridize with domain-dependent heuristics to make an efficient implementation for a specific issue, such as Job-shop Scheduling Problem (JSP) [5,6]. GAs often perform well on all types of combinatorial optimization problems because they do not make any assumption about the underlying fitness landscape [14,45]. Many of the traditional parallel GAs run on multi-core machines, massively parallel cluster machines, or grid computing environments. However, recent

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advances in microprocessor devices have made it possible to use general-purpose graphics processing units (GPGPUs) for parallel GAs. Simply GPUs are lowcost, massively parallel, many-core processors and they can execute thousands of threads in parallel in single instruction multiple threads manner [4,49]. With GPUs acceleration, it becomes possible to use parallel GAs to solve real timebased optimization problems. In this paper we applied Multiobjective Evolutionary Algorithms with Hybrid Sampling Strategy [47] for finding the best scheduled timetable for the train network, accelerated with GPU computing, as one of case studies. The rest of this paper is organized as follows: Sect. 2 summarizes the literature review on genetic algorithms with GPU. Section 3 introduces parallel hybrid GA with learning with GPU acceleration for a case study in which the train scheduling problem is described with a mathematical model and the multiobjective GA approach with hybrid sampling strategy is developed to solve it. Section 4 summarizes numerical experiments of Parallel MoGA for a train scheduling problem. Conclusions of this paper is given in Sect. 5.

2

Literature Review for Accelerating GAs with GPU

Many of early works on parallel GAs with GPU and CUDA (Computer Unified Devices Architecture) can be viewed as a kind of extension of CPU approaches onto GPU, therefore, which do not fully exploit the computation power of GPU architecture because of less understanding of the fundamental features of GPU computing architecture [7]. Munawar et al. [28] reported GA approach with local search for solving Max-Sat problem and Wong [42] reported multi-objective Evolutionary Algorithm for Multi-objective Optimization problem with GPUs, respectively. Wong and Wong [44] reported hybrid GA for solving nonlinear Optimization problem and Pedemonte et al. [33] reported binary GA for solving one max problem with GPUs, respectively. Kruger et al. reported generic local search algorithm (memetic algorithm) on a single GPU chip [23], Munawar, et al. proposed adaptive resolution microgenetic Algorithm with tabu search to solve mixed integer non-linear programming (MINLP) problems using GPU [29] and Tsutsui and Fujimoto [41] reported an analytical study of parallel GA with independent runs on GPUs with implementation techniques for massively parallel multi-objective optimization. Sharma and Collet [38] implemented parallel multi-objective optimization algorithm massively. Wong and Cui [43] also reported data mining using parallel multi-objective evolutionary algorithms on GPU and Sato et al. reported acceleration of genetic algorithms for sudoku solution on many-core processors [36]. Kromer, et al. proposed many-threaded differential evolution (DE) on the GPU [21] and Solomon, et al. reported scheduling using multiple particle swarm optimization (PSO) with memetic features on GPU [39]. Tsutsui and Fujimoto [40] reported Ant Colony Optimization (ACO) with Tabu search on GPUs for fast solution of the Quadratic Assignment Problem (QAP) and Pedemonte, et al. proposed new ideas in parallel metaheuristics on GPU: systolic genetic search [34].

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Kromer et al. reported the brief survey on the nature-inspired metaheuristics such as GA, DE, PSO and SA on GPU [22]. Mittal and Vetter [26] reported a survey paper on CPU-GPU heterogeneous computing techniques in ACM Computing Surveys and Ortega, et al. proposed Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU for solving multiobjective optimization problems [31]. Moreno et al. proposed a sequential Best Order Sorting (BOS) for treating Non-dominated sorting (NDS) to solve multiobjective optimization problem [27]. Cheng and Gen surveyed the recent advances on Genetic Algorithms with GPU [4,49]. Nitisiri, et al. reported a parallel multiobjective genetic algorithm with learning-based mutation for solving railway scheduling problem [30]. Now we listed the related works in accelerating Genetic Algorithms with GPU in Table 1.

3 3.1

Parallel Hybrid GA with Learning Accelerated by GPU: Case Study Problem Description and Mathematical Model

We consider the train scheduling problem for a double-track railway system. Each track is operated in a single direction. The system consists of 2S stations where each station is only employed for one operation direction, 1 −→ S, and S + 1 −→ 2S. There is one platform in the station for each direction, and there is no by-pass track so that the trains cannot overtake each other. Stations 1 and S are connected with the turnaround track. Safety headway constraints between consecutive trains running on the same track are their operating cycle c at station 1 in up-direction, pick up the passengers and travel to next consecutive station until reaching station S. After that, the train will turn around and go back to the origin station (2S) using the down-direction track and then wait for the next operating cycle. The overall objectives of the scheduling model is to obtain the feasible timetable of the railway system [30]. The quality of service is modeled through two objective functions: (a) the average passenger waiting time and (b) the number of operating cycles. min z1 =

p end 

N  C  2S 

p=pstart c=1 k=1 i=1

min z2 =

PA (ai (p) · (tD cki − ti (p)))/

p end 

2S 

ai (p)

(1)

i=1 N  2S 

x1ki (p)

(2)

p=pstart k=1 i=1

subject to D R tA cki+1 = tcki + tcki , ∀c, k, i ≥ 2

(3)

A S T tD cki = tcki + tcki + t , ∀c, k, i

(4)

Survey on GA with GPU

Train scheduling problem

Accelerating GA Parallel MoEA-HSS

Comp. & Indus. Eng.

Comp. & Indus. Eng.

J. Global Optimization

ACM Comp. Surveys

Int. J Parallel Program

Massively Parallel EC

Nitisiri, et al. (2019) [30]

Non-dominated sorting (NDS)

CPU/GPU Heteroge. Comp

GA, DE, PSO, SA

Massively Parallel EC

Massively Parallel EC

Multiobjective optimization problem Sequential Best Order Sort, NDS J. Global Optimization

Particle Swarm Optimization

Massively Parallel EC Massively Parallel EC

Cheng and Gen (2019) [4, 49]

Task matching problem

Solomon, et al. (2013) [39]

Many-threaded DE

Massively Parallel EC

Moreno, et al. (2018) [27]

Linear ordering problem

Kromer, et al. (2013) [21]

Parallel Multi-Objective EA Coarse-grained GA

Tasks schedule problem

Sudoku puzzle problem

Sato, et al. (2013) [36]

Massively Parallel EC Massively Parallel EC

Survey

Data Mining problem

Wong and Cui (2013) [43]

Parallel GA model Archive-based Stoch Rank

Ortega, et al. (2017) [31]

Multi-Obj Optimization Prob

Sharma and Collet (2013) [38]

Massively Parallel EC

Massively Parallel EC

Mittal and Vetter (2015) [26]

Quadratic assign problem

Tsutsui, et al. (2013) [41]

Adaptive Resolution GA

Memetic Alg., Local Search

Systolic Genetic Search

Mixed Int Nonlinear Opt.

Munawar, et al. (2013) [29]

GECCO 2011

Study Comp Intel. 187

Survey

Nonlinear. Opt. problem

Kruger, et al. (2013) [23]

Binary Genetic Algorithm

Hybrid GA, Fast Evo Prog

Knapsack problem

One max problem

Pedemonte, et al. (2011) [33]

Genet. Prog & Evo Mach. GECCO 2009

Kromer, et al. (2014) [22]

Nonlinear. Opt. problem

Wong and Wong (2009) [44]

GA & Local Search Multi-Obj. Evolutionary Alg.

Pedemonte, et al. (2013) [34]

Multi-Obj. Opt. problem

Wong (2009) [42]

Journal/Conference

ACO with Tabu Search

Max-Sat problem

Munawar, et al. (2009) [28]

Evolutionary algorithms

Tsutsui and Fujimoto (2013) [41] Quadratic assign problem

Problem model

Authors (Year)

Table 1. Accelerating genetic algorithms with GPU

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Rmax tRmin ≤ tR , ∀c, k, i i cki ≤ ti

(5)

D max hmin ≤ tD , ∀c, k, i ck+1,i − tcki ≤ h

(6)

A max hmin ≤ tA , ∀c, k, i ck+1,i − tcki ≤ h

(7)

PA W tD cki − ti (p) − M xcki (p) ≤ tcki , ∀c, k, i, p ∈ [pstart , pend ]

(8)

D tA ck,S+1 − tck+1,i ≥ 0, ∀c, k

(9)

D R S W tA cki , tcki , tcki , tcki , tcki ≥ 0, ∀c, k, i

(10)

xcki (p) ∈ 0, 1, ∀c, k, i, p ∈ [pstart , pend ]

(11)

Objective functions are Eq. (1) to minimize average waiting time for the passenger of the railway line and Eq. (2) to minimize a total number of the operating cycles during the operating period. Since the more trains operate during the day, result in less waiting time for the passenger but increase the operational cost for the company. Equation (3) indicates the train arrival time is equal to the time required to travel between stations and the departure time from the previous station. Equation (4) guarantees that the departure time is equal to the arrival time and dwelling time, including the turnaround time for the first and last station. Equation (5) is the speed limit for a train traveling between two consecutive stations. Equations (6) and (7) are the safety headway for two consecutive trains. Equation (8) denotes that when the departure of the k th train is earlier than tPA i (p), the left-hand side is negative and, the minimization objective will force the right-hand side (the waiting time) to be zero. On the other hand, if xcki (p) = 1, passengers are able to get on the k th train or earlier trains, and there is no one waiting. Equation (9) implies that only one train could turn around at the station. Equations (10) and (11) impose the nonnegative restriction. 3.2

MoEA-HSS Approach

In this section, we briefly introduce a multiobjective genetic algorithm and parallel computation to solve the railway scheduling model. Genetic algorithms are stochastic search methods based on principles of natural selection and recombination which attempt to find the optimal solution to the problem [17]. The population is evaluated, and the best solutions are selected to reproduce and mate to form the next generation. Over a number of generations, good traits dominate the population, resulting in an increase in the quality of the solutions [14]. GA have been received considerable attention as a novel approach to the manufacturing scheduling problems [13,15] and it required less computational resource than the mathematical formulation approach.

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Multiobjective Evolutionary Algorithm with Hybrid Sampling Strategy: For evaluating multiple objectives in the railway scheduling model, we applied MoEA-HSS (Multiobjective Evolutionary Algorithm with Hybrid Sampling Strategy [13,15,47] combined Vector Evaluated Genetic Algorithm [37] which prefers the solutions in the edge area of the Pareto front, and the sampling strategy according to a Pareto dominating and dominated relationship-based fitness function (PDDR-FF) which has the tendency converging toward the central area of the Pareto front as shown in Fig. 1. The PDDR-FF value of the j th individual Gj can be calculated by the following the fitness assignment function: eval(Gj ) = q(Gj ) + 1/(p(Gj ) + 1), j = 1, 2, · · · , popSize

(12)

where q(Gj ): number of chromosomes which can be dominated by Gj , p(Gj ): number of chromosomes which can dominate Gj , popSize: total number of chromosomes in the population pool. The MoEA-HSS and two classical multi-objective algorithms, NSGA-II [10]

Fig. 1. Visual description of MoEA-HSS

and SPEA2 [48] had been discussed in Zhang, Gen, and Jo [47]. The SPEA2 algorithm depends on the raw fitness assignment mechanism and density mechanism where the individuals strength, raw fitness, and the distance need to be calculated. Since the complicated computation of distance value and pruning scheme in updating archive phase, SPEA2 requires more computational time than NSGA-II which depend on the Pareto ranking and the crowding distance. However, The MoEA-HSS, which use two simple sampling strategies, requires the least computational time while preserving both the convergence and distribution performances.

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Parallel MoEA-HSS Approach

CUDA is a parallel computing platform and application programming interface (API) model that allows a CUDA-enabled GPU to be used for general purpose processing [7]. It is accessible through CUDA-accelerated libraries and extensions to industry-standard programming languages. Each processing unit executes the same code specified by the kernel function in Single Instruction, Multiple Threads (SIMT) fashions. The main benefit of GPU computing is a significant speed up in the calculation. A typical processing flow of a CUDA program follows the following pattern: (1) Copy data from CPU memory to GPU memory. (2) Invoke kernels to operate on the data stored in GPU memory. (3) Copy data back from GPU memory to CPU memory. When a kernel function is launched from the host side, execution is moved to a GPU where a large number of threads are generated, and each thread executes the statements specified by the kernel function. All threads spawned by a single kernel launch are called a grid. All threads in a grid share the same global memory space and are grouped into a block which shares data efficiently via shared memory. A grid is organized as a 2D array of blocks, and a block is organized as a 3D array of threads. However, there are restrictions on the maximum number of threads in one block. So, threads need to be partitioned into several thread blocks with the same size, then organized the grid dimension for the maximum performance [7]. Parallel MoEA-HSS Approach: For evaluating multiple objectives in the railway scheduling model, we applied parallel MoEA-HSS with GPU under CUDA as follows: (1) Genetic representation For designing a chromosome, we define it as a C × 2S–length matrix G, in which C is the total number of train operating cycle and S is the number of the station (Fig. 2). We first randomly generate the initial population using a uniform random number between 0 and 1. This way we can create a feasible solution from the chromosome without repairing process. Each chromosome will be used to

Fig. 2. The genetic representation

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calculate the train arrival and departure time. Where the traveling time between stations and headway between two consecutive trains can be calculated by: Rmin tR + gci (tRmax − tRmin ), ∀c, k, i = {1, · · · , Cmax S} cki = ti i i

(13)

hc = hmin +gci (hmax −hmin ), ∀c, i = {0, S +1, 2S +1, · · · , (Cmax −1)S +1} (14) The next operating train schedule will use the previous consecutive train timetable as a reference. The calculations will be the same until all the trains are scheduled. (2) Evaluation process The evaluation process is parallelized, where each chromosome in the population is calculated by the same decoding routine because this process needs a lot of computational time compared to other processes. In order to parallelize the task of evaluating the fitness function, the population is stored in GPU global memory, and each chromosome in a generation is assigned to a GPU thread to be evaluated independently. (3) Crossover process Two-point cut crossover is used in the crossover process. Two threads will be paired to create two chromosomes for the next generation by crossover two randomly selected chromosomes from the mating pool. The number of threads created by the kernel is equal to the number of populations. (4) Learning-based mutation process The ML techniques can be incorporated into different EAs in various ways, and they also affect EAs on various aspects. In many application, ML enhanced techniques have been proven to be advantageous in both convergence speed and solution quality. The surveys of ML-technique enhanced-EAs can be found in Jourdan, et al. [20], Zhang, et al. [46] and Lin and Gen [24]. We enhanced the mutation process by using the learning algorithm to improve the quality of the evolutional process. The randomly chosen position of the chromosome is replaced with the value given by the learning algorithm. As introduced in previous Subsection, the archive mechanism uses A(t) to store the chromosomes with good PDDR-FF value which is similar to an elitist sampling strategy. These chromosomes will be used to teach the chromosome in the next generation. The learning algorithm mimics the teaching and learning process of a typical class, the learner. Chromosome learns from the teacher. In each generation t, one chromosome with the best fitness value (GT j ) is selected to be the teachers of  all offspring. The new mutated chromosome (Gj )will be generated from each offspring GT j , as follows:

Advances in Hybrid Genetic Algorithms with Learning and GPU 

T L Gj = GT j + σ(Gj − Gj )

331

(15)

where j = 1, 2, · · · , popSize, GLj = GT j and σ is a parameter scaling factor in range [0, 1]. Then, if the new mutated chromosome has a better fitness value than the old offspring chromosome, the new mutated chromosome will be selected for the next generation. (5) Selection process The chromosomes of A(t) and P (t) are combined to form a temporary archive A (t). Then, the PDDR-FF values of all chromosomes in A (t) are calculated and sorted in an ascending order. The smallest |A(t)| chromosomes in A (t) are copied to form A(t + 1). Then, the Pareto optimal solution E(P, C) is created by using non-dominated routine. The fitness value from each generation will be used to update both of the A(t) and E(P, C). After obtaining the E(P, C), we use the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach [19] to find the best compromised train timetable from the Pareto optimal set. This approach considered the preference of the decision maker among the objective functions [8]. The overall procedure of the proposed algorithm is illustrated in Fig. 3.

Fig. 3. The pMoEA-HSS procedure in the pseudo code

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4 4.1

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Numerical Experiments Case Study of Train Scheduling

We consider the train scheduling problem for a double-track railway system in the case study of the BTS line for a larger case as shown in Fig. 4 and the demands in terms of average number of passengers in Fig. 5. The case study of the train scheduling will be demonstrated to evaluate the proposed algorithm in which two parallelized algorithms (pMoEA-HSS and pMoEA-HSS with Learning-based mutation), together with three sequential CPU algorithms (MoEA-HSS, SPEA2, and NSGA-II) are conducted 10 times to compare the results. The same selection, crossover, and archive mechanisms are used. The single point mutation process is used for all the sequential CPU algorithms. The parameters of both algorithms are the same. The archive sizes are set to be half of the population size. All the computation are performed on the NVIDIA Quadro K620 GPU and a 2.10 GHz Intel Xeon E5-2620 CPU on Windows 10 Pro OS. The code was written and compiled in Python 3.6 with pyCUDA 2017.1. The adopted parameters are listed as in Table 2. The following two measures were considered to evaluate the performance of the approaches. Table 2. The list of adopted parameters Population size

100

Maximum generation 1000 Archive size

50

Crossover probability 0.8 Mutation probability 0.3 GPU Block sizes

32

TOPSIS weight (wq ) (0.7, 0.3), (0.5, 0.5), (0.3, 0.7)

M1) The average compromised distance (Dc (Qr )) is used to calculate an average minimum distance of the solutions Qr from Q∗ in Euclidean n-dimensional distances. The smaller Dc (Qr ) means that the compromised schedule is superior for the decision maker since the best alternative should have the shortest distance from the ideal solution, where Qr is a solution set of each approach (r = 1, 2, · · · , 5), and Q∗ is a combination of all Pareto sets that were obtained using all compared approaches. M2) The computational time used to obtain solutions. The BTS Silom Line consists of 13 stations (S = 14), start at National Stadium (W1) in central Bangkok and travel southward to the last station S12

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Fig. 4. BTS Silom line, which operates in center part of the city, has higher passenger demand

Fig. 5. Demands in terms of average number of passengers

at Bang Wa station with the total length of 13 km. The present BTS system operates daily from 05:00 to 24:00. The regular timetable the Bangkok BTS Silom transit line is shown in Fig. 6, the headways during normal operation is 15 mins, and 10 mins from 7:00 to 9:00 and from 17:00 to 22:00. The total number of operating cycles is 91 cycles. The running times between two adjacent stations for Bangkok ARL transit line are given in Table 3. The dwelling times at each station are 30 s.

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Fig. 6. Regular timetabling scheme for Bangkok BTS transit line Table 3. Running times between two stations for BTS transit line OD station # Running time (s) OD station # Running time (s)

4.2

W1 – CEN

60

S12 – S11

120

CEN – A1

180

S11 – S10

120

S1 – S2

120

S10 – S9

120

S2 – S3

120

S9 – S8

60

S3 – S4

120

S8 – S7

180

S4 – S5

180

S7 – S6

120

S5 – S6

120

S6 – S5

120

S6 – S7

120

S5 – S4

180

S7 – S8

180

S4 – S3

120

S8 – S9

60

S3 – S2

120

S9 – S10

120

S2 – S1

120

S10 – S11

120

S1 – CEN

180

S11 – S12

120

CEN – W1

60

Experimental Results

From experimental results, under the same operation period, when we add more trips to the line, the average passenger waiting time is decreased. This is usual fact that higher train frequency or shorter headway time reduce passenger waiting time at the stations, which improves the service quality, but will potentially increase the number of operational trains which leads to higher operational costs. This can be seen that it shows the objectives of the best-compromised solution obtained from each algorithm. To compare the performance of the purposed algorithm, Pareto Frontiers of all algorithms are created from combining all of the obtained Pareto set with 10 runs together and the average compromised distance

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is used to evaluate the combined Pareto solution set with the best compromised solution obtained from the TOPSIS approach [8,19]. The best compromised timetable for the BTS line obtained from pMoEAHSS + ML algorithm with wq = (0.5, 0.5) is shown in Fig. 7. The headway time is varied in different time periods, for instance, the headway is shorter during 6:00 – 9:00 and 18:00 – 21:00 which is caused by the increased passenger demands as shown in Fig. 8. The performance of the learning mutation strategy is measured using Relative Improvement (RI), for this case study and parameters setting the pMoEA-HSS+ML can obtain the best compromised solution from the wq = (0.7, 0.3) and (0.5, 0.5). Sequential MoEA-HSS obtained the best compromised solution with wq = (0.3, 0.7). The pMoEA-HSS+ML have smaller Dc (Qr ) values compared with the other approaches in most cases, the SPEA2 and MoEA-HSS tend to do better in the wq = (0.3, 0.7) setting. The same results can be seen in the Case 2 (Table 4). The pMoEA-HSS+ML still be able to obtain the smaller Dc (Qr ) values compared with the other approach.

Fig. 7. The best compromised timetable for BTS transit line for pMoEA-HSS + ML

Fig. 8. Average compromised distance (Dc (Qr )) for Case 2: (b) wq = (0.5, 0.5)

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wq

SPEA2 NSGAII MoEA-HSS pMoEA-HSS pMoEA-HSS.ML RI (%)

(0.7, 0.3) 0.686

0.699

0.798

0.723

0.638

−11.06

(0.5, 0.5) 0.340

0.339

0.330

0.329

0.273

−17.02

(0.3, 0.7) 0.933

0.887

0.816

0.879

0.736

−16.27

From Table 5, it is clear that both parallelized algorithms (with and without learning-based mutation) are much faster than all of the sequential algorithm because, during the evaluation process, the fitness values needed to be calculated after the time tables creation process. Since each operating train timetable needs the previous consecutive train timetable as a reference, these timetable creation and fitness value evaluation processes took a lot of time to be calculated sequentially by the CPU. Separating all the evaluation processes into sub-processes (by allocating chromosomes to the GPU threads), the timetable can be created and then evaluated separately without waiting for the previous chromosome to be completely evaluated. This results in a significant speed up to the computational time. The speedup is even higher for larger if the number of chromosomes in the population is increasing. Moreover, the learning-based mutation process does not affect the computational time. In the larger problem case, the computational time for all sequential algorithms is increased from more data that need to be calculated. Table 5. The average computational time Average CPU Time SPEA2 NSGAII MoEA-HSS pMoEA-HSS.ML [s] per generation Case 1

12.08

10.77

10.20

0.04

Case 2

20.20

20.08

19.49

0.05

But the computational time for parallelized algorithms is almost the same. The results show the potential of the parallelized computation using the GPU unit. The parallelized algorithms can be used in more complex railway network or in the situation that needs very high computational time, like rescheduling the network after an accident.

5

Conclusions

Scheduling is one of the important and complex combinatorial optimization problem (COP) model, where it can have a major impact on the productivity of a manufacturing process. The most well known models of scheduling are confirmed

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as NP-hard or NP-complete problems. The target of scheduling is to find a schedule with the best performance through selecting a proper resource for each operation, the operation sequence for each resource and the beginning time. Genetic algorithm is one of the most efficient methods among metaheuristics for solving the real-world scheduling problems. In this paper we briefly surveyed the literature on genetic algorithms (GAs) with GPU (Graphics Processing Unit) acceleration. As a case study of Hybrid GA with GPU, we introduced parallel multiobjective evolutionary algorithm with hybrid sampling strategy and learning-based mutation (pMoEA-HSS with ML) based on GPU acceleration to solve the train scheduling model. For choosing a best compromised solution from the Pareto optimal solution set, we combined the TOPSIS method. The main objective is to obtain the best compromised solution to satisfy both the passenger satisfaction and train operational cost by minimizing the total averaged passenger waiting time and the total number of train operating cycle. Acknowledgements. This work is partly supported by Grant-in-Aid for Scientific Res. (C) of Japan Society of Promotion of Sci. (JSPS: No. 19K12148) and Thailand National Science and Technology Scholarship.

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Parameter and Mixture Component Estimation in Spatial Hidden Markov Models: A Comparative Analysis of Computational Methods Eugene A. Opoku1(B) , Syed Ejaz Ahmed2 , Trisalyn Nelson3 , and Farouk S. Nathoo1 1

Department of Mathematics and Statistics, University of Victoria, Victoria, BC V8W 2Y2, Canada [email protected] 2 Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada 3 School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, USA

Abstract. Hidden Markov models incorporating the Potts model for the labelling process are an important class of mixture models in spatial statistics. These models have been applied to problems in statistical mechanics, image analysis and disease mapping, among other areas. Jointly estimating the model parameters, the discrete state variables and the number of states (number of mixture components) is recognized as a difficult combinatorial optimization problem. We make comparisons between iterated conditional modes (ICM), simulated annealing (SA) and hybrid ICM with ant colony system (ACS-ICM) optimization for pixel labelling, parameter estimation and mixture component estimation. These comparisons are made for different levels of spatial dependence in the underlying true image. Our studies demonstrate that estimation based on ACS-ICM when carefully tuned exhibits performance that is uniformly superior to both ICM as well as a carefully tuned SA algorithm. Keywords: Ant colony system optimization · Hidden Markov random field · Iterated conditional modes · Mixture model · Potts model · Pseudo-likelihood · Simulated annealing

1

Introduction

The investigation of optimization algorithms for spatial hidden Markov models (HMMs) for the pixel/voxel-labeling problem is an important area of research and has many applications including those in image analysis where a Potts model c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 340–355, 2020. https://doi.org/10.1007/978-3-030-49829-0_25

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[29] is often used as a prior for the latent states of an image [1,26]. We consider here the setting where the data constitute a 2-dimensional image with each pixel represented by a continuous measurement and where the goal is to classify each pixel into one of a finite number of states. A discussion of various algorithms and applications for dealing with such problems can be found in [6,19,27]. A novel aspect of our study is that we consider the case where the number of latent states is unknown and we develop an estimator based on automatic pruning of redundant mixture components. The iterated conditional modes algorithm (ICM) [3] and the expectation maximization (EM) algorithm [6,7] will converge to a local optimum and they are the most widely used techniques for parameter estimation within this setting. ICM is a deterministic algorithm that iteratively maximizes the full conditional densities of model parameters and latent variables. While computationally efficient and widely used, ICM and EM can be very sensitive to initial values. A number of global optimization methods have been investigated including the simulated annealing algorithm [12] and the genetic algorithm [15]. A Bayesian framework is considered in [23] where the authors propose a general method of estimation applicable to the case of hidden data called the Iterative Conditional Estimation (ICE) algorithm. Their approach combines ICE with unsupervised fuzzy Bayesian image segmentation using hidden fuzzy Markov fields. [18] propose a clonal selection algorithm (CSA) and the use of Markov Chain Monte Carlo (MCMC) for HMM estimation for applications to brain magnetic resonance (MR) image segmentation. Their proposed approach employs a three-step iterative process that consists of MCMC-based class label estimation, bias field correction and CSA-based model parameter estimation. Moving beyond point estimation, [11] develop a reversible jump MCMC algorithm for fully Bayesian inference for a mixture of Poisson distributions with the Potts model used as a prior for mixture allocations and with an unknown number of latent states in the model handled using reversible jump MCMC. [14] develop a Bayesian framework that employs a novel stochastic search algorithm for computing estimates of the hidden MRF model parameters that also incorporates the EM algorithm for maximizing the posterior density. This procedure is applied to the estimation of HMMs for images where each pixel has an associated multivariate observation with the likelihood based on multivariate distributions. [31] propose MRF-based stereo algorithms that apply an iterative algorithm for MAP estimation. To overcome drawbacks associated with local algorithms such as ICM and EM, one alternative is the simulated annealing (SA) algorithm [12]. SA is a stochastic algorithm for combinatorial optimization that applies Monte Carlo sampling at each iteration to a modified objective function that corresponds to the original objective function raised to the power 1/Tj at successively decreasing values of the temperature Tj . The algorithm is based on initially setting the temperature to relatively high values leading to easier movement across the parameter space and then decreasing according to a specified cooling schedule until the temperature is close to zero.

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The SA algorithm can use either the Metropolis algorithm or Gibbs sampling and the two sampling procedures when used within SA have been shown to be asymptotically equivalent in some settings [17] but in general Gibbs-SA is different from the standard SA algorithm that uses the Metropolis algorithm. For the Potts-Gaussian mixture model Gibbs-SA is extremely convenient computationally when combined with a chequerboard updating scheme and we will thus focus on the Gibbs sampling variant of SA in the rest of the paper. While SA can theoretically reach a global optimum for some theoretically chosen cooling schedules [24], this algorithm is not a panacea as the choice of an optimal cooling schedule can be difficult in practice with parameter spaces of high-dimension. This is the case for the spatial mixture model considered here. Theoretically optimal cooling schedules are too slow to be of practical use and can be difficult to approximate so more practical faster empirically driven schedules must be used instead [10]. Thus even when well-tuned empirically the algorithm may fail to find a global optimum with very complex objective functions such as the log-posterior density of a spatial hidden Markov model incorporating the Potts model. The primary focus of our work lies with the Ant Colony System (ACS) optimization algorithm, a search algorithm based on the behaviour of real ants searching for food [8,19] and its application to spatial hidden Markov models when the number of mixture components is either known or unknown. ACS is a population-based approach based on a group of ants each constructing solutions (pixel labellings and parameter estimates) using pheromone information accumulated by the entire group of ants. Each ant is guided by a common function representing the distribution of pheromone which serves as a mechanism for the ants to communicate with each other regarding the quality of their estimates. In the variant of ACS considered here, we also incorporate the ICM algorithm within each iteration of ACS to conduct a local search that serves to improve the quality of the solutions found as well as estimate the mixture model parameters. We study the performance of ACS within the context of a Gaussian spatial mixture model (GMM) for continuous data over pixels in a 2-dimensional grid. The GMM incorporates a labelling process allocating each pixel to one of K latent states. The labelling process is assumed to follow a Potts model which allows for spatial dependence among neighbouring pixels with a hyperparameter, known as the inverse temperature, that controls the degree of spatial dependence. In addition to the ACS algorithm, we also consider the estimates obtained from Gibbs-SA and ICM and make comparisons. [30] propose a spatiotemporal model of land use change based on ant colony optimization using Markov chains and cellular automata. An important contribution related to our work is [9] who develop an ant colony optimization algorithm ‘AntMarkov’ for temporal hidden Markov models and compare their proposed algorithm with the Baum-Welch algorithm, Viterbi-Training, the structured nonnegative matrix factorization algorithm and the Tabu-Search method. An important distinction between their work and our work is our consideration of a spatial rather than temporal hidden Markov model. [13] develop a hybrid

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ant colony optimization and Baum-Welch algorithm for estimation in temporal hidden Markov models. [22] develop an artificial bee colony (ABC) algorithm for estimation of a 1-dimensional hidden Markov model developed for hand gesture recognition applications. [4] consider the ACS algorithm for a temporal hidden Markov model classifier and focus on dynamic adaptation of the algorithm’s tuning parameters. Closely related to our work is the work of [19] who consider ant colony optimization for image segmentation based on a Markov random field. There are several key differences between their work and ours. First, we consider the case where the number of mixture components is unknown and one has the goal of estimating the number of latent states. Second, we consider estimation of pixel labels and mixture model parameters which leads to an ACS-ICM algorithm; whereas, [19] only describe algorithms for the pixel labelling (image segmentation in their case) problem via ACS. Third, we carefully consider the tuning of both the ACS-ICM and Gibbs-SA algorithms carefully and adopt formal procedures for optimizing over tuning parameters. Fourth, we examine differences in the relative performance of ACS-ICM and competing algorithms under different levels of spatial dependence in the true scene. The remainder of the paper proceeds as follows. In Sect. 2 we provide more detail on the Gaussian-Potts spatial mixture model. Section 3 presents a description of the ICM, Gibbs-SA and ACS-ICM algorithms within the context of the Gaussian-Potts mixture model. Section 4 presents simulation studies comparing the algorithms under different levels of spatial dependence in the true image and the evaluation of mixture component estimation. The paper concludes with a discussion in Sect. 5.

2

Statistical Model

Let y = (y1 , y2 , . . . , yn ) be a vector of continuous values representing a 2dimensional image with n pixels and with yi being the value of the image at the ith pixel. We assume that yi follows a Gaussian-Potts mixture model so that at the first level the data are distributed as ind

yi |Z, μ, σ 2 ∼

K 

N (μ , σ2 )I(Zi =1) .

(1)

=1

Pixels are assigned to mixture components through a labelling process     Z = (Z1 , Z2 , . . . , Zn ) where Zi is a vector of K binary variables indicating the K mixture component to which observation yi has been assigned with l=1 Zi = 1. We assume that the allocation process follows a Potts model having the following probability mass function:  exp{β h∼j δ(Zj , Zh )}  P (Z|β) = , δ(Zj , Zh ) = 2Zj Zh − 1, G(β)

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where G(β) is the normalizing constant for this probability mass function, β ≥ 0 is a hyper-parameter, known as the inverse temperature parameter, which governs the degree of spatial dependence, and i ∼ j indicates that pixel i and j are neighbors. In what follows we assume a first-order neighborhood structure over a 2-dimensional regular grid of pixels. The model parameters are the component specific means and variances, μ = 2  ) . Priors completing the model spec(μ1 , μ2 , ....., μK ) and σ 2 = (σ12 , σ22 , ....., σK ification are assigned as follows: iid

σ2 ∼ Inverse-Gamma(aσ2 , bσ2 ),  = 1, 2, ...K, iid

μ ∼ N (vμ , wμ ),  = 1, ..., K. For the current study we assume β is known. In practice its value can be varied as part of a sensitivity analysis. The model unknowns are thus: 2 Θ = {Z, {μ1 , μ2 , ..., μK }, {σ12 , σ22 , ..., σK }}.

The number of mixture components is estimated using automatic pruning of mixture components in the optimization algorithm. Assuming the value of K used in the algorithms is larger than the true number of mixture components we obtain the estimated pixel labelings Zˆ and then count the number of non-empty ˆ = mixture components to obtain an estimate of the number of latent states K  K n ˆij > 0}. After estimation the K ˆ non-empty mixture components Z I{ j=1 i=1 are re-ordered according to the estimated component means in decreasing order.

3

Estimation

The MAP estimate is obtained as   ˆ = argmax log P Θ|y = argmax log P(Z, μ, σ 2 , y) Θ Z,μ,σ 2

Θ

where P(Z, μ, σ 2 , y) is the joint probability of Θ and the data y and takes the form P (Θ, y) = P (Z, μ, σ 2 , y) = P (y|Z, μ, σ 2 ) × P (Z|β) × P (μ) × P (σ 2 ) n  = [ P (yi |Z, μ, σ 2 )] × Potts(Z|β) × P (μ) × P (σ 2 ) i=1

≈[

n 

i=1

P (yi |Z, μ, σ 2 )] × PL(Z|β) ×

K  =1

P (μ )P (σ2 )

Optimization in Spatial Hidden Markov Models

=

n  K  i=1

×

K 

   N (yi ; μ , σ2 )I(Zi =1) × P (Zi |Z(−i) , β)

345

(2)

=1

IG(σ2 ; aσ2 , bσ2 ) ×

=1

K 

N (μ ; wμ , vμ )

(3)

=1

Where Potts(Z; β) denotes the joint probability mass function of the Potts model with inverse temperature parameter β evaluated at Z and PL(Z|β) is the corresponding pseudolikelihood approximation [2] that we use moving forward; N (y; μ, σ 2 ) denotes the density of the normal distribution with mean μ and variance σ 2 evaluated at y; IG(σ2 ; aσ2 , bσ2 ) denotes the density of the inverse-gamma distribution with parameters aσ2 and bσ2 evaluated at σ2 . The pseudolikelihood approximation is used to avoid the difficult computation of the normalizing constant G(β) associated with the Potts model. Equations (1.2) and (1.3) together comprise the objective function that we wish to maximize over Θ = {Z, μ, σ 2 }. 3.1

Iterated Conditional Modes

The iterated conditional modes (ICM) algorithm [3] is well known and well studied. It proceeds by iteratively maximizing full conditional distributions of the model parameters and latent variables. For the spatial model under consideration, the labelling process variables Z are updated using an efficient chequerboard updating scheme [3,26]. Within this scheme Z is partitioned into two blocks Z = {ZW , ZB } according to a 2-dimensional chequerboard arrangement, where ZW corresponds to the ‘white’ pixels and ZB corresponds to the ‘black’ pixels. Under a Markov random field with first-order neighbourhood structure the elements of ZW are conditionally independent given ZB , and vice versa. This allows for simultaneous updating of all elements of ZW followed by simultaneous updating of all elements of ZB and this can be made relatively fast using multiple cores. Convergence of the algorithm is based on monitoring the relative change in the objective function. The ICM algorithm for the spatial hidden Markov model is presented in Algorithm 1. 3.2

Annealed Gibbs Sampling

Simulated annealing (SA) is developed in [12] and is a stochastic algorithm for combinatorial optimization [20]. The algorithm is inspired by the annealing process in which materials are raised to higher energy levels and then cooled in search of an optimal low energy configuration. This notion is implemented through the introduction of a temperature parameter that is used to provide a rescaling of the objective function that may allow the algorithm to avoid local optima. At higher values of the temperature parameter the rescaled objective function has a flatter more uniform shape which makes it easy for the algorithm to

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move around the parameter space and explore the values of the function. As the temperature parameter moves towards zero the rescaled function becomes increasingly concentrated around small neighbourhoods of its local optima. We work with a variant of SA that applies the Gibbs sampler at each iteration, which we call Gibbs-SA, successively at decreasing values of the temperature parameter. The algorithm is run in stages and each stage consists of a sequence of Gibbs sampling iterations with the target distribution being proportional to 1 P (Z, μ, σ 2 , y) Tj , where Tj is the temperature for stage j. Initially, this temperature is set to a relatively high value which encourages the Gibbs draws to move around the parameter space. The value of Tj is decreased according to a carefully chosen schedule and as the temperature approaches zero the system freezes near an optimum of the objective function. The sequence of Tj values is known as the cooling schedule and in what follows we create this sequence based on the rule Tnew = Told ∗ k for k ∈ (0, 1) and where k is a tuning parameter. The tuning parameters for the algorithm are thus k and the initial temperature T0 and these are chosen using an outer level optimization carried out over the tuning parameters. We optimize over the tuning parameters (To , k) using the Nelder-Mead optimization algorithm [25] which is implemented by using the ‘optim’ function in the R programming language [28]. 3.3

Ant Colony System - Iterated Conditional Modes

Ant Colony System (ACS) optimization is a population based metaheuristic method that can be used to find approximate solutions to combinatorial optimization problems. This algorithm was introduced by [8] as an approach to the travelling salesmen problem and is inspired by the behaviour of an ant colony based on how ants attempt to find an optimal path to a food source. The ACS algorithm is based on a set of agents each representing an ant searching for solutions to the problem of maximizing the posterior density. A key idea is the leaving of a marker representing a pheromone trail quantifying the quality of a path found by a given agent. The process of constructing a solution is stochastic and is biased by this pheromone trail which is represented by a function in the algorithm. Ants modify the pheromone trail (and thus the function that represents it) when a particular path or estimate is chosen and this represents information available to the other ants that are also searching for solutions. It is through the pheromone trail that the ants communicate information about the solutions chosen and the objective function. Ants follow certain paths probabilistically and the probability of a given path depends on the current value of the pheromone trail. As more ants find the same path it becomes reinforced through corresponding changes to the pheromone trail. The ants incrementally find an optimal solution through the evolving pheromone function. Our proposed algorithm for the spatial hidden Markov model combines ACS with ICM where the model parameters are assumed fixed and the mixture allocations variables are updated using ACS with a set number of ants and then

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the ICM algorithm is used for both updating the model parameters and also for a local search over the mixture allocation variables. Thus the update steps corresponding to ACS are combined with running ICM to convergence at each iteration. The combination of ACS for updating the pixel labels with ICM for updating the mixture model parameters is a key difference between our algorithm and that described in [19]. The algorithm has four tuning parameters. The first denoted qo ∈ (0, 1) controls the degree of stochasticity with larger values corresponding to less stochasticity and thus less random exploration of the parameter space. When a solution is chosen another tuning parameter τ0 controls the amount of pheromone reinforcing this solution in the information available to the other ants. A third tuning parameter ρ controls the evaporation of pheromone and finally a fourth tuning parameter Nants controls the number of ants. The number of ants used for this study is 20 (Nants = 20). As with Gibbs-SA the remaining tuning parameters (qo , τ0 , ρ) for ACS-ICM are chosen using an outer level optimization using the Nelder-Mead algorithm.

4

Simulation Studies

The algorithms I-III described in the previous section are compared using two simulation studies. In the first study we fix the number of mixture components at the true value (chosen as Ktrue = 3) and focus on parameter estimation and pixel labelling while in the second study the algorithms are run with K = 10 and the data are generated based on Ktrue = 3 and we compare the sampling ˆ SA and K ˆ ACS . The simulated data are based on a 100 ˆ ICM , K distributions of K (10 × 10) pixel image. In each study we run three sets of simulations each based on 100 simulation replicates with the data simulated from the Gaussian Potts mixture model with three mixture components. The level of spatial dependence in the underlying image varies across the three sets with the true pixel labels Z drawn from the Potts model (a single draw is used for each of the three sets) by varying the inverse-temperature parameter as β = 0, β = 0.2 and β = 1.1. The same datasets are used for each of the two studies with the difference being that K is fixed at the true value (Ktrue = 3)) in study I and K = 10 in study II ˆ Given the true labels, each where we focus on the sampling distribution of K. data replicate is simulated from the Gaussian mixture components with μ1 = 5, μ2 = 7 and μ3 = 9 and σ12 = 1, σ22 = 2 and σ32 = 3. For each dataset we fit the model using ICM, Gibbs-SA and ACS-ICM. To ensure a fair comparison, the same starting values are used to initialize all of the algorithms and all algorithms are run to convergence of the objective function. The pixel labelings are randomly initialized independently with equal probability for each class. Given the random labelings, the mixture component means are taken as the average of the data values for those pixels assigned to a given component and the mixture component variances are taken as the corresponding

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Algorithm 1 - ICM algorithm 1. Θ ← Set Initial Value 2. Converged ← 0 3. While Converged = 0 do 4. for l = 1, . . . . K do n n    I (Zil =1)   I (Zil =1)(yi −μl )2  2 i=1 i=1 + b + a σl ← 2 / 2 + 1 σ σ 2 2 end for 5. for l = 1, . . . . K do n   I (Zil =1)yi i=1 + μl ← σ2 l

wμ vμ



2

2

D , where D =



n  i=1

I (Zil =1) σl2

+

1 vμ

−1

end for 6. Let B denote the indices for ’black’ voxels and W denote the indices for ’white’ voxels. 7. For κ ∈ B simultaneously Zκq ← 1 and Zκl ← 0, ∀l = q where q = argmaxh∈{1,...,K} P (h), and P (h) =

    −1 n 2 σh ×exp − 1 v∈δκ Zvh i=1 (yi −μh ) +4β 2    K  n (y −μ )2 +4β σ −1 ×exp − 1 l v∈δ Zvl i=1 i 2 l=1 l κ

where δκ contains the indices for the neighbours of pixel κ. end for 8. For κ ∈ W simultaneously Zκq ← 1 and Zκl ← 0, ∀l = q where q = argmaxh∈{1,...,K} P (h), and P (h) =

    −1 n 2 σh ×exp − 1 v∈δκ Zvh i=1 (yi −μh ) +4β 2    K  n (y −μ )2 +4β σ −1 ×exp − 1 l v∈δ Zvl i=1 i l=1 l 2 κ

end for 9. check for convergence; set Converged = 1 if converged. end while

sample variances. For pixels in which the random labelings result in a mixture component having no pixels assigned to it, the prior mode is used as the initial value for the component model parameters.

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Algorithm 2. Gibbs Simulated Annealing 1. Set tuning parameters: Initial temperature T0 and scaling constant k 2. Θ ← Initial Value 3. At iteration j, run N iterations of the Gibbs sampler with 2

∝ P (Z, μ, σ , y)

1 Tj

target

Gibbs sampling: Begin for l=1, . . . . K do σl2 ∼ Inverse-Gamma(al , bl ) n 

where

al

=

i=1

I (Zil =1)

+

2Tj

n 

aσ2 +1 Tj

− 1 and

bl

=

i=1

I (Zil =1)(yi −μl )2

+

2

bσ 2 Tj

End for Begin for l=1, . . . . K do μl ∼ N (ml , Dl2 ) n   I (Zil =1)yi ml = i=1 T σ2 + j

l

wμ Tj ×vμ



Dl2 ,



and Dl2 =

n  i=1

I (Zil =1)

Tj ×σl2

+

1 Tj ×vμ

−1

End for Use the chequerboard scheme to sample Z based on the pixel-specific full conditional distributions: Zi ∼ multinomial(1, prob = (P [Zi1 ], . . . , P [ZiK ]) ), i = 1, . . . , n  −1/Tj  P [Zih ]

=

P (Zih

=

1)

=

σh

K

l=1

×exp −

σl−1 ×exp



n 2 4β  1 v∈δκ Zvh i=1 (yi −μh ) + Tj 2Tj σ 2 h  4β  1 n 2 − v∈δκ Zvl i=1 (yi −μl ) + Tj 2Tj σ 2 h





where δκ contains the indices for the neighbours of pixel κ. Set Θ based on the values of Z, μ, σ 2 obtained from the final iteration of Gibbs sampling. 4. Until Tj ≈ 0. Update Tj = kTj−1 and go back to step 3. Return final values of σl2 , μl and Zv

To compare the performance of the algorithms, we first store the optimized objective function value obtained from each of the three algorithms for each of the 300 datasets (100 for each level of spatial dependence). In addition to the objective function values we compute for each pixel, the proportion of simulation replicates in which the estimated label is equal to the true label. We then examine the distribution of these proportions across the image and this is displayed in

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Algorithm 3 - ACS-ICM Algorithm 1. Θ ← Initial Value; set tuning parameters τo , qo , ρ and Nants . 2. Initialize pheromone information τ (i, ) = τo , for each (i, ) ∈ {1, . . . n} × {1, . . . K} representing information gathered by ants about the pixel labelling. 3. Construct candidate solutions for each of Nants ants. For ant j, we find a candidate (j) (j) (j) pixel labelling Z (j) = (Z1 , Z2 , . . . , Zn ) . • Construct candidate by assigning label l to pixel s using the rule:  arg maxu τ (s, u) if q ≤ qo = RANDOM if q > qo where if q > qo the label for pixel s is drawn randomly from {1, . . . , K} with probability τ (s, ) , p(s, ) =  u∈Λ τ (s, u) and where q ∼ uniform[0, 1]. • Assuming pixel s is assigned label l set: τ (s, ) = (1 − ρ)τ (s, ) + ρτo and for all k = l set:

τ (s, k) = (1 − ρ)τ (s, k)

where ρ is a tuning parameter in (0,1), which represents evaporation of the pheromone trail and τo > 0. 4. Run ICM to convergence on the solutions obtained from all ants while also updating mixture component parameters μ, σ 2 . 5. For all Nants solutions, evaluate the quality of each ant’s solution using objective (j) function: OBJ(Z (j) , μ(j) , σ 2 ). Keep track of the best value. The current solution for each ant serves as the starting value for the next iteration. 6. Apply a global updating of the pheromone function. For the best ever solution (s, ) update the function as: τ (s, ) = (1 − ρ)τ (s, ) + ρτo and for all k = l set:

τ (s, k) = (1 − ρ)τ (s, k).

Check for convergence. Go back to step 3. 7. Return pixel labelling Z and model parameters μ and σ 2 from the best ever solution.

Fig. 1. A clear pattern emerges showing that ACS-ICM gives the most accurate pixel labelings, followed by Gibbs-SA and then ICM and this ordering is constant across levels of spatial dependence.

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Fig. 1. Distribution (across 100 simulation replicates) of the proportion of correctly labelled pixels with β = 0 (left), β = 0.2 (center) and β = 1.1 (right).

Figure 2 shows three pairs plots comparing the final objective function values obtained from each of the algorithms for the three different levels of spatial dependence. Again, a clear pattern emerges showing that ACS-ICM yields the highest objective function values in almost all cases, followed by Gibbs-SA and then followed by ICM. It is clear that ICM underperforms substantially compared to ACS-ICM and Gibbs-SA as expected and it is also clear that ACS-ICM has the best overall performance. For mixture component estimation, Table 1 compares the bias and meanˆ SA and K ˆ ACS for the three different levels of spatial ˆ ICM , K squared-error of K dependence. We observe that the estimator of the number of mixture components obtained from ACS-ICM exhibits the best performance in terms of both bias ˆ SA ) < Bias(K ˆ ICM ) and MSE(K ˆ ACS ) < ˆ ACS ) < Bias(K and MSE with Bias(K ˆ ICM ) and that this ordering is seen at all three levels of ˆ SA ) < MSE(K MSE(K spatial dependence. We also note that the bias is positive in all cases so that the number of mixture components is over-estimated. With respect to computation time, with all algorithms programmed in the R software and run on an Intel E5-2683 v4 Broadwell 2.1 Ghz processsor with 16 GB RAM the average (over simulation replicates) computation time to convergence for a single image is 15 min for the ICM algorithm, 50 min for the Gibbs-SA algorithm and 180 min for ACS-ICM.

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Fig. 2. Comparison of objective function values obtained for each dataset and algorithm with β = 0 (top), β = 0.2 (middle) and β = 1.1 (bottom).

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Table 1. Bias and Mean Square Error (MSE) of estimated number of mixture compoˆ from the 100 simulation replicates with different spatial correlations when nents (K) the algorithms are run with K = 10. β=0

ICM SA

ACS-ICM

ˆ Bias(K) 3.13 2.03 1.64 ˆ MSE(K) 10.47 4.61 3.10 β = 0.2 ICM SA ACS-ICM ˆ Bias(K) 2.86 1.89 1.42 ˆ 8.70 3.93 2.30 MSE(K) β = 1.1 ICM SA ACS-ICM ˆ Bias(K) 2.69 1.76 1.32 ˆ 7.71 3.46 1.96 MSE(K)

5

Discussion

We have studied the problem of computation for MAP estimation for the spatial hidden Markov model with both known and unknown number of mixture components. Our studies demonstrate a superior performance of the ACS-ICM algorithm when compared to Gibbs-SA and ICM algorithms in terms of objective function values, pixel labelling accuracy and mixture component estimation. This relative performance in the three algorithms appears constant across different levels of spatial dependence. Our most important result is the demonstration that the ACS-ICM outperforms Gibbs-SA when both algorithms are carefully tuned with an outer-level optimization that uses the Nelder-Mead algorithm to select tuning parameters. While theoretical results associated with simulated annealing allow for convergence to a global optimum for some tuning schedule, in practice this can be difficult to achieve even after careful tuning as demonstrated in our studies. ACS-ICM exhibits superior performance with an equal effort made in tuning both algorithms. Our results suggest that ACS-ICM is a useful and potentially powerful approach for MAP estimation with spatial mixture models and may be a preferred approach when the increased computation time is feasible. We are currently extending our investigation to the development of an ACSICM algorithm for computing solutions to inverse problems where existing approaches have used the ICM algorithm in combination with spatial mixture models [26]. The algorithm may have considerable potential for application to other statistical problems involving combinatorial optimization such as MAP estimation with spike-and-slab variable selection [21] and optimization for deep learning [16]. Along these lines [5] develop an ant colony optimization algorithm for training recurrent neural networks. These and other statistical and experimental design problems are potentially fruitful areas for further study of ACS-ICM.

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Acknowledgements. Research is supported NSERC, the Visual and Automated Disease Analytics (VADA) graduate training program and CANSSI.

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Sustainable Water Allocation Under Multi-disciplinary Framework: Dealing with Uncertainties in Decision Making and Optimization Liming Yao(B) , Zerui Su, and Xudong Chen Business School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. Evidence shows that studies on sustainable water allocation require multi-disciplinary expertise to achieve a combined perspective of natural and social science. Inspired by sustainable water engineering, this study incorporates a multi-disciplinary decision making framework for sustainable water allocation, and provides an example to demonstrate the proposed paradigm. Initially, experts in Hydrology, Meteorology, Geography, Water Resource Management, Sociology, Economics, and Environmental Sustainability are gathered in different workshops to discuss suitable criteria for consideration in water allocation schemes. The uncertainties in their linguistic expressions are accounted for by using multiple criteria decision making. Subsequently, the indicators and thresholds determined during the decision making process are transformed into parameters for a robust optimization model. Finally, a sample model that deals with the uncertainties in water allocation is shown. Under the proposed framework, multi-disciplinary expertise is critical and we recommend its use in future water allocation projects. Keywords: Sustainable water allocation · Decision making framework · Multiple criteria decision making · Robust optimization

1

Introduction

Water is essential for life. Confronted by environmental and social uncertainties, such as the urban heat island effect, accelerating urban expansion, land loss, we must develop improved water resource engineering and conservation methods. A unified environmental and social framework is required to accomplish future sustainable development goals. Existing ecological losses and climate change uncertainties are significant challenges. Moreover, water scarcity [11] and existing environmental concerns are yet to be addressed, increasing the need for stronger water management schemes. A “sustainable water management” framework considers the economic, environmental, and social benefits of hydrological c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 356–372, 2020. https://doi.org/10.1007/978-3-030-49829-0_26

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infrastructure considering multi-dimensional uncertainties [18], and prescribes new methods for the future management of water resources. Water allocation projects that manage the uneven distribution of water resources and satisfy the needs of multiple stakeholders face increasing challenges due to future uncertainties and poor decision making. This can only be addressed using a more sustainable framework in the future. The rising need to ensure public water security has led several researchers to analyze the decision making process in water management. In the 20th century, numerous water resource engineering methods were used to prevent hydrological threats [31]. To address ecological losses, several successful methods were applied to ecosystem management [20], water quality management [23], freshwater conservation [16], crop irrigation [10], etc. In addition, water-use efficiency was also considered, along with the optimization of water allocation. Hu et al. modified the traditional DEA model by incorporating bi-level programming and used it to improve water use and wastewater treatment efficiency [12]. Water has also been attached to other elements while studying sustainability. Zhang and Xu evaluated the efficiency of sustainable water management using the HFTODIM method, incorporating the synthesis of sustainable development, i.e., society, economy, and ecological environment [33]. In the field of management science, optimization models and other related algorithms for water allocation have been thoroughly studied and applied to water management procedures. However, room for further advancement still exists.

Fig. 1. Keyword network for literature related to “water allocation” in WOS.

A WOS search with “water allocation” as the subject, and “core collection” as the database type selection yields a total of 11217 published articles on “water allocation”. VOSviewer, a visualizing tool for scientific literature, was used to analyze hotspots in the relevant research and determine the number of related

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item groups (shown in Fig. 1). Water allocation is vital to the growth and productivity of plants, as indicated by the keywords “photosynthesis”, “nitrogen”, “stomatal conductance”, “gas exchange”, “carbon allocation”, etc., as well as the lifecycle mechanisms of water-based life-forms, such as reproduction, evolution, adaptation, etc. Water allocation and management largely support the functions of water resources such as water consumption, trading, irrigation etc., by the conservation, pricing, optimization, simulation, and governance of water resources. Furthermore, climate change, land-use change, and other uncertainties have a significant effect on water resources and are studied using system dynamics, water-footprint, life-cycle assessment, stochastic programming, etc. Under the framework of sustainability, factors such as energy, biodiversity, rainfall, and other natural elements are also included in the study of water allocation systems. However, anthropogenic factors, such as water “economics” and “markets”, only represent a small proportion of these studies. This suggests that the links between water resources and social benefits have not been thoroughly studied. Sustainable water allocation procedures that incorporate multi-dimensional elements from academia, society, and other sections are inevitable. From the perspective of urban hydrologists, human disturbances such as urbanization can impact the physical structure of streams, thereby influencing the hydrology in urban areas [15]. The increased threat of drought has led hydrologists to believe that groundwater depletion may influence future water security to a large extent [7]. Consequently, there is an urgent need to precisely measure available and allocated water resources. Uncertainties such as climate change pose new challenges to meteorologists, who need to diagnose increasingly variable meteorological symptoms, and prescribe new projections and precautions. A professional workshop with a multi-disciplinary panel of experts must be established and applied in further studies. This paper is organized as follows. A detailed problem statement is described in Sect. 2. The procedure of this research and an introduction to the approach used in the research paradigm are laid out in Sect. 3. In Sect. 4, the decision making paradigm is defined and analyzed. Section 5 lists the conclusions and future implications, as well as the shortcomings of this study.

2

Problem Statement

To address water scarcity, experts have proposed that we increase the efficiency of the use of available water, thereby enabling additional water allocation or reallocation [13]. If a river basin is regarded as a whole system, variable flows and other uncertainties can pose challenges to water allocation [19]. The process of water allocation consists of the description and definition of the problem, model construction, algorithm development, case studies, etc. [29]. Stakeholders in the traditional decision making framework for water allocation include those in charge of the higher or lower water allocation departments [30]. To improve the water allocation process, we must consider the following: (1) Water management is not an independent discipline and requires support from other

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disciplines such as Hydrology, Geography, Sociology, etc., to improve water allocation decision making; (2) The leaders in water allocation departments tend to make profit-driven decisions, indicating a likely bias in decision making; and (3) Sustainable water allocation should be resilient against the potential risks of real-world uncertainties such as rainfall, runoff change, and temperature change.

Fig. 2. Basic framework of water allocation and relative influencing factors.

Considering a single-layer decision-making system [34], the total amount of available water resources is denoted by S, which is allocated and transported to sub-areas shown as sub-i. The basic demand for water resources in sub-i is denoted by Di and the amount of allocated water to sub-areas is denoted by Qi . Similarly, ωi represents the weight of sub-i in water allocation. Considering water exchanges between each sub-area, a simplified mechanism is incorporated in which the out-flows of water resources are denoted by Outi while the in-flows are denoted by Ini . Uncertainties that may affect the benefits of decision making in water allocation, such as precipitation, temperature, and unpredictable natural disasters, are denoted by ε (shown in Fig. 2). Inspired by other successful attempts at sustainable water engineering, this study aims to establish a new framework that incorporates a multi-disciplinary panel of experts in the decision making process for water allocation. Furthermore, the traditional decision making process is modified, eliminating the potential bias in voting. The concept of resilience will then be combined with sustainable water allocation, to account for stochastic interruptions or uncertainties.

3

Research Framework

The United Nations (UN) defines sustainable development as development that meets the needs of the present without compromising the ability of the future

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generations. Since issues concerning water resources are quite controversial, countries that share water resources have been working on methods to ensure sustainable use, while satisfying the needs of all member states [26]. Water management has long been viewed as a contest between engineers and ecologists. The sustainability of water management projects is evaluated from a win-win context, wherein economic profits and ecological benefits are both achieved simultaneously. As mentioned earlier, sustainable water allocation is a combination of natural and social sciences and should be robust enough to deal with environmental uncertainties such as variable flows, temperature, and precipitation. Compared to traditional water allocation methods, this study proposes a newly established multi-disciplinary decision making framework, which ensures that the criteria and parameters considered in the optimization process are more persuasive from the perspectives of natural science, social science, and economic profits. As shown in Fig. 3, experts participating in the decision making are divided into three groups, i.e., Team A, Team B, and Team C. Team A consists of experts in Hydrology, Geography, and Meteorology who provide natural science insights to evaluate the theoretical and technical feasibility of water allocation schemes. Team B consists of experts in Economics, Ecology, and Sociology who inspect sectional sustainable capabilities and provide a sustainable development perspective. Water management experts in Team C participate in the discussions and perform a final optimization of the water allocation scheme. In the event of uncertainties in the linguistic expressions of the experts during the optimization process, a fuzzy multiple criteria decision making framework is used to achieve sustainable water allocation. A robust optimization of the sample model is performed to illustrate the proposed paradigm in a practical way. The overall procedure is shown in Fig. 4.

4 4.1

Decision Making Paradigm Scenario Illustration

This study considers a river basin that supplies two sub-areas. The fairness of water allocation schemes and the profits generated from both sub-areas are considered in the decision making process. The experts selected for the multidisciplinary decision making framework (shown in Fig. 3) are invited to provide scientific support and accurate references for instruction. Besides, future uncertainties such as varying river flow may hinder the process and final profits of the water allocation scheme. Initially, the experts will propose an optional criteria set and assign an initial rating for the basic feasibility, intermediate effects on implementation, and long-term sustainability of the water allocation scheme. Subsequently, thresholds-presented as interval numbers-will be assigned by the experts. We assume that there are nine experts, divided into three teams, i.e., Team A, Team B, and Team C. Profiles of each expert, including their expertise and authorized weights, are given in Table 1. The three expert groups are considered equally important and given the same weightage while evaluating criteria and thresholds. As uncertainties exist in the linguistic expressions of the experts, a fuzzy set, i.e., fuzzy linguistic expressions, are used as the input of the decision

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making process. Once the criteria and thresholds are settled, an optimization model will be established considering the numerical illustration of uncertainties. Table 1. Illustration of experts in different groups Team A

A2

A3

Expertise Hydrology Weights 0.4

A1

Meteorology 0.3

Geography 0.3

Team B

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Expertise Economy Weights 0.33

Sociology 0.33

Ecology 0.33

Team C

C2

C3

Water pollution management 0.2

Water allocation management 0.4

B1

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Expertise Water resources management Weights 0.4

Fig. 3. Multi-disciplinary decision making framework for sustainable water allocation.

4.2

Decision Making Process

(1) Decision scaling Uncertainties have long been viewed as biggest challenge in the decision making process. Traditional solutions to uncertainties primarily involve a

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top-down climate risk assessment that applies general circulation models (GCMs) to predict future precipitation and temperature. While several materials have been attached to diversified GCM projections, these methods continue to encounter problems. As indicated by Brown et al., utilizing the results of the projections while considering the requirements of multiple decision makers is quite difficult. To account for uncertain climate change, a decision-scaling approach that provides a framework to compensate for traditional decision processing has been used in adaptive decision processing to prevent hazards [5]. Under the new decision making framework established by Brown et al., climate information is incorporated by building links from the bottom-up using a stochastic vulnerability analysis and GCM projections. This strengthens the decision making framework against climate uncertainties, with newly added climate information. Simultaneously, the reliability of alternative schemes is assessed considering the probability of future risks to arrive at an optimal scheme. Poff et al. applied decision-scaling to sustainable water management considering future uncertainties and realized the conceptual framework proposed by Brown et al. [18]. Specifically, an eco-engineering decision scaling (EEDS) approach that includes five steps was established within the framework of a multi-stakeholder decision making process. Inspired by Brown et al. and Poff et al., we established a three-step decision making framework for water allocation. Initially, experts in Hydrology, Geography, and Meteorology select the basic indicators and thresholds to assess reliability. The indicators and thresholds that may influence the benefits of water allocation are selected. Next, experts in Economics, Ecology, and Sociology assess the overall sustainability of the optional schemes to provide a thorough scaling of sustainable water allocation. Finally, experts in Water Resource Management are introduced to the discussions. (2) Hesitant fuzzy linguistic term set Occasionally, the opinions of the experts may not appear as numerical ratings and thus cannot be directly treated as inputs. Instead, their linguistic expressions can be transformed and added to the decision making matrix. Rodrguez et al. proposed a multicriteria linguistic decision-making model to assess comparative terms that are represented by a Hesitant fuzzy linguistic term set (HFLTS) [32]. Suppose T is a linguistic term set, see Eq. (1). T = {b1 : poor, b2 : weak, b3 : fairly weak, b4 : medium, b5 : fairly good, b6 : good, b7 : excellent}

(1)

Essentially, a multi-criteria linguistic decision-making problem consists of a finite set of alternatives, O = {o1 , · · · , om }, where each alternative is defined by means of a finite set of criteria, C = {c1 , · · · , cn }.

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Fig. 4. Overall procedure of the new decision making paradigm.

Three phases are organized as follows: (1) Transformation phase: HFLTS establishment; (2) Aggregation phase: HFLTS aggregation; and (3) Exploitation phase: Alternative ranking. Opinions can be represented by linguistic expressions such as “better than good” or “worse than weak” which may include more than one term. For example, let T be a linguistic term set and X = {poor, weak, fairly weak} be an HFLTS of T , which is a fuzzy version of “worse than medium”. Transformation of a linguistic term set to deterministic ratings is shown in Table 2 and the processing of fuzzy linguistic expressions such as “better than fairly good”, is achieved by simply reorganizing the numerical ratings by a weighted average.

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2

3

4

5

6

7

(3) Simple multi-attribute rating technique method For complex and poorly-defined problems with multiple interrelated criteria, multiple criteria decision making (MCDM), an important sub-discipline of operations research, has been proposed [1]. MCDM is a sum of decisionmaking techniques and consists of Multiple Objective Decision Making (MODM) to design the best solution, and Multiple Attribute Decision Making (MADM) to select the best alternatives [28]. While handling MCDM problems, multiple conflicting criteria must be considered in the decisionmaking process and weights for each criteria must be given in the process of opinion integration [27]. Of the typical or modern solutions to MCDM problems, the Simple Multi-Attribute Rating Technique (SMART) method introduced by Winterfeldt and Edwards in 1986 is a simple tool that enables the aggregated prioritization of action alternatives and budget allocation [24]. Due to its ease of use, we apply the SMART model for further implementation of the criteria and schemes (alternatives) established in the process of decision scaling. Due to its simplicity in responding to the needs of decision makers [14], the SMART method has been applied to and tested along with the MADM. Myllyviita et al. incorporated the SMART method in the study of life cycle assessment, along with the Analytic Hierarchy Process (AHP) [17]. To assess the sustainability of tertiary wastewater treatment technologies, Plakas et al. used the SMART method to assign weights to selected sustainability indicators [4]. More recently, Borissova and Keremedchiev applied the SMART method to evaluate and rank students based on their theoretical knowledge and practical skills [8]. In accordance with other methods applied to MADM problems, SMART contains five major steps to rank alternatives. Step 1. Establish a basic matrix of alternatives and attributes. ⎤ ⎡ x11 · · · x1j · · · x1n ⎢ .. . . .. . . .. ⎥ ⎢ . . . . . ⎥ ⎥ ⎢ · · · x · · · xin ⎥ x (2) D=⎢ ij ⎥ ⎢ i1 ⎥ ⎢ . . . . . ⎣ .. . . .. . . .. ⎦ xm1 · · · xmj · · · xmn where D is the decision making matrix; xij is the element of attribute i in alternative j (i = 1, · · · , m; j = 1, · · · , n). Step 2. Rating the attributes. To begin with, the minimum and maximum values assigned for all attributes are pre-defined and denoted by Xmin and Xmax , respectively, where the decision

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makers rate the attributes in the interval of (Xmin , Xmax ). For calculation, the entire decision-making interval is divided into sub-intervals with equal lengths as follows: (3) Xmin , Xmin + r0 , Xmin + r1 , · · · Then, interval r can be calculated as: rq − rq−1 = εrq−1

(4)

Geometric progression is given as follows: rq = (1 + ε)rq−1 = (1 + ε)2 rq−2 = (1 + ε)q r0

(5)

Finally, the relationship between minimum and maximum values can be expressed as: Xmax = rq + Xmin (6) Step 3. Calculating the performance score of alternatives. Once we obtain the ratings for each attribute assigned by the decision makers, further processing of the matrix is conducted to judge each attribute. The qualitative attributes are ranked as shown in Table 2 and Eq. 7 is applied to the ranking of the attributes:  Xq − Xmin ) ∗ 2μ (7) q = log2 ( Xmax − Xmin where Xq represents the value of each alternative in the rated attribute and μ indicates the total number of intervals. For cost attributes, the effective weight is calculated as: yij = Xmax − q

(8)

For benefit attributes, the effective weight is calculated as: yij = Xmin + q

(9)

Step 4. Calculating the normalized weights of alternatives. Given the rank of each attribute, the weightage for each attribute can be calculated as follows: √ (10) ωj = ( 2)pj where Pj represents the rank of each alternative j (j = 1, · · · , n) given by a decision maker. Then, the given weights of the attributes are integrated as: √ ( 2)pj (11) wj =

n √ p j 2 j=1

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Step 5. Final ranking of alternatives. By processing the effective weights and normalized weights, the final ranking of alternatives is obtained as: n  wj ∗ yij (12) Fij = j=1

This concludes the decision making process. Specifically, a numerical example is shown as follows: Considering the robust criteria set established by the expert groups, further discussions are held over the scaling of the optional criteria, denoted by O(O1 , O2 , · · · , Oi ). Subsequently, attributes of each criterion are obtained from the inner discussions of each expert group, i.e., Team A, Team B, and Team C. Attributes obtained from the opinions of Team A are denoted by a(a1 , a2 , · · · , ai ), Team B by b(b1 , b2 , · · · , bi ), and Team C by c(c1 , c2 , · · · , ci ). Next, the experts assign ratings to each attribute of optional criteria using fuzzy linguistic expressions. We assume that there are three optional criteria that need to be further specified considering sustainable water allocation, evaluated by a total of nine attributes (see Table 3). Table 3. Example of original HFLTS ratings O1

O2

O3

a1 better than fairly good fairly weak

good

a2 good

fairly good

worse than fairly weak

a3 fairly good

better than fairly good fairly good

b1 good

fairly good

better than fairly good

b2 good

excellent

fairly weak

b3 fairly good

good

good

c1 weak

good

fairly weak

c2 medium

better than fairly good medium

c3 fairly weak

good

good

Using the SMART method, performance scores and normalized weights of alternatives are presented in Table 4 and Table 5, respectively. The overall performance of the alternatives are calculated: O1 = 4.613, O2 = 4.917 and O3 = 4.979. Evidently, O1 < O2 < O3 , which indicates that from the perspective of the expert groups, O3 is the optimal selection of the three alternatives. As for other criteria, the same procedures are conducted with different dimensions of expertise to find the most appropriate criteria to evaluate the basic feasibility, intermediate effects on implementation, and long-term sustainability of water allocation. The selected criteria are then used in the process of robust optimization as a form of parameter setting.

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Table 4. Performance scores a1

a2

a3

b1

b2

b3

c1

c2

c3

O1 6.3536 6.2895 1.5261 6.1520 1.8480 6.4739 1.5261 6.3536 5.8301 O2 6.5305 6.7843 2.7105 6.4739 1.1255 5.7370 1.5261 6.8301 6.2895 O3 6.1520 6.7843 1.3626 6.4150 1.2630 6.4739 2.2630 6.7370 6.6881 Table 5. Normalized weights a1

a2

a3

b1

b2

b3

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c2

c3

O1 0.1155 0.1090 0.1296 0.0971 0.0971 0.1296 0.1296 0.1155 0.0771 O2 0.1126 0.1503 0.0474 0.1063 0.1687 0.0597 0.1063 0.1593 0.0894 O3 0.0765 0.1445 0.1215 0.0964 0.1364 0.1022 0.0573 0.1364 0.1287

4.3

Optimization Process

(1) Robust optimization As mentioned earlier, uncertainties in water allocation can hinder the process of sustainability and must be tackled. One way to deal with uncertainties is to design a “robust” system or scheme that remains feasible and strong despite parameter changes [6]. Robust optimization has been used as an optimization model in energy management systems [22], agricultural water resource management [25], etc., for single-stage optimization problems, where uncertainties are defined as inequality constraints subject to a user-defined probability [9]. Unlike typical assumptions in deterministic programming, robust optimization contains unknown parameters that may influence the results. Essentially, uncertainties in robust optimization can be presented as interval numbers [2]. If x denotes a closed and bound set of real numbers, an interval number x± with known upper and lower bounds but with unknown distribution information can be defined as an interval for x as: x± = [x− , x+ ] = b ∈ x|x− ≤ x ≤ x+

(13)

where x− and x+ are the lower and upper bounds of x± . As Ben-Tal and Nemirovski demonstrated, two representative scenarios of uncertainty are “unknown-but-bounded” and “random” [3]. The “unknown-butbounded” elements in the decision making matrix are subject to uncertainty and xij − ε¯ xij , x ¯ij + ε¯ xij ](0 ≤ ε ≤ 1), where x ¯ij is a nominal x ˜ij , which ranges from [¯ value that appears at most ε¯ xij . Generally, the uncertainty set is specified as follows: xij = x ¯ij + ε¯ xij ηij }(i, j, η ∈ ϕ) (14) φ = {˜ xij |˜ and ϕ = {|ηij | ≤ 1,

n  j=1

|ηij | ≤ τj }(−1 ≤ ηij ≤ 1)

(15)

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where τj denotes the budget of uncertainty. When τj = 0, ηij is equivalent to 0; when τj = |Ji |, i is completely protected from uncertainty; when τj ∈ [0, |Ji |], a trade-off exists [21]. An integrated linear form of the robust optimization model is as follows: max Z = cx n n   a ¯ij xij + ξi τi + ζij ≤ bi , s.t. j=1

j=1

ξi + ζij ≥ εaij xij , x ¯ij − ε¯ xij ≤ x ¯ij ≤ x ¯ij + ε¯ xij ,

(16)

xij , ξi ζij ≥ 0 where ξi and ζij are additional variables. When they are both equal to 0 or the ε is equivalent to 0, the robust problem alters to another problem, i.e. the nominal linear problem. In terms of uncertainties appearing in water allocation, we applied the robust optimization model as a solution to uncertain water supplies, water demand, variable meteorological indicators, and other influencing factors related to overall sustainability. (2) Objective function Sustainable water allocation considers fairness, robustness, and socioeconomic benefits simultaneously. The robustness of a scheme is reflected in its ability to sustain basic functions in the face of uncertainties. It can be solved using robust optimization, especially considering environmental uncertainties. In terms of fairness, the objective of this study is to achieve the integrated satisfaction of the higher authorities and the lower stakeholders. Moreover, the socio-economic benefits are also taken into consideration as one of the objectives. Maximizing the integrated sustainability: max F = Fecon + Fsoci + Fecol

(17)

where F represents the overall sustainability of water allocation with the decision making variable Qi , while Fecon , Fsoci , and Fecol denote the economic, social, and ecological profits, respectively. Generally, Fecon consists of indicators that illustrate economic gains of the regional entities, factories, and other sections. For example, the economic experts in Team B may perform an accurate and persuasive measurement of regional and sectional economic gains from the water allocation scheme. Similarly, Fsoci represents the concerns of local citizens, including the amount of water available to them and the impact of the allocated water on their personal incomes. The most relevant indicators are selected and added to Fsoci . Fecol represents the benefits for the ecological environment and incorporates the unit return of ecological water consumption, the proportion of ecologically-benefited areas from water allocation, etc.

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Minimizing the integrated loss: ˜=L ˜ econ + L ˜ soci + L ˜ ecol min L

(18)

˜ represents the overall loss in sustainability. L ˜ econ , which is usually where L presented as an interval number with considerable upper and lower boundaries, represents a conceptual indicator that may influence the economic profit from water allocation. For example, sub-areas with lesser amounts of allocated water may lose incomes from water-related industries, which may be restricted within ˜ soci represents the a certain range, depending on the local policies. Similarly, L ˜ ecol represents the ecological loss to the sustainability of the local society and L loss of sustaining uneven water distribution. (3) Constraints Generally, typical water allocation programming contains of constraints like available water resources, least demands of sub-areas determined by population, industries, etc. As shown in Fig. 2, a sample model of water allocation can be established by considering the primary water allocation and point-topoint water exchange. For robust optimization, variables such as river flows, precipitation, temperature, etc., can be transformed into interval numbers. Water availability constraints: the total amount of water allocated to each subarea cannot exceed the available resources of the basin. S˜ is an interval variable that appears in interval numbers and indicates a dynamic variation in water allocation. n  ˜ + S˜κ ) ˜ Qi (S˜ = Stotal + P˜ − E (19) S≥ i=1

where Stotal is the total available water resources excluding the basic ecological demand of the river basin. P˜ denotes the dynamic precipitation within the river ˜ represents the evaporation of water observed due to temperature basin, while E variation. S˜κ contains other influencing factors that vary within an interval. Water allocation constraints: the total amount of water allocated to each sub-area cannot be below their basic demand, which is defined by population, industry, and other water-related requirements. ˜ i (D ˜i = D ˜ citizen + D ˜ industry + D ˜ κ) ˜i ≥ D Q

(20)

˜ i represents the total allocated water to sub − i and consists of D ˜ citizen , where D ˜ industry , and D ˜ κ. D ˜ citizen denotes the basic water consumption of citizens D and is influenced by the in-flows and out-flows of population and the seasons. ˜ industry denotes the basic water consumption of industries and is influenced by D ˜ + −D ˜− ˜ κ is equivalent to D the number of factories and other economic factors. D κ κ + ˜ where Dκ represents the in-flows from water exchange (Ini ) and other dynamic ˜ κ− represents the out-flows from water exchange factors and variations, and D (Outi ) and other dynamic factors and variations.

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Satisfaction constraints: complete the minimal objective of sectional and regional satisfaction. n  I˜ ≥ I˜min (21) i=1

where I˜ denotes the actual satisfaction of sectional managers and I˜min represents the total satisfaction of the stakeholders of the water allocation scheme. (4) Optimization model Therefore, a sample robust optimization model for water allocation that considers uncertainties is established as follows: max F = Fecon + Fsoci + Fecol ˜ =L ˜ econ + L ˜ soci + L ˜ ecol min L s.t. n 

˜ = Stotal + P ˜−E ˜+S ˜κ ) ≤ S ˜ Qi ( S

i=1

˜i ≥ D ˜ i (D ˜i = D ˜ citizen + D ˜ industry + D ˜ κ) Q n 

(22)

I˜ ≥ I˜min

i=1

˜ S ˜κ , P ˜ , E, ˜ Q ˜i, D ˜ i, D ˜ citizen , D ˜ industry , D ˜ κ , I, ˜ I˜min }) γ ¯ − ε¯ γ ≤γ ¯ ≤γ ¯ + ε¯ γ (γ = {S, Qi ≥ 0

˜ are functions of Qi and γ represents a range number set which where F and L ˜ Q ˜i, D ˜ i, D ˜ citizen , D ˜ industry , D ˜ κ , I, ˜ and I˜min . ˜ ˜ includes S, Sκ , P˜ , E,

5

Conclusions and Future Implications

Inspired by sustainable water engineering, this study incorporated multidisciplinary expertise in the decision making framework for sustainable water allocation, and demonstrated an example in dealing with the uncertainties that appear in linguistic expressions of the experts and water allocation processes. Experts in Hydrology, Meteorology, Geography, and Water Resource Management decide which indicators are valuable and how much the settled thresholds determine the efficiency of water allocation. Experts in Sociology, Economics, and Environmental Sustainability add to the discussion with their concerns about the fairness and benefits to local citizens, as well as the natural environment. Subsequently, their opinions are transferred into objective functions and constraints for a robust optimization process. A sample robust optimization model for sustainable water allocation, which incorporates parameters obtained through discussion and evaluation was demonstrated. This study has two shortcomings. First, this paper is limited to conceptual innovation and lacks empirical examination. This can be rectified in a future study with the establishment of an expert workshop. Second, the sample model must be further extended with specific parameters and original data to test its feasibility and robustness.

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As this study aims to provide new ideas for further studies, trans-disciplinary studies in sustainable water allocation, such as Hydrology-Water Resource Management, Hydrology-Meteorology-Water Resource Management, and HydrologyMeteorology-Geography-Water Resource Management may be conducted in the future. While the work performed in this study is limited, we will continue to study this subject and find solutions to the existing shortcomings. Acknowledgements. The work is supported by the National Natural Science Foundation of China (Grant No. 71771157), Funding of Sichuan University (Grant No. skqx201726 and 2019hhs-19), and Social Science Funding of Sichuan Province (Grant No. SC19TJ005).

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A Hybrid Model for Online Merchandise Recommendation Based on Ordination and Cluster Analysis Siqi Hu1 , Shihang Wang2 , and Zhineng Hu1(B) 1

2

Uncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, People’s Republic of China [email protected] Business School, Sichuan University, Chengdu 610064, People’s Republic of China

Abstract. With the continuous development of modern information technology, online shopping is becoming more and more popular. With more and more online products, how to rank and recommend online products is particularly important. This paper proposed a hybrid model combining unconstrained ordination analysis and cluster analysis. Ordination analysis is used to explain the relationship between online merchandise and its indexes; then cluster analysis is implemented to classify the results of the ranking analysis. Therefore, the buyer can directly understand the situation of the product in terms of the product index and the store index from the bi-plots. The proposed model solves the neglect of the link between commodities and their indexes in traditional rankings. Buyers can purchase goods accurately according to their needs. Keywords: Online products · Rank analysis · Ordination analysis

1

· Hybrid model · Cluster

Introduction

Online shopping is becoming more and more convenient, making more and more people choose to shop online [19]. On various online shopping platforms, (such as Taobao, JD. com, Suning Tesco, etc.), there are tens of thousands of products, and the number of the products is constantly increasing. The product search ranking system is an important part for online shopping platforms. It has become more and more important how to search for the user’s search keywords, retrieve the products associated with them, and effectively recommend the products [14]. Good product recommendations can put the most relevant products first, which not only meets the shopping needs of the end users, but also increases the possibility of related products being purchased. Therefore, it is necessary to study the ranking recommendation based on online products. However, in the research on commodity ranking, researchers mostly ignore the characteristics c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 373–383, 2020. https://doi.org/10.1007/978-3-030-49829-0_27

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of attributes. The traditional method of product ordering has caused multiple objective indexes not to be fully reflected by integrating weighting methods, which is not conducive to buyers to find out where the advantages of the products are and whether they meet their own purchasing needs. This paper focuses on exploring the links between commodities and commodity indexes in order to help consumers choose products more easily.

2

Literature Review

Online product rankings are complicated because of involving multiple product indexes [6]. Therefore, Peiliang Tian proposed a method for researching the problem of commodity ranking based on opinion mining [16]. In addition, B. K. Mohanty and K. Passi have adopted a fuzzy algorithm based on network information to deal with the problem of product ranking [12]. The comprehensive ranking of most e-commerce companies today uses the “rank aggregation” idea in web page ranking [5,13]. However, “rank aggregation” ignores the impact of commodity indexes on commodities. Therefore, it is a better choice to conduct a joint analysis of sample commodities and their indexes. Ordination is the process of arranging vegetation samples by one or more ecological gradients [1,8,9]. The ordination analysis mainly reflects the relationship between plots and species in low-dimensional space as realistically as possible, and has matured in ecology [4,18]. The ordination analysis can be a good aid for product classification [10]. It laid the foundation for the subsequent clustering of commodities. The Unconstrained Ordination analysis methods, CA (Correspondence analysis), PCA (Principal Component Analysis), PCoA (Principal Coordinate Analysis) are applicable to the commodity data [2,7]. However, Ordination analysis does not explain product classification well, so cluster analysis needs to be introduced as well. Cluster analysis is often used to analyze multivariate data and find the same group or class among them [3]. Hierarchical clustering can help us cluster highquality data at different levels [15]. At the same time, for the numerical data in this paper, the method based on hierarchical clustering Ward’s Linkage is a good choice [17]. Although different types of sample data can be obtained in cluster analysis, the correlation between sample data and indexes cannot be obtained. Therefore, based on the advantages and disadvantages of ordination analysis and cluster analysis being complementary, this paper proposed a hybrid ordinationclustering model to better explain the relationship between commodities and their indexes.

3

Data Handing

The data is from Sichuan Taoshihui E-commerce Co., Ltd. The company was established in February 2016 and is located in Guangming New District, Xingwen County, Yibin City, Sichuan Province, covering an area of 1,000 square meters.

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The company adopts the support mode of “e-commerce + industry organization + farmers”, “e-commerce + leading enterprises + farmers”. E-commerce poverty alleviation products have been promoted throughout the county to help farmers achieve poverty alleviation. The company has absorbed and assisted more than 100 small and medium-sized agricultural product breeding farmers to sell on the Internet. A product called bacon was obtained from Taoshihui. Ranked products are the same except for the eight indexes in the paper. The matrix of index values of this product is M = (Mij )a∗b , where a is the number of products, a = 30; b is the number of product indexes, b = 8, Mi1 is the monthly sales index (unit: piece); Mi2 is the price index (unit: yuan); Mi3 is Number of comments; Mi4 is the favorable rate index; Mi5 indicates the number of store followers index; Mi6 indicates the store credit rating index (1–20); Mi7 indicates the transportation costs index; Mi8 indicates the store turnover rate index. In addition, this paper classed the attribute characteristics of ranked products into two categories, one is the product index, such as price, monthly sales etc; The other is the store index, such as store followers index, store turnover rate, and number of store followers. Next, from the two different perspectives, the relationship between the product and its index will be analyzed, and the combination of these two aspects can better recommend the product to the buyer.

4

The Model

This section describes the design of the proposed model, the results obtained by the model, and the methods for conducting exploration analysis. To analyze the relationship between commodities and their attribute indexes, a hybrid model of ranking and clustering can be shown as three steps (Fig. 1).

Standardized

TSH

Raw commodity data

Processed data

Ordinadion analysis

Clustering analysis

Commodity recommendation

Fig. 1. Flowchart of the proposed model

(1) Step 1: Data processing Usually there are two categories of positive indexes and negative indexes. The larger the positive index value, the better the index; and the smaller the negative index value, the better the index. Among the eight indexes, monthly sales,

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the number of comments, favorable rate, number of followers, store credit rating, and turnover rate are positive indexes, while price and transportation costs are negative indexes. To better and more intuitively observe the impact of product attribute characteristics on product ranking, all positive indexes need to be converted into negative indexes. Besides the following formula for normalization processing, the resulting matrix is Q = (Qij )p∗q . 1) Standardization of positive indexes Qij =

max Qij − Qij ; max Qij − min Qij

2) Standardization of negative indexes Qij =

Qij − min Qij ; max Qij − min Qij

where max Qij and min Qij respectively represent the maximum and minimum values of the corresponding quantities of each index in the sorted product. (2) Step 2: Ordination Analysis After completing the above work, the data was converted to the same scale without negative and missing values, then Ordination analysis can be used. The Unconstrained Ordination analysis methods, CA (Correspondence analysis), PCA (Principal Component Analysis), PCoA (Principal Coordinate Analysis) are applicable to the derived matrix. Like other dimensionality reduction algorithms, these methods can map variables and samples to twoor three-dimensional space while preserving the original information as much as possible. In two dimensions, we compare the percentage of information held by CA, PCA, and PCoA. Therefore, CA was chosen to complete the model (Table 1).

Table 1. The evaluation of each ordination analysis methods Group

CA

PCA

PCoA

Product index 80.3% 67.4 49.5% 100% 89.3% 62.7% Store index

CA can more intuitively analyze samples, variables, and the relationship between variables and samples. First, the closer a point is to the origin, the fewer features it has. Second, adjacent samples or variables usually have similarities, and the sample’s preference for each variable can be measured according to their relative distance. In addition, the cosine law can be used to obtain sample correlation and variable correlation based on their angle to the origin.

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(3) Step3: Ward’s Linkage Clustering based on Ordination Analysis Finally, Ward’s Linkage clustering method is applied to the low-dimensional space obtained by CA. Ward’s Linkage is a hierarchical clustering method [17]. In order to determine the optimal clustering of Ward’s Linkage, the concept of “cluster gain” is applied [11]. The larger the cluster gain, the better the model performance. The optimal amount of clustering is usually chosen at the point of convergence or inflection. Ward’s Linkage clustering results on product indexes and store indexes. (Fig. 2 and Fig. 3). The results obtained by CA are clustered by ward to clarify the boundaries between classes. It can better explain the results of CA.

Fig. 2. Ward’s Linkage results of product indexes

Fig. 3. Ward’s Linkage results of store indexes

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Results and Discussion The Results of Integration Step

Although there is a large difference in monthly sales, it means that some products are very popular and some are not so popular. on the whole, the monthly sales of most products are not large, the prices are moderate, and the praise rate is above 80%, indicating that buyers are satisfied with these products (Table 2). Table 2. The descriptive statistical analysis for product indexes Primary indexes

Mean

Std

Min

25%

75%

Max

Monthly Sales Price Number of comments Favorable rate Transportation costs

219.25 61.4 76.15 86.26 6.425

219.4724 16.9746 53.1307 6.8833 4.9441

31.00 29.9 18.00 55.56 0.000

67.25 48.0 31.50 84.49 5.000

348.75 73.5 111.25 89.77 8.000

981.00 103.0 193.00 97.67 30.000

There is a large difference between the number of followers in the store. The smallest number of followers is only one, and the largest can reach 16,000, indicating that buyers are selective about the stores they follow. Most shops have good credit, which means they can be trusted. In addition, for those who have already purchased the store and have a good product impression, most buyers will make a second purchase (Table 3). Table 3. The descriptive statistical analysis for store indexes Primary indexes

Mean

Std

Min 25%

75%

Max

Number of followers 2533.3 4130.96 1.0 362.2 1447.2 16000.0 2.74 0.00 7.00 10.00 13.00 Store credit rating 8.25 0.00 4.00 23.00 100.00 Store turnover rate 16.07 19.27

5.2

The Results of CA

Obviously, all products and their indexes are mapped on a two-dimensional coordinate chart (Fig. 4). A product that is close to the index indicates that this product performs well on this index. For example, it can be clearly seen that the favorable rate of products such as products 10, 18, and 22 perform well; 2, 3, 7, 8, 17, 31, 21, 24, 20, 23, 25, 29, 33 and other goods performed well in monthly sales. Commodities 9, 39, 12, 11, 1, 14, 34, etc. are more balanced in

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Fig. 4. Bi-plot of product indexes by correspondence analysis

all aspects of commodity indexes. Among them, the monthly sales volume of the index and the number of comments are closer to each other on the map, and they are similar. According to the law of cosine, the monthly sales volume is highly correlated with the number of comments.

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While paying attention to the quality of the products, buyers are also concerned about the credibility of the store (Fig. 5). Store credit mainly reflects store credit rating, turnover rate, and number of followers from three aspects. The turnover rate of product 39,35,18 is relatively high, and the number of following products 28, 22, 26, 10, 16, 40, 20, 30, 34 is relatively high. The indexes

Fig. 5. Bi-plot of store indexes by correspondence analysis

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for commodities 23, 29, 15 and other commodities are relatively balanced, and there are no indexes with special preferences. 5.3

The Results of Hybrid Model

Based on CA, we then use ward for clustering. While ward classifies commodities into categories, it helps to strengthen the interpretation between variables and indexes, and the boundaries between different commodity categories will become clearer. For commodity indexes, the best results are achieved when commodities are divided into six categories (Fig. 6). Combining the results in the CA chart, we can clearly see that the preference indexes of various commodities. For example, the favorable rate of class 2 is better and the transportation cost is lower. The monthly sales and favorable rate of class 5 are better. The number of comments and monthly sales of class 4 and class 6 are good. Class 3 has a good performance in terms of price. However, the performance of class1 indexes is relatively poor. (Fig. 6)

Fig. 6. Ward’s clustering result after CA (for product)

The results are consistent with the previous analysis of commodity indexes (Fig. 7), class 1 and class 4 have a better performance in store return rate and attention; class 2 has a good performance on the credit rating of products; however, indexes in class 3 are relatively poor. (Fig. 7). Combining the results in the two figures, We can make product recommendations for different groups of people; for example, we can see that the products

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Fig. 7. Wards clustering result after CA (For store)

9, 19, 35, 39 are cost-effective, and the stores are trustworthy. Students who have no income ability are suitable to buy 7, 8, 17, 20, 23, 24, 27 and other commodities have high sales and good store credit, but the price is a little expensive. They are suitable for people with certain ability to pay, such as office workers. At the same time, combining the two indexes to analyze the goods, we can more accurately recommend for the needs of buyers.

6

Conclusion

According to the back-end data feedback of rural e-commerce, the proposed model accurately reflected the actual situation of each commodity. CA is designed for small data set analysis, which is fully suitable for this application scenario. Therefore, CA is introduced as the key component of the hybrid model. Because CA can’t classify the data accurately, cluster analysis is introduced for supplementary interpretation. Comparing with the comprehensive ranking of large e-commerce companies such as Taobao, this model reduces all indexes of all commodities to twodimensional space, and converts the original absolute ranking of weighted integration into relative ranking. It is also different from the single-factor ranking of them. They only consider the influence of a single factor on the ranking. The results recommended by the ranking often cannot meet the needs of buyers. Therefore, considering all the indexes, the proposed model also focused on embodying the superior indexes of all products, so that the recommendation results can make the buyer more satisfied.

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Acknowledgements. This research was supported by the projects 2019skzx-pt171.

References 1. Austin, M.: On non-linear species response models in ordination. Vegetatio 33(1), 33–41 (1976) 2. Austin, M.P.: Continuum concept, ordination methods, and niche theory. Annu. Rev. Ecol. Syst. 16(1), 39–61 (1985) 3. Everitt, B.: Cluster analysis. Qual. Quan. 14(1), 75–100 (1980) 4. Dale, M.: On objectives of methods of ordination. Vegetatio 30(1), 15–32 (1975) 5. Fagin, R., et al.: Searching the workplace web. In: Proceedings of the 12th International Conference on World Wide Web, pp. 366–375 (2003) 6. Feng, Q., Hwang, K., Dai, Y.: Rainbow product ranking for upgrading e-commerce. IEEE Internet Comput. 13(5), 72–80 (2009) 7. Gauch Jr., H., Whittaker, R.: Comparison of ordination techniques. Ecology 53(5), 868–875 (1972) 8. Goodall, D.W.: Objective methods for the classification of vegetation. iii. An essay in the use of factor analysis. Aust. J. Bot. 2(3), 304–324 (1954) 9. Goodall, D.W.: The continuum and the individualistic association. Vegetatio 11(5– 6), 297–316 (1963) 10. Whittaker, R.H.: Gradient analysis of vegetation. Biol. Rev. Camb. Philos. Soc. 49(2), 207–264 (1967) 11. Jung, Y., Park, H., Du, D.Z., Drake, B.L.: A decision criterion for the optimal number of clusters in hierarchical clustering. J. Global Optim. 25(1), 91–111 (2003) 12. Mohanty, B., Passi, K.: Web based information for product ranking in e-business: a fuzzy approach. In: Proceedings of the 8th International Conference on Electronic Commerce: The New E-commerce: Innovations for Conquering Current Barriers, Obstacles and Limitations to Conducting Successful Business on the Internet, pp. 558–563 (2006) 13. Nagmoti, R., Teredesai, A., De Cock, M.: Ranking approaches for microblog search. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 153–157. IEEE (2010) 14. Ryu, Y.U.: A hierarchical constraint satisfaction approach to product selection for electronic shopping support. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 29(6), 525–532 (1999) 15. Strauss, T., von Maltitz, M.J.: Generalising wards method for use with Manhattan distances. PloS One 12(1), e0168288 (2017) 16. Tian, P., Liu, Y., Liu, M., Zhu, S.: Research of product ranking technology based on opinion mining. In: 2009 Second International Conference on Intelligent Computation Technology and Automation, vol. 4, pp. 239–243. IEEE (2009) 17. Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963) 18. Zavadskas, E.K., Mardani, A., Turskis, Z., Jusoh, A., Nor, K.M.: Development of topsis method to solve complicated decision-making problemsan overview on developments from 2000 to 2015. Int. J. Inf. Technol. Decis. Mak. 15(03), 645–682 (2016) 19. Zhang, K., Narayanan, R., Choudhary, A.N.: Voice of the customers: mining online customer reviews for product feature-based ranking. WOSN 10, 11–11 (2010)

Model Selection and Post-estimation via Pretesting: Ridge Regression Pannipa Rintara1(B) , Supranee Lisawadi1 , and Syed Ejaz Ahmed2 1

2

Department of Mathematics and Statistics, Thammasat University, Klong Luang, Pathum Thani, Thailand [email protected] Faculty of Mathematics and Science, Brock University, St. Catharines, ON, Canada

Abstract. The goal of this study is to improve the efficiency of parameter estimation in a gamma regression model, when there was uncertainty about the quality of subspace information and multicollinearity was present. Ridge-type of pretest estimation strategy was applied and a Monte Carlo simulation was conducted to evaluate the proposed estimators. These estimators outperformed the classical ridge regression estimator. The suggested strategy was applied to a real dataset to test the practicality of the estimators.

Keywords: Linear shrinkage information

1

· Pretest · Shrinkage pretest · Subspace

Introduction

Generalized linear models (GLMs) are an extension of the framework of a linear regression model where the response variable is assumed to follow an exponential family distribution. Maximum likelihood estimation is commonly used to obtain the unbiased estimator of the regression parameters. However, it is common that the predictors are intercorrelated, which indicates that multicollinearity is present. Such problems yield an unduly large variance of the maximum likelihood estomator (MLE). To address this, Hoerl and Kennard [7] first proposed the classical ridge regression method for the linear regression model and Segerstedt [15] applied the method to GLMs. Therefore, we have a full model containing all predictors and the estimator from this model is known as the unrestricted ridge regression estimator. Nevertheless, there are some predictors which may not have an influence on the response variable, so that the efficiency of full model estimation may decrease. To improve the quality of the estimators, we can use the subspace information to identify the effective and ineffective predictors. The source of this information may be the experience of the researcher, previous studies, or model selection techniques. We can treat such information in the estimation as a c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 384–395, 2020. https://doi.org/10.1007/978-3-030-49829-0_28

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restriction, which yields a restricted estimator. The model has only the effective predictors, known as a submodel. In cases with multicollinearity, we investigate the restricted ridge regression estimator of Sarkar [14]. However, the situation is commonly encountered in practice, we are uncertain of the accuracy of subspace information. The main focus of this work is to test the validity of such information before incorporation into the estimation. If the information is accepted, then the parameter is estimated from the submodel. Conversely, the parameter is estimated from the full model. Therefore, pretest estimation strategy is suggested, following Bancroft [4] and Ahmed [1]. This strategy has been applied to many statistical models by researchers including Lisawadi et al. [9] and Reangsephet et al. [12] for logistic regression, Yüzbaşı et al. [18] for linear regression, Yüzbaşı et al. [17] for partially linear regression, and Reangsephet et al. [13] for negative binomial regression. Ahmed [2] discussed all strategies in a range of contexts. For relevant work, see Hossain et al. [8], Raheem et al. [11], Yüzbaşı and Ahmed [16], Ahmed and Yüzbaşı [6], and Amin et al. [3]. The literature on ridge-type pretest estimation strategy in a gamma regression model is limited. In this study, we proposed pretest strategy for parameter estimation in a gamma regression model, when subspace information is available but has an unknown degree of uncertainty. The organization of the paper is as follows. The gamma regression model is introduced in Sect. 2. Pretest estimation strategy is discussed in Sect. 3. We conducted Monte Carlo simulations to study the performance of the proposed estimators, and these are reported in Sect. 4. A real data example is given in Sect. 5, and the conclusions are provided in Sect. 6.

2

Gamma Regression Model

Let Y1 , Y2 , . . . , Yn be independent random variables follow a gamma distribution with shape parameter ν > 0 and rate parameter λi > 0, denoted as Yi ∼ G (ν, λi ) for i = 1, 2, ..., n. The following probability density function is consistent with the parameters: λνi ν−1 −λi yi y f (yi ; ν, λi ) = e , yi > 0. (1) Γ (ν) i The mean and variance are E [Yi ] = ν/λi = μi and V [Yi ] = ν/λ2i , respectively. Given that λi = ν/μi and the dispersion parameter φ = 1/ν, the above equation can be rearranged as a function of μi and φ and written in the exponential form as   ⎧   ⎫ 1 1 ⎪ ⎪ ⎪ ⎪ ⎨ yi μ − ln μ ⎬ i i (2) f (yi ; μi , φ) = exp + c(yi , φ) , ⎪ ⎪ −φ ⎪ ⎪ ⎩ ⎭ where c(yi , φ) = ((1 − φ) /φ) ln yi − (1/φ) ln φ − ln Γ (1/φ) and the canonical link function is the reciprocal function 1/μi = xi β. This is the linear combination

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of the predictors xi = (1, xi1 , xi2 , . . . , xip ) , and β = {β0 , β1 , β2 , . . . , βp } ∈ Rp+1 is a (p + 1) × 1 vector with unknown regression coefficients. However, the  alternative log link function μi = ex i β is used rather than the canonical link since it confirms that μi > 0. The corresponding log-likelihood of Eq. (2) is then given by n

 1  (3)  (β) = −yi e−x i β − xi β + c (yi , φ) . φ i=1 The maximum likelihood estimator (MLE) is obtained by maximizing Eq. (3): n

∂ (β) 1  −x i β yi e = − 1 xi = 0. (4) ∂β φ i=1 Applying the iteratively reweighted least squares, the MLE of β is obtained: ˆ n X)−1 X  W ˆ n z, ˆ βˆUE = (X  W

(5)

ˆi ˆ n = I and the ith element of vector zˆ becomes zˆi = x βˆ + yi − μ where W and i μ ˆi  ˆ μ ˆi = ex i β .

3

Pretest Estimation Strategies

In practice, predictors may be highly intercorrelated, which can yield a phenomenon known as multicollinearity. The variance of MLE increases until it becomes unstable. Unrestricted ridge regression is an alternative estimator. Segerstedt [15] proposed the following unrestricted ridge regression estimator (URRE) for GLMs: ˆ n X + κIp ]−1 X  W ˆ n zˆ βˆUE (κ) = [X  W = Rn (κ)βˆUE ,

(6) (7)

where βˆUE is the MLE in Eq. (5) and κ ≥ 0 is the ridge parameter. If κ = 0, then the URRE is the MLE. If κ → ∞, then the URRE is zero. As noted above, the some predictors may not influence the response variable, so should be eliminated from the model to improve estimation efficiency. Then, β belongs to the subspace defined by Rβ = r, where R is a q × (p + 1) matrix and r is a q × 1 vector of known constant. Without loss of generality, r may be a zero vector. In the presence of multicollinearity, the corresponding restricted ridge regression estimator (RRRE) of β takes the following form: βˆRE (κ) = Rn (κ)βˆRE . Here, βˆRE is the restricted MLE, so that  −1 ˆ n X)−1 R R(X  W ˆ n X)−1 R (RβˆUE − r). βˆRE = βˆUE − (X  W

(8)

(9)

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Our work focuses on a special case that all coefficients except the intercept are zero. Therefore, β belongs to the subspace defined by Rβ = 0, where R = [0p×1 , Ip×p ], or βp = 0p , excepting the intercept. In practice, the accuracy of the subspace information is unknown, and the pretest method requires testing before incorporation into the estimation. This is used to remove the uncertainty surrounding the information. The pretest ridge regression estimator (PTRRE) for β is derived as follows: βˆPTE (κ) = Rn (κ)βˆPTE = βˆUE (κ)I(Ln > Ln,α ) + βˆRE (κ)I(Ln ≤ Ln,α ).

(10) (11)

Here, βˆPTE = βˆUE I(Ln > Ln,α ) + βˆRE I(Ln ≤ Ln,α ) is the pretest estimator based on the MLE of β, I(·) the indicator function, and Ln a general statistic for testing the null hypothesis H0 : Rβ = r. We adopt the Wald test statistic [5] to test the null hypothesis H0 : Rβ = 0:  ˆ ˆ Ln = (RβˆUE ) (φR(X Wn X)−1 R )−1 RβˆUE ,

where φˆ =

n

 1 (n − p − 1) i=1



yi − μ ˆi μ ˆi

(12)

2 .

Under the null hypothesis, Ln follows the chi-square distribution with p degrees of freedom. Ln,α is the upper α-level critical value of the chi-square distribution with p degrees of freedom. The linear shrinkage ridge regression estimator (LSRRE) is a linear combination of URRE and RRRE. The LSRRE for β is derived as βˆLSE (κ) = π βˆRE (κ) + (1 − π)βˆUE (κ).

(13)

where π ∈ [0, 1] denotes the shrinkage intensity, or the degree of confidence in the subspace information. This value can be set by the researcher, based on personal confidence in the accuracy of the information. Ahmed [1] introduced a shrinkage pretest estimator which substitutes the linear shrinkage estimator into the restricted estimator in Eq. (11). The shrinkage pretest ridge regression estimator (SPTRRE) of β is then βˆSPTE (κ) = βˆUE (κ)I(Ln > Ln,α ) + βˆLSE (κ)I(Ln ≤ Ln,α ).

(14)

Its performance was found to superior to that of the PTRRE across a large portion of the parameter space. For selection of the ridge parameter κ, Amin et al [3] recommended the following ridge estimator with the minimum mean square error:  1 p   1 p+1 , (15) κ= mj j=1 where mj =



 ˆ αˆ2 and α ˆ j is the jth element of γ βˆUE , where γ is the eigenφ/ j

 ˆ n X. vector of X W

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Monte Carlo Simulation

A Monte Carlo simulation was conducted to evaluate the performance of the proposed estimators. All calculations were run on the R statistical program. The relative mean square efficiency (RMSE) was used as the criterion when comparing their performance, with the mean square error of the URRE as benchmark.  The RMSE of βˆ (κ) is then defined as   ˆUE (κ)   M SE β    . RM SE βˆUE (κ), βˆ (κ) = (16) M SE βˆ (κ) If the RMSE is greater than one, estimator βˆ (κ) is superior to the URRE. The response variable was generated from a gamma distribution Yi ∼  G(ν, ν/μi ), such that μi = ex i β for i = 1, 2, . . . , n with sample size n = 100 and shape parameters ν = 1 and 1.5. The predictors were drawn using the following formula: xij = (1 − ρ2 )1/2 zij + ρzip ,

(17)

where ρ2 represents the correlation between the predictors setting ρ = 0.5, 0.7, and 0.9, and zij are independent standard normal random numbers. The number of predictors was set as p = 4, 8, and 12, the shrinkage intensity π = 0.25, 0.50, and 0.75 and the significance level α = 0.05 and 0.10. To study the behavior of the proposed estimators, the RMSEs were reported as a function of Δ∗ = ||β − β 0 ||. Δ∗ is the divergence between the simulated model and the submodel under the null hypothesis, where a simulated parameter β = (0.5, βp ) such that βp = (Δ∗ , 0p−1 ), the submodel parameter β 0 = (0.5, 0p ) , and || · || is the Euclidean norm. The simulation was iterated 5,000 times to provide stable results. 4.1

Simulation Analysis

We would like to evaluate the performance of the proposed estimation strategy when the size of Δ∗ is changed. Tables 1, 2 and 3 show the RMSEs of the proposed estimators when Δ∗ = 0 and shape parameter ν = 1. In Table 4, the RMSEs of the proposed estimators when Δ∗ ≥ 0 and shape parameter ν = 1.5, ρ = 0.5 and α = 0.05. Other results are shown in Fig. 1. The results can be summarized as follows: 1. When the subspace information was correct, so that Δ∗ was zero, the proposed estimators outperformed the URRE and improved in efficiency as p increased. 2. As can be seen in Fig. 1, when the subspace information was correct, LSRRE dominated all other estimators as measured by RMSE. As Δ∗ moved away from zero, its RMSE decreased and converged on zero. However, the RMSEs converged to zero more slowly when the shrinkage intensity was small.

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Table 1. RMSEs of proposed estimators with respect to βˆUE (κ) when Δ∗ = 0, ν = 1, n = 100 and ρ = 0.5 Number of predictors (p) 4 8 12

Estimator PTRRE

α = 0.05 α = 0.10

3.639 5.166 5.640 2.842 3.806 4.108

LSRRE

π = 0.25 π = 0.50 π = 0.75

1.578 1.668 1.699 2.687 3.190 3.395 4.647 7.054 8.469

SPTRRE π π π π π π

= 0.25 = 0.25 = 0.50 = 0.50 = 0.75 = 0.75

α = 0.05 α = 0.10 α = 0.05 α = 0.10 α = 0.05 α = 0.10

1.465 1.396 2.193 1.946 2.750 2.319

1.545 1.476 2.531 2.237 3.421 2.833

1.562 1.495 2.610 2.311 3.594 2.974

Table 2. RMSEs of proposed estimators with respect to βˆUE (κ) when Δ∗ = 0, ν = 1, n = 100 and ρ = 0.7 Number of Predictors (p) 4 8 12

Estimator PTRRE

α = 0.05 α = 0.10

4.298 5.930 3.179 4.167

LSRRE

π = 0.25 π = 0.50 π = 0.75

1.635 1.704 1.727 2.992 3.427 3.592 5.961 8.719 10.219

SPTRRE π π π π π π

= 0.25 = 0.25 = 0.50 = 0.50 = 0.75 = 0.75

α = 0.05 α = 0.10 α = 0.05 α = 0.10 α = 0.05 α = 0.10

1.505 1.428 2.356 2.058 3.061 2.509

1.572 1.498 2.656 2.326 3.697 3.003

6.298 4.412

1.582 1.511 2.709 2.381 3.818 3.111

3. For fixed α and p, PTRRE dominated SPTRRE as measured by RMSE when the subspace information was correct or nearly correct, so that Δ∗ was at zero or near zero. However, SPTRRE dominated PTRRE in some range of Δ∗ . 4. In Table 4, the RMSEs of PTRRE and SPTRRE initially fell below one as Δ∗ increased from zero. In this phase, PTRRE outperformed SPTRRE. As Δ∗ increased further, SPTRRE began to outperform PTRRE. As Δ∗ increased further, the RMSEs of the two estimators converged to one.

390

P. Rintara et al. p = 4 and ρ = 0.7

p = 4 and ρ = 0.9 10.1

3.6

4.6

7.6

2.4

RMSE

6.1

RMSE

RMSE

p = 4 and ρ = 0.5 4.8

3.1

1.2 1.0

1.5

0.0

0.0

5.0

2.5

1.0 1.0

0.0

0.5

1.0

Δ*

1.5

2.0

0.0 0.0

Δ*

1.5

2.0

0.0

12.6

5.5

6.8

9.4

RMSE

9.0

3.7

4.5

2.3

1.8 1.0

0.5

1.0

Δ*

1.5

2.0

0.0 0.0

0.5

1.0

Δ*

1.5

2.0

0.0

p = 12 and ρ = 0.7

p = 12 and ρ = 0.5

8.0

10.2

5.3

2.7 1.0

0.0

0.0 0.0

0.5

1.0

Δ*

1.5

2.0

Estimator

RMSE

6.7

RMSE

13.6

1.0

0.5

1.0

Δ*

1.5

2.0

p = 12 and ρ = 0.9

10.6

2.2

2.0

6.3

8.9

4.5

1.5

1.0

0.0 0.0

1.0

Δ*

3.1

1.0

0.0

0.5

p = 8 and ρ = 0.9

7.4

RMSE

RMSE

1.0

p = 8 and ρ = 0.7

p = 8 and ρ = 0.5

RMSE

0.5

6.8

3.4 1.0 0.0 0.0

0.5

1.0

Δ*

1.5

2.0

LSRRE (a)

LSRRE (c)

SPTRRE (a)

LSRRE (b)

PTRRE

SPTRRE (b)

0.0

0.5

1.0

Δ*

1.5

2.0

SPTRRE (c)

Fig. 1. RMSEs of proposed estimators with respect to βˆUE (κ) as a function of Δ∗ when ν = 1.5 and the significance leval α = 0.05. (·) is the shrinkage intensity such that (a) π = 0.25, (b) π = 0.50, and (c) π = 0.75.

5. The RMSE of PTRRE decreased as α increased, whereas, the RMSE of SPTRRE increased as π increased but α decreased. 6. Since the RRRE only has the intercept while the URRE has the coefficients of all predictors, their MSEs are very different. Furthermore, the LSRRE is a linear combination of RRRE and URRE dependent on the shrinkage intensity. Hence, when there was a large shrinkage intensity, the MSEs of LSRRE and

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Table 3. RMSEs of proposed estimators with respect to βˆUE (κ) when Δ∗ = 0, ν = 1, n = 100 and ρ = 0.9 Number of Predictors (p) 4 8 12

Estimator PTRRE

α = 0.05 α = 0.10

5.834 3.847

LSRRE

π = 0.25 π = 0.50 π = 0.75

1.723 1.751 1.760 3.562 3.779 3.850 9.906 12.377 13.395

SPTRRE π π π π π π

= 0.25 = 0.25 = 0.50 = 0.50 = 0.75 = 0.75

α = 0.05 α = 0.10 α = 0.05 α = 0.10 α = 0.05 α = 0.10

1.569 1.479 2.642 2.247 3.664 2.852

7.326 4.760

1.607 1.528 2.838 2.454 4.127 3.259

7.369 4.881

1.608 1.533 2.842 2.477 4.139 3.308

Table 4. RMSEs of proposed estimators with respect to βˆUE (κ) when ν = 1.5, ρ = 0.5, and α = 0.5 p Δ∗

Estimator PTRRE LSRRE SPTRRE π = 0.25 π = 0.50 π = 0.75 π = 0.25 π = 0.50 π = 0.75

4 0.00 0.05 0.10 0.15 0.20 0.40 0.60 0.80 1.00 1.25 1.50 1.75 2.00

3.592 2.863 1.824 1.183 0.858 0.793 0.991 1.000 1.000 1.000 1.000 1.000 1.000

1.586 1.576 1.549 1.506 1.450 1.156 0.862 0.633 0.471 0.335 0.248 0.189 0.149

2.727 2.622 2.356 2.017 1.679 0.780 0.412 0.247 0.163 0.106 0.075 0.055 0.043

4.796 4.156 2.975 2.019 1.393 0.445 0.208 0.119 0.077 0.049 0.034 0.025 0.020

1.462 1.433 1.359 1.248 1.131 0.970 0.998 1.000 1.000 1.000 1.000 1.000 1.000

2.181 2.047 1.747 1.410 1.145 0.922 0.996 1.000 1.000 1.000 1.000 1.000 1.000

2.728 2.453 1.913 1.418 1.090 0.886 0.995 1.000 1.000 1.000 1.000 1.000 1.000

8 0.00 5.282 0.05 4.232 0.10 2.792

1.676 1.670 1.654

3.240 3.160 2.963

7.355 6.543 5.024

1.550 1.523 1.457

2.554 2.411 2.107

3.469 3.139 2.520 (continued)

392

P. Rintara et al. Table 4. (continued) Δ∗

Estimator PTRRE LSRRE SPTRRE π = 0.25 π = 0.50 π = 0.75 π = 0.25 π = 0.50 π = 0.75

0.15 0.20 0.40 0.60 0.80 1.00 1.25 1.50 1.75 2.00

1.857 1.324 0.906 0.994 1.000 1.000 1.000 1.000 1.000 1.000

1.630 1.599 1.413 1.185 0.966 0.780 0.598 0.465 0.367 0.295

2.691 2.388 1.353 0.786 0.496 0.335 0.223 0.158 0.117 0.090

3.641 2.634 0.914 0.438 0.253 0.164 0.106 0.074 0.054 0.042

1.366 1.255 1.004 0.999 1.000 1.000 1.000 1.000 1.000 1.000

1.767 1.453 0.988 0.998 1.000 1.000 1.000 1.000 1.000 1.000

1.942 1.495 0.969 0.997 1.000 1.000 1.000 1.000 1.000 1.000

12 0.00 0.05 0.10 0.15 0.20 0.40 0.60 0.80 1.00 1.25 1.50 1.75 2.00

5.868 5.032 3.496 2.325 1.616 0.985 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1.708 1.705 1.698 1.684 1.666 1.548 1.383 1.202 1.027 0.836 0.679 0.555 0.452

3.451 3.412 3.289 3.098 2.864 1.881 1.193 0.787 0.547 0.370 0.264 0.197 0.151

8.908 8.331 6.895 5.340 4.053 1.521 0.743 0.432 0.281 0.181 0.126 0.093 0.070

1.570 1.551 1.494 1.401 1.281 1.011 1.000 1.000 1.000 1.000 1.000 1.000 1.000

2.647 2.548 2.273 1.910 1.553 1.011 1.000 1.000 1.000 1.000 1.000 1.000 1.000

3.677 3.437 2.848 2.197 1.662 1.007 1.000 1.000 1.000 1.000 1.000 1.000 1.000

p

URRE were also very different. In other words, the RMSE of LSRRE was large when the shrinkage intensity was large.

5

Real Data Example

To appraise the performance of the pretest estimation strategy in a real application, we used a subsample of a dataset from Ouellet et al. [10]. This dataset is available on https://ssc.ca/en/case-study/determinants-presence-and-volumebrown-fat-human. In this study, the brown fat mass was taken as the response variable and consisted of n = 315 after zero was excluded. The response variable had a gamma

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393

distribution based on a p-value 0.112 for the chi-square test and using 7 predictors: diabetes (0 = no and 1 = yes), age (year), weight (kg), height (cm), BMI, glycemia (mmol/L), and LBW. The subspace information was obtained by applying the BIC and it was found that no predictors were selected into the model. To evaluate the performance of the proposed estimators, we used the resampling bootstrap method to estimate parameter β. We drew m = 100 bootstrap rows from the dataset with replacement and N = 2, 000 replications and set the significance level α = 0.05. The relative prediction error (RPE) was calculated to compare the performance of the proposed estimators: RP E(βˆUE (κ)) =

P E(βˆUE (κ)) . P E(βˆ (κ))

(18)

Table 5. Correlation coefficient matrix of the predictors Predictors Age

Weight Height BMI

0.265

−0.278

Weight

−0.178

1.000

0.529 0.870

0.012

0.854

Height

−0.365

0.529

1.000 0.053 −0.069

0.807

0.010

0.870

0.053 1.000

0.061

0.534

0.265

0.012 −0.069 0.061

1.000

0.020

0.854

0.020

1.000

Age

BMI Glycemy LBW

1.000 −0.178 −0.365 0.010

Glycemy LBW

−0.278

0.807 0.534

The correlation matrix of some predictors is presented in Table 5. As can be seen, multicollinearity was present. The RPEs in Table 6 show all the proposed estimators were superior to the URRE and the LSRRE performed best. The SPTRRE performed better than PTRRE, suggesting that Δ∗ was far from zero and the subspace information given by BIC was unreliable. However, The RPEs of PTRRE and SPTRRE were greater than one. These results confirmed the simulation results, when the subspace information was assumed to be incorrect, or the Δ∗ was far from zero. Table 6. RPEs of proposed estimators with respect to URRE Criterion Estimator PTRRE LSRRE SPTRRE π = 0.25 π = 0.50 π = 0.75 π = 0.25 π = 0.50 π = 0.75 RPE

1.020

1.206

1.238

1.216

1.040

1.045

1.036

394

6

P. Rintara et al.

Conclusions

We analyzed the application of pretest ridge-type estimation strategy to the gamma regression model, when multicollinearity is present and the accuracy of the subspace information is unknown. We focused on the special case in which the subspace information βp = 0p . We examined the performance of the proposed estimators using a Monte Carlo simulation and applied the proposed estimators to a real dataset. The simulation results confirmed that the proposed estimators were more efficient than URRE when the subspace information was correct or nearly correct. For a fixed significance level, PTRRE outperformed SPTRRE when the subspace information was correct or nearly correct, whereas SPTRRE dominated PTRRE in some part of the parameter space. The pretest method was as effective as the URRE when the subspace information is incorrect. The linear shrinkage estimator was sensitive to the quality of the subspace information because of the shrinkage intensity. The analysis of the real dataset produced results consistent with the simulation results. Acknowledgements. The research of Professor S. Ejaz Ahmed was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. The authors are grateful for the financial support of Kasetsart University, Chalermphrakiat Sakon Nakhon Province Campus, Thailand.

References 1. Ahmed, S.E.: Shrinkage preliminary test estimation in multivariate normal distributions. J. Stat. Comput. Simul. 43(3–4), 177–195 (1992) 2. Ahmed, S.E.: Penalty, Shrinkage and Pretest Strategies: Variable Selection and Estimation. Springer, Heidelberg (2014) 3. Amin, M., Qasim, M., Amanullah, M., Afzal, S.: Performance of some ridge estimators for the gamma regression model. Stat. Pap. 1–30 (2017) 4. Bancroft, T.A.: On biases in estimation due to the use of preliminary tests of significance. Ann. Math. Stat. 15(2), 190–204 (1944) 5. De Jong, P., Heller, G.Z., et al.: Generalized Linear Models for Insurance Data. Cambridge Books (2008) 6. Ejaz Ahmed, S., Yüzbaşı, B.: Big data analytics: integrating penalty strategies. Int. J. Manag. Sci. Eng. Manag. 11(2), 105–115 (2016) 7. Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970) 8. Hossain, S., Ahmed, S.E., Doksum, K.A.: Shrinkage, pretest, and penalty estimators in generalized linear models. Stat. Methodol. 24, 52–68 (2015) 9. Lisawadi, S., Shah, M.K.A., Ahmed, S.E.: Model selection and post estimation based on a pretest for logistic regression models. J. Stat. Comput. Simul. 86(17), 3495–3511 (2016) 10. Ouellet, V., Routhier-Labadie, A., Bellemare, W., Lakhal-Chaieb, L., Turcotte, E., Carpentier, A.C., Richard, D.: Outdoor temperature, age, sex, body mass index, and diabetic status determine the prevalence, mass, and glucose-uptake activity of 18F-FDG-detected bat in humans. J. Clin. Endocrinol. Metabol. 96(1), 192–199 (2011)

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11. Raheem, E., Saleh, A., et al.: Penalty, shrinkage, and preliminary test estimators under full model hypothesis. arXiv preprint arXiv:150306910 (2015) 12. Reangsephet, O., Lisawadi, S., Ahmed, S.E.: A comparison of pretest, stein-type and penalty estimators in logistic regression model. In: International Conference on Management Science and Engineering Management, pp. 19–34. Springer, Heidelberg (2017) 13. Reangsephet, O., Lisawadi, S., Ahmed, S.E.: Improving estimation of regression parameters in negative binomial regression model. In: International Conference on Management Science and Engineering Management, pp. 265–275. Springer, Heidelberg (2018) 14. Sarkar, N.: A new estimator combining the ridge regression and the restricted least squares methods of estimation. Commun. Stat.-Theory Methods 21(7), 1987–2000 (1992) 15. Segerstedt, B.: On ordinary ridge regression in generalized linear models. Commun. Stat.-Theory Methods 21(8), 2227–2246 (1992) 16. Yüzbaşı, B., Ahmed, S.E.: Shrinkage and penalized estimation in semi-parametric models with multicollinear data. J. Stat. Comput. Simul. 86(17), 3543–3561 (2016) 17. Yüzbaşı, B., Ahmed, SE., Aydın, D.: Ridge-type pretest and shrinkage estimations in partially linear models. Statistical Papers pp. 1–30 (2017) 18. Yüzbaşı, B., Ahmed, S.E., Güngör, M.: Improved penalty strategies in linear regression models. REVSTAT–Stat. J. 15(2), 251–276 (2017)

Opening Margin Trading Business Probability Forecasts Based on Decision Tree Model–A Case Study of S Securities Company Dan Zhang(B) , Zhi Yong, Shuying Deng, and Yue He Business School of Sichuan University, Chengdu 610065, People’s Republic of China [email protected]

Abstract. More margin trading customers can bring greater value contribution. Hence find potential customers can creative more benefits for the company. Based on customer trade data, this paper first compares common properties, assets, operations, profitability and risk appetite between margin trading customers and ordinary customers. Then forecast the probability of opening margin trading business for ordinary customers by decision tree model. Finally, the prediction results are verified. Empirical studies have shown that predictions coverage rate up to 94% and Improve the company’s business promotion success rate 4.29 times.

Keywords: Decision tree Data mining

1

· Potential customers · Securities company ·

Introduction

Securities margin trading is also known as securities credit trading or margin trading, refers to the provision of guarantees by investors to securities companies qualified for securities margin trading, the act of borrowing money to buy securities (margin trading) or borrowing securities and selling them (securities trading). Including securities companies to investors margin, securities and financial institutions to securities companies’ margin, securities. With the rapid development of China’s capital market and the continuous improvement of the legal construction of securities market, major securities companies officially launched securities margin trading business in January 2010. Margin trading customers have a large amount of capital and higher commission contributions than ordinary customers. In addition, they also contribute high interest rates, belongings to the company’s high value customers. According to statistics, the margin trading customers of S securities company account for 5% of the total customers of the company, while the owned assets account for 25% of the total assets of the customers of the company, and the average monthly commission contribution of c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 396–406, 2020. https://doi.org/10.1007/978-3-030-49829-0_29

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margin trading customers account for 37% of the total commission of the company (the total commission of the ordinary customers include the general account of the margin trading customers). Margin trading customers have made great contributions to the company. Therefore, mining potential customers of margin trading customers can help securities companies to promote opening margin trading business more accurately and bring more benefits to the companies.

2

Literature Review

Data mining technology can effectively extract relevant information from huge and messy data, which has been widely used in various industries to mining potential target customers. For example, Cho et al. proposed a personalized recommendation system based on weighted frequent pattern mining. Different weights are defined for each transaction, and weighted association rules are generated by mining. Use the RFM model for customer analysis to identify potential customers and provide more accurate recommendations for customers [3]. Lisa nazahroun et al. used online customer data and customer relationship management (CRM) to identify the best customers. Identifying potential customers through segmentation improves the company’s profits [19]. S. T. Yang et al. developed a personal business information management model with four modules: key customer identification, potential customer identification, user category identification and user busyness identification. TSP (the technology and service providers) can identify potential customers and provide personalized business information [18]. Horng-jinh Chang et al. generated data on the clickstream rules of loyal customers based on their web logs. By analyzing and observing the web logs of potential customers and comparing them with the clickstream rules of loyal customers, they could more easily target potential customers who might be interested in star products in the near future [2]. Christy et al. conducted RFM analysis on the transaction data, and then used the traditional k-means and fuzzy c-means to cluster them. Customer segmentation identifies the company’s potential customers, which helps to better understand customer needs and increase the company’s revenue [4]. Data mining technology also can solve practical problems. For example, BUCKINX W et al used Logistic regression, Automatic Correlation determination (ARD) Neural Network and Random Forest classification techniques and established a model to predict and analyze the loss of behavioral loyal customers. Studies have shown that increased customer retention is profitable [1]. Liu Chen et al. used rough set theory to analyze the relationship between stock returns and financial attribute data of 20 listed companies in the same industry in 2011, eliminating some redundant attributes. Then, using the financial data of 2012, the selected stocks were scored by the multi-attribute decision making method. The ranking results were completely consistent with the actual stock returns, proving the effectiveness and accuracy of the method [10]. For the evaluation of different customer segmentation methods, accuracy often plays a key role, but it is unable to accurately distinguish customer types. Makoto Mizuno et al.

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overcome this defect by citing two different indicators: recall rate and accuracy [13]. Foreign scholars also have a lot of research in related aspects. For example, scholar Siaw Linglo et al. ranked high value potential customers on Twitter by fuzzy matching, LDA and support vector machine [12]. Aslihan Dursun et al., using the data mining technology to analyze the customers who can bring a profit to the hotel. They divided the customer into eight types of “loyal customer”, “summer loyal customer”, “high-potential customers”, “new customer”, and provided different customer management strategies for the hotel[ [6]. David F. Mu˜ noz modeled, simulated and analyzed the new securities settlement system through the linear programming model, which reduced the settlement cost [14]. Shui Hua Han et al. used the decision tree model to group typical customers based on customer value to identify high-value customers [7]. Taeho Hong et al. proposed a method to combine SOM and k-means clustering together, and grouped customers of the online store according to factors that affect customers’ purchase intention [9]. Benlan Ha et al used support vector machine to predict the loss of bank customers [8]. Although data mining technology is widely used and the research on securities industry is beginning to rise, there are few related researches on product target customer mining of securities companies, especially for the target customer mining of margin trading business. Based on the customer transaction data of the securities company, this paper analyzes and studies the characteristics of margin trading customers, finds the difference between margin trading customers and ordinary customers, and the indexes with large differences are selected to be classified and predicted by the decision tree, to find out the potential customers.

3

The Difference Between the Margin Trading Customers and the General Customers

This section makes a statistical analysis of the basic attributes, assets, contributions, trading habits and profitability of the margin trading customers. On the one hand, it helps S securities company to better understand the general situation and characteristics of the margin trading customers, on the other hand, it compares with the ordinary customers, and finds the differences between the two, which lays a good foundation for predicting the potential margin trading customers. We extracted the data of ordinary customers in the ordinary account from January 1, 2014 to June 30, 2015, and the data of margin trading customers in the margin trading accounts from January 1, 2014 to June 30, 2015. Through comparative analysis, it is found that there are the following differences between the two: 1. The active customers of margin trading business have only 60% of the opening account customers, which is higher than the 45% active customers of the ordinary customers, but 40% of margin trading accounts are still dormant.

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2. Basic attribute: Compared with the ordinary customer, the proportion of margin trading customers aged 25 to 34 is less. We can further pay attention to customers in this age group and promote their transformation into margin trading customers. In addition, 62% of margin trading customers were male, while 49% of ordinary customers were male, indicating that male clients were more likely to open margin trading accounts than female customers. The average opening time of margin trading customers is 8.9 years, which is 1.2 years longer than the 7.7 years of ordinary customers, indicating that old shareholders are more likely to become margin trading customers. 3. Asset situation: margin trading customers account for 5% of the company’s total customers, the total daily average net assets are 25.6 billion, the average daily assets of ordinary customers are 102.4 billion, that is, 5% of margin trading customers assets account for 25% of the total assets of the company’s customers. In addition, the average monthly commission of margin trading customers is 74.28 million, and the average monthly commission of the ordinary customers is 201 million, that is, 5% of the customers contribute 37% of the commission (the total commission of the ordinary customers includes the ordinary accounts of margin trading customers). The contribution of margin trading customers to the company is extremely high. 4. Risk appetite: margin trading customers with the largest assets are customers under the age of 24, which are significantly different from those of ordinary customers. Young customers under the age of 35 have a higher proportion of positions and stock ownership concentration, and a lower proportion of performance guarantee, which all indicate that young margin trading customers have a higher risk appetite and are suitable for recommending high-risk products to these customers. With the increase of age, the risk appetite of margin trading customers gradually decreases. 5. Trading habits: the average annual turnover rate of margin trading customers is 4.25, 1.32 times that of the ordinary customers. Because of the particularity of margin trading business, customers will do more operations to try to create more value in a limited time. 6. Profitability: the overall profitability trend of margin trading customers is roughly the same as that of ordinary customers, that is, the number of profitable customers is more, but the margin of loss of the customers is larger. Due to the leverage effect, the profit and loss range of margin trading customers are larger than ordinary customers. Young customers have large assets but low yield, which is worthy of investment advisers’ attention. We can recommend the company’s financial products to these customers.

4 4.1

Data Preparation Index Selection

In order to better distinguish between the margin trading customers and the ordinary customers, synthesis of multiple articles [5,11,15,16] and opinions of professional staff, 14 indexes of the five aspects of the common properties, the

400

D. Zhang et al. Table 1. Index and calculation method

Classification

Sequence number

Index

Common properties

1

Sex

2 3

Age Ordinary account opening time

Assets

5

Daily average assets

AVG (margin trading total assets per day)

Contribution condition

6

Monthly commission

SUM (daily commission)/daily commission

Position habit

7

Position ratio

8

Concentration degree of holding

9

Shareholding exclusivity

10

Average daily number of shares

AVG (daily market value of securities)/daily average assets SUM (the market value of a stock with the first three proportion of positions.)/SUM (total market value of all stocks) SUM (the market value of a stock with the first proportion of positions)/SUM (total market value of all stocks) AVG (number of shares held per day)

11

Monthly average SUM (daily average number of applications number of applications for for new shares new shares)/number of transaction months Common delegation MAX (number of mode delegation mode) Monthly average (Average daily trading turnover rate volume/daily average assets)*250/12

Trading habit

12 13

Profitability capacity

14

Annualized rate of return

Computing method

(1+ (total profit/days of transaction)/daily average assets)ˆ250 - 1

asset condition, the operation preference, the contribution condition and the profitability capacity are selected for the basic characteristic analysis of the customer. The specific indicators and calculation methods are shown in Table 1.

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4.2

401

Data Extraction

After opening the margin trading account, the operation habits of the ordinary account may change. In order to eliminate this effect, it is better to compare that transaction characteristics of margin trading customers and the ordinary customer in the ordinary account, this paper selects margin trading customers who open accounts before April 1, 2015, extracts the transaction data of ordinary accounts from January 1, 2014 to before the opening of margin trading accounts, removes the customers with the turnover rate and the yield of 0, and the 30540 people are finally obtained as the target samples. Since the number of margin trading customers account for a small proportion of the total customers, in order to increase the concentration of the target sample, improve the behavior performance of the target sample, the article randomly extract 30540 ordinary customers with normal transaction data which from their account opening to June 30, 2015. The ratio of margin trading customers and ordinary customers is 1:1. (1) Data extraction cycle General customer: January 1, 2014 to June 30, 2015 Margin trading customers: January 1, 2014 one day before the opening of the account. (2) Sample capacity The total analysis sample was 61080 people, and the training set and test set were distributed at 7:3. There were 42,756 people in the training set and 18,324 people in the test set. The proportion of both margin trading customers and general customers in the training set and test set is 1:1.

5 5.1

Forecast of Potential Margin Trading Customers Model

A decision tree is a tree structure in which each tree node can be a leaf node, it corresponds to a certain category, or it can correspond to a division. The sample set corresponding to this node is divided into several subsets, and each subset corresponds to a node [20]. The decision tree is used to classify the 42756 people in the training set, and the tree growth method is CRT. Through repeated experiments, the final decision tree uses 6 indicators to classify customers, 49 nodes and 25 terminal nodessix of these nodes can distinguish potential margin trading customers from general customers. The specific classification rules are shown in Table 2: It can be found from the classification rules that customers with the largest assets of more than 500,000 yuan (node 2 and node 6) have a higher probability of opening margin trading accounts. As can be seen from Table 2, customers with the largest assets of more than 500,000 yuan with large daily trading volume, high proportion of open positions and high daily yield are more likely to open margin trading accounts. In addition, customers with the largest assets of less than 500,000 yuan are also likely to become margin trading customers. Focus on

402

D. Zhang et al. Table 2. Classification rules of potential margin trading customers

Node Daily average assets (yuan)

Maximum Average assets daily (yuan) trading volume (yuan)

Position Average Annualized The prob- Customer ratio daily rate turnover ability of characterof return opening istics margin trading business(%) >0.95

1

93462

93462

6234

5

>93462

93462

>498236

>12.547

73.8

Highly active customer

87.2

Potential customers

0.95

>0.0026

64.2

Experience customer

82.6

High net worth customer

customers with a position ratio of more than 0.95, an annual turnover rate of more than 12.547, and a daily average return of more than 0.0026 (node 1 and node 5), which have higher risk appetite, high activity, and higher returns, and are more likely to accept the margin trading business. According to the main characteristics of the classification rules, customers can be divided into high active customers, potential customers, low return customers, low risk customers, experienced customers and high net worth customers. 5.2

Model Evaluation

The results of the decision tree classification are shown in Table 3, a common capture rate (recall rate), an accuracy rate, an F value, and a test set are used to evaluate the prediction effect. (1) Capture rate The capture rate refers to the customer who has already opened the margin trading account and is predicted to be the range of the margin trading customers, with a value of 0-1, the closer the value is 1, and the higher the capture rate. The capture rate of this model is: Capture rate =

predicted and actually the number of margin trading customers actual number of margin trading customers

It shows that the prediction results cover 83% of margin trading customers, and the prediction results are good.

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Table 3. Decision tree classification results Observed

Predicted General customer Margin trading customers

General customer

16364

5014

3586

17792

Margin trading customers

(2) Accuracy rate Accuracy rate refers to how many of the people who predict margin trading customers are the actual customers of margin trading customers, with the value is 0-1, the closer the value is 1, the higher the accuracy rate. The accuracy rate of this model is: Accuracy =

predicted and actually the number of margin trading customers predicted number of margin trading customers

It shows that the accuracy rate of the prediction results of margin trading customers is 78%, and the prediction results are good. The reason why the accuracy rate is not as high as the capture rate is that the boundary between margin trading customers and the ordinary customers is not obvious enough. Among the randomly selected customers, there will be some potential margin trading customers, thus reducing the accuracy rate. But the ordinary customers that are predicted to be margin trading customers are the potential customers we need. (3) F value The F-value comprehensively considered the two indexes of capture rate and accuracy rate, which can reflect the accuracy of the model as a whole. The F value of this model is: F=

83.2% × 78% × 2 accurancyrate × capturerate × 2 = = 80.5% accurancyrate + capturerate 83.2% + 78%

The F value is more than 80%, indicating that the model prediction result is good. In fact, the article has also tried to use Logistic regression method for prediction, but the prediction effect is not as good as a decision tree model. Therefore, the paper chooses the prediction results of the decision tree model. (4) Check test set The generated decision tree classification rules are applied to 18324 people in the test set, the predicted results are shown in Table 4. Table 4. Classification results of decision trees Observed

Predicted General customer Margin trading customers

General customer

6690

2472

Margin trading customers 1530

7632

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The capture rate of the prediction results is 83.3%, the accuracy rate is 75.5% and the F value is 80%, which indicate that the prediction result of the model is good and can be used for the actual prediction. 5.3

Empirical Application

The classification rule will be applied to all active customers of S securities company, excluding those who have opened margin trading accounts before June 30, 2015. Among the remaining ordinary customers, 23.3% of potential margin trading customers were found to meet the classification rules. Of these, 94% of the new margin trading customers are included, meaning that the actual coverage of the forecast results is high, and the effect is good. The actual test shows that before the application of the model, the success rate of the company in promoting the margin trading business is 5.2%. At present, it is only promoted to the predicted potential customers, and the success rate is 22.4%, which increases the success rate by 4.29 times. It also shows that the forecast results can provide effective decision-making basis for securities companies. Table 5 shows the proportion of various customers to open margin trading business from July 1, 2015 to October 31, 2015. It can be found that the second and sixth types of customers account for a relatively high proportion, and their largest assets are also above 500,000, in line with the current state to open margin trading business necessary conditions, it is recommended to focus on these two types of customers. Table 5. Test of prediction effect of potential margin trading customers Classification

Customer characteristics Proportion (%) Percentage of potential customers in this category (%)

1

Highly active customer

0

0

2

Potential customers

2.3

0.86

3

Low yield customer

2.1

0.06

4

Low risk customer

2.1

0.13

5

Experience customer

0.4

0.06

6

High net worth customer 93.1

Total

6

1.74

100

Conclusions

Some scholars propose that the model analysis obtained from data mining may be helpful to the decision-making of enterprises [17]. This paper analyzes and studies the characteristics of the margin trading customers and finds that there are certain differences between the margin trading customers and the ordinary

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customers in basic attributes, assets, operations, risk appetite and profitability. Then these different indicators were incorporated into the decision tree model to obtain the corresponding decision tree classification rules. It is possible to predict the probability of each customer’s opening margin trading business, and potential margin trading customers can be mined. It is found that customers with large daily trading volume, high proportion of positions and high daily average return of more than 500000 of the largest assets are more likely to open margin trading accounts, and it is suggested that attention should be paid to them. Customers with high risk appetite, high activity and high yield with the largest asset below 500,000 yuan are more likely to open margin trading accounts and focus on customers with relatively high probability of opening. In the future, the operation management department can match the relevant index data of the customer with the classification rules of the decision tree, so as to obtain the corresponding opening probability of the customer and promote margin trading business for customers with high probability. The empirical study shows that the results of this study increase the success rate of the promotion of S securities company’s margin trading business by 4.29 times, indicating that the prediction results can provide effective decision-making basis for securities companies. The innovation of this paper is to use the trading data of securities companies to calculate customer indicators. The probability of opening margin trading business was predicted through decision tree, to dig out potential margin trading customers and effectively improve the promotion success rate of margin trading business of securities companies. The deficiency of this paper is that it only considers five main indicators of basic attributes, asset status, operational preference, contribution and profitability, and there may be some indicators that can make the prediction effect better. In the future, more index sets should be added to further improve the prediction accuracy.

References 1. Buckinx, W., Van den Poel, D.: Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur. J. Oper. Res. 164(1), 252–268 (2003) 2. Chang, H.J., Hung, L.P., Ho, C.L.: An anticipation model of potential customers’ purchasing behavior based on clustering analysis and association rules analysis. Expert Syst. Appl. 32(3), 753–764 (2007) 3. Cho, Y.S., Moon, S.C.: Weighted mining frequent pattern based customers RFM score for personalized ucommerce recommendation system. J. Converg. 42(4), 387– 400 (2013) 4. Christy, A.J., Umamakeswari, A., Priyatharsini, L., Neyaa, A.: RFM ranking-an effective approach to customer segmentation. J. King Saud Univ.-Comput. Inf. Sci. 3(1), 57–63 (2018) 5. Dun, J.: Research on customer hierarchical management of Citic securities Donghai road business department. Lanzhou University Technology, pp. 10–12 (2018). (in Chinese)

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6. Dursun, A., Caber, M.: Using data mining techniques for profiling profitable hotel customers: an application of RFM analysis. Tour. Manag. Perspect. 18(4), 153–160 (2016) 7. Han, S.H., Lu, S.X., Leung, S.C.: Segmentation of telecom customers based on customer value by decision tree model. Expert Syst. Appl. 39(4), 3964–3973 (2012) 8. Hea, B., Shic, Y., Wan, Q., Zhao, X.: Prediction of customer attrition of commercial banks based on SVM model. Procedia Comput. Sci. 31(1), 423–430 (2014) 9. Hong, T., Kim, E.: Segmenting customers in online stores based on factors that affect the customer’s intention to purchase. Expert Syst. Appl. 39(2), 2127–2131 (2012) 10. Liu, C., Huang, H.: Approach for stock investment decisions based on SVM. In: 2014 International Conference on System Engineering and Management Science, vol. 34, pp. 228–242 (2014). (in Chinese) 11. Liu, F.: Empirical research on customer segmentation in securities industry based on cluster analysis. J. Chifeng Univ. 8–12 (2016). (in Chinese) 12. Lo, S.L., Cornforth, D., Chiong, R.: Identifying the high-value social audience from twitter through text-mining methods. In: Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, vol. 1, vol. 14, pp. 325–339. Springer (2014) 13. Mizuno, M., Saji, A., Sumita, U., Suzuki, H.: Optimal threshold analysis of segmentation methods for identifying target customers. Eur. J. Oper. Res. 186(1), 358–379 (2008) 14. Mu˜ noz, D.F., Palacios, A., de Lascurain, M.: Modeling, simulation and analysis of a securities settlement system: the case of central securities depository of Mexico. J. Econ. Finan. Adm. Sci. 17(33), 48–59 (2012) 15. Peng, J.: Research on customer segmentation of h securities company based on data mining. Jinan University, pp. 6–19 (2017). (in Chinese) 16. Qiu, J., Wang, Y.: Empirical research on customer segmentation in securities industry based on customer life cycle theory. Shanghai Management Science, pp. 7–35 (2013). (in Chinese) 17. Wu, D.: Application study on bank’s CRM based on data mining technology. In: 2011 International Conference on Electric Information and Control Engineering, vol. 34, pp. 64–77. IEEE Computer Society (2011) 18. Yang, S.T., Hou, J.L.: A business model for potential customers identification and personalized knowledge provision of TSPs. IFAC Proc. Vol. 42(4), 656–661 (2009) 19. Zahrotun, L.: Implementation of data mining technique for customer relationship management (CRM) on online shop tokodiapers. com with fuzzy c-means clustering. In: 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 299–303. IEEE (2017) 20. Zhang, X.: Reversible data hiding in encrypted image. IEEE Sig. Process. Lett. 34(2), 255–257 (2011). (in Chinese)

The Herd Effect on Chinese Firms’ OFDI - A Data Mining Approach Jie Jiang1 , Cangyu Wang1 , Junshan Liu1 , and Lei Zhang2(B) 1

Business School, Sichuan University, Chengdu 610064, People’s Republic of China 2 College of Computer Science, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. When Chinese firms make OFDI decisions, their investment motives and layouts are often influenced by peers or trends, resulting in investment agglomeration and a “herd effect”, which can alter the knowledge of a host country’s risk, influence the risk attitudes of the Chinese firms, and affect their foreign investment decisions. To quantify the Chinese Outward Foreign Direct Investment (OFDI) herd effect, four dynamic indexes were developed at both industry and country levels based on a combined dataset from 2004 to 2015 for 1207 Chinese OFDI events. Data mining was then used to determine the links between Chinese OFDI volume and the herd effect and to examine the heterogeneous characteristics of the host countries, industries and firms. Using a random forest method, 50 decision attributes that affected the firms’ investment volume were identified, which were then incorporated in an optimized BP neural network to generate a Chinese OFDI decision-making model. It was found that: (1) there was an obvious herd effect in Chinese OFDI associated with host country and industry selection; (2) when a firm invested, it tended to choose a host country that has a smaller political risk and higher degree of labor freedom and globalization; (3) large firms with low efficiency tended to make larger OFDI decisions.

Keywords: Chinese OFDI country heterogeneity

1

· Data mining · Location decision · Host

Introduction

Since the reform and opening up in 1979, Chinese outward foreign direct investment (OFDI) has developed rapidly. However, the problem of low investment quality and low efficiency is becoming more and more prominent. Facing the great difference of host country’s political, economic, cultural and legal environment, Chinese OFDI is characterized as high risk preference, large scale of transaction volume, relatively converging industries and regions [12]. In view of the uncertainty of host country’s market, irreversibility of investment and spillover c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 407–422, 2020. https://doi.org/10.1007/978-3-030-49829-0_30

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effect of experience, Firms often adopt the following-up mode when they make OFDI decisions. Research shows that when Chinese firms make OFDI decisions, their investment motives and investment layouts are often influenced by peers or trends, resulting in different levels of investment agglomeration, forming “herd effect” [29]. The herd effect may change the cognition of host country’s risk, influence the risk attitude of Chinese firms, and then affect their foreign investment decisions. The side effect of herd investment will cause the waste of resources and even increase the investment risk of firms if too much concentrating in a certain industry or region, resulting in poor performance and low efficiency of OFDI [39]. The scientific decision-making of firms’ OFDI and policy support system from the government need to be optimized to help diversify the OFDI location and industry. Traditional international direct investment theory states that enterprise OFDI is generally risk averse [13]. However, previous research has found that Chinese firms have tended to invest in high-risk host countries as they often base their OFDI location selections on the investment experience of other firms [18,23,24]. Using cross term variables such as host country natural resources, strategic resources and economic level, some studies sought evidence that Chinese firms had a traditional risk-averse mode, and argued that the risk preference of Chinese OFDI was an illusion [38]. However, most studies have found that there is a “herding behavior” in OFDI investments in some host countries and industries. The contribution of this paper is twofold. Firstly, Although the existing literature on the host country risk and Chinese OFDI’s motivation is rich and profound, research that investigates Chinese OFDI’s herd effect on the firm level (how concentrated Chinese OFDI on country level and industry level), measure and identify the herd effect as well as evaluate its impact on Firms’ OFDI decision is lacked. This paper quantifies the measurement of Chinese firms OFDI “herd effect” index on the industry level and country level based on over 1200 Chinese OFDI events by listed firms during 2004–2015. Then it investigates the linkages between Chinese OFDI’s volume and herd effect as well as considering the heterogeneity characteristics of host countries, industries and firms. Secondly, Considering the complexity of OFDI decision-making, data mining and machine learning instead of traditional statistic method is adopted with more than 77 variables considered, and retrieves 50 core attributes to form the trisection OFDI volume evaluation model. The importance of herd effect on both country level and industry level for Chinese firms’ OFDI is confirmed. In the era that Chinese government plans to focus on upgrading the Chinese OFDI from volume to quality, we believe our research can shed light on firms’ rational OFDI decisions and provide powerful reference for the implementation of “going out” strategy and “Belt and Road Initiative”. The remainder of this paper is structured as follows. Section 2 reviews the relevant theoretical and empirical literature, Sect. 3 describes the chosen sample and data, and Sect. 4 presents the data-mining techniques, and Sect. 5 concludes the study.

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Literature Review

The “herd effect” refers to decision-making behavior in which the behavioral subject ignores private information and imitates the action of others when there is incomplete information. The “herd effect” is related to the herd mentality, which can easily lead to blind obedience. Faced with the significant uncertainty of multinational investments and asymmetric information, firms often follow previous companies’ actions when making their own OFDI decisions. Existing empirical research on herd effects has been mostly focused on the financial and securities market; however, there have been some studies that have investigated the overseas investment behavior of firms and assessed the aggregation of corporate investment in certain regions or industries [27]. While there has been little previous research on OFDI herd effects, there has been significant research on the networked production coordination approach used by multinational corporations in specific agglomeration areas to achieve optimal economies of scale. Therefore, this agglomeration effect has become a new factor when an enterprise is considering OFDI, which is generally reflected in their choice of investment location and the industry sector, with enterprises in the same industry sector tending to follow similar investment strategies. It has been suggested that these various industry agglomerations are part of the multinational global value chain, and there have been many studies focused on external direct investment and industrial chain aggregation. For example, Puga and Venables [28] established a two-country economic model to theoretically analyze the influence of agglomerated economies on multinational corporation locations, and found that based on the supply – demand relationship and the trade cost of intermediate products, firms were willing to invest in production locations near upstream suppliers or downstream buyers. Barrell and Pain [2], Guimar˜ aes, Figueiredo and Woodward [11] respectively verified that the agglomeration economy was an important factor in Japanese, Irish, American, and Portuguese transnational investment decision-making, and suggested that governments could use this “imitation propensity” to adopt reasonable guidance and focused industrial policies to promote industrial agglomeration. With the United States and/or Japan as the research objects, Wheeler and Mody [36], Henisz and Delios [15] all verified that the agglomeration effect and the pull of industrially related firms played an important role when selecting investment locations. Gross, Raff and Ryan [10] analyzed the direct investment location choices in Europe of Japanese manufacturing and non-manufacturing firms from an industry perspective from 1970 to 1995, and found that Japanese manufacturers tended to choose markets in which other Japanese manufacturers from the same industry were already established. Therefore, while it has been widely found that enterprise foreign direct investment tends to cluster in a certain region, there have been no comprehensive or scientific explanations of the follow-up effects of this type of herding. Most studies on Chinese OFDI investment locations and industry concentrations has found that Chinese OFDI has a risk characteristic; that is, Chinese OFDI has often been focused on high-risk host countries [4]. It has also been

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found that Chinese OFDI firms select their markets based on the investment experience of others; that is, there has been a recognized “herd effect” associated with Chinese OFDI [25]. Liu [26] proposed a surge phenomenon, which was similar to “the herd effect”, and referred to firms in rapidly developing countries reaching a consensus on new and promising industry investments and forming a rational “surge phenomenon” in their investment choices whereby they clustered their investments because of information asymmetry. Zhang [40] examined Chinese foreign direct investment industrial agglomerations by calculating a static and dynamic agglomeration index and found that manufacturing and service industry OFDI was expanding, and the direct investment in the service industry had a stable and continuous agglomeration effect. To examine the effects of aggregation, Fang [7] selected all A-share listed companies from 1999 to 2009 to examine if there had been over-aggregation and the influence on operating performances, and found that the over-aggregation of corporate investments led to industry performance deterioration. Xie and Liu [37] applied spatial econometric methods to empirically examine the factors affecting Chinese OFDI and the trade investment mechanism and verified that there was an agglomeration effect, concluding that China should continue to maintain its continuous OFDI dynamics, focus on experience accumulation, and be guided by the agglomeration effect. Although there have been significant research advances in understanding host country risk and Chinese OFDI, and there has been consensus on the foreign investment decision-making behavior of firms, these studies have been mostly based on country-level data and static regression analyses; however, there have been fewer dynamic analyses of company characteristics and corporate behavior. Herd effect studies have been focused on securities investment behaviors and there have been very few studies on the “herd effect” associated with long-term asset investments and especially foreign direct investments. Therefore, this paper employed data mining techniques to deal with the complex OFDI variables and then simulated a Chinese OFDI decision-making prediction model to identify the “herd effect” in Chinese OFDI, study its associated attributes, and examine the heterogeneous characteristics of the host countries, industries and firms.

3 3.1

Data Selection and Research Design Sample Selection

This paper sets up a preliminary data set that contains 1209 OFDI events from 2004–2015 by Chinese listed firms. The name, investment volume, year, host-country and other investment-related information are extracted from List of Chinese Foreign Investment Enterprises collected by Ministry of Commerce, and then the firm-level data including financial ratios, firm characteristics and structure are matched through their year reports from CSMAR database. Hostcountry data are acquired from IFS, World Bank, OECD etc. to match these OFDI’s locations.

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411

Herd Effect Index Variables

This paper focused on both country and industry level herd effects to determine if Chinese firms invested more in countries with a larger Chinese OFDI flow percentage or in industries with a larger share of industry-related OFDI in a certain year. Unlike previous research that sought to identify the herd effect by examining the OFDI in a host country [6,17,30] or took the experience of other firms’ OFDI as the dummy variable to identify the herd effect [29], in this study, it was postulated that the herd effect occurs when firms follow OFDI investment decisions made by other firms in previous years. Four indices were developed that considered both the number of firms and the investment volumes, as follows. (1) Herdnumber bycountryt,j : the number of Chinese firms with OFDI in country j in year t − 1 over the total number of Chinese firms with OFDI in year t − 1 in all sample countries. Nt−1,j Herdnumber bycountryt,j =  n Nt−1,j j=1

(2) Herdvolume bycountryt,j : the OFDI volume of Chinese firms in country j in year t − 1 over the total OFDI volume of Chinese firms in year t − 1 in all sample countries. Vt−1,j Herdvolume bycountryt,j =  n Vt−1,j j=1

(3) Herdnumber byindustryt,j,k : the number of Chinese firms with OFDI in industry k in country j in year t − 1 over the total number of Chinese firms with OFDI in year t − 1 in country j. Nt−1,j,k Herdnumber byindustryt,j,k =  n Nt−1,j,k k=1

(4) Herdvolume byindustryt,j,k : the OFDI volume of Chinese firms in industry k in country j in year t − 1 over the total OFDI volume of Chinese firms in year t − 1 in country j. Vt−1,j,k Herdvolume byindustryt,j,k =  n Vt−1,j,k k=1

3.3

Other Variables

To investigate the relationships between the country-level, industry level and firm level herd effects and Chinese OFDI, 77 initial attributes including the

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herd effect variables were collected, and after the feature subset selection, 50 core attributes were included in the modeling. The adopted attributes and corresponding symbols are detailed in Appendix Table 3 (Note: Due to the length of paper, we give the definition and source of the important attributes which we are interested in, and just list other core attributes in Appendix Table 3. All country-level data were lagged by one year as t − 1 yearly data.

4 4.1

Research Design and Results Analysis Research Design

Recently, the interdisciplinary research on computer technology and other disciplines has attracted the attention of scholars, especially using data mining methods to detect suspect data, supplement missing data, and build predictive models [5,6,8,21]. In our research, determinations of Chinese firm OFDI are complex and imprecise as relevant factors are related to business behavior, characteristics, and performance, the industry sector characteristics and host country heterogeneity. As mainstream social science research has generally adopted statistical methods to describe or examine the relationships between the independent and explained variables, it has often made impractical assumptions [22]. Further, as the statistics from regressions generally only represent average results [32], it is not possible to identify the contextual relationships for the specific relationships related to individual firms. To overcome these problems, soft computing data mining methods have emerged to resolve real-world financial problems [35] because compared to traditional statistics, they are able to consider all relevant and interrelated criteria to solve the problems. Therefore, to comprehensively test the existence of a “herd effect” in Chinese OFDI, to study the Chinese OFDI “herd effect” theoretical model, and to identify the heterogeneous host countries, industries and firm characteristics, this study employed machine learning and data mining technique. 4.2

Establish a Classification Forecast Model

This paper builds a classification prediction model based on C4.5 algorithm. Two models were established: “Country, Industry, Firm”-“Investment Volume” model; “Country, Firm”-“Investment Volume” model. The two models were established to investigate the impact of various factors on the amount of investment. Due to a large number of missing values in the original data, Wrapper algorithm is used to select the attributes of the data after processing the missing values. Core impact attributes for the investment volume are selected as the final experimental data. The experimental data is divided into two parts: the training set and the test set. The training set is used for the establishment of the BP neural network model and the optimization of the model; the test set is used for the evaluation of the prediction accuracy of the established model. To ensure the authenticity of the experiment, 70% of the total sample was randomly selected as the training set, and the remaining 30% was used as the test set.

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(1) Attribute selection based on Wrapper algorithm To facilitate data visualization and improve the performance of the classification prediction model, n fold cross validation measures were used to evaluate the impact of the attributes on the results [33]. The training data is split in n approximately equally sized partitions. The induction algorithm is then run n times each time using n partitions as the training set and the other partition as the test set the accuracy results from each of the n runs are then averaged to produce the estimated accuracy. Note that no knowledge of the induction algorithm is necessary except the ability to test the resulting structure on the validation sets. Since the experimental data contains all 77 attributes, this paper uses C4.5 algorithm with 5 fold cross validation to evaluate the attribute subsets. The C4.5 can be referred as the statistic Classifier. This algorithm uses gain radio for feature selection and to construct the decision tree. It handles both continuous and discrete features. C4.5 algorithm is widely used because of its quick classification and high precision [31]. Finally, an optimal attribute subset that is suitable for the BP neural network algorithm and has a large impact on the “lcap” is obtained. Step 1. Discretize attributes. In order to use the classification algorithm to predict the “lcap” level, this paper discretizes values of categorical attribute into 3 levels –“high”, “middle”, “low”. The maximum value of “lcap” in original data is 27.012981, the minimum is 5.0344863. So this paper takes value between 5 and 12 as “low”, between 12 and 20 as “middle”, and the value higher than 20 as “high”. Step 2. Uses C4.5 algorithm as a subset selection algorithm of wrapper model, and obtain the best subset by 5 fold cross validation. Step 3. Algorithm compared to select the optimal algorithm. Three classifiers are used: decision tree (DT), BPN, KNN. The evaluation parameters of each algorithm are shown in Table 1 and Table 2. Table 1. “Country, Industry, Firm”–“Investment Volume” models comparison Attributes

Decision Tree All

Subset

Back-propagation KNN Neural Network All Subset All

Subset

Correctly Classified Instances

64.961% 66.9291% 66.929% 66.929% 64.1732% 64.961%

Precision

0.641

0.667

0.674

0.671

0.633

0.634

Recall

0.650

0.669

0.669

0.669

0.642

0.650

TP Rate

0.650

0.669

0.669

0.669

0.642

0.650

FP Rate

0.279

0.253

0.250

0.260

0.289

0.286

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J. Jiang et al. Table 2. “Country, Firm”–“Investment Volume” models comparison Attributes

Decision Tree All

Subset

Back-propagation KNN Neural Network All Subset All

Subset

Correctly Classified Instances

66.929% 65.748% 65.748% 64.961% 61.417% 66.535%

Precision

0.661

0.654

0.664

0.667

0.614

0.666

Recall

0.669

0.657

0.657

0.650

0.614

0.665

TP Rate

0.669

0.657

0.657

0.650

0.614

0.666

FP Rate

0.237

0.237

0.241

0.233

0.287

0.243

Step 4. After the comparison of Step 3, find that the Decision Tree models and BPN models are similar in accuracy. The overall research steps are summarized in Fig. 1.

Fig. 1. Illustration of the research framework

4.3

Empirical Results Analysis and Discussions

(1) Core attributes identified Using wrapper method we introduced earlier, this paper regards the lcap as the decision attribute, the others as the condition attributes to select the core condition attributes which have significant impact on the firms’ OFDI volume. The selected core attributes (based on the Wrapper method) and the corresponding symbols are summarized in detail in Appendix Table 3.

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(2) Herd effect identification With respect to the four indices that we developed for identifying the herd effect, data mining approach selected three as core attributes to lcap, namely herdnumber bycountry, herdvolume bycountry and hervolume byindustry. The country level herd effect showed a strong pattern, indicating that Chinese firms tended to invest more in a host country when the previous Chinese OFDI (investment volume) had been high. As uncertainties are a major obstacle when “going abroad”, previous experience from their own or other firms can improve cross-border investment confidence, efficiency and information asymmetry is generally minimized when Chinese companies are already operating in the host country, and links with other Chinese firms can strengthen market power, optimize the supply chain, and guarantee better performance. This result was consistent with previous studies that have found that Chinese firms prefer to invest in host countries that have higher Chinese OFDI concentrations [26,29].

Fig. 2. Distribution of the herd effect

On the industry level, only herdvolume byindustry is selected as core attribute for decision. The pattern in Fig. 2 shows a polarized distribution which means Chinese firms tends to invest either in the host country that has little concentration in that certain industry or also has herd effect in the industry level. This finding is very interesting and it explains that Chinese firms tend to invest in two circumstances related to industry level in the host country: (1) invest in the industry that has already had too much concentration from Chinese firms compared to other industries. It further means Chinese firms OFDI is not very diversified in industry level by mainly focused on manufacturing sector in our sample (supporting Fig. 2). (2) investment in the industry that has little concentration from Chinese firms in the previous year. This could also reveal that the supporting system between Chinese firms in these countries is quite poor. Chinese firms’ OFDI decision hasn’t distributed strategically along supply chain in a certain industry. (3) Other host country characteristics and Chinese OFDI The results also find that some host country characteristics are core attributes to Chinese OFDI. Larger investment from China would come to countries with

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higher globalization level, investment freedom, and more obviously, trade freedom. Political environment variables including voice and accountability, rule of law are selected as core attributes for investment decision. It depicts that Chinese firms do consider political risks and take host countries with better government credit and law environment as investment destination. This result is not consistent with most of the existing literature which proved that Chinese OFDI has a risk-preference attitude [3,4,18,23]. Host countries that have signed bilateral investment treaties with China have been absorbing more Chinese OFDI. Infrastructure facilities in host country tend to relate to middle-volume Chinese investment. In addition, Chinese OFDI tends to invest in the host countries with relatively low technology level. These host countries’ characteristics have been discovered by former researches using statistical method. Figure 3 gives a visualized illustration of these relationships.

Fig. 3. Core attributes of the host country characteristics

(4) Firm characteristics and Chinese OFDI In this paper, core attributes of firm characteristics including financial status and firm’s governance structure are also derived from the preliminary data set. The linkages that we found between the financial statuses of invested firms and invest volume are in divergence with what the classical investment theories reveal. Figure 4 shows firms with lower Tobin Q, market value, asset turnover rate and productivity (T f p lp) are found to invest more abroad. Or in other words, the firms which invested more cross-boarderly are the ones have lower efficiency. Firms with bigger total asset and revenue tend to invest more in host countries. So we could get a sketch that Chinese firm that invested more internationally are large firms with low efficiency. This is not in line with the existing literature which argues that firm would like to invest when it has high productivity or efficiency [1,14,16]. We believe this finding can also provide a supporting proof to the recent argument that Chinese OFDI has poor performance financially [19,20]. Firms with low efficiency can hardly generate a synergy effect when they conduct direct investment internationally [9,34].

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Fig. 4. Core attributes of the firm characteristics

(5) Prediction model based on C4.5

Fig. 5. “Country, Industry, Firm”-“Investment Volume” prediction model

A classification prediction model is established for the data sets that attributes are selected based on the Wrapper method. Randomly select 70% as the training set and 30% as the test set. After the test, the correct rate of the “Country, Industry, Firm”-“Investment Volume” decision tree model based on the C4.5 algorithm is 68%, and the correct rate of “Country, Firm”-“Investment Volume” decision tree model is 67%. So we take “Country, Industry, Firm”“Investment Volume” decision tree model as the prediction model. Figure 5 shows herdvolume bycountry as a core measurement for herd effect on the country level has a significant impact on firms’ OFDI decisions. Weaker herd effect in the host countries linked with more medium-sized OFDI. While strong herd effect combined with freer business environment, larger asset and relatively low productivity generated larger medium and large-sized OFDI. The prediction model

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based on our data set still needs larger samples to generate a more scientific OFDI decision-making supporting system.

5

Conclusions and Implications

This paper developed four dynamic Chinese OFDI herd indexes at industry and country levels. Using a machine learning method, 77 variables that affect investment decisions were introduced in the preliminary data set. Then, using the wrapper method, 50 multiple feature decision attributes that affected investment volumes at host country, industry, and firm levels were identified, which were then incorporated in an optimized BP neural network to generate a Chinese OFDI decision-making, the analysis from which gave rise to the following conclusions. (1) A Chinese OFDI herd effect was reflected in location choice and industry sector investment, which indicated that when making OFDI decisions, Chinese firms tend to invest in host countries in which there had been previous concentrated investment. However, the industry herd effect showed a polarized pattern, with Chinese firms either choosing to invest in highly concentrated industries or low concentrated industries. (2) Some Chinese OFDI risk aversion characteristics were identified; for example, when a firm wishes to pursue OFDI, it tends to choose a host country that has a smaller political risk, a higher degree of trade and investment freedom, and established globalized trade. To further optimize the industrial and spatial layout of Chinese OFDI, both country and industry level herd effects need to be examined and considered, as previous analyses have found poor Chinese OFDI industry level performances. Therefore, the relevant government authorities and industry associations need to develop guiding policies to improve OFDI decisions, strategically guide firms to optimize their position in the global value chain through considered OFDI, and amplify the role of the leading firms in the related industry. The country level OFDI herd effect could be seen to be influenced by factors such as the political environment, the bilateral trade and investment relationships, the infrastructure and technological levels, the service industry developments in the host country and other factors; therefore, sustainable cooperative relationships with the main host countries should be developed to upgrade Chinese OFDI. At the same time, a dynamic supervision and decision-making aid mechanism should be developed for Chinese firms to expand and gain competence internationally under a systematic framework. Using data mining approach can help us find the key influencing factors from many attributes, but the mechanism that these key attributes affect investment needs to be further studied, and the establishment of a dynamic risk warning mechanism for investment combined with the scientific methods is also a direction worthy of future research. Acknowledgements. This work was supported by National Social Science Foundation (Grant No. 19BJY100).

Appendix

Author’s calculation

herdvolume bycountry

It measures the ratio of output value of the industry of service to all industries’ total output value

obor = 1 means the investment took place in belt and road countries; Otherwise obor = 0

BIT = 1 means China has signed a bilateral investment treaty with the host country; Otherwise BIT = 0

It measures nation’s overall level of infrastructure construction

It measures the host country’s level of technological development

It measures the host country’s political environmentb

share of service

obor

BIT

inf ra

tech

PE

a

It represents which industry the target firm belongs to

industry

Host Country Characteristics

ICRG

The United Nations

IMF

Ministry of Commerce

Ministry of Commerce

World Bank

CSMAR

Author’s calculation

herdnumber byindustry the number of Chinese firms with OFDI in industry k in country j in year t − 1 over the total number of Chinese firms with OFDI in year t − 1 in country j

the OFDI volume of Chinese firms in country j in year t − 1 over the total OFDI volume of Chinese firms in year t − 1 in all sample countries

Author’s calculation

the number of Chinese firms with OFDI in country j in year t − 1 over the total number of Chinese firms with OFDI in year t − 1 in all sample countries

herdnumber bycountry

CSMAR, Statistical Bulletin of China’s Outward Foreign Direct Investment

Original Source of Data

Firm’s OFDI investment volume (takes the logarithm)

Definitions

lcap

Herd Effect

Attributes

Table 3. Core attribute selected by Wrapper method The Herd Effect on Chinese Firms’ OFDI - A Data Mining Approach 419

Definitions

It measures firm’s total factor productivity using LP method, higher tf p lp implies high efficiency

tf p lp

total resource llabour

import

lproductivity add

value

mean bycpi

export

leverage

rgdpb ycpi

skewr bycpi

varr bycpi

number of senior managers

proportion of regulators’ share

proportion of managers’ share

regulators’ share

ltotalshare

roe proportion of senior managers Note: a The attributes of tech includes indexes of the number of internet users, high tech exports percent of GDP, internet subscribers per 100 people, mobile phone subscribers per 100 people. b PE involves 6 core attributes: voice and accountability; rule of law; overall globalization; business freedom; investment freedom; trade freedom.

senior managers’ share

proportion of independent board

number of board number of independent board

EPS

f di

cpi

prgdp growth

Other Core Attributes

CSMAR Author’s calculation

CSMAR

It measures firm’s total assets in the year of the investment event (takes the logarithm)

CSMAR

ltotalasset

It measures whether the market value of firm’s asset overvalued or undervalued tobinq = value of an individual stock (or the stock market as a whole)/ net assets at replacement cost

Original Source of Data

assetturnoverrate Higher asset turnover rate implies superior efficiency

tobinq

Firm Characteristics

Attributes

Table 3. (continued)

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Analysis of Variant Data Mining Methods for Depiction of Fraud Qurat Ul Ain1 , Muhammad Azam Zia1(B) , Naeem Asghar2 , and Asim Saleem3 1

2 3

Department of Computer Science, University of Agriculture, Faisalabad, Pakistan [email protected] Department of IT, Government College University Faisalabad, Faisalabad, Pakistan School of Information and Software Engineering, University of Electronic Science and Technology, Chengdu, People’s Republic of China

Abstract. Financial fraud is a growing problem with far-reaching concerns in the financial sector. Online transaction is the basic problem that raises many fraudulent quires around the world which cause loss of money to the people. These transactions generated huge volume of complex data in daily life. The depiction of fraud from credit card is still a key challenge due to two main reasons: firstly, profiles of ordinary and fraudulent behavior changes with the Passage of time, and secondly highly skewed credit card fraud records. Therefore, this study considered this challenge and proposed the solution to identify the fraudulent transactions through the credit cards using data mining techniques. Data mining has played a significant role in identifying credit card fraud from online transactions. Dataset collected from the publically available source and refine it. The employed classifiers are Naive Bayes, Bayes net, Logistic regression, Random forest, Decision tree, support vector machine, Decision stump, K- Nearest Neighbor, J48 and Binary Classification Technique. These techniques are applied on the preprocessed data. This data consists of 284,785 credit card transactions. Extensive experiments were conducted. The accuracy of each classifier was recorded in order to perform comparison. Our empirical analysis spotlights that K-NN outperforms in term of accuracy which is 99.95% than other classifiers. The findings of this study would be useful for the banking sector. Keywords: Data mining · Credit card fraud · Ensembling techniques · Prediction · Behavior · Financial sector

1 Introduction Internet reliance has increased the number of credit card transactions but also elevated the number of credit card frauds while transactions performed online or offline [6]. Despite credit card transactions being a common payment method, new computing approaches to deal with credit card fraud have been highlighted. For industries such as loans, banking, e-commerce, insurance and manufacturing, there are various frauddetection technologies and applications that prevent fraud. Data mining is a popular c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 423–432, 2020. https://doi.org/10.1007/978-3-030-49829-0_31

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method used to resolve the problem of credit fraud [1, 2]. Fraud extends to many businesses, including the retail, financial and insurance industries and public authorities. This analysis is thus motivated by the main challenge described above. Most studies have used a form of misclassification to test the different solution. The existing data about transaction and certain operational issues pose many challenges for developing the fraud detection system. Precision is still a key challenge that needs to be addressed. So, current study is motivated from the aforementioned issues. The aim of this study is to consider the said problem. The approaches that the financial institution recommends will be of grateful because it can improve the accuracy of the obtained results. Kho and Via 2017 [4] described that the advent of the credit card business chip card model (Europay-MasterCard-VISA) largely solved the old magnetic card technology problem. Some reports, however, begin to question the design and implementation of the EMV. This paper indicates that there must be a detection method to record possible anomaly a fallback in the event that technology fails. Although the Random Tree and J48 only achieved the highest accuracy value of 94,32% and 93,50% respectively, several classificatory were evaluated during the model development. The J48 is more suitable to understand transaction log data through the analysis of both (2) classification modules. Sharma et al., 2018 [8] suggested that Credit card fraud leads to of millions of dollars in misfortune for electronic shippers, which is a crucial area in our current age, where practically any person needs to physically or via the internet to handle the account. Through advancing the calculations of machinery learning, analysts slowly find complicated ways to identify extortion, but the use of this approach is often scarce. The paper defines the fraudulent accounts by means of classification algorithms and then uses the sorting technique to improve the tests’ accuracy. Bee search and genetic algorithms have been used to pick from the large dataset the necessary characteristics. In different aspects, the reduced data set has been analyzed. To minimize variation, the ensemble’s learning methods are applied and the implications for the detection of fraud by bagging, stacking and voting are the optimal process. De sa et al., 2018 [9] presented that Fraud-BNC, a Bayesian Network Classifier (BNC) algorithm designed to deal with an actual problem with credit card fraud detection. A hyper-heuristic evolutionary algorithm (HHEA) was used to construct fraudBNCs automatically. The information of the BNC algorithms is arranged in a taxonomy and the best grouping of these modules for a specific data set is pursued. Fraud-BNC was generated spontaneously using the PagSeguro, the most popular online expense service from Brazil, and verified along with two cost-sensitive classification policies. The findings were compared to 7 other algorithms and evaluated in view of the question of data arrangement and the method’s commercial efficiency. Fraud-BNC was the best algorithm for a good deal between the two viewpoints, increasing the economic efficiencies of the current company by up to 72,64%. Mekterovic et al., 2018 [11] said that the problem of credit card fraud is the billions of dollars lost annually. It also cause of fraudulent transaction that are exponential growing day to day. It is also constantly changing as the software and usage patterns change over time, making CCFD a particularly difficult issue. However, these methods will be extended by data mining techniques for fraud detection. Fraud detection has historically

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been based solely on rules on domain detection. This paper discusses the issue of the CCFD with its conventional issues and state-of - the-art resolution. We review recent documents and provide a systematic overview of the related features for fraud detection and data mining. This research is basically to detect the credit card fraud transaction from the dataset of ULB machine learning by applying the different data mining algorithms to check and evaluate the accuracy and specificity and then compare the accuracy level to state the best algorithm to solve the problem of credit card fraud. 1.1 Contributions of the Proposed Study Firstly, dataset collected from the ULB machine learning. Then refine the dataset and process the data according to the requirement. Secondly, the simulation work performed in Weka tool which provides different methods to identify, collect, cluster, group rules and display information pre-processing. This proposed methodology includes various strategies such as Decision Trees, Binary Classification and K-Nearest Neighbor (K-NN), Naïve Bayes, Bayes net, Logistic regression, Random forest, support vector machine, Decision stump and J48. Thirdly, Extensive experiments are performed and accuracy of each classifier recorded. Our empirical analysis spotlights that K-NN outperforms in term of accuracy which is 99.95% than other classifiers.

2 Background of Variant Classifiers Data mining techniques and classifiers can be stated as an algorithm that are used for implementation and classification [3]. Algorithms are implemented as mathematical function and it is an instance of supervised learning where training set of correctly identified observation is available. 2.1 Naïve Bayes (NB) Naïve Bayes is an algorithm for the classification function that is simple and dominant. Even if we run on a set of data with millions of accounts with a certain number of attribute then try to approach Naïve Bayes. Naïve Bayes is a master-learning algorithm used to predict the class of prospects by using training datasets of defined target category. In particular, we can conclude that the naïve Bayes does not rely on the occurrence or absence of distinct attributes in the same array. This technique is called naive because it intelligently acknowledges the equality of class-specified attributes. 2.2 Bayes Net (BN) Bayesian networks (BNs) provide a strong graphical framework for encoding the probabilistic relationship between a set of variables. Nonetheless, widely learned Bayesian network classifiers (BNCs) using probability ratings typically only achieve average accuracy, since the ratings are less relevant to the category but rather a general inference

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problem. Bayes net is also called a graphical model. It used to fabricate a model from dataset. It is also used to speak to irregular inspection and under their conditional and compliance by coordinated non-cyclic diagram. 2.3

K-Nearest Neighbor (K-NN)

The K-nearest (KNN) algorithm is used to measure the number of nearest neighbors in a class of a sample data point based on a k deference. The category given by data points is defined by several closest neighbors. The data samples should be present in the memory at pause. Such data samples are assigned weights according to the sample information range [10]. A k-classifying neighbor explores the pattern space for the ksamples closest to the unidentified sample when an unidentified sample is given. In terms of Euclidean distance, “Closeness” is established. 2.4

Logistic Regression (LR)

Logistic regression is a supervised rating method that returns the probability of a binary dependent variable, estimated from a logistic regression independent data set, which predicts the resemblance of an outcome with two values-zero or one, yes or none and false or real [7]. These are parallels to linear regression, but since a straight line is obtained in linear regression, logistic regression reveals the curve. Logistic regression The application of one or more predictors or independent variable is based on the estimation, logical regression generates logistic curves that differentiate between zero and one. Regression is a model of regression in which the variable of dependency is categorical and the relationship between several independent variables is evaluated. 2.5

Random Forest (RF)

Collection of decision trees is Random Forest. Many decision-making bodies have been established and the results compiled to achieve an end result. Whenever a new example is marked, it’s put in the forest as a tree. All the trees are graded separately for a category. Select the object with the highest efficiency. In case of large and uneven data sets with different characteristics the classifier has good computer speed and is simple to use. The collected data is correct because it extracts the output variations from all decision-making trees that are forests and also integrates further inputs into the final forecast. 2.6

Decision Tree (D-tree)

Decision tree is a research system with a tree structure or a hierarchical one. It is mostly used for classifying and forecasting processes. The assembly of decision-tab classification systems does not need dominance information or constraint, so exploratory knowledge exploration is sufficient [12]. The main advantages of Decision Trees are that this method represents acquired knowledge meaningfully and therefore facilitates the drawing up of IF THEN rules. Decision trees are the most efficient research and data mining methodologies.

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2.7 J48 The study of large data sets is systematic. The classification is used to manage data. Tree modelling sometimes helps to predict new data. This paper concentrates on the J48 algorithm which is used to construct univariate decision-making bodies. Imagine having a set of analysts or self-determining variables and a list of goals or reliant variables. Then you can guess the target variable of a new dataset record by using a choice tree like J48 for this dataset. 2.8 Support Vector Machine (SVM) SVM is a popular regression, classification machine learning algorithm. It is a controlled algorithm of learning that analyzes data used to classify and regress [5]. The modeling of SVM requires two stages: first, to train a data set and then to achieve a model and then to predict the information of a sample dataset using this method. A Support Vector Machine (SVM), formally defined as a distinct, discriminatory classifier, represents the training data points in the space and maps them to ensure a divide between the points of different types. 2.9 Decision Table (D-table) Testing of the decision table is a code research method used to test device comport ability of different input combinations. This is a systematic approach in the form of tabular captured the various input combinations and the corresponding device actions (output). This is why it is called a Cause-Effect Table for better test analysis of cause and effect. Decision Table is a table of inputs against rules/cases/test conditions. 2.10

Ensembling Techniques

Ensemble methods have been improved, combining various models to produce more precise results. These methods provide more accurate results. By combining several models to create multiple models. The main reason for implementing these approaches is that the template is like an organizational group here.

3 Research Methodology First the credit card data set obtained from publically available platform. The dataset cleaned and checked on the basis of a data set that includes eliminating redundancy, completing empty rows, translating the parameter into factors or classes, and splitting the data into 2 sections. Different models are employed using Weka. These models are Naïve Bayes, Bayes Net, KNN, Logistic Regression, Random forest, Decision tree, J48, SVM and Decision table. The accuracy of each classifier is noted then comparison is made on the basis of accuracy value. ULB Machine Learning Group is responsible for the dataset. The data set covers European cardholders’ credit card transactions during September 2013, and a dataset of 284,786 transactions occurring within two days be

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given. The data set is extremely unbalanced and biased to the positive class which are 0.173% of the transaction data in fraud cases. It only includes numerical input variables (continuous) which are converted to 28 main components as a result of the Principal Component Analysis (PCA). And in this analysis, a total of 30 inputs are used. A parameter of each profile use that represents customers’ spending habit along with the days of the month, hours of the day, geographic locations and form of the merchant in which the transaction is performed is a behavioral feature of the card. Those variables are then used to create a model that differentiates fraudulent activities. Due to Confidentiality issue the detail and background information of the feature may not be provided. The time function stores the seconds between each transaction and the first transaction. The amount is the transaction amount. Feature ‘class’ is a conditional class target and it takes 1 for positive (fraud) and 0 for negative (non-fraud) instances. Nine classification models are being established in this analysis, based on Naïve Bayes, Bayes net, KNN, logistic regression, Decision tree, J48, SVM and the decision table. 70 per cent of the data set is for training purposes and 30 per cent for validation and analysis is used to evaluate these models. The quality of the nine classifier is evaluated by using accuracy and comparison analysis. Figure 1 shows the step by step procedure of proposed work.

Fig. 1. Research architecture

4 Experimental Results This section discuses about experimental part of this work. Numerous carried out in this work based on accuracy. Obtained results are based on the Performance measurement

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Table 1. Performance measurement of different algorithms Performance NB

BN

K-NN LR

RF

D-TREE J48

SVM D-TABLE

TP rate

0.978 0.994 1

0.977 0.986 0.955

0.927 0.975 0.999

FP rate

0.177 0.192 0.23

0.177 0.407 0.77

0.108 0.179 0.407

Precision

0.998 0.998 0.999 0.996 0.997 0.995

Recall

0.987 0.994 1

0.977 0.986 0.955

0.991 0.975 0.999

F-measure

0.987 0.996 0.999 0.987 0.905 0.955

0.991 0.991 0.999

MCC

0.206 0.385 0.829 0.206 0

ROC

0.971 0.974 0.885 0.923 0.984 0.992

1

0.987 0.962

PRC

0.999 0.999 0.999 0.923 0.984 0.992

1

0.996 0.999

0.878

0.991 0.996 0.999

0.932 0.912 0.708

Fig. 2. Performance measurement

and accuracy measurement of different algorithms. The accuracy measurement of various data mining algorithm on given data has been analyzed by the help of Weka tool. Table 1 shows that values of performance measurement which evaluate the accuracy, sensitivity, precision and specificity by applying the different data mining classifiers that are mention above on the target class of dataset in WEKA. It shows all the weighted average values which are true positive rate means correctly classified instance and false positive rate means incorrectly classified instance, precision and recall value equivalent to the TP rate, F-measure which is balance measure that is balance performance of the target class. MCC value which is the measurement of correlation coefficient between the observed and predicted values and quality of class. ROC value which is receiver operating curve value which examine the performance of classifier generally. At last the PRC precision recall value which represent the behavior of the classifier that check the class is healthy or not.

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97.75%

BN

99.43%

K-NN

99.95%

LR

97.75%

RF

98.52%

D-tree

95.45%

J48

92.65%

SVM

97.43%

D-table

99.92%

Fig. 3. Comparison of accuracy of classifiers

4.1

Impact of Ensemble the Classifiers

In this study, the output compared using a 70% training and 30% test dataset using different classifiers. The calculation of precision and consistency in the Fig. 2 can be seen. Table 2 shows the comparative results of each algorithm The Fig. 3 shows the correctly classified instances using the different type of classification algorithm. Some of them are used in previous study while some are used in proposed study. The comparative analysis clearly indicates that proposed algorithms accuracy much better than the previous studies. Figure 3 clearly indicates correctly classified instance of KNN algorithm achieve maximum accuracy 99.95% than others. The minimum correctly classified instance is 0.042%. That’s why, we used KNN for a better result and predict the place where default payment rate is so high. By using the different type of algorithm it is clear that the KNN classification algorithm gives better and accurate result. KNN 99.95% accuracy while others achieve low. It detects fraud transactions in the dataset and non-fraud transactions in the form of Legit or fraud.

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5 Conclusion With the use of modern technologies, online frauds are rapidly increasing in financial sector. There are certain reasons behind this problem. So, this study paid attention to this issue that overlooked in the past. Publically available dataset employed to perform this research work. Extensive experiment conducted and accuracy of each classifier recorded. The accuracy of each algorithm was critically analyzed and compared in terms of accuracy. Naïve Bayes has the accuracy 97.75%, Bayes net has the accuracy 99.43%, logistic regression has 97.75% random forest has 98.52%, Decision tree has 95.45% accuracy, J48 has 92.65% accuracy while SVM shows accuracy of 97.43% and Decision table shows accuracy of 99.92% but the best accuracy obtained by the KNN which is 99.95%. as indicated by Fig. 3 Moreover, K-NN shows the high and precise accuracy in the problem of credit card fraud detection. It would also be beneficial to use more preprocessing techniques. The Bayes net algorithm still suffers from the imbalanced dataset problem and needs further preprocessing in order to achieve the highest performance and it could have been of great interest.

6 Significance of the Study The findings of this work will be useful for banking sectors because it can help to minimize the issue and to predict at early stage.

7 Future Study In future, we will extend our work and emphasis to develop a system that will facilitate the financial organizations to detect fraud rate accurately that were happened in the past few years. Acknowledgments. I would like to thank to my mentor Dr. M. Azam Zia to provide necessary support, motivation and infrastructure to carry out the research work. I also want to thank my loving parents for their continuous help and support.

References 1. Amanze, B.C., Asogwa, D.C., Chukwuneke, C.I.: Credit card fraud detection system using intelligent agents and enhanced security features. Int. J. Trend Res. Dev. 5(3), 524–530 (2018) 2. Anushree, B., Kumar, R.: A novel machine learning approach to detect credit card fraud using ECSVM. J. Eng. 8(11), 54–62 (2018) 3. Bathala, S.B., Nagendra, M., Kandakatla, M.: A review on banking sector. Int. J. Eng. Tech. 3(6), 681–688 (2017) 4. de Sa, A.G.C., Adriano, C.M., et al.: A customized classification algorithm for credit card fraud detection. Eng. Appl. Artif. Intell. 72, 21–29 (2018) 5. Jain, R.B.G., Dubey, S.: A hybrid approach for credit card fraud detection using rough set and decision tree technique (2016)

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6. Kaneri, A., Anugrah, S., et al.: Fraud detection in online credit card payment. Int. Res. J. Eng. Technol. 5(3), 2921–2923 (2018) 7. Khare, N., Sait, S.Y.: Credit card fraud detection using machine learning models and collating machine learning models. Int. J. Pure Appl. Math. 118(20), 825–838 (2018) 8. Kho, J.R.D., Vea, L.: Credit card fraud detection based on transaction behavior. In: IEEE Region 10 Conference, pp. 1880–884 (2017) 9. Mekterovic, I., Brkic, K., Baranovic, M.: A systematic review of data mining approaches to credit card fraud detection. WSEAS Trans. Bus. Econ. 15, 437–444 (2018) 10. Navamani, C., Krishnan, S.: Credit card nearest neighbor based outlier detection techniques. Int. J. Comput. Tech. 5(2), 56–60 (2018) 11. Sharma, S., Mittal, P., Geetika, G.: An approach to detect credit card frauds using attribute selection and ensemble techniques. Int. J. Comput. Appl. 180(21), 1–6 (2018) 12. Vimala, S., Sharmili, K.C.: Survey paper for credit card fraud detection using data mining techniques. Int. J. Innov. Res. Sci. Eng. Technol. 6(11), 357–364 (2017)

Imputation Method Based on Sliding Window for Right-Censored Data Syed Ejaz Ahmed1 , Dursun Aydın2 , and Ersin Yılmaz2(B) 1

2

Department of Mathematics and Statistics, Brock University, St. Catharines, Canada Department of Statistics, Mugla Sitki Kocman University, Mugla, Turkey [email protected]

Abstract. Censored data arise in almost all important statistical analyses. For example, in patient-based studies, biostatistics data often subject to right censoring due to the detection limits, or to incomplete data. In the context of regression analysis, improper handling of these problems may lead to biased parameter estimates. Recently, imputation techniques are popularly used to impute censoring observations and the data are then analyzed through techniques that can handle censoring. In this sense, we introduce a new imputation strategy called sliding window method based on predictive model imputation (SWPM). In the present study, to assess the success of the proposed imputation method, the classical predictive model (PM) is used as a benchmark method. Hence, we compared two imputation methods for evaluating the right-censored data. The focus here is to assess and analyze through simulation and real data studies the performances of our imputation techniques based on different censoring levels and sample sizes. Keywords: Sliding window imputation · Censored data

1

· Imputation · Predictive model

Introduction

One of the most important criteria for data quality is the completeness of the data. In data science applications, it is commonly assumed that all values are fully measured. Although there have been many studies on the improvement of data quality (see, for example, the studies of [6,9,13]), it can be said that censorship observations are still neglected in many areas of application, especially in the field of biostatistics. As is known, right censored observations may occur, especially in biostatistics, for different reasons such as withdrawal of the observed subject (or patient) or equipment failure. As a result, analyzing or ignoring the right censored data without any adjustment can lead to biased and unreliable results. The problem of right-censored data is an important challenge in terms of modelling data. The presence of right censored observations is a common issue c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 433–446, 2020. https://doi.org/10.1007/978-3-030-49829-0_32

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in the real application areas, especially in the social sciences, bioinformatics or signal processing. Imputation methods for the right censored values are of great importance for the successful analysis of these data. In terms of data completeness, the contributions of this article are a new sliding window method based on predictive model imputation (SWPM) for completing the right censored data points in the missing dataset. Furthermore, in order to evaluate the success of the method introduced in this article, the classical predictive model (CPM) introduced by Bø et al., [3] is used as a benchmark method. It should be noted that a number of authors have studied the imputation methods for missing data. Examples of some recent studies include the following: Troyanskaya et al., [14] introduced the kNN imputation method. Wang et al., [15] proposed the basis of an imputation method for support vector machines. Cai et al., [5] model and advanced iterative local-least squares method. Burgette and Reiter [4] proposed sequential regression trees to deal with missing data. Khan et al., [10] showed an imputation method based on factor analysis and so on. Also, see Bertismas et al., [2] for a detailed list of imputation methods. This study differs from other studies in the literature by two aspects. First, SWPM imputation is used for right censored data; this means that the method can use some additional information when completing the right censored observation. It can be counted as an advantage for reducing the deviation of the imputed data points. Second, the sliding window procedure can be adapted to various datasets with varying window widths. Remain of the paper is organized as follows. Both methods, SWPM and PM imputation methods are given in Sect. 2. In order to measure the performances of the imputation methods evaluation criteria are presented in Sect. 3. Section 4 includes the numerical experiments and results of the simulations. In Sect. 5, outcomes of the real data example are given and finally, conclusions and discussions are explained in Sect. 6.

2

Materials and Methods

Let us consider the completely observed data points as (x1 , x2 , ..., xn ) and values of censoring variable (c1 , c2 , ..., cn ). In this case, partially observed data points can be determined as follows  1 if xi < ci (1) ti = min (xi ,ci ) , i = 1, . . . , n,δi = 0 if xi ≥ ci where ti ’s are the values of incomplete dataset and δi ’s are called as a censoring indicator which takes value “0” if observation is censored and “1” otherwise. Throughout this paper, we consider the following observed data (xi ,ci , ti ,δi ). In this section, the fundamentals of the proposed imputation method and its difference from other solution techniques for censorship are briefly explained. In this context, it can be said that methods for dealing with censored data can be divided into three different categories. The first is to delete the censored data

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points from the database, which can causes large biased results. The second considers the replacing the distribution of the censored data with a predictor of it. The Kaplan-Meier estimator [8], weighted observations (see [12]) or data transformation (see [11]) methods are widely used in this group. The third is imputation methods, which are mostly developed for missing data problems, but are also very useful for censored data. For the right censored data, the kNN imputation method and predictive model imputation methods are used and good results are obtained with these methods (see [1]). Similarly, in this paper, the SWPM imputation method is introduced and also used to overcome the right censored data points. The next two sections describe the CPM and proposed SWPM imputation methods, respectively. As mentioned before, the CPM is considered as a benchmark method to make a meaningful comparison with the SWPM. 2.1

CPM Imputation

The CPM is an imputation method that works using with ordinary least squares (OLS) method. It estimates a linear regression model for only observed values of the dataset (tj , j = 1, . . . , k) where “k” is the number of uncensored values. The CPM then imputes the right-censored observations with in-sample estimation. In this context, the form linear regression model as an imputation method can be shown as follows t∗j = vjT β ∗ + ε∗j , j = 1, . . . k

(2)

where VjT are (1 × p) dimensional vectors of the predictors. For simplicity, the predictor variable in this study consists of sequential values in the interval [0, 1], T β ∗ = (β0 , β1 , . . . , βp ) is the vector of the regression coefficients and ε∗i ∼ N (0, 1) is the random error term for CPM. Note that the estimator of the regression coefficients vector β ∗ defined in Eq. (2) can be obtained by minimizing the least squares (LS) criterion objective function LS (β ∗ ) =

k  

t∗j − vj β

2

= (t∗ − vβ) (t∗ − vβ) T

(3)

j=1

  Assuming the inheritability vT v , standard least-squares theory provides the solution to the Eq. (3), given by  −1 T ∗ βˆ∗ = vT v v t

(4)

Hence, the fitted values are given by ˆ t∗ = vβˆ∗ = H∗ t∗

(5)

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 −1 T where H∗ = v vT v v . After obtaining the vector of fitted values, ˆ t∗ , data points matched with δi = 0 can be imputed with obtained linear regression model and replaced with censored ones. Thus, complete dataset is obtained. In addition, some important features related to the use of OLS for imputation can be listed as follows. i. Because of OLS is an important method, it provides simplicity for interpretation. ii. If xi ∼ N (μx , σx ), then imputed (estimated) values will be unbiased and consistent. In addition, there is a reliable confidence interval for each imputed data point. It should be noted that the CPM has some disadvantages. Because of the assumption of linearity, when the structure of the data breaks the rule of linearity, it may not be imputed censored observations correctly. In addition, all imputations are carried out by only one model that can causes unstable imputed values for data sets containing heteroscedasticity. Note that SWPM has a capacity for solving these problems. Details are given in the next section. Ahmed et al. [1] produced an algorithm for performing CPM given in Appendix. 2.2

Imputation Based on Sliding Window

This paper introduces a new method to impute right-censored observations by a sliding window (SW) method based on predictive model. The SW is an important method in data science, especially in data mining applications, as it provides the latest information on censored observations. The sliding window method includes a fixed window size on the data points and only works locally with the data points placed in the specified window then moves to the next window. This local operation feature of the SW distinguishes it from other imputation methods and provides an advantage for data sets with unstable variance. The new SWPM method, combined with SW and predictive model (PM), provides more flexibility than the conventional PM method. Modeling technique can be determined according to linearity or nonlinearity properties of the data. The SWPM methodology in a similar way to CPM. Assume  can be described   that the data pairs vj , t∗j , j = 1, . . . , n are the uncensored part of the data. In the sliding window method, for window size w and window both variable vectors can be written as t∗r = (tr , tr+1 , . . . tr+w−1 ) vr = (vr , vr+1 , . . . , vr+w−1 ) T

T

where t∗r and vr are vectors of the observations in rth window. As r increases, the specified window slides in the data stream. The number of windows changes according to window size (w) and can be calculated as nw = (n − w + 1). Therefore, in the SWPM approach, the number of windows (nw ) denotes the number of model to be estimated. Note that it is very important to choose the right window size here. In this study, values of “w” are determined as three levels:

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small, medium and large according to censoring level. When the censoring level increases, “w” gets small values and takes large values otherwise. Once the parameters of the SW have been determined, the OLS method is applied to the observations in each window. The obtained linear models are then used to impute the right censored observations. Thus, each censored observation can be estimated with different regression models that provide a local approach to the data points. In this case, the SWPM model can be written as follows ˆ t∗r = vrT βr + εr , r =1, 2, . . . , nw

(6)

where βr = (β1r , β2r , . . . , βpr ) regression coefficient for the rth window and εr ∼ N(0, σr2 ) . From this, the estimator βr of regression coefficients for each window can be estimated similar to Eq. (4), in the following manner, T

βˆr = (vrT vr )−1 vrT t∗r

(7)

and the fitted values are given by t∗r = vrT βˆr = Hr t∗r

(8)

where Hr is a hat matrix that can be described as Hr = vr (vrT vr )−1 vrT . It seems that right censored observations can be imputed by the obtained models. For example, if q th observation is censored, it can be obtained as follows: ˆ xsw q = vq βr(q)

(9)

where βˆr(q) denotes estimated regression coefficients for rth window that contains q th observation. An algorithm for imputing with SWPM is given in appendix. Detailed information can be seen from the attached algorithm.

3

Evaluation Criteria

This section includes some evaluation criteria to measure performances of the imputation methods in terms of accuracy of imputed values. In this study, mean absolute error (MAE), square root of mean square error (RMSE) and imputation accuracy (IA) criteria are used as measurement tools. Each criterion is considered ) and the assumed to measure the difference between the imputed values (ximp i values of incomplete observations (xi ). Therefore, it can be said that a major contribution of this study is provided by simulation experiments, because it is not possible to know the true values of missing observations in real data applications. Mathematical formulas of the mentioned criteria are given below (Table 1):

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Formula  nc    imp  1 Mean absolute error (MAE) − x x  i i nc  i=1 2 nc   1 xi − ximp Square-root of mean square error (RMSE) i nc i=1

Imputation accuracy (IA)

1 nc

nc x −ximp  | i i | i=1

xi

where nc represents the number of the right-censored data points.

4

Numerical Experiments

A simulation study is conducted to determine that the proposed SWPM method is an appropriate imputation technique. Furthermore, the performance of the SWPM method is compared with the benchmark method PM. In order to realize these ideas, the right censored data generation process is performed step by step as follows. Step 1. Generate vi ’s from a uniform distribution such as vj ∼ U [a, b] where, a and b can be arbitrarily selected according to researcher. Step 2. Generate xi ’s from xi = β0 + β1 vj + εi , i =1, 2, . . . , n , which are T assumed completely observed dataset. Here, β = (2, 3) regression coefficients that are used for only generating dataset, not to be estimated and εi ∼ N(0, 1) represents random error term(s). Step 3. Generate the δi ’s from Binomial distribution such as δi ∼ B (n, (1 − C.R.)), where C.R. denotes the censoring ratios determined as three levels C.R. = (0.10, 0.25, 0.60).  Step 4. Produce the censoring variable ci from a normal distribution N μx , σ 2x , where μx and σx2 represent the mean and variance of xi ’s, respectively. Here the detail is generating censored data points bigger than xi ’s which is needed a small iteration. Accordingly, if (δi = 0), value of ci ’s should be generated again and again until it is ensured that ci > xi . Step 5. Thus, incomplete observations can be obtained, as defined in Eq. (1). Note that simulation study begins after five steps have been completed. In this simulation study, we generate 1000 random samples of size n = 25, 50 and 150. Results obtained from the simulation experiments are summarized in the following tables and figures. It should be noted that the results in Table 2 are given for comparing the introduced SWPM imputation method with classical PM method that can be thought of as a benchmark case. The findings reported in Table 2 show the finite sample performances of the SWPM and PM imputation techniques based on three different censoring ratios at 10%, 25% and 60%. For each performance measure, the best score is indicated with an asterisk (*). In general, one can clearly see that SWPM works well especially for samples of size n = 150 under each censoring levels. When the results are examined in detail, it appears that

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PM gives better scores for some simulation configurations, and in some cases both methods give similar results. Table 2 reveals that the effect of the censoring tends to decrease the performance measures of the imputation methods. The scores from methods declines as the censoring level increases. In addition, these scores are improved as the sample size increases. In this context, these results are supported by Figs. 1-3 to observe the step-by-step operation of the imputation methods. Table 2. Performance scores obtained from SWPM and PM methods C.R. (%) 10%

25%

60%

n

Method/ IA criterion

25

SWPM PM

0.213* 0.304* 0.783* 0.275 0.759* 1.075 0.243* 1.140* 1.103* 0.562 0.682 1.945 0.251* 0.775 0.985* 0.352 1.767 1.639

50

SWPM PM

0.197 0.163* 0.625 0.169* 0.375* 0.598* 0.212 0.194* 0.188 0.616* 0.425 0.76 1.428 0.212

150 SWPM PM

RMSE MAE IA

RMSE MAE IA

RMSE MAE

0.731* 0.834* 0.76 0.852

0.163* 0.204* 0.701* 0.201* 0.415* 0.769* 0.152* 0.587* 0.605* 0.225 0.282 1.001 0.22 0.458 0.818 0.184 0.693 0.726

Fig. 1. Line plots of the SWPM and PM methods for evaluation criteria based on C.R. = 10%.

Figures 1, 2 and 3 represent the line graphs for the IA, MAE, and RMSE evaluation criteria, respectively, for all sample sizes and censoring levels. In each figure, the χ-axis shows the sample size (n) and the y-axis shows the score of the corresponding performance measurement. When the figures are examined carefully, it can be seen that the numerical values from evaluation criteria increase as censorship rates increase. This case is a traditional result for censoring data analysis. One interesting point here is that the SWPM is less affected by the sample size, compared to PM, especially for C.R. = 10% and 60%. This can be explained by the non-parametric nature of the SWPM operating with the changeable window size parameter. This means that SWPM method can adapt to data more easily than PM.

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Fig. 2. Similar to Fig. 1 but for C.R. = 25%.

Fig. 3. Line plots of the numerical scores from the evaluation criteria for C.R. = 60%

Of course, in some simulation configurations, for example, C.R. = 25% and n = 25 or C.R. = 60% and n = 50, the PM gives better results than the SWPM (see Fig. 2). These outcomes may also be caused by inappropriate window size selection. In this study, the window size parameter is intuitively selected and has three levels, as previously mentioned, but should be optimally selected. This is also one of the future plans of this study. However, Fig. 4 is shown to see the effect of window size on SWPM performance using the specified performance metrics. The panels in Fig. 4 are designed to see the effects of both sample sizes and censoring rates. Therefore, one can think that the left side of the figure represents the influence of sample size (plots at the top of the left obtained for n = 50 and n = 150, when C.R. = 10%) and right side of the figure denotes influence of the censoring rate (plots at the right-side drawn for C.R. = 10% and C.R. = 60%, when n = 50). In this context, for the settings of this experimental design, it can be said that there is an almost positive linear relationship between censorship rates and window size (w) parameter. Likewise, a similar relationship exists between the parameter w and the sample sizes This is an important result because it provides an ability to define behaviors of the parameter w. In this sense, it is necessary to select w. For this, one of several selection methods in the literature, for example, general cross-validation or Akaike information criterion can be used.

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Fig. 4. Effects of window size parameter (w) to RMSE scores for different simulation configurations.

5

Real Data Application

This section uses an example from the real world to better understand how SWPM works to impute the censored observations. In this context, Stanford Heart Transplant Data, which is introduced to the literature by Crowley and Hu (1977) [7], is used as a real data. This dataset has sample of size n = 172, with many covariates. However, since the aim of this study is to complete censored response (survival time of patients) observations, we use age (in years) as explanatory variable here. Hence, to assess the effect of age on the survival time of patients PM and SWPM models can be defined as (P M ) : timei = β0 + β1 agei + εi , i =1, 2, ..., 172 (SW P M ) : timeij = β0j + β1j ageij + εij , i =1, 2, ..., w, j =1, 2, ..., nw

(10)

where nw = (n − w + 1) indicates the number of windows and for this data the window size is determined as 50 according to the simulation results. In this case, nw = (172 − 50 + 1) = 123. This means that the SWPM will estimate the 123 linear models for making imputation. To provide prior knowledge about the dataset, a scatter plot is given below and completely observed and right-censored part of the data is indicated with different colors. As can be seen in Fig. 5, there are many right censored observations. The data includes 97 censored data points, which means a censorship rate of 56.39%.

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It should be noted that this dataset is very similar to the simulation configuration obtained from n = 150 and C.R. = 60%. This makes it easy to interpret the real data study. Due to censored points, there is an important problem in measuring the performances of SWPM and PM methods for real data. Because the evaluation criteria used here use original (completely observed) data to measure the error between imputed and real values. Therefore, it should be emphasized that the results of the real data are less reliable than the simulation study. The solution to this problem is one of the future work plans. Note also that the numerical values obtained from the evaluation criteria based on SPM and PM for real data study are given in Table 3.

Fig. 5. Scatter plot of Stanford Heart Transplant Data with censored and uncensored data points.

From the results presented in Table 3, one can see that SWPM gives better scores than PM for all criteria. Here, the imputation is repeated four times to see the effect of the window size on the performance of SWPM and the smaller sized window (i.e., w = 4) appears to be better for the given data set. Once again, however, it can be remembered that these measurement tools are used without completely observed data set. In this context, in order to clearly understand the imputation process, the missing data set and the imputed ones by aforementioned methods are given in Fig. 6. In Fig. 6 one sees that the PM method makes censored observations almost linearly distributed points (i.e., imputed observation points denoted by green asterisk). Although such a distribution cannot be counted as satisfactory imputation performance, these points may provide important scores in terms of evaluation criteria. On the other hand, as can be seen from the same figure, it is clear that the SWPM works successfully in the specified local windows. The imputed

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Table 3. Performance scores from stanford heart transplant data imputed by SWPM and PM Criterion IA

MAE

RMSE

Method

SWPM PM

SWPM

w=4

20.106* 37.915

296.546* 416.157

w = 15

38.512

37.915* 389.441* 416.157

416.157

348.970*

w = 30

42.48

37.915* 414.028* 416.157

349.303

348.970*

w = 60

49.108

37.915* 471.963

416.157* 393.786

348.970*

PM

SWPM

PM

301.076* 348.97

Fig. 6. Scatter plot of real data points and imputed observations by SWPM and PM methods

values of the SWPM distributed in accordance with real observations yield the higher imputed values for almost every censored observation.

6

Concluded Remarks

This article focuses on completing the right censored data points with a new method SWPM and compared with PM to observe the behavior of the method and its differences from classical methods. In this section, some concluded remarks from simulation experiments and real data application are presented. In general frame, it is clear that SWPM shows better imputation performance than PM. In this context, some important inferences, conclusions and recommendations are given as follows:

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• It is seen that SWPM has important advantages on imputation such as changeable window size parameter (from that it can be said that SWPM has a nonparametric nature) and works locally on determined windows. Thus, SWPM can catch the variation of data more correctly than PM. Note that, PM uses all of the data and it imputes the censored data points using by in-sample estimation. • In the context of simulation studies, we see that SWPM gives satisfactory results for almost all simulation combinations. Note that PM gives better results than SWPM in some configurations such as (C.R. = 25%, n = 25), and (C.R. = 60%, n = 50). This can be explained with unsuitable choice of the parameter w. • As in the simulation study, SWPM is superior to PM in Stanford Transplant Data application. It should also be said that the real data sample is very similar to the simulation configuration of n = 150 and C.R. = 60%. So, it gives us more reliable results.

Appendix Algorithm 4. CPM imputation for right-censored data Input: Right-censored data points ti , i = 1, ..., n; with associated censoring indicator δi ; values of predictor variable vi . T xcpm ,x ˆcpm , ..., x ˆcpm Output: Imputed dataset x ˆcpm = (ˆ nc ) 1 2 1: begin 2: for (i = 1 to n) do 3: if (δi = 1) do 4: obtain ti with t∗i = ti 5: obtain t∗i with t∗i = ti 6: end(for the for loop in step 2) 7: Estimate the vector of the regression coefficients β ∗ with Eq. (6) 8: for (i = 1 to n) do 9: if (δi = 0) do 10: Estimate right-censored observation for ith value with using βˆ∗ and obtain cpm x ˆi 11: Replace the estimated value (ˆ xcpm ) with censored one ti i 12: end (for the for loop in step 8) T xcpm ,x ˆcpm , ..., x ˆcpm 13: Return x ˆcpm = (ˆ nc ) 1 2 14: end

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Algorithm 5. SWPM imputation for right-censored data Input: Right-censored data points ti with, Corresponding δi = I(xi < ci ), Values of predictor variable vi , window size parameter w sw sw T Output: Imputed dataset x ˆsw = (xsw 1 , x2 , ..., xnc ) 1: begin 2: for (i = 1 to n) do 3: if (δi = 1) do (Obtained the uncensored part of the data) 4: obtain ti with t∗i = ti 5: obtain t∗i with t∗i = ti 6: end 7: Determine the number of windows (nw ) with (n − w + 1) 8: for (j = 1 to nw ) do 9: Estimate the βr∗ for j th window ˆsw = vj βˆr 10: obtain the fitted values for the j th window by x j 11: end 12: for (j = 1 to w) do 13: if (δi = 0) do ˆsw (replacing the censored ones by imputed value) 14: ti = x i 15: if (δi = 1) do 16: ti = t∗i 17: end (for the for loop in step 12) sw sw T 18: Return x ˆsw = (xsw 1 , x2 , ..., xnc ) 19: end

References 1. Ahmed, S.E., Aydin, D., Yılmaz, E.: Nonparametric regression estimates based on imputation techniques for right-censored data. In: International Conference on Management Science and Engineering Management, pp 109–120. Springer (2019) 2. Bertsimas, D., Pawlowski, C., Zhuo, Y.D.: From predictive methods to missing data imputation: an optimization approach. J. Mach. Learn. Res. 18(1), 7133– 7171 (2017) 3. Bø, T.H., Dysvik, B., Jonassen, I.: LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res. 32(3), e34 (2004) 4. Burgette, L.F., Reiter, J.P.: Multiple imputation for missing data via sequential regression trees. Am. J. Epidemiol. 172(9), 1070–1076 (2010) 5. Cai, Z., Heydari, M., Lin, G.: Iterated local least squares microarray missing value imputation. J. Bioinf. Comput. Biol. 4(05), 935–957 (2006) 6. Cheng, R., Chen, J., Xie, X.: Cleaning uncertain data with quality guarantees. Proc. VLDB Endow. 1(1), 722–735 (2008) 7. Crowley, J., Hu, M.: Covariance analysis of heart transplant survival data. J. Am. Stat. Assoc. 72(357), 27–36 (1977) 8. Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958) 9. Keller, S., Korkmaz, G., Orr, M., Schroeder, A., Shipp, S.: The evolution of data quality: understanding the transdisciplinary origins of data quality concepts and approaches. Ann. Rev. Stat. Appl. 4, 85–108 (2017)

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10. Khan, M.E.E., Bouchard, G., Murphy, K.P., Marlin, B.M.: Variational bounds for mixed-data factor analysis. In: Advances in Neural Information Processing Systems, pp. 1108–1116 (2010) 11. Koul, H., Vv, S., Van Ryzin, J., et al.: Regression analysis with randomly rightcensored data. Ann. Stat. 9(6), 1276–1288 (1981) 12. Miller, R.G.: Least squares regression with censored data. Biometrika 63(3), 449– 464 (1976) 13. Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002) 14. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001) 15. Wang, X., Li, A., Jiang, Z., Feng, H.: Missing value estimation for DNA microarray gene expression data by support vector regression imputation and orthogonal coding scheme. BMC Bioinf. 7(1), 32 (2006)

The Behaviour of Solutions to Degenerate Double Nonlinear Parabolic Equations Tahir Gadjiev(B) , Yasin Rustamov, and Aybeniz Yangaliyeva ANAS Institute of Mathematics and Mechanics, Baku, Azerbaijan [email protected]

Abstract. We consider local behaviour of solutions to degenerate double nonlinear parabolic equations, where weight function is replaced with a double condition which supports a Poincare inequality. We give Harnack’s inequality for certain degenerate of double nonlinear parabolic equations. We used is well known that Moser’s tecnique is essentially based on the combination of a Sobolev and a Caccioppoli type inequalities. We also is established the local Holder continuity of a weak solution is a consequence of the Harnack’s inequality. However, due ∂(u p−1 ) to the nonlinearity of the term ∂t when p  2, it is not clear for the double nonlinear equations. Keywords: Degenerate · Double nonlinear · Parabolic equations · Harnack’s inequality

1 Introduction We study the Harnack’s inequality of nonnegative weak solutions to the degenerate double nonlinear parabolic equation   ∂ u p−1 = div A (x, t, u, Du) , 1 < p < ∞, (1) ∂t where is a Caratheodory function and satisfies the conditions A (x, t, u, Du) Du ≥ C1 ω (x) |Du| p A (x, t, u, Du) ≤ C2 ω (x) |Du| p−1

(2)

where C1 , C2 are positive constants, ω (x) is Mackenhount’s weights function. When p = 2 we have the degenerate heat equation. Due to the nonlinearity of the ∂(u p−1 ) term ∂t we cannot add a constant to a solution. We know, Eq. (1) in nondegenerate case had first been studied by Trudinger in [16], where he proved a Harnack inequality for nonnegative weak solutions. The proof was based on Moser’s celebrated work [13] and used a parabolic version of the John-Nirenberg lemma. Later the proof of the John-Nirenberg’s lemma was simplified by Fabes and Garofalo, see [4]. We give a relatively simple proof for Harnack’s inequality using the approach of Moser in [16]. The c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 447–459, 2020. https://doi.org/10.1007/978-3-030-49829-0_33

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parabolic John-Nirenberg lemma is replaced with a lemma due to the Bombieri in [2]. The weights are doubling and to support a Poincare inequality. The corresponding result in the elliptic case for measures induced by Muckenhoupt’s weights had been studied by Fabes, Kenig and Serapioni in [5]. The weighted theory in the parabolic case has been studied by Chiarenza and Serapioni in [3]. However, in their approach the role of the measure is somewhat different compared to ours and equation is linear. A very interesting question, whether also the necessity holds in this case. Moreover, the weights satisfy condition are doubling and to support a Poincare’s inequality are rather standard assumptions in analysis on metric spaces. It is well known that Moser’s tecnique is essentially based on the combination of a Sobolev and a Caccioppoli type inequalities. We take a full advantange of a metric space result, which states that the double property of weights and the Poincare’s inequality imply a Sobolev type inequality, see [1, 11, 14, 15]. See also [6]. The presented results is a generalization of previous work [7–10]. It is well-known that the local Holder continuity of a weak solution is a consequence of the Harnack’s inequality when p = 2 , see [13]. However, due to the nonlinearity of ∂(u p−1 ) the term ∂t when p  2, it is not clear how to modify the same proof for the double nonlinear Eq. (1). The paper is organized as follows. In Sect. 2, we give some definitions and auxillary results, also parabolic Harnack’s inequality. Later we is consider weighted Poincare’s inequality is a consequence of the double property and use some modification of an lemma due to Bombieri. In Sect. 3, we study estimates for super and sub solutions. In Sect. 4, we give a weak Harnack’s inequality and proof of based theorem. In Sect. 5, we give conclusion. Thus we is proved Harnack’s inequality for degenerate of double nonlinear parabolic equations and the local Hölder continuity of a weak solution.

2 Preliminaries Let ω (x) is Muckenhoupt’s weights and suppose that Ω is an open set in Rn . The 1 (Ω) is defined as completion of C ∞ (Ω) with respect to weight in Sobolev space W p,ω the Sobolev norm   1p   1p 1 (Ω) = uW p,ω |u| p ωdx + |Du| p ωdx Ω

Ω

1 (Ω) if it belongs to A function belongs to the local weight in Sobolev space W p,ω,loc 1   W p,ω (Ω ) for every open subset Ω of Ω, whose closure is a compact subset of Ω. The ◦

weight in Sobolev space with zero boundary values W 1p,ψ (Ω) is a completion of C0∞ (Ω) with respect to the Sobolev’s norm. For the basic properties of weighted Sobolev spaces we refer to [12].   1 (Ω) , t1 < t2 , the space of functions such that for We denote by L p t1 , t2 ; W p,ω 1 (Ω) and almost every t, t1 < t < t2 , the function u (x, t) belongs to W p,ω  t2  (|u (x, t)| p + |Du (x, t)| p ) ωdxdt < ∞. t1

Ω

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  p 1 (Ω) is clear. Let t1 < t2 and 1 < p < The definition for the space Lloc t1 , t2 ; W p,ω,loc   p 1 (Ω) is a weak ∞. A nonnegative function u (x, t) which belongs to Lloc t1 , t2 ; W p,ω,loc solution to (1) in Ω × (t1 , t2 ) if   t2   ∂η A (x, t, u, Du) · Dη − u p−1 ωdxdt = 0, (3) ∂t t1 Ω for all η ∈ C0∞ (Ω × (t1 , t2 )). Further, we say that u (x, t) is a super solution to Eq. (1) if the integral Eq. (3) is nonnegative for all η ∈ C0∞ (Ω × (t1 , t2 )) with η ≥ 0. If this integral is nonpositive, we say that u (x, t) is a sub solution. If the test function η vanishes only on the lateral boundary ∂Ω × (τ1 , τ2 ), where t1 < τ1 < τ2 < t2 , then the boundary terms are following   1 τ1 +σ p−1 u (x, τ1 ) η (x, τ1 ) ω (x) dx = lim u (x, t) p−1 η (x, t) ω (x) dxdt σ→0 σ τ Ω 1 and

 Ω

1 σ→0 σ



u (x, τ2 ) p−1 η (x, τ2 ) ω (x) dx = lim

τ2

τ2 −σ

u (x, t) p−1 η (x, t) ω (x) dxdt.

In the case of a super solution to the double nonlinear Eq. (1) the condition becomes 

τ2

τ1



 Ω

|Du| p−2 DuDηω (x) dxdt +

τ2 Ω



u p−1 ηω (x) dx

− τ=τ1

τ2 τ1

 u p−1 Ω

∂η ω (x) dxdt ≥ 0 (4) ∂t

for almost every τ1 , τ2 with t1 < τ1 < τ2 < t2 . The weight ω (x) is doubling, if there exists a constant C0 ≥ 1 such that ω (B (z, 2R)) ≤ C0 ω (B (z, R)) ,

(5)

for every z ∈ Rn and R ≥ 0. Here B (z, R) denotes the open ball with center z and radius R. The dimension related to a double weight is defined as dμ = log2 C0 . Note that in the case of the Lebesgue measure the dimension is n. The weight is said to be support a weak (1, p)-Poincare inequality if there exist constants P0 > 0 and τ ≥ 1 such that –∫ v − vB(z,R) ω(x)dx ≤ P0 R

B(z,R)

 –∫ |Dv| ω(x)dx p

 1p

,

(6)

B(z,R)

1 (Rn ) , z ∈ Rn and R > 0. Here we use the notation for every v ∈ W p,ω,loc

vB(z,R)

1 = –∫ vω(x)dx = (B (z, R)) ω B(z,R)

 vω (x) dx B(z,R)

The word weak refers to the possibility that τ > 1. If τ = 1, the space is said to support a (1, p) - Poincare inequality.

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Let 0 < σ ≤ 1 , τ ∈ R and B (z, R) be a ball in Rn . We denote U = B (z, R) × (τ − r p , τ + r p ) ,   1 1 1 1 σU + = B (z, σr) × τ + r p − (σr) p , τ + r p + (σr) p , 2 2 2 2 and



1 1 1 1 σU = B (z, σr) × τ − r p − (σr) p , τ − r p + (σr) p 2 2 2 2 −



Now we give parabolic Harnack’s inequality. Theorem 1. Let 1 < p < ∞ and assume that the weight ω is Mackenhoupt, doubling and supports a weak (1, p) -Poincare inequality. Let u ≥ ρ > 0 be a weak solution to Eq. (4) in U and let 0 < σ < 1. Then we have ess sup u ≤ C3 ess inf u, +

(7)

σU

σ U−

where the constant C3 depends only on p, C0 , P0 and σ. Note carefully that the constants in Eq. (7) is independent of ρ. A modification of the proof shows that the technical assumption u ≥ ρ can be removed and that the result holds for all nonnegative solutions. In nonweights case proof is due to Trudinger [16]. The following weighted Poincare’s inequality is a consequence of the double property and the (1, p) is Poincare inequality.  θ 1 (B (z, R)). Let ϕ (x) = 1 − |x−z| , whereθ > 0. Then Theorem 2. Suppose that u ∈ W p,ω R there exists a constant C = C (p, C0 , P0 , θ) such that for all 0 < r < R p –∫ u − uϕ ϕ (x) ω (x) dx ≤ C4 r p –∫ |Du| p ϕ (x) ω (x) dx, B(z,r)

B(z,r)



where

B(z,r)

uψ =

uϕω (x) dx

B(z,r)

ϕω (x) dx

.

Also we use the following modification of an abstract lemma due to Bombieri [2]. Lemma 1. Let ν be a Borel measure and θ, A and γ be positive constants, 0 < δ < 1 and 0 < q ≤ ∞. Let Uσ be bounded measurable sets with Uσ ⊂ Uσ for 0 < δ ≤ σ < σ ≤ 1. Moreover, if q < ∞, we assume that the double condition ν (U1 ) ≤ Aν (Uδ ) holds. Let f be a positive measurable function on U1 which satisfies the reverse Holder’s inequality  q

–∫ f dν

U σ

 1q



A ≤ –∫ f s dν (σ − σ )θ Uσ

 1s

with 0 < s < q. Assume further that f satisfies 

 Aν (Uδ ) nu x ∈ U1 log f > λ ≤ λγ

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for all λ > 0. Then  1q

 q

–∫ f dν

≤ C5

Uδ

where C5 depends only on θ, δ, γ, q and A. Proof. We denote   1q ψ = ψ (σ) = log –∫ f q dν Uσ

Hölder’s inequality gives 1 –∫ f dv = (U ν σ) Uσ



s

1 f dν + ν (Uσ )



s

log f < ψ2

 ψs 



A ≤ exp + exp (ψs) (ψ/ 2)γ 2

log f > ψ2

f s dν

(q−s)/ q

Let ψ be so large that 0 < log (ψγ / (A · 2γ )) ≤ q · ψ. The obtained lower bound on ψ depends on A, γ and q. We call it A1 . If we choose S =

2 −1 ψ log (ψγ / (A · 2γ )) 3

and use reverse Hölder’s inequality we get a proof of lemma. Lemma is proved.

3 Estimates for Super and Sub-solutions The following Caccioppoli type estimates for super and sub solutions are essentially consequences of choosing test function in integral identity Eq. (3). Lemma 2. Assume that u ≥ ρ > 0 is a super solution in Ω × (t1 , t2 ). Then v = u−1 is a sub solution. Proof. We take the η = u2(1−p) ϕ, where ϕ ∈ C0∞ (Ω × (t1 , t2 )) with ϕ ≥ 0. Then after calculation and a substitution in Eq. (4) leads to  t2   t2  u2(1−p) |Du| p−2 DuDϕω (x) dxdt − 2 (p − 1) |Du| p u1−2p ϕω (x) dxdt t1

Ω

t1

Ω

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t2

+2 (p − 1)



t1

∂u u ϕ ω (x) dxdt − ∂t Ω −p



t2

 u1−p Ω

t1

∂ϕ ω (x) dxdt ≥ 0. ∂t

An integration by parts, we obtain   t2   p−2 p−1 ∂ϕ − ω (x) dxdt ≥ 0. |Du| DvDϕ − v ∂t t1 Ω Lemma is proved. Lemma 3. Assume that u ≥ ρ > 0 is a super solution in Ω × (t1 , t2 ). Let ε > 0 with ε  p − 1. Then there exists a constant C6 = C6 (p, ε) such that  t2   ess sup u p−1−ε ϕ p ω (x) dx + |Du| p u−ε−1 ϕ p ω (x) dxdt t1 t1 such that   1 u p−1−ε (x, τ) ϕ p (x, τ) ω (x) dx ≥ ess sup u p−1−ε ϕ p ω (x) dx, 2 Ω Ω t1 p. Proof. The proof is based on use of Sobolev inequality and Caccioppoli’s estimate. Let γ = 2 − p/k. We fix σ and divide the interval (σ , σ) into k parts by setting   1 − γ− j . σ0 = σ, σk = σ , σ j = σ − σ − σ 1 − γ−k We denote Q j = σ j · Q = B j × T j and fix k later. We choose test functions ϕ j   γj , where supp ϕ j ⊂ Q j , 0 ≤ ϕ j ≤ 1 in Q j , ϕ j = 1 in Q j+1 , Dϕ j ≤ C10 r(σ−σ ) ,  j p γ ∂ϕ j ≤ C10 in Q j . Let α = p − 1 − ε and 0 < ε < p − 1. Later we use Holders ∂t T r(σ−σ ) inequality, Sobolev inequality estimate, also since the weight ω (x)  with Caccioppoli’s  is doubling and σ j+1 ≥ min δ, (γ + 1)−1 σ j , we obtain  Q j+1

⎛ ⎞γ  ⎜⎜⎜ ⎟⎟⎟ jp γ ⎜ ⎟⎟⎟ α uγα ω(x)dxdt ≤ C11 ⎜⎜⎜⎜⎜ u ω(x)dxdt ⎟⎟⎟ . p  ⎝ (σ − σ ) ⎠

(8)

Qj

Careful study of the proof of Lemma 3 shows that the constant C11 does not depend on α. The next step is to iterate Eq. (8). Observe that the condition 0 < α < p − 1 must

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be satisfied. This gives the upper bound q0 = γ (p − 1) for q. For the iteration, we fix q and s with q > s, and k such that s · γk−1 ≤ q ≤ s · γk . Choose ρ0 such that ρ0 ≤ s and q = γk ρ0 . Denote ρ j = γ j ρ0 for j − 0, 1, . . . , k. Then we have ⎛ ⎞ 1q ⎛ ⎞ ρ1  ⎜⎜⎜ ⎟⎟⎟ ⎜⎜⎜ ⎟⎟⎟ 0 (k) C ⎜⎜⎜ ⎟ ⎜ ⎟ 12 ⎟ ⎜ q ρ0 ⎟ ⎜ u ω(x)dxdt⎟⎟⎟⎟⎟ , ⎜⎜⎜ u ω(x)dxdt⎟⎟⎟ ≤ ⎜⎜⎜ γ∗  ⎝ ⎠ ⎝ (σ − σ ) ⎠ σQ

Qk

  p·γ− j    γ∗ k−1 pγ −j · j=0 γ j+1 , γ∗ = p · k−1 = γ−1 where C12 (k) − C11 1 − γ−k . j=0 γ Obviously C12 (k) is uniformly bounded on k. From Hölder’s inequality we obtain ⎛ ⎞ 1q   ρ1 ⎜⎜⎜  ⎟⎟ 0 C13 ⎜⎜⎜⎜ uq ω(x)dxdt⎟⎟⎟⎟⎟ ≤ ∗ ⎜⎜⎝ ⎟⎟⎠ γ (σ − σ ) σ Q

⎛ ⎞ 1s ⎜⎜⎜ ⎟⎟ ⎜⎜⎜⎜ us ω(x)dxdt⎟⎟⎟⎟⎟ . ⎜⎜⎝ ⎟⎟⎠ σQ

As sγk−1 ≤ ρ0 γk , then ρ0 ≥ s/ γ and consequently the required estimate follows with θ = pγ2 (γ − 1). Lemma is proved. The proof of the following bound for the essential supremum is based on the standard Moser iteration scheme. Lemma 5. Assume that u ≥ ρ > 0 is a subsolution in Q and 0 < δ < 1. Then there exist positive constants C14 (p, C0 , P0 , T, δ) and θ = θ (p, C0 ) such that ⎛ ⎞ 1s  1s ⎜⎜ ⎟⎟⎟ ⎜ C14 ⎜⎜⎜ ⎟⎟⎟ s ess sup u ≤ u ω(x)dxdt ⎜ ⎟⎟⎟ , ⎜ ⎠ (σ − σ )θ ⎜⎝ σ Q 

σQ

for all 0 < δ ≤ σ < σ ≤ 1 and s > 0. Proof. We can choose T = 1. Let the choices of test functions and σ j be the same as in the proof of Lemma 4 with the exception that    σ j = σ − σ − σ 1 − γ − j . As in the proof of Lemma 4 from the Sobolev inequality and from corresponding to Lemma 3 for subsolutions we obtain ⎛ ⎞γ   ⎜⎜⎜ ⎟⎟⎟ p jp γ α ⎜ ⎟⎟⎟ α uγα ω(x)dxdt ≤ C15 ⎜⎜⎜⎜⎜ u ω(x)dxdt (9) ⎟⎟⎟ , p  ⎝ (σ − σ ) ⎠ Q j+1

Qj

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where α = p − 1 − ε, ε ≥ 1, γ = 2 − p/ k . In Lemma 3 the constant is singular as ε is close to 0. We deliberately avoid this singularity by choosing ε ≥ 1. Moreover, we choose α j = p · γ j , j = 0, 1, . . . . . We iterate the inequality Eq. (9) and obtain ⎛ ⎞ 1p −2 j/γ j  γ−1 +γ−2 +···+γ−k+1 k−1 ⎜⎜⎜  ⎟⎟⎟ j−0 γ (σ − σ ) ⎜⎜⎜ ⎟⎟⎟ q ⎜⎜⎜ u ω(x)dxdt⎟⎟⎟ ≤ C16 ⎝ ⎠ σQ0

⎛ ⎞ k1 ⎜⎜⎜ ⎟⎟⎟ γ ·p k ⎜⎜⎜ ⎟ γ p ⎜⎜⎜ u ω(x)dxdt⎟⎟⎟⎟⎟ . ⎝ ⎠ Qk

Let k tend to infinity and we get the result for s ≥ p from Holders inequality. If s < p, then ⎛ ⎞ 1p  1p ⎜⎜ ⎟⎟⎟ ⎜ C17 ⎜⎜⎜ ⎟⎟⎟ p ess sup u ≤ u ω(x)dxdt ⎜ ⎟⎟⎟ ⎜ ⎠ (σ − σ )θ ⎝⎜ σ Q 

σQ



p−s ess sup u ≤ 2p σQ

 (p−s)  p

2p p−s

  (p−s) p

C17 (σ − σ )θ

  1s C17 1 ≤ ess sup u + 2 (σ − σ )θ σQ

⎛ ⎞ 1p  1p ⎜⎜ ⎟⎟⎟ ⎜⎜⎜ ⎜⎜⎜ u s ω(x)dxdt⎟⎟⎟⎟⎟ ⎜⎝ ⎟⎠ σQ

⎛ ⎞ 1s ⎜⎜⎜ ⎟⎟⎟ ⎜⎜⎜ ⎟ ⎜⎜⎜ u s ω(x)dxdt⎟⎟⎟⎟⎟ , ⎝ ⎠ σQ

where we used Young’s inequality. By iteration we obtain the result. Lemma is proved. We already have the reverse Holder inequalities for both super and subsolutions. Let 0 < σ ≤ 1, τ ∈ R, T > 0 and B (z, r) be a ball in Rn . We set Q = (z, B r) × (τ − T r p , τ + T r p ), σ Q+ = B (z, σr) × (τ, τ + T (σ r) p ) and σ Q− = B (z, σr) × (τ − T (σ r) p , τ) Let dν = ω (x) dxdt. Lemma 6. Assume that u ≥ ρ > 0 is a supersolution in Q and let ϕ (x, t) = ϕ (x) =  |x−z| 1 − 2 (1+σ)r , where 0 < σ ≤ 1 and (x, t) ∈ B (z, r) × (τ − (σ r) p , τ + (σ r) p ). Let t

β = B(z,r) log u (x, τ) ϕ p (x) ω (x) dx. Then there exist constants C18 (p, C0 , P0 , σ, T ) and C19 (p, C0 , σ, T ) such that for every λ > 0  C18    ν (x, t) ∈ σQ− / log u (x, t) > λ + β + C19 ≤ p−1 ν σ · Q− λ and

  C18   ν (x, t) ∈ σQ+ / log u (x, t) < −λ + β − C19 ≤ p−1 ν σ · Q+− . λ

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Proof. Let N =



ϕ p (x) ω (x) dx. Then

B(x,r)



1−σ − 1+σ

p ω (B (z, σr)) ≤ N ≤ ω (B (z, r)) .

We denote v (x, t) = log u (x, t) − β and V (t) = N1 B(z,r) v (x, τ) ϕ p (x) ω (x) dx. Remark that V (τ) = 0. Since ϕ depends on t, we obtain from following inequality  τ2  τ2  p p p vϕ ω (x) dx |Dv| ϕ ω (x) dxdt − τ1

 ≤ C20 that

Ω

τ2  τ1



t2 t1

Ω



Ω

|Dϕ| p ω (x) dxdt + C20

τ2 

τ1

τ=τ1

p−1 ∂ϕ |v| ϕ ω (x) dxdt, ∂t Ω





t2

|Dv| ϕ ω (x) dxdt − p

B(z, r)

vϕ ω (x) dx

p



t2

≤ C21 t1

p

B(z, r)



(10)

t=t1

|Dϕ| p ω (x) dxdt, B(z, r)

where τ − (σr) p ≤ t1 ≤ t2 ≤ τ + (σr) p . Furthermore, Theorem 2 yields   1 p p |Dv| ϕ ω (x) dx ≥ |v − V (t)| p ϕ p ω (x) dx C22 r p B(z, r) B(z, r)  (1 − σ) p ≥ |v − V (t)| p ω (x) dx. C22 r p B(z,σ r) It follows that 1 C22 N · r p



t2 t1

≤ C23

 |v − V (t)| p ω (x) dxdt + V (t1 ) − V (t2 ) B(z, r)

(t2 − t1 ) ω (B (z, r)) t2 − t1 ≤ C24 , rp N T (σr) p

here we used the fact that N ≥ 2−pC0−2 ω (B (z, r)). We denote w1 (x, t) = v (x, t) + t−τ t−τ C24 T (σr) p and w2 ( t) = V ( t) + C 24 T (σr) p . Then we obtain  t2  1 |w1 (x, t) − w2 (t)| p ω (x) dxdt + w2 (t1 ) − w2 (t2 ) ≤ 0. C22 N · r p t1 B(z, r) We have for almost every t with t1 < t < t2  t2  1 |w1 (x, t) − w2 (t)| p ω (x) dxdt − w2 (t) ≤ 0. C22 N · r p t1 B(z, r)

(11)

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457

Let Eλ− (t) = {(x, t) ∈ σQ− /w1 (x, t) > λ} . We observe that      |w1 (x, t) − w2 (t)| p ω (x) dx ≥ (λ − w2 (t)) p ω Eλ− (t) ≥ ω Eλ− (t) λ p , B(z, r)

because w2 (t) < w2 (τ) = 0, as τ > t > t − (σr) p . Thus we have   ω Eλ− (t) w2 (t) + C25 ≤ 0, − (λ − w2 (t)) p Nr p for almost every π > t > t − (σr) p . We integrate this inequality over the interval (τ − (σr) p , τ) and obtain    τ ν Eλ− C27 ≤ C26 (λ − w2 (t))−(p−1) . p ≤ p t=τ−(σ·r) N·r λ p−1 This implies   C28   ν (x, t) ∈ σQ− / log u (x, t) > λ + β + C24 ≤ p−1 ν σ · Q− . λ Let Eλ+ (t) = {(x, t) ∈ σQ+ /w1 (x, t) < −λ} . Later by doing corresponding calculations we obtain   C29   ν (x, t) ∈ σQ+ / log u (x, t) < −λ + β − C24 ≤ p−1 ν σ · Q+ . λ Lemma is proved.

4 Harnack’s Inequality In the beginning we will give a weak Harnack inequality and use the same notation as in Theorem 1. Theorem 3. Assume that u ≥ ρ > 0 be a supersolutin in U. Then there exist constants C30 (p, C0 , P0 , q, δ) and q0 = (p − 1) (2 − p/ k) , k > p , where ⎧ log2 C0 ⎪ ⎨ log2 C0 −p , 1 < p < log2 C0 , k=⎪ ⎩2 , p ≥ log2 C0 such that ⎛ ⎞ 1q ⎜⎜⎜  ⎟⎟⎟ ⎜⎜⎜ ⎟ uq ω(x)dxdt⎟⎟⎟⎟ ≤ C30 ess inf u, ⎜⎜⎝ ⎠ δ U+ δ U−

for 0 < δ < 1 and 0 < q < q0 .

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Proof. We fix 0 < δ < 1. Let ϕ be as in the assumptions of Lemma  6 and  let  + −1  β and C30 be the corresponding constants. We define v = u exp β − C30 and    . v− = u exp −β − C30 We apply Lemma 6 for the function u and get: "   ! 1+δ C30 1 + δ + + + · U / log v (x, t) > λ ≤ p−1 ν ·U ν (x, t) ∈ 2 2 λ !

and

(x, t) ∈

ν

1+δ · U − / log v− (x, t) > λ 2

" ≤

  C30 1 + δ − · U ν . 2 λ p−1

Here we also used a fact that   ν (B (z, σr) × (τ, τ ± (σr) p )) ≤ C30 ν δU ± . Lemma 2 implies that v+ is a subsolution in U. Consequently, Lemma 5 gives ⎛ ⎞ 1s  ⎜⎜⎜ ⎟⎟⎟  + s + ⎜ C30 ⎟⎟⎟ ess sup v ≤ ⎜⎜⎜⎜ ω(x)dxdt v ⎟⎟⎠ , ⎝ (σ − σ )θ. σ U + σU +



when δ < σ < σ ≤ (1 + δ) / 2 and s > 0. Note that we have chosen a suitable parameter T to match the time scales in various lemmas. Now let’s use Lemma 1 and obtain ess sup v+ ≤ C30 . δU +

(12)

From the Lemma 4 for v− we have that ⎛ ⎞ 1q ⎛ ⎞ 1s  ⎜⎜⎜  ⎟⎟⎟ ⎜⎜⎜ ⎟⎟⎟  − q  − s ⎜⎜⎜ ⎟⎟⎟ ⎜⎜⎜ C30 ⎟⎟⎟ ω(x)dxdt⎟⎟ ≤ ⎜⎜ ω(x)dxdt v v ⎜⎜⎝ ⎟⎟⎠ , ⎠ ⎝ (σ − σ )θ. σU +

σ U −

when δ < σ < σ ≤ (1 + δ) / 2 and 0 < s < q < q0 . From Lemma 1 obtain ⎞ 1q ⎛ ⎟⎟⎟ ⎜⎜⎜  ⎟⎟⎟ ⎜⎜⎜  − q ω(x)dxdt v ⎟⎟⎠ ≤ C30 . ⎜⎜⎝ δU −

By multiplying this with Eq. (12) we get that: ⎛ ⎞1 ⎜⎜⎜  ⎟⎟⎟ ⎜⎜⎜ ⎟ q inf u ⎜⎜⎝ u ω(x)dxdt⎟⎟⎟⎟⎠ ≤ C30 ess δU + δU −

and the result follows. Theorem 3 is proved. Now we are ready to prove the full Harnack inequality. Proof of Theorem 1. We apply 3 with δ = (1 + σ) / 2. Then the result follows from Lemma 5.

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5 Conclusion The main purpose of the paper are established Harnack’s inequality for degenerate double nonlinear parabolic equations and the local Hölder conditnuity of a weak solutions. We used is well known that Moser’s tecnique is essentially based on the combination of a Sobolev and a Caccioppoli type inequalities. We also is established the local Holder continuity of a weak solution is a consequence of the Harnack’s inequality. However, ∂(u p−1 ) due to the nonlinearity of the term ∂t when p  2, it is not clear for the double nonlinear equations.

References 1. Bakry, D., Coulhon, T., Ledoux, M., Saloff-Coste, L.: Sobolev inequalities in disguise. Indiana Univ. Math. J. 44(4), 1033–1074 (1995) 2. BoMBIERI, E., Giusti, E.: Harnack’s inequality for elliptic differential equations on minimal surfaces. Invent. Math. 15(1), 24–46 (1972) 3. Chiarenza, F.M., Serapioni, R.P.: A Harnack inequality for degenerate parabolic equations. Commun. Partial Differ. Equ. 9(8), 719–749 (1984) 4. Fabes, E.B., Garofalo, N.: Parabolic BMO and Harnack’s inequality. Proc. Am. Math. Soc. 95(1), 3–69 (1985) 5. Fabes, E.B., Kenig, C.E., Serapioni, R.P.: The local regularity of solutions of degenerate elliptic equations. Commun. Stat.-Theory Methods 7(1), 77–116 (1982) 6. Gadjiev, T., Yangalieva, A.: Regularity of solutions of degenerate parabolic nonlinear equations and removability of solutions. Appl. Comput. Math. 6(3) (2017) 7. Gadjiev, T., Galandarova, S., Guliyev, V.: Dirichlet boundary value problems for uniformly elliptic equations in modified local generalized Sobolevorrey spaces. Electron. J. Qual. Theory Differ. Equ. 2017(21), 1–17 (2019) 8. Gadjiev, T., Galandarova, S., Guliyev, V.: Regularity in generalized Morrey spaces of solutions to higher order nondivergence elliptic equations with VMO coefficients. Electron. J. Qual. Theory Differ. Equ. 55, 1–17 (2019) 9. Gadjiev, T., Guliev, V., Suleymanova, K.: The Dirichlet problem for the uniformly elliptic equation in generalized weighted Morrey spaces. Educational Studies in Mathematics (2020, in appear) 10. Guliyev, V.S., Gadjiev, T.S., Serbetci, A.: The dirichlet problem for the uniformly higherorder elliptic equations in generalized weighted Sobolev-Morrey spaces. Nonlinear Stud. 26(4), 831–842 (2019) 11. Hajlasz, P., Koskela, P.: Sobolev Met Poincare. American Mathematical Society, vol. 688 (2000) 12. Heinonen, J., Kipelainen, T., Martio, O.: Nonlinear Potential Theory of Degenerate Elliptic Equations. Courier Dover Publications (2018) 13. Moser, J.: A harnack inequality for parabolic differential equations. Commun. Pure Appl. Math. 17(1), 101–134 (1964) 14. Saloff-Coste, L.: A note on Poincare, Sobolev and Harnack inequalities (1992) 15. Saloff-Coste, L.: Aspects of Sobolev-Type Inequalities, vol. 289. Cambridge University Press, Cambridge (2002) 16. Trudinger, N.S.: Pointwise estimates and quasilinear parabolic equations. Commun. Pure Appl. Math. 21(3), 205–226 (1968)

Fault Detection and Identification for Maintenance Management Isaac Segovia Ramirez and Fausto Pedro Garcia Marquez(B) Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain [email protected]

Abstract. Photovoltaic solar energy is increasing the energy production due to the technological advances, together to the initial investment reduction. Solar farms are being installed with larger production capacity, improving the technical challenge for developing correct and efficient maintenance management. The photovoltaic maintenance management requires to increase the reliability and reduce the operating costs. The photovoltaic panels inspection with unmanned aerial vehicles is an efficient condition monitoring technique, analyzing large areas and obtaining accurate thermographic images. Due to the large amount of data, it is necessary the use of image processing algorithms for automatic identification of faults. Despite these advances, it is required the identification of the type and the importance of the fault. This information will be used by the plant operators for developing efficient maintenance management plans. The novelty developed in this work is a robust decision system for photovoltaic maintenance management, based on the combination of image processing for fault detection and statistic techniques. The first phase of the methodology is the extraction of interest areas or possible faults with neural networks trained for this purpose. The second phase develops the statistical analysis of the radiometric data of the area detected as possible fault with neural network. The radiometry data of these areas will be analyzed with statistic models with the aim of detecting patterns for detect identification and quantification. A real case study of a solar plant is presented, and the results obtained with this methodology provide the positioning and importance of each defect, probing the strength of the method. Keywords: Infrared thermography · Solar photovoltaic energy · Condition monitoring system · Remotely piloted aircraft · Neural network · Photovoltaic management

1 Introduction The worldwide electricity demand is increasing every year and the conventional energy sources are causing an elevated environmental impact. The electric generation from renewable energies is fundamental for reducing gas emissions, getting the zeroemission agreement in 2050 [17]. Photovoltaic (PV) solar energy is one of the most significant renewable energy sources, and this technology has a fundamental role in the energy transition. The solar energy is growing due to the reduction of panel prices, the c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 460–469, 2020. https://doi.org/10.1007/978-3-030-49829-0_34

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development of novel energy storage solutions and the increment of the energy production reliability [5]. In 2018 was installed 102,4 GW, and the previsions for 2019 are around 114,5GW. The PV solar contribution to global electricity generation was increased in more than 2%. Asia continues leading the global market, mainly in China and Japan, followed by the United States [8]. It is expected to rise to 180GW in a global medium scenario in 2023, see Fig. 1.

200 180 160 140

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Fig. 1. World annual solar PV market and forecasting for 2019–2023 [8]

Due to advances in manufacturing process, PV systems have increased its competitiveness. The efficient of silicon cells is between 5–19% although it is still low in comparison with other renewable energies [22]. The productivity of the PV farms is based on the maintenance costs, initial investment, the lifetime, the financing and loan conditions. The reduction of the operation and maintenance (O&M) costs is required to reach the competitiveness [16], and to ensure the economic feasibility of the PV solar plant [3]. The current size of solar farms is larger every year, and an efficient maintenance management plan is required for ensuring the reliability [24]. The PV performance in long-term is affected by different faults that reduce the energy production of the plant. The adverse environment conditions of the PV farms reduce the crystalline-Si modules performance. PV panels present faults when the modules are working in unusual conditions. The main PV failures are hot spots, module open circuited, short circuited, delaminated, broken or fault cell, shadowing effect, bubble, presence of dust and open-circuit bypass diode [12]. Electrical failures or presence of dust in the panels produce variations in the superficial temperature and reductions in the generated power [25]. The deposition of dust or dirt is a critical problem for the industry, since it varies the solar radiation angle reducing the electricity production. The effect of dust on PV performance has been studied in different countries, and the percentage depends on the time period and the particle size, determining that finer particles have more influence in the efficiency mitigation. The hot spot detection is the main

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goal of the maintenance industry to reduce the downtimes and identify faults before the increment of the severity. Current condition monitoring systems (CMS) employ different sensors and data acquisition systems, evaluating different parameters of the PV panel and energy production for further analysis [7]. Supervisory Control and Data Acquisition (SCADA) system provides suitable data about the real state of the system for improving the maintenance management plans [18]. SCADA system in PV farms uses different sensors and subsystems for monitoring the electrical production to the panels. With this monitoring technique, it is possible to identify certain faults, but the state of the panel surface it is no considered in the analysis. Between a 33–43% of the PV module failures are located in the cells and glass of the PV module [4], therefore, it is needed advanced CMS for inspecting superficial faults. Diagnostic algorithms use the data acquired for obtaining the fundamental information, e.g. electric generation simulations tests comparing the model with the real situation [14], neural networks [2, 9] and fuzzy logic [23]. The infrared (IR) thermography is one of the most suitable technique for inspecting solar farms and detect heat patterns, preventing the development of failures [15]. The thermography is a non-destructive technique employed in the industry based on the detection of the infrared energy, invisible for the human view. This energy is emitted by the surface of a body. This information is transformed into thermal values. The infrared cameras capture radiometric thermal images, or thermograms, where the temperature values are displayed with different colour scale. This type of cameras can also provide the temperature in each pixel of the image. IR is applied in several industrial fields, such as civil buildings, manufacturing and welding inspection, among others, due to its easy implementation, non-contact technique and the possibility of extract information in thermal images and data from radiometry. Despite these advantages, the inspection rate is not enough for the current plant requirements. The thermography analysis is developed manually by technicians with elevated costs and time as a result of the larger size of current solar farms. This technique combined with unmanned aerial vehicles (UAV), or drones, makes possible to cover more areas, ensuring the reliability and efficiency of the measurement process. UAVs have a great establishment in several industrial fields, highlighting PV maintenance, due to its capacity of covering large areas, carrying different CMSs, data capture automatization, favourable legislation and flexibility in control and design. Different researches are focused on novel CMS in UAVs [26], increasing the efficiency of aerial inspections. Due to the amount of data generated in the inspection of large solar farms, novel digital technologies based on big data analysis are needed for post-processing phases [28]. The neural networks have expanded its range of applications in the last 15 years, being applicated in complex and nonlinear problems [2]. The Region-based Convolutional Neural Network (R-CNN) can learn from training using examples for pattern identification in several application fields. Traditional computational activities are based on pre-stablished rules [13], but R-CNN can work with problems with discontinuous data. With this algorithm is possible to detect objects with great accuracy and classifying the type of detected object. The training with image level identification by the user is fundamental for adapting the R-CNN to the required detection [21]. J. Muñoz et al. studied the risk of hot spots, that can cause irreversible damage in modules, appearing in PV solar cells and also in resistive sol-

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der bonds [27]. The method presented in this paper allows to detect and locate faults automatically. Literature shows similar techniques to locate faults. Similar works can be found in the literature [20], however, they do not employ the region-R-CNN. Most of the works about thermography inspection of PV solar panels focus on the analysis of individual panels. In those works, different methods were applied to demonstrate the capabilities of thermography, for example, studying the efficiency of the panel, detecting relative hot regions in panels or identifying general faults in PV modules [1]. Several authors present analogous methodologies for fault detection in PV panels. Kim et al. [11] analyse the thermal images taken by a thermal camera and a UAV, and they develop an image segmentation considering only the area of the panel. The PV panels are simulated as polygons and the information outside these areas is not considered. The statistical analysis is focused in all the panel area, losing efficiency and increasing the computational load. Kaplani [10] analyses the degradation effect with I-V curve, IR thermography and an algorithm is developed for detecting discoloration in PV cells. Huerta et al. [6] proposes a recurrent convolutional neural network for the identification of hot spots. Segovia et al [29] developed a real case study based of simulating dust in PV panel and analyse the thermal data. The regions of interest (ROI), where it is possible to find faults, are selected manually. However, the proposed method is employed in a real PV solar plant, where many panels can be analyzed together. Therefore, the main contribution of this paper with respect to the literature is the development of an intelligent algorithm that detects hot areas of solar panels. The algorithm is tested in a real PV solar farm. The novelties proposed in this article are resumed in the following points: • The implantation of Neural Network as solution for hot spot identification in the solar maintenance using aerial images taken by a UAV. The image identification of the hot spot is combined with statistical analysis of the data. This methodology allows the fault detection and identification by thermal patterns. The union between image detection and statistical analysis allows the reliability of the fault detection and the efficiency of the numerical analysis. • The analysis with basic statistics reduces the computational costs and allows the fault identification and classification regarding on the severity and importance.

2 R-CNN and Statistical Analysis: Real Case Study The methodology proposed in this work uses the information extracted from the image identification algorithm as input for the analysis of the radiometric data. This approach combines image fault detection and radiometric analysis of the detected fault. For this reason, this method is only applied in radiometry thermographic images, where it is possible to detect the accurate thermal value in each pixel. The first phase is based on the image fault identification by means of R-CNN. The neural network is trained for identifying hot spots in the panels, and the pixel location of the identified fault is storage for further analysis. The GPS positioning is storage for further location of the failures. In the second phase, the pixels with detected failures are presented and the thermal values are analysed with different statistic tools for extracting patterns. This

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Training

Data prefiltering

R-CNN

Temperature data

bbox

Fig. 2. Methodology diagram

process is automatic, and the user only have to upload the information to the system and the results will be generated. Figure 2 shows a diagram of the process. The design and development of novel neural network is not the main objective of this paper. The R-CNN developed by Huerta et al. [6] to validate the approach presented in this paper. The fault detection by neural network provides three variables about the failure: bbox, score and label. The score variable shows the confident level of the results. In this case, it is ensured that the results will have a 70% of confident. The bbox matrix has the information about the pixel location of the fault in the thermogram. Label is the categorial array assigned to the bounding boxes and, in this case, classify in panel or fault. The fault is only detectable if it is in the area detected as panel, in order to ensure that no false failures are considered. The information of bbox will be used in the second phase of the analysis. In this phase, the thermal information of the coordinates given by bbox is storage. It is pretended to use basic configurations in order to reduce the computational load of all the operations and develop the statistical analysis. The mean value, variance and standard deviation are common statistical variables employed in may application fields and in the image distribution [19]. The application of boxplot analysis in the performing of temperature fluctuation is a fundamental tool to detect graphically thermal patterns. The case study presented in this work is based on the aerial thermographic inspection developed in a real solar farm in 2017. Figure 3 shows a thermogram acquired by a UAV and a thermographic camera. The images are prefiltered and orientated for the suitable application of the R-CNN. The pixel data about the location of panels and faults identified by the neural network is applied in the boxplot graphic. Figure 4 compares the distribution of the temperature fluctuation of the faults showed in Fig. 3. Both thermal patterns present the same distribution due to the similar-

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Fig. 3. Thermogram with detected failures

ities between failures. The number of outliers and the values are quantified to determine the importance of the faults. Fault showed in Fig. 4 (b) presents high number of outliers and this fault is considered with more importance than the fault presented in Fig. 4 (a).

(a)

(b)

Fig. 4. (a) Fault 1. (b) Fault 2

The comparison between panels with faults and panels with no faults is presented in Fig. 5. The distribution of the panel with faults in the higher positions proves the presence of several pixels with overheat. The outliers in the bottom part of the diagram are produced by the difference between the square form of the R-CNN detector and the form of this specific type of panel, capturing data of the ground. Despite this effect, the information about the real state of the panel is acquired and it is possible to identify critical panels regarding on the number of outside outliers. Figure 6 shows the same panel displayed in the Fig. 3 at higher altitude. This image allows the validation of the method at different altitude. In this case, other PV panel with faults is detected and the results are compared. Figure 7 evaluates the boxplots of the PV panel 1 faults. The number of outliers is reduced in comparison with Fig. 4 because of the higher altitude decrease the regions of interest size and the number of detected pixels.

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(a)

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Fig. 5. (a) Panel with faults. (b) Panel with no fault

Fa t2

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Fig. 6. Thermogram with different panels affected

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Fig. 7. (a) Panel 1 fault 1. (b) Panel 1 fault 2

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Figure 8 shows the hot spots in the other PV module. The fault 2 has outliers with temperature values lower than Q1 due to the area selected by the algorithm. The fault is close to the panel limit made with aluminium and the temperature values are reduced because of reduced emissivity of the aluminium. Despite this issue, it is possible to identify the failure. In this scenario, fault 1 can be detected clearly than fault 2. Figure 9 compares the three panels affected. The PV panel with no faults in Fig. 9 (a) shows outliers with lower values than the median due to the square form of the detector considering the values behind the panel. Boxplots in Fig. 9 (b)–9 (c) has similar fluctuation but the fault in Fig. 9 (c) has more relevance since a greater number of outliers are detected and therefore, i.e. more area of the PV panel is overheated.

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Fig. 8. (a) Panel 2 fault 1. (b) Panel 2 fault 2

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Fig. 9. (a) Panel, free-fault. (b) Panel 2, fault 1. (c) Panel 3, fault 2.

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3 Conclusions Photovoltaic solar energy maintenance is required to reach the feasibility of the solar plants. Novel condition monitoring systems are necessary for fault detection and diagnosis. Aerial thermography is a novel technique based on the analysis of images done by a thermographic camera embedded in an unmanned automatic vehicle. The volume of the data generated implies robust and complex algorithms. This paper proposes an algorithm combining neural network for detecting the fault positioning together with statistical analysis of the radiometric data for quantifying the importance of the fault. The methodology is tested using real thermograms, and different faults are analysed and quantified using the algorithm. The results show the efficiency and accuracy of this approach. Acknowledgements. The work reported herewith has been financially by the Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

References 1. Ancuta, F., Cepisca, C.: Fault analysis possibilities for PV panels. In: Proceedings of the 2011 3rd International Youth Conference on Energetics (IYCE), pp. 1–5. IEEE (2011) 2. Arcos Jiménez, A., Gómez Muñoz, C.Q., García Márquez, F.P.: Machine learning for wind turbine blades maintenance management. Energies 11(1), 13 (2018) 3. Branker, K., Pathak, M., Pearce, J.M.: A review of solar photovoltaic levelized cost of electricity. Renew. Sustain. Energy Rev. 15(9), 4470–4482 (2011) 4. DeGraaff, D., Lacerda, R., et al.: Degradation mechanisms in SI module technologies observed in the field; their analysis and statistics. In: NREL 2011 Photovoltaic Module Reliability Workshop, p. 20 (2011) 5. Europe, S.P.: Global market outlook for solar power/2019-2023. Solar Power Europe, Brussels, Belgium, Technical report (2019) 6. Herraiz, Á.H., Marugán, A.P., Márquez, F.P.G.: Optimal productivity in solar power plants based on machine learning and engineering management. In: International Conference on Management Science and Engineering Management, pp. 983–994. Springer (2018) 7. Herraiz, Á.H., Marugán, A.P., Márquez, F.P.G.: Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure. Renew. Energy 153, 334–348 (2020) 8. Jäger-Waldau, A.: Snapshot of photovoltaicsmarch 2017. Sustainability 9(5), 783 (2017) 9. Jiménez, A.A., Gómez, C.Q., Márquez, F.P.G.: Concentrated solar plants management: big data and neural network. In: Renewable Energies, pp. 63–81. Springer (2018) 10. Kaplani, E.: Detection of degradation effects in field-aged c-SI solar cells through IR thermography and digital image processing. Int. J. Photoenergy 2012, 11 (2012) 11. Kim, D., Youn, J., Kim, C.: Automatic fault recognition of photovoltaic modules based on statistical analysis of UAV thermography. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 42, 179 (2017) 12. Köntges, M., Kurtz, S., et al.: Review of failures of photovoltaic modules (2014) 13. Márquez, F.P.G., Muñoz, J.M.C.: A pattern recognition and data analysis method for maintenance management. Int. J. Syst. Sci. 43(6), 1014–1028 (2012)

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14. Márquez, F.P.G., Pedregal, D.J.: Applied RCM 2 algorithms based on statistical methods. Int. J. Autom. Comput. 4(2), 109–116 (2007) 15. Márquez, F.P.G., Ramírez, I.S.: Condition monitoring system for solar power plants with radiometric and thermographic sensors embedded in unmanned aerial vehicles. Measurement 139, 152–162 (2019) 16. Márquez, F.P.G., Pardo, I.P.G., Nieto, M.R.M.: Competitiveness based on logistic management: a real case study. Ann. Oper. Res. 233(1), 157–169 (2015) 17. Márquez, F.P.G., Karyotakis, A., Papaelias, M.: Renewable Energies: Business Outlook 2050. Springer (2018) 18. Marugán, A.P., Márquez, F.P.G.: Scada and artificial neural networks for maintenance management. In: International Conference on Management Science and Engineering Management, pp. 912–919. Springer (2017) 19. Marugán, A.P., Márquez, F.P.G., Papaelias, M.: Multivariable analysis for advanced analytics of wind turbine management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 319–328. Springer (2017) 20. Marugán, A.P., Márquez, F.P.G., et al.: A survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822–1836 (2018) 21. Marugán, A.P., Chacón, A.M.P., Márquez, F.P.G.: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab. Eng. Syst. Saf. 191(106), 574 (2019) 22. Messenger, R.A., Abtahi, A.: Photovoltaic Systems Engineering. CRC Press, Boca Raton (2017) 23. Mohammedi, K., Benmessaoud, T., et al.: Fuzzy logic applied to SCADA systems (2017) 24. Muñoz, C.Q.G., Márquez, F.P.G.: Future maintenance management in renewable energies. In: Renewable energies, pp. 149–159. Springer (2018) 25. Muñoz, C.Q.G., Marquez, F.P.G., et al.: A new condition monitoring approach for maintenance management in concentrate solar plants. In: Proceedings of the Ninth International Conference on Management Science and Engineering Management, pp. 999–1008. Springer (2015) 26. Muñoz, C.Q.G., Marquez, F.P.G., et al.: New pipe notch detection and location method for short distances employing ultrasonic guided waves. Acta Acustica United Acustica 103(5), 772–781 (2017) 27. Muñoz, J., Lorenzo, E., et al.: An investigation into hot-spots in two large grid-connected pv plants. Prog. Photovoltaics Res. Appl. 16(8), 693–701 (2008) 28. Pliego Marugán, A., García Márquez, F.P.: Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22(11), 1622–1635 (2019) 29. Ramírez, I.S., Marugán, A.P., Márquez, F.P.G.: Remotely piloted aircraft system and engineering management: a real case study. In: International Conference on Management Science and Engineering Management, pp. 1173–1185. Springer (2018)

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management Isaac Segovia Ramirez and Fausto Pedro Garcia Marquez(B) Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain [email protected]

Abstract. Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks. Keywords: Wind turbine · Maintenance management Neural network · Wind turbine management

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Introduction

Wind energy is growing renewable energy due to greater heights and powers of current wind turbines (WT), with new and moderns installations [24]. It is expected new installations, with more than 55 GW every year in the world until 2023 for onshore and offshore, see Fig. 1 [26]. Offshore installation is increasing its capacity with the associated technical issues in installation and maintenance, and it is expected to reach an energy production of 2182 TWh by 2030. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 470–480, 2020. https://doi.org/10.1007/978-3-030-49829-0_35

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The WT is composed of several subsystems, transforming wind energy into electric energy [32]. Wind farms are located in areas with hard environments with proper wind conditions and, consequently, each WT may present issues related to energy losses, e.g. mechanical failures and blade icing [1,12]. The rotor blades, electrical devices, plant control system, hydraulic and sensors congregate more than 50% of total failures [4,33]. The maintenance operations have important associated costs, high risks for human resources in the WT access and the energy production losses due to downtimes [34]. The WT operation and maintenance (O&M) costs are between 10%C25% of the total costs [9]. Due to the working conditions, the efficiency and security of O&M activities are reduced, and it is required novel failure prediction techniques for avoiding downtimes and increasing the reliability of the installations [31]. Novel technological solutions are needed to increase the competitiveness based on the maintenance cost reduction for ensuring the efficient positioning of this technology in the energy market [7]. The improvement in the maintenance management operations will allow to reach the competitiveness in wind power in terms of reliability, lifetime and availability [28]. Condition monitoring system (CMS) is employed for failure detection, allowing proper maintenance management and the reduction of down-times [6,13]. The monitoring techniques are based in performance monitoring, vibration (for gearbox and mechanic monitoring), acoustic emissions (this analysis provides data about waves generated by the failures) [5,17], thermal, oil and ultrasonic analysis [25], among others. Several types of maintenance operations are developed for incrementing the reliability of the energy production [10,27]. The method analysis uses mathematical models developed with the comprehension of the physical behaviour of the WT. Monitoring established with data system is developed analysing the historical data and mathematical algorithms

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are applied for pattern recognition. The volume and diversity type of the data provided for the CMS involves different analysis for extracting the real condition of the WT [14]. Different researches and techniques are focused in the analysis by components. Fault Tree Analysis (FTA) is a qualitative analysis for representing graphically the connections between the effect of the produced faults to the components. FTA is a technique performed by binary decision diagrams that allow the quantitative analysis and the identification of the critical components of each WT, identifying the critical risks with by gates [15,18]. Supervisory control and data acquisition (SCADA) system integrates all the sensors and measurement systems, and the data from the WT is received and storage. The SCADA system monitors signals and alarms with a range acquisition period per minute o per ten minutes. The alarm activations display the anomalies or failures detected by the sensors although CMS may induce false alarms due to wrong analysis or non-defined failures [20]. This issue reduces the veracity of the system, being necessary novel algorithms for false alarm identification [29]. The generation of false alarms by SCADA system is a fundamental issue due to unnecessary stops, false interventions by the maintenance team and loss of productivity. Several researches are based on the detection and reduction of WTs false alarms [22] with different rules for the reset of certain number of alarms. SCADA data are used in the control of the WT for improving the operation and the reliability of this method is probed in different researches [23], although it is required novel analysis techniques for pattern identification [19]. Due to the amount of data, intelligent algorithms and signal processing techniques are included in the analysis for early failure detection [11,30]. Machine learning algorithms are applied for data processing in order to extract valuable information. Artificial neural networks (ANNs) are computational models based on the nerve system formed by neurons in human brains. Several neurons are connected to each other with different weights and layers. The design of ANNs is determined by the connections and the transfer function between neurons, being the number of neurons a fundamental parameter in the neural network performance. ANNs are trained using specific datasets, and the network modifies the weight of each layer and the connections between neurons. ANN are able to deal with complex problems with reduced operational response times, and it is not required the determination of the analytical function of the model for the data treatment. This algorithm works with variety of input and output information proving the suitability of the method. For these reason, the ANNs are established in several application fields, e.g. power electronics [2], photovoltaic energy [8], medicine and image processing, due to its suitability and efficiency. It is probed the effectiveness and strength of ANNs in the patter identification of failures in WT [21]. With an efficient training, it is possible to identify patterns for prediction, classification and forecasting. Zhang and Wang [35] propose a fault detection model using ANNs with SCADA data in main WT bearing system. The novelty proposed is based on the combination of different method analysis for fault detection and diagnosis employing SCADA data. It is pretended a better ANN accuracy by means of basic statistical analysis of the data before the

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use of the ANN. Initial statistical analysis for selecting critical alarms for further analysis and principal component analysis (PCA) will be applied for reducing the dimensions of the data [3]. The ANN application provides valuable information for pattern identification. A real case of WT with failure is presented and analysed with the method presented, analysing the results obtained and probing the reliability of the method.

2

SCADA Fault Detection Methodology and Case Study

The data analysis is a key factor for WT maintenance management. The methodology proposed in this work is based on the SCADA data signals with PCA for the identification of the causes of alarm activation for further analysis based on ANN. The signals will be filtered and evaluated with PCA with the aim of reducing de volume of the data and improving the efficiency of the ANN. The results of the ANN will be used in early detection of failures. The first phase is the alarm filtering for identifying the critical alarms with Pareto chart and the activation information of the alarms. Initial alarm analysis is determined for detecting critical alarms for further analysis. Pareto chart is applied to identify the critical alarms. The number of activations, time of activation and difference between alarms will give the alarm for study. Once the alarm set is done, it is studied the period of interest, analysing the time between alarms and filtering the alarms with no interest in the study, e.g. alarms with reduced activation period with periods of time with no alarms. The second phase is signal analysis. Different signals are pre-selected and it is pretended to analyse the behaviour of these signals in the periods with no alarm activation. Using PValue, it is probed the importance of several signals and the signal with no required values will be deleted from the study. The third phase is based on the identification of the interest range of the data, as it is possible to see in Fig. 2. Due to the extension period between alarms, it is necessary a synchronization between alarm and associated signals, and then a proper selection of the signal period for increasing the reliability of the analysis method. It is considered in this model that the signal data closer to the alarm activation have more relevance in the failure causing. Two different zones have been designed: the first zone is the area with no influence in the alarm activations. The second zone includes the cause of the alarm activation. PCA is applied when it is specified the possible alarm activation periods in the signal data. PCA analysis is a dimension reduction algorithm that allows the transformation of an amount of data into smaller groups that hold most of the source information [16]. PCA is a multivariate analysis with different linear combinations and transformations of the original dataset A real case study is considered for testing the reliability of the methodology analysing the SCADA data extracted from a real WT. This turbine has a rated power of 2 MW and different alarms are identified in the system. Pareto chart is applied in different alarms in Fig. 3, analysing the number of activations, the period of the activated alarm, the maximum period of the alarm and the difference between alarms.

474

I. Segovia Ramirez and F. P. Garcia Marquez SIGNAL PERIOD BETWEEN ALARMS ALARM

ALARM

ACTIVATION

ACTIVATION

Possible alarm activation period

Training zone or safe period

Fig. 2. Interest period diagram

(a)

(b)

Maximum alarm period

104

Average period without alarms

8

98%

7

86%

6

74%

5

61%

4

49%

3

37%

2

25%

1

12%

0

0% Alarm 1

(c)

Alarm 3

Alarm 2

(d)

Fig. 3. (a) Alarms activation. (b) Average alarm period. (c) Maximum period of each alarm activation. (d) Average period without alarms.

Alarm 2 presents an elevated number of activations in comparison with other alarms. Alarm 1 is activated less periods, but the period of each activation is higher compared to Alarm 3 and Alarm 3. Alarm 1 and Alarm 2 present similar values, although Alarm 1 shows more distance between each activation. The period between alarms is fundamental for ensuring the proper activation

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of the alarm, therefore, decreased periods produce reduced information about the failure. This characteristic allows a greater range of data for the study and, for this reason, Alarm 2 is studied in detail in this paper. Once the alarm is selected, the first phase of the methodology filters the alarm for the acquisition of valuable information, see Table 1. Table 1. Alarm information Number of data

597618 (415 days)

Number of activations

10

Average period time of activation 2282 Mean deviation

30039

Average period between alarms

352322

Mean deviation

64431.38

The average period of time and average period between alarms do not provide valuable information since there are different alarms with elevated values. Figure 4 shows that the initial alarms have reduced separation. The most activation periods of the alarms are located in 3 and 8.

900 200000

800 700

150000

Periods

600 500

100000

400 300

50000

200 100 0

0 1

2

3

4

5

6

7

8

9

10

Alarm acƟvaƟon Period Alarm acƟvaƟon period

SeparaƟon between alarms

Fig. 4. Alarm analysis: alarm activation period and time separation between each alarm

A minimum period between two alarm activations must be guaranteed to have a training and possible alarm activation periods. With this previous analysis, it is possible to prefilter alarms and select only the most fundamental alarms.

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For this particular case study, the alarm separation threshold is stablished in 700 periods due to the alarm analysis. All the alarms with less separation are considered not relevant for this study.

3

Results

A basic scenario with no statistical analysis is simulated for further comparison with the methodology proposed in this work. In this case, the data is not filtered or analysed with PCA or alarm positioning. Figure 5 shows the ANN diagram. For the initial case without statistical analysis, it is determined a basic ANN with ten hidden layer and the input and output distribution required for the selected data. Input

Signals prefiltered with PCA. Alarms periods of interest.

. .

Hidden

Output

. . .

Fig. 5. Neural network diagram

The performance of the ANN shown in Fig. 6 is very low and the possibilities of finding patterns are reduced due to the reduced epoch and not stabilization of the network. The ANN is not suitable for this situation and since there are no validations or tests allowed. Once the basic ANN is developed, it is employed PCA to the initial dataset for improving the efficiency of the network. The application of PCA provides a valuable information about the detection of failures. For this particular case, it is proposed a dataset of signals related with the alarm. The signals provided for this study have different values and nature, and there is no information about the relevant signals or the signal with more influence in the alarm development. For this reason, it is applied PCA, with the aim of reducing the data volume and obtain an improvement in the ANN efficiency. Figure 7 indicates that 97% of the dataset could be explained with one principal component. The initial dataset is transformed into one principal components with the aim of obtaining an efficient data selection to be used as inputs in the ANN.

477

Mean Squared Error (mse)

Supervisory Control and Data Acquisition Analysis

Percentage of data

Fig. 6. Performance of the neural network with no statistical analysis

Fig. 7. Percentage of the data explained with different principal component

This new dataset will be used in the ANN design and development. Figure 8 shows the performance of the ANN with the dataset treated by the statistical analysis. Figure 8 shows that the ANN develops a validation and test with adequate accuracy although it is required an ANN with more efficiency and reduced errors for ensuring the pattern detection.

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Fig. 8. Performance of the neural network with no statistical analysis

4

Conclusions

The data generated in the supervisory control and data acquisition requires of algorithms and methods for obtaining valuable information applicable in the operation and maintenance management. The volume and variety of the data increment the importance of advanced algorithm for data analysis. This paper presents a method combined statistical analysis and advanced algorithm for pattern recognition. The first phase is based on the alarm identification for the use of this information in the signal combination. The interest periods of the alarm are defined and identified in the dataset. The initial dataset of signals is reduced using principal component analysis. The new dataset is implanted in a basic neural network defined for this study. A real case study is applied using different alarms and signals, and studying the results obtained with the data with no initial statistical analysis. The analysis between the performance of both situations validates the approach. Acknowledgements. The work reported herewith has been financially by the Direcci´ on General de Universidades, Investigaci´ on e Innovaci´ on of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

References 1. Arcos Jim´enez, A., G´ omez Mu˜ noz, C.Q., Garc´ıa M´ arquez, F.P.: Machine learning for wind turbine blades maintenance management. Energies 11(1), 13 (2018) 2. Bose, B.K.: Neural network applications in power electronics and motor drives–an introduction and perspective. IEEE Trans. Ind. Electron. 54(1), 14–33 (2007)

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3. Garc´ıa M´ arquez, F.P., Garc´ıa-Pardo, I.P.: Principal component analysis applied to filtered signals for maintenance management. Qual. Reliab. Eng. Int. 26(6), 523–527 (2010) 4. Garc´ıa M´ arquez, F.P., Pliego Marug´ an, A., et al.: Optimal dynamic analysis of electrical/electronic components in wind turbines. Energies 10(8), 1111 (2017) 5. G´ omez, C., Garc´ıa, F., et al.: A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers. Eksploatacja i Niezawodno´s´c 19 (2017) 6. de la Hermosa Gonzalez, R.R., M´ arquez, F.P.G., et al.: Pattern recognition by wavelet transforms using macro fibre composites transducers. Mech. Syst. Sig. Process. 48(1–2), 339–350 (2014) 7. de la Hermosa Gonz´ alez, R.R., M´ arquez, F.P.G., et al.: Maintenance management of wind turbines structures via MFCs and wavelet transforms. Renew. Sustain. Energy Rev. 48, 472–482 (2015) ´ 8. Herraiz, A.H., Marug´ an, A.P., M´ arquez, F.P.G.: Optimal productivity in solar power plants based on machine learning and engineering management. In: International Conference on Management Science and Engineering Management, pp. 983–994. Springer, Heidelberg (2018) 9. Irena, I.: Renewable energy technologies: cost analysis series. Concentrating Solar Power (2012) 10. JantaraJunior, V., Basoalto, H., et al.: Evaluating the challenges associated with the long-term reliable operation of industrial wind turbine gearboxes. In: IOP Conference Series: Materials Science and Engineering, vol. 454, p. 012094. IOP Publishing (2018) 11. Jim´enez, A.A., Mu˜ noz, C.Q.G., et al.: Artificial intelligence for concentrated solar plant maintenance management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 125–134. Springer, Heidelberg (2017) 12. Jim´enez, A.A., Mu˜ noz, C.Q.G., M´ arquez, F.P.G.: Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers. Reliab. Eng. Syst. Saf. 184, 2–12 (2019) 13. Marquez, F.G.: An approach to remote condition monitoring systems management (2006) 14. Marquez, F.G., Singh, V., Papaelias, M.: A review of wind turbine maintenance management procedures. In: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, pp. 1–14 (2011) 15. M´ arquez, F.G., Papaelias, J., Hermosa, R.R.: Wind turbines maintenance management based on FTA and BDD. In: International Conference on Renewable Energies and Power Quality (ICREPQ 2012), pp. 4–6 (2012) 16. M´ arquez, F.P.G.: A new method for maintenance management employing principal component analysis. Struct. Durability Health Monit. 6(2), 89–99 (2010) 17. M´ arquez, F.P.G., Mu˜ noz, J.M.C.: A pattern recognition and data analysis method for maintenance management. Int. J. Syst. Sci. 43(6), 1014–1028 (2012) 18. M´ arquez, F.P.G., P´erez, J.M.P., et al.: Identification of critical components of wind turbines using FTA over the time. Renew. Energy 87, 869–883 (2016) 19. Marug´ an, A.P., M´ arquez, F.P.G.: SCADA and artificial neural networks for maintenance management. In: International Conference on Management Science and Engineering Management, pp. 912–919. Springer, Heidelberg (2017)

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20. Marug´ an, A.P., M´ arquez, F.P.G., Papaelias, M.: Multivariable analysis for advanced analytics of wind turbine management. In: Proceedings of the Tenth International Conference on Management Science and Engineering Management, pp. 319–328. Springer, Heidelberg (2017) 21. Marug´ an, A.P., M´ arquez, F.P.G., et al.: A survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822–1836 (2018) 22. Marug´ an, A.P., Chac´ on, A.M.P., M´ arquez, F.P.G.: Reliability analysis of detecting false alarms that employ neural networks: a real case study on wind turbines. Reliab. Eng. Syst. Saf. 191(106), 574 (2019) 23. Mohammedi, K., Benmessaoud, T., et al.: Fuzzy logic applied to SCADA systems (2017) 24. Mu˜ noz, C.Q.G., M´ arquez, F.P.G.: Future maintenance management in renewable energies. In: Renewable Energies, pp. 149–159. Springer, Heidelberg (2018) 25. Mu˜ noz, C.Q.G., Jim´enez, A.A., M´ arquez, F.P.G.: Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis. Renew. Energy 116, 42–54 (2018) 26. Ohlenforst, K., Council, G.W.E.: Global wind report 2019 (2019) 27. Pedregal, D.J., Garc´ıa, F.P., Roberts, C.: An algorithmic approach for maintenance management based on advanced state space systems and harmonic regressions. Ann. Oper. Res. 166(1), 109–124 (2009) 28. P´erez, J.M.P., M´ arquez, F.P.G., Hern´ andez, D.R.: Economic viability analysis for icing blades detection in wind turbines. J. Clean. Prod. 135, 1150–1160 (2016) 29. Pliego Marug´ an, A., Garc´ıa M´ arquez, F.P.: Advanced analytics for detection and diagnosis of false alarms and faults: a real case study. Wind Energy 22(11), 1622– 1635 (2019) 30. Pliego Marug´ an, A., Garc´ıa M´ arquez, F.P., Lorente, J.: Decision making process via binary decision diagram. Int. J. Manag. Sci. Eng. Manag. 10(1), 3–8 (2015) 31. Pliego Marug´ an, A., Garc´ıa M´ arquez, F.P., Lev, B.: Optimal decision-making via binary decision diagrams for investments under a risky environment. Int. J. Prod. Res. 55(18), 5271–5286 (2017) 32. Polinder, H., Ferreira, J.A., et al.: Trends in wind turbine generator systems. IEEE J. Emerg. Sel. Topics Power Electron. 1(3), 174–185 (2013) 33. Tchakoua, P., Wamkeue, R., et al.: Wind turbine condition monitoring: state-ofthe-art review, new trends, and future challenges. Energies 7(4), 2595–2630 (2014) 34. Walford, C.A.: Wind turbine reliability: understanding and minimizing wind turbine operation and maintenance costs. Technical report, Sandia National Laboratories (2006) 35. Zhang, Z.Y., Wang, K.S.: Wind turbine fault detection based on SCADA data analysis using ANN. Adv. Manuf. 2(1), 70–78 (2014)

Assessing the Relative Performance of Penalty and Non-penalty Estimators in a Partially Linear Model Siwaporn Phukongtong1(B) , Supranee Lisawadi1 , and Syed Ejaz Ahmed2 1

Department of Mathematics and Statistics, Thammasat University, Klong Luang, Pathum Thani, Thailand [email protected] 2 Department of Mathematics and Statistics, Brock University, St. Catharines, ON, Canada

Abstract. We investigated the linear shrinkage and shrinkage pretest estimators in a partially linear model, when it is a priori suspected that the regression coefficient may be restricted to a subspace. Using Monte Carlo simulations, we compared their performance with those of some penalty estimators. The proposed estimators were more efficient than the penalty estimators when the number of non-significant predictors was large. The shrinkage pretest estimator is suggested for practical applications, since its performance was robust against the reliability of the restriction. The proposed estimators were also applied to a real dataset to confirm their practicality. Keywords: Shrinkage pretest · Linear shrinkage · Penalty estimation · Partially linear model · Monte Carlo simulation

1

Introduction

Semiparametric models have received considerable recent attention in the statistical and related literature. In these models, the mean response is assumed to be linearly related to some covariates, whereas the relation to additional variables is characterized by nonparametric functions. They are thus more flexible than parametric models. Partially linear models (PLMs) are one of the most widely-used semiparametric models. Over the last few decades, interest in PLM has grown significantly. The first application of PLM was in Engle et al. [6]. Since then, such models have been widely studied in the literature. H¨ ardle et al. [8] conducted a survey on the theory and applications of the PLM in fields including economics, geology, and biology. Ahmed et al. [4] suggested the pretest, shrinkage, and penalty estimators in the PLM. Zhu et al. [16] and Aydın et al. [5] studied the estimation procedure for PLMs with high-dimensional data. The partially linear regression model considered here is yi = xTi β + g(ti ) + εi , i = 1, . . . , n. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 481–491, 2020. https://doi.org/10.1007/978-3-030-49829-0_36

(1)

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Here, yi are responses, xi = (xi1 , . . . , xip ) are the p-dimensional covariates, T β = (β1 , . . . , βp ) is an unknown vector of parameters, ti are the nonparametric covariate effects, and it is assumed that 0 ≤ t1 ≤ . . . ≤ tn ≤ 1, and g(.) is a smooth function. The error terms εi are independent and identically distributed (i.i.d.) with mean 0 and common variance σ 2 . In real applications, multiple predictors are common. Some contribute significantly to the overall prediction, while others make little or no contribution, and may be left aside. It is commonly assumed that the significant predictors can be detected from prior information or by applying variable selection techniques. T The parameter vector β is partitioned as β = (β1T , β2T ) , where β1 is the coefficient vector of significant predictors and β2 of non-significant predictors. Here, β1 and β2 have dimensions p1 and p2 respectively, and p1 + p2 = p. There are two models in consideration: a full model that includes all p predictors and a submodel that includes only p1 predictors. A key point is that the use of submodel estimation improves efficiency if the submodel is correct, but is less efficient when the submodel incorrectly represents the data at hand. To address this issue, one option is to apply a preliminary or pretest strategy that uses a test to decide between a full model or submodel estimator. Ahmed [1] developed the shrinkage pretest estimator from the pretest estimator, which improved significantly on the pretest in terms of the size of the test. Another option is based on a linear shrinkage strategy that uses a linear combination of full model and submodel estimators. Penalty strategies that provide simultaneous variable selection and estimation of submodel parameters have become popular in recent years, and these procedures are effective when the model is sparse. Ahmed et al. [4] were the first to compare the performance of pretest and penalty (LASSO) estimators in the PLM, and reported the pretest to be more efficient. The estimators, based on the pretest, linear shrinkage, and penalty strategies, have been applied in a range of contexts. We refer the interested reader to Ahmed [2], Ahmed and Y¨ uzba¸sı [3], Lisawadi et al. [9], Reangsephet et al. [11], and Y¨ uzba¸sı et al. [14], among others. In this paper, our principle contribution is to efficiently estimate the regression coefficients in a partially linear model, when it is a priori suspected that the coefficient may be restricted to a subspace. We propose the linear shrinkage and shrinkage pretest estimators, and compare their performance with those of the penalty estimators including the smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) estimators, via a Monte Carlo simulation. The paper is organized as follows. The profile likelihood estimators are introduced in Sect. 2. The proposed estimators are also discussed. The results of the Monte Carlo simulation are reported in Sect. 3. A real-data example is analyzed in Sect. 4. Finally, conclusions are presented in Sect. 5.

2

Estimation Strategies

In this section, we present efficient estimators from linear shrinkage and shrinkage pretest strategies in a PLM when it is a priori suspected that the regression

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coefficient may be restricted to a subspace. We first estimated the nonparametric component using the smoothing splines approach, which is attractive for many reasons. Speckman’s [12] introduction of the principle of adding a penalty term to a sum of squares or to a log-likelihood is applicable in a wide range of linear and nonlinear problems. The method simply seems to work well and is easy to implement. For smoothing splines-based estimation in PLM, a solution can be obtained by minimizing the following equation over β and g: SS(β, g) =

n  

yi −

xTi β

2 − g(ti ) + λ

i=1

1

2

(g  (t)) dt.

(2)

0

Here, the smoothing parameter λ > 0 controls the tradeoff between fidelity to the data and the roughness of the function estimate. It is often estimated by using cross-validation (CV) or generalized cross-validation (GCV). We applied the smoothing splines method of Speckman [12] to estimate g and the estimator T T is given by gˆ(·, β) = Sλ (y − Xβ ), where X = (x1 , . . . , xn ) , y = (y1 , . . . , yn ) , and Sλ is the smoother matrix. Assume that εi ∼ i.i.d. N (0, σ 2 ). For a given β, using the result of gˆ, the log-likelihood function is given by l(β, σ 2 ) = −

n 1 T ln(2πσ 2 ) − 2 (y − Xβ − gˆ) (y − Xβ − gˆ), 2 2σ

(3)

ˆ Maximizing (3) with respect to β provides the profile likelihood estimator β, given by ˜ T X) ˜ −1 X ˜ T y, ˜ βˆ = (X (4) ˜ = (I − Sλ ) X, y˜ = (I − Sλ ) y. where X 2.1

Full and Sub-model Estimators

The full model (FM) estimator βˆ1FM of β1 is given by −1  ˜1 ˜ T M2 y, ˜ T M2 X ˜ βˆ1FM = X X 1 1

(5)

 −1 ˜2 X ˜ 1 is composed of the first p1 row vectors ˜2 ˜T, X ˜TX where M2 = I − X X 2 2 ˜ and X ˜ 2 of the last p2 row vectors. of X, The submodel (SM) estimator βˆ1SM of β1 is obtained by maximizing (3) with respect to β under the restriction β2 = 0p2 . This has the form −1  ˜1 ˜ T y. ˜TX X βˆ1SM = X 1 1 ˜

(6)

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Linear Shrinkage Estimator

The linear shrinkage (LS) estimator βˆ1LS of β1 is a linear combination of βˆ1FM and βˆ1SM , and is defined as βˆ1LS = π βˆ1SM + (1 − π)βˆ1FM ,

(7)

where the constant π is the shrinkage intensity and π ∈ [0, 1]. The value of π may be assigned by the researcher based on subjective estimation of the prior information at hand, or chosen to minimize the mean squared error of the estimator. If π = 0, then the LS estimator is the FM estimator. Conversely, the LS estimator is the SM estimator when π = 1. 2.3

Shrinkage Pretest Estimator

To construct the shrinkage pretest estimator, we first introduce the following profile likelihood ratio statistic for testing H0 : β2 = 0p2 . ˆ σ ˆ 2 ) − l(βˆ1SM , σ ˆ12 )}. Tn = 2{l(β, Here, σ ˆ2 =

n−1

n  i=1

(8)

n  ˆ 2, σ (yi − xTi βˆ − gˆ(ti , β)) ˆ12 = n−1 (yi − xTi βˆ1SM − i=1

2 ˆ σ gˆ1 (ti , βˆ1SM )) , l(β, ˆ 2 ) and l(βˆ1SM , σ ˆ12 ) are the profile log-likelihood values at βˆ SM and βˆ1 . Under the null hypothesis H0 , the distribution of Tn follows a chisquared distribution with p2 degrees of freedom as n → ∞. The shrinkage pretest (SP) estimator βˆ1SP of β1 is defined as

βˆ1SP = βˆ1FM − π( βˆ1FM − βˆ1SM ) I(Tn ≤ χ2p2 ,α ),

(9)

or in alternative form βˆ1SP = βˆ1FM − ( βˆ1FM − βˆ1LS ) I(Tn ≤ χ2p2 ,α ).

(10)

Here, I(.) is an indicator function. The SP estimator uses the pretest strategy to choose between the FM and LS estimators. It is the FM estimator when H0 is rejected, and takes the value of the LS estimator otherwise. When π = 1, the shrinkage pretest estimator simplifies to the pretest (PT) estimator, given by βˆ1PT = βˆ1FM − ( βˆ1FM − βˆ1SM ) I(Tn ≤ χ2p2 ,α ).

(11)

The shrinkage pretest estimator is a significant improvement on the pretest estimator in terms of the size of the test α.

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485

Penalty Estimators

We now introduce two well-known penalty estimators, the smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). Fan and Li [7] proposed the SCAD to overcome the lack of oracle properties (consistency in variable selection and asymptotic normality) in the LASSO method. The SCAD estimator βˆSCAD of β is derived as follows: ⎡ ⎤ p  ρa,γ (|βj |)⎦ , (12) βˆSCAD = argmin ⎣−l(β, σ 2 ) + γ β

j=1

where ρa,γ (·) is the SCAD penalty with a tuning parameter γ, and whose first derivative for some a > 2 and x > 0 is given by 

(aγ − x)+  I(x > γ) . ρa,γ (x) = γ I(x ≤ γ) + (a − 1)γ Zhang [15] proposed a MCP estimator, given by ⎡ ⎤ p  βˆMCP = argmin ⎣−l(β, σ 2 ) + γ ργ (|βj |, ν)⎦ , β

(13)

j=1

where ργ (ν) is the MCP penalty given by ργ (ν) =

t 0

(γ − x/ν)+ dx, where ν is

a regularization parameter.

3

Monte Carlo Simulations

We simulated the partially linear regression model with different numbers of predictors, based on Model (1). At a sample size n = 100,√εi were simulated from i.i.d. N (0, 1), xis = ζis + ξi with ζis ∼ N (0, 1), ξi = ti / 2+ ti for s = 1, . . . , p,  2π . i = 1, . . . , n, ti = (i − 0.5)/n, and g(ti ) = ti (1 − ti ) sin ti +0.05 Consider the null hypothesis H0 : βj = 0, j = p1 + 1, . . . , p with p = p1 + p2 . T T We partitioned the regression coefficients as β = (β1T , β2T ) = (β1T , 0Tp2 ) . We   T defined Δ∗ = β − β (0) , where β (0) = (β1 T , 0Tp2 ) and · is the Euclidean norm. Δ∗ is essentially a measure of how far we are from the null hypothesis. Samples were generated from those distributions in which Δ∗ ∈ [0, 2] and case I: β1 = (0.75, −1, 1.25) and β2 = 0p2 , case II: β1 = (0.6, −1, −1.3, 2) and β2 = (b, 0p2 −1 ) so that Δ∗ = b2 .

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We set p2 = 5, 10, 15, 20, π = 0.25, 0.50, 0.75, 1 and α = 0.01, 0.05, 0.1. Each configuration was iterated 2,000 times to obtain stable results. All computations were conducted using the R statistical program (R Development Core Team [10]). To evaluate the performance of the proposed estimators, we used the mean squared error (MSE) criterion. We defined the relative mean squared efficiency (RMSE) of an estimator βˆ1∗ with respect to βˆ1FM as follows: MSE ( βˆ1FM ) RMSE( βˆ1FM : βˆ1∗ ) = , MSE ( βˆ1∗ ) where βˆ1∗ is any one of estimators βˆ1SM , βˆ1LS , or βˆ1SP . If the RMSE( βˆ1FM : βˆ1∗ ) is larger than one, then estimator βˆ1∗ outperforms βˆ1FM . 3.1

Case I: Parameter Space Information Is Correct

In this subsection, we considered the situation in which the parameter space information is correct (Δ∗ = 0). We note that the performance of the proposed estimators was compared with those of the penalty estimators only for Δ∗ = 0 because, according to Ahmed et al. [4], the penalty estimators do not take advantage of the fact that β is partitioned into significant coefficients and nonsignificant coefficients, and are thus at a disadvantage when Δ∗ > 0. The tuning parameter γ of the two penalty estimators was estimated using 10-fold cross validation. The number of predictors was (p1 , p2 ) = (3, 5), (3, 10), (3, 15), and (3, 20), with the results reported in Table 1. We found that all estimators performed better than the FM estimator, and their RMSE increased as the number of nonsignificant predictors p2 increased. The SM estimator had the highest RMSE in all configurations. The RMSE of the LS estimator was an increasing function of the shrinkage intensity π, and was equal to that of SM when π = 1. This estimator also dominated the SP estimator for fixed π. The performance of the SP estimator depended on the size of the test α and the magnitude of π. Its RMSE increased as α decreased and π increased. We observed that, at small values of α and large values of π, the proposed and penalty estimators were comparable when p2 was small. However, as p2 increased, the proposed estimators became more efficient. 3.2

Case II: Parameter Space Information May Be Incorrect

Next, we considered the situation in which the parameter space information may be incorrect (Δ∗ ≥ 0). The number of predictors was (p1 , p2 ) = (4, 5), (4, 10), (4, 15), and (4, 20). For brevity, we present only the results of case π = 0.75 in Table 2 and their graphical representation in Fig. 1 where the SP estimators at α = 0.01, 0.05, and 0.1 are denoted by SP1, SP2, and SP3, respectively. Our main findings were as follows:

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Table 1. RMSEs of estimators with respect to βˆ1FM when Δ∗ = 0. Estimator

Number of non-significant predictors (p2 ) 5 10 15 20

SM

1.1437 1.2295 1.4784 1.6760

LS

π π π π

SP

π = 0.25 α = 0.01 α = 0.05 α = 0.10 π = 0.50 α = 0.01 α = 0.05 α = 0.10 π = 0.75 α = 0.01 α = 0.05 α = 0.10 π = 1.00 α = 0.01 α = 0.05 α = 0.10

= 0.25 = 0.50 = 0.75 = 1.00

1.0570 1.1024 1.1322 1.1437

1.0899 1.1643 1.2133 1.2295

1.1624 1.3162 1.4316 1.4784

1.2141 1.4334 1.6077 1.6760

1.0522 1.0455 1.0370 1.0932 1.0809 1.0651 1.1199 1.1038 1.0827 1.1300 1.1125 1.0886

1.0837 1.0734 1.0634 1.1525 1.1328 1.1136 1.1980 1.1716 1.1457 1.2137 1.1850 1.1562

1.1470 1.1230 1.1031 1.2829 1.2319 1.1916 1.3831 1.3094 1.2530 1.4237 1.3397 1.2771

1.1862 1.1467 1.1196 1.3684 1.2807 1.2237 1.5077 1.3770 1.2952 1.5614 1.4117 1.3195

SCAD

1.0966 1.1694 1.3206 1.3809

MCP

1.0926 1.1441 1.2942 1.3679

(i) The SM dominated other estimators at or near Δ∗ = 0, but its RMSE decreased sharply to zero as Δ∗ increased. (ii) Similarly, as Δ∗ increased, the RMSE of the LS estimator decreased to zero more slowly than that of SM. (iii) The size of the test α and the shrinkage intensity π impacted the performance of the SP estimator only when Δ∗ was zero or nearly zero. As Δ∗ increased, the RMSE of the SP estimator dropped below one, then increased to approach one. The SP estimator outperformed the SM and LS when Δ∗ was large.

4

Real Data Example

The proposed estimators were applied to a subsample of the household gasoline consumption dataset from Yatchew [13]. This data comprised n = 953 households, with a nonzero number of licensed drivers, vehicles, and distance driven in Ontario, Canada. We considered the ten variables listed in Table 3.

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Table 2. RMSEs of estimators with respect to βˆ1FM when Δ∗ ≥ 0 for π = 0.75. p2 Δ∗ SM

LS

SP α = 0.01 α = 0.05 α = 0.10

5

0.0 0.1 0.3 0.5 0.7 1.0 1.5 2.0

1.1588 1.1026 0.8416 0.5769 0.3936 0.2352 0.1193 0.0708

1.1475 1.1119 0.9405 0.7268 0.5446 0.3562 0.1944 0.1192

1.1356 1.0989 0.9414 0.9601 0.9960 1.0000 1.0000 1.0000

1.1180 1.0725 0.9584 0.9851 0.9991 1.0000 1.0000 1.0000

1.0963 1.0543 0.9671 0.9921 0.9991 1.0000 1.0000 1.0000

10 0.0 0.1 0.3 0.5 0.7 1.0 1.5 2.0

1.3742 1.3303 1.1342 0.8862 0.6722 0.4450 0.2450 0.1509

1.3412 1.3143 1.1946 1.0211 0.8443 0.6184 0.3763 0.2442

1.3030 1.2773 1.1101 0.9898 0.9932 1.0000 1.0000 1.0000

1.2539 1.2208 1.0605 0.9901 0.9977 1.0000 1.0000 1.0000

1.2124 1.1773 1.0501 0.9938 0.9996 1.0000 1.0000 1.0000

15 0.0 0.1 0.3 0.5 0.7 1.0 1.5 2.0

1.4300 1.3733 1.0984 0.7915 0.5604 0.3450 0.1785 0.1073

1.3985 1.3639 1.1911 0.9594 0.7467 0.5071 0.2855 0.1787

1.3435 1.3002 1.0812 0.9659 0.9904 1.0000 1.0000 1.0000

1.2632 1.2211 1.0448 0.9839 0.9970 1.0000 1.0000 1.0000

1.2191 1.1729 1.0238 0.9922 0.9981 1.0000 1.0000 1.0000

20 0.0 0.1 0.3 0.5 0.7 1.0 1.5 2.0

1.7184 1.6957 1.4524 1.1169 0.8261 0.5304 0.2823 0.1705

1.6405 1.6320 1.5015 1.2821 1.0464 0.7506 0.4421 0.2804

1.5359 1.5025 1.2639 1.0579 0.9962 1.0000 1.0000 1.0000

1.3906 1.3468 1.1622 1.0189 0.9981 1.0000 1.0000 1.0000

1.3118 1.2736 1.1176 1.0097 0.9991 1.0000 1.0000 1.0000

We applied variable selection based on the Akaike information criterion (AIC) to obtain a candidate submodel that contained three significant predictors: price, paid, and income. The price was selected as a nonparametric variable.

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Fig. 1. RMSEs of estimators with respect to βˆ1FM when Δ∗ ≥ 0 for π = 0.75.

In an effort to appraise the performance of the proposed estimators, we conducted bootstrapping with 500 replicates. We then used the relative prediction error (RPE) of βˆ1∗ with respect to βˆ1FM : RPE( βˆ1FM : βˆ1∗ ) =

MSPE( βˆ1FM ) , MSPE( βˆ∗ ) 1

where MSPE denotes the mean square prediction error. If the RPE > 1, then βˆ1∗ outperforms βˆ1FM . We assumed α = 0.05 and π = 0.75. Table 4 confirms the expectation that the RPE of the SM estimator was highest. The LS estimator outperformed the SP and penalty estimators. The RPE of the SP estimator was quite low because the parameter space information was nearly, but not exactly, true. Furthermore, all estimators were superior to the FM, and the results supported those from the simulations.

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The log of distance traveled per month

Predictor variables driver

Number of licensed drivers in household

hhsize

Number of members of household

price

Price of a liter of gasoline

income

Annual household income

gaspur

Number of monthly liters of gasoline purchased

paid

Total price paid for fuel

veh

Number of vehicles in household

urban

Dummy variable for urban dwellers (1 if urban, otherwise 0)

age

Dummy variable for age of the first member of the household roster that drives the selected vehicle (1 if age > 65, otherwise 0) Table 4. RPEs of estimators with respect to βˆ1FM . Estimator SM RPE

5

LS

SP

SCAD MCP

1.5396 1.3601 1.0829 1.1019 1.1436

Conclusions

In this study, Monte Carlo simulations were carried out to compare the relative performance of the submodel, linear shrinkage, shrinkage pretest, and penalty estimators with respect to the full model estimator in a partially linear model, when it is a priori suspected that the regression coefficient may be restricted to a subspace. The submodel estimator was shown to perform best at or near the restriction, but was inferior to the shrinkage pretest estimator as one moved away from the restriction. The linear shrinkage estimator was also less efficient than the shrinkage pretest estimator when the restriction was incorrect. At small values of the size of the test α and large values of the shrinkage intensity π, the proposed estimators were more efficient than the penalty estimators when the number of non-significant predictors p2 was large. The shrinkage pretest estimator is suggested for use in practical situations since its performance was robust, regardless of the reliability of the restriction. Acknowledgements. The research work of S. Ejaz Ahmed was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Thammasat University under the Bualuang ASEAN Chair Professorship grant.

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References 1. Ahmed, S.E.: Shrinkage preliminary test estimation in multivariate normal distributions. J. Stat. Comput. Simul. 43(3–4), 177–195 (1992) 2. Ahmed, S.E.: Penalty, Shrinkage and Pretest Strategies: Variable Selection and Estimation. Springer, Cham (2014) 3. Ahmed, S.E., Y¨ uzba¸sı, B.: Big data analytics: integrating penalty strategies. Int. J. Manag. Sci. Eng. Manag. 11(2), 105–115 (2016) 4. Ahmed, S.E., Doksum, K.A., Hossain, S., You, J.: Shrinkage, pretest and absolute penalty estimators in partially linear models. Aust. N. Z. J. Stat. 49(4), 435–454 (2007) 5. Aydın, D., Ahmed, S.E., Yılmaz, E.: Estimation of semiparametric regression model with right-censored high-dimensional data. J. Stat. Comput. Simul. 89(6), 985–1004 (2019) 6. Engle, R.F., Granger, C.W., Rice, J., Weiss, A.: Semiparametric estimates of the relation between weather and electricity sales. J. Am. Stat. Assoc. 81(394), 310– 320 (1986) 7. Fan, J., Li, R.: Variable selection via nonconcave penalized likelihood and its oracle properties. J. Am. Stat. Assoc. 96(456), 1348–1360 (2001) 8. H¨ ardle, W., Liang, H., Gao, J.: Partially Linear Models. Springer-Physica-Verlag, Heidelberg (2000) 9. Lisawadi, S., Kashif Ali Shah, M., Ahmed, S.E.: Model selection and post estimation based on a pretest for logistic regression models. J. Stat. Comput. Simul. 86(17), 3495–3511 (2016) 10. R Development Core Team: R: A language and environment for statistical computing (2018) 11. Reangsephet, O., Lisawadi, S., Ahmed, S.E.: Improving estimation of regression parameters in negative binomial regression model. In: International Conference on Management Science and Engineering Management, pp. 265–275 (2018) 12. Speckman, P.: Kernel smoothing in partial linear models. J. Royal. Stat. Soc.: Ser. B (Methodol.) 50(3), 413–436 (1988) 13. Yatchew, A.: Semiparametric Regression for the Applied Econometrician. Cambridge University Press, Cambridge (2003) 14. Y¨ uzba¸sı, B., Arashi, M., Ahmed, S.E.: Shrinkage estimation strategies in generalised ridge regression models: low/high-dimension regime. Int. Stat. Rev. 88(1), 229–251 (2020) 15. Zhang, C.H.: Nearly unbiased variable selection under minimax concave penalty. Ann. Stat. 38(2), 894–942 (2010) 16. Zhu, Y., Yu, Z., Cheng, G.: High dimensional inference in partially linear models. SSRN Electron. J. (2017). https://doi.org/10.2139/ssrn.3015397

Improving the Performance of Least Squares Estimator in a Nonlinear Regression Model Janjira Piladaeng1(B) , Supranee Lisawadi1 , and Syed Ejaz Ahmed2 1

Department of Mathematics and Statistics, Thammasat University, Khlong Luang, Pathum Thani, Thailand [email protected] 2 Department of Mathematics and Statistics, Brock University, St. Catharines, ON, Canada

Abstract. In this study, we proposed novel preliminary test and shrinkage strategies for efficient estimation for multiple nonlinear regression models of the Cobb-Douglas type. A Monte Carlo simulation was conducted to evaluate the performance of the proposed estimators. To assess their practicality, they were applied to a real dataset. In simulations, the submodel estimator outperformed the other estimators when the subspace information was true. The preliminary test and shrinkage strategies outperformed the widely-used Least Absolute Shrinkage and Selection Operator in this case. Conversely, the shrinkage estimators outperformed the other estimators in most of the parameter space when the subspace information was untrue.

Keywords: Cobb-Douglas model Monte Carlo simulation

1

· Uncertain prior information ·

Introduction

Many econometric models are nonlinear. The Cobb-Douglas (C-D) function is one such model, and perhaps the most widely-used in economics. It has been applied to econometric analysis of utility and production functions in growth, development, macroeconomics, public finance, labour, and many other applied areas. In this study, we considered the estimation of regression parameters in a C-D model with many predictors, some of which may have no influence on the response variable. If it is a priori known or suspected that some regressors do not significantly contribute to predicting the response variable, a submodel excluding these regression coefficients may be adequate. It is often true that the submodel estimator is a considerable improvement over the full model estimator. However, if the uncertain prior information (UPI) is incorrect, the estimators based on c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 492–502, 2020. https://doi.org/10.1007/978-3-030-49829-0_37

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a submodel may be biased and inefficient. We therefore developed improved estimation strategies for the regression parameters when some of the information about the parameter space is uncertain. One strategy for eliminating uncertainty about the prior information is the use of a preliminary test to remove the inactive parameters [1]. The choice between a full model and submodel is based on the validity of the UPI, and the result is a compromise between the two. The key problem with preliminary estimation is that the UPI is tested before the estimator is chosen. An alternative approach is shrinkage strategy which incorporates whatever UPI is available into the estimation process. These may be smooth considered versions of the preliminary test estimation. The Least Absolute Shrinkage and Selection Operator (LASSO) is a penalty method that is widely used for variable selection and estimation. It performs well if the model is sparse. The use of preliminary test, shrinkage, and LASSO strategies in low-dimensional models are discussed in [2,3,11,13]. Applications of these strategies are also applied in high-dimensional include [4,12]. In this study, we applied the preliminary test and shrinkage estimation strategies to a nonlinear regression model (C-D) model under UPI. The resulting estimators were compared with those from classical nonlinear least squares and LASSO. The rest of this paper is organized as follows. In Sect. 2, we describe the model and estimation strategies. The results of Monte Carlo simulations are reported in Sect. 3. Application to a real dataset is reported in Sect. 4. We offer concluding remarks in Sect. 5.

2

Model and Estimation Strategies

Assume that we know the form of the nonlinear regression function, but unknown parameters are present. In case, the function does not necessarily possess intercepts, and the number of parameters does not usually equal the number of regressors. The general form of the nonlinear regression model is yi = h(xi , θ) + εi , for i = 1, 2, ..., n, where yi is the dependent variable, xi = (xi1 , xi2 , ..., xik ) is a k × 1 vector of independent variables for the ith subject, θ = (θ1 , θ2 , ..., θp ) is a p × 1 vector of regression coefficients, and εi is a random error term which has N ∼ (0, σ 2 ). Here, p is the number of parameters and k the number of regressors. In unrestricted form, the C-D function can be written as follows: yi = θ 1

p 

θ

j xi,j−1 + εi .

j=2

Here, xi must be a positive real number and may refer to goods produced, goods consumed, etc. A more detailed discussion can be found in [7], and applications

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to the C-D model in [5,9,16]. The residual sum of squares to be minimized can now be written as S(θ) =

n 

[yi − h(xi , θ)]

2

i=1

= [y − h(x, θ)] [y − h(x, θ)], with

⎡ ⎤ ⎤ h(x1 , θ) y1 ⎢ h(x2 , θ) ⎥ ⎢ y2 ⎥ ⎢ ⎢ ⎥ ⎥ y = ⎢ . ⎥ and h(x, θ) = ⎢ ⎥. .. ⎣ ⎣ .. ⎦ ⎦ . ⎡

h(xn , θ)

yn

This minimization problem is not as straightforward as the linear case since h(x, θ) is a nonlinear function of θ. Parameter estimation in a nonlinear regression model may therefore require the use of iterative methods, as explicit formulas for the estimates are generally not available. The most widely-used approach to least squares (LS) fitting for nonlinear models is the Gauss-Newton (GN) nonlinear least squares. In a nonlinear regression, a set of starting values or initial guesses for θ must be supplied. It is helpful if the starting values are set close to the true LS estimate. In the GN method, the sum of squares error function is as follows: SS(θ) = [w − F β] [w − F β],

ˆ F = ∂h(x,θ )

ˆ The derivative of the where w = y − h(x, θ), , and β = θ − θ. ∂θ θ =θˆ

residual sum of squares SS(θ) with respect to θ is obtained by ∂SS(θ) ∂ = [w − F β] [w − F β] = 0. ∂θ ∂θ More detailed information about the nonlinear regression model is available in [6,10,14]. In this study, we partitioned the unknown parameter θ into (θ1 , θ2 ) , where θ1 is a vector of active parameters and θ2 a vector of inactive parameters. These have dimensions q and r respectively, such that q + r = p. We are interested in using the information on θ2 to estimate θ1 when θ2 is zero. This may be mathematically written in terms of a constraint on θ, such that θ2 = 0. 2.1

Full Model and Submodel Estimators

The full model (FM) estimator of θ or θˆFM is found by solving the GN iterative equation with the final-iteration of nonlinear least squares estimators. This gives  −1 θˆFM = (F  F ) F  F θˆ(k) + w . (1)

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From Eq. (1), the FM estimator of θ1 , denoted as θˆ1FM , is

   −1 θˆ1FM = (F1 F1 ) F1 F1 θˆ1(k) + w1 − F2 θˆ2FM − θˆ2(k) . Under the restriction θ2 = 0, the submodel (SM) estimator can also be obtained using the GN iterative method. Applying the Lagrange multiplier, the SM estimator is given by −1 θˆ1SM = θˆ1FM + (F1 F1 ) F1 F2 θˆ2FM .

2.2

Preliminary Test and Shrinkage Estimators

We set the restriction in the form of a testable hypothesis H0 : θ2 = 0 against H1 : θ2 = 0. The test statistics is defined as follows:

 −1 (θˆ2FM ) F2 F2 − F2 F1 (F1 F1 ) F1 F2 θˆ2FM Ψn = , (2) s2e where s2e = (y − yˆ) (y − yˆ)/(n − p) is the estimator of σ 2 . If the null hypothesis is true, Ψn converges to a chi-square distribution with r degrees of freedom as n → ∞. Here, r is the number of restrictions on the submodel estimator or the number of inactive parameters. The pretest or preliminary test (PT) estimator of θ1 is a discontinuous function of UE and RE and is defined as follows: θˆ1PT = θˆ1FM − (θˆ1FM − θˆ1SM )I(Ψn ≤ cα )  SM θˆ1 if Ψn ≤ cα = ˆFM if Ψn > cα . θ1 Here, Ψn is the test statistic, cα is the critical value for a test of size α, and I(·) is an indicator function. In PT estimation, the prior information is tested before the estimator is selected. In practice, this means that the PT estimator is the submodel estimator if Ψn ≤ cα , and the full model estimator if Ψn > cα . The shrinkage estimator, introduced below, is a smoothed version of the PT estimator. It takes a mixed approach by shrinking the full model estimator to a plausible alternative estimator or submodel estimator. The shrinkage (S) estimator or Stein-type estimator of θ1 is defined as: θˆ1S = θˆ1SM + {1 − ηΨn−1 }(θˆ1FM − θˆ1SM ), where η = r − 2, r ≥ 3, and Ψn is as defined in Eq. (2). One problem with shrinkage estimation is that, if ηΨn−1 is larger than unity, the shrinkage factor {1− ηΨn−1 } will be negative; the change of sign affects interpretability. To overcome this, the following positive-part shrinkage (S+ ) estimator has been suggested: + + θˆ1S = θˆ1SM + {1 − ηΨn−1 } (θˆ1FM − θˆ1SM ),

+

where {1 − ηΨn−1 } = max(0, 1 − ηΨn−1 ).

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2.3

Penalty Estimator

For a given penalty function φ(θ) and penalty parameter τ , the general form of the objective function can be written as: 

S(θ) = [y − h(x, θ)] [y − h(x, θ)] + τ φ(θ). Here, the penalty function is in the form φ(θ) =

p  j=1

|θj |ν , where ν > 0. For

ν = 1, we obtain LASSO by minimizing the penalized residual sum of squares. The LASSO estimator of θ is given by ⎫ ⎧ p n ⎬ ⎨  2 θˆLASSO = arg min [yi − h(xi , θ)] + τ |θj | , ⎭ ⎩ θ i=1

j=1

where τ > 0 is a tuning parameter which controls the amount of shrinkage p  and |θj | = θ1 is the vector L1 -norm. For nonlinear regression models, an j=1

efficient algorithm for estimating the parameter by solving the L1 -regularized nonlinear least-squares problem has been proposed [15].

3

Monte Carlo Simulations

The performance of the preliminary test, shrinkage, and positive-part shrinkage estimators were examined using Monte Carlo simulations. The response values were simulated from the C-D model as follows: θ

p yi = θ1 (xθi12 )(xθi23 )...(xi,p−1 ) + εi ,

i.i.d

i.i.d

where xij ∼ N (8, 1) and εi ∼ N (0, 1). Under the null hypothesis H0 : θ2 = 0, the true values of θ1 in the simulation were set to θ1 = (0.75, 0.75, 0.75) for Δsim = 0 or subspace information is correct. If Δsim > 0, the subspace information is incorrect. In a second simulation, we set θ1 = (2, 0.75, 0.75, 0.25, 0.25), so that both correct and incorrect subspace information was present. The values of α were set to 0.01, 0.05, and 0.10 for the PT estimator. Using a sample size of 100, the simulations were run 5,000 times to obtain stable results. We defined     Δsim = θ − θ (0)  as the divergence between the simulation model and the candidate subspace model, where θ is the parameter in the simulation model, θ (0) = (θ1 , 0) , and  ·  is the Euclidean norm. After each run, the mean square error (MSE) of all estimators was calculated. The performance of the estimators was measured by comparing the relative mean

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+ squared error (RMSE) of θˆ1FM with those of θˆ1 , i.e., θˆ1SM , θˆ1PT , θˆ1S , θˆ1S , and LASSO  θˆ1 . The RMSE of θˆ1 was defined as

MSE(θˆ1FM ) RMSE(θˆ1 ) = RMSE(θˆ1FM , θˆ1 ) = . MSE(θˆ1 ) An RMSE larger than one indicates that θˆ1 outperformed θˆ1FM . 3.1

Subspace Information Is Correct (Δsim = 0)

When the submodel is true, so that Δsim = 0, the RMSEs of θˆ1SM , θˆ1PT , θˆ1S , + θˆ1S , and θˆ1LASSO were computed using active parameter q = 3 and inactive parameters r = 3, 5, 7, 11, 15. The RMSEs of all proposed parameters for five pairings of q and r are shown in Table 1. The RMSEs increased as r increased and the SM estimator dominated all other proposed estimators. The PT estimator performed better when α was small. The RMSE of the S+ estimator was greater than that of the shrinkage estimator. The LASSO estimator outperformed the PT estimator at α = 0.10, when there were many inactive parameters, but its performance was below that of the PT estimator when r was small. + Table 1. RMSEs of θˆ1SM , θˆ1PT , θˆ1S , θˆ1S , and θˆ1LASSO with respect to θˆ1FM for q = 3, n = 100, and N = 5, 000

Estimator

Number of inactive parameters (r) 3 5 7 11 15

SM

2.1282 2.9442 4.1282 6.1506 9.2070

PT

α = 0.01 2.0129 2.7479 3.3068 5.1958 6.4817 α = 0.05 1.7464 2.2764 2.5147 3.6119 4.2670 α = 0.10 1.6103 1.9821 2.1772 2.8501 3.4045

S S

1.1428 1.6196 2.0874 3.1280 4.1787 +

LASSO

3.2

1.3105 1.8919 2.3975 3.6637 5.1194 1.1259 1.4889 2.0706 2.9061 3.8244

Both Correct and Incorrect Subspace Information Is Present (Δsim ≥ 0)

The performance of the estimators was investigated when Δsim > 0. The simulation model comprised q = 5 active parameters and r − 1 inactive parameters, with r set to 5, 10, 15, and 20. Here, θ2 = (θ6 , 0) where θ6 is a scalar and that can have multiple values. The values of Δsim were set to equal θ6 , and lay

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+ Table 2. RMSEs of θˆ1SM , θˆ1PT , θˆ1S , and θˆ1S with respect to θˆ1FM for incorrect subspace information, with q = 5, n = 100, N = 5, 000, and (r − 1) = 14 inactive parameters

Δsim SM

PT S α = 0.01 α = 0.05 α = 0.10

S+

0.000 5.3858 4.3151

3.3895

2.7619

3.1309 3.7729

0.005 4.5900 3.7297

2.8476

2.3938

2.8617 3.3669

0.010 3.1972 2.4915

2.0200

1.7306

2.3868 2.6420

0.015 2.0988 1.6511

1.3846

1.2860

1.9500 2.0435

0.020 1.3980 1.1400

1.0768

1.0393

1.6392 1.6647

0.025 0.9650 0.9301

0.9405

0.9510

1.4380 1.4433

0.030 0.6913 0.8707

0.9380

0.9650

1.3092 1.3100

0.035 0.5117 0.9151

0.9706

0.9875

1.2249 1.2249

0.040 0.3895 0.9717

0.9938

0.9966

1.1679 1.1679

0.045 0.3035 0.9934

0.9989

0.9992

1.1282 1.1282

0.050 0.2412 0.9984

1.0000

1.0000

1.0998 1.0998

0.055 0.1949 1.0000

1.0000

1.0000

1.0789 1.0789

0.060 0.1598 1.0000

1.0000

1.0000

1.0632 1.0632

0.065 0.1326 1.0000

1.0000

1.0000

1.0512 1.0512

0.070 0.1112 1.0000

1.0000

1.0000

1.0418 1.0418

0.075 0.0941 1.0000

1.0000

1.0000

1.0345 1.0345

0.080 0.0803 1.0000

1.0000

1.0000

1.0286 1.0286

0.085 0.0690 1.0000

1.0000

1.0000

1.0239 1.0239

0.090 0.0597 1.0000

1.0000

1.0000

1.0200 1.0200

0.095 0.0520 1.0000

1.0000

1.0000

1.0168 1.0168

0.100 0.0455 1.0000

1.0000

1.0000

1.0142 1.0142

between 0 and 0.1. The RMSEs of the proposed estimators for r = 15 are shown in Table 2. Figure 1 gives a graphical representation of the simulation results for each r when α = 0.05. As Δsim moved away from 0, the RMSE of the submodel converged on 0 and the curve of RMSE(θˆ1SM ) fell below the horizontal line at RMSE = 1. The RMSE of the PT estimator first fell below the horizontal line, then increased to 1. When Δsim was close to zero, the RMSE of PT exceeded those of the S and S+ estimators in a small part of the parameter space. Conversely, the S and S+ estimators outperformed the PT estimator when sim was far from zero. The S+ estimator was initially superior to the shrinkage Δ estimator when Δsim > 0, but converged to the same values as Δsim increased. This suggests that the S+ estimator is preferred, as its performance remains robust even if the assumed model is grossly wrong.

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Note that the LASSO estimator was not applied when Δsim > 0, because this estimator makes no use of subspace information.

+ Fig. 1. RMSEs of θˆ1SM , θˆ1PT , θˆ1S , and θˆ1S with respect to θˆ1FM when the subspace sim = θ6 . Here q = 5, α = 0.05, and r − 1 are inactive information is incorrect, as Δ parameters

4

Application to Real Data

Economic data was taken from the Federal Reserve Bank of St. Louis [8] from Q1 1980 to Q2 2019, giving a sample size of 158 quarters. As shown in Table 3, these data contained a dependent variable of real gross domestic product (GDP) and 11 independent variables. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select variables. We considered the submodel generated from AIC or BIC, only if it satisfied the condition r ≥ 3. As can be seen from Table 4, eight predictors were active in the AIC and BIC submodels. We therefore set θ2 = (θHFO , θHE , θSLG ) = (0, 0, 0), to denote the inactive parameters under these submodels. Overall, there were nine active parameters, θ1 = (θ1 , θLS , θGS , θHR , θUR , θAO , θHVL , θTS , θFG ), three inactive parameters, and

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J. Piladaeng et al. Table 3. Variables in the United States economic data

Variable Description Dependent variable GDP

Real Gross Domestic Product

Independent variable LS

Nonfinancial Corporations Sector: Labour Share

GS

Gross Saving as a Percentage of Gross National Income

HR

Homeownership Rate for the United States

UR

Unemployment Rate: Aged 25–54: All Persons for the United States

AO

Automobile Output

HVL

Households; Vacant Land, Level

TS

Treasury Securities; Assets Personal Sector

FG

Federal Government; Cash and Monetary Assets Other than Treasury operating cash; asset, Level

HFO

Household Financial Obligations as a Percent of Disposable Personal Income

HE

Hourly Earnings: Manufacturing for the United States

SLG

State and Local Governments, excluding Employee Retirement Funds; Farm Mortgages; Asset, Level

Table 4. Variable selection results for the United States economic data Method

Active predictor

Full model LS, GS, HR, UR, AO, HVL, TS, FG, HFO, HE, SLG AIC

LS, GS, HR, UR, AO, HVL, TS, FG

BIC

LS, GS, HR, UR, AO, HVL, TS, FG

12 total parameters. The relative mean square prediction error (RMSPE) was used to compare the performance of the proposed estimators with that of the FM estimator: RMSPE(θˆ1FM , θˆ1 ) =

[y − f (x, θˆ1FM )] [y − f (x, θˆ1FM )] . [y − f (x, θˆ )] [y − f (x, θˆ )] 1

1

An RMSPE greater than one means that θˆ1 dominates θˆ1FM , where θˆ1 is one + ˆ of θ1SM , θˆ1PT , θˆ1S , θˆ1S , or θˆ1LASSO . The result from the C-D nonlinear regression ˆ is the true parameter value. coefficient (θ) To investigate the performance of the estimators for the nine active parameters, we set m = 100 bootstrap rows with replacement for N = 1, 000 runs, and set α to 0.01, 0.05, and 0.10. Table 5 compares the performance of the estimators. Unsurprisingly, the SM estimator had the largest RMSPE, since this estimator assumes that the subspace information is true. The PT strategy dominated

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the shrinkage, positive-part shrinkage, and LASSO strategies, while the performance of LASSO was below that of the other estimators. The RMSPE of the SM estimator was superior to that of LASSO, suggesting that the LASSO estimator introduced underfitting by eliminating too many significant predictors. Table 5. The RMSPE of the estimators with respect to θˆ1FM when q = 9, r = 3, m = 100, and N = 1, 000 Estimator SM RMSPE

5

PT S α = 0.01 α = 0.05 α = 0.10

3.0910 1.9262

1.5440

1.3518

S+

LASSO

1.1951 1.2469 1.1155

Concluding Remarks

This study applied preliminary test, shrinkage, positive-part shrinkage, and LASSO estimation strategies for the special case of a nonlinear model. This effectiveness was compared using simulations, and by application to a real dataset. Our results suggest that the submodel estimator dominates the preliminary test, shrinkage, positive-part shrinkage, and LASSO estimators when the UPI is true. The shrinkage and positive-part shrinkage estimators outperformed the LASSO estimator. However, the LASSO estimator outperformed the preliminary test estimator only at α = 0.10, when number of inactive parameter was large. If the UPI was incorrect, the efficiency of the submodel estimator reduced, falling below that of the full model estimator. The preliminary test estimator performed poorly for some values of Δsim , but matched the full model estimator when Δsim was large. The positive-part shrinkage estimator outperformed the shrinkage estimator, and both outperformed the full model, submodel, and preliminary test estimators when Δsim was large. The real data example produced results that were consistent with those from the simulations. Acknowledgements. The research of S. Ejaz Ahmed was financially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Thammasat University under the Bualuang ASEAN Chair Professorship grants.

References 1. Ahmed, S.E.: Penalty, Shrinkage and Pretest Strategies: Variable Selection and Estimation. Springer, Cham (2014) 2. Ahmed, S.E., Nicol, C.J.: An application of shrinkage estimation to the nonlinear regression model. Comput. Stat. Data Anal. 56(11), 3309–3321 (2012) 3. Ahmed, S.E., Raheem, S.E.: Shrinkage and absolute penalty estimation in linear regression models. Wiley Interdiscip. Rev.: Comput. Stat. 4(6), 541–553 (2012)

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4. Ahmed, S.E., Y¨ uzba¸sı, B.: Big data analytics: integrating penalty strategies. Int. J. Manag. Sci. Eng. Manag. 11(2), 105–115 (2016) 5. Cheng, M.L., Han, Y.: A modified Cobb-Douglas production function model and its application. IMA J. Manag. Math. 25(3), 353–365 (2013) 6. Draper, N.R., Smith, H.: Applied Regression Analysis. Wiley, Hoboken (1998) 7. Durlauf, S.N., Blume, L.E.: The New Palgrave Dictionary of Economics, 2nd edn., vol. 1. Basingstoke, Palgrave Macmillan (2008) 8. Federal Reserve Bank of St. Louis: Federal reserve economic data, 19 November 2019. https://fred.stlouisfed.org/ 9. Hossain, M.M., Majumder, A.K., Basak, T.: An application of non-linear CobbDouglas production function to selected manufacturing industries in Bangladesh. Open J. Stat. 2(4), 460–468 (2012) 10. Myers, R.H., Montgomery, D.C., et al.: Generalized Linear Models with Applications in Engineering and the Sciences, 2nd edn. Wiley, Hoboken (2010) 11. Reangsephet, O., Lisawadi, S., Ahmed, S.E.: Improving estimation of regression parameters in negative binomial regression model. In: International Conference on Management Science and Engineering Management, pp. 265–275. Springer (2018) 12. Reangsephet, O., Lisawadi, S., Ahmed, S.E.: Weak signals in high-dimensional logistic regression models. In: International Conference on Management Science and Engineering Management, pp. 121–133. Springer (2019) 13. Reangsephet, O., Lisawadi, S., Ahmed, S.: Adaptive estimation strategies in gamma regression model. J. Stat. Theory Pract. 14(1), 8 (2020) 14. Seber, G.A.F., Wild, C.J.: Nonlinear Regression. Wiley, Hoboken (2003) 15. Yang, Z., Wang, Z., et al.: Sparse nonlinear regression: parameter estimation and asymptotic inference. arXiv preprint arXiv:151104514 (2015) 16. Yasar, A., Bilgili, M., Simsek, E.: Water demand forecasting based on stepwise multiple nonlinear regression analysis. Arab. J. Sci. Eng. 37(8), 2333–2341 (2012)

A Mathematical Model of Soil Fertility Yasin Rustamov1(B) , Tahir Gadjiev2 , and Sheker Askerova3 1

2

Institute of Control Systems, Institute of Mathematics and Mechanics, B. Vahabzade, 9, Baku, Azerbaijan [email protected] Institute of Mathematics and Mechanics, B. Vahabzade, 9, Baku, Azerbaijan 3 Ganja Agrarian University, Ozan str., 103, Ganja, Azerbaijan

Abstract. In the paper the mathematical model for investigation of soil fertility is constructed. The model is based on differential equations (simple and delayed), which allow to estimate agrochemical parameters and the dynamics of the organic matter in the soil.

Keywords: Fertility Differential equations

1

· Soil · Agrochemical parameter · Vegetation · · Nonlinearity · Open system · Heterogeneity

Introduction

Mathematical modeling of natural processes is widely developing in all the branches of science. This is due to attempts at understanding the deeper knowledge of the natural processes, the possibility of their quantitative characterization, forecasting and managing the natural processes. It is in the soil that all the possible energetic and material connections between the atmospheric and the subsoil phenomena are strongly manifested. It is the most important link in the implementation of all the dynamic relationships, processes and possible changes. Unlike a simple set of elements, the soil as a system, possesses an important property, namely integrity. Fertility can be mentioned as an example of the emergent property of the system. The fertility of the soil is determined by the content of nutrients necessary for the plants which grow in it, the level of the stable moisture, the presence of the air in the soil, and the structure of the soil, which determine the possibility of root development. The level of fertility depends on the percentage of the humus content - a complex of organic compounds formed as a result of the decomposition, by microorganisms, of the residues of a plant and animal origin. Fertility of the soil is determined by the content of nutrients necessary for plants in it, the level of stable moisture, the presence of air in the soil, the structure and structure of the soil, which determine the possibility of root development. The soil is an open, heterogeneous and non-linear system. This type of research was conducted by [2–6,8–13].

c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 503–510, 2020. https://doi.org/10.1007/978-3-030-49829-0_38

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The Construction of a Model

Most soil characteristics are related to the properties of the parent rock. Fertility depends mostly on the following: humus, pH, nitrogen, phosphorus, potassium and other components. Also, the soil fertility is conditioned by an optimal and constant soil moisture. In [7], we showed how to water a rock for increasing its optimal fertility. Water is a prerequisite for soil formation and soil fertility. If there is too little water, then the decomposition of the organic matter and the fixation of nitrogen stop. With an excess of water, the organic matter is fermented. Therefore, when constructing a model, we take into account its fundamental characteristics, which can lead to an abrupt change in soil fertility. This is shown by entering into the right side of the equation describing the dynamics of soil fertility special functions. In general terms, it will be the following system of balance equations: dPi = fi (humus, N, F, K, pH, W, Φ1 , CO2 ) (1) dt   i = 1, n - is a measure of fertility, W - is the soil moisture, and where P i   ΦI l = 1, m - is the rate of photosynthesis, which is determined by the intensity of the diffusion flux of the CO2 to chloroplasts from the atmos. The system integrates with the initial conditions of the form: Pi |t=0 = P0i ,

i = 1, n

(2)

For ΦI by Chartier (see [13], Moncy and Saeki [5], Curry [3,4] are received different empirical formulas. Some models also take into account the effect of mineral nutrition on the photosynthesis. So, in [10], an accounting method for nitrogen and phosphorus is proposed.

3

The Model Described by Ordinary Differential Equations

We propose the following models, in the form of an ordinary and delayed differential equation: dP = a1 − a2 · P − a3 · P 2 (3) dt with an initial condition: (4) P (t0 ) = 0 Here, P (t) is a measure of soil fertility, the constants a1 , a2 , a3 - depend on the following factors: pH, body weight, photosynthesis rate, intensity of the diffusion flux of the CO2 , calcium, phosphorus and soil moisture.

A Mathematical Model of Soil Fertility

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In the first case, the solution is as follows: first, using transformation, Eq. (3) is reduced to dP = a1 − a2 · P − a3 · P 2 , dt if a22 + a1 > 0 and a3 > 0. 4a3 From these conditions, it follows that if there is more humus in the soil, then the fertility will increase. Then, the solution to the problem is as follows:   1 2 P = A(1 − A(t+c) ) − a2 , 2a3 e  where, A = a22 + 4a1 a3 . This solution actually shows that the fertility decreases with the decrease of: humus, photosynthesis rate, intensity of the diffusion flow of the CO2 , calcium, phosphorus, soil moisture.

4

The Model Described by Delayed Differential Equations

Now, we consider problem Eq. (5), Eq. (6), with the initial function Eq. (7): dP = a1 − a2 · P (t) − a3 · P 2 (t − τ ) dt

(5)

P (t) = φ0 (t) at t0 − τ ≤ t ≤ t0

(6)

φ0 (t) =

1 √ e 2a π · t

x2 4a2 t

(7)

We assume that in different layers of the soil, the elements of fertility are different and change by leaps. This is obvious, since fertility varies in different layers. In addition, the equation is delayed, since after photosynthesis or tillage, a certain time passes for the impact on fertility. Equation (5) can be reduced to the form dP = a1 − a2 · P (t) − a3 · φ2 (t − τ ) dt t 0 ≤ t ≤ t0 + τ

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P (t0 ) = φ(t0 ) t ∈ [t0 , t0 + τ ] If you enter the function −a2 (t−t0 )

P1 (t) = e

t

e−a2 (t−s) (a1 − a3 · φ2 (s − τ ))ds

· φ(t0 ) + t0

then, Eq. (5) can be rewritten in the form dP = a1 − a2 · P (t) − a3 · P1 2 (t − τ ). dt

(8)

In the simplest case for the equation dH dP + a1 · P + =f dt dt

(9)

with an initial condition P (t0 ) = 0, here, H - is a known quantity. It was used Laplace transformation ∞

∞

−st dP

e 0

=a

dt

−st

e

∞ P dt +

0

e−st f dt.

0

From here, integrating by parts, we obtain −st

e

P

|∞ 0

∞ +s

−st

e

∞ P dt = a

0

−st

e

∞ P dt +

0

e−st f dt.

0

Introducing the notation ∞ L(P ) =

e−st P dt, L(f ) =

0

∞

e−st f dt,

0

we find L(P ) =

L(f ) c + . s−a s−a

Applying the inverse Laplace transform and the convolution theorem, we have t P (t) = f (t) + P (t − s)dG(s), (10) 0

A Mathematical Model of Soil Fertility

507

where we have the Stieltjes integral. If G (s) is a step function in a finite number of points 0 < t1 < t2 < · · · < tk , then, Eq. (11) can be written as P (t) = f (t) +

k 

gi P (t − ti ),

(11)

i=1

Moreover, P (t) = 0 f or t < 0 . The solution obtained by the Laplace transforms  L(f )est ds P (t) = 1 − L(dG) (G)

can be re-written as

 P (t) = (G)

L(f )est ds , N −st i 1− gi e i=1

where gi - horse racing. If there exists a constant c1 = const > 0, such as that on the segment [0, t0 ], |f (t)| ≤ c1 , then, there exists a unique solution to the equation on 0 ≤ t ≤ t0 . To prove this, we can use the method of successive approximations P0 (t) = f (t), t Pn+1 (t) = f (t) +

P0 (t − s)φ(s)ds. 0

Here, we need the condition t0 |φ(s)|ds < ∞. 0

We consider two sub cases: in the first, the numbers ti are commensurable, in the second, they are disproportionate. If the numbers ti are comparable, then the action of the root S = r corresponds to the set of roots located at equal distances from each other S = r ± iktT0 , k = 1, 2... Therefore, if we assume that there is one simple real root r, then P (t) has the form

∞  f (t ) exp 1 − (r + ikT )t dt 1 0 i i exp [(r + ikT0 )i] ∞  0 . P (t) = N k=−∞ gi ti e−rti i=1

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To justify the contour shift, we use the N. Wiener theorem. If ti are incommensurable, then using [1] we are getting required result. Take the function F (t) as follows F (t) = f (t) −

dH . dt

Thus F (t) depends on all known quantities on the right-hand side of Eq. (9) and assume that F (t) is the simplest discontinuous function, for example, a step function. We consider the derivative of the piecewise an absolutely continuous function F (t) with the piecewise continuous derivative F  (t) at the discontinuity points t1 , t2 , . . . and the corresponding horse racing h1 , h2 , . . .. Introduce the function  F1 (t) = F (t) − hk · θ(t − tk ), k

where θ(t) =

1, t>0 0, t30?

Yes

Mean> Median?

Skew(X)>0? No

No

Applicability Verification

Criterion 3

Criterion 2

Criterion 1

No Data Do Not Obey Benford's Law

Extend Sample Data through Simulation

No

End Data Obey Benford's Law

Yes

Yes

Criterion 4

Criterion 5

Criterion 6

Yes No Amout Restriction?

Yes Naturally Generated?

Not SelfCorrelated?

No

No

Yes

No

First-Digit Chi-Squared Test

Hypothesis Testing is Passed?

Second-Digit

Calculate Percentages of Top Three Digits

Third-Digit

Fig. 2. Framework of sales data fraud detection. Table 5. Extended sample data statistics and applicability verification result. Data group

Sample size (N )

Skewness Mean Median Criteria (RMB 1012 ) (RMB 1012 ) 1 2 3

4

5

6

1

100

0.65

9.7

7.2

     

2

350

0.64

122.2

91.4

     

3 670 0.64 450.0 336.9 Note:  means satisfy, × means do not satisfy

     

100, 350, and 670. Applicability verification was then conducted on these three group of data, and all of them satisfy all the 6 criteria. Data statistics and applicability verification result are shown in Table 5. The percentages of the top three digits for the three groups of extended sample data were then calculated as shown in Fig. 3. It can be found that all the percentages of the top three digits for the three groups of data fit pretty well as compared with the corresponding probability distributions of Benford’s Law for each of the first-, second-, and third digit scenarios respectively. When the sample size increases, the data even fit better. The chi-squared test on these data further conformed the above facts (See Table 6). It seems the percentages of the higher-order digit scenarios normally have a better goodness of fit since the corresponding chi-squared test statistics are significantly smaller as compared with the first-digit scenario, except for the third-digit scenario when sample size N = 350. The hypothesis testing results showed that the null hypotheses for all of the top three digit scenarios based on the three groups of extended sample data were accepted for all three significance levels (i.e., α = 0.01, α = 0.05, and α = 0.1), which implied that there was no statistical evidence that the sample data do not obey Benford’s Law. Based on the above data analysis result, we have reason to believe that Alibaba did not fake the Tmall “Double Eleven” Day data since the probability of doing that should be less than 10%.

Did Alibaba Fake the Tmall “Double Eleven” Data?

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Fig. 3. Percentages of the top three digits for the three groups of extended sample data (N = 100, N = 350, N = 670). Table 6. Chi-squared test result for the three groups of extended sample data. Data Sample Digit position Chi-squared test group size (N ) Statistic χ2i (N ) Critical value (χ2df,α ) α = 0.01 α = 0.05 α = 0.1

5

1

N = 100 First Second Third

10.2 9.28 7.54

20.1 21.7 21.7

15.5 16.9 16.9

13.4 14.7 14.7

2

N = 350 First Second Third

7.19 1.48 1.68

20.1 21.7 21.7

15.5 16.9 16.9

13.4 14.7 14.7

3

N = 670 First Second Third

3.70 2.10 0.39

20.1 21.7 21.7

15.5 16.9 16.9

13.4 14.7 14.7

Conclusions and Future Research

This paper developed a methodological framework for detecting sales data fraud based on Benford’s Law. Six criteria were half quantitatively and half qualitatively defined according to the work made by Nigrini and Mittermaier [24], and Wallace [25] for verifying the applicability of the data. If the original data do not

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have sufficiently large enough sample size, a simulation method will be employed by using a regression model to extend the sample size. The skewness, mean, and median of the data will be then checked to guarantee that the data satisfy all the criteria including not restricted in amount, not influenced by human related factors, and not self-correlated. The verified data will be used to calculate the percentages of the top three digits which were compared with the Benford distribution. Finally, hypothesis testing will be conducted to show whether the data obey Benford’s Law or not. If the null hypothesis is accepted, it implies that there is no statistical significant evidence to support data fraud existed. The Tmall “Double Eleven” Day GMV historical data was employed as a case study. Since the size of the original sample data was small, quadratic curve model was employed as the simulation method to extend the data into three groups with sample sizes as N = 100, N = 350, and N = 670. All the three groups of the extended sample data passed the applicability verification. The percentages of the top three digits for these data were calculated and showed similar trends and pretty well goodness of fit as compared with the corresponding probability distributions of Benford’s Law. The chi-squared test result confirmed to the facts. The result suggested that Alibaba did not fake the Tmall “Double Eleven” data. This framework may also be applicable to detect other types of anomaly data. Future research could be focused on improving the method through extending the detection scope, combining with other models, and strengthening the explanation of test results. Acknowledgements. This research was supported by the National Natural Science Foundation of China (Grant Nos. 71971150, 71501137), the Youth Program of National Social Science Foundation (Grant No. 19CJL047), the Humanities and Social Sciences Programs of Ministry of Education in China (Grant No. 17XJC790016), the Sichuan Science and Technology Program (Grant No. 2019JDR0167), the project of Research Center for System Sciences and Enterprise Development (Grant No. Xq16B05), Sichuan University (Grant Nos. 2019hhs-16, skqy201647), the Fundamental Research Funds for the Central Universities of China (Grant Nos. 20826041C4201, SXYPY202004, 20826041D4134), and Sichuan Social Science Planning Project (Grant No. SC18TJ014). The authors would like to give our great appreciation to the editors and anonymous referees for their helpful and constructive comments and suggestions, which have helped to improve this article.

References 1. Xu, X., Li, Q., Peng, L., et al.: The impact of informational incentives and social influence on consumer behavior during Alibaba’s online shopping carnival. Comput. Hum. Behav. 76, 245–254 (2017) 2. Wu, J., Li, Q., Wei, K.: Alibaba’s IT platform and electronic commerce synergy in driving “Singles’ Day”. J. Organiz. Comput. Electron. Commer. 26(3), 193–202 (2016) 3. China Internet Watch: China Double 11 (Singles’ Day) Insights (2012). https:// www.chinainternetwatch.com/tag/double-11/

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4. Alizila: By the numbers: 2019.11.11 global shopping festival (2019). https://www. alizila.com/by-the-numbers-2019-11-11-global-shopping-festival/ 5. China Internet Watch: China Double 11 shopping festival statistics 2019: best-selling brands (2019). https://www.chinainternetwatch.com/29999/double11-2019/ 6. Xue, Y.: Alibaba takes legal action against Singles’ Day sales skeptics (2019). https://www.sixthtone.com/news/1004841/alibaba-takes-legal-action-againstsingles-day-sales-skeptics 7. Li, C.: Tmall responds to double 11 data fraud: insulting intelligence has started the judicial process (2019). https://www.jqknews.com/news/311771-Tmall responds to double 11 data fraud insulting intelligence has started the judicial process.html 8. Diekmann, A.: Not the first digit! Using Benford’s Law to detect fraudulent scientific data. J. Appl. Stat. 34(3), 321–329 (2007) 9. Newcomb, S.: Note on the frequency of use of the different digits in natural numbers. Am. J. Math. 4, 39–40 (1881) 10. Benford, F.: The law of anomalous numbers. Proc. Am. Philos. Soc. 78, 551–572 (1938) 11. Hill, T.: A statistical derivation of the significant-digit law. Stat. Sci. 10, 354–363 (1995a) 12. Hill, T.: Base invariance implies Benford’s Law. Proc. Am. Math. Soc. 123, 887– 895 (1995b) 13. Hill, T.: The first digit phenomenon. Am. Sci. 86, 358–363 (1998) 14. Shao, L., Ma, B.: The significant digit law in statistical physics. Phys. A 389, 3109–3116 (2010) 15. Lin, F., Lin, L., Yeh, C., Wang, T.: Does the board of directors as Fat Cats exert more earnings management? Evidence from Benford’s Law. Q. Rev. Econ. Finance 68, 158–170 (2018) 16. Geyer, C., Williamson, P.: Detecting fraud in data sets using Benford’s Law. Commun. Stat.-Simul. Comput. 33(1), 229–246 (2004) 17. Badal-Valero, E., Alvarez-Jareno, J., Pavia, J.: Combining Benford’s Law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci. Int. 282, 24–34 (2018) 18. Kaiser, M.: Benford’s Law as an indicator of survey reliability-can we trust our data? J. Econ. Surv. 33(5), 1602–1618 (2019) 19. Deckert, J., Myagkov, M., Ordeshook, P.: Benford’s Law and the detection of election fraud. Polit. Anal. 19, 245–268 (2011) 20. Alipour, A., Alipour, S.: Application of Benford’s Law in analyzing geotechnical data. Civ. Eng. Infrastruct. J. 52(2), 323–334 (2019) 21. Pavlovic, V., Knezevic, G., Joksimovic, M., Joksimovic, D.: Fraud detection in financial statements applying Benford’s Law with Monte Carlo simulation. Accid. Anal. Prev. 73, 351–358 (2019) 22. Liu, Y., Wu, X., Zeng, W.: Detecting statistical data anormality by combining Benford’s Law and panel data models. Stat. Res. 11, 74–78 (2012). (in Chinese) 23. Li, F., Han, S., et al.: Application of Benford’s Law in data analysis. J. Phys.: Conf. Ser. 1168(3), 032133 (2019) 24. Nigrini, M., Mittermaier, L.: The use of Benford’s Law as an aid in analytical procedures. Audit.-A J. Pract. Theory 16(2), 52–67 (1997) 25. Wallace, W.: Assessing the quality of data used for benchmarking and decisionmaking. J. Gov. Financ. Manag. 51(3), 16–22 (2002)

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26. Durtschi, C., Hillison, W., Pacini, C.: The effective use of Benford’s Law to assist in detecting fraud in accounting data. J. Forensic Account. 99(99), 17–34 (2004) 27. Cohen, D.: An explanation of the first digit phenomenon. J. Comb. Theory 20, 367–370 (1976) 28. Albright, S., Winston, W.: Business Analytics: Data Analysis and Decision Making, 6th edn. Cengage Learning, Boston (2017)

The Identification of the Company Profile Listed on the Romanian Stock Exchange Involved in CSR Actions Nuc˘a Dumitrit¸a1(B) , Grosu Maria2 , Mihalciuc Camelia3 , and Apetri Ani¸soara3 1

Department of Accounting, Academy of Economic Studies of Moldova, Chi¸sin˘ au, Republic of Moldova [email protected] 2 Faculty of Economics and Business Administration, “Alexandru Ioan Cuza” University, Ia¸si, Romania 3 Department of Accounting, Audit and Finance, Faculty of Economic Sciences and Public Administration, “Stefan cel Mare” University, Suceava, Romania

Abstract. The transition of society, in general, and the economy, in particular, towards a sustainable model of development is a necessity, caused by imminent threats related to the climate change crisis that is affecting us today. Economic entities, through the models of sustainable development adopted, are considered important actors in supporting a sustainable economy, which respond not only to the stakeholders, but to the community in general. Based on these considerations, in our scientific approach, the problematic approach presents, after reviewing the specialized literature, the sustainable development model adopted by the companies listed on the Bucharest Stock Exchange (BSE) on the regulated market, as a result of the measures imposed, in particular, at EU level, but also on its own initiative according to the 2030 Agenda. Keywords: Corporate social responsibilities · Sustainable business model · Sustainable development · Sustainable development goals

1

Introduction

Sustainable development and corporate social responsibility are compatible elements and points of view that need to be taken into consideration in the company’s development strategy, with benefits for both the company, the environment, and all stakeholder categories. Corporate Social Responsibility is crucial because it supports the mission and vision of the company and pleads for sustainable development [8]. Sustainable development refers to maintaining the existence of the ecosystem as well as providing for human needs [16] and requires radical and systemic innovations, integrated in the concept of business models, which thus offers an analytical tool provided in a holistic framework, which will c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 525–540, 2020. https://doi.org/10.1007/978-3-030-49829-0_40

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allow to evaluate the interaction between the different aspects that companies combine to create an ecological, economical and social value [14]. The specialized literature attempts in many ways to highlight the importance of corporate social responsibility in business strategy. Some authors even believe that “Corporate Social Responsibility initiatives must be integrated and internalized by the organization so that they are placed at the heart of the organization” [12], to gain benefits such as: improving corporate reputation, gaining customer confidence, increasing employee motivation or quota market. Taking into account the perception of corporate social responsibility until 2006, Galbreath [15] develops four strategy models (Income maximization, Strategic altruistic model, Bilateral approach to the phenomenon, Convergence of the different interests of the direct and indirect shareholders). Strategic corporate social responsibility is usually identified through a long - term process based on continuous dialogue with stakeholders on the one hand and on the formation of social reports on the other [30]. A corporation that successfully integrates sustainable development into its strategy, both in terms of its operation and the types of goods it sells, it is called a corporation that promotes sustainability [7]. Applying the concept of sustainable development at the company level takes into consideration the corporate responsibility - resulting from the need for companies to adapt to a company’s survival in a context in which globalization and civic activism are increasingly changing radically [5]. This type of approach has been taken up at the level of large companies around the world [24], the concept of sustainable development focusing more on the objective of sustainable and inclusive economic growth [19]. As the concept became wide larger, it focused more on economic development, social development and environmental protection for future generations [10]. An interesting approach is also given by author Shaker [28], who points out that sustainability is related to the equilibrium between human being and environment. A recent study [37] focuses more on identifying laws, rules and regulations in both international and national legal frameworks and aims to analyze the impact of CSR compliance especially on human rights, the environment and sustainable development. The three aspects of sustainability (economic, environmental and social) are translated into an approach to corporate sustainability, which has been presented as the ultimate goal for corporations [13,33]. A profitable business activity would also reveal financial resources that can be used later to promote the concept of sustainable development in society [25]. The points targeted in the part for the research methodology refer to: target population, sample extracted, variables identified, data source, as well as data analysis methods. Based on the results identified in the specialized literature consulted, the following research hypotheses are proposed for testing in this study: (1) At the level of the Romanian companies listed on the Bucharest Stock Exchange BSE on the regulated market, a profile of the company can be identified according to the object of activity, the sustainable development strategy

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adopted, the management system and the involvement in actions regarding the respect of human rights and the fight against corruption. (2) The activity object of the companies listed on the BSE on the regulated market influences the number of sustainable development goals. (3) The strategy of sustainable development adopted at one point and the number of sustainable development goals in which a company is involved in a given period contributes to the increase of its financial performance in the immediate period. The econometric study shows, first of all, the extent to which the number of sustainable development goals is influenced by the activity object of the companies included in the sample, and secondly, it emphasizes that the sustainable development strategy adopted by the companies included in the sample and the number of sustainable development goals in which they are involved contributes to the increase of financial performance, in the immediate period, which may be the basis for new sustainable development initiatives.

2

Literature Review

Carroll’s conceptual model [2] comprehensively describes essential aspects of corporate social performance, presenting the notions of ethical and discretionary responsibilities, placing ethical and discretionary expectations into a rational economic and legal framework. Other papers of Carroll [3,11] presented the extent to which three models of management morality - Immoral Management, Moral Management, and Amoral Management - are extant in the European business environment. The corporate model of sustainable business practices is based on model proposed by Svensson and Wood [31], namely the business ethics model. Wood [36] also proposed an ethical model based on 2 concepts: commitment and partnerships, that represented a commitment to partnerships with all stakeholders both internal and external in an attempt to enhance the level of ethical business practices. Another empirical study [35] of Fortune 1000 companies examined a number of factors related to ethics codes and policy statements, including their usage, age, rate of revision, degree of dissemination, and employee acknowledgement of the policy. Bocken et al. [9] defined a business model taken into account three main elements: the value proposition, value creation and delivery and value capture, these three factors being presented also by Richardson [29]. Rasmussen [27] showed that business models are concerned with how the company defines its competitive strategy by designing the product or service it offers on its market, how it is perceived for it, what it costs to produce, how it differs from other companies by proposing value and how the company integrates its own value chain with those of another company in a value network. In Teece’s opinion [32], the business model consists in defining the manner by which the company delivers value to customers, entices customers to pay for value, and converts those payments to

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profit. Building on existing literature, Zott and Amit, [38] conceptualize a company’s business model as a system of interdependent activities that transcends the focal company and spans its boundaries. A holistic, multi-level, boundary-spanning and dynamic description of business models is presented also by Beattie and Smith [4], this concept of business model is viewed as a key driver of value in the knowledge economy and, therefore, a crucial element of the business reporting model. In terms of business modeling, this is the managerial equivalent of the scientific method, the starting point being the hypothesis, which will be tested in action and revised when necessary [21]. In another study [20], it was examined whether the CEE entities integrate economic, social and environmental aspects/policies in business activities in accordance with the principles of sustainable development. New business models, with a strong focus on sustainability, are currently emerging and include [6]: green product/process-based models; waste regeneration systems; alternative energy-based systems; efficiency optimization by ICT; functional sales and management services; innovative financing schemes; sustainable mobility systems; industrial symbiosis; and green neighborhoods and cities. Some studies [1] confirm that economic entities publish social and environmental information only to be within the limits of legality in the field in which they operate. In 2018, the focus was on eliminating inequality and workplace unpleasantness. The year 2018 brought, in addition, from the perspective of corporate social responsibility, and a significant concern regarding the protection of personal data [23].

3

Research Methodology: Population, Sample, Variables, Data Source, Data Analysis Methods

In this section of the paper, the authors conducted a study to show the extent to which the Romanian companies listed on the Bucharest Stock Exchange on the regulated market, adopted the objectives imposed by the 2030 Agenda. The General Assembly of the United Nations adopted in September 2015 a resolution establishing the sustainable development plan by 2030, the main objectives of the 2030 Agenda being presented in Fig. 1 [18,34]. Sustainability assessment is a growing concern worldwide, with the implementation of the United Nations 2030 Agenda, thus finding the appropriate tools to ensure full coverage of environmental, social and economic issues in light of cultural, historical perspectives - retrospective and prospective - respectively institutional and to allow the participation of several stakeholders. Business Sustainability Goals have become more and more important to companies, but also to business stakeholders, such a business attitude, aimed at enhancing fair relations with stakeholders. Based on the results identified in the specialized literature consulted, the following research hypothesis is proposed for testing and validation: At the level of the Romanian companies listed on the BSE on the regulated market [22],

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Fig. 1. Sustainable development goals (Source: author’s elaboration on the basis of Sustainable Development Goals (SDGs) [18, 34].)

a profile of the company can be identified according to the object of activity, the sustainable development strategy adopted, the management system and the involvement in actions regarding the respect of human rights and the fight against corruption. In order to test and validate the proposed research hypotheses, this study uses a statistical approach [17] which involves identifying the analyzed population and selecting the sample, choosing variables, establishing data analysis methods, collecting and data processing, and, finally, obtaining research results and interpreting them. 3.1

The Population Studied and Analyzed Sample

In this study, the analyzed population is represented by all the companies listed on the Bucharest Stock Exchange - BVB. The selected sample includes only the companies on the regulated market. Of the 81 companies listed at the end of the financial year 2018, a number of 7 companies were excluded, for which the data were unavailable. Thus, the analyzed sample comprises 74 listed companies, for which data were collected for the financial year 2018. Depending on the object of activity, the analyzed sample comprises 48 companies in the field of production, 23 companies in the field of services and 3 companies in the field of trade. After a more analytical classification of the objects of activity, the analyzed sample comprises 37 companies active in the manufacturing industry, 13 companies in the service field, 7 companies in the energy-oil field, 5 companies in the chemical-pharmaceutical field, 5 entities in the financial services field and 3 companies in the field of trade (see Fig. 2).

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Fig. 2. The sample analyzed on activity objects

From the Fig. 2 presented (a and b), it can be observed that the manufacturing industry holds the largest share in the analyzed sample. At the opposite pole are the entities active in the field of trade. 3.2

The Variables Analyzed, the Models Proposed for Testing and the Data Source

In order to test and validate the proposed research hypothesis, the study aims to identify a profile of the Romanian company listed on the BSE on the regulated market, at the end of 2018, according to the adopted sustainable development strategy, the management system and the involvement in actions on respecting human rights and combating corruption. Thus, in order to identify the associations between the activity object of the companies included in the sample, the sustainable development strategy, the management system and the involvement in actions regarding the respect of human rights and the fight against corruption, methods of multivariate data analysis are used [26], namely: Factorial Analysis of Correspondence (FAC) and Factorial Analysis of Multiple Correspondence (FAMC). Then, to test the influence of the activity object of listed companies on the number of sustainable development goals and to test the contribution of the sustainable development strategy adopted at a given time and the number of sustainable development goals in which a company is involved in a given period increasing the financial performance of listed companies in the immediate period, the simple and multiple linear regression models [17] used are the following: N o Obji = α + βAct F ield2i + εi

(1)

ROEi = β0 + β1 SD Strategyi + β2 N o Obji + εi

(2)

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where: N o Obj is the total number of sustainable development goals in which the company i has been involved, and i = 1...74; Act F ield2 is the field of activity of the company identified according to the seven types: manufacturing industry, services, energy-oil, chemicalpharmaceutical, financial services and trade; ROEi is the Return On Equity, which for the half-yearly report for the financial year 2019 is classified as: high, medium and low; α, β, βi=0,...,2 represents the parameters of the regression models; εi represents the error component, ε ∼ N (0, 1). Table 1. List of identified variables and their description (Source: own processing) Variable symbol Variable description

Categories

Act Field1

Activity Field1

Act Field2

Act Field2

Production Services Trade Manufacturing Industry Services Energy-Oil Chemical-Pharmaceutical Financial Services Building Trade

Gs Inn Gs Com Gs Envi Gs Empl Gs Edu Gs Sports Gs Health Gs Cult SD Strategy

Goals in Innovation Goals in Community Environmental Goals Goals on Employees Goals in Education Goals in Sports Goals in Health Goals in Culture Sustainable Declared and Involved Development Declared Strategy Undeclared Mg System Quality, Environment, Integrated System Health and Safety Quality and Environment Management System Undeclared Safety Health Safety and Health at Work Soc Dial Social Dialogue Discrim Discrimination Hum Dev Voc Tr Human Development and Vocational Training Code Ethics Code of Ethics and Integrity Anti Corr Pol Anti-Corruption Policies Decl NAS Declaration of Adherence to the National Anti-Corruption Strategy 2016–2020

The data were collected manually from the non - financial reports and/or from the annual reports of the companies included in the analyzed sample, and the data analysis was performed with SPSS 22.0 software.

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The Table 1 presents the variables identified and their description, which are the basis of the processing carried out.

4

Results and Discussions

For testing the research hypothesis, qualitative variables, described in Table 1, were taken into account, with the exception of the Return on Equity (ROE), but which was transformed from numerical variable into categorical variable, being set at intervals as: low, medium or high. The variable: “Activity Field” was broken down once in three directions: Production, Services and Trade, and to identify the profile of the company involved in sustainable development, a classification of the domains of activity in seven directions was used, as they are mentioned in the table describing the variables. Regarding the Sustainable Development Goals in which the companies included in the sample were and are involved, the development was made on eight types (Innovation, Community, Environment, Employees, Education, Sport, Health and Culture), according to the entities reporting in this direction. The variable: “Sustainable Development Strategy” was identified on the three values, according to the reporting made by companies, considering that only some of them declare that they are involved in sustainable development actions, without mentioning the projects in which they are involved, in while other companies declare that they have adopted a certain sustainable development strategy and are also involved in different projects in this direction. Regarding the Quality, Environment, Health and Safety Management System, some companies have implemented an Integrated System, others have only implemented the Quality and Environmental Management System or do not declare anything in this direction. Considering that entities with an average number of employees greater than 500 were required to report information on Respecting Human Rights and Fighting Corruption, variables regarding the compliance of these aspects were also identified. Thus, for the Respect of Human Rights, the variables are focused on ensuring Health and Safety at Work, Social Dialogue, Non-discrimination and actions aimed at Human Development and Vocational Training. For the actions of the companies that aim at preventing and combating Corruption, the variables identified are aimed at the existence of the Codes of Ethics and Integrity, the Anti-Bribery Policies, but also the adherence or not of the companies to the National Anti-Corruption Strategy (NAS) 2016–2020. Following the data analysis, the main results consider: the presentation of descriptive statistics for the variables used, the identification of associations between the activity object of the companies included in the sample, the sustainable development strategy, the management system and the involvement in actions regarding the respect of human rights and combating corruption (Table 2). From the previous table, it can be observed that for the sample analyzed during the period considered, the companies that operate in the field of production have the highest weight, 66%, in the case where the field of activity is divided in

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three directions and 50%, in the case in which the business area of the companies is divided in seven directions. The smallest share in the analyzed sample is held by the companies that carry out commercial activity, of only 4%. From an analysis of the sustainable development goals for the companies in the analyzed sample, it can be seen that the highest share is held by the environmental protection goals, in a percentage of 84%, followed by goals oriented employees, 74%. A significant weight is also held by the innovation-oriented goals, at a percentage of 65% at the level of the sample analyzed. Lower weights have sustainable development goals oriented sport and culture. Regarding the sustainable development strategy, only 49% of the companies analyzed only declare that they have adopted such a strategy, 41% are actively involved in sustainable development projects on different goals, and 10% do not make statements in this direction. Table 2. Descriptive statistics on the analyzed variables (Source: own processing in SPSS 22.0)

(continued)

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Table 2. (continued)

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47% of the companies included in the sample implemented the Integrated Quality, Environment, Health and Safety Management System, 22% of the companies only partially implemented the Management System, oriented on Quality and Environment, and 31% do not make statements in this direction. Regarding the respect of Human Rights, over 50% of the companies included in the sample report that they aim to ensure both the safety and health of the employees, the social dialogue, as well as non-discrimination and human development and vocational training. Reporting on corruption and bribery issues highlighted that over 40% of the entities included in the sample adopted Codes of Ethics and Integrity and established Anti-Corruption Policies and only 8% of the analyzed entities adhered to the National Anti-Corruption Strategy (NAS) 2016–2020. ROE, as a performance indicator taken in the study, indicates that 38% of companies have a high level, 22%, an average level, and 40% have a low level. The associations between the object of activity of the companies included in the sample for the analyzed period, the sustainable development strategy, the management system and the involvement in actions regarding the respect of human rights and the fight against corruption are presented in the diagrams below (see Figs. 3, 4, 5).

Fig. 3. The association between the Activity Field and the Sustainable Development Strategy and The associations between the Activity Field and the Goals of Sustainable Development (Source: own processing in SPSS 22.0, using FAC)

From the previous Figure, it can be seen that the entities that are active in the fields of energy - oil, financial services and trade declare that they have adopted a Sustainable Development Strategy and are actively involved in projects in this direction. Companies that have as their object of activity the production, the chemical-pharmaceutical field or the services, except the financial ones, are characterized by the fact that they have a Sustainable Development Strategy adopted only at declarative level. The entities in the field of construction do not make statements in this regard. By type of goals, the involvement of entities in certain fields of activity highlights the fact that those active in the field of production are most often involved in three of the eight goals, namely Environment,

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Employees and Innovation, those in the field of services, but also of trade is most commonly involved in the other five of the eight goals.

Fig. 4. The association between the Activity Field and the Management System and The associations between the Activity Field and aspect for Human Rights (Source: own processing in SPSS 22.0, using FAC and FAMC)

Regarding the adopted Management System (see Fig. 4 (a)), the entities in the chemical-pharmaceutical and energy-petroleum fields have implemented an Integrated Quality, Environment, Health and Safety Management system, those in the field of trade and production have implemented a Quality Management System- Environment, those in the field of services, including bail services, do not make statements in this regard. The association between the object of activity and the respect for human rights (see Fig. 4 (b)), highlights the fact that the entities included in the sample that are active in the field of trade do not make any mention of the four variables identified as characteristic for human rights. In contrast, those in the field of production, but also of services, report that they act as a percentage of 50% in this direction. The same interpretation is also valid for reporting actions concerning the fight against corruption and bribery. The entities included in the sample active in the field of trade do not make any mention of the three variables identified as characteristic for combating corruption. In contrast, those in the field of production, but also of services, report that they act as a percentage of 50% in this direction. All the above interpretations allow the validation of the Hypothesis formulated: At the level of the Romanian companies listed on the BSE on the regulated market, a profile of the company can be identified according to the object of activity, the sustainable development strategy adopted, the management system and the involvement in actions regarding the respect of human rights and the fight against corruption. Thus, at the level of the sample analyzed, the media shows that a company active in the energy - oil field is actively involved in the sustainable development strategy, has implemented an integrated management system and initiates, in part, actions regarding the respect of human rights and

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Fig. 5. The associations between the Activity Field and the fight against Corruption (Source: own processing in SPSS 22.0, using FAMC)

the fight against corruption and corruption bribery. The dependence of the number of sustainable development goals on the field of activity of the companies included in the sample is analyzed using a simple linear regression model, and the results obtained from the statistical processing are presented in Table 3. Table 3. Parameters estimates of the first regression model (Eq. (1)) Variable included in the model β

S.E. df Sig. R2

Activity Field2

.129 1

.300

Constant 2.958 .448 1 M odel: N o Obji = α + βAct F ield2i + εi )

.023 .070 .000

The obtained model requires some explanations and comments. First, according to the presented results, we observe that the value of Sig. is less than 0.05, so the relationship between the two variables considered is significant or, in other words, the model is statistically significant. However, the determination ratio R2 of 0.07 shows that the dependent variable (No Obj ) is only 7% influenced by the independent variable (Act Field2 ). The difference is explained by the influences of other variables not included in the model. Therefore, the number of sustainable development goals involving the listed companies at the BSE included in the sample depends to a small extent on the activity object of the entity. More important is the association of the types of goals on certain fields of activity. The influence of the sustainable development strategy adopted at a given time and the number of sustainable development goals in which a company is involved in a given period on its financial performance, in the immediate period, is analyzed using a multiple linear regression model, and the results obtained from the statistical processing are presented in Table 4.

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Variable included in the model

β

Objectives number

.303 .025 2

Sustainable development strategy .183 .088 2

.000 .683 .041

Constant .530 .175 2 .004 M odel: ROEi = β0 + β1 SD Strategyi + β2 N o Obji + εi )

Like the first model, the second model requires some explanation and comment. And in this case it is observed that the value of Sig. is less than 0.05, so the relationship between the variables considered is significant. In addition, the determination ratio R2 of 0.68 shows that the dependent variable (ROE) is influenced 68% by the independent variables (SD Strategy and No Obj ). The difference is explained by the influences of other variables not included in the model. As a result, the model explains that the sustainable development strategy adopted at the level of the companies included in the sample, as well as the number of sustainable development goals in which they are involved, can contribute to increasing the financial performance of these entities, respectively upon the increase the return on equity for the average entities included in the sample in the immediately following reporting period.

5

Conclusion

The corporate responsibility of the corporations has undergone an alert development, evolving from a little known phenomenon and, even less, practiced, towards a controversial subject, on the basis of which numerous works have been elaborated, constituting a fundamental strategic priority in the business process nowadays. The econometric studies carried out according to the article have revealed that, at the level of 2018, for the Romanian companies listed on the BSE on the regulated market, a profile of the company can be identified according to the activity object, the sustainable development strategy adopted, the system of management and involvement in actions regarding the respect of human rights and the fight against corruption. This profile is the following: at the level of the sample analyzed, the media shows that a company that activates in the energy-oil field is actively involved in the strategy of sustainable development, has implemented an integrated management system and initiates, in part, actions regarding the observance of human rights and the fight against corruption. Of course, things can be improved, and companies play a vital role in society. Testing the influence of the activity object of the companies listed on the BSE on the regulated market on the number of sustainable development goals has shown that the number of the sustainable development goals involving the listed companies on the BSE included in the sample depends to a small extent on the object of activity of the entity. More important is the association of the

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goals types on certain fields of activity. In contrast, the sustainable development strategy adopted at one point and the number of sustainable development goals in which a company is involved in a given period contributes to increasing its financial performance in the immediate period, which may be the basis for our sustainable development initiatives. The overall conclusion resulting from this work is as follows: adopting a sustainable development model can contribute both to the success of a corporation and to tackle the global challenges the whole society is facing with, by increasing the overall level of wellfare.

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Department Efficiency Evaluation of Chinese Commercial Bank Based on EBM-DEA Model Ying Li(B) , Miao Wu, Jin Liu, Xingling Hu, and Suichuan Zhou Business School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. Financial industry is the core industry of modern economy, among which commercial Banks play a pillar role. The full entry of foreign Banks into China and the continuous deepening of China’s banking reform have led to great opportunities and challenges for China’s banking industry. Improving the efficiency of commercial banks is the key for promoting the development of commercial banks. This study took GY bank as an example, sampled the data of GY bank and its branches from 2016 to 2018, used EBM (Epsilon-Based Measure) DEA model to analyze the efficiency of commercial Banks. By comparison, it is found that the efficiency of most branches has been significantly improved, while the efficiency of a few branches has not changed much. Therefore, this study puts forward some suggestions to promote the efficiency improvement of GY bank.

Keywords: Commercial bank model

1

· Efficiency evaluation · EBM-DEA

Introduction

Finance occupies the core position in the modern economic industry, and the basis of financial market is commercial Banks. Commercial Banks are the financial institutions that play an important role in China’s financial system. China’s financial system has undergone a series of changes and development since its reform and opening up in 1978. As the financial system continues to improve and mature, the banking industry has also matured and improved, playing an increasingly critical role in society and the economy. Although China’s commercial banks have developed rapidly, they still face many problems. (1) The complex and changeable economic environment means that the development of commercial Banks needs more preparation and motivation. International financial integration magnifies the fragility of the banking industry, and China’s reform has brought downward pressure on the development of the real economy. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 541–560, 2020. https://doi.org/10.1007/978-3-030-49829-0_41

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(2) The competitiveness of Chinese banks (in terms of capital adequacy ratio, profitability and innovation ability) is lower than that of excellent foreign banks. (3) The proportion of direct financing in the capital market is constantly increasing, companies can have more options for direct supply of funds when they need funds, and their dependence on banks is decreasing. Therefore, banks should seek new growth points to ensure their continued operation. (4) The development of Internet technology has promoted the rapid growth of financial technology. The rapid progress of Internet finance has greatly affected the traditional banking industry. Commercial banks must quickly improve their efficiency to deal with the impact. According to the above analysis, this study will use DEA model to measure operating efficiency of banks and study the efficiency of inclusive financial loans, non-performing loans and employees of GY bank, to provide suggestions for GY bank to adapt to the competitive market, so that GY bank can improve its efficiency in a targeted way to enhance its comprehensive competitiveness. This study also hopes to enrich the existing literature on bank efficiency and provide reference for China’s small and medium-sized Banks to adjust their operating efficiency.

2

Literature Review

In the research field of bank efficiency, many scholars have adopted a variety of DEA methods to evaluate the efficiency of Banks. For example, Pastor [16] proposed a new DEA-based model, which decomposed risks into internal and external risks in order to obtain efficiency measures for adjusting risks and environment. Giokas [4] used DEA method to measure the operational efficiency of 171 retail bank branches of a large commercial bank in Greece when providing products of the same quality in different commercial market environments. An et al. [1] used the two-stage DEA method to measure the slack-based efficiency of Chinese commercial Banks from 2008 to 2012. The results showed that the improvement of deposits utilization was the main reason for the performance improvement of Chinese commercial Banks in this period. Chao et al. [2] used the DNSBM-DEA model to analyze the efficiency of 27 Taiwan Banks from 2005 to 2011. The results showed that the creative ability of intellectual capital began to improve from 2008 to 2010, but the profitability of Banks had been deteriorating since the 2008 financial crisis, which indicated that intellectual capital investment became the main reason for Banks to maintain and enhance competitiveness during the financial crisis. Li et al. [11] used SBM-DEA to explore the operational efficiency of 37 banks in Taiwan from 2012 to 2016. The results showed that the average efficiency of SBM-max model was higher the SBM-min model. Yu et al. [26] evaluated the operational efficiency of Bank of China’s credit risk based on DEA. The results showed that there are differences in the operating efficiency of banking groups. The operating efficiency of rural commercial banks has declined since it was listed. The main reason for the low operating efficiency

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of state-owned commercial banks and rural commercial banks is weak credit risk control, and credit risk technology should be improved. Kwateng et al. [8] used DEA to assess the bank level cost and IT efficiency in Indian. The survey results showed that the bank achieved an average level of 99.1% cost efficiency for the sampled period. In addition, research has found that there are both short-term and long-term relationships between IT efficiency and cost performance. In the research of using DEA model to evaluate bank efficiency, some scholars also analyzed and compared the efficiency of different kinds of Banks. Stewart et al. [21] measured the efficiency of Vietnamese Banks from 1999 to 2009. The results showed that the efficiency of non-state-owned Banks was higher than that of state-owned Banks, and the efficiency of large Banks was higher than that of small and medium-sized Banks. Halkos et al. [5] used DEA model to study the efficiency of mergers and acquisitions of Japanese regional Banks from 2000 to 2008. The results showed that the performance of small Banks after merger was often better than that of large Banks. Mahendru and Bhatia [15] used DEA method to study the cost, revenue and profit efficiency of Indian commercial Banks from 2012 to 2013. The results showed that the cost and profit efficiency of foreign Banks in India was higher than that of domestic Banks, but the revenue efficiency was lower than that of domestic Banks. Lee et al. [9] compared the efficiency of 18 Korean Commercial Banks from 2010 to 2014. The results showed that most Korean Banks began to show a recovery trend from 2011, and their performance in recent years was relatively similar. In the three categories of National Banks, Regional Banks and Special Banks, Special Banks show the characteristics of high performance, which are mainly determined by Market Access and ownership type. Li et al. [12] compared the efficiency of financial holding and nonfinancial holding banks. The results showed that the average efficiency and technology gaps of nonfinancial holding banks are better than financial holding banks. The nonfinancial holding banks are more efficient in investment and other income. Singh and Thaker [20] used a two-stage DEA to assess the profit efficiency (PE) of the Bank of India Group. The results showed that the profit efficiency of large state-owned banks, private banks and foreign banks is higher than that of small and medium banks. Besides, some scholars have studied the influence of internal and external factors on bank efficiency through DEA model. Sathye and Sathye [19] collected data from 2007 to 2011 and analyzed the technical efficiency of Indian Banks with DEA method. The results showed that state ownership, foreign Banks and loan quality all had negative effects on efficiency, among which loan quality had the most significant negative effect on efficiency. Wanke et al. [24] studied the efficiency of 13 Mozambican Banks from 2003 to 2011. He results showed that the bank’s market share and cost structure (labor price and capital price) had a significant impact on the situational variables, and market share had a positive impact on the efficiency, while the high cost led to the low efficiency of the bank of Mozambique. Hassan and Jreisat [6] used the data of 14 Egyptian Banks from 1997 to 2013 as samples to study the determinants of the cost-effectiveness of the banking industry by using the two-stage DEA method. The results showed

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that the number of branches had a negative impact on the bank’s cost efficiency, while loans, return on equity (ROE), good management style and age had a positive impact. In addition to using the DEA model to analyze bank efficiency, some scholars also use SAR model, SFA method, Malmquist productivity index method, and other methods to evaluate bank efficiency or analyze factors affecting banking competition. Li [10] used SAR model to study the factors affecting competition in China’s banking industry. The results show that China’s banking competition has obvious spatial correlation characteristics and significant spatial clustering. Human capital, economic growth, financial development scale, and foreign direct investment all have significant positive effects on increasing the degree of competition. Government intervention has a significant negative impact, while fixed asset investment has no significant effect. Titova [22] used stochastic frontier analysis (SFA) to analyze whether the board of directors of American Banks was related to the efficiency of Banks from 2007 to 2013. The study showed that the size of the board of directors was in a U-shaped relationship with the efficiency of Banks. Kamarudin et al. [7] studied the productivity levels of Islamic Banks in southeast Asia (Brunei, Indonesia and Malaysia) from 2006 to 2014 by using DEA method and Malmquist productivity index method. The results showed that foreign Banks have higher efficiency changes, so their productivity is higher than domestic Banks. In Islamic banks, the main reason for the increase in efficiency is productivity. The main factors affecting the productivity of Islamic Banks are liquidity, capitalization and the world financial crisis. Vidyarthi and Tiwari [23] used DEA and Tobit regression to assess the economic efficiency of 37 BSE-listed Indian banks during 2005–2018 and their relationship with intellectual capital. Tobit regression results showed that human capital, structural capital and relational capital have positive and moderate effects on efficiency. Dincer et al. [3] developed a hybrid analysis model based on fuzzy analysis network process and DEA to evaluate the efficiency of Turkish deposit Banks. The results showed that the efficiency results of bank activities vary according to the competitiveness and the adoption of new technologies, the vast majority of deposit Banks are inefficient. In the existing research abroad, parametric analysis method and nonparametric analysis method are widely used. Foreign scholars tend to use empirical research to measure the business performance of commercial Banks by describing specific indicators and using econometric models to study the “costbenefit” relationship, which is generally called efficiency. Chinese scholars have also carried out a series of studies on the efficiency of commercial Banks [13,14,17,18,25,27–29]. In the early stage, most of the methods used by Chinese scholars to measure the efficiency of commercial Banks were fixed bank analysis method and financial proportion method. With the continuous development of econometrics, frontier analysis methods, such as parametric analysis method and non-parametric analysis method (such as DEA method), have been widely applied in the exploration of bank efficiency.

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This study draws on the existing domestic and foreign literatures to broaden the vision of measuring the efficiency of commercial Banks. However, few researches have been focused on the efficiency of the branches of a specific bank, especially on a new urban commercial Banks in China. Therefore, the study on the efficiency of bank branches can provide reference for improving the operational efficiency of Banks based on a data of selected 24 branches of GY bank as a sample to conduct research and measure their operating efficiency.

3

Method and Model

Traditionally, DEA model can be divided into Radial DEA model, represented by CCR and BCC, and non-radial DEA model, represented by SBM. The two models have their own disadvantages. Radial DEA model ignores non-radial slacks when evaluating the efficiency value, while non-radial DEA (such as SBM) fails to consider radial characteristics when evaluating the efficiency value of slacks. In order to solve the shortcomings of radial and non-radial models, Tone and Tsutsui (2010) proposed EBM-DEA model, including input-oriented, output-oriented and non-oriented. The model architecture is as follows. 3.1

Input-Oriented EBM

The input orientation of EBM is to compare and analyze the utilization of input resources at the same output level. Suppose there are n decision making units DM Uj = (DM U1 , DM U2 , · · · , DM Uk , · · · , DM Un ), m types Xj = (X1j , X2j , · · · , Xmj ) are used to produce s types of outputs Yj = (Y1j , Y2j , · · · , Ysj ), the efficiency value of decision-making unit is: γ ∗ = min θ − ε 0,λ,s−

i

i=1

i

Xio

(1)

Subject to θXo − Xλ − S − = 0

(2)

Y λ ≥ yo

(3)

λ≥0

(4)

S [Dual]

m  w− s−

−

≥0

(5)

λ1 + λ2 + · · · + λn = 1

(6)

γ ∗ = max uyo

(7)

Subject to vxo = 1

(8)

− vX + uY ≤ 0 εx wi vi ≥ (i = 1, · · · , m) xio vi ≥ 0

(9)

(11)

λ1 + λ2 + · · · + λn = 1

(12)

v,u

(10)

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Y : represents the output items of DMU; X : represents the input items of DMU; S − : represents the slack variable; m  Wi− = 1(∀Wi− ≥ 0). Wi− : represents the input weight, and i=1

εx is the combination of ray θ and the non-radial slack. If γ ∗ of DM Uo is equal to 1, the decision-making unit has EBM efficiency. If inefficient decisionmaking units want to achieve the optimal efficiency goal, it needs to make the following adjustment: Xo∗ = Xλ∗ = θ∗ Xo − S −∗ . If ε∗ = 0, EBM is the CCR model. If θ = 1 and ε = 1, EBM is the SBM model. However, EBM-DEA model is mainly to determine ε∗ and w− , this model is based on the affinity matrix to estimate ε∗ and w− . Then use the value of ε∗ and w− , to determine the efficiency value of EBM model and all the parameters and values. Here are five steps: Step 1. Establish the target value of VRS efficiency of DMU, which means that n DMU have VRS efficiency: ⎡ ⎤ x ¯1 ⎤ ⎡ ⎢ ··· ⎥ ¯1n x ¯11 · · · x ⎢ ⎥   ⎥ ⎢ x ¯m1 · · · x ¯mn ⎥ ⎢ x ¯m ⎥ x ¯ ⎢ ⎥ ⎢ (13) = =⎣ ⎥ y¯11 · · · y¯1n ⎦ ⎢ y¯ ⎢ y¯1 ⎥ ⎣ ··· ⎦ y¯s1 · · · y¯sn y¯s Step 2. Establish affinity matrix: Under the input orientation, affinity matrix is S = [Sij ] ∈ Rmxm , Sij = ¯ j )(i, j = 1, · · · , m), all matrices S must satisfy 1 ≥ Sij ≥ 0. ¯i, X S(X Step 3. Use eigenvalue and eigenvector to calculate the affinity matrix: We mainly use Perron-Frobenius theory to find out the eigenvalue ρx and eigenvector Wx of S (Wx ≥ 0, m > ρx ≥ 1). Step 4. Calculate εx and W − : m − ρx , if m > 1 m−1 = 0, if m = 1

εx =

wx W− =  m wxi

(14)

(15)

i=1

Step 5. Use the value of εx and W model and all the parameters.

−

to calculate the efficiency value of EBM

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Output-Oriented EBM

The output guidance of EBM is to compare the achievement status of output at the same input level. Under the same condition as input orientation, the efficiency value of decision-making unit is: s  wi+ s+ 1 i = max η + ε (16) y ∗ τ y η,λ,s+ io i=1 Subject to Xλ ≤ Xo

(17)

ηyo − Y λ + S = 0

(18)

+

λ1 + λ2 + · · · + λn = 1

(19)

λ≤0

(20)

S+ ≥ 0

(21)

Y : represents the output items of DMU; X : represents the input items of DMU; S + : represents excess variable; m  Wi+ = 1(∀Wi+ ≥ 0). Wi+ : represents the output weight, and i=1

εy is the combination of ray θ and the non-radial slack. If γ ∗ of DM Uo is equal to 1, the decision-making unit has EBM efficiency. If inefficient decisionmaking units want to achieve the optimal efficiency goal, it needs to make the following adjustment: Yo∗ = Y λ∗ = θ∗ yo − S +∗ . The five steps of calculating εy and W + are as follows: Step 1. Establish the target value of VRS efficiency of DMU: ⎡ ⎤ x ¯1 ⎡ ⎤ ⎢ ··· ⎥ x ¯11 · · · x ¯1n ⎢ ⎥   ⎢x ⎢x ⎥ x ¯ ¯ · · · x ¯ ¯m ⎥ m1 mn ⎥ ⎢ ⎥ ⎢ = =⎣ ⎥ y¯ y¯11 · · · y¯1n ⎦ ⎢ ⎢ y¯1 ⎥ ⎣ ··· ⎦ y¯s1 · · · y¯sn y¯s

(22)

Step 2. Establish affinity matrix: Under the output orientation, affinity matrix is Sij = S(¯ yi , y¯j )(i, j = 1, · · · , s). All matrices S must satisfy 1 ≥ Sij ≥ 0. Step 3. Use eigenvalue and eigenvector to calculate rhoy and Wy . Step 4. Calculate εy and W + . S − ρy , if S > 1 S−1 = 0, if S = 1

εy =

Wy W+ =  s Wyi

(23)

(24)

i=1

Step 5. Use the value of εy and W + to calculate the efficiency value of EBM model and all the parameters.

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3.3

Non-oriented EBM

Under the same condition as input and output orientation, the efficiency value of decision-making unit is: K∗ =

θ − εx min

o,η,λ,s− ,s+

η + εy

m 

wi− s− i xio

i=1 s 

wi+ s+ i yio

i=1

Subject to θXo − Xλ − S − = 0

(26)

ηYo − Yλ + S = 0

(27)

λ1 + λ2 + · · · + λn = 1

(28)

+

λ ≥ 0, S Y X S− S+ Wi− Wi+ εx εy

: : : : : : : :

represents represents represents represents represents represents represents represents

(25)

−

≥ 0, S

+

≥0

(29)

the output items of DMU; the input items of DMU; the slack variable; excess variable;  the input weight, and Wi− = 1(∀i Wi− ≥ 0); the output weight, and Wi+ = 1(∀i Wi+ ≥ 0); the combination of ray θ and the non-radial slack; the combination of ray η and the non-radial slack.

If K ∗ of DM Uo is equal to 1, the decision-making unit has EBM efficiency. If inefficient decision-making units want to achieve the optimal efficiency goal, it needs to make the following adjustment: Xo∗ = Xλ∗ = θ∗ Xo − S −∗ Yo∗

∗

∗

= Y λ = η yo + S

(30)

+

−

The five steps of calculating εx , W , εy and W

+

(31) are as follows:

Step 1. Establish the target value of VRS efficiency of DMU: ⎡ ⎤ x ¯1 ⎡ ⎤ ⎢ ··· ⎥ x ¯11 · · · x ¯1n ⎢ ⎥   ⎢x ⎢x ⎥ x ¯ ¯ · · · x ¯ ¯m ⎥ m1 mn ⎥ ⎢ ⎥ ⎢ = =⎣ ⎥ y¯ y¯11 · · · y¯1n ⎦ ⎢ ⎢ y¯1 ⎥ ⎣ ··· ⎦ y¯s1 · · · y¯sn y¯s Step 2. Establish affinity matrix: Affinity matrix is: S = [Sij ] ∈ Rmxm Sij = S(¯ xi , x ¯j )(i, j = 1, · · · , m) Tij = [Tij ] ∈ R

sxs

Tij = T (¯ yi , y¯j )(i, j = 1, · · · , s)

(32)

(33) (34) (35) (36)

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Step 3. Use eigenvalue and eigenvector to calculate ρx , Wx , ρy and Wy . Step 4. Calculate εx , W − , εy and W + m − ρx , if m > 1 m−1 = 0, if m = 1

εx =

Wx W− =  m Wxi

(37)

(38)

i=1

S − ρy , if S > 1 S−1 = 0, if S = 1

εy =

Wy W+ =  s Wyi

(39)

(40)

i=1

−

Step 5. Use the value of εx , W , εy and W + to calculate the efficiency value of EBM model and all the parameters.

4 4.1

Empirical Analysis Data Sources and Basic Statistical Analysis

This paper selected 24 branches of Guizhou GY bank from 2016 to 2018 as research samples, including branches of AS, BJ, BY, CD, DY, GA, GSH, HX, KL, KY, LM, LPS, QZ, SL, TR, WD, XF, XW, XY, YYB, YY, ZN, ZS and ZY. Data of deposits comes from the statistics of each branch by the corporate finance department of the head office. Data of loans come from the annual statistics of the credit management system. Data of employees come from the statistics of the human resources department of the head office. Data of inclusive financial loans come from the statistics of various branches of inclusive finance. Data of non-performing loans come from the end-of-the-year classified ledger of risk management department (the last three types are selected). Data of other incomes come from other income reports of the planning and finance department of the head office. Figures 1, 2 and 3 show the statistical analysis of the input and output indicators of each branch from 2016 to 2018. As Fig. 1 shows, both the average and the minimum value of deposits rose to the highest value in 2017 and declined slightly in 2018. The maximum deposits remained stable in 2016 and 2017, and increased significantly in 2018. The change of total deposits was not obvious, basically remained stable. According to the statistical analysis from 2016 to 2018, the average number of employees was about 200, and the maximum number was slightly increased. The number of employees in each branch remained relatively stable.

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Fig. 1. Statistical analysis of the number of deposits and the number of employees

Fig. 2. Statistical analysis of the number of loans and non-performing loans

Fig. 3. Statistical analysis of the number of inclusive financial and other income

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As can be seen from Fig. 2, the maximum and average values of loans and nonperforming loans increased significantly from 2016 to 2018, and the average value of loans increased more than that of non-performing loans, but the maximum value increased as much as the number of non-performing loans. Both the maximum and minimum values of inclusive financial loans rose slightly, but the average value of inclusive financial loans fluctuated and rose, reaching a three-year high in 2018, which meant that the total amount of inclusive financial loans increased significantly. As Fig. 3 shows, the average of other incomes remained stable and grew slightly, while the maximum of other incomes rose rapidly and reached its maximum in 2018. Notably, the gap between the maximum and minimum values of deposits, other incomes, loans and non-performing loans has been widening significantly, which means that the gap between branches has been widening. 4.2

Results and Analysis

(1) Overall efficiency evaluation

Fig. 4. Annual efficiency scores of each branch from 2016 to 2018

As Fig. 4 shows the efficiency scores of each branch from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, most of them have improved. In 2016, 10 branches had an efficiency score of 1, and by 2018 14 had a score of 1. The score of AS increased from 0.70 in 2016 to 1 in 2017, with a large and stable increase. The scores of BY, BJ, CD and GSH improved to 1 in 2017, and the scores of these branches improved significantly. QZ, TR, XF and YY all scored 1 in these three years. However, the scores of some branches showed a downward trend from 2016 to 2018: KL, LM, WD, YYB, ZN, ZS. Among them, YYB had the largest decrease, with a decrease of 0.23. Among the 24 branches, DY, KL, KY, LM, SL, YYB and ZS still have

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great room for improvement. The average score of 24 Banks fell to its lowest level (0.63) in these three years in 2017 and rose to its highest level (0.80) in these three years in 2018. (2) Employees and deposits analysis

Fig. 5. Efficiency scores of the index of deposits from 2016 to 2018

As Fig. 5 shows the efficiency scores of deposits of each bank from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, most of them have improved. In 2016, there were 10 branches with an efficiency score of 1 for deposits, and 15 Banks with an efficiency score of 1 for deposits by 2018. The score of GSH increased from 0.45 in 2016 to 1 in 2018, with a large increase. But in 2017, the score was 0.30, indicating that the score of GSH was unstable. The scores of AS, BJ, BY and GA improved to 1 in 2018, the scores of these branches improved significantly. LPS, QZ, TR, WD, XF, XW and YY all scored 1 in these three years. The scores of some branches showed a downward trend from 2016 to 2018: LM, SL, YYB, ZN, ZS. Among them, ZN had the largest decrease, with a decrease of 0.5. Among the 24 branches, DY, KL, KY, LM, SL, YYB and ZS still have great room for improvement. The average score of 24 Banks fell to its lowest level (0.82) in these three years in 2017 and rose to its highest level (0.85) in these three years in 2018. As Fig. 6 shows the efficiency scores of employees of each bank from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, most of them have improved. In 2016, there were 10 branches with an efficiency score of 1 for employees, and 15 Banks with an efficiency score of 1 for employees by 2018. The score of AS increased from 0.69 in 2016 to 1 in 2017, with a large and stable increase. The scores of BY, BJ, CD and GSH improved to 1 in 2017, the scores of these branches improved significantly. QZ, TR, XF and YY all scored 1 in these three years. The scores of DY, KL, KY and ZY rose steadily over the past three years. Only LM’s score dropped, from 0.55 in 2016 to 0.43

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Fig. 6. Comparison of the efficiency scores of the index of employees from 2016 to 2018

in 2018. Among the 24 branches, DY, KL, KY, LM, SL, YYB and ZY still have great room for improvement. The average score of 24 Banks rose to 0.84 in 2017 and continued to rise to its highest level (0.91) in these three years in 2018. (3) Loans, inclusive financial loans and non-performing loans analysis

Fig. 7. Comparison of the efficiency scores of the index of loans from 2016 to 2018

As Fig. 7 shows the efficiency scores of loans of each bank from 2016 to 2018. It can be seen that scores of most Banks have improved and remained stable. In 2016, there were 10 branches with an efficiency score of 1 for loans, and 15 Banks with an efficiency score of 1 for loans by 2018. The score of GA improved from 0.69 in 2016 to 1 in 2017, maintaining a steady growth in three years. The

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scores of AS, BY, BJ, GA, CD and GSH improved to 1 in 2017, among which the scores of GA and GSH improved significantly. LPS, QZ, TR, XF and YY all scored 1 in these three years. The scores of some branches showed a downward trend from 2016 to 2018: LM decreased from 0.76 to 0.73, and ZS decreased from 0.91 to 0.73. Among the 24 branches, DY, KL, KY, LM, SL, YYB and ZS still have great room for improvement. The average score of 24 Banks showed no significant change in 2017, rising to the highest level (0.94) in these three years in 2018.

Fig. 8. Comparison of the efficiency scores of the index of inclusive financial loans from 2016 to 2018

As Fig. 8 shows the efficiency scores of inclusive financial loans of each bank from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, most of them have improved. In 2016, there were 10 branches with an efficiency score of 1 for inclusive financial loans, and 14 Banks with an efficiency score of 1 for inclusive financial loans by 2018. The score of GA improved from 0.50 in 2016 to 1 in 2018, maintaining a steady growth in three years. The scores of BY, BJ, CD and GSH improved to 1 in 2017, and the scores of these branches improved significantly. QZ, TR, XF, XW and YY all scored 1 in these three years. The scores of some branches showed a downward trend from 2016 to 2018: WD, YYB, ZN. Among them, WD had the largest decrease, with a decrease of 0.44. Among the 24 branches, KL, KY, LM, SL, YYB and ZY still have great room for improvement. The average score of 24 Banks fell to its lowest level (0.81) in these three years in 2017 and rose to its highest level (0.90) in these three years in 2018. As Fig. 9 shows the efficiency scores of non-performing loans of each bank from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, the efficiency scores of non-performing loans of most Banks are

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Fig. 9. Comparison of the efficiency scores of the index of non-performing loans from 2016 to 2018

improved. In 2016, there were 10 branches with an efficiency score of 1 for nonperforming loans, and 14 Banks with an efficiency score of 1 for non-performing loans by 2018. The score of CD increased from 0.18 in 2016 to 1 in 2017, with a large and stable increase. The scores of AS, BY, BJ, CD and GSH were improved to 1 in 2017, while the scores of BY, CD, KY, SL and YYB were significantly improved. LPS, QZ, TR, XF, XW and YY all scored 1 for these three years. The scores of some branches showed a downward trend from 2016 to 2018: WD decreased from 1 to 0.86, and ZS decreased from 0.90 to 0.89. Among the 24 branches, KL, KY, LM, SL and YYB still have great room for improvement. The average score of 24 Banks fell to its lowest level (0.80) in these three years in 2017 and rose to its highest level (0.92) in these three years in 2018. (4) Analysis of index of other incomes As Fig. 10 shows the efficiency scores of other incomes of each bank from 2016 to 2018. It can be seen that although the overall scores of these Banks are different, most of them have improved their scores. In 2016, there were 10 branches with an efficiency score of 1 for other incomes, and 15 Banks with an efficiency score of 1 for other incomes by 2018. From 2016 to 2018, The score of BJ increased from 0.62 to 1, and BYs increased from 0.66 to 1, with a large increase. The scores of AS and CD improved to 1 in 2017, and the scores of these two branches improved significantly. QZ, TR, WD, XF and YY all scored 1 for these three years. The scores of some branches showed a downward trend from 2016 to 2018: DY, KL, YYB, ZY. Among them, ZY had the largest decrease, with a decrease of 0.2. Among the 24 branches, DY, KL, KY, LM, SL, YYB, ZS and ZY still have great room for improvement. The average score of 24 Banks fell to its lowest level (0.78) in these three years in 2017 and rose to its highest level (0.88) in these three years in 2018.

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Fig. 10. Comparison of the efficiency scores of the index of other incomes from 2016 to 2018

5

Conclusions

Table 1 summarizes and compares the efficiency of input-output indexes such as average annual efficiency, deposits, employees, loans, inclusive financial loans, other incomes and non-performing loans of each branch, and serves as the basis for policy suggestions on efficiency improvement of each branch. There are some main conclusions as following: (1) The average annual efficiency score of QZ, TR, XF and YY were 1, maintaining the best among all branches. If the existing input and output are maintained, the input-output index can reach the optimum. (2) The average annual efficiency scores of AS, CD, HX, XY and ZN were high, with scores of 0.9021, 0.9225, 0.9407, 0.9369 and 0.9607, The average annual efficiency scores of BJ, BY, GA, GSH, LPS and XW were at the same level, with scores of 0.7908, 0.7801, 0.7980, 0.7905, 0.7369 and 0.7725. (3) The average annual efficiency scores of LM and ZS were 0.3192 and 0.1148, respectively. The efficiency scores of ZS in terms of employees, loans, inclusive financial loans and non-performing loans were in the middle level, but the efficiency scores in terms of deposits and other incomes were the lowest among all branches, and the annual efficiency scores were also the lowest among all branches. The efficiency scores of LM were between 0.4 and 0.7. LM should make more efforts than other branches to improve the annual efficiency scores.

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Table 1. Average efficiency scores of each branch

6

DMU Average annual efficiency

Deposits Employees Loans Inclusive Other financial incomes loans

Nonperforming loans

AS

0.9021

0.9469

0.8998

0.9597 0.9082

0.9597

0.9469

BJ

0.7908

0.8608

0.9594

0.9673 0.8476

0.7162

0.9594

BY

0.7801

0.9139

0.9603

0.9679 0.7630

0.7281

0.7611

CD

0.9225

0.9807

0.9089

0.9827 0.8674

0.8596

0.7280

DY

0.5253

0.8846

0.7162

0.8237 0.9146

0.6914

0.8846

GA

0.7980

0.9140

0.9510

0.8617 0.7010

0.9619

0.9524

GSH

0.7905

0.5881

0.8871

0.8694 0.8869

0.9222

0.7925

HX

0.9407

0.9683

0.9683

0.9504 0.9734

0.9734

0.9683

KL

0.4256

0.7990

0.4121

0.8574 0.8020

0.5249

0.7990

KY

0.5365

0.7050

0.6864

0.8184 0.7032

0.8184

0.7050

LM

0.3192

0.5235

0.5235

0.7571 0.7064

0.6065

0.4490

LPS

0.7369

1.0000

0.8856

1.0000 0.9164

0.8345

1.0000

QZ

1.0000

1.0000

1.0000

1.0000 1.0000

1.0000

1.0000

SL

0.4814

0.6005

0.6711

0.8020 0.6719

0.7560

0.4220

TR

1.0000

1.0000

1.0000

1.0000 1.0000

1.0000

1.0000

WD

0.8796

0.9978

0.9667

0.9979 0.7088

0.9979

0.7419

XF

1.0000

1.0000

1.0000

1.0000 1.0000

1.0000

1.0000

XW

0.7725

1.0000

0.9547

0.9500 1.0000

0.8354

1.0000

XY

0.9369

0.9682

0.9442

0.9733 0.9733

0.9275

0.9682

YYB 0.4948

0.5708

0.7929

0.8592 0.5926

0.6547

0.3919

YY

1.0000

1.0000

1.0000

1.0000 1.0000

1.0000

1.0000

ZN

0.9607

0.8352

0.9888

0.9895 0.9253

0.9895

0.9888

ZS

0.1148

0.2812

0.9311

0.8361 0.8893

0.5015

0.9311

ZY

0.5870

0.7804

0.6672

0.8525 0.6694

0.7556

0.7804

Managerial Suggestions

Based on the results, this study puts forward the following policy suggestions and management decision-making reference. Each branches of the back should implement management measures and decisions that suit its own characteristics according to the overall efficiency of different branches. For banks with an overall efficiency of 1 for three consecutive years, they should summarize successful experiences and learn from the experience of benchmarking companies to form a development model that suits them. Banks with low overall efficiency should aim at the input-output indicators with

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the worst efficiency scores, and formulate corresponding management measures to improve and improve the overall efficiency and index efficiency. For banks with medium efficiency, on the basis of maintaining the existing results, they take measures to address outstanding issues and improve overall efficiency and departmental efficiency. From the analysis of the efficiency scores of the non-performing loans of each branch, most branches had low efficiency scores. The efficiency scores of YYB branch, SL branch and LM branch were even about 0.4. Therefore, we can find that the non-performing loans is still a big problem for many branches. YYB, SL and LM should focus on reducing the non-performing loans. By measuring the efficiency of non-performing loans, we find that the efficiency of commercial Banks is directly affected by non-performing loans, and the verification and reduction of non-performing loans can effectively improve the operating efficiency of Banks. Commercial Banks should establish a sound credit fund delivery system and management system in the working process. They can also learn from some foreign Banks to transfer and reduce loan risks by securitizing non-performing loans. When using financial leverage to generate profits, the provision ratio of non-performing assets should be increased and the asset adequacy ratio should be enhanced, so as to continuously strengthen the stability and safety of the bank. From the efficiency analysis results, it can be seen that the ZY branch, YYB branch, SL branch, CM branch, KY branch, KL branch and DY branch of GY bank have redundant staff input. Therefore GY bank should streamline its organization, reasonably arrange its business outlets and staff, and appropriately reduce the redundant staff of its inefficient business outlets to achieve the purpose of reducing operating expenses and improving cost management ability. Banks can use attractive remuneration and growth career development models to attract more outstanding talents, thereby improving the quality and capabilities of employees. At the same time, banks can also stimulate employees’ enthusiasm for work and loyalty to the organization according to the characteristics of employees. Acknowledgements. This paper is supported by China Nature Science Fund (Grant No. 71773082).

References 1. An, Q., Chen, H., et al.: Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Ann. Oper. Res. 235(1), 13–35 (2015) 2. Chao, C.M., Yu, M.M., Wu, H.N.: An application of the dynamic network DEA model: the case of banks in Taiwan. Emerg. Markets Finance Trade 51(sup1), S133–S151 (2015) 3. Dincer, H., Hacioglu, U., et al.: Developing a hybrid analytics approach to measure the efficiency of deposit banks. J. Bus. Res. 104, 131–145 (2019)

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4. Giokas, D.I.: Cost efficiency impact of bank branch characteristics and location: an illustrative application to Greek bank branches. Manag. Finance 34(3), 172–185 (2008) 5. Halkos, G.E., Matousek, R., Tzeremes, N.G.: Pre-evaluating technical efficiency gains from possible mergers and acquisitions: evidence from Japanese regional banks. Rev. Quant. Finance Account. 46(1), 47–77 (2016) 6. Hassan, I., Jreisat, A.: Cost efficiency of the Egyptian banking sector: a panel data analysis. Int. J. Econ. Financ. Issues 6(3), 861–871 (2016) 7. Kamarudin, F., Hue, C.Z., et al.: Does productivity of Islamic banks endure progress or regress? Empirical evidence using data envelopment analysis based malmquist productivity index. Humanomics 33(1), 84–111 (2017) 8. Kwateng, K.O., Agyei, J., Amanor, K.: Examining the efficiency of IT applications and bank performance. Ind. Manag. Data Syst. 119(9), 2072–2090 (2019) 9. Lee, Y.J., Joo, S.J., Park, H.G.: An application of data envelopment analysis for Korean banks with negative data. Benchmarking Int. J. 24(4), 1052–1064 (2017) 10. Li, Y.: The spatial econometric analysis of China’s banking competition and its influential factors. Sustainability 7(12), 16771–16782 (2015) 11. Li, Y., Chiu, Y., et al.: Market share and performance in Taiwanese banks: min/max SBM DEA. TOP 27(2), 233–252 (2019) 12. Li, Y., Chiu, Y., et al.: The operating efficiency of financial holding and nonfinancial holding banks - Epsilou-based measure metafrontier data envelopment analysis model. Manag. Decis. Econ. 40(5), 488–499 (2019) 13. Liu, H.: A measurement to the efficiency of the commercial banks in China by DEA approach. Econ. Sci. 26(6), 48–58 (2004). (in Chinese) 14. Luo, D.: Empirical study on the efficiency of commercial banks based on DEA. Manag. Sci. 18(2), 39–45 (2005). (in Chinese) 15. Mahendru, M., Bhatia, A.: Cost, revenue and profit efficiency analysis of Indian scheduled commercial banks. Int. J. Law Manag. 59(3), 442–462 (2017) 16. Pastor, J.M.: Credit risk and efficiency in the European banking system: a threestage analysis. Appl. Financ. Econ. 12(12), 895–911 (2002) 17. Qin, W., Ouyang, J.: Market structure, efficiency, and performance of China’s commercial bank. Econ. Sci. 4, 34–45 (2001). (in Chinese) 18. Qiu, Z., Zhang, A.: Research on the X-efficiency of Chinese commercial banks based on FDH method. J. Financ. Res. 11, 91–102 (2009). (in Chinese) 19. Sathye, S., Sathye, M.: Loan quality, ownership and efficiency of Indian banks: a bootstrap truncated regression approach. Indian J. Econ. Bus. 14(2), 289–306 (2015) 20. Singh, P.K., Thaker, K.: Profit efficiency and determinants of Indian banks; a truncated bootstrap and data envelopment analysis. Cogent Econ. Finance 8(1) (2020). https://doi.org/10.1080/23322039.2020.1724242 21. Stewart, C., Matousek, R., Nguyen, T.N.: Efficiency in the Vietnamese banking system: a DEA double bootstrap approach. Res. Int. Bus. Finance 36, 96–111 (2016) 22. Titova, Y.: Are board characteristics relevant for banking efficiency? Evidence from the US. Corporate Governance (2016) 23. Vidyarthi, H., Tiwari, R.: Cost, revenue, and profit efficiency characteristics, and intellectual capital in Indian banks. J. Intellect. Cap. 21(1), 1–22 (2019) 24. Wanke, P., Barros, C.P., Emrouznejad, A.: Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: a case of Mozambican banks. Eur. J. Oper. Res. 249(1), 378–389 (2016)

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Risk Evaluation of Technology Innovation Project on Aspect of Life Cycle Based on Multi-dimensional Extensible Matter-Element Model Liping Li, Xiaofeng Li, and Qisheng Chen(B) Business School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. It is well acknowledged that technological innovation is the driving force of enterprise’s sustainable and competitive development. However, risk always goes with the innovation. In order to identify and evaluate risk comprehensively and systematically, our paper assess the risk from the perspective of life cycle process. Moreover, based on the matter-element and extension set theory, a multi-dimensional extensible matter-element model and extensible evaluation method are proposed to evaluate the risks index at different life cycle stages. Such method can accurately evaluate the risks at different life cycle stages and provide new method and angle for analyzing and reducing risks of technology innovation project. Both the theoretical analysis and practical results have verified the feasibility and effectiveness of the model. Keywords: Risk evaluation · Multi-dimensional extensible matter-element model · Technological innovation project · Life cycle

1

Introduction

The risk management of technological innovation project have been focused universally not only by scholars but also by practitioners. In order to preoccupy an advantageous position in the fierce market competition, enterprises must carry out technological innovation [9]. However, technological innovation project is always carried out with a variety of risk, which may induce tremendous losses [4]. In this situation, it is necessary to make the proper and comprehensive risk management. With the scientific risk management, enterprises are able to make decisions such as whether to suspend or continue the technological innovation projects and ensuring the technological innovation project smoothly going [6,10]. According to the International Organization for Standardization’ assertion, risk management can be integrated into three main parts, namely risk identification, risk evaluation and risk response [7]. We have to admit that technological innovation project has a life cycle and different stages such as initiation stage, c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 561–574, 2020. https://doi.org/10.1007/978-3-030-49829-0_42

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development stage, production stage and market stage, etc [16]. These stages are filled with different kinds of risk such as technology risk, management risk, manufacture, finance risk, policy risk and market risk. Because these stages are featured with dynamic continuity, the impact level and consequences of the same risk may be different in different stages. For example, the market risk exists all of life cycle, but in market stage, it has high index weight compared to other stages. However, traditional researches study the risk identification with dividing the risk into some parts such as technology risk, market risk, etc. instead of making integrated and dynamic identification and evaluation, which may bring the bias on the index weight evaluated. For example, in the traditional researches, the weight of market risk is the same in all of technological innovation project. Based on this research status, we try to make the risk identification, construct the risk index system and determine the weight of risk indices from the perspective of life cycle, which can comprehensive and systematic evaluation of risks. In order to determine the risk level objectively and accurately, there are several model establishment of technological innovation risk analyzation and evaluation put forward by scholars, such as an integrated leapfrogging mode [28], agent-based modeling [27], a rectangular array analysis method [23], VIKOR method [8], and probabilistic linguistic information sets with VIKOR method [19]. Also, the failure mode and effects analysis (FMEA) is a well-known and common method for analyzing and assessing the risks [2]. Matter-element mode is an emerging subject created by Chinese scholar Wen [26], which uses formal tools to examine the regularity problems under the combined effects of multiple factors from both qualitative and quantitative perspectives, and this method is used in the risk evaluation. On the basis of the matter-element and extension set theory, our paper constructs the multi-dimensional main characteristic elementmatrix matrix, and firstly used it to evaluate the risk level at different life cycle stages of technological innovation project. The contributions of this paper can be summarized in two folds. (1) This paper constructs a risk factor index system for the various stages of a technological innovation project from a full life cycle perspective, which induce the risk identification and evaluation comprehensive and systematic. (2) A multi-dimensional extensible matter-element model and extensible evaluation method are proposed to evaluate the risk at different life cycle stages of a technological innovation project relying on the matter-element and extension set theory. This method can not only describe the state of various risk factors in the process of technological innovation in a formal manner, but also quantitatively and accurately evaluate the risks at different stages so as to comprehensively reflect their advantages and disadvantages. This paper is organized as follows. In Sect. 2, we make risk identification of technological innovation project based on life cycle. In Sect. 3, we construct the risk index system and determine the weight of risk indices. In Sect. 4, we build a multi-dimensional extensible matter-element model for the risk evaluation of enterprise technological innovation project at different life cycle stages.

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In Sect. 5, the extensible measurement method is used for the enterprise technological innovation risk evaluation. In Sect. 6, the multi-dimensional extensible matter-element model and the extensible evaluation method is applied to solve a practical problem. Some conclusions and future studies are summarized in Sect. 7.

2

Risk Identification of Technological Innovation Project Based on Life Cycle

The risk of enterprise technological innovation project refers to the loss possibility induced by uncertain enterprise external risk such as market risk, environmental factors and enterprise internal risk [1,13,18,20,30]. On the perspective of life cycle, some scholars are inclined to divide the process of technological innovation into two stages: technical stage and market stage (or business risk); typical examples include the technological innovation two-dimensional theory proposed by Moriaty and Kosnik [21]. Some Chinese scholars propose to divide the life cycle into three stages: technical stage, production stage and market stage [29]. In this paper, based on the life cycle theory and the characteristics of technological innovation project, we divide the life cycle of a technological innovation project into four stages, namely the initiation stage, development stage, production stage and market stage. (1) Risk at the initiation stage. The risk at this stage mainly is as follows. The enterprise makes incorrect forecast about the macroeconomic situation or the innovation project is not supported by relevant policies; The enterprise’s prediction about the market of innovative products is inaccurate and incompliant with actual customer needs; The technology is far ahead reality and incompatible with current market needs; The enterprise is lack of sufficient feasibility study or rigorous screening about innovative ideas; The decisionmaking involves mistakes, etc. (2) Risk at the development stage. The main risk at this stage is as follows. The enterprise’s development capability or project organization ability is not strong enough to solve technical difficulties and ensure the success of key technology research. The mistakes in product design and trial production leads to failure of project development. The complexity of technology results in high cost, etc. (3) Risk at the production stage. The main risks at this stage is as follows. The design principle of the innovative product is unreasonable and the technical performance is unstable and unreliable, the supply of raw materials and parts is in great difficulty; the pilot and small batch production are not completed due to equipment insufficiency, technical weakness, process deficiency and other factors; the cost increases greatly due to process adjustment or substantial increase of new equipment even if the pilot can be completed; the production cost is excessively high due to quality deficiency, instability or low pass rate in mass production; the enterprise has difficulty obtaining credit funds in order to start or expand production.

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(4) Risk at the market stage. The main risk at this stage is as follows. The industry of the innovative product is in recession and the market capacity is small; the enterprise is lack of effective marketing strategies and sales channels; too many competitors or the competitors are too strong; the product price is lack of competitive advantage due to high production cost; the emergence of alternative products shortens the product life cycle; the weakness in capital operation leads to financial constraints; the adjustment of policy environment results in sales difficulty or product withdraw.

3

Construct the Risk Index System and Determine the Weight of Risk Indices

The technological innovation process may involve a number of risk factors, and the correlation among these factors is complicated. Therefore, the construction of risk factor index system must comply with the following principles: (1) the systematic and comprehensive principle; (2) the concise and scientific principle; (3) the feasible and valid principle; and (4) the combination of quantitative and qualitative principle [24]. The risk factor index system is constructed for different life cycle stages of an enterprise technological innovation project (initiation stage, development stage, production stage and market stage) in accordance with the above-mentioned principles and relevant literatures [22,25]. Then, the established system is refined, modified, supplemented and finalized by expert survey, statistical analysis and other methods. The final establishment is shown in Table 1. The relative importance of each risk factor differs at different life cycle stages of the technological innovation project [11]. In order to accurately reflect the importance of each risk factor, the weight should be assigned appropriately. In this paper, the analytic hierarchy process [3,12,15,17] is employed to determine the weight of each risk factor index, as shown in Table 1.

4

4.1

The Multi-dimensional Extensible Matter-Element Model for the Risk Evaluation of Enterprise Technological Innovation Project at Different Life Cycle Stages Build a Multi-dimensional Extensible Matter-Element Matrix

Analyzing a large number of daily instances with conflict proves that things themselves, their features and the corresponding value should be considered together to solve the problem with more appropriate description and formalized process. Therefore, as the basic elements of describing things, the matter-element model ‘R’, which is composed of name of matter ‘N ’, its features ‘c’ and the corresponding value triples ‘h’ is referred here [14]. R = (N, c, h)

(1)

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Table 1. The risk index system for life cycle of technological innovation project Life cycle T

Risk factor index xT k

Index weight WT k

Life cycle T

Risk factor index xT k

Index weight WT k

Initiation stage (T = 1)

Macroeconomic situation x11

0.113

Production stage (T = 3)

Production scale x31

0.089

Degree of policy support x12

0.121

Management capability x32

0.129

Accuracy of market information x13

0.12

Degree of supply difficulty of raw materials and parts x33

0.077

Accuracy of technical forecast x14

0.132

Production technical advancement x34

0.094

Degree of technical advancement x15

0.131

New product technology stability x35

0.119

Feasibility demonstration & planning x16

0.126

New product quality performance x36

0.117

Management level x17

0.142

New product production cost x37

0.116

Scientific and democratic decision-making level x18

0.115

New product production cycle x38

0.082

Difficulty of credit source x39

0.089

Capital strength x310

0.088

Policy environmental changes x41

0.074

Development stage (T = 2)

Capability of project leader x21

0.106

Market stage (T = 4)

Project organization & management capacity x22

0.082

Industry prosperity degree x42

0.072

Progress control capability x23

0.075

Market size x43

0.073

Timeliness of project funding x24

0.076

Competitor strength x44

0.08

Technical complexity & difficulty x25

0.105

0.078

Technology development cost x26

0.098

Number of competitors x45 Competitors’ unfair competition activities x46

Technical level x27

0.097

Changes in consumer demand x47

0.079

Difficulty and complexity of pilot test x28

0.098

New product life cycle x48

0.071

Technical collaboration capability x29

0.082

New product price x49

0.069

Strength of research personnel x210

0.113

Reputation and visibility x410

0.084

Treatment of research personnel x211

0.068

Marketing capability x411

0.074

Capital operation capability x412

0.083

Management capability x413

0.096

0.067

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If f represents the number of all features of matter N and e represents the number of main features of matter N . Obviously, e < f . Thus, the main features of N are c1 , c2 , . . . , ce , its corresponding value are h1 , h2 , . . . , he and the main characteristics of the matter-element matrix is as follows: ⎤ ⎡ ⎤ ⎡ R1 N, c1 , h 1 ⎢ ⎥ ⎥ ⎢ c , h 2 2⎥ ⎢ R2 ⎥ ⎢ (2) R=⎢ .. .. ⎥ = ⎢ .. ⎥ ⎣ . . ⎦ ⎣. ⎦ ce , he Re R represents the main characteristics of the matter-element matrix, Ri = (N, ci , hi ) (i = 1, 2, . . . , e) represents R points and main features of the matterelement matrix. 4.2

Establishment of the Classical and Extensional Matter-Elements Model

The risk involved in the implementation process of technological innovation project is unstable and are always undergoing dynamic changes. Therefore, it is possible to grasp the main characteristic that reflects the risk state of technological innovation project C that is, the various risk factors of technological innovation project C to describe the quantitative changing process of the risk using the established multi-dimensional main characteristic matter-element matrix. Then, based on risk evaluation, the level of technological innovation risk can be determined. Assuming there are m risk factors at the T -th stage (T = 1, 2, 3, 4 in this paper) of the life cycle of a technological innovation project, namely XT 1 , XT 2 , . . . XT m . On the basis of these indexes, the risk of enterprise technological innovation is quantitatively divided into n levels by experts or in accordance with the statistical clustering analysis, which can be described as the qualitative and quantitative risk evaluation matter-element model as follows (referred to as “classical domain matter-element matrix”): ⎡ ⎤ ⎡ ⎤ No j xT 1 No j xT 1 Vo j1 ⎢ ⎢ xT 2 Vo j2 ⎥ xT 2 ⎥ ⎢ ⎥ ⎢ ⎥ = (3) Ro j = ⎢ ⎢ ⎥ ⎥ .. .. .. .. ⎣ ⎣ ⎦ ⎦ . . . . xT m Vo jm

xT m

Where, Ro j denotes the matter-element model of the enterprise technological innovation project at the T -th stage of the life cycle when the risk level equals j; No j represents the j-th level risk; Vo jk = represent the range of value of the k-th risk factor index xT k at the T -th stage of the life cycle of the innovation project when the risk level equals j; j = 1, 2, . . . , n; k = 1, 2, . . . , m. The matter-element model formed by the range of value of the various risk factor indexes of the innovation project at the T -th stage of the life cycle, that

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is, the range of value when the various risk factor indexes are at risk level 1 to level n (referred to as the “node domain matter-element matrix”) is: ⎡ ⎤ ⎡ ⎤ Np xT 1 Np xT 1 Vp1 ⎢ ⎢ xT 2 Vp2 ⎥ xT 2 ⎥ ⎢ ⎥ ⎢ ⎥ (4) Ro j = ⎢ ⎥ .. .. ⎥ = ⎢ .. .. ⎣ ⎣ ⎦ ⎦ . . . . xT m Vpm xT m Where, RP denotes the node domain of the risk evaluation matter-element model of enterprise technological innovation project at the T -th stage of the life cycle; NP represents all risk levels of the enterprise technological innovation project; Vpk = represents the range of value of the risk factor index xT k in NP ; Vo jk ⊂ Vpk , j = 1, 2, . . . , n; k = 1, 2, . . . , m. 4.3

Establishment of Matter-Element Matrix for the Technological Innovation Project

For a target technical innovation project at the T -th stage of the life cycle, the detected data or analysis results of the various risk factor indexes can be expressed by the following matter-element matrix: ⎡ ⎤ N x T 1 v1 ⎢ xT 2 v2 ⎥ ⎢ ⎥ (5) R=⎢ .. .. ⎥ ⎣ . . ⎦ xT m vm Where, N denotes the risk of the target enterprise technological innovation project at the T -th stage of the life cycle; vk represents the evaluation value of the k-th risk factor index xT k of the target enterprise technological innovation project at the T -th stage of the life cycle (k = 1, 2, . . . , m).

5

The Extensible Measurement Method for the Enterprise Technological Innovation Risk

After establishing the matter-element model, the next question is how to specifically evaluate the risk level of technology innovation project at the T stage of life cycle. In this regard, it is necessary to calculate the “approach degree” of the matter-element matrix (Eq. (5)) and the classical domain matter-element matrix (Eq. (3)). In this paper, the elementary dependent function of Extenics is used to calculate the approach degree. 5.1

Definition of Approach Degree

Let p(vk, Vo jk ) = |vk −

ao jk + bo jk 1 | − (bo jk − ao jk ) 2 2

(k = 1, 2, ..., m;

j = 1, 2, ..., n)

(6)

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p(vk, Vpk ) = |vk −

1 apk + bpk | − (bpk − apk ) 2 2

(k = 1, 2, ..., m;

j = 1, 2, ..., n) (7)

Which denote the “approach degree” of point vk with interval Vo jk and Vpk respectively. For example, p(vk , Vpk ) ≥ 0 indicates that vk is not in interval Vpk ; p(vk , Vpk ) ≤ 0 indicates that vk is within interval Vpk . Meanwhile, different negative values indicate different positions of vk in interval Vpk . 5.2

Establishment of Correlation

Let Kj (vk ) =

p(vk , Vo jk ) p(vk , Vpk ) − p(vk , Vo jk )

j = 1, 2, . . . , n; k = 1, 2, . . . , m

(8)

Which denotes the correlation between the k-th factor index xT k of the risk level of technology innovation project at the T stage of life cycle and j level risk, −∞ < Kj (vk ) < +∞. Kj (vk ) ≥ 0 indicates that vk belongs to Vo jk ; a greater value of Kj (vk ) suggests that vk has more properties of Vo jk . Kj (vk ) ≤ 0 indicates that vk does not belong to Vo jk ; a smaller value of Kj (vk ) suggests that vk is farther away from interval Vo jk . 5.3

Risk Level Determination in Life Cycle

According to Eq. (8), the correlation matrix between the evaluation indexes and the performance levels of the risk level of technology innovation project at the T stage of life cycle can be calculated as follows: K = [Kj (vk )]m×n

(9)

According to the aforementioned correlation matrix: max Kj (vk ) = K i0 (vk ) = K ∗ (vk )

1≤j≤n

(10)

K i0 (vk ) indicates that the k-th evaluation index xT k of the risk level of technology innovation project at the T stage of life cycle is located at level i0 . Because the influence degree of risk factors index on the success of technology innovation project at the T stage of life cycle is different, it is needed to sum the correlation of different risk factors index to comprehensively measure the risk of technical innovation projects at this stage of life cycle. m  WTk = 1) is the weight coefficient of index xT k of the risk level If WTk ( k=1

of technology innovation project at the T stage of life cycle. The correlation

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between the risk level of technology innovation project at the T stage of life cycle to be evaluated and the j-th level of performance is: Kj (R) =

m

WTk Kj (vk )

(11)

k=1

According to the following equation, Kj0 (R) = max Kj (R)

(12)

1≤j≤n

The risk level of technology innovation project at the T stage of life cycle to be evaluated belongs to level j0 .

6

Empirical Study

Chengdu Chiffo Electronics Co., Ltd. (Chiffo Electronics) is one of the top three gas water heater manufacturers in China. It is a high-tech and industrial pillar enterprise in Chengdu, owning the import/export business right. Chiffo Electronics carried out a number of technological innovation projects and developed a series of gas water heater products since 2000. In this study, one of their technological innovation projects, the silicon rubber insulation material QFM1098D gas water heater, is selected as the case and the established model is used to evaluate the risk of this project at different stages of the life cycle. The risk of enterprise technological innovation is divided into 5 levels, corresponding to very-low risk, low risk, moderate risk, high risk and very-high risk respectively. Table 1 shows the risk factors of the enterprise technological innovation project at different stages of the life cycle. When the risk factor index is at risk level 1 (very-low risk), risk level 2 (low risk), risk level 3 (moderate risk), risk level 4 (high risk) and risk level 5 (very-high risk), the corresponding evaluation value (range of value) is v1 , v2 , v3 , v4 and v5 , respectively, where v1 ∈ [0, 1] , v2 ∈ (1, 2] , v3 ∈ (2, 3] , v4 ∈ (3, 4] , v5 ∈ (4, 5]. 6.1

Risk Evaluation at the Initiation Stage

(1) Determination of the classical and extensional matter-elements model According to the range of values on risk factors for the technological innovation project at the initiation stage index xT k (T = 1, k = 1, 2, ..., 8), the classical domain matter-element matrix of the risk evaluation for the enterprise technological innovation project can be obtained as follows: ⎡ ⎡ ⎤ ⎤ N01 x11 0, 1 N02 x11 1, 2 ⎢ ⎢ x12 0, 1 ⎥ x12 1, 2 ⎥ ⎢ ⎢ ⎥ ⎥ R01 = ⎢ .. .. ⎥ R02 = ⎢ .. .. ⎥ ⎣ ⎣ . . ⎦ . . ⎦ x18 0, 1

x18 1, 2

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⎡

R03

⎡ ⎤ ⎤ N03 x11 2, 3 N04 x11 3, 4 ⎢ ⎢ x12 2, 3 ⎥ x12 3, 4 ⎥ ⎢ ⎢ ⎥ ⎥ R =⎢ = ⎢ ⎥ .. .. .. .. ⎥ 04 ⎣ ⎣ ⎦ . . . . ⎦ x18 2, 3 x18 3, 4 ⎡

R05

⎤ N05 x11 4, 5 ⎢ x12 4, 5 ⎥ ⎢ ⎥ =⎢ .. .. ⎥ ⎣ . . ⎦ x18 4, 5

According to the allowed range of values for risk evaluation for the enterprise technological innovation project indexes, the domain matter-element matrix can be obtained as follows: ⎡ ⎤ Np x11 0, 5 ⎢ x12 0, 5 ⎥ ⎢ ⎥ Rp = ⎢ .. .. ⎥ ⎣ . . ⎦ x18 0, 5 (2) Establishment of matter-element matrix for the technological innovation project The evaluation values of the various risk factor indexes of the target company (Chiffo Electronics)’s technological innovation project (silicon rubber insulation material QFM1098D gas water heater) are determined by the following method: A total of 7 experts who are familiar with the target innovation project are invited to rate the risk factor index xT k (T = 1, k = 1, 2, . . . , 8) for the initiation stage. If an expert considers the index x1k to be at risk level 1 (very low risk), then the rating of this index is u1 (u1 ∈ [0, 1]); if the expert considers the index x1k to be at risk level 2 (low risk), then the rating of this index is u2 (u2 ∈ (1, 2]); and so on so forth... if the expert considers the index x1k to be at risk level 5 (very high risk), then the rating of this index is u5 (u5 ∈ (4, 5]). Assume that the i-th (i = 1, 2, ..., 7) expert rates the risk factor index x1k at 7  the initiation stage as ui , then the evaluation value of x1k is vk = 17 ui . i=1

According to the method above, the 7 experts who are familiar with the target innovation project (silicon rubber insulation material QFM1098D gas water heater) are asked to rate x1k for the initiation stage. The rating results are collected to calculate the mean value. Then, the evaluation values of the various risk factor indexes of the target project at the initiation stage can be obtained and expressed by the following matter-element matrix:

Risk Evaluation of Technology Innovation Project on Aspect of Life Cycle

⎡

N x11 ⎢ x12 ⎢ ⎢ x13 ⎢ ⎢ x14 R=⎢ ⎢ x15 ⎢ ⎢ x16 ⎢ ⎣ x17 x18

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⎤ 0.75 1.36 ⎥ ⎥ 1.81 ⎥ ⎥ 2.89 ⎥ ⎥ 1.45 ⎥ ⎥ 2.16 ⎥ ⎥ 1.31 ⎦ 2.29

(3) Determination of the risk level of the target technological innovation project The risk correlation degree matrix of the target technological innovation project at the initiation stage must be calculated first before evaluating the corresponding risk at this stage. Then, the comprehensive risk correlation degree can be calculated based on the correlation degree matrix. Lastly, the risk level of the technological innovation project at the initiation stage can be determined by the comprehensive risk correction degree. a. Calculation of the risk correlation degree matrix The correlation between the various risk factor indexes and

degree matrix  risk levels K = Kj (vk )8×5 is calculated for the target innovation project at the initiation stage according to Eq. (8). The results are as follows: ⎡ ⎤ 0.50 −0.25 −0.63 −0.75 − 0.81 ⎢ −0.21 0.36 −0.32 −0.55 − 0.66 ⎥ ⎢ ⎥ ⎢ −0.31 0.12 −0.10 −0.40 − 0.55 ⎥ ⎢ ⎥

 ⎢ −0.47 −0.30 0.05 −0.05 − 0.34 ⎥ ⎢ ⎥ K = Kj (vk )8×5 = ⎢ ⎥ ⎢ −0.24 0.45 −0.28 −0.52 − 0.64 ⎥ ⎢ −0.35 −0.07 0.08 −0.28 − 0.46 ⎥ ⎢ ⎥ ⎣ −0.19 0.31 −0.35 −0.56 − 0.67 ⎦ −0.36 −0.11 0.15 −0.24 − 0.43 b. Calculation of the risk correlation degree matrix In Sect. 3, the AHP method is used to determine the weight coefficient W1k (k = 1, 2, . . . , 8) for each risk factor index x1k for the target innovation project at the initiation stage: W = (W11 , W12, . . . , W18 ) = (0.113, 0.121, 0.120, 0.132, 0.131, 0.126, 0.142, 0.115) From Eq. (11), the correlation degree between the initiation stage of the target innovation project and the j-th level risk is: Kj (R) =

8

W1k Kj (vk ) = (−0.2117, 0.0711, − 0.1715, − 0.4147, − 0.5684)

k=1

That is, K1 (R) = −0.2117, K4 (R) = −0.4147,

K2 (R) = 0.0711, K3 (R) = −0.1715 K5 (R) = −0.5684

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Then, Kj0 (R) = max Kj (R) = K2 (R) = 0.0711 1≤j≤5

It can be seen that the risk level of the target innovation project (silicon rubber insulation material QFM1098D gas water heater) of the target company (Chiffo Electronics) at the initiation stage is 2, i.e., low risk. 6.2

Risk Evaluation for the Target Innovation Project at Other Stages of the Life Cycle

Similarly, the risk level of the target innovation project (silicon rubber insulation material QFM1098D gas water heater) of the target company (Chiffo Electronics) at stage 2, 3 and 4 (i.e., development stage, production stage, market stage) can be also calculated using the above method. The detailed calculation process is not presented due to space constraint. The results indicate that the risk level is 4 (high risk) at stage 2 (development stage), 3 (moderate risk) at stage 3 (production stage) and 2 (low risk) at stage 4 (market stage) respectively. The results are basically in line with the actual situation of the target technological innovation project—at the initiation stage of this project, the company management generally believed that the risk level of this project was low, but during the development process, they encountered many difficult technical problems and were exposed to a high risk. The management then adjusted their development strategy timely and established a joint R&D center with a university. After six months of research, all the technical problems were finally solved. At present, this product has high competitiveness in the market and has achieved good sales. Overall, this technological innovation project has been successful.

7

Conclusion

On the basis of extenics matter-element and extension set theory, this paper constructed the extension matter-element model and the extension evaluation method for the risk evaluation on enterprise technical innovation project at different stages of life cycle. The research finding suggestion is in three-fold. Firstly, the established method can comprehensively consider the various risk state at different stages of life cycle during the technical innovation process and can objectively and accurately evaluate the risk level at different stages of life cycle. Secondly, the established matter-element model is efficient, practical and simple to use without requiring a large number of state evaluation samples. Meanwhile, the evaluation results are intuitive; Thirdly, this method provides a new approach for the performance evaluation of risk evaluation on enterprise technical innovation project, and has a good application prospect. In the future study, we propose several more reasonable operational laws, some novel decision-making methods and apply these new methods into the risk

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evaluation of technological innovation project. Additionally, studying the consensus reaching methods in large-scale group decision making with probabilistic linguistic information [5] is also an interesting research field. Acknowledgements. The work was supported by National Social Science Foundation of China (grant no. 16BGL024) and Sichuan Province System Science and Enterprise Development Research Center (grant no. Xq16C13).

References 1. Abdel-Basset, M., Gunasekaran, M., et al.: A framework for risk assessment, management and evaluation: economic tool for quantifying risks in supply chain. Future Gen. Comput. Syst. 90, 489–502 (2019) 2. Adamczak, S., Bochnia, J., Kaczmarska, B.: An analysis of tensile test results to assess the innovation risk for an additive manufacturing technology. Metrol. Meas. Syst. 22(1), 127–138 (2015) 3. Al-Rawashdeh, T.A., Al’azzeh, F.M., Al-Qatawneh, S.M.: Evaluation of ERP systems quality model using analytic hierarchy process (AHP) technique. J. Softw. Eng. Appl. 7, 225–232 (2014) 4. AliAhmadi, A., et al.: National innovation systems in Iran: challenges and approaches. Int. J. Ind. Eng. Manag. Sci. 6(1), 1–23 (2019) 5. Bai, C., Zhang, R., et al.: Interval-valued probabilistic linguistic term sets in multicriteria group decision making. Int. J. Intell. Syst. 33(6), 1301–1321 (2018) 6. Banda, W.: An integrated framework comprising of AHP, expert questionnaire survey and sensitivity analysis for risk assessment in mining projects. Int. J. Manag. Sci. Eng. Manag. 14(3), 180–192 (2019) 7. Bi, K., Huang, P., Ye, H.: Risk identification, evaluation and response of lowcarbon technological innovation under the global value chain: a case of the Chinese manufacturing industry. Technol. Forecast. Soc. Change 100, 238–248 (2015) 8. Biswas, T.K., Zaman, K.: A fuzzy-based risk assessment methodology for construction projects under epistemic uncertainty. Int. J. Fuzzy Syst. 21(4), 1221–1240 (2019) 9. Cavaco, N.M., Machado, V.C.: Sustainable competitiveness based on resilience and innovation-an alternative approach. Int. J. Manag. Sci. Eng. Manag. 10(2), 155– 164 (2015) 10. Galli, B.J., Battiloro, G.: Economic decision-making and the impact of risk management: how they relate to each other. Int. J. Serv. Sci. Manag. Eng. Technol. (IJSSMET) 10(3), 1–13 (2019) 11. Golichenko, O., Samovoleva, S.: Mapping risk factors of innovation activity enterprises. Int. J. Innov. Reg. Dev. 5(2), 149–164 (2013) 12. Ho, W., Ma, X.: The state-of-the-art integrations and applications of the analytic hierarchy process. Eur. J. Oper. Res. 267(2), 399–414 (2018) 13. Horvat, S., Doˇsen, D.O.: Perceived risk influence on the consumer attitude to private labels in the product’s life cycle growth stage. Econ. Bus. Rev. 15(4), 267 (2013) 14. Li, J., Ji, C., Wang, L.: Matter-element model of integrated risk assessment for flood control systems. In: Coastal Hydrology and Processes: Proceedings of the AIH 25th Anniversary Meeting & International Conference “Challenges in Coastal Hydrology and Water Quality”, vol. 8, pp. 342–352. Water Resources Publication (2008)

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15. Kim, S.K.: Analytic hierarchy process expansion for innovation performance measurement framework. J. Eng., 1–6 (2013) 16. Krasyuk, I., Kirillova, T., et al.: Life cycle management in network retail enterprise based on introduction of innovations. In: IOP Conference Series: Materials Science and Engineering, vol. 497, p. 012125. IOP Publishing (2019) 17. Li, Y., Wang, X.: Risk assessment for public-private partnership projects: using a fuzzy analytic hierarchical process method and expert opinion in China. J. Risk Res. 21(8), 952–973 (2018) 18. Matthews, H.S., Lave, L., MacLean, H.: Life cycle impact assessment: a challenge for risk analysts. Risk Anal. Int. J. 22(5), 853–860 (2002) 19. Mi, X., Liao, H., et al.: Probabilistic linguistic information fusion: a survey on aggregation operators in terms of principles, definitions, classifications, applications, and challenges. Int. J. Intell. Syst. 35, 529–556 (2020) 20. Miorando, R.F., Ribeiro, J.L.D., Cortimiglia, M.N.: An economic-probabilistic model for risk analysis in technological innovation projects. Technovation 34(8), 485–498 (2014) 21. Moriarty, R.T., Kosnik, T.J.: High-tech marketing: concepts, continuity, and change. MIT Sloan Manag. Rev. 30(4), 7 (1989) 22. Shen, N., Feng, D.: Recognition of process innovation risk factors in manufacturing enterprise under the circumstance of informatization. Metall. Min. Ind. 7, 88–95 (2015) 23. Tao, W.: Classification research and rectangular array analysis methed of technology innovation risk. Sci. Res. Manag. 2, 40–45 (1999) 24. Tchankova, L.: Risk identification-basic stage in risk management. Environ. Manag. Health 13, 290–297 (2002) 25. Wang, J., Lin, W., Huang, Y.H.: A performance-oriented risk management framework for innovative R&D projects. Technovation 30(11–12), 601–611 (2010) 26. Wen, C., Yong, S.: Extenics: its significance in science and prospects in application. J. HARBIN Inst. Technol. 38(7), 1079–1086 (2006) 27. Wu, D.D., Kefan, X., et al.: Modeling technological innovation risks of an entrepreneurial team using system dynamics: an agent-based perspective. Technol. Forecast. Soc. Change 77(6), 857–869 (2010) 28. Wu, D.D., Chen, Y., et al.: Risk in integrated leapfrogging mode of technological innovation. Kybernetes 41, 1423–1439 (2012) 29. Huang, Y.: Project management: based on the life period and based on the functions management. J. Zhengzhou Inst. Light Ind. (Soc. Sci.) 5, 26–29 (2004) 30. Zou, P.X., Wang, S., Fang, D.: A life-cycle risk management framework for PPP infrastructure projects. J. Financ. Manag. Property Constr. 13, 123–142 (2008)

Part III: Decision Analysis

Research on Consensus Mechanism of Diagnosis and Treatment Conclusion of Consultation Yueyu Li1(B) , Xiyang Li1 , Qianjun Bu1 , and Ling Kuang2 1

Business School, Sichuan University, Chengdu 610064, Sichuan, People’s Republic of China [email protected] 2 Affiliated Hospital of University of Electronic Science and Technology Operational Department, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan, People’s Republic of China

Abstract. This paper is to explore how to form the three typical consensus mechanisms of diagnosis and treatment conclusion between consultation specialists in the process of consultation for intractable disease. After reviewing the medical records, each consultation specialist draws his or her own subjective preference of diagnosis and treatment conclusion, which is the subjective probability of diagnosing intractable disease. Meanwhile, each consultation specialist has his or her own authoritative endowment. With the descriptive research paradigm, credible preference and Bayes’ theorem, the scientific nature of the three typical consensus mechanisms to reach on the diagnosis and treatment conclusion for the consultation specialist is discussed theoretically. The first of the typical consensus mechanisms is reasonable; The second is reasonable when the inconsistency is an absolute minority, while the relative minority cannot guarantee the rationality; The third that the diagnosis and treatment conclusions confirmed by the authoritative consultation specialist can only be said to be helpless reasonable, but has the risk of arbitrary when it is difficult to reach a consensus. The three typical consensus mechanisms on diagnosis and treatment conclusion are determined by the preferences and authoritative endowments of consultation specialists. Keywords: Consultation specialist · Preference of diagnosis and treatment conclusion · Authoritative endowment · Three typical consensus mechanisms

1 Introduction and Objectives It is difficult to confirm the diagnosis when the clinical attending or treating doctors encounter a complicated disease, or intractable and rare disease involved other professionals. To prevent delay in diagnosis and treatment or even misdiagnosis and mistreatment, a consultation application will be submitted. The department will invite a number of doctors (specialists), clinical pharmacists, and out-of-hospital specialists with clinical experience or relevant professional departments to form a consultation group to participate in the discussion of diagnosis and treatment. Here, doctors, pharmacists, and in and out-of-hospital specialists will be collectively referred to as consultation specialists. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 577–587, 2020. https://doi.org/10.1007/978-3-030-49829-0_43

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The purpose of the consultation is to solve the problems of timeliness and accuracy of clinical diagnosis and treatment of intractable disease by drawing on collective wisdom and absorb all useful ideas, and to reduce and correct misdiagnosis and mistreatment, improve medical quality, as well as avoid medical disputes. The consultation mode includes the traditional inter-hospital consultation, multi-disciplinary team (MDT) and remote consultation mode [3, 9, 17]. Generally, the doctor or department applying for consultation should complete the collation of relevant medical records (including text, data and images of medical history records, physician’s order sheet (POS), nursing records, accurate and high-quality auxiliary inspections, etc.) before the consultation, so that the consultation specialists can review, understand, analyze and judge the intractable disease. The consultation procedure is that first the clinical attending doctor and department introduce the patient‘s condition, various inspection indicators, self-diagnosis, and the purpose of the consultation, etc. then the specialists will inquire about the medical history, conduct detailed physical examination, and check the medical records. To be able to speak freely during the discussion process, the doctor with low grade speaks first, and then the senior and prestigious specialists analyze and present the opinions (the latters have greater credibility and weight). The quality of diagnosis and treatment conclusion for the consultation depends on both the integrity and accuracy of the medical records and on the medical professional level of the consultation specialists, as well as the consensus mechanism to reach the diagnosis and treatment conclusion. The survey of Research Projects (No. 17YJA630048, skzx2016-sb37, skzx2017sb221 and 2019skzx-pt162. Hereinafter referred to as Research Projects) found that the consultation specialists have at least three typical consensus mechanisms for reaching on the diagnosis and treatment conclusion during the consultation of the hospital departments that have the ability to receive intractable diseases: (1) Consensus Mechanism 1: If the diagnosis and treatment conclusion of the consultation specialists is consistent with the self-diagnosis conclusion of the clinical attending doctor, the credibility of the self-diagnosis conclusion will increase. Otherwise the self-diagnosis conclusion is rejected; (2) Consensus Mechanism 2: When a few number of consultation specialists have different conclusions of diagnosis and treatment, the principle that the minority is subordinate to the majority should be followed, and choosing the consensus of diagnosis and treatment conclusion recognized by most consultation specialists; (3) Consensus Mechanism 3: The consultation specialists have heated debates with each other and unable to reach an agreement or majority-approved consensus on diagnosis and treatment conclusion. At this time, the final judgment is made by an authoritative consultation specialist with the highest level, position and the most discourse power. Few studies have looked at this issue of the three typical consensus mechanisms for reaching on diagnosis and treatment conclusion at home and abroad. Therefore, this paper will make a theoretical analysis of the scientific decision-making of the three typical consensus mechanisms. For the convenience of discussion, this paper assumes that the medical records of intractable disease at the time of consultation are basically complete and accurate. The consultation specialists have a higher professional level, and the information of the consultation process is fully communicated.

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2 Materials and Methods The number of consultation specialists except for the clinical attending doctor should be three or more. Consultation is a kind of group decision for a single goal of multiperson and with communication. Consultation specialists usually have the authoritative endowment characteristics of the “late-maturity” in the medical industry, and meanwhile, they are decision-making individuals with different preferences for the diagnosis and treatment conclusion [13]. The consultation mainly focused on the decisionmaking problems of diagnosis and treatment for the semi-structured and unstructured intractable diseases with complex conditions. The number of consultation specialists, the preferences of diagnosis and treatment conclusion, and the authoritative endowment will directly affect the accuracy of diagnosis and treatment conclusion and the consensus mechanism of the decision-making behavior of the consultation. Strictly speaking, before the intractable disease is cured, the diagnosis and treatment conclusions drawn by all consultation specialists are subjective judgments. 2.1 The Preference of the Diagnosis and Treatment Conclusion of Consultation Specialists Let a group of consultation specialists are S 1 , S 2 , · · · , S n . Their preferences of the diagnosis and treatment conclusion for patient with intractable disease H can be described by the subjective probabilities: P1 , P2 , · · · , Pn , given by them, and 0 ≤ Pi ≤ 1. Furthermore, Pi = 1 indicates that the diagnosis and treatment conclusion of the consultation specialist S i is most likely to be intractable disease H subjectively, and Pi = 0 indicates that the conclusion of S i is least likely to be H. Let the clinical attending or treating doctor is S 1 . Before the consultation the clinical attending doctor usually owns the subjective preference of self-diagnosis conclusion for the intractable disease H, which is represented by P1 and is the prior probability of judging the intractable disease H. Generally, P1 ≤ 0.60 typically between 0.40 and 0.60, and that means it is difficult to confirm the intractable disease H for the clinical attending doctor. 2.2 The Credibility of Consultation Specialists Consultation specialists generally have different authoritative endowment described comprehensively by objective and subjective credibility. This objective and subjective credibility (hereinafter referred to as credibility) is a kind of psychological credibility or weight, which is used to measure the relative psychological importance of the consultation specialists in the consultation group. Objective credibility refers to the evaluation of anchoring psychological credibility of specialists from the aspects of professional titles, academic qualifications, positions, medical experience, academic and scientific research achievements, which is an objective psychology faith. It can fix the authoritative endowment of specialists on a certain psychological trust value of the members of consultation specialists [12, 16, 19]. Subjective credibility refers to the assessment of the psychological credibility of the specialist S i , perceived by the members of the consultation specialists, when S i makes specific diagnosis and treatment analysis about

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intractable disease H during the consultation. The value of subjective credibility fluctuates in the value the objective credibility [4, 8, 14, 15]. Let the credibility of the consultation specialists S 1 , S 2 , · · · , S n are represented by C1 ,C2 , · · · ,Cn , respectively, and 0 ≤ Ci ≤ 1. For the intractable disease H, Ci = 100% = 1 means that the specialist S i has absolute authority and the highest credibility, Ci = 100% = 0 indicates that the specialist S i is the least authoritative and least credible. Generally, the higher the credibility of consultation specialist is, the higher the psychological confidence of people for him or her. From the survey of Research Projects, this paper found the credibility Ci of consultation specialists who can usually diagnose and treat intractable diseases should be greater or equal to 80%, otherwise, their professional skills are not enough and should not be invited to the group of consultation specialists. The credibility of the top authoritative consultation specialist or department specialist for a certain type of intractable disease is Ci = 1 in some cases. The professional level of the clinical attending doctor S 1 who can accept the intractable disease H is not too bad, and its credibility is C1 ≥ 70%. Otherwise, the patient should be referred to other high-level doctors or hospitals. 2.3

Credible Preference of Diagnosis and Treatment Conclusion About Consultation Specialists

Considering the different credibility Ci or the relative importance of consultation specialist S i in the group of consultation specialists, this paper defines that the credible preference Pi (H) of the conclusion of diagnosis and treatment for the intractable disease H is described by the inner product (or utility value) of the preference Pi of the conclusion of diagnosis and treatment and the credibility Ci of specialist S i , Pi (H) = Pi × Ci [5]. It can be explained that the subjective probability Pi of the conclusion of diagnosis and treatment for the intractable disease H that given by S i will be psychologically discounted by its credibility Ci . For example, if the clinical attending doctor S 1 owns P1 = 0.60 and his or her credibility C1 = 70%, the credible preference of the self-diagnosis conclusion is P1 (H) = P1 × C1 = 0.60 × 0.70 = 0.42. If the authoritative consultation specialist S i has Pi = 0.90 and credibility Ci = 100%, then the credible preference is Pi (H) = Pi × Ci = 0.90 × 1 = 0.90, and the subjective probability Pi of the conclusion of diagnosis and treatment does not have a credibility psychological discount. The credible preference of the consultation specialists S 1 , S 2 , · · · , S n are represented by P1 (H), P2 (H), · · · , Pn (H), respectively. 2.4

Bayes’s Theorem, Conditional Probability and Posterior Probability of Consultation

At the consultation, the famous Bayes’s theorem can be used to infer and verify the subjective probability of the diagnosis and treatment conclusion for the intractable disease H, which means the posterior probability [7, 18, 22]. The normative model is shown in Eq. (1) [2, 6, 11]. Pi (H |E ) =

Pi (H) · Pi (E |H ) . Pi (H) · Pi (E |H ) + Pi (∼ H) · Pi (E |∼ H )

(1)

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In the Eq. (1), an intractable disease is denoted as H. The clinical attending doctor or consultation specialist S i has his or her credible preference Pi (H) of the diagnosis and treatment conclusion for the intractable disease H. Not suffering from intractable disease H is denoted as ∼ H. The diagnosis and treatment as well as judgment argument provided by the consultation specialist S i during the consultation are expressed in E. Then under the condition of the intractable disease H, the conditional probability of the diagnosis and treatment as well as judgment argument E is denoted as Pi (E|H). After the diagnosis and treatment as well as judgment of the consultation specialist S i , the posterior probability of the diagnosis and treatment conclusion for the intractable disease H was denoted as Pi (H|E). The decision-making idea of Bayes’ theorem is to continuously iterate and correct the subjective diagnosis and treatment probability P1 of the clinical attending doctor S 1 based on the newly obtained diagnosis and treatment as well as judgments, and the posterior probability Pi (H|E) of diagnosis and treatment conclusion for the intractable disease H is finally obtained after consultation [1, 10, 20, 21, 23]. 2.5 Typical Consensus Mechanism Analysis of Diagnosis and Treatment Conclusion As mentioned above, there are at least three typical consensus mechanisms for consultation specialists to reach on a diagnosis and treatment conclusion. Detailed theoretical analysis is given below. Suppose the consultation group consists of the clinical attending doctor S 1 and the five consultation specialists S 2 , S 3 , · · · , S 6 . At the same time, the clinical attending doctor S 1 has P1 = 0.60, the credibility C1 = 70%, and the credible preference P1 (H) = 0.60 × 0.70 = 0.42. When the diagnosis and treatment conclusion of the five consultation specialists S 2 , S 3 , · · · , S 6 are consistent with the clinical attending doctor S 1 , it takes Pi = 0.80, otherwise takes Pi = 0.20. (1) Consensus Mechanism 1: consistent conclusion of consultation specialists When the self-diagnosis conclusion of clinical attending doctor is consistent with the conclusions of all consultation specialists, the preferences P2 , P3 , · · · , P6 , credibility C2 ,C3 , · · · ,C6 and the credible preferences P2 (H), P2 3(H), · · · , P6 (H) of the five consultation specialists S 2 , S 3 , · · · , S 6 are shown in Table 1. The posterior probability Pi (H|E) of the consultation specialists S 2 , S 3 , · · · , S 6 is iterated and calculated respectively by using Eq. (1), as shown in Table 1. From Table 1, when iterating to the fifth consultation specialist S 6 , the posterior probability P6 (H|E) = 0.96 has increased the credibility of the self-diagnosis conclusion of the clinical attending doctor S 1 , and all consultation specialists reach no objection on the conclusion of intractable disease H. On the contrary, when the self-diagnosis conclusion of the clinical attending doctor is inconsistent with the conclusions of all consultation specialists, Table 2 shows the preferences, credibility and credible preferences of the five consultation specialists. Equation (1) is used to iterate and calculate the posterior probability Pi (H|E) of consultation specialists, as shown in Table 2. From Table 2, when iterating to the second consultation specialist S 3 , the posterior probability P3 (H|E) = 0.03, and the self-diagnosis conclusion of the clinical attending doctor S 1 has been negated.

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Table 1. Analysis of consensus mechanism for consistent diagnosis and treatment conclusion S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.8 0.8

0.64

S 3 0.8 0.8

0.64

0.56 0.7

S 4 0.8 0.85 0.68

0.83

S 5 0.8 0.85 0.68

0.91

S 6 0.8 0.9

0.96

0.72

Table 2. Analysis of consensus mechanism for inconsistent diagnosis and treatment conclusion S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.2 0.8

0.16

S 3 0.2 0.8

0.12

0.16

0.03

S 4 0.2 0.85 0.17

0.01

S 5 0.2 0.85 0.17

0

S 6 0.2 0.9

0

0.18

The final posterior probability P6 (H|E) = 0.00 means the consensus diagnosis and treatment conclusion is that there is no intractable disease H after the consultation. (2) Consensus Mechanism 2: A small part of consultation specialists have different conclusions In this case, the meaning of this “a small part” is very important for the consensus mechanism of the diagnosis and treatment conclusion. The absolute or relative few of “a small part” result in which the final posterior probability Pi (H|E) of the diagnosis and treatment conclusion is different. “A Small Part” is Absolute Few Suppose that only one of the five consultation specialists has the different preference from the rest, and include the consistency and inconsistency of the self-diagnosis conclusion between the clinical attending doctors and the majority of the consultation specialists, as shown in Table 3 and Table 4. The posterior probability Pi (H|E) of the consultation specialist is iterated and calculated by using Eq. (1), as shown in Table 3 and Table 4. S 2 ∼ S 6 are respectively exchanged as the one consultation specialist with inconsistent diagnosis and treatment conclusion. The final posterior probability P6 (H|E) of the diagnosis and treatment conclusion fluctuates between 0.74 and 0.69, which accord with the principle in which the minority is subordinate to the majority. Similarly, S 2 ∼ S 6 are respectively exchanged as one consultation specialist with consistent diagnosis and treatment conclusion. The final posterior probability P6 (H|E)

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Table 3. Analysis of consensus mechanism in which only one consultation specialist has inconsistent S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.2 0.8

0.16

S 3 0.8 0.8

0.64

0.12 0.2

S 4 0.8 0.85 0.68

0.34

S 5 0.8 0.85 0.68

0.53

S 6 0.8 0.9 0.72 0.74 Table 4. Analysis of consensus mechanism in which only one consultation specialist has consistent S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.8 0.8

0.64

S 3 0.2 0.8

0.16

0.56 0.2

S 4 0.2 0.85 0.17

0.05

S 5 0.2 0.85 0.17

0.01

S 6 0.2 0.9

0

0.18

of the diagnosis and treatment conclusion always is 0.00, which accord with the principle in which the minority is subordinate to the majority. “A Small Part” is Relative Few Suppose that two of the five consultation specialists have the different preferences from the rest, and also include with the consistency and inconsistency of the selfdiagnosis conclusion between the clinical attending doctors and the majority of the consultation specialists, as shown in Table 5 and Table 6. The posterior probability Pi (H|E) of the consultation specialist is iterated and calculated by using Eq. (1), as shown in Table 5 and Table 6. Table 5. Analysis of consensus mechanism in which two consultation specialists have inconsistent S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.2 0.8

0.16

0.12

S 3 0.2 0.8

0.16

0.03

S 4 0.8 0.85 0.68

0.05

S 5 0.8 0.85 0.68

0.11

S 6 0.8 0.9

0.23

0.72

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Table 6. Analysis of consensus mechanism in which two consultation specialists have consistent S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.8 0.8

0.64

S 3 0.8 0.8

0.64

0.56 0.7

S 4 0.2 0.85 0.17

0.32

S 5 0.2 0.85 0.17

0.09

S 6 0.2 0.9

0.02

0.18

S 2 ∼ S 6 are respectively exchanged as the combination of the two consultation specialists with inconsistent diagnosis and treatment conclusion. The final posterior probability P6 (H|E) of the diagnosis and treatment conclusion fluctuates between 0.23 and 0.18. Obviously, this situation does not accord with the principle in which the minority is subordinate to the majority. Similarly, S 2 ∼ S 6 are respectively exchanged as a combination of two consultation specialists with consistent diagnosis and treatment conclusion. The final posterior probability P6 (H|E) of the diagnosis and treatment conclusion fluctuates between 0.02 and 0.03, which accord with the principle in which the minority is subordinate to the majority. From the above analysis, it can be seen that when the “a small part” of consultation specialists are absolute few, it is reasonable to use the principle in which the minority is subordinate to the majority to select the consultation conclusions recognized by most specialists as the consensus. But when the “a small part” of consultation specialists are relative few, it is unreasonable to use the same principle. (3) Consensus Mechanism 3: Conclusion judged by authoritative consultation specialist without the consensus In this case, authoritative consultation specialist S 6 with the highest level, position and the most discourse power has P6 = 1.00 or P6 = 0.00, and C6 = 100%. Equation (1) is used to iterate and calculate the posterior probability P6 (H|E) of consultation specialists, as shown in Table 7. Table 7. Analysis of consensus mechanism judged by authoritative consultation specialist S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6

0.7

0.42

S 2 0.8

0.8

0.64

S 3 0.8

0.8

0.64

S 4 0.2

0.85 0.17

S 5 0.2

0.85 0.17

S 6 1.00 or 0.00 1

1

0.56 0.7 0.32 0.09 1.00 or 0.00

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S 2 ∼ S 5 are respectively exchanged as a combination of two consultation specialists with inconsistent diagnosis and treatment conclusion. It is found that the posterior probability P5 (H|E) fluctuates between 0.09 ∼ 0.11, indicating that there is no consensus and the final diagnosis and treatment conclusion for the intractable disease H must be judged by an authoritative consultation specialist S 6 with the highest level, position and the most discourse power. Obviously, the medical professional level of the authoritative consultation specialists has the greatest impact on the correctness for the diagnosis and treatment conclusion. Due to the complexity and uncertainty of the intractable disease H, the consensus mechanism given by an authoritative consultation specialist may cover up some of fuzzy diagnosis factors, and so the risk is still relatively large. (4) Consultation specialists are difficult to diagnose on the conclusion There are also the diagnosis and treatment conclusions of all specialists S 2 , S 3 , · · · , S 6 as the same as the self-diagnosis conclusions of the clinical attending doctor S 1 and they all can’t confirm the intractable disease H. At this time, it takes Pi = 0.60 and the Eq. (1) is used to iterate and calculate the posterior probability Pi (H|E) of consultation specialists, shown in Table 8. Table 8. Analysis of the consensus mechanism that all consultation specialists are difficult to diagnose S i Pi

Ci

Pi (H) Pi (H|E)

S 1 0.6 0.7

0.42

S 2 0.6 0.8

0.48

S 3 0.6 0.8

0.48

0.38

S 4 0.6 0.85 0.51

0.39

S 5 0.6 0.85 0.51

0.4

S 6 0.6 1

0.5

0.6

0.4

The final posterior probability P6 (H|E) = 0.50 of the diagnosis and treatment conclusion indicates that the group of consultation specialists is difficult to confirm the intractable disease H. This is the worst case for the patient, and the patient is either referred to another hospital or other specialists to continue to treat or not diagnosed without confirmed diagnosis. 2.6 Number of Consultation Specialists If there are only three or four consultation specialists except for the clinical attending doctor S 1 , and when one or two of them have different conclusions of diagnosis and treatment, the three typical consensus mechanisms to reach the consensus of diagnosis and treatment conclusion are false theoretically unless there is an authoritative consultation specialist in them. These situations can be verified by readers themselves and this paper will not be repeated. Therefore, the number of consultation specialists participating in the consultation is preferably five or more, and preferably odd.

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3 Conclusion The first of three typical consensus mechanisms for reaching on the diagnosis and treatment conclusion is reasonable. The second consensus mechanism adopting the principle in which the minority is subordinate to the majority is reasonable when the specialist of the inconsistency is absolute few, but it’s not guaranteed to be reasonable when relative few. The third consensus mechanism judged by authoritative consultation specialist can only be helplessly reasonable when it‘s difficult to reach a consensus on diagnosis and treatment conclusion but there are arbitrary risks. Due to limited length, this paper does not discuss the method of generating and evaluating the preference and credibility of the consultation specialists. Instead, it theoretically supposes the typical values of preference and credibility in the analysis process of the consensus mechanism of the diagnosis and treatment conclusion. Acknowledgments. This research is supported by (1): 2017 Research Project of Humanities and Social Science of Education Ministry: Research of medical guidance service system based on the cognitive behavior of patients on medical guidance (Project No.: 17YJA630048) and (2): Project of basic scientific research business fee for the Central Universities, Sichuan University (Project No. skzx2016-sb37, skzx2017-sb221, 2019skzx-pt162).

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13. Jianhua, L., Zeyang, L.: The application of unascertained rational number to determining subjective weight of index. Inf. Res. 40(3), 23–37 (2014) 14. Lealem, M., Andrew, D., et al.: Credibility, replicability and reproducibility in simulation for biomedicine and clinical applications in neuroscience. Front. Neuroinformatics 12, 18 (2018) 15. Li, S., Jia, R.: Research on the reliability of medical expert. Li Shizhen Med. Mater. Med. Res. 22(5), 1222–1223 (2011) 16. Li, T., Liu, Y., Huang, J.: Study on the improvement on the qualitative judgment of LEC method based on the expert credibility. J. Civ. Eng. Manag. 32(3), 44–48 (2015) 17. Loh, D., Hogg, F., et al.: Two-year experience of multi-disciplinary team (MDT) outcomes for brain metastases in a tertiary neuro-oncology centre. Br. J. Neurosurg. 32(1), 53–60 (2018) 18. Paul, A., Devon, B., Slater, J.J.: Bayesian networks for risk prediction using real-world data: a tool for precision medicine. Value Health 22(4), 439–445 (2019) 19. Qinfan, W., Jiali, F., et al.: A kind of evaluation and decision model based on analysis and learning of attribute coordinate. J. Nanjing Univ. (Nat. Sci.) 39(2), 182–188 (2003) 20. Rens, V.S., Winter, S.D., Oisin, R.: A systematic review of Bayesian articles in psychology: the last 25 years. Psychol. Methods 22(2), 217–239 (2017) 21. Rodriguez-Cruz, S.E., Montreuil, R.S.: Assessing the quality and reliability of the DEA drug identification process. Forensic Chem. 6, 36–43 (2017) 22. Vladutescu, S., Siminica, M., Dumitru, A.: Information gain vs. information loss. In: 6th LUMEN International Conference on Rethinking Social Action Core Values, pp. 1373–1377 (2015) 23. Wong, D.F., Caroline, S., Leanne, B.: Peer-reviewed validation of a comprehensive framework for disaster evaluation typologies. Prehospital Disaster Med. 34(3), 230–240 (2019)

The Approval Evaluation of Agricultural Project Based on the Integration of 2-Tuples and GR Guoqiang Xiong1 , Yue Cao2(B) , Ying Yang1 , and Yang Chai1 1

College of Economics and Management, Xi’an University of Technology, Xi’an 710048, People’s Republic of China 2 Shaanxi Aviation Industry Asset Management CO., LTD., Xi’an 710048, People’s Republic of China [email protected]

Abstract. Aiming at the practical problems that there are few index information and fuzzy evaluation criteria in the approval evaluation of agricultural project, this paper proposes a suitable method based on the integration of 2-tuples and grey correlation (GR). Firstly, the FAHP method and the goal-planning model correct the subjective weights of the evaluation index. Then, WWA operator of 2-tuples is used to gather the evaluation information of internal and external dimensions, and construct the superiority analysis matrix of the project. Otherwise, because of the shortcomings of the traditional gray correlation analysis method (GR), the paper proposes an approval evaluation method that uses the integration of 2-tuples and GR to calculate the gray comprehensive correlation degree between the superiority analysis value of each project and the positive and negative gray bullseyes. Finally, taking the agricultural and cultural tourism project as an example carry out the application analysis. The results show that the grey comprehensive correlation coefficient calculated by 2-tuples/GR evaluation method can comprehensively and effectively aggregate the fuzzy evaluation information of the project, which can effectively reduce the subjectivity and uncertainty of decision-making and provide scientific basis for project sorting and selection. Keywords: 2-tuples · Grey relation analysis (GR) · Integration method of 2-tuples and GR · Project approval evaluation

1 Introduction Project approval valuation is an important step of agricultural project construction. It includes multi-attribute linguistic information, which has some problems such as little index information, difficult to set the evaluation standard. In addition, because climate is unpredictable all the time, the agricultural project shows character of ambiguity and uncertainty. Result in overcoming this decision-making dilemma in evaluation, Deng (1982) proposed the grey relational analysis method [2], and Herrera (2000) set the 2-tuple fuzzy linguistic representation model [5]. In recent years, some scholars have improved or applied the two methods respectively. For example: to avoid information c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 588–600, 2020. https://doi.org/10.1007/978-3-030-49829-0_44

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and precision losing in aggregation, Wu and Wu et al. (2018) proposed a linguistic multiple attribute group decision-making method based on single-valued neutrophil binary semantic evaluation information, so that avoiding the loss of binary semantic information and accuracy during the aggregation process [14]; Feng (2018) aims at the multiple attribute decision making problems with hesitant fuzzy 2-tuple linguistic set, which is based on entropy weight [4]; Wang (2019) addressed the information fusion involving the interrelationship between aggregated terms and the prioritization relationship among decision makers under hesitant 2-tuple linguistic situation by establishing the hesitant 2-tuple linguistic Bonferroni mean operator and prioritized weighted hesitant 2-tuple linguistic Bonferroni mean operator [13]. Sohaib (2019) made assessment on the choice of cloud service models for small-to-medium-sized businesses by proposing a 2-tuple fuzzy linguistic multi-criteria group decision-making method [8]; Suo (2019) evaluated the CI risk of critical infrastructure by integrating 2-tuple linguistic with DEMATEL [11]; Sun (2019) established a multi-objective evaluation model based on GR and AHP to evaluate and make decisions on the scheme of agricultural nonpoint-source pollution control projects [10]; Liu (2019) integrated GR and TOPSIS model to evaluate the regional agricultural water and soil resource projects of 15 farms [6]. Most of present studies were evaluated through a single method, 2-tuples or GR. With the application of these methods, it was found that both methods exist limitations. In recent years, although some scholars have combined the 2-tuple linguistic model with gray relational analysis and applied them to group decision making problems [9, 12], there are few achievements in the area of agricultural projects evaluation. Among the process of project establishment and evaluation of agricultural projects, the evaluation information in different language formats is often put forward according to the evaluation preference of experts, taking into account the different understanding of experts on the project. Therefore, this paper intends to adopt the method of combining 2-tuples and GR and integrate the risk preference and risk awareness of decision makers into the fuzzy evaluation information for agricultural project establishment evaluation, so as to solve the deviation caused by the one-sided evaluation information and decision makers’ preference.

2 Model Construction 2.1 Binary Semantics and Related Operators The binary semantic evaluation model is a two-tuples (s, α ) consisting of a language term s ∈ S and a real number α ∈ [−0.5, 0.5]. The binary semantics effectively solve the ambiguity of project decision-making, and has apparently advantages in reducing decision loss and improving decision accuracy and objectivity. Definition 1. Supposing si ∈ S1 as a language term, it can be described in the form of two-tuples:

ϑ : S1 → (S1 × [−0.5, 0.5]), ϑ (si ) = (si , 0)/si ∈ S1

(1)

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Definition 2. Supposing S1 as the set of language terms, and β ∈ [0, τ ] as the symbolic integration operation value, then the two-tuples obtained by the function Δ can be used to express the equivalent information of β :

Δ : [0, τ ] → S1 × [ −0.5, 0.5) 

Δ (β ) =

si i = round(β ) α = β − i, α ∈ [−0.5, 0.5)

(2)

(3)

“Round” is a rounding operator, the subscript of si is closest to β , and α is the symbol conversion value. Definition 3. Supposing the language term set S1 and the two-tuples (si , α ), there is always an inverse function Δ −1 made the two-tuples (si , α ) restores its equivalent value β ∈ [0, τ ]:

Δ −1 : S1 × [−0.5, 0.5) → [0, τ ]

(4)

Δ −1 (si , β ) = i + α = β

(5)

Definition 4. Assuming that the non-negative odd additive language evaluation scale is S = {Sα |α = 0, 1, · · ·, τ }

(6)

Sα indicates the language term, and the Sτ indicates the upper limit of the language scale, τ is a positive integer, and the algorithm corresponding to “s” is: 1) Sα ⊕ Sβ = Sα + β 2) λ · Sα = Sλ ·α And, 1) if i > j, si > s j ; 2) si > s j , then max(si , s j ) = si 3) si < s j , then min(si , s j ) = si 2.2

Traditional Grey Relational Analysis Method

The grey correlation is applied to analyze the similarity between the geometric relationship of the project data sequence and the geometry of the curve, which is an indicator of describing the close relationship between system factors. The related concepts are as follows: Definition 5. Assuming Xi as the system factor, and the observation data on the sequence number “k” is k = 1, 2, · · ·, n. Then Xi (k) = (xi (1), xi (2), · · ·, xi (n)) is the sequence of related factor behaviors in the system.

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Definition 6. Assuming that the sequences Xi = (xi (1), xi (2), · · ·, xi (n)) and X j = (x j (1), x j (2), · · ·, x j (n)) exist, the zero images of starting point are: Xi0 = (xi0 (1), xi0 (2), · · ·, xi0 (n)) X j0 = (x0j (1), x0j (2), · · ·, x0j (n)) Among them xi0 (k) = xi (k) − xi (1), x0j (k) = x j (k) − x j (1), k = 1, 2, · · ·, n. Property 1: For any given non-negative single index data sequence, it has the property of number preserving, sequence preserving and difference preserving. The specific proof process is not repeated. Definition 7. Set the sequence of system behavior as X0 = (x0 (1), x0 (2), · · ·, x0 (n)) and the sequence of system-related factors as Xi (k) = (xi (1), xi (2), · · ·, xi (n)). If ξ0i (k) is existed, make that

ξ0i (k) =

min min |X0 (k) − Xi (k)| + ρ max max |X0 (k) − Xi (k)| i

i

k

k

|X0 (k) − Xi (k)| + ρ max max |X0 (k) − Xi (k)| i

γ (X0 , Xi ) =

(7)

k

1 n ∑ ξ0i n i=1

(8)

k = 1, 2, · · ·,t; i = 1, 2, · · ·, m, j = 1, 2, · · ·, n

ξ0i (k) is called correlation coefficient and γ is grey correlation degree. The main idea of gray target decision-making is to find a bulls-eye as a standard model in the gray target, and then calculate the bulls-eye distance between each decision point and the bulls-eye point in the gray target, after that we could determine the order by comparing the bulls-eye distance.   k = max{ (r +¯  Definition 8. If r+ i j ri j ) 2 1 ≤ i ≤ n}, and the corresponding decision + + value is denoted as [ri j , r¯i j ], then 1 2 2 + + + + + (⊕), r+ (⊕), · · ·, r+ (⊕)} = {[ + r+ = {r+ i0 1 , r¯i0 1 ], [ i0 2 , r¯i0 2 ], · · ·, [ i0 k , r¯i0 k ]}

is the optimal effect vector of grey target decision, denoted as the positive target.   k = min{ (r +¯  Definition 9. If r− i j ri j ) 2 1 ≤ i ≤ n}, and the corresponding decision − − value is denoted as [ri j , r¯i j ], then 1 2 2 − − − − − (⊕), r− (⊕), · · ·, r− (⊕)} = {[ − r− = {r− i0 1 , r¯i0 1 ], [ i0 2 , r¯i0 2 ], · · ·, [ i0 k , r¯i0 k ]}

It is the worst effect vector of grey target decision and is denoted as the negative target.

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Integrated Model of Binary Semantics and Grey Relational Analysis

Setting the collection of projects for evaluation as X; X = {x1 , x2 , ..., xn } as item set; xn represents the Nth alternative item; T = {T1 , T2 , ..., Tl } is the collection of experts participating in the evaluation, Tl is the l-th expert, and the expert weight is t = {t1 ,t2 , ...,tl }. We suppose the evaluation index set of the project is I = {I1 , I2 , ..., In }, where Ii represents the ith indicator, and the indicator concentration is ω = {ω1 , ω1 , · · ·, ωn }T . (1) Determination and correction of index weight In this paper, FAHP method [1] is used to calculate the subjective weights assigned by ‘l’ evaluation experts to each index. The evaluation experts used the fuzzy scale in Table 1 to construct the fuzzy judgment matrix of trigonometric functions, which represents the trigonometric fuzzy number compared by the kth expert with the index I and j. Then we take the maximum and minimum weights given by the experts each indicator to construct the subjective weight range  of the indicator  in 1 ≤ ω 1 ≤ ω 1 , ω 2 ≤ ω 2 ≤ ω 2 , · · ·, ω i ≤ ω i ≤ ω i i H = ωmin max max max Where ω repremin min sents the weight range of the ith index. Table 1. Fuzzy scale of trigonometric functions Fuzzy scale Definition

Instruction

0.1

Extremely unimportant Compared with the two elements, the former is extremely less unimportant than the latter

0.2

Strong unimportant

0.3

Obviously unimportant Compared with the two elements, the former is obviously less important than the latter

0.4

Slightly less important Compared with the two elements, the former is slightly less important than the latter

0.5

As important

Compared with the two elements, the two elements is equally important

0.6

A little important

Compared with the two elements, the former is slightly more important than the latter

0.7

Obviously important

Compared with the two elements, the former is obviously more important than the latter

0.8

Highly important

Compared with the two elements, the former is highly more important than the latter

0.9

Extremely important

Compared with the two elements, the former is extremely more important than the latter

Compared with the two elements, the former is stronger unimportant than the latter

In order to correct the decision bias caused by subjective preference, this paper constructs the target planning model to correct the subjective weight of the evaluation index. The model is as follows:

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4

min J  = ∑ d1+j + d1−j + d2+j + d2−j j=1 ⎧ ς Lj (ω ) − d1+j + d1−j = 0, j = 1, 2, · · ·, n ⎪ ⎪ ⎪ ⎪ ⎪ ς Rj (ω ) − d2+j + d2−j = 0, j = 1, 2, · · ·, n ⎪ ⎪ ⎪ ⎪ d + ≥ 0, d − ≥ 0, j = 1, 2, · · ·, n ⎨ 1j 1j d2+j ≥ 0, d2−j ≥ 0, j = 1, 2, · · ·, n ⎪ ⎪ ⎪ d1+j d1−j = 0, d2+j d2−j = 0, j = 1, 2, · · ·, n ⎪ ⎪ ⎪ ⎪ ω ∈H ⎪ ⎪ ⎩ n ∑i=1 ωi = 1, ωi ≥ 0(i = 1, 2, · · ·, n)

(9)

(2) Integration of decision-makers’ preference and multidimensional evaluation Among the process of analyzing decision-making problems, decision-makers often confront the problems which raised by multiple factors: The first is how to effectively deal with the subjective preference of decision-makers; Second, how to effectively gather “excellent”, “good”, “poor” and other natural language evaluation information, and adopt effective methods to discriminate and analyze the pros and cons of the alternatives. Due to the subjective preference of decision makers, the decision results are often biased. In order to process the preference information of decision-makers, this paper proposes the concept of relative superiority degree, which is defined as the degree to which one scheme is superior (inferior) to other schemes in a set of schemes. The decisionmakers constructs the project dominance matrix by comparing the advantages of the schemes in pairs, and converts it into the group dominance matrix Qˆ = (qˆi j )n×n by using the binary semantic WWA operators. Definition 10. If (s1 , s2 , ···, sn ) is a set of aggregated binary semantics, WWA operators of aggregating binary semantics are defined as follows: 1 (10) WWA(sx1 , sx2 , · · ·, sxn ) = (sx1 ⊕ sx2 ⊕ · · · ⊕ sxn ) = s 1 ∑n xi n i=1 n In terms of evaluation information integration, most project evaluations on binary semantics only start from one dimension, such as only considering the condition of each index attribute within the project [3, 7], or constructing score from the overall perspective of the project [15]. Scarcely literatures comprehensively integrate the score of project attribute and the quality of the project and consider the influence of information interaction from internal and external dimensions. This paper proposes to transform the project comprehensive attribute value z˜k (ω ) into a comprehensive attribute value unit matrix and combine it with the group dominance matrix to construct a project advantage analysis matrix. By constructing the project advantage analysis matrix, it can effectively reflect the comprehensive score of the project in the context of information interaction. Definition 11. Setting the project’s comprehensive attribute vector unit matrix E = (zk ) and group dominance matrix Q = (qi j ), then the project advantage analysis matrix is: ⎞ ⎛ z¯1 qˆ12 . . . qˆ1n ⎟ ⎜ ˆ qˆi j ) = ⎜ qˆ21 z¯2 . . . qˆ2n ⎟ (11) L(li j ) = E(¯zi ) + Q( ⎝ ... ... ... ... ⎠ qˆn1 qˆn2 . . . z¯n

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Among them, ⎞ ⎛ z¯1 0 · · · 0 0 ⎜ 0 z¯2 · · · 0 ⎟ ⎜ qˆ21 ⎟ ˆ ⎜ E(¯zi ) = ⎜ ⎝ · · · · · · · · · · · · ⎠ Q(qi j ) = ⎝ · · · qˆn1 0 0 · · · z¯n ⎛

qˆ12 0 ··· qˆn2

⎞ · · · qˆ1n · · · qˆ2n ⎟ ⎟ ··· ··· ⎠ ··· 0

(12)

(3) Determination of complementary matrix of advantage analysis This paper introduces the concept of complementary matrix and transforms the language decision matrix into reciprocal matrix to transform and operate the aggregated natural language evaluation information. Definition 12. S = {s0 , s1 , ···, s2g } is a collection of orderly language assessment, si ∈ S is No. i language phrases, the corresponding subscript ‘i’ can be obtained from the function: I:S→N I(si ) = i, si ∈ S

(13)

The “N” is a set of integers. Definition 13. Assume that the elements in the language decision matrix–Pk constitute the following matrix: Qk = [qkij ]n×n

 qkij = 0.5 + (I(pkij ) − gk ) 2

(14)

where pkij ∈ S, i, j = 1, 2, · · ·, n, the Qk is called the derived matrix of language judgment matrix or the complementary matrix.

3 Integrate 2-Tuples/GR Calculation Steps for Project Approval Evaluation This paper combines project dominance with decision makers’ preference tendencies, and fully considers the relationship between decision makers’ preferences and project strengths and weaknesses. Then we proposes a multi-dimensional project evaluation model based on decision makers’ preference for binary semantics and grey relational analysis. It could be divided to 7 steps: Step 1. According to the fuzzy analytic hierarchy process proposed in Sect. 2.3, the subjective weight range H of each index is determined, and the target planning model is established to solve the index weight correction value. Step 2. The T decision was made from the index attributes of each candidate item and candidate’s superiority to other items, which is from the internal and external dimensions, then making construction of the uncertainty language decision matrix (k) R˜ k = (˜ri j )m×n and the dominance matrix Qk = (qi j (k) )n×n .

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Step 3. The ULWA and the WWA operator are used to respectively assemble the uncertainty language decision matrix and the project dominance matrix. Then we construct the group uncertainty language decision matrix and the group R = (ri j )m×n project dominance matrix Q = (qi j )n×n . Then we determine the positive target center uncertainty decision matrix R˜ + (ri j )m×n , the negative target center uncertainty language decision matrix R˜ − (ri j )m×n , and the positive and negative bullseye project dominance matrix Q+ (ri j )n×n , Q− (ri j )m×n . Step 4. Calculating the uncertainty attribute value z˜k (ω ) of each item, the positive target value of the bull’s-eye uncertainty comprehensive attribute value z˜+ (ω ), and the negative target-value uncertainty comprehensive attribute value z˜− (ω ), and the LA operator is used to convert each uncertain comprehensive attribute value into average comprehensive attribute value z¯(ω ). Among them, z˜ j (ω ) = ω1 r˜1 j ⊕ ω2 r˜2 j ⊕ · · · ⊕ ωm r˜m j  LA(˜z1 (ω ), z˜2 (ω ), · · ·, z˜ j (ω ))=1 j[˜z1 (ω ) ⊕ z˜2 (ω ) ⊕ · · · ⊕ z˜ j (ω )] = z¯(ω )

(15)

(16)

Step 5. Building the advantage analysis matrix L = (li j )n×n , and determining the positive and negative bulls-eye advantage analysis matrices L+ (li j ), L− (li j ); then using the formula 5 to convert the matrices L(li j ), L+ (li j ) and L− (li j ) into complementary matrices. Step 6. Calculating the gray correlation coefficient of each item, then we get the gray relation relative closeness C+ . C+ =

γ+ γ+ + γ−

(17)

Step 7. Sorting the gray relation relative closeness in descending order and selecting the optimal item.

4 Model Application 4.1 The Construction of Evaluation Index Yang Ling agricultural tourism demonstration district, is the first agricultural high-tech industry demonstration district in China. Since 2010s, Yang Ling’s government has been devoting to develop the agricultural tourism industry. At present, Yang Lin has become well-known in China for the agricultural tourism. This paper choose Yang Ling’ agricultural tourism development as research object, using CNKI database as the statistical source and the search criteria of literatures then summarize the relevant literatures on the evaluation of agricultural literature and tourism and the evaluation of project establishment. According to the current national standards (such as Tourism Resource Classification, Survey and Evaluation (GB/T 18972-2003), New Smart City Evaluation Index and Tourism Planning General Principles (GB/T 18971-2003)) and scientific, systematic, completeness, universality, prospective evaluation principles, the scientific,

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systematic, complete, universal and forward-looking evaluation principles, this paper set seven evaluation indicators for the agricultural tourism project in the Yang Ling Demonstration District in Shaanxi Province: The scale of resources (I1 ), historical and cultural values (I2 ), agricultural tourism experience value (I3 ), project location and traffic convenience (I4 ), basis of project (I5 ), organizational management and risk response (I6 ), comprehensive benefits (I7 ). 4.2

Evaluation and Screening of Alternative Projects

We suppose that there are 3 experts, they evaluated the 7 attributes of 4 alternative projects X = {x1 , x2 , x3 , x4 }, and the Expert Weight is t = {t1 ,t2 ,t3 } = {1/3, 1/3, 1/3} to facilitate decision making. This paper assumes that all decision makers adopt seven-level non-negative additive language evaluation matrix S = {S0 = extreme, S1 = very poor, S2 = poor, S3 = average, S4 = good, S5 = very good, S6 = excellent} to evaluate alternative projects. It could be divided to 8 steps: Step 1. Three experts used FAHP method to determine the subjective weight assignment of indicators, and took the upper and lower boundary of the assignment result to construct the range of each indicator as: H = {0.121 ≤ ω1 ≤ 0.128, 0.169 ≤ ω2 ≤ 0.176 0.141 ≤ ω3 ≤ 0.149, 0.133 ≤ ω4 ≤ 0.135, 0.114 ≤ ω5 ≤ 0.1170.106 ≤ ω6 ≤ 0.1090.199 ≤ ω7 ≤ 0.205} (18) LINGO software is used to establish the target planning model: 4

min J  = ∑ d1+j + d1−j + d2+j + d2−j j=1 ⎧ − + 4 ω + 4.67 ω2 + 5ω3 + · · · + 5ω6 + 5ω7 − d11 + d12 =0 1 ⎪ ⎪ ⎪ − + ⎪ ω + 5.67 ω + 5.67 ω + · · · + 5.67 ω + 5.67 ω 5.67 1 2 3 17 − d21 + d21 = 0 16 ⎪ ⎪ ⎪ ⎪ · · · · · · · · · · · · · · · · ·· ⎪ ⎪ ⎪ − + ⎪ + d14 =0 4.67ω1 + 4.33ω2 + 4ω3 + · · · + 4.33ω16 + 4.67ω17 − d14 ⎪ ⎪ ⎪ − + ⎪ 5.67ω1 + 5ω2 + 5.33ω3 + · · · + 5.33ω16 + 5.33ω17 − d24 + d24 =0 ⎨ d1+j ≥ 0, d1−j ≥ 0, d2+j ≥ 0, d2−j ≥ 0, j = 1, 2, · · ·, 4 ⎪ ⎪ d1+j · d1−j =0d2+j · d2−j =0, j = 1, 2, · · ·, 4 ⎪ ⎪ ⎪ ⎪ ⎪ 0.121 ≤ ω1 ≤ 0.128 ⎪ ⎪ ⎪ ⎪ ··················· ⎪ ⎪ ⎪ ⎪ 0.199 ≤ ω7 ≤ 0.205 ⎪ ⎩ ω1 + ω2 + · · · + ω7 = 1

(19)

By solving the objective programming model, the modified weight of the attribute can be obtained as:

ω  = (0.125, 0.173, 0.145, 0.1350.117, 0.106, 0.199)T − − − + + + + and d11 = 4.7073, d11 = 0, d21 = 5.535, d21 = 0, d12 = 3.8302, d12 = 0, d22 = 4.6193, − − − − + + + + = d22 = 0, d13 = 3.5226, d13 = 0, d23 = 4.4612, d23 = 0, d14 = 4.2646, d14 = 0, d24 − 5, 1804, d24 = 0.

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Step 2. three experts evaluated the indicators and advantages of the four alternative projects. Finally it could be obtained as three uncertainty language decision matrix (l) (l) R˜ (l) = (˜ri j )7×4 and three advantage matrix Q(l) = (qi j )4×4 (l = 1, 2, 3): ⎛ ⎜ ⎜ ⎜ ⎜ (1) ˜ R =⎜ ⎜ ⎜ ⎜ ⎝

⎛ ⎞ [S5 , S6 ] [S3 , S4 ] [S2 , S4 ] [S4 , S6 ] ⎜ [S5 , S6 ] [S4 , S4 ] [S4 , S5 ] [S4 , S5 ] ⎟ ⎜ ⎟ ⎜ ⎟ [S5 , S6 ] [S2 , S2 ] [S2 , S3 ] [S4 , S5 ] ⎟ ⎜ (2) ⎟ ˜ [S3 , S5 ] [S5 , S6 ] [S4 , S4 ] [S3 , S4 ] ⎟ R = ⎜ ⎜ ⎜ [S5 , S6 ] [S4 , S5 ] [S5 , S5 ] [S3 , S4 ] ⎟ ⎜ ⎟ ⎝ ⎠ [S5 , S6 ] [S4 , S4 ] [S3 , S4 ] [S4 , S5 ] [S5 , S5 ] [S5 , S5 ] [S4 , S4 ] [S5 , S6 ]

⎞ [S4 , S6 ] [S3 , S5 ] [S2 , S4 ] [S5 , S6 ] [S4 , S5 ] [S4 , S5 ] [S4 , S5 ] [S5 , S5 ] ⎟ ⎟ [S5 , S5 ] [S3 , S3 ] [S2 , S3 ] [S4 , S6 ] ⎟ ⎟ [S4 , S5 ] [S5 , S6 ] [S5 , S5 ] [S4 , S4 ] ⎟ ⎟ [S5 , S6 ] [S4 , S5 ] [S3 , S5 ] [S4 , S6 ] ⎟ ⎟ [S5 , S6 ] [S4 , S5 ] [S3 , S4 ] [S4 , S5 ] ⎠ [S5 , S6 ] [S4 , S5 ] [S4 , S5 ] [S4 , S5 ]

⎛

⎞ [S3 , S5 ] [S4 , S5 ] [S3 , S4 ] [S5 , S5 ] ⎜ [S5 , S6 ] [S4 , S5 ] [S4 , S5 ] [S4 , S5 ] ⎟ ⎜ ⎟ ⎜ [S5 , S6 ] [S2 , S3 ] [S3 , S4 ] [S4 , S5 ] ⎟ ⎜ ⎟ ⎟ R˜ (3) = ⎜ ⎜ [S4 , S4 ] [S4 , S5 ] [S4 , S5 ] [S4 , S5 ] ⎟ ⎜ [S5 , S5 ] [S4 , S5 ] [S4 , S5 ] [S5 , S6 ] ⎟ ⎜ ⎟ ⎝ [S5 , S5 ] [S4 , S4 ] [S4 , S4 ] [S5 , S6 ] ⎠ [S6 , S6 ] [S4 , S5 ] [S4 , S5 ] [S5 , S5 ] ⎛

0 ⎜ S 3 Q(1) = ⎜ ⎝ S2 S3

S3 0 S1 S2

S4 S5 0 S2

⎛ ⎞ 0 S3 ⎜ S4 ⎟ S ⎟ Q(2) = ⎜ 3 ⎝ S1 S4 ⎠ 0 S3

S3 0 S2 S2

S5 S4 0 S3

⎛ ⎞ 0 S3 ⎜ S4 ⎟ S ⎟ Q(3) = ⎜ 2 ⎝ S4 S3 ⎠ 0 S2

S4 0 S2 S3

S2 S4 0 S4

⎞ S4 S3 ⎟ ⎟ S2 ⎠ 0

(20)

Step 3. ULWA operator is used to construct group of uncertain language decision matrix: ⎛ ⎞ [S4 , S5.67 ] [S3.33 , S5.67 ] [S2.33 , S4 ] [S4.67 , S5.67 ] ⎜ [S4.67 , S5.67 ] [S4 , S4.67 ] [S4 , S5 ] [S4.33 , S5 ] ⎟ ⎜ ⎟ ⎜ [S5 , S5.67 ] [S2.33 , S2.67 ] [S2.33 , S3.33 ] [S4 , S5.33 ] ⎟ ⎜ ⎟ ⎟ (21) R = (ri j )7×4 = ⎜ ⎜ [S3.67 , S4.67 ] [S4.67 , S5.67 ] [S4.33 , S4.67 ] [S3.67 , S4.33 ] ⎟ ⎜ [S5 , S5.67 ] ⎟ [S , S ] [S , S ] [S , S ] 4 5 4 5 5 5.33 ⎟ ⎜ ⎝ [S5 , S5.6 ] [S4 , S4.33 ] [S3.33 , S4 ] [S4.33 , S5.33 ] ⎠ [S5.33 , S5.67 ] [S4.33 , S5 ] [S4 , S4.67 ] [S4.67 , S5.33 ] WWA operator is used to construct group of projects dominance matrix: ⎞ ⎛ 0 S3.33 S3.67 S3.33 ⎜ S2.67 0 S4.33 S3.67 ⎟ ⎟ Q = (qi j )m×n = ⎜ ⎝ S2.33 S1.67 0 S3 ⎠ S2.67 S2.33 S3 0

(22)

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The positive and negative target language decision items and dominance matrices are constructed as follows: R˜ + (˜ri j ) = ([S4.67 , S5.67 ], [S4.67 , S5.67 ], [S5 , S5.76 ], [S4.67 , S5.67 ], [S5 , S5.67 ], [S5 , S5.6 ], [S5.33 , S5.67 ])T

(23)

R˜ − (˜ri j ) = ([S2.33 , S4 ], [S4 , S4.67 ], [S2.33 , S2.67 ], [S3.67 , S4.33 ], [S4 , S5 ], [S3.33 , S4 ], [S4 , S4.67 ])T

(24)

⎛

0 ⎜ S3 + Q (qi j ) = ⎜ ⎝ S4 S3

S4 0 S2 S3

S5 S5 0 S4

⎛ ⎞ S4 0 ⎜ S2 S4 ⎟ − ⎟ Q (qi j ) = ⎜ ⎝ S1 S4 ⎠ 0 S2

S3 0 S1 S2

S2 S4 0 S1

⎞ S3 S3 ⎟ ⎟ S2 ⎠ 0

(25)

Step 4. the uncertain comprehensive attribute values of each project, the maximum item and minimum item are as follows: z˜1 (ω ) = [S4.70 , S5.54 ] , z˜2 (ω ) = [S3.83 , S4.62 ] , z˜3 (ω ) = [S3.52 , S4.46 ] , z˜4 (ω ) = [S4.26 , S5.18 ]

z˜+ (ω ) = [S4.92 , S5.22 ], z˜− (ω ) = [S3.43 , S4.22 ]

(26)

LA operator is used to convert the uncertain comprehensive attribute value into the average comprehensive attribute value: z¯1 (ω ) = LA [S4.70 , S5.54 ] = S5.12 , z¯2 (ω ) = LA [S3.83 , S4.62 ] = S4.23

z¯3 (ω ) = LA [S3.52 , S4.46 ] = S3.99 , z¯4 (ω ) = LA [S4.26 , S5.18 ] = S4.72

z¯+ (ω ) = LA[S5 , S6 ] = S5.29 , z¯− (ω ) = LA[S3.10 , S3.71 ] = S3.83

(27)

Step 5. constructing the advantage analysis matrix and the maximum and minimum advantage analysis matrix. ⎛

⎛ ⎞ ⎞ 0.82 0.56 0.61 0.56 0.88 0.67 0.83 0.67 ⎜ 0.44 0.71 0.72 0.61 ⎟ +  ⎜ 0.5 0.88 0.83 0.67 ⎟ ⎜ ⎟ ⎟ L = ⎜ ⎝ 0.39 0.28 0.67 0.5 ⎠ L (li j ) = ⎝ 0.67 0.33 0.88 0.67 ⎠ 0.44 0.39 0.5 0.79 0.5 0.5 0.67 0.88 ⎛

S3.83 ⎜ S2 − L (li j ) = ⎜ ⎝ S1 S2

S3 S3.83 S1 S2

S2 S4 S3.83 S1

⎞ S3 S3 ⎟ ⎟ S2 ⎠ S3.83

(28)

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Step 6. using Eq. (5) to convert the matrices L, L+ (li j ) and L− (li j ) into complementary matrices L , L+ (li j ) and L− (li j ). ⎛

⎛ ⎞ ⎞ 0.82 0.56 0.61 0.56 0.88 0.67 0.83 0.67 ⎜ 0.44 0.71 0.72 0.61 ⎟ +  ⎜ ⎟ ⎟ L (li j ) = ⎜ 0.5 0.88 0.83 0.67 ⎟ L = ⎜ ⎝ 0.39 0.28 0.67 0.5 ⎠ ⎝ 0.67 0.33 0.88 0.67 ⎠ 0.44 0.39 0.5 0.79 0.5 0.5 0.67 0.88 ⎛

⎞ 0.64 0.5 0.33 0.5 ⎜ 0.33 0.64 0.67 0.5 ⎟  ⎟ L− (li j ) = ⎜ ⎝ 0.17 0.17 0.64 0.33 ⎠ 0.67 0.67 0.84 0.64

(29)

Step 7. using Eq. (2) and Eq. (7) to calculate the relative closeness coefficient of grey correlation of each item (Table 2): Table 2. Grey correlation coefficient of each item Item Positive grey Negative grey Grey correlation degree of correlation coefficient correlation coefficient relative closeness x1

0.7195

0.9062

0.4426

x2

0.8223

0.8422

0.494

x3

0.556

0.6814

0.4493

x4

0.7474

0.8396

0.471

Step 8. sorting the relative closeness degree in descending order of grey correlation, and obtain: x2  x4  x3  x1 , therefore, item 2 is the optimal project.

5 Conclusion In summary, this paper aims at the decision makers’ preference and decision information uncertainty of project evaluation. It combines the binary semantics with the grey relational analysis model to construct a project evaluation model. The main contributions of this paper as follows: 1) this paper introduces the target planning model to correct the subjective weights of the evaluation indicators determined through FAHP method. 2) This paper transforms the evaluation information into a binary semantic form, and refers the concept of project advantage analysis value. By constructing the project advantage analysis matrix, the project comprehensive evaluation value is combined with the project dominance, which refers a new idea of multidimensional fuzzy decision information integration. 3) By introducing the gray correlation model, we calculated the gray comprehensive correlation degree between the value of each candidate project and the value of the positive and negative gray bullseyes.

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After that, we processed sorting and selection of the project proposal, which extends the application scope of the gray relational analysis model. In addition, through comparing with previous studies, we considers more about the interaction among attribute weights, decision preferences and evaluation information when we set the evaluation model. It helps to avoid the subjectivity and uncertainty in the project decision-making process and to ensure the completeness and scientificalness of decision-making results. Acknowledgements. The work is partially supported by the Shaanxi Philosophy and Social Science Research Base Project (Grant No. 18JZ044).

References 1. Calik, A., Pehlivan, N.Y., Kahraman, C.: An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey. Technol. Econ. Dev. Econ. 24(4), 1280–1302 (2018) 2. Deng, J.: Control problems of grey system. Syst. Control Lett. 1(5), 288–294 (1982) 3. Dutta, B., Guha, D., Mesiar, R.: A model based on linguistic 2-tuples for dealing with heterogeneous relationship among attributes in multi-expert decision making. IEEE Trans. Fuzzy Syst. 23(5), 1817–1831 (2014) 4. Feng, X.Q., Liu, Q., et al.: Hesitant fuzzy 2-tuple linguistic multiple attribute decision making method. Oper. Res. Manag. Sci. 27(1), 17–22 (2018) 5. Herrera, F., Mart´ınez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000) 6. Liu, D., Qi, X., et al.: A resilience evaluation method for a combined regional agricultural water and soil resource system based on weighted mahalanobis distance and a gray-topsis model. J. Clean. Prod. 229, 667–679 (2019) 7. Lu, C., You, J.X., et al.: Health-care waste treatment technology selection using the interval 2-tuple induced topsis method. Int. J. Environ. Res. Public Health 13(6), 1–16 (2016) 8. Sohaib, O., Naderpour, M., Hussain, W., Martinez, L.: Cloud computing model selection for E-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method. Comput. Ind. Eng. 132, 47–58 (2019) 9. Su, Z.: Method for multiple attribute group decision making based on relational analysis of two-tuple linguistic representation. Stat. Decis. 479(11), 62–65 (2017) 10. Sun, X., Hu, Z., et al.: Optimization of pollutant reduction system for controlling agricultural non-point-source pollution based on grey relational analysis combined with analytic hierarchy process. J. Environ. Manag. 243, 370–380 (2019) 11. Suo, W., Zhang, J., Sun, X.: Risk assessment of critical infrastructures in a complex interdependent scenario: a four-stage hybrid decision support approach. Saf. Sci. 120, 692–705 (2019) 12. Wang, H.H., Zhu, J.J., Fang, Z.G.: Aggregation of multi-stage linguistic evaluation information based on grey incidence degree. Control Decis. 28(1), 109–114 (2013) 13. Wang, L., Wang, Y., Pedrycz, W.: Hesitant 2-tuple linguistic bonferroni operators and their utilization in group decision making. Appl. Soft Comput. 77, 653–664 (2019) 14. Wu, Q., Wu, P., et al.: Some new Hamacher aggregation operators under single-valued neutrosophic 2-tuple linguistic environment and their applications to multi-attribute group decision making. Comput. Ind. Eng. 116, 144–162 (2018) 15. Xu, Z.: EOWA and EOWG operators for aggregating linguistic labels based on linguistic preference relations. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 12(06), 791–810 (2008)

The Influence of Referral Cognition on Referral Intention Among Outpatient Patients: An Empirical Research Xinli Zhang1 , Lingyun Zhang1 , Zhen Zeng1 , and Yeli Chen2(B) 1

Business School, Sichuan University, Chengdu 610065, People’s Republic of China 2 West China Hospital of Sichuan University, Chengdu 610065, People’s Republic of China [email protected]

Abstract. Two-way referral is essentially a method of medical reform led by the government to optimize and integrate urban medical resources. The low downward referral intention can be attributed to patients’ insufficient understanding of the primary health care service and the low cognition of referral. In this paper, we conducted a questionnaire with 8 items of demographic characteristics, referral cognition and referral intention among outpatients in large hospitals, and adopted the followup of two years in 2014 and 2017. Based on the sample data of two years, we set up the logistics regression models for referral cognition and intention respectively to certify that there existed the influence of referral cognition on the referral intention; then constructed an incremental utility index on the basis of referral cognition increments and referral intention increments. We strive to detect the patients’ attribute type causing this influence in the demographic characteristics by using the method of empirical analysis, hence provide access for referral intention intervention for different people.

Keywords: Cognition-intention utility utility analysis

1

· Classification · Incremental

Introduction

Referral system is the main pattern of medical health service, and most of the referral service adopt the ‘gatekeeper policy’ with the engagement of general practitioners, while some countries adopt the system of ‘willing to compliance, dual referral’. As for the referral system in China, two-way referral includes upward referral and downward referral. However, in the referral service, the acceptance ratio of upward referral has exceeded 80%, at the same time the ratio for the downward referral only takes up around 10% [15]. The two-way referral has the difficult situation of “no downward referral, it’s easier tRen2010Currento c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 601–613, 2020. https://doi.org/10.1007/978-3-030-49829-0_45

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transfer up than transfer down”, which has become one of the main factors affecting the development of primary health care institutions. According to the cognitive psychology, the formation of intention is influenced by the cognition for certain things [12], and the cognition is the dominator of the intention and has a direct influence on the formation of intention [19]. In the referral system, referral cognition refers to the patients’ knowledge and understanding of the referral, and the referral intention means whether patients are willing to accept referral from high-level hospitals to low-level hospitals. Researches have shown that in this “patient-centered” and “willing to compliance” referral service, the patients’ intention has a decisive role and influential utility on their referral behavior [10]. In consequence, the influential research on patients’ intention towards the improvement of referral acceptance behavior has an important significance. Zhou [21] proves that the willingness of patients with chronic diseases to make downward referral is mainly affected by the resource allocation of community health service institutions, patients’ perception, accessibility of referral service and the participation of medical staff, and the influence degree of each factor on the willingness of patients with chronic diseases to make downward referral is different [21]. Li (2019) found that patients’ cognition of two-way referral is not high, and education level and medical insurance type are the main influencing factors. Patients generally have the attitude that they are willing to transfer upward rather than downward, and the medical level and cost have the strongest guidance to patients’ referral choice [20]. Zhao’s study (2019) shows that both medical staff and patients have low awareness of two-way referral, which is related to the incomplete formation of relevant mechanisms and norms. The influencing factors of two-way referral awareness of medical staff and patients mainly include publicity, relevant policies, patients’ trust in primary medical institutions, convenience, etc. [11]. It is found that referral intention is influenced by the demographic characteristics. In that way, whether the transmitting utility of cognition towards intention has a remarkable significance on the demographic features or not? Whether there exists the influential utility difference under the patients’ classification? The accurate answers towards the above questions have an important value for the identification of utility rule of referral cognition-intention and the establishment of referral service with categorical guidance for the patients. We try to find out the type of patient attributes that lead to this effect in demographic characteristics, so as to provide a way to improve the referral willingness of different populations, and provide a basis for promoting the implementation of two-way referral system. In this paper, Sect. 2 introduces the method of empirical regression analysis and the incremental utility index, Sect. 3 presents the empirical research process in a certain large hospital, Sect. 4 discusses the research results, and Sect. 5 gives the conclusion and the future research trends.

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Method Questionnaire

In November 2014 and November 2017, a random questionnaire survey was conducted among outpatients in the same department of a large hospital in China. The questionnaire is divided into two parts: the first part selects 8 demographic characteristics, including gender, age, education level, monthly income per capita of the family, household registration, resident location, insurance status. Each type (which is represented by m) has k item demographic characteristics. Consequently, the k-th classification option under the m-th characteristic can be denoted as D(m, k) (see Table 1); the second part includes two issues, A. Do you know the referral service, yes/no. B. Are you willing to return to primary healthcare institutions, yes/no.

Table 1. Demographic characteristics Characteristics of patients m

D(m, k)

Characteristics of patients m

D(m, k)

Gender (m = 1)

Male Female

Monthly income (m = 5)

1). When there is a small increase in their referral cognition, there will be a large increase in their referral intention. The percentage increase of referral intention of characteristic patients was lower than that of referral cognition below the line, and the sensitivity of this group to referral cognition was lower (ICIR < 1). Therefore, when this group’s referral cognition increases, their referral intention will not significantly increase, or even decrease. c. Utility Sensitivity Analysis. Based on the standard of average value of 1.9, in order to find out the characteristic patients who have a greater contribution to the positive effect of utility in the sample population, introduce the straight line y = 1.9x. The characteristic patients lied above the straight line have a greater contribution to the measurement of the overall utility of the sample, ICIR is greater than 1.9, the average level, which means that the overall positive effect of referral cognition on the referral intention is reflected in the sample as a whole, which is attributed to the characteristic patients with greater ICIR.

4

Discussion

(1) Cause Analysis of Cognition Increase. The research found that the overall level of referral cognitive and referral intention increased by 9.7% and 18.6%

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in 2017 compared with 2014. To a certain extent, the promotion of patients’ referral cognition level was attributed to the achievements of earlier outpatient service improvement, the hospital had constructed new outpatient platform and set up referral office, to some extent, this measure had broadened the channel of knowing referral service for the patients [3]. (2) Cause Analysis of Intention Increase. The cognition-intention logistic regression coefficients were 0.517 and 1.282 in 2014 and 2017, this was in accordance with the results of ‘referral cognition would influence referral intention’ which was put forward by cognitive psychology and behavioral psychology [1,8]. (3) Referral cognition has a positive utility and significance on referral intention. The taking value interval of ICIR with different demographic characteristics was [−79.0, 12.8], with variance 3.3. It indicated that with different demographic characteristics, there existed a utility discrepancy in the influence of the referral cognition on referral intention. Partial featured group reflected a negative influential relationship, cognition guidance for this part of patient could not increase the referral intention, it needed further research. According to the analysis in Subsect. 3.3. (2), the patient classification of cognitionintention incremental utility can be obtained, as showed in Fig. 2. The positive effect of referral cognition on referral intention is 82.4%. 44.2% of referral intention was more sensitive to referral cognition, which can be divided into type I patients. These patients were mainly young-and-middle aged, secondary and higher education level, middle income, new rural cooperative medical system (NRCMS) and people living in the province. They had a strong cognitive ability [13], and had a high satisfaction with primary health services [2]. They are easily affected by external active intervention and are willing to refer to primary health service institutions. The different medical resources and constructions have resulted in a difference between the two districts in terms of the willingness of individuals to make their first visit to PCIs. Strengthening the service capabilities of PCIs remains a priority [16]. About 38.2% of the patients with characteristic features were not sensitive to the cognition of referral, and they could be divided into type II. These patients are characterized by middle-aged and elderly people, low education level, low income, living outside the province, and the main types of medical insurance of them are social insurance and new rural cooperative medical insurance (NRCMS). The pro-rich distribution of health literacy was mainly attributable to education background, whereas income inequalities contributed most to the pro-rich distribution of health-information seeking among an urban population, whereas income inequalities contributed most to the pro-rich distribution of health-information seeking among an urban population. These patients had a high degree of trust in community general practitioners [5] and were quite satisfied with primary health care services [2]. They prefer to refer to primary health care institutions for rehabilitation treatment. Establishing and preserving connections with physicians is essential

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to creating a strong referral network and a steady supply of patients [9]. In addition, public interventions in China to reduce inequality in health literacy and HISBs (health-information seeking behavior) among the urban population, coupled with easily accessible information sources on health, warrant further attention from policymakers [18]. About 14.7% of the patients had negative correlation between the intention of referral and the cognition of referral. This group could be divided into type III. These patients have high educational background, high income, commercial insurance and work in enterprises. This group has strong cognitive ability, they will pay close attention to the medical effect [17]. They have economic strength and the ability to select high-quality large hospitals, and they are unwilling to be placed in primary health care institutions. To change this situation, first of all, the primary health care institutions need to improve high-end medical services to meet their needs and achieve their expectations of medical level. (4) Limitation of This Study. The respondents of this study were randomly selected patients from the same outpatient waiting room of the same large hospital in different years, there existed difference of these two sample groups in aspect of demographic characteristics. Therefore, the relationship between patients’ choice of referral behavior, referral cognition, referral intention and referral behavior can be summarized as follows: Patients’ referral cognition will affect their referral intention, and referral intention will affect their referral behavior. This paper mainly studies the transitive relation of the previous step, and the transitive relation of the latter step needs further study.

5

Conclusion

This paper argues that patients’ cognition of referral usually has a positive impact on the intention to refer, so cognitive guidance has a general basic role in improving the willingness to refer. However, there are significant differences in the cognition-intention of demographic information. Therefore, individualized cognitive guidance should be carried out according to demographic characteristics. In addition, the incremental utility index of cognition-intention proposed in this paper shows the effect difference of demographic informatics, and provides the classification of cognitive intention compliance utility for patients, so as to provide the basis for the improvement of patients’ cognitive classification management level and intention. However, the research on the relationship between referral intention and referral behavior needs to be continued. Acknowledgements. The author gratefully acknowledges Xinli Zhang and Yeli Chen’s efforts on the paper collection and classification, Zhen Zeng and Lingyun Zhang’s efforts on data collation and analysis, and Lingyun Zhang’s efforts on the chart drawing and improving. This work was supported by the research project from Sichuan University (Reference:2018hhs-54).

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References 1. Abraham, I.L.: Cognitive set and clinical inference: referral information may not (always) affect psychosocial assessment. Soc. Behav. Pers. Int. J. 14(1), 51–58 (1986) 2. Baltaci, D., Eroz, R., et al.: Association between patients’ sociodemographic characteristics and their satisfaction with primary health care services in Turkey. J. Kuwait Med. Assoc. 45(4), 291–299 (2013) 3. Cheng, N., Liu, Z., et al.: Developing dual referral and improving outpatient care. Chin. Hosp. 20(4), 4–5 (2016). (in Chinese) 4. Combariza, J.F., Toro, L.F., et al.: Cost-effectiveness analysis of interventions for prevention of invasive aspergillosis among leukemia patients during hospital construction activities. Eur. J. Haematol. 100(2), 140–146 (2018) 5. Croker, J.E., Swancutt, D.R., et al.: Factors affecting patients’ trust and confidence in GPs: evidence from the English national GP patient survey. BMJ Open 3(5), e002762 (2013) 6. de Crupp´e, W., Geraedts, M.: Hospital choice in Germany from the patient’s perspective: a cross-sectional study. BMC Health Serv. Res. 17(1), 720 (2017) 7. Hemadeh, R., Hammoud, R., et al.: Patient satisfaction with primary healthcare services in Lebanon. Int. J. Health Plann. Manag. 34(1), e423–e435 (2018) 8. Huang, Q., Lu, F., et al.: Analysis on cognition and willingness of hierarchicalmedical system among hospital patients. Chin. Primary Healthc. 30(7), 1–3 (2016) 9. Kahn, M.J., Baum, N.: Obtaining and maintaining referrals from other physicians. In: The Business Basics of Building and Managing a Healthcare Practice, pp. 81– 94. Springer (2020) 10. Lever, J., Krzywinski, M., Altman, N.: Points of significance: logistic regression. Nat. Methods 13(7), 541–542 (2016) 11. Li, W.: Two-way referral recognition of patients and its influencing factors under the cooperation of urban hospitals. China Health Ind. 16(03), 67–69 (2019). (in Chinese) 12. Miller, G.A., Galanter, E., Pribram, K.H.: Plans and the structure of behavior. Am. J. Psychol. 19(75), 338–340 (1961) 13. Mortensen, E.L., Flensborg-Madsen, T., et al.: The relationship between cognitive ability and demographic factors in late midlife. J. Aging Health 26(1), 37–53 (2014) 14. Plumpton, C.O., Brown, I., et al.: Economic evaluation of a behavior-modifying intervention to enhance antiepileptic drug adherence. Epilepsy Behav. 45, 180–186 (2015) 15. Ren, X., Li, J.: Current problems of dual referral and countermeasures in new medical service care reformations. Chin. Health Econ. 29(4), 44–46 (2010). (in Chinese) 16. Song, H., Zuo, X., et al.: The willingness of patients to make the first visit to primary care institutions and its influencing factors in Beijing medical alliances: a comparative study of Beijing’s medical resource-rich and scarce regions. BMC Health Serv. Res. 19(1), 1–11 (2019). Article number: 361 17. Sun, J., Zhang, L., et al.: Differences in the cognitive evaluation of the doctorpatient relationship between the medical side and the contracting parties. Chin. Ment. Health J. 30(7), 486–491 (2016) 18. Tang, C., Wu, X., et al.: Examining income-related inequality in health literacy and health-information seeking among urban population in China. BMC Public Health 19(1), 221 (2019). (in Chinese)

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The Influence of Sugar-Free Label Formats and Colors on Consumers’ Acceptance of Sugar-Free Foods Ping Liu1 , Hong Wang2(B) , Wei Li1 , and Bo Hu3 1

Business School of Sichuan University, Chengdu 640000, People’s Republic of China 2 Business School of Chengdu University of Technology, Chengdu 640000, People’s Republic of China [email protected] 3 College of Management Science, Chengdu University of Technology, Chengdu 640000, People’s Republic of China

Abstract. The purpose of this study is to evaluate the influence of sugar-free label design on consumers’ acceptance of sugar-free foods. The sugar-free food labels of 2 products (caramel treats and herbal tea) were analyzed using a factorial design with 2 3-level variables: sugar-free label format (direct, sweetener and indirect) and color (red, green and black). A total of 436 participants (48.9% males, Mage = 20.35, SD = 1.84) from a Chinese university participated in the study. A general linear model and a level of significance of 0.05 were used to analyze the data. The results suggested that when the sugar-free label was in the direct format, the color green was the best fit. However, when the sugar-free label was in the sweetener format, the participants’ acceptance of the red label was higher. Similarly, the color red was also the best fit for the indirect format. These conclusions can provide theoretical references for food enterprises to promote the effects of sugar-free food information communications, the design of sugar-free labels, and the consumption of sugar-free foods. Keywords: Sugar-free food

1

· Food label · Design · Food acceptance

Introduction

Sugar consumption has tripled worldwide over the past 50 years [17] and has been linked to the increasing prevalence of obesity, type 2 diabetes, and cardiovascular diseases worldwide [9,15,23,32]. Sugar added to processed foods has been identified as the main source of sugar in the diet [24]. Increasing numbers of countries and food manufacturers have decided to reduce the sugar content in food. In 2017, many countries, including the United Kingdom and France, joined the ranks of those imposing a “sugar tax”. In 2018, soft drink manufacturers, including Coca-Cola, announced in Australia that they would cut the c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 614–626, 2020. https://doi.org/10.1007/978-3-030-49829-0_46

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sugar content in their beverage products by 20% by 2025, and more than 80% of non-alcoholic beverage manufacturers signed the joint commitment agreement. In addition, food companies are increasing their research and development efforts to actively find suitable methods of reducing sugar. Thus, we can see an increasing tendency to be concerned about sugar-free foods. As the attention paid to sugar-free foods has increased, how to improve consumers’ acceptance of sugar-free foods has become a very practical issue. Consumers usually reject low-sugar or sugar-free foods that fail to meet their sensory and hedonic expectations compared to general healthy food products [43]. Eliminating sugar often results in products that have sensory properties that are incomparable to standard products and affect consumers’ acceptance [13]. With the help of production technology, it has been found that the replacement of sucrose with xylitol and maltitol in foods is the most accepted in sensory evaluation [25] Therefore, to some extent, replacing sucrose in food with different sweeteners can improve consumers’ acceptance of sugar-free foods. Adding sweeteners to take place of sucrose in food has been a common method for food manufacturers. In China, as long as the amount of sweetener meets the national standard, the foods containing sweetener instead of sucrose can still be considered as sugar-free foods. In fact, large amounts of sugar-free foods in the market contain sweeteners. However, for sugar-free foods with sweeteners, food producers may use different formats of sugar-free labels when communicating the sugar-free information of the food to consumers. In addition, considering another important design factor, i.e., color, manufacturers also apply different colors in the design of sugar-free labels. Therefore, for the sugar-free foods with sweeteners, considering both label formats and label colors, do different formats and colors of sugar-free labels affect consumers’ acceptance of them? Is there be a match-up effect between the two design factors? Based on research on methods of health information communication in food advertising [6], claims of sugar reduction in food [20], and sugar-free labels employed in practice, three kinds of sugar-free label formats have been found: direct format, indirect format, and sweetener format. Direct format sugar-free labels directly state that there are no sugars in the foods by using terms such as “sugar-free” or “zero”. Indirect format sugar-free labels indirectly indicate that there are no sugars in the foods by using terms such as “light” or other similar terms. Sweetener format signifies that specific sweeteners have replaced the sucrose in the foods by using terms such as “xylitol” or the names of other sweeteners. These three kinds of sugar-free label formats are common in reality, and many manufacturers apply them optionally (see Fig. 1). The design of the visual characteristics of food and food packaging will affect consumers’ food perception and purchase behavior. These design factors have been widely studied, such as label [5,33], package size [21], information on package [12], visual imagery [29], shape [40], and color [1,19,30]. In the existing literature, no previous work has directly evaluated the effects of these three kinds

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of sugar-free labels with different colors on the acceptance of sugar-free foods. The current study aims at bridging this gap. The present study contributes to the food label literature by demonstrating that the sugar-free label formats and colors do affect food perception and acceptance. It also contributes to the literature about healthy food by introducing a new way to alter consumers’ healthy food choice behaviors. Our findings provide suggestions for sugar-free food product marketers by introducing some preferred design ways for the sugar-free label. The next section details the theoretical background and is followed by one study. The last section discusses the contributions of this research to theory and practice, and finally, the limitations and future directions of this research are concluded.

2

Research Hypotheses

Because the purchase of food is usually a quick decision, consumers’ perception of the food is often based on some concise and prominent information, such as the label on the front of the package [11]. We assume that consumers’ perceived healthiness and tastiness of sugar-free food is mainly affected by the sugar-free label and its format. Compared with sugar-free labels in the indirect format and sweetener format, sugar-free labels in the direct format make people feel that the labeled food is less sweet and healthier. These inferences can be explained by association theory. Association theory suggests that if two events always occur together, a connection will be formed between them, which enables consumers to predict one event with the other [34]. Our experiences in life tell us that sugar is the main source of tastiness but too much sugar is bad for our health. According to association theory, the sugar content of food is often related to the level of tastiness and healthiness of food products. People will infer the tastiness and healthiness of foods based on the information related to the sugar content. Direct format sugar-free labels that use terms such as “sugar-free” have a higher positive association with healthiness and a higher negative association with tastiness. These associations will promote consumers’ healthiness perception but reduce consumers’ taste perception for the labeled food. In contrast,

Fig. 1. Sugar-free label formats [(a): “Sugar-free” for direct format; (b): “Xylitol” for sweetener format; (c): “Light” for indirect format]

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the indirect format and sweetener format of sugar-free labels are indirect ways of communication that try to avoid stating up-front that the food does not contain sugar. Information communication is more euphemistic. These communications have relatively less impact on consumers’ perceptions. The positive association between these sugar-free labels and healthiness is not that close, and the negative association between these sugar-free labels with tastiness is also weaker. Therefore, consumers’ taste perception of food products with the indirect and sweetener formats is higher than those with the direct format. In contrast, consumers’ healthiness perception of food products with the indirect and sweetener formats is lower than those with the direct format. Thus, we infer that the food with the direct format sugar-free label is perceived as being healthier, and the food with the indirect format or sweetener format sugar-free labels is perceived as being taster. Additionally, the color of food labels plays an important role in food perception [28]. There is a natural connection between red and sweetness (e.g., the red color increases during fruit ripening and increased sweetness), and experience with the taste of this natural color helps explain why red affects the perception of sweetness [18]. Existing studies show that warm colors, such as red foods and drinks, are often associated with pleasurable attributes that stimulate sensory characteristics, such as a sweet taste [36]. In contrast, cool colors (such as green) are associated with healthiness perceptions produced during cognitive judgments [28]. In addition, red-packaged food is considered to be sweeter, while green-packaged food is perceived as being related to healthiness [14]. Therefore, red contribute to conveying the tastiness of the food, while green is instrumental in communicating the healthiness of food. The match-up effect theory suggests that the match-up effect occurs when consumers feel that a product and a certain informational cue in an advertisement are well-matched [4]. Consumers evaluate advertisements more favorably when the advertised products and the products’ related cue in the ad show high and strong associations [4]. On the basis of match-up effect theory, we assume that there is a match-up effect between the different cues of food products, which can enhance the evaluation of the products. Therefore, we infer that the different perceptions resulting from the label formats may further interact with the label colors and ultimately affect the acceptance of sugar-free foods. Three main colors, i.e., green, red and black, are usually applied in sugarfree labels in practice. We hypothesize that the color of sugar-free labels will interact with the label format and impact consumers’ acceptance of the foods. Direct format labels express explicit sugar-free information and should thus be fitted with green, as the color of green indicates the healthiness of the food to consumers. Therefore, the effects of direct format sugar-free labels may be better when they are presented in green. In contrast, the indirect format and sweetener format labels emphasize the tastiness of sugar-free foods, which is in accordance with the symbolic meaning of red. Thus, the effects of these two kinds of sugarfree labels may be better when they are presented in red. We hypothesized as follows:

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Hypothesis 1. For direct format sugar-free labels, a green color (vs. red vs. black) results in higher (lower) levels of acceptance toward the labeled sugar-free food. Hypothesis 2. For sweetener format sugar-free label, a red color (vs. green vs. black) results in a higher (lower) level of acceptance toward the labeled sugar-free food. Hypothesis 3. For indirect format sugar-free labels, a red color (vs. green vs. black) results in higher (lower) levels of acceptance toward the labeled sugarfree food.

3 3.1

Methods Experimental Design

An experimental study using a 3 (sugar-free label formats: direct format vs. sweetener format vs. indirect format) × 3 (sugar-free label color: red vs. green vs. black) between-subjects design was conducted. Two non-meaningful fictitious brands, “Bissen” and “Laloo”, were chosen to avoid any potential bias due to consumers’ previous experiences with commercial products [4,38,39]. Two kinds of common sugar-free foods, caramel treats and herbal tea, were selected as the experimental foods. Before the main study, one pretest was conducted to design the stimuli and check whether our manipulation was successful. Pretest. In the pretest, first, we designed stimuli only under the condition of a black sugar-free label. The stimuli consisted of pictures of the front of the package showing sugar-free labels. The stimuli contained the sugar-free label, the product name, the brand name, the volume/ weight, and a food picture. The only difference across the different trials was the sugar-free label format, specifically, direct format (“sugar-free”), sweetener format (“xylitol”) and indirect format (“light”). The sugar-free label text was printed in a 44-point bold font in black on a light background [10,39]. The stimuli pictures were designed by professional designers, and their size was 24.5 × 21 mm. Second, we determined the control variables and their measures. We adopted the familiarity, attitude, and visual complexity of the product packages as the control variables [3,16,22,27,31]. These variables were all measured on 7-point Likert scales. We employed four items and six items to measure the packages’ familiarity and attitude, respectively [27]. The visual complexity of the packages was measured by a single item [22]. Finally, we tried to assure that the stimuli were different only in their sugarfree label formats. We randomly assigned 150 participants (48% males, Mage = 19.73, SD = 1.56) to the three conditions. The participants browsed the package pictures and completed the items measuring the control variables. There were no significant differences found in the three caramel treatment packages with different sugar-free label formats for either familiarity (Cronbach s α = 0.823) [F (2, 147) = 1.09, P = 0.341], visual complexity [F (2, 147) = 0.09, P = 0.913], or attitude (Cronbach s α = 0.885) [F (2, 147) = 0.37, P = 0.692]. Similarly,

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there were no significant differences found in the three herbal tea packages for either familiarity (Cronbach s α = 0.834) [F (2, 147) = 0.48, P = 0.623], visual complexity [F (2, 147) = 1.85, P = 0.163], or attitude (Cronbach s α = 0.899) [F (2, 147) = 0.58, P = 0.566]. The pretest indicated that the experimental stimuli of the sugar-free label formats were designed successfully. Some of the stimuli are shown in Fig. 2.

Fig. 2. Stimulus materials for the three conditions [(a) “sugar-free” for direct format, (b)“xylitol” for sweetener format v. (c) “light” for indirect format] of the caramel treats between-subjects design

Stimuli Development. On the basis of the three fundamental sugar-free label formats in black obtained through the pretest, we designed different colored labels for each format, and the colors used were red [RGB(255, 0, 0)], green [RGB(0, 225, 0), and black [RGB(0, 0, 0)]. We obtained nine pictures of the package for each sugar-free product. Table 1 shows the characteristics of each of the sugar-free labels considered in the study in terms of the design variables of the joint analysis. Table 1. Characteristics of labels in terms of variables considered in the study for each target sugar-free food Products

Label format

Camarel treats or herbal tea Direct format Sweetener format Indirect format Direct format Sweetener format Indirect format Direct format Sweetener format Indirect format

Label color red red red green green green black black black

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Data Collection

Participants. In the main study, the participants were undergraduates recruited from a Chinese university via social platforms. The participants took part in the experiment for a nominal payment. Eight subjects dropped out of the study, and 16 subjects had poor responses. Therefore, these observations were excluded. We obtained valid data from a final sample of 436 participants (48.9% males, Mage = 20.35, SD = 1.84), and each condition contained approximately 48 participants. All participants had normal vision. Procedures. The participants were assigned to one of the nine experimental conditions. There were no differences in procedures among the nine groups except for the difference in the stimuli. First, the participants received a brief instruction and signed an informed consent form. Second, the participants were told to browse one of the nine stimuli. In particular, each stimulus was accompanied by a nutrients table in which the index of sugar was zero and a list of ingredients in which xylitol was displayed as a main ingredient. Therefore, we can ensure that the characteristics of the experimental food were essentially the same. Then, the participants were asked to answer questions about the control variables of the packaging. Third, the participants answered questions about their acceptance of the products and provided basic demographic information. Finally, to test whether personal factors affected the acceptance of sugar-free products, five covariates (health consciousness, nutrition knowledge, product involvement personal hunger/thirst and prior attitude toward sugar-free products) were measured. Measures. All questions employed 7-point Likert-type rating scales. Health consciousness was measured with 10 items [8] (Cronbach s α = 0.891). Nutrition knowledge was measured with 13 questions in a true-false format [7]. Product involvement [37], personal hunger and thirst [35] were measured with 1 item. Prior attitude toward sugar-free products was measured with 6 items from an attitude scale [27] (Cronbach s α = 0.816). Finally, sugar-free product acceptance was measured with 5 items from a product acceptance scale [26] (Cronbach s α = 0.923). 3.3

Data Analysis

The researchers evaluated the influence of the design variables on sugar-free food acceptance using a generalized linear model. Two-way analysis of variance was conducted to explore the interaction of the sugar-free label formats and colors. All data analyses were carried out in the statistical software package SPSS version 21. A 95% confidence level was considered.

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Results Control Variables

The analysis of variance for the three control variables of the package showed that there are no significant differences in these factors (P s > 0.05). The conclusions above again validate the successful design of the stimuli. 4.2

Effects of Sugar-Free Label Formats

As shown in Table 2, an analysis of variance for the covariates showed that there were no significant differences in health consciousness, nutrition knowledge, product involvement, hunger/thirst and prior attitude toward sugar-free products. We first examined whether the different sugar-free label formats affected the participants’ acceptance of sugar-free products. As shown in Table 2, there is a main effect of sugar-free label format on the acceptance of the “Bissen” sugarfree caramel treats [F (2, 433) = 3.12, P = 0.045, partial η 2 = 0.014]. Similarly, sugar-free label format has a main effect on the acceptance of the “Laloo” herbal tea [F (2, 433) = 3.03, P = 0.046, partial η 2 = 0.013]. 4.3

Effects of Sugar-Free Label Colors

We then examined whether the different sugar-free label colors affected the participants’ acceptance of sugar-free products. As shown in Table 2, the sugar-free label color has a significant impact on the acceptance of the sugar-free caramel treats [F (2, 433) = 13.27, P = 0.000, partial η 2 = 0.058]. We obtained similar results for the sugar-free herbal tea [F (2, 433) = 18.39, P = 0.0000, partial η 2 = 0.078]. 4.4

Interaction of Sugar-Free Label Formats and Color

As shown in Table 2, the interaction of the sugar-free label format and the label color has a significant effect on the acceptance of both the sugar-free caramel treats [F (2, 427) = 8.05, P = 0.000, partial η 2 = 0.070] and the herbal tea [F (2, 427) = 8.30, P = 0.000, partial η 2 = 0.072]. Then, we conducted simple effects analyses on the effect of different colors on the acceptance of sugar-free food for each sugar-free label format. As shown in Table 3, for the direct format, the sugar-free label colors have a significant impact on the acceptance of the sugar-free caramel treats [F (2, 429) = 20.93, P = 0.000] and the herbal tea [F (2, 249) = 26.39, P = 0.000]. Specifically, the participants’ acceptance of sugar-free foods with green labels is higher than that for those with red or black labels, which indicates that for the direct format, the color green is the best fit. Thus, hypothesis1 was supported. For the sweetener format, the sugar-free label colors have a significant impact on the acceptance of the sugar-free caramel treats [F (2, 429) = 5.12, P = 0.006] and the herbal tea [F (2, 429) = 5.56, P = 0.004]. Specifically, the participants’ acceptance of

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Table 2. Influence of sugar-free label formats and colors on the acceptance of sugar-free foods Product Effect

Caramel treats Herbal tea

Health consciousness

0.05 ns

Nutrition knowledge

2.17 ns

2.86 ns

Product involvement

0.01 ns

1.54 ns

Hunger/thirst

1.33 ns

0.26 ns

Prior attitude

1.35 ns

1.57 ns

Label format

3.12*

3.03*

Label color

13.27***

18.39***

3.73 ns

Label format label color 8.05*** 8.30*** Notes: Ns indicates that the effect on food acceptance scores was not significant (P > 0.05). *P < 0.01; ***P < 0.001. F values are shown for the model. Table 3. Scores of sugar-free food acceptance in terms of different label formats and colors Label format Product

Label color

Direct format Sweetener Indirect format format

Camarel treats

Red

4.18B

5.04A

5.02A

Green Black

5.16A 3.48C

4.64B 4.20B

4.36B 4.43B

Red 4.23B 5.00A 4.91A A B Green 5.16 4.68 4.31B Black 3.34C 4.16B 4.28B Notes: Participants (N = 436) were students recruited from a Chinese university. A − C Scores of sugar-free food acceptance with different uppercase superscripts within the same column for one product are significantly different according to a generalized linear model (P < 0.05). Herbal tea

sugar-free foods with red labels is higher than that for those with black or green labels, which indicates that for the sweetener format, the color red the best fit. Thus, hypothesis2 was supported. For the indirect format, the sugar-free label colors have a significant impact on the acceptance of the sugar-free caramel treats [F (2, 429) = 3.96, P = 0.020] and the herbal tea [F (2, 429) = 4.04, P = 0.018]. Specifically, the participants’ acceptance of sugar-free foods with red labels is higher than that for those with black or green labels. This indicates that for the indirect format, the color red is the best fit. Thus, hypothesis 3 was supported.

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Discussion Conclusions

The results of the present study are conducive to nutritional communications. We obtained these results from a study that examined both food and drink. We found that there is a match-up effect between sugar-free label formats and label colors. The direct format expresses explicit sugar-free information, and it is best fitted with green sugar-free labels, as green suggests the healthiness of food products. Therefore, the effects of direct sugar-free labels in green are better. The indirect format and sweetener format emphasize the taste perception of sugarfree foods, and they are therefore in accordance with the symbolic meaning of red. Thus, the effects of these two kinds of sugar-free labels are better when they are in red. On the basis of association theory and match-up effect theory, we found that there is a match-up effect between the different cues of food products, which can enhance the evaluation of the products. Based on the consistency between a higher perception of health caused by sugar-free labels in the direct format and the higher perception of health resulting from the color green, we found a match-up effect between the color green and sugar-free labels in the direct format. Based on the consistency between a higher perception of taste caused by sugar-free labels in the indirect and sweetener formats and a higher perception of taste resulting from the color red, we found a match-up effect between the color red and sugar-free labels in the indirect and sweetener formats. In general, we found that the match-up effect between colors and product characteristics [2] also exists in the relationship between sugar-free label formats and the color of their label. The results of the current research enrich the applications of association theory and match-up effect theory in healthy food marketing. 5.2

Implications for Research and Practice

As far as we know, this research is among the few to study the influence of sugarfree label designs with different formats and colors on the acceptance of sugar-free foods and to provide a theoretical basis for enterprises in designing sugar-free labels. Previous studies on the acceptance of sugar-free foods have been mainly based on the technical level of food production to find ways to improve the taste of sugar-free foods [25,41,42]. Under the premise of not changing sugar-free foods, a research gap still exists regarding how to use communication strategies to improve consumers’ acceptance of existing sugar-free foods. In this paper, research on the design of sugar-free labels fills in the blanks related to improving the acceptance of sugar-free foods from the view of nutritional communication strategies and enriches the research field of healthy food consumption. The results from the current work stress that sugar-free label design influences consumers’ acceptance of sugar-free food products. These results suggest that strategies to regulate label design are needed. In particular, it is important to stress that the formats of sugar-free labels may influence consumers’

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perception of the tastiness and healthiness of sugar-free food products and their acceptance of these products. If the direct format is required, the most suitable color is green, followed by red. When the indirect format or sweetener format is required, the most suitable color is red, followed by green. 5.3

Limitations and Future Directions

There were some limitations to the study. First, the researchers selected participants using a convenience sample from a university in China, which limits the generalizability of the findings. Second, only two kinds of food were considered instead of a wide range of different types of products. Finally, acceptance was considered as a dependent variable, but there was no further discussion about the influence of the sugar-free label design on attitude, purchase intention or purchase behavior. Further research should be conducted to expand the findings of the current research to different populations in different countries to increase generalizability and to evaluate the influence of other label characteristics, such as the relative location of the labels to the brand logo. Second, different types of food can be used in future research to test the validity of the current research findings in different food scenarios. In addition, many different experimental methods can be used to verify the current results. Eye-tracking experiments can be used to detect consumers’ attention and interest in sugar-free labels. Field experiments can be used to test the effect of different text label designs on consumers’ purchasing behaviors for sugar-free food. Finally, the influence of sugar-free label formats and the colors of the labels on the acceptance of sugar-free foods may have boundary conditions, which is also a direction worthy of attention in future research.

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Study on the Effect of Exposure to Death Information and Perceived Personal Control on Healthy Food Choices Shouwei Li1(B) , Ping Liu2 , and Yan Guo3 1

2

Chengdu Agricultural College, Chengdu 611130, People’s Republic of China [email protected] Business School, Sichuan University, Chengdu 610064, People’s Republic of China 3 College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, People’s Republic of China

Abstract. The Terror Management Health Model (TMHM) suggests that when one is exposed to death information, the individual’s defensive behavior about health will produce two results: the health behaviororiented outcomes and the threat avoidance-oriented outcomes. Focusing on food choice, this paper examined the interaction effect of exposure to death information and perceived personal control on the choice of healthy food. The results showed that, for individuals with high perceived personal control, exposure to death information (vs.no) promotes healthy food choices. For individuals with low perceived personal control, exposure to death information (vs.no) can’t promote healthy food choices anymore. Keywords: Terror management health model Healthy food · Food choice · Perceived control

1

· Mortality salience ·

Introduction

People are often inevitably exposed to death information in their daily life. Such death information exposure is sometimes indirect, such as media reports after natural disasters (e.g., earthquakes, tsunamis, typhoons) or other disasters (e.g., wars, air crashes, gas explosions, the spread of influenza). Even if individuals do not actively seek this kind of information, they will inevitably receive them [25]. In addition, people are sometimes exposed to death information directly, such as learning about other people’s illnesses, passing away and feeling their bodies aging [15]. Does exposure to death information affect consumers’ health behavior? The Terror Management Health Model (TMHM) points out that when one is exposed to death information and facing the threat of death thoughts in consciousness, he may adopt one of the two kinds of defense modes about health: health behaviororiented defense mode and threat-avoidance defense mode. These two defense c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 627–639, 2020. https://doi.org/10.1007/978-3-030-49829-0_47

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modes will produce healthy and unhealthy behaviors respectively [17]. On the one hand, after exposing to death information, people tend to adopt healthpromoting behaviors, such as the reduction of smoking behavior of non-habitual smokers [4], increase of intentions to exercise [2], and preference to high-SPF sun products [36]. This is a positive way of coping. Individuals hope to promote health by reducing health-threatening behaviors, so as to achieve psychological defense against the threat of death thoughts. However, on the contrary, people may increase their intentions to engage in risky behavior, such as alcohol and drug abuse [22], scuba diving [32], and increase of habitual smokers’ smoking intensity [4]. For coping with the threat brought by the death thoughts in consciousness, individuals avoid their self-awareness by engaging in healththreatening behaviors, thus achieving psychological defense. Diet is related closely to human health. Food types have great impacts on consumers’ health. So, does exposure to death information affect consumers’ healthy food choices? Under what conditions can the impact of exposure to death information really play a role? For answering these questions, this study focuses on the effect of death information exposure on healthy food selection. Moreover, individuals with different perceived personal control have different reactions to death information exposure [33]. Therefore, this paper takes perceived personal control as a moderator, studying the boundary conditions of death information exposure affecting consumers’ choice of healthy food. The current study contributes to Terror Management Health Model, by studying the influence of conscious death thoughts without delay on healthy food choices and clarifying the boundary conditions of death information exposure promoting healthy food choice, enriching the application fields of TMHM. The present research also contributes to healthy food literature, by introducing a new way to alter consumers’ healthy food choice behaviors. Our findings provide suggestions for healthy food product marketers by introducing some new advertisement ways and promotion ways for healthy food. The next section details the theoretical background and is followed by one study. The last section discusses the contributions of this research to theory and practice, and finally, the limitations and future directions of this research are concluded.

2 2.1

Literature Review and Research Hypotheses Effects of Death Information Exposure on Healthy Behavior

Research found that exposure to death-related information and thoughts of death substantially influence human behavior and psychological well-being [30], for example, mortality salience affects the collective behavior in the aftermath of terrorist attacks [13], adoption of subjectivist moral judgments [46], retirement savings decisions [37], mating strategy [47], risky driving intentions [14], satisfaction with life [42], and attitude toward product innovation [5]. Moreover, implications for understanding the implicit processes initiated in response to mortality salience are discussed [20]. Among these researches, TMHM is a representative model to discuss the effect of exposure to death-related information

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on health behavior. TMHM combines the health-oriented model with the selforiented model [17]. It provides a theoretical basis to understand the complex factors affecting health decision-making in our daily life. TMHM holds the point that when one is exposed to death information and facing the threat of death thoughts in consciousness, he may adopt one of the two kinds of defense modes about health: health behavior-oriented defense and threat-avoidance defense. When death information is exposed, individuals will escape the threat of death thoughts about health by avoiding it. They try to avoid arousing selfconsciousness as much as possible in order to weaken the influence of death thoughts in consciousness. Leventhal’s research suggests that individuals may cope with the emotional threats of health risks by “eating and drinking”, which “dull the awareness of external dangers” [23]. Other studies have also shown links between avoidance motivation and eating unhealthy foods, avoidance motivation and watching TV, avoidance motivation and alcoholism [11], and even avoidance motivation and overeating [40]. Subjects who participate in boring tasks increase their craving for snacks rather than choosing to eat healthy foods [34]. It is worth noting that individuals may escape from conscious death thoughts by engaging in health risk behaviors. For instance, providing participants with sweets immediately after death information exposure reduces subsequent defensive responses [21]. Habitual smokers increase their smoking intensity after death information exposure [4]. However, after exposure to death information, individuals may defend themselves by health intentions and health behaviors. A healthier body is often associated with a longer life, which can effectively defend against death thoughts in consciousness. For example, reducing smoking is an effective way to reduce health risks, so non-habitual smokers respond to death thoughts by reducing their smoking intensity [4]. Furthermore, after being exposed to death information, participants reported that they preferred to exercise more [2] and purchase high-SPF sun products [36]. 2.2

Interaction of Death Information Exposure and Perceived Personal Control

TMHM also suggests that when faced with conscious death thoughts, the individual’s forthcoming behavior should be able to reduce the link between health risk and death thoughts. In the health field related to death threats, the perceived effectiveness of health behavior (i.e., defensive effect) can moderate the impact of death thoughts on health behavior. Perceived personal control refers to the degree to which an individual feels he can predict, explain, influence and change the occurrence and development of external events in order to achieve the desired results [7]. Perceived personal control can predict positive outcomes including resilience, motivation, cognitive functioning, and life satisfaction [19,24,27,39]. It also plays an important role in enhancing mental and physical health, such as perceived personal control is a mediator of the relationship between shared group identity and well-being [38]. A person with higher perceived personal control can ward off or ameliorate

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unhealthy mental states. Individuals who are deprived of control become helpless, passive, and withdrawn [18]. Besides, perceived personal control has an impact on physical health. For example, perceived control is the best-performing variable in the theory of planned behavior in predicting health outcomes [16]. We speculate that when exposed to death information, consumers choose whether healthy or unhealthy food will rely on their perceived personal control. When they are exposed to death information, individuals with high perceived personal control (e.g., internal locus of control) would see potentially dangerous situations as riskier. That’s because exposed to death information should make them experience dissonance between the belief that one is in control of their life and the reminder that one does not control their death. Given their tendency for control, they would exercise their choice to avoid such situations. When individuals with low perceived personal control (e.g., external locus of control) are exposed to death information, they may take greater risks assuming that their choices are not relevant to the potentially dangerous outcomes because they see their fate as controlled by external factors [33]. In the case of death information exposure and healthy food choice, consumers with high perceived personal control believe in their ability to cope with health threats and are willing to take active defensive measures. They are more likely to generate the motivation to maintain health and choose healthy food. While consumers with low perceived personal control are more inclined to think that they can’t resist health threats and adopt negative defensive methods. They are more likely to generate the motivation of avoidance and choose less healthy food. Thus, we proposed the following hypotheses: Hypothesis 1. For individuals with high perceived personal control, exposure to death information (vs.no) promotes consumers’ healthy food choices. Hypothesis 2. For individuals with low perceived personal control, exposure to death information (vs.no) can’t promote consumers’ healthy food choices anymore.

3 3.1

Methods Materials

Foods. Prior to the main experiment, one pretest was conducted to select healthy and unhealthy foods. Six groups of food were preliminarily screened in this study. Subjects (N = 35) were asked to determine which of the two foods in each group was healthier. Food groups were: beef sandwiches vs. vegetable sandwiches, chocolate cookies vs. coarse grain biscuits, sandwich bread vs. wholewheat bread, ordinary cola vs. sugar-free cola, fruit milk drinks vs. pure milk, and tea drinks vs. mineral water. The results showed that 82.86% of the subjects thought the vegetable sandwich was healthier, 97.14% of the subjects thought coarse grain biscuit was healthier, 94.29% of the subjects thought whole-wheat bread was healthier, 94.29% of the subjects thought sugar-free cola was healthier, 97.14% of the subjects thought pure milk was healthier, and 91.43% of the

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subjects thought mineral water was healthier. This showed that these foods can be used in the main experiment. Manipulation of Death Information Exposure. We manipulated the exposure of death information by asking college students to imagine themselves suffering from incurable diseases (vs. failing an important exam) [28,35,44]. More concretely, exposure to death information was manipulated by having participants answer two open-ended questions about their own death: “Please briefly describe the emotions that the thought of your death arouses in you” and “What you think will happen to you as you physically die and once you are physically dead.” Control participants responded to questions about a different aversive topic, failing an important exam, which allowed us to further assess the uniqueness of death (relative to general aversive events) in eliciting the effects of interest [44]. 3.2

Participants

The participants of this study were students recruited from a Chinese university. A total of 168 participants took part in this study for a nominal payment. They were randomly assigned to 2 (death information: exposure vs. control) × continuous (perceived personal control) conditions. Six subjects dropped out of the study, and 4 subjects had poor responses. Therefore, these samples were excluded. We obtained valid data from a final sample of 158 participants (46.8% males, Mage = 21.25, SDage = 2.01). 3.3

Procedure

The study was conducted online via the Wenjuanxing website. In the first session, all participants were told that they could withdraw from the experiment at any time if the items cause any discomfort. In the second session, the participants received a brief instruction and signed an informed consent form. After reading and signing an informed consent form, participants were randomly assigned to one of the two conditions. Before participants received the manipulation, we measured their perceived personal control. Then the experimental group was exposed to death information and the control group was exposed to information about failing an important exam. In the third session, participants’ positive and negative moods were measured. Then, based on the foods selected from the pre-test, the subjects were asked to imagine that they were going to buy foods and choose one of the two foods from each given pair. After completing the food selection, the subjects were asked to judge which of the two foods in each group was healthier. Finally, we measured the background variables and control variables in order to test whether personal factors would affect their food decisions. Five control variables (attitude toward healthy food, health consciousness, personal hunger, personal thirst, and personal health state) were measured.

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3.4

Measures

All questions employed 7-point Likert-type rating scales unless otherwise indicated. Perceived Personal Control. Perceived personal control was assessed using three items, including “I feel in control of my life”; “I am free to live my life how I wish”; “My experiences in life are due to my own actions” [18] (Cronbach s α = 0.77). Positive and Negative Affect. Participants in both conditions completed the Positive and Negative Affect Form [45] immediately after the death information exposure (or control) inductions. The PANAS asks participants to indicate the extent to which they currently feel a variety of emotions (e.g., excited, scared) on a 1 (very slightly or not at all) to 7 (extremely) scale. The instrument consists of 10 negative emotions (Cronbach s α = 0.86) and 10 positive emotions (Cronbach s α = 0.88), providing an assessment of both positive and negative affect. We obtained the positive affect score and negative affect score by averaging the items belonging to each of these two subscales. Healthy Food Choices. Referring to the coding methods of previous research, the choice of unhealthy food was coded as 0, the choice of healthy food was coded as 1. The coded number of food choice were added up to form the number of healthy food choices [26]. Control Variables. Attitude toward healthy food was measured with four items (bad/good, dislike/like, unfavorable/favorable and negative/positive) (Cronbach s α = 0.87) [9]. Health consciousness (I try to avoid foods that are unhealthy) [9], personal hunger and thirst (I am hungry/thirsty now) [43], and personal health state (I am in good health) were measured with one item respectively and rating on a 7-point scale ranging from 1 (definitely disagree) to 7 (definitely agree). Control variables were measured to check whether these variables had an impact on healthy food choices. Background Variables. Participants were asked about their age and gender.

4

Results

Control Variables. There were no significant differences in health consciousness, personal health, attitude toward healthy food, hunger, and thirst between the two conditions (PS > 0.1; see more details in Table 1). Foods. The results showed that 87.34% of the subjects thought the vegetable sandwich was healthier, 96.20% of the subjects thought coarse grain biscuit was healthier, 91.77% of the subjects thought whole-wheat bread was healthier, 93.67% of the subjects thought sugar-free cola was healthier, 94.93% of the subjects thought pure milk was healthier, and 87.34% of the subjects thought mineral water was healthier. This indicated that the healthy and unhealthy foods provided were easily recognized by the subjects.

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Table 1. Summary of results in the study Mexposure Mcontrol t(156) P Health consciousness 5.77 5.41 Personal health Attitude toward healthy food 5.19 3.19 Hunger 3.66 Thirst 3.05 Negative effect Positive effect 4.00 3.58 Healthy food choices N 79 ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001

5.90 5.25 5.17 3.10 3.73 2.64 4.14 2.44 79

−0.79 0.77 0.08 0.31 −0.26 2.16 −0.82 4.65 −

0.432 0.443 0.938 0.754 0.794 0.032∗ 0.412 0.000∗∗∗ −

Healthy Food Choices. The healthy food choices in the death information exposure group (Mexposure = 3.58, SD = 1.61) was significantly more than that in the control group [(Mexposure = 2.44, SD = 1.47), t(156) = 4.65, P = 0.000]. The result suggests that exposure to death information (vs.no) promoted consumers’ healthy food choices. Impact of Positive and Negative Affect. The score of negative moods in the group of death information exposure (Mexposure−negative = 3.05, SD = 1.13) was significantly higher than that in the control group [(Mcontrol−negative = 2.64, SD = 1.24), t(156) = 2.16, P = 0.032]. While positive effect was no significant between the two groups [(Mexposure−positive = 4.00, SD = 1.07), (Mcontrol−positive = 4.14, SD = 1.18), t(156) = −0.82, P = 0.412]. The negative affect caused by death information exposure in the experimental group were different from those of the control group. When we conducted an analysis of covariance with the affect scores as covariates [28], the healthy food amounts choose in the group of death information exposure was still significantly more than that in the control group [F (154) = 22.99, P = 0.000]. Thus, this finding was not caused by affective differences. Moderation of Perceived Personal Control. Subjects with high level of perceived personal control and low level of perceived personal control were categorized by a median split of the perceived personal control score: subjects scoring higher than the mediation (a score of 4) were classified as high level of perceived personal control, and those scoring lower than the median were classified as low level of perceived personal control. We conducted ANOVA to test the interaction between exposure of death information and perceived personal control. We found that the interaction between these two variables on healthy food choice was significant [F (1, 154) = 17.25, P = 0.000]. Further simple effect analysis showed that, for consumers with high level of perceived personal control, choice of healthy food in experimental group (Mexposure = 4.46, SD = 1.13) was significantly more than control group [(Mcontrol = 2.51, SD = 1.30), F (1, 155) =

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38.10, P = 0.000]. While for consumers with low level of perceived personal control, there was no significant difference between the two groups [(Mexposure = 2.49, SD = 1.46), (Mcontrol = 2.38, SD = 1.61), F (1, 155) = 0.36, P = 0.55] (see Fig. 1).

Fig. 1. Moderation of perceived personal control. (Death information: Exposure vs. Control; Perceived personal control: High vs. Low)

The results showed that perceived personal control plays a moderating role in the process of death information exposure affecting healthy food selection. For individuals with a high level of perceived personal control, exposure to death information results in increasing their choices of healthy food. For individuals with a low level of perceived personal control, exposure to death information doesn’t result in increasing the choices of healthy food any more. Hypothesis 1 and Hypothesis 2 were supported.

5 5.1

General Discussion Conclusions

Across experiments with food and drink, we demonstrated that perceived personal control plays a moderating role in the effect of death information exposure on healthy food selection. For individuals with high perceived personal control, exposure to death information (vs.no) promotes healthy food choice. For individuals with low perceived personal control, exposure to death information (vs.no) can’t promote healthy food choices anymore.

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Theoretical Implications

In this paper, TMHM was applied to consumer’s healthy food choices, which enriches the application field of TMHM. Previous studies have shown that conscious death thought can lead to health behavior-oriented response, thereby reducing smoking [4], increasing the willingness to exercise [2] and use sunscreen [36]. There are only a few studies, such as Ferraro et al., explored the effects of death thoughts on self-control, which involved healthy food choices [12]. Besides, McCabe et al. explored the impact of exposure to death information on the purchase of nutritious foods [31]. But both of them take the unconscious death thoughts after a delay as an independent variable. This paper studied the influence of conscious death thoughts without delay on healthy food choices, which enriches the application fields of TMHM. As TMHM predicts, the propensity of the response to affect death-related cognition is critical. Variables relevant to coping with the health threat and its perceived association with death should moderate health responses. To date, research relevant to this hypothesis has examined the effect of conscious mortality thoughts on health outcomes as a function of perceived vulnerability [3], health optimism, response efficacy [10], active coping strategies [1], and age. In addition to the factors above, we think perceived control can also play a moderating role. Perceived personal control is a stable personal trait [8]. A positive sense of control over one’s life is essential for maintaining health and well-being [6]. Relevant research indicates that mortality salience increased the actual risktaking of individuals with low perceived personal control. Individuals with high perceived personal control show decreased risk-taking in the mortality salience condition [33]. Results of our research further validate the research conclusions related to mortality salience and perceived personal control, enrich the TMHM, and clarify the boundary conditions of death information exposure promoting healthy food choices. 5.3

Practical Implications

We found that disaster or death-related information is an effective way to promote healthy food choices. Healthy food manufactures can carry out public welfare activities in disaster areas shortly after major disastrous events, such as earthquakes, typhoons and so on. In addition, in order to achieve a better propaganda effect, the best advertising time for healthy food brands is after news programs rather than other entertainment and variety shows, because news programs often contain death-related reports. Besides, one research shows that when using persuasive fear information, death-related cognition can be aroused without explicitly mention of death. The same effect can be achieved by indicating that the behavior has serious consequences related to health [41]. This makes the application of death information exposure to promote healthy food choices more flexible. We found that for individuals with low perceived personal control, exposure to death information could not increase the choice of healthy food. While the

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individual’s sense of personal control may be affected by other external factors [29]. Therefore, we can enhance the individual’s perceived personal control and make them respond to death information exposure positively. For example, in product advertising practice, we can focus on meeting the needs of consumers in disaster areas for perceived personal control. Thus increase consumers’ preference for brands and products. 5.4

Limitations and Future Directions

In this paper, we only discussed the influence of death thoughts in consciousness and individual defensive response on the choice of healthy food. There was no discussion on the choice of healthy food by unconscious death thoughts and defensive modes. The TMHM points out that unconscious death thoughts can also lead to healthy and unhealthy behaviors. Cultural world view and self-esteem are defensive ways for individuals to cope with unconscious death thoughts, which will affect their health decision-making after death information is exposed. In the future, we can explore the influence of unconscious death thoughts on healthy food choices and their boundary conditions. We can even study the influence of two different types of death thoughts on healthy food choices simultaneously. The subjects of the present study are mainly college students. There are some differences between the subjects and ordinary consumers, and further tests are needed in the field and real marketing scenarios. Acknowledgements. The authors are grateful to the study participants for their cooperation.

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Evaluation of Modern Service Industry Under Economic Transformation Based on Catastrophe Series Method Xiaoning Yang1 , Yingchun Chen1 , and Lu Gan1,2(B)

2

1 College of Architecture and Urban-Rural Planning, Sichuan Agricultural University, Dujiangyan 611830, People’s Republic of China ganlu [email protected] Business School, Sichuan University, Chengdu 610064, People’s Republic of China

Abstract. With the proportion of the service industry market increasing, there is an imperative need to explore how to develop and innovate the service industry in an economically stable environment. To solve the comprehensive evaluation issue of the modern service industry and determine the key influencing factors, a comprehensive evaluation system for the development capability of the service industry was established. Firstly, the entropy method was adopted by this research to empowerment the various indicators. Secondly, the research used the principal component analysis method to determine the correlation between these indicators. Thirdly, catastrophe progression method was applied to the classification and indicator level calculation of indicator system. Finally, a case study is tested to validate the effectiveness of the model. The results show that the fluctuation of the development environment has a great impact on the development capability of the service industry. And the policy and force majeure are two major factors in market volatility. Finding and circumventing negative influences has become the key to improving the service industry in the context of economic transformation. Keywords: Service industries Industrial economics

1

· Government policy · Indicators ·

Introduction

The importance of the development of the service industry for the construction of the contemporary economic market is well known. The current market transformation for the development of the economy is the general trend. Just as the modern economic growth mentioned by Simon Kuznets is actually an overall change in the economic structure, it is not just an industrial revolution or a revolution in the service industry [5]. For the exploration of the service industry, some of the current service industry evaluation systems do not consider the demographic factors, so it is not conducive to horizontal comparisons between regions. Besides, the development level of the producer service industry and c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 640–654, 2020. https://doi.org/10.1007/978-3-030-49829-0_48

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the knowledge service industry are also ignored by most systems, causing that these systems could not reflect the development quality of the current regional service industry [1]. At present, most research in the service industry selects specific industries or groups as research objects. Other studies tend to analyze the time series of research service industry development. The rest mainly focuses on studying the sample data of each region. For the purpose of the comparison of service industry research in different regions, the service industry evaluation system constructed by Deng mainly involves four aspects: specialization level, development level, growth potential, and basic conditions. It comprehensively compares the advantages and disadvantages of the Chinese service industry in the east and west. Besides, it also explores the defects of the central region [2]. However, due to the lack of a comprehensive and objective evaluation system, it is difficult to meet the actual needs of the current development of the service industry. In addition, in the context of big data, the lack of empirical investigation and analysis has led to a failure to fully understand modern service industry activities. Meanwhile, the nature, humanities and policy support of service industry are independent and interconnected, which results in a certain contradiction in the evaluation. Therefore, how to analyse regional characteristics, locate indicators selection principles, and scientifically evaluate development capabilities of regional service industry have become a valuable research area. Considering the interrelation and restriction between the nature, culture and policy support of different regions, and the contradictory in the formation of indicator system, the catastrophe series method suitable for solving multi-objective contradiction is selected as the data analysis method, making the conclusion of indicator level more effective. To evaluate the development ability of the service industry reasonably, this paper selects Sichuan Ya’an, represented by eco-tourism service industry, as the research area and analyzes the impact of ecotourism on the development capability of modern service industry. It is notable that the inadequacy of the previous comprehensive evaluation system was avoided successfully. The research framework is shown in Fig. 1. Compared with the traditional service component evaluation methods such as principal component analysis, entropy method and fuzzy analytic hierarchy process, the catastrophe progression method can solve the contradictory and complex multi-objective decision-making problem. And the inherent contradiction between the indicators, which is unresolvable for other current evaluation methods, can be solved by the above-mentioned method. In addition, there are still many other complex situations that need to be addressed. For example, the differences in regional development, regional resources and regional humanities, the contradiction between the service industry, and differential development goals. To handle these situations, this paper will focus on the study area, select the factors with greater influence as representative indicators and highlight the regional characteristics. These measures are beneficial to intuitively reflect the development of the service industry in the research area.

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Fig. 1. Research framework

The other parts of this paper are composed as follows. Section 2 elaborates the current research on the service industry. Section 3 lists the index system construction according to the characteristics of the research area. Section 4 introduces the research method used in this paper. Section 5 describes the substitution of research area data into the model, the level of indicators is derived and summarized and analyzed, and relevant policy recommendations are proposed. Finally, Sect. 6 summarizes the conclusions and discusses the limitations of this study and the direction of further research.

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Literature Review

Nowadays, there is an urgent need to make appropriate adjustments to China’s economic order to participate in higher-level international cooperation. Therefore, the transition of the market from an industrial economy to a service-oriented economy has become an important measure for China’s development. The 12 measures to accelerate the development of the national service industry were put forward in the planning outline. At present, majority of domestic and foreign scholars have conducted relevant research on the development of the service industry. Lupo analyzed how to improve the competitiveness and profitability of

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the medical service industry from four aspects: health care personnel, compassion, interpersonal relationship and logistics service [8]. Based on im & Kim’s QUESC model and Yong and Donna’s SSQRS model, Shu accurately evaluated the quality of the fitness service industry from environmental quality, employee service quality, course quality and result quality [14]. Wood Peter explored that the economic development of London’s cities depends on labor intensity and knowledge, while service industry competitiveness and innovation dominated in the economic development of cities from an industry perspective [11]. Feng & etc. constructed the evaluation index system of China’s service industry development from four aspects: development scale, industrial structure, growth rate and economic benefit, and evaluated the development level of service industry in 31 provinces and autonomous regions in China [3]. Qing Ye evaluated the level and stage of service industry modernization in G20 countries since 1990 according to the service content, service quality, and service management [1]. Yang & etc. empirically analyzed the growth of total factor productivity in China’s service industry through the nonparametric Malmquist index method. And the result shows that information technology is an important factor for the difference in the service industry [12]. Using the Display Comparative Advantage Index (RCA), Belay Seyoum explored the competitive advantage in the development of the service sector in the total Chinese [10]. To clarify the status and prospects of the service industry, A.P Layton & etc. established an integrated indicator of the overall economic activities of the service industry, and a growth cycle chronology was constructed to make corresponding predictions of changes [6]. The above studies mainly focus on the specific service industry and the overall situation of the service industry. Different models and research methods were used by scholars to make corresponding predictions on service development and prospects. However, most of these researches lack analysis of the constraints on the development of the service industry in the research locations and the impact of the local superior industry. Therefore, the existing comprehensive evaluation system is not suitable for regional research.

3

Indicator System

To better match the current development status of the modern service industry, it has become a key issue to study how to reasonably and effectively plan and rectify the service industry. Therefore, this paper focuses on analyzing the impact of eco-tourism on the development capacity of the modern service industry. According to Ya’an statistical yearbook and literature review, combined with the actual situation of ya’an service industry, a comprehensive evaluation system is established. At last, three first-level indicators, seven second-level indicators and 19 third-level indicators are constructed. The evaluation index system is shown in Fig. 2.

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Fig. 2. Indicator system

4 4.1

Methodology Determine the Weight

The text will use the entropy method to assign weights of the indicators to minimize the impact of subjective factors. The details for realizing the entropy method are as follows: (1) Data normalization processing, processing data into 0–1 to eliminate the influence of different dimensions and directions [7]. (2) Determine the equation selection by considering the positive and negative effects of the indicator. (3) Calculate the weight of each indicator. The formula is as follows: Deviation standardization: x=

X − min , max − min

(1)

where max is the maximum value of the variable and min is the minimum value of the variable.

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Determine the positive and negative properties of indicators: Positive indicator: xij = (xij − min{xj })/(max{xj } − min{xj })

(2)

Negative indicator: xij = (max{xj } − xij )/(max{xj } − min{xj })

(3)

xij represents the value of the index j in the sample of the i year, max{xj } represents the maximum value of the j index, and min{xj } represents the minimum value of the j index. Index proportion: xij Pij = n i=1

xij

(i = 1, 2, ..., n; j = 1, 2, ..., m)

Pij represents the proportion of the i sample of the j indicator. The entropy of the index: n Pij ln(Pij ) Ej = −K i=1

K = 1/ ln(n), Ej ≥ 0.

(4)

(5)

(6)

Ej represents the entropy of the j indicator. Entropy redundancy of information: Dj = 1 − Ej

(7)

Dj represents the utility value of the indicator. Weight of index: Dj Wj = m i=1

Dj

(8)

Wj represents the weight of the j indicator. 4.2

Determine Complementarities of Indicators

The correlation coefficient between the indicators is determined through the principal component analysis method. According to the principle of application of catastrophe progression, if the correlation coefficient between the indicators is less than 0.5, that is, there is no obvious correlation between the indicators. In this case, the non-complementary principle is selected. The value attained by calculation adopts the principle of “large and medium take small”. On the contrary, the principle of “small medium and large” is adopted for comparison when there is complementarity between the indicators.

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Determine the Mutation Type

The catastrophe progression method is a comprehensive evaluation method which uses the total membership function to rank and analyze the evaluation objectives. The total membership function can be obtained by a comprehensive calculation on mutation fuzzy membership function using normalized formula. For the generation of mutated fuzzy membership function, the first step is a multi-level contradiction decomposition of the evaluation target. Besides, catastrophe theory and fuzzy mathematics are also involved. There are four common forms of mutation. Folding mutation: f (x) = x3 + ax

(9)

Point mutation: f (x) = x4 + ax2 + bx

(10)

Swallowtail mutation: f (x) = x5 + ax3 + bx2 + cx

(11)

Butterfly Mutation: f (x) = x6 + ax4 + bx3 + cx2 + dx,

(12)

where x represents the state variable of the system, f (x) refers to the potential function of the state variable x, a, b, c, d is the control variable of the state variable x. By obtaining the first derivative of the potential function f (x) of the catastrophe model, let f  (x) = 0 get its equilibrium surface; find the second derivative of f (x) and let f  (x) = 0 get the singular point at the same time, f  (x) = 0 and f  (x) = 0 can obtain the system’s bifurcation equation, indicating the state change of the system when the value of the control variable satisfies the bifurcation setting equation [9]. The fork set equation is expressed by the control variable represented by the state variable, and the fork set equation in the decomposition form can be obtained. However, the fork set equation of decomposition form cannot calculate the contradiction control variables, so it is necessary to combine the fork set equation of the decomposition form of each mutant model with the fuzzy membership function to obtain the formula of attribution. The indicator model in this paper is divided into four kinds according to the mutation theory. √ (13) Folding mutation: Xa = a Point mutation: Xa = Swallowtail mutation: Xa = Butterfly Mutation: Xa =

√

√

√

a, Xb =

a, Xb =

a, Xb =

√ 3 b

√ √ 3 b, Xc = 4 c

√ √ √ 3 5 b, Xc = 4 c, Xd = d

(14) (15) (16)

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Case Study Study Area

The study area, Ya’an City, Sichuan Province, is shown in Fig. 3. This region with two districts and six counties is 120 km away from Chengdu and its total area is 15046 square kilometers. As a famous historical and cultural city in Sichuan and “China’s outstanding tourist city”, Ya’an has been committed to eco-tourism services. And eco-tourism shares the same importance with characteristic agriculture for urban development. However, various practical factors, such as different resource reserves and different development priorities, lead to an unbalanced development of the service industry. Meanwhile, changes in policy orientation and people’s attitudes to life have had a huge impact on the development of the service industry.

Fig. 3. Map of Ya’an, Sichuan

5.2

Result

This article obtains the 2011–2017 Ya’an Statistical Yearbook through the official website of the Ya’an Municipal Bureau of Statistics, screening the data required for the indicators, confirming the authority and accuracy of the data, and standardizing the data, the index weights are sorted according to the entropy value and the mutation type is established. As shown in Table 1 below. According to the calculation rules of the mutation theory, we obtained the change trends of the service industry development capacity index and all aspects of the impact index for Ya’an City 2011–2017, including the development environment, development level and development potential. As shown in Table 2 and Fig. 4.

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Fig. 4. Trends in systems Table 1. Post-empowerment indicator system and mutation type Primary indicator

Secondary indicators

Three-level indicator

A1 (0.384888) Complementary swallowtail mutation

B1 (0.170004) Non-complementary swallowtail mutation

C1 (0.094309) C2 (0.033370)

B2 (0.154237) Non-complementary swallowtail mutation B3 (0.060647) Non-complementary cusp mutation A2 (0.317998) Complementary cusp mutation

B4 (0.159458) Complementary swallowtail mutation B5 (0.158540) Complementary butterfly mutation

A3 (0.297113) Complementary cusp mutation

B6 (0.172951) Non-complementary cusp mutation B7 (0.124163) Non-complementary cusp mutation

C3 (0.042325) C4 (0.073210) C5 (0.048772) C6 (0.032256) C7 (0.033152) C8 (0.027496) C9 (0.069920) C10 (0.055669) C11 (0.033869) C12 (0.058370) C13 (0.039680) C14 (0.034489) C15 (0.026000) C16 (0.123856) C17 (0.049094) C18 (0.081922) C19 (0.042241)

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Table 2. Index of indicators for each system Years Development Level of Development Service industry environment level development potential level capability level 2011 2012 2013 2014 2015 2016 2017

5.3

0.073150239 0.751949520 0.472444593 0.866096562 0.651902091 0.515612149 0.796385529

0.568472978 0.774508889 0.662020598 0.791168748 0.834549777 0.854352129 0.789490218

0.438912190 0.069577483 0.385134879 0.402111227 0.107497115 0.548380108 0.602971019

0.270463009 0.513590733 0.687346050 0.796318031 0.572597176 0.718061382 0.881199231

Discussions

(1) The development environment of the service sector In 2012, Sichuan Province decided to build a strong province in the western modern service industry (Fig. 5). With the support of the policy, the development environment of the Ya’an service industry has been greatly improved. Accumulated many notices on information services, pension services, health services, and eco-tourism. Compared with 2011, the per capita urban road area, telecommunications coverage and green coverage rate in infrastructure conditions increased by 10.1%, 6.0%, and 32.1%. In the first quarter of 2013, the service industry realized an added value of 182.33 billion yuan, a year-on-year increase of 8.4%, which was 0.1% points higher than the national total. In 2013, the Lushan earthquake in Ya’an caused a huge environmental turmoil, and the post-disaster reconstruction work became the key point of urban development. In 2014, the government issued the “Sichuan Province Service Industry Development Four-Year Action Plan (2014–2017)”. In 2014, the index of government investment in the service industry reached 0.778, which is the crucial factor for the remarkable improvement of service industry development. However, the service industry’s capacity, development environment and development potential showed a downward trend in 2015. To cope with this dilemma, in 2016, Ya’an released the “Main Points of the Service Industry Development in the Province in 2016”, and pointed that the development of the service industry should be promoted from five aspects: in-depth, accelerated, vigorous, sustained and integrated, aiming to achieve a 10% increase in the added value of the city’s service industry. The goal is to complete the supply-side structural reform of the service industry, such as “de-capacity, de-stocking, deleveraging, cost reduction, and short-boarding”. The service industry gathers and integrates development, and the tourism industry breaks through development. The total investment of the service industry exceeds 2.5 billion yuan. The local service industry “goes out” to vigorously develop service trade and service outsourcing, and strengthen the cultivation of talents. Eventually, compared with 2015, The city’s service industry’s total production increased by 2.827 billion yuan, capacity index of Ya’an

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service industry increased by 25.4% in 2016, achieving a huge breakthrough. By 2017, the indicators of the development environment tend to be balanced, which provides a good ecological environment for the development of Ya’an service industry.

Fig. 5. Changes in development environment subsystems

Generally, the overall development environment of Ya’an service industry in the period of 2011–2017 is turbulent, and the above situation is not conducive to the stable development of the service industry. Therefore, the following four points are the key to improve the development capability of the service industry. The first point is to maintain a stable environment for development. The second is to strengthen infrastructure construction. The third is to strengthen the government’s emphasis on the service industry. The last is to increase the overall vitality of the service industry. (2) Level of development in the service sector During the period of 2011–2012, the development level led the service industry to achieve steady growth (Fig. 6). In 2013, a 7.0-magnitude earthquake occurred in Lushan, Ya’an, causing a total of 1.52 million people to be affected. Many cultural monuments were destroyed, causing tremendous damage to Ya’an’s overall economic income and structure. The city’s tertiary industry’s GDP fell by 4.2% from the previous year, and investment fell by nearly 5%. Compared with 2012, the annual income of eco-cultural tourism in Ya’an decreased by 11.4% and the development level index decreased by 14.5%. In 2014, the government decentralized the “Lushan Earthquake Recovery and Reconstruction Master Plan Implementation Project (Adjusted Edition)”, with a total investment of 75.98 billion yuan and 2,188 reconstruction projects. Focusing

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Fig. 6. Changes in the development level subsystems

on infrastructure, industrial reconstruction, cultural tourism, and disaster relief reconstruction projects, the reconstruction will be completed in 2016, including 12.71 billion yuan for scenic infrastructure construction and 2.54 billion yuan for cultural tourism industry projects. A large part of the investment was transferred to the service industry. However, along with the orderly reconstruction of the post-disaster reconstruction, the residents’ lives have recovered well and the development level has achieved the annual increase. The Ya’an service industry development level index in 2016 is up nearly 30% from 2013. The Ya’an ecotourism income value double the 2013 level and the service industry’s income rises by 21.2% points of GDP. Apparently, the tourism industry led the city’s service industry to grow rapidly. Despite all this, the proportion of tertiary industry in Ya’an in 2017 is still lower than the national and provincial average by 12.2 and 10.4% points. The imbalance of development is serious and the stability of development is declining. In addition, the lack of emerging consumer hotspots is also one of the reasons for this development dilemma. Li presented that the proportion of the traditional service industry has declined, and emerging industries have become the main driving force for the structural upgrading of the tertiary industry [4]. The growth value of Ya’an strategic emerging industries accounts for a low proportion of GDP and the growth rate is slow, leading to a decline in development levels. From another perspective, all indicators are at a stable growth level based on the analysis of the development level of Ya’an service industry from 2011 to 2017. The activity of the market is owing to the national policy support and the rise of the people’s consumption index. Excellent industrial competitiveness will ultimately be reflected in the ability of services, enterprises, and industries [13]. Consequently, improving the competitiveness of Ya’an industry is the primary task to cover the shortcomings of the development level.

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(3). Growth potential in the service sector The service industry development potential subsystem is shown in Fig. 7, With the gradual formation of a good development environment, the service industry in Ya’an City has increased year by year. By 2017, the overall service industry growth rate of Ya’an City has reached 33.6%, and the overall trend is good. However, there were two declines in 2012 and 2015 respectively. In 2012, the focus of work shifted to energy conservation and emission reduction, and the “Responsibility System for Energy Conservation and Emission Reduction Targets in Sichuan Province” was issued. The work of Ya’an City highlighted the theme of energy conservation and invested a large number of funds to eliminate backward production capacity. It is planned to reduce energy consumption by 15% in 2015 compared to 2010. In 2015, Ya’an City vigorously promoted the grassroots deepening reform tasks, focused on improving the urban and rural planning mechanism, improving the social security system, and reforming the land-use system. The city’s development priorities have shifted, so fluctuations in development potential have made the index level in 2016 not change much compared to 2011. The development potential index increased by only 10% points in five years. Fortunately, development potential trends showed signs of an upturn in the period of 2015–2017, providing excellent reserve power for the development of the service industry.

Fig. 7. Changes in development potential subsystems

Compared with the development environment and development level, the development potential of Ya’an service industry in 2011–2017 is still at a low level, which has a slightly low impact on the overall service industry. Thus, popularizing the current situation of economic transition and the development trend of the service industry, as well as improving education level is necessary for providing the reserve force for innovative development of the service industry. Moreover, it is an imperative need to steadily improve the growth level of the service industry for improving the development potential.

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Conclusion

At present, the country makes every effort to promote economic transformation, and developing the service industry is the trend of the ages. Belay Seyoum has ever proposed that many developing countries have advantages in the tourism service industry, but financial and commercial services still have room for improvement [10]. Therefore, each region should also make a corresponding assessment based on the development capability of the service industry and strengthen the advantages and remedy the shortcomings. This paper selects Sichuan Ya’an as the research area and establishes a comprehensive evaluation index system for evaluating the development ability of the service industry. Then the mutation series method and entropy method integrated with principal component analysis method are used to analyze the collected data. The result shows the importance of the development environment for the development of the service industry and the great impact of market changes caused by policy orientation and natural disasters. For further study, more data from extra years need to be considered to improve the accuracy of the result since this paper only selected the data from 2011 to 2017. Moreover, different regions with the same characteristics of service industry development should be taken as the study area for analysis to form a horizontal comparison of service industry development capabilities. Acknowledgements. The research was supported by the Foundation of Yaan Philosophy and Social Science Research Planning Funds (Grant No. YA20190029), the Foundation of Chengdu Science and Technology (Grant No. 2017-RK00-00274-ZF), the Foundation of Chengdu Science and Technology (Grant No. 2019-RK00-00311-ZF), and the Foundation of Chengdu Philosophy and Social Science Planning Research Funds (Grant No. 2019L12).

References 1. Ye, Q.: Evaluation of service industry modernization in G20 countries. The 15th China Modernization Research Forum, Beijing China (2017). (in Chinese) 2. Deng, Z., Hu, S., Zhang, W.: Evaluation index system and empirical analysis of modern service industry in China. Technol. Econ. 31(10), 60–63+105 (2012). (in Chinese) 3. Feng, H., Sun, W.: Research on service industry development evaluation index system and development level of china’s provinces and regions. Dongyue Tribune 31(12), 5–9 (2010). (in Chinese) 4. Jiangfan, L.: Industrial structure upgrading and tertiary industry modernization. J. Sun Yatsen Univ. (Soc. Sci. Edn.) 04, 124–130+144 (2005). (in Chinese) 5. Kuznets, S., Murphy, J.T.: Modern economic growth: rate, structure, and spread, vol. 2. Yale University Press, New Haven (1966) 6. Layton, A.P., Moore, G.H.: Leading indicators for the service sector. J. Bus. Econ. Stat. 7(3), 379–386 (1989) 7. Li, Y., Kappas, M., Li, Y.: Exploring the coastal urban resilience and transformation of coupled human-environment systems. J. Cleaner Prod. 195, 1505–1511 (2018)

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8. Lupo, T.: A fuzzy framework to evaluate service quality in the healthcare industry: an empirical case of public hospital service evaluation in sicily. Appl. Soft Comput. 40, 468–478 (2016) 9. Ningning, W.: Study on competitiveness evaluation of high-tech service industry based on mutation progression method. Ph.D. thesis, Hefei University of Technology, Hefei (2017) 10. Seyoum, B.: Revealed comparative advantage and competitiveness in services: a study with special emphasis on developing countries. J. Econ. Stud. 34(5), 376–388 (2007) 11. Wood, P.: Service competitiveness and urban innovation policies in the UK: the implications of the ‘London paradox’. Regional Stud. 43(8), 1047–1059 (2009) 12. Xiangyang, Y., Xiang, X.: An empirical analysis of the growth of total factor productivity in China’s service industry. Economist 03, 68–76 (2006). (in Chinese) 13. Yuanqiang, L.: T-S fuzzy prediction on competitive structure model and evaluation system of emerging industry innovation alliance. Cogn. Syst. Res. 52, 192–197 (2018) 14. Zongli, S.: A comprehensive assessment of commercial health & fitness clubs service quality. Int. J. Manag. Sci. Eng. Manag. 7(4), 263–267 (2012)

Research on Evaluation and Influencing Factors of Provincial Technological Innovation Capability in China Lihong Wang and Hongchang Mei(B) School of Management, Chongqing Technology and Business University, Chongqing 400067, People’s Republic of China m [email protected]

Abstract. Regional technological innovation capability is the core element of promoting developmen. Under the circumstances of the transformation of international trade relations and the continuous expansion of China’s opening to the outside world, this paper constructs an evaluation index system of China’s provincial technological innovation capability from four aspects of innovation input, environment, technological achievement output and value transformation, and analyzes the distribution of technological innovation capability and its influencing factors by using principal component analysis (PCA) and cluster analysis. The results show that, at present, China’s technological innovation capability is quite different between regions and within regions, and technological innovation input and value transformation contribute greatly to the development of the regional technological innovation capability. The innovation environment and technological achievement output need to be improved and strengthened. In the end, the paper puts forward some suggestions on improving the overall level of China’s technological innovation capability. Keywords: Provincial technological innovation capability · Evaluation index system · Principal component analysis · Influencing factor

1

Introduction

In December 2018, a spokesman for the State Ministry of Commerce, Gao Feng, said at a regular conference on trade relief and industrial development that China was facing complex and severe trade frictions. Since the beginning of 2018, the United States and its partner countries have increased their friction with China by means of tariff barriers, technological blockades and suppression of high-tech enterprises, thus restricting the market access of Chinese-made products. The US-China trade conflict has escalated from tariff war to a technological war and a strategic competition between the two giants [17]. Trade friction affects China’s economic development by affecting foreign investment [20], products export, c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 655–669, 2020. https://doi.org/10.1007/978-3-030-49829-0_49

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employment, etc. In addition, due to the influence of the “three-phase superposition” in China, the investment in China, especially in research and development, is insufficient in staying power and the economic growth expectation is lowered. Schumpeter (1912), the initiator of modern innovation theory, believes that innovation is the recombination of production conditions and factors and is the core of economic development [14]. Innovation can not only promote the development and growth of regional economy [16], but also is an important driving force to promote the sustainable and high-quality development of China’s economy. Therefore, enhancing regional technological innovation capability is an inevitable choice to realize high-quality economic development and enhance China’s economic international competitiveness. In the 2018 Global Competitiveness Report released by the World Economic Forum, China ranks 13th in competitiveness. Obviously, as the world’s second largest economy, China still has a long way to go in its innovation-driven development. There are many factors that affect the development of innovation capability. Environmental factors [8,18], regional economic structure, R&D intensity [3], leadership [9], venture investment [12] all have different degrees of impact on it. However, at present, there are few researches on influencing factors based on the development stage of technological innovation capability itself. Under this background, it is very necessary to clarify the distribution and current situation of technological innovation capability, analyze the phased factors and short boards that affect its development from the perspective of the development path of technological innovation, and seek the path to enhance China’s economic international competitiveness by strengthening technological innovation capability. The contributions of this paper is mainly reflected in the following two aspects. Firstly, the existing literatures on the evaluation of technological innovation capability are mostly based on qualitative methods to select evaluation indicators. This paper uses correlation-principal component analysis to build a quantitative model and quantify the distribution of provincial technological innovation capability in China, thus clarifying its distribution and regional differences. Secondly, in terms of influencing factors of technological innovation capability, most of the existing researches focus on external factors. From the perspective of the development path of technological innovation, this paper is committed to analyzing the internal phased factors and the short boards of its own development, getting a deeper understanding of the development situation of China’s technological innovation capability, and putting forward suggestions on how to enhance China’s technological innovation capability. The structure of the rest of this paper is as follows: The second part is literature review. The third part is the construction of evaluation index system. The fourth part is empirical analysis. The fifth part is the research conclusions and suggestions.

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2 2.1

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Literature Review Evaluation of Regional Technological Innovation Capability

Regional technological innovation ability is the ability to transform technological innovation achievements into new products, new processes and new services within a specific region and a specific space. Utterback (1974) [15] syllogism divides the process of technological innovation into three processes: the formation of new ideas, the transformation of technological achievements and marketization. Rbobert (1988) [13] regards technological innovation as a management process of creating new ideas, forming new ideas, forming processing models and realizing commercialization. Fu Jiaji, a Chinese scholar, divides the technological innovation capability into six process capabilities: input, research and development, innovation tendency, innovation management, manufacturing and marketing. In the research on the evaluation of regional technological innovation, some scholars use a single index to study, for example, Mikel Buesa et al. (2006) [2] use the number of patents as an index to measure Spain’s regional innovation capability, and Chen et al. (2009) [4] also evaluate the technological innovation capability of China’s eight major economic regions based on data of patent technology. More scholars are doing research through the construction of evaluation index system. Liu et al. (2011) [10] construct an evaluation system including innovation input capacity, technological achievement output capacity and industrial achievement output capacity, and measure the development of regional technological innovation in China with topsis method improved by entropy weight. Bai (2012) [1] uses factor analysis method to analyze the technological innovation capability of different regions in China from five main indicators: innovation environment, knowledge creation, knowledge acquisition, enterprise innovation and innovation benefits. Huang et al.(2015) [7] make a comparative analysis of the four urban agglomerations in Sichuan province by establishing an evaluation index system including innovation investment, transformation of scientific and technological achievements, industrialization of high-technology, economic and social development and environment, and using AHP and fuzzy comprehensive evaluation methods. Yi et al. (2016) chose an improved objective order analysis weighting method to evaluate the regional innovation capability of 11 provinces in eastern China from three aspects: innovation input, innovation output and innovation environment. In order to make up for the shortcomings of incomplete data, few samples and grey characteristics of data, Jin (2019) [6] uses Deng’s grey model to measure China’s provincial technological innovation capability after constructing an evaluation index system that includes four dimensions of technological innovation input, output, supporting environment and sustainability. It can be seen that, at present, scholars generally believe that the development of regional technological innovation capability is affected by many factors, so it is mostly measured by evaluation system. However, through combing the literature, it is found that in the index selection of the evaluation system, most

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scholars select the indexes by qualitative methods, and the scientificity of the index system needs to be strengthened. 2.2

Influencing Factors of Regional Technological Innovation

Schumpeter believes that entrepreneur is the key factor in determining the success of innovation. Frankel (1955) stresses that vertical integration of enterprises is conducive to the realization of technological innovation [5]. Schmookler (1966) proposes that market demand will affect the development of technological innovation [11]. Modern scholars have also conducted a large number of studies. Chen et al. (2014) study the impact of environmental factors on innovation. Adverse environmental factors have made China’s regional innovation system perform poorly in terms of both technological creation efficiency and technological commercialization efficiency on the provincial average [8]. Yu et al. (2016) analyze the impact of environmental policies on regional technological innovation. The results show that different types of environmental policies have different degrees of promotion on technological innovation across the country [18]. Chen et al. (2017) have found that regional economic structure, R&D intensity and economic development would affect China’s innovation-driven development based on the analysis of 30 provincial panel data [3]. Lakshman (2019) finds that the leadership has a strong impact on learning and innovation [9]. Pan et al. (2019) test the dynamic relationship between venture capital and China’s regional technological innovation, and find that the total amount of venture capital will promote regional economic innovation, and this promotion has a significant lag [12]. It can be seen that in the research on the influencing factors of innovation, most scholars start with external factors, while the research on the influencing factors based on the self-development stage of technological innovation capability is less. In addition, although some studies have evaluated and compared the regional technological innovation capability, they have not made an in-depth analysis of the influencing factors, nor have they compared the differences in the regional technological innovation capability.

3

The Construction of Evaluation Index System

Following the objectivity, comprehensiveness, feasibility of index selection, and availability, continuity and integrity of data, and combining with the definition of technological innovation, this paper regards technological innovation as a process from the proposal of new ideas to the commercialization of new products. In this paper, 60 indexes ware initially selected, and the initial indexes are screened by correlation-principal component analysis method to form the final evaluation model. 3.1

Logic Model of System Construction

The logical frame of the comprehensive evaluation index system constructed in this paper is shown in Fig. 1, in which technological innovation investment is the

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foundation, technological innovation environment is the support, technological achievements are the direct manifestation of the success of technological innovation, and the value transformation of technological innovation embodies the process from technological achievements to commercialization of new products.

3.2

Index Selection of Evaluation Model

After summarizing the high-frequency indexes of technological innovation capability evaluation proposed by the existing researches and international authoritative organizations, this paper makes a preliminary selection of the indexes. Investment is the initial stage of technological innovation, and is also the foundation and guarantee for obtaining strong technological strength, generally including investment in funds and human resources. In this paper, 16 tertiary indicators including equivalent full-time equivalent of R&D personnel and investment intensity of R&D funds are initially selected. Environment is the support for technological innovation, including policy environment, education environment, innovation atmosphere, etc. This paper initially selects 19 threelevel indicators such as the total registered investment of foreign-invested enterprises to obtain information reflecting the innovation environment from different angles. Only when technological achievements are transformed and realized in actual production, can technological innovation input be transformed into technological innovation output and new productivity be formed. In this paper, 14 three-level indicators such as patents, scientific and technological papers and works are initially set up. The output of technological innovation includes both the output of scientific and technological achievements and the output of industrial achievements, but in order to obtain industrial achievements, the value of scientific and technological achievements must be effectively transformed. Therefore, this paper chooses the value transformation index to consider the ability of each region’s technological achievements to realize the commercialization of new products. Under the index of technological innovation transformation capability,

Fig. 1. Logical model of evaluation index system

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11 third-level indexes are initially set, including the sales share of new products and the proportion of new product exports to total exports. 3.3

Optimization of Index System Based on Correlation-Principal Component Analysis

In this paper, the composite statistical method, correlation-principal component analysis, is used to optimize the initial index system, to delete indexes with high correlation and low factor load, and to avoid duplication of information. The specific optimization idea is shown in Fig. 2. Correlation-principal component analysis plays a significant role in the construction of evaluation index system. For example, Chi et al. (2012) construct an evaluation system on the all-round development of human beings based on this method. Correlation analysis is to calculate the correlation coefficient between each three-level index. And if the correlation coefficient is large, one of the indexes will be deleted, and if the correlation is small, both indexes will be retained. Pearson coefficient (γ) between indexes is calculated by SPSS software, and set the critical value to 0.9. If γ > 0.9, one of the indexes is deleted, otherwise it is retained. After calculation, 14 indicators including the number of new product development projects of regulated industrial enterprises and the proportion of external expenditure of research and development funds to GDP were deleted. Principal component analysis is the secondary screening of the remaining indicators, includes calculating the factor load value of each three-level indicator to the principal component, and deleting the indicator with smaller absolute value of factor load. There are four secondary indexes in this paper. According to the five subordinate indexes of each secondary index, the top five indexes with larger factor load value are selected as the criteria of its tertiary indexes. The final evaluation index system of this paper is shown in Table 1. According to the principle of factor analysis, the variance of data is used to measure the amount of information that the index can provide. The index of information contribution rate is used to measure the rationality of the evaluation index system: 85918591209621.30 × 100% = 99.99% 85917634016806.60 Where, IN represents the contribution rate of information. TrSs is the sum of variance of the screened indexes, TrSh is the sum of variance of the original IN = trSs/trSh =

Fig. 2. Optimization of index system

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indexes. Using the correlation-principal component analysis method, this paper finally selected 20 indexes from 60 initial indexes to form the final evaluation index system, and the value of IN was 99.99%, that is, 30% of the indexes finally reflected 99% of the original information, and the index construction is reasonable.

4 4.1

Empirical Analysis Research Objects and Data Sources

Considering the availability and completeness of data, the research objects of this paper are 30 provinces and municipalities directly under the central government in China. The data are mainly from the China Statistical Yearbook, China Science and Technology Statistical Yearbook and official website of the National Bureau of Statistics. 4.2

Evaluation Method

In the analysis containing multiple variables, the principal component analysis method can extract a few principal components containing most of the information of the original variables through dimension reduction, thus making the problem analysis simpler. In addition, KMO and Balet tests are used to analyze the data structure. The results show that KMO test coefficient is 0.774 and Bartlett’s test result is P < 0.001, which show that the data structure is reasonable for principal component analysis. Therefore, this paper chooses principal component analysis to study. Principal component analysis is also widely used, for example, Zhang (2016) [19] uses principal component analysis to evaluate the financial performance of China’s life insurance to identify the importance of financial indicators to the company’s financial performance. 4.3

Evaluation of China’s Provincial Technological Innovation Capability

In this paper, SPSS 25.0 is used to analyze the data, and principal component analysis is used to obtain the total variance decomposition table of provincial technological innovation capability (Table 2). As can be seen from the table, three principal components have been extracted and 86.74% of the original information has been explained. Among them, the cumulative contribution rate of the first principal component is 64.684%, which includes all technical innovation input, technical innovation value transformation indicators, and some technical innovation environment and achievement output indicators. The cumulative contribution rate of the second and third principal components is 22.06%, and there are large load values in the three indicators, namely, the proportion of professional and higher education, the turnover of technology market, and the total of professional and technical personnel in public economic enterprises and institutions.

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Table 1. Evaluation index system of provincial technological innovation capability Primary indicator

Secondary indicators

Tertiary indicators

Input capacity of Full-time equivalent of R&D technological personnel innovation (A)

Technological innovation capability

Technological Innovation Environment(B)

Unit Man-year

Intensity of investment in R&D funds

%

Contract amount for foreign technology introduction

Ten thousand dollars

Proportion of R&D personnel to employees

%

The proportion of R&D expenditure to the main business income of regulate industrial enterprises

%

Number of faculty in ordinary universities

People

Total professional and technical personnel of public economic enterprises and institutions

People

Total registered investment of foreign-invested enterprises

100 million dollars

Proportion of industrial % enterprises with R&D institutions

Output of technical results (C)

Proportion of employees with secondary education or above

%

Technology market technology flow area (contract amount)

Ten thousand yuan

University R&D staff published scientific papers

Piece

Invention patent applications

Item

Turnover in technology market

Ten thousand yuan

Number of valid invention patents Item of enterprises Value

Profitability of high-tech industry 100 million yuan

Transformation of Technological Innovation (D)

Proportion of high-tech enterprises’ operating income to total income

%

Export of new products by regulate industrial enterprises

Ten thousand yuan

Export of high-tech products

Ten thousand dollars

gdp per capita

Yuan

(1) Distribution of China’s Provincial Technological Innovation Capability In order to make a comprehensive evaluation of China’s provincial technological innovation capability, the paper obtains the principal component scores of each province, and calculates the comprehensive evaluation value with the

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Table 2. Total variance decomposition of provincial technological innovation capability Component Initial eigenvalue Total

Variance percentage

Extract load sum of squares Accumulation % Total

Variance percentage

Accumulation %

1

12.937 64.684

64.684

12.937 64.684

64.684

2

2.868 14.340

79.024

2.868 14.340

79.024

3

1.544

86.745

1.544

86.745

7.721

7.721

variance contribution ratio of each principal component Ω = Φi /Σpi Φi as the weight. As some scores of the main components are negative after standardization, the paper uses Xu’s (2009) method for reference and adds the absolute value of a minimum negative number to all the data. This way will not change the analysis results. In this paper, the minimum score is −2.344. Therefore, all data are shifted to the right by three units, and the final scores are shown in Table 3. Judging from the comprehensive evaluation results (Table 3), there are obvious differences in the distribution of China’s technological innovation capability between the east and the west. The top five provinces and municipalities in terms of technological innovation capability are Beijing, Guangdong, Jiangsu, Shanghai and Zhejiang, all cities in eastern China. Among them, Beijing, with its geographical advantages, political advantages and historical development level, has created a good atmosphere and environment for the development of its technological innovation capability, and coupled with the blessing of technological innovation investment, it ranks first. The bottom five provinces are Shanxi, Gansu, Qinghai, Ningxia and Xinjiang, which are all located in the west of China. The R&D funds and human resources in the western region are both insufficient. The intensity of R&D funds in Qinghai in 2017 is 0.68%, accounting for only 12% of Beijing. Moreover, the equivalent full-time equivalent of R&D personnel in the western region only accounts for 12.93% of the national total, so the technological innovation capability in the western region is weak. Moreover, according to the comprehensive score in 2017, the total technological innovation of 11 provinces the eastern region accounts for about 45% of the national total. Beijing scores 2.37 times as much as Qinghai. The eastern provinces have stronger technological innovation capability. As can also be seen from Fig. 3, there is an imbalance between the eastern region and western region in provincial technological innovation capacity, and the eastern region is generally stronger. In order to further reveal the distribution characteristics of technological innovation capability among regions in our country, K- means clustering analysis is carried out based on the 2017 evaluation results, and the results are shown in Table 4. According to Table 4, most provinces in the eastern region belong to Area A and B, but Hebei and Hainan belong to Area C. In 2017, Hebei’s total economic volume is 340.1632 billion yuan, ranking 8th in the total regional economic

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F1

F2

F3

Synthesis score

Beijing

4.859 6.688 4.638 5.142

Tianjin

3.130 4.345 1.574 3.192

Hebei

2.673 2.563 3.946 2.768

Shanxi

2.372 2.844 3.169 2.521

Inner Mongolia 2.223 3.185 2.661 2.421 Liaoning

2.757 3.141 3.467 2.884

Jilin

2.306 3.146 3.045 2.511

Heilongjiang

2.308 2.874 3.406 2.499

Shanghai

4.198 4.780 1.677 4.070

Jiangsu

5.268 1.970 3.114 4.531

Zhejiang

4.027 3.049 2.324 3.713

Anhui

3.112 2.527 2.818 2.989

Fujian

2.990 3.126 2.532 2.972

Jiangxi

2.511 2.546 3.022 2.562

Shandong

3.693 2.284 5.026 3.579

Henan

2.952 1.950 4.358 2.912

Hubei

3.140 3.259 4.219 3.256

Hunan

3.042 2.751 3.143 3.003

Guangdong

6.014 0.656 1.600 4.735

Guangxi

2.295 2.586 3.255 2.429

Hinan

2.004 3.074 2.022 2.182

Chongqing

3.248 3.226 0.934 3.038

Sichuan

3.031 2.442 4.431 3.058

Guizhou

2.245 2.600 2.986 2.369

Yunnan

2.333 2.476 3.138 2.428

Shaanxi

2.915 3.389 4.017 3.091

Gansu

2.143 2.808 2.992 2.328

Qinghai

1.944 3.241 2.059 2.169

Ningxia

2.192 3.369 1.736 2.346

Xinjiang

2.076 3.105 2.691 2.301

volume of our country. In the same year, its technological innovation ability ranks 14th. As a major economic province, Hebei’s technological innovation ability is obviously weak. Most provinces in the western region belong to Area C, while Sichuan, Chongqing and Shaanxi have stronger capabilities and belong to Area B. Thus, the development of technological innovation capability in the region is not balanced. In order to deeply analyze the causes of the above phenomena,

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Table 4. Comparison of K-means clustering and regional distribution K-means clustering

Regional distribution

Area A Beijing, Shanghai, Jiangsu, Guangdong

East

Area B Tianjin, Liaoning, Zhejiang, Anhui, Fujian, Shandong, Henan, Hubei, Hunan, Chongqing, Sichuan, Shaanxi

Midland Shanxi, Jinlin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan

Area C Hebei, Shanxi, Inner Mongolia, West Jilin, Heilongjiang, Jiangxi, Guangxi, Hainan, Guizhou, Yunnan, Gansu, Qinghai, Ningxia, Xinjiang

Beijing, Tianjin, Heibei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan

Sichuan, Chongqing, Guizhou, Yunnan, Shaanxi, Gansu, Inner Mongolia, Qinghai, Ningxia, Xinjiang, Guangxi

this paper uses entropy method to calculate the process scores of technological innovation, and analyzes the main influencing factors from four aspects of technological innovation investment, environment, technological achievements and value transformation in Hebei, Hainan, Sichuan, Chongqing and Shanxi. Entropy method is a method to calculate the weight of each index according to the variation degree of each index. In this method, entropy and uncertainty are inversely proportional to the amount of information. The larger the amount of information, the smaller the uncertainty and entropy, and vice versa. Here, the deviation standardization method is used to standardize the data, and the calculated scores of each secondary index are shown in Table 5. Sichuan, Chongqing and Shaanxi, which are located in the western region, belong to Area B, while Hebei and Hainan, which are located in the eastern

Fig. 3. Distribution of technological innovation capability in China

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L. Wang and H. Mei Table 5. Secondary index score of provincial technological innovation ability Province

Investment in technological innovation

Technological innovation environment

Output of technological achievements

Transformation of technological innovation value

Hebei

0.033

0.037

0.025

0.026

Hainan

0.004

0.011

0.002

0.010

Sichuan

0.045

0.037

0.065

0.043

Chongqing

0.130

0.033

0.022

0.076

Shaanxi

0.043

0.034

0.050

0.031

Eastern average 0.104

0.067

0.087

0.086

Western average 0.029

0.023

0.020

0.022

region, belong to Area C. According to Table 5, Hebei’s scores do not reach the eastern average, of which the technical innovation environment score accounted for 55.5% of the eastern average and the technical achievement output score only accounted for 28.55%. Thus, in the process of developing Hebei’s technological innovation capability, the difference between the output capability and the average level in the eastern region is the largest. Therefore, in order to improve Hebei’s technological innovation capability, we should focus on the output of technological achievements, which can be improved by increasing the output of patents, works, scientific papers, etc. Hainan ranks in the bottom of China’s overall score. As an eastern city, its scores of technological innovation process do not reach the average level in the western region. The scores of its technological innovation input and output of scientific and technological achievements are even less optimistic. Hainan’s technological innovation input is only 4.25% of the average level in the eastern region, and the output level of technological achievements is only 1.85%. Chongqing, which is located in the western region, also has a relatively low output score of technological innovation achievements. However, Chongqing has a high level of comprehensive capability due to its large investment and high level of transformation of innovation value. The technological innovation process of Sichuan and Shaanxi is relatively balanced, and both have a high level in the west. Therefore, the technological innovation capability of the two places is stronger than that of other provinces in the same region. It can be seen that the output of technological achievements is a short board that limits the technological innovation ability of Hebei and Hainan. Enhancing the output of technological achievements will greatly promote the improvement of their technological innovation ability. Moreover, the balanced development of the technological innovation process will also effectively promote the development of the region’s comprehensive capabilities.

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Fig. 4. The chart of principal component change trend

(2) Influencing Factors of Regional Technological Innovation Ability In order to further analyze the factors affecting the distribution of China’s provincial technological innovation capability, the change trend chart of each principal component is drawn according to the factor scores data in Table 4, as shown in Fig. 4. From the figure, it can be seen that F1 and the comprehensive score curve have a relatively consistent change trend, which indicates that China’s provincial technological innovation capability is largely determined by the indicators explained by the first principal component, and the indicators with larger load in first principal component are all technological innovation input, technological innovation value transformation, and some technological innovation environment and achievement indicators. The trend of F2 and F3 curves deviates greatly from the comprehensive score curve, which indicates that the second and third principal components have insufficient influence on China’s provincial technological innovation capability, mainly due to the deficiencies in the three indicators of technology market turnover, and the proportion of professional technicians and employees in public-owned economic enterprises and institutions with professional qualifications or above, which cannot well promote the development of China’s provincial technological innovation capability. Therefore, in order to further enhance China’s provincial technological innovation capability, it is not only necessary to continue to invest heavily in technological innovation and strengthen the ability to realize the value transformation, but also more important to further improve the technological innovation environment and increase the output of technological achievements, thus promoting the overall development of China’s provincial technological innovation capability.

5

Conclusions and Suggestions

In this paper, a quantitative evaluation index system of provincial technological innovation capability is established, the distribution of China’s technological innovation capability is evaluated, and the influencing factors are analyzed in depth. The main conclusions are:

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Firstly, there are great differences in technological innovation capability between the east and west of China, and the development level in the region is not balanced. Hebei, located in the eastern part of China, ranks 17th among 30 provinces in 2017 in terms of technological innovation capability, ranking behind some central and western regions and belonging to Area C, while other provinces in the eastern part all belong to Area A and B. Secondly, the development of China’s technological innovation capability is short in stages, which is manifested in the fact that the input of technological innovation and the environment contribute greatly to the comprehensive capability, but the development of the output of technological achievements and the transformation of value in two stages are not sufficient. The unbalanced development in stages greatly limits the development of it. Based on the above problems, in order to narrow the differences in the development of China’s inter-regional and intra-regional technological innovation capabilities and improve the overall level of China’s technological innovation capabilities, the following suggestions are put forward: Firstly, increase innovation investment in the western region. Investment is not only the initial stage of innovation, but also the part with the greatest difference between the east and the west. In 2017, the investment in the western region is only 27.8% of the evaluation value in the eastern region. Increasing investment, especially in R&D personnel, funds and foreign technology introduction, is the most important thing to promote the development of technological innovation in the west. At the same time, the western region can also realizes the overall improvement of its technological innovation capability through rational allocation of scientific and technological resources and local financial resources of various provinces, increasing the intensity of investment in innovation, and strengthening technological exchanges and cooperation. Secondly, improve the technological innovation environment in the central and eastern regions and further enhance the capacity of technological achievements output. The environment and the output of technological achievements have become the short boards in the development of China’s technological innovation capacity. Innovation input and value transformation have contributed greatly to it. Promoting the output level of technological achievements and improving the environment will further promote the development of technological innovation capacity in the central and eastern regions. Thirdly, promote the application of technological innovation achievements and the flow of technological innovation resources to the central and western regions, and promote the coordinated development and overall improvement of technological innovation capabilities in the eastern, central and western regions. The eastern region has a good technological innovation environment and rich innovation resources. Through project funds and poverty alleviation through science and technology, the flow of technological innovation resources between the eastern, central and western regions can be realized, the innovation ability in the western region can be improved.

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References 1. Bai, J.: Evaluation and comparison of China’s regional technological innovation capability. Sci. Manag. Res. 30(1), 15–18 (2012). (in Chinese) 2. Buesa, M., Heijs, J., et al.: Regional systems of innovation and the knowledge production function: the Spanish case. Technovation 26(4), 463–472 (2006) 3. Chen, X., Liu, Z., Ma, C.: Chinese innovation-driving factors: regional structure, innovation effect, and economic development-empirical research based on panel data. Ann. Regional Sci. 59(1), 43–68 (2017) 4. Chen, Y., Yang, Z., et al.: A patent based evaluation of technological innovation capability in eight economic regions in PR China. World Patent Inf. 31(2), 104–110 (2009) 5. Frankel, M.: Obsolescence and technological change in a maturing economy. Am. Econ. Rev. 45(3), 296–319 (1955) 6. Gold, J.: Measurement of provincial technological innovation capability based on grey relational model. Stat. Decis. 35(4), 59–62 (2019). (in Chinese) 7. Huang, H.: Comprehensive evaluation of regional technological innovation capability based on fuzzy hierarchical comprehensive evaluation method-taking sichuan province’s four major urban agglomerations as an example. Soc. Sci. 12, 72–77 (2015). (in Chinese) 8. Kaihua, C., Mingting, K.: Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure. Ann. Regional Sci. 52(2), 627–657 (2014) 9. Lakshman, C., Rai, S.: The influence of leadership on learning and innovation: evidence from India. Asian Bus. Manag. 1–32 (2019) 10. Liu, B., Pan, P., Li, L.: Evaluation and gap measurement of regional technological innovation capability in China. Sci. Technol. Progress Countermeasures 28(8), 124– 128 (2011). (in Chinese) 11. Schmookler, J.: Invention and Economics Growth. Harvard University Press, Cambridge (1966) 12. Pan, L., Sun, L.: Analysis of the impact of venture capital on regional technological innovation-based on the empirical analysis of China’s nearly 20 years of data (2019). (in Chinese) 13. Roberts, E.B.: Managing invention and innovation. Res.-Technol. Manag. 31(1), 1–27 (1988) 14. Schumpeter, J.A.: Theory of Economic Development. Routledge, Abingdon (2017) 15. Utterback, J.M.: Innovation in industry and the diffusion of technology. Science 183(4125), 620–626 (1974) 16. Werker, C., Athreye, S.: Marshall’s disciples: knowledge and innovation driving regional economic development and growth. J. Evol. Econ. 14(5), 505–523 (2004) 17. Yong, W.: Interpreting US-China trade war background, negotiations and consequences. China Int. Strategy Rev. 1(1), 111–125 (2019) 18. Yu, W., Chen, Q., Chen, H.: Analysis of the impact of different environmental policy tools on technological innovation-an empirical study based on China’s provincial panel data from 2004 to 2011. Manag. Rev. 28(1), 53–61 (2016). (in Chinese) 19. Zhang, T.: Financial evaluation of life insurance companies-ridit method based on principal component analysis. Manag. World 3, 182–183 (2016). (in Chinese) 20. Zhen, Y., Chen, M.: Impact of trade friction on china’s foreign direct investment: an empirical study based on overseas anti-dumping against China. World Econ. Res. (12), 108–120+133

Digital Trust Mediated by the Platform in the Sharing Economy from a Consumer Perspective Xiaodan Liu1 , Chunhui Yuan1(B) , Muhammad Hafeez1 , and Ch. Muhammad Nadeem Faisal2 1 School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, People’s Republic of China [email protected] 2 Department of Computer Science, National Textile University, Faisalabad, Pakistan

Abstract. There are many pieces of research on antecedents and mechanisms of digital trust, which are essential pillars of e-commerce. However, sharing economy is a new peer-to-peer online business model, which only has the transfer of usage rights rather than property rights. Therefore, it is important to create and enhance a trusted environment to promote successful transactions in the context of the emerging sharing economy. This research work proposes a theoretical trust model and evaluates it empirically with 289 valid samples. It intends to explore consumer’s trust in the platform, trust transfer from the platform to suppliers, and the impact of trust on the intention of participating in the sharing economy. The trustworthiness of the platform is strongly related to consumer’s trust in the platform. Structural assurance also has a strong impact on the trustworthiness of the platform and trust in the platform. Situational normality affects the intention of participating in the sharing economy but not affects trust in the platform and suppliers. Disposition to trust affects trust in the platform but not in suppliers. Trust transfer from the platform to suppliers is strongly supported. It is implied to strengthen the presentation of trustworthiness and structural guarantee mechanism of the platform to guarantee the sustainable development of the sharing economy. Keywords: Sharing economy · Digital trust · Risk Structural assurance · Disposition · E-commerce

1

· Trustworthiness ·

Introduction

In recent decades, there is a rapidly increasing phenomenon called “sharing economy” driven by the mobile intelligent terminal technology. Instead of property rights transfer, it is an access-based consumption where consumers only pay for item usage or service not for possession or maintenance, which reduces transaction costs for consumers [5]. The sharing economy links massive and scatted c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 670–684, 2020. https://doi.org/10.1007/978-3-030-49829-0_50

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individual strangers to transact through web intermediary platforms. It brings unlimited prospects for the development of economy, society, and ecological environment respectively. Ensuring its sustainable and healthy development is important, especially in the situation of imperfect laws, regulations, and market mechanisms for the sharing economy. Trust is crucial for the sharing economy to achieve long-term sustainable growth, and there is abundant literature on trust research in e-commerce since the 1990s [6]. However, owing to trust depending on the context [11], it is meaningful to study the trust-building mechanism in the new context of the sharing economy. The sharing economy had its characteristics, e.g., the decentralization of resources and participants, online matching and offline fulfillment, and access not ownership, which increases uncertainty and risk of transaction in the sharing economy. The question is whether antecedents of trust-building in e-commerce can be applied in the sharing economy directly. If it has changed, what are they In online transactions, the intermediary platform plays an important role in mediating the relationship between consumers and suppliers [12,16]. Based on trust theory of Mayer et al. (1995), McKnight et al. (2002), and Zucker (1986), the current research focuses on the analysis of antecedents to build trust in the platform and the influence of trust in the platform on trust in suppliers, accordingly to help the practical business operation in the sharing economy [10,11,19]. The rest of the paper will be organized as follows. The second section is the literature review about the sharing economy and digital trust. The next is the research model and hypothesis, followed by data analysis and result discussion, and finally is conclusions, limitations, and future research suggestions.

2

Theoretical Background and Literature Review

In recent years, the sharing economy has increased rapidly in many fields as a new economic model driven by mobile technology development. In practice, the sharing economy presents different disruptive innovations on production and consumption fields in China, such as car-hailing from Didi chuxing (Didi), takeout from Meituan, P2P Internet finance loan, and crowd-sourcing. In academic research, researchers give different definitions according to their research perspectives, such as collaborative consumption, accessed-based consumption, and peer to peer (P2P) [2,5]. In this research, the sharing economy is defined by analyzing its typical common features in the light of existing literature and business practices. Three characteristics are from the above academic literature: (1) access, not ownership; (2) change of trading object: durable goods, time, and service; (3) massive participation. Another three characteristics are summarized from the practice: (4) supply and demand redistribution; (5) online matching and offline fulfillment; (6) target community has online shopping experience. Therefore, in this paper, the sharing economy is defined as linking and re-matching demand and supply with the idle resources and cognitive surplus from massive and scattered individuals or organizations mediated by the network technology platform.

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The above characteristics lead to higher uncertainty and new risk, which pose a major challenge to the sustainable development of the sharing economy. Trust plays a critical role in decreasing online trading complexity and promoting successful transactions in the sharing economy [6,8,18]. The outbreak of many negative events would destroy trust, participation intention, and consumer confidence in the sharing economy. Hence, there is the theoretical and practical significance in studying the trust-building mechanism concerning the new context of the sharing economy. Mayer et al. (1995) define trust as the willingness to be vulnerable under conditions of risk and interdependence, which distinguishes trust from trust behavior from psychology and social psychology view [10]. Building trust willingness includes the trustworthiness presence of a trustee (a party to be trusted), and the trustworthiness perception process of a trustor (a trusting party) influenced by the personal trust propensity and contextual factors [10]. Most of the transactions in the sharing economy take place in P2P operation mode. Trading objects involve personal services and access to personal assets or durable goods, which may lead to the risk of life safety and property safety. The intermediary platform needs to provide appropriate security measures to build a trusted transaction environment on the platform [12]. Businesses or organizations tend to have higher trustworthiness and credibility than individuals [14]. A consumer firstly produces trust in the platform and then transfers to trust in suppliers on the platform. Strohmaier et al. (2019) examine the impact of institutional mechanisms of the platform on trust and distrust in the context of crowdfunding [16]. Kong et al. (2019) find information quality and transaction safety from structural assurance have a positive influence on trust in the sharing commerce [8]. The mediation role of the platform is investigated in M¨ ohlmann (2016)’s research about the trust in the P2P collaborative consumption [12]. We consider structural assurance and situational normality as contextual factors for online trust in the sharing economy [11]. From the characteristics of a trustee, the trustworthiness of the platform is considered, including perceived ability, perceived integrity, perceived benevolence, and perceived predictability [10,11]. Moreover, trust is generated from multiple interactions between a trustor and a trustee. Disposition to trust is considered as a factor from the trustor based on psychological perception and cognitive process [3,9,12].

3

Research Model and Hypothesis

Based on the Theory of Reasoned Action (TRA), McKnight et al. (2002) examine trusting beliefs that lead to trusting intentions, which result in trust-related behaviors [11]. This research investigates the antecedents of trust belief and its influence on behavioral intention. In the sharing economy, transactions are finished among strangers, and the characteristics of the sharing economy also result in higher uncertainty. The intermediary platform plays an important role in trust-building and transaction

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safety assurance [16]. Therefore, in the model of Fig. 1, the present study analyzed antecedents of trust in the platform from a consumer perspective. Afterward, it also investigated trust transfer from trust in the platform to trust in suppliers and the influence of trust on participation intention. 3.1

Trustworthiness of Platform

Trustworthiness is an objective and intrinsic trait possessed by a trustee. A trustor perceives the trustworthiness of a trustee according to various credible signals presented by the trustee and then decides the extent to which the trustee successfully fulfills transaction commitment. Trustworthiness is defined as the motivation (or lack thereof) to lie and is recognized as three main sub-constructs: ability, integrity, and benevolence [10]. Predictability is also discussed as a factor of trustworthiness [10,11]. (1) Perceived Ability Ability is defined as a group of skills, competencies, and characteristics that enable a party to influence within some specific domain [10]. Perceived ability is an important cognitive clue to indicate the reliability of a trustee. It gives the trustor confidence that the trustee can deliver commitments and fulfill expectations. In the sharing economy, the ability is presented by the platform, which makes participants feel safe and risk-controlled to trade on the platform. Owing to domain specificity of ability, an intermediary platform needs to provide the technical capability, feedback and emergency measures, and excellent service capability, to guarantee safe and successful transactions. The technical capability includes reliable technological infrastructure, perceived usefulness, and perceived ease of use [8]. The feedback and emergency measures include a reputation feedback mechanism, rapid emergency response, and insurance [17]. Excellent service capability refers to information quality, customer service quality, and immediate response [18]. Also, some works of literature examine that the scale of the platform has a positive impact on trust [9,12]. Based on the above analysis, we develop four instruments to measure perceived ability and propose the following hypothesis.

Perceived ability

H1 H2

Perceived integrity

H3

H5B

Trustworthiness of platform H5A

H5C

Consumer’ s trust in the platform H8A

Structural assurance

Perceived benevolence

H6A

Consumer’ s trust in suppliers

H4

Perceived predictability

H6B

Situational normality

H6C H8B H7A

Disposition to trust

H7B

Intention of participating in the sharing economy

Fig. 1. Trust building model in the sharing economy

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Hypothesis 1. Perceived ability is positively related to the trustworthiness of the platform. (2) Perceived Integrity Integrity is the practice of being honest, showing consistent and uncompromising adherence to strong moral and ethical principles. During the trust belief building, a trustee has more real information about his/her reliability than a trustor, referred to as information asymmetry. Integrity implies possibilities that a trustee can satisfy the expectation of a trustor [10]. It is also a reflection of reputation accumulated through multiple interactions. Among massive transactions on the platform, a trustee is responsible for his/her actions related to reputation. Perceived integrity can enhance the consumer’s confidence in the fulfillment of trust behavior as expected. Hypothesis 2. Perceived integrity is positively related to the trustworthiness of the platform. (3) Perceived Benevolence Benevolence is defined as the extent to which a party is believed to want to do good for the trusting party, aside from an egocentric profit motive [10]. Both integrity and benevolence reflect ethical traits. Benevolence refers to motives and is based on altruism, whereas integrity refers to keeping commitments and not lying [11]. Benevolence makes a trustor believe that a trustee can behave on behalf of the trustor. In the sharing economy, the benevolence of the platform makes participants believe that the platform can put their concerns and interests in the first place. Hypothesis 3. Perceived benevolence is positively related to the trustworthiness of the platform. (4) Perceived Predictability Predictability is the consistency between future and present behavior. A trustor can predict what a trustee will do based on the current experience. Trust is the expectation of a trustor to a trustee, and expectation refers to future behavior. McKnight et al. (2002) also suggest predictability should be considered for trust belief in the adjusted model [11]. When predictability of individual behavior increases, the uncertainty of behavior will be weakened greatly. We add perceived predictability as a sub-construct into trustworthiness in the sharing economy context. Hypothesis 4. Perceived predictability is positively related to the trustworthiness of the platform. 3.2

Structural Assurance

In the sharing economy, structural assurance is defined as structures like guarantees, regulations, promises, and other procedures established by a platform to promote successful transactions and protect the interests of participants [11]. Compared with the traditional consanguinity society, many social and economic

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interactions happen among strangers on the Internet in modern society. In a weak-tie community, it is intended to believe in the system control than individuals [14]. Structural assurance can enhance a sense of security and decrease the perception of the uncertainty of online transactions. Trust was increasingly produced by creating rules and formal structures [19]. For trust in the platform, structural assurance is an important signal to present the trustworthiness of the platform. It makes consumers believe the platform can concern and protect their interests. For example, in the online car-hailing platform, it is necessary to pass the background screening and professional certification for drivers. Besides, many car-hailing platforms implement deposit systems and insurance compensation measures to protect the rights and interests of consumers after emergent incidents. These structural assurances make consumers feel guaranteed, which improves their trust beliefs and promotes participation in the sharing economy. In this paper, we propose structural assurance not only positively affects the trustworthiness of the platform but also consumer’s trust in the platform. Hypothesis 5A. Structural assurance is positively related to the trustworthiness of the platform. Hypothesis 5B. Trustworthiness of the platform is positively related to consumer’s trust in the platform. Hypothesis 5C. Structural assurance is positively related to consumer’s trust in the platform. 3.3

Situational Normality

Perception of high situational normality implies that a consumer believes the Internet environment is appropriate and well ordered, as explained by Mcknight et al. (2002) [11]. After decades of exploration and development, e-commerce has established a set of security mechanisms for online transactions. In the sharing economy, the user community has accumulated rich experience of online transactions from e-commerce, which boosts the confidence of consumers on Internet security. It is favorable for a consumer to participate in the sharing economy quickly. It is believed that platforms and suppliers in the online environment have the attributes of trustworthiness. We propose situational normality positively affects consumer’s trust in the platform and suppliers. Also, it positively affects the intention of participating in the sharing economy. Hypothesis 6A. Situational normality is positively related to consumer’s trust in the platform. Hypothesis 6B. Situational normality is positively related to consumer’s trust in suppliers. Hypothesis 6C. Situational normality is positively related to the intention of participating in the sharing economy.

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Disposition to Trust

Disposition to trust is the general willingness of individuals to trust others, and it is a relatively stable personality trait based on the general experience and social cognition of a trustor in the past life. It plays an important role especially in the initial trust stage without direct interactional experience. In the online trust research, some empirical results are inconsistent about the influence of disposition to trust on online trust formation. Some studies find that disposition to trust has a positive impact on the initial trust when lacking interaction experience [3]. However, Koufaris and Hampton-Sosa (2004) don’t find statistical support for the hypothesis that disposition to trust affects online initial trust [9]. In the sharing economy, participants have online transaction experience and only lack relevant experience on the unique characteristics of the sharing economy, such as online matching and offline fulfillment. It needs to be verified for the direction and extent of the impact of disposition to trust in the sharing economy. We assume that disposition to trust has a positive correlation with consumer’s trust in the platform and suppliers. Hypothesis 7A. Disposition to trust is positively related to consumer’s trust in the platform. Hypothesis 7B. Disposition to trust is positively related to consumer’s trust in suppliers. 3.5

Trust, Trust Transfer, and Behavioral Intention

In a broad sense, trust refers to the good expectation of an individual on the behavior of others, in many cases, based on previous interactions. In this paper, we focus on consumer’s trust in the platform in sharing economy and trust transfer from the platform to suppliers. There are some theoretical and empirical supports for trust transfer. Ba (2001) proposes transfer-based trust and points out that trust transfer means that if users trust the community of a site, they also trust members of the site [1]. The trust and distrust transfer from the intermediary to founders are examined in the crowdfunding context [16]. Trust can be transferred from a reliable third party to two new counterparties without contact before, which is an effective way to establish trust. Therefore, we propose that consumer’s trust in the platform has a positive impact on consumer’s trust in suppliers on it. Behavioral intention is the willingness to carry out a specific action. In the sharing economy, the participation intention of a consumer is defined as the willingness to trade with suppliers on the platform to obtain goods or services to meet his/her requirements, including information retrieval of products or services, provision of individual information, and payment for products or services online [11]. We assume that consumer’s trust in suppliers affects positively the intention of participating in the sharing economy. Hypothesis 8A. Consumer’s trust in the platform is positively related to consumer’s trust in suppliers. Hypothesis 8B. Consumer’s trust in suppliers is positively related to the intention of participating in the sharing economy.

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Methodology and Result Discussion Pretest and Data Collection

The present study adopted the structured questionnaire survey based on psychological measurement to collect data samples with real experience of the sharing economy to ensure the validity of respondents [7,15]. The questionnaire includes 34 instruments derived from literature except for 8 newly developed instruments. All instruments were five-point Likert-type scales from strongly disagree (1) to strongly agree (5). A pretest was conducted among peer researchers and graduate students in the laboratory, and 32 respondent samples were collected. We discussed semantic expression and understanding of instruments with respondents and then adjusted the description of some instruments to guarantee content validity. Since consumers on sharing economy platforms are individuals (peer to peer), we randomly sample from an online-survey websites owing to 2.6 million sample members [7,15]. To differentiate users with trading experience in sharing economy platforms, an item was set: “Please choose a sharing economy platform you are familiar with”, options include Didi (Express or Hitch), Xiaozhu.com, Airbnb, Uber, P2P Internet finance loan, Meituan takeout, zbj.com (crowdsourcing platform), Other platforms (The suppliers are individuals), and Never use it”, which cover the major sharing economy platforms in China. Another item was set to distinguish the role of participant: “Are you a consumer or a supplier in the sharing economy platform?”, and options include “A consumer” and “A supplier”. 304 samples are collected and 5 samples without using experience were abandoned. Among the remaining 299 samples, 10 samples from the supplier role were removed and 289 valid samples were reserved for the following empirical research using SPSS and AMOS. 4.2

Results and Discussion

Table 1 is the demographic description of the samples. There are 55.4% females and 44.6% males. Didi platform (47.1%) and Meituan platform (45.3%) are used by most respondents, which are also the two popular representative sharing economy platforms in China. The instrument validation is shown in Table 2. Two instruments of the ability construct were dropped because their factor loadings are less than 0.5. The factor loadings of remaining instruments are greater than 0.5 significantly (p < 0.001), Cronbach’s Alpha values are greater than 0.7 except the ability construct, and composite reliability of all constructs are greater than 0.5. The reliability of measurement is accepted. Figure 2 and Table 3 show the SEM path correlation and hypothesis testing results. The 11 theoretical hypotheses are supported and the 3 hypotheses are rejected. It indicates that situational normality has a significant positive impact on the intention of participating in the sharing economy, but has no significant impact on consumer’s trust in the platform and suppliers on it. Disposition to

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0.890 0.939***

Perceived integrity

0.465

Trustworthiness of platform 0.961

0.963***

***

Consumer’ s trust in the platform

0.438***

0.905***

Structural assurance

Perceived benevolence

-0.035 Perceived predictability

0.554***

Consumer’ s trust in suppliers

0.070 Situational normality

0.520*** 0.146** Disposition to trust

0.210** 0.111

Intention of participating in the sharing economy

Fig. 2. AMOS analysis result of research model (*p < 0.05, **p < 0.01, ***p < 0.001) Table 1. Demographic description (N = 289 samples) Attribute

Value

Gender

Female

160

55.4

Male

129

44.6

Age

Under 18 18–25

Education level

26–30

67

23.2

31–40

136

47.1

41–50

22

7.6

51–60

4

1.4

Above 60

1

0.3

Junior school and below

3 11 207

1 3.8 71.6

Master

52

18

Doctor

16

5.5

Under 5000

73

25.3

5000–10000

107

10000–15000

56

19.4

15000–20000

23

8

Above 20000

30

10.4

Married Single

Choose a familiar sharing economy platform

0 20.4

Bachelor

Marital status

0 59

High school

Income

Frequency Percentage (%)

Didi(Express or Hitch)

37

202

69.9

87

30.1

136

47.1

Xiaozhu.com

9

3.1

Airbnb

6

2.1

Uber

1

0.3

P2P Internet finance loan

2

0.7

Meituan takeout

131

45.3

zbj.com(crowdsourcing platform)

2

0.7

Other platforms(The suppliers are individuals)

2

0.7

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Table 2. Measurement model results Construct

No. of items

Perceived ability

2

Perceived integrity

Perceived benevolence

3

3

Item Loading C.R. Ab2

0.514***

Ab3

0.734*** 7.621

SE

CA

CR

AVE

3.498 0.676 0.548 0.565 0.402 0.225

Int1

0.732***

Int2

0.774*** 14.493 0.0710

Int3

3.218 0.766 0.810 0.785 0.549

0.716*** 11.226 0.092

Ben1 0.736*** Ben2 0.610*** 9.898

Mean SD

3.261 0.714 0.725 0.742 0.492 0.086

Ben3 0.749*** 12.269 0.087 Perceived predictability 3

Pre1 0.888***

3.069 0.744 0.710 0.839 0.636

Pre2 0.754*** 11.598 0.071 Pre3 0.743*** 10.092 0.078 Structural assurance

Situational normality

Disposition to trust

5

3

3

Consumer’s trust in the 3 platform

Sa1

0.644***

Sa2

0.753*** 12.431 0.085

3.190 0.733 0.813 0.813 0.467

Sa3

0.613*** 9.143

Sa4

0.731*** 10.565 0.099

Sa5

0.665*** 9.756

Sn1

0.757***

Sn2

0.854*** 13.244 0.078

Sn3

0.711*** 11.569 0.077

0.091 0.094 3.435 0.757 0.823 0.819 0.603

Dis1 0.746***

3.386 0.707 0.735 0.739 0.486

Dis2 0.672*** 8.778

0.123

Dis3 0.671*** 8.774

0.112

Trp1 0.828***

3.149 0.768 0.767 0.803 0.577

Trp2 0.693*** 12.864 0.076 Trp3 0.751*** 12.583 0.072

Consumer’s trust in suppliers

4

Trs1 0.691***

3.194 0.731 0.817 0.783 0.475

Trs2 0.660*** 13.474 0.071 Trs3 0.702*** 11.073 0.106 Trs4 0.703*** 10.522 0.103

Intention of participating in the sharing economy

3

Ci1

0.771***

Ci2

0.708*** 8.751

3.790 0.621 0.693 0.750 0.501 0.122

Ci3 0.638*** 7.975 0.134 Note: C.R.: t-value, CA: Cronbach’s Alpha, CR: Composite Reliability. Goodness-of-fit statistics: CFI = 0.913, TLI = 0.902, RMSEA = 0.056, RMR = 0.055, GFI = 0.844, AGFI = 0.811. ***p < 0.001.

trust has a significant impact on consumer’s trust in the platform but has no significant impact on trust in suppliers. Factors Analysis on the Trustworthiness of the Platform in the Sharing Economy Perceived ability (0.890, p < 0.001), perceived integrity (0.939, p < 0.001), and perceived benevolence (0.963, p < 0.001) have higher factor loadings on the trustworthiness of the platform than perceived predictability (0.554, p < 0.001), which are three essential antecedents to affect the trustworthiness presence of the platform. Structural assurance has a direct effect on consumer’s trust in the

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X. Liu et al. Table 3. Results of SEM path correlation testing

Hypothesis Path

Standardized Standard coefficient error

H1

Perceived ability -> Trustworthiness of platform

0.890***

0.23

3.869

Accepted

H2

Perceived integrity -> Trustworthiness of platform

0.939***

0.245

7.376

Accepted

H3

Perceived benevolence -> Trustworthiness of platform

0.963***

0.255

7.142

Accepted

H4

Perceived predictability -> Trustworthiness of platform

0.554***

0.214

6.533

Accepted

H5A

Structural assurance -> Trustworthiness of platform

0.961***

0.274

7.068

Accepted

H5B

The trustworthiness of platform -> Consumer’s trust in the platform

0.438***

0.090

5.206

Accepted

H5C

Structural assurance -> Consumer’s trust in the platform

0.465***

0.080

5.812

Accepted

H6A

Situational normality -> Consumer’s trust in the platform

-0.035

0.058

−0.630

Rejected

H6B

Situational normality -> Consumer’s trust in suppliers

0.070

0.051

1.166

Rejected

H6C

Situational normality -> Intention of participating in the sharing economy

0.210**

0.061

2.604

Accepted

H7A

Disposition to trust -> Consumer’s trust in the platform

0.146**

0.062

2.865

Accepted

H7B

Disposition to trust -> Consumer’s trust in suppliers

0.111

0.055

2.005

Rejected

H8A

Consumer’s trust in the platform -> Consumer’s trust in suppliers

0.905***

0.067

11.064

Accepted

Consumer’s trust in 0.520*** suppliers -> Intention of participating in the sharing economy Note: *p < 0.05, **p < 0.01, ***p < 0.001.

0.076

6.079

Accepted

H8B

C.R. (t-value)

Result

platform and also has an indirect effect on it through the trustworthiness of the platform. Structural assurance provides a credible guarantee environment for successful transactions in the sharing economy, especially when suitable laws and regulations from the government have not been established in the early stage of development. In the study of trust in e-commerce and modern society,

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institution-based trust or social control is an important antecedent in trustbuilding. It has significance to prove the importance of structural assurance for trust-building in the sharing economy. For entrepreneurs or practitioners in the sharing economy, fair structural assurance should be established to ensure the rapid development of the sharing economy. Ability, integrity, benevolence, and structural assurance are the perception of trustworthiness based on past or current behavior of the platform. However, predictability is the forecast of future behavior, which implies more uncertainty. It has less impact on the trustworthiness of the platform. A trustor judges the possibility of expectation satisfied based on the predictability of a trustee’s behavior, or the possibility of future consistency between words and deeds. However, due to the uncertainty of predictability itself, it has a significant impact on the trustworthiness of the platform, but the extent is not as great as the other three variables. Impact of Institutional Context on Trust Belief in the Sharing Economy Institution-based trust includes structural assurance and situational normality [11]. Empirical results show that the direct impact of structural assurance on consumer’s trust in the platform is significantly supported with a coefficient of 0.438 (p < 0.001). Moreover, structural assurance has a significant impact on the trustworthiness of the platform with a coefficient of 0.961 (p < 0.001), which has a positive effect on consumer’s trust in platform with a coefficient of 0.465 (p < 0.001). However, situational normality has no direct effect on consumer’s trust in platform and suppliers and its impact on the intention of participating in the sharing economy is supported with a coefficient of 0.210 (p < 0.01). Because participants in the sharing economy context are strangers, structural assurance is more important than that in an acquaintance community [11,19]. Pavlou and Gefen (2004) consider that institutional trust is the most important mode of trust to create a business environment when lacking familiarity [13]. Sztompka (1999) proposes that trust in institutions, also known as procedural trust, is based on the belief that if the program is followed and it will produce the best results [14]. Structural assurance is a type of mechanism to ensure the normal operation of the system. It is not only the protection mechanism and punishment measure to guarantee the promise of trust to be fulfilled, but also the reliability of trustworthiness presence. In the sharing economy, the intermediary platform plays a rule-maker role for massive peers participating in sharing activities on it. Situational normality has no direct effect on trust in the platform, which is explained that experience in a safe online environment directly influences behavioral intention. One characteristic of the sharing economy is that the target community has a rich online trading experience. A consumer has a safer perception of online shopping in the Internet environment. The existence of this basic trust has become the consensus of the consumer’s community. The experienced consumers would consider more rationally whether they need products or services on the platform rather than from impulse or curiosity. Therefore, sit-

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uational normality affects the intention of participating in the sharing economy but has no impact on consumer’s trust in the platform and suppliers owing to the existence of the basic trust in the online community. Impact of Personal Traits on Trust Belief in the Sharing Economy The impact of disposition to trust on trust in the platform is supported with a coefficient of 0.146 (p < 0.01), but has no significant impact on trust in suppliers. In the process of establishing the trust relationship between a consumer and a platform, disposition to trust has a lighter impact than trustworthiness and structural assurance. Mayer et al. (1995) propose that trust behavior itself is a kind of risk behavior [10]. From the view of reducing risk, the trustworthiness presence of a trustee and structural assurance can better decrease uncertainty. Disposition to trust is a subjective tendency of a trustor. It doesn’t mean the reduction of objective risk even though an individual has a high trust tendency. On the contrary, an extremely high disposition to trust would put an individual into a vulnerable position, which would further destroy his/her trust belief. Analysis of Trust Transfer and Behavioral Intention in the Sharing Economy There is a strong trust transfer from trust in the platform to trust in suppliers, which is strongly supported by a coefficient of 0.905 (p < 0.001). DelgadoM´ arquez et al. (2012) argue that trust transfer occurs when an agent trusts an unknown agent because the unknown agent is related to a trusted agent [4]. It is strongly supported that trust is transferred from an intermediary to funders in the empirical research of Strohmaier et al. (2019) [16]. In the sharing economy, interactions exist among massive unacquainted peers, and a trusted third-platform plays an essential role to promote trust-building among peers through trust transfer. Usually, an individual has a stronger trust perception of business institutions than that of other individuals. For a platform, it is more important to build consumer’s trust in the platform through security certification, transaction protection, insurance, brand image, etc., rather than directly build trust among massive unacquainted peers. From the empirical results, it is also concluded that consumers trust in suppliers has a strong influence on the intention of participating in the sharing economy with a coefficient of 0.520 (p < 0.001). In the sharing economy, trust is directly related to participation intention [11]. Participation intention implies that it has a high probability to choose products and services from suppliers trusted after a rational decision.

5

Conclusion and Future Research

The present research aspires to explore consumer’s trust and participation intention in the sharing economy, along with trust transfer from trust in the platform to trust in suppliers. A theoretical trust model in the sharing economy is proposed and estimated empirically. Based on the empirical evaluation, we draw out five conclusions. First, the role of the platform is essential to build trust and promote successful transactions in the sharing economy. It is necessary to improve

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consumer perception on the trustworthiness of the platform in the aspects of ability, integrity, benevolence, and structural assurance respectively. Second, how to build effective structural assurance is very important in the sharing economy. It not only affects the trustworthiness of the platform but also directly affects consumer’s trust in the platform. It requires business practitioners to pay more attention to it, and specific structural guarantees vary from industry to industry. Third, it is easier to establish consumer’s trust in the platform than trust in suppliers. A consumer has a higher-level perception of the reliability of commercial organizations. Therefore, we should focus on establishing the credibility of the platform to promote transactions among peers in the sharing economy. Fourth, there is no link between situational normality and consumer’s trust in the platform and suppliers. However, situational normality is related to the intention of participating in the sharing economy, which should be paid more attention to how to provide products and services necessary for the rational and experienced consumer community. Finally, disposition to trust has only an impact on consumer’s trust in the platform. It reminds us to promote the presence of the platform’s trustworthiness and it’s easier for consumers to perceive. In this paper, there are also some limitations and future research implications. Risk should be considered during trust-building [10]. Besides, an order is made online among strangers, whereas fulfillment is finished offline in the sharing economy. Suppliers face a high risk of property safety and even life safety, which is different from that in e-commerce. It is meaningful to study trust in the sharing economy from the view of a supplier.

References 1. Ba, S.: Establishing online trust through a community responsibility system. Decis. Support Syst. 31(3), 323–336 (2001) 2. Botsman, R., Rogers, R.: What’s Mine Is Yours: The Rise of Collaborative Consumption. Harper Collins Publishers, New York (2011) 3. Chen, Y., Barnes, S.: Initial trust and online buyer behaviour. Ind. Manag. Data Syst. 107(1), 21–36 (2007) 4. DelgadoMarquez, B.L., Hurtadotorres, N.E., AragonCorrea, J.A.: The dynamic nature of trust transfer: measurement and the influence of reciprocity. Decis. Support Syst. 54(1), 226–234 (2012) 5. Fleura, B., Eckhardt, G.M.: Access-based consumption: the case of car sharing. J. Consumer Res. 39(4), 881–898 (2012) 6. Hawlitschek, F., Notheisen, B., Teubner, T.: The limits of trust-free systems: a literature review on blockchain technology and trust in the sharing economy. Electron. Commer. Res. Appl. 29, 50–63 (2018) 7. Iqbal, K., Peng, H., Hafeez, M., Khan, I.: Empirically analyzing the future intentions of Pakistani students to stay or leave: evidence from China. In: International Conference on Management Science and Engineering Management, pp. 759–769. Springer, Heidelberg (2019) 8. Kong, Y., Wang, Y., Hajli, S., Featherman, M.: In sharing economy we trust: examining the effect of social and technical enablers on millennials’ trust in sharing commerce. Comput. Hum. Behav. (2019)

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9. Koufaris, M., Hampton-Sosa, W.: The development of initial trust in an online company by new customers. Inf. Manag. 41(3), 377–397 (2004) 10. Mayer, R.C., Davis, J.H., Schoorman, F.D.: An integrative model of organizational trust. Acad. Manag. Rev. 20(3), 709–734 (1995) 11. Mcknight, D.H., Choudhury, V., Kacmar, C.: Developing and validating trust measures for e-commerce: an integrative typology. Inf. Syst. Res. 13(3), 334–359 (2002) 12. Mohlmann, M.: Digital trust and peer-to-peer collaborative consumption platforms: a mediation analysis. SSRN Working Paper 2813367 (2016). http://papers. ssrn.com/sol3/papers.cfm?abstract id=2813367 13. Pavlou, P.A., Gefen, D.: Building effective online marketplaces with institutionbased trust. Inf. Syst. Res. 15(1), 37–59 (2004) 14. Paxton, P.: Trust: A Sociological Theory. Cambridge University Press, Cambridge (1999) 15. Razika, M., Yang, Q., Hafeez, M.: Evaluating the global product development challenges through social commerce. In: International Conference on Management Science and Engineering Management, pp. 783–793. Springer, Heidelberg (2019) 16. Strohmaier, D., Zeng, J., Hafeez, M.: Trust, distrust, and crowdfunding: a study on perceptions of institutional mechanisms. Telematics Inform. 43, 101252 (2019) 17. Yang, S., Lee, K., Lee, H., Koo, C.: In airbnb we trust: understanding consumers’ trust-attachment building mechanisms in the sharing economy. Int. J. Hospitality Manag. 83, 198–209 (2018) 18. Zhang, L., Yan, Q., Zhang, L.: A computational framework for understanding antecedents of guests’ perceived trust towards hosts on airbnb. Decis. Support Syst. 115, 105–116 (2018) 19. Zucker, L.G.: Production of trust: institutional sources of economic structure. Res. Organ. Behav. 8(2), 53–111 (1986)

Market Feedback, Investor Compensation and Decisions of Subsequent Private Placement: Based on the Analysis of Mediating Effects Ying Zhang1(B) and Chunming He2 1

2

Business School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected] Jincheng College, Sichuan University, Chengdu 610071, People’s Republic of China

Abstract. Whether the private placement by listed companies can be successfully accomplished not only depends on the issuance characteristics and market environment but also is closely related to the expected returns of investors. When a listed company makes a decision on multiple private placements, it shall not only take into account the investor compensation effect of the current private placement but also make reference to the impact of the market performance of the previous issuance on the determination of the investors’ returns. In combination with a large number of research results and under the assumption of rational expectation and from the perspective of mediating effects, this paper takes the analysis of investors’ returns as the breakthrough point, uses market feedback and investor compensation (usually reflected in the discount rate of issuance) as the direct effect variable and mediating effect variable, and constructs a mediating effect model of market feedback, investor compensation and decisions of subsequent private placement. The conclusion shows that the market performance of the previous private placement has a positive feedback effect on the subsequent private placement decisions, and there exists a phenomenon that issuers cater to investors’ expected returns through the mediating path of investor compensation. Keywords: Private placement Investor compensation

1

· Mediating effects · Market feedback ·

Introduction

Private placement is characterized by simple issuance procedure, convenient operation, time and cost saving and easy control, and it has become the main form of China’s SEO. In developed capital markets such as Europe and the United States, private placement is a very mature means of SEO. Some scholars found that in developed capital markets the long-term market performance c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 685–700, 2020. https://doi.org/10.1007/978-3-030-49829-0_51

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of private placements performed poorer than investors’ expectations, that may be related to investors’ investment sentiment, the blind optimism of investors can easily stimulate the maladjustment between endogenous problems of issuer and external environment and encourage the issuer towards a low-issue discount strategy [5,6,9]. In China, there also exists the similar phenomenon of poor long-term market performance after a private placement [24]. Due to the holding period of restricted securities, the long-term market performance of the private placement shares significantly affects the investment returns of investors. The long-term market performance is not only reflected in the market recognition of the private placement items but also in the issuer’s operational management capabilities and potential risk levels. Therefore, the long-term market performance of private placement shares during the holding period becomes the key factor for investors to participate in subsequent issuances whether or not and is also the guarantee for issuer’s subsequent issuances [3]. Issuers formulate private placement’s issuance strategies and discount rate levels based on the estimation of the long-term market performance of private placement shares, under the expectation of continuous private placement, the issuers must also consider the feedback effect of the previous private placement. While the investors usually make decisions according to the comprehensive utility of the discount rate levels and the expected long-term market performance, and the expected total returns must meet or exceed the minimum returns requirement. Under normal circumstances, expected total returns of investors can be divided into the long-term market performance (market feedback effect) after the previous private placement and the investor compensation by current private placement. Discount on issuance can be viewed as the compensation for private placement investors1 . When the market performance is poor and the market response differs greatly from the expectation, investors’ willingness to participate in private placement can only be stimulated by increasing the investor compensation to meet the minimum expected returns requirements. Thus, starting from analysis of investors’ returns and in the perspective of mediating effects, this paper takes market feedback and investor compensation as direct effects variable and mediating effects variable, and builds the mediating effect model of market feedback, investor compensation and decisions of subsequent private placement. It makes use of the characteristics of mediating effects to simplify the prediction model of private placement decisions. The main innovations of this paper are as follows: Firstly, the mediating effects are applied for the first time to predict and analyze the private placement decisions, clarifying the core factors that affect the success of private placement for issuers under the complex economic background. Secondly, it takes the discount rate of private placement as a mediating variable for indirect feedback effect to reduce the difficulty of analyzing complex variables affecting information asymmetry so as to make the research model more concise. Thirdly, taking the market feedback variables of the previous private placement as direct 1

In this paper, the discount of private placement represents the investor compensation.

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feedback variables can better reflect the impact of investor sentiment changes on investment decisions, making the selection of parameters of mediating effect model universally applicable.

2

Theoretical Analysis and Hypotheses

At present, it is generally believed that the private placement decisions of listed companies are mainly determined by the financing needs of the company and the external environment. In related research on the analysis of corporate financing needs, it is usually assumed that under the background of incomplete information, companies make equity financing decisions based on their own business strategy and financial situation, with rationality or based on rational sentiment. Wruck [21] analyzed the private placement of listed companies from the perspective of ownership concentration for the first time, and believed that the private placement would lead to an increase in the concentration of the company’s equity, promote the interests of major shareholders to be more consistent with the interests of the company, so as to obtain the virtuous supervision on enterprises by the private placement, alleviate the agency problem and enhance the corporate value. The external market environment includes the behavior and sentiment of trading counterparties and so on. Private placement issuers need to raise investor sentiment to achieve high-quality private placement, and market feedback is an indicator of investor sentiment. High returns on investment signifies the value of private placement shares, which can encourage managers to raise additional funds. Jegadeesh et al. [13] found earlier that the high returns after initial public offering (IPO) is positively correlated with the probability of secondary equity offering (SEO). Based on the basic operation information of the enterprise, Hovakimian and Hutton [10] took the investment returns rate in the previous year of equity Issuance as a feedback variable, and built a feedback prediction model for the five-year long-term market performance, and accurately predicted that the long-term market performance in the next five years is positively correlated with the investment returns rate issued in the previous year. So it can be seen that the subsequent private placement decisions of enterprises are related to the market returns of previous private placement. According to above analysis, the first hypothesis can be proposed: Hypothesis 1. Among the factors that influence the decision of private placement, market feedback has a direct impact on the private placement decision. Through the research on mature private placement market, it is found that a lower discount or premium usually brings higher abnormal returns, while a higher discount may only bring lower abnormal returns or even a loss. However, it seems difficult for investors to be unaffected by the attractive discount [8, 15]. Generally, the management knows the value of the company better than the potential investors, and also clearly knows whether the company’s value is undervalued, so he can set the offering discount according to their own needs to

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obtain maximum benefits [16]. Anderson et al. [1] believed that the more difficult it is to evaluate the enterprise value, the higher the issue discount rate is, and the greater the investment risk is. Meidan D [15] explained this phenomenon with the theory of information asymmetry. When the degree of information asymmetry is high, institutional investors will investigate the issuer. If it is found that the overall growth level of the enterprise is seriously underestimated, usually they will buy shares at a higher price in order to obtain higher positive returns in later period, it means they will accept a lower discount rate. If the overall level of the enterprise is seriously overvalued, investors will demand a higher discount rate. According to above analysis, the second hypothesis can be proposed: Hypothesis 2. The higher the discount rate of private placement issuance is, the lower the investor’s dependence on the information symmetry will be, and the higher the probability of successful issuance of private placement will be. Subrahmanyam and Titman [19] believed that the incidental information from the public equity market is an important factor for listing. Information of stock price generated during IPO could help consumers identify the quality of company products [18]. Bommel and Vermaelen [20] proposed a market feedback model based on this pattern, that is, the issuer set a low IPO price to induce investors to produce information concerning the issuer, and then the issuer reached the expected IPO financing level by using market feedback in investment decision. Based on rationality about investment, private placement financing usually performs better in market for a short term due to the early compensation effect, in turn, the feedback information motivates investors to subscribe for private placement shares. When studying the chemical behavior of molecules, Ingold C and Ingold E [11] found that there is an electron transfer in molecules with unsaturated systems under normal circumstances, and he referred to the effects produced by this electron transfer as “electronic strain”, which is prototype of the mediating effects. The proposal of mediating effects opens up a new gate for economic research. In the process of using mediating variable analysis, the original theories explaining similar phenomena can be integrated into the mechanism of action, thus enriching the existing theoretical system and making research and analysis more significant both in theory and practice. Yang et al. [23] based on the data of Chinese listed companies from 2003 to 2012, tested the impact of corporate governance on the competitive effect of cash holdings from the perspective of mediating effects of capital investment. It is also found that cash holding has a competitive effect, and capital investment has mediating effects in giving full play to the competitive advantage of cash holding in the product market. Based on the data of 360 MSMEs(the micro, small, and medium enterprises), Islam and Ganguly [12] established the relation between the factors under the utility of loan, utilization of fund, and capital formation in the MSMEs, and found that utilization of loan is playing a mediating role in the relation between utility of loan service and capital formation, and loan services provided by public sector banks are influencing to utilize the fund effectively by the entrepreneurs

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but not directly influencing in the formation of capital. This paper finds out the significant variables that affect the subsequent private placement of issuer through the mediating variable analysis method for the first time, which provides a valuable reference for private placement enterprises to formulate a private placement framework and also helps investors to choose private placement shares. Based on the above analysis, the third hypothesis is proposed: Hypothesis 3. Investor compensation has significant mediating effects among company characteristics, market environment and subsequent private placement decisions, which will boost the issuer’s motivation to implement private placement and increase the success probability of private placement.

3 3.1

Model Design Main Variables and Definitions

When the mediating effects method is applied to study the subsequent private placement decisions, reference variables that reflect the comprehensive income of private placement and the compensation level of private placement must be used. This paper predicts whether private placement enterprises can successfully implement private placement with early market feedback and the level of discount rate of issuance. This paper sets the dependent variables of private placement decisions as 0–1 for whether to implement private placement within 3 years, which not only meets the time requirement of private placement but also reflects the timing of private placement decisions. Selection of explanatory variables is based on the core variables that affect the long-term market performance of private placement and take the discount rate of issuance as the mediating variable that affects the private placement decisions. According to relevant research, the main variables used in this paper are shown in Table 1 below. 3.2

Model Design

The secondary market may offer feedback for private placement, and the investors’ abnormal returns in the holding period are the precondition for their decisions to participate in the subscription of private placement shares. In the process of private placement, the total returns of investors can be divided into two parts—the discount of private placement and the long-term market performance during the holding period. The higher current yield for investors may enable them to maintain higher enthusiasms and increase the probability of accepting a low discount rate in subsequent private placement [22]. Compared with IPO issuance, the long-term market performance of private placement is generally poor. Under the dual effects of asymmetric information between enterprises and investors and uncertainty in the stock market, private placement enterprises usually use the issuance discount as compensation for private placement

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Y. Zhang and C. He Table 1. Relevant variable definitions

Variable category

Name

Definition

Dependent variable

Y

Represent the 0–1 variables for whether the subsequent private placement is successful within 3 years after the private placement. If the private placement is successfully implemented again within 3 years after the current private placement, the value is 1; otherwise, it is 0

Mediating variable

DIS

Expressed by discount rate of issuance, that is, the growth rate of the closing price on private placement date to the issue price, this paper is expressed as follows: Issueprice Discount = 1 − Closingpriceontheprivateplacementdate

Explanatory PR 1 variable PR

The secondary stock market returns of one year before the private placement The secondary stock market returns one year after the private placement SIZE Company scale. The natural logarithm of the total share capital of the company before private placement is considered as a factor to measure the scale of company Isu P Issue price BHAR Buy-and-hold abnormal returns for 18 months after private placement, are calculated based on the closing price on the private placement date BHAR IsuP Buy-and-hold abnormal returns for 18 months after private placement, are calculated based on the issue price of private placement (Isu P) MB Company’s mark-to-book ratio includes the company’s growth opportunities and pricing errors, reflecting the company’s growth potential in future

investors. When the market performance is poor and the market response is significantly different from expectations, investors’ expected returns must reach or exceed their minimum expected level, so as to make investors keep high emotion towards private placement and help the subsequent private placement proceed smoothly [7]. So, it is necessary for private placement enterprises to ensure the long-term market performance (market feedback effect) of current private placement and investor compensation (mainly reflected by the issuance discount) at next private placement when they are planning a subsequent private placement. Through the above analysis, we learn that there are two paths to influence the decisions whether the subsequent private placement can be successfully issued or not: market feedback and investor compensation-the mediating path. Referring to the existing research models [4,17], the mediating effects of investor compensation is shown in Fig. 1:

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Fig. 1. Schematic diagram of mediating effects of investor compensation

It can be seen from Fig. 1 that the path c indicates the feedback effect of market response on private placement decisions. In addition, investor compensation for the previous private placement can usually send signals to investors such as insufficient confidence of private placement enterprises, large information asymmetry, and willingness for long-term and stable cooperation. It may also give a certain feedback on subsequent private placement decisions. It is further speculated that the scale of investor compensation can also affect the expected returns of the subsequent private placement, and give an indirect feedback for the subsequent private placement, that is, the path a → b. Based on the application of the above-mentioned mediating effects theory, this paper uses the “Causal Steps Approach” proposed by Baron and Kenny [2] to analyze the mediating effects of the issue discount on the subsequent decision of private placement. According to the test process of the mediating effect model, the following three regression equations must be tested in turn.  Controli (1) Y = c × P R− 1 +  Controli (2) DIS = a × P R− 1 +  Controli (3) Y = c × P R− 1 + b × DIS + Based on Eqs. (1)-(3) and according to the design idea of step-by-step method, regression equations are used to test the direct impact between the two pairs of variables: P R 1 and Y , P R 1 and DIS; as well as the indirect impact between variables P R 1, DIS and Y . Thus, the test procedure is as follows: Step 1. Test the coefficient c in Eq. (1). Step 2. Test the coefficient a in Eq. (2) and the coefficient b in Eq. (3). If the coefficient c is significant and the coefficients a and b are also significant, it indicates that the mediating effects are significant; Step 3. If the coefficient c in Eq. (3) is not significant, it is a complete mediation; otherwise, it is a partial mediation.

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By the test of Eq. (1), we can obtain the significance of the long-term market performance of current private placement on the decisions of subsequent private placement. By the test of Eq. (2), we can obtain the significance of the longterm market performance of current private placement on the discount rate of the same private placement. By the test of Eq. (3), we can determine the type of this mediating effect model, that is, whether it belongs to complete mediation.

4

Empirical Analysis

4.1

Sample Selection and Data Sources

In this paper, we collected the data of 3802 China’s listed companies that successfully issued private placements from 2006 to 2017. Basic data such as issue date and issue price were downloaded from Wind database. The BHAR data were downloaded from JQData [14]. For two issuances of the same listed company’s simultaneous announcement of the pre-plan, they were merged into one issuance, which combined 706 samples, then removing samples with severe missing data, after processing, 2772 valid samples were obtained, 1907 for the first offering and 865 for the subsequent offering. Some samples with missing control variables are retained, the reason is that some estimation methods, such as bootstrap, can better deal with the situation of the missing data, thus retaining as much original data as possible. 4.2

Correlation and Descriptive Statistical Analysis

In accordance with the premise assumption and variables selection, correlation analysis is carried out for the current and subsequent private placement, as shown in Tables 2 and 3: Table 2. Correlation analysis of explanatory variables for current private placement Y

PR

PR 1

Y

1

PR

0.152∗∗∗

PR 1

−0.194∗∗∗ −0.579∗∗∗ 1

BHAR

0.204∗∗∗

BHAR IsuP 0.099∗∗∗ MB SIZE

0.04 −0.031

BHAR

BHAR IsuP MB

SIZE

1 0.761∗∗∗

−0.342∗∗∗ 1

0.043∗

−0.030

0.247∗∗∗

1

0.019

0.106

0.074∗∗

1

−0.001

0.100∗∗∗ 1

∗

0.097

∗∗∗

−0.118

∗∗∗

0.077

∗∗∗

−0.126

Note: ∗ Significant at 5% level, ∗∗ Significant at 1% level, and Sources: Wind database and JQData.

∗∗∗

Significant at 0.1% level.

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Table 3. Correlation analysis of explanatory variables for subsequent private placement Y

PR

PR 1

Y

1

PR

0.225∗∗∗

PR 1

−0.200∗∗∗ −0.765∗∗∗ 1

BHAR

0.243∗∗∗ ∗∗

BHAR IsuP 0.094

BHAR

BHAR IsuP MB

SIZE

1 0.606∗∗∗

−0.485∗∗∗ 1

∗∗∗

0.253

−0.176∗∗∗ 0.446∗∗∗ 1

MB

0.134∗∗∗

0.082∗

−0.081∗

0.088∗∗ 0.070∗

1

SIZE

0.016

−0.018

−0.011

−0.022

−0.215∗∗∗ 1

Note: ∗ Significant at 5% level, ∗∗ Significant at 1% level, and Sources: Wind database and JQData.

−0.022

∗∗∗

Significant at 0.1% level.

According to the correlation analysis results of explanatory variables in Tables 2 and 3, the explanatory variables involved in the model are significantly correlated, i.e. the level of linear correlation meets the statistical requirements. Before subscribing for the private placement shares, investors usually screen the investment targets based on the expected abnormal returns of the holding period, and also conduct judgments on the private placement shares according to the order of private placement. When the expected abnormal returns in the holding period is lower than the average market level, it is difficult for investors to subscribe the shares, and the issuer will face a large probability of failure of the private placement. Further analysis of market data shows that descriptive statistics of private placement data are shown in Table 4: Table 4. Descriptive statistics of private placement Descriptive statistics of the first private Descriptive statistics of subsequent placement private placement Count Mean

Sd

Y

1907

0.265

0.441 0

Min

Max

Count Mean

Sd

1

865

0.208

0.406 0.000

PR

1906

0.187

PR 1

1906

BHAR

1840

Min

Max

0.936 −0.825 17.737

865

0.011

0.495 −0.793 5.524

0.111

0.591 −0.947 4.727

865

0.160

0.463 −0.847 3.828

0.105

−0.050 0.517 −1.031 4.583

1.000

0.869 −1.936 16.001

812

BHAR IsuP 1841

−0.084 0.919 −3.330 16.002

812

−0.140 0.582 −1.180 3.339

MB

1905

0.563

1.963 −0.195 82.560

865

0.515

0.208 0.057

1.151

SIZE

1907

3.353

1.093 −0.521 7.742

865

4.046

0.929 0.949

8.031

Sources: Wind database and JQData.

From Table 4, it shows that more than 26.5% of the enterprises successfully implemented the private placement again within 3 years after the first private placement. Which is higher than the statistical results of non-first private

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placement—20.8%. It indicates that the total returns of the first private placement are higher than that of the subsequent private placement. Given that the total returns of investors in the first private placement have reached expectations, the investors remain highly enthusiastic about the subsequent private placement. BHAR and BHAR IsuP of the first private placement are both significantly better than the statistical data of the subsequent private placement, it further proves that the abnormal returns in the holding period of the current private placement of enterprise have positive feedback effect on the subsequent private placement. In the same way, the secondary stock market returns one year after the private placement (PR) have a feedback effect on the successful implementation of the subsequent private placement. By the statistical description, it can be estimated that good market feedback is conducive to promoting the private placement decisions of enterprises. 4.3

Test Results of Mediating Effects

In accordance with the setting of the mediating effect model, the Stata data analysis software is applied to carry out the test and analysis in this paper. The test process is divided into two steps. The first step is to analyze the causal determinants of whether the listed company successfully implemented a private placement within three years after the first private placement (Y), taking the sample of the first private placement as the analysis object. The second step is to analyze the causal determinants of whether the listed company successfully implemented a private placement within three years after the non-first private placement (Y), using the same indicators. Through model analysis, we expect to accomplish the following goals: on the one hand, test and compare the impacts of direct effect factors (such as corporate characteristics and market feedback) and mediating effects on the success of secondary offering after private placement; on the other hand, through the comparison between the samples in the first private placement and the subsequent private placement, we can further verify the impact of information asymmetry on the private placement, so as to guide investors’ emotional trends and prevent excessive emotions from continuously stimulating the chaos of private placement financing. (1) Mediating effect analysis of samples for the first private placement According to the mediating effect model, this paper analyzes the causal determinants of whether the listed company successfully implemented a private placement within three years after the first private placement(Y), and the analysis results are shown in Table 5, the Sobel-Goodman mediating effect test is shown in Table 6:

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Table 5. Mediating effect test of samples of the first private placement Model (1) Y

Model (2) DIS

Model (3) Y

PR 1 −0.923∗∗∗ (−7.43)

8.768∗∗∗ (7.98)

−1.064∗∗∗ (−8.25)

SIZE −0.234∗∗∗ (−4.44)

−2.527∗∗∗ (−4.23)

−0.204∗∗∗ (−3.81)

−0.110 (−0.33)

−0.0814 (−0.86)

MB

−0.0984 (−1.02)

0.0123∗∗∗ (6.03)

DIS Cons −0.171 (−0.91)

30.93∗∗∗ (14.66)

−0.582∗∗ (−2.86)

N

1904

1904

1904

R2

0.0376

Note: ∗ Significant at 5% level, ∗∗ Significant at 1% level, and Sources: Wind database and JQData.

∗∗∗

Significant at 0.1% level.

Table 6. Sobel-Goodman mediating effect test for the first private placement Coef

Std. Err

Z

P >Z

Sobel

0.0189

0.0038

4.976

6.489e−7

Goodman − 1 Aroian

0.0189

0.0038

4.951

7.367e−7

Goodman − 2

0.0189

0.0038

5.001

5.701e−7

a

8.1545

1.0928

7.4621

8.5e−14

b

0.0023

0.0003

6.6776

2.4e−11

Indirect effect

0.0189

0.0038

4.9761

6.5e−7

Direct effect

−0.1636

0.0168

−9.7196

0

Total effect

−0.1447

0.0168

−8.6207

0

Sources: Wind database and JQData.

In the Sobel test results, the mediating effect and direct effect have opposite impacts on the first private placement decision. The ratio of mediating effect to direct effect is 11.58%. From the analysis results, it can be seen that the mediating effect of this model is significant under the sample data. Private placement provides a simple and efficient equity financing method for the financial market, while under the action of non-performing financing motivation, financial chaos such as “face-off phenomenon after money encirclement” and “pledge” also reduces investors’ investment sentiment. After the first private placement, chaos such as money encirclement effect disturbed investors’ cognition, thus the market feedback showed direct negative feedback effect.

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Model (2) DIS

Model (3) Y

PR 1 −1.286∗∗∗ (−5.37)

6.395∗∗∗ (4.03)

−1.393∗∗∗ (−5.72)

SIZE −0.235∗ (−2.44)

−3.821∗∗∗ (−4.82)

−0.175 (−1.80)

MB

−1.562 (−0.44)

0.638 (1.49)

0.581 (1.37)

0.0144∗∗∗ (3.39)

DIS Cons −0.597 (−1.42)

34.21∗∗∗ (9.63)

−1.151∗ (−2.53)

N

865

865

865

R2

0.0421

Note: ∗ Significant at 5% level, ∗∗ Significant at 1% level, and Sources: Wind database and JQData.

∗∗∗

Significant at 0.1% level.

Table 8. Sobel-Goodman mediating effect test for the first private placement Coef

Std. Err

Z

P >Z

Sobel

0.0136

0.0052

2.624

0.0087

Goodman − 1 Aroian

0.0136

0.0053

2.577

0.1000

Goodman − 2

0.0136

0.0051

2.673

0.0076

a

5.759

1.5914

3.6188

0.0003

b

0.0024

0.0006

3.8093

0.0001

Indirect ef f ect

0.0136

0.0052

2.6237

0.0087

Direct ef f ect

−0.1888

0.0292

−6.4581

1.1e−10

T otal ef f ect

−0.1752

0.0292

−5.9909

2.1e−9

Sources: Wind database and JQData.

(2) Mediating effect of samples in the subsequent private placement Using the same data of the first private placement, we analyzes the causal determinants of whether the listed company successfully implemented a private placement within three years after the non-first private placement(Y), and the analysis results are shown in Table 7, the Sobel-Goodman mediating effect test results are shown in Table 8: In accordance with results of Tables 7 and 8, similar to the effect of the first private placement, the mediating effect and direct effect also have opposite impacts on subsequent private placement decisions. The ratio of mediating effect to direct effect is 7.21%, and both are significant.

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After the first private placement, the market feedback has a direct negative feedback effect since the poor long-term market performance has become the main cognition. However, through investor compensation, the market feedback effect has weakened the effect of long-term market performance. Compared with the first private placement, the proportion of direct effect has increased, while the indirect mediating effects have weakened relatively. It can be concluded that the investor compensation plays a key role in the smooth implementation of private placement. The results of empirical analysis show that: (1) the enterprises’ characteristics and market environment have a significant impact on investor compensation, and especially the market feedback effect has a strong negative impact on the success of subsequent private placement; (2) investor compensation has a significant mediating effect among the enterprises’ characteristics, market environment and subsequent private placement decisions, the average mediating effect has a positive effect on the successful implement of subsequent private placement, which is about one tenth of the market feedback effect. In general, according to the analysis results of this chapter, appropriately raising investor compensation is beneficial to the subsequent private placement financing of the listed enterprises.

5

Robustness Test

Sobel test is a new method for edge detection, which plays an important role in the mediating effect test. The method can better detect the edge, i.e. it has high accuracy in 3 aspects: no missing of true edges, no checking of false edges and accurate edge positioning. However, the Bootstrap method proposed by Preacher and Hayes [17] can also achieve the efficient test of mediating effects. Bootstrap is a popular statistical method in modern statistics. It has obvious advantages and good results in small samples. Based on the Bootstrap method, this paper tests the robustness of the mediating effect model of private placement market feedback, investor compensation and decisions of subsequent private placement. 5.1

Robustness Analysis of Mediating Effects of Private Placement

The Stata data analysis software was used to analyze the robustness of the model. The results are shown in Table 9:

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Y. Zhang and C. He Table 9. Mediating effect robustness test of samples in private placement Mediating effect robustness test of the first private placement Model (1) Model (2) Model (3) Y DIS Y

Mediating effect robustness test of subsequent private placement Model (1) Model (2) Model (3) Y DIS Y

PR 1 −0.660∗∗∗ (−6.52)

8.768∗∗∗ (7.98)

−0.729∗∗∗ (−7.00)

−1.154∗∗∗ (−5.36)

6.395∗∗∗ (4.03)

−1.209∗∗∗ (−5.55)

SIZE −0.309∗∗∗ (−6.38)

−2.527∗∗∗ (−4.23)

−0.293∗∗∗ (−6.01)

−0.371∗∗∗ (−4.05)

−3.821∗∗∗ (−4.82)

−0.337∗∗∗ (−3.64)

−0.110 (−0.33)

−0.0573 (−0.84)

1.153∗∗ (2.88)

−1.562 (−0.44)

1.179∗∗ (2.94)

−0.0634 (−0.87)

MB

0.00667∗∗∗ (3.75)

DIS

0.0081∗∗∗ (2.10)

Cons 0.506∗∗ (2.95)

30.93∗∗∗ (14.66)

0.294 (1.63)

−0.0369 (−0.09)

34.21∗∗∗ (9.63)

−0.339 (−0.80)

N

1904

1904

865

865

865

R2 Note:

1904

0.0376 ∗

Significant at 5% level,

0.0421 ∗∗

Significant at 1% level, and

∗∗∗

Significant at 0.1% level.

According to results of robustness analysis in Table 9, the feedback variables, size and discount rate of the first private placement under mediating effects are significant at 0.1%, and the model is robust. Compared with the test results of the first private placement, the discount rate level of the subsequent private placement in the mediating effect model has decreased significantly, but it is also significant at the level of 5%, and the model is robust. 5.2

Robustness Test of Bootstrap Method for Private Placement

According to the test principle of Bootstrap method, the mediating effect model in this paper is rechecked. The test results are shown in Table 10: Table 10. Robustness test of bootstrap method for samples in private placement Mediating effect robustness test of the first private placement Ef f ect

M ean

Mediating effect robustness test of subsequent private placement

[95% Conf. Interval] M ean

[95% Conf. Interval]

Totel Effect

−0.154 −0.182 −0.125

−0.152 −0.193 −0.112

Average Mediation

0.016

0.011

Average Direct Effect

−0.170 −0.198 −0.143

−0.163 −0.202 −0.124

% of Tot Eff mediated

−0.104 −0.129 −0.088

−0.074 −0.101 −0.058

0.010

0.023

0.004

0.021

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699

From Table 10, Bootstrap’s results are basically consistent with those based on Sobel-Goodman. The consistency of the results of the two tests further shows that the model design and results are highly robust, and the original model is robust and credible.

6

Conclusions

In this paper, the empirical analysis results of the mediating effect model show that: (1) the enterprises’ characteristics and market environment have a significant impact on the investor compensation, especially the market feedback effect has a strong negative impact on the success of subsequent private placement. (2) Investor compensation has a significant mediating effect on the enterprises’ characteristics, market environment and subsequent private placement decisions. The average mediating effect which is about one tenth of the market feedback effect has a positive effect on the successful offering of the subsequent private placement. (3) With the increase of the number of private placement offerings, the average percentages of direct effect of market feedback and mediating effects of investor compensation both show a downward trend, that is, the degree of impact on the success of the enterprise’s future private placement decreases gradually. Based on the theory of mediating effects, and combined with the existing mainstream research results, this paper takes the market feedback as a direct variable and the investor compensation as a mediating variable to find out the interaction among the enterprise market feedback, investor compensation and decisions of subsequent private placement, thereby providing a theoretical basis for the successful completion of multiple rounds of private placement financing by private placement enterprises, and also providing suggestions and references for investors to select private placement shares and standardize the private placement market. Acknowledgements. The authors are grateful for the help and support from Li Delong and Li Yiguang. Any errors are the sole responsibility of the authors.

References 1. Anderson, H.D., Rose, L.C., Cahan, S.F.: Differential shareholder wealth and volume effects surrounding private equity placements in New Zealand. Pac.-Basin Financ. J. 14(4), 367–394 (2006) 2. Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173 (1986) 3. Bayar, O., Chemmanur, T.J., Liu, M.H.: A theory of capital structure, price impact, and long-run stock returns under heterogeneous beliefs. Rev. Corporate Financ. Stud. 4(2), 258–320 (2015) 4. Calvo-Mora, A., Ruiz-Moreno, C., et al.: Mediation effect of TQM technical factors in excellence management systems. J. Bus. Res. 67(5), 769–774 (2014)

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5. Chou, D.W., Gombola, M., Liu, F.Y.: Long-run underperformance following private equity placements: the role of growth opportunities. Q. Rev. Econ. Finan. 49(3), 1113–1128 (2009) 6. Chuang, K.S.: Private placements, market discounts and firm performance: the perspective of corporate life cycle analysis. Rev. Quant. Financ. Acc. 54(2), 541– 564 (2020) 7. Han, J., Pan, Z., Zhang, G.: Divergence of opinion and long-run performance of private placements: evidence from the auction market. J. Finan. Res. 42(2), 271– 302 (2019) 8. Hertzel, M., Smith, R.L.: Market discounts and shareholder gains for placing equity privately. J. Finan. 48(2), 459–485 (1993) 9. Hertzel, M., Lemmon, M., et al.: Long-run performance following private placements of equity. J. Finan. 57(6), 2595–2617 (2002) 10. Hovakimian, A., Hutton, I.: Market feedback and equity issuance: evidence from repeat equity issues. J. Financ. Quant. Anal. 45(3), 739–762 (2010) 11. Ingold, C.K., Ingold, E.H.: CLXIX–the nature of the alternating effect in carbon chains. Part V. a discussion of aromatic substitution with special reference to the respective roles of polar and non-polar dissociation; and a further study of the relative directive efficiencies of oxygen and nitrogen. J. Chem. Soc. (Resumed) 129, 1310–1328 (1926) 12. Islam, S., Ganguly, D.: Mediating effect of utilisation in the relation between loan services from PSBs and capital formation of MSMEs: a study of Purba and Paschim Medinipur districts of West Bengal. J. Glob. Entrepr. Res. 9(1), 57 (2019) 13. Jegadeesh, N., Weinstein, M., Welch, I.: An empirical investigation of IPO returns and subsequent equity offerings. J. Financ. Econ. 34(2), 153–175 (1993) 14. JQData (2019). https://www.joinquant.com 15. Meidan, D.: The informativeness of offer characteristics versus investor identity in PIPE transactions. Available at SSRN 894689 (2006) 16. Myers, S.C., Majluf, N.S.: Corporate financing and investment decisions when firms have informationthat investors do not have. Technical report, National Bureau of Economic Research (1984) 17. Preacher, K.J., Hayes, A.F.: SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Methods Instrum. Comput. 36(4), 717–731 (2004) 18. Stoughton, N.M., Wong, K.P., Zechner, J.: IPOs and product quality. J. Bus. 74(3), 375–408 (2001) 19. Subrahmanyam, A., Titman, S.: The going-public decision and the development of financial markets. J. Finan. 54(3), 1045–1082 (1999) 20. Van Bommel, J., Vermaelen, T.: Post-IPO capital expenditures and market feedback. J. Bank. Finan. 27(2), 275–305 (2003) 21. Wruck, K.H.: Equity ownership concentration and firm value: evidence from private equity financings. J. Financ. Econ. 23(1), 3–28 (1989) 22. Xu, B., Yu, J.: Is it the interest transfer of major shareholders or the optimism of investors that drives up the discount of private placement: evidence from China’s securities market. Finan. Trade Econ 4, 40–6 (2010). (in Chinese) 23. Yang, X., et al.: Corporate governance and competition effect of cash holdings- an empirical study based on the mediating effect of capital investment. China Ind. Econ. 1, 121–133 (2015). (in Chinese) 24. Yu, J., et al.: Study on major shareholder speculation, market opportunity selection and announcement effect of private placement. J. Zhongnan Univ. Econ. Law 5, 126–133 (2015). (in Chinese)

Research on the Impact of Gamification Application Interaction on B2C Mobile’s Continued Using Intention Jingdong Chen, Anbang Wang, and Mo Chen(B) Faculty of Economy and Management, Xi’an University of Technology, Xi’an 710054, People’s Republic of China [email protected] Abstract. Gamification as a new marketing method has been widely used on many B2C mobile terminals. This article explores the impact of gamification application interactivity on the continued using intention of B2C mobile users. The results show that the human-computer interaction of the gamified application on the B2C mobile terminal has a positive impact on the cognitive, emotional, and behavioral dimensions of customer engagement; the interpersonal interaction of the gamified application on the B2C mobile terminal has a positive impact on the cognitive, emotional, and behavioral dimensions of customer engagement; the emotional and behavior dimensions of customer engagement have a positive impact on the continued using intention of B2C mobile users. The research takes multi-dimensional customer engagement as the starting point, enriches the relevant theories of B2C mobile users’ continued using intention, and provides effective theoretical guidance for B2C mobile terminals to improve the interactivity of gamification applications from the perspective of customer engagement. Keywords: Gamification applications · Interaction engagement · Continued using intention

1

· Customer

Introduction

In the era of the mobile Internet, B2C mobile application software plays an important role in business operations. It is not only an important point of contact between enterprises and consumers, but also a major source of traffic for many enterprises (especially Internet companies). With the operation of B2C mobile terminal, enterprises can achieve the purpose of brand promotion, traffic conversion and profitability growth and so on. With the rapid popularization of smartphones, the number of B2C mobile terminals has spurted up. How to enhance users’ willingness to continue using a certain B2C mobile terminal has become a problem faced enterprises and operators. To solve this problem, many companies have begun to try to add gamification to B2C mobile terminals. The so-called “Gamification” refers to “the way to c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 701–715, 2020. https://doi.org/10.1007/978-3-030-49829-0_52

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apply game design elements, mechanisms or principles to non-game content” [4]. With gamified applications, companies immerse users and promote deeper interaction with B2C mobile and brand content. In the process of “playing”, deepen the user’s awareness of the brand, and ultimately affect their attitude and behavior towards B2C mobile terminal products and brands [5]. It can be seen that gamification provides new methods for enterprises to solve problems such as low retention rate of B2C mobile end users and weak willingness to continue using. Today, gamification is widely used in marketing practice, but related theoretical research is relatively lagging behind. Existing research has explored the definition of gamification in different application scenarios and the impact of game elements (points, leaderboards, medals, etc.) on the gamification application itself, but the impact mechanism of gamification applications on B2C mobile terminals is rarely involved. In fact, interactivity is a core feature of gamified applications and an important indicator for judging the quality of a gamified application. It is of great theoretical significance and practical value to study how the interactivity of gamified applications affects the continued using intention of B2C mobile terminals. To this end, this article divides the interactivity in gamification applications into human-computer interaction and interpersonal interaction, and explores the impact of two types of interactivity on the continued using intention of B2C mobile users. And innovative introduction of customer engagement as an intermediary variable, in-depth analysis of the above-mentioned impact of the intermediate mechanism. The research conclusions will enrich the theory of gamified marketing and guide related practices. This study is divided into five parts, the first part is the introduction; the second part is the literature review; the third part is the research hypothesis and model construction; the fourth part is the research design and empirical analysis; the fifth part is the conclusion.

2 2.1

Literature Review Gamification Application Interactivity (GAI)

There are two perspectives of system design and user experience in the understanding of gamification [3]. From the perspective of system design, gamification is an addiction system composed of different game mechanisms [17]; From the perspective of user experience, piecing together game mechanics is not enough to be called gamification. Gamification must also give people the feeling of playing games [22]. Combining the two perspectives of system design and user experience, we believe that gamification application is an application that makes use of game design elements, mechanisms or principles to bring a sense of game to users. Through the combination of different game mechanics, non-game content can also present game-like interactivity, which is one of the basic characteristics of games and gamification applications [12]. According to the user interaction dimension of Internet products, this study can divide the interactivity of gamified applications into human-computer interaction and interpersonal interaction.

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Man-machine interaction originally refers to the interaction between users and various virtual contents in the computer [6]. With the development of mobile devices, human-computer interaction is no longer limited to the computer environment, but also can be a variety of mobile devices. Therefore, in this paper, the man-machine interaction in gamification application is mediated by mobile devices, and the interaction between users and gamification application content. Human interaction is the interaction between users of gamified applications [14]. This interaction takes place mainly on the mobile Internet, but it can also take place offline. 2.2

Customer Engagement (CE)

The use of the concept of engagement dates back to the 17th century, when it was used to describe moral or legal obligations, duties, engagements, employment, and military conflicts [2]. The earliest use of the word “Engagement” in marketing practice can be traced back to 2001, when Appelbaum (2001) pointed out that customer engagement consists of rational loyalty and emotional attachment. Subsequently, “Engagement” quickly became a focus of research in the field of marketing, leading to a series of concepts including customer engagement, customer engagement behaviors, customer brand engagement and so on. Business and academia have different definitions for the concept of customer engagement. From an organizational perspective, the business community defines customer engagement as “activities to enhance interaction and promote customers’ emotional, psychological or material investment in the brand”; However, from the perspective of system, the academia interprets customer engagement as customer and organization representative and others The tendency of customers to participate in collaborative knowledge exchange processes. In fact, different scholars have different definitions of customer engagement, as shown in Table 1. In addition to the research and elaboration of the concept of customer engagement, many scholars at home and abroad are also concerned about the measurement of customer engagement. Measuring customer engagement requires identifying its dimensions. At present, the academic research results on customer engagement dimension are mainly divided into two categories: single dimension and multi-dimension. The research shows 40% of scholars identified customer engagement as a single dimensional concept. The definition of single dimension can be divided into two types: psychological level and line level. For example, Pham and Avnet (2009) mainly defined customer engagement from the perspective of behavior, believing that the dimension measurement of behavior is conducive to the observation and analysis. Van Doorn et al. (2010) believe that customer engagement is an expression of behavior rather than a reflection of psychology [20]. Beckers et al. (2014) believe that customer engagement reflects the change of customers’ psychological activities, which drives the generation of behaviors [1]. More scholars believe that multi-dimension can better reflect the meaning of customer engagement. Patterson et al. (2006) proposed that customer engagement consists of four components, namely concentration, dedication, activity and communication [16]; Vivek (2009) proposed three dimensions of customer engagement, including enthusiasm, conscious participation and

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J. Chen et al. Table 1. Concepts and definitions of engagement in the marketing field Scholar

Concept

Patterson et al. (2006)

Customer The customer is the degree of physical, engagement cognitive, and emotional representation in the relationship of the service organization

Bowden (2009)

Customer It is a psychological process that describes engagement the forming mechanism of new customers’ Process loyalty to the service brand and the maintenance mechanism of repeated customers’ loyalty to the service brand

Pham and Avnet Customer (2009) behavior

Definition

A engagement seems to result from a class of actions or withdrawal from a target

Higgins and scholer (2009)

Engagement The state of being involved in, occupied, fully assimilated, or absorbed by a target object, resulting in some attraction or repulsion. Highly compatible individuals will approach or reject the target object, thus increasing or decreasing the value of the target object

Vivek, Beatty et al. (2010)

Consumer The intensity of involvement and connection engagement between the individual and the organization’s offerings and activities (initiated by the customer or organization)

Mollen and Wilson (2010)

Online Customers’ cognitive and emotional brands commitment to a positive relationship with engagement the brand. The behavior of a customer toward a brand or business beyond buying is driven by motivational factors such as word-of-mouth campaigns, referrals, helping other customers, blogging, and writing reviews

Van Doorn et al. Customer The mental state of customer motivation, (2010) engagement brand relevance and situational dependence behavior is characterized by some degree of cognition, emotion and behavioral activity in the interaction with the brand Holllebeek (2011)

Customer The customer is the degree of physical, brand cognitive, and emotional representation in engagement the relationship of the service organization

communication and interaction. Kumar et al. (2010) introduced the concept of customer engagement value and believed that customer impact value, lifetime value, recommendation value and knowledge value together constituted the value of customer engagement. Hollebeek (2014) put forward the three dimensions of customer engagement, namely cognition, emotion and behavior. In different

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705

environments, the level of compatibility between customers and enterprises or brands is different [8]. Among them, the three dimensions of cognition, emotion and behavior are increasingly recognized and gradually become the standards used by scholars at home and abroad to measure customer engagement. However, few researches focus on the influence of the interaction of game application on the user’s continued using intention from the perspective of engagement. The research takes multi-dimensional customer engagement as the starting point, enriches the relevant theories of B2C mobile users’ continued using intention, and provides effective theoretical guidance for B2C mobile terminals to improve the interactivity of gamification applications from the perspective of customer engagement. 2.3

Continued Using Intention (CUI)

Domestic scholars mainly take the expectation confirmation theory and technology acceptance model as the theoretical basis to carry out research on the willingness to continue to use. The main research areas are social apps, travel apps, news apps and other B2C mobile terminals. Based on the expectation confirmation model, some scholars have constructed a theoretical research model of WeChat users’ continued using intention. Bao (2016) will be PPM model integration and expected to confirm model, the micro credit continues to use the influence factors of willingness, the double perspective to explore inner demand and external environment, bears fruit found that information seeking, recreation, social interaction and kill time motivation significantly influence WeChat continued use [23]. Meng (2018) studied the continuous use behavior of mobile social media users, integrated the ECM model and self-determination theory, and built the research model. The research results showed that users’ willingness and habit of continuous use directly affected users’ continuous use behavior [13]. Xiao (2016) research results show that satisfaction significantly directly affects users’ intention of continuous use and the improvement of immersion can drive the improvement of users’ satisfaction and continuous intention. In addition, the interaction of different dimensions also has different effects on the formation of user satisfaction and immersion [9]. Kim Min-Sun, Kim Yu-jeong (2012) research results show that the frequency of social interaction to Shared vision and social networking sites for intention to have a positive influence, the frequency of social interaction and the relationship between the trust has not been confirmed. The study found that “common vision” plays an important role in building users’ confidence and shaping users’ continuous use, and the trust of other users can significantly affect users’ continuous intention of SNS. The environmental generality of mobile payment services has been shown to be effective in eliciting positive emotions (fun) from users and influencing the intention to continue using them. The effect on bipolar emotion (anxiety) is partially confirmed [10].

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Research Hypothesis and Model Construction Gamification Application Interactivity and Customer Engagement

Sawhney et al. (2005) once studied the effect of customer engagement on product innovation in the network environment, and believed that network platform could become the pre-influencing factor of customer engagement, including interactivity, enhanced reach, persistence, speed and flexibility. Furthermore, enterprises can design the network platform based on the above characteristics, so that suitable customers can have a more positive impact on the product innovation of enterprises [19]. The research puts forward the concept model of customer brand engagement [7]. Vivek et al. (2012) mainly analyzed the antecedent variables generated by customer engagement from the perspective of customers, mainly including customer involvement and customer participation, which once again provided theoretical support for customer involvement as a pre-factor of customer engagement [21]. Berger et al. (2017) believe that gamified interaction can enable customers to align with the company’s brand, thus having a significant impact on selfbrand connection. They argue that flow experiences can drive users’ emotional and cognitive responses to a particular activity. Therefore, in the network environment, flow experience generated by gamification interaction, including high interactivity and optimal challenge, will have an impact on the emotional and cognitive dimensions of customer brand engagement. Based on the above theoretical derivation, the following hypotheses are proposed: Hypothesis 1a. Gamification application of human-computer interaction has a significant positive effect on cognitive engagement; Hypothesis 1b. Gamification application of human-computer interaction has a significant positive impact on emotional engagement; Hypothesis 1c. Gamification application of human-computer interaction has a significant positive impact on behavioral engagement; Hypothesis 2a. Gamification of interpersonal interaction has a significant positive effect on cognitive engagement; Hypothesis 2b. Gamification of interpersonal interaction has a significant positive effect on emotional engagement; Hypothesis 2c. Gamification of interpersonal interaction has a significant positive effect on behavioral engagement. 3.2

Customer Engagement and Continuous Use Intention

According to most scholars agree with the theory of construction, to customers satisfaction, loyalty, trust, attachment, self-product brand contact could be the result of customers agree with variable [11]. There are more literature shows that these factors has a positive influence on users continue to use behavior,

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thus can be speculated that the customer will engagement is to influence users continue to use, it has gained a degree of empirical support, is also a Reitz in his online customer agree with the concept of model, prove they engagement has positive impact to the users continue to use Facebook [18]. Therefore, the following hypothesis is proposed in this paper: Hypothesis 3a. Cognitive Engagement has a significant positive effect on the willingness to continue using; Hypothesis 3b. Emotional Engagement has a significant positive effect on the willingness to continue using; Hypothesis 3c. Behavioral Engagement has a significant positive effect on the willingness to continue using. Based on the above research assumptions, the theoretical model of this study is constructed as shown in Fig. 1. Gamification Application Interactivity HumanComputer Interaction Interpersonal Interaction

Customer Engagement Cognitive Engagement Emotional Engagement

Continued Using Intention

Behavioral Engagement

Fig. 1. Theoretical model

4 4.1

Research Design and Empirical Analysis Variable Design and Measurement

All variables in the questionnaire were modified and improved by referring to the existing mature scales and combining with the gamification application scenarios. The details are as follows: (1) human-computer interaction (HCI) of gamification applications. Based on the study of Yun (2007), the measurement was made from the aspects of operation fluency, interface prompt clarity and timeliness of feedback. There were three measurement items in total [24]; (2) The interpersonal interactivity of gamification applications refers to the research by Nambisan and Robert (2009) [15], which measures users’ interactions in gamification applications, online and offline communication, and willingness to share with others. There are three measurement items; CE was measured by the scale developed by Hollebeek (2014), which included three dimensions of cognition, emotion and behavior, and ten items. Continuance Intention (CI) of B2C mobile terminal. According to the research of Alraimi et al. (2015), users’ willingness to use B2C mobile terminal now, stability and future willingness to use B2C mobile terminal were measured from three measurement items. After pre-testing and adjustment, the questionnaire finally determined the final item (see Table 2).

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Variable

Items

Human-computer interaction

HCI1: I think the overall operation of the Yun (2007), ant forest is very smooth Robert (2009) HCI2: I can get multiple cues when I’m operating an ant forest HCI3: when I click on a feature, it gives me quick results II1: when I use ant forest, other users respond positively to my behavior human interaction II2: I will share my experience of using ant forest with other users (both online and offline) II3: I will participate in discussions related to ant forest (both online and offline) COG1: ant forest reminds me of alipay first COG2: when I use payment software, I think more about alipay COG3: ant forest stimulated my interest in more information about alipay EMO1: when I use the ant forest, I feel very positive EMO2: the ant forest makes me happy EMO3: when I use the ant forest, I enjoy it. Hollebeek EMO4: I’m proud to plant trees in the ant forest

Interpersonal Interaction

Cognitive engagement

Emotional engagement

Reference

Behavioral engagement

BEH1: compared to other software Hollebeek (2014) applications, I spent a lot of time using the antsen (2014) forest. BEH2: whenever I use ant forest, I usually use alipay for other functions BEH3: using ant forest is one of the most common recreational activities I use when using alipay one

Continued using intention

CI1: I am willing to continue to use Alraimi et al. Alipay (2015) CI2: I will at least keep using Alipay as it is now CI3: I will often use Alipay in the future

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Data Collection and Statistical Analysis

According to the concept of gamification application and the availability of actual data collection, we finally selected the gamification application with the ant forest as the research object. Ant forest is a gamification app launched by alipay. In the game, everyone starts with a small sapling, the sapling needs to get enough green energy (the user’s low-carbon row can be converted into green energy) to grow into a big tree, the user can finally claim a real sapling planted in Alxa. In this study, a total of 350 questionnaires were collected and screened. Finally, 315 valid questionnaires were obtained, with a valid questionnaire rate of 90%. The overall structural characteristics of the samples are shown in Table 3: Table 3. Descriptive analysis of sample characteristics

4.3

Item

Type

Number of samples Percentage

Gender

Female male

180 135

57.14% 42.86%

Education

College and below 65 Undergraduate 190 Graduate and above 60

20.63% 60.32% 19.05%

Occupation

Students Non-school student

200 115

63.49% 36.51%

Monthly income Under 2000 yuan 130 2000–4000 yuan 70 4001–6000 yuan 48 6001–8000 yuan 35 8001–10000 yuan 20 10,000 yuan or more 12

41.27% 22.22% 15.24% 11.11% 6.35% 3.81%

Reliability and Validity Analysis

Reliability refers to the consistency, stability and reliability of test results. The measure index of reliability is the reliability coefficient. The higher the reliability coefficient is, the higher the reliability is. In this study, Cronbach’s alpha value was used to test the reliability. It is generally believed that the minimum acceptable value is Cronbach’s alpha between 0.65 and 0.7, and the reliability is better when the alpha value is larger than 0.7. Due to the use of mature scale in this study, confirmatory factor analysis of structural equation was used to verify the validity. The results of reliability and validity tests are shown in Table 4. Table 4 the consistency coefficient of each measurement index to the measurement concept is above 0.7, indicating that the scale has good reliability. In the validity analysis, according to the recommendation of Hair et al.’s judgment criteria for factor loading, it is acceptable for the standardized factor loading

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to reach a significant level above 0.05. As can be seen from Table 4, from the specific fitting results, the standardized load of each indicator or dimension on the corresponding latent variables was between 0.701 and 0.920, and all passed the significance test at the P < 0.001 level, so the validity of the scale could meet the requirements. Table 4. Reliability and validity test results Variable

4.4

Index

Factor loading Cronbach’s α

Human-computer interaction HCI1 HCI2 HCI3

0.763

0.805 0.824*** 0.788***

Interpersonal interaction

II1 II2 II3

0.736

0.759 0.814*** 0.728***

Cognitive engagement

COG1 0.803 COG2 COG3

0.755 0.701*** 0.837***

Emotional engagement

EMO1 0.896 EMO2 EMO3 EMO4

0.84 0.843*** 0.788*** 0.856***

Behavioral engagement

BEH1 0.889 BEH2 BEH3

0.813 0.920*** 0.867***

Continued using intention

CI1 CI2 CI3

0.909 0.832*** 0.813***

0.87

Path Analysis and Hypothesis Verification

In this paper, the structural equation model is used to test the proposed model and hypothesis. The independent variables in the model include two variables of gamification application man-machine interaction and interpersonal interaction, the intermediate variables include cognitive engagement, emotional engagement and behavioral engagement, and the outcome variable is the continued using intention. AMOS18.0 was used for path analysis to calculate the path coefficients among variables in the model and their significant manifestations. The analysis results of path coefficients between variables in this study and corresponding T and P values are shown in Table 5.

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Table 5. Path coefficient analysis result Paths

Standardized coefficients (β)

t value p value

Human-Computer Interaction → Cognitive Engagement

0.817

19.04

Human-Computer Interaction → Emotional Engagement

0.718

13.788 ***

Human-Computer Interaction → Behavioral Engagement

0.811

19.34

Interpersonal Interaction → Cognitive Engagement

0.701

13.601 ***

Interpersonal Interaction → Emotional Engagement

0.394

4.507

***

Interpersonal Interaction → Behavioral Engagement

0.435

6.995

***

Cognitive Engagement → Continued Using Intention

0.052

0.856

0.094

Emotional Engagement → Continued Using Intention

0.413

7.394

***

Behavioral Engagement → Continued Using Intention

0.314

4.358

***

***

***

It can be seen from Table 5 that the human-computer interaction of gamification applications in B2C mobile terminals has a positive impact on the cognitive, emotional, and behavioral dimensions of customer engagement (β = 0.817, p < 0.001; β = 0.718, p < 0.001; β = 0.811, p < 0.001;), so the original hypotheses H1a, H1b, and H1c are all true; the interpersonal interaction of gamified applications in the B2C mobile terminal has a positive impact on the cognitive, emotional, and behavioral dimensions of customer engagement (β = 0.701, p < 0.001; β = 0.394, p < 0.001; β = 0.435, p < 0.001;), so the original hypotheses H2a, H2b, and H2c are all true; the cognitive engagement has no significant impact on the willingness to continue using B2C mobile terminals (β = 0.052, p > 0.001;), so H3a is not established; emotional engagement and behavioral engagement have a significant positive impact on the continued using intention B2C mobile terminals (β = 0.413, p < 0.001; β = 0.314, p < 0.001). Therefore, the null hypothesis H3b and H3c are true.

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Conclusion Model Results and Conclusion

Through empirical research methods, AMOS and SPSS data analysis software were used to analyze and test the collected data. The results of hypothesis testing are shown in Table 6. Table 6. Hypothesis test result Label Hypothesis H1a

Human-Computer Interaction has a significant positive impact on Cognitive Engagement

H1b

Human-Computer Interaction has a significant positive impact on Emotional Engagement

H1c

Human-Computer Interaction has a significant positive impact on Behavioral Engagement

H2a

Interpersonal Interaction has a significant positive impact on Cognitive Engagement

H2b

Interpersonal Interaction has a significant positive impact on Emotional Engagement

H2c

Interpersonal Interaction has a significant positive impact on Behavioral Engagement

H3a

Cognitive Engagement has a significant positive impact on Continued Using Intention

H3b

Emotional Engagement has a significant positive impact on Continued Using Intention

H3c

Behavioral Engagement has a significant positive impact on Continued Using Intention

Test results √ √ √ √ √ √ × √ √

Previous studies on the impact of gamification have generally focused on the game elements themselves, and explored the impact of game elements on gamification applications, such as the impact of social elements on users of gamification sports platforms, and the impact of game content on user behavior of gamification applications. At the same time, most studies only focus on the impact of gamification applications themselves, but ignore the impact of gamification applications on B2C mobile terminals. From a more macro perspective, this paper studies the influence of inter mobility of gamification applications on users’ willingness to continue using B2C mobile terminals, and draws the following conclusions: (1) The human-computer interaction of gamified applications on the B2C mobile terminal has a positive impact on the cognitive, emotional, and behavioral

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dimensions of customer engagement; the interpersonal interaction of gamified applications on the B2C mobile terminal has a cognitive and emotional impact on customer engagement. The behavior dimension has a positive impact. In the previous related research on the definition of gamification, although it showed that gameplay is the key to gamification applications, it did not explain what factors make gamification applications produce gameplay. We verified empirically that interactivity is an important factor in making gamified content feel like a game. (2) Emotional engagement and behavioral engagement have a significant positive impact on the willingness to continue using B2C mobile terminals. The understanding of customer engagement is the dynamic process of interaction and interaction with the enterprise. B2C mobile users have emotional and behavioral links to the website, which makes them willing to play B2C mobile games. High-engagement customers are more willing to continue playing B2C mobile games because they can trust the website and get pleasure from the B2C mobile games, which makes them continue to use B2C mobile terminals. (3) The impact of cognitive engagement on the willingness to continue using B2C mobile terminals is not significant. To explore the reasons, we can consider the following two aspects. On the one hand, in theory, compared to the two dimensions of emotion and behavior, the cognitive dimension reflects a more superficial state of mind, rather than what is obtained after careful consideration, and it is necessary to obtain the willingness to continue using B2C mobile terminals. It can only be obtained after experience and discrimination, so the impact of cognition on the continued using intention of B2C mobile terminals is minimal; on the other hand, various gamified applications provided by various B2C mobile terminals are numerous, and users need to pass actual use before they can actually feel To whether to use it again, so focus on behavior rather than psychology. 5.2

Implications

According to the research conclusions in this paper, the human-computer interaction of gamified applications in B2C mobile terminals has a positive impact on the cognitive, emotional, and behavioral dimensions of customer engagement; the interpersonal interactivity of gamified applications in B2C mobile terminals has a positive impact on customer engagement. Cognitive, emotional, and behavioral dimensions have a positive impact. Therefore, managers of B2C mobile terminals should fully realize this. There are two opinions on how B2C mobile terminals can attract and retain users: First, companies should add more interactive elements to the design of gamified applications to improve the human-computer interaction of B2C mobile gamified applications; second, for gamified applications In other words, interpersonal interaction also plays an important role in it. Therefore, how to increase social elements and promote user interaction and communication is an issue that enterprises should also consider when designing gamification applications.

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From the perspective of gamification application interaction and the introduction of customer engagement, this study conducts an empirical study on the influencing factors of the continued using intention B2C mobile terminals. Relevant researches in the past have been detailed. Although some results have been obtained, due to the constraints of subjective and objective conditions, there are still some shortcomings such as that the model fitting indicators have not reached the optimal level, which needs to be further studied further and improved.

References 1. Beckers, S.F., Risselada, H., Verhoef, P.C.: Customer engagement: a new frontier in customer value management. In: Handbook of Service Marketing Research, Edward Elgar Publishing (2014) 2. Brodie, R.J., Hollebeek, L.D., et al.: Customer engagement: conceptual domain, fundamental propositions, and implications for research. J. Serv. Res. 14(3), 252– 271 (2011) 3. Ning, C., Tong, N.: Summary and prospect of foreign gamification marketing research. Foreign Econ. Manag. 39(10), 72–85 (2017). (in Chinese) 4. Deterding, S., Dixon, D., et al.: From game design elements to gamefulness: defining “gamification”. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, pp. 9–15 (2011) 5. Gross, M.L.: Advergames and the effects of game-product congruity. Comput. Hum. Behav. 26(6), 1259–1265 (2010) 6. Hoffman, D.L., Novak, T.P.: Marketing in hypermedia computer-mediated environments: conceptual foundations. J. Mark. 60(3), 50–68 (1996) 7. Hollebeek, L.: Exploring customer brand engagement: definition and themes. J. Strateg. Mark. 19(7), 555–573 (2011) 8. Hollebeek, L.D., Glynn, M.S., Brodie, R.J.: Consumer brand engagement in social media: conceptualization, scale development and validation. J. Interact. Mark. 28(2), 149–165 (2014) 9. Xiao, H.: Study on the influencing factors of the willingness to use WeChat public account users. Southwest University (2016). (in Chinese) 10. Kim, M.S., Yj, K.: Effect of social capital on users’ continuance intention to SNS. Internet E-commer. Res. 12(4), 1–22 (2012) 11. Lin, T.C., Wu, S., et al.: The integration of value-based adoption and expectationconfirmation models: an example of IPTV continuance intention. Decis. Support Syst. 54(1), 63–75 (2012) 12. McMillan, S.J., Hwang, J.S., Lee, G.: Effects of structural and perceptual factors on attitudes toward the website. J. Adver. Res. 43(4), 400–409 (2003) 13. Meng, M., Zhu, Q.: Research on the continuous use behavior of mobile social media users. Mod. Inf. 38(01), 5–18 (2018). (in Chinese) 14. Nambisan, S., Baron, R.A.: Interactions in virtual customer environments: implications for product support and customer relationship management. J. Interact. Mark. 21(2), 42–62 (2007) 15. Nambisan, S., Baron, R.A.: Virtual customer environments: testing a model of voluntary participation in value co-creation activities. J. Prod. Innov. Manage 26(4), 388–406 (2009)

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16. Patterson, P., Yu, T., De Ruyter, K.: Understanding customer engagement in services. In: Proceedings of ANZMAC 2006 Conference on Advancing Theory, Maintaining Relevance, pp. 4–6, Brisbane (2006) 17. Penenberg, A.L.: Play at Work: How Games Inspire Breakthrough Thinking. Portfolio (2015) 18. Reitz, A.R.: Online consumer engagement: understanding the antecedents and outcomes. Ph.D. thesis, Colorado State University, Libraries (2012) 19. Sawhney, M., Verona, G., Prandelli, E.: Collaborating to create: the Internet as a platform for customer engagement in product innovation. J. Interact. Mark. 19(4), 4–17 (2005) 20. Van Doorn, J., Lemon, K.N., et al.: Customer engagement behavior: theoretical foundations and research directions. J. Serv. Res. 13(3), 253–266 (2010) 21. Vivek, S.D., Beatty, S.E., Morgan, R.M.: Customer engagement: exploring customer relationships beyond purchase. J. Mark. Theory Pract. 20(2), 122–146 (2012) 22. Werbach, K.: Redefining gamification: a process approach. In: International Conference on Persuasive Technology, pp. 266–272. Springer (2012) 23. Bao, Y.: Research on the factors influencing the willingness of WeChat users’ continuous use. Harbin Institute of Technology (2016) (in Chinese) 24. Yun, G.W.: Interactivity concepts examined: response time, hypertext, role taking, and multimodality. Media Psychol. 9(3), 527–548 (2007)

Research on the Influence of Product Design on Purchase Intention Based on Customer Satisfaction Mo Chen, Jingdong Chen, and Zhihu Li(B) Faculty of Economy and Management, Xi’an University of Technology, Xi’an 710054, People’s Republic of China [email protected]

Abstract. As the concept of “double innovation” is deeply rooted in the hearts of the people, consumers will pay more attention to the innovation of products. In order to satisfy consumers’ pursuit of innovation, there are various designs of enterprise products. To explore the influence of product design on purchase intention, the SEM is utilized to carry out an empirical research on the drive and influence of customer engagement on continued purchase intention based on the multi-dimensional product design perspective of functional design, aesthetic design and symbolic design with the introduction of customer satisfaction as a mediator variable. The empirical results show that the function, aesthetic and symbolic dimensions of product design have a significant positive impact on customer satisfaction; customer satisfaction has a significant positive impact on purchase intention; product design function, aesthetics and symbolism directly promote purchase intention effect. When the whole industry is custom-made for furniture, the multi-dimensional perspective research based on customer satisfaction can not only make up for the shortcomings of existing research, but also has certain significance for guiding enterprises to carry out product design practice.

Keywords: Product design satisfaction · Purchase

1

· Furniture customization · Customer

Introduction

Design is the medium of communication between enterprises and consumers. Design not only provides various benefits for enterprises and consumers, but also changes life. Nowadays, when the features and prices of products can not be distinguished from each other, design becomes the only factor that can distinguish products. Fifteen years ago, the competition among enterprises was about price, today it is about quality, and in the future it is about design. The importance of product design is particularly suitable for today’s market because it has become the main way to make products unique. Brown, President of IDEO, c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 716–730, 2020. https://doi.org/10.1007/978-3-030-49829-0_53

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a famous American design company, pointed out that we should think about problems from the perspective of design, and turn them into the commodity and customer value of market opportunities to meet the needs of customers. It can be seen that the product design ability of an enterprise affects the market share of its products. The product design ability of an enterprise is the source of its competitive advantage and an important driving force of its performance, Product design is increasingly regarded as an important strategic tool by managers to create deeper emotional value between enterprises and customers. With the development of social technology and its impact on people more and more in-depth, customer demand begins to show diversification and variability. Products designed based on customer demand will increase customer’s desire to buy, and customer satisfaction will greatly improve after experiencing products. In the existing literature, the concept and dimensions of product design have been studied in depth. These studies are mainly based on the theoretical deduction. The research of product design is also a hot topic in the academic circles at home and abroad. Bloch et al. [4] constructed the consumer response model of product design from the perspective of consumer behavior, pointed out that product modeling can trigger the psychological response of consumers, thus triggering their behavioral response. At the same time, the impact of product modeling on consumer psychological response will be regulated by many factors. At present, there are many literatures indicating that product design has a positive impact on the purchase intention of customers Ring. This paper summarizes the main dimensions of furniture customized product design, combined with customer satisfaction as an intermediary variable, constructs the relationship model between product design and purchase intention, based on the dominant logic of customer satisfaction, and explores the factors affecting customer purchase intention from the perspective of product design. If Furniture Customization companies want to achieve good operation and good performance, they need customers’ support and purchase. The marketing strategy of the furniture customization enterprise influences the customer’s purchase behavior. Based on the good situation of the development of the home furnishing industry, it is a breakthrough to enhance its dynamic competitiveness to promote the sustainable development of the furniture enterprise.

2 2.1

Literature Review Product Design

In the past, the concept of product design was only defined from one or two dimensions. Landwehr [14] made a single evaluation on product design from the aspect of aesthetics, and thought that product design is the design on product aesthetics. Chitturi [6] et al. estimated the product design from the selected dimension, and thought that the product design is the design of the product enjoyment and practical attributes. However, with the in-depth study of product design concept, the recent concept research of product design has extended beyond one dimension and two dimensions. Bloch pointed out that product

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design is a three-dimensional concept, that is, product design consists of three dimensions: aesthetic dimension, functional dimension and symbolic dimension. Srinivasan [19] used this division dimension in his research, but this research only focused on one product and used highly specific items to measure the dimension of product design, which is not applicable. On the basis of these existing studies, based on the Gestalt theory, through a large number of literature review, we theoretically derived the definition of product design and product design three dimensions, and through qualitative interviews with consumers, we verified the definition, and finally verified the applicability of the scale through empirical research. Consumers’ views on product design and the elements that make up the product can be visible or invisible. The definition of product design by Homburg includes both visual elements and non visual elements of the product. The research of Homburg breaks through the single dimension perspective of product design, so the definition of product design by Homburg and other scholars is more comprehensive. Homburg et al. [10] provide a standard and applicable product design measurement method, which can be applied in practice and can develop research on product design. Therefore, this study adopts the more comprehensive concept of product design by scholars such as Homburg, and holds that product design refers to a set of basic elements of products that consumers can perceive, which are composed of aesthetic, functional and symbolic dimensions. Aesthetic dimension refers to the appearance and aesthetic feeling of products that can be perceived by consumers, that is, the product itself has the attribute that can make the spectators perceive the beauty [1]. Function dimension refers to the consumer’s perception of the performance of the product to achieve its functions. Symbolic dimension refers to the perceptual information formed by a product, which is the self-image formed by consumers on the basis of themselves and other elements. 2.2

Customer Satisfaction

As early as the 1950s, customer satisfaction (CS) has been recognized and concerned by the world [17]. Scholars’ understanding of customer satisfaction mostly revolves around the “expectation difference” paradigm. The basic connotation of this paradigm is that customer expectations form a reference point for comparison and judgment of products and services. Customer satisfaction is perceived as a subjective feeling. It describes the degree to which the customer’s expectation of a particular purchase touch is satisfied [16]. Oliver and Linda [2] thought that customer satisfaction is “a psychological state”. When the expectation formed by the customer according to the consumption experience is consistent with the consumption experience, it will produce “a emotional state”. Henry Assel [18] thought that when the actual consumption effect of the goods reaches the expectation of the customer, it will lead to customer satisfaction. Otherwise, it will lead to customer dissatisfaction. Henry Assel also believes that when the actual consumption effect of goods meets the expectations of consumers, it will lead to satisfaction, otherwise, it will lead to

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customer dissatisfaction. According to Philip Kotler [13], customer satisfaction “refers to a person’s feeling state of pleasure or disappointment after comparing the perceived effect of a product with his expectation”. 2.3

Furniture Customization

The word “custom” originated in Saville street, which means tailoring for individual customers [20]. Savile row is a shopping district in Mayfair, central London, famous for its traditional bespoke tailoring business. With the development of the times, the meaning of the word “customized” has been gradually enriched, such as customized clothing, gifts, even customized skin color, customized vegetables, etc., which caters to people’s psychology of pursuing quality and personality. Customization is the real personalized consumption. Among the “top 10 technologies to change the future” predicted by the United States, “personalized customization” is ranked first, and its market position is increasingly recognized by people. Professor Xie [23] of Zhongnanlin University completed the master’s thesis of research on the implementation of mass customization in furniture enterprises. This paper puts forward the strategy of mass customization in the process reform and organizational structure reform of furniture enterprises, and expounds the goal and way of realizing mass customization in enterprises. In 2006, the “mass customization production of furniture enterprises” project was launched in Guangzhou. As the undertaker of the project, Yuanfang software took weishang furniture group as the pilot enterprise. After a year and a half of research and development, it developed an integrated solution suitable for panel furniture production enterprises to realize the “mass customization” production mode. After the application of mass customization, the production capacity of weishang cabinets increased compared with that before With 4 times increase, the delivery time is shortened from 20 days to 10 days, the error rate is reduced from 30% to 10%, the utilization rate of plates is increased from 70% to 88%, and the daily production order receiving quantity is increased from 30 to 150.

3 3.1

Research Hypothesis and Model Construction Product Design and Customer Satisfaction

With the in-depth study of product design concept, Bloch believes that product design consists of three dimensions, namely aesthetic dimension, functional dimension and symbolic dimension. Homburg et al. [5] have defined and measured the concept of product design. They think that product design refers to a set of basic elements of products that consumers can perceive. These basic elements are composed of aesthetic, functional and symbolic dimensions. In this study, block, Humburg and other scholars take a more comprehensive division method to divide product design into aesthetic dimension, functional dimension and symbolic dimension. Product evaluation refers to a kind of non intelligent

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and automatic subjective perceptual evaluation made by consumers for products [7]. Yeung et al. concluded that product design affects consumers’ evaluation of the product, and then through evaluation to influence their impression of the brand product [24]. Gao Wei, a domestic scholar, thinks that for product design, every consumer has their own concerns, some people pay attention to the aesthetic feeling of the product, some people pay attention to the function of the product, some people pay attention to the symbolic meaning of the product and so on. Based on their respective concerns, consumers will evaluate the product [8]. Hypothesis 1a. Functional design has a positive impact on customer satisfaction. Hypothesis 1b. Aesthetic design has a positive impact on customer satisfaction. Hypothesis 1c. Symbolic design has a positive impact on customer satisfaction. 3.2

Customer Satisfaction and Purchase Intention

Tse and Wilton [21] thinks that customer satisfaction is the evaluation of the difference between the expectation quality formed by the customer before the purchase behavior and the perceived quality after the consumption. Westbrook and Reily [9] believed that customer satisfaction is an emotional response, which is accompanied or produced by the psychological impact of product display and the overall shopping environment on consumers during the purchase process. Then it is extended that customer satisfaction will play a positive role in their purchase intention. When customers experience enterprise products, a good sense of experience will improve customer satisfaction and form customers The reason of customers’ positive purchase intention. Therefore, this paper proposes the following research hypotheses: Hypothesis 2. Customer satisfaction plays a positive role in promoting purchase intention. 3.3

Product Design and Purchase Intention

From the perspective of consumer perception, marketing research on product design focuses on consumer cognition, emotion and consumer behavior [22]. Bloch pointed out in 1995 that product modeling design is the decisive factor for commercial success of products, and built a “consumer response model of product modeling design”, as shown in Fig. 1. The model points out that product modeling design will lead to consumer psychological response, and then affect consumer behavior response. Positive cognitive response will lead to positive emotional response, and positive emotional response will lead to positive behavioral response. At the same time, the psychological reaction of consumers caused by product modeling design will be regulated by many factors, such as consumers’ personal preference and taste, environmental factors, marketing plans and so on.

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Hypothesis 3a. Functional design has a positive impact on purchase intention. Hypothesis 3b. Aesthetic design has a positive impact on purchase intention. Hypothesis 3c. Symbolic design has a positive impact on purchase intention. According to the above research hypothesis, the theoretical model of this study is constructed as shown in Fig. 1.

Product design Customer satisfaction functional design

Aesthetic design

Symbolic design

H3c

Purchase intention

Fig. 1. Theoretical model

4 4.1

Research Design and Empirical Analysis Variable Design and Measurement

Referring to the view that the product design can be divided into aesthetic dimension, functional dimension and symbolic dimension, the author uses three dimensions and nine specific measurement indicators to measure(see Table 1). As shown in Table 2, we know that customer satisfaction is an emotional cognition of consumers after consumption. Therefore, based on the research of Taylor, haksik, etc., this paper constructs the customer satisfaction index as follows: customer satisfaction measurement project. Fornell and Johnson [12] believe that satisfaction is an expression of the level of likes or dislikes after purchase, and it is based on their overall attitude towards the accumulated experience. The analysis of these scholars focuses on the cognition of expectation and actual difference. Customer satisfaction can be seen as an emotional response to the consumer experience. Woodruff et al. [11] pointed out in a study that customers use emotions to express satisfaction. The above research shows that customer satisfaction is an emotional cognition of consumers after consumption. If the product after consumption is not as expected, consumers will have dissatisfied cognition after comparison. If they can exceed their cognition and expectation level, customers will feel satisfied.

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M. Chen et al. Table 1. Operational questions of product design

Measurement dimension

Operational Item variables

Aeshetic design

Q1 Q2

functional design

Q3 Q4 Q5

Symbolic design

Q6 Q7

Q8 Q9

Reference source

This set of furniture is custom-made Homburg and eye-catching (2015) [10] This set of furniture is custom-made and beautiful This set of furniture looks attractive This set of furniture has good customization performance This set of furniture can be customized to play its role This set of furniture custom practical This set of furniture customization helped me build a unique personal image This set of furniture is custom made me different from others This set of furniture customization is a fitting reflection of my personal achievements

Table 2. Customer satisfaction operational questions Measurement dimension

Operational Item variables

Reference source

Customer satisfaction

CS1

Fornell and Johnson 1984; Woodruff et al. (1995) [11, 12]

CS2

CS3

I think the overall performance of the custom service for this furniture is good I am satisfied with the custom furniture platform that i am currently serving The service provided by the custom furniture generally meets my expectations

The willingness to buy represents the subjective willingness of consumers to buy tourism products or services when they participate in tourism virtual community browsing information and interact with each other. This study mainly considers the pre-tourism purchase behavior, mainly referring to zeithsml et al. [25] and the research scale of Jia Li [15], as shown in Table 3:

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Table 3. Willing to buy operational questions Measurement dimension

Operational Item variables

Reference source

Purchase intention

PI1

Zeithsml et al., (1996); Li Jia(2015) [15, 25]

PI2

PI3

4.2

If I need to buy a travel product, I will buy it in the travel community I would like to browse the travel website to find the products or services I need I’d love to make a purchase on this site

Data Collection and Statistical Analysis

This questionnaire mainly includes three parts: the first part defines the concept of tourism virtual community; the second part is the questionnaire, which measures product design, customer satisfaction and purchase intention. The third part is the basic characteristics of the respondents. Based on the relevant mature scales of previous studies, combined with the actual situation of tourism virtual community, the initial questionnaire is formed. The scoring method of the scale has been more mature. In this study, Likert 5 scale is selected. All items are measured by Likert 5 scale: 5 represents “very consistent”, 1 represents “very inconsistent”. This questionnaire is mainly distributed through the combination of online survey and field survey: first, use the questionnaire star website to carry out online questionnaire survey, mainly through WeChat, Weibo and other social media; second, in order to ensure the uniform distribution of the questionnaire samples, take the way of field distribution, select areas with large local traffic for distribution. A total of 1000 questionnaires were sent out, 923 questionnaires were collected, and 886 effective questionnaires were finally obtained, with an effective rate of 88.60%. Through the analysis of effective questionnaires, the descriptive statistical results of the distribution of respondents are shown in Table 4:

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M. Chen et al. Table 4. Descriptive analysis of sample characteristics Features

Category

Frequency Percentage

Gender

Male Female

441 445

49.8 50.2

Marital status

Married Unmarried

486 400

54.9 45.1

Education level

High school/technical secondary school Undergraduate/Junior College Master degree or above

250

28.2

461

52.0

85

9.6

RMB 3000 and below

140

15.8

RMB 3000-5000 RMB 5000-7000 RMB 7000 and above

344 283 119

38.8 31.9 13.4

Family monthly income

4.3

Reliability and Validity Analysis

In the pre investigation process, the reliability and validity of the research scale have been preliminarily tested, but this is not enough. In order to confirm the reliability and validity of the data, we need to use the valid sample data to test the reliability and validity of the research scale again. For the reliability test of the scale in this study, the item to item correlation coefficient (CICT), Cronbach’s a value, average variance extraction (AVE) and combined reliability (CR) are usually used to test; for the convergence validity of the scale in this study, the confirmatory factor analysis is used to test; for the difference validity of the scale in this study, the correlation coefficient between the scales is used to add and subtract twice the standard error (a No I.0 is included for verification. In addition, the scale in this study is based on the existing research of domestic and foreign scholars. All the scales in this study adopt the methods of translation and back translation to ensure the accurate expression of the scale sentences, and discuss and modify the items and expressions of the scale in the form of symposiums, and refer to the opinions of experts and modify and improve the scale in combination with the situation of China to ensure the quantity Accuracy of table description. According to the results shown in Table 5 the paper satisfies these judgment criteria, indicating that the measurement model has good polymerization validity. From the specific fitting results in Table 4, standard factor loadings of each index or dimension on the corresponding latent variable are between 0.711-0.938, and all pass the significance test at P < 0.001 level, so the validity of the scale can meet the requirements.

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Table 5. Reliability and validity analysis Variable

Item Factor loading Cronbach’s α KMO

Aesthetic design

Q1 Q2 Q3

0.845 0.855 0.817

0.745

0.858

functional design

Q4 Q5 Q6

0.736 0.843 0.71

0.883

0.901

Symbolic design

Q7 Q8 Q9

0.743 0.857 0.864

0.83

0.883

Customer satisfaction CS1 0.841 CS2 0.754 CS3 0.777

0.885

0.893

Purchase intention

0.872

0.832

PI1 PI2 PI3

0.777 0.868 0.966

Note: ∗ ∗ ∗p < .001 Table 6. Analysis of path coefficients T value P value

Route

Standardization coefficient (β)

Aesthetic design→Purchase intention

0.825

19.16

***

functional design→Purchase intention

0.726

13.8

***

Symbolic design→Purchase intention

0.808

19.332

Aesthetic design→Customer satisfaction

0.070

1.120

0.262

functional design→Customer satisfaction

0.389

4.495

***

Symbolic design→Customer satisfaction

0.426

6.987

***

Customer satisfaction→Purchase intention 0.786

11.838

***

***

Note: ∗ ∗ ∗p < .001

4.4

Path Analysis and Hypothesis Verification

In this paper, the structural equation model is used to test the model and hypothesis. In the model, the independent variables include aesthetic, functional and symbolic variables, the intermediary variables include customer satisfaction, and the outcome variable purchase intention. Amos18.0 is used for path analysis to calculate the path coefficient and its significant performance among the variables in the model. The results of path coefficient analysis among the variables in this study and the corresponding T and P values are shown in Table 6. From Table 6, it can be seen that aesthetics, function and symbol based on customer satisfaction have a significant positive impact on purchase intention

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(β = 0.825, P < 0.001; β = 0.726, P < 0.001; β = 0.808, P < 0.001), so the original hypothesis H1a, H1B and H1C are all tenable. The influence of aesthetic design on customer satisfaction is not significant (β = 0.070, P > 0.001; β = 0.045, P > 0.001), so H2A and H2A ’are not tenable; functional design has significant positive influence on customer satisfaction (β = 0.402, P < 0.001; β = 0.389, P < 0.001); symbolic design has significant positive influence on customer satisfaction (β = 0.318, P < 0.001; β = 0.426, P < 0.001), so the original hypothesis is H2B, H2B’. Both H2C and H2C ’are established. Customer satisfaction has a significant positive effect on purchase intention (β = 0.780, P < 0.001; β = 0.786, P < 0.001), that is, H3A and H3B are established. 4.5

Mediating Effect Test

According to the causal stepwise regression method proposed by Baron & Kenny to verify the mediating effect, Wen Zhonglin, a domestic scholar, also proposed a method to test the mediating variable on the basis of this method [3]: if the path coefficient C of the independent variable to the dependent variable is significant, and the path coefficient a of the independent variable to the intermediate variable and the path coefficient b of the intermediate variable to the dependent variable are significant, then the influence of the independent variable on the dependent variable has one Part of it is realized through intermediary variable, and the path coefficient C of independent variable to dependent variable is obtained after introducing intermediary variable. If C  is significant and significantly smaller than C, then the influence of independent variable on dependent variable is only partially through intermediary variable; otherwise, if C  is not significant, then the influence of independent variable on dependent variable is completely through intermediary variable. In order to test the mediating effect of customer satisfaction between product design and purchase intention, this study uses the causal stepwise regression method to verify the mediating effect of customer satisfaction, and constructs the following multiple regression model with continuous purchase intention as the dependent variable: CP I = β1 + c E + bP V CP I = β2 + c B + bP V According to Table 6. the positive effect of cognition on practical value and hedonic value is not significant, which does not meet the premise of the above test intermediary variable (i.e. a is not significant), so we will not continue to discuss it. After adding perceived value, as shown in Table 7, the influence of emotional satisfaction and customer satisfaction on purchase intention is still significant, and the standardization coefficient of independent variables is significantly reduced, so it can be considered that perceived value plays a part of intermediary role in emotional satisfaction and behavioral satisfaction on purchase intention.

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Table 7. Mediating role of perceived value Label Path

CE’s β Significance Mediating role

1

E→CPI 0.726 E→PV→CPI 0.145

0.000 0.000

Partial mediation

2

B→CPI 0.808 B→PV→CPI 0.240

0.000 0.000

Partial mediation

Note: CE: customer engagement; E: emotion engagement; B: behavior engagement; PV: perceived value; CPI: continued purchase intention

5 5.1

Conclusions and Implications Conclusions

In order to facilitate the analysis and discussion of this part, the assumptions proposed above are summarized in the form of hypothesis test results table, as shown in Table 8. Table 8. Hypothesis test results Hypothesis H1a Aesthetic design has a positive impact on customer satisfaction H1b Functional design has a positive impact on customer satisfaction H1c Symbolic design has a positive impact on customer satisfaction H2

Customer satisfaction plays a positive role in promoting purchase intent

H3a Aesthetic design has a positive effect on purchase intent H3b Functional design has a positive effect on purchase intent H3c Symbolic design has a positive impact on purchase intent

Test result √ √ × √ √ √ √

Based on the above empirical results, the following conclusions can be drawn: (1) The three dimensions of product design in Furniture Customization have significant influence on purchase intention, but the degree is different. Among them, functional design has the most influence on purchase intention, while aesthetic design and symbolic design have the second. Functional design is based on the actual needs of customers. This mode of production is conducive to solving the actual problems of customers and promoting the willingness of customers to buy products. The three dimensions of design affect the purchase intention of consumers in varying degrees. Innovative furniture

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customization enterprises should focus on promoting and encouraging these three kinds of design, and strive to build and increase the stickiness of users to the enterprise furniture products. (2) Customer satisfaction has a positive effect on purchase intention. The results show that customer satisfaction mediates the influence of aesthetic dimension and functional dimension on product purchase intention. When the customer is satisfied with the aesthetic and functional dimension of furniture product, the customer will be willing to buy the product to experience and promote the customer’s purchase intention. (3) The aesthetic design and functional design in Furniture Customization have a significant impact on customer satisfaction. Aesthetic design is the product design based on the aesthetic concept of the times, so that the furniture of the house can meet the needs of customers for the appearance of the product, and meet the changes of customers with the aesthetic of the times, so as to naturally improve customer satisfaction; functional design is the humanized design to meet the needs of customers, according to the problems customers encounter when using the product to design for the purpose of improving customers for the home Have the experience of customized products, so as to improve customer satisfaction. However, symbolic interaction has no significant impact on customer satisfaction, which may be due to the fact that customer satisfaction does not enable consumers to obtain instrumental and practical needs, does not cause consumers’ purchase intention, so it has no significant impact on customer satisfaction. 5.2

Implications

Based on the conclusions of the above analysis, this study provides the following management suggestions for business managers: (1) Enterprise managers should view and understand the connotation of product design from multiple perspectives, and strengthen the cooperation among design department, R & D department and marketing department. Product design is the basic component of a product and a multi-dimensional structure of consumer perception and organization, including aesthetic, functional and symbolic dimensions. The extension of this concept may be different from many managers’ understanding of product design, which may be limited to the design of product aesthetic dimension. Through the conclusion of this study, we can see that the influence of functional dimension and symbolic dimension on product evaluation and product emotion is greater than aesthetic dimension. Therefore, from the theoretical and empirical point of view, this kind of understanding is not enough. A correct understanding of its connotation can help managers avoid subjective assumptions on product design. Therefore, enterprises should actively pay attention to all three dimensions, and view and understand the connotation of product design in multiple aspects. To maximize the efficiency of product design, we should benefit from the cooperation among design department, R & D department and marketing department. Chinese style.

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(2) Enterprises should attach great importance to consumers’ evaluation of products and emotional response, and develop consumers’ empathy. As consumers tend to mature, today’s market presents a “buyer’s market” feature. Therefore, no matter what products or services enterprises provide to the market, the starting point should be around consumers, so that the evaluation and emotional response of consumers to products is particularly important. Develop the empathy of consumers, consciously contact with consumers and potential consumers, deeply understand and observe the evaluation and emotions of consumers on products, link product design with customer value, apply the results of customer value research to product design strategy in product development stage, reduce the risk of developing products and improve the competitiveness of products. Chinese style. (3) The practice of enterprise product design should consider the influence of product category characteristics and consumer personality characteristics, so as to guide enterprises to use these factors in new product design for innovation and improvement. If product design and development want to create good benefits, the analysis and positioning of different products and different groups of people has become an indispensable part, which to a certain extent determines the future product performance and life cycle. For the enterprises that produce hedonic products, the practice of product design should pay more attention to the aesthetic experience of consumers and the transmission of symbolic value of products, so as to achieve the purpose of pleasing consumers and win long-term market performance. For consumers with unique needs, enterprises can authorize more consumers, guide consumers to participate in product design, so as to strengthen the interaction with consumers, enable consumers to enhance the sense of ownership of products in the interaction with enterprises, and then bring positive word-of-mouth to the products of enterprises. Based on the main dimensions of furniture customized product design, combined with customer satisfaction as an intermediary variable, this study constructs a relationship model between product design and purchase intention, and conducts an empirical study on the influencing factors of customers’ continuous purchase intention. The previous related research has been refined, although some achievements have been achieved, but due to the constraints of subjective and objective conditions, there are still some deficiencies such as the model fitting index does not reach the best, etc., which need to be further studied and improved.

References 1. Adjoul, O., Benfriha, K., Aoussat, A.: Algorithmic strategy for optimizing product design considering the production costs. Int. J. Interact. Des. Manuf. (IJIDeM) 13(4), 1313–1329 (2019) 2. Andrew, H.: Oliver Russell and Linda ward. Br. J. Learn. Disabil. 29(2), 43–44 (2001)

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3. Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173 (1986) 4. Bloch, P.H.: Seeking the ideal form: product design and consumer response. J. Mark. 95(3), 16–29 (1995) 5. Bloch, P.H.: Product design and marketing: reflections after fifteen years. J. Prod. Innov. Manag. 28(3), 378–380 (2011) 6. Chitturi, R., Raghunathan, R., Mahajan, V.: Delight by design: the role of hedonic versus utilitarian benefits. J. Mark. 72(3), 48–63 (2008) 7. Desmet, P.: A multilayered model of product emotions. Des. J. 6(2), 4–13 (2003) 8. Gao, W.: Research on the influence of product modeling for emotional design on customer emotion and behavior. Tianjin University, Tianjin (2010). (in Chinese) 9. Heimburger, C.R., Westbrook, R.: Isaiah’s Vision Of Peace in Biblical and Modern International Relations: Swords Into Plowshares, vol. 11 (2010) 10. Homburg, C., Schwemmle, M., Kuehnl, C.: New product design: concept, measurement, and consequences. J. Mark. 79(3), 41–56 (2015) 11. James, T.O., Sasser, W.E.: Why satisfied customer defect. Harv. Bus. Rev. 71(6), 88–99 (1995) 12. Kano, N., Seraku, N., et al.: Attractive quality and must be quality. J. Jpn. Soc. Qual. Control 14, 39–48 (1984) 13. Kotler, P.: Reconceptualizing marketing: an interview with Philip Kotler. Eur. Manag. J. 12(4), 353–361 (1994) 14. Landwehr, J.R., Wentzel, D., Herrmann, A.: The tipping point of design: how product design and brands interact to affect consumers preferences. Psychol. Amp Mark. 29(6), 422–433 (2012) 15. Li, J.: The influence of Internet word-of-mouth and value creation on consumers’ willingness to purchase under social network. Beijing University of Posts and Telecommunications (2015). (in Chinese) 16. Michaels, P.: Apple: what recession. Macworld 29(1), 3–20 (2012) 17. Rahmani, D., Abadi, M.Q.H., Hosseininezhad, S.J.: Joint decision on product greenness strategies and pricing in a dual-channel supply chain: a robust possibilistic approach. J. Clean. Prod. 256(120), 437 (2020) 18. Soria-Castro, S.M., Lebeau, B., et al.: Organic/inorganic heterogeneous silica-based photoredox catalyst for Aza-Henry reactions. Eur. J. Org. Chem. 2020(10), 1572– 1578 (2020) 19. Srinivasan, R., Lilien, G.L., et al.: The total product design concept and an application to the auto market. J. Prod. Innov. Manag. 29, 3–20 (2012) 20. Stamolampros, P., Dousios, D., et al.: The joint effect of consumer and service providers’ culture on online service evaluations: a response surface analysis. Tour. Manag. 78(104), 057 (2020) 21. Tse, W.: Seeking the ideal form: product design and consumer response. J. Mark. 59(3), 16–29 (1988) 22. Wen, Z., Zhang, L., et al.: The test procedure of intermediary effect and its application. J. Psychol. 5, 614–620 (2004). (in Chinese) 23. Xie, A.: Research on the implementation of mass customization in furniture enterprises “d”. Master’s thesis of Central South Forestry University (2005). (in Chinese) 24. Yeung, C.W., Wyer Jr., R.S.: Does loving a brand mean loving its products? The role of brand-elicited affect in brand extension evaluations. J. Mark. Res. 42(4), 495–506 (2005) 25. Zeithaml, V.A., Berry, L.L., Parasuraman, A.: The behavioral consequences of service quality. J. Mark. 60(2), 31–46 (1996)

Effects of the Recommendation Label Prominence on Online Hotel Booking Intention: An Eye-Tracking Study Luoyi Xiong, Chenzhu Zhao, and Li Huang(B) Tourism School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. Online travel agencies (OTAs) rely on marketing clues to reduce the consumer uncertainty perceptions of online travel-related products. The recommendation label served as the information provided by the official platform has a profound effect on consumers’ purchase decisions. The purpose of this article is to explore the impact of the recommendation label prominence (RLP) on consumer visual attention and booking intention, and investigate the moderating effect of online comment valence (OCV). Eye movement is closely related to the transfer of visual attention, so it is employed to record consumers’ visual attention. This research uses a 3 (recommendation label prominence: without vs. low vs. high) × 3 (online comment valence: low vs. medium vs. high) design to conduct the experiments. The main findings showed the following:(1) The recommendation label can significantly improve the visual attention to the entire OTA advertisements(ads) and inspire the purchase intention;(2) Online comment valence moderates the effect of the recommendation label prominence on both visual attention to the entire OTA advertisements and purchase intention. Only when the online comment valence is medium, high recommendation label prominence can significantly increase the attention to the entire OTA advertisements; and when the online comment valence are medium and high, the recommendation label can significantly raise consumers’ purchase intention. The results suggest that OTAs can enhance the advertising effectiveness by adding the official recommendation label and increasing the label’s prominence. Still, when the online comment valence of the hotel is low, the official recommendation label cannot “save” it. Keywords: Recommendation label prominence · Comment valence Eye tracking · Online travel agencies · Decision-making · Visual attention

c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 731–743, 2020. https://doi.org/10.1007/978-3-030-49829-0_54

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Introduction

The Internet has become the main channel for the tourism business. Online channels contributed 52.3% of primary hotel brand bookings in 2010 [42], and much of that business was conducted through online travel agencies (OTAs). Hotel choice is a complex behavior that is influenced by several interrelated factors [37]. Hence, it is of great interest and importance for firms to understand how consumers make decisions when booking hotels online. In the contemporary world, a large number of hotels can be acquired without significant effort [42]. The hotel offers a typical experiential service [25]. Since consumers cannot accurately assess the content and quality of their services before booking, they need to refer to a large amount of information in the decision-making process to reduce perceived risks [18]. On this occasion, third party recommendation information plays a vital role in attracting consumers’ attention and providing information that shapes expectations and influence purchase decisions [29]. However, numerous studies focus solely on the influence of user recommendation information on consumers’ online hotel booking intentions [2,32,46,47]. Zhang et al. (2011) [48] found that user recommendations, such as online comment valence and review browsing indexes, had a significant impact on consumers’ online hotel booking intentions. Jabr and Zheng (2014) [16] found that product reviews are usually deemed as key and reliable information references for consumers, leading to more than 90% of online customers read reviews before making purchase decisions [10]. There is still little understanding of how official recommendations affect consumers’ beliefs and behavior. Furthermore, whether the relationship between official recommendations and consumer recommendations is additive or complementary is remaining. Thus, recognizing this omission in existing hotel research, our study focuses on how does the RLP capture visual attention and influence the booking intention and the moderating effect of the OCV with an eye-tracking technique. The rest of this paper is fourfold organized. Firstly, we review the theoretical background and present the research hypotheses. Secondly, we describe the research method and the experimental procedure. Thirdly, we analyze and discuss the results of the experiment. Lastly, we conclude with suggestions for future research, and limitations and implications for future research are discussed.

2 2.1

Literature Review and Hypotheses Online Hotel Booking

As a typical experiential service product, the hotel is especially suitable for online sales [36]. And professional hotel online booking platform has become an essential channel for room sales [35]. Hotel products have intangible characteristics, so consumers are faced with higher perceived risk in purchase, especially need more information to assist decision-making [3]. Therefore, when consumers purchase such experiential service products in the network environment, the information has a more significant impact on their decision-making [28].

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The Impact of RLP on Visual Attention and Online Hotel Booking Intention

The use of the Internet and the full application of information and communications technologies strengthen information dissemination between suppliers and consumers [41]. In the e-commerce context, the role of the recommendation label is similar to that of salespersons in retail stores [19]. Chen et al. pointed out that online recommendation information can significantly affect the online sales of books [8]. The recommendation label can help OTAs to invoke important emotions of customers and not only improve their satisfaction with the hotel but also increase the average amount of booking [24]. Previous studies showed that online recommendation labels offered by online retailers are more influential than the recommendation from experts or other consumers, so recommendation labels offered by online retailers are considered as a successful type of product recommendation [9,39]. Prior research suggests that salient stimuli can arouse users’ attention automatically and thereby affect their information acquisition behavior [4,17]. Accordingly, a highlighted label is likely to attract more considerable attention from consumers [46]. In summary, recommendation labels were likely to significantly affect consumer’s purchase intentions because of their ability to reduce the cognitive burden of sifting through multiple alternatives [13,20]. Highlighting recommendation labels may lead consumers to pay more attention to OTA ads. Therefore, we hypothesize, Hypothesis 1. RLP has a significantly positive impact on attention toward OTA ads. Hypothesis 2. RLP has a significantly positive impact on consumer booking intention (Fig. 1). 2.3

The Moderating Effect of Online Comment Valence

The society is increasingly relying on the polymerized opinions of others online [12]. Contributions made by users on platforms facilitate the interaction between like-minded community members who share purchasing interests, thus promoting the decision-making process [1,12]. As a quality information cue from other consumers, the relevant literature has proven the influence of online consumer reviews and ratings on purchasing decisions, representing a fundamental driver of hotel selection [2,11,21,27]. In the online environment, positive reviews favorably affect consumer attitudes toward a hotel and lead to higher booking intentions [22,43,45]. Research has revealed that products are selected twice as often if they are recommended by others, and this influence is dependent on the type of recommendation source [39]. As an official recommendation, the excessive prominence of the recommendation label makes consumers aware of the marketing intention

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of the platform, thus causing suspicion and distrust [46]. Meanwhile, if consumers are exposed to negative reviews for extended periods, marketers will suffer poor reputations [6,40]. Therefore, we hypothesize, Hypothesis 3. OCV moderates the relationship between RLP and attention to OTA ads. Hypothesis 4. OCV moderates the relationship between RLP and consumer booking intention (Fig. 1).

3 3.1

Methodology Research Design

The objective of this study is to examine the influence of the RLP and OCV on consumer visual attention and purchase intention. Since a within-subjects design is commonly used in eye-tracking research [23,30], we employed a 3(recommendation label prominence: without vs. low vs. high) × 3(online comment valence: low vs. medium vs. high) within-subjects experimental design to test these hypotheses, which included nine conditions correspondingly (see Table 1 for the nine groups). A total of 14 stimuli were involved in our main experiment, of which 9 were created as targets, i.e., one for each condition, and the other 5 were added as fillers entirely unrelated to our experimental purpose. Each participant was exposed to 9 target stimuli while going through all pages on screen. Using an eye-tracking technique, participants’ visual attention was measured. 3.2

Stimulus Material and Pre-test

All experimental materials were drawn from an OTA (Ctrip.com) to ensure the authenticity of them. These experimental materials contained information

Fig. 1. Conceptual framework.

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Table 1. Groups for different manipulation conditions Group RLP

OCV

Number of target Picture ID Fictitious hotel name

1

Absence Medium 1

1-1

Zhiyou hotel

2

Low

High

1

1-2

Haoyun hotel

3

Absence High

1

1-3

Moting hotel

4

High

Low

1

1-4

Ruidao hotel

5

Absence Low

1

1-5

Nanya hotel

6

High

High

1

1-6

Shangming hotel

7

Low

Medium 1

1-7

Juntai hotel

8

Low

Low

1

1-8

Liyue hotel

9

High

Medium 1

1-9

Minglun hotel

regarding the hotel name, online comment valence, picture of bedroom, booking button and recommendation label. Other confounding variables were deleted. To control the influence of price and brand, we used the fictitious brand for each hotel and used the booking button to replace the price label. Since we adopted this scenario-based experimental design, similar hotel pictures are selected from to real OTA sites to control the influence of pictures. To determine the level of the moderating variable, we used Python to crawl the hotel ratings of China’s top 10 most visited cities on Ctrip.com. Finally, we take the quartile as the low level, the median as the medium level, and the three quarters as the high level. For RLP, because the attributes or alternatives that are located near the end of the list either vertically or horizontally receive less attention [33]. Therefore, the high prominence recommendation label is located in the upper left corner of the OTA ads, and the length is half of the total page length. In contrast, the low prominence recommendation label is located in the lower right corner of the page and the length is one-quarter of the total page length. Other than that, their other physical characteristics are the same. Furthermore, we adopt the ABBA balance method to avoid the position effect and the learning effect [7,31]. Meanwhile, two fillers were inserted at the beginning and end of the sequence to avoid primacy effect, and others were inserted the sequence by random, which prevent participants from guessing the purpose of the experiment [14]. 3.3

Participants

The student sample is typical in the past studies through eye-tracking technique [15,44]. Participants (N = 45) were a convenience sample of undergraduate and postgraduate students at a southwestern Chinese university in exchange for a gift to participate in the experiment. And all invitation messages were sent through QQ and WeChat three days before the experiment. After excluded 3 participants from the analyses because of poor gaze data, we keep a useable sample of 42

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participants for further analysis. Participants ranged in age from 18 to 25 years (38.1% women). All participants were with normal or corrected to normal vision, and no significant differences among demographic variables were apparent, which met the requirement of experimental design. To get effective eye-tracking results, participants were naive to the real purpose of our experiment [14]. 3.4

Apparatus and Procedure

Our main experiment was conducted in the Consumer Behavior Lab of a southwestern Chinese university, which equipped with desktop-mounted EyeLink 1000 plus system (https://www.sr-research.com/products/eyelink-1000-plus/). It is a video-based eye tracker, sampling binocularly at up to 2000 Hz. In our eyetracking experiment, the EyeLink 1000 plus eye tracker with the sampling rate of 1000 Hz was used to collect the monocular data. The Desktop Mount below the display was located at about 60 cm from the participants. All participants were seated in front of a 17-inch monitor with a resolution of 1280 × 1024 pixels, and the screen distance was set to 70 cm. At this distance, 2◦ of the visual angle subtended approximately 103 pixels (∼2.7 cm). The EyeLink 1000 determines the start and end of fixations and saccades according to an algorithm based on the acceleration velocity of the eye. The acceleration velocity threshold can be set by the researcher, and this data is converted into units of degrees per second. In the present study, the default threshold of 30◦ per second was used, which is typical for cognitive research [23]. Firstly, all potential participants were asked to read a brief introduction to the experiment process and complete an informed consent document to participation. Then, guiding participants into the lab, the researcher conducted a calibration of eye tracking by adjusting participants’ positions such as grip height and chair positions. The following step is eye-movement tracking. The participants were first lead to an instruction page that explained the experimental scenario: Suppose you plan to book a hotel. Then, you were instructed to visit the online travel agency website and browse hotel information as you like. After you score the intention to book hotel, rated on a seven-point Likert scale (1 = not likely to book and 7 = likely to book), the website would automatically jump to the next hotel. After they had browsed all pages, they were asked to complete a questionnaire. In this questionnaire, demographic information including age, gender and other information were collected from the participants. Then, participants’ traveling experience and internet using experience were assessed. As a manipulation check, the participants were asked to review nine OTA ads in a booklet and meanwhile complete a two-item, seven-point semantic differential scale measuring their perception of online hotel comment valence and the prominence of recommendation label. In the final, they were asked to make a purchase decision from 9 hotel targets.

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737

Measures

Each of the choice sets was considered as an area of interest (AOI) [26]. We set up the surface sizes of all AOIs at 120% of the actual area, to keep a small AOI margin and balance the ratio of true and false positive fixations [34], because of possible noise in the eye-tracking data, the possibility of peripheral attention [38], variations in calibration, and variations in weight gaze samples. For the hypotheses, we considered the ad AOI which refers to the ad as a whole. Further, participants’ attention was quantified by (1) the fixation frequency, which is the total number of fixations within the AOI; (2) the total fixation duration, which is the summed duration of all fixations within the AOI.

4 4.1

Data Analysis and Results Manipulation Checks

Manipulation check was carried out to examine the validity of the independent variable and moderating variable in our research. ANOVA was performed to assess participants’ understanding of the RLP and OCV. The results showed that regarding the RLP, the mean score for high-level RLP (M ean = 4.54, S D = 1.412) was significantly higher than that for low-level RLP (M ean = 3.71, S D = 1.419, F (1, 250) = 21.417, p = 0.000 < 0.001). Regarding the OCV, the mean score for high-level (M ean = 5.84, S D = 1.176) was significantly higher than for medium-level and low-level (M ean = 3.01, S D = 1.149); the mean score for medium-level (M ean = 4.74, S D = 0.940) was significantly higher than for low-level (M ean = 3.01, S D = 1.149, F (2, 357) = 215.041, p = 0.000 < 0.001). The manipulation was successful. 4.2

Heat Map Analysis

Before evaluating the eye-tracking quantitative data, participants’ visual attention to all OTA ads can be represented with heat maps. The heat map is an effective tool to visualize eye-tracking data, which can illustrate visual attention by manifesting the fixation locations and fixation durations across regions within the stimuli [5]. Different colors represent the degree of the participants’ fixations: red color is for the highest level of fixation; yellow color is for the medium level; green color is for the lowest level. The results of the eye-tracking heat maps revealed that participants paid more attention to names of the hotels as well as pictures in the OTA ads. Moreover, the effects of the presence of recommendation labels and comment valence labels could be shown from the heat map results. 4.3

Descriptive Statistics

In this section, 42 participants were determined to be usable and included in the following analyses. All participants’ visual attention toward the OTA ads were

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measured by the fixation frequency (average number of fixations) and the fixation duration (average total fixation duration). The average number of fixations ranged from 4 to 55 (M ean = 15.42, S D = 8.513), and the total fixation duration ranged from 311 to 12561.00 ms (M ean = 3334.64 ms, S D = 1873.998 ms). Participants’ booking intentions ranged from 1 to 7 (M ean = 4.21, S D = 1.448). Table 2 shows descriptive statistics for the fixation frequency and fixation duration (in milliseconds) by online comment valence in high/medium/low conditions with different RLP. Table 2. Descriptive statistics of fixation indicators (n = 42) Conditions High online comment valence

Medium online comment valence

Low online comment valence

Fixation frequency

Fixation frequency

Fixation frequency

Fixation duration

Fixation duration

Fixation duration

High

14.48 (8.497)

3100.00 20.19 (1470.866) (10.930)

4285.21 15.12 (2515.711) (6.957)

3259.95 (1586.630)

Low

15.83 (7.477)

3510.98 15.45 (1745.187) (7.613)

3262.38 16.00 (1750.579) (9.069)

3633.88 (2198.939)

RLP

Absence

13.83 3022.69 14.74 3126.86 13.10 2809.83 (7.454) (1763.277) (9.139) (1903.734) (7.599) (1435.546) Note: Values are the arithmetic mean (Mean) and the standard deviation (SD) in parenthesis.

4.4

Hypothesis Testing

(1) The main effect of the recommendation label prominence (RLP) on OTA ads’ attention and booking intention. Variance analysis ware conducted to compare the visual attention and booking intention of different OTA ads. The results suggested that the average number of fixations on OTA ads with the high RLP (M eanhigh = 16.60, S Dhigh = 9.237) were significantly more than those without the recommendation label (M eanabsence = 14.74, S Dabsence = 9.139; F (2, 375) = 3.328, p = 0.035 < 0.05); the average total fixation duration on OTA ads with the high RLP (M eanhigh = 3548.39 ms, S Dhigh = 1972.077 ms) and the low RLP (M eanlow = 3469.08 ms, S Dlow = 1901.107 ms) were significantly longer than those without the recommendation label (M eanabsence = 2986.46 ms, S Dabsence = 1972.077 ms; F (2, 375) = 3.360, P = 0.036 < 0.05). Therefore, hypothesis 1 was supported. And the result suggested that the booking intention with the high RLP (M eanhigh = 4.44, S Dhigh = 1.456) were significantly higher than those without the recommendation label (M eanabsence = 4.02, S Dabsence = 1.356; F (2, 375) = 2.868, p = 0.058 < 0.1). Hypothesis 2 was therefore supported.

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Table 3. Summary of test results Fixation frequency Fixation duration Booking intention Variables

F

RLP

3.442 0.033∗∗

P

3.411 0.034∗∗

5.202 0.049∗∗

Online Comment Valence

2.553 0.079∗

1.387 0.251

1.834 0.000∗∗∗

RLP × OnlineCommentV alence 1.875 0.114

Note: ∗ p < 0.1;

∗∗

p < 0.05;

∗∗∗

F

2.229

P

0.065∗

F

P

0.738 0.024∗∗

p < 0.001

(2) The moderating effect of online comment valence (OCV) on OTA ads’ attention and booking intention. The multivariate test of the MANOVA was performed to investigate the moderating effect of OCV. Table 3 summarizes the results of the main and interaction effects of the MANOVA test, including the F-value and significance level. Firstly we tested the main effects of RLP on the fixation frequency and fixation duration of OTA ads and the booking intention. Specifically, the RLP had a significant main effect on the average number of fixations (F (2, 369) = 3.442, p = 0.033 < 0.05), the total fixation duration (F (2, 369) = 3.441, p = 0.034 < 0.05), and booking intention (F (2, 369) = 3.051, P = 0.049 < 0.05). Therefore, MANOVA produced exactly the same hypothesis test results as above about H1, H2. Secondly, the moderating effect of OCV between RLP and visual attention toward OTA ads is analyzed (as shown in Fig. 2(a)). The results showed significant interaction effects between OCV and RLP on the total fixation duration (F (4, 369) = 2.229, p = 0.065 < 0.1). Further analysis indicated that, when OCV of the hotel were low, the average total fixation duration on OTA ads with the low RLP (M eanlow = 3633.88 ms S Dlow = 2198.939 ms) was significantly longer than one without the recommendation label (M eanabsence = 2809.83 ms, S Dabsence = 1435.546 ms; F (4, 369) = 2.229, p = 0.065 < 0.1); when OCV of the hotel were medium, the average total fixation duration on OTA ads with the high RLP (M eanhigh = 4285.21 ms, S Dhigh = 2515.711 ms) is significantly longer than one with the low RLP (M eanlow = 3262.38 ms, S Dlow = 1750.579 ms) and without the recommendation label (M eanabsence = 3126.86 ms, S Dabsence = 1903.734 ms; F (4, 369) = 2.229, p = 0.065 < 0.1). Hence, hypothesis 3 was confirmed. Thirdly we analyzed the moderating effect of OCV between RLP and booking intention (as shown in Fig. 2(b)). The results showed significant interactions effect between OCV and RLP on the booking intention (F (4, 369) = 2.846, p = 0.024 < 0.05). Further analysis indicated that, when OCV of the hotel were medium, the booking intention with the high RLP (M eanhigh = 4.45, S Dhigh = 1.435) is significantly higher than one with the low RLP (M eanlow = 3.86, S Dlow = 1.389; F (4, 369) = 2.846, p = 0.024 < 0.05); when OCV of the hotel were high, both the booking intention with the high RLP (M eanhigh = 5.14, S Dhigh = 1.072) and low RLP (M eanlow = 4.67, S Dlow = 1.541) are significantly higher than one without the recommendation label

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(M eanabsence = 4.05, S Dabsence = 1.513; F (4, 369) = 2.846, p = 0.024 < 0.05). Hence, hypothesis 4 was confirmed.

(a) Moderating effect of OCV on the relationship between the

(b) Moderating effect of OCV on the relationship between

RLP and fixation duration on the OTA ads

the RLP and booking intention

Fig. 2. Two-way interactions between OCV with the visual attention and booking intention.

5

Discussion and Contributions

Previous studies mainly discussed the effects of Consumer Recommendations (CR), while Provider Recommendations (PRs) are being neglected to some extent. Draw on the previous eye-tracking studies, the primary objective of this research is to understand the influences of the RLP on consumer visual attention and purchase intention as well as the moderating effects of OCV of the hotel. Theatrically, we have the following two findings. Firstly, the recommendation label can significantly improve the visual attention to the entire OTA ads and inspire the purchase intention. Secondly, the OCV moderates the effect of the RLP on both visual attention to the entire OTA ads and purchase intention. Only when the OCV is medium, high RLP can significantly increase the overall attention to the entire OTA ads; and only when the OCV is medium or high, the recommendation label significantly raises consumers’ purchase intention. With respect to practice implications, our work provides managers of OTAs with some practical guidance about how to benefit from actively recommending target hotels. When selecting a targeted hotel for a recommendation, consider its OCV. Only when a hotel has a medium or high OCV, it can be recommended. And the effect will be better if the RLP is improved.

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Our research offers two contributions. Firstly, based on the original research, this paper verified the mutual function of platform recommendation labels and consumer recommendation information from the physiological and behavioral aspects, and improved the research framework of the influence of multiinformation clues on consumers’ online hotel booking intention under the network environment. Secondly, we provide a reference for OTAs to adjust the display strategy of recommendation labels and improve the recommendation mechanism of hotels.

6

Limitations and Future Research

There are some limitations to the research. The first limitation of this study is regarding the samples. We only recruited students. Future research could investigate how other social groups view OTAs recommendation labels. Moreover, another limitation is the type of stimuli. We only studied OTA platforms, and future studies can investigate whether the results are applicable to other platforms (such as e-commerce platforms). Acknowledgements. This study was supported by grants from the National Natural Science Foundation of China (to YANG Yang) (No. 71502019), Cultural and Social Foundation of National Education Department (to YANG Yang and LI Jixin) (No. 17YJA630031), Innovation Spark Project of Sichuan University (to YANG Yang) (No. 2018hhf-37), and Scientific Research Project for Talent Introduction of Sichuan University(to YANG Yang) (No. 20822041A4222).

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Research on CEO Power and Charitable Donation: Evidence from China Furong Guo, Shengdao Gan, Chengyan Zhan, and Ziyang Li(B) Business School, Sichuan University, Chengdu 610064, People’s Republic of China lzy [email protected]

Abstract. As an important decision made by the enterprise, charitable donation is influenced by many factors, such as the current strategies as well as the characteristics of CEOs. Taking the data of China’s A-share listed companies from 2008 to 2017, this paper uses factor analysis to measure CEO power and empirically tests the impact of CEO power on corporate philanthropy. Our results show that CEO power is positively related to donations from corporation, in other words CEOs with greater power are more likely to make generous donations. We further discover that CEOs of state-owned enterprises (SEO) as well as those from central and western China are more inclined to make charitable donations. This paper contributes by increasing the understanding on CEO power’s influence over donations in the context of China’s transitional economy. Foundations are laid for the virtuous mechanism of the charitable donation.

Keywords: CEO power Principal-agent theory

1

· Factor analysis · Charitable donation ·

Introduction

2008 Wenchuan earthquake started a wave of charitable donations in China. According to the 2017 China Charitable Donation Report released by China Charity Federation, China’s total charitable donations continued to grow from 2012 to 2017. In 2017, China received a total of 149.986 billion Yuan from home and abroad. Donation per person was 107.90 Yuan, which was 7.11% higher than that of the last year. Among them, 64.23% of donations come from enterprises, indicating that corporate donations constitute the majority of the charitable donations. Table 1 presents statistics form China’s charitable donations from 2012 to 2017. The data comes from each year’s ‘China Charitable Contributions Report’. Companies can benefit from charitable donation. It not only serves as the advertisement, but also generates publicity, enhances corporate image [19], attracts potential customers, increases employee loyalty [2] and improves corporate performance. Charitable donation also helps companies obtain government c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 744–756, 2020. https://doi.org/10.1007/978-3-030-49829-0_55

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Table 1. Statistics of charitable donation in China from 2012 to 2017 (million) 2012 Total amount

817

Donation from business 474.38 Percentage

2013

2014

2015

2016

2017

989.42

1042

1108.57 1392.94 1499.86

689.33

702.8

783.85

908.2

963.36

58.06% 69.67% 67.45% 70.70% 65.20% 64.23%

subsidies [13], tax relief, which contributes to profit maximization [1]. In addition, corporate donations can send the signal that the business has sufficient cash flow and the company is in good financial status [17,20]. Though charitable donation benefits enterprise in many ways, it consumes the resources of the enterprise, increasing the labor cost and agency cost. There is a large empirical literature on the association between charitable donations and enterprise development. It mainly focused on exploring the motivation of charitable donation at the corporate level, and little attention is allocated to the analysis of CEO’s impact. CEOs directly implement the strategy and often have great influences over firm’s blueprint. The degree of influence primarily depends on CEO power. Therefore this paper fills the gap by exploring the impact of CEO’s power on charitable donation. Considering the economy of China, we dig deeper and focus on the influence of different property rights. Also as the developments of enterprises are affected by the geographic factors, we explore the potential impacts of different regions. As a result, this paper utilizes the data of China’s A-share listed companies from 2008 to 2017, and uses factor analysis to measure CEO power so as to test the influence of CEO power on corporate donation empirically. Our results show that CEO power has positive impacts on charitable donations. We further test the role that property rights and regions play in such mechanism and discover that CEOs of state-owned enterprises (SEO) as well as those from central and western China tend to focus more on the positive effects of charitable donations. This paper enriches the agency theory by contributing to the understanding over CEO power’s influence on charitable donation in the context of transitional economy in China. It also reveals the underlying causes of the increasing trend of corporate donations. The remainder of the paper is organized as follows: We first review the literature and develop our hypotheses in Sect. 2. Then we discuss our data and the methodology in Sect. 3. Finally, we present our empirical results in Sect. 4 and conclude the research in Sect. 5.

2

Theoretical Analysis and Hypotheses Development

According to the principal-agent theory, there are conflicts between CEOs’ and shareholders’ interests. Therefore CEOs tend to make decisions for their own benefits in company management [10]. Acting as advertisements, charitable donations can significantly enhance the reputation of the business, which is

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greatly effective for both the company and CEO [2]. By analyzing U.S. companies, Brown, Helland and Smith (2006) found that in addition to generating publicity, CEOs and directors also utilizes charitable donation to expand their social networks, which improves the management effectiveness, establishes and maintains stable political and commercial relationships, and increases political legitimacy and performance [2]. To certain degree, charitable contribution wins social respect and recognition for the CEO, and makes him stand out among others [4]. Charitable donation is also an important method through which the executives build and maintain intergovernmental relationships [3,18]. In China, there is no campaign contribution and lobbying, thus political connection built through bribery bears greater legal risk [5]. As a result, to build a political relationship with the government, CEOs must first gain the recognition from society and government, and charitable donation is clearly an excellent choice [18]. Charitable giving has long been considered as an important expression of social responsibility and one of the core elements of corporate citizenship. It is not only legal and in line with public values, but also boosts corporate image and reputation [12]. At the same time, CEOs can also obtain political status, scarce social resources and social status through charitable donations [7,14]. Charitable donations can be beneficial to the development of enterprises, but they will also increase the cost. Expenditure on setting up a separate department to manage donations exemplifies the increasing labor cost [2]. From the perspective of the product market, the increased costs will ultimately be transferred to consumers or shareholders, consequently the price advantage and the internal driving force will be affected [9], resulting in weaker performance. Additionally, if inappropriate decisions are made due to incomplete information, the mass outflow of cash may lead to unstable or even broken operation chains [11]. Since the market value of the senior management is considered in proportion to their past operating performance in the labor market, some conservative CEOs tend to adopt a more conservative attitude towards the company’s investment decisions in an effort to maintain stable growth of the company. Charitable donation is therefore reduced [15]. As CEOs wield great power and directly execute the strategies of companies, they often exert great influence over the strategic decisions. CEO’s influence mainly depends on their power, which is an important factor in transforming their personal decisions into an executable corporate strategy. The strategic decisions and implementation are critical to the company’s competitive advantages, among which power plays the key role. The principal-agent theory states that the development of productivity leads to the division of labor. The principals entrust their resources (or capital) to the agents for business management, resulting in the separation of ownership and management rights. As the agents’ power increases, they gain more control over resources. However, since the interests of agents are not identical with those of principals, the agents are more willing to maximize their own utility by keeping the resources inside the enterprise and build their ‘empire’. Therefore they tend to reduce charitable donation, which is a drain on the company’s resource.

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Hence this paper proposes: Hypothesis 1a. All other conditions being the same, the greater the power of the CEO, the higher level of charitable donation. Hypothesis 1b. All other conditions being the same, the greater the power of the CEO, the lower level of charitable donations.

3 3.1

Methodology Sample and Data

Since 2008 Wenchuan earthquake set off a wave of charitable donations in China, the study period of this paper is set as 2008–2017. GDP per capita comes from the National Bureau of Statistics. As for statistics of marketization (marketgov), the scores of the relations between government and market are compiled by Fan Gang; the rest of the data are from China Stock Market & Accounting Research (CSMAR) Database. The following observations are eliminated: (1) listed companies in the financial industry and ST. (2) companies that went public in 2017. (3) main variables data missing. Finally we obtained 4499 observations. 3.2

Variables Definitions

(1) Dependent variable Charitable donation(Donation), This paper refers to prior studies [13], and adopts Lndon to measure the amount of charitable donation: the proxy variable is calculated as the natural logarithm of the donations amount plus 1. Dondummy is used as the measure of firms’ willingness of donation, which equals 1 if a firm makes charitable contribution and 0 otherwise. (2) Independent variable This paper uses Finkelstein’s method [8] and measures CEO’s power from four dimensions: ownership, organizational, expert, and prestige power. We use eight indexes to represent the level of CEO’s power [16]. The definition of each variable is showed in Table 2. This paper carries out the principal component analysis on the above eight indexes. The scores are used as the measure of CEO’s power. The larger number indicates greater authority. Concrete analysis is as follows: First of all, to determine whether the above indicators are suitable for factor analysis, this paper performs KMO (Kaiser-Meyer-Olkin) and Bartlett value test. According to the results of Table 2, KMO value of 0.610 is greater than 0.5, it is suitable for factor analysis. The Bartlett value is significant at the 0.01 level, indicating that there is a collinearity problem between these variables, and it is necessary to perform the main component analysis (Table 3). Then, on the condition that the eigenvalue should be greater than 1, we choose three principal components. Combined with the score from Table 4, the

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F. Guo et al. Table 2. Definition of CEO’s power dimension

Variable

Power dimension

Symbol

Indicator meaning

Ownership rights

CEOs holding shares Shareholding ratio of the largest shareholder

Indshare

If CEOs hold the equity of the company, it is 1; otherwise, it is 0 If the proportion of the largest shareholder is less than the industry average, it is 1; otherwise, it is 0

Organizational Dual power

Board size

Expert power

Indfirst

Dual

Condirect

Educational level Edu Title

Prestige power Concurrent post

Founder status

If the CEO concurrently serves as the chairman or vice-chairman of the company, it is 1; otherwise, it is 0 If the board size exceeds the industry average, it is 1; otherwise, it is 0 If the CEO has a master’s degree or above, it is 1; otherwise, it is 0 If the CEO has a high level professional title, it is 1; otherwise, it is 0

Title

Concurrent

Founder

If the CEO works part-time outside the company, it is 1; otherwise, 0 If the CEO worked in the company at the time of the IPO, it is 1; otherwise, it is 0

Table 3. Kaiser–Meyer–Olkin (KMO) and Bartlett spherical test results Test method

Index

Result

KMO test

KMO

0.610

Bartlett test of sphericity

1876.860 Degrees of freedom 28 p-value

0.000

principal components are expressed as linear combinations of variables shown as F1, F2, and F3. F 1 = 0.5076 × Indshare + 0.0249Indf irst + 0.5581 × Dual + 0.3650 × Condirect + 0.5090 × Edu + 0.4058 × T itle + 0.4809 × Concurrent + 0.6200 × F ounder F 2 = 0.4086 × Indshare + 0.5167 × Indf irst + 0.3161 × Dual − 0.0912 × Condirect − 0.4881 × Edu − 0.6168 × T itle − 0.0524 × Concurrent + 0.2589 × F ounder

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Table 4. Scoring table Variable

Factor1 Factor2 Factor3

Indshare

0.5076

0.4086

0.3064

Indfirst

0.0249

0.5167

0.5903 −0.1709

Dual

0.5581

0.3161

Condirect

0.3650

−0.0912 −0.4153

Edu

0.5090

−0.4881 0.3746

Title

0.4058

−0.6168 0.3650

Concurrent 0.4809

−0.0524 −0.4083

Founder

0.2589

0.6200

−0.1060

F 3 = 0.3064 × Indshare + 0.5903 × Indf irst − 0.1709 × Dual − 0.4153 × Condirect + 0.3746 × Edu + 0.3650 × T itle − 0.4083 × Concurrent − 0.1060 × F ounder At last, the weighting coefficient is calculated as the proportion of each factor’s variance contribution rate in that of all factors. And we get the comprehensive score of CEO’s power. P ower = 0.2127 × Indshare + 0.1759 × Indf irst + 0.1388 × Dual − 0.025 × Condirect + 0.0730 × Edu + 0.0582 × T itle + 0.0280 × Concurrent + 0.1509 × F ounder. (3) Control variable Referring to the previous study conducted by Pan Yue (2017) [19], Li Sihai (2016) [13] and Yi-Chen (2018) [6], we add control variables of corporate governance, financial situation and external management to the model, including: internal corporate governance (lninterdex): we take the log of DIB internal control index; total asset return rate (roaa): net profit/total asset balance; growth (growth): (current year’s operating income-previous year’s operating income)/previous year’s operating income; Free cash flow (freecash): net increase in cash and cash equivalents-net cash flow from fund-raising activities; property rights: dummy variables, takes 1 when the ultimate control of listed companies is SOE(stateowned enterprise) and 0 otherwise; Marketization (marketgov): The score represents relationship between government and the market in the Yangang index, The age of listing (age) and the logarithm of the province’s GDP per capita value (lngdp) [6,13,19]. In order to control the impact of macroeconomic and regional factors, this paper also controls the year and provinces. The specific meaning of each variable is presented in Table 5. 3.3

Model Design

To investigate the relationship between CEO’s power and charitable donation, based on the research results of Pan et al. (2017) [19] and Li et al. (2016) [13], we build the following model:

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F. Guo et al. Table 5. The definitions and descriptions of the main variables

Definition

Variable

Description

Charitable donation

Donation

Lndon is the logarithm of charitable donations plus 1; Dondum is a virtual variable, which equals 1 if the enterprise participates in donation; and 0 otherwise

CEO power

Power

Comprehensive score calculated under the principal component analysis method mentioned above

Corporate governance

Lninterdex The logarithm of DIB internal control index

Return on assets

Roaa

Net profit divided by total assets

Free cash flow

Freecash

Net increase in cash and cash equivalents cash inflow from investment activity

Growth

Growth

Growth rate of revenue = (revenue of period t−revenue of period t−1)/revenue of period t−1

Property right

Soe

Dummy variable, which equals 1 if a firm’s ultimate shareholder is the government and 0 otherwise

Marketization process

Marketgov Scores complied by Fan Gang

Age

Age

Years of listing

Per capita GDP

Lngdp

The logarithm of the province’s per capita GDP in which the company is located



Year Province



Year

Controlling the influence of macroeconomic

Province Controlling the influence of province

Donationi,t = β0 + β1 poweri,t + β2 Lninterdexi,t + β3 Roaai,t  + β4 Growthi,t + β5 Soei,t + β6 Marketgovi,t + β7 Agei,t + β8 Lngdpi,t + Year + Province + ε To test the hypothesis, we should focus on β1 in the model. If β1 is significantly negative, it indicates that CEO power suppresses corporate charitable donations; if β1 is significantly positive, it implies that CEO power increases the level of corporate charitable donations.

4 4.1

Regression Results and Analysis Descriptive Statistics

Table 6 presents the descriptive statistics. The average value of don dum is 0.395, indicating that 39.5% of enterprises made charitable contributions. However, the amount of donations varies significantly among companies, as evidenced by the standard deviation (2.660) and the great difference between minimum (0.00) and maximum (9.270) of lndon. The average value of property rights (soe) is 0.624, which shows SOEs accounted for 62.4% of the sample companies.

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Table 6. Descriptive statistics of major variables Variable

4.2

N

Mean

P50

Sd

Max

Min

lndon

4499 1.930

0

2.660

9.270

0

Don dum

4499 0.395

0

0.489

1

0

Power

4499 −0.0165 −0.0946 0.579

1.560

−0.984

6.900

3.420

lninterdex 4499 6.530

6.540

0.152

Roaa

0.0379

0.0529 0.636

4499 0.0464

−0.349

Growth

4499 30.50

0.118

2007

134607 −1

Freecash

4499 0.169

0.137

0.120

0.810

0.00263

lngdp

4499 10.90

11

0.476

11.80

9.090

Age

4499 15.50

16

5.730

27

1

Marketgov 4499 6.940

6.870

1.590

8.590

−6.330

Soe

1

0.484

1

0

4499 0.624

Analysis of Pearson Correlation Coefficient

Table 7 presents the test of the Pearson correlation coefficient for the sample. It can be concluded that CEO power is significantly positive related to the level of donation, indicating that the greater the power of management, the higher the level of corporate charitable donations. Hypothesis 1a is therefore verified, but further proof is still needed. Table 7. Pearson coefficient test lndon lndon

0.057∗∗∗ lninterdex 0.194∗∗∗ Roaa 0.144∗∗∗ Power

Growth

0.012

Freecash

−0.007

lngdp Age Market Soe

Power

lninte

Roaa

lngdp

Age

Market

Soe

1 0.003 0.101∗∗∗

1 0.322∗∗∗ 1

−0.0150 0.150∗∗∗

0.0170 −0.003 0.052∗∗∗ 0.289∗∗∗

−0.037 0.036∗ −0.0150 −0.039∗∗ −0.364∗∗∗ 0.055∗∗∗ 0.002 0.094∗∗∗ 0.067∗∗∗ −0.012 −0.285∗∗∗ 0.072∗∗∗

1 −0.004 1

0.00200 0.0130 −0.114∗∗∗ 0.0140 0.070∗∗∗ 0.00700 −0.180∗∗∗ 0.0110

Robust t-statistics in parentheses,

4.3

Growth Freecash

1

∗∗∗

p < 0.01,

0.030∗ 1 −0.181∗∗∗ −0.100∗∗ 1 0.00100 0.437∗∗∗ −0.033∗ 1 −0.162∗∗∗ −0.0170 0.338∗∗∗ −0.077∗∗∗ 1

∗∗

p < 0.05, ∗ p < 0.1

Regression Results

The regression results of CEO’s power’s impact on charitable donation are shown in Table 8. Columns (1) and (3) present results without control variables, and columns (2) and (4) present results after adding control variables. As evidenced by columns (1) and (2), the CEO’s power (POWER) is positively and significantly correlated with the amount of the charitable donation (lndon) at the

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F. Guo et al. Table 8. Robustness tests Variable

(1) Indon

(2) Indon

Power

0.290∗∗∗ 0.248∗∗∗ (4.22) (3.28)

(3) (4) don dum don dum 0.210∗∗∗ (3.77)

0.193∗∗∗ (3.14)

Ininterdex

2.563∗∗∗ (6.47)

1.493∗∗∗ (4.45)

Roaa

4.177∗∗∗ (4.70)

2.437∗∗∗ (3.33)

Growth

0.000∗∗∗ (12.25)

0.004 (1.41)

Freecash

−1.391∗∗∗ (−3.88)

−0.853∗∗∗ (−2.92)

Ingdp

0.676 (−1.60)

0.074 (−1.64)

Age

−0.013 (0.96)

−0.011 (0.12)

Marketgov

−0.142∗ (−1.95)

0.506 (1.54)

Soe

−0.057 (−0.61)

0.009 (0.11

Year and Province Yes

Yes

Yes

Yes

Constant

3.183∗∗∗ −19.603∗∗∗ 0.513∗∗ (11.22) (−2.57) (2.39)

−13.920∗∗∗ (−2.67)

Observations

4,499

4,437

4,437

4,499

R-squared 0.05 0.09 0.0349 0.0479 Robust t-statistics in parentheses, ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

level of 1%. This shows that the greater the CEO’s power, the higher the firm’s charitable donation. Besides, the coefficient (0.248) indicates that 1% increase in CEO’s power will result in 0.248% increase of the amount of charitable donations. Column (3) and (4) show that the CEO’s power (power) is positively correlated with enterprise’s donation will (don-dum) at the level of 1%. It manifests that the enterprises are more likely to make donations when CEOs have access to greater power. The conclusions above verify the Hypothesis 1a. For other variables, the quality of internal control (lninterdex), return on total assets (roaa), and corporate growth (growth) were significantly positively related to corporate donations; freecash was significantly negatively related to donations, which is consistent with previous literature. It worth noting that when the dependent variable is Lndon, we use the Ordinary Least Squares (OLS) model; when the dependent variable is Don dum, we use the logistics method for regression.

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Table 9. Regression results for the hypothesis Variable

(1) Indon

(2) Indon

Power

0.245∗∗∗ 0.199∗∗∗ (3.43) (2.50)

(3) (4) don dum don dum 0.181∗∗∗ (3.08)

0.164∗∗∗ (2.51)

Ininterdex

2.236∗∗∗ (5.81)

1.318∗∗∗ (3.81)

Roaa

4.231∗∗∗ (4.57)

2.636∗∗∗ (3.43)

Growth

0.000∗∗∗ (12.53)

0.004 (1.43)

Freecash

−1.309∗∗∗ (−3.44)

−0.907∗∗∗ (−2.91)

Ingdp

1.273 (1.52)

0.594 (0.75)

Age

−0.016∗ (−1.81)

−0.013∗ (−1.88)

Marketgov

−0.298∗ (−2.16)

0.239 (0.81)

Soe

−0.046 (−0.47)

0.030 (0.36)

Year and Province Yes

Yes ∗∗∗

Yes ∗∗∗

Yes ∗∗

Constant

1.700 (6.90)

−23.858 (−2.82)

−0.568 (−2.71)

−17.968∗∗∗ (−2.68)

Observations

3,959

3,899

3,959

3,899

R-squared 0.04 0.07 – – Robust t-statistics in parentheses, ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

4.4

Robustness Test

This paper uses two methods (Dondum and lndon) to measure charitable donation, therefore the results already have a certain degree of robustness. Also, in order to eliminate the impact of the great earthquake, this paper refers to previous researchers(Pan Yue et al.) and excludes the observations from 2008, 2010 and 2013, namely the period during which catastrophic earthquakes strike Wenchuan, Yushu and Ya’an, yielding 1110 observations. Table 9 illustrates the reexamined results that are consistent with the previous results. 4.5

Further Test

To measure the impact of CEO power on charitable donations under different property rights and in different regions, we conducted a group test. The results

0.000∗∗∗

(5.18)

(4.71)

0.000∗∗∗

(8,63)

(0.12)

0.388

(1.22)

(0.80)

−0.249∗

(−1.78)

2.76)

(1.97)

(12.12)

(1.17)

0.007∗∗∗ 0.016

Yes

(−1.16)

−0.251

(0.52)

0.692

(−2.15)

(7.41) (2.49) (10.13)

0.000∗∗∗

0.11

R-squared

–

2,770 ∗∗∗

(−2.13)

Robust t-statistics in parentheses,

2,770

(−1.92)

p < 0.01,

0.10

1,667

(−1.30)

1.676

Yes

(1.17)

∗∗

–

0.08

2,965

p < 0.05, ∗ p < 0.1

1,663

(−0.72) (−2.76)

−33.097∗∗∗

Yes

(−1.35)

−0.477

(−0.20) (1.21) 0.507

−16.675∗ −14.170∗∗ −17.379 −7.271

Yes

0.091

(−2.88)

0.677

Observations

Constant

(1.59)

3.799∗∗∗ 2.248∗∗∗ 2.643∗∗∗

(3.83)

3.574∗∗∗

(1.53)

(1.05)

0.023

(2.01)

1.676∗∗∗

(3.69)

1.554∗∗∗

(1.52)

–

2,965

(−1.04)

−11.287

Yes

(0.14)

0.027

(0.15)

0.176

(3.43)

5.299∗∗∗

0.880

(1.04)

dondum 0.112

(−3.83)

3.371∗∗∗

(4.84)

2.336∗∗∗

(0.91)

0.140

−0.238

(3.79)

2.378∗∗∗

0.105

(−1.93) (−5.69)

1.526∗∗∗

(3.98)

0.108

0.14

1,472

(−0.63)

−5.811

Yes

(−1.08)

−0.128

(0.09)

0.088

(1.29)

0.862

(−6.53)

−0.011∗∗∗

(5.43)

6.832∗∗∗

(3.16)

1.205∗∗∗

(2.77)

0.374∗∗∗

Indon

–

1,472

(−0.59)

−5.592

Yes

(−2.83)

−0.429∗∗∗

(0.03)

0.029

(0.62)

0.324

(−2.41)

−0.013∗∗∗

(3.10)

3.707∗∗∗

(2.23)

1.022∗∗∗

(2.91)

0.335∗∗∗

dondum

Eastern China Eastern China Non-Eastern China Non-Eastern China

dondum Indon

SOE

(−3.65)

(3.53)

0.395∗∗∗

Indon

SOE

−1.391∗∗∗

0.288∗∗∗

Indon

−1.729∗∗∗ −1.126∗∗∗ −1.216∗∗ −0.948∗ −2.400∗ ∗ ∗

SOE

dondum

SOE

Year and province Yes

Marketgov

Ingdp

Freecash

Growth

Roaa

Ininterdex

Power

Variables

Table 10. Results of group testing

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are shown in Table 10. The first four columns are the results of group property inspections, and the last four columns are the results of regional inspections. The results indicate that compared with non-SEO, the power of CEOs from SEO can significantly increase the charitable donations of enterprises. The diversification of SEO goals may explain the phenomenon, as SOEs may be directed by the government to make charitable donations in the period of major natural disasters. In addition, executives of SOE are usually appointed by the government. This mechanism gives executives more incentives to gain authority’s recognition by increasing donations. The regional inspection results indicate that compared with developed provinces in eastern China, the power of CEOs in the central and western regions is significantly positively related to charitable donations. The underlying reason may be that in comparison with enterprises from developed region, those from developing areas may gain more benefits from shouldering social responsibility like making donations.

5

Conclusions

Charitable donation is an important expression of corporate social responsibility and one of the core elements of corporate citizenship. It benefits enterprises in many ways, which makes it critical to the sustainable development of the business. This paper uses statistics of Chinese A-share listed companies from 2008 to 2017 and conducts empirical analysis on the impact of CEO’s power on corporate donations. The results show that the greater the CEO’s power is, the more the enterprise will donate. We further test the influence of property rights and geographical factors. The results indicate that CEO power has more positive impacts on corporate donation in SOEs, and power of CEOs from western China is significantly and positively correlated with donations. This paper opens up a door to the study of CEO power and charitable donation. We further test the influence of property right and geographic factors on such mechanism in the economic context of China. Still we find this paper limiting for the following reason: it only tests CEO power’s influence on charitable donations, and fails to incorporating the impacts of other CEO’s personal factors, which is the potential area for future studies. Acknowledgements. This work was supported by the National Natural Science Foundation of China (71902128); the Fundamental Research Fund of Central Universities (YJ201872); the Humanities and Social Sciences Project of the Ministry of Education in China (18YJC790081), and the Innovation Spark Project of Sichuan University (2018hhf-49). We are grateful to anonymous reviewers whose valuable suggestions have led to a considerable improvement in the organization and presentation of this manuscript. The authors declare no conflict of interest.

References 1. Albuquerque, R., Koskinen, Y., Zhang, C.: Corporate social responsibility and firm risk: theory and empirical evidence. Manag. Sci. 65(10), 4451–4469 (2019)

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2. Brown, W.O., Helland, E., Smith, J.K.: Corporate philanthropic practices. J. Corp. Finance 12(5), 855–877 (2006) 3. Burt, R.S., Bian, Y., Opper, S.: More or less Guanxi: trust is 60% network context, 10% individual difference. Soc. Netw. 54, 12–25 (2018) 4. Campbell, L., Gulas, C.S., Gruca, T.S.: Corporate giving behavior and decisionmaker social consciousness. J. Bus. Ethics 19(4), 375–383 (1999) 5. Chen, J., Dong, W., Tong, J.Y., Zhang, F.F.: Corporate philanthropy and investment efficiency: empirical evidence from China. Pac.-Basin Finance J. 51, 392–409 (2018) 6. Chen, Y., Hung, M., Wang, Y.: The effect of mandatory CSR disclosure on firm profitability and social externalities: evidence from China. J. Account. Econ. 65, 169–190 (2018). (in Chinese) 7. Dai, Y., Pan, Y., Feng, S.: Are Chinese enterprises’ charitable donations “political contributions”? – Evidence from the replacements of the municipal party secretaries. Econ. Res. J. 2, 74–86 (2014). (in Chinese) 8. Finkelstein, S.: Power in top management teams: dimensions, measurement, and validation. Acad. Manag. J. 35(3), 505–538 (1992) 9. Gao, S.: Theoretical analysis and strategy discussion on corporate charitable donation. J. Changsha Univ. 4, 26–29 (2017). (in Chinese) 10. Jensen, M.C., Meckling, W.H.: Theory of the firm: CEO’s behavior, agency costs and ownership structure, no. 4, pp. 305–260. Social Science Electronic Publishing (1976) 11. Lai, E., Wang, F.: Discussion on corporate charitable donation behavior based on cost-benefit analysis. Taiwan Agric. Res. 117, 49–52 (2012). (in Chinese) 12. Lamond, D., Dwyer, R., Arendt, S., Brettel, M.: Understanding the influence of corporate social responsibility on corporate identity, image, and firm performance. Manag. Decis. 48, 1469–1492 (2010) 13. Li, S., Chen, X., Song, X.: The generosity of the poor: a strategic study on the motive. Manag. World 5, 116–127+140 (2016). (in Chinese) 14. Liang, J., Chen, S., Gai, Q.: Political participation, governance structure of private enterprises and charitable giving. Mang. World 7, 109–118 (2010). (in Chinese) 15. Liu, H., Bao, Y.: Research on the influence of CEO reputation on the financial performance of listed companies. Hebei Enterp. 5, 81–82 (2018). (in Chinese) 16. Liu, Y., Yao, H.: Executive power, audit committee professionalism and internal control deficiencies. Nankai Manag. Rev. 17, 4–12 (2014). (in Chinese) 17. Lys, T., Naughton, J.P., Wang, C.: Signaling through corporate accountability reporting. J. Account. Econ. 60(1), 56–72 (2015) 18. Ma, D., Parish, W.L.: Tocquevillian moments: charitable contributions by Chinese private entrepreneurs. Soc. Forces 85(2), 943–964 (2006) 19. Pan, Y., Weng, R., Liu, S.: The selfish goodwill: new evidence from corporate philanthropy in typhoon. China Ind. Econ. 5, 133–151 (2017). (in Chinese) 20. Shapira, R.: Corporate philanthropy as signaling and co-optation. Fordham Law Rev. 80, 1889–1939 (2012)

Influencing Factors of Fresh Food Online Repurchase Intention Weiping Yu1(B) , Wenyang Bian1 , Wenjie Li2 , and Xiaoyun Han1 1

2

Sichuan University, Chengdu 610064, People’s Republic of China [email protected] Sun Yat-sen University, Guangzhou 510080, People’s Republic of China

Abstract. In this research we use comment text mining of fresh food from JD.com to study the impacts of consumers’ repurchase intention. A total of 6260 repurchase comments of fresh foods from fruits, vegetables, meat, and seafood were screened out from 79539 comments. We find that: (1) The frequency distribution of the fresh food repurchase comments text fits “The Long Tail”; (2) The high frequency keywords at the head of distribution curve reflect the basic attributes of the product (delicious, fresh, taste, etc). The intermediate frequency keywords reflect the basic attributes of the product and the basic services of the business. The large number of low-frequency keywords at the end of distribution curve reflect excess and diversified additional services that affect consumers’ willingness to repurchase; (3) There are differences in the factors affecting the repurchase willingness among the four types of fresh food. The difference reflected in the distribution of long tail curves and the frequency of keywords. Keywords: E-commerce mining

1

· Fresh food · Repurchase intent · Text

Introduction

In recent years, online shopping has become an important channel of shopping for consumers. Especially, mobile e-commerce has penetrated into consumers’ daily life and promoted the expansion of fresh food e-commerce market. The annual average growth rate of fresh food e-commerce market in China is over 50%. In 2017, the transaction scale of fresh food e-commerce market reached 139.13 billion RMB. E-commerce can more effectively meet the requirements of consumers for fresh food and improve their sense of happiness. When consumers buy fresh food online, they attach great importance to product quality and safety [17]. However, fresh food has the characteristics of low standardization, strong timeliness, and easy to deterioration or decay [4], which greatly improves the logistics difficulty and consumers’ perceived risk of product quality. So the consumers tend to distrust the food quality, which leads to lower online purchase intention and repurchase frequency. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 757–770, 2020. https://doi.org/10.1007/978-3-030-49829-0_56

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Many scholars have studied the willingness of online purchase. They have adopted many theories like Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), Technology Acceptance Model (TAM). Some scholars uses these theories to study online purchase of fresh food and so on. Oresanya studied the factors that promote and hinder students’ online purchase of agricultural products [10]. Based on the reference effect, Zhao researched consumers’ willingness to buy agricultural products online [18]. Fresh food has stronger characteristics of experience goods and credence goods, which is more suitable for shopping through mobile e-commerce [11]. But few researches study the willingness to repurchase fresh food, though online repurchase intention has been researched by academia. The increase of consumers’ repurchase intention is conducive to improving profits, reducing marketing costs and raising the price of consumers’ willingness to pay for products [14]. And repurchase intention is an important antecedent variable of consumer loyalty [16]. It’s necessary to figure out what factors affect the willingness to repurchase fresh food. In general, Technology Acceptance Model (TAM), Consistency Theory (CT), Customer Perceived Theory (CPV) are used to study online repurchase intention. For example, Lee et al found that perceived value, perceived ease of use, perceived usefulness, firm reputation, privacy, trust and functionality significantly influence online repurchase intention [6]. However, is there a new method to study the factors affecting the repurchase willingness? This is a question worth exploring. Online comments of consumers are an effective form of feedback, and are often used by e-commerce enterprises to collect consumer information [9]. The hot topics and topics implied in the comments reflect the purchase status and focus of consumers [5]. Thus, we extract and analyse the repurchase intention influencing factors of fresh foods including fruits, vegetables, meat and seafood through online comment text mining [15]. The innovations of this paper are as follows: (1) This paper focus on consumers’ online fresh food repurchase intention. (2) The influencing factors were extracted through online comments text mining. (3) This paper use the long tail theory to analyse keyword frequency and then extract the influencing factors from different level.

2 2.1

Method Text Mining

Text mining is one of the important means of content analysis. Content analysis developed with the emergence of the Internet. The digital environment of the network provides the necessary condition for transforming text into data. Researchers combine the traditional content analysis with network information to form the network content analysis. Text mining is a research method that converts qualitative and unstructured data such as text, image and HTML into quantitative and structured data [13]. Unstructured text information obtained from social media, news, blogs, BBS and other channels can be transformed into

Influencing Factors

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analyzable data, which can provide guidance for future marketing activities with the help of the related market prediction [7]. 2.2

The Long Tail

Chris Anderson first proposed “The Long Tail” and used this theory to analyze the economic situation of some platforms like Amazon [1]. The long tail theory is a breakthrough and innovation to the traditional Pareto’s Principle in the era of Internet. Due to the limitation of energy and cost in traditional business, people attach great importance to the “Pareto’s Principle”. When setting up goals and marketing activities, they will focus on a small number of important customers or products at the top of the curve, and seldom consider a large number of ordinary customers or products. However, things go different in the e-commerce environment. The 80% of ordinary customers can bring major profits [12]. Since the cost of consumer information acquisition and store attention have been greatly reduced, many non-mainstream, small-volume products with a large variety will also attract consumers’ attention. Therefore, the products and consumers of the long tail part have great potential benefits. Long tail pattern exists in the vocabulary used by international tourists to describe Chinese tourist destinations. The destination image is dominated by a few very popular phrases, but also contains a large number of niche words. Tourists who use niche words are more likely to go to specific destinations [3]. The long tail theory is helpful for this paper to understand the factors that affect the fresh food repurchase intention from different levels. 2.3

Data

The consumer online comments of JD.com are selected as the data source, for JD.com has a strong advantage in the field of fresh food e-commerce. We select fresh food of fruits, vegetables, meat, seafood as object [8], collect the the top 100 sellers of each kind on JD.com by January 23, 2019, and collect 200 comments on the first 20 pages of each product from the website of the above merchants respectively. Finally, 20427 fruit comments, 19425 vegetable comments, 19872 meat comments and 19635 seafood comments were collected, a total of 79359. Then text data preprocessing, including data cleaning, word segmentation, removal of stop words, part of speech tagging. Data cleansing is the removal of duplicate or unrelated records from text. Before the processing of Chinese text, it is necessary to divide Chinese text into words and divide sentences into multiple elements according to certain rules or algorithms. After word segmentation, a large number of function words with no practical meaning will appear in the text. Therefore, a large number of irrelevant words need to be removed in combination with the Chinese stop word list to finally form an effective analysis corpus. Before data cleaning, it is necessary to use functions such as Excel’s “filter” to extract text related to “repurchase” from a large number of comments according to keywords. Before the formal screening, the 400 comments data were manually screened. Table 1 shows the phrase closely related to “repurchase”.

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W. Yu et al. Table 1. Phrase closely related to “repurchase” Keywords Phrase Again

Buy again, come again, order again, purchase again, reorder again, stock again

More

Buy more, order more, purchase more, want more, stock more, more buying, more coming

Continue

Continue to buy, continue to order, continue to visit

Other

Reorder, repurchase, reordering

After filtering the words above, there will still be irrelevant comments including “re-evaluation, re-review, re-comment, no more buying, no more coming” that do not conform to the purpose of the research, and they will also be deleted. After data preprocessing, some meaningless comments still exist in the comments, such as completely repeated or meaningless comments, which need to be deleted in combination with the sorting function of Excel. Finally, 6260 valid fresh food review data were retained, including 1342 fruit comments, 1497 vegetable comments, 1701 seafood comments and 1720 meat comments. Repurchase accounts for about 8% of all comments.

3 3.1

Analysis Keyword Classification

The keyword classification of fresh food comment aims to reflect the influencing factors of consumer repurchase more clearly and concisely. Comment is an important carrier for consumers to convey information, while the keywords can reflect the important repurchase factors. According to the statistical results, this paper divides keywords into 7 categories: 1) Product, including description of taste, size and freshness. The description of taste includes “crispy, moist, soft and waxy, delicious, sweet”. Descriptions of size include “size, big” etc. The description of freshness mainly appears in the meat and seafood such as “fresh, tender”. 2) Logistic Service, including the description of consignment, distribution, logistics services, etc. Consignment includes description of the consign efficiency of the merchant after the consumer orders, including “order, consignment, consignment speed”, etc. Distribution describes the circulation speed of goods in the logistics process, such as “logistics, speed, distribution”, etc. Logistics service describes the perfection of logistics service and the brand of logistics service, such as “courier, Jingdong logistics, SF express, delivery, home delivery”, etc. 3) Price, which is used to describe the product price perceived by customers. Some words simply highlight cheap, such as “affordable, cheap, price, coupons”. There are also words that express the cost performance of products, such as “cost-effective, cheap and fine, cost performance” and so on. 4) The Jingdong Brand, reflects consumer trust in Jingdong brand, like “Jingdong, Jingdong logistics, Jingdong

Influencing Factors

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proprietary”. 5) Brand of Merchants, including trust in product brand, mainly appears in fresh meat products, reflecting brand loyalty to producers of meat products, such as “brand, sign”. It also includes the evaluation of the service quality of the brand merchants themselves, such as “service attitude, customer service, seller”. 6) Packaging, which is used to evaluate the packing tightness and aesthetics of seafood. The packing tightness is described as “sealed”. Description of package aesthetics include “exquisite, delicate”, etc. 7) Present, mainly appears in the seafood due to the scarcity and low accessibility of it. Because it has higher transportation and maintenance costs, higher value, so many people treat seafood as gifts, like “present, gift box”, etc. 3.2

The Long Tail

The text of the four kinds of fresh food was summarized to conduct word segmentation and keyword frequency statistics. The top 800 keywords were retained, including 106 words appear less than 10 times, 645 words appear 10-50 times, and the rest above 50. The keywords frequency distribution is shown in Fig. 1, which exactly fits “The Long Tail”. The high frequency keywords occupy the head of the long tail curve while low frequency keywords at the end.

Fig. 1. Keywords frequency distribution.

Combined with the long tail theory and the morphological characteristics of long-tail curve, 800 keywords were divided into three groups. The first group was made up of top six high-frequency keywords, including “delicious, Jingdong, fresh, taste, packaging, logistics”. Then follows intermediate frequency keywords ranked 7-50, like “express, price, quality, flavor” and so on. The third group of 750 low-frequency keywords ranked 51-800, including “huge, thoughtful, aftersales, whole family, authentic, full reduction” and so on. According to the distribution of the long tail curve, it can be seen that there are three levels of fresh food repurchase intention influencing factors. The high frequency words at the top of the curve mainly show consumers’ judgment on

762

W. Yu et al. Table 2. Summary of high-frequency keywords NO. Word

Freq

%Total NO. Word

Freq %Total

1

Delicious

1672 2.04%

26

Service Attitude

147

0.18%

2

Jingdong

1347 1.64%

27

Courier

138

0.17%

3

Fresh

993 1.21%

28

Cost Performance

135

0.16%

4

Taste

907 1.11%

29

Customer Service

125

0.15%

5

Package

851 1.04%

30

Cheap and Cheerful

117

0.14%

6

Logistics

738 0.90%

31

JingDong Logistics

117

0.14%

7

Express

531 0.65%

32

Present

111

0.14%

8

Price

520 0.63%

33

Tasty

107

0.13%

9

Quality

469 0.57%

34

Jingdong Proprietary 102

0.12%

10

Flavor

448 0.55%

35

Delicacy

97

0.12%

11

Speed

359 0.44%

36

Brand

92

0.11%

12

Size

357 0.43%

37

Distribution

91

0.11%

13

Delivery

349 0.43%

38

Family

91

0.11%

14

Affordable

306 0.37%

39

Weight

84

0.10%

15

Cheap

266 0.32%

40

Sign

82

0.10%

16

Consignment

227 0.28%

41

Gift Box

80

0.10%

17

Meat Quality

218 0.27%

42

Sealed

79

0.10%

18

Children

217 0.26%

43

Exquisite

79

0.10%

19

Qualified

216 0.26%

44

Uniform

69

0.08%

20

Cost-effective

212 0.26%

45

Family Members

67

0.08%

21

Laddie

201 0.24%

46

Sweet

62

0.08%

22

Friends

193 0.24%

47

Palatable

62

0.08%

23

Product

190 0.23%

48

Sweet and Sour

61

0.07%

24

Seller

178 0.22%

49

Fresh and Tender

52

0.06%

25

Folk

154 0.19%

50

Coupon

48

0.06%

taste, which reflect the taste orientation of fresh food. Intermediate frequency keywords in the middle of the curve pay more attention to the basic products and basic services of fresh food sellers, such as express, logistics speed, consign speed, product quality and so on. A large number of low-frequency keywords at the end of the curve consider more about the additional services of fresh food sellers, which often exceed the expectations of consumers, such as aftersales service, thoughtfulness and so on. These factors not only affect customers’ repurchase intention, but also affect the fresh food sellers’ brand building.

Influencing Factors

3.3

763

Repurchase Intention Influencer

The text of four kinds of fresh food was summarized for keyword frequency statistics. The top 50 were selected to form Table 2, which contains 6 highfrequency keywords and 44 intermediate-frequency keywords. (1) High frequency keywords Among the top 6 high-frequency keywords, “delicious”, “fresh”, “taste” mean the taste and flavor of fresh food, “Jingdong” means the brand of Jingdong, “package” means the packaging of fresh food and “logistics” means the logistic service. “Delicious”, “fresh” and “taste” appear 1672, 993 and 907 times, all of which belong to “Product”, occupying 3 positions among the top 6, indicating that consumers have a significant preference when repurchasing fresh food. Now the overall strength of the national economy is rising, people’s consumption is upgrading. They pay much more attention to product quality and usage experience. Therefore, when consumers make repurchase decisions, their first consideration is the taste and quality of fresh food. The frequency of “Jingdong” is 1325, belongs to “The Jingdong Brand”, indicating the platform brand of Jingdong. It has an efficient business admittance system, business reputation evaluation system and product supervision system, which brings strong endorsement effect to the fresh food merchants. The words “package” and “logistics” come from “Logistics Service” and “Packaging”, appear 851 and 738 times. For fresh food is time-sensitive and perishable, high-quality packaging and logistics can maximize the freshness of food. On the other hand, consumers can feel respected from good packaging. Therefore, consumers attach great important to packaging and logistic service. (2) Intermediate frequency keywords The word frequency decline of 44 intermediate keywords becomes slow, and docks naturally with the low frequency keywords at the end of the curve. Among them, 13 keywords are “Product” for the most. These words still stress the importance of product quality, such as food taste, fruit size, etc., and mutually confirm with high frequency keywords. With increased purchasing power, consumers consider most about the taste and quality of fresh food when repurchase. Then 7 “Logistic Service” and 7 “Brand of Merchants” tied for 2nd. Logistic Service, include “speed, delivery, distribution, laddie”, etc., is a necessary and basic service for fresh food e-commerce. It’s also an important factor that determine the freshness of food and consumer usage experience. So it will be considered by consumers. Brand of Merchants, include “Seller, service attitude, customer service” and so on show the service-oriented repurchase intention of consumers. Because when product quality and logistics are similar, the service of merchants is an important area of differentiation advantage. Thus the service determines consumers’ repurchase intention. Behind them are 6 keywords belong to “Price”, which shows that consumers attach more importance to service level than to price. It’s also because the increased purchasing power, price is no longer a priority. So consumers with the ability to consume fresh food have relatively low price sensitivity and are willing to pay a corresponding premium for high-quality service.

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After meeting the basic service demand, consumers begin to consider the costeffectiveness of the price. The rest of intermediate keywords are 5 Present, 4 Packaging and 2 Jingdong Brand. Consumers consider present only at certain times, so present relatively is not an important factor for repurchase consideration. In intermediate frequency keywords, “Packaging” mainly contains words like “gift box, sealed, exquisite”, etc., which provide added value to packaging so it’s also not a priority. Finally, Jingdong Brand, represents consumers’ trust in the Jingdong brand. (3) Low frequency keywords There are 750 low-frequency keywords in total, with low frequency and no obvious characteristics. Therefore, it is difficult to find the influencing factors of consumers’ repurchase. From the perspective of the long tail theory, a large number of low-frequency words cover the end of the curve, but together they have a broader explanatory effect. Low frequency keywords account for 42.27% of the total word frequency, which is much larger than middle frequency keywords (11.57%) and high frequency keywords (5.99%). From the analysis of the factors influencing the potential repurchase intention, it can be seen that the characteristics of low-frequency words are stronger than those of high-frequency words. We found that the low-frequency words express products or services that exceed consumer expectations, and further refine the influencing factors of middle and high-frequency keywords. For example, “value for money, excellent quality and reasonable price” reflects the premium experience brought by the price and quality. “Enthusiasm, introduction, patience, intimacy, thoughtfulness, after-sales service” reflect the shopping experience brought by merchants’ customer service quality to consumers, as highlighted in the Webqual e-commerce service scale proposed by Barnes and Vidgen (2002) [2]. There is also expressions related to festivals such as “New Year goods, Spring Festival”.etc, which reflects the special demands of consumers during the festival. “Very fast, the same day, one day, transportation, the next day” is the recognition of the logistics speed, while “freezing, cold chain, defreezing” and other comments are the approval of the cold chain logistics. Both of them are important factors affecting consumers’ willingness to repurchase. “Beautiful, elegant, design”, reflected the appearance and design of the product packaging, which provide added value to consumers. The number of low-frequency special words is large, which reflects the more diversified repurchase factors, and affirmed with high-frequency keywords and intermediate frequency keywords. It is also the concrete expression of various keywords, showing the specific scenes and specific elements that affect consumers’ repurchase intentions. 3.4

Comparisons Between Categories

The comments text of four fresh food online shopping was imported for word segmentation and word frequency analysis. After removing some meaningless words, high-frequency keywords were obtained, and the keywords ranked top 20 were extracted respectively to form Table 3.

113

103

Flavor

Price

63

62

61

58

56

Delivery

Moisture

Sweet and Sour

Children

Bad Fruit

65

128

Logistics

Speed

129

Express

72

150

Package

73

152

Size

Fruit

173

Apple

Cheap

214

Taste

74

304

Quality

320

Fresh

0.31%

0.32%

0.33%

0.34%

0.35%

0.36%

0.39%

0.73%

0.41%

0.56%

0.62%

0.70%

0.71%

0.82%

0.83%

0.95%

1.17%

1.67%

1.75%

Soft and Waxy

Laddie

Qualified

Affordable

Product

Cheap

Goods

Children

Speed

Price

Delivery

Quality

Express

Flavor

Package

Logistics

Fresh

Taste

Jingdong

Delicious

Jingdong

2.28%

Freq %CAT Words

416

Delicious

Vegetable

Words

Fruit

38

38

39

39

42

42

45

45

59

87

90

92

100

109

116

128

154

198

289

591

0.26%

0.26%

0.27%

0.27%

0.29%

0.29%

0.31%

0.31%

0.41%

0.60%

0.62%

0.64%

0.69%

0.75%

0.80%

0.89%

1.06%

1.37%

2.00%

4.09%

154

165

169

187

287

403

71

78

83

93

109

109

123

133

Children

Laddie

Qualified

54

63

64

Cost-effective 67

Hot-pot

Cheap

Speed

Affordable

Delivery

Flavor

Quality

Express

Seafood

0.26%

0.30%

0.31%

0.32%

0.34%

0.37%

0.40%

0.44%

0.52%

0.52%

0.59%

0.63%

0.65%

0.74%

0.79%

0.81%

0.89%

1.37%

1.92%

2.05%

Product

Service Attitude

Delivery

Seller

Present

Friends

Consignme-nt

Flavor

Affordable

Speed

Express

Price

Size

Quality

Taste

Delicious

Logistics

Jingdong

Fresh

Package

Freq %CAT Words 429

Meat Quality 137

Price

Fresh

Logistics

Package

Taste

Jingdong

Delicious

Freq %CAT Words

Meat

Table 3. High frequency keywords of four fresh food

78

79

87

102

105

107

113

117

133

152

169

176

177

180

208

236

313

335

370

0.30%

0.31%

0.34%

0.39%

0.41%

0.41%

0.44%

0.45%

0.51%

0.59%

0.65%

0.68%

0.68%

0.70%

0.80%

0.91%

1.21%

1.29%

1.43%

1.54%

Freq %CAT 398

Influencing Factors 765

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W. Yu et al.

“Delicious” has the highest word frequency and proportion on the whole, ranking 1st in the number of word frequency in fruits, vegetables and meat, and 5th in the seafood. There is only one keyword whose frequency is more than 500, which belongs to vegetable. There are 9 keywords with frequency between 300 and 500, 3 of which belong to fruit, 2 to meat and 4 to seafood. There are 38 keywords with frequency between 100 and 300, 8 of which belong to fruit, 7 to vegetable, 10 to meat and 13 to seafood. There are 32 keywords with frequency below 100, 9 of which belong to fruit, 12 belong to vegetable, 8 belong to meat and 3 belong to seafood. The top four high-frequency words for fruit and vegetable are “Jingdong”, “delicious”, “taste” and “fresh”, emphasis the taste and freshness. The top 4 keywords of meat are “delicious”, “Jingdong”, “taste” and “packaging”. In addition to food taste, meat quality has higher demand for preservation so consumers attach great importance to packaging. The top 4 of seafood are “Jingdong”, “fresh”, “packaging” and “logistics”, which emphasize more about the package and logistics of seafood. The high-frequency keywords of the top 50 fresh food categories were selected to draw the word frequency distribution curve, as shown in Fig. 2.

Fig. 2. High frequency keywords distribution of four fresh food.

As can be seen from the figure, there are significant differences in the distribution of curves, especially in top 20. The frequency of the first 3 keywords in vegetable drop fast and then gradually stabilized, while seafood declines slowly with a relatively smoothly transition of the curve. Thus it can be seen that vegetables shows a dual structure under the dominant repurchase influencing factor, while meat, seafood and fruit are closer to the multiple structure. In order to better describe the difference among the four fresh food, the high and intermediate frequency keywords ranked in the top 50 in each category were classified and processed to obtain the cumulative frequency distribution of the different types of keywords as shown in Fig. 3. It can be seen that the overall trend of the four fresh foods in different categories of keywords is similar, but there are significant differences in specific

Influencing Factors

767

one. Among the category of product, word frequency of fruit is the highest, and it can be seen that consumers have higher requirements on the taste of fruit. Logistics services increase in the order of fruits, vegetables, meat, seafood, because the demand of preservation in four kinds of fresh food are increasing. For example, meat and seafood are easy to rot, need to delivery quickly and keep fresh in the process. In terms of packaging and present, the frequency of seafood is significantly higher than other categories. Seafood always have a further transportation distance to consumers, and it is prone to deterioration, so that the process of logistics preservation has become a business must think about. Therefore, a variety of frozen packaging, tight vacuum packaging and so on has become a necessary means, but also what consumers focus on. Due to the specific origin, transportation difficulty, high cost of preservation, and the long-term formation of high value, seafood at a specific time also become a consumer gift choice. As we can see in Table 2, the package is ranked 5 and logistics 6. While in Fig. 3, it’s clear that the category “Logistics Service” is more than “Packaging”. That is because in the Logistics Service, consumers also value speed, delivery, consignment, etc. in all kinds of fresh food. These are important components of logistics service with higher priority. So these words have relatively higher frequency. In Packaging, what consumers value is the tightness and artistry of packaging apart from “package”, like “sealed, exquisite”, etc. These are added value that won’t be considered first, and mostly appear in meat and seafood. Words like that have relatively lower frequency. So in sum, Logistics Service is more than Packaging.

4

Discussion

This paper obtained consumers’ repurchase comments on four kinds of fresh food including fruit, vegetable, meat and seafood from the platform of Jingdong. Through text mining, this paper studied the influencing factors of consumers’ willingness to repurchase fresh food and reached the following conclusions:

Fig. 3. The cumulative of different types of keywords.

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First, the keyword frequency distribution of consumer fresh food repurchase review has the characteristic of typical “long tail curve”. A small number of intermediate and high frequency keywords in the head of curve reflect the main factors influencing consumers’ willingness to buy fresh food again. A large number of low-frequency keywords form the long tail part of the curve, they are not only the specific expression of intermediate and high frequency keywords, but also reflect the diversified demands of consumers. Second, there are clear repurchase influencing factors when making fresh food repurchase choices. The high frequency keywords in the head of the “long tail curve” show that when consumers repurchase fresh food, food quality such as taste, flavor and size become the primary decision factors. After that, consumers begin to pay more attention to basic services such as logistics and customer services, etc., with relatively low sensitivity to price. This is due to the improvement of consumers’ purchasing power, so consumers pursuit more of food quality rather than just satiety. When buying fresh food, consumers can, to some extent, ignore the price and focus more on the taste, flavor and so on. They are even willing to pay more for choosing fresh food that can bring excellent tongue experience. In addition, consumers also value basic services such as customer service, logistics efficiency, packaging quality. Then, on the basis of dominant repurchase factor, whether a business can provide more or diversified services than customers expect is an important factor that affects consumers’ repurchase intention. The analysis of a large number of low-frequency keywords in the tail of the “long tail curve” shows that consumers not only pay attention to the overall perception of basic products and basic services, but also pay attention to the details of services. When the service details exceed consumers’ expectations and even give a shock, consumers will be willing to repurchase. In addition, providing diversified added value such as gifts, sense of design and beauty can effectively shape online brands and form unique brand recognition. At last, there are category differences in influencing factors of fresh food repurchase intention. Different kind of fresh food have different priorities in repurchase decisions. Consumers pay more attention to fruit taste than other fresh food, more consideration will be given to fresh logistics of meat and seafood. As for fresh seafood, the focus on packaging and gifts is significantly higher than other fresh food. We have also some suggestions. First, e-commerce sellers should stress more on quality control of fresh food. The primary factor influencing consumers’ willingness to repurchase is product. Product quality is the cornerstone of continuous purchase, more important than the perception of price and service. Only when the taste, flavor, freshness of fresh food satisfied their tongues will consumers consider the next purchase. Second, merchants need to improve the basic services of fresh food businesses. In addition to the taste of the product itself, service attitude, logistics services are important factors affecting consumer repurchase. Consumers attach more importance to basic services such as customer service and logistics than to prices. Therefore, businesses can strengthen service marketing

Influencing Factors

769

and improve consumers’ perception of service. Choose reliable logistics to ensure that consumers enjoy fast and efficient logistics services. Third, strengthen the e-commerce brand of fresh food. The fresh food on JD platform has a low entry threshold and a high level of product homogeneity, which easily leads to fierce competition. The sources of competitive advantages of merchants mainly include food taste, logistics speed, customer services and even the brand effect of JD itself. However, the competition in these aspects is difficult to form barriers and bring long-term brand premium. Therefore, fresh food stores with high brand recognition rarely appear in existing platforms. And when making a repurchase decision, consumers consider different with different kind of fresh food. So fresh food businesses can provide diversified and differentiated additional services. Build their own product system, make targeted propaganda according to their feature, and form fresh food brands. However, this paper has some limitations. First, the time span of data is not long enough, so we can’t study the temporal trend of features. Then, we mainly focus on sellers, didn’t study the commonality and difference of different consumers. These will be explored in future studies. Acknowledgements. The work was supported by: National Key Social Science Foundation of China (Grant No.: 18AGL010).

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Research on the Impact of Taobao Live Broadcasting on College Students’ Online Consumption Behavior Based on TAM Model Fumin Deng, Yaqi Wang, and Xuedong Liang(B) School of Business, Sichuan University, Chengdu 610064, People’s Republic of China [email protected]

Abstract. Through real-time and timely two-way transmission, Taobao Live has formed a better interaction with consumers, thus motivating consumers and bringing about a better consumer experience. At the same time, as the number of users of Taobao Live increased sharply, more benefits have been brought to other participants, which have provided more employment opportunities and increasing sales for people and online merchants respectively. Based on the TAM model, a qualitative and quantitative analysis was conducted through questionnaire surveys, attempting to find out the factors that affect college students’ consuming behavior on Taobao Live, and provide for Taobao Live Platform and other participants to attract consumers. Here are some certain references and suggestions to promote the development of Taobao Live: Taobao Live Platform should focus on optimizing the design of Taobao Live’s homepage and interactive functions, and standardize the quality of the platform’s products; Taobao’s self-employed merchants should try to cooperate with online celebrities; if they choose themselves to be the anchor, merchants should take full advantage of the Taobao Live Platform to enhance interaction with the audience. Keywords: TAM model experience

1

· E-commerce live broadcast · Consumer

Introduction

Since 2016, the number of online live broadcast users has exploded; the live broadcast marketing market size in 2019 was expected to reach 5.06 billion; some surveys showed that about 64% of users would watch online live broadcasts with the purpose of consumption. In China, Taobao Live is one of the most mature platforms among e-commerce live broadcast platforms. The post-90s generation is currently the main force of online consumption, which can easily accept new things and have more time for relax, causing their chances of watching live broadcasts much greater. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 771–782, 2020. https://doi.org/10.1007/978-3-030-49829-0_57

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Although a lot of quantitative research on the behavior of traditional online shopping have been analyzed, some qualitative analyses on the current situation, form and value of various online live broadcast marketing have only been started to conduct in the past two years. Therefore, with the atmosphere that the scale of online live broadcast will continue to expand and the public is gradually accepting online live broadcast consumption, empirical studies was conducted taking Taobao Live as an example. From the perspective of college students’ consumption, this paper made suggestions for the development of e-commerce live broadcast, which has a more practical and specific guiding role. Reviewing the past literatures, this study found out the factors that influence college students’ consumption behavior on Taobao Live. Through building a theoretical model based on the TAM model, this article, in sequence, proposed hypotheses, defined variables, designed a questionnaire, and conducted questionnaire distribution and data collection. Reliability and validity tests were performed on the data; the model was modified by the structural equation model using AMOS 19.0, and the results were obtained and discussed accordingly. Finally, suggestions were made for the development of e-commerce live broadcast.

2 2.1

Literature Review Theory of Technology Acceptance Model

Davis [1] borrowing related theories such as self-efficacy theory and expectation theory proposed the technology acceptance model (TAM) based on the TRA theory, which effectively explains the acceptance of information technology by the behavior of users. In the TAM, external variables instead of the original subjective norms variables (normative beliefs, compliance motivation) can affect users’ internal beliefs (perceived usefulness, perceived ease of use), and then affects their attitudes and behavioral intention, ultimately affecting its actual use of information technology.

Fig. 1. TAM model

According to the TAM (seen from Fig. 1), the determinants of behavioral attitudes are perceived usefulness and perceived ease of use, both of which are

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key factors. Moreover, perceived ease of use could play an indirect role through having a direct impact on perceived usefulness. Based on the extended technology acceptance model, four variables were added: network interactivity, opinion leader, trust, and perceived price. Network interactivity refers to the degree of interaction among participants, communication media, participants and information, the impact of which on the online shopping willingness was considered important in Sun [9]. Because opinion leaders don’t represent business interests, their opinions are also considered more reliable than those obtained directly from marketers. Therefore, given the interpersonal, informal, and verbal influence of opinion leaders, Wang [11] thought they were essential to the success of new products. Lin [3] found that the perceived price level has a negative impact on users’ attitudes and intentions. Some research showed that that as more and more consumers started to seek product information from the Internet to assist decision-making, the credibility and reliability of information would affect consumers. So that KHAMPHEERA [6] thought it was of great practical significance to study trust in this type of research. 2.2

Research on the Status of Webcast

For the study of webcasting, He [12] qualitatively explored the impact of webcasting on college student consumption; other scholars described the current status of webcasting and summarized the innovation of webcasting. As for the research on Taobao Live, Xie et al. [13] analyzed the problems of Taobao Live, and discussed the corresponding strategies qualitatively according to the problem; Ding et al. [8] took a specific Taobao store as an example, the live broadcast strategies and effects of which were analyzed and discussed. To sum up, due to the emergence of online live broadcast marketing only in recent years, these studies have still stayed at the qualitative analysis stage, the depth of which is limited to the summarizing of individual phenomena. However, from the whole perspective of the live broadcast platform, extensive data support is much more important. Therefore, this article used the improved TAM model to carry out a combination of theoretical and empirical analysis, and attempted to discuss the factors affecting college students’ shopping behavior on Taobao Live.

3 3.1

Design of a Research Model Model Design

Based on the previous analysis, Fig. 2 shows a research model based on the TAM, which explains the consumption behavior of college students on Taobao Live.

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Fig. 2. Empirical model

3.2

Propose Assumption

Based on the background of Taobao Live Broadcasting, this article made assumptions on the relationship in the model (seen from Table 1). As for the relationship (H8) between network interaction and perceived usefulness, the explanation is as following: In the discussion of the marketing value of the live broadcast platform in Luo [4], during the interaction with consumers, anchors can answer questions from Table 1. Hypothetical relationship Number Hypothesis

Literature resources

H1

Taobao live streaming platform quality has a positive Ren [7] impact on perceived ease of use

H2

Taobao live broadcast platform quality has a positive Ren [7] impact on perceived usefulness

H3

Perceived ease of use has a positive impact on perceived usefulness

Wang [11]

H4

Perceived ease of use has a positive impact on online shopping intention

Davis [1]

H5

Perceived usefulness has a positive effect on online shopping intention

Davis [1]

H6

Perceived price has a negative impact on online shopping intention

KHAMPHEERA [6]

H7

Network interactivity has a positive impact on trust

Luo [4]

H8

Network interactivity has a positive impact on perceived usefulness

assumed by this paper

H9

Opinion leaders have a positive impact on trust

Wang [11]

H10

Trust has a positive impact on online shopping intention

KHAMPHEERA [6]

H11

Online shopping intentions have a positive impact on actual online shopping

Davis [1]

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the audience in a timely manner, greatly increasing their affinity and reducing distance between them and audience. It can be speculated that in the process of watching Taobao Live, consumers often gain a lot of useful knowledge about the products because of the enthusiastic response of the anchor. Therefore, Hypothesis 8 was proposed: college students’ network interaction has a significant positive impact on the usefulness of perception. Table 2. Model variables and definitions Variable

Definition

Taobao Live platform quality Layout and function design of Taobao Live Perceived ease of use

In the online shopping process on Taobao Live, consumers believe that the process is relatively easy and the process is easy to master

Perceived usefulness

Consumers believe that shopping on Taobao Live can improve the efficiency and effectiveness of their shopping, and make transactions and experiences more convenient

Perceived price

Consumers feel price discounts during online shopping on Taobao Live

Network interactivity

Consumers can keep good contact with merchants on Taobao Live

Opinion leader

Consumers can be attracted by opinion leaders on Taobao Liv

Trust

Consumers have confidence that their benefits can be protected when shopping on Taobao Live

Online shopping intention

Consumers’ willingness to shop on Taobao Live

Online shopping

Consumers’ behavior of shopping on Taobao Live

3.3

Research Design

(1) Variable definitions The definitions of each variable are shown in Table 2. (2) Questionnaire Based on a large number of previous literature studies, this article formed a total of 27 variable items (seen from Table 3), 8 items of which were redesigned combining with specific Taobao Live backgrounds. Each item was used the five-point method proposed by Likert, which is used to measure the respondents’ degree of approval. One point indicates “complete disagreement” and five points indicate “complete agreement”.

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Variable

Item

Taobao Live platform quality

NQ1: The interactive function on Taobao Live is perfect, and I can easily watch and buy NQ2: The quality of Taobao Live is high, and the types of products are suitable NQ3: The products’ information is clear and complete, and it is easy to search for the information you want on Taobao Live

Perceived ease of use

PEOU1: Taobao Live is friendly and easy to operate PEOU2: It’s easy to search for information about any products and buy goods from Taobao Live PEOU3: I can easily interact with anchors or sellers who I am interested in on Taobao Live PEOU4: Overall, I think shopping on Taobao Live is easy

Perceived usefulness

PU1: I think Taobao Live is useful for myself PU2: Shopping on Taobao Live can not only shorten the search time of related information, but also provides access to a large amount of product information, which is convenient for product comparison PU3: Taobao Live provides more authentic and visual product information (such as size, freight, insurance, etc.) PU4: I can get more useful information or knowledge through Taobao Live PU5: I can often find my new demand through Taobao Live

Perceived price

PP1: I can buy cost-effective products on Taobao Live PP2: Anchors or sellers on Taobao Live often promote and offer discounts

Network interactivity

NI1: Anchors or sellers on Taobao Live can immediately respond to my questions NI2: Anchors or sellers on Taobao Live are passionate about me and I can feel at home

Opinion leader

OL1: Compared to watching a special live broadcast of a certain store, I am more inclined to watch different recommendations of online celebrities on Taobao Live OL2: I am willing to accept the influencers who I’m following to recommend new products to me

Trust

TT1: Overall, shopping on Taobao Live is more at ease and is trustworthy TT2: I believe anchors who I’m following will recommend me good products

Online shopping intention

OI1: I will try shopping on Taobao Live, or continue to shopping on Taobao Live OI2: I will convince people around to join Taobao Live OI3: I am willing to shop on Taobao Live, and I am passionate about shopping on Taobao Live

Online shopping

OS1: I like shopping on Taobao Live and enjoy the happiness it brings OS2: I would recommend the good products in Taobao Live to those around me OS3: If I’m not familiar enough with the product itself and need to know more product information, I will first think of Taobao Live OS4: I often use Taobao Live to select my interested product and often buy them on Taobao Live

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Empirical Research Issue and Collection of Questionnaires

College students were the survey respondents to do offline and online (e.g., WeChat and QQ) questionnaire. A total of 211 questionnaires were issued, 200 of which were valid questionnaires and the effective questionnaire rate was 94.8. 4.2

Data Analysis

(1) Reliability analysis The commonly used reliability coefficient Cronbach α coefficient was used to test the reliability of the questionnaire. Reliability analysis was performed on the questionnaire data using SPSS 19.0. It was found that the Cronbach values of all variables were higher than 0.7, indicating that the credibility of setting items for all variables was good. (2) Validity analysis SPSS 19.0 was used to perform sample adequacy tests (KMO) on related variables to be factored. By using Bartlett sphere test, it was found that the KMO value of each variable was greater than 0.7, indicating that it is suitable for factor analysis. Principal component analysis was used to perform exploratory factor analysis on the relevant variables, and then the maximum variance method for rotation analysis was carried out. According to the results, all factor loads are greater than 0.6, so the items need not be modified. (3) Structural equation model analysis a. Modeling AMOS 19.0 was used for structural analysis of this model, which involved 9 variables including Taobao live broadcast platform quality, network interactivity, opinion leaders, etc. The 27 items in the questionnaire were used as measurement indicators for each variable, and error variables were set. The basic structural equation model was finally obtained as shown in Fig. 3. b. Model fit analysis and model modification Two indicators that can best represent the fitness of the questionnaire data and the model were selected as the test indicators of the fitness measurement: the ratio of the chi-square value to the degree of freedom and the root mean square table of approximate errors (RMSEA). According to the covariances term of the Modification Indices in the AMOS 19.0 output report, the model was modified to make it fit. c. Path saliency analysis and model modification In conjunction with the report given by AMOS 19.0, a significance analysis of the hypotheses proposed above can be provided. It is known that if the absolute

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Fig. 3. The basic structural equation model

value of C.R. is less than 1.96 and the P value is greater than 0.05, the path relationship of the model is not significant; when the absolute value of C.R. is greater than 1.96 and the P value is less than 0.05, the path relationship of the model is significant. As 45% of the hypothetical relationships in the preliminary model are not significant, after analyzing with the basic model and making multiple iterations of the model, this article decided to remove H2, H4, H6, and H7, and at the same time, retain H5 and H9. After running the final model on AMOS 19.0, the fitting state of the final model was found to reach normal indicators, indicating that the final model has a good degree of fit. The path coefficient test of the final model is shown in Table 4: Table 4. Variable path coefficient test results Relation-ship Number

Hypothetical regression path

Standard path factor

Value of C.R.

Value of P

Statistical significance

1

Taobao Live platform quality → Perceived ease of use

0.071

10.277

***

Significant

2

Perceived ease of use → Perceived usefulness

0.075

5.58

***

Significant

3

Perceived usefulness → Online shopping intention

0.092

3.959

***

Significant

4

Network interactivity → Perceived usefulness

0.069

4.657

***

Significant

5

Opinion leader → Trust

0.062

14.374

***

Significant

6

Trust → Online shopping intention

0.084

9.309

***

Significant

Online shopping intention → 0.066 15.234 Online shopping Note: *** indicates a significant difference at a significant level of 0.01

***

Significant

7

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Fig. 4. Schematic diagram of the final model

(4) Impact analysis Based on the research results, the final research model is shown in Fig. 4. This article proposes a total of 11 hypothetical situations, 4 of which are not supported by the empirical data of this study. The explanations are as following: a. The reason why H2 cannot be established: As a technology to be applied to Taobao shopping, the purpose of online shopping live broadcast is not as single as some platforms for entertainment activities, such as Yingke Live and Zanthoxy Live. The main purpose of Taobao Live is to attract users and then guide viewers to buy more products. Therefore, viewers who want to buy some products will not find the platform useful simply because the interface design of Taobao Live is reasonable or the interaction functions are complete. It depends on whether the anchor can make full use of these functions to enhance interaction with customers. Therefore, in general, the direct impact of Taobao live broadcast platform quality on perceived usefulness will not be significant. b. The reason why H4 cannot be established: Since this research is aimed at young college students who have a strong ability to accept new things, any platform may be relatively easy for them to master. When they only found Taobao Live easy to use, they may not have enough motivation to buy things on it. In addition, when Shi and Pan [8] used the TAM model to study the B2C ecommerce technology, the final model they derived also showed that only the perceived ease of use positively affected perceived usefulness, thus affecting behavioral intentions. Therefore, given that some scholars also have come to a similar conclusion, coupled with the specific background of Taobao Live, it is possible that the perceived positive impact of ease of use on online shopping intentions is not significant.

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c. The reason why H6 cannot be established: People can already enjoy many discounts in the traditional online shopping environment, although Taobao Live would also give appropriate coupons. So consumers may not watch Taobao Live for price reasons, but choose it because of other attractive factors. From empirical research on consumer purchasing behavior in Soutar and Sweeney [10], it can be seen that perceived price is actually only one of the four dimensions of perceived value. Meng [5] studied the influence of opinion leaders on trust, and believed that the emotional and functional values brought by opinion leaders have a significant impact on trust. It can be inferred that in the environment of Taobao Live, the impact of emotional value and functional value on online shopping intention be greater than that of the perceived price on online shopping intention. Therefore, perceived prices may have no significant effect on online shopping willingness. d. The reason why H7 cannot be established: On Taobao Live, the positive impact of network interaction on trust is not significant, but the revised model shows that interaction can indirectly affect trust through interaction with opinion leaders. The anchors on Taobao Live are mainly divided into internet celebrities (opinion leaders) and shop self-hosted anchors. The research in Ding and Liu [2] shows that because the Internet celebrities have shared and interacted on social platforms before, with a certain number of fans, it is easier to gain the trust of the audience. Therefore, in the context of Taobao Live, the interaction of opinion leaders may be more useful than the interaction of direct commercial intention. So the interactivity of the network may have an indirect impact on trust through influencers, but its direct impact is not significant.

5

Conclusion

In the research process, because the regression analysis method provided by SPSS cannot analyze the entire model as a whole, nor can it show possible indirect effects, AMOS was finally used for structural equation model analysis, and there was no separate regression analysis of each dependent variables. According to the results of research, the degree of influence of trust on online shopping intentions is far greater than other factors, which is also different from the conclusions of previous studies in the traditional online shopping environment. That shows that in the context of Taobao Live, trust is one of the most important factors for whether college students are willing to choose Taobao Live to shop on. In addition, research proves that network interactivity has a positive impact on perceived usefulness, which validates the previous qualitative discussion and also shows that network interactivity plays an important role in Taobao Live.

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Finally, in the process of using AMOS to modify the model, it was found that there was an interactive relationship between network interactivity and platform quality, network interactivity and opinion leaders. And the interaction impact between interaction and opinion leaders is stronger. On the one hand, it shows that Taobao Live needs to pay attention to the interactive function of the platform. On the other hand, it also shows that if the Internet celebrities can make good use of the interactivity of the network, it can greatly stimulate the consumption of college students. In conclusion, from the perspective of Taobao Live, emphasis should be placed on optimizing the design of Taobao Live homepage and interactive functions, and standardizing the quality of platform products. From the perspective of Taobao merchants, they should try to cooperate with Internet celebrities on Taobao Live when they market to college students. If they choose themselves as anchors, they should make full use of the advantages of the platform to enhance the interaction with the audience and meet the emotional needs of consumers, so as to enhance their purchase intention. Acknowledgements. I would like to express my gratitude to all those who have helped me during the writing of this thesis. I gratefully acknowledge the help of my supervisor professor Fumin Deng and Mr. Xin Cui. I do appreciate their patience, encouragements, and professional instructions during my thesis writing. Also, I would like to thank Mr. Xuedong Liang, who kindly gave me a hand when I was making the questionnaire among the college students. Last but not the least, my gratitude also extends to my family and friends who have been assisting, supporting and caring for me all of my life.

References 1. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 319–340 (1989). (in Chinese) 2. Ding, Q., Liu, Y.: Application analysis of taobao live broadcasting in e-commerce clothing brands taking taobao women’s clothing “MG Elephant” as an example. Theatre House 24, 228–229 (2017). (in Chinese) 3. Lin, Y.: Research on user behavior of mobile taxi software. Master’s thesis, Xiamen University (2014). (in Chinese) 4. Luo, J.: Analysis of marketing value of live broadcast platform. New Media Res. 4(05), 53–54 (2018). (in Chinese) 5. Meng, F.: A study on the impact of opinion leaders on purchase intention in a social business environment. Ph.D. thesis, Nanjing University (2012). (in Chinese) 6. Khampheera, M.: Research on the factors influencing Thai cross-border ecommerce consumers’ buying behavior. Master’s thesis, Zhejiang University (2019). (in Chinese) 7. Ren, K.: Research on the influence of online shopping environment on consumers’ online shopping willing. Jiangsu Bus. Theory 02, 20–23 (2015). (in Chinese) 8. Shi, F., Pan, Y.: Application of TAM in B2C E-commerce adoption intention research. China Manag. Inf. 24, 104–106 (2009) 9. Sun, Z.: Research on college students’ online shopping consumption based on TAM Model. Shanghai University of Engineering and Technology (2016). (in Chinese)

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10. Sweeney, J.C., Soutar, G.N.: Consumer perceived value: the development of a multiple item scale. 77(2), 203–220 (2001) 11. Wang, J.: An analysis of the influencing factors of college students’ consumption behavior in the internet red economy based on the self-media environment. Master’s thesis, Beijing University of Posts and Telecommunications (2019). (in Chinese) 12. Xu, Y., Zhang, Z., et al.: Factors of college students’ willingness to pay for online online courses. Obser. Manag. 32, 117–118 (2018). (in Chinese) 13. Xie, H., Liu, N.: Problems existing in the development of internet commerce. Comput. Knowl. Technol. 14(31), 274–276 (2018). (in Chinese)

Integrated Reporting – An Influencing Factor on the Solvency and Liquidity of a Company and Its Role in the Managerial Decision-Making Process Mih˘ail˘a Svetlana1 , Tanas˘a Simona-Maria (Brˆınzaru)2(B) , Grosu Veronica2 , and Timofte Cristina (Coca)2 1

2

Department of Accounting, Academy of Economic Studies of Moldova, Chisin˘au, Republic of Moldova Department of Accounting, Audit and Finance, Faculty of Economic Sciences and Public Administration, “Stefan cel Mare” University, Suceava, Romania tanasa [email protected]

Abstract. Corporate reporting has evolved over time as a result of the global economic situation and of the pressure coming from the stakeholders of a company. In this context, the concept of integrated reporting emerged, which, all companies can now voluntarily adopt, except for the South African ones. The International Integrated Reporting Council (IIRC) is the main body that developed the first conceptual framework for integrated reporting in 2013. The main objective of this research is the analysis of the solvency and liquidity of the companies that have adopted integrated reporting, considering the classification of the companies by their business sector. The analyzed sample consists of 13 business sector comprising 56 companies from Europe, North and South America, over 2015–2017. The result of the research reflects that the adoption of integrated reporting does not represent a significant influence factor on the solvency and liquidity of the analyzed business sectors, but can certainly lead over time to liquidity improvement and to the reduction of the insolvency risk. Integrated reporting is a management tool that, if properly used, can bring both external and internal benefits to a company’s economic activity and to its financial performance. Keywords: Integrated reporting · Liquidity · Solvency · Managerial decision

1 Introduction The global economic crisis has affected not only the economy, but it has also influenced corporate reporting. Organizations around the world have understood that they need to change the way they do business, that traditional financial reporting has been outdated, the sustainability and information requirements of stakeholders leading to a new approach to business. Non-financial reporting has taken many forms besides the inclusion of non-financial information in the annual reports, such as social responsibility reports, c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 783–794, 2020. https://doi.org/10.1007/978-3-030-49829-0_58

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environmental balance sheets, social balance sheets, corporate governance reports or sustainability reports. However, they were not enough to keep up with the changes in the world economy and with the desire to reach a sustainable economy. Thus, in September 2009, representatives of major global organizations (ACCA, Global Reporting Initiative (GRI), Harvard University, KPMG, PricewaterhouseCoopers, SustainAbility, The Prince’s Accounting for Sustainability Forum, etc.) discussed at a meeting held in London on how to integrate financial reporting with non-financial reporting. At this meeting, an international body was established, meant to bring together organizations with relevant experience that are recognized in the fields of transparency, accounting and financial reporting. The main objective was developing a conceptual framework for integrated reporting (financial and non-financial), materialized in the establishment of the International Integrated Reporting Council (IIRC) as a first step in the process of achieving a sustainable economy [7]. IIRC developed the Conceptual Framework on Voluntary Integrated Reporting in 2013 and argues that integrated reporting promotes a more coherent and efficient approach to corporate reporting and aims to improve the quality of the information available to financial capital providers in order to enable more efficient capital allocation [16]. The long-term vision of IIRC is a world in which the concept of integrated thinking is implemented in business practices in the public sector, but also, privately so that integrated reporting becomes a norm of corporate reporting. The communication resulting from integrated reporting has as a main objective supporting the decisions of the longterm investors regarding the allocation of financial capital, and it aims to align with the long-term public interest, as well as to create and maintain the short, medium and long term value [2]. The main objective of this research is the analysis of the solvency and liquidity of the companies that have adopted integrated reporting, considering the classification of the companies by their business sector. Secondly, the aim was to highlight how IR influences managers’ decisions regarding the solvency and liquidity of the companies. This paper contributes to the development of international literature on the topic of IR through the results of the research which show that IR is not a major influence factor on the solvency and liquidity of a company and that managers should pay more attention to this concept so as to obtain benefits that will lead to the improvement of the companys financial situation.

2 Literature Review The concept of integrated reporting has evolved both theoretically and in the practice of organizations around the world. Eccles and Serafeim state that integrated reporting is focused on creating value for all stakeholders on medium and long term, through a single report accessible to all stakeholders that shows how social and environmental performance, together with good governance contribute to high financial performance [8]. In the most recent research studies, the concept of integrated reporting can be found associated with information asymmetry, the cost of capital or materiality. Garc´ıa-S´anchez and Noguera-G´amez demonstrated that there is a negative relationship between the integrated report and the information asymmetry and that the effect of

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the integrated report is statistically more significant compared to the information asymmetry in the countries with strong investor protection [12]. The same authors conducted a study in 2017, using a sample of 995 companies, on the relationship of integrated reporting and the cost of capital. The results of the study showed that integrated reporting implies a lower cost of capital, confirming the use of integrated information in decision-making; information asymmetry problems and previous capital costs determine companies to publish integrated reports in order to reduce the current and future cost of capital [11]. The studies published by Steenkamp (2018) and Fasan and Mio (2016) focused on the materiality of the integrated reports. The correlation between materiality and independent variables such as legal environment, ROA, total assets, size and diversity of the board of directors, board meetings, independent directors, industry was researched in the study of Fasan and Mio. The results showed that the main factors of disclosure of the material elements are the industry and certain characteristics of the board (size and diversity), while the legal environment in which companies operate does not play a significant role. Furthermore, the authors of the study concluded that all the companies in the IIRC Pilot Program revealed more materiality information than their competitors who did not join the program [10]. Instead, Steenkamp argues that, regardless of materiality disclosure factors, the level of presentation of each significant element for the company and for the other stakeholders, within the integrated reports, suggests that each one of them has a unique story of value creation and that IR are unique, which makes them difficult to be compared [20]. In the Romanian literature, there can be found studies that deal with the problem of assurance of integrated reports [5] or that analyze the degree of compliance of the integrated reports published in 2015 by the European companies with the IIRC conceptual framework [3, 19]. It should be noted that the integrated reports have a large number of pages, although they should be the opposite, because they must be concise, that is, to present the most important financial and non-financial information, but sufficient to give a complete picture of an organization. The study of Garc´ıa-S´anchez et al. (2018) aimed to analyze whether the trend to adopt integrated reporting depends on management and if this relationship is influenced by internal control mechanisms (for example, the board of directors) or by external factors (the level of investor protection) [14]. Most of the research studies focus on factors that influence companies’ decisions to adopt integrated reporting, on the compliance of the reports published by the companies with the international framework on IIRC reporting, on the credibility or asymmetry of information. Therefore, there are fewer research studies related to the integrated performance of a company or from other perspectives that involve financial implications. Furthermore, several authors identified some weaknesses of IR and brought criticism to the IIRC framework. For example, the disclosure information risk that can lead to loss of competitive advantages, damage to the company’s reputation, even a negative influence on the price of shares; the time needed to ensure a good collaboration between the departments of a company that leads to qualitative IR [6] or increased costs for corporate reporting [13]. The conceptual framework elaborated by IIRC (2013) put the long-term investors in front of other categories of stakeholders [18] and the lack of a well-defined indicator system by which we can analyze the performance, as well as the G4 standard from GRI [15], are among the criticisms of this framework.

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These elements determined us to analyze the situation of the companies that apply integrated reporting, according to their business sector, through the main indicators of solvency and liquidity.

3 Research Methodology The main objective of this research is the analysis of the economic and financial situation of the companies that have adopted integrated reporting, depending on their specific business sector, in terms of solvency and liquidity. The management of a company is an important factor in adopting the concept of integrated reporting because its decisions can influence how IR contributes or not to the improvement of a company’s activity. Thus, two hypotheses were advanced for testing, as follows: Hypothesis 1. Companies that have adopted integrated reporting register an improvement of the solvency indicators, recording values between optimal parameters. Hypothesis 2. Companies that have adopted integrated reporting register an improvement of the liquidity indicators, recording values between optimal parameters. In order to test the two working hypotheses we created a sample of 56 companies operating in different business sectors out of the 214 presented through the IR Examples Database [17], as companies that have adopted the principles of integrated reporting. The equation comprises companies on three continents as follows: 3 companies in South America, 2 companies in North America and 51 companies in Europe. The main selection criterion applied was the presence of the fully adopted integrated report according to the framework elaborated by IIRC or of the report in which references are made to this framework. The companies that applied the IR framework according to KING III were eliminated. The period included in the research study is 2015–2017. The companies included in the sample are presented according to business sectors in Table 1. Table 1. Classification of the companies included in the sample according to their business sector No. Business sector

Number of companies Percentage

1

Financial services

8

14.3%

2

Utilities

8

14.3%

3

Consumer goods

6

10.8%

4

Transport

5

8.9%

5

Mining/basic material

5

8.9%

6

Technology & telecommunications

5

8.9%

7

Oil & gas

4

7.1%

8

Chemicals

3

5.4%

9

Construction & materials

3

5.4% (continued)

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Table 1. (continued) No. Business sector Number of companies Percentage 10

Industrial

3

5.4%

11

Other services

3

5.4%

12

Retail

2

3.5%

13

Healthcare

1

1.8%

14 Total 56 *source: own elaboration

100%

Accounting data and information were collected from the integrated reports and consolidated financial statements available on each company’s website through the IR Examples Database and subsequently, the analyzed indicators were calculated in Microsoft Excel 2010. Indicators extracted from the financial statements presented in a currency other than the euro were converted as such, using the National Bank of Romania’s exchange rate valid at the end of December of each year for the period 2015–2017. The liquidity and solvency indicators were calculated for each company and subsequently an average was determined for each business sector mentioned in Table 1. The calculation formulas used in the study are presented in Table 2 below. Table 2. The indicators used in the analysis Category

No. Indicators

Formula

Optimal reference parameters

Indicators of liquidity and solvency

1

General Solvency Ratio (GSR)

TA CD

Minimum 1.66 Normal 2.00

2

Financial Solvency Ratio (FSR) Current Liquidity Ratio (CLR)

4

Quick Liquidity Ratio (QLR)

5

Immediate Liquidity (IL)

TA T FD Current assets Current liabilities CCE+MS+AR Current liabilities CCE Short−term financial debt

Minimum 2.00

3

1.2–1.8 0.8 0.85–1.15

Notations

TA – total assets; Ci – invested capital; KP – permanent capital; CD – current debts; TFD – total financial debts; CCE – cash & cash equivalents; MS – marketable Securities; AR – account receivables

Purpose

Determining and explaining the evolution of each indicator separately on each business sector, in order to establish a trend

*source: [1]

4 Results and Discussions The motivation of adopting integrated reporting by a company should not only reflect the external benefits such as stakeholders’ external pressure on the information requirements or the alignment with the new corporate reporting trend [9]. IR also includes internal benefits such as improving the internal decision-making process by interconnecting the departments of an organization, promoting and implementing a sustainable

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strategy throughout the organization or identifying cost savings by bringing together the analysis of financial and non-financial information [4]. Therefore, IR involves integrated information meant to support stakeholders’ decision-making process, and managers should take advantage of this information to streamline the company’s internal activity that should be reflected by the solvency and liquidity indicators. In order to test the advanced hypotheses, the solvency and liquidity indicators were analyzed for the sampled 13 business sectors, for 2015–2017, taking into account the optimal parameters range value and the trend of the indicators.

Fig. 1. Financial services *source: own elaboration

Fig. 2. Utilities *source: own elaboration

The business sector presented in Fig. 1: Financial services reaches very high financial solvency rates, of 21.28 in 2017, which shows the high capacity of companies to cover their financial debts but generally, values below 2 are registered, which suggests that there are difficulties in covering the total debt. From the liquidity point of view, companies in this sector are at the minimum accepted level of general and immediate liquidity. The intermediate liquidity indicator is not presented because it has insignificant values. Within the Utilities sector (Fig. 2), companies’ liquidity indicators show an ascending trend but do not register values within the accepted optimal parameters. In terms of solvency, companies record values below the minimum acceptable level of total debt coverage capacity from total assets and values above the minimum 2 for FSR. It is clear that integrated reporting has not led to the improvement of the activity of the companies included in this sample as far as solvency and liquidity are concerned. Esch et al. state that integrated information used in external integrated reporting can also be used for internal purposes in order to help companies make sustainable decisions [9]. As far as solvency and liquidity are concerned, one can resort to this integrated information, for the quantitative component of the data but not only that. Information on the policy of indebtedness adopted by a company, the conditions accepted in case of company credit, the use of the current or total assets, the deadlines applied in case of debt recovery, etc. are also available so that the management can take the most appropriate decisions to reduce the risk of insolvency or bankruptcy. In order to avoid default risk, companies have to register general solvency over 1.66, respectively over 2 in what concerns the financial solvency indicator, which is the case

Integrated Reporting - An Influencing Factor

Fig. 3. Consumer good *source: own elaboration

Fig. 4. Transport *source: own elaboration

Fig. 5. Technology & telecommunications *source: own elaboration

Fig. 6. Mining *source: own elaboration

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for the business sector Consumer goods (Fig. 3). As for liquidity, there can be noticed an improvement of the indicators in 2016 and 2017 compared to 2015 because they exceed the minimum accepted values. Figure 4 shows the Transport sector where the average of the solvency ratios of the companies assures them a favorable position against the risk of insolvency or bankruptcy. In addition, an improvement of the general and intermediate liquidity over 2015–2017 is observed, only the immediate liquidity (IL) having an oscillating trend registering a decrease of 1.51 in 2017 compared to 2016. Figure 5 below shows the Technology & Telecommunications business sector where oscillating evolutions of solvency and liquidity indicators are observed. From the point of view of the general solvency, the average of the sector is below the normal level of 2, demonstrating that there are difficulties in covering the debts on account of the assets, but the financial solvency registers higher values, at least the financial debts can be covered based on the total assets. Companies in this field have liquidity problems where averages below the minimum thresholds are recorded, except for IL, meaning at least short-term loans can be paid based on existing availabilities. There are some problems regarding solvency and liquidity, thus, the management did not capitalize the integrated reporting tool, in such a way as to improve these indicators.

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The Mining business sector (Fig. 6) is characterized by a high average of IL, registering a value of 21.65 in 2017, thus showing the increased ability of the companies to repay short-term loans from existing availabilities. On the one hand, in the case of general liquidity indicator, there is a trend that oscillates over 2015–2017, but with values above 1, that shows that there are sufficient current assets necessary to pay the current debts by the companies in this sector. On the other hand, the intermediate liquidity is affected, average values below the threshold of the minimum 0.8 being recorded. Overall, out of the five analyzed indicators, four show an evolution with low averages in 2016 compared to 2015 and 2017 and the QLR indicator has considerably decreased. Solvency indicators are above the minimum accepted thresholds.

Fig. 7. Oil & Gas *source: own elaboration

Fig. 8. Chemicals *source: own elaboration

As for the Oil & Gas business sector (Fig. 7), there can be noticed an ascending trend of both solvency and liquidity indicators. Analyzing each indicator, there can be observed that there are difficulties in covering the maturity debt based on total resources because indicators are below the accepted minimum, except for 2016 and 2017 for financial solvency ratio where the threshold of 2 is exceeded. In addition, a more efficient management of the total or current assets of the companies, more specifically the availability, can be noted in 2017, as liquidity indicators exceed the minimum accepted thresholds. However, it can be stated that, management has used integrated reporting to improve its ability to cover short, medium and long-term debts. The same thing cannot be said about the companies in the Chemical sector (Fig. 8), where solvency indicators have an oscillating trend, but the risk of insolvency does not exist because indicators exceed the minimum values over 2015–2017. On the other hand, the average liquidity of the companies decreased until 2017, although the values are among the accepted parameters range, which shows us their diminished capacity of repaying the short-term debts. The Industrial sector is characterized by an oscillating trend of the average of solvency indicators (Fig. 9), however being positioned far from the risk of insolvency. Some problems can be observed in what concerns the liquidity indicators that record values around or below the thresholds, suggesting an inefficient use of the current assets needed to cover short-term debt.

Integrated Reporting - An Influencing Factor

Fig. 9. Industrial *source: own elaboration

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Fig. 10. Construction & material *source: own elaboration

The solvency and liquidity of the companies in the Construction & Material business sector (Fig. 10) shows an oscillating trend, but the average of each indicator is above the minimum allowed limit, which suggests that there is no risk of insolvency or inability to cover short-term debt. The companies included in the Other Services sector (Fig. 11) have a downward trend for the general solvency average, reaching the limit in 2017 regarding the risk of general insolvency. At the same time, their ability to cover their total financial liabilities by their total assets decreases significantly until 2017, but without falling below the accepted minimum threshold. The companies are better placed with values above the accepted minimum in what concerns liquidity indicators. The immediate liquidity has an ascending evolution that suggests the ability of the companies to cover their shortterm loans on account of cash and bank accounts. The values of the solvency and liquidity indicators show the financial efficiency of the companies’ economic activities, and no risk of insolvency for the Retail business sector (Fig. 12). In the last business sector Healthcare (Fig. 13), the indicators calculated show an oscillating trend but the values do not fall significantly below the minimum accepted thresholds, thus registering no risk of insolvency. The attention falls on the FSR indicator where values are over 35, suggesting a high capacity of the company to cover its financial debts based on its total assets. Integrated reporting involves a short, medium and long-term vision that properly understood and applied by the management of the company should lead to improving its ability to cover short, medium and long-term debts at the agreed maturity. Therefore, we conclude that if IR is properly implemented at the level of the whole company, there should be noticed an increase of value for all the parties concerned, and an improvement of the solvency and liquidity indicators, all things equal.

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Fig. 11. Other services *source: own elaboration

Fig. 12. Retail *source: own elaboration

Fig. 13. Healthcare *source: own elaboration

5 Conclusions According to the analysis carried out on the 13 business sectors that included 56 companies, we reached the conclusion that integrated reporting does not constitute a major influence factor on the solvency and liquidity of a company, as the studied indicators did not improve over the analyzed period 2015–2017 for the selected sample. Finally, the two hypotheses are not validated, as it cannot be concluded that integrated reporting leads to the improvement of the solvency and liquidity indicators, as within the 13 business sectors included in the study, there are more oscillating and descending trends than an improvement of the indicators. Specifically, in the case of the solvency indicators calculated for each business sector, there were mainly oscillating and descending trends, except for the Financial Services and Oil & Gas sectors, where an increase of these indicators was registered over the three analyzed years, but below the minimally accepted thresholds. This is also the case for the liquidity indicators, except for the Utilities and Oil & Gas sectors, where there has been an ascending trend for all the three calculated indicators. There can also be noticed an improvement of the values of a single liquidity indicator, out of the three liquidity indicators that were studied, over the optimal parameters range, in the case of several business sectors (Transport, Industrial, Other Services). Nonetheless, it cannot be stated that the

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adoption of integrated reporting will clearly lead to an increase of the efficiency of a company’s activity in terms of its solvency and liquidity. Integrated reporting is a factor that influences managerial decisions because it involves the approach of a new policy, a new management vision on how to do business, however, it does not lead to the improvement of the financial situation of companies in terms of solvency and liquidity. The role of integrated reporting in the decision-making process of managers is to provide integrated information that gives a complete picture of the company’s activity, in order for them to make the best decisions regarding its activity and in order to ensure sustainability. Moreover, we consider that IR must be implemented in the entire activity of an organization through the concept of integrated thinking, in order to influence not only the way of reporting, but also its results. Given that integrated reporting is in the process of being adopted internationally, the effects of this phenomenon will be visible in a longer period of time. In this context, as a limit of this research study, only a period of three years was analyzed, 2015–2017, as per data availability. Adopting integrated reporting can be useful in improving the financial situation of a company and strengthening the relationship with the stakeholders, but only if properly used, by not considering it as a simple management policy adopted solely to meet the external pressure of the stakeholders and that of the international market. Acknowledgements. This work is supported by project POCU 125040, entitled “Development of the tertiary university education to support the economic growth–PROGRESSIO”, co-financed by the European Social Fund under the Human Capital Operational Program 2014–2020.

References 1. B´ırsan, M.: Analiza economico-financiar˘a: instrument ´ın managementul performant al firmei, pp. 251–254. Didactic and Pedagogical Publishing Ltd., Bucharest (2013). Table 2, The indicators used in the analysis 2. Botez, D.: Raportarea integrat˘a-sfˆars¸it sau un nou ˆınceput pentru raportarea financiar˘a? Financ. Audit Mag. 98(2), 25–29 (2013) 3. Bratu, A.: Empirical study regarding the integrated reporting practices in Europe. Financ. Audit Mag. 4(148), 613–627 (2017) 4. Druckman, P., Fries, J.: Integrated reporting: the future of corporate reporting? In: Eccler, R., Cheng, B., Saltzman, D. (eds.) The Landscape of Integrated Reporting: Reflections and Next Steps, pp. 81–85. Harvard College, Cambridge (2010) 5. Dumitru, M., Guse, R.: Assurance of integrated reports: the state of the art. Financ. Audit Mag. 2(134), 227–234 (2016) 6. Eccles, R., Armbrester, K.: Integrated reporting in the cloud: two disruptive ideas combined. IESE Insight 8, 13–20 (2011) 7. Eccles, R.G., Krzus, M.P.: One Report: Integrated Reporting for a Sustainable Strategy. Wiley, Hoboken (2010) 8. Eccles, R.G., Serafeim, G.: Accelerating the adoption of integrated reporting, pp. 70–91. InnoVatio Publishing Ltd. (2011) 9. Esch, M., Schnellb¨acher, B., Wald, A.: Does integrated reporting information influence internal decision making? An experimental study of investment behavior. Bus. Strategy Environ. 28(4), 599–610 (2019)

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10. Fasan, M., Mio, C.: Fostering stakeholder engagement: the role of materiality disclosure in integrated reporting. Bus. Strategy Environ. 26(3), 288–305 (2017) 11. Garc´ıa-S´anchez, I.M., Noguera-G´amez, L.: Integrated information and the cost of capital. Int. Bus. Rev. 26(5), 959–975 (2017) 12. Garc´ıa-S´anchez, I.M., Noguera-G´amez, L.: Integrated reporting and stakeholder engagement: the effect on information asymmetry. Corp. Soc. Responsib. Environ. Manag. 24(5), 395–413 (2017) 13. Garc´ıa-S´anchez, I.M., Rodr´ıguez-Ariza, L., Fr´ıas-Aceituno, J.V.: The cultural system and integrated reporting. Int. Bus. Rev. 22(5), 828–838 (2013) 14. Garc´ıa-S´anchez, I.M., Mart´ınez-Ferrero, J., Garcia-Benau, M.A.: Integrated reporting: the mediating role of the board of directors and investor protection on managerial discretion in munificent environments. Corp. Soc. Responsib. Environ. Manag. 26(1), 29–45 (2018) 15. Grosu, V., Tanas˘a, S.M.: Performanta prin prisma raport˘arii integrate. O abordarea a cadrului elaborat de Consiliul International pentru Raportare Integrat˘a. In: The VII Edition of The International Scientific Conference Accounting and Auditing in the Globalized Conditions: Realities and Prospects for Development, Chisinau, Republic of Moldova, 19–20 April 2018 (2018) 16. IIRC: the international IR framework, pp. 1–37 (2013). http://integratedreporting.org/wpcontent/uploads/2015/03/13-12-08-the-international-ir-framework-2-1.pdf 17. IR examples database (2019). http://examples.integratedreporting.org/home 18. Katsikas, E., Rossi, F.M., Orelli, R.L.: Towards Integrated Reporting: Accounting Change in the Public Sector. Springer, Cham (2017) 19. Sofian, I., Dumitru, M.: The compliance of the integrated reports issued by European financial companies with the international integrated reporting framework. Sustainability 9(8), 13–19 (2017) 20. Steenkamp, N.: Top ten South African companies’ disclosure of materiality determination process and material issues in integrated reports. J. Intellect. Cap. 19(2), 230–247 (2018)

Bidding Strategy of Wind Power with Uncertain Supply in the Spot Electricity Market Tingting Liu, Jingqi Dai, Lurong Fan(B) , and Ruolan Li Business School, Sichuan University, Chengdu 610064, People’s Republic of China [email protected] Abstract. In order to enhance operational efficiency and break up the monopoly, Chinese electricity market has undergone reforms since 2002. This paper considers a spot electricity market in a discrete-time setting that includes two types of power generation firms, renewable firms (wind generators) and conventional firms (thermal power plants). A stochastic bi-level model for an upper-level wind power producer aiming profit maximization and lower-level maximizing the social welfare has been proposed in this work. This strategic wind producer participates as a pricemaker in the day-ahead market, and a deviator in the balancing market to compensate for production deviations due to wind power uncertainty. Accordingly, the potential analysis considering wind power output scenarios, price mechanisms, coordinated bidding strategy, and policy implications are investigated. This paper could guide a better integration of renewable energy into the electricity market.

Keywords: Electricity marketing optimization

1

· Wind energy · Bi-level

Introduction

Growing penetration of renewable energy (RE) occurs in the energy generation system to respond to climate change and increasing demand, a number of countries have implemented various supporting mechanisms and policies to provide incentive or subsidy for RE generation [17]. However, though feasible for modest penetration levels, the incentives will become flawed as RE penetration increases; and, the instantaneous balance of electricity generation and consumption can only be maintained at the expense of grid security (frequency disorder) or economic efficiency (power curtailment) for that the available RE supply is inflexible at any given time. As the RE technology matures and becomes costcompetitive, subsidies turn out to be less relevant and some countries plan to cut or decline the subsidies for RE [9]. Good solutions to further develop RE could be participating in the electricity market [14,18]. Considering the advantages of near-zero variable production costs due to free fuel source and that can c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 795–806, 2020. https://doi.org/10.1007/978-3-030-49829-0_59

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be forecasted in advance, RE can make very competitive offers and achieve priority dispatch in an electricity market by utilizing its marginal cost advantage. Meanwhile, affected by the low electricity prices resulting from RE’s competitive bids, other less competitive conventional power sources have to reduce their output as much as possible. In contrast, when RE output is low, clearing price in the electricity market rises, and all types of flexible power can be stimulated to generate as much as possible. The electricity market goes through different development in countries. With the promulgation of “Opinions for Further Reformation on Electric Power Industry” (No. 9 document) by the State Council in 2015 [3], China’s electricity market starts a process of deregulation. The new regulation sets the guiding principles for the liberalisation of the wholesale and retail electricity market, while the government would only control the transmission of electricity. The new market rules has triggered a lot of response in the electricity sector and promote the enthusiasm for RE participation, because the priority for RE consumption as a price taker in the spot market is guaranteed to achieve clean energy system. Several provincial and municipal governments are moving fast and becoming demonstration sites for electricity reform, including Shen Zhen city, Inner Mongolia Autonomous Region, Gui Zhou and Yun nan Province. The spot market refers to the market that focuses on short-term and instant electricity transactions, which is an important part of the electric power trading mechanism [12]. Firstly, in a day-ahead stage, the Independent System Operator (ISO) can accurately decide the operation and starting conditions of each generating unit, and analyze the network structure of power systems. Secondly, the balancing market can guide the power generators to meet the requirement of electric system peak regulation initiatively by the balancing electricity price signals, and lay a mechanism for the implementation of demand response. Thirdly, accurately reflecting the real electrical market supply and demand in different time scale, the price signal in the spot market is produced by the sufficient competition of all the market participants, which can provide an effective quantitative reference for optimizing resource allocation, planning investment. In a deregulated electricity market, the generation companies operate under a high competition degree due to the nodal variations of electricity prices in order to obtain the best profit bidding in the day-ahead market. For the RE producer, the power output and market-clearing electricity price uncertainties are to be addressed in order to commit how much to produce to formulate a realistic bid. Because in case of excessive or moderate bids, other producers must reduce or increase production to fill the deviation, implying penalties causing economic losses [6]. For thermal power producers, only market-clearing electricity price uncertainties have to be addressed. Therefore, the new market rules could greatly influence the consumption and benefits of RE. This paper puts forward a bi-level model towards the bidding behavior of wind power producers (aiming maximizing the profits) and the market clearing process completed by ISO to minimize the total power costs. The differences of wind producers and thermal plants considering bidding strategy are explained. The penalty rates

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associated with under-supply and over-supply, and the output power of renewable energy sources are characterized by scenarios generation, which could have a great influence on the bidding strategy. The joint bidding strategy between two kinds of producers should also be analyzed. Finally, the policy implications are presented.

2

Market Behavior

The spot market is divided into a day-ahead market and a balancing market. The day-head market is the main trading platform, using “one day” as a suitable advanced time to organize the market, in which the market participants can more accurately predict their power generation capacity and electricity demand to form a trading plan which is adaptive and executable to the power system operation. Complementarily, the balancing market allows the short-term covering of dispatched power that does not materialize due to equipment failures or the intermittent nature of RE sources (e.g., wind or solar power plants). This paper considers the spot electricity market consisting of a renewable (wind) firm and an inflexible conventional (thermal) firm in a discrete-time setting. There are n units in total, which include Nr ≥ 2 wind units indexed by i = 1, 2, . . . , Nr , Ni ≥ 2 conventional units indexed by k = Nr + 1, . . . , Nr + Ni . Following three main important differences between a wind firm and a conventional firm exist: First, different from a conventional firm, the available supply of a wind firm is uncertain in the future. Second, compared to a conventional firm, a renewable firm incurs a negligibly small production cost. Third, a renewable firm can change its committed production schedule in every day-ahead market. However, a conventional firm does not change its production schedule that frequently for the start-stop cost and ramp rate. In fact, it continuously produces electricity at a constant rate for a long time because changing the production quantity leads to excessive operational inefficiencies for the firm [16]. The producers participant in the day-ahead market by providing the offer quantity and price, then the ISO manages the market clearing process to maximize the social welfare based on supply-demand balance and other constraints. However, compared to thermal units, the wind power producer (WPP) has no control over its uncertain production level. Due to this uncertainty, it is usual that the WPP gets scheduled a lower/higher production in the day-ahead market than its actual production. To analyze the penalty effect of this market for lack of accurate power output prediction, balancing market allows WPP to correct the deviation by buying/selling the electricity deficit/surplus. In this paper, the buying and selling price in the balancing market is considered to be related to day-ahead market accepted price using a separate coefficient. It is noteworthy that in order to simulate the real mode and create motivation for participation with actual production level in the day-ahead market, and with consideration of the penalty effect for production surplus or shortage compared to the planned value, the balancing market price has been assumed as less than or equal to the maximum accepted price in day-ahead market and also equal to or greater than the maximum accepted price in day-ahead market upon purchase.

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It has been assumed in this paper that producers present a maximal M block bid to the day-ahead market for each unit. After receiving generation bids and demand bids from retailers, ISO can solve market clearing problems and determines the winning power for any unit. Note that it is stipulated in this study that the producers in balancing market do not bid the price and they purchase or sell energy based on balancing market prices, which are dependent on the maximum accepted price in day-ahead market. Thus, this problem is converted into a bi-level model in which WPP maximizes his profit from the participation in day-ahead and balancing markets at the upper-level and the ISO deals with market clearing at the lower-level in order to minimize payable costs to the producers (Fig. 1).

Day-ahead Market (D-1) 10:30

9:30

Real-time Market (D) 13:00

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B ($/MWh)

Boundary condition Prior information notice Offer (Supply&Demand) Market clearing WPP

Bm

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Q m,b

Q m,e

Q (MW)

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Profit maximization Imbalance cost

Offering strategy

Rival

17:30

Trade

each fifteen-minute

Balancing service

Social cost minimization

Power balance

Unit constriants

Bided electricity

System constriants

Market clearing price

Fig. 1. Market behavior

3 3.1

Bi-level Model Model Assumptions

Three assumptions for the model building are as follows: (1) Total power generation should be offered, that is, the holding capacity is not allowed. (2) Producers submit the unit parameters to ISO, including the rated power output, power generation limitation, wind power forecast, ramp-up data, star-up and shut-down costs, minimum continuous start-up and shutdown times and so on. (3) It is assumed that ISO clears the market with a uniform price system (everyone is paid at the level of the highest accepted bid, i.e. the price at which the supply and demand curves cross).

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As a decision maker in the upper level, the strategic WPP determines the offering curve, which includes the price Bi,t and quantity Qi,t in each block m, to maximize the expected profit subject to constraints as well as the additional constraints that qi,t,m are solutions of the lower level problem and p∗t are dual variables of the power balance equations for the day-ahead market. Profit maximization model of WPP can be presented as: Nr  M ax πw,t = (p∗t qi,t + Ii,t ) i=1  + a a − qi,t ≥ 0 pt (qi,t − qi,t ), qi,t Ii,t = − a a pt (qi,t − qi,t ), (qi,t − qi,t ) < 0 s.t. (Qm,b − Qm ,b )(Bm − Bm ) ≥ 0, m ≥ m Qm,e − Qm,b ≥ (Qra − Qmin ) ∗ 10% Qm,b = Qm−1,e m≤M

p∗t qi,t represents the revenue obtained from selling wind power in the dayahead market, which is computed as the wind power cleared in this market times the day-ahead market price, which is the dual variable associated with the power balance constraints. Ii,t is the imbalance cost/profit of purchasing/selling energy in the balancing market due to the wind power uncertainty. It is computed as the difference of the wind power cleared in the day-ahead market (qi,t ) and the a ), times the balancing market price. The constraints power actually generated (qi,t impose the monotone non-decreasing offer strategy, the block capacity limitation and the maximal block number. The selling and buying price in the balancing market is considered to be related to the day-ahead market maximum accepted price using a separate coefficient: rt+ = rt−

=

p+ t , rt+ p∗ t p− t , rt− p∗ t

≤1 ≥1

Note that definitions are consistent under the assumption that the hourly electricity prices in the day-ahead market are positive, i.e., p∗t > 0. This corresponds to the general behavior of energy prices in most electricity markets, as negative price is not a common phenomenon. After the wind producer bids its offering price and quantity, the ISO computes the wind power to be cleared in the day-ahead market (qi,t ) so as to maximize the expected social welfare subject to the constraints. The offering price and quantity (Bi,t,m ; Qi,t,m ) are considered to be constants, and the day-ahead prices p∗t and bided power for each producer can be determined in the lower level.

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Market Clearing Model

At the lower level, ISO aims to maximize the social welfare, which is minimizing electricity costs Ω. The objective comprises of two parts in which the first part consists of payable costs to wind power units and the second part is related to the costs of conventional generators. These costs are calculated as the product of the offered price (that coincides with the variable cost) times the output from the corresponding plant. Apart from the operational costs, the star-up and shutdown costs for thermal units are considered. The ISO should allocate production level to each of wind-power units and conventional plants in such a way that in addition to minimization of costs, the related constraints to be observed as well.

Min Ω =

 M N T r    t=1

m=1

N r +Ni

Bi,t,m qi,t,m +

i=1

 Bk,t,m qk,t,m

k=Nr +1

Power balancing equations of the day-ahead market Nr 

qi,t +

i=1 M 

m=1

Nr +Ni k=Nr +1

qk,t = Dt

qi,t,m = qi,t ,

M  m=1

qk,t,m = qk,t

Qn,m,b ≤ qn,t,m ≤ Qn,m,e The energy balance (demand = total generation) is imposed by equality () whose associate dual variable p∗t represents the day-ahead market clearing price. Unit n’s bided value in block m should be in the scope of the offered range. Positive and negative reserve capacity On the premise of ensuring the system power balance, avoiding the system load forecasting deviation and the supply & demand unbalanced fluctuation caused by various actual operation accidents, the whole system generally needs to have a certain capacity reserve. It is necessary to ensure that the total daily boot capacity meets the system’s minimum backup capacity. Positive reserve capacity constraints can be described as: N r +Ni

max αk,t qk,t ≥ Dt + RtU

k=Nr +1

αk,t is a binary parameter describing the ON/OFF status of unit k, αk,t = 1 max is unit k’s maximal power output in time means ON and 0 means OFF. qk,t U period t. Rt is the system’s positive reserve capacity requirements.

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Negative reserve capacity constraints can be described as: N r +Ni

min αk,t qk,t ≤ Dt − RtD

k=Nr +1 min qk,t is unit k’s minimal power output in time period t, RtD is system’s negative reserve capacity requirements.

Spinning reserve capacity Nr +Ni k=Nr +1 +Ni Nr k=Nr +1



max min ΔqkU , qk,t+1 − qk,t ≥ ΔS RtU

min ≥ ΔS RtD min ΔqkD , qk,t − qk,t+1

The maximal ramp-up and ramp-down rates of unit k are ΔqkU and ΔqkD , S RtU and S RtD are respectively the required up and down spinning reserve. Output constraint There is power generation limitation for each unit: min max αk,t qk,t ≤ qk,t ≤ αk,t qk,t max 0 ≤ qi,t ≤ αi,t qi,t

Minimum continuous start-up and shutdown time D − (αk,t − αk,t−1 ) TD ≥ 0 Tk,t U Tk,t − (αk,t−1 − αk,t ) TU ≥ 0 D U TD and TU is the minimal shutdown time and start-up time, Tk,t and Tk,t is unit k’s continuous shutdown and start-up time in time period t.

Maximum units’ start-up and shutdown times N r +Ni

|αk,t − αk,t−1 | ≤ g

k=Nr +1

So the lower-level market clearing model is presented as: Min Ω = Nr  qi,t +

i=1 +Ni Nr

k=Nr +1

T



t=1 Nr +Ni

k=Nr +1

M m=1



Nr i=1

qk,t = Dt

max αk,t qk,t ≥ Dt + RtU

Bi,t,m qi,t,m +

Nr +Ni

k=Nr +1 Bk,t,m qk,t,m



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min αk,t qk,t ≤ Dt − RtD



max min ΔqkU , qk,t+1 − qk,t ≥ ΔS RtU

min ≥ ΔS RtD min ΔqkD , qk,t − qk,t+1

k=Nr +1 min αk,t qk,t

max ≤ qk,t ≤ αk,t qk,t max 0 ≤ qi,t ≤ αi,t qi,t D Tk,t − (αk,t − αk,t−1 ) TD ≥ 0 U Tk,t − (αk,t−1 − αk,t ) TU ≥ 0 Nr +Ni |αk,t − αk,t−1 | ≤ g k=Nr +1

4

Constructive Analysis

In the proposed methodology, an offering and bidding strategy is developed for a WPP which submits the optimal offers to the day-ahead market in the competitive trading floor. The objective of the WPP is to maximize its profit through its interactions while the objective of ISO is to minimize the energy production cost during scheduling horizon. WPP needs to adjust deviations of day-ahead market in balancing market when the wind generation and other stochastic parameters become more accurate near the real time. The potential analysis based on related research are discussed in this Section. 4.1

Wind Power Output Scenarios

Wind power forecasting in the day-ahead market is necessary to provide system operators with useful estimations of available wind power. The prediction accuracy is very important for having a negative relationship with the benefits, as the deviation could result in economic loss in the balancing market. Many approaches have been developed and reported in the literature to provide short-time wind power forecasts. Available methods can be generally classified into three categories, i.e., physical numerical weather prediction (NWP) models, data-driven models with NWP data and historical data as inputs and data-driven models based only on historical data. The NWP models predict meteorological variables including wind speed, wind direction, temperature, humidity and some other variables, and wind power forecasts can be obtained based on the performance of wind turbines using NWP results, but this could need extensive calculation and much time. A large amount of research use data-driven methods to predict the future wind power of a wind farm by combing relatively NWP data and historical wind farm data, and promising forecasting results have been obtained for up to several days horizon. Data-driven approaches generally build statistical models that can represent the behavior of the wind power time series based on the historical data. Many statistical models and machine learning models have

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been studied for this purpose, such as Autoregressive Moving Average (ARMA), which is used mostly for modeling on hourly scales [4]; Artificial Neural Networks (ANNs) [2]; Support Vector Machines (SVMs) [7]. As researches have been done using these methods and the results are promising, multiple scenarios could be investigated for wind power generation, in which one scenario is considered as the base condition. Other scenarios usually pursue a normal distribution with a standard deviation from the actual value. Therefore, the impacts of accurate power output prediction on the benefits of WPP could be shown and analyzed. 4.2

Price Mechanism

To analyze the cost of over-supply and under-supply based on the bided value, the penalty mechanism is set, which has been modeled at different rates. Different penalty rates could reflect the risk that power producers faced and when there are variances, the strategy of whether increasing or decreasing production should be chosen to maximize profits. Scenarios can be assumed as the following example: • βt+ = 0.30, βt− = 0.15, producer is exposed to the further penalty in surplus production than defect of production. • βt+ = 0.15, βt− = 0.30. In this way, the producer is exposed to the further penalty in defect of production than surplus production. • βt+ = 0.25, βt− = 0.25. Equivalently, in this state, the value of penalty is the same for either surplus or defect of power production in any scenario. • βt+ = 0, βt− = 0. At this state, no penalty is designated for deviation energy. As discussed in [16], increasing and imposing a market-based undersupply penalty rate, which is common in many electricity markets in the United States, can result in commitment inflation by renewable firms and expected reliability degradation in markets. Thus, a fixed undersupply penalty rate is more effective in improving the reliability than a market-based penalty rate. However, the specific rules are still needed to be made based on market differentiation. The price mechanism is very important to motivate all the producers bidding at true generations and avoid the arbitrage behavior. Otherwise, the producers will take great risks bringing about by the uncertainty of market price. Indicated in the Denmark market [1], the settlement of unbalanced power in the market has formed a punishment mechanism for RE to a certain extent, inspiring RE producers to improve the forecast accuracy of power generation and reduce the impact on system imbalance. 4.3

Joint Bidding Strategies

The uncoordinated approach implies that two bids have to be submitted independently, one for the wind farm plant and the other for the thermal plant. While the coordinated approach has only one bid to be submitted, having an expected profit higher. Also, the coordinated approach does not represent a burden in computational resources when compared with the uncoordinated one. There has

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been many researches towards the joint bidding strategy for different energy resources, also called as cooperative game [15]. A. Esmaeily et al. presented a linear programming framework for self-scheduling of a hydro-thermal system to prove the profitability of integrated scheduling compared to non-integrated one, whereas the electricity market prices and forced outages of generating units have been considered as uncertain sources [5]. A joint planning and operation strategy of wind power producers and hydro power plants was provided in [11], and the storage system was effective in improving wind farm dispatch in the Australian electricity market [8]. Renewable energy’s participation in the power market will be a gradual process according to the direction of China’s power system reform, as has also been the international experience. Based on the provided model, the possibility of joint bidding of the WPP and thermal producer should be considered. It is expected that the strategy of cooperated bidding can effectively help wind power companies to obtain more profits when participating in the market. The low marginal cost of wind power generation can help coordinated generators expand the range of the quotation function and increase the flexibility in the day-ahead market. Thermal power with rapid adjustment capabilities can compensate for the uncertainty of actual wind power output, making wind power behave more actively in the market. 4.4

Policy Implications

From the experience of the development of foreign RE sources, mechanisms of fixed on-grid electricity prices and unified purchase and marketing by grid companies without participation in the electricity market could provide the greatest incentives for RE industries. However, this is mainly applicable to the early stage of RE development, which can promote a rapid scale increasing. While the penetration of RE reaches a level, participating in the spot market is an inevitable choice for sustainable development [13]. China started the market reform for a short time, related policies to encourage RE consumption are developed while it needs time to test the validity and be more comprehensive. Renewable energy’s participation in the power market will be a gradual process according to the direction of China’s power system reform, as has also been the international experience. For example, in the early stage, RE participates in the power markets by bidding a production curve without quoting a price and take priority in market clearing as a price taker, to enable priority for renewable consumption. When the spot market is mature and stable, newly added renewable energy projects will likely participate in the power markets by bidding both quantity and price. The different mechanisms could be investigated and the results compared in the model in this paper. For example, Document 9 emphasizes the importance of ancillary services, and the need to improve the compensation mechanisms for ancillary services based on the idea of ‘Shared Responsibility and Shared Gains’. This could greatly help integrate RE for controlling uncertainty, and also motivate thermal power plants in the competition for peak regulation. Therefore, the constraints for ancillary services

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trading can be included in the model to do this analysis. The further improvement of inter-regional and inter-provincial electricity trading mechanisms is also supported, while the failure of the on-time construction scheme of Jing-Jin-Ji Electricity Market and the regional electricity trading center presented many challenges [10]: 1) Intra-provincial electricity trading generates more taxes for provincial governments than inter-provincial electricity trading. 2) The potential electricity supply has exceeded national demand in recent years. Some provinces are not willing to import electricity from other provinces, as this will reduce the revenue of local thermal power plants. 3) The trading volume/price is difficult to settle, and there are high costs of transmission and line losses for inter-provincial electricity flows. While these problems should be solved with the joint effort of research, industries and governments.

5

Conclusion

This paper proposes a bi-level multi-time scale scheduling method for solving the offering strategy and the self-scheduling problem of a price-taker wind power and thermal power producer. This strategic WPP participates as a price-maker in the day-ahead market, and a deviator in the balancing market to compensate for production deviations due to wind power uncertainty. The lower level problem optimizes the schedule of the wind and thermal units and determines the dayahead prices, considering the energy balance and strict unit constraints. Based on the model, potential analysis is investigated, including the methods to characterize a set of scenarios solving the uncertainties associated with balancing market prices and the wind power output. The feasibility of joint wind-thermal bidding strategies is also discussed. Finally, the policy implications to RE participating in the spot market including ancillary services and inter-provincial electricity trading are analyzed. In the future study, the real case study based on trading data is expected to be implemented for model validation. As China has begun to shift from fixed feed-in tariffs to tendering and subsidy-free renewables, it is important to continue to complete and consolidate existing spot markets and focus on mobilizing flexible sources in the current power system to better integrate renewable energy, including smart grid, conventional power plants, demand response, and energy storage.

References 1. Bjerregaard, C., Møller, N.F.: The impact of EU’s energy labeling policy: an econometric analysis of increased transparency in the market for cold appliances in Denmark. Energy Policy 128, 891–899 (2019) 2. Chitsaz, H., Amjady, N., Zareipour, H.: Wind power forecast using wavelet neural network trained by improved clonal selection algorithm. Energy Convers. Manag. 89, 588–598 (2015) 3. CPC Central Committee CSC: opinions on further deepening the power sector reform (2016). http://tgs.ndrc.gov.cn/zywj/201601/t20160129 773852.html

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4. Erdem, E., Shi, J.: ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88(4), 1405–1414 (2011) 5. Esmaeily, A., Ahmadi, A., et al.: Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate. Energy 122, 182–193 (2017) 6. Utility Wind Interest Group: wind power and electricity markets (2011) 7. Hu, Q., Zhang, S., et al.: Noise model based ν-support vector regression with its application to short-term wind speed forecasting. Neural Netw. 57, 1–11 (2014) 8. Khatamianfar, A., Khalid, M., et al.: Improving wind farm dispatch in the Australian electricity market with battery energy storage using model predictive control. IEEE Trans. Sustain. Energy 4(3), 745–755 (2013) 9. Murray, B.C., Cropper, M.L., et al.: How effective are US renewable energy subsidies in cutting greenhouse gases? Am. Econ. Rev. 104(5), 569–74 (2014) 10. NDRC: management measures for full purchase of guaranteed generation (Fagainengyuan [2016], no. 625) (2016) 11. de la Nieta, A.A.S., Contreras, J., Mu˜ noz, J.I.: Optimal coordinated wind-hydro bidding strategies in day-ahead markets. IEEE Trans. Power Syst. 28(2), 798–809 (2012) 12. Peng, X., Tao, X.: Cooperative game of electricity retailers in China’s spot electricity market. Energy 145, 152–170 (2018) 13. RAP: low-carbon power sector regulation: international experience from Brazil, Europe, and the United States. Beijing, China (2014) 14. Shaghayegh, Z., Ettore, B., et al.: The impacts of an integrated European adjustment market for electricity under high share of renewables. Energy Policy 136 (2020). https://doi.org/10.1016/j.enpol.2019.111055 15. Street, A., Lima, D.A., et al.: Sharing quotas of a renewable energy hedge pool: a cooperative game theory approach. In: 2011 IEEE Trondheim PowerTech, pp. 1–6. IEEE (2011) 16. Sunar, N., Birge, J.R.: Strategic commitment to a production schedule with uncertain supply and demand: renewable energy in day-ahead electricity markets. Manag. Sci. 65(2), 714–734 (2019) 17. Yang, X., He, L., et al.: Effect of government subsidies on renewable energy investments: the threshold effect. Energy Policy 132, 156–166 (2019) 18. Zhang, Y., Farnoosh, A.: Analyzing the dynamic impact of electricity futures on revenue and risk of renewable energy in China. Energy Policy 132, 678–690 (2019)

How Does Environmental Regulation Enhance Firms’ Competitiveness Through Innovation Incentive in China? A Nonlinear Mediating Model for the Porter Hypothesis Die Hu1 , Maoyan She2(B) , and Xue Yang2 1

2

School of Economics and Management, Fuzhou University, Fuzhou 350108, People’s Republic of China Business School, Sichuan University, Chengdu 610065, People’s Republic of China [email protected]

Abstract. Both developed and developing countries have introduced various policies to require enterprises to protect environment by providing green products and service. However, environmental protection is often seen as an obstacle to business development. Professor Michael Porter put forward that this dilemma can be alleviated through innovation. Environmental regulation can enhance firms’ competitiveness by spurring their innovation. It is well known as the Porter Hypothesis which has been examined in many previous studies. However, existing empirical evidence still remains inconsistent. To reconcile the inconsistent result, this study proposes a nonlinear mediating model for the Porter Hypothesis from a holistic perspective. Based on Chinese industry-level panel data of 35 industrial sectors between 2001 and 2010, the empirical results show that environmental regulation has a nonlinear effect on innovation; and innovation shows a nonlinear association with competitiveness. Generally, innovation plays a full and nonlinear mediating role in relationship between environmental regulation and competitiveness. This study reveals the relationships among environmental regulation, innovation, and firms’ competitiveness, which helps government to formulate policies and firms to develop sustainably. Keywords: Environmental regulation · Firms’ competitiveness Innovation · Porter hypothesis · China

1

·

Introduction

In recent years, the serious problems of climate change and environmental pollution have drawn wide attention from all over the world [9]. To reduce environment pollution, various policies and regulations have been issued by the government to c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 807–818, 2020. https://doi.org/10.1007/978-3-030-49829-0_60

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require firms to fulfill their social responsibilities and adhere to sustainable development [23]. However, a conventional viewpoint was that environmental regulation negatively affects productivity and competitiveness by incurring costs to firms and restricting their investment opportunities. Porter challenged this view and argued that well-designed regulations could actually stimulate innovation, thereby reducing costs, improving the quality of products and finally helping firms gain a competitive advantage in the market [19]. The idea that environmental regulation can improve firms’ competitiveness via its impact on innovation has become known as the Porter Hypothesis. Till now, scholars have tested and verified the Porter Hypothesis with different propositions [23]. Most studies demonstrate that environmental regulation has a significant positive effect on innovation and competitiveness [21], but others demonstrate a negative relationship, an inverted U-shaped relationship, or even none at all [25]. The resulting inconsistence might be explained from various angles. The present study is an effort to reconcile this inconsistent result from a nonlinear model. According to original thought of the Porter Hypothesis, neither nonlinear causal effect nor a simple linear mediating effect in current literature can convincingly explain the complex linkage between environmental regulation, innovation and competitiveness, thus leads to mixed results. As Porter and Linde first described, environmental regulation spurs innovation, and then innovation promotes competitiveness [3]. However, some earlier researchers only investigated the relationship between environmental regulation and innovation [15]. Despite the latecomers have focused on the causal link between environmental regulation and competitiveness [26], or further taken innovation into account [21], a majority of studies simply analyze the relationship among environmental regulation, innovation and competitiveness using linear or nonlinear causal model. In recent years, several studies have provided a mediating effect of environmental innovation [12], which means that environmental regulation influences competitiveness indirectly via innovation, but they have only been tested regarding the linear mediating effect. Due to evidence of nonlinear associations among those variables can be found in some literature [4], a nonlinear mediating model should be established to explore the association between environmental regulation, innovation and competitiveness in the Porter Hypothesis. Thus, this paper analyzes the Porter Hypothesis in a holistic framework while considering the nonlinear mediating role of innovation. Specifically, we propose that there might be a nonlinear relationship between environmental regulation and innovation, as well as between innovation and competitiveness. Meanwhile, innovation plays a nonlinear mediating role in the relationship between environmental regulation and competitiveness. We employ industry-level panel data for 35 Chinese industries from 2001 to 2010 to investigate the Porter Hypothesis and estimate the nonlinear linkages between environmental regulation, innovation and competitiveness. This study has several contributions. First, this paper reconciles current inconsistent result by proposing and investigating a nonlinear mediating effect of innovation on the relationship between environmental regulation and com-

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petitiveness. Second, based on a holistic framework, we test and verify dynamic structure of the Porter Hypothesis, which can better help us know entirely about the effect of environmental regulation on competitiveness mediated by innovation. Meanwhile, this study also enriches existing research of Porter Hypothesis. Third, unlike previous industry-level analyses, our sector-level panel data for Chinese high-pollution industries in three main sectors captures the effects of industrial specific environmental regulations on sectorial productivity mediated by innovation, that allows us to obtain greater insight on empirical validity of the Porter Hypothesis in developing countries. The remainder of this paper is organized as follows. In Sect. 2, we review relevant literature on the Porter Hypothesis. Section 3 provides the data and methodology. We present the empirical results and our analysis in Sect. 4. Finally, several aspects and implications of our research are discussed.

2

Literature Review

According to Jaffe and Palmer, the Porter Hypothesis is distinguished among “narrow”, “weak” and “strong” versions [13]. Specifically, the “narrow” version of Porter Hypothesis is that only these environmental regulations which focus on outcomes not processes can stimulate innovation of firms, while a “weak” version highlights the positive relationship between environmental regulation and innovation. In what has been called the “strong” version of Porter Hypothesis, it is noted that innovation induced by well-designed environmental regulations can more than offset any additional regulatory costs, and consequently, often leads to an increase in firm competitiveness and productivity. Following these versions of Jaffe and Palmer, two main groups of studies have investigated the Porter Hypothesis and get mixed results. Those testing the “weak” version focus on the relationship between environmental regulation and innovation, have basically reached a consensus that environmental regulation has a positive effect on innovation in the context of developed countries [16], despite a small amount of different evidence, such as an inverted U-shaped relationship [18]. For the “strong” version, which concentrates on how environmental regulations affect productivity and competitiveness, the empirical evidence is more mixed. Some studies concluded that environmental regulation leads to a productivity slowdown or efficiency losses [6], others detected positive results [2], while still others found no discernible relationship between environmental regulation and productivity. More recently, there are emerging papers that test both “weak” and “strong” versions of the Porter Hypothesis and examine the relationship between environmental regulation, innovation and competitiveness simultaneously, but still get inconsistent results. For instance, through investigating both innovation and productivity responses to environmental regulation, Yang et al. provide empirical evidence on both “weak” and “strong” versions [27]. Lanoie et al. also find strong support for the “weak” version, but no support for the “strong” version [17]. Finally, Ramanathan et al. examine the relationships between environmental regulation, innovation and private sustainability benefits at the firm level [20],

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the results of which show that firms innovatively respond to environmental regulations are better able to reap the private benefits of sustainability and develop their competitive advantage. Thus the “strong” version is supported here. Although the existing studies can help us understand the association of Porter Hypothesis from a comprehensive perspective, there are three aspects to note concerning the inconsistent result of these empirical literatures. First, we do not know whether the innovation incurred by environmental regulation is good or bad for productivity and competitiveness. Therefore, the group of studies focusing on the “weak” version cannot provide an overall test for the Porter Hypothesis. Second, when testing the “strong” version, these studies did not properly consider the effect of innovation on the relationship between environmental regulation and competitiveness in empirical model. The results thus cannot explain whether the effect of environmental regulation on competitiveness comes from innovation or is due to other reasons. Hence, the linkage between environmental regulation, innovation and competitiveness in the Porter Hypothesis is still unclear. Third, a handful of papers, which test both versions of the Porter Hypothesis, employ Porter’s viewpoint that environmental regulation stimulates innovation and theoretically leading to an increase in competitiveness. Nevertheless, we find that these empirical studies test the relationship of these three constructs based on separate models instead of a holistic framework. Accordingly, in the following section, we develop a mediating model taking into account that the innovation is associated with both environmental regulation and competitiveness. In this paper, aligning with Porter’s original idea, we propose that a mediating model could be an appropriate approach to test both “weak” and “strong” versions of the Porter Hypothesis in a holistic framework. Actually, the Porter Hypothesis is a mediating process in which environmental regulation causes innovation, and then innovation causes competitiveness. In theory, mediation is defined as a process that “influences of an antecedent are transmitted to a consequence through an intervening variable” [14]. From the point of view of Porter Hypothesis, the indirect effect of government environmental regulation on competitiveness is formed by spurring firms to adopt innovation strategies. Therefore, theoretically, using a mediating model to examine the Porter Hypothesis is more appropriate. In fact, some pioneering scholars have attempted to use a mediating model to disentangle the linkage of environmental regulation - innovation - competitiveness and test the Porter Hypothesis in recent years [7]. Relying on a linear mediating test, they find that innovation mediates the relationship between certain environmental pressure forces and firms’ performance. However, their model settings and conclusions still remain mixed. Some results indicate significant negative relationship between government environmental regulation and environmental innovation strategy which positively effect firms’ business performance. While, other study verifies that corporate’s green product innovation mediates the positive relationship between firm environmental ethics and its competitive advantage.

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Facing these mixed result, we further propose a nonlinear mediating model to reconcile the complicated relationship between environmental regulation, innovation and competitiveness. Thanks to the method developed by Hayes and Preacher [10], which makes our study possible in practice. They introduced a new approach to estimate nonlinear mediating effects in the behavioral research, which has been adopted in many research fields [1]. Based on this method put forward by Hayes and Preacher, we draw a conceptual model to present the overall logic of our research and clearly illustrate the nonlinear mediating effect of innovation on the relationship between environmental regulation and competitiveness. Unlike some other studies, this research framework has a comprehensive perspective. As is shown in Fig. 1, our conceptual mediating model is informed by both “weak” and “strong” version of the Porter Hypothesis. Different from general mediating model, we try to explain the mediating role of innovation via a nonlinear analysis.

Fig. 1. The conceptual mediating model

3 3.1

Data and Methods Data Collection and Sample Selection

The data used in this study is from 35 industrial sectors of China for the period of 2001 to 2010. We collect these data from annual yearbooks released by the National Bureau of Statistics, including China Statistical Yearbook, China Statistical Yearbook on Science and Technology, China Statistical Yearbook on Environment. In order to adopt uniform caliber data, as well as consider the availability of research data, we select panel data of 35 industrial sectors spanning ten years, from 2001 to 2010. 3.2

Variable Measurements

Environmental Regulation We use operation cost of facilities for the treatment of waste gas and water as a proxy for environmental regulation (ER) in our

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research data. To eliminate the dimension and effects of different variances, this variable is operationalized as the natural logarithm of the cost of facilities for the treatment of waste gas and water [27]. Innovation To proxy for innovation, we use industrial R&D expenditures taken from China Statistical Yearbook on Science and Technology. Hence, as a mediator, innovation is operationalized as the natural logarithm of R&D expenditures (R&D) in our study. Industrial Competitiveness is measured by Total Factor Productivity (TFP) [21]. Here, we use data envelopment analysis (DEA model) to measure the TFP of 35 industrial sectors. Based on prior studies [24], when calculating TFP. We use capital inputs (original value of fixed assets, 100 million RMB), labor inputs (annual average employed persons, 10,000 persons), energy inputs (total energy consumption, 10 thousand tons of SCE) as input indicators. And use gross industrial output (changeless price compared to 2001) and the reciprocal of the environmental pollution index as output indicators. The smaller this index value is, the better. In addition, in order to address the unobserved heterogeneity, the Size, Market and Fcapital at industrial level are controlled, which are measured by the nature logarithm of the gross industrial output value, the proportion of unprofitable firms to the total number of firms and the proportion of foreign capital to firms’ total capital. Besides, controlled industry dummy variables and year dummy variables are also introduced to our empirical research. 3.3

Methods

In this study, we use three nonlinear empirical models in empirical research on the basis of mediation model introduced by Hayes and Preacher [10]. Specifically, we employ three nonlinear empirical models containing quadratic term to examine the total effect of environmental regulation on competitiveness (Eq. (1)), the impact of environmental regulation on innovation (Eq. (2)) and its influence on competitiveness via innovation (Eq. (3)). T F P = θ1 + c1 ER + c2 ER2 +



βi Controlsi +εi

(1)

βi Controlsi +λi

(2)

i

R&D = θ2 + a1 ER + a2 ER2 +

 i

T F P = θ3 + c1 ER + c2 ER2 + b1 R&D + b2 R&D2 +



βi Controlsi +ηi

(3)

i

where i denotes industries, TFP is total factor productivity, ER is short for environmental regulation, R&D represents innovation, Controls refers to control variables.

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For the Eq. (3), we use an instrument for innovation due to suspected simultaneity between R&D and TFP. The instrument variable we design is the average R&D expenditures in the same category of sector (INSTRUMENT R&D). This is assumed to be correlated with firms’ decision to undertake innovation in the specific category of sector, but to have an insignificant impact on the productivity of these sectors. This type of instrument variable has been notably used in the industrial organization and environmental literature to purge the simultaneity problem [17]. Hence, the Eq. (3) should be adjusted to: T F P = θ3 + c1 ER + c2 ER2 + b1 F IT R&D + b2 F IT R&D2 +



βi Controlsi +ηi

i

(4) where F IT R&D is the fitted value of R&D while using the instrument in the first stage of the regression. In this regression, IN ST RU M EN T R&D, ER with 1-year lag, and control variables are all included. Given that the dependent variable TFP is censored ranging from 0 to 1, a panel-based Tobit model is used to investigate the relationships which TFP involved [22]. While testing the linkage between ER and R&D, OLS model is selected here.

4

Empirical Results

Table 1 provides descriptive statistics and correlation matrix for all of the described variables. A multicollinearity diagnostic test demonstrates that the highest Variance Inflation Factor (VIF) is 5.12, which is far below the critical point of 10 [8]. The regression results are presented in Table 2. Following the mediating effect test procedure, the relationship between ER and TFP is tested in Model 1 and Model 2 (Eq. (1)). Then the effects of ER on R&D are shown in Model 3 and Model 4 (Eq. (2)). In Model 5, we construct complete linkage, including dependent variable TFP, independent variables ER and FIT R&D, and their squares (Eq. (4)). Considering dynamic structure of the Porter Hypothesis [11], we employ the data for ER with 1-year lag from Model 1 to Model 4, while replace ER and FIT R&D with their 2- and 1- year lags respectively in Model 5. The coefficient for ER is insignificant in Model 1, but is positive and significant (c1 = 0.1394, p < 0.05) in Model 2. Meanwhile, the coefficient of ER2 is negative and significant (c2 = −0.0062, p < 0.05). Based on coefficients in Model 2, we draw a curve in Fig. 2 (a). It can be seen that both regression results and the curve show a nonlinear relationship between ER and TFP.

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Variables

Min

Max

Mean

S.D.

1

TFP

0.138

1

0.613

0.236

1

R&D

8.267

16.10

12.59

1.689

0.013

2

3

4

5

1

ER

6.924

15.33

11.06

1.685

−0.121 0.590

1

Size

4.126

10.78

7.862

1.396

0.109

0.673

Market

0.009

0.559

0.181

0.097

−0.318 −0.307 −0.048 −0.300 1

0.627

1

Fcapital 0 0.528 0.114 0.112 0.158 −0.129 −0.368 −0.099 −0.140 Note: Year and Industry dummies are not shown in the table.

The effect of ER on R&D is tested in Model 3, an insignificant coefficient is found for the variable ER, while it is positive and significant (a1 = 0.3607, p < 0.1) in Model 4. Similarly, the coefficient of ER2 is negative and significant (a2 = −0.0168, p < 0.1). The curve in Fig. 2 (b) demonstrates that there is also a nonlinear relationship between ER and R&D. Combined with the results of Model 1–5 from Table 2, the mediating effect of innovation on the relationship between environmental regulation and competitiveness can be examined. First, in Model 1 and Model 2, it can be found that ER has a significant nonlinear effect on TFP. Then, results in Model 3 and Model 4 show more significant nonlinear relationship between ER and R&D. Finally, when both ER and R&D are introduced in Model 5, the related coefficients of ER become insignificant(c1 = 0.0430, p > 0.1; c2 = −0.0023, p > 0.1), while the relationship between mediator R&D and dependent variable TFP is still nonlinear(b1 = 0.2436, p < 0.01; b2 = −0.0090, p < 0.01) (the curve in Fig. 2(c) shows this nonlinear relationship). According to Baron and Kenny, we can safely conclude that innovation plays a full and nonlinear mediating role in relationship between environmental regulation and competitiveness [5].

(a)

(b)

(c)

Fig. 2. The nonlinear relationship among ER, R&D and TFP

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Table 2. Regression results

ER−1

(1) TFP

(2) TFP

(3) R&D

(4) R&D

0.0111

0.1394**

0.0066

0.3607*

(0.841)

(2.219)

(0.181)

(1.944)

2 ER−1

−0.0062**

−0.0168*

(−2.086)

(−1.945)

ER−2

(5) TFP

0.0430 (0.697)

2 ER−2

−0.0023 (−0.763)

F IT R&D−1

0.2436*** (2.883)

2 F IT R&D−1

−0.0090*** (−2.855) 0.0510***

1.0835***

1.1043***

−0.0188

Size

0.0424*** (3.098)

(3.590)

(27.959)

(27.575)

(−0.510)

Market

−0.3962***

−0.3490***

0.4111

0.5309*

−0.5391***

(−3.513)

(−3.059)

(1.297)

(1.652)

(−3.730)

Fcapital

−0.0940

−0.0817

−1.9341***

−1.9121*** 0.0593

(−0.489)

(−0.429)

(−3.742)

(−3.710)

(0.271)

Constant

0.2655**

−0.4607

4.1526***

2.1550**

−0.9319

(2.078)

(−1.247)

(12.032)

(1.991)

(−1.477)

Year

Control

Control

Control

Control

Control

Industry

Control

Control

Control

Control

Control

Chi2

54.28

53.99

1934

1955

120.0

Observations

315

315

313

313

279

No. of indus- 35 35 35 35 35 try Note: z-statistics are in parentheses. Observations are not equal because of 1- or 2year lags and the existing of some missing values in R&D. ***p < 0.01; **p < 0.05; *p < 0.1

5

Conclusion and Discussion

In this study, we proposed a nonlinear mediating effect of innovation on the relationship between environmental regulation and competitiveness, as well as investigated the association between environmental regulation and competitiveness through innovation in a coherent framework. Based on the industry-level panel data of 35 industrial sectors in China, it has been proved that innovation

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plays a full nonlinear mediating role in the linkage between environmental regulation and competitiveness. Specifically, environmental regulation which has the operation cost of facilities for environmental treatment as a proxy, has a nonlinear effect on innovation input, such as R&D expenditures, afterwards, this innovation input shows a nonlinear correlation with competitiveness as well. Just as core viewpoint of the Porter Hypothesis, well-designed environmental regulations will stimulate firms’ innovation, and innovation can but will not necessarily offset the regulatory costs. This study has following contributions. First, previous empirical studies have investigated the Porter Hypothesis by testing both the “weak” and “strong” versions, but the results are inconsistent. This study adds to the prior literature by highlighting the nonlinear mediating role of innovation on the relationship between environmental regulation and competitiveness. Both “weak” and “strong” versions are evidenced under this nonlinear mediating model. Second, while some research has disentangled the linkage of environmental regulation innovation - competitiveness by using a mediating model, none of them has paid attention to the nonlinear relationship. This paper is among the first attempts to test the Porter Hypothesis based on a nonlinear mediating model, and enlarge the scope of an inverted U-shaped relationship from environmental regulation - innovation, environmental regulation - productivity to three constructs (environmental regulation, innovation, competitiveness). Third, our sector-level panel data for 35 Chinese high-pollution industries captures the effects of industrial specific environmental regulations on sectorial productivity mediated by innovation, that allows us to obtain greater insight on empirical validity of the Porter Hypothesis in developing countries. Our results also have some implications from the managerial perspective. In practice, our nonlinear results indicate that the conflict between environment and productivity does not absolutely exist in developing countries. Just as Porter and Linde first declared that, well-designed environmental regulations can actually enhance competitiveness. On the one hand, a dilemma between environment and economic growth can be solved if there are well-designed regulations. It is more important to distinguish effective and ineffective environmental policies as well as adopt or reenact effective policies which can enhance innovation, than strictly require companies to protect the environment. On the other hand, when adopting positive innovation strategies to address environmental issues, too much innovation investment is potentially not good for firms and may undermine their ability to profit. Still, there are a few limitations in our study, which in turn offer directions for future research. First, restricted by data sources, we use industry-level data to test the Porter Hypothesis following prior studies while its original argument focuses on firms. Next, our study verifies the Porter Hypothesis in a nonlinear mediating model by using data from China. Future studies can examine this full model in other developed or emerging economies to generalize an extended Porter Hypothesis model. Last but not least, on the one hand, we use costs of facilities for the treatment of pollution abatement as the proxy for environmental

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regulations, which might not capture environmental policies in a satisfactory way. On the other hand, we employ R&D expenditures to measure innovation in our research, which may lead to two issues. The one is that we only used technological input of a particular industry, and it cannot fully reflect innovation activities of firms. Acknowledgement. This work was supported by the National Natural Science Foundation (grant number: 71904137) and Fuzhou University Fund (grant number: 510824).

References 1. Agarwal, R., Campbell, B.A., et al.: What do i take with me? The mediating effect of spin-out team size and tenure on the founder-firm performance relationship. Acad. Manag. J. 59(3), 1060–1087 (2016) 2. Albrizio, S., Kozluk, T., Zipperer, V.: Environmental policies and productivity growth: evidence across industries and firms. J. Environ. Econ. Manag. 81, 209– 226 (2017) 3. Ambec, S., Cohen, M.A., et al.: The porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness? Rev. Environ. Econ. Policy 7(1), 2–22 (2013) 4. Artz, K.W., Norman, P.M., et al.: A longitudinal study of the impact of R&D, patents, and product innovation on firm performance. J. Product Innov. Manag. 27(5), 725–740 (2010) 5. Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173 (1986) 6. Broberg, T., Marklund, P.O., et al.: Testing the porter hypothesis: the effects of environmental investments on efficiency in Swedish industry. J. Prod. Anal. 40(1), 43–56 (2013) 7. Chang, C.H.: The influence of corporate environmental ethics on competitive advantage: the mediation role of green innovation. J. Bus. Ethics 104(3), 361– 370 (2011) 8. Chatterjee, S., Price, B.: Regression Analysis by Example. Wiley, New York (1991) 9. Demirel, P., Danisman, G.O.: Eco-innovation and firm growth in the circular economy: evidence from European small-and medium-sized enterprises. Bus. Strategy Environ. 28(8), 1608–1618 (2019) 10. Hayes, A.F., Preacher, K.J.: Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivariate Behav. Res. 45(4), 627–660 (2010) 11. Horv´ athov´ a, E.: The impact of environmental performance on firm performance: short-term costs and long-term benefits? Ecol. Econ. 84, 91–97 (2012) 12. Hu, D., Wang, Y., Li, Y.: How does open innovation modify the relationship between environmental regulations and productivity? Bus. Strategy Environ. 26(8), 1132–1143 (2017) 13. Jaffe, A.B., Palmer, K.: Environmental regulation and innovation: a panel data study. Rev. Econ. Stat. 79(4), 610–619 (1997) 14. James, L.R., Brett, J.M.: Mediators, moderators, and tests for mediation. J. Appl. Psychol. 69(2), 307 (1984)

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15. Johnstone, N., Haˇsˇciˇc, I., Popp, D.: Renewable energy policies and technological innovation: evidence based on patent counts. Environ. Resour. Econ. 45(1), 133– 155 (2010) 16. Kneller, R., Manderson, E.: Environmental regulations and innovation activity in UK manufacturing industries. Resour. Energy Econ. 34(2), 211–235 (2012) 17. Lanoie, P., Laurent-Lucchetti, J., Naa, J.: Environmental policy, innovation and performance: new insights on the porter hypothesis. J. Econ. Manag. Strategy 20(3), 803–842 (2011) 18. Perino, G., Requate, T.: Does more stringent environmental regulation induce or reduce technology adoption? When the rate of technology adoption is inverted U-shaped. J. Environ. Econ. Manag. 64(3), 456–467 (2012) 19. Porter, M.E.: America s green strategy. Reader in Business and the Environment 33 (1991) 20. Ramanathan, R., He, Q., et al.: Environmental regulations, innovation and firm performance: a revisit of the porter hypothesis. J. Cleaner Prod. 155, 79–92 (2017) 21. Rubashkina, Y., Galeotti, M., Verdolini, E.: Environmental regulation and competitiveness: empirical evidence on the porter hypothesis from European manufacturing sectors. Energy Policy 83, 288–300 (2015) 22. Schiavo, S.: Common currencies and FDI flows. Oxford Econ. Pap. 59(3), 536–560 (2007) 23. Stoever, J., Weche, J.P.: Environmental regulation and sustainable competitiveness: evaluating the role of firm-level green investments in the context of the porter hypothesis. Environ. Resour. Econ. 70(2), 429–455 (2018) 24. Sueyoshi, T., Yuan, Y., Goto, M.: A literature study for DEA applied to energy and environment. Energy Econ. 62, 104–124 (2017) 25. Wang, Y., Shen, N.: Environmental regulation and environmental productivity: the case of China. Renew. Sustain. Energy Rev. 62, 758–766 (2016) 26. Xie, R., Yuan, Y., Huang, J.: Different types of environmental regulations and heterogeneous influence on “green” productivity: evidence from China. Ecol. Econ. 132, 104–112 (2017) 27. Yang, C.H., Tseng, Y.H., Chen, C.P.: Environmental regulations, induced R&D, and productivity: evidence from Taiwan’s manufacturing industries. Resour. Energy Econ. 34(4), 514–532 (2012)

Which Kind of Sponsor Brand Crisis Most Decreases Sport-Event Brand Evaluation?—The Moderating Effects of Brand Relationship Norms Bo Hu1 , Xueling Jiang2(B) , and Hong Wang3 1

2

College of Management Science, Chengdu University of Technology, Chengdu 610000, People’s Republic of China Business School, Sichuan University, Chengdu 610000, People’s Republic of China [email protected] 3 Business School, Chengdu University of Technology, Chengdu 610000, People’s Republic of China

Abstract. This paper discusses the impact of sponsor brand crises on sport-event brand evaluation, and the important moderating role of brand relationship norms. A scenario experiment is performed to analyze the impact of sponsor brand crisis attribution (morality attribution vs ability attribution) on the sport-event brand evaluation as well as the moderating effects of brand relationship norms (exchange brand relationship norms vs. communal brand relationship norms). It extends the study of sponsorship crises and defines sponsor brand crises clearly. The conclusions of this paper can provide theoretical references for sport-event organizers to deal with sponsor brand crises. Keywords: Sport-event · Sponsor brand · Crisis attribution Sport-event brand evaluation · Brand relationship norms

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Introduction

Sponsorship is a cash and/or in-kind fee paid to a property (typically in sports, arts, entertainment or causes) in return for access to the exploitable commercial potential associated with that property [6]. Sponsorship as a new way of marketing starts to play a prominent role, and it might helps sponsors build a competitive advantage over their rivals [9]. Furthermore, the strategic value of sponsorship is to enhance brand awareness and brand images [15]. As more and more global corporations attempt to enhance brand awareness and brand image, global corporate sponsorship costs have been steadily increasing, reaching over 6.0 billion dollars in 2016 and 6.28 billion dollars in 2017 [23]. However, as sponsorship becomes more popular, corporations are faced with increasing sponsor brand crises. Sponsor brand crises refer to the negative incidents that would affect the image of the sponsor brand and hence arouse the c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 819–830, 2020. https://doi.org/10.1007/978-3-030-49829-0_61

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mistrust in the sponsor brand [19]. Some studies show that sponsor brand crises have an impact on sport-event brand evaluation [22]. When controversial sports sponsorships emerge, they can adversely affect the sponsor team and sponsor brand [16]. The lack of a link between sports sponsorship and social responsibility can negatively affect sponsor attitudes [8]. For instance, Toshiba paid US$1.05 billion as U.S. user’s compensation due to the U.S. consumers’ collective prosecution with respect to defective features of laptop software, and this sponsor brand crisis made 2000 Asian Cup less attractive. As sponsor brand crises can decrease sport-event brand evaluation, we have to figure out which kind of sponsor brand crisis would most decrease the sport-event brand evaluation. Attribution theory provides a plausible tool to solve this issue. In the field of sports sponsorships, people may develop their own beliefs regarding whether the sponsorship initiative is morally appropriate or not [25]. Once the sponsor brand crisis happens, consumers will form two kinds of attributions to the sponsor brand crisis: one is the ability attribution and the other is the moral attribution [12]. Specifically, ability attribution signifies that incapability causes the brand crisis. For example, Volkswagen was the sponsor of the CBA (China Basketball Association) in 2013. Unfortunately, it was reported that its products had some problems with direct sift gearbox due to the immature technology. This sponsor brand crisis is attributed to the poor technology ability of Volkswagen. In contrast, morality attribution signifies that immorality causes the brand crisis [21]. MacDonald was the sponsor of the 2014 Olympic Games but was found using rotten meat to make hamburgers. Obviously, consumers attribute this negative incident to the lack of morality. Sponsor image accounts for 90% of the total effect of sports involvement on purchase intention [3]. Sport involvement moderates the effectiveness of sponsorevent congruence on sponsor credibility, influencing attitude toward the sponsor and intention to purchase the sponsor’s product [11]. Gwinner and Eaton [7] document that if the image or function of a sponsor brand is similar to a sport-event, the image of the sponsor brand may transfer to the image of the sport-event brand. It must be noted that the relationship between consumers and the sponsor brand has an influence on this transfer process [20]. The relationship between consumers and the sponsor brand can be measured by brand relationship norms. Brand relationship norm means consumer groups’ expectations to a specific brand [1], which are relevant to both consumers and brands. As prior studies have suggested, brand relationship norms can be divided into communal brand relationship norms and exchange brand relationship norms [14]. Communal brand relationship norms mean that the relationship between consumers and a brand is excellent, stable and routine, and consumers do agree with the vision of the brand are willing to pay. In contrast, exchange brand relationship norms mean that the relationship between consumers and a brand is fast, short-term and high profitable, and consumers attach much significance to the benefit that the brand can bring to consumers [2].

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When the sponsor brand crisis happens, how the sport-event organizers take corrective measures to deal with the crisis becomes very important. However, in the past, when researching the impact of sponsor brand crisis on the evaluation of sports event brands, it did not distinguish the impact of different crisis types on the evaluation of event brands, so it was not possible to give the players effective strategies to deal with the crisis. In addition, the brand relationship norms is an important indicator for measuring brand relationships, and whether the difference will affect the effect of the sponsor brand crisis on the evaluation mechanism of the event brand has not been considered. In order to solve the above problems, this paper uses attribution theory as the basis, clearly defines two attribution modes: moral attribution and ability attribution. It takes the sponsorship crisis as the specific research object, the brand relationship norms as the moderator, and the effects of two types of crisis attributions on the evaluation of event brands in the norms of exchange and communal brand relationships are studied experimentally. It expands the research on sponsorship crisis, enriches the relevant theories of sports-event brand evaluation, and provides important theoretical guidance for sports-event organizers to cope with the sponsor brand crisis.

2 2.1

Literature Review Crisis Attribution

Among the studies about crises, many explore crises from the perspective of attribution. Tucker et al. [22] analyze responsibility attribution. It emphasizes that consumers’ response to a failed product is spontaneous, and it is stable and controllable. When the crisis attribution is not clear, high brand loyalty will reduce responsibility attribution. Similarly, Lewicki et al. [13] find that crisis attribution is influenced by consumers’ (demographic) characteristics such as their ages, gender, cultural background, moral values and attitudes towards crises. By the same token, the attributes of crises will affect crisis attribution. For instance, crisis severity, crisis product category, and origin have an impact on consumers’ judgment on crises. To differentiate the essential reasons that cause crises, Tomlinson [21] divides crisis attribution into ability attribution and moral attribution. Ability attribution refers to the success or failure that can be traced back to ability. The fact that ability is high or low determines the way to deal with crises. Morality attribution refers to the success or failure that can be traced back to morality. Morality is the standard to judge which is right or wrong and determines the bottom line of the society. Morality attribution is rooted in everyone’s heart. When crises are unlikely to be attributed to the ability completely, morality attribution will evolve. From this aspect, morality attribution is an important supplement to the ability attribution. On these grounds, this paper utilizes Tomlinson’s way to divide sponsor brand crisis attribution into ability attribution and morality attribution. In addition, it treats sponsor brand crisis attribution as an independent variable and sport-event brand evaluation as a dependent variable, hoping to find the influence of sponsor brand crisis attribution on sport-event brand evaluation [14].

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Moderating Role of Brand Relationship Norms

Brand relationship norms mean consumer groups’ expectations for a specific brand [1], which are relevant to both consumers and brands. They can be divided into communal brand relationship norms and exchange brand relationship norms [14]. Communal brand relationship norms signify that the relationship between consumers and brand is excellent, stable and routine, and consumers do agree with the vision of the brand and are willing to pay. Under the condition of communal brand relationship norms, there is high emotional stickiness between the brand and the consumers who rely on the brand. Moreover, consumers have a special affection for the brand. Communal brand relationship norms focus on a kind of “parasitic” relationship [4]. To be more specific, consumers are parasitic on faith, reputation and brand vision, and have “co-prosperity and disgrace” emotional ties. As can be seen, under the condition of communal brand relationship norms, compared with ability attribution, moral attribution has a more profound impact on attribution. Moral attribution is deeper in the heart of consumers, who will agree with the brand more. When a sponsor brand under communal brand relationship norms is involved with a morally attributed crisis, consumers will feel being deceived. Moreover, this emotion of being cheated will transfer to sport-event brand evaluation, and jeopardize the sport-event brand sharply. Therefore, the following hypothesis is proposed: Hypothesis 1. Under the condition of communal brand relationship norms, compared with ability attribution, morality attribution has a more negative impact on sport-event brand evaluation. In contrast, exchange brand relationship norms mean that the relationship between consumers and the brand is fast, short-term and high profitable, and consumers attach much significance to the benefit that the brand can bring to consumers [2]. Exchange brand relationship norms focus on a kind of “beneficial” relationships [26]. As can be seen, under the condition of exchange brand relationship norms, compared with moral attribution, ability attribution plays a more predominant role in attribution [24]. From this point of view, when the sponsor brand under exchange brand relationship norms encounters brand crisis and ability attribution happens, the “beneficial ties” between the sponsor brand and consumers will break up, and the unsatisfied and distrustful feelings among consumers about the sponsor brand tend to transfer to the sport-event brand, thus degrading the sport-event brand evaluation. Hence, the following hypothesis is made: Hypothesis 2. Under the condition of exchange brand relationship norms, compared with morality attribution, ability attribution has a more negative impact on sport-event brand evaluation. Brand relationship norms signify that consumers have a certain expectation for the brand. Simultaneously, consumers have a certain perception of the value

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of the brand [10]. When a crisis happens to the sponsor brand, the initial perception of the brand value, influenced by different crisis attribution, will have different impacts on sport-event brand evaluation. Thus, the following hypothesis is made: Hypothesis 3. Brand relationship norms play a moderating role in the process where the sponsor brand crisis attribution influences on sport-event brand evaluation (Fig. 1).

Brand Relationship Norms (Exchange versus Communal) Sponsor Brand Crisis Attribution (Morality Attribution versus Ability Attribution)

Sport-event Brand Evaluation

Fig. 1. Conceptual model

3 3.1

Empirical Research Experimental Group Design

This paper analyses the impact of sponsor brand crisis attribution on sportevent brand evaluation, and identifies the moderating effect of brand relationship norms. Therefore, this paper applies between-group design as 2 (sponsor brand crisis attribution: moral attribution & ability attribution) × 2 (brand relationship norms: communal relationship norms & exchange relationship norms). 3.2

Stimuli Design

In this study, a brand of drinking water is selected as the sponsor brand for a case study because of the high universality of the product category, familiarity, and product involvement. Moreover, to eliminate the potential influence of prior impressions of the relevant brand, the brand employed in the current study is fictitious and called “Brand A”. In addition, CBA (China Basketball Association) is utilized as the experimental sport-event stimulus out of two considerations: a) participants are familiar with this sport-event stimulus (CBA), thus ensuring

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the authenticity of this stimulus; b) participants’ attitude towards the sportevent should be moderate in order to avoid the influence of extreme attitude of participants (too high or too low). There are two types of experimental stimuli: the stimulus of sponsor brand crisis attribution and the stimulus of brand relationship norms. With respect to the former, two descriptions are given as follow: Stimulus of Sponsor Brand Crises Moral Attribution. CBA (China Basketball Association), a basketball league home and away beyond one year, was founded in 1995 by the Chinese Basketball Association. It is the Chinese premier men’s professional basketball league as well as Asia’s. Consequently, the size, management, operation of CBA is the best among Chinese basketball leagues. It is reported that Brand A (a brand of drinking water) has sponsored CBA for three years in succession. However, China State Food and Drug Administration (CSFDA) issued warnings on brand A to the public that the content of iodide exceeded the national standard in April 2014. Specifically, the content of iodide in each bottle per liter was 20% more than the standard of Sanitary Standards of Using Food Additives at less than 0.05 mg per liter. This violation may cause moderate diarrhea. However, there were no cases reported yet. To find out why the content of iodide exceeded the national standard, a journalist made secret inquiries. Later on, he found that to save cost, Brand A outsourced producing tasks to other small producers that did not have a production license. More seriously, Brand A also omitted the routine detection of iodide regulated by the Sanitary Standards of Using Food Additives. As the reporter evaluated, Brand A succeeded in saving 30% production cost by outsourcing producing tasks to other small producers and omitting the routine detection of iodide. Stimulus of Sponsor Brand Crisis Ability Attribution. CBA (China Basketball Association), a basketball league home and away beyond one year, was founded in 1995 by the Chinese Basketball Association. It is the Chinese premier men’s professional basketball league as well as Asia’s. Consequently, the size, management, operation of CBA is the best among Chinese basketball leagues. It is reported that Brand A (a brand of drinking water) has sponsored CBA for three years in succession. However, China State Food and Drug Administration (CSFDA) issued warnings on Brand A to the public that the content of iodide exceeded the national standard in April 2014. Specifically, the content of iodide in each bottle per liter was 20% more than the standard set by Sanitary Standards of Using Food Additives at less than 0.05 mg per liter. This violation may cause moderate diarrhea. However, there were no cases reported yet. To find out why the content of iodide exceeded the national standard, a journalist made secret inquiries. Later on, he found that Brand A produced drinking water in accordance with the national standard. Although the production facilities were not scrapped, production facilities were sharply perishing because of the long-term overload operation. Brand

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A had developed an equipment renewal plan before this brand crisis happened. Unfortunately, bank loans were not approved yet, which directly caused the brand crisis. According to the previous studies [1,5] and the reality of the drinking water industry, we formulate the stimulus of brand relationship norms. Stimulus of Communal Brand Relationship Norm. There are 27 million consumers to date focusing on the Weibo account (Weibo is a popular social platform in China similar to Twitter) of Brand A. The number of messages that are forwarded and left is more than 10 million every day. The Weibo account of Brand A launches an activity named “You give a like, I donate 1 yuan”. In other words, when a consumer gives a like, Brand A will donate 1 yuan to povertystricken areas. There are 230 million consumers who have given likes for now, and Brand A has donated 230 million yuan. Imagine this, you agree with Brand A very much and treat Brand A as your first choice. Simultaneously, you are a follower of the Weibo account of Brand A and always transfer the message of Brand A through Weibo. When it comes to this activity, you reckon that it is not only meaningful but also interesting. Stimulus of Exchange Brand Relationship Norm. Brand A has established 3 production bases in the national primary water source protection area, and signed the water conservation agreement with the local government. The aim of the agreement is to make sure that there are no industrial enterprises within fifty kilometers around production bases. Moreover, Brand A has developed dozens of patents to ensure that the quality of Brand A’s drinking water is pure and natural. On top of that, Brand A emphasizes that the notion of “good water comes from a good source”. Spontaneously, the natural and clean source and high quality of Brand A has been emphasized. Imagine this, you think highly of the pure and natural source of Brand A and treat Brand A as your first choice. Although the price of Brand A’s water product has been raised to 0.5 yuan per bottle, you still reckon that it is worth buying. 3.3

Scale Design

This paper utilizes 7 Likert scales to measure all kinds of stimulus and sportevent brand evaluation. The first measure is the familiarity with and the attitude towards the sport-event. With respect to the former, the item of measuring the familiarity with the sport-event is “I am extremely familiar with CBA” according to the research of Roehm [18]. With respect to the measure of consumers’ attitude towards the sport-event, three items adapted from the research of Pitt et al. [17] are employed in this study (“I think CBA is pretty good”, “I think CBA is very positive”, and “I like CBA very much”). The second measure is the sponsor brand crisis attribution. Inasmuch as we do not explore the condition of coexistence of moral attribution and ability attribution, it is proper that we just design one kind scale of attribution. This paper designs the moral attribution scale. When the score of moral attribution is higher than 4, it means moral attribution. When the score of moral attribution

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is equal to or lower than 4, it means ability attribution. Based on the research of Aggarwal [2], the items of measuring sponsor brand crisis attribution are “This crisis reflects that Brand A is lack of morality”, “Low morality causes this brand crisis”, and “This crisis reflects that Brand A sacrifices consumers’ health for profit”. The third is brand relationship norms. Based on the research of Pankaj and Aggarwal [1], the items of measuring communal brand relationship norms are “Brand A is a very special brand”, “Brand A concerns about consumers very much”, “Brand A likes its consumers very much”. The items of measuring exchange brand relationship norms are “Brand A values for money”, “Brand A is trusted for its quality, and consumers are attracted by its quality”, “Brand A deserves to buy”. The last is the sport-event brand evaluation. The items of measuring sportevent brand evaluation are “The sponsorship of Brand A makes CBA more attractive”, “As a consumer of Brand A, I will keep focusing on the next season”. 3.4

Pretest

A pretest was held in a university in Chengdu, in which 30 undergraduate students participated in the experiment. They are 16 males and 14 females, all aged between 18 and 22 years old. We divided 30 students into 2 groups. One group read the stimulus of moral attribution*communal brand relationship norms; the other group read the stimulus of ability attribution*exchange brand relationship norms. Firstly, we can see that the familiarity with and the attitude towards the sport-event is manipulated successfully, with an average score of 4.48 and 3.65 and a standard deviation of 1.55 and 1.68, respectively. These data prove that the enrolled students are familiar with CBA and have a moderate attitude towards CBA. Secondly, variance analysis shows that various stimuli are manipulated successfully. Significant differences in the perception of sponsor brand crisis attribution between two groups of students (Mability∗exchange = 4.67, Mmoral∗communal = 3.43; F (1, 28) = 9.25, p < 0.05), significant differences in the perception of communal brand relationships between two groups of students (Mmoral∗communal = 5.13, Mability∗exchange = 3.82; F (1, 28) = 13.44, p < 0.05), and significant differences in the perception of exchange brand relationships between two groups of students (Mmoral∗communal = 3.54, Mability∗exchange = 4.43; F (1, 28) = 9.28, p < 0.05) are found.

4 4.1

Data Analysis Sample Description

The experiment was held in a university in Chengdu, and 280 undergraduates participated in the experiment. They are all between 18 and 22 years old, and it is similar to the pretest. We reject the sample who did not answer the whole questionnaire and so on. In the end, 266 valid questionnaires were obtained.

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Manipulation Check

Firstly, the familiarity with and the attitude towards are manipulated successfully, with an average score of 4.25 and 3.71 and a standard deviation of 1.67 and 1.92 respectively. These data prove that students are familiar with CBA and their attitude towards CBA is moderate. Secondly, variance analysis shows that various stimuli are manipulated successfully. Significant differences in the perception of sponsor brand crisis attribution between two groups of students (Mmoral∗communal = 4.82, Mability∗exchange = 3.55; F (1, 264) = 22.34, p < 0.05), significant differences in the perception of communal brand relationships between two groups of students (Mmoral∗communal = 4.98, Mability∗exchange = 3.62; F (1, 264) = 13.80, p < 0.05), and significant differences in the perception of exchange brand relationships between two groups of students (Mmoral∗communal = 3.72, Mability∗exchange = 4.83; F (1, 264) = 6.21, p < 0.05) are found. 4.3

Variable Description

All scales in this paper are extracted from previous studies and hence are reliable. In the reliability analysis, the reliability of sponsor brand crises, brand relationship norms, sport-event brand evaluation, and all scales is 0.85, 0.83, 0.85, and 0.81, respectively. Therefore, the reliability of this paper is high. 4.4

Hypothesis Test

First, we test Hypothesis 1. One way ANOVA shows that under the condition of communal brand relationship norms, compared with ability attribution, morality attribution has a more negative impact on sport-event brand evaluation (Mmoral∗communal = 3.06, Mability∗communal = 3.84; F (1, 144) = 15.22, p < 0.05). Therefore, Hypothesis 1 is supported. Second, we test Hypothesis 2. One way ANOVA shows that under the condition of exchange brand relationship norms, compared with morality attribution, ability attribution has a more negative impact on sport-event brand evaluation (Mability∗exchange = 3.22, Mmorale∗exchange = 3.86; F (1, 120) = 17.62, p < 0.05). Therefore, Hypothesis 2 is supported. At last, we test Hypothesis 3. To verify the moderating role of brand relationship norms, we test the influence of the interaction of brand relationship norms and sponsor brand crisis attribution on sport-event brand evaluation. If the interaction is significant, it proves that brand relationship norms play a moderating role in the process where repair strategy influences on sport-event brand evaluation. Regression analysis shows that the regression model is significant (p < 0.01), and the regression coefficient of the interaction of brand relationship norms and sponsor brand crisis attribution is significant (r = 0.52, p < 0.01). Therefore, Hypothesis 3 is supported.

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Discussion

In recent years, sponsor brand crises happen frequently. What the organizers should do after sponsor brand crises happen is a significant question. Correspondingly, crisis attribution is an effective way to solve this problem. Previous studies investigating sponsor brand crises are always from the perspective of spillover, but rare is from the perspective of attribution. This paper takes brand relationship norms as a moderating variable and empirically investigates the impact of sponsor brand crisis attribution on sport-event brand evaluation. The study result will provide important theoretical implications for organizers of sport-event to deal with the aftermath of sponsor brand crises. The findings of this study are summarized below. First, under the condition of communal brand relationship norms, compared with ability attribution, morality attribution has a more negative impact on sport-event brand evaluation. Communal brand relationship norms signify that the relationship between consumers and the brand is excellent, stable, routine, and consumers do agree with the vision of the brand and are willing to pay. Under the condition of communal brand relationship norms, there is high emotional stickiness between the brand and consumers. Moreover, consumers have a special affection for the brand. Communal brand relationship norms focus on a kind of “parasitic” relationship. When a sponsor brand crisis happens, moral attribution will pose a more serious negative impact on a sport-event brand. In addition, under the condition of exchange brand relationship norms, compared with morality attribution, ability attribution has a more negative impact on sport-event brand evaluation. Exchange brand relationship norms highlight a kind of “beneficial” relationship. As can be seen, under the condition of exchange brand relationship norms, compared with moral attribution, ability attribution has a deeper impact on attribution. When a sponsor brand crisis happens, ability attribution will have a more serious negative impact on the sport-event brand. At last, brand relationship norms play a moderating role in the process where sponsor brand crisis attribution influences on sport-event brand evaluation. It signifies that consumers have a certain expectation for the brand. Simultaneously, consumers have a certain perception of brand value [10]. When a crisis happens to the sponsor brand, the initial perception of the value of sponsor brand is influenced by different kinds of crisis attribution. When sponsor crises happen, in addition to helping sponsors correct misbehaviors, sport-event organizers should find out which crisis attribution predominates, moral attribution or ability attribution. If moral attribution holds sway over the consumers, sport-event organizers should urge sponsors to take actions such as discounting and promoting to form exchange brand relationship norms with consumers in a short term. Otherwise, they should urge the sponsor to take actions such as charitable donation and promotion of the brand concept to form communal brand relationship norms with consumers in a short term. There are three limitations in this research. First, the sponsor brand in the stimuli is a fictitious brand (Brand A), and there is a big difference between a real brand and a fictitious brand. Second, the generalizability of the research

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conclusion should be verified. Drinking water only represents one kind of product; hence we need to verify other kinds of products to see whether the result is universal. Third, the samples are students. Although students are common participants in researches and the samples of students can improve the homogeneity of samples and reduce the impact of other interfering variables, the problem of sample representation still exists. Thus, subsequent researches can utilize nonstudent samples to verify the research model and improve external validity.

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Development Potential of Biomass Energy in Western Ethnic Regions by PSR Model Yanting Yuan(B) and Han Meng School of Management, Southwest Minzu University, Chengdu 610041, People’s Republic of China [email protected]

Abstract. As one of the most promising renewable energy source, biomass energy has great potential. In this case, it’s essential for the western ethnic regions in China to initiate a better use of the biomass energy, which will protect the environment, drive the economic growth, and reduce the regional disparities. With a certain industrial basis, the conditions for the development of biomass energy in China’s western ethnic regions are unique. For better development of biomass energy in these areas, the government should optimize the industrial structure of biomass energy and attach importance both to the talent training and the improvement of technical capabilities. Besides, the government should adjust measures to the local conditions and execute multi-development. Keywords: Western ethnic regions

1

· Biomass energy

Introduction

As the important cornerstone of the development of production activities, energy is a material basis that human beings depend on. Due to the rapid development of the global economy, the increasing demand for energy has led to the natural resource depletion [7,21], which has gradually become an bottleneck restricting the sustainable development [18]. Under the threat of increasing environmental pollution and sharp consumption of traditional disposable fossil energy, vigorous development of clean energy is the only way for countries around the world to achieve sustainable development [5,22]. Therefore, the development and utilization of biomass energy has become one of the important topics for countries to compete in [13,15,17,19]. Biomass energy, which is clean, efficient and sustainable, is a type of renewable energy. It is mainly produced from natural sources-energy crops, biomass, wastes and by-products, macro algae, microalgae, seaweeds and aquatic plants- capable of replacing fossil energy [12]. As one of the strategic emerging industries, there major topics: “energy”, “environment” and “agriculture” are related to the bio-energy industry [3]. Ever since the development of western regions in 2000, western ethnic regions have always taken natural resources as their regional comparative advantage [8]. c The Editor(s) (if applicable) and The Author(s), under exclusive license  to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 831–841, 2020. https://doi.org/10.1007/978-3-030-49829-0_62

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In order to achieve better modernity of civilization, the compression of time in ethnic areas occurred in an extremely condensed manner [2,11]. At the same time, the government developed the strategy of “natural resource-oriented industrial dependence and economical growth” [20], which only focused on immediate interests, causing the problems of repeated construction, ecological destruction, environmental degradation and resource depletion [1]. The emergence of these problems directly forced the energy industry in western ethnic regions to transform. Therefore, to promote the development of the biomass energy industry can be beneficial to reduce environmental pollution and protect the living environment of western ethnic minorities. Besides, as it ensures a stable energy supply, a industrial support for China’s western development strategy can be brought into light [10].

2

Characteristics of Western Ethnic Regions

The western ethnic regions are with lagging economic indicators and sparse population, it’s urgent to drive the economy growth in these areas [4]. Generally speaking, the regions are mainly involves five ethnic provinces, including Sichuan, Yunnan, Guizhou, Gansu, Qinghai and other five ethnic autonomous regions that contains Guangxi, Tibet, Ningxia, Xinjiang, Inner Mongolia. As the regions under resource-oriented strategy for many years, the resources in western regions have been excessively depleted and the ecological environment has been severely damaged, this local condition has become the major obstacle to the economic growth in western regions [9]. The competence of the population in the regions is low, with high rate of illiteracy and semi-illiteracy. In this case, although the western ethnic regions have abundant labor, yet with low level of education and health services, the development has been severely restricted [14]. As it shown in Tables 1 and 2, the western ethnic regions in China have obvious regional characteristics of natural, economic, and human resources. As it shown in Table 2, the distribution of biomass energy in western ethnic regions has the following characteristics: (1) Generally speaking, the distribution of biomass energy in the western ethnic regions is relatively uneven, combine with large inter-provincial differences. Sichuan, Yunnan, and Tibet in the southwest of the regions occupy a large advantage in biomass energy, accounting for about half of the entire ethnic regions. Moreover, among the regions, Sichuan ranks the first in all types of biomass energy. (2) The distribution of straw in western ethnic areas is basically the same as that of crops, mainly in some provinces and cities in the southwest. From the perspective of total biomass energy, the proportion of straw resources is not high. Animal manure resources are mainly distributed in provinces with developed animal husbandry and aquaculture industries. Due to different factors such as the ecological environment, traditional feeding habits and national customs, poultry breed and the types of manure resources have changed between regions. Forest resources are distributed in major forest areas, of which Sichuan, Yunnan and Tibet account for most of the reserves.

Development Potential of Biomass Energy

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Table 1. Economic overview of western ethnic regions in 2017 (Data source: China Statistical Yearbook) Region

Regional GDP per capita (yuan)

Disposable income of urban residents (yuan)

Disposable income of rural residents (yuan)

Added value of primary industry structure (Billion yuan)

Added value of secondary industry structure (Billion yuan)

Added value of Tertiary industry structure (Billion yuan)

Tibet

39267

30671.03

10330.21

122.72

513.65

674.55

Ningxia

50765

29472.28

10737.89

250.62

1580.57

1612.37

Xinjiang

44941

30774.8

11045.3

1551.84

4330.89

4999.23

Sichuan

44651

30726.87

12226.92

4262.35

14328.13

18389.74

Yunnan

37136

30995.88

9862.17

2032.27

5428.14

7833

Gansu

28497

27763.4

8076.06

859.75

2561.79

4038.36

Inner Mongolia

63764

35670.02

12584.29

1649.77

6399.68

8046.76

Guizhou

37956

29079.84

8869.1

2032.27

5428.41

6080.42

Guangxi

38102

30502.07

11325.46

2878.3

7450.85

8194.11

Qinghai

44047

29168.86

9462.3

238.41

1162.41

1224.01

National average

59201

36391.69

13432.43

62099.5

332742.7

425912.1

Western Ethnic regions

42912.6

30482.505

10451.97

1587.83

4918.452

6109.255

83.76%

77.81%

Proportion 72.49% of Western Ethnic regions

(3) The utilization of biomass energy in western ethnic regions is mainly focused on improving rural living conditions. Most of the biomass energy is straightly used in traditional utilization and direct combustion, which not only reduces the utilization efficiency, but also seriously threatens the ecological environment and the health of residents in the western ethnic areas. Therefore, to improve the technologies of biomass energy utilization such as biodiesel and biomass power generation, and to efficiently take use of biomass energy in the future has become great potential in western ethnic regions. (4) Besides to the abundance in biomass energy resources, there’s also a large number of other renewable energy sources in the western ethnic regions. In this case, combining with other renewable energy sources such as solar energy and wind energy, efficient development and utilization of biomass energy is of great significance for solving the energy problems in western ethnic regions.

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Table 2. Distribution differences of biological resources in western ethnic regions Biomass energy types

Range (10∧4t)

Areas covered

Straw

>4000 2000–4000 0–2000 >30000

Sichuan Inner Mongolia, Yunnan, Guangxi, Xinjiang Guizhou, Gansu, Ningxia, Tibet, Qinghai Sichuan

Animal manure

10000–30000 0–10000

Inner Mongolia, Yunnan, Guangxi, Xinjiang, Guizhou Gansu, Tibet, Qinghai, Ningxia

>30000

Tibet, Sichuan, Yunnan

10000–30000 0–10000

Inner Mongolia Guangxi, Xinjiang, Gansu, Guizhou, Qinghai, Ningxia

Wood firewood

3 3.1

Index System for the Evaluation of the Development Potential of Biomass Energy Regional PSR Model

PSR stands for pressure-state-response model and was proposed by the United Nations Economic Cooperation and Development Agency in the mid-1970s. The model is currently widely used in the area of sustainable development and assess of energy resource utilization [16]. Among which the pressure index is used to indicate human activities that cause certain pressure on the environment, the status index is used to indicate the system status in the process of sustainable development, and the response index is used to indicate the human respond to the sustainable development under certain economical strategies. 3.2

Data Source

Based on the national and provincial annual statistic report of energy, economy and environmental protection in 2017. The data on the official website of the Chinese National Data is also used in addition. The above evaluation index system is used to evaluate the development potential of biomass energy in western provinces and regions. 3.3

Construction of Evaluation Index System of PSR Model

(1) Stress index The pressure indicators includes three aspects: population pressure, environmental pressure and resource pressure. Combine the population growth rate with its’ amount, whether the development of biomass energy can meet the energy

Development Potential of Biomass Energy

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demand bought by the expansion of population can be obtained. Environmental pressure is considered in terms of per capita nitrogen oxides emissions, carbon dioxide emissions, and e-liquid emissions. As the development of biomass energy can improve the ecological environment to a certain extent, we take the environmental pressure as a reciprocal and turn the environmental pressure into an indicator that threatens the development of biomass energy. We considered the resource pressure from three aspects, the production of crude oil, raw coal, and natural gas. It reflects the competitive relationship between traditional fossil energy and biomass energy in the energy market. These indicators have a positive impact on the development. (2) Status index Status index is mainly measured from the following parts: resources, economy, industry, and the degree of interference each part is affected from the outside world. The reserve of biomass energy raw materials is the basis for the development of biomass energy. This article selects four indicators that can indirectly reflect the amount of biomass raw materials, including the number of livestock, sewage discharge, forest resources, and grain output. These indicators have positive impact on the development potential of biomass energy. The economic status of per capita GDP and residents’ consumption level can reflect the overall economic operation. As the development of biomass energy requires large-scale capital investment, the economic status of each region has a positive impact on the development potential of biomass energy. In the mean time, the industrial status is considered to consist of the regional power generation capacity, the biomass power grid-connected generation capacity, and the thermal power installed capacity, reflecting not only the current development status of biomass energy, but also the competition of other industries under the development of biomass energy generation (Table 3). (3) Response index Response index refers to external influences that improve the development of biomass energy. It is composed of highway mileage, the number of labor, environmental expenditure, the number of patent applications and R&D expenditure as a percentage of GSP. It reflects the government’s political and financial support on the development of biomass energy, as well as the local achievement in the progress of science and technology. Each index has a positive impact on the development potential of biomass energy. The specific index content selected in the index system is shown in Table 4.

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Table 3. Evaluation index system for biomass energy regional development potential System layer

Subsystem

Evaluation of Stress Population biomass energy index (B1) pressure (C1) development potential in Environmental China(A) pressure (C2)

Resource pressure (C3)

Status Resource index (B2) status (C4)

Index layer

Relationship Weights with system layer

Population growth rate (D1)%

-

0.0433

Population at the end of the year(D2)- Ten thousand people

0.0249

CO2 emissions per capita(D3) -T/Ten thousand people

0.0345

NOx emissions per capita (D4)-T/Ten thousand people

0.0286

Soot emissions(D5)-T

-

0.0327

Crude oil production (D6)-Ten thou- sand T

0.0185

Raw coal production (D7) -Ten thou- sand T

0.0181

Natural gas production (D8) - Billion M3

0.018

Large and medium-sized livestock at + the end of the year(D9)-Ten thousand heads

0.048

Total sewage thousand T

0.0643

Forest hectares

discharge(D10)-Ten +

area(D11)-Ten

thousand +

Grain yield (D12) -Ten thousand T

Response Index (B3)

0.0645

Economic state GDP per capita (D13) -Yuan/people + (C5) Household consumption level (D14) - + Yuan

0.041

Industrial status (C6)

- +

0.04

Biomass power generation grid- + connected capacity (D16) - Ten thousand KW.H

0.09

Thermal power installed capacity (D17) - Ten thousand KW.H

0.026

District power Billion KW.H

generation

(D15)

Social response Number of labor (D18) -Ten thousand + (C7) people

Technology response (C8)

4

+

0.049

0.033

0.062

Highway mileage(D19) -Ten thousand + KM

0.04

Environmental expenditure (D20 - 100 + million yuan

0.067

R & D expenditure as a percentage of + GDP(D21)%

0.046

Number of patent applications (D22) - + Pieces

0.11

Entropy Weight TOPSIS Method

This paper introduces the entropy weight method and TOPSIS method into the index system for the study of biomass energy potential in western ethnic regions. The entropy weight method is used to determine the weight of evaluation indices [6]. TOPSIS is used to comprehensively reflect the potential of biomass energy development in western ethnic regions. The specific calculation steps are as follows.

Development Potential of Biomass Energy

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(1) M regions and n indices are selected to represent the biomass energy development potential of these regions. Construct the original matrix of evaluation indices. ⎡

x11 ⎢ x21 ⎢ X=⎢ . ⎣ ..

x12 x22 .. .

xn1 xn2

⎤ · · · x1n · · · x2n ⎥ ⎥ .. .. ⎥ . . ⎦ · · · xnm n×m

(1)

(2) Normalization of index data. Normalize the positive indices,  Xij =

xij − min xij max xij − min xij

(2)

max xij − xij max xij − min xij

(3)

Normalize the reverse index,  Xij =

where: i = 1, 2, ..., m; j = 1, 2, ..., n (3) Calculate the weight of each index. First calculate the characteristic proportion of the j-th index in the i-th region: xij 1 P  ij =  ;k = − m ln m xij

(4)

i=1

Then you can find the entropy of the j-th index:

ej = k

m

Pij ln Pij

(5)

i=1

Finally, find the entropy weight of the j-th index:

wj =

1 − ej , j = 1, 2, ..., n n  n− ej i=1

(6)

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Y. Yuan and H. Meng

(4) Construction of weighted normative decision matrix yki = xki wk

(7)

(5) To determine positive and negative ideal solutions, let be positive ideal solutions and be negative ideal solutions where k = 1, 2, ..., 22. Then: + yki = max1≤i≤m (yki )

(8)

− yki = min1≤i≤m (yki )

(9)

(6) Calculate the Euclidean distance between the evaluation object and the optimal solution. And Euclidean distance of positive ideal solution and negative ideal solution, respectively

m  +  yk − yki ∧ 2 = d+ k

(10)

m   − dk = yki − yk− ∧ 2

(11)

k=1

k=1

(7) Calculate the closeness of the ideal solution. Then cji =

d− i + , i = 1, 2, ..., m d− i + di

(12)

The closeness cij indicates the distance between the evaluation object and the positive ideal solution. The closer the closeness is to 1, the better the potential of developing biomass energy in the area. On the contrary, the closer the closeness is to 0, the less the potential of developing biomass energy in the area.

5

Empirical Results and Analysis of the Evaluation of Biomass Energy Regional Development Potential in the Western Ethnic Areas

Table 4 Overall evaluation results, the relative closeness and ranking of each index of the criterion layer, and the optimal solution (Table 5).

Development Potential of Biomass Energy

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Table 4. Overall evaluation results, the relative closeness and ranking of each index of the criterion layer, and the optimal solution Area

Stress index (B1)

Status index (B2)

Impact index (B3)

Overview

Closeness

Closeness

Ranking

Closeness

Ranking

Closeness 0.257

Ranking

Ranking

Tibet

0.768

1

0.136

11

0.013

11

Ningxia

0.413

9

0.207

9

0.146

9

0.17

11

8

Xinjiang

0.142

11

0.367

6

0.208

8

0.221

10

Sichuan

0.734

2

0.817

1

0.981

1

0.959

1

Yunnan

0.727

3

0.562

4

0.536

3

0.561

4

Gansu

0.665

5

0.208

8

0.309

7

0.301

7

Inner Mongolia

0.413

10

0.81

2

0.358

5

0.585

3

Guizhou

0.612

7

0.28

7

0.374

4

0.353

6

Guangxi

0.683

4

0.454

5

0.316

6

0.424

5

Qinghai

0.661

6

0.159

10

0.084

10

0.234

9

National average

0.503

8

0.577

3

0.682

2

0.729

2

Table 5. Relative closeness of each index of the element layer and the optimal solution Area

Population Environmental Resource

Resource

Economic

Industrial

Social

pressure

pressure

pressure

status

status

status

influence

Technology influence

(C1)

(C2)

(C3)

(C4)

(C5)

(C6)

(C7)

(C8)

Tibet

0.377

0.888

1.000

0.224

0.225

0.21

0.071

0.000

Ningxia

0.503

0.318

0.985

0.058

0.684

0.17

0.06

0.178

Xinjiang

0.295

0.333

0.397

0.391

0.458

0.241

0.247

0.127

Sichuan

0.617

0.771

0.873

0.855

0.488

0.771

1

0.919

Yunnan

0.554

0.749

0.989

0.577

0.257

0.353

0.717

0.249

Gansu

0.704

0.744

0.577

0.244

0.144

0.25

0.343

0.222

Inner Mongolia

0.872

0.19

0.83

0.664

1

0.429

0.498

0.127

Guizhou

0.569

0.605

0.963

0.3

0.328

0.274

0.479

0.202

Guangxi

0.351

0.777

0.99

0.447

0.321

0.528

0.387

0.236

Qinghai

0.54

0.643

0.875

0.104

0.481

0.209

0.094

0.091

National average

0.703

0.448

0.782

0.477

0.889

0.778

0.604

0.905

6

Analysis of Evaluation Results

Based on the comprehensive scores of various provinces and regions in 2017, the development potential level of biomass and energy in the western provinces and regions can be divided into four grades. A comprehensive comparison of pressure, status, and response rankings of the western ethnic regions shows that the status and response of the Sichuan index is far ahead of other provinces. As a region abundant in reserves of biomass materials, Sichuan has good economic strength and strong industrial scale. It highly invested in the development for the comprehensive environmental protection by

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not only supporting the research and anti-pollution efforts of the local groups, but also providing considerable funds for the local educational institutions on the talents training. Making it ranks the first in terms of biomass development potential. However, as the most populous province in the western regions with heavy industrial concentration, Sichuan ranks high in all aspects of pressure indicators. Therefore, when developing the biomass energy plan, it will face greater environmental pressures caused by the population. Although Yunnan is under great pressure in the development of biomass energy, it still has a better condition due to its’ abundance in raw materials and policy support on environmental protection by the government. On the contrary, under less industrial pressure yet relatively poor scientific and technical conditions, Inner Mongolia’s situation is not optimistic. It should increase investment in scientific research and attach importance to the educating and training of talents in energy areas. Tibet, Xinjiang, Gansu, Guizhou, Guangxi, and Qinghai have the average potential for the development of biomass energy. These provinces should continue their efforts so as to reach the goal of economic improvement and scientific innovation. It is also necessary for them to increase capital investment in environmental protection and energy conservation continuously, so that the development of biomass energy industrial can be achieved. The province with the poorest biomass energy development potential is Ningxia. Although the pressure indicators, status indicators, and response indicators for the development of biomass energy in this region are the lowest among the western provinces, its’ situation remains arduous. Acknowledgement. The work was supported by the Program of the Fundamental Research Funds for the Central Universities, Southwest University for Nationalities No. [2014SZYQN49]. The authors are indebted to the editors and reviewers for their valuable comments and suggestions.

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Development Potential of Biomass Energy

841

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Author Index

A Abdullayeva, Narmina, 274 Ahmed, Danish, 66 Ahmed, Syed Ejaz, 340, 384, 433, 481, 492 Anişoara, Apetri, 525 Asghar, Naeem, 423 Askerova, Sheker, 503 Aydın, Dursun, 433 Aziz, Nazrina, 286 Azmi, Nur Atikah Binti Mohamad, 286 B Bian, Wenyang, 757 Bu, Qianjun, 577 C Camelia, Mihalciuc, 525 Cao, Yue, 588 Chai, Yang, 588 Chanthamith, Bouasone, 105 Chen, Jingdong, 701, 716 Chen, Mo, 701, 716 Chen, Qisheng, 561 Chen, Xudong, 356 Chen, Yeli, 601 Chen, Yiding, 182 Chen, Yingchun, 640 Chen, Zhan, 259 Cheng, John R., 322 Cristina (Coca), Timofte, 783 D Dai, Jingqi, 795 Dang, Xinghua, 48 Deng, Fumin, 771

Deng, Shuying, 396 Desai, Mohammad Ahmed, 32 Dumitriţa, Nucă, 525 F Faisal, Ch. Muhammad Nadeem, 670 Fan, Lurong, 795 G Gadjiev, Tahir, 447, 503 Gan, Lu, 640 Gan, Shengdao, 744 Garcia Marquez, Fausto Pedro, 460, 470 Gen, Mitsuo, 322 Guo, Furong, 744 Guo, Yan, 627 H Hafeez, Muhammad, 66, 670 Haidery, Asmara, 13 Hajiyev, Asaf, 274 Han, Xiaoyun, 757 Haron, Nur Ain Binti, 286 He, Chunming, 685 He, Dongmei, 157 He, Yanqiu, 81 He, Yue, 396 He, Yupei, 119 Hu, Bo, 614, 819 Hu, Die, 807 Hu, Ruifeng, 224 Hu, Siqi, 373 Hu, Xingling, 541 Hu, Zhineng, 373 Huang, Li, 731

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 J. Xu et al. (Eds.): ICMSEM 2020, AISC 1190, pp. 843–845, 2020. https://doi.org/10.1007/978-3-030-49829-0

844 J Jia, Luoyi, 142 Jiang, Jie, 407 Jiang, Qiang, 142 Jiang, Xueling, 819 K Kamran, Asif, 13, 32 Khan, M. Ahad Hayat, 32 Khelfaoui, Issam, 66 Khoo, Zhiwen, 259 Kuang, Ling, 577 L Li, Liping, 561 Li, Ruolan, 795 Li, Shan, 171 Li, Shouwei, 627 Li, Shuanghai, 130 Li, Wei, 614 Li, Wenjie, 757 Li, Xiaofeng, 561 Li, Xiyang, 577 Li, Ying, 541 Li, Yueyu, 577 Li, Zhihu, 716 Li, Ziyang, 744 Li, Zongmin, 93 Liang, Chengyi, 81 Liang, Shidi, 142 Liang, Xuedong, 771 Lin, Zhenlong, 130 Lisawadi, Supranee, 384, 481, 492 Liu, Jin, 541 Liu, Jing, 240 Liu, Junshan, 157, 407 Liu, Ping, 614, 627 Liu, Tingting, 795 Liu, Xiaodan, 670 Liu, Xiaolu, 93 Liu, Yunqiang, 81, 309 M Ma, Chenwei, 105 Mao, Liqin, 197 Maria, Grosu, 525 Mei, Hongchang, 182, 655 Meng, Han, 831

Author Index N Nathoo, Farouk S., 340 Nelson, Trisalyn, 340 Nitisiri, Krisanarach, 322 O Ohwada, Hayato, 322 Opoku, Eugene A., 340 P Phukongtong, Siwaporn, 481 Piladaeng, Janjira, 492 Pu, Yu, 119 Q Qureshi, Muhammad Fazal, 32 R Rintara, Pannipa, 384 Rizvi, S. M. Ahsan, 13 Rustamov, Yasin, 274, 447, 503 S Saleem, Asim, 423 Sarker, Md Nazirul Islam, 105 Segovia Ramirez, Isaac, 460, 470 Shao, Xiaoyu, 309 She, Maoyan, 807 Si, Dongyang, 210 Simona-Maria (Brînzaru), Tanasă, 783 Su, Zerui, 356 Svetlana, Mihăilă, 783 Syed, Nadeem A., 13 U Ul Ain, Qurat, 423 V Veronica, Grosu, 783 W Wang, Wang, Wang, Wang, Wang, Wang, Wang,

Anbang, 701 Cangyu, 407 Hong, 614, 819 Lihong, 655 Na, 224 Shihang, 373 Wenli, 48

Author Index Wang, Yaqi, 771 Wang, Yile, 157 Wang, Yuandi, 224 Wei, Yuzhu, 182 Wu, Lan, 197 Wu, Miao, 541 Wu, Min, 105 X Xie, Mute, 171 Xie, Yuantao, 66 Xiong, Guoqiang, 588 Xiong, Luoyi, 731 Xu, Jiuping, 1 Xu, Xinxin, 259, 511 Xu, Yanyan, 240 Y Yang, Lan, 119 Yang, Xiaoning, 640 Yang, Xue, 807 Yang, Ying, 588 Yangaliyeva, Aybeniz, 447 Yao, Liming, 356 Yılmaz, Ersin, 433

845 Yong, Zhi, 396 You, Ming, 309 Yu, Lingli, 197 Yu, Weiping, 210, 757 Yuan, Chunhui, 670 Yuan, Yanting, 831 Z Zeng, Zhen, 601 Zeng, Ziqiang, 511 Zhan, Chengyan, 744 Zhang, Dan, 396 Zhang, Lei, 407 Zhang, Lingyun, 601 Zhang, Qianyou, 259 Zhang, Qin, 157 Zhang, Xiaomei, 48 Zhang, Xinli, 601 Zhang, Ying, 685 Zhao, Chenzhu, 731 Zhou, Suichuan, 541 Zhu, Chengzheng, 240 Zia, Muhammad Azam, 423 Zinicovscaia, Inga, 297 Zu, Xu, 119, 210