Business Intelligence and Information Technology: Proceedings of BIIT 2022 981993415X, 9789819934157

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Smart Innovation, Systems and Technologies 358

Aboul Ella Hassanien Dequan Zheng Zhijie Zhao Zhipeng Fan   Editors

Business Intelligence and Information Technology Proceedings of BIIT 2022

123

Smart Innovation, Systems and Technologies Volume 358

Series Editors Robert J. Howlett, KES International Research, Shoreham-by-Sea, UK Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK

The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability. The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form. Volumes on interdisciplinary research combining two or more of these areas is particularly sought. The series covers systems and paradigms that employ knowledge and intelligence in a broad sense. Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community. It also focusses on the knowledge-transfer methodologies and innovation strategies employed to make this happen effectively. The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities. The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions. High quality content is an essential feature for all book proposals accepted for the series. It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles. Indexed by SCOPUS, EI Compendex, INSPEC, WTI Frankfurt eG, zbMATH, Japanese Science and Technology Agency (JST), SCImago, DBLP. All books published in the series are submitted for consideration in Web of Science.

Aboul Ella Hassanien · Dequan Zheng · Zhijie Zhao · Zhipeng Fan Editors

Business Intelligence and Information Technology Proceedings of BIIT 2022

Editors Aboul Ella Hassanien Faculty of Computer and AI Cairo University Giza, Egypt Zhijie Zhao School of Computer and Information Engineering Harbin University of Commerce Harbin, Heilongjiang, China

Dequan Zheng School of Computer and Information Engineering Harbin University of Commerce Harbin, Heilongjiang, China Zhipeng Fan School of Computer and Information Engineering Harbin University of Commerce Harbin, Heilongjiang, China

ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-981-99-3415-7 ISBN 978-981-99-3416-4 (eBook) https://doi.org/10.1007/978-981-99-3416-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are reserved 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, expressed 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Organization Committee

General Chair Dequan Zheng, Harbin University of Commerce, China

General Co-chairs Sabah Mohammed, Lakehead University, Canada Jina Fiaidhi, Lakehead University, Canada Luis Javier García Villalba, Universidad Complutense de Madrid, Spain Yaoqun Xu, Harbin University of Commerce, China Zeguo Qiu, Harbin University of Commerce, China Ping Han, Harbin University of Commerce, China

Technical Program Committee Chairs Zhijie Zhao, Harbin University of Commerce, China Subramaniam Ganesan, Oakland University, USA

Technical Program Committee Co-chairs Weipeng Jing, Northeast Forestry University, China B. H. Kang, University of Tasmania, Australia Naseer Al-Jawad, The University of Buckingham, UK

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Organization Committee

Publication Chairs Tai-Hoon Kim, Beijing Jiaotong University, China Aboul Ella Hassanien, SRGE, Egypt

Publication Co-chairs Zhipeng Fan, Harbin University of Commerce, China Carlos Ramos, ISEP/IPP, Portugal Mohamed Hamdi, Elgazala Innovation Center, Tunisia

Local Organizing Committee Chairs Xiaodong Su, Harbin University of Commerce, China Haitao Xin, Harbin University of Commerce, China Tai-Hoon Kim, Beijing Jiaotong University, China

Technical Program Committee Members Aboul Ella Hassanien, College of Business Administration, Kuwait Ajith Abraham, Norwegian University of Science and Technology, India Alicja Wieczorkowska, PJIIT, Poland Antonio Coronato, ICAR-CNR, Italy Arbib, Michael, University of Southern California, USA Ashesh Mahidadia, University of New South Wales, Australia Brian King, Purdue University, USA Bong-Hyun Kim, Seowon University, Korea Chantana Chantrapornchai, Silpakorn University, Thailand Charalampos Z. Patrikakis, National Technical University of Athens, Greece Chengcui Zhang, University of Alabama at Birmingham, USA Chris van Aart, Sogeti B.V, Netherlands Ching-Hsien Hsu, Asia University, Taiwan D. Manivannan, University of Kentucky, USA Duman Hakan, T Research and Technology, Germany Gerald Schaefer, Aston University, UK Giovanni Cagalaban, Hannam University, Korea Hakan Duman, British Telecom, UK Han-Chieh Chao, National Ilan University, Taiwan

Organization Committee

Hideyuki Suzuki, The University of Tokyo, Japan Ismail Khalil Ibrahim, Institute of Telecooperation, Austria James B. D. Joshi, University of Pittsburgh, USA Jason Sigfred, Surigao Sur Polytechnic State College, Philippines Javier Garcia-Villalba, Complutense University of Madrid, Spain Jemal H. Abawajy, Deakin University, Australia Ali Saberi, Distinguished Researcher in Iranian Researchers Network, Iran Naveen K. Chilamkurti, La Trobe University, Australia Noskov Mikhail Fedorovic, Siberian Federal University, Russia Kamal Karkonasasi, Universiti Sains Malaysia, Malaysia Harald Kitzmann, Kazakh University, Kazakhstan Andrii Bieliatynskyi, National Aviation University, Ukraine Anelia Kurteva, University of Innsbruck, Austria Uduak Augustine UMOH, University of Uyo, Nigeria Muhammad Hashim, National Textile University, Pakistan Globa Larysa, National Technical University of Ukraine, Ukraine Alex Mathew, Bethany College, USA Levleva Olga T., Southern Federal University, Russia Tan Xiao Jian, Tunku Abdul Rahman University College (TARUC), Malaysia Antonio Lucadamo, University of Sannio, Italia Anna Crisci, University of Naples Federico II, Italia Mahfujur Rahman, Daffodil International University, Bangladesh Nassira Achich, University of Sfax, Tunisia

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Preface

This volume constitutes the refereed proceedings of the 2022 International Conference on Business Intelligence and Information Technology (BIIT 2022) held in Harbin, China, during December 17–18, 2022. BIIT 2022 is organized by the School of Computer and Information Engineering, Harbin University of Commerce, and supported by Scientific Research Group in Egypt (SRGE), Egypt. BIIT is organized to provide an international forum that brings together those actively involved in the areas of interest, report on up-to-the-minute innovations and developments, summarize the state of the art, and exchange ideas and advances in all aspects of business intelligence and information technologies. The papers cover current research in business intelligence and decision making, digital economy and digital technology, electronic commerce technology and application, image analysis and processing, information technology and applications, machine learning and deep learning and their applications, smart economy and data analysis, and artificial intelligence technology. As well as the regular submission to the main conference, there are eight special sessions have been organized. The conference proceedings has seven main tracks: Part I—Business Intelligence and Decision Making Part II—Digital Economy and Digital Technology Part III—Electronic Commerce Technology and Application Part IV—Image Analysis and Processing Part V—Information Technology and Applications Part VI—Machine Learning and Deep Learning and Their Applications Part VII—Smart Economy and Data Analysis On average, all submissions were reviewed by at least two reviewers, with no distinction between papers submitted for all conference tracks. We are convinced that the quality and diversity of the topics covered will satisfy both the attendees and the readers of this conference proceedings. We express our sincere thanks to the plenary speakers, workshop/session chairs, and International Program Committee members for helping us to formulate a rich technical program. We want to extend our sincere appreciation for the outstanding work contributed over many months by the Organizing Committee: local organization chair and publicity chair. We also wish to ix

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express our appreciation to the SRGE members for their assistance. We want to emphasize that the success of BITT 2022 would not have been possible without the support of many committed volunteers who generously contributed their time, expertise, and resources toward making the conference an unqualified success. Finally, thanks to the Springer team for their support in all stages of the production of the proceedings. We hope that you will enjoy the conference program. Giza, Egypt

Aboul Ella Hassanien [email protected]

Harbin, China

Prof. Dequan Zheng [email protected]

Harbin, China

Dr. Zhipeng Fan [email protected]

Harbin, China

Prof. Zhijie Zhao [email protected]

Contents

Part I 1

2

Business Intelligence and Decision Making

Research on the Incentive Mechanism of Publishing Editors Based on Psychological Contract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shang-kun Lu and Zai-ming Yang 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 An Analysis of the Influencing Factors of Publishing Organization Editors’ Psychological Contract . . . . . . . . . . . . . . . . 1.3.1 Dominant Factors in Publishing Organizations . . . . . . . 1.3.2 Editing Team’s Own Hidden Factors . . . . . . . . . . . . . . . . 1.4 Data Sources and Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Editorial Incentive Mechanisms for Publishing Organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Reconstructing the Corporate Culture Ecology of Integrated Publishing . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Establish a “People-Oriented” Management Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Create High-Trust Organizational Working Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Optimize Performance Management System and Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of China’s Digital Agriculture Development Level Under the Entropy Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jialin Dong 2.1 Introduction and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Research Methods and Data Sources . . . . . . . . . . . . . . . . . . . . . . . .

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2.2.1 2.2.2

Indicator System Construction . . . . . . . . . . . . . . . . . . . . . Determination of Evaluation Index Weights Based on Entropy Value Method . . . . . . . . . . . . . . . . . . . 2.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Analysis of Evaluation Results . . . . . . . . . . . . . . . . . . . . . 2.3.2 Time Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3 Time Series Evolution Analysis . . . . . . . . . . . . . . . . . . . . 2.4 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

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Research on Patent Cooperation Network of Patent-Intensive Industries in Heilongjiang Province from the Perspective of Complex Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ye Tian, Xiaojia Wang, and Wei Chen 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Construction of Patent Cooperation Network . . . . . . . . . . . . . . . . . 3.2.1 Research Tools and Methods . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Empirical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Static Analysis of Patent Cooperation Network . . . . . . . 3.3.2 Evolution Analysis of Patent Cooperation Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study of Egg Product Supply Chain Benefit Allocation Based on Fuzzy Shapley Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Defa Cai and Zepeng Wang 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Shapley Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Numerical Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Classical Shapley Value Method for Egg Product Supply Chain Revenue Allocation . . . . . . . . . . . . . . . . . . 4.3.2 Modified Shapley Value Allocation . . . . . . . . . . . . . . . . . 4.4 Conclusion and Insights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Trilateral Evolutionary Game Analysis of Personal Information Use and Protection in the Digital Economy . . . . . . . . . . . Shiying Zhang and Yao Tian 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Model Design of Trilateral Game . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Model Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Parameter Variable Setting . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Decision Variables Setting . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Trilateral Evolution Game Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Evolution Equations in Dynamic Game Processes Based on Government Perspective . . . . . . . . . 5.4.2 The Evolution Equation in the Dynamic Game Process of the Source of Personal Information . . . . . . . 5.4.3 The Evolution Equation in the Dynamic Game Process of Businesses (C) . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Analysis of Evolution Path of Trilateral Dynamic Game . . . . . . . 5.5.1 Path Evolution of Government-Driven Three-Party Dynamic Game . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Analysis of the Evolution Path of the Government-Driven Tripartite Dynamic Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research on the Evaluation of Tourism Digital Content Marketing Based on PCA-AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kewen Liu and Hui Xian 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Digital Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Digital Content Marketing . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Evaluation of Digital Content Marketing . . . . . . . . . . . . 6.3 Indicator System Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Evaluation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Selection of Evaluation Indicators . . . . . . . . . . . . . . . . . . 6.3.3 Determination of Index Weights . . . . . . . . . . . . . . . . . . . 6.4 Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Evaluation Process and Result Analysis . . . . . . . . . . . . . 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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8

Contents

Research on the Performance Evaluation of Digital Trade Based on the “VHSD-EM” Model Under Dual Circulation . . . . . . . . Yijun Xiang and Jiayi Yuan 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Meaning of Digital Trade Under Dual Circulation . . . . . . . . . . . . . 7.2.1 Definition of Digital Trade . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Characteristics of Digital Trade Under Dual Circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Performance Evaluation Mechanism of Digital Trade . . . . . . . . . . 7.3.1 Construction of Performance Evaluation System for Digital Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Measurement Method of Digital Trade . . . . . . . . . . . . . . 7.4 Evaluation of the Present Situation of China’s Digital Trade Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 The Advantages of Digital Infrastructure . . . . . . . . . . . . 7.4.2 The Advantages of the Large Scale of Market . . . . . . . . 7.4.3 The Advantages of the Huge Industrial Scale . . . . . . . . 7.4.4 The Insufficiency of the Key and Core Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.5 The Highlighted Importance of Data Security and Privacy Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Measures to Promote the Development of Digital Trade . . . . . . . . 7.5.1 Increasing Technological Innovation in Digital Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Deepening International Cooperation on Digital Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.3 Upgrading the Security Guarantee System of Digital Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research on the Distribution of Income from Agricultural Supply Chains Under the Integration of Agriculture Based on the Revised Shapley Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Xuan Sun and Nan Xu 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Literature Review on Agricultural Supply Chain . . . . . . . . . . . . . . 8.3 Shapley Model and Revenue Distribution . . . . . . . . . . . . . . . . . . . . 8.3.1 Introduction to the Shapley Method . . . . . . . . . . . . . . . . 8.3.2 Agricultural Supply Chain Node Revenue Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Modification of the Shapley Model . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Influencing Factors Affecting the Distribution of Supply Chain Benefits . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 Shapley Value Correction Model . . . . . . . . . . . . . . . . . . .

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Example Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 AHP Hierarchical Analysis Method to Calculate the Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Correction of Shapley Value . . . . . . . . . . . . . . . . . . . . . . . 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II 9

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Digital Economy and Digital Technology

Analysis of the Evaluation of the Effect of International Cooperation on the Cultivation of Talents in Vocational Colleges Under the Construction of “Double-High” . . . . . . . . . . . . . . . Lixia Yang, Jiao Liu, Chaohong Liu, and Shaoqing Tian 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Establishment of the Indicators . . . . . . . . . . . . . . . . . . . . 9.2.2 Data Processing and Analysis Methods . . . . . . . . . . . . . 9.3 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Entropy Right Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 The TOPSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Data Analysis of Talent Training Effect . . . . . . . . . . . . . . . . . . . . . . 9.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4.2 Calculating the Entropy Weights . . . . . . . . . . . . . . . . . . . 9.4.3 Construction Based on the TOPSIS Model . . . . . . . . . . 9.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

10 The Impact of Digital Finance on Energy Intensity——New Evidence from China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi Qu, Aizhi Li, and Kai Ning 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Variable Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Measurement Regression Results . . . . . . . . . . . . . . . . . . 10.3.2 Heterogeneity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109 109 112 112 113 113 113 114 115 115 116 117 119 120 122 123 123 124 124 125 126 126 126 128 129 129

11 Research on the Influencing Factors of China’s Digital Trade Development Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Lanlan Zhou, Feifei Chen, and Chengwen Kang 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

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11.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Analysis of Influencing Factors of China’s Digital Trade Development Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Suggestions for Accelerating the Development of Digital Trade in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Accelerate the Construction of Digital Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.2 Increase Financial Support and Talent Training . . . . . . . 11.5.3 Upgrade the Digital Industry Structure . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Research on the Coupling and Coordinated Development of the Digital Economy and Rural Revitalization . . . . . . . . . . . . . . . . . Long Yin and Jinwen Yao 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Theoretical Construction of the Coupling of the Digital Economy and Rural Revitalization . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Empirical Analysis of the Coupling Between the Digital Economy and Rural Revitalization . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.1 Construction of Indicator System and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.2 Model Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3.3 Discussion of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 The Impact of the Digital Economy on TFP in China’s Equipment Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zengfan Liu and Shimiao Zhang 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Reviews of Relevant Research Literature . . . . . . . . . . . . . . . . . . . . 13.2.1 The Definition and Statistical Standard of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.2 The Concept of TFP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.3 Researches Related to Digital Economy and Equipment Manufacturing TFP . . . . . . . . . . . . . . . . 13.3 Study Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.1 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.2 Variable Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.3 Data Sources and Processing . . . . . . . . . . . . . . . . . . . . . . 13.4 Analysis of TFP and Digital Economy Development Level . . . . . 13.4.1 Static Efficiency Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 Dynamic Efficiency Analysis . . . . . . . . . . . . . . . . . . . . . .

132 133 134 137 138 138 138 139 141 141 143 144 144 144 147 148 148 148 149 151 151 152 152 153 153 154 154 154 156 156 156 159

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13.4.3 Data Processing for Digital Economy . . . . . . . . . . . . . . . 13.4.4 Digital Economy Analysis . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Analysis of Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.1 Empirical Tests Based as a Whole and Sub-Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5.2 Regional Heterogeneity Analysis . . . . . . . . . . . . . . . . . . . 13.6 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 A Study on the Impact of Digital Finance on Green Technology Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Wang and Xiaohu Bian 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Theoretical Analysis and Research Hypothesis . . . . . . . . . . . . . . . 14.2.1 Green Technology Innovation and Digital Finance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 The Inclusive Function of Digital Finance for the Growth of Urban Green Technology Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3.1 Variable Definitions and Data Descriptions . . . . . . . . . . 14.3.2 Model Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4 The Impact of Digital Finance on Green Technology Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.1 Return to Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.2 Robustness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.3 Endogeneity Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Inclusive Attributes of Digital Finance for Urban Green Technology Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.6 Research Findings and Policy Recommendations . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Research on the Relationship Between the Digital Economy, Industrial Innovation Capability, and Servitization of the Advanced Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . HuiMei Qu, YueYing Cui, and Wei Chen 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Research Hypotheses and Theoretical Models . . . . . . . . . . . . . . . . 15.3.1 Research Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.2 Sample Selection and Data Sources . . . . . . . . . . . . . . . . 15.3.3 Variable Indicator Description . . . . . . . . . . . . . . . . . . . . . 15.4 Empirical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.1 Digital Economy, Industrial Innovation Capability, and Servitization of Advanced Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . .

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161 162 162 163 163 165 168 171 171 173 173

173 174 174 175 175 175 176 176 177 178 179

181 181 182 183 183 184 185 188

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15.4.2 Test for Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 Research Conclusions and Countermeasures . . . . . . . . . . . . . . . . . 15.5.1 Research Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.2 Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Research on the Performance of Digital Economy in Heilongjiang Province on the Development of the Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fangyu Dong and Dejun Song 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Review of Relevant Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3 The Construction of Index System and Model . . . . . . . . . . . . . . . . 16.3.1 Data Sources and Evaluation Methods . . . . . . . . . . . . . . 16.3.2 Variable Selection and Index System . . . . . . . . . . . . . . . 16.3.3 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.1 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.4.2 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5.2 Policy Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Nature-Inspired Optimization Methods in Digital Filters Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adam Slowik and Aneta Hapka 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Digital Filters: FIR and IIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.3 Nature-Inspired Optimization Methods . . . . . . . . . . . . . . . . . . . . . . 17.4 Application of Nature-Inspired Optimization Methods to Digital Filters Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 The Impact of Urban Factor Market Distortion on the Innovation Efficiency of Industrial Enterprises in Heilongjiang Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yin Zhao, Yanhua Li, Zelong Ti, and Xiaojia Wang 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Evaluation of Distortion Degree of Urban Factor Market in Heilongjiang Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2.1 Model Principle and Setting . . . . . . . . . . . . . . . . . . . . . . . 18.2.2 Variable Selection and Data Processing . . . . . . . . . . . . . 18.2.3 Measurement Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

190 191 191 191 192

195 195 196 197 197 198 199 199 199 201 201 201 202 203 205 205 207 208 209 212 212

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18.3 Measurement of Innovation Efficiency of Urban Industrial Enterprises in Heilongjiang Province . . . . . . . . . . . . . . . . . . . . . . . . 18.3.1 Selection of Research Methods . . . . . . . . . . . . . . . . . . . . 18.3.2 Variable Selection and Data Sources . . . . . . . . . . . . . . . . 18.3.3 Measurement Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part III Electronic Commerce Technology and Application 19 Optimal Decision of Fresh Agricultural Products’ Supply Chain Under Stochastic Demand and Online Pre-selling . . . . . . . . . . Han Xiuping, Yang Guang, and Xu Hang 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Model Formation and Basic Assumptions . . . . . . . . . . . . . . . . . . . . 19.3 Model Establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3.1 Centralized Decision-Making . . . . . . . . . . . . . . . . . . . . . . 19.3.2 Decentralized Decision-Making . . . . . . . . . . . . . . . . . . . 19.4 Numerical Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.4.1 Impacts of Pre-sale Discount Rate on Sales Price and Network Service Level . . . . . . . . . . . . . . . . . . . . . . . . 19.4.2 The Effect of Pre-sale Discount Rate on the Optimal Profit of Each Member in the Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Research on the Impact of Cross-Border Data Flow Restrictions on Digital Service Trade and Its Countermeasures . . . . Hui-ying Yang and Xi-yuan Tian 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2 Analysis on the Current Situation of Restrictions on Digital Service Trade and Cross-Border Data Flow . . . . . . . . . . . . . . . . . . 20.2.1 Development Status of Digital Service Trade . . . . . . . . 20.2.2 Current Situation of Cross-Border Data Flow Restrictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3 Analysis of the Impact of Cross-Border Digital Flow on Digital Service Trade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3.1 Raising the Trade Cost of Enterprises . . . . . . . . . . . . . . . 20.3.2 Hinder the Improvement of Production Efficiency . . . . 20.3.3 Reduce the Scale of Export Trade . . . . . . . . . . . . . . . . . . 20.4 Econometric Models and Variable Demarcation . . . . . . . . . . . . . . . 20.4.1 Measurement Model Setting . . . . . . . . . . . . . . . . . . . . . . . 20.4.2 Index Meaning and Data Source Description . . . . . . . . . 20.4.3 Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

231 232 233 234 234 236 238 238

239 240 240 243 243 244 244 245 246 246 247 247 248 248 248 249

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20.5 Results Inspection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.5.1 Full Sample Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.5.2 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.6 Conclusions and Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Research on Trade Facilitation of Cross-Border Export B2C E-Commerce Based on Entropy Weight Method . . . . . . . . . . . . . . . . . Li Dai, Xu Zhang, and Yanhua Li 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Research Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Model Construction and Variable Introduction . . . . . . . . . . . . . . . . 21.3.1 Data Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3.2 Entropy Weighting Method . . . . . . . . . . . . . . . . . . . . . . . 21.3.3 Trade Facilitation Level Comparison Based on Entropy Weight Method . . . . . . . . . . . . . . . . . . . . . . . . 21.3.4 Application of Fixed Effect Model Based on Entropy Weight Method . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Research on Pre-sales Services and Pricing Strategies for Fresh Produce e-Commerce Under Different Game Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hang Xu, Guang Yang, and Xiuping Han 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Model Formation and Basic Assumptions . . . . . . . . . . . . . . . . . . . . 22.2.1 Consumer Utility Analysis . . . . . . . . . . . . . . . . . . . . . . . . 22.2.2 Basic Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Model Establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3.1 Service and Pre-sales Strategies in a Cooperative Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3.2 Service and Pre-sales Strategies for E-tailer-led Stackelberg Gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3.3 Nash Gaming’s Service and Pre-sales Strategy . . . . . . . 22.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

250 250 250 250 253 255 255 256 257 257 257 261 261 263 264 264

267 268 269 269 270 271 271 272 274 275 276 277

23 Study on Dynamic Pricing of Perishable Goods Based on Consumer Strategic Behavior in Two Purchasing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Jian Wang and Wen Hu 23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 23.1.1 Dynamic Pricing of Perishable Goods . . . . . . . . . . . . . . 280

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23.1.2 The Purpose of the Study . . . . . . . . . . . . . . . . . . . . . . . . . 23.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3 Theory and Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3.1 A Subsection Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.4 Dynamic Pricing of Perishable Products on Consumer Strategic Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.4.1 The Pricing of Consumer Strategic Behavior in Determine Environment . . . . . . . . . . . . . . . . . . . . . . . . 23.4.2 Pricing in Uncertainty Condition with Consumer Strategic Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.4.3 The Purchase Decision of Strategic Consumers . . . . . . 23.4.4 Optimal Dynamic Pricing Decision of E-retailers with Uncertain Demand . . . . . . . . . . . . . . . . . . . . . . . . . . 23.5 Numerical Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.5.1 The Influence of Consumer Strategic Behavior on Retailers Profits in the Market . . . . . . . . . . . . . . . . . . 23.5.2 Consumer Strategy Behavior Under Out of Stock Risk with Uncertain Environment . . . . . . . . . . . . . . . . . . 23.6 Conclusion and Prospect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Challenges and Solutions for Arabic Natural Language Processing in Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sallam AL-Sarayreh, Azza Mohamed, and Khaled Shaalan 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.2 Problem Identification and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 24.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.4.1 Criteria for Acceptance and Removal . . . . . . . . . . . . . . . 24.4.2 Data Sources and Search Strategies . . . . . . . . . . . . . . . . . 24.4.3 Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.6 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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281 281 282 282 283 283 285 285 286 287 288 289 291 292 293 293 293 294 295 295 297 297 297 299 299 300 301

Part IV Image Analysis and Processing 25 MSAN: Multi-stage Human Pose Estimation Universal Network Based on Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . Yuru Zhang, Jiayuan Zhao, Xiaodong Su, Shizhou Li, Yurong Zhang, and Hongyan Xu 25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.1 Human Pose Estimation . . . . . . . . . . . . . . . . . . . . . . . . . .

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25.2.2

Human Pose Estimation Based on Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.3 Attention Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3.2 Backbone Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.3.3 Channel Spatial Attention Network . . . . . . . . . . . . . . . . . 25.3.4 Multi-stage Self-attentive Network . . . . . . . . . . . . . . . . . 25.3.5 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.4.1 Datasets and Evaluation Indicators . . . . . . . . . . . . . . . . . 25.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 An Image Matting Algorithm Based on Inception-ResNet-V2 Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guilin Yao and Ruiguo Huang 26.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3 Matting Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3.1 Image Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3.2 Trimap Generate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3.3 Overall Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . 26.3.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 An Improved Image Colorization Algorithm Based on Pix2Pix . . . . Haitao Xin and Zixuan Zhang 27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2 Related Algorithm Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.1 Lab Color Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.2 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.3 Instance Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.2.4 Partial Convolution-Based Padding . . . . . . . . . . . . . . . . . 27.3 Network Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.4.1 Setups and Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27.4.2 Experimental Process and Evaluation Index . . . . . . . . . 27.4.3 Experimental Results and Analysis . . . . . . . . . . . . . . . . . 27.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

308 309 310 310 310 311 313 314 315 315 316 319 320 323 323 324 326 326 327 328 331 331 333 333 335 335 336 336 337 337 338 338 340 340 340 341 343 344

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28 Quality Evaluation Algorithm of Human Video Motion Image Segmentation Based on Visual Perception . . . . . . . . . . . . . . . . . . . . . . . Qingwei Wang, Xinyu Wang, Zhifeng Lv, and Dahai Tan 28.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2 Feature Detection and PR of HVMI . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.1 Detection of ECF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.2.2 Pixel PR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.3 Quality Evaluation of HVMI Segmentation . . . . . . . . . . . . . . . . . . 28.3.1 Blur Feature Extraction of HVMIs . . . . . . . . . . . . . . . . . 28.3.2 Visual Perception of Image Segmentation . . . . . . . . . . . 28.4 Experiments Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.4.1 Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.4.2 Evaluation Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.4.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Target Tracking Method for Human Motion Image Based on Kalman Filtering Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qingwei Wang, Xinyu Wang, Dahai Tan, and Zhifeng Lv 29.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2 Target Tracking Method of Human Motion Image . . . . . . . . . . . . . 29.2.1 Human Motion Image Feature Acquisition . . . . . . . . . . 29.2.2 Target Feature Recognition Algorithm of Human Motion Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.2.3 Realization of Object Tracking in Human Motion Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sabah Mohammed, Sudeepthi Govathoti, K. V. Satyanarayana, and Eali Stephen Neal Joshua 30.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.3 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.4 Methodology of the Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . 30.5 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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31 Lung Cancer Classification Using Improvised CNN . . . . . . . . . . . . . . Subramaniam Ganesan, Eali Stephen Neal Joshua, K. V. Satyanarayana, and V. Nagu 31.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.1 Basic Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3.2 CSPNet—Cross Stage Partial Network . . . . . . . . . . . . . . 31.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part V

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386 386 387 387 388 390 391 391

Information Technology and Applications

32 Research on IoT Data Collection Middleware Based on Microservice Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Haihao and Sun Xu 32.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2.1 IoT Data Collection Middleware Based on SOA . . . . . . 32.2.2 IoT Data Collection Middleware Based on Microservice Architecture . . . . . . . . . . . . . . . . . . . . . . 32.3 Design and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3.1 Overall Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.3.2 Features of the Main Microservice . . . . . . . . . . . . . . . . . 32.4 Microservice Component Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.4.1 Device Description File . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.4.2 Communication Design Between Microservices . . . . . . 32.4.3 Design of Communication Microservice . . . . . . . . . . . . 32.4.4 Design of Load Balancing . . . . . . . . . . . . . . . . . . . . . . . . 32.4.5 IoT Access Device Capability Evaluation Model . . . . . 32.5 Experimental Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.5.1 Functional Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.5.2 Testing of IoT Access Device Capability Evaluation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 The Daily Tourist Predicting Based on Classification Model . . . . . . . Yin-chao Ma and Lian-bin Zhou 33.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33.3.1 Input Variables Determining . . . . . . . . . . . . . . . . . . . . . . . 33.3.2 Prediction Results Analysis . . . . . . . . . . . . . . . . . . . . . . .

395 395 396 396 396 397 397 398 399 399 399 400 401 401 402 402 404 405 405 407 407 409 410 410 411

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33.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413 34 Information Asymmetry Simulation of Comprehensive Transparency with Meta Model in a Trader Behavior Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haoyang Du 34.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.2 Model Variable Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.2.1 Sample Selection and Data Sources . . . . . . . . . . . . . . . . 34.2.2 Variable Definition and Model Building . . . . . . . . . . . . . 34.3 Model Parameter Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.3.1 Explanatory Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.3.2 Control Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.3.3 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.4.1 Descriptive Statistical Analysis . . . . . . . . . . . . . . . . . . . . 34.4.2 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.4.3 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Implementation of Business Data Sharing Based on Blockchain and Improvement of Consensus Algorithm . . . . . . . . . Biying Zhang, Bowen Zhang, and Lei Zhang 35.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.2 Related Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.3 Data-Sharing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.4 PoLe-Pre-Train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.4.1 Pre-train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.4.2 Train . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.4.3 Reword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.4.4 Security Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.5 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.5.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . . 35.5.2 Training Script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.5.3 Experiment Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

415 416 417 417 417 418 419 421 421 423 423 424 424 428 429 431 431 432 433 434 434 435 435 436 436 436 437 437 439 440

36 Knowledge Structure and Research Progress in Mental Workload (MWL) Using CiteSpace Based on Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Yingying Wei, Huimei Qu, Song Wang, and Changdong Xu 36.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 36.2 Mental Workload Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443

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36.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36.3.1 Bibliometric Study of MWL . . . . . . . . . . . . . . . . . . . . . . 36.3.2 Web of Science (WoS)-Based Investigation . . . . . . . . . . 36.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36.4.1 Document Co-citation Analysis (DCA) . . . . . . . . . . . . . 36.4.2 Keyword Co-occurrence Analysis . . . . . . . . . . . . . . . . . . 36.4.3 Hotspots and Emerging Trends of MWL . . . . . . . . . . . . 36.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

444 444 444 445 445 446 448 449 450

37 Predicting Social Events Using Analyses of Arabic Dailies . . . . . . . . . Renata Avros, Dan Lemberg, Elena V. Ravve, and Zeev Volkovich 37.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.2 Time Series Newspapers Patterning . . . . . . . . . . . . . . . . . . . . . . . . . 37.2.1 Regression Mean Rank Dependency . . . . . . . . . . . . . . . . 37.2.2 Modified Thompson Tau Test . . . . . . . . . . . . . . . . . . . . . . 37.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.4 Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.4.1 Parameters Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.5 Discussions and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

451

38 Barrage Sentiment Analysis Based on Snow NLP—An Example of Liu’s Fitness Video . . . . . . . . . . . . . . . . . . . . . . . Lixia Zhang and Yuxuan Zhang 38.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.2.1 Chinese Bullet Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.2.2 Sentiment Analysis Based on the Sentiment Dictionary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.3.1 Data Crawling and Pre-processing . . . . . . . . . . . . . . . . . 38.3.2 Sentiment Analysis of Pop-Up Comments . . . . . . . . . . . 38.3.3 Data Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Statistical Analysis of COVID-19 Pandemic Lockdown on Rural Undergraduate Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Hamdi, Nakka Marline Joys, Debnath Bhattacharyya, and N. Thirupathi Rao 39.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.2.1 Inadequate Familiarity with Digital Technology . . . . . . 39.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

451 454 454 457 458 461 463 469 469 471 471 472 472 472 473 473 474 476 478 479 481

481 482 483 483 484

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39.3.1 Method of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.3.2 Design and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

484 485 489 490

40 Detection of COVID-19 Using SIS-CODE Algorithm . . . . . . . . . . . . . Daegeon Kim and Sudeepthi Govathoti 40.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.3.1 Specimen Image Smoothing (SIS) Algorithm . . . . . . . . 40.3.2 Coronavirus Detection (CODE) Algorithm . . . . . . . . . . 40.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.4.1 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

491

41 Nanotechnology and the Application of Information Technology for Sustainable Innovation in Agriculture . . . . . . . . . . . . Pushan Kumar Dutta, Ujan Banerjee, Banani Manna, Kabyashree Hazarika, Bimalangshu Das, and Sudipta Roy 41.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2 Application of Nanotechnology in the Field of Information Science in Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Implementation of Nanoparticles in Agriculture . . . . . . . . . . . . . . 41.3.1 Nanobiosensors in Agriculture . . . . . . . . . . . . . . . . . . . . . 41.3.2 Encapsulation of Nanotechnology in Agriculture . . . . . 41.3.3 Nanoherbicides Growth and Monitoring . . . . . . . . . . . . 41.3.4 Nanoparticles in Sustained Agriculture Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Application of Metal-Based Nanomaterials . . . . . . . . . . . . . . . . . . 41.4.1 Plant Pest Management and Nano-insecticidal Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4.2 Nanomaterials as Antimicrobials . . . . . . . . . . . . . . . . . . . 41.4.3 Nanofertilizers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4.4 Robust Biosensor Networks . . . . . . . . . . . . . . . . . . . . . . . 41.4.5 Nanotechnology in Diagnosis and Management of Plant Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4.6 Innovative Drug Administration System . . . . . . . . . . . . . 41.4.7 Nanotechnology in Food Technology . . . . . . . . . . . . . . . 41.4.8 Nanopesticides in Agriculture . . . . . . . . . . . . . . . . . . . . . 41.4.9 Delivery of Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4.10 Targeting Specific Cells . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4.11 Health and Environmental Concerns Related to Nanoparticles Include . . . . . . . . . . . . . . . . . . . . . . . . . .

492 493 495 495 496 497 498 500 501 503

504 505 507 507 508 508 509 509 510 510 511 512 512 513 514 514 515 515 516

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41.5 A Comparative Study of Approaches to Merge Nanomaterials to Various Agricultural Sectors . . . . . . . . . . . . . . . . 41.5.1 Constraints in the Field of Nanotechnology . . . . . . . . . . 41.6 Challenges Encountered by Nanomaterials Associated with Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Implementing Solar Panel Surface Dust Cleaning Innovation Using a Solar Innovation Framework Model . . . . . . . . . . . . . . . . . . . . . P. K. Dutta, Sujatra Dey, Sayani Majumder, Pritha Sen, and Sudipta Roy 42.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Theories Addressed in Solar Innovation for Solar Panel Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Determinant Factors that Affect the Solar Panel Cleaning Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3.1 Dry Cleaning of Solar Panels . . . . . . . . . . . . . . . . . . . . . . 42.3.2 Soap-Less Brushes and Sponges . . . . . . . . . . . . . . . . . . . 42.3.3 Parameters Affecting Dust Accumulation . . . . . . . . . . . 42.4 Methodology in Solar Panel Cleaning Process . . . . . . . . . . . . . . . . 42.4.1 Silica Sol: Making of Silica Sol: Different Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.4.2 Properties of Silica Sol . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Quality of Service Analysis in a Fog Computing Network with Breakdown and Vacation Interruption . . . . . . . . . . . . . . . . . . . . . . Hibat Eallah Mohtadi, Mohamed Hanini, and Abdelkrim Haqiq 43.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.2 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.3 Model Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 The Adaptation Process of Multicultural Married Migrant Women’s University Life Culture Based on Grounded Theory . . . . . Chungyun Kim 44.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.3.1 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.3.2 Respondents of the Study . . . . . . . . . . . . . . . . . . . . . . . . . 44.3.3 Reliability of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.3.4 Reliability and Validity of the Study . . . . . . . . . . . . . . . .

517 517 518 519 520 523

524 525 527 528 528 529 530 531 533 535 536 539 539 541 543 546 546 549 549 550 552 552 552 553 553

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44.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.1 Causal Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.2 Contextual Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.3 Central Phenomenon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.4 Interventional Condition . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.5 Action/Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.4.6 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Statistical Data Analysis Method for Factors Affecting Geriatric Nursing Performance of Tertiary Hospital . . . . . . . . . . . . . . Inok Kim and Hyunli Kim 45.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.1 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.2 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.3 Survey Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.4 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.5 Ethical Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.2.6 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.3.1 Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.3.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part VI

554 554 556 556 557 557 558 559 561 563 564 565 565 565 566 566 567 567 567 567 568 571 575 576

Machine Learning and Deep Learning and Their Applications

46 The Effect of Colored Noise on Self-feedback Chaotic Neural Networks with Legendre Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Zhang, Bin Chen, Yao-qun Xu, Si-fan Wei, and Lan Li 46.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.2.1 Nonlinear Self-feedback Chaotic Neurons . . . . . . . . . . . 46.2.2 Self-feedback Chaotic Neuron Model with Colored Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.2.3 Self-feedback Chaotic Neural Network Model with Colored Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.3 Comparison and Analysis of the Dynamics of Two Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46.3.1 Nonlinear Self-feedback Chaotic Neurons . . . . . . . . . . . 46.3.2 Colored Noise Nonlinear Self-feedback Chaotic Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

581 581 582 582 584 585 586 586 587

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46.4 Application of Self-feedback Transient Chaotic Neural Networks with Legendre Functions and Colored Noise . . . . . . . . 589 46.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594 47 Structured Financial Products that Can Hedge Against Drought Risk: Pricing Approach Based on Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengge Yao, Jiayuan Liang, Liqing Xue, and Jun Zhou 47.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47.3 Pricing Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47.3.1 Pricing of Fixed-Income Securities . . . . . . . . . . . . . . . . . 47.3.2 Pricing of Rainfall Put Barrier Options . . . . . . . . . . . . . . 47.4 Empirical Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47.4.1 Weather-Structured Financial Product Design . . . . . . . . 47.4.2 Pricing of Weather-Structured Financial Products . . . . . 47.5 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 47.5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47.5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Automatic Generation of Ancient Poetry Based on Generative Adversarial Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yannan Li, Dequan Zheng, Feng Yu, and Rong Han 48.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.2.1 Generative Adversarial Network . . . . . . . . . . . . . . . . . . . 48.2.2 Sequence Generative Adversarial Network . . . . . . . . . . 48.2.3 Long Short-Term Memory . . . . . . . . . . . . . . . . . . . . . . . . 48.2.4 Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.2.5 Evaluation Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.3.1 Encoder Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.3.2 Decoder Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.4 Experiment and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.4.2 Experimental Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

595 595 596 597 598 598 601 601 601 603 603 604 604 607 607 608 608 610 610 610 611 611 611 612 613 613 614 615 616 616

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49 A Factor Analysis and Support Vector Machine Based Method for Studying Life Satisfaction of the Elderly . . . . . . . . . . . . . . Jialin Zhang, Lei Li, Xingke Qu, and Yueyang Zhang 49.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.2 Modeling Methods and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . 49.2.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.2.2 Support Vector Machine (SVM) . . . . . . . . . . . . . . . . . . . 49.2.3 Particle Swarm Optimization Parameters . . . . . . . . . . . . 49.2.4 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.3.1 Factor Analysis of Dimensionality Reduction Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.3.2 PSO-SVM Results Analysis . . . . . . . . . . . . . . . . . . . . . . . 49.3.3 Performance Comparison Analysis with Other Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Leakage Detection of Water Supply Network Based on Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiao Qing Wu and Li Ge 50.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.2 Model Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.2.1 Method Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.2.2 Model Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.3 Experiment and Result Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.3.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . . 50.3.2 Training Super Parameter Setting . . . . . . . . . . . . . . . . . . 50.3.3 Conclusion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Recurrent Neural Network Deep Learning Approach for Classifying Early-Stage Malicious Ransomware Malware . . . . . . Gulshan Kumar and V. Nagu 51.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.1.1 Relapse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2 Existing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3.1 Advantages of Proposed System . . . . . . . . . . . . . . . . . . . 51.4 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5 Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5.1 Exploratory Data Evaluation . . . . . . . . . . . . . . . . . . . . . . 51.5.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5.3 Feature Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5.4 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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51.6 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 51.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 648 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651 52 Automatic Clustering of Hyperspectral Images Using Quantum Reptile Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tulika Dutta, Siddhartha Bhattacharyya, Bijaya Ketan Panigrahi, and Aboul Ella Hassanien 52.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3 Important Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3.1 Quantum Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.3.2 Reptile Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 52.3.3 Correlation-Based Cluster Validity Index (CRI) . . . . . . 52.4 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.4.1 Band Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.4.2 Qubit and Qutrit Reptile Search Algorithm . . . . . . . . . . 52.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.6 Discussions and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Portfolio Optimization Using Quantum-Inspired Modified Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhishek Gunjan, Siddhartha Bhattacharyya, and Aboul Ella Hassanien 53.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.2 Motivation and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3.1 Arithmetic Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3.2 Heuristic Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.3.3 Limitations of Arithmetic and Heuristic Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.4 Quantum-Inspired Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . 53.5 Experimental Results and Findings . . . . . . . . . . . . . . . . . . . . . . . . . 53.6 Discussions and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part VII

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54 The Impact of Digital Finance on Carbon Emissions Intensity: Evidence from 30 Provinces in China . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianrui Zhang and Yi Qu 54.1 Introduction and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 54.2 Model and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2.1 Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2.2 Variable Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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54.3 Empirical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.3.1 The Changing Characteristics of DF . . . . . . . . . . . . . . . . 54.3.2 The Impact of DF on CEI . . . . . . . . . . . . . . . . . . . . . . . . . 54.3.3 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.3.4 Analysis of Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . 54.4 Conclusions and Policy Recommendations . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 The Empirical Research of Digital Inclusive Finance on the Coordinated Development of Regional Economy . . . . . . . . . . . Zhang Xiaofeng and Zhang Chunyu 55.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.2 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.2.1 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.2.2 Variable Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.2.3 Model Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.2.4 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.3.1 Empirical Analysis of Panel Linear Regression . . . . . . . 55.3.2 Empirical Analysis of Nonlinear Threshold Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.4 Conclusions and Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55.4.2 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Evaluation of China’s Provincial Digital Economy Development Level in the Post-epidemic Era: Evidence from Chinese 31 Provinces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jialin Dong 56.1 Introduction and Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 56.2 Model Construction and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.2.1 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.2.2 Description of Indicators and Data Sources . . . . . . . . . . 56.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.3.1 Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.3.2 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.4 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 56.4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56.4.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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697 697 698 698 699 700 700 702 704 704 705 706

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57 Research on the Performance of Targeted Poverty Alleviation from the Perspective of Financial Support Based on 20 Provinces in Central-Western China . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sha Lou, Chen Cao, and Dehua Zhang 57.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.2 Data Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.2.2 Selection of Input–Output Index . . . . . . . . . . . . . . . . . . . 57.2.3 Selection of Researching Objects . . . . . . . . . . . . . . . . . . 57.2.4 Data Resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.3 Performance Analysis of Targeted Poverty Alleviation Based on the Global Malmquist Index . . . . . . . . . . . . . . . . . . . . . . . 57.3.1 Analysis of the Results of Comprehensive Efficiency of Targeted Poverty Alleviation in 20 Regions of Central-Western China . . . . . . . . . . . . . . . . . 57.3.2 Analysis on the Spatial Distribution of TFP in Each Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.3.3 Performance Decomposition Index Analysis of Targeted Poverty Alleviation in Each Area of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.4 Conclusions and Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . 57.4.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.4.2 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Digital Finance, Financing Constraints, and Small and Medium Enterprise Technology Innovation—An Empirical Study Based on SME and GEM Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengge Yao, Jiayuan Liang, Linlin Ma, and Zhenhuan Chen 58.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.2 Research Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.2.1 Digital Finance, Financing Constraints on SME Technology Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.3 Model and Variable Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.3.1 Sample Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.3.2 Core Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.3.3 Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.3.4 Variable Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4.1 Benchmark Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4.2 Mechanism Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4.3 Endogeneity Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4.4 Robustness Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.4.5 Heterogeneity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . .

707 707 710 710 712 712 713 713

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58.5 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 58.5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.5.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 The Impact of Digital Finance on Financial Industry Agglomeration: Based on Panel Data Research in Jilin Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Yin and Qiang Chen 59.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59.2 Research Methodology and Data Sources . . . . . . . . . . . . . . . . . . . . 59.2.1 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 59.2.2 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59.3 Analysis of Financial Industry Agglomeration Level . . . . . . . . . . . 59.3.1 Analysis of Location Entropy Value . . . . . . . . . . . . . . . . 59.3.2 Spatial Autocorrelation Test . . . . . . . . . . . . . . . . . . . . . . . 59.4 Analysis of the Relationship Between Digital Finance and Financial Agglomeration in Jilin Province . . . . . . . . . . . . . . . . 59.5 Conclusion and Insight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 The Impact of Digital Economy Development on Economic Growth: An Empirical Study Based on the Yangtze River Economic Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yijun Xiang and Guangming Shi 60.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.2 Calculation and Analysis of the Development of the Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.2.1 Evaluation Index System . . . . . . . . . . . . . . . . . . . . . . . . . . 60.2.2 Research Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.2.3 Measurement Results and Analysis . . . . . . . . . . . . . . . . . 60.3 Empirical Analysis of Digital Economy on Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.3.1 Model Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.3.2 Variable Selection and Data Description . . . . . . . . . . . . 60.3.3 Empirical Results and Analysis . . . . . . . . . . . . . . . . . . . . 60.4 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 60.4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60.4.2 Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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61 An Empirical Study on the Impact of Digital Inclusive Finance on China’s Cultural Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Long Yin and Honghao Xin 61.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 61.2 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 762

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61.3 Variables, Data, and Empirical Models . . . . . . . . . . . . . . . . . . . . . . 61.3.1 Variable Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.2 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.3 Establishment of Model . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Empirical Testing and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4.1 Unit Root Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4.2 D Cointegration Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4.3 Linear Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . 61.5 Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . 61.5.1 Government Level Recommendations . . . . . . . . . . . . . . 61.5.2 Financial Institution Level Recommendations . . . . . . . . 61.5.3 Cultural Enterprise Level Recommendations . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Technical Efficiency Evaluation of Electronic and Communication Equipment Manufacturing Industry: Empirical Analysis Based on Provincial Panel Data of China from 2011 to 2021 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Boyang Men and Hong Lan 62.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Evaluation Method and Index Design . . . . . . . . . . . . . . . . . . . . . . . 62.2.1 Evaluation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2.2 Evaluation Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Evaluation of Innovation Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . 62.3.1 Time Evolution Characteristics of Innovation Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3.2 Comparative Analysis of Technological Innovation in Each Region . . . . . . . . . . . . . . . . . . . . . . . . 62.4 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.4.2 Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Systematic Review of Automatic Arabic Text Summarization Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Khaled J. Abdelqader, Azza Mohamed, and Khaled Shaalan 63.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.1.1 The Architecture of Automatic Text Summarization (ATS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.1.2 Why Automatic Arabic Text Summarization (AATS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.1.3 About the Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.2.1 Data Sources and Search Strategy . . . . . . . . . . . . . . . . . . 63.2.2 Data Coding and Quality Assessment . . . . . . . . . . . . . . . 63.2.3 Inclusion and Exclusion Criteria . . . . . . . . . . . . . . . . . . .

763 763 763 764 764 764 765 766 767 767 768 768 768

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Contents

63.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.1 RQ1: Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.2 RQ2: Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.3 RQ3: Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.4 RQ4: Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.5 RQ5: Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.6 RQ6: Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3.7 RQ7: Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Artificial Intelligence–Internet of Things Enabled Mosquito-Based Diseases Identification Trap . . . . . . . . . . . . . . . . . . . . M. Krishnaveni, P. Subashini, T. T. Dhivyaprabha, B. Gayathre, and K. Manimegalai 64.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.2 Literature Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.3 Proposed Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.3.1 Machine Learning (ML) Phase . . . . . . . . . . . . . . . . . . . . 64.3.2 Internet of Things (IoT) Phase . . . . . . . . . . . . . . . . . . . . . 64.4 Experimental Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 64.5 Conclusion and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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788 788 789 789 790 790 791 791 792 793 794 797

798 801 803 804 810 812 814 815

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817

About the Editors

Aboul Ella Hassanien is a professor at the faculty of Commuter and AI, Cairo University, Egypt. He has authored/co-authored over 1000 research publications in peer-reviewed reputed journals, book chapters, and conference proceedings. His research interest includes machine learning and applications in climate change, medical image processing, IoT, drones, and drug discovery. Dequan Zheng has a B.Sc. (Harbin, China), an M.Sc. (Harbin, China), and a Ph.D. (Harbin, China) in Computer Science and Technology. He has a long experience in teaching various computing courses at Harbin University of Commerce and Harbin Institute of Technology. The courses he has taught have included artificial intelligence, data structures and algorithms, programming, business intelligence, and data analytics. He supervises undergraduate and postgraduate projects. Dr. Dequan Zheng has natural language processing research in artificial intelligence and business data analytics in electronic commerce, including human–machine dialogue, information extraction, data mining, etc. Zhijie Zhao, doctor of engineering, visiting scholar of New York University, director of e-commerce and information processing laboratory, is currently a professor at Harbin University of Commerce. He has more than 100 scientific research papers published in well-known journals and conferences, over 11 books, and 7 invention patents. He has presided over and participated in more than 30 National Natural Science Foundation of China and other scientific research projects and won many scientific research awards. His main research fields include medical image processing, character image recognition, intelligent information processing, business intelligence, online reviews, and consumer engagement.

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About the Editors

Zhipeng Fan, Ph.D., is an associate professor, member of the Key Laboratory of Electronic Commerce and Information Processing; member of China Computer Federation; associate editor of the International Journal of Smart Business and Technology; and SCI Journal Reviewer. His main research fields include business intelligence, blockchain theory and application, and signal and information processing. In recent years, he has published more than 20 papers in journals and 2 textbooks. He has completed more than 10 scientific research projects and obtained 2 national invention patents and 2 software copyrights.

Part I

Business Intelligence and Decision Making

Chapter 1

Research on the Incentive Mechanism of Publishing Editors Based on Psychological Contract Shang-kun Lu and Zai-ming Yang

Abstract In the era of the knowledge economy, the editor is the core resource element of the publishing organization. Taking the different dimensions of the psychological contract as the knowledge effect of editors in publishing organizations, a conceptual model to explore the satisfaction of editors and publishing organizations is established, and the empirical test is carried out by means of questionnaires. The research results show that the cultural identity of the publishing organization, the people-oriented management concept, and the high trust of the organization have a significant positive impact on the editor’s satisfaction; the working environment of the publishing organization has a certain mediating effect on the editor’s satisfaction. The research results clarify the mechanism of the relationship between the publishing organization and the editor, reveal the essence of affecting the editor’s satisfaction, and provide theoretical guidance for the management practice of the publishing organization.

1.1 Introduction Cultural self-confidence is the foundation of cultural construction, and cultural construction is the premise of cultural power. The development experience of human history has proved that culture is the eternal force for the cohesion and development of a country and a nation. The national “14th Five-Year Plan” outline puts forward the development goal of “prospering and developing cultural undertakings and cultural industries, and improving the country’s cultural soft power.” Entering the new era, the people’s growing cultural needs have become an important engine for high-quality cultural development. As an important industry for recording history, S. Lu (B) Harbin Engineering University, Harbin 150001, China e-mail: [email protected] S. Lu · Z. Yang Harbin University of Commerce, Harbin 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_1

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inheriting civilization, popularizing knowledge, disseminating truth, and educating people, the publishing industry is the foundation of all cultural development and an indispensable knowledge force in the evolution of all social civilizations [1]. Accelerating the construction of a modern publishing industry system and stimulating the subjective initiative of editorial elements in publishing organizations are the keys to achieving high-quality development of publishing.

1.2 Literature Review Foreign scholars’ research on psychological contract theory started earlier. The organizational psychologist Argyris [2] first proposed the concept of the psychological contract, which means that both parties do not express their intentions directly through some obvious form, but through psychological suggestion, the two parties perceive and recognize each other. A set of invisible power-duty relationship agreements formed based on their respective expectations. Robinson [3] further pointed out that this belief refers to the exchange relationship between employees’ explicit and intrinsic employee contributions and organizational incentives, commitment, understanding and perception. Domestic scholars Chen Jiazhou [4] believed the core of the psychological contract is the implicit and unwritten mutual responsibility of both parties. Zou Bin [5] proposed that the core editor is the typical knowledge worker in the publishing organization. Ma [6] believed that there is a symbiotic relationship between enterprises and knowledge workers. Fu and Zhong [7] believe that the breakdown of psychological contract has a significant negative impact on employee knowledge sharing. Based on the above literature research ideas, this study attempts to use the structural equation model as a measurement model for the psychological contract fit of the editorial team of the publishing unit, to perform statistical significance tests on the path coefficient or load coefficient, and to explain the model results, so as to avoid the subjectivity of expert scoring data. Sexuality is too strong.

1.3 An Analysis of the Influencing Factors of Publishing Organization Editors’ Psychological Contract 1.3.1 Dominant Factors in Publishing Organizations Publishing organization and management capabilities. It is mainly reflected in the personality charm, way of thinking and management level of the leaders of the publishing organization, which determine the cultural style and humanistic environment of the publishing organization, and have an important impact on the fit between the publishing organization and the editor. The reform of the publishing system has

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been continuously deepened; the management mechanism and assessment mechanism have been gradually strengthened, and the management performance assessment system has comprehensively evaluated the workability, work attitude, and task indicators of the editorial team. The editorial career is an important indicator for the editorial team to realize its own value and personality development. To sum up, respect for editorial needs, freedom, and dignity is the realization of the professional development of editors and the high-quality development of publishing.

1.3.2 Editing Team’s Own Hidden Factors The editor’s recognition of the cultural atmosphere, justice, fairness and value of the publishing organization, the scientific index evaluation and assessment system, the primacy of the editor’s status and the goal of the editor’s development and cultivation are the key elements of the editor’s psychological fit. After the transformation, the salary system, and social recognition of the publishing organization, editors have more expectations for pay and remuneration. A more scientific and reasonable incentive mechanism is the endogenous power of editing. The psychological contract of self-development and realization of editors is to stimulate the potential of topic selection, planning, and editing quality, apply professional knowledge and professional skills to high-quality publications, and obtain the affirmation of publishing organizations, authors and readers. Therefore, publishing high-quality books and gaining social recognition is the highest-level psychological contract for editors.

1.4 Data Sources and Model Building 1.4.1 Data Sources Based on combining domestic and foreign research theoretical results, the sample data is collected by means of a survey and questionnaire survey, and the questionnaire is continuously revised and improved through reliability and validity tests. The research took the form of a combination of questionnaires and interviews. In this study, editors of publishing organizations were sampled for research. A total of 530 questionnaires were distributed, 515 questionnaires were returned, and 509 valid questionnaires were obtained. The research object positions are divided into four directions, including 291 book editors, 156 journal editors, 21 quality inspection editors, and 41 proofreading editors. After excluding the questionnaires with irregular answers, the basic information of the obtained valid samples is given in Table 1.1.

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Table 1.1 Basic information of respondents Variable

Distribution type

Percentage (%)

Number of valid samples

Gender

Male

30.0

154

Female

70.0

355

Education

Undergraduate and below

51.0

158

Postgraduate

44.9

138

Job title

PhD student

4.1

13

None

8.6

43

Assistant editor

25.3

129

Editor

43.8

223

Deputy editor

17.7

91

4.5

23

3 years or less

25.1

128

3–5 years

35.2

180

6–10 years

29.4

149

10+ years

10.3

52

Editing Working years

1.4.2 Model Building Variables and Model Settings. Through the questionnaire survey, it was found that factors such as resignation intention, job performance, work environment, job satisfaction, and publishing organization management can significantly affect the editor’s psychological contract fit. The corresponding measurement variables of each factor are given in Table 1.2. Model parameter estimation and its significance test. The data from the previous survey papers were imported into AMOS 23.0 software, and parameter estimation and model fitting tests were performed according to the theoretical model diagram. Since the effective number of questionnaires collected in this survey is 509 and the sample size is sufficient, the maximum likelihood estimation method is selected, and the coefficient value is selected as a standardized coefficient. After the initial fitting of the hypothetical model, the path coefficients of the structural variables are shown in Fig. 1.1. The parameter estimation results are given in Tables 1.3 and 1.4. The model evaluation should first check whether the parameters estimated in the model results have statistical significance. It is necessary to perform a statistical significance test on the path coefficient or load coefficient. Passing the parameter test is the premise of model fitting. The test results are given in Tables 1.3 and 1.4, where SRW in Table 1.3 stands for Standardized Regression Weights. It can be seen from Tables 1.3 and 1.4 that the CR values of the parameter estimates of almost all paths are greater than 2 and pass the significance test, which provides a good premise for the model fitting of the subsequent structural equations.

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Table 1.2 Latent variables and measured variables Latent variable

Measure variable

Latent variable

Measure variable

Willingness to leave

a1 Job security a2 Colleague collaboration a3 Career planning

Work performance

a4 Task performance a5 Relationship performance

Psychological contract

a6 Editors’ expectations for organizations a7 Organizations expect from editors a8 Editor’s formal contract with the organization

Publishing organization management

a9 Assessment plan a10 Institutional stability a11 Institutional effectiveness

Job satisfaction

a12 Remuneration package a13 Scientific research conditions a14 Scientific research training a15 Promotion space

Working environment

a16 Organizational culture a17 organizational climate a18 Collaboration system

Fig. 1.1 Results of structural variable path coefficients after fitting the model Table 1.3 Hypothesis test results of the research model Path

SRW

S.E.

C.R.

P

Is it established

Resign 0, businesses will choose an illegal strategy. Individuals are less aware of the ways in which information can be compromised and stolen at an early stage, so they are more cautious about data sharing. In addition, in the early days, electronic devices were used less and produced less data, so the overall picture was of individuals providing information negatively. Thus, when R2 + C2 − C2 < 0, the individual (P) chooses negatively providing strategies.

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Analysis of the Transition Stage of the Government to the Regulatory Strategy (1, 0, 0). As the information market expands, illegal use of information is on the rise, which will lead to more social conflicts and difficulties for government management. As a result, governments have successively enacted regulations on the use of personal information. Among these regulations, the government has increased penalties for companies that break the law, which will improve the management efficiency and enhance the public image of the government. To manage the information market efficiently, the government will establish a reasonable division of departments, such as supervision, which will facilitate market management. Therefore, when R1 −C1 + k13 > 0, the government is more inclined to opt for a strict regulatory strategy. The government has opted for a heavy-handed regulatory strategy. However, if current laws, regulations and regulatory authorities are poorly constrained, chaos will remain in the information market. The possibility of personal information to be abused or stolen is too high. If the companies do not adopt standardized strategies, the personal information will not receive corresponding benefits. Therefore, individuals do not employ positive information sharing strategies. Thus, when R2 +C2 −C2 < 0, the individuals adopt a strategy of negatively providing personal information. The government opts for a heavy-handed regulatory strategy with inadequate penalties, and the net benefit to businesses from legitimate use of data may not be as high as that from the illegal use strategy. Second, if a peer adopts an illegal strategy, the business that chooses a legal strategy will lose its competitive advantage. For the above reasons, companies often resort to illegal use tactics based on the Prisoner’s Dilemma theory. And if the fees that individuals have to pay for adopting illegal strategies are not high, it will also drive businesses to adopt illegal strategies. Therefore, when −R3 + C3 + w + S32 + w  − C3 − S13 − K 13 > 0, the personal information user (C) will adopt a strategy of illegally obtaining and using information. Analysis of the transition stage of the direction of individuals to positively provide personal information (1, 1, 0). As the government opts for a strict supervision strategy and sets strict information security laws to punish violations, it has shifted from a strategy of rewards and few penalties to one of mainly punishment for the illegal behaviors of businesses. The above behaviors signal to the individuals that the market is becoming healthy, so the individuals will adopt positive sharing strategies. Thus, when R2 +C2 −C2 > 0, the individual adopts a positively providing strategy. The government opted for a heavy-handed regulatory strategy. Meanwhile, this phase is dominated by penalized strategies. However, companies will still choose to use illegal tactics if the penalties imposed by the government are insufficient, or if the benefits of illegal use are much higher than the costs of using the information illegally. So, when −R3 + C3 + w + S32 + w  − C3 − S13 − K 13 > 0, the personal information user (C) will adopt a strategy of illegally obtaining and using information. Analysis of the transition stage of the direction of business to legally obtain and use personal information (1, 0, 1). The government opts for a strict monitoring strategy, but individuals will not trust the government if past violations have been mishandled. Individuals believe that even with regulation, the cost of self-harm would

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be elevated if they actively share information. Meanwhile, individuals still adopt a negative sharing attitude, because they do not believe that companies will use the information legally and pay a reasonable fee for its use. Therefore, when R2 + C2 − C2 < 0, the individuals choose to negatively provide strategies. The government has opted for a heavy-handed regulatory strategy, and this phase is dominated by penalties. As the chaos deepens, the government’s methods of punishment will be more severe. As a result, any violation of personal information will be immediately detected and severely punished. As soon as the cost of punishment becomes greater than the profit from illegal use of information, businesses will choose to adopt a legitimate use strategy. Therefore, when −R3 + C3 + w + S32 + w  − C3 − S13 − K 13 < 0, the personal information user (C) will take legal use strategy. Analysis of the transition stage of strategy to strict government supervision, legal use by businesses, and positive provision of information by individuals (1, 1, 1). As the market for personal information develops, the government will take tougher measures to curb market behavior and improve management efficiency. From the above analysis, it can be seen that it is more effective to regulate the acquisition and use behavior of businesses and increase the penalties at a later stage of the market development, so the government will choose a strict regulatory strategy. When R1 −C1 +k13 > 0, the government is more willing to choose a strict supervision strategy. Through strict punishment and rational management, bad behavior in the information market has been less. Individuals believe that effective government regulation can reduce the possibility of illegal use and the costs they incur from these actions. Meanwhile, due to positive supervision, businesses will give individuals reasonable subsidies when using shared information, so individuals will actively share. Hence, when R2 + C2 − C2 > 0, individuals adopt a positive providing strategy. The government has increased penalties and reasonably regulated the market so that violations are quickly detected and punished, so that the penalty for adopting an illegal strategy is greater than the profit generated by the violation. This also prevents other companies in the same industry from choosing the illegal strategy, and the business adopts the legal usage strategy. Thus, when −R3 + C3 + w + S32 + w  − C3 − S13 − K 13 < 0, the businesses will adopt a legal strategy.

5.6 Conclusions and Suggestions 5.6.1 Conclusions This paper constructed the evolution process of a dynamic game model dominated by the government (G), the source of personal information (P), and the user of personal information (C). Through additional analysis of the evolution path of the governmentdriven tripartite game, this paper put forward suggestions from the perspective of the tripartite. With the above analysis, the summary analysis is as follows.

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In the course of the dynamic evolution of the government-driven dynamic game, the government should implement a strict supervision strategy. The government administers businesses to adopt legal strategies to obtain and use personal information by setting up reward and punishment systems and refining management departments. The government should consider the benefits that could be derived from illegal acts. If the benefits are too large, the government needs to adopt a system of severe penalties. A company’s legal actions can only be motivated if the cost of the company’s illegal strategy is higher than the profit. Timely detection and strict handling of violations can convince the individual that the cost of positive sharing information is lower than the profit, thus giving individuals an incentive to share. Meanwhile, the three parties can reach a Pareto optimal strategy equilibrium.

5.6.2 Suggestions Based on the above analysis, the policy recommendations of this study are as follows. From the perspective of government policymakers, they need to subdivide the various management functions and strengthen the executive ability of each department. In terms of specific measures, first, the government needs to set up an early warning department to supervise the information behavior of various businesses, and set up an early warning indicator system to provide timely warning and interventions for actions that exceed the indicators. Second, the relevant law and regulation establishment departments need to establish detailed and strict bills of personal information. The bills need to deal with violations rigorously on a reasonable basis. From a business-related perspective, this paper suggests that companies involved in the use of personal information should improve their personal information protection systems in the course of their development, and use this as core competitiveness in their later development. Because, in the later stage of the personal information market, the government will increase the punishment for information abuse and limit the use rights of companies that use information illegally, which means that companies with favorable information usage conditions have more advantages in the information market. In addition, favorable usage conditions and legal usage behaviors can win the trust of individuals and thus gain additional rights to share information. From an individual-related perspective, the government needs to increase information protection advocacy to ensure better awareness among individuals. In addition, the government needs to promptly publicize the results of penalties for violations. With regular promotions, active signals of information market can be released to individuals, which can motivate individuals to actively share personal information.

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References 1. Ma, L.: Talking about the civil law protection of personal information. Legal Vis. 27(2), 186– 187 (2019) 2. Liu, W.D.: Exploring the criminal law protection of personal information. Legal Vis. 26(2), 227–228 (2019) 3. Dong, H.Y.: Research on administrative law protection of citizens’ personal information. Legal Syst. Soc 23(4), 279–282 (2016) 4. Yang, C.Q.: Reflections on legal protection of personal information under the background of informatization. Legal Vis. 25(2), 229–231 (2019) 5. Wu, H.J.: Online personal information security research. Comput. Telecommun. 5(3), 37–39 (2019) 6. Zhao, W.G.: Laws and regulations in the information society: personal privacy and intellectual property protection. Inf. Technol. Educ. China Z3, 22–24 (2019) 7. Solove, D.J.: The new vulnerability: data security and personal information. Soc. Sci. Electron. Publ. 18, 123–124 (2008) 8. Solove, D.J.: The future of reputation: gossip, rumor, and privacy on the Internet. Soc. Sci. Electron. Publ. 3, 982–992 (2009) 9. Paul, M.S.: Privacy inalienability and personal data chips. Harvard Law Rev. 5, 117 (2004) 10. Parent, O.: Personal information protection & electronic documents act. Health Law Can. 2, 12–14 (2001) 11. Brian, S., Richard, B.: Information technology and the social construction of information privacy. J. Acc. Public Policy 20(4–5), 295–322 (2001) 12. Estevez, P.A., Held, C.M., Perez, C.A.: Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Syst. Appl. 31(2), 337–344 (2006) 13. Li, Y.F., Wang, X.Z.: Experience and enlightenment of European and American personal information protection laws and policies in public health emergencies. Inf. Doc. Serv. 43(3), 9 (2022)

Chapter 6

Research on the Evaluation of Tourism Digital Content Marketing Based on PCA-AHP Kewen Liu and Hui Xian

Abstract A crucial marketing tactic for the growth of the tourism industry is digital content marketing (DCM). To improve the evaluation system of digital content marketing of scenic tourism, based on tourism digital content marketing theory, the PCA-AHP model is applied to evaluate the DCM of 5A tourist attractions in three provinces in northeastern China. The AHP index system is constructed from information practicality, user experience, and social function, and the principal components are extracted by combining PCA: platform effect, technology effect, audiovisual experience, and interactive experience and the comprehensive weight of the index is calculated. The results show that social function dominates DCM, and the influence of user experience is better than information utility. Platform effect and interactive experience have a significant influence on DCM, and the effect of audiovisual experience is the weakest. The objective is to improve the DCM’s evaluation methodology and system, as well as the digital content service it provides to the travel and tourism sector.

6.1 Introduction Tourism destinations may now efficiently engage with guests through DCM due to the quick development of digital media. Tourist destinations develop effective marketing strategies by sharing practical marketing information and enlisting the cooperation of the media. DCM is available in many different formats, including graphic and video formats. Graphic push marketing provides entertainment-functional and socialfunctional information to satiate tourists’ wants for destinations [1]. Contrarily, the depth of video content and interaction with tourists can provide tourists with a more

K. Liu (B) · H. Xian School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_6

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emotional travel experience and inspire them to travel in several ways. Therefore, video marketing is more successful [2]. DCM communication channels are divided into three types: search communication, push communication, and interactive communication. Tourist attractions’ websites are the most popular resource for tourists looking for travel-related information and they are critical to tourism digital content marketing. Academics examine the spatial distribution of picturesque locations and tourist satisfaction in terms of website information completeness, communication technology, social environment, and tourism resource [3]. The digital economy has increased network coverage, assisting in the long-term growth of tourism as an information-intensive industry. Network coverage is the technological underpinning that directly influences tourists’ information-seeking behavior and serves as a precursor to their interaction with digital content. Current study on the digital economy and tourism, as well as the implications of information technology development on smart destinations, focuses on the regional economy and high-quality development [4]. However, research on the intersection between the digital economy and DCM in tourist attractions is limited. Social media allows for both push communication and interactive communication. WeChat marketing is defined by high precision, private domain, and interactive immediacy, and it can respond instantaneously to consumer feedback. The WeChat official account regularly pushes material with the information function, transaction function, and social function to develop long-term relationships with tourists and achieve the transmission of information value and emotional value [5]. From the standpoint of destination promotion and management among other things, Yana Wengel and C. Zhu evaluated the positive effect of user-generated content (UGC) and professionally generated content (PGC) of Jitterbug on destination image communication [6, 7]. The study on social media contains the transmission of information value and the destination image perception of tourists, etc., and the study of the marketing evaluation system of tourism digital information is not yet complete. This study chooses assessment indices from the perspectives of information practicality, user experience, and social function to develop a theoretical framework for the use of DCM and the digital economy in the tourism sector, builds a scientific evaluation system, objectively examines the DCM of social media and the websites of tourist attractions, and efficiently assesses the impact of tourism DCM in the context of the digital economy.

6.2 Literature Review 6.2.1 Digital Content Digital content is produced when audio, video, text, images, and other informational pieces are coupled with an application function and real-world value using the Internet and other media information technologies as a carrier. Logic thought,

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emotion, inventiveness, and other informational components all contribute to its existence, recording, and digital communication. Intangibility, heterogeneity, and simultaneous production and consumption are characteristics of digital content [8], and the word “digital products” refers to both the distribution method and the actual act of purchasing items [9]. As mentioned by Pardo Abad C J (2020) et al., “Tourism digital products are not intended to replace tourism resources or tourist experiences, but rather serve as a technical support and a medium that helps tourism companies to define strategic focus, increase digital interaction with tourists, deliver information about the destination to tourists, enable effective management of tourist attractions, and spread a distinctive digital cultural concept” [10].

6.2.2 Digital Content Marketing Digital content marketing: Adhering to the customer-centric principle, effective brand-related content is pushed to current or potential customers on digital platforms with the use of digital technology in order to achieve the management process of increasing user engagement [11]. Tourism companies analyze the number of likes, retweets, comments, and other behaviors generated by tourists browsing information through a detailed investigation of the useful, entertaining, and authentic characteristics of digital information to accurately assess tourist engagement and target customers in segments for precise marketing and motivate tourists to make online purchase decisions [12, 13].

6.2.3 Evaluation of Digital Content Marketing Evaluation studies take into account DCM’s forms, sources, marketing channels, and functions. Because of social media, digital tourism content has expanded from a simple website image to videos broadcast on tourist destination websites and social media. Travelers, corporations, and destination management organizations are among the publishers of tourism digital content. User-generated content (UGC) and professionally generated content (PGC) are two types of digital content. Visitors’ reactions to digital content are directly tied to the marketing effect, and assessment studies of digital content marketing focus on visitors’ feelings of experience with digital content, destination image perception, and visitors’ intention to travel and purchase. There is no clear standard for digital content evaluation methodologies and indicators. Using a single qualitative or quantitative method for evaluation in such multivariate issues can cause the evaluation results to be too dependent on the researcher or ignore the meaning and impact in a specific semantic context, so most studies use a combination of qualitative and quantitative approaches to construct evaluation models.

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Scholars screened for different evaluation indicators, covering four main dimensions: basic information, e-commerce information, communication and interaction information, and online application information, specifically including location, contact information, virtual sightseeing, weather, and environment of tourism destinations; promotional activities, online reservations; online consultation, complaint handling, applications, official websites, social media, and other specific information. Daries N assessed golf tourism websites for infrastructure information, ecommerce information, and interactive information using website content analysis (WCA) and principal component analysis (PCA), implying that the website serves as the center of basic information delivery and social media serves as the center of relationship building with tourists [14]. However, this evaluation method website content analysis uses an objective analysis method and lacks evaluation originating from subjective experience. Mathew V assessed the influence of digital content marketing on tourists’ propensity to purchase tourism products using the extended technology acceptance model (TAM). This technique uses a questionnaire to collect tourists’ subjective judgments, but it lacks an objective investigation of the substance of digital marketing in scenic sites, as well as an assessment of the correlation between indicators and their relevance [15]. Tellis used principal component analysis (PCA) to construct a model of the drivers of the viral spread of social media video content, analyzing the impact of the video’s information content, emotional content, and business content on the motivation of video sharing [16], but such models are limited by the sample size and cannot obtain the interrelationship between each type of information. Xiao estimated the influence of visual effects of UGC in picture form on destination image using hierarchical analysis (AHP) with correlation among criteria (CRITIC), but the approach is under-researched for digital content in video form [17]. The PCA-AHP technique with PCA structure-driven weight ranking offers more technical benefits and a stronger correlation between AHP and PCA [18]. To order to generate index weights in a mixed subjective and objective model, this study blends hierarchical analysis, a standard method for subjective weight assignment, with principal component analysis as its major research methodology. Existing literature has revealed the evaluation of DCM from the perspectives of digital information technology, information form, media, and consumer value perception and consumption behavior; on this basis, this paper combines the theory of functional evaluation of tourism websites and business activities into the index system and combines tourism destinations’ digital information services.

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6.3 Indicator System Construction 6.3.1 Evaluation Model The weights of digital content evaluation indexes are determined by comparing the independent use of subjective and objective methods to the combined use of subjective and objective methods [19], and scholars have demonstrated that the evaluation effect of the combined subjective and objective methods is superior [20, 21]. In the mid-1970s, T. L. Saaty proposed the AHP as a qualitative and quantitative method that quantifies researchers’ experience and systematizes and hierarchizes the analysis from the target, which is suitable for complicated target structures and data scarcity, but in practice, the randomness of the evaluation process as well as the subjective uncertainty and vagueness of the researchers’ perceptions are unavoidable. Principal component analysis (PCA) was introduced and applied to non-random variables by Karl and Pearson (1901), and Hotelling (1933) expanded the concept to random vectors. It is a multivariate statistical analysis mused that uses the concept of dimensionality reduction to condense many indicators into a small number of composite indicators. The method computes weights based on the magnitude of factor contribution, simplifying the complex index structure while avoiding experimental outcomes that rely on researchers’ subjective experience. The combined PCA-AHP method, on the one hand, can analyze the complex nature content of individual DCM evaluation index system indicators in detail and obtain subjective weights; on the other hand, PCA can simplify the index system, extract the principal components based on objective data, calculate the DCM evaluation score of each sample, and obtain the objective weights of each indicator. Finally, utilizing subjective and objective weights for normalization, the comprehensive weights of each indicator can be determined.

6.3.2 Selection of Evaluation Indicators Traditional tourism websites often categorize information dimensions based on product features, fundamental product information, supplementary service transaction information, and visitor communication information. Tourist attractions’ websites serve as a basic channel for basic information transmission, and the core of DCM contains information practicality, social function, and user experience [22]. First, information practicality is classified based on its content, which includes transaction information to cover the basic needs of tourists as well as audiovisual information to enhance the information experience [23]. The transaction information on the website of tourist attractions includes information about online products such as online ticket booking, accommodation, food and beverage, entertainment, travel routes, car/boat, weather, and tourist souvenirs [24], demonstrating the richness of transaction information [25]; the audiovisual function includes information about

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audiovisual services like dynamic pictures, videos, audio, maps, 360° videos, and 360° virtual tours [26]. Second, user attention reflects the user’s Internet experience. The Baidu index was employed by Koying Zeng and Lijun Zhou to analyze the web attention of tourists to tourist attractions in three northeastern provinces, and the outcomes ranged from high to poor in Liaoning, Heilongjiang, and Jilin [27]. Network coverage is crucial for web attention [28], and a greater number of fixed broadband access households implies greater network coverage of the destination and a better website user experience. Third, the social function is mirrored in the design of media platforms, and the fast development of emerging media platforms, such as WeChat, TikTok, and Weibo has resulted in the formation of new entertainment, fragmentation, and emotional DCM business models. Using WeChat and TikTok as examples, social media digital content is focused on delivering value, which is quantified using user behavior data such as clicks, views, and interactions, as well as the number of tweets. Using data such as video length and the number of comments, Xu et al. designed a typological framework for tourist digital content [29]. Wang contended that the time it takes to create a video account represents a company’s financial capacity for DCM [30]. The social function evaluation parameters chosen in this paper include account creation length, average monthly tweets and the total number of tweets, number of videos posted, average video length, and number of viewer interactions. The digital content indicators listed above are utilized to construct the digital content marketing evaluation system displayed in Fig. 6.1.

Fig. 6.1 Tourism DCM evaluation system

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6.3.3 Determination of Index Weights First, the DCM evaluation model is built using yaahp software, then subjective weights are derived using hierarchical analysis. A judgment matrix is established, a hierarchical ranking is performed, the indications at the same and different levels are assessed and rated, and the subjective weights of each indicator are decided. The objective weights were then calculated. The original data of the collected scenic digital contents were investigated for factor feasibility using SPSS, and the degree of correlation between each variable was validated using the KMO test and Bartlett’s spherical test to establish whether each index was suitable for factor analysis. The model is acceptable if the KMO value is more than 0.5 and the P value of Bartlett’s spherical test is less than 0.05. Third, dimensionality reduction factor analysis was used to pick principal components from the original data with uniform magnitudes in order to compute eigenvalues, eigenvectors, and variance contribution rates. The primary components of DCM of 5A tourist attractions are extracted using the principle that the eigenvalue is more than 1 and the cumulative variance contribution rate is greater than 85%. The scores of each primary component and each case site are calculated using the factor rotation loading matrix of secondary indicators, and the objective weights of each indication are calculated. PCA relies on theoretical arguments based on empirical data, whereas AHP uses subjective weighting based on the nature of each indicator. As a result, by multiplying and normalizing the objective weights with the subjective weights to combine the subjective and objective weights, this study increases the weights’ dependability and credibility. The calculating formula is as follows: αi βi W = n i=1 αi βi

(6.1)

where W is the weight value obtained from the combination assignment; αi is the weight value obtained from the subjective assignment; βi is the weight value obtained from the objective assignment; and n is the number of evaluation indicators.

6.4 Empirical Study 6.4.1 Data Collection This paper screened 5A tourism attractions in Heilongjiang, Jilin, and Liaoning provinces, and those lacking independent government websites and social media applications were removed. Sun Island Scenic Area, Jingpohu Scenic Area, The Wudalianchi Scenic Area, Scenic Area of the Sea of Forest and Grotesque Stones along the Tangwang River, Arctic Village Scenic Area, and Tiger Head Scenic Area in

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Heilongjiang Province; the Puppet Manchu Palace Museum in Jilin Province; Benxi Water Cave, Golden Pebble Beach Scenic Area, Tiger Beach Ocean Park in Liaoning Province; to ensure the information’s integrity, digital economy construction data such as statistical annual reports and work reports of the cities where the tourist attractions are located were collected from January 1 to July 31, 2022, and graphic data of the tourist attractions’ websites and the WeChat official account as well as TikTok video data were recorded.

6.4.2 Evaluation Process and Result Analysis First, the subjective weights were computed. The weights between different levels of indicators in the evaluation model were determined after yaahp software completed a consistency test on the index system’s subjective weighting data, as shown in Table 6.1. The objective weights were determined. The index system collected data and imported it into SPSS software for factor analysis feasibility testing; the results showed that the KMO value was greater than 0.5, and the statistic of Bartlett’s Table 6.1 AHP subjective weights of tourism DCM evaluation system

First-level indicator

Weight and ranking

Second-level indicator

Information Practicality A1

0.109(3)

Transaction 0.054(5) information X1

User experience A2

0.345(2)

Social function A3

0.547(1)

Weight and ranking

Audiovisual 0.054(9) information X2 User attention X3

0.230(3)

Network coverage X4

0.115(2)

Average monthly tweets X5

0.039(10)

Creation length 0.029(8) X6 Total number of 0.041(4) tweets X7 Number of videos posted X8

0.124(6)

Viewer interactions X9

0.290(1)

Average video length X10

0.025(7)

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spherical test was 123.114, with a significant P value of 0.005, indicating that the index system could be subjected to principal component analysis. The data were standardized using the Z-score method and descriptive data analysis in SPSS to eliminate the influence of data with different magnitudes. To test the common factor variance commonality, the factor commonality ranged between 0.763 and 0.952, suggesting a high correlation between the indicators, which is consistent with the premise of applying principal component analysis. Table 6.2 gives the typical roots and variance contribution rates of the correlation coefficient matrix produced following dimensionality reduction analysis of the standardized data of each index. The four components f 1, f 2, f 3, and f 4 with eigenvalues of 3.771, 2.478, 1.587, and 1.026 have cumulative contribution rates of more than 80% and can appropriately represent most of the data information. The analysis of the initial factor loading matrix is displayed in Table 6.3, and we know that component 1 is more related to the total number of WeChat tweets, average monthly WeChat tweets, transaction information, and the number of videos posted. As a result, it might be noted as the platform effect. Component 2 is more connected with network coverage, user attention, and the length of time it takes to create a WeChat official account, all of which are related to the level of digital information development of tourist attractions, so it may be noted as a technology effect. Component 3 is primarily made up of the average length of TikTok videos and the number of audiovisual information services on the websites of tourist attractions, therefore it is classified as an audiovisual experience. Component 4 is related to users’ interactions with TikTok, and high-quality online communication content can promote consumers’ effective knowledge of tourist attractions and provide a data source for UGC management of tourist attractions, so component 4 is recorded as an interactive experience. In order to facilitate the ranking of weights, the objective weights of each principal component were calculated by summing the standardized data of the four principal components of platform effect, technology effect, audiovisual experience, and interactive experience after absolute value and weighting the factor variance contribution rate and eigenvalue. The comprehensive weighting shows that the number of online comments of users on short video platforms, i.e., interactive experience, has the highest weighting, indicating that interactive experience is the primary channel of digital content promotion and occupies the primary position in digital content marketing. Network coverage and user attention are ranked second and third, demonstrating that the advancement Table 6.2 Total variance explained Component

Initial eigenvalues

Extraction sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

1

3.771

37.706

37.706

3.771

37.706

37.706

2

2.478

24.778

62.484

2.478

24.778

62.484

3

1.587

15.874

78.357

1.587

15.874

78.357

4

1.026

10.263

88.620

1.026

10.263

88.620

0.713 0.811 0.007

0.236

0.971

0.697

− 0.142

− 0.401

− 0.109

0.833

− 0.037

0.055

0.481

− 0.018

0.141

0.171

Z (X5)

Z (X3)

Z (X4)

Z (X6)

Z (X10)

Z (X2)

Z (X9)

0.948

− 0.133 0.214

− 0.028

0.935

− 0.197

− 0.156

− 0.077

0.040

0.325

0.320

0.500

0.069

0.049

0.060

0.092

0.076

0.060

0.067

0.092

0.073

− 0.212 0.162

Objective weighting

Interactive experience

− 0.108

0.264

0.265

− 0.269

0.228

0.839

Z (X8)

− 0.175

0.866

Z (X7)

0.069

0.049

Audiovisual experience

0.873

Technology effect

Z (X1)

Platform effect

Table 6.3 Rotated component matrix and comprehensive weights

0.272

0.027

0.068

0.028

0.230

0.115

0.021

0.070

0.087

0.081

Subjective weighting

0.706

0.010

0.017

0.009

0.110

0.045

0.006

0.024

0.042

0.031

Combined weighting

1

8

7

9

2

3

10

6

4

5

Combined ranking

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of Internet technology is the foundation of DCM. Three platform effect indicators, two technology effect indicators, and one interactive experience factor make up the top six indicators, with the total number of WeChat tweets, the richness of transactional information on websites, and the number of videos posted all belonging to the platform effect but coming from three different channels. As a result, the DCM media platform effect has a substantial impact. The audiovisual experience was rated poorly, and the average length of videos and audiovisual content on websites had a lesser impact on digital content marketing. The platform effect factor and the technology effect factor are represented by the length of time it takes to create a WeChat official account and the average number of monthly tweets, respectively. The rotation matrix results show that, despite belonging to the same factor the influence of different indicators differs significantly. Finally, the total DCM score of each tourist attraction area is computed. After normalization, the four principal component scores were calculated using rotation matrix coefficients and raw data. The formula for calculation is: f 1 = 0.45 × Z (X 1) + 0.446 × Z (X 7) + 0.432 × Z (X 8) + 0.429 × Z (X 5) + 0.019 × Z (X 3) + 0.028 × Z (X 4) + 0.248 × Z (X 6) − 0.009 × Z (X 10) + 0.073 × Z (X 2) + 0.089 × Z (X 9) (6.2) f 2 = 0.03 × Z (X 1) − 0.111 × Z (X 7) + 0.145 × Z (X 8) + 0.150 × Z (X 5) + 0.617 × Z (X 3) + 0.602 × Z (X 4) + 0.443 × Z (X 6) − 0.090 × Z (X 10) − 0.055 × Z (X 2) − 0.069 × Z (X 9)

(6.3)

f 3 = 0.03 × Z (X 1) − 0.139 × Z (X 7) + 0.182 × Z (X 8) + 0.188 × Z (X 5) + 0.773 × Z (X 3) + 0.755 × Z (X 4) + 0.555 × Z (X 6) − 0.113 × Z (X 10) − 0.319 × Z (X 2) − 0.087 × Z (X 9) (6.4) f 4 = 0.04 × Z (X 1) − 0.173 × Z (X 7) + 0.225 × Z (X 8) + 0.233 × Z (X 5) + 0.959 × Z (X 3) + 0.936 × Z (X 4) + 0.688 × Z (X 6) − 0.140 × Z (X 10) − 0.396 × Z (X 2) − 0.108 × Z (X 9)

(6.5)

Table 6.1 gives the results of calculating the comprehensive score of each tourist attraction using the scores of each principal component and the variance contribution rate. The formula below is used to compute the total score of tourist attractions. F = 0.377 × f 1 + 0.248 × f 2 + 0.159 × f 3 + 0.103 × f 4

(6.6)

Firstly, Benxi Water Cave is the most significant platform effect as the core influence factor of digital content marketing of tourist attractions, with the greatest comprehensive score, according to the comprehensive score of each tourist attraction.

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Second, Sun Island Scenic Area shows the best technology effect, audiovisual experience, and interactive experience, and ranks second overall, with the platform effect requiring to be improved. The Wudalianchi Scenic Area and Puppet Manchu Palace Museum Scenic Area, Shenyang Expo Garden, and Tiger Head Scenic Area, on the other hand, only have one high score of platforms effect and should focus on strengthening the balanced growth of the other three components. It demonstrates that a balanced expansion of each influencing component has a direct impact on DCM results. Finally, Jingpohu Scenic Area, Arctic Village Scenic Area, and Scenic Area of the Sea of Forest and Grotesque Stones along the Tangwang River rank in the center, with low scores for each primary component, indicating a lack of digital content marketing and the need to build all DCM activities. Golden Pebble Beach Scenic Area and Tiger Beach Ocean Park have the lowest scores for digital content marketing tactics and require global strategy (Table 6.4). Table 6.4 Evaluation of tourist attractions DCM comprehensive score Tourist attractions

Platform effect

Benxi Water Cave

19.246

0.112

0.274

− 0.246

7.302 (1)

Sun Island Scenic Area

1.006

4.240

5.314

6.590

2.954 (2)

The Wudalianchi Scenic Area

2.851

0.310

0.389

0.479

1.263 (3)

The Puppet Manchu Palace Museum

1.426

0.670

0.840

1.040

0.945 (4)

Shenyang Expo Garden

1.425

0.069

0.090

0.583

0.629 (5)

Jingpohu Scenic Area

0.682

0.807

− 0.057

− 0.069

0.441 (6)

Tiger Head Scenic Area

1.056

− 0.335

− 0.420

− 0.522

0.194 (7)

Arctic Village Scenic Area

0.055

− 0.308

− 0.387

− 0.479

− 0.166 (8)

Scenic Area of the Sea of Forest and Grotesque Stones along the Tangwang River

− 0.328

− 0.355

− 0.444

− 0.550

− 0.339 (9)

Golden Pebble Beach Scenic Area

− 1.310

− 0.966

− 1.211

− 1.501

− 1.081 (10)

Tiger Beach Ocean Park in Liaoning Province

− 1.347

− 1.341

− 1.680

− 2.083

− 1.322 (11)

Technology effect

Audiovisual experience

Interactive experience

Comprehensive score and ranking

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According to the comprehensive scenic ranking results, the platform impact drives the comprehensive score ranking the highest, which differs greatly from the indicator assessment effect, and the influence of interactive experience is less than the media effect. The impact of audiovisual experience is weaker, as is the impact of technology. Due to the short sample size and the uneven distribution of 5A tourist attractions in each province, the DCM evaluation status at the provincial level is not explored.

6.5 Conclusion Based on digital content marketing theory, this paper examines scholars’ research methods and contents according to DCM channels and forms, and it employs a combination of subjective and objective methods to build an evaluation system to assess the tourism digital content marketing of 5A tourist attractions in three northeastern provinces. To begin, the theoretical enhancement of the digital content marketing evaluation system; DCM is deconstructed into three dimensions of information practicality, user experience, and social media effect, and digital technology is incorporated into the evaluation dimension of digital content user experience by combining information technology evaluation methods in the digital economy. Secondly, by the PCA the subjective weighting technique of AHP is improved, and the subjective and objective weighting of ten secondary indicators is combined, with the results underlining the relevance of the social media effect, interaction channels, and push channels of DCM The construction of practical information on the tourist attractions is still inadequate, and transaction information usually requires the support of mobile devices at the order payment stage, which also reflects the disadvantage of the public channel on the web side compared with the mobile side, so the search channel construction needs to be strengthened further. Finally, this study develops a principal component scoring technique for tourist DCM that integrates platform effect, technology effect, audiovisual experience, and interactive experience as the basis of tourism DCM scoring. A positive platform effect score can directly influence the overall score, demonstrating the value of social media. Users value interactive experiences, and instant interactive features can boost the effectiveness of digital content marketing. Social media is an important DCM carrier; instantaneous interaction can effectively improve visitors’ digital content service experience. Tourist attractions should realize the sustainable development of each publicity channel and achieve balanced development in terms of the post of high-quality digital content, mastery of digital technology, and platform operation level to improve the competitiveness of tourism DCM and the overall strength of destination marketing. While advancements to the PCA-AHP model can compensate for the method’s reliance on subjective behavior to some extent, research on small samples still needs to be improved. In the future, we should evaluate a broader range of study objects, look into more digital content functions, and conduct in-depth research on tourist

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experience and demographic features for locations with various geographical and cultural origins. Acknowledgements This paper was supported by (1) Heilongjiang Province Philosophy and Social Science Research Planning Project (21GLE297) and (2) Harbin Commercial University Doctoral Research Initiation Fund Project (2019DS034).

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19. Gregoriades, A.: Supporting digital content marketing and messaging through topic modeling and decision trees. Expert Syst. Appl. 184, 115546 (2021) 20. Wang, W.H., Wang, Y.: Research and application of multi-criteria evaluation model based on PCA-AHP-IE. J. Zhejiang Univ. Technol. 47(06), 591–596 (2019). (in Chinese) 21. Chen, G.F., Wei, H.: Tunnel gas risk assessment based on EW-AHP and unascertained measurement theory. Nonferr. Met. Sci. Eng. 12(5), 89–95 (2021). (in Chinese) 22. Liao, B.Y.: Digital Content Marketing. Science Press, Beijing (2019). (in Chinese) 23. Tee, M., Chaw, L.Y.: Generation Z’s Perspective on Tourists’ Knowledge Sharing and Service Excellence in Tourism. Springer, Cham (2021) 24. Ageeva, E.: Evaluating the factors of corporate website favorability: a case of UK and Russia. J. Cetacean Res. Manag. 22(5), 687–715 (2019) 25. Fox, A.K., Nakhata, C., Deitz, G.D.: Eat, drink, and create content: a multi-method exploration of visual social media marketing content. Int. J. Advert. 38(3), 450–470 (2019) 26. Zeng, K.Y., Zhou, L.J.: Analysis on network attention of class 5A and 4A tourist spots in Northeast China based on Baidu index. J. Northeast Normal Univ. (Nat. Sci. Ed.) 51(01), 133–138 (2019). (in Chinese) 27. Shen, Y.C., Wu, X.Y.: Temporal and spatial characteristics of network attention on tourist satisfaction in China. Economy Geography. 39(02), 232–240 (2019). (in Chinese) 28. Wang, F.Q., Jiang, J.H.: How does the internet short video business model realize value creation? A comparative case study of Douyin and Kuaishou. Foreign Econ. Manag. 43(02), 3–19 (2021). (in Chinese) 29. Xu, D.: Reaching audiences through travel vlogs: the perspective of involvement. Tour. Manag. 86, 104326 (2021) 30. Wang, R., Chan-Olmsted, S.: Content marketing strategy of branded Youtube channels. J. Media Bus. Stud. 17(3–4), 294–316 (2020)

Chapter 7

Research on the Performance Evaluation of Digital Trade Based on the “VHSD-EM” Model Under Dual Circulation Yijun Xiang and Jiayi Yuan

Abstract The digital economy becomes a driving force of global economic development, while digital trade also becomes a new factor to reshape the global value chain and enhance international competitiveness. Digital trade plays a key role in the formation of a new development pattern of dual circulation and achieving highquality economic development in China. Studies on the evaluation of China’s digital trade development becomes an important topic. By analyzing the definition and characteristics of digital trade, this paper designs an evaluation index system of digital trade development under the target framework of promoting high-quality development of foreign trade and building a new pattern of dual circulation. The system contains 4 dimensions, 10 elements, and 27 specific indicators, including the environment, conditions, capabilities, and cooperation level of the digital trade development. The paper also proposes to use the “VHSD-EM” model to measure the development level of digital trade and finds that China’s digital trade the remarkable advantages in digital infrastructure, the scale of the market and the industrial scale. However, the international competitiveness of key and core technologies, data security and personal privacy protection should be further improved. China should take some effective measures to promote technological innovation, deepen international cooperation, and improve the security system in order to achieve digital trade development.

7.1 Introduction With the development of economic globalization and digital technology, such as big data, cloud computing, and the Internet of things, the traditional business model and trade pattern undergo profound changes, while there is a boom in global digital trade. Y. Xiang · J. Yuan (B) School of Economics, Harbin University of Commerce, Harbin 150028, Heilongjiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_7

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Digital trade is globally growing much faster than traditional trade. Trade in digital services is driving global trade to develop toward the direction of servitization. A variety of new business forms and models emerge and innovate in China, such as live streaming e-commerce, cross-border e-commerce, and social e-commerce, which make remarkable achievements in promoting consumption, stabilizing foreign trade, and ensuring employment. Strong demand for digital services such as online office, online education, and remote consultation caused by COVID-19 in 2020 also plays an important role in helping to prevent and control the epidemic, promoting the return to work and production and optimizing government governance. China’s digital trade is facing unprecedented opportunities and challenges. It is not only a new force to promote steady economic growth and the high quality of foreign trade development, but also an important bridge between domestic and foreign economic circulation in the Digital Age, which is helpful to accelerate building a new development pattern of dual circulation. The Ministry of Commerce of China issued the 14th Five-Year Plan for the Development of Trade in Services, clearly proposing that the development of digital trade is a key to the high-quality development of trade in services in October 2021. China Academy of Information and Communication Technology released Digital Trade Development and Cooperation Report 2022 to promote deepening cooperation and common development of global digital trade in September 2022, which indicates China’s trade in cross-border digital services shows strong growth momentum. The total value of China’s digital services imports and exports reached US $359.7 billion accounting for 43.2% of total services imports and exports in 2021. Therefore, it becomes an important topic to study in the evaluation of China’s digital trade development under dual circulation. In order to objectively evaluate the development level of China’s digital trade and discover its internal development law and dynamic development trend, the paper focuses on analyzing the definition and characteristics of digital trade, also constructs the evaluation index system of digital trade development, explores its measurement method, finally puts forward the countermeasures to promote the development of digital trade based on the evaluation of the present situation of China’s digital trade.

7.2 Meaning of Digital Trade Under Dual Circulation 7.2.1 Definition of Digital Trade Global digital trade develops rapidly and becomes an important link for the opening and cooperation of the global digital economy, exerting a profound impact on the innovation of the global science and technology industry and the international division of labor in recent years. Digital trade is a new form of international trade empowered by digital technology, mainly pulled by data flow, carried by modern information

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networks, and strongly supported by the digital platform. It is a revolutionary change of trade mode, and its meaning develops and enriches unceasingly. The concept of digital trade was first proposed by Weber, an American scholar, who defined it as a product or service delivered digitally by the Internet in 2010. According to Digital Trade in the US and the Global Economy issued by the United States International Trade Commission in 2013, digital trade is comprised of digital content, social media, search engines, and other products and services. The second report in 2014 expanded the scope of digital trade to include cross-border data flows and physical products sold over the Internet. Since then, the digital trade measurement manuals of the WTO, OECD, and IMF divided digital trade into digital order trade, digital intermediary platform, and digital delivery trade, which added trade intermediary platforms that provide sellers and buyers with interactive services into its definition [1]. Chinese scholars define digital trade from multiple perspectives. From a global value chain perspective, it is believed that digital trade is the digital upgrading of digital products and services entering into the global value chain system and forming the digital product value chain and the global value chain system [2]. Digital trade is also divided into digital goods trade, digital services trade, and data trade. Trade in digital goods refers to cross-border e-commerce. Trade in digital services includes trade in digitized services and trade in digital content services. Data trade means the flow of data across borders [3]. From this point of view, the definition of digital trade has been improved along with the development of network infrastructure and the growth of digital trade. In a narrow sense, it includes only products and services delivered through digital technology. In a broad sense, digital goods and services, cross-border data flow and digital trade platforms are all included in the definition of digital trade.

7.2.2 Characteristics of Digital Trade Under Dual Circulation Digital trade is a senior form of the evolution of international trade in the digital age. Its obvious advantage lies in breaking the limitation of transaction time and space, and greatly reducing the cost of trade, which largely promotes global import and export development. Compared with traditional trade, its basic features are reflected in the following four aspects. Firstly, it needs the support of the Internet and logistics environment which improves transaction efficiency. Secondly, it requires a higher quality of trade talent, because the core of digital trade is digital technology, and a sufficient reserve of digital talent is the prerequisite for the development of digital technology. Thirdly, the digitization of trade mode, namely the digital transformation of the whole trade process and the whole industrial chain, leads to the lower cost of trade, better efficiency, and more diverse subjects. Fourthly, the digitalization of trade objects means that elements and services in the form of data become important

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objects in international trade, including ICT services, other services and data elements enabled by ICT [4]. In order to build a new development pattern, China should conform to the trend of economic globalization and the evolution of the division of labor, and should play a role in the new round of technological and industrial revolution, including the digital technology revolution and its application in practice, which gives digital trade a new requirement. Under the conditions of open development, the digitization of trade promotes the evolution of the global division of labor, the digital revolution and digital technology strengthen the trend of the global factor division, globalization, and fragmentation of services and reshape the trade rules [5]. Digital trade should not only realize the digital transformation of the whole process of foreign trade, but also realize the digital transformation of the whole industry chain, the whole value chain and the whole supply chain, which profoundly changes the production, distribution, exchange and consumption, and thus promotes a better linkage development between domestic and international circulation.

7.3 Performance Evaluation Mechanism of Digital Trade 7.3.1 Construction of Performance Evaluation System for Digital Trade Based on the above analysis of the requirements of digital trade development under dual circulation, considering the measurability of the index system and the availability of data, referring to the existing research on the evaluation of the level of development of digital trade, the paper designs the evaluation index system of digital trade development under the framework of promoting high-quality development of foreign trade and building dual circulation, including four dimensions, such as digital trade development environment, conditions, capacity and cooperation level, and 10 elements and 27 specific indicators (see Table 7.1).

7.3.2 Measurement Method of Digital Trade The existing evaluation for the development of digital trade mostly uses the entropy method [6–8] which is an important static evaluation method. The method can determine the weight based on the information content of each evaluation index, to reflect the differences between the indexes well, but it cannot achieve the dynamic comparison of evaluation results. While the “Vertical and Horizontal” Scatter Degree method as a dynamic evaluation method can integrate the time factor into the weight value determination process and maximumly reflect the differences between the evaluated

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Table 7.1 Performance evaluation index system of digital trade development Primary index

Secondary index

Tertiary index

Direction

The environment of digital trade development

Transportation infrastructure

Logistics performance index

+

Power consumption

+

Information network infrastructure

Quantity of domain names

+

Quantity of broadband subscribers

+

Length of long-distance fiber optic cable per unit area

+

Innovation conditions

The proportion of R&D investment in GDP

+

Quantity of patent applications

+

Quantity of talents in R&D

+

Internet and related services investment

+

The total output value of electronic equipment manufacturing

+

Conditions of digital trade development

Industry supply conditions

Total revenue of telecommunications + business Total revenue of software business Consumption GDP per capita demand conditions Consumption expenditure per capita E-commerce sales revenue Online retail sales revenue

+ + + + +

The capacity of digital Scale and structure The proportion of ICT products trade development of digital trade exports in total exports of products

+

The proportion of ICT services exports in total exports of services

+

The proportion of export of digital services in total export of services

+

Competitiveness of digital trade

Trade competitiveness index

+

Export growth advantage index

+

Degree of openness of foreign trade

The total value of import and export of goods in GDP

+

The total value of import and export of services in GDP

+

Digital trade barriers

Government online services index

+

Digital trade restrictions index

−

Digital trust risks

Information technology regulation

+

Global network security index

+

Level of digital trade cooperation

The density of secure Internet servers +

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objects in different years. However, it only depends on the evaluation matrix to determine the weight of the indicators, which cannot reflect the quantity of information contained in each indicator. The paper puts forward the adoption of the “Vertical and Horizontal” Scatter Degree method and Entropy Method, “VHSD-EM” model [9] to evaluate the development level of China’s digital trade. The advantages of the “VHSD-EM” model are as follows. Firstly, for the sequential stereo data, the influence of time factors on weight values can make the evaluation results dynamically comparable. This model can achieve the dynamic comparison of evaluation results over different years. Secondly, by examining the information contained in the indicators, the degree of importance of each indicator in the evaluation object in each year can be well clarified. The model can reflect the differences in the information contained in each index, so as to effectively identify the development differences and deficiencies for each region. Thirdly, considering the possible measurement bias of a single evaluation method, the combined evaluation method can optimize the weight value to a certain extent. This model can reduce the measurement error caused by a single evaluation method, and then make the evaluation result more scientific and reasonable. Therefore, the “VHSD-EM” model can fully unify the advantages of the two evaluation methods. China’s digital trade develops rapidly and there are obvious differences among different regions. The “VHSD-EM” evaluation model can not only realize the dynamic comparison of evaluation results among different years but also reflect the difference of information contained in each index, so as to effectively identify the development differences and deficiencies of different regions. Besides, it can also reduce the measurement error caused by a single evaluation method and make the evaluation result more scientific and reasonable. The Basic Principle of VHSD Evaluation Model. Study on a certain system problem involves Qk years, Qm evaluated objects and Qn basic indexes, then these indexes can be arranged into time aggregated data according to the investigated time. Let the basic index value matrix of the index system be: X = xi j (tk ),

(i = 1, 2, . . . , m; j = 1, 2, . . . , n; k = 1, 2, . . . , K )

(7.1)

x ij (t k ) is the value of item No. j of sample No. i in the year No. k. Let us denote the specific name of sample No. i, then the time series data is shown in Table 7.2. Firstly, non-dimensional treatment for the experimental data. The formula is: yi jk =

xi jk − xi−jk σi jk

, i = 1, 2, . . . , m; j = 1, 2, . . . , n; k = 1, 2, . . .

(7.2)

yijk denotes the index value of item No. j of sample No. i in the year No. k after standardization, xi−jk is the average value of item No. j in the year No. k, σ ijk is the standard deviation of item No. j in the year No. k.

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Table 7.2 Time series three-dimensional data t1

t2

…

tk

x1 x2 … xn

x1 x2 … xn

…

x1 x2 … xn

u1

x 11 (t 1 ) x 12 (t 1 ) … x 1n (t 1 )

x 11 (t 2 )x 12 (t 2 ) … x 1n (t 2 )

…

x 11 (t k )x 12 (t k ) … x 1n (t k )

u2

x 21 (t 1 ) x 22 (t 1 ) … x 2n (t 1 )

x 21 (t 2 )x 22 (t 2 ) … x 2n (t 2 )

…

x 21 (t k )x 22 (t k ) … x 2n (t k )

…

…

…

…

…

um

x m1 (t 1 )x m2 (t 1 ) … x mn (t 1 )

x m1 (t 2 )x m2 (t 2 ) … x mn (t 2 )

…

x m1 (t k )x m2 (t k ) … x mn (t k )

Sample

Secondly, the determination of the weight of the indicator. Let the comprehensive evaluation function be: z i (tk ) =

n 

δ j yi j (tk )

(7.3)

j=1

δ j is the index weight, zi (t k ) is the comprehensive evaluation value of sample No. i in the year No. k. We use the method of the sum of squares of total deviations to maximize the differences among the samples. σ2 =

m K  K   (z i (tk ) − z)2 = δ T Hk δ = δ T H δ k=1 i=1

(7.4)

k=1

K δ = (δ1 , δ2 , . . . , δn )T , H = k=1 Hk is a symmetric matrix, Hk = AkT Ak , k = 1, 2, …, K. Under the condition that δ T δ = 1, σ 2 gets the maximal value, when taking the eigenvector corresponding to the largest eigenvalue of matrix H. Then, the weight vector δ j is obtained by normalizing the eigenvector. The Basic Principle of EM Evaluation Model. For the index data after nondimensional treatment, the degree of variation is calculated by yi jk vi jk = m i=1 yi jk

(7.5)

vijk is the characteristic proportion of index No. j of evaluation object No. i in the year No. k. Therefore, the EM value of index No. j,   1  vi jk ln vi jk In(m) i=1 m

E jk = −

(7.6)

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  When vijk = 0 or 1, let vijk ln vi jk = 0. Let the difference coefficient of the index is Djk , then D jk = 1 − E jk

(7.7)

The bigger Djk , the more information of the object to be evaluated of index j, the more weight should be given to it, and the more important the index is in the evaluation system. The EM weight of the index is D jk w jk = n j=1 D jk

(7.8)

The Basic Principle of “VHSD-EM” Evaluation Model. Based on the weight δ j calculated by the VHSD method and the weight of each index in each year wjk determined by the EM method, group them into a matrix C jk by year. ⎡

C jk

δ1 · · · ⎢ .. . . =⎣ . . δn · · ·

⎤ w1k .. ⎥ . ⎦

wnk

(7.9) n×2

Sum up the elements of each row in C jk and calculate the arithmetic mean, then get the final weight of each basic index W jk , as for the comprehensive evaluation value of each object in each year, the linear weighted method is used, to sum up, the weighted values layer by layer, and the final evaluation value Pmk is obtained.

7.4 Evaluation of the Present Situation of China’s Digital Trade Development 7.4.1 The Advantages of Digital Infrastructure China has huge advantages in the aspect of network communication, storage and computing and integration application for the digital infrastructure. China takes the lead in information infrastructure and builds the world’s largest fiber optic and fourthgeneration (4G) mobile communications networks and accelerates the construction and application of fifth-generation (5G) mobile communications networks. The quantity of 5G patents by Chinese companies accounts for about 34 percent of the total quantity in the world, which is the largest share, well ahead of South Korea and the US. In addition, China is gradually closing the gap with the United States in the field of the construction of super-large data centers.

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7.4.2 The Advantages of the Large Scale of Market China is the second largest economy with a population of more than 1.4 billion, there are over 400 million middle-income population. With the growth of China’s open economy and the expansion of its middle-income group, the potential of consumer demand will be fully released, which could activate a positive effect of demand-driven innovation. A large amount of consumer demand inevitably leads to economies of scale and a strong demand-driven innovation effect, which gives impetus to digital technology change and technological progress as well as its penetration and integration in other industries.

7.4.3 The Advantages of the Huge Industrial Scale China has the largest manufacturing industry in the world. Among the more than 500 kinds of major industrial products in the world, China ranks first in the output of more than 220 kinds of products. China has a complete industrial system which deeply integrates with the global supply chain and shows a huge advantage in the scale of the manufacturing industry. While the huge industrial scale provides the related industrial support for the digital technology progress, there is a huge development space to realize the digital transformation relying on digital technology. According to the China Internet Development Report (2021), China’s big data, artificial intelligence, cloud computing and Internet of things industries all developed rapidly and lay at the top of the list in many industries.

7.4.4 The Insufficiency of the Key and Core Technologies Although China has advantages in the construction of digital infrastructure, there is a relatively low self-sufficiency rate in many keys and core components and technologies. Particularly, high-end chips, basic software, core components, and so on are highly dependent on imports, as well as some of the core technologies in the global software industry chain. There are obvious differences between China and the developed countries in the basic and high-end fields of digital technology, which will affect China’s greater voice in building the global digital trade rules system and cause a contradiction between the supply and demand structure. In the final analysis, it is due to the shortage of talent in related industries, especially in the fields of big data, artificial intelligence, and other new information technology, which restricts the high-quality development of the digital industry chain.

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7.4.5 The Highlighted Importance of Data Security and Privacy Protection Building the dual circulation causes a more frequent flow of labor, information and data between the domestic and international markets, and a more efficient global allocation of resources. With expanding the scope of cross-border trade, the national boundaries of data flows become blurred. In this situation, more attention is paid to personal privacy protection and data security. China’s digital trade starts late and develops rapidly, but the formulation of digital trade rules lags behind the market practice of digital enterprises, so the legislation of data security and data management should be improved urgently, especially in mobile payments, and crossborder payments. There are strong interest appeals to the prevention of data risk, the protection of data subjects’ rights and interests, and the maintenance of national security.

7.5 Measures to Promote the Development of Digital Trade 7.5.1 Increasing Technological Innovation in Digital Trade Digital technological progress and innovation are a great support for the development of digital trade. Firstly, China should speed up the construction of new infrastructure and consolidate the foundation for the development of digital trade. Secondly, strengthening independent R&D and technological innovation to further enhance the level of digital technology. Supporting and encouraging the leading information technology enterprises to increase capital investment and technological R&D in cutting-edge fields. In addition, China should accelerate the digitization transformation of traditional service industries and layout emerging key digital technologies and digital service industries, promote the wider application of digital technologies in the media, healthcare, logistics, and others, to expand the scale of trade in digital services and enrich the kinds of trade in goods and services.

7.5.2 Deepening International Cooperation on Digital Trade China should actively promote global digital trade to deepen cooperation and common development. Firstly, upholding the concept of win–win cooperation and building more international consensus on digital trade development, opening wider to the outside world. Secondly, expanding access to digital dividends and development gains. We should increase investment in digital industries in the Belt and Road and other emerging markets, upgrade the digitalization of emerging markets,

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and participate in international cooperation on global digital trade. Thirdly, strengthening the coordination of international rules and building a universally accepted digital trade rules system. We should actively conduct bilateral or multilateral negotiations on regional digital trade rules. Fourthly, we should jointly build platforms for international exchange and cooperation and expand cooperation space for digital trade.

7.5.3 Upgrading the Security Guarantee System of Digital Trade China should speed up the construction of the digital governance system, promote institutional openness of the digital market and further improve the relevant policies and regulations. It is key to implement the relevant laws and regulations on crossborder data transmission for realizing the safe and orderly flow of cross-border data. For example, speeding up the protection of personal privacy and the implementation of the Data Security Act, and breaking down inter-regional information barriers based on ensuring the security of data information to promote the orderly flow of multi-industry, multi-field, cross-sector, and cross-level data, forming a unified and standardized domestic data and information market. Furthermore, raising awareness of personal information security and protection to ensure that information is used by the law and regulations. Acknowledgements This work was supported by the grants of the National Social Science Fund Project (18BJL094).

References 1. Zheng, W., Zhao, Y.: Digital trade: a study of international trends and our country’s development path. Int. Trade 4, 56–63 (2020) 2. Wang, X.W., Gong, W.C.: Foreign experience in the development of digital trade and its enlightenment. Guizhou Soc. Sci. 363(3), 132–138 (2020) 3. Dong, X.J., Guo, X.J.: The developing trend of digital trade between the United States, Japan and Europe and China’s countermeasures. Int. Trade 3, 27–35 (2021) 4. Zhao, X.Q., Zhang, X.Q., Lin, Z.G.: Opportunities, challenges and suggestions for China’s digital trade under the new development pattern of dual circulation. Econ. Restruct. 4, 22–28 (2021) 5. Tan, D.H., Dai, X.: Digitization of trade: a new engine for effective leakage of dual circulation. Int. Trade 1, 18–25 (2022) 6. Han, K., Han, Y.L., Cai, C.Q.: China’s digital trade development level measurement and dynamic evolution. Stat. Decis.-Mak. 20, 155–159 (2022) 7. Feng, Z.X., Duan, D.Y.: Study on the evaluation of digital trade development and the influencing factors—based on panel data of 49 countries. J. Beijing Univ. Technol. Sci. (Soc. Sci. Ed.) 22(4), 100–115 (2022)

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8. Zhang, D.P., Zheng, X.Y.: The measurement of digital trade development level and its influencing factors: a case study of Zhejiang Province. J. Zhejiang Univ. Sci. Technol. 32(4), 249–256 (2020) 9. Tang, X.B., Wang, Y.N., Tang, X.W.: China provincial economic high-quality development evaluation research. Sci. Res. Manag. 41(11), 44–55 (2020)

Chapter 8

Research on the Distribution of Income from Agricultural Supply Chains Under the Integration of Agriculture Based on the Revised Shapley Method Li Xuan Sun and Nan Xu

Abstract At present, China is in a new development stage and pursuing high-quality development of agriculture, but the imperfect integration of resources in the supply chain of agricultural products and the incomplete traceability system have led to the constraint of integrated development of agriculture. Based on the cooperation game between enterprises of different nodes in the supply chain, this paper first uses the Shapley value method to calculate the benefit distribution among agricultural products processors, wholesale markets and retailers. Secondly, the contribution degree of risk management, contribution degree of collaborative management and contribution degree of social benefits are proposed for the future development of agricultural integration, and the weight values are calculated by using AHP hierarchical analysis. The results show that the benefits of the improved model tend to favor the agricultural wholesale market, which helps to enhance the construction of the core functions of the wholesale market, which is in line with the integrated development of agricultural products and makes the operation of the agricultural supply chain more reasonable and effective.

8.1 Introduction In the great historical period of building a moderately prosperous society, China is entering a new development period, and the requirements of high-quality social development make agricultural products rich in variety, but also ensure better quality. The scale of China’s agricultural products distribution market is huge, but the uneven development of the market, poor intensification and incomplete coverage of modern

L. X. Sun · N. Xu (B) College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_8

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means have led to a low level of agricultural integration for a long time. And a highlevel agricultural integration model can enhance the integration of information and resources in the supply chain, save operating costs and increase the added value of agricultural products. Agricultural wholesale markets occupy a central position in the agricultural products supply chain and are the main channel of the agricultural products distribution market, as well as the core hub of agricultural integration. Along with the rapid development of science and technology, the construction of agricultural wholesale markets has failed to develop simultaneously, and the service system and development scale have stagnated, which is not in line with the requirements of high-quality development. Most of the agricultural wholesale markets are not clearly divided into functional areas as the internal environment is simple, cold chain and preservation function is not enough. Where the profit model is also single, the lack of value-added services, the redundant costs that represent the loss of a larger profit space. Thus, modern management methods are missing and fail to communicate effectively with digital IoT technology, which leads to an incomplete tracking system. Based on the various problems exposed by the agricultural wholesale markets, the reasons for insufficient investment and construction and backward infrastructure cannot be ignored, but the more important reason is that the integration structure of the agricultural supply chain is imperfect and the benefit distribution model is unreasonable. In this paper, we emphasize the synergistic management function of the agricultural wholesale market and redistribute the benefits in the agricultural supply chain to achieve the optimization of the overall benefit distribution decision, so as to promote the subjects in the agricultural supply chain to optimize and upgrade in their respective fields and finally deepen the development of agricultural integration.

8.2 Literature Review on Agricultural Supply Chain Levi R empirically evaluates the UMP reform of the Indian government, employing a difference-in-differences method, and demonstrates that such reform measures are beneficial to the development of high-quality agricultural products [1]. Zhou J concluded that sampling collection and information disclosure can help enhance the traceability of agricultural products by collecting various data from Chinese wholesale fish markets [2]. Septiani WD is based on the development of communication technology, emphasizing that electronic agricultural wholesale markets can better sell high-quality agricultural products [3]. Liu C used survey data analysis to strengthen the integration of artificial intelligence and agricultural wholesale markets, which helps to open up online and offline distribution channels [4]. Zeng Y proposed the use of cloud computing technology to strengthen the innovation of trading, supply and demand and operation mechanisms by analyzing the existing agricultural distribution models [5]. Salamin O analyzed the operating environment of the national agricultural wholesale market in Ukraine and proposed to expand the construction

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of agricultural wholesale market functions [6]. Ngango J used Rwandan agricultural retail and wholesale data, combined with linear methods and asymmetric error correction model framework approach to provide theoretical recommendations for policymakers [7]. Zhang Ping used data envelopment analysis (DEA) to conduct an example analysis of the circulation efficiency and evolutionary trends of agricultural wholesale markets, emphasizing the optimization of resource factor allocation in agricultural wholesale markets to enhance resource utilization efficiency [8]. Sano Y used an oligopoly model to assess the economic welfare impact of wholesale markets to benefit producers and consumers and facilitate the development of industrial organizations [9]. Ao Guiyan et al. conducted an empirical analysis using a mediating effects analysis model and concluded that organizational models and vertical integration can have a significant positive impact on farmers’ income, with a significant increase in operating income after participating in business organizations [10]. Barcho MK proposed the development of agro-industrial complexes for structural transformation based on the establishment of market structures and mechanisms that can contribute to agricultural competitiveness [11]. Pinmanee S proposed an internal and external supply chain integration model to improve logistics performance by summarizing agricultural supply chain integration models [12]. Derso B analysis of agricultural extension system integration in the Amhara Region found that the level of interorganizational linkages and coordination must be strengthened to play a role in achieving strong integration [13]. Staatz JM through his study of regional agricultural integration found that regional trade policies are the main challenge for West African countries [14]. Hedoui MA identified the limitations and challenges that hinder the trade integration of countries around the Mediterranean by assessing the complementarity of agricultural trade to EU countries [15]. Ziyadin S affirmed the effectiveness of EU agricultural integration while making some recommendations for the development of ecological agriculture [16]. Hamulczuk M uses the concept of spatial integration to analyze the homogeneity of goods and trade costs that hinder agricultural integration [17]. Aboah J discusses using system dynamics modeling and concludes that the pursuit of agricultural diversification and forward integration strategies is beneficial for agricultural value chains [18]. Kabakchieva T uses Bulgaria as an object to reveal the impact of the integration process on the impact of agricultural trade and regionalism dynamics in the EU region [19]. Kryvenko N focuses on the common agricultural policy and the characteristics of the EU, suggesting that the establishment of a common market with appropriate regulation is necessary [20]. Rational benefit distribution is a prerequisite for the development of agricultural integration, and the Shapley value method is widely used in the process of benefit distribution in agricultural supply chains. George V proposes that contract governance should be improved to enhance the fairness of benefit distribution by analyzing contract farming [21]. Zavalloni M uses the article that applies the Shapley value and the Nash-Harsanyi solution to propose incentive schemes to improve benefit distribution rationality [22]. Zhou Yewang measured the contribution of each subject in the alliance and concluded that participation in the alliance can improve business returns and promote the sustainable development of the egg industry [23]. Huan

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Meili et al. used the Shapley value method to achieve revenue Pareto improvement in the revenue distribution of the raw milk supply chain, and finally found the optimal decision [24]. Zhu Yiqing et al. used the improved Shapley model to improve the ecological operation of the agricultural e-commerce supply chain and the rationality of revenue allocation [25]. Bai proposed a correction factor to modify the traditional Shapley method to make the benefit distribution among supply chain subjects more reasonable under new retail [26]. Xu Zhongwen et al. established a supply chain coordination model under a decentralized and centralized market environment and proved that a centralized market can improve the total profit of an agricultural supply chain through imitation row numerical analysis [27]. Ge J constructed a comprehensive benefit distribution model to improve the Shapley value and combined with the Topsis method to optimize the benefit distribution of supply chain enterprises [28]. In summary, many domestic and foreign scholars have conducted more studies on the distribution of benefits among agricultural wholesale markets, agricultural integration and agricultural supply chains, which provide more theoretical guidance for the research ideas in this paper. However, there are not many studies that take the agricultural wholesale market as the object and combine the integration of the agricultural supply chain structure with the benefit distribution of each participating subject. This paper takes agricultural integration in China as the background and agricultural wholesale market as the ore, uses the Shapley value method to calculate and analyze the contribution of the agricultural wholesale market in the agricultural supply chain and adjusts it by introducing influencing factors to make the distribution of benefits of the agricultural supply chain more reasonable and the operation of agricultural supply chain more synchronized with the development of agricultural integration.

8.3 Shapley Model and Revenue Distribution 8.3.1 Introduction to the Shapley Method When the core node enterprises of the agricultural supply chain form an alliance with the agricultural wholesale market as the leader, and play a synergistic function, the overall revenue of the alliance will increase and the revenue distribution will be more reasonable, which is a typical cooperative game problem, and the Shapley value model has a significant role in the process of solving the cooperative game problem. Shapley value method was proposed by economist L.S. Shapley in 1953 and it is most widely used in cooperative games, especially in the problem of coalition revenue distribution. When individual economic agents form a coalition, the return of each agent and the overall coalition return will increase and the overall coalition return will be larger as more agents join, and the return will be reasonably distributed

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by calculating the value of each agent’s contribution to the coalition. It is defined as follows: Suppose there are n subjects in the agricultural products supply chain, let the set N = {1, 2, 3, …, n}, n subjects form a subset s of alliances with each other, υ(s) represents the revenue of each alliance s in the agricultural products supply chain and the function υ(s) satisfies the following conditions: υ(φ) = 0

(8.1)

υ(s1 + s2 ) ≥ υ(s1 ) + υ(s2 ), s1 ∩ s2 = φ .

(8.2)

Equation (8.1) indicates that the overall cooperation revenue of the supply chain is 0 when there is no alliance and Eq. (8.2) indicates that the revenue obtained by the alliance subjects involved in the cooperation is not less than the revenue of each subject when they operate individually, and the game strategy is as follows. ϕ(υ) = {ϕ1 (υ), ϕ2 (υ), ϕ3 (υ), ϕ4 (υ), . . . , ϕn (υ)} n Σ

ϕi (υ) = υ(i ), ϕi (υ) ≥ υ(i ), i = 1, 2, 3, . . . , n

(8.3) (8.4)

i=1

Equation (8.3) indicates that the sum of the overall returns in the alliance is equal to the sum of the returns of each member after forming the alliance, and Eq. (8.4) indicates that the returns allocated to each member participating in the alliance are greater than or equal to the returns of each member when operating individually. ϕi (υ) =

Σ

w(|s|)[υ(s) − υ(s\i )], i = 1, 2, 3, . . . , n w(|s|) =

(n−|s|)!(|s|−1)! n!

(8.5) (8.6)

Equation (8.5) is the benefit distribution model of the Shapley value method. w|s| in Eq. (8.6) is the weighting factor, |s| is the number of coalitions formed in the supply chain, n is the number of members in the coalition, υ(s) is the benefit generated by each coalition s in the supply chain, and υ(s\i) is the benefit generated by subtracting subject i from the supply chain coalition s.

8.3.2 Agricultural Supply Chain Node Revenue Calculation The three core nodes of processor A, wholesale market B and retailer C are selected for the arithmetic analysis. Most of the profits in the actual market operation are occupied by processing enterprises and retailers, and the wholesale market has the

96 Table 8.1 Separate operating income

Table 8.2 Cooperative business revenue

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Subjects

Individual earnings/million

A

60

B

30

C

90

Subjects

Cooperation income/10,000 yuan

A+B

120

A+C

180

B+C

150

A+B+C

360

smallest proportion of operating income. Therefore, it is reasonable to assume that the revenues obtained by three nodes, namely agricultural products processor A, agricultural products wholesale market B and agricultural products retailer C when they operate independently are 600,000 yuan, 300,000 yuan and 900,000 yuan, respectively; the revenues generated by agricultural products processor A and agricultural products wholesale market B when they cooperate are 1.2 million; the revenue generated when agricultural product processor A and agricultural product retailer C work together is 1.8 million yuan; the revenue generated when agricultural product wholesale market B and agricultural product retailer C work together is 1.5 million yuan; the revenue generated when agricultural product processor A, agricultural product wholesale market B and agricultural product retailer C reach a cooperative alliance is 3.6 million yuan. The sum of the returns obtained by different node enterprises is equal to the sum of the overall returns of the agricultural products supply chain. The distribution of the revenues of the selected nodal enterprises after operating individually and participating in cooperation is given in Tables 8.1 and 8.2. When the supply chain node enterprises operate individually, the agricultural product processors and agricultural product retailers have the largest share of benefit distribution due to more operational inputs, which are 600,000 yuan and 900,000 yuan, respectively. In contrast, the wholesale market of agricultural products has less input and the benefit distribution is only 300,000 yuan. Table 8.2 gives the revenue generated by the three node enterprises in the supply chain when they cooperate two by two, respectively, and the revenue obtained when the three form an alliance. Substituting the above data into the Shapley value formula, the gains of the three levels of nodes in the supply chain are calculated separately, and the results are given in Tables 8.3, 8.4 and 8.5. According to the calculation, the benefit that agricultural product processor A can get in the alliance is ϕ 1 = 20 + 15 + 15 + 70 = 1.2 million yuan. The revenue that wholesale agricultural market B can get in the alliance is ϕ 2 = 10 + 10 + 10 + 60 = 900,000 yuan.

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Table 8.3 Agricultural processors A participating alliance revenue s

A

A&B

A&C

A&B&C

υ(s)

60

120

180

360

υ(s\i )

0

30

90

150

υ(s) − υ(s\i )

60

90

90

210

|s|

1

2

2

3

w(|s|)

1\3

1\6

1\6

1\3

w(|s|)[υ(s) − υ(s\i )]

20

15

15

70

Table 8.4 Wholesale agricultural market B participating alliance revenue s

B

A&B

B&C

A&B&C

υ(s)

30

120

150

360

υ(s\i )

0

60

90

180

υ(s) − υ(s\i )

30

60

60

180

|s|

1

2

2

3

w(|s|)

1\3

1\6

1\6

1\3

w(|s|)[υ(s) − υ(s\i )]

10

10

10

60

Table 8.5 Agricultural retailer C participation alliance revenue s

C

A&C

B&C

A&B&C

υ(s)

90

180

150

360

υ(s\i )

0

60

30

120

υ(s) − υ(s\i )

90

120

120

240

|s|

1

2

2

3

w(|s|)

1\3

1\6

1\6

1\3

w(|s|)[υ(s) − υ(s\i )]

30

20

20

80

The revenue that agricultural retailer C can receive in the alliance is ϕ 3 = 30 + 20 + 20 + 80 = 1.5 million yuan. The above results show that the benefits obtained by each core node enterprise after participating in alliance cooperation are significantly higher than those obtained when operating alone, which is in line with the development of the agricultural integration model. However, as the core of the agricultural product supply chain, the agricultural product wholesale market obtains the lowest revenue compared with other core nodes. When the agricultural products wholesale market is optimized and upgraded around agricultural integration as the core. Its proper functions of distribution, transportation, storage, information service, and traceability supervision are given full play. The current revenue distribution model does not apply to the restructured organization model and the agricultural products wholesale market cannot maintain a normal

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operation which will still hinder the development of agricultural integration process at the end, so the development of agricultural products integration process should be addressed. Therefore, the traditional Shapley value revenue model should be revised for the role played by the wholesale market of agricultural products, so that the benefit distribution can be more rationalized and the cooperation of each node enterprise in the agricultural products supply chain can be regulated.

8.4 Modification of the Shapley Model 8.4.1 Influencing Factors Affecting the Distribution of Supply Chain Benefits Risk management is integrated into all aspects of agricultural supply chain management, operation and production inputs, and effective risk management can reduce risk losses and interrupt the collateral risks generated by risk sources. In the distribution and circulation of agricultural products, the risks arising from the traceability link are always continuous. At this time, the wholesale market of agricultural products should assume more functions such as risk warning, investigation, control and disposal. Therefore, the risk factor is an important factor that cannot be avoided, and the positive and negative relationship of risk disposal is directly proportional to the revenue, which is one of the important factors that must be considered when using the Shapley value method to correct the supply chain revenue. Collaborative management is the act of linking different links in the agricultural supply chain, or nodes with unique advantages, to share information and technical resources, so as to improve the competitiveness and overall profitability of the industry. The integration of information and resources is the core of collaborative management and the ultimate goal of agricultural integration. However, the application of collaborative operations centered on agricultural wholesale markets in the agricultural field is relatively small. Collaborative management of agricultural supply chains is crucial for either supply chain companies or supervisory authorities, and can positively improve and enhance the quality and safety of agricultural products as long as a mutually beneficial symbiotic relationship is reached. This paper argues that in relatively mature agricultural products supply chain operation, collaborative management is an important factor that can influence the stability of the agricultural products supply chain, and the higher the degree of participation of the subject, the more obvious the positive effect. In recent years, with the high-quality development of China’s economy, environmental benefits have been advocated more and more. Healthy, high-quality agricultural cultivation can create good economic benefits and also create healthy ecological agricultural development. However, social benefits are often overlooked, social benefits can actively promote social stability, improve people’s well-being and promote the role of socially sound and healthy development. There is no more prominent

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role in agriculture than that of stabilizing people’s social life, improving people’s livelihoods and creating a healthy quality of life that cannot be considered solely from an economic and environmental perspective.

8.4.2 Shapley Value Correction Model The Shapley value method avoids equal distribution and takes into account the marginal contributions of the participating subjects, but the lesser number of influencing factors considered makes the final distribution of benefits limited. Given the shortcomings of the traditional Shapley value method, this paper proposes three influencing factors of risk disposition contribution, collaborative management contribution and social benefit contribution, and uses AHP hierarchical analysis to calculate the weights of the influencing factors to further amend the benefit distribution model of Shapley value method. Let ∆γi be the comprehensive influence factor of the ith subject, ωi be the weight of the ith influence factor, ai , bi , ci be the measured values of risk disposition contribution, collaborative management contribution and social benefit contribution, respectively, and the number of supply chain members is n. With the agricultural wholesale market as the core, the benefit distribution model of the improved Shapley value method is as follows: ,

ϕi (υ) = ϕi (υ) + υ(N ) ∗ ∆γi

(8.7)

∆γi = γi − 1\n

(8.8)

⎞ ω1 γi = (ai , bi , ci )⎝ ω2 ⎠ ω3

(8.9)

⎛

ϕi, (υ) is the gain obtained by the ith subject in the cooperative game [N , υ] after correction, ∆γi = γi − 1\n indicates the difference between the comprehensive evaluation index of the subject and the average value, that is the comprehensive Σith n ∆γi = 0;Σ γi is the comprehensive evaluation value of the correction factor and i=1 n ∆γi = 1. three factors of the ith member, and i=1

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8.5 Example Analysis 8.5.1 AHP Hierarchical Analysis Method to Calculate the Weights First, the influence factor weights are calculated. Expert evaluation method was used to judge the importance of influencing factors, and the judgment principles are given in Table 8.6. According to the judgment principle, every two contribution degree indicators are compared to form a judgment matrix. Where ai j indicates the importance degree of factor ai compared with a j after two-by-two comparison, the quantified value is 1 when two factors are of the equally important compared, 3 when slightly important, 5 when ai is more important compared with a j , 7 when ai is strongly important compared with a j , 9 when ai is extremely important compared with a j ; and between two adjacent important degrees can be taken as 2, 4, 6, 8. This time, expert evaluation method is applied to compare the above three contribution degrees of importance, and the comparison results are given in Table 8.7. After forming the judgment matrix, the summation by column is performed for each column in Table 8.7 to obtain the summation set {5.33, 1.42, 10}, followed by the normalization by column according to the sum of each column, that is, each column of data in Table 8.7 is divided by the sum of the corresponding column, and the results are given in Table 8.8. According to the data results in Table 8.7, each column of data was normalized by column, and each data item was divided by the sum of each column, and subsequently Table 8.6 Judgment principles

Table 8.7 Judgment matrix

Quantified values

Importance Equally important

1

Slightly important

3

More important

5

Strongly important

7

Extremely important

9

Median of two adjacent judgments

2, 4, 6, 8

Inverse

aij = 1/aij

Contribution

Risk disposition

Collaborative management

Social benefits

Risk disposition 1

1/4

3

Collaborative management

4

1

6

Social benefits

1/3

1/6

1

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Table 8.8 Normalized matrix by column Contribution

Risk disposition

Collaborative management

Social benefits

Weights

Risk disposition

0.19

0.18

0.30

0.221

Collaborative management

0.75

0.70

0.60

0.685

Social benefits

0.06

0.12

0.10

0.094

averaged for each row to find the weights ω of the corresponding influencing factors, as given in Table 8.8. According to the calculation, the set of weights of the three influencing factors is ω = {0.221, 0.685, 0.094}. From the weight concentration, we can see that the contribution of collaborative management accounts for the largest proportion, the contribution of risk disposition is the second and the contribution of social benefits is the smallest. It indicates that when the contribution of collaborative management plays a central role, it has a greater positive effect on the smooth operation of each main body upstream and downstream of the agricultural products supply chain. When the level of collaborative management is improved, it can directly improve the risk disposition ability of each main body in the agricultural products supply chain, and it can also play a good role in promoting the social level and indirectly improve the service level and social benefits of agricultural products market circulation. The contribution of risk disposition benefits from the improvement of the collaborative management function, which can quickly intervene to deal with risks promptly and avoid the occurrence of collateral and secondary risks, also can reduce the occurrence of agricultural quality problems and traceability problems. The contribution of social benefits accounts for the smallest percentage, mainly thanks to the current contribution of both being optimally handled, the improvement of the ability to coordinate in the early stage and the timely disposal of risks in the later stage, so that social benefits can be indirectly increased, thus benefiting society and enhancing people’s happiness index and social value.

8.5.2 Correction of Shapley Value It is assumed that when agricultural product processors, agricultural product wholesale markets and agricultural product retailers form an alliance, each service function is optimized and upgraded. According to the expert evaluation method, the affiliation relationship of the three-level core nodes to the three influencing factors is evaluated, and the evaluation results are given in Table 8.9. According to Eq. (8.9), the calculation of the comprehensive evaluation value γ is performed.

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Table 8.9 Influence factor affiliation

Risk disposition

Collaborative management

Social benefits

Agricultural processor A

0.3

0.3

0.2

Wholesale market B

0.4

0.5

0.6

Agri-product retailer C

0.3

0.2

0.2

| || | | 0.3 0.3 0.2 || 0.221 | | || | γ = || 0.4 0.5 0.6 |||| 0.685 || | 0.3 0.2 0.2 || 0.094 | where comprehensive Σ3evaluation value γ set = {0.291, 0.487, 0.222}, which satisfies γ = 1. the requirement of i=1 Σn Since the total supply chain revenue is υ(N ) = i=1 ϕi (υ) = 360 million yuan, the revised revenue values are calculated by substituting the impact factors according to Eq. (8.7), respectively. ϕ1, (υ) = ϕ1 (υ) + υ(N ) · (γ1 − 1/3) = 120 + 360(0.291 − 1/3) = 104.88 (Million yuan) Similarly: ϕ2, (υ) = ϕ2 (υ) + υ(N ) · (γ2 − 1/3) = 90 + 360(0.487 − 1/3) = 145.44 (Million yuan) ϕ3, (υ) = ϕ3 (υ) + υ(N ) · (γ3 − 1/3) = 150 + 360(0.222 − 1/3) = 110.04 (Million yuan). In the supply chain model with the agricultural wholesale market as the core, the returns of agricultural product processor A, agricultural product wholesale market B and agricultural product retailer C are all higher than the returns when they operate separately. In the process of vertical integration, agricultural product processors can concentrate their resources on the deep processing of agricultural products, create added value of products and sell agricultural products more quickly and safely by using the advantages of transportation and information coordination to reduce stagnation and loss, and increase the revenue from 600,000 to 1,048,800 yuan. The wholesale market of agricultural products can give full play to the role of a hub in the organization and coordination, providing information matching, storage and transportation services for upstream and downstream enterprises, it can also better improve the

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profitability in the agricultural supply chain, from 300,000 to 1,454,400 yuan. Agricultural retailers can match price information, yield, origin, greenness and other quality information of agricultural products from the agricultural wholesale market, and purchase agricultural products corresponding to market demand more accurately, thus realizing fast and convenient purchases as well as accurate sales, and raising revenue from 900,000 to 1,100,400 yuan.

8.6 Conclusion With the rapid development of China’s modernization, the stability and innovation of agricultural development are self-evident. This paper mainly focuses on the current low degree of integration of the agricultural products circulation market and the unreasonable structure of the agricultural products supply chain. On the one hand, China’s agricultural regional development is stepped and uneven, causing the agricultural market to fail to become a unified whole, and the core supply chain hub is missing, making it difficult to create regional scale integration, resulting in a slow development of the agricultural industry with high quality. On the other hand, the core node agricultural wholesale markets are not modernized enough, many service functions are missing, so they cannot assume the responsibility of collaborative scheduling and play a marginal role, which leads to a disadvantage in the distribution of agricultural supply chain revenue all the time. This paper starts by analyzing the role that agricultural wholesale markets should play. Only through agricultural wholesale markets to improve the information end, logistics end and traceability end of the supply chain can we accelerate the development of agricultural integration. Therefore, we select agricultural product processors, wholesale markets and retailers as the research objects and propose three factors that affect the long-term smooth operation of the agricultural product supply chain, namely the contribution of risk disposition, the contribution of collaborative management and the contribution of social benefits, to amend the traditional Shapley value model and improve the uneven distribution of benefits in the agricultural product supply chain. The data results show that when supply chain benefits are reasonably distributed, agricultural wholesale markets can upgrade infrastructure, integrate supply chain resources and accelerate the transformation of service functions. It can give full play to its coordination role, which helps to manage the circulation of agricultural products and improve the quality, safety and traceability guarantee system.

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References 1. Levi, R.: The impact of unifying agricultural wholesale markets on prices and farmers’ profitability. Proc. Natl. Acad. Sci. 5 (2020) 2. Zhou, J.: Effects of regulatory policy mixes on traceability adoption in wholesale markets: food safety inspection and information disclosure. Food Policy 107 (2022) 3. Septiani, W.D.: Designing of agricultural product e-marketplace by using UCD method. J. Tek. Komputer 7(1) (2021) 4. Liu, C.: The effective marketing channels of agricultural products in the artificial intelligence environment (2020) 5. Zeng, Y., You, M.: Study on the circulation of agricultural products based on cloud computing from the perspective of rural revitalization strategy. IOP Conf. Ser.: Earth Environ. Sci. 697(1) (2021) 6. Salamin, O.: State regulation of agriculture in Ukraine. Econ. Scope (2021) 7. Ngango, J., Hong, S.: Price transmission in domestic agricultural markets: the case of retail and wholesale markets of maize in Rwanda. Korean J. Agric. Sci. 47(3) (2020) 8. Zhang, P.: Research on circulation efficiency and evolution trend of agricultural products wholesale market in China. J. Bus. Econ. (2019) 9. Sano, Y., Sato, T., Kawasaki, K.: Estimating the degree of market power in the vegetable market in Japan. J. Agric. Resour. Econ. Rev. (2022) 10. Ao, G., Liu, Q., Qin, L.: Organization model, vertical integration, and farmers’ income growth: empirical evidence from large-scale farmers in Lin’an, China. PLOS ONE (2021) 11. Barcho, M.K.: Basic directions for forming perspective forms of agricultural integration. Entrep. Sustain. Issues 8 (2020) 12. Pinmanee, S.: A Comprehensive Model of Supply Chain Integration in Agricultural Industry (2018) 13. Derso, B.: Assessing Stakeholders Integration in Practicing Agricultural Extension System in Selected Districts of North Gondar Zone (2017) 14. Staatz, J.M., Diallo, B., Me-Nsope, N.M.: Strengthening Regional Agricultural Integration in West Africa: Key Findings & Policy Implications Moving from Barriers to Greater Cooperation Across West Africa (2017) 15. Hedoui, M.A., Natos, D., Mattas, K.: Assessing agricultural trade integration among EU and Mediterranean countries: countries signed under the Agadir agreements. In: 164th EAAE Seminar: Preserving Ecosystem Services via Sustainable Agro-food Chains (2018) 16. Ziyadin, S., Kabasheva, N.: The basis for initiating the eurasian integration of the agricultural sector. Public Admin. Issues (2018) 17. Hamulczuk, M.: Spatial integration of agricultural commodity markets—methodological problems. Probl. Agric. Econ. (2020) 18. Aboah, J.: Ex-ante impact of on-farm diversification and forward integration on agricultural value chain resilience: a system dynamics approach. Agric. Syst. 189(2) (2021) 19. Kabakchieva, T.: The impact of European integration on agricultural commodities trading in the periphery of the European Union. Monographic Library. Knowledge and Business. Varna (2021) 20. Kryvenko, N.: Eu Integration: The Meaning of the Common Agricultural Policy (2019) 21. George, V.: Who Benefits in Contract Farming? A Perspective of Sunflower and Sorghum in Central Tanzania, vol. 3 (2017) 22. Zavalloni, M., Raggi, M., Viaggi, D.: Assessing collective measures in rural policy: the effect of minimum participation rules on the distribution of benefits from irrigation infrastructure. Sustainability 9(1) (2016) 23. Zhou, Y.W.: Research on the benefit distribution mechanism of layer chicken supply chain based on improved Shapley value method. Chinese J. Livest. Ecol. 40(08), 60–64 (2019) 24. Huan, M.L.: Revenue distribution mechanism of fresh milk supply chain based on revised Shapley value. China Dairy Ind. 48(01), 32–37 (2020)

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25. Zhu, Y.Q.: Research on the revenue distribution mechanism of agricultural product e-commerce supply chain from an ecological perspective. J. Price Mon. 11, 81–86 (2021) 26. Bai, X.J.: Benefit distribution strategy of supply chain under new retail based on improved Shapley value method. Pract. Underst. Math. 49(14), 88–96 (2019) 27. Xu, Z.: An improved Shapley value method for a green supply chain income distribution mechanism. Int. J. Environ. Res. Public Health 15(9), 1976 (2018) 28. Ge, J., Gao, G.: Study on benefit distribution of improved Shapley value in fresh agricultural cold chain. J. Bus. Admin. 3(4), 8 (2020)

Part II

Digital Economy and Digital Technology

Chapter 9

Analysis of the Evaluation of the Effect of International Cooperation on the Cultivation of Talents in Vocational Colleges Under the Construction of “Double-High” Lixia Yang, Jiao Liu, Chaohong Liu, and Shaoqing Tian

Abstract Objective: To explore the Double-High program’s impact on the talent cultivation effect. Methods: Through the visual analysis of the documents collected in the CNKI database, 45 papers related to the construction of Double-High and talent evaluation published in core journals were used as research objects, and the keyword co-occurrence mapping was drawn up through CiteSpace. The entropy weight method and TOPSIS method were combined to evaluate the effect of talent cultivation in Hainan Vocational College of Economics and Trade before and after the “Double-High plan” based on the talent training effect evaluation index system. The results and conclusions: Hainan Vocational College of Economics and Business’s talent cultivation effect has significantly improved after the construction of the “Double-High” program.

9.1 Introduction On 24 January, 2019, the State Council issued the “National Implementation Plan for Vocational Education Reform,” proposing to launch the implementation of the “Plan for the Construction of High-level Vocational Colleges and Specializations with Chinese Characteristics” (referred to as the “Double-High Plan”). To serve the needs of building a modernized economic system and higher quality and fuller employment, the State has concentrated its efforts on building many higher vocational schools and high-level professional clusters with Chinese characteristics and world-class L. Yang Hainan College of Economics and Business, Haikou, China J. Liu · C. Liu · S. Tian (B) Hainan University, Haikou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_9

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Fig. 9.1 Keyword co-present map about the construction of “Double-High”

standards to lead the reform and support development, and to drive the deepening reform of vocational education. In this context, it is more and more important to analyze the results of the implementation of the “Double-High School Programme.” By consulting the China Knowledge Resources Database (CNKI database) and setting the search time range like 2019–2021, the search theme words were “DoubleHigh construction” and “effect evaluation,” and 180 core journals were retrieved. One hundred and eighty articles were retrieved. CiteSpace software was used to sort out these 180 articles to draw a keyword co-occurrence map [1], as shown in Fig. 9.1. From the keyword co-occurrence chart and keyword frequency table, it can be seen that in the construction of “Double-High,” the topics researched by experts and scholars mainly focus on higher vocational institutions, talent cultivation, integration of industry and education, teacher training, and so on. The specific research directions are as follows. In the research on the theme of talent training, Li Bing et al. proposed the establishment of a market-oriented “multi-cultivation, three-dimensional integration, and collaborative education” model for the cultivation of mechanical talents in institutions under the construction of a “Double-High” plan [2]. Zhu Shanyuan et al. “The research and development of vocational education talents is a strategy of ‘value-added empowerment’ in the context of the national ‘Double-High plan’” [3]. Li Yuyin et al. in [4] used the principal component analysis (PCA) and established an evaluation model to study the talent cultivation of the 17 institutions declaring the double-high education including finance, economics and commerce colleges. Under the theme of industry-education integration, Li Weiwei et al. summarized the realistic basis of

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industry-education integration in vocational education in the context of the “DoubleHigh plan” from three aspects: industrial foundation, vocational education system, and type of education transformation and pointed out four paths to promote in-depth integration of industry-education in the “Double-High plan” [5]. Lv Luping et al.: The quality evaluation system of the integration of industry and education in higher education institutions under the background of a “Double-High plan” is studied from the perspective of the lack of standards and the lack of concrete, practical cases [6]. Gu Guangfu and Zou Jiquan analyze the connotation of the deep integration of industry and education in higher education under the construction of a “Double-High plan” and put forward the implementation strategies for the integration of industry and education in higher education [7]. In the main body of teacher training, Qin Huawei et al. analyzed the meaning of “three teachings” (teachers, teaching materials, and teaching methods) in the context of the “Double-High plan” from the perspective of the reform of “three teachings” [8]. The logic and promotion strategy of teachers’ professional development in higher education institutions are analyzed from the perspective that the professional development of teachers in higher education institutions is unified and synchronized with the construction of the “Double-High plan” [9]; Hu Lina et al. analyze the logic and promotion strategy of teachers’ professional development in higher education institutions from the perspective of giving social service functions to higher education institutions [10]. Pan Liyun analyzed the innovation strategies of the organization of teaching innovation teams in higher education institutions under the background of “Double-High” construction from the perspective of reconstructing grass-roots teaching organizations [11]. Under the theme of teaching mode, Du Juan analyzed the path of integrating distance and open education with vocational education in the context of “Internet+” [12]; Rao Chuchu and Lan Yeshen explored the connotation of Huang Yanpei’s thought of vocational education to explore the ways of cultivating the spirit of higher vocational students [13]; Wenbo Sun Billett Stephen analyzes the factors that influence young people’s decisions on after-school pathways and preferred careers from the perspective of skill shortages in some occupations [14]; Mattias Nylund focuses on the development of vocational education and the context of transition in the Nordic countries [15]. The above research was conducted on the dilemma of talent cultivation under the “Double-High plan,” the integration method of industry and education integration, the construction of teachers’ teams, and the innovation of teaching mode. However, none of these studies involved the comparison of the talent cultivation effect of vocational colleges before and after the construction of the “Double-High plan.” Therefore, based on the conceptual model of the evaluation index system of the talent cultivation effect constructed in the previous study, this study uses the entropy weight method and TOPSIS method to compare the talent cultivation effect with that of 16 other financial colleges that declared Double-High. This study uses the entropy weight method and TOPSIS method to compare the talent training effect of Hainan College of Economics and Business and Technology with those of 16 other financial institutions that have declared Double-High (before 2018 and after 2020).

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9.2 Data and Methods 9.2.1 Establishment of the Indicators By consulting the China Knowledge Resources General database (CNKI database), the search time range was set from 2010 to 2021. The search topics were “talent training” and “effect evaluation,” and 45 core journals were retrieved. Through the combing of 45 documents, it is found that most focus on the quality of talent training and the effect evaluation research of talent training mode. Therefore, based on drawing lessons from the research results of other scholars and combined with the index system of Double-High declaration and filling, a set of talent training effect evaluation index systems of talent cooperation is constructed. The index system has four first-level indicators, the basic situation of the school, teachers, teaching mode, and employment. Each primary index has also decomposed many secondary indicators, as shown in Table 9.1. Table 9.1 Evaluation indexes of talent training effect Level 1

Level 2

Level 3 Type

Base situation

Student average teaching, scientific research, and auxiliary housing area (square meter/student)

X1

Positive

Both teaching and scientific research instruments (Yuan/ X2 student)

Positive

Faculty

Foster pattern

Employment status

The proportion of full-time teachers is (%)

X3

Positive

The proportion of part-time teachers is (%)

X4

Negative

A teacher is more than a (%)

X5

Negative

The number of part-time teachers teaching courses in the X6 academic year Scale (%) of the total number of class hours

Negative

The proportion of students on orders (%)

X7

Positive

Number of courses jointly developed by the school and enterprise (individual)

X8

Positive

The number of graduates employed by the cooperative enterprises accounts for the new graduates The proportion of employees is (%)

X9

Positive

The initial employment rate of fresh graduates is (%)

X10

Positive

The employment rate of the last graduates half a year later is (%)

X11

Positive

The proportion of employment in this province and city is (%)

X12

Positive

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9.2.2 Data Processing and Analysis Methods Data were processed using the entropy and TOPSIS methods, and the relative proximity was ranked by relative proximity after calculating each year. The specific steps are: (1) weight the entropy with the entropy method; (2) establish the normalized data matrix; (3) identify the optimal scheme and the worst scheme in the effective scheme; (4) calculate the distance between the evaluation object and the optimal scheme; and (5) obtain the relative proximity as the basis for the evaluation [16].

9.3 Research Methods 9.3.1 Entropy Right Method Entropy weight method [17] is the assignment to the weight coefficient in the comprehensive evaluation, which can effectively reflect the system’s change law and the evaluation effect more truly and objectively. The following steps are taken: Step 1: With m regions to be evaluated, the original data matrix is defined as: ⎞ X 11 · · · X 1n ⎟ ⎜ = ⎝ ... . . . ... ⎠ X m1 · · · X mn m×n ⎛

{ } X = X i j m×n

(9.1)

Step 2: Data standardization [18]. The data from each index were normalized for a given n metrics, among which, X 1 , X 2 , . . . , X n X i = {X 1 , X 2 , . . . , X n }: {

X −min(X )

ij i Positive index: Yi j = max(X i )−min(X i ) max(X i )−X i j Negative index : Yi j = max(X i )−min(X i)

(9.2)

Step 3: Calculate the proportion of the ith value under the item j index: Xi j Pi j = Σ m i=1

Xi j

, 0 ≤ Pi j ≤ 1

(9.3)

Step 4: Calculate the entropy value of each index: Hj = −

m 1 Σ Pi j ln Pi j , 0 ≤ H j ≤ 1 ln n i=1

Step 5: Calculate the weight coefficient of each index:

(9.4)

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1 − Hj ω j = Σ n j=1 1 − H j

(9.5)

9.3.2 The TOPSIS TOPSIS law [18] is the abbreviation of Technique for Order Preference by Similarity to the Ideal Solution, a technique approximating the ideal solution, which is a multi-objective decision method. The basic idea of the method is to define the ideal and negative solutions to the decision problem and then find a scheme in the feasible scheme nearest to the ideal solution and the farthest from the negative ideal solution. The ideal solution is generally the best scheme. Each solution’s corresponding properties are at least the best value; the negative ideal solution is the assumed worst solution. The corresponding properties are at least not better than the best value in each scheme. The decision rule of scheme queuing compares the practical and ideal solutions with the negative ideal solution. If a feasible solution is closest to the ideal solution and far away from the negative ideal solution, this solution is the satisfactory solution of the scheme set. Step 1: Normalization All metrics were normalized according to equation [19] process and established the matrix. The formula is as follows: fi j Z i, j = /Σ n i=1

(9.6) f i2j

The i = 1, 2, 3, …, n, for the number of schools evaluated, n = 17; j = 1, 2, 3, …, m, for the serial number of the index evaluated, m = 12. Step 2: Constructing a standardized weighted decision matrix Z i j = W j Z i, j , i = 1, . . . , n; j = 1, . . . , m

(9.7)

W j is the right for the jth. Step 3: Deriving optimal and inferior solutions. Determine the index optimal and worst scheme, constituting the optimal and worst value vector, respectively. If the element Z in the decision matrix Z i j larger value indicates that the better the scheme, then ) { ( } Z + = Z 1+ , Z 2+ , . . . , Z m+ = max Z i j | j = 1, 2, . . . , m

(9.8)

( } ) { Z − = Z 1− , Z 2− , . . . , Z m− = min Z i j | j = 1, 2, . . . , m

(9.9)

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Step 4: Calculating the ideal solutions and the negative ideal solutions of the evaluation object. The calculation between the data and the resulting optimal and worst scheme calculates the annual distance from the optimal and worst values, respectively [20]. The calculation method is performed as follows: Si+

⎡ |Σ )2 | m ( Z i j − Z +j =√

(9.10)

j=1

⎡ |Σ )2 | m ( − Z i j − Z −j Si = √

(9.11)

j=1

Step 5: Calculating the relative proximity. Count C i , and by the relative proximity C for each protocol, sort the size to find out the satisfactory solution. Taking the value between 0 and 1, the closer 1 means the closer the evaluation object is to the optimal level; otherwise, the closer 0 means the closer the evaluation object is to the worst level. Ci =

Si− Si+ + Si−

(9.12)

9.4 Data Analysis of Talent Training Effect 9.4.1 Data College numbers from 1 to 17 are Anhui Industrial and Commercial Vocational College, Anhui Vocational and Technical College of Commerce and Trade, Beijing Finance and Trade Vocational College, Guangdong Vocational and Industry Technical College, Guangxi Economic and Trade Vocational and Technical College, Hainan Economic and Trade Vocational and Technical College, Jiangsu Economic and Trade Vocational and Technical College, Jiangxi Vocational College of Finance and Economics, Ningxia Vocational and Technical College of Finance and Economics, Shandong Business Vocational and Technical College, Shandong Foreign Trade Vocational College, Shanxi Provincial Finance and Taxation Junior College, Changsha Business and Tourism Vocational and Technical College, Zhejiang Finance Vocational College, Zhejiang Economic Vocational and Technical College, Zhejiang Economic and Trade Vocational and Technical College, and Zhejiang Business Vocational and Technical College. X1–X12 respectively correspond to the 12 indicators in Table 9.1. Among them, the data of each college are

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Table 9.2 Talent status in 17 universities from 2017–2018 X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

X12

1

9.28

8215.21 71.13 23.26 17.27 12.66

4.72

2

9.79

6945.85 79.86 45.65 15.17 42.43

7.60 115.00 27.05 97.59 100.00 64.65

3

29.41 32,612.40 60.29

4

10.10

79.00 37.01 99.34

99.94 84.20

1.49

96.00 13.56 94.68

99.42 95.03

9.05 13,304.50 56.44 21.07 17.97 27.99

6.36

11.00 17.79 90.25

91.97 73.16

6.24

22.00 15.23 94.46

94.61 14.79

1.29 175.00 10.71 98.21

99.07 57.58

1.10

45.00

6.70 98.06

98.46 49.15

0.00

53.00 27.77 96.10

99.53 96.82

10 12.35 12,204.40 86.68 28.32 16.43 27.38 12.38 175.00 25.96 99.51

99.05 79.49

11 10.93

6381.77 60.34 64.73 15.19 21.73 64.39 103.00 28.20 98.02

97.24 88.93

7186.04 52.49 13.96 17.81

9.59 91.34

95.42 40.12

6.55 27.39

51.00 72.50 93.63

92.72 75.85

14 13.61 13,038.90 90.26 82.56 12.48 46.91 13.92

93.00 29.26 98.38

98.27 82.13

15 16.81 20,209.60 81.98 68.86 14.04 27.92

1.83 184.00 28.61 98.80

98.77 80.01

16 12.76 15,496.20 88.83 49.71 14.94 27.45

7.45 114.00 15.82 97.90

98.38 77.81

17 14.37 15,182.90 87.17 42.46 15.24 22.21

7.16 177.00 18.60 95.50

97.87 80.15

9110.59 74.59 71.40

9.74

6

14.79

7

17.89 10,816.90 92.71 76.09 14.31 40.97

8

9.00

9

7599.23 83.09 27.57 21.75

9.69 11.71

97.03 66.01

7.71 34.58

5

5.05

34.00 24.50 89.32

5803.39 74.22 56.36 15.01 50.19

12.07 12,845.80 33.51

9.68

12

1.37

3.98 17.76

13 17.79 11,033.30 96.31 15.58 17.09

3.46

3.14

1.30

21.00

Table 9.3 Talent training status of Hainan Economic and Trade Vocational and Technical College from 2019–2020 X1

X2

X3

X4

X5

X6

X7

X8

X9

X10

X11

X12

12.56

10,345

78.35

5.4

18.9

3.1

26.43

89

24.46

81.00

85.40

85.40

used for reference through the official website query and the data in the paper [21] (Tables 9.2 and 9.3).

9.4.2 Calculating the Entropy Weights Step 1: Construct the talent training raw data matrix X from 2017–2018 and 2019–2020 according to the Formula (9.1): Raw Data Matrix X, 2017–2018 X:

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⎛

⎞ 9.28 8215.21 · · · 66.01 ⎜ .. .. .. ⎟ ⎜ . ⎟ . ··· . ⎟ ⎜ ⎜ ⎟ ⎝ 14.79 7599.23 . . . 67.41 ⎠ ··· ··· ··· ··· Raw Data Matrix X 2019–2020 X: ⎛

⎞ 9.28 8215.21 · · · 66.01 ⎜ .. .. .. ⎟ ⎜ . ⎟ . ··· . ⎟ ⎜ ⎜ ⎟ ⎝ 12.56 10345 . . . 72.52 ⎠ ··· ··· ··· ··· Step 2: Standardized data from 2017–2018 and 2019–2020 are calculated according to Eq. (9.2): student teaching research and auxiliary housing area (square meters/students), teaching, scientific research equipment value (yuan/ student), double quality teacher proportion (%), the total class (%), order students (%), school-enterprise standard development courses (a), cooperative enterprises accept graduates employment (%), fresh graduates first employment rate (%), six-year graduate employment (%), fresh graduates in the province employment proportion (%) are a positive indicator, the higher, the better; and the proportion of part-time teachers, the proportion of student teachers, and the proportion of parttime teachers in the total number of professional courses are negative indicators, the lower, the better; Step 3: The proportion of Formula (9.3) of the index of item j from 2017–2018 and 2019–2020; Step 4: Calculate the entropy value of each index according to Formula (9.4); Step 5: Calculate the weight coefficient of each index using formula (9.5).

9.4.3 Construction Based on the TOPSIS Model Step 1: With 12 indexes, such as the average student teaching and auxiliary housing area (square 2/student) from 2017–2018 and 2019–2020, we normalized and established all indexes according to Formula (9.6). Step 2: A normalized weighted decision matrix is intended to be constructed in combination with Formula (9.7). Step 3: According to the above result set, the best and worst schemes for 2017– 2018 and 2019–2020 can be obtained. Optimum and worst scheme for 2017–2018: = (0.03679, 0.04484, 0.02735, 0.03561, 0.02995, 0.03674, 0.05816, 0.03431, 0.05130, 0.02210, 0.02206, 0.02767)

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= (0.01126, 0.00798, 0.00952, 0.00172, 0.01062, 0.00100, 0.00000, 0.00205, 0.00474, 0.00000, 0.02029, 0.01146) Optimal and worst schemes for 2019–2020: = (0.03671, 0.04496, 0.02736, 0.03571, 0.02468, 0.03663, 0.05597, 0.03429, 0.05088, 0.02286, 0.02259, 0.02750) = (0.01123, 0.00800, 0.00952, 0.00172, 0.01007, 0.00226, 0.00000, 0.00205, 0.00470, 0.01861, 0.01930, 0.01139). Step 4: According to Formula (9.9) and (9.10), the optimal scheme and the worst scheme are calculated to calculate the relative proximity of the annual index value and the optimal value vector (Tables 9.4 and 9.5). Table 9.4 Relative proximity to index and optimal values for different years from 2017 to 2018 No.

S+

S−

Ci

Sort

1

0.0904218418

0.0257646878

0.2217527958

16

2

0.0784397283

0.0448919542

0.3639936900

9

3

0.0752785305

0.0588142051

0.4386084364

5

4

0.0862207919

0.0440552874

0.3381686619

11

5

0.0883883582

0.0302956224

0.2552629452

13 14

6

0.0935756507

0.0292837410

0.2383516688

7

0.0809549479

0.0557611226

0.4078607761

6

8

0.0925054858

0.0429552229

0.3171046668

12 15

9

0.0941120566

0.0294281695

0.2382071854

10

0.0738156648

0.0470520420

0.3892854695

7

11

0.0611024675

0.0686023222

0.5289112479

1 17

12

0.1024446630

0.0158155348

0.1337350609

13

0.0689806735

0.0603730327

0.4667282792

2

14

0.0678711903

0.0567560752

0.4554065676

3

15

0.0717637552

0.0570500235

0.4428875861

4

16

0.0775383277

0.0423299943

0.3531374561

10

17

0.0798363491

0.0468029552

0.3695768501

8

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Table 9.5 Relative proximity to index and optimal values for different years from 2019 to 2020 No.

S+

S−

Ci

Sort

1

0.0889694601

0.0257646878

0.2245598911

16

2

0.0767308673

0.0448919542

0.3691079820

9

3

0.0732009164

0.0588142051

0.4455111235

5

4

0.0837021653

0.0440552874

0.3448353616

11

5

0.0869782472

0.0302956224

0.2583322486

14

6

0.0795065311

0.0292837410

0.2691760987

13

7

0.0788383467

0.0557611226

0.4142744610

6

8

0.0906762254

0.0429552229

0.3214454637

12

9

0.0925463339

0.0294281695

0.2412649260

15

10

0.0723059538

0.0470520420

0.3942093837

7

11

0.0604868576

0.0686023222

0.5314335585

1

12

0.1009714049

0.0158155348

0.1354221182

17

13

0.0681450896

0.0603730327

0.4697627979

2

14

0.0659300832

0.0567560752

0.4626118868

3

15

0.0694584340

0.0570500235

0.4509581779

4

16

0.0756883873

0.0423299943

0.3586728926

10

17

0.0748855510

0.0468029552

0.3846127844

8

9.5 Discussion Changes in the Talent Training Status of Hainan Economic and Trade Vocational and Technical College from 2019–2020: (1) In the basic situation of the primary index, the teaching research and auxiliary housing area from the average student was reduced from 14.79 (square meters/ student) in 2018 to 12.56 (square meters/student), and the teaching and research instruments and equipment value increased from 7599.23 (yuan/student) to 10,345 (yuan/student). This is due to the expansion of Hainan Economics and Trade under the influence of the plan and its attraction for graduates; the construction of “Double-High” also provides financial support for the maritime economy and trade. (2) The proportion of full-time teachers in the Grade I index changed from 83.09 to 78.09%; the proportion of part-time teachers decreased from 27.57 to 5.4%; the student ratio changed from 21.75 to 18.9%; and the proportion of parttime teachers changed from 1.37 to 3.1%. Double teacher, higher vocational education teacher refers to “double” certificate teacher or “double title” teacher, that is, teacher + intermediate or above technical position (or vocational qualification). The proportion declined because of the enrollment of more highly educated teachers by Hainan Vocational College of Economics and Trade under the influence of the “Double-High” program. Teachers with high education

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generally have no experience in working in enterprises and rarely have intermediate technical positions or above. The proportion of the number of doubleteacher teachers in the sea economy and trade is still relatively large, although it has decreased but the proportion is small; the proportion of part-time teachers has dropped significantly, because although the number of part-time teachers has also increased, no large proportion of total teachers has increased. “Student to teacher ratio” is an important data in the school teaching work. To some extent, it reflects the scale of Chinese higher education and the utilization efficiency of human resources in colleges and universities. It also reflects the quality of colleges and universities from one side. In the “student-division ratio,” Shanghai economy and trade have decreased compared to 2018. This is due to the sea economic and trade expansion of more teachers under the “Double-High” plan, and the proportion of teachers increased more than the proportion of students increased; the increase in the number of part-time teachers to the total number of professional courses is due to the international cooperation of international trade majors in maritime economy and trade. (3) The order students in the first-level index training mode (the order talent training mode conducted by the national administration or institution or enterprise through the relevant employment enterprises and service years agreed in the contract with the school or individual students). The proportion changed from 6.24% in 2018 to 26.43%; co-enterprise courses increased from 22 to 89; and graduates increased from 15.23% to 24.46%. It can be seen from the data that the proportion of older students has increased significantly, which is due to more enterprises’ cooperation with the sea economy and trade under the “DoubleHigh” plan; the increase of school-enterprise joint development courses is due to the number of international trade courses in the “Double-High” from separate teaching to cooperation with foreign schools. (4) The initial employment rate of fresh graduates in the primary index employment situation decreased from 94.46% to 81%; the employment rate of fresh graduates decreased from 94.61% to 85.4% after half a year; and the employment rate (% in the province and city) increased from 67.41% to 72.52%. This is due to the impact of the COVID-19 epidemic in 2019, all walks of life have been affected to varying degrees, and more overseas economic and trade students come from local areas. Local graduates also find more jobs in the province due to the epidemic difficulties.

9.6 Conclusion Although implementing the “Double-High” plan has brought great opportunities to the development of Hainan Economic and Trade Vocational and Technical College, there are still problems, such as excessive urgency and insufficient experience in the implementation process. The present recommendation is made as follows: (1) Rationalizing the use of allocated funds to improve teaching standards

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In the school infrastructure construction, it increased investment to create a comfortable learning environment in the teaching management, combined with the expansion of higher vocational enrollment to promote the implementation of classified management and information management. In the construction of a teaching team: in addition to the “double division” talents jointly built by schools and enterprises, there is also a professional talent training team in combination with the characteristic majors of the school. We will implement the bilingual teacher training plan, increase the training of teachers, and improve their internationalization level. (2) In-depth implementation of professional cluster construction strategy and implementation of industry-education integration We should focus on talent training, comprehensively improve the level of scientific research, create distinctive campus culture, and promote international cooperation. We will constantly optimize the professional structure, realize resource sharing, reconstruct the governance system, and pool the characteristics of running schools. Schools should integrate resources and teach students according to their aptitude to achieve the integration of industry and education. Talent training standards are integrated with the talent needs of enterprises, professional innovation and development, enterprise transformation and upgrading, international exchanges, and the expansion of enterprises’ overseas undertakings. (3) Make full use of modern technology to break the shadow of the epidemic Due to the epidemic’s impact, the employment rate of graduates is lower than in previous years. Schools should actively use modern science and technologies such as the Internet and big data to provide technical services for students during the epidemic so that graduates can conduct interviews and employment smoothly. (4) Build characteristic brands and promote the international direction Vocational colleges should position themselves, fully realize their running characteristics, expand their running advantages according to the development needs, find the correct running orientation, highlight the characteristics of running schools, and create the school’s brand image. Innovate the teaching mode, actively introduce excellent international school philosophy, export Chinese culture to the international world, build a bridge of education cooperation at home and abroad, and realize the high-quality development of higher vocational education. Acknowledgements This work was supported by the 2020 Hainan College of Economics and Business Research Project (hnjmk2020201), 2021 China Education International Communication Association (2021-011), 2020 Hainan Province Higher Education and Teaching Reform Research Project (Hnjg2020-13), and 2020 Hainan University Education and Teaching Reform Research Key Project (hdjy2033).

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Chapter 10

The Impact of Digital Finance on Energy Intensity——New Evidence from China Yi Qu, Aizhi Li, and Kai Ning

Abstract Energy is a necessary resource for social development and has an irreplaceable impact on the social operation and economic development. Using the data of 26 China’s provinces from 2011 to 2020, this study analyses the effect of digital finance on energy intensity by fixed effect model. The results of this study have the following points: firstly, digital finance has a significant promoting effect on energy intensity. Secondly, from the subdimension of digital finance, it is mainly usage depth that significantly promotes energy intensity. In contrast, the level of digitization services level significantly inhibits energy intensity. As for digital finance coverage, its effect on energy intensity can be ignored. Finally, this study also found that the function of digital finance in energy intensity exists heterogeneous. In the eastern areas, the effect is not significant, while in the middle-western areas, digital finance has significantly promoted energy intensity.

10.1 Introduction Reducing energy intensity is a key way to achieve the goals of “Carbon Peak” and “Carbon Neutralization” in China. Energy intensity is the intensity of energy consumption, which is measured by the proportion of each unit of GDP in the total regional energy consumption. Energy intensity can reflect energy efficiency. If the energy intensity is lower, the energy utilization efficiency is higher, and economic growth is less dependent on energy. In recent years, a downward trend in energy intensity has been shown in China. From 1978 to 2016, energy intensity dropped by 76.4% [1], which is an important change. However, on the whole, China’s energy intensity is still at a high value, and there is a rebound in energy intensity [1]. Existing literature has discussed the elements affecting energy intensity from multiple angles, including Y. Qu · K. Ning (B) Harbin Vocational College of Science and Technology, Harbin, Heilongjiang 150300, PR China e-mail: [email protected] A. Li Harbin University of Commerce, Harbin, Heilongjiang 150028, PR China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_10

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economic level [2], industrial structure [3], technological progress [4], household consumption [5], financial development [6], etc. With the continuous development of the Internet and industrial reform, financial business innovation has produced a new service pattern. Digital finance can usefully correct the problems of “field matching” and “stage mismatch” in traditional finance, further alleviate financing constraints, and promote the development of innovation and entrepreneurship activities and the promotion of enterprise technological innovation. Meanwhile, digital finance can alleviate household credit constraints and decrease the cost of transactions, which will contribute to the upgrading of household consumption. These processes will affect the energy intensity. By summarizing the existing literature, we found a lack of relevant studies on the impact of digital finance on energy intensity. Therefore, this study uses econometric methods to explore the effect and discusses the existing regional heterogeneity. This study possesses three contributions: first, the digital inclusive financial index is used by us to measure the development level of digital finance and study its impact on energy intensity, which enriches the reference material of the environmental effects of digital finance; Secondly, this work analyzes the influence of the sub-dimensioning of digital finance on energy intensity; Finally, geographical location is divided in an exogenous way to study the difference of the effect.

10.2 Research Method 10.2.1 Variable Description Explained variable. Energy intensity (EI). Using real GDP as a unit of measurement, energy intensity is calculated [7]. EI =

Energy consumption Real GDP

(10.1)

Explaining variable. Digital finance (DF). For estimating the digital finance level, this study used the digital inclusive finance index. There are three sub-dimensions under the index: coverage breadth, usage depth and digitization services level. Control variables. According to STIRPAT framework [8], we include economic development level (GPDG), R&D level (R&D), and population density (Pop) into the control variables. Moreover, taking the role of the government into account, we also control government intervention (Gov). Economic development is estimated by the regional per capita GDP; R&D level is measured by R&D investment; An annual count of people is used to determine population density; Government intervention is measured by government general budget expenditure. To ensure the robustness of data, all control variables are logarithmic.

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Table 10.1 Descriptive statistics Varible

Obs

EI

234

Mean 1.478

Std 0.796

Min 0.662

Max 4.921

lnDF

234

5.120

0.683

2.909

5.960

lncoverage

234

4.948

0.856

0.673

5.893

lnusage

234

5.098

0.653

1.911

6.003

lndigtization

234

lnPgdp

234

lnGov

234

lnR&D

234

lnPop

234

5.467 10.51 8.032 14.20 8.283

0.686

2.754

0.400

9.787

0.566

6.559

1.417 0.753

10.96 6.342

6.117 11.55 9.175 16.96 9.352

Referring to the study of Li et al. [9] and Wang et al. [10], the relevant data on digital Inclusive finance in the above variables are derived from the China Digital finance research center of Peking University. Other data are obtained from China Statistical Yearbook. Taking into account the particular characteristics of municipalities directly under the central government, Beijing, Shanghai, Tianjin, and Shenzhen are excluded from this paper. Due to the lack of data, this study also excluded Tibet, Hong Kong, Macao and Taiwan. Therefore, the research area designed in this paper is the remaining 26 provinces, and the research period is from 2011 to 2020. A description of the variables is given in Table 10.1.

10.2.2 Model According to STIRPAT model [8], we take economic development level (GPDG), R&D level (R&D), population density (Pop) and other sociological factors (government intervention (Gov) is considered in this paper) as control variables into the model, and finally build a model as follows: E Iit = β0 + β1 ln D Fit + β2 X it + εit

(10.2)

where EI is energy intensity and DF is digital finance. X represents the control variables (GPDG, R&D, Pop, Gov). Since the differences between different provinces and at different times will affect energy intensity, this study takes the matter forward to control the time effect and provincial effect, that is, this paper formed a fixed-effects model.

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10.3 Empirical Results 10.3.1 Measurement Regression Results In this paper, Benchmark regression is performed using the fixed effect model, as shown in Table 10.2. From column (1) (2), a 1% significance level indicates that digital finance’s regression coefficients have a positive relationship with energy intensity. This implies that the increasing digital finance level will promote energy intensity. Digital finance eased financial constraints, increased enterprises’ and households’ liquidity, and accelerated the operation efficiency of enterprises, thus promoting the improvement of the economy and household consumption demand. Thus, the demand for energy increases. Columns (3)–(8) reflect the role of the subdimension on energy intensity. Energy intensity is not significantly influenced by coverage breadth, and the regression coefficient is not significant. The usage depth significantly promoted the energy intensity (passing the 1% significance level test). The usage depth mainly reflects the actual use of digital finance by enterprises or individuals. It includes payment, monetary fund, insurance, credit, investment and other services. It is the dimension that can most directly stimulate household consumption and promote enterprise financing. Therefore, the usage depth is the main way for digital finance to promote energy intensity. Contrary to the usage depth, the level of digital support services significantly inhibits energy intensity. Digital support services are embodied in the process of financial transactions through the convenience and mobility of digital finance. With the deepening of digitization services level, paperless trading methods are becoming more and more extensive. At the same time, digitization services level can reduce transaction costs. It can, therefore, reduce energy intensity significantly. It inspires us that the energy or environmental effects brought by digital finance in the development process have two-sided effects. The focus of the project is how to fully exploit the “green effect” and continue to expand.

10.3.2 Heterogeneity Analysis The economic development level and industrial structure of eastern, central and western China differ greatly. Under such a background, there is also a clear gap between regions when it comes to digital finance. Therefore, it is necessary to explore whether there is regional heterogeneity in the energy effect produced by digital finance. In the eastern region, digital finance has no statistically significant impact on energy intensity (see Fig. 3). Accordingly, digital finance conducts a limited impact on energy intensity in the eastern parts of the country. In the middle-western regions, digital finance plays a significant role in promoting energy efficiency. The eastern region already enjoys sufficient financial services and information, so the impact is not significant. Moreover, combined with the Environmental Kuznets curve, the eastern area has a high level of economic and scientific and technological level, which

0.477

234 0.246

0.475

234

−77.33*** (11.72)

−77.32***

(11.64)

234

(1.349)

(1.339)

234 0.649

(0.087)

(0.086) 8.695***

(0.085)

8.639***

−0.335***

−0.430***

−0.425***

0.686***

0.482

234

(11.39)

−74.58***

(1.317)

8.137***

(0.246)

0.655** (0.254)

0.650**

(0.229)

0.630***

0.328*** (0.088)

0.441***

(6)

(0.094)

(5)

(0.252)

(0.236)

(0.235)

*, **, and *** indicate statistical significance at 10%, 5%, and1% respectively

234

0.425

Obs

0.063 (0.051)

0.085 (0.058)

(4)

0.695***

(0.130)

(0.147)

(3)

0.668***

0.257**

(2)

0.336**

EI

(1)

R2

Constant

lnPop

lnR&D

lnGov

lnPGDP

lndigtization

lnusage

lncoverage

lnDF

Variable

Table 10.2 Results of a regression analysis of digital finance and energy intensity

0.278

234

(0.099)

(0.107)

0.474

234

(11.70)

−74.56***

(1.350)

8.331***

(0.088)

−0.382***

(0.254)

0.724***

(0.234)

0.703***

−0.224**

(8)

−0.306***

(7)

10 The Impact of Digital Finance on Energy Intensity——New Evidence … 127

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Table 10.3 Regional heterogeneity test Variable

EI East

lnDF

Middle

West

0.021

0.009

1.936***

1.690**

0.227**

0.407*

(0.145)

(0.138)

(0.581)

(0.651)

(0.412)

(0.316)

lnPGDP

0.461** (0.190)

(1.172)

(0.486)

lnGov

−0.361*

−0.672

2.088***

(0.202)

(0.430)

(0.561)

lnR&D lnPop

0.264

0.877*

−0.177*

0.205

0.0881

(0.0886)

(0.206)

(0.178)

−0.587

−2.374

13.01***

(1.505)

(4.466)

(2.548)

0.464

6.400

−5.438***

15.23

1.054

−127.2***

(0.568)

(13.52)

(2.003)

(33.88)

(1.336)

(20.45)

Obs

81

81

81

81

72

72

R2

0.663

0.570

0.329

0.367

0.375

0.848

Constant

*, **, and *** indicate statistical significance at 10%, 5%, and1% respectively

is likely to have passed the inflexion point of environmental effects. Therefore, digital finance does not have a significant energy effect. In contrast, middle-western areas still experience extensive economic development. The increasing level of digital finance has created more growth opportunities for the central and western regions. However, when pursuing economic development, the rational planning of resource use and the “green effect” is often ignored. Moreover, the economic conditions in middle-western areas may not allow them to make good coordination between resources and economic development. This also reflects a phenomenon: there is a greater concentration on the influence of digital finance (including its positive and negative impacts) in economically underdeveloped areas.

10.3.3 Robustness Test This chapter applies three channels to verify the robustness of the results. First, we reduce the data of explanatory variables by 1%, the extreme values are removed to make the data more stable. Secondly, explanatory variables are lagged by one period for regression tests to weaken the reverse causal effect. Finally, the sys GMM model is used to test the data again to solve the endogenous problem. The results of the three methods are consistent with the above. Therefore, the results of this paper have certain reliability.

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10.4 Conclusion The combination of technology and traditional finance spawned a fresh financial model, digital finance, which inevitably aroused scholars’ interest in its environmental effects. This study analyzes the effect of digital amount on energy intensity, as well as its regional heterogeneity by using the fixed effect model, and arrived three conclusions: (1) Digital finance will significantly promote energy intensity. (2) From the subdimension of digital finance, it is mainly the usage depth that significantly promotes energy intensity. In contrast, the digitization services level significantly inhibits energy intensity. As for the coverage breadth, its effect on energy intensity is negligible. (3) The effect exists regional heterogeneity. We explore the eastern, middle and western areas, and find that the energy effect produced by digital finance in the eastern area is not significant. In middle-western regions, digital finance has greatly improved energy intensity. This further emphasizes the importance of financial resource allocation and resource environment regulation. Based on these conclusions, we put forward several policy recommendations. First, digital finance development needs to be improved and digital financial resources should be used to protect resources and the environment. Secondly, strengthen the regulation of resources and the environment. Middle-western areas are in urgent need of development, and relatively lax regulations on industry and the environment. Therefore, we should strengthen the regulation of resources and the environment in these areas. Finally, the flow of digital financial resources should be strengthened, science and technology among regions, strengthen interregional cooperation, and jointly promote the decline of energy intensity. For the eastern region, while maintaining its own development, it should help middle-western areas to develop their digital finance, thus enhancing its quality, making energy utilization develop in a cleaner and more efficient way, jointly reducing energy intensity and playing a green effect. For middle—western areas, while giving full play to the advantages brought by digital finance, we should gradually guide the development of energy towards energy conservation and environmental protection. Acknowledgements The funding sponsored by Key Project in 2022 of the “14th Five Year Plan” of Educational Science in Heilongjiang (ZJB1422248), and General Project in 2021 of the “14th Five Year Plan” of Vocational Education Association in Heilongjiang (HZJG2021036).

References 1. Li, M., Gao, Y., Liu, S.: China’s energy intensity changes in 1997–2015: non-vertical adjusted structural decomposition analysis based on input-output tables. Struct. Chang. Econ. Dyn. 53, 222–236 (2020) 2. Mahmood, T., Ahmad, E.: The relationship of energy intensity with economic growth: evidence for European economies. Energ. Strat. Rev. 20, 90–98 (2018) 3. Li, K., Lin, B.Q.: The nonlinear impacts of industrial structure on China’s energy intensity. Energy (Oxford) 69, 258–265 (2014)

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4. Wang, H., Zhao, X.G., Ren, L.Z., Fan, J., Lu, F.: The impact of technological progress on energy intensity in China (2005–2016): evidence from a geographically and temporally weighted regression model. Energy (Oxford). 226, 120362 (2021) 5. Ding, Q., Cai, W., Wang, C.: Impact of household consumption activities on energy consumption in China—Evidence from the lifestyle perspective and input-output analysis. Energy Procedia 105, 3384–3390 (2017) 6. Aller, C., Jesus Herrerias, M., Ordez, J.: The effect of financial development on energy intensity in China. Energy J. (Cambridge, Mass) 39(S1), 25–38 (2018) 7. Zhang, P., Wang, X., Zhang, N., Wang, Y.: China’s energy intensity target allocation needs improvement! lessons from the convergence analysis of energy intensity across Chinese Provinces. J. Clean. Prod. 223, 610–619 (2019) 8. Dietz, T., Rosa, E.A.: Effects of population and affluence on CO2 emissions. Proc. Natl. Acad. Sci. USA 94, 175–179 (1997) 9. Li, J., Wu, Y., Xiao, J.J.: The impact of digital finance on household consumption: evidence from China. Econ. Model. 86, 317–326 (2020) 10. Wang, Z.R., Zhang, D.H., Wang, J.C.: How does digital finance impact the leverage of Chinese households? Appl. Econ. Lett. 12, 1–4 (2021)

Chapter 11

Research on the Influencing Factors of China’s Digital Trade Development Level Lanlan Zhou, Feifei Chen, and Chengwen Kang

Abstract With the rapid development of the digital economy, digital trade has become an important force affecting economic development. Based on the technology–organization–environment (TOE) framework, this paper selects 9 index variables that affect the development of China’s digital trade and uses the grey relational model to quantitatively analyze the relationship between China’s digital trade development and its influencing factors. The grey correlation degree in descending order is GDP per capita, mobile cellular network subscriptions, number of R&D personnel, the added value of the tertiary industry, the proportion of R&D expenditure in GDP, fixed broadband users, the actual use of foreign capital, and proportion of government research expenditure in total financial expenditure, total import and export trade/ GDP, and is closely related to China’s digital trade. It shows that national economic strength, information infrastructure, the number of researchers and capital investment, and industrial structure have a greater impact on China’s digital trade. On this basis, this paper finally puts forward suggestions for accelerating the development of China’s digital trade from three aspects: infrastructure construction, capital support and personnel training, and industrial structure.

11.1 Introduction At present, the whole world is setting off an era of change with the theme of digital trade and industrial interconnection. The digital economy strategy with digital trade as the core is gradually receiving high attention from the state. The report of the 19th National Congress of the Communist Party of China included ‘Digital China’ in the government work report for the first time. Digital China will be an important driving force for high-quality economic development in the new era. In 2021, the scale of China’s digital economy reached 7.1 trillion yuan, ranking second in the world. L. Zhou (B) · F. Chen · C. Kang Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_11

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Among them, the scale of digital trade reached 1854.39 billion yuan, an increase of 40.1% year-on-year, 32% points higher than the year-on-year growth rate of the digital economy (8.2%), and the pulling effect on the digital economy is on the rise. From this point of view, the development of digital trade is not only an inherent requirement of China’s economic development but also an effective means to deal with the complex and changeable international economic situation. Therefore, this paper attempts to explore the factors related to the development level of China’s digital trade and uses the grey correlation analysis method to analyze the correlation between different influencing factors and the development of China’s digital trade, to provide feasible suggestions for China’s development of digital trade.

11.2 Literature Review Digital trade, the existing research mainly focuses on the definition and connotation of digital trade, the analysis of digital trade rules, digital trade barriers, the relationship between digital trade and the global value chain, foreign investment, and so on. The earliest proponent of digital trade was Weber (2010), an American scholar, who defined digital trade as products or services delivered digitally through the Internet [1]. Since then, the definition of digital trade has been continuously improved. Today, digital goods and services, cross-border data flow, and digital trade platforms are all included in the concept connotation of digital trade. This makes the measurement of digital trade difficult and difficult to unify. At present, most scholars will refer to the statistical data of digital delivery trade defined in the digital trade measurement manual jointly issued by OECD, WTO, and IMF for relevant analysis. Wang Aihua (2021) measured the scale of cross-border digital trade between China and Japan according to the cross-border digital order trade-cross-border digital delivery trade framework [2]. Peng Yu, Lv Yifan (2021) and others used UNCTAD’s BOP classification of the balance of payments service trade to select relevant digital service trade sectors for research [3, 4]. Some scholars have collected data on indicators related to digital trade through the compilation of composite indicators to conduct a comprehensive evaluation of digital trade. The advantage of the former is that it is easier to obtain data, which can be obtained through official databases such as UNCTAD, OECD, and WTO, while the latter can comprehensively reflect the infrastructure and innovation capabilities related to digital trade. On the influencing factors of digital trade development, Chen Chaofan and Liu Hao (2018) believe that data localization, intellectual property protection, and obstacles to foreign direct investment are the limiting factors for the development of global digital trade [5]. Lu Jing and Fu Nuo (2018) used an improved gravity model to study the influencing factors of digital trade, and the results show that the important determinants of digital trade development include factors such as technical level, system, and culture [6]. Based on Michael Porter’s ‘Diamond Model’, Lan Qingxin and Dou Kai (2019) selected six indicators through stepwise regression of 10 factors to empirically analyze the influencing factors of China’s digital trade international competitiveness. The results

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show that the mismatch of talent systems will lead to a significant negative effect of human capital on the international competitiveness of digital trade [7]. Yue Yunsong and Zhao Jiahan (2020) used the two-step system GMM method to study the determinants of digital service exports. The results show that the level of service industry development, foreign direct investment, and the level of informatization are positive factors affecting the export of digital services [8]. In a word, the existing research results provide a good reference for the research on the measurement and influencing factors of China’s digital trade development level, but there are still some breakthroughs. The focus of the research is still on the use of comprehensive index compilation methods to evaluate digital trade. There are few discussions on the scale of digital trade and its influencing factors, and there is a lack of dynamic research on the development of digital trade.

11.3 Research Methods Grey relational analysis is one of the basic methods of grey system theory. Grey system theory proposes the concept of grey relational analysis between various subsystems, aiming to find numerical values between various subsystems in the system through analysis relation. Therefore, the grey relational analysis method quantifies a series of trends in the development and change of a system and judges whether various subsystems are closely related through the similarity of the geometric curves. If the curves are similar, the greater the corresponding serial correlation degree is proved. Grey relational analysis is mainly divided into the following six steps: (1) Determine the comparison sequence and the reference sequence; (2) Dimensionless processing of raw data (two methods are mainly used); (a) Initialization processing: xi (k) = xi (k)/xi (1), k = 1, 2...n; i = 0, 1, 2...m (b) Average processing: xi (k) = xi (k)/xi , k = 1, 2...n; i = 0, 1, 2...m where k corresponds to the period and i corresponds to a row in the comparison sequence (that is, a feature). (3) Calculate the absolute difference between |the comparison sequence and the | reference sequence: Δ j (i ) = |x j (i) − x0 (i )|; (4) Calculate the grey correlation coefficient; (5) Obtain the grey relational degree; (6) To sort out the grey relational degree and analyze the results. In 1990, Tornatzky and Fleisher proposed the technology–organization–environment (TOE) framework, which divides the factors affecting the adoption of technological innovation into three categories: technology, organization, and environment and then explains the organizational technology integration and adoption behaviour [9]. Digital trade can be regarded as technological innovation behaviour. Therefore,

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this paper selects relevant influencing factor variables from the three aspects of technology, organization, and environment of the TOE framework to conduct a qualitative analysis of digital trade at home and abroad. Digital trade needs digital technology, and this paper draws on the indicators of Yu Yulan (2019), Jiang Xiaojuan (2019), and Lan Qingxin (2019) to measure technical factors and finally selects six factors to measure technical factors: number of R&D personnel, the proportion of R&D expenditure in GDP, actual use of foreign capital, fixed broadband subscribers, and mobile cellular network subscriptions [10]. Organizational factors refer to the organizational composition, organizational scale, and decision-making support related to technology trade, organizational factors can be divided into the industrial structure and government support, and this paper is expressed by the added value of the tertiary industry and the proportion of government scientific research expenditure in total fiscal expenditure. Environmental factors refer to the macro environment in which the activity is carried out, the development of digital trade is inseparable from the support of a country’s economic strength, and trade openness will also have a certain impact on the digital trade volume. Therefore, this paper selects GDP per capita and total import and export trade/GDP as influencing factors of digital trade. So far, there is no unified measurement standard for digital trade, the development level of digital trade is difficult to accurately measure with data and to measure the convenience of measurement, and this paper will select the export value of digital delivery of service trade as a substitute value. Based on the availability of data and the measurability of variables, this paper will analyze the data from 2010 to 2020. Table 11.1 describes each variable.

11.4 Analysis of Influencing Factors of China’s Digital Trade Development Level According to the calculation process of the above grey relational analysis method, this paper selects the 11-year digital delivery service trade export volume from 2010 to 2020 as the reference sequence, denoted as X 0 = {X 0 (1),…, X 0 (n)}; the 9 influencing factor indicators in Table 11.1 are used as a comparison sequence, denoted as X i = {X i (1),…, X i (n)}; among them, n = 11, i represents i-th index, i = 1,…,9. In this paper, the mean value will be used to carry out the dimensionless processing of the data, so that the various factors are comparable so that the two-level maximum difference and the minimum difference are determined by calculating the absolute difference between each comparison sequence and the corresponding element of the reference sequence one by one. M = maxi maxk Δi (k) = 2.2082 m = maxi maxk Δi (k) = 0 Thus, the relationship coefficient ζi (k) = m + ρ M/Δi (k) + ρ M, where i = 1,…, 9, k = 1,…, 11, is the resolution coefficient, ρ ∈ (0, 1), usually ρ = 0.5, the obtained grey. The

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Table 11.1 Factors influencing the development of digital trade based on the TOE framework Indicator category

Specific indicators and units

Data sources

Digital trade

X0: Digitally delivered service trade exports ($100 million)

UNCTAD

Technical factors

X1: Number of R&D personnel (10,000 people)

National Bureau of Statistics

X2: The proportion of R&D expenditure in GDP (%)

World Bank Database

X3: Actual use of foreign capital ($100 million)

National Bureau of Statistics

X4: Fixed broadband subscribers (per 100 people)

World Bank Database

X5: Mobile cellular subscriptions (per 100 people)

World Bank Database

X6: The added value of the tertiary industry (billion US dollars)

National Bureau of Statistics

X7: The proportion of government scientific research expenditure in total fiscal expenditure

National Bureau of Statistics

X8: Total import and export trade/GDP

National Bureau of Statistics

X9: GDP per capita

National Bureau of Statistics

Organizational factors

Environmental factors

correlation coefficients are shown in Table 11.2. Secondly, through the grey relan Σ ζi (k), the grey relational degree between China’s tional degree formula γi = n1 k=1

digital trade development and ten influencing factors is obtained, as shown in Table 11.3. According to the grey correlation degree results in Table 11.2 and the actual meanings contained in each variable, the following results can be drawn: The grey correlation degree values of the nine influencing factor indicators are distributed between 0.53 and 0.86, all greater than 0.5, which indicates that the selected nine indicators make a difference in the development of China’s digital trade and are positively correlated. Among them, the correlation degree of the four indicators of the number of R&D personnel (X 1 ), mobile cellular network subscriptions (X 5 ), tertiary industry added value (X 6) , and per capita GDP (X 9 ) is all above 0.76. It shows that these influencing factors are highly correlated with digital trade. Next, a detailed analysis is carried out. It can be seen from the table that among the environmental factors, the per capita GDP has the greatest impact on the comprehensive development level of digital trade, and the correlation is 0.8989, indicating

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Table 11.2 Grey correlation coefficients 1

X1

X2

X3

X4

X5

X6

X7

X8

X9

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

1.0000

2

0.8549

0.9346

0.8221

0.9362

0.8651

0.8990

0.7518

0.7652

0.8914

3

0.9939

0.8488

0.8109

0.9151

0.9925

0.9377

0.7698

0.7427

0.9870

4

0.9544

0.8051

0.7484

0.9635

0.9667

0.9143

0.7023

0.6516

0.9807

5

0.7931

0.8900

0.6215

0.8600

0.7976

0.9890

0.5717

0.5358

0.8388

6

0.8736

0.7234

0.6888

0.6633

0.8650

0.7702

0.5930

0.5325

0.9981

7

0.9048

0.6305

0.6845

0.5452

0.9222

0.6604

0.6063

0.5139

0.8938

8

0.8357

0.5869

0.6382

0.4526

0.8756

0.6173

0.5627

0.4805

0.8672

9

0.6374

0.6725

0.4908

0.5591

0.6921

0.7185

0.4478

0.3858

0.8599

10

0.6244

0.6111

0.4538

0.5283

0.6470

0.6923

0.4224

0.3558

0.8249

11

0.5973

0.5664

0.4288

0.5130

0.5625

0.7412

0.4177

0.3333

0.7455

Table 11.3 Grey correlation degree between digital trade development and influencing factors Relevance (γ) sort X1: Number of R&D personnel (10, 000 years)

0.8245

3

X2: R&D investment (100 million yuan)

0.7517

5

X3: Actual use of foreign capital ($100 million)

0.6716

7

X4: Fixed broadband subscribers (per 100 people)

0.7215

6

X5: Mobile cellular subscriptions (per 100 people)

0.8351

2

X6: The added value of the tertiary industry (billion US dollars)

0.8127

4

X7: The proportion of government scientific research expenditure in total 0.6223 fiscal expenditure

8

X8: Total import and export trade/GDP

0.5725

9

X9: GDP per capita

0.8989

1

that the impact of economic development on digital trade is significant, and the rapid economic development provides a strong foundation for digital trade. Among the technical factors, the number of mobile cellular network subscriptions has the second highest impact on the comprehensive development level of digital trade, which shows that the number of mobile cellular network subscriptions has a strong positive correlation with the development level of China’s digital trade. The development of information technology has led to the development of digital platforms based on the network, accelerated the circulation of digital products, reduced the cost of digital trade, promoted the integration of digital and various industries, and will inevitably lead to the development of digital trade. The correlation between the R&D expenditure and the number of scientific researchers in the technical factor is 0.8245 and 0.7517, respectively. The reason is inseparable from the technical characteristics of its digital trade. Digital trade

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relies on digital technology for development and renewal. The more R&D investment and scientific research personnel, the more attention is paid to the development of technology. A large amount of cost and human resources can bring technological output and innovation, thus driving the development of digital trade. The value added correlation of the tertiary industry in organizational factors ranks fourth. With the transformation of traditional trade to digital trade, the upgrading and optimization of the industrial structure of digital trade will greatly drive the development of digital trade. The fixed broadband users (per 100 people), the actual utilization of foreign capital (100 million US dollars) in the technical factor, and the proportion of government scientific research expenditure in the total fiscal expenditure in the organizational factor are 0.7215, 0.6716, and 0.6223, respectively. At present, these factors are weak in promoting digital trade. This result may be influenced by the sample size of this study. Among the nine development factors, the degree of trade openness has the least correlation with digital trade, indicating that the level of foreign trade openness has a relatively weak impact on digital trade. For reasons of national security, data security, privacy protection, etc., China’s intervention in the cross-border flow of data has largely restricted the level of opening up of regional digital trade. Therefore, compared with other factors, the impact of the level of opening up on China’s digital trade lags significantly. In summary, this paper uses the grey correlation model to analyze the relationship between China’s digital trade development and its influencing factors and draws the following conclusions: First, national economic strength, information infrastructure, and the number of researchers are important factors driving the development of digital trade, and they are also the basis for digital trade to some degree. Second, industrial structure and R&D investment are key factors driving the highquality development of digital trade. Third, policy support and trade openness have less impact on digital trade than other factors. But in fact, the development of digital trade is inseparable from them.

11.5 Suggestions for Accelerating the Development of Digital Trade in China Based on the analysis of the influencing factors of the development of digital trade, we find that the development of digital trade is affected by many factors, and the influence of each factor is different. Based on this, we should adopt reasonable policies and measure to promote the development of digital trade.

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11.5.1 Accelerate the Construction of Digital Infrastructure The development of digital trade is closely related to the construction of digital infrastructure, and China should further promote the construction of digital infrastructure, actively promote the growth of Internet penetration in rural areas, further reduce the cost of using digital infrastructure, and narrow the digital divide. Promote the integrated development of agriculture, industry, service industries, and other industries with digital technology, vigorously develop digital trade-related industries, push the upgrading of industrial structure, accelerate the application of industrial Internet, Internet of things technology, blockchain technology, and cloud computing, improve the digital infrastructure of various industries, and continuously make big advances in the development of digital technology and digital economy.

11.5.2 Increase Financial Support and Talent Training Talent and technology spending has a greater impact on digital trade. So to realize the long-acting development of digital trade, we must increase fiscal and taxation policies to support digital technology innovation, promote the construction of digital platforms, realize information sharing, and reduce transaction costs, and establish a supply chain service platform to improve the efficiency of commodity circulation. Secondly, strengthening support for digital enterprises can reduce the actual cost of technological innovation of digital enterprises through government subsidies, lowering of loan thresholds, and tax incentives. Finally, strengthen the cultivation of digital technical talents and digital trade management talents. Set up a training plan for interdisciplinary talents, and gradually complete the training of talents [11].

11.5.3 Upgrade the Digital Industry Structure Digital trade relies on the development of the digital industry. The optimization and upgrading of industrial structure will promote the prosperity and development of digital services and digital commodity-related industries. Therefore, it is necessary to take measures to further optimize the structure of the digital industry: first, speed up the development of core industries of digital trade, including the software and information industry, cross-border e-commerce, and Internet industry; second, the government should strengthen macro guidance and allow full play to the leading role of the market, improve the supply structure of enterprises according to changes in supply and demand, and increase the competitiveness of enterprises; third, promote the connection between trade platforms and customs and national inspections, simplify complex procedures, realize the transformation of Zhejiang Province’s

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product exports from labour-intensive to technology-intensive, and improve digital International competitiveness of trade.

References 1. Weber, R.H.: Digital trade in WTO-law-taking stock and looking ahead. Asian J. WTO Int. Health Law Policy 5(1), 1–24 (2010) 2. Wang, A.H., Wang, Y.Z.: Analysis on scale measurement of cross-border digital trade between China and Japan. Contemp. Econ. Jpn. 40(01), 43–55 (2021) 3. Sheng, Y.L., Peng, Y., Gao, J., Chen, L.X.: New impetus to digital trade development: RTA digital trade rules in the ascendant-analysis report of global digital trade promotion index (2020). World Econ. Res. 40(01), 3–16+134 (2021) 4. Lv, Y.F., Fang, R.N., Wang, D.: Topological structure characteristics, and influence mechanism of global digital service trade network. Quant. Econ. Tech. Econ. Res. 38(10), 128–147 (2021) 5. Chen, C.F., Hao, L.: Global digital trade development situation, limiting factors and China’s countermeasures. Theor. Res. 35(05), 48–55 (2018) 6. Jing, L., Nuo, F.: Rise of global digital trade: analysis of development pattern and influencing factors. Soc. Sci. Front. 41(11), 57–66+281+2 (2018) 7. Lan, Q.X., Dou, K.: An empirical study on international competitiveness of china’s digital trade based on diamond model. Soc. Sci. 41(03), 44–54 (2019). Yue, G.Y., Zhao, J.H.: Research on the characteristics and influencing factors of digital service export-analysis based on transnational panel data. Shanghai Econ. Res. 39(08), 106–118 (2020) 8. Min, C.: Research on the promotion mechanism of digital trade to China’s industrial servitization based on network readiness index. Res. Prod. 35(05), 15–18+53 (2020) 9. Yu, Y.L., Yang, Y.Q.: Error correction model analysis of the impact of technological innovation on digital trade competitiveness. ShenZhen Soc. Sci. 3(03), 50–58 (2020) 10. Jiang, X.J., Luo, L.B.: Globalization of services in the internet age-new engines, acceleration and great power competitiveness. China Soc. Sci. 40(02), 68–91+205–206 (2019) 11. Yoon, J.Y., Joung, S.: A study of purchase intention of eco-friendly products: a cross-cultural investigation between Korea and China. Int. J. Smart Bus. Technol. 7(2), 61–66 (2019)

Chapter 12

Research on the Coupling and Coordinated Development of the Digital Economy and Rural Revitalization Long Yin

and Jinwen Yao

Abstract In the process of rural revitalization, the development elements in the digital economy are added, and the development advantages of the digital economy are used to drive the development of rural revitalization, thereby promoting the overall revitalization of the countryside. According to the relevant data from 2011 to 2020 that have been found, extract appropriate indicators and establish appropriate models to measure and analyze their development index and coupling coordination degree, respectively. The degree of mutual dependence and mutual restriction between the two systems of my country’s digital economy and rural revitalization is in a stage of steady rise, but all are still relatively low. Therefore, explore ways in which coupled and coordinated development can be effectively brought into play.

12.1 Introduction As the most active key production factor, data are coupled and resonated with the development of traditional industries. Digital innovation with digital industrialization and industrial digitization as the main line injects new momentum into the highquality development of various industries. The digital economy is becoming the core engine that leads and promotes economic and social development. In 2021, the growth rate of many core industries of the digital economy in my country will exceed 20%, and the total market value of listed companies in the digital economy will reach 14.5 trillion yuan. The potential of the digital industry to lead development is fully demonstrated. As an essential part of China’s road to rejuvenation, rural construction requires not only policy guidance but also a lot of technical and financial support. By clarifying the relationship of coordinated development between the two and discussing a series of problems that may occur in their coupled and coordinated L. Yin (B) · J. Yao College of Economics, Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_12

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development, we will promote the digital transformation of the “three rural” fields and form a modern new countryside. Regarding the digital economy and rural revitalization, many people who have ideas in this regard conduct more detailed discussions on the possible relationship between. Two from a theoretical point of view are based on the connotation of the two. For example, Zhao and Ding (2021) believe that digitalization can improve the quality, efficiency, and competitiveness of agriculture and promote the overall revitalization of rural areas by fully infiltrating digital transformation and innovative development concepts and creating a digital inclusive atmosphere [1]. Zhou (2021) believes that the advantages of the digital cultural industry are fully utilized to optimize the upgrading of the rural industrial structure and promote economic and cultural development in rural areas [2]. Wu (2021) believes that China’s application of digital technology to rural industry exhibitions to promote the development of a rural economy is an efficient and intelligent internal integration state and a unique dynamic process [3]. Chen (2021) believes that the synergy of digital technology and technological innovation can be used to accelerate the mutual development of the digital economy and rural industries, to realize the sustainable development of agriculture and rural areas [4]. Feng and Xu (2021) believe that building a digital village is an inevitable choice for rural revitalization. The digital economy empowers the digital environment and infrastructure construction in the countryside and provides a new solution for China to solve the “three rural” problems [5]. Based on sufficient relevant theories, some scholars have begun to use statistical econometric models to transform qualitative research between the digital economy and rural revitalization into quantitative research. First, measure the degree of development of the digital economy. Wang and She (2021) contends that the digital economy has greatly changed the layout of my country’s regional economic development, and the industrial development, comprehensive development, and self-development results of each region are good [6]. Liu et al. (2022) believe that the development of the national digital economy mainly presents the characteristics of a stepped distribution, and the gap in the degree of development of adjacent provinces (autonomous regions and municipalities) is expanding year by year [7]. Xue et al. (2022) believe that the overall level of rural revitalization and development in China shows a fluctuating upward trend. Among them, the gap between the eastern regions is the largest, and it is necessary to fully consider the own conditions of each region to improve rural development capacity [8]. Chen et al. (2021) believe that the increasing gap between urban and rural residents’ income and expenditure is a common problem in economically developed areas, and it is the general trend to carry out a rural revitalization strategy [9]. The correlation between them needs to be further revealed, and there is never enough discussion about this part. Li et al. (2022) believe that the coupled and coordinated development of digital villages and rural revitalization will help to stimulate the development of rural industries [10]. Through the above discussion, it can be concluded that many previous people who have experience in this field have summarized a lot of theoretical knowledge, but only a small number of

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people have combined this theoretical knowledge with their own practical experience. Suggestions and optimal paths on how to effectively coordinate the two are almost impossible to achieve, and more quantitative analysis is needed. Therefore, based on some conclusions drawn by those interested in this aspect, this article uses some appropriate models and methods to analyze the degree of mutual dependence and mutual restriction between the two systems of my country’s digital economy and rural revitalization the development provides limited reference.

12.2 Theoretical Construction of the Coupling of the Digital Economy and Rural Revitalization Data have the characteristics of low cost, wide source, high efficiency, and high quality, so it can become the most important factor of production in the digital economy. At present, the digital economy mainly starts from three aspects: transforming agricultural production methods, ensuring farmers’ lives, and improving rural government services. It improves the digitalization level of agriculture, meets farmers’ needs for material and spiritual development, and improves rural governance efficiency to promote the comprehensive revitalization of rural areas. The digital economy helps rural industries revitalize by promoting the digitization and intelligence of rural industries. As a crucial new production factor in the digital economy era, data have the advantages of easy collection and fast dissemination. With the help of high-level digital infrastructure, data elements can be disseminated immediately, effectively, and quickly, promoting large-area coverage. The network and widely linked information nodes coordinate with traditional production factors to achieve full integration of the two, change the backwardness of agricultural production, and realize the digitization and efficiency of agricultural production through the reform of the rural industrial structure and the stimulation of industrial development vitality. These digital technologies can rely on their advanced nature to drive the progress of traditional industries in rural areas and can effectively connect with the Internet to revise important core technologies, adjust all agricultural production methods and processes, and prepare for the realization of modern agriculture. On the other hand, the smooth development of rural areas can change the use of digital technology, and the production of rural industries is full of vitality so that the digital economy will progress in an effective environment. The government has always paid close attention to the renovation and improvement of the countryside. It has collected and sorted out a lot of data on the production and life of rural residents in various places to understand and discuss the actual situation in the countryside. If digital technology can be applied here, then the decisions made in the future will be accurate. The rate will be greatly improved, and more experience in rural governance will be summed up.

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12.3 Empirical Analysis of the Coupling Between the Digital Economy and Rural Revitalization 12.3.1 Construction of Indicator System and Data Sources Today’s digital economy plays a key role in reorganizing global factor resources, reshaping the global economic structure, and changing the global competition pattern by taking advantage of its advantages of rapid development, wide coverage, and deep influence. At present, my country’s economic development has been injected with strong vitality due to the digital economy, which has absorbed a lot of experience for the progress of the whole society and economic recovery. People’s production and way of life are very different, and we can fully recombine and allocate various element resources by taking advantage of their special properties of fast spread, large coverage, and strong influence. Almost all villages in my country have serious problems of insufficient resources and insufficient kinetic energy. Therefore, it is essential to launch fast and efficient strategic decisions and always pay attention to the advantageous resources of the villages such as culture, environment, industry so that the villages can take the initiative to improve themselves and their consciousness. There is no difference between the city itself and the city itself, and the goal of transforming from the backward old countryside to the modernized new countryside is realized. Referring to the research results of Zhang et al. (2022) [10], 22 secondary indicators are designed to study how the development is (Table 12.1). The original data used in this study mainly come from the China Statistical Yearbook and the National Bureau of Statistics.

12.3.2 Model Introduction This paper can use the entropy weight method to get the weight of all indicators. First, the data must be standardized, and the calculation formula is as follows: ( ) xi j − min xi j ( ) + 0.0001 ( ) Positive indicators : Q i j = (12.1) max xi j − min xi j ( ) max xi j − xi j ( ) ( ) + 0.0001 Negative indicators : Q i j = (12.2) max xi j − min xi j Secondly, calculate the level of development, the calculation formula is as follows: Qi j pi j = Σn i=1 Q i j

(12.3)

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Table1 Indicator system Variable

Level indicators

The secondary indicators

Attribute

Weight

Digital economy(X)

Digital infrastructure

X 1 Mobile phone penetration

+

0.025

X 2 Internet Broadband Access users

+

0.045

X 3 Length of optical cable

+

0.043

X 4 Number of domain names

+

0.030

X 5 Full-time equivalent + of R&D personnel

0.030

X 6 Internal expenditure + on R&D is equal to GDP

0.026

X 7 Number of patent + applications authorized

0.044

X 8 Technology Market Turnover

+

0.049

X 9 Proportion of the added value of tertiary industry

+

0.038

X 10 The ratio of fixed assets to GDP

+

0.288

X 11 The proportion of undergraduate and junior college students in the total population

+

0.040

X 12 Total imports and exports as a percentage of GDP

+

0.067

Y1 Labor productivity

+

0.022

Y2 Land productivity

+

0.032

Y3 Total mechanical power per capita

+

0.027

Y4 Proportion of the added value of primary industry

+

0.038

Y5 Living area per capita

+

0.018

Y6 Urban–rural Income – ratio

0.028

Digital technological innovation

Application of digital industry

Rural revitalization(Y )

Prosperous industry

Life rich

Y7 Engel coefficient

–

0.026 (continued)

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Table1 (continued) Variable

Level indicators

The secondary indicators

Attribute

Weight

Ecological livable

Y8 Health clinic staff of + thousand village

0.023

Y9 Comprehensive population coverage of television programs

+

0.024

Y10 Forest coverage

+

0.037

( ) 1 Σ pi j ln pi j ln(n) i=1

(12.4)

gj = 1 − ej

(12.5)

1 − ej ) w j = Σn ( j=1 1 − e j

(12.6)

m

ej = −

U=

n Σ

w j × Qi j

(12.7)

j=1

Among them, pi j is the proportion of each index, e j is the entropy value of the indicator, g j is the index difference coefficient, w j is the weight of each indicator, and U is the development index of each subsystem. In this paper, the coupled coordination degree model can seem used to measure how well the two systems developed in coordination. The specific model form is as follows: D= / C=

√ C×T

√ U1 U2 2 U1 U2 = ( U1 +U2 )2 U1 + U2

(12.8)

(12.9)

2

T = αU1 + βU2

(12.10)

Among them, D and C can be understood as coupling coordination degree and coupling degree, T is the comprehensive coordination index of digital inclusive finance and innovative development, and α and β are undetermined weight coefficients. It is equally important as rural revitalization, so take α = β = 0.5.

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12.3.3 Discussion of Results Overall, the digital economy development index is higher than the rural revitalization development index, but the gap between the two is gradually narrowing, indicating that the growth rate of rural revitalization is greater than that of the rural digital economy evolution. However, the overall development index of the two subsystems is generally low and the growth rate is relatively slow (see Table 12.2). The coupling degree has been stable above 0.9 in most years, only the coupling degree in 2012 was relatively low, and it was still at a high level of coupling in general, indicating that the two subsystems of China’s digital economy and rural revitalization are closely interdependent and interdependent role relationship, that is, there is a strong correlation between the two. However, this does not mean that they can develop well in coordination, and the degree of coupling coordination is affected by multiple factors. By carefully discussing the empirical conclusions in the above table, we can simply understand that the relationship between the digital economy and rural revitalization is not close enough to rely on and restrict each other, but as time accumulates, the relationship between them becomes closer, and we should continue to implement them efficiently following the relevant policies of the two (Table 12.3). Table 12.2 Development index of digital economy–rural revitalization subsystem Year

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

X

0.009

0.015

0.021

0.024

0.028

0.028

0.029

0.035

0.040

0.046

Y

0.019

0.312

0.027

0.030

0.034

0.039

0.047

0.059

0.070

0.085

Table 12.3 Coupling coordination degree of the digital economy–rural revitalization coupling system Year

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

C

0.928

0.424

0.992

0.993

0.995

0.986

0.973

0.967

0.962

0.954

D

0.114

0.263

0.155

0.164

0.177

0.183

0.193

0.213

0.230

0.250

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12.4 Conclusions and Recommendations 12.4.1 Conclusion The digital economy and rural revitalization are two coupled subjects, which are cross-coupled through the coupling process of key activities in the external environment and the internal environment. The digital economy includes digital infrastructure, digital technology innovation, and digital industrial applications. Rural revitalization includes industrial prosperity, ecological livability, and affluent life. From the coupling measurement, it can be concluded that there is a strong correlation between the digital economy and rural revitalization, while their coupling coordination is generally low. The empirical results show that China’s digital economy–rural revitalization coupling system is in the late stage of adjustment and is expected to enter a stage of high-speed and high-level coordinated development after 2022. This important change needs to be taken into account when formulating policies.

12.4.2 Recommendations In terms of theory and practice, this paper provides a certain reference for policy adjustment and policy formulation, aiming at a series of problems related to the combination of the digital economy and rural revitalization. First, increase support for the digital economy and rural revitalization and development, and adhere to innovative development models. According to the evaluation indicators of the two systems, the development of both systems has reached a relatively high level, and the follow-up development must maintain the speed of development and ensure the quality of development, among which innovation must play an important role. According to the characteristics of each rural area, starting from their real living environment and situation, we will create suitable financial products that they can consume. At the same time, some basic equipment related to the development of the digital economy in the past needs to be regularly maintained and repaired, other new facilities such as mobile communication and artificial intelligence should be put into use as soon as possible, and all the resources currently possessed should be fully and properly used. Make adequate preparations for the perfect integration of the digital economy and rural areas in the future. Secondly, full-coverage broadband and complete network facilities can ensure smooth information exchange, laying a foundation for the subsequent coordination between the two, which is conducive to reducing the occurrence of information asymmetry in economic activities. For rural areas with a poor economic environment, we can use the characteristics of residents to give them some necessary subsidies, to encourage them to use digital technology products such as the Internet, to achieve the purpose of increasing the popularity of digital technology products in rural areas. Finally, starting from the coordination level of the digital economy and rural revitalization, we will move toward a

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new development mode. The coupled and coordinated development of my country’s digital economy and rural revitalization requires extensive knowledge education and publicity. If you have the knowledge and skills related to this aspect, not only will mathematical knowledge be familiar to everyone, but also the economic and cultural undertakings of the countryside will be well advanced. More professional and technical talents are cultivated to prepare for future career development, which can ensure the progress of cooperation between the digital economy and rural revitalization. Acknowledgements This work was supported by The Harbin University of Commerce Doctoral Research Start-up Grant Project: Research on Carbon Emission Linkage and Differentiated Emission Reduction Paths of China’s Urban Agglomerations under Dual Carbon Tar-gets (Project No. 22BQ53).

References 1. Zhao, D.Q., Ding, Y.W.: The mechanism, path, and countermeasures of digital promotion of rural revitalization. J. Hunan Univ. Sci. Technol. (Soc. Sci. Ed.) 24(06), 112–120 (2021) 2. Zhou, J.: The mechanism and path of digital cultural industry empowering rural revitalization strategy. Rural Econ. 10–16 (2021) 3. Wu, X.X.: Research on the integrated development of digital economy and rural industry. Southwest Finan. 78–88 (2021) 4. Chen, Y.M.: Mechanism innovation of the integrated development of digital economy and rural industries. Issues Agric. Econo. 81–91 (2021) 5. Feng, C.R., Xu, H.Y.: The current practical dilemma and breakthrough path of digital village construction. J. Yunnan Normal Univ. (Philos. Soc. Sci. Ed.) 53(05), 93–102 (2021) 6. Wang, J.J., She, G.J.: Measurement and regional comparison of my country’s digital economy development level. Chin. Circ. Econ. 35(08), 3–17 (2021) 7. Liu, C.K., Jiang, Y., Zhang, Q.H., Zhu, X.F.: A statistical measurement of digital economy development level and research on spatial and temporal evolution trend. Ind. Technol. Econ. 41(02), 129–136 (2022) 8. Xue, L.F., C, Z.F., Y, C.: Regional differences and dynamic evolution analysis of china’s rural revitalization development level. China’s Agric. Resour. Zoning. 1–15 (2022) 9. Chen, J.L., Lin, Y., Shi, H.H.: Research on the comprehensive evaluation of the development level of rural revitalization in the Yangtze river delta region. East China Econ. Manage. 34(03), 16–22 (2020) 10. Li, Y.L., Wen, W., Gao, W.X.: Time series adaptability analysis of the coupled and coordinated development of digital villages and rural revitalization. Agric. Econ. Manage. 1–12 (2022)

Chapter 13

The Impact of the Digital Economy on TFP in China’s Equipment Manufacturing Industry Zengfan Liu and Shimiao Zhang

Abstract Measure the changes in the equipment manufacturing industry’s total factor productivity (TFP) based on the data from 30 Chinese provinces and cities from 2005 to 2020. Use the entropy value method to determine the digital economy, and empirically test the impact of the digital economy on the TFP of China’s equipment manufacturing industry. The empirical results suggest that: TFP grows by an average of 3.8% per annum within the study area, mainly contributed by technological progress(tech); the digital economy has a remarkable boost to the TFP. Meanwhile digital economy significantly boosts the growth of equipment manufacturing TFP in western, with the least impact in eastern. There exists industry and regional heterogeneity.

13.1 Introduction The 2022 Report on the Work of the Government states that it is essential to boost the digital economy, accelerate industrial Internet’s development and improve technological innovation, and supply capacity of key software and hardware [1]. Digitally is becoming a new engine to drive economic growth. The White Paper on the Development of China’s Digital Economy once point out that the growth in the scale of the digital economy will reach to 4.55 billion yuan in 2021, increasing 16.2% over 2020. Among them, the digitalization of industry accounts for more than 80% of digital economy, and it can be concluded that the degree of optimization about digital economy structure increases year by year [2]. According to CAICT’s forecast, China’s digital economy will reach to 60 trillion yuan around 2025. Digital economy is one of the main elements of supply-side structural reform, and its hard power reflects the country’s comprehensive strength in the digital era, promoting Z. Liu (B) · S. Zhang Harbin University of Commerce, Harbin, China S. Zhang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_13

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economic growth of high quality. By the way, digital economy is also a stabilizer to cope with the downward pressure of economy. It can alleviate the negative impact of the demographic dividend, the difficulties in upgrading the "troika" of investment, the export and consumption, and the bottleneck in transformation of the traditional economic structure [3]. In particular, during the outbreak of the COVID-19, digital forms such as online offices, teleconferencing and contactless parcels have emerged. The government uses big data for flow regulation, precise prevention and control. In this context digital economy can reduce the gathering of people, mitigate the risk of epidemic transmission and stabilize economic growth, allowing it to consolidate its central position in the national economy. China’s economy has shifted from the stage of high-speed growth to the stage of high-quality development. The urgent requirement about improving TFP and the maintenance of a higher level of TFP is crucial to China’s economic development [4]. The industries provide production technology for economic development. It’s critical to the country’s level on economic development. It is a national major tool, the core of industry and the lifeline of the national economy. Exploring the digital economy’s impact on equipment manufacturing industry and promoting digital economy’s deep integration with the real economy is of great value in accelerating the construction of high-quality economic and social growth [5]. Digital economy not only greatly reduces the incoming and outgoing business’s friction for equipment manufacturing enterprises, bringing new challenges to market operations, but also eases the pressure on rising manufacturing costs and traditional equipment manufacturing enterprises that are overly dependent on the demographic dividend. Digital economy is to build equipment manufacturing industry to flourish “base”. And under the east wind of digital economy, equipment manufacturing industry will be from “can be” to “big as”.

13.2 Reviews of Relevant Research Literature 13.2.1 The Definition and Statistical Standard of the Digital Economy Tapscott first introduces the term “digital economy” based on the idea that Information and Communication Technologies (ICT) are widely used in economic activities in 1996. In 1998 Report “The Emerging Digital Economy” issued by the US Department of Commerce, which gave shape to the term digital economy. The digital economy is the next major economic form after the agricultural and industrial economies. There is currently no standard for statistics about digital economy at home or abroad: the OECD defines digital economy as the realization and execution of trade in goods and services through e-commerce on the Internet in 2013, with the main elements relating to competition and regulation in digital markets as well as network effects, interoperability, open and closed platforms. The US

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Bureau of Economic Analysis in 2018 defines digital economy based on the Internet, related information and ICT, measuring it mainly in terms of digital infrastructure, e-commerce and digital media dimensions. CAICT classifies it into industrialization and industrial digitization in 2021, focusing on the value added of the information industry, the integration and application of digital technologies with other industries. At the same time, academics have also conducted researches on digital economy measurement, and scholars have defined digital economy developments from different dimensions: Yuan Huiwen and Gao Bo construct four dimensional indicators of digital infrastructure, digital industry development, digital enterprise application and digital penetration and then they measure digital economy development’s level in each province of China by the entropy value method [6]. The approach of Li Yingjie and Han Ping is similar to this operation above and then ceforecast the development of the digital economy from 2019 to 2028 through a grey forecasting model [7]. Liu Chengkun and Jiang Yue et al. use entropy TOPSIS to measure digital economy’s develpoment level based on basic kinetic energy, resource-based kinetic energy, technology-based kinetic energy, integration-based kinetic energy and service-based kinetic energy [8].

13.2.2 The Concept of TFP TFP is considered to be the core driver of long-term economic growth and is used to express the change in output with multiple inputs. It is an overall performance of integrated production efficiency and a more comprehensive representation of the quantitative correlation between overall inputs and outputs at the level for economic growth. It is one of the major indicators of a country or region’s quality development [9]. Färe et al. (1994) first applies the DEA model to measuring TFP, using the Malmquist index to measure TFP and decomposing it into three drivers, which were derived using non-parametric accounting methods [10]. Nowadays, estimating TFP is one of the important issues in economics researches.

13.2.3 Researches Related to Digital Economy and Equipment Manufacturing TFP Wang Jun, Zhu Jie and Luo Xian study the spatial–temporal evolution of digital economy and conclude that digital economy development level shows an incremental pattern, but varies widely between different economic zones [11]. Cai Ling and Wang Ping use PCA and standardization to obtain an index of digital economy development indicators for prefecture-level cities in China, and use the DEA to obtain TFP index. And the results show that digital economy significantly enhances green TFP within cities [12]. Wang Xiaoling and Han Ping analyses the level of integration and

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development of digital economy and equipment manufacturing industry in northeast. They find that the growth rate of integration and development of both from 2013 to 2019 is overly from mild imbalance to moderate integration [13]. Combing through the literature, it can be seen that the academic community is quite rich in studies about the digital economy alone and the relationship between digital economy and the equipment manufacturing industry. But there is less research on the heterogeneity of digital economy on its TFP by subdividing the equipment manufacturing industry into seven major industries, and less research from the four regions of eastern, central, western and northeastern to analyses. So the empirical evidence on this still has some research value. Based on this, this paper uses Chinese 30 provinces and cities’s data between 2005 and 2020 to sum up and uses this as a sample for empirical analysis to reveal the digital economy’s influence on the TFP of China’s equipment manufacturing industry.

13.3 Study Setting 13.3.1 Model Construction This paper presents the model’s construction shown: TFPit = α0 + α1 Scoreit + β X it + εit

(13.1)

where TFPit denotes the development of TFP of the equipment manufacturing industry in province i in year t, Score denotes the comprehensive score of digital economy development level, X denotes other control variables affecting the equipment manufacturing industry’s TFP, ε denotes the random disturbance term, α and β denote the parameter values of the variables to be estimated.

13.3.2 Variable Description Explanatory variable: equipment manufacturing industry’s total factor productivity (TFP). According to the Solow residual value method, the main business income of the equipment manufacturing industry is taken as the output factor. The net value of fixed assets is taken as the capital input, and the average number of all employees is taken as the labour input, both of which are taken as the input factors to build a TFP indicator system from 2005 to 2020. Core explanatory variable: the comprehensive score of digital economy development level (Score). The current construction of indicators for the digital economy development index does not meet a uniform international standard and the biggest data is unstructured data [14]. So, we refer to Alibaba and Xia Jiechang et al. (2022)

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to build a consolidated assessment index system for digital economy in China’s provinces and cities (See Table 13.1). Control variables: GDP per capita (pgdp), an increase in GDP per capita means that the economy is growing by leaps and bounds. Fiscal affluence can affect how much the digital economy invests in equipment manufacturing. The level of urbanisation (czh), the influx of rural population into the cities will accelerate the urbanisation process and subsequently bring cheap labor. The demographic dividend will affect the change of TFP in the equipment manufacturing industry. Industrial structure (cyjg), the value’s proportion added in the secondary industry to GDP is chosen as a tool for measuring industrial structure. Table 13.1 Digital economy level index system 1st-level indicator Digital infrastructure

Digital connectedness

2st-level indicator

Property

Length of long distance fibre optic cable routes

Kilometre

+

Number of mobile phone subscribers

Million

+

Total postal services

Billion

+

Total telecoms business

Billion

+

Number of people with Internet access

Million

+

Number of Internet domain names

Million

+

Number of Internet broadband access ports

Million

+

Total technology contract turn over

Million

+

software product revenue

Million

+

Software business revenue

Million

+

Revenue from information technology services

Million

+

Billion

+

Digital research R&D expenditure

Digital talent

Unit

R&D expenditure intensity

%

+

Amount of foreign technology introduction contract

US$ million

+

Technology market turnover

Billion

+

Number of patent applications by domestic applicants Piece

+

Number of professional and technical staff in public economy (state-owned) enterprises and institutions

People

+

R&D staff full-time equivalents

People

+

Average number of students enrolled in tertiary education per 100,000 population

People

+

Number of general higher education schools

people

+

State financial resources for education

million

+

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Foreign business dependency (fdi), changing in the proportion of foreign capital investment will affect the direction of R&D in industries, which in turn will have an impact on TFP. Therefore, the proportion of foreign capital to a paid-in capital of industrial enterprises above size is chosen as a tool for measuring industrial structure. Export density value (exp), the increase or decrease of export range means the international level of China’s equipment manufacturing industry, so the proportion of export density value to total equipment manufacturing industry’s output value is taken as the index of export density value.

13.3.3 Data Sources and Processing Selecting 30 Chinese provinces and cities’s data (except Tibet, Hong Kong, Macao and Taiwan) from 2005 to 2020 for seven major sub-sectors of the equipment manufacturing industry, including metal products industry, general, professional, transportation, electrical machinery and equipment manufacturing, computer, communication and other electronic, and instrumentation. These relevant data from EPS, CEInet Statistics Database, China Industry Statistical Yearbook, China Statistical Yearbook and China Economic Census Yearbook 2018. Missing data is supplemented by linear interpolation. Take “Ln” for czh and pgdp respectively. In some years, the net value of fixed assets is not available. Drawing on the treatment of Wang Wanjun, original cost of fixed assets—accumulated depreciation is used to replace the net value of fixed assets for 2011–2016 and 2017 [15].

13.4 Analysis of TFP and Digital Economy Development Level China’s equipment manufacturing industry TFP changes are measured by DEAP. The comprehensive scores are calculated by the entropy method. Results are presented in Tables 13.2, 13.3 and 13.4.

13.4.1 Static Efficiency Analysis 1. From the comprehensive efficiency index, China’s equipment manufacturing industry has not reached DEA efficiency effective in 2005 and 2020. The average value of crste is 0.923 and 0.924, which is a low growth rate in general. By provinces, the supply efficiency of different provinces and cities is different, including four and three provinces and cities in 2005 and 2020 respectively to reach the production frontier surface. Among them, Qinghai, Guizhou and

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Table 13.2 Values for 2005 and 2020 in 30 provinces and cities Province and city

2005

2020

Crste

Vrste

Scale

Crste

Vrste

Scale

Beijing

0.949

1

0.949

1

1

1

Tianjin

1

1

1

0.687

0.690

0.995

Hebei

0.562

0.563

0.999

0.689

0.691

0.996

Shanxi

0.340

0.347

0.980

0.606

0.621

0.976

Inner Mongolia

0.478

0.493

0.969

0.531

0.554

0.960

Liaoning

0.41

0.414

0.991

0.523

0.525

0.997

Jilin

0.611

0.644

0.949

0.934

0.935

0.998

Heilongjiang

0.510

0.512

0.995

0.544

0.569

0.958

Shanghai

0.873

1

0.873

0.832

1

0.832

Jiangsu

0.823

1

0.823

0.652

1

0.652

Zhejiang

0.752

0.754

0.996

0.662

0.696

0.952

Anhui

0.663

0.665

0.998

0.630

0.631

1

Fujian

1

1

1

0.706

0.707

0.998

Jiangxi

0.464

0.467

0.993

0.768

0.770

0.998

Shandong

0.941

0.951

0.989

0.800

0.800

1

Henan

0.607

0.609

0.996

0.732

0.734

0.997

Hubei

0.570

0.570

0.999

0.575

0.616

0.933

Hunan

0.454

0.455

0.996

0.814

0.816

0.998

Guangdong

1

1

1

1

1

1

Guangxi

0.775

0.779

0.994

0.869

0.882

0.985

Hainan

1

1

1

0.53

1

0.53

Chongqing

0.604

0.605

0.998

0.73

0.73

0.999

Sichuan

0.622

0.623

0.999

0.642

0.690

0.930

Guizhou

0.33

0.353

0.934

0.517

0.544

0.95

Yunnan

0.559

0.626

0.893

0.644

0.672

0.958

Shaanxi

0.363

0.364

0.996

0.49

0.493

0.996

Gansu

0.306

0.381

0.803

0.304

0.366

0.831

Qinghai

0.296

1

0.296

0.529

1

0.529

Ningxia

0.349

0.624

0.559

0.411

0.522

0.787

Xinjiang

0.411

0.57

0.721

1

1

1

National average

0.649

0.707

0.923

0.678

0.742

0.924

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Table 13.3 Malmquist productivity averages for sub-sectors, 2005–2020 Industry

Effch

Techch

Pech

Sech

TFP

Metal products

1.011

1.027

1.008

1.003

1.038

General

1.010

1.033

1.009

1.000

1.043

Professional

1.020

1.030

1.019

1.001

1.051

Transportation

1.022

1.054

1.017

1.005

1.077

Electrical machinery

1.005

1.027

1.004

1.001

1.032

Computer, communication and other electronic

0.997

1.018

1.000

0.997

1.014

Instrumentation

1.004

1.009

1.000

1.004

1.013

Equipment manufacturing industry

1.010

1.028

1.008

1.002

1.038

Table 13.4 Comprehensive score and ranking of digital economy development level by provinces and cities Province and city

Score

Rank

Province and city

Score

Rank

Beijing

0.360

3

Henan

0.259

7

Tianjin

0.184

18

Hubei

0.243

9

Hebei

0.236

10

Hunan

0.231

11

Shanxi

0.178

20

Guangdong

0.457

1

Inner Mongolia

0.158

24

Guangxi

0.180

19

Liaoning

0.230

12

Hainan

0.099

29

Jilin

0.164

23

Chongqing

0.176

21

Heilongjiang

0.186

16

Sichuan

0.276

6

Shanghai

0.257

8

Guizhou

0.155

25

Jiangsu

0.383

2

Yunnan

0.176

21

Zhejiang

0.289

5

Shaanxi

0.220

13

Anhui

0.219

14

Gansu

0.147

26

Fujian

0.213

15

Qinghai

0.098

30

Jiangxi

0.185

17

Ningxia

0.101

28

Shandong

0.336

4

Xinjiang

0.143

27

National average

0.109

Gansu are in a relatively backward position in terms of overall efficiency. So, the quality of their supply governance program and the development of an effective reward and punishment system should be improved, striving to rationalize the distribution of inputs and outputs to achieve the best results. 2. The technical efficiency (vrste) is in a growth mode. In 2020, the technical efficiency is 0.742, which is 0.258 different from the production frontier surface, indicating that China’s equipment manufacturing industry technology still needs to be improved and there is much room for progress. In 2005 and 2020, technical efficiency effective provinces are 8 and 7 respectively, indicating that these

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provinces and cities are more technologically advanced and established inputs to maximize output [16]. Guizhou and Gansu have the lowest technical efficiency values, at 0.353 and 0.381 in 2005, 0.544 and 0.366 in 2020 respectively, implying that they are seriously below the national average and the technical level remains to be enhanced. 3. The scale efficiency (scale) reflects the extent to which each province utilizes its resources. The scale has increased slightly from 0.923 in 2005 to 0.924 in 2020, with four and five provinces and cities reaching the optimum scale efficiency in 2005 and 2020 respectively. Qinghai’s scale efficiency is at the bottom of China. So the government ought to further enhance investments to reach its optimal scale of input and output. China’s equipment manufacturing industry’s low technology content and low value-added hinder the expansion of the equipment manufacturing industry scale and inhibit scale efficiency to reach effectively. Tianjin, Zhejiang and Jiangsu have increasing scale payoffs, which indicates that it should be reasonable for them to increase their investment in the industry. And these places still have the space and strength to bear the pressure of the equipment manufacturing industry. Gansu and Sichuan have decreasing scale payoffs. According to Hu Yanan and Yu Donghua, this suggests that it is not being used effectively, possibly due to their low level of investment funding. Resource mismatch and efficiency losses are clearly evident here. Input funds should be used wisely to promote industrial upgrading and economic growth in these provinces [17].

13.4.2 Dynamic Efficiency Analysis The Malmquist index dynamically reflects the trends of the TFP and decomposition indicators. Overall, the TFP index is greater than 1 every year, indicating that China’s equipment manufacturing industry development is in an upward phase. The improvement of technology level and management decisions are in the best mode, and provinces are actively responding to the national call of “Made in China 2025”. China’s equipment manufacturing industry’s average value on TFP from 2005 to 2020 is 1.038. Attributed to the decomposition index, the main reason for the improvement is technological progress change (techch). The average value of techch from 2005 to 2020 is 1.028, an increase of 2.8% and the largest proportion of all decomposition indicators. It’s a fact that accelerating the implementation of technological progress is the primary step to increase TFP. In terms of techch decomposition metrics, pech and sech grow by an average of 0.8% and 0.2% per year. By industry, transportation sees the most significant increase in TFP, with tfp of 1.077, an increase of 7.7%. The techch has the largest comparative impact. TFP in the metal products, general, special and electrical machinery and equipment manufacturing increase by 3.8%, 4.3%, 5.1% and 3.2% respectively, with tech making

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the largest contribution to the increase in TFP across the four industries. Overall TFP in computer, communication and other electronic are improved, increased by 1.4%. The main contributor to the indicator is the techch, with a slight decline in both technical efficiency change (effch) and sech, suggesting that the gains from the removal of pech from techch in this sector are essentially limiting TFP growth [18]. In comparison, instrumentation manufacturing sees the least improvement in TFP at 1.3%, mainly due to a 0.9% increase in techch and both effch and sech improve by 0.4%, which means a reasonable growth range exists [19]. The dynamics of TFP and the size of its decomposition indicators vary slightly because of the different technologies, earnings, national emphasis and other external factors embodied in different industries. Thus, the focus and importance of the TFP impact paths for different segments are also different [20]. By the time, TFP changes fluctuate widely (See Fig. 13.1). TFP falls relatively for two consecutive years from 2006 to 2008, from 1.120 in the first period to 1.100 in the second period and finally to 0.992 in the third period, resulting in different contribution efficiency of the decomposition indicators of slow TFP growth. Between 2005 and 2006 effch, techch, pech and sech all contribute, of which effch has contributed the most. The increase of TFP in 2005–2006 comes from effch, techch and pech, with sech playing a dampening role, declining by 0.9%. This is referred to as the drag effect.

Fig. 13.1 Rate of changes in TFP and its decomposition indicators, 2005–2020

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13.4.3 Data Processing for Digital Economy Considering the magnitudes of different indicators are different, the data for the 21 secondary indicators are standardized and normalized, and the indicator attributes are all positive, so the following treatment is done: X i jt =

xi jt − min(xi jt ) + 0.0000000001 max(xi jt ) − min(xi jt )

(13.2)

where i is the province, j is the indicator and t are the time. The i province and city accounts for the j indicator wijt in the t year. X i jt m

wi jt = n i=1

t=1

X i jt

(13.3)

Information entropy ej and redundancy d j for indicator j for province and city i in year t: n m 1  ej = − (wi jt ∗ ln wi jt ) ln n i=1 t=1

(13.4)

dj = 1 − ej

(13.5)

where n is the year figure and m is the figure of province and city, and then calculate the weight ϕ j for indicator j: dj ϕ j = m j=1

dj

, j = 1, 2, ..., m

(13.6)

Digital economy development level composite score: Score =

n 

ϕ j × wi jt

(13.7)

j=1

In this paper, we use state to find out the comprehensive score of each province and city’s digital economy development level, which ranges from 0 to 1. The higher the score, the higher degree of digital economic growth.

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13.4.4 Digital Economy Analysis The results (see Table 13.4 and Fig. 13.2) show that China’s overall score for the digital economy’s development has increased from 0.140 in 2005 to 0.303 in 2020. The digital economy as a whole is moving upwards. By region, the eastern is the main development region, followed by the central and northeastern, with a 2020 Score of 0.432 for the eastern and 0.313 for the central. The difference between the western and the northeast is very small. From the provinces and cities, the top rankings are Guangdong, Jiangsu and Beijing, which is consistent with the reality of the situation. While the lower rankings are mostly in the West, such as Qinghai, Ningxia and Xinjiang. The Yangtze River Delta, the Pearl River Delta and the BeijingTianjin-Hebei regions have a good concentration of industries, forming an interactive phenomenon of digital economy development. They are expected to generate digital radiation and drive the digital economy’s development in the surrounding cities, narrowing the scope of the “digital divide”.

13.5 Analysis of Empirical Results With the purpose of exploring the digital economy’s influence on TFP, the empirical evidence is made up of two parts: firstly, study the extent of digital economy influence on TFP in China’s equipment manufacturing industry as a whole and by industry segment; secondly, research the regional heterogeneity about the digital economy on TFP in China’s equipment manufacturing industry.

Fig. 13.2 Score for digital economy development level by region 2005–2020

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13.5.1 Empirical Tests Based as a Whole and Sub-Sectors Use the state to analyses the extent to which the digital economy affects TFP as a whole and its seven sub-sectors from 2005 to 2020 (See Table 13.5). From Table 13.5, the digital economy on TFP’s overall impact is remarkably positive. This suggests that the digital economy has a catalytic role in TFP. By industry, the coefficients of transportation and instrumentation are significantly negative, probably due to the fact that the focus of these two industries is in the high-tech industry. They require industrial innovation and the smaller scale of the digital economy at this stage cannot promote the increase of TFP in high-end equipment manufacturing. In terms of control variables, the effect of GDP per capita (lnpgdp) on TFP in China and its seven sub-sectors is significantly negative, which is inconsistent with Wang Jun’s (2021) study. The possible reason is that even though the economy is growing rapidly, the country does not pay much attention to the industries and previously did not vigorously promote their importance. The impact of urbanization (lnczh) on the TFP in China and its seven sub-sectors is significantly positive, indicating that the demographic dividend is still intact. The impact of industrial structure (cyjg) is significantly positive as a whole, but the coefficient is negative for the transport and instrumentation. The impact of foreign dependence (fdi) on TFP in China is significantly negative, indicating that foreign dependence has a smaller role in promoting the equipment manufacturing industry, while the coefficients of professional and transportation are significantly positive. This may be due to the lack of technical requirements of the industries in which foreign investors are stationed in China. This industries belong to the lower end of the “smile curve” and inhibit TFP’s growing in equipment manufacturing. The effect of export density value (exp) on China’s overall equipment manufacturing industry TFP is significantly negative, the impact on the instrumentation is significantly positive. The reason may be that China is relatively skilled in the instrumentation industry’s growth and has the core expertise. So the increase in export density value can promote the instrumentation’s TFP increase.

13.5.2 Regional Heterogeneity Analysis As the level of economic development in China’s regions varies greatly and significant differences exist. The overall Chinese samples are specifically divided into four regions, namely eastern, central, western and northeastern, for analysis to examine the regional heterogeneity of the digital economy’s influence on TFP in China’s equipment manufacturing industry (See Table 13.6). By Hausman, test Prob > chi2 = 0.0000 < 0.05, the original hypothesis “H 0 : ui is not correlated with x it , zi ” is rejected and a fixed effects model is used to test. From Table 13.6, it is found that the digital economy’s influence on TFP in the eastern is not significant and there is inhibition about them. The main reason may be that the digital economy has been applied to the equipment manufacturing industry for

−1.457 (−1.57)

(−070)

(−1.10)

−1.325a

−0.307a (−0.09) −0.811a (−0.23) −1.059a (−0.21) 1.110a (0.27)

exp

_cons

(−1.89)

−0.999a (−0.57)

(−0.06)

(1.36)

(−1.18)

(0.06)

−1.707a

0.070a

2.305b (0.71)

Note a , b , c denote at 5%, 10%, 1% levels respectively; t-values in brackets

−2.312b

−0.084b

2.650a

(−1.44)

fdi

(1.05)

2.051a (0.52)

2.488b

(−0.88)

5.238b (1.91)

(2.79)

(−2.66)

−0.641a

4.584a

−1.074b

1.538b (0.57)

(0.90)

cyjg

(1.99)

(−1.76)

−0.800b

lnczh

(0.36)

3.223b

−1.682a

lnpgdp

(1.56)

0.728a

tfp3

−1.095a

(0.41)

0.359c

tfp2

szjj

1.073b

tfp1

0.279a

Variable tfp

Table 13.5 China overall and sectoral regression results (−0.16)

(2.79)

(−2.55)

1.157a (0.50)

(−1.51)

(0.71)

−1.237b

0.444a

−1.426 (−0.78)

3.072a

(1.48)

(−0.99) 1.533a (0.22)

−2.443b

−2.511 (−1.33)

3.844a (0.69)

4.928b

(−1.50)

(0.54)

−1.925b

0.750a

−0.075b −1.080a

tfp5

tfp4

(0.79)

(−0.47) 0.272 c (0.12)

(−0.41) −0.394b (−0.23)

−2.570a (−0.51)

2.927a (0.97)

−0.586a (−0.11) −3.492a (−0.55)

−0.851a

−0.554a

1.324b (0.33)

1.914a

−0.962b (−0.77)

tfp7 (−0.62) −0.528b (−0.46)

(0.08) −0.580a

0.081c

tfp6

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Table 13.6 Eastern, central, western and northeastern sub-regional regression results Variable

Eastern

Central

Western

Northeastern

szjj

−4.533 (−0.94)

−1.536b (−0.44)

6.395c (0.65)

6.286c (1.70)

lnpgdp

−0.031a (−0.03)

0.642a (0.98)

−0.589b (−0.86)

−1.515c (−3.49) 8.699a (2.49)

lnczh

7.500 (1.22)

−1.422 (−0.49)

−0.133a

cyjg

−3.476b

−1.414b

4.425b

fdi

5.470b (1.43)

10.816 (1.18)

−30.711 (−1.51)

1.141 (0.78)

exp

1.378a (0.26)

0.125 (0.04)

7.844a (1.19)

−7.258a (−2.79)

_cons

−28.481 (−1.28)

0.447b

5.431 (0.73)

−20.238b (−1.93)

(−0.64)

(−0.82)

(0.07)

(−0.05)

(1.62)

2.147a (2.58)

Note ***, **, * denote at 1%, 5%, 10% levels respectively; t-values in brackets

a rather short-term time. Technical proficiency needs to be improved. In contrast, the provinces in the eastern have a high level of economic development and are composed of elements such as perfect talent introduction policies, excellent infrastructures and advanced enterprise management methods, which makes the effect of improving their TFP in the east through a certain measure slow. And inhibitory effects may even occur. The digital economy’s influence on TFP in the western and northeastern is notably strong, indicating that the digital economy rising scale can promote TFP growth in this region. The main reason may be the region’s relatively backward level, a large number of talent loss, and the lack of scientific and technological innovation capacity. The digital economy can effectively improve this situation because the positive effect of its application in the equipment manufacturing industry is more obvious. Overall comparison of the regression results in eastern, central, western and northeastern find that digital economy generates the smallest impact on TFP in the eastern, followed by central and the largest in western, satisfying the law of increasing eastern, central, northeast and western. This indicates that the primary digital economy has a less positive impact on the TFP of the more developed areas of the equipment manufacturing economy. Throughout the whole, the digital economy has yet to improve its contribution to China’s equipment manufacturing industry and has not brought the digital economy into full play in all regions.

13.6 Conclusions and Policy Recommendations Based on China’s equipment manufacturing industry data from 2005 to 2020, this paper measures TFP’s change in the equipment manufacturing industry through DEAP and empirically analyzes the degree of the digital economy’s influence on TFP from three perspectives: nationwide, by industry and by region. The findings show that: firstly, China’s equipment manufacturing industry TFP grows at an average annual rate of 3.8% within areas, mainly contributed by technological progress; secondly, China’s digital economy grows by 21.5%, with an average

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annual growth of 28.1% in eastern, 21.9% in central, 16.6% in western and 19.4% in northeastern; thirdly, the digital economy can significantly enhance the growth of TFP and significantly it inhibits the transportation and instrumentation; fourthly, the digital economy’s influence on the four major regions shows a pattern of increasing from eastern, central, western and northeastern. They have industry heterogeneity and regional heterogeneity. According to the results of this research, the following recommendations are made: As part of the equipment, the system is strictly closed, the lack of external communication connections and data sharing makes the system’s open transformation and data sharing difficult. These issues affect the equipment manufacturing interconnection and transformation process. Therefore it is necessary to increase the scale of the digital economy and try to amplify the solutions of the significant contribution of the digital economy to TFP. At this stage of China’s digital economy development level in the rise, the lower digital economy scale cannot improve TFP level in eastern and central. So, the eastern and central equipment manufacturing enterprises should focus on scaling up digital economy, putting key core technologies in their own hands, to avoid the “neck” status quo. Improve the level of digital economy and thus promote TFP growth. Equipment manufacturing enterprises in the western and northeastern should focus on the degree of integration between the digital economy and the equipment manufacturing industry. The huge policy dividend and market drive released by the digital economy industry will promote a significant increase in the TFP. The extensive use of the digital economy in different aspects of research and development, production, promotion will improve the digital, networked and intelligent level. Integrating digital economy with the equipment manufacturing industry in depth can reduce the huge cost pressure of producing equipment manufacturing. Equipment manufacturing enterprises need increase investments in digital economy technology and develop a sound digital economy management system supported by digitalization. The quality and speed of enterprise service management capabilities can be improved and “equipment manufacturing + service” integration can be promoted through the construction of “order-based, express, network” mode. Enterprises should improve the level of the digital economy’s development to the practical application of equipment manufacturing and actively change cadres and workers’ attitude. The level of quality education of all staff should be improved by carrying out knowledge competitions for relevant positions. So the staff can constantly adapt to the digital, networked and intelligent development requirements in terms of thinking and ability[21]. The government should increase the supply-side reform of the digital economy and equipment manufacturing industry, pulling domestic demand through the increased quality of the digital economy and maintaining the competitive advantage of the equipment manufacturing supply chains and production chains. In the midst of today’s unprecedented changes in the century, the dynamic external environment including the uncertainty of trade frictions and strong inflation in the US has put the economy under tremendous downward pressure [22]. To cope with this situation, the government should continue to play the role of fiscal policy in guiding the digital economy, strongly encouraging provinces and municipalities to tilt their finances

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towards local digital economy research. Enterprise confidence can be promoted by reducing taxes and fees, developing reasonable financing channels, and supporting private enterprise financing. Stabilizing the balance of income and expenditure and promoting TFP growth is one of the main things at the moment. Program Code //Program Digital Economy Entropy Method: global positive_var a1 a2 a3 a4 b1 b2 b3 b4 b5 b6 b7 c1 c2 c3 c4 c5 d1 d2 d3 d4 d5 global all_var $positive_var foreach i in $positive_var { qui sum ‘i’ gen x_‘i’=(‘i’-r(min))/(r(max)-r(min))} foreach i in $all_var { egen ‘i’_sum=sum(x_‘i’) gen y_‘i’=x_‘i’/‘i’_sum} gen n=_N foreach i in $all_var {gen y_lny_‘i’=y_‘i’*ln(y_ ‘i’) replace y_lny_‘i’=0 if x_‘i’==0} foreach i in $all_var { egen y_lny_‘i’_sum=sum(y_lny_‘i’)} foreach i in $all_var { gen E_‘i’= -1/ln(n)*y_lny_‘i’_sum} foreach i in $all_var { gen d_‘i’= 1-E_‘i’} egen d_sum = rowtotal(d_*) foreach i in $all_var { gen W_‘i’= d_‘i’/d_sum} foreach i in $all_var { gen Score_‘i’= x_‘i’*W_‘i’} egen Score=rowtotal(Score_*) gen lnpgdp=ln(pgdp) gen lnczh=ln(czh) tsset year // National overall data reg tfp szjj lnpgdp lnczh cyjg fdi exp reg tfp1 szjj lnpgdp lnczh cyjg fdi exp reg tfp2 szjj lnpgdp lnczh cyjg fdi exp reg tfp3 szjj lnpgdp lnczh cyjg fdi exp reg tfp4 szjj lnpgdp lnczh cyjg fdi exp reg tfp5 szjj lnpgdp lnczh cyjg fdi exp reg tfp6 szjj lnpgdp lnczh cyjg fdi exp reg tfp7 szjj lnpgdp lnczh cyjg fdi exp// Sub-sectors reg tfp szjj lnpgdp lnczh cyjg fdi exp

168

est store m_ols xtreg tfp szjj lnpgdp lnczh cyjg fdi est store m_fe xtreg tfp szjj lnpgdp lnczh cyjg fdi est store m_re est table m_ols m_fe m_re, b(%6.3f) 0.01) hausman m_fe m_re // Hausman Testing xtset area year xtreg tfp szjj lnpgdp lnczh cyjg fdi 1, fe est store m1_fe xtreg tfp szjj lnpgdp lnczh cyjg fdi 2, fe est store m2_fe xtreg tfp szjj lnpgdp lnczh cyjg fdi 3, fe est store m3_fe xtreg tfp szjj lnpgdp lnczh cyjg fdi 4, fe est store m4_fe est table m1_fe m2_fe m3_fe m4_fe, star(0.1 0.05 0.01) //Sub-region

Z. Liu and S. Zhang

exp, fe exp, re star(0.1 0.05

exp if area == exp if area == exp if area == exp if area == b(%6.3f)

Acknowledgements This work was supported by the grants of the Study on The Impact and Countermeasures of Global Value Chain Reconstruction Under Sino-US Trade Frictions On Equipment Manufacturing Industry In Heilongjiang Province (18JYB148).

References 1. Chinese government website. http://www.gov.cn, last updated 1 Mar 2022 2. Zhang, W., Zhao, S., Wan, X., Yao, Y.: Study on the effect of digital economy on high-quality economic development in China. PLoS ONE 16(9), e0257365 (2021) 3. Cheng, W.X., Qian, X.F.: Digital economy and green total factor productivity growth in Chinese industry (in Chinese). Inouiry Econ Issues 42(08), 124–140 (2021) 4. Li, L.S., Bao, Y.F., Liu, J.: A study on the impact of intelligence on total factor productivity in China’s manufacturing industry (in Chinese). Stud. Sci. Sci. 38(04), 609–618+722 (2020) 5. Xu, G.T., Lu, T.J., Liu, Y.M.: Symmetric reciprocal symbiosis mode of china’s digital economy and real economy based on the logistic model. Symmetry 13(7), 1136 (2021) 6. Yuan, H.W., Gao, B.: Digital economy development and innovation efficiency improvement in high-tech industries: an empirical test based on provincial panel data in China (in Chinese). Sci. Technol. Prog. Policy 39(10), 61–71 (2022) 7. Li, Y.J., Han, P.: Comprehensive evaluation and forecast of China’s digital economy development (in Chinese). Stat. Decis. 38(02), 90–94 (2022)

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8. Liu, C.K., Jiang, Y., Zhang, Q.H., Zhu, X.F.: A study on the statistical measurement of the development level of digital economy and the spatial and temporal evolution trend (in Chinese). J Ind. Technol. Econ. 41(02), 129–136 (2022) 9. Wan, Y.Y.: Research on the Evaluation of Total Factor Productivity and Influencing Factors of Equipment Manufacturing Industry in Hebei Province (in Chinese). Hebei University, BaoDing (2020) 10. Fare, R., Grosskopf, S., Norris, M.: Productivity growth, technical progress and efficiency change in industrialized countries. Am. Econ. Rev. 84, 66–83 (1994) 11. Wang, J., Zhu, J., Luo, X.: Measuring the development level and evolution of China’s digital economy (in Chinese). J. Quant. Tech. Econ. 38(07), 26–42 (2021) 12. Cai, L., Wang, P.: The digital economy and Urban green total factor productivity: impact mechanisms and empirical evidence (in Chinese). Stat. Decis. 38(09), 11–16 (2022) 13. Wang, X.L., Han, P.: Study on the integration and development of digital economy and equipment manufacturing industry-Northeast China as an example (in Chinese). J. Technical Econ. Manage. 05, 105–110 (2022) 14. Donggun, Y., Jaehwan, K.: A study on the changes in the appraisal industry in the era of the 4th industrial revolution—Focus on the factors affecting intention to adopt big data in the appraisal field. Int. J. Smart Bus. Technol. 7(1), 65–72 (2019) 15. Wang, W.J.: Cost structure and cost reduction of Chinese industrial firms—A comparative analysis based on labor and non-labor costs (in Chinese). Ind. Econ. Rev. 12(02), 120–132 (2021) 16. Guan, L.J., Zhao, W.: Evaluation of the efficiency of rural infrastructure supply based on DEA-Malmquist (in Chinese). Stat. Decis. 36(04), 172–175 (2020) 17. Hu, Y.N., Yu, D.H.: Biased technological progress, factor allocation structure and total factor productivity improvement: the case of China’s equipment manufacturing industry (in Chinese). Soft Sci. 35(07), 1–9 (2021) 18. Lu, S.C., Guan, N.: Research on the business performance of listed companies in equipment manufacturing industry based on DEA-Tobit–an analysis of empirical data from equipment manufacturing industry from 2005 to 2010 (in Chinese). J. Ind. Technol. Econ. 31(02), 108–115 (2012) 19. Xu, X.W., Qi, L.Q., Jiang, S.B.: The empirical analysis of optimal capital structure of the equipment manufacturing industry listed companies. Int. J. u - e Serv. Sci. Technol. NADIA 8(6), 71–78 (2015) 20. Li, S.M., Li Q.: Total factor productivity measurement and improvement path for China’s equipment manufacturing industry (in Chinese). J. Harbin Univ. Commer. Soc. Sci. Edition (02), 54–61+88 (2019) 21. Zhou, R., Tang, D., Da, D.: Research on China’s manufacturing industry moving towards the middle and high-end of the GVC driven by digital economy. Sustainability 14(13), 7717 (2022) 22. Fan, X., Liu, Y., Dai, M.: Research on improving strategy of technology innovation capability of equipment manufacturing industry in Liaoning Province. J. Phys. Conf. Ser. 1885(2), 022004 (2021)

Chapter 14

A Study on the Impact of Digital Finance on Green Technology Innovation Wei Wang and Xiaohu Bian

Abstract This study uses panel data from Chinese cities to research the impact of digital finance on green technology innovation. The empirical results show that digital finance helps to enhance green technology innovation and has a greater effect on green technology innovation activities in small and medium-sized cities, which is because digital finance can use the new generation of information technology to overcome the “siphoning effect” of large cities and enhances the inclusive ability of “diffusion effect” to green technology innovation. The findings of the text can provide empirical evidence and policy implications for how to use digital finance to drive green technology innovation.

14.1 Introduction The 18th Party Congress clearly put forward that innovation can play a strategic supporting role in improving social productivity and comprehensive national power and stressed the need to implement an innovation-driven development strategy. Existing studies have also shown that technological innovation is an important driving force for sustained economic growth and can provide a guarantee for China’s highquality economic development under the current severe world economic situation [1]. However, China has shortcomings in basic technological innovation and industrialization, whose innovation capacity cannot adapt to the needs of high-quality development [2]. Based on this situation, many experts and scholars have started to study the constraints on technological innovation, and most of the studies have shown that financing constraints have an inhibiting effect on corporate innovation. Innovation is characterized by sunken inputs, information asymmetry, and long investment cycles, so innovation activities require stable and sufficient financial resources as a guarantee [3], but at the same time, they are prone to face the problem of a shortage of W. Wang (B) · X. Bian Department of Economics, Harbin University of Commerce, Harbin, Heilongjiang, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_14

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exogenous financing [4]. As an important part of exogenous financing, bank credit effectively plays the function of information disclosure, which should have alleviated the problems of adverse selection and moral hazard caused by information asymmetry. But, credit rent-seeking will instead exacerbate the inhibiting effect of financing constraints on enterprise innovation in China for its underdeveloped financial market [5]. It is worth mentioning that stable sources of finance are extremely important for innovation activities [6]. Since China proposed the “double carbon” strategy, green technology innovation has been the key to developing a low carbon economy [7] and has become an important driver to achieving China’s and even the world’s “carbon neutrality” [8]. Compared with general innovation activities, the process of green technology innovation is more complex and costly, requiring more continuous investment in innovation resources [9] and stronger support from the financial system. However, financial resources are frequently misallocated in China’s traditional financial services model [10]. For example, commercial banks are willing to grant loans to state-owned enterprises with relatively low productivity, instead of private enterprises with relatively higher productivity [11]. Secondly, capital flows excessively into infrastructure, real estate, and other fields, making it difficult for manufacturing industries to receive sufficient support. With the rapid development of the new generation of information technology, finance has derived an inclusive financial model—Digital Finance (DF). With the characteristics of sharing, convenience, low cost, and low threshold, digital finance can pinpoint user profiles, refine risk pricing, and centralize business processes [12], which can improve the misallocation of financial resources and expand the service scope of financial institutions. Consequently, digital finance has become one of the frontier issues of current academic research. Initially, most of the research on digital finance was in the form of theoretical discussions, mainly focusing on its development status [13], impact factors [14], risk identification and control [15], etc. With the availability of authoritative indicators for measuring digital finance, empirical research has been conducted, including the impact of digital finance on innovation [16], employment [17], banking behavior [18], economic growth [19], and so on, which has produced fruitful academic results. In the research on the impact of DF on innovation, whether through urban panel data [20] or firm sample data [21], the innovation incentive effect of DF can be found, but there are few studies that cut through the urban dimension. Moreover, after China put forward the strategic goal of “double carbon”, innovation activities should be more focused on green technology innovation. This study, therefore, focuses on the impact of DF on green technology innovation, in order to build a bridge between them and to examine the inclusive function of DF on green technology innovation in cities, which can provide theoretical support and practical experience for digital finance to promote coordinated regional development and provide an important driving force for China to achieve the “carbon neutrality” goal.

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14.2 Theoretical Analysis and Research Hypothesis 14.2.1 Green Technology Innovation and Digital Finance Financial reform helps to improve the efficiency of capital allocation [22]. The path of DF on green technology innovation can be manifested in two aspects: “incremental supplementation” and “stock optimization”. The “incremental supplement” refers to digital finance supported by the new generation of information technology, which can effectively screen the massive amount of data in the market with high efficiency and low cost, to expand and absorb the financial resources in the market. Through the realization of the “long tail effect” [23] and the use of third-party payments, a tremendous amount of “small and scattered” investors in the financial market can be consolidated into an effective supply, providing a richer financing channel for green technology innovation. The “stock optimization” refers to the optimization and quality improvement of business processes in the traditional financial sector by DF. This is because DF can effectively hedge risks of adverse selection and moral hazard brought by the information asymmetry, improve the transparency of information between the financial sector and financing agents [24], and reduce the cost of green technology innovation for economic agents. Moreover, digital finance can break the boundary constraints of traditional finance and provide an optimal allocation of financial resources for green technology innovation activities by speeding up the flow of funds and improving credit mismatch. In addition, by reducing biased credit decisions in the credit approval process [25] and hardening the soft budget constraint of economic agents, digital finance can force traditional financial institutions to transform and upgrade, alleviate the mismatch of financial resources, improve the allocation efficiency of financial resources, and thus effectively promote green technology innovation. According to the above analysis, the study puts forward research hypothesis H1: DF helps to improve the level of green technology innovation.

14.2.2 The Inclusive Function of Digital Finance for the Growth of Urban Green Technology Innovation In the process of urbanization in China, the “siphon effect” and the “diffusion effect” are prevalent. The “siphon effect” between cities causes a large influx of resources into large cities, while small cities are constrained by the relative lack of quality resources. With the continuous expansion of the regional development gap, the “siphon effect” is becoming more and more serious, inhibiting the momentum of green technology innovation in small-medium cities around large cities. On the contrary, the “diffusion effect” can give full play to the external economic advantages of small-medium cities, such as strong spatial extension and low labor costs, which can promote the green technology innovation activities of large cities to the surrounding areas. The development of digital finance enables the flow of traditional

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economic factors to break through the limitations of time and space and can effectively identify the resource endowment of small and medium-sized cities in specific areas, enhancing their contribution to green technology innovation. Both the “siphon effect” and “diffusion effect” take the transfer of traditional economic factors as an important means. At the same time, the development of DF has enabled traditional economic factors to break through the constraints of time and space, which can effectively identify the resource endowments and factor advantages of small and medium-sized cities in specific areas and enhance their attractiveness to green technology innovation activities. To a certain extent, it suppresses the “siphon effect” and enhances the “diffusion effect” of large cities, increasing the possibility for small and medium-sized cities to access financial resources promoting their green technology innovation capacity. According to the above analysis, the study puts forward research hypothesis H2: digital finance has the inclusive nature of promoting green technology innovation in cities.

14.3 Study Design 14.3.1 Variable Definitions and Data Descriptions Two hundred and eighty-nine prefecture-level cities in China from 2011 to 2020 are selected for the study, and the definition and data source of variables are as follows. Explanatory variable: green technological innovation (GI). In this study, the patent database of the State Intellectual Property Office is used to identify the green patent applications in each city, which is based on the green patent list and international classification code published by the World Intellectual Property Organization (WIPO). Core explanatory variable: digital finance (DF). The study uses the Peking University Digital Financial Inclusion Index of China compiled by the Institute of Digital Finance at Peking University to measure DF. Control variables (Control). The control variables chosen for this paper include the following. Industrial structure (Industry) is measured by the ratio of the output value of the tertiary industry to that of the secondary industry. Financial Development (FinDev) is measured by the proportion of loan balance in GDP. Population size (LnPeo) is measured as the logarithm of the resident population. The natural rate of population growth (Growth) is directly given by China Statistical Yearbook. The intensity of fiscal science and technology expenditure (SciEdu) is measured as a proportion of government fiscal expenditure on science and education to GDP.

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The degree of economic development (LnGDP) is measured as the logarithm of GDP.

14.3.2 Model Setting In order to verify the above assumptions, the following benchmark regression model is built:  GIit = α + β1 DFit + β j Controlit + μi + γt + εit (14.1) j

GI refers to green technology innovation, DF refers to digital finance, control includes the above six control variables, μi stands for the city fixed effects, γt stands for the year-fixed effects, and ε is a random error term. To mitigate the endogeneity of the variables and eliminate the effect of heteroscedastic influence between cities, a two-way fixed effects’ model and robust standard errors are both used in this paper. With the purpose of testing the research hypothesis H2, the following moderating effect model is constructed: GIit = α + β1 DFit + β2 Big Cityit × DFit  + β j Controlit + μi + γt + εit

(14.2)

j

where Big City is a moderating variable to measure the number of large cities around small and medium-sized cities.

14.4 The Impact of Digital Finance on Green Technology Innovation 14.4.1 Return to Baseline Table 14.1 shows the results of a benchmark regression. Examined by controlling for cities and year-fixed effects, controlling for variables, the regression coefficient for digital finance is found to be significantly positive, proving that the development of DF has a significant influence on enhancing the level of green technology innovation.

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Table 14.1 Baseline regression results DF

(1)

(2)

(3)

(4)

0.044a (2.056)

0.041c (1.867)

0.069b (3.389)

0.027a (1.399)

Industry

0.339b (5.539)

0.019 (0.288)

−0.041 (−0.785)

FinDev

0.300b (4.271)

0.456b (6.493)

0.102b (2.743)

SciEdu

12.612b (3.338)

−3.475c (−1.732)

7.659c (1.958)

LnPeo

0.385c (1.862)

−0.088 (-0.940)

0.531b (2.617)

LnGDP

1.560b

1.398b

0.756b (8.168)

_cons

6.930b

Urban fixed effects

Control

(73.613)

Year-fixed Control effects

(36.488)

−25.727b

(−16.165)

(18.010)

−18.278b

(−24.935) −10.648b (−5.040)

Control

No control

Control

No control

Control

Control

Note a , b , and c denote significance at the level of 5%, 1%, and 10%, respectively, with t-values in brackets after accounting for clustering robust standard errors, as in the table below

Table 14.2 Robust regression results DF R2

(1)

(2)

(3)

0.074a (2.72)

0.059b (2.86)

0.070a (2.92)

0.469

0.740

0.715

a, b,

c

Note and denote significance at the level of 5%, 1%, and 10%, respectively, with t-values in brackets after accounting for clustering robust standard errors, as in the table below

14.4.2 Robustness Tests Considering the long cycle of green technology innovation activities, the study applies two-way fixed effects and regresses Digital Finance (DF) again after lagging one period (column 2) and two periods (column 3) in order to test the dynamic effect of DF on green technology innovation. Table 14.2 presents that the regression coefficient of DF is still significantly positive, which is the same as above. It proves that the results of the benchmark regression are robust, and DF can steadily empower green technology innovation.

14.4.3 Endogeneity Test Considering the endogenous problem caused by the causal relationship between variables, this paper takes Digital Finance (DF) lag one period as its instrument variable and then performs 2GLS. Table 14.3 can be seen that after the endogenous

14 A Study on the Impact of Digital Finance on Green Technology Innovation Table 14.3 Endogeneity test DF R2 a, b,

(1)

(2)

0.116a (3.50)

0.086a (2.61)

0.1986

0.2472

177

c

Note and denote significance at the level of 5%, 1%, and 10%, respectively, with t-values in brackets after accounting for clustering robust standard errors, as in the table below

test of instrumental variables, the promotion of DF to green technology innovation is stronger than before, which again verifies the reliability of the regression results.

14.5 Inclusive Attributes of Digital Finance for Urban Green Technology Innovation Firstly, the sample is divided into small–medium cities and large cities according to their urban population to test the control variables and two-way fixed effects, respectively. The regression results for small and medium-sized cities are shown in column (1) of Table 14.4, and those for large cities are presented in column (2). The results show that the role of DF development in small and medium-sized cities in promoting green technology innovation is higher than that in large cities. This may be due to the fact that small and medium-sized cities are actively enhancing their own resource accessibility and regional collaboration in the development of DF so that their green technology innovation activities are not significantly affected by the “siphoning effect” of large cities. This ensures that green technology innovation activities can be regionally coordinated, which can demonstrate the inclusive nature of DF in promoting green innovation in cities. Secondly, in order to further investigate how DF affects the “siphon effect” and “diffusion effect” between cities, this paper calculates the number of large cities within 100, 200, and 300 km around small–medium cities, respectively, introduces the moderating variable Big City, and then conducts an empirical study again, which is referred to the practice of Shi et al.[26]. The results of Table 14.5 show that the more the number of big cities around small and medium-sized cities, the greater the impetus of digital finance to green technology innovation. This indicates that Table 14.4 Examination based on small-medium cities and large cities

(1)

(2)

DF

0.100a (2.74)

0.028b (2.54)

R2

0.732

0.924

Note a , b , and c denote significance at the level of 5%, 1%, and 10%, respectively, with t-values in brackets after accounting for clustering robust standard errors, as in the table below

178 Table 14.5 Examination of the “siphon effect” and “diffusion effect”

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DF

(1)

(2)

0.007 (0.09)

0.241b (4.00) 0.392b (4.50)

(3)

BigCity100*DF 0.087a (1.82) 0.195b (5.68)

BigCity200*DF

0.226b (6.24)

BigCity300*DF R2

0.734 a, b,

0.739

0.744

c

Note and denote significance at the level of 5%, 1%, and 10%, respectively, with t-values in brackets after accounting for clustering robust standard errors, as in the table below

spatial penetration and resource allocation inclusiveness of DF overcome the “siphon effect” and enhance the “diffusion effect” of big cities, allowing green technology innovation in small and medium-sized cities to enjoy more dividends from digital finance development.

14.6 Research Findings and Policy Recommendations The study using Chinese urban panel data empirically analyzes the impact of digital finance on green technology innovation in the context of global efforts to make the goal of “carbon neutrality” possible. The main conclusions are as follows: Firstly, digital finance has a significant incentive effect on green technology innovation. Secondly, DF can overcome the guiding role of city size, factor endowment, and other aspects in green technology innovation activities, which is reflected in the suppression of the “siphon effect” of big cities on the surrounding small–medium cities and the enhancement of the “diffusion effect”, so that the development of DF in small–medium cities can promote green technology innovation strongly. It brings into play the inclusive nature of DF in enabling green technology innovation, which is in line with the concept of coordinated regional development. Based on the above findings, the study has the following policy implications: First, the pace of digital transformation of traditional finance should be accelerated, and the advantages of the new generation of information technology can be fully utilized through policy guidance, high-level institutional reform, and the attraction of advanced talents to complement the shortcomings of traditional financial development to enhance the adaptability of financial resource supply and financial demand. Real, which can inject new vitality into green technology innovation activities. Secondly, on the premise of keeping the bottom line, we should give digital finance adequate policy support, expand the financial application scenarios of emerging technologies, accelerate their application layout at the channel and scene ends, and create an ecosystem with emerging technologies as the core, so that digital finance can precisely dock with the subject of green technology innovation and enhance the sense of user experience in all aspects. Thirdly, we should

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not neglect the regulation of digital finance. Precisely because financial regulation always lags behind financial innovation [27], there is a greater need for continuous prudent management by regulators, the formulation of targeted and forward-looking contingency plans, and a focus on process regulation, especially for green technology innovation, which is a hot innovation activity involving economic agents. It not only contributes to the completeness of theoretical research, but also provides a reliable guarantee for DF to stimulate green technological innovation.

References 1. Zhang, J., Hou, Y., Liu, P.L., He, J.W., Zhuo, X.: Target requirements and strategic paths for high-quality development. Manage. World. (7) (2019) 2. Nie, X.H., Jiang, P., Zheng, X.J.: Research on digital finance and the level of regional technological innovation. Financ. Res. 3, 132–150 (2021) 3. Wang, Y.Z., Luo, N.S., Liu, W.B.: What kind of leverage ratio is conducive to corporate innovation. China Ind. Econ. 3, (2019) 4. Hall, B.H., Lerner, J.: The financing of R&D and innovation. Handb. Econ. Innov. 1, 609–639 (2010) 5. Zhang, X., Liu, B.B., Wang, T.: Credit rent-seeking, financing constraints and firm innovation. Econ. Res. 5, 161–174 (2017) 6. Ma, G.R, Liu, M., Yang, E.Y.: Bank credit, credit crunch and corporate R&D. Financ. Res. (07), (2014) 7. Xia, J.: Research on the constraints of technological innovation and countermeasures for the development of low-carbon economy in China. J. Wuhan Univ. Technol. (Soc. Sci. Ed.). 25(01), 1–6+134 (2012) 8. Gu, J.H., Chai H.Q.: Green innovation impact effects of digital finance. Soft Sci. 1–10 (2022) 9. Ma, Y., Hou, G., Yin, Q.: The sources of green management innovation: does internal efficiency demand pull or external knowledge supply push? J. Clean. Prod. 202(8), 582–590 (2018) 10. Han, R.D., Bo, F.: Can regional financial reforms alleviate capital allocation distortions? Int. Financ. Stud. 10, 14–23 (2020) 11. Franklin, A., Jun, Q., Qian, M.: Law, finance, and economic growth in China. J. Financ. Econ. 77(1), 57–116 (2004) 12. Demertzis, M., Merler, S., Wolff, G.B.: Capital markets union and the fintech opportunity. J. Finan. Regul. 4(1), 157–165 (2018) 13. Guo, F., Kong, S.T., Wang, T.: General patterns and regional disparity of internet finance development in China: evidence from the peking university internet finance development index. China Econ. J. 9(3), 1–19 (2016) 14. Ge, H.P., Zhu, H.W.: A study of provincial differences and influencing factors of digital inclusive Finance in China. New Finance 2, 47–53 (2018) 15. Jiao, J.P., Sun, T.Q., Huang, T.T.: Digital currency and inclusive financial developmenttheoretical framework, international practice and regulatory system. Fin. Regul. Res. 7, 19–35 (2015) 16. Chen, L., Wang, T.P., Wu, Y.M., Xie, J.Z.: Government subsidies, digital inclusive finance and corporate innovation—an empirical analysis based on information manufacturing listed companies. Contemp. Econ. Res. 01, 107–117 (2022) 17. Guo, Q., Meng, S.C., Mao, Y.F.: Can the development of digital inclusive finance contribute to the improvement of employment quality? J. Shanghai Univ. Finance Econ. 24(01), 61–75+152 (2022) 18. Qiu, H., H, Y.P., Ji, Y.: The impact of fintech on bank behavior: a perspective based on internet finance. Finan. Res. (11), 17–30 (2018)

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19. Yang, G., Zhang, H.Y.: Digital inclusive finance, regional innovation and economic growth. Stat. Decis. Making. 38(02), 155–158 (2022) 20. Wu, S.F., Yue, C.G., Zhou, Q.: Digital finance and urban innovation —evidence from the city level in China. China Sci. Technol. Forum. 04, 128–136 (2022) 21. Xiong, Z.D., Li, Q.F.: The impact of digital finance on corporate technological innovationevidence based on 370 listed companies in digital creative industries. J. Hunan Agric. Univ. (Soc. Sci. Ed.) 23(03), 80–89 (2022) 22. Shenoy, R.R., Mohammed, S., Fiaidhi, J.: Fintech credit scoring techniques for evaluating P2P loan applications—a python machine learning ensemble approach. Int. J. Smart Bus. Technol. 6(1), 49–68 (2018) 23. Huang, Y.P., Qiu, H.: Big tech credit: a new framework for credit risk management. Manage. World 2, 12–21 (2021) 24. Sutherland, A.: Does credit reporting lead to a decline in relationship lending? Evidence from information sharing technology. J. Account. Econ. 1, 123–141 (2018) 25. Li, C.T., Yan, X.W., Song, M., Yang, W.: Fintech and corporate innovation—evidence from NISB listed companies. China Ind. Econ. 1, 81–98 (2020) 26. Shi, W., Du, J.M., Shuang, P.: How can digital economy promote green innovation—empirical evidence from Chinese Cities. Finan. Econo. 1–14 (2020) 27. Li, Y.: Improving the financial system is a prerequisite for the healthy development of Fintech. Tsinghua Fin. Rev. (5) (2019)

Chapter 15

Research on the Relationship Between the Digital Economy, Industrial Innovation Capability, and Servitization of the Advanced Manufacturing Industry HuiMei Qu, YueYing Cui , and Wei Chen

Abstract Currently, the digital economy has become a key force in changing the global competition pattern and deep integration with advanced manufacturing through digital technology, and it has become a new form of economy that accelerates the development of advanced manufacturing. This paper explores the relationship between digital economy, industrial innovation capabilities and advanced manufacturing servitization, through regression method. The research results reflect the following information: from an overall point of view, the development of the digital economy significantly promotes the servitization level of advanced manufacturing, and the ability of industrial innovation plays an intermediary role in the relationship between the digital economy and the servitization of advanced manufacturing.

15.1 Introduction In 2021, the added value of China’s manufacturing industry has accounted for nearly 30% of the worlds, but China is still not among the “manufacturing powers”. The fundamental reason is that China’s low-end manufacturing industry accounts for too much. As a high-end industry in the manufacturing base, advanced manufacturing is a critical engine for China’s manufacturing industry to get out of the “low-end locking”. In 2021, the scale of China’s digital economy reached 45.5 trillion RMB, accounting for about 39% of the GDP. Its supporting force in the national economy is obvious. H. Qu · Y. Cui (B) · W. Chen College of Management, East University of Heilongjiang, Harbin 150066, China e-mail: [email protected] W. Chen College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_15

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From the perspective of the internal environment, the low-cost development model cannot meet the needs of China’s economic and social development, and advanced manufacturing is a key force. A series of leading documents, including《the “Guiding Opinions of the State Council on Deepening the Internet + Advanced Manufacturing Industry” to develop the Industrial Internet》 , provide a guiding light for the development direction of advanced manufacturing under the background of digital economy. From the external environment, foreign developed countries have put forward clear plans for their own development strategy of digital economy to promote advanced manufacturing. And European and American countries have taken a series of containment measures against the development of China’s digital technology. The international economic situation and domestic economy development situation have confirmed the necessity of developing the digital economy and advanced manufacturing in China. Driven by the digital economy, servitization of advanced manufacturing has become an inevitable trend. The phenomenon of servitization has different levels of development in both industrialized countries and developing countries. The manufacturing industries of various countries gradually tend to rely on the appreciation of service value to enhance the market competitiveness. And the digital economy has gradually become a powerful driving force for the servitization of advanced manufacturing. And the development of the digital economy will also drive the industrial innovation capabilities of advanced manufacturing, which will further affect the level of servitization development of advanced manufacturing. The digital economy and industrial innovation have become important driving forces for the high-quality development of China’s advanced manufacturing industry in the new era. Therefore, it is necessary to explore the impact of the digital economy on the servitization of China’s advanced manufacturing industries.

15.2 Literature Review Regarding the connotation of servitization, Vandermenwe believes that traditional manufacturing enterprises have changed from simply providing tangible goods to customers, and the market service package trend of providing “goods + services + support” to customers is servitization. Most of the later research is based on this extension [1]. Guo [2] believes that the input of intermediate service elements such as finance and management consulting into the production of advanced manufacturing is the input servitization of advanced manufacturing, and the output servitization of advanced manufacturing is the product service system, servitization manufacturing, etc. Its essence is the input and output of average service value in manufacturing. There are relatively few studies on the relationship between the servitization of advanced manufacturing and the digital economy. For example, Zhao in [3] proved the digital development has significantly improved the level of enterprise servitization. Li in [4] believes that under the advantages of the developed digital economy, it is necessary to promote the transition from manufacturing servitization to service

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productization In addition, the research on advanced manufacturing servitization and industrial innovation is relatively abundant. Li in [5] pointed out that the input of relevant elements of innovation has an obvious influence on the innovation of hightech industries. Xia in [6] believes that the improvement of industrial innovation capabilities cannot be ignored by high-tech enterprises. With the research on digital economy and industrial innovation, Han in [7] believes that digital economy can help the industry to form a catch-up effect and a demonstration effect and promote the development of industrial innovation. Yuan’s [8] shows that the development of the digital economy can effectively improve the innovation efficiency of high-tech industries. From the literature review, the digital economy is beneficial to industrial innovation and servitization of advanced manufacturing. However, the existing research on the relationship between the three is relatively rare, especially the research on the transmission and intermediary role of industrial innovation ability in the process of digital economy promoting advanced manufacturing servitization. In order to expand the existing research, this paper selects advanced manufacturing as the research object to study the relationship between digital economy, industrial innovation capability, and advanced manufacturing servitization, so as to provide reference and information for advanced manufacturing servitization.

15.3 Research Hypotheses and Theoretical Models 15.3.1 Research Hypotheses The in-depth development of the digital economy is profoundly changing the basic concepts of advanced manufacturing and has become a powerful driving force for the high-quality development of China’s manufacturing industry [9]. Servitization is an effective development way for the manufacturing industry. Now, with the wide application of big data and cloud computing, the digital economy can help advanced manufacturing industries provide service value to consumers more accurately [2], to help the servitization process of advanced manufacturing. On the one hand, it is to precisely locate the development direction of servitization of advanced manufacturing. On the other hand, it is to help digitize the manufacturing process of advanced manufacturing. By integrating the manufacturing process with digital technology, the transformation from advanced manufacturing to intelligent manufacturing and digital manufacturing will be realized. The digital economy has become a powerful guarantee for improving the servitization level of advanced manufacturing and realizing value-added value chain. Therefore, Hypothesis 1 is proposed: H1: The digital economy plays a positive role in promoting the servitization level of advanced manufacturing. With the increasing complexity of the external environment of advanced manufacturing industry, advanced manufacturing urgently needs to improve its industrial

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innovation capabilities to cope with external changes. In the process of servitization, industrial innovation is a supporting capability for strategy implementation and the leading driving force for servitization of advanced manufacturing industries. Industrial innovation can promote service innovation by optimizing service processes, efficiency, and building core technologies, enhancing the core competitive advantage of advanced manufacturing, and providing servitization products around this advantage. The servitization development of advanced manufacturing without the support of industrial innovation is likely to have the risk of premature industrial structure [10]. At the same time, servitization itself has an innovative effect, which can reversely improve the innovation capability of the industry. From the conclusions of most research results, there is a positive and direct correlation between industrial innovation and servitization level, especially for advanced manufacturing [11]. Therefore, Hypothesis 2 is proposed: H2: High industrial innovation capability is beneficial to promoting the servitization of advanced manufacturing. In addition to directly promoting the servitization of advanced manufacturing, the digital economy can also indirectly promote the servitization of advanced manufacturing by enhancing industrial innovation capabilities. Emerging digital technologies provide rich knowledge resources and application scenarios for industrial innovation in advanced manufacturing [12], thus accelerating the servitization process of advanced manufacturing [8]. First, industrial innovation provides technical and knowledge support for the digital economy to promote the servitization of advanced manufacturing. Digital technologies characterized by digitization and intelligence can accelerate the innovation of service products and promote the development of advanced manufacturing services. Second, industrial innovation weakens the barriers for the digital economy to drive the servitization of advanced manufacturing. Therefore, Hypothesis 3 is proposed: H3: The digital economy is mediated by industrial innovation capabilities, which together have a significant positive impact on the servitization of advanced manufacturing.

15.3.2 Sample Selection and Data Sources Advanced manufacturing industry includes advanced manufacturing formed by the introduction or integration of modern science and technology from traditional manufacturing, as well as leading advanced manufacturing developed by emerging science and technology. Considering the previous scholars’ definition of advanced manufacturing industry and the lack of relevant industry data in some countries in the WIOD database, for the convenience of calculation, this paper simplifies the classification of 58 industries in WIOD according to the “Industry Classification of National Economy” and the industry classification of the WIOD database. c11, c17–c22 are advanced manufacturing industries, and c25, c27–c28, and c42–c56 are producer services. This paper chooses the producer service industry, which cooperates more

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closely with advanced manufacturing industry, as a major source industry of service value. The index data in this paper come from the “China Industrial Statistical Yearbook”, “China Science and Technology Statistical Yearbook”, “China Statistical Yearbook”, “China High-tech Industry Statistical Yearbook”, and the 2016 WIOD database.

15.3.3 Variable Indicator Description (1) Servitization level of advanced manufacturing industry According to previous literature, the servitization of advanced manufacturing can be divided into input servitization and output servitization. This paper mainly focuses on the input servitization of advanced manufacturing, referring to the research results of Qi Liangqun [13, 14], the essence of advanced manufacturing servitization is the input and output of service value in advanced manufacturing industry, and service value actually enters advanced manufacturing industry with service added value as the carrier. Therefore, based on the MRIO model and referring to the research results of Wang Zhi, Wu Yongliang and others [15–17], this paper uses the added value input to replace the intermediate product input to construct a trade value-added decomposition framework, and the specific measurement methods are as follows: Assuming the world exist G countries, each country has N industries, X is the output matrix, Y is the final demand matrix,1 x sr represents the output of various industries produced by country s and acquired by country r, ysr represents the final demand of country r for various industries in country s, B stands for the Leontief inverse matrix, V stands for the added value coefficient matrix, then V s represents the proportion of value added per unit output of each industry in country s. Let Vˆs is a diagonal matrix whose diagonal entries are the added value of each industry in country s. “&” means that the items in the corresponding rows in the matrix are multiplied but not summed. The subscript in the formula is the country, and 12 1 Yr represents the value added of the superscript is the industry. For example, vs1 bsr industry 1 in countries exported to industry 2 in country r. Therefore, the horizontal summation of the matrix is the total value added of a certain industry in countries exported to all industries in country r, and the vertical summation is the total value added of all industries in country s exported to a certain industry in country r [17]. Then, the decomposition framework (1) of the total output value added is obtained: China belongs to the eighth country in WIOD (2016), when s, r = 8, it means China; when i, j = 4, it means advanced manufacturing industry; when i, j = 8, it means producer services. Then, the servitization level of China’s advanced manufacturing industry is the ratio of the input of value added of domestic and foreign producer services in the

1

For the detailed derivation process, please refer to the research of Yongliang Wu (2018).

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output of advanced manufacturing industry to the sum of the value-added input of factors of all industries in all countries. The calculation formula is shown in (15.2). ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨

⎡ 1 11 1 1 12 1 v1 b11 Y1 v1 b11 Y1 ⎢ 2 21 2 2 22 2 ⎢ v1 b11 Y1 v1 b11 Y1 ⎢ ⎢ . . ⎢ . . ⎣ . . n1 Y n v n bn2 Y n v1n b11 1 1 11 1 . Vˆ S Bsr &Yr = . . ⎪ ⎪ ⎪ ⎡ 1 11 1 1 12 1 ⎪ ⎪ b Y v b v ⎪ ⎪ G G1 1 G G1 Y1 ⎪ ⎢ 2 21 2 2 22 2 ⎪ ⎪ ⎢ vG bG1 Y1 vG bG1 Y1 ⎪ ⎪ ⎢ ⎪ ⎪ ⎪⎢ . . ⎪ ⎪⎢ . . ⎪ ⎪ ⎣ . . ⎪ ⎪ ⎩ n n1 n n n2 vG bG1 Y1 vG bG1 Y1n

⎤

⎡ 1 11 1 1 12 1 v1 b1G YG v1 b1G YG ⎢ 2 21 2 2 22 2 ⎢ v1 b1G YG v1 b1G YG ⎢ ··· ⎢ . . ⎢ . . ⎣ . . n1 Y n v n bn2 Y n v1n b1G G 1 1G G . .. . . . ⎤ ⎡ 1 b1n Y 1 1 b11 Y 1 v 1 b12 Y 1 · · · vG v G1 1 G GG G G GG G ⎢ 2 21 2 2 22 2 2 b2n Y 2 ⎥ · · · vG ⎢ vG bGG YG vG bGG YG G1 1 ⎥ ⎥ ⎢ ⎥ ··· ⎢ . . . .. ⎥ ⎢ . . . . ⎦ ⎣ . . . n nn n n n1 n n n2 n vG bGG YG vG bGG YG · · · vG bG1 Y1

1n Y 1 · · · v11 b11 1 2n 2 · · · v1 b11 Y12 . .. . . . nn Y n · · · v1n b11 1

⎥ ⎥ ⎥ ⎥ ⎥ ⎦

1n Y 1 v11 b1G G 2n Y 2 2 v1 b1G G . .. . . . nn Y n · · · v1n b1G G

··· ···

⎤ ⎫ ⎪ ⎪ ⎪ ⎥ ⎪ ⎪ ⎥ ⎪ ⎪ ⎥ ⎪ ⎪ ⎥ ⎪ ⎪ ⎥ ⎪ ⎪ ⎦ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎬

1 b1n Y 1 vG GG G 2 b2n Y 2 vG GG G . .. . . . n nn n · · · vG bGG YG

··· ···

⎪ ⎪ ⎤⎪ ⎪ ⎪ ⎪ ⎪ ⎥⎪ ⎪ ⎥⎪ ⎪ ⎥⎪ ⎪ ⎥⎪ ⎪ ⎥⎪ ⎪ ⎪ ⎦⎪ ⎪ ⎪ ⎭

(15.1)

44 

ser − input = fser − input + dser − input =

vs8 bs8 48 Y88 s=1 8 44   vsi bsi 48 Y8i s=1 i=1

(15.2)

(2) Digital economy According to existing literature, the entropy method is used to construct a digital economy measurement index system. The entropy method determines the corresponding weights according to the data information, which has advantages in processing index data. Referring to the research of existing literature [8], the index evaluation system is constructed from digital infrastructure and digital industry development to measure the level of digital economy, see Table 3.1 for details. Considering the differences in the selected indicator units, the data are first standardized and then measured by the entropy method, and finally, the digital economy level is obtained (Table 15.1). Table 15.1 Measurement system of digital economy level indicator First-level indicator Secondary indicators Digital economy

Digital infrastructure

Three-level indicator Optical cable line length Number of Internet broadband access ports Express business income Number of mobile phone base stations

Digital industry development Software business revenue Information technology revenue Telecom business revenue Number of electronic information industry enterprises

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(3) Industrial innovation capability Industrial innovation ability (Inno) is the mediating variable. At this stage, the commonly used measurement methods of industrial innovation level are mainly innovation input method and innovation output method. The innovation input indicator generally selects R&D investment, including R&D funds, etc. [19, 20]; the innovation output indicator generally selects the number of patent applications, etc. This paper chooses the innovation input index and finally chooses to use the ratio of R&D expenditure and main business income of each manufacturing sector to represent the industrial innovation capability. (4) Control variables According to relevant literature research, the capital allocation structure (cap), the level of service economy (ser), and the degree of industry competition (com), which have important impact on the servitization of advanced manufacturing, are selected as control variables. Among them, the industry competition degree (com) is measured by the industry concentration degree, which is represented by the Herfindahl index; the capital distribution structure (cap) is measured by dividing the net value of fixed assets in a industry by the number of employees in the industry; the level of service economy (ser) is measured by the proportion of the tertiary industry in GDP. (5) Model construction According to the assumptions H1, H2, and H3, a multiple linear regression model is established to verify the interrelationship between the digital economy, industrial innovation capabilities, and advanced manufacturing servitization. The model is as follows: Ser − input = α1 + α11 Dig + α12 Com + α13 Cap + α14 Ser + ε1

(15.3)

Inno = α2 + α21 Dig + α22 Com + α23 Cap + α24 Ser + ε2

(15.4)

Ser − input = α3 + α31 Dig + α32 Inno + α33 Com + α34 Cap + α35 Ser + ε3 (15.5) Table 15.2 shows the descriptive statistics of each variable.

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Table 15.2 Descriptive statistics for each variable Variable

Average value

Standard deviation

Minimum value

Maximum value

Ser-input

0.141

0.014

0.112

0.159

Dig

0.273

0.276

0.103

0.985

Inno

0.011

0.002

0.008

0.015

Ser

0.433

0.023

0.398

0.483

Com

0.254

0.024

0.211

0.305

Cap

0.297

0.058

0.114

0.363

15.4 Empirical Research 15.4.1 Digital Economy, Industrial Innovation Capability, and Servitization of Advanced Manufacturing Industry (1) Digital Economy and Servitization of Advanced Manufacturing industry Take Dig, Ser, Cap, and Com as the explanatory variables and Ser-input as the explained variable for regression processing. The results are shown in Table 4.1. The results show that the digital economy has a significant positive role in promoting servitization of advanced manufacturing, compared with traditional manufacturing, and the servitization of advanced manufacturing pays more attention to personalization and precision. In the context of the digital economy, new-generation information technologies such as mobile communications, Internet of Things, and big data can help advanced manufacturing more accurately grasp the relevant information [18], avoid risks, and seize opportunities. It helps advanced manufacturing industries accurately grasp the development trends and future trends of the industry, improve the industrial operation efficiency and servitization of advanced manufacturing industries, and realize value chain value-added [2]. In addition, the impact of each control variable on the servitization of advanced manufacturing is positive and significant. Suppose H1 is proved (Table 15.3). (2) Digital Economy and Industrial Innovation Capability Take Dig, Ser, Cap, and Com as the explanatory variables and Inno as the explained variable for regression processing. The results are shown in Table 4.2. The results show that the digital economy can significantly enhance industrial innovation capabilities. First of all, the digital economy can improve the information transparency of advanced manufacturing, stimulate healthy competition among industries, promote continuous innovation in advanced manufacturing, and ensure that it remains competitive in market competition. Secondly, the digital economy can organically combine the production end and the consumption end, so that the advanced manufacturing

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Table 15.3 Regression results of the digital economy on the servitization of advanced manufacturing Explanatory variable

Unstandardized coefficients B

Standardized coefficient

t

Sig

VIF

Standard error

Constant

0.077

0.076

–

1.005

0.000***

–

Dig

0.034

0.013

0.694

2.696

0.002**

2.519

Ser

0.255

0.122

0.436

2.082

0.006*

1.67

Com

0.21

0.135

0.378

1.559

0.08*

2.232

Cap

0.058

0.049

0.247

1.192

0.000***

1.63

Adj_R2 F

0.632 F =.007 7

P* < 0.1, P** < 0.05, p*** < 0.01

industry can obtain market information in a timely manner, conduct a comprehensive analysis of market demand through digital technology, accelerate breakthroughs in core technologies, and comprehensively enhance industrial innovation capabilities. Finally, the digital economy can significantly improve the production efficiency and industrial innovation capabilities of advanced manufacturing [2]. Industrial big data and user big data can guide the direction of industrial innovation in advanced manufacturing, so that innovation results can truly meet market demand. Suppose H2 is proved and as shown in Table 15.4, the regression result of digital economy on industrial innovation capability is presented. Table 15.4 Regression result of digital economy on industrial innovation capability Explanatory variables

Unstandardized coefficients B

Standardized Coefficient

t

Sig

VIF

Standard Error

Constant

−0.032

0.016

–

−1.934

0.082*

–

Dig

0.007

0.003

0.851

2.653

0.024**

2.519

Ser

0.073

0.026

0.728

2.788

0.019**

1.67

Com

0.033

0.029

0.34

1.127

0.069*

2.232

Cap

0.002

0.01

0.061

0.236

0.000***

1.63

Adj_R2

0.527

F

F = 5.675

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Table 15.5 Digital economy, industrial innovation capabilities, and return to advanced manufacturing servitization Explanatory variables

Unstandardized coefficients B

Standardized coefficient

T

Sig

VIF

Standard Error

Constant

0.004

0.079

–

0.046

0.000***

–

Dig

0.016

0.015

0.322

1.085

0.011***

4.292

Inno

2.53

1.301

0.436

1.945

0.044*

2.447

Ser

0.440

0.144

0.754

3.052

0.014**

2.968

Com

0.128

0.127

0.229

1.008

0.018**

2.515

0.043

0.273

1.489

0.043**

1.639

Cap

0.064

Adj_R2

0.712

F

F = 7.923

(3) Digital economy, industrial innovation capability, servitization of advanced manufacturing industry Take Dig, Inno, Ser, Cap, and Com as explanatory variables, and the mediating effect of Inno on Dig and Ser-input is verified. The results are shown in Table 4.3. The results show that industrial innovation capability plays a partial mediating role in the promotion of the digital economy to the servitization of advanced manufacturing. Now, advanced manufacturing is facing more intense market competition. The digital economy further promotes the servitization by promoting industrial innovation, reducing information asymmetry, and improving the allocation efficiency of capital factors [8]. The industrial innovation effect brought by the digital economy is an inexhaustible driving force for continuously improving the level of servitization of advanced manufacturing. Suppose H3 is proved (Table 15.5).

15.4.2 Test for Robustness This paper further tests the robustness of the above regression results and uses the complete consumption coefficient instead of the servitization level of advanced manufacturing in the paper to re-regress. The test results are shown in Table 15.6. The results show that the results obtained by changing the outcome variables are generally consistent with the original model, and only the significance of some variables has changed slightly, which further proves that the empirical conclusions are robust and reliable.

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Table 15.6 Digital economy, industrial innovation capabilities, and return to advanced manufacturing servitization Explanatory variables

Unstandardized Coefficients B

Standard error

Standardized coefficient

t

Sig

VIF

Constant

0.066

0.074

–

0.892

0.075*

–

Dig

0.013

0.014

0.155

0.982

0.026**

4.153

Inno

0.879

1.215

0.086

0.723

0.063*

2.447

Ser

0.208

0.135

0.783

5.948

0.02**

2.968

Com

0.354

0.118

0.362

2.989

0.062*

2.515

Cap

0.125

0.04

0.302

3.092

0.013**

1.639

Adj_R2

0.628

F

F = 6.423

15.5 Research Conclusions and Countermeasures 15.5.1 Research Conclusion This paper takes the advanced manufacturing industry as the research object and empirically analyzes the relationship between the digital economy, industrial innovation capabilities, and the servitization of advanced manufacturing industries. Overall, the improvement of the digital economy and industrial innovation capabilities has promoted servitization of advanced manufacturing industry. In addition, the digital economy can effectively promote the improvement of the innovation capability of the advanced manufacturing industry. It can promote the servitization level of the advanced manufacturing industry by enhancing the industrial innovation capability, that is, the industrial innovation capability has played a role in mediating role.

15.5.2 Suggestions From the government level, we must firmly grasp the opportunities of the digital economy. First, we will improve relevant policies and increase financial allocation. The high-quality development for servitization of advanced manufacturing requires the guidance of the government. Second, further encourage the development of highend producer services. Enhance the adaptability of servitization [13], and in the context of the digital economy, the industry will further promote the development of servitization of advanced manufacturing while improving its innovation capabilities. Third, strengthen the construction of the industrial chain. A good industrial chain can provide good software, hardware, and related supply systems for the digital economy

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and advanced manufacturing and escort the servitization development of advanced manufacturing. From the industrial level, advanced manufacturing should take advantage of the digital economy to nurture an ecological environment for industrial innovation, especially service innovation. The global value chain is transitioning from product economy to service economy. Compared with traditional manufacturing, advanced manufacturing can better integrate resources and realize value added, which is more advantageous. At the same time, the reserve of industrial innovation is of great value to the development of the industry. The existing industrial innovation can guarantee the current market, and the reserve of industrial innovation determines the future of the industry. Through the intelligent and digital upgrade of advanced manufacturing industry, we can find the innovative growth point of the industry, innovate the industrial form and production mode, promote industrial innovation, and then improve the servitization development of advanced manufacturing industry. Acknowledgements This study was partly supported by the Innovation Team Construction Project of the Scientific Research Department of Heilongjiang Oriental University (Project No.: HDFKYTD202108) and the 2020 Heilongjiang Provincial Higher Education Teaching Reform Key Entrustment Project (Project No.: SJGZ20200138).

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Chapter 16

Research on the Performance of Digital Economy in Heilongjiang Province on the Development of the Manufacturing Industry Fangyu Dong and Dejun Song

Abstract By analyzing the digital economy and the development of manufacturing industry in Heilongjiang Province, the construction of 13 cities in Heilongjiang Province in 2013–2020 individual fixed effects model, the empirical results show that the development of the manufacturing industry is positively impacted by the digital economy, and it is concluded that the digital technology innovation level, the digital infrastructure level and the digital science and technology development level can promote the development of manufacturing industry in Heilongjiang Province. Some recommendations are made to enhance the performance level of manufacturing development in Heilongjiang Province, such as improving the digital infrastructure, enhancing the digital economy’s level of development, strengthening the talent training, and promoting the thourogh integration of the digital economy and manufacturing industry.

16.1 Introduction Recently, the digital economy has been tremendous growth. It is imperative to actively promote the simultaneous growth of digital economy and substantial economy, grasp the direction of digitalization, promote the digitalization of manufacturing、 service and other industries, and seize the digital economy’s development opportunities. As an important base in northeast China, Heilongjiang Province closely follows the pace of the development of digital economy and actively facilitatess the transformation and development of the manufacturing industry in Heilongjiang Province. The government of Heilongjiang Province has also issued “Digital Longjiang” and other measures to encourage the growth of the digital economy, manufacturing and other industries. Under the support of government policy, the growth of digital economy F. Dong (B) · D. Song Harbin University of Commerce, Harbin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_16

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has a good momentum. Therefore, how to integrate the integration and penetration between the two industries to help improve the performance of manufacturing industry has become a new hot topic to be solved in our research.

16.2 Review of Relevant Literature At the present stage, the research mostly focuses on the role of the digital economy in fostering the manufacturing sector’s superior development and the development path of integration of the two industries. The main research results are as follows: Shen Yunhong and Huang Wei believe that digital infrastructure can cut down on the amount of time needed to gather information to achieve the purpose of promoting the advanced transformation of manufacturing enterprises [1]. Gou Bo and Hong Gongxiang pointed out that the growth of digital can improve the production mode of manufacturing industry in the form of demand-driven, and the scientific research achievements of digital economy can encourage the modernization and upgrading of low-cost manufacturing industries. [2]. Zheng Jiliang and Li Ruxian showed that the digital economy has favorable effects on the servitization of manufacturing industry, and the level of manufacturing servitization is affected by the development of the current and early digital economy, and that digital economy has a certain enabling effect on promoting the servitization of regional manufacturing sector [3]. Feng Weiyi believes that the implementation of digital operation can be achieved through industrial Internet and consumer participation [4]. Shi Yupeng believes that digital economy should promote the customization and diversification of manufacturing production to improve the supply efficiency of manufacturing industry. Meanwhile, manufacturing enterprises should actively learn and apply digital technology to realize the digital transformation of traditional enterprises [5]. Liao Xinlin and Yang Zhengyuan believe that digital economy can significantly promote the transformation and upgrading of manufacturing industry [6]. Cao Yueling and Zhao Beibei showed that promoting industrial digital transformation is the development path to realize the deep integration of digital and substantial economy [7]. Wang Yuxiang and Jiang Jian believe that the effective integration of digital economy and manufacturing can accelerate the digital transformation of manufacturing enterprises [8]. Mahmoud Zadeh thinks the digitization of industry can better improve the sales [9]. In summary, the researches of domestic scholars mainly focus on analyzing the effect of digital economy on economic development and the integrated development path of digital economy and manufacturing industry from a macro perspective. There are few researches attention to the effect of digital economy on development and manufacturing industry performance. Therefore, it is the focus of the paper to research the influence of digital economy on manufacturing development performance in Heilongjiang Province.

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16.3 The Construction of Index System and Model 16.3.1 Data Sources and Evaluation Methods According to relevant literature and the state of Heilongjiang Province’s digital economy in terms of development, the following research hypotheses are presented in this study: H 1 : The digital technology innovation level has a positive effect on the development of manufacturing industry. H 2 : The digital infrastructure level has a positive effect on the development of manufacturing industry. H 3 : The digital science and technology development level has a positive effect on the development of manufacturing industry. Data sources. The research data of this paper come from Heilongjiang Statistical Yearbook and Heilongjiang Science and Technology Innovation Network Platform. Considering the prior hypotheses, this article selects the original data from 2013 to 2020 for research. The research object is 13 prefecture-level cities in Heilongjiang Province, and 104 samples of data in total are gathered. The index in the original data is converted in the unit of the average exchange rate of the year, and interpolation method is used to complete the missing values in the data collection. Improved entropy method evaluation method. Indicator description: X pi j is represented as the jth indicator of city i in the pth year, d is t represented as the year span, the number of cities is recorded as n, and the number of indicators is recorded as m. (1) Standardization of indicators: article  

X pi j =

X pi j , index J is the positive index that bigger the better X pj max X pj min , index J is the negative index as small as possible X pi j

(16.1)

(2) Determination of index entropy value: d  n  H j = −k [Y pi j ln pi j ], k = p=1 i=1



X pi j 1 , Y pi j = d n  ln(dn) p=1 i=1 X pi j

(16.2)

(3) Determination of the utility value of indicator information: G j = 1 − Hj (4) Determination of index weight:

(16.3)

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Gj W j = m j=1

Gj

(16.4)

(5) Determination of comprehensive score: Z pi =

m   (W j X pi j )

(16.5)

j=1

16.3.2 Variable Selection and Index System (1) Explained variable Manufacturing MFG (Manufacturing). Industrial added value is used to represent the development performance level of the manufacturing industry, and the corresponding data are standardized [10]. (2) Explanatory variables: 1. Digital technology innovation level innov. Three indicators are selected: the level of science and technology expenditure, the quantity of patent applications, and the number of college students are the three indicators that are chosen. The improved entropy method’s complete score is used to assess the level of innov. 2. Digital infrastructure level infra. Four indicators are selected: the total postal service、 fixed Internet broadband access total telecom service, and yearend mobile phone users. The improved entropy method’s complete score is used to assess the level of infra. 3. Digital science and technology development level dist. The number of high-tech enterprises and R&D’s full-time equivalent were selected. The improved entropy method’s complete score is used to assess the level of dist (Table 16.1). (3) Control variables: The following control variables are added in this paper to weaken the possible analysis error caused by the omission of variables: (1) Education investment level EIL: The ratio of local finance science and technology expenditure and education expenditure to local finance public expenditure is adopted to measure EIL. Talent cultivation can maximize the development level of manufacturing industry. (2) Government participation GI: GI is measured by the ratio of local financial public expenditure to GDP, and the government promotes the development of manufacturing industry through financial subsidies and other means. (3) Foreign trade dependence FTD: The FTD is measured by the per capita GDP. (4) The economic development level ED: ED of each region is measured by per capita GDP.

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Table 16.1 Digital economy evaluation index system of Heilongjiang Province The target layer

Index layer

Unit

Digital technology Level of science and technology expenditure innovation level The quantity of patent applications Number of college students Digital infrastructure level

Index Weights attribute

%

+

0.095

Item

+

0.386

People

+

0.519

Total postal service

Billion +

0.317

Total telecom service

Billion +

0.287

Fixed Internet broadband access

Million +

0.188

Year-end mobile phone users Digital science and technology development level

Million +

0.208

Number of One high-tech enterprises

+

0.491

R&D

+

0.509

Man year

16.3.3 Model Construction In order to study the three important factors of the digital technology innovation level, digital infrastructure level and digital science and technology development level on the manufacting industry. The following measurement model is set as follows: MFGit = αβ0 innovit + +β1 EILit + β2 GIit + β3 FTDit + β4 EDit + εit

(16.6)

MFGit = α + β0 infrait + β1 EILit + β2 GIit + β3 FTDit + β4 EDit + εit

(16.7)

MFGit = α + β0 distit + β1 EILit + β2 GIit + β3 FTDit + β4 EDit + εit

(16.8)

Among them, i stands for each municipal area (i = 1, 2, ···, 13), t stands for the year (t = 2013, 2014, ···,2020), α and β are parameters to be estimated, ε is the residual.

16.4 Empirical Analysis 16.4.1 Regression Analysis In this work, panel data are processed and evaluated, and the individual fixed effect model is used for regression analysis by STATA software. Determine how the three index systems have affected the development of manufacturing industry under the digital economy (Table 16.2).

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Table 16.2 Regression results Variable

Model (1)

innov

0.8150a (3.19)

Model (2)

Model (3)

1.2848a (3.84)

infra

1.7234a (6.84)

dist EIL

−0.4419 (−0.68)

−0.0818 (−1.27)

−0.0005 (−0.01)

GI

−0.3489a

−0.0570 (−0.47)

−0.2080b (−2.69)

FTD

−0.0251 (−0.50)

−0.04625 (−0.94)

−0.0093 (0.22)

ED

0.2452a (5.24)

0.2330a (5.07)

0.1783a (4.32)

Constant term

−2.4343a (−5.68)

−2.5616a (−5.46)

−1.8912a (−4.56)

Observations

104

104

104

R-squared

0.1188

0.4647

0.4902

Note (1)

a, b

(−4.06)

c

and represent significant at 1%, 5% and 10% levels respectively; (2) () is t value

The first one in the table is the regression result of the digital technology innovation level on the development performance level of manufacturing industry. The result shows that it has passed the significance level test of 1%. And when the digital technology innovation level increases by one unit, the high-quality development level of the manufacturing industry increases by 1.2848 units. That is, the digital technology innovation level is positively connected with the development level of the manufacturing industry. Hypothesis 1 has been verified. The second one in the table is the regression result of digital infrastructure level on the development performance level of manufacturing industry. The result shows that it has passed the 5% significance level test. And digital infrastructure level of regression coefficient is 0.8150, That is, when the digital infrastructure level increases by one unit, the high-quality development level of the manufacturing industry increases by 0.8150 units. This promotes that the construction of digital infrastructure and the increasing investment in digital infrastructure level will help to improve the level of manufacturing industry development. Hypothesis 2 has been verified. The third one in the table is the regression result of the digital science and technology development level on the development level of manufacturing industry, which passes the 1% significance level test. The regression coefficient of the digital science and technology of explanatory variable is 1.7234, indicating that if the digital science and technology development level increases by 1 unit, the development level of manufacturing industry increases by 1.7234 units. That means it has the greatest influence on the growth of manufacturing industry. Hypothesis 3 has been verified. Considering the regression outcomes in the table, the regression coefficients of the economic development level of the control variables are 0.2452、 0.2330 and 0.1783 respectively, which are significant at the 1% level. The improvement of the economic development level of residents will also promote the improvement of the development level of the manufacturing industry.

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Table 16.3 Robustness test Variable

Model (1)

innov

1.099a (3.75)

Model (2)

Model (3)

1.3995a (3.53)

infra

0.8521a (2.40)

dist EIL

0.0036 (0.05)

– 0.0366 (−0.48)

0.0280 (0.36)

GI

−0.3970a

−0.0932 (−0.64)

−0.3740a (−3.44)

FTD

0.0409 (0.71)

0.0176 (0.30)

0.0571 (0.94)

ED

0.3220a (6.00)

0.3115a (5.73)

0.2981a (5.13)

Constant term

−3.1186a (−5.68)

−3.2523a (−5.86)

0.2981a (−4.86)

Observations

65

65

65

R-squared

0.4327

0.4236

0.3814

Note (1)

a, b

(−4.02)

c

and represent significant at 1%, 5% and 10% levels respectively; (2) () is t value

16.4.2 Robustness Test To guarantee the accuracy of the research findings, the gross industrial output value is re-used as the explained variable, and the regression analysis was used to test the robustness of the model. The test findings demonstrate that there is no change in the direction of the explanatory variable’s relationship to the explained variable’s value of industrial production, which is consistent with the previous conclusion, indicating that the empirical results are relatively reliable (Table 16.3).

16.5 Conclusions and Suggestions 16.5.1 Conclusion Through the above research, the following conclusions can be drawn: First, the impact of digital technology innovation level on manufacturing performance is very obvious. Enhancing the level and breaking down the barriers within the manufacturing industry with the high technology of digital economy can reduce environmental pollution and energy waste, and thus effectively improve the productivity of the manufacturing industry. Second, the development of digital infrastructure can hasten the flow of data, production, and other processes as well as the growth of the manufacturing industry. It can also break down barriers between large and small manufacturing enterprises, promote information exchange and data transmission, and help to improve the performance and development level of the manufacturing industry. Third, the digital science and technology can improve the level of development at a lower cost into use in the manufacturing industry development, the level of high and new technology

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enterprise development, the better, the more high quality manpower gathered themselves together, and can be directly improve manufacturing innovation potential, and generally in the current scientific and technological achievements to show, applied to manufacturing industry according to a relatively small hysteresis effect. It can be quickly implemented to raise the actual production level of manufacturing industry.

16.5.2 Policy Recommendations Considering the aforementioned findings, combined with the actual development conditions in Heilongjiang Province, this essay makes the following helpful recommendations. (1) Improving the digital infrastructure. According to the situation of different cities in Heilongjiang Province, the digital infrastructure construction should be carried out. Improve the level of the province’s digital infrastructure, strengthen the building of infrastructure, enhance the quality of rural broadband networks, and signal towers. Increase construction activity and use of the Internet, cloud computing, and big data digital infrastructure, fiber optic cable and other facilities for the improvement of digital technology to provide solid support environment. (2) Improve the development level of the digital economy. The government should strengthen the fund policy support and seize the construction opportunity. Improve the regional investment environment of digital economy, deepen cooperation with high-tech enterprises such as Alibaba and other groups, learn advanced technology and knowledge. We should seize the new opportunities presented by the new round of industrial transformation and scientific and technological revolution, create new growth sectors, create a kind of digital character-transformation system and deterioration.The information technologys can be used to improve the maturity of products and the performance of manufacturing enterprises. (3) Strengthen personnel training. Strengthen the universities and research institutes in Heilongjiang Province to train the composite talents of digital technology and manufacturing industry.We can strengthen the innovation consciousness of talents, play up the abilities’ capacity for innovation, and guide the training and supply of digital technology professionals. According to the development needs of digital economy, it focuses on the training leading scientific research talents in this field to reserve strength for the development of Heilongjiang Province. (4) Further integrating the digital economy with manufacturing. Deep integration is the key to improve the performance and development of manufacturing industry. Depending on the traits of their own businesses, manufacturing enterprises choose the appropriate digital economy development mode, and use digital technology to improve the innovation and development level of manufacturing industry. Reasonable application of digital technology to the production process,

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sales and other levels of the enterprise. In-depth research and development of intelligent, service-oriented, grid industrial equipment to reduce manufacturing production costs, intelligent manufacturing to replace traditional manufacturing to improve production efficiency. Make good use of digital technology development to promote the improvement of manufacturing performance level.

References 1. Shen, Y., Huang, L.: Research on the impact of digital economy level on the optimization and upgrading of manufacturing industrial structure: based on the panel data of Zhejiang Province from 2008 to 2017. Sci. Technol. Manage. Res. 40(03), 147–154 (2020) 2. Gao, B., Hong, G.: An empirical study on digital economy promoting high-quality development of manufacturing industry: based on panel data analysis of Anhui Province from 2010 to 2020. J. Anhui Adm. Univ. 05, 60–68 (2021) 3. Zheng, J., Li, R.: Research on the servitization development of digital economy enabling manufacturing industry: A case study of Beijing. J. Kunming Univ. Sci. Technol. (Soc. Sci. Ed.) 1–11 (2022) 4. Feng, W.: Digital transformation path and countermeasures of manufacturing industry under the background of digital economy. Contemp. Econ. Res. 1–8, (2022) 5. Shi, Y.: Integrated development of digital economy and manufacturing industry: path and suggestion. People’s Forum Acad. Front. 06, 34–39 (2021) 6. Liao, X., Yang, Z.: Effect measure and realization path of digital economy enabling manufacturing transformation and upgrading in Yangtze River Delta. East China Econ. Manage. 35(06), 22–30 (2021) 7. Cao, Y., Zhao, B.: Research on the development path of deep integration of digital economy and real economy in Hebei Province. Rural Econ. Sci. Technol. 32(10), 165–166 (2021) 8. Wang, Y., Jiang, J.: Research on the digital transformation path of manufacturing enterprises in the era of digital economy. Nat. Circ. Econ. 28, 135–137 (2021) 9. Zadeh, M.: A new sales forecasting method for industrial supply chain. Int. J. Smart Bus. Technol. 9(2), 1–12 (2021) 10. Fu, W., Liu, Y.: A study on the coupling and coordination of industrial digitalization and high quality development of manufacturing industry: an empirical analysis based on Yangtze River Delta region. East China Econ. Manage. 35(12), 19–29 (2021)

Chapter 17

Nature-Inspired Optimization Methods in Digital Filters Design Adam Slowik and Aneta Hapka

Abstract The article presents the popularity of nature-based optimization methods in the context of issues related to digital filters. An analysis was made concerning the number of publications in popular scientific databases. Attention was paid to publications that are related to nature-inspired optimization methods and their application to digital filters design. The results obtained from scientific databases make it possible to identify the most popular optimization methods in the context of their applications to digital filters. In this paper, we also point out the thematic areas in which the articles related to nature-inspired optimization methods and digital filters are published more often.

17.1 Introduction Digital filters are used in a very wide spectrum of electronic systems. We can divide them into two groups: infinite impulse response (IIR) filters and finite impulse response (FIR) filters. In IIR digital filters, each sample of the output signal depends on previous samples of the input and output signals. Therefore, there is feedback in these filters, and due to this feedback instabilities and oscillations of infinite duration can arise at the output of a given filter. IIR filters are very efficient and require a much smaller, compared to FIR filters, number of multiplications for calculating a single sample of the output signal (while ensuring the required frequency response). From a hardware point of view, this means that IIR filters are very fast, they allow real-time operation, and can operate at much higher sampling rates than FIR filters [1, 2].

A. Slowik (B) · A. Hapka Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland e-mail: [email protected] A. Hapka e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_17

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When designing filters, it is demanded that they meet, as closely as possible, the assumptions set in the design phase. Such assumptions are: stop band width, pass band width, ripple in the pass band, attenuation value in the stop band [3, 4]. In addition, in the case of IIR filters, it is also demanded that they be stable, i.e., that there are no oscillations of infinite duration at the output of a given filter. The stability of an IIR filter is guaranteed by placing all poles of the transfer function inside the unit circle on the z-plane. Of course, in FIR filters, their stability is guaranteed by the fact that the transfer function is represented in polynomial form. When designing filters with typical amplitude characteristics, existing approximations can be used: Butterworth, Chebyshev (with ripples in the pass or stop band) or Cauer [3, 5]. However, the problem becomes more complicated when the filter must to have unusual amplitude characteristics such as in amplitude or phase equalizers, hearing aids, audiophile equipment; then typical approximations become useless. In addition, when a given digital filter is implemented in a Digital Signal Processing (DSP) system that works in Q.M numerical format, we can expect with high probability that so-called rounding errors will arise. IIR filters, whose sensitivity in this regard is greater than that of FIR filters, are particularly prone to such errors. The consequence of rounding errors will be a digital filter (implemented in the DSP system), which with its parameters will differ from the digital filter that was originally intended to be implemented in the DSP system. Also note that the function describing the digital filter design problem is a multimodal function [6, 7]. This causes that algorithms based on gradient methods will easily get stuck in local extremes. Among other things, to avoid the inconveniences described earlier, optimization methods based on nature have been used for some time to design digital filters. We are talking about evolutionary algorithms [8–14] or swarm intelligence algorithms [15, 16]. As an example, the works [17–22] can be mentioned here. The work [17] presents the use of the simulated annealing algorithm for the design of digital filters. Karaboga in paper [18] presented the use of a differential evolution algorithm to design IIR digital filters. On the other hand, in paper [19], Karaboga et al. made a comparison of the effectiveness of using the genetic algorithm and the differential evolution algorithm in terms of designing FIR digital filters. Serbet et al. in paper [20] presented the use of swarm intelligence algorithm for the design of IIR digital filters. The paper [21] showed the use of a differential evolution algorithm with a specialized crossover operator in the design of IIR digital filters. Morikawa et al. in paper [22] presented an ant colony optimization (ACO) algorithm for designing FIR filters. This article presents an overview of the popularity of nature-inspired optimization algorithms in terms of digital filter design. The article is divided into the following sections. Section 17.2 presents general information on FIR and IIR digital filters. Section 17.3 briefly discusses how nature-inspired optimization algorithms work. Section 17.4 presents the popularity of nature-inspired optimization methods in the digital filter design problem and indicates areas where they are particularly applicable. The article concludes with Sect. 17.5, which presents a summary of the paper.

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17.2 Digital Filters: FIR and IIR We can divide digital filters into finite impulse response (FIR) filters and infinite impulse response (IIR) filters. The structure of an FIR digital filter is shown in Fig. 17.1. The filter shown in Fig. 17.1 is an nth-order filter, x[n] is the vector of input samples, y[n] represents the vector of output samples, z −1 are the delay elements, and bi (i ∈ [0; n]) are the so-called filter coefficients. The transfer function H (z) of the FIR filter is as follows: H (z) = b0 + b1 · z −1 + b2 · z −2 + · · · + bn−1 · z −(n−1) + bn · z −n

(17.1)

During digital FIR filters design, the main goal is to select the values of bi coefficients in such a way that the given filter meets the assigned design assumptions with minimal error. In the case of IIR filters, their structure is as shown in Fig. 17.2. As in the FIR filter, the IIR filter (shown in Fig. 17.2) is an nth-order filter, x[n] is the input sample vector, y[n] is the output sample vector, z −1 are the delay elements, while bi (i ∈ [0; n]) and a j ( j ∈ [1; n]) are the so-called filter coefficients. The transfer function H (z) of the IIR filter is as follows:

H (z) =

b0 + b1 · z −1 + b2 · z −2 + · · · + bn−1 · z −(n−1) + bn · z −n ( ) 1 − a1 · z −1 + a2 · z −2 + · · · + an−1 · z −(n−1) + an · z −n

(17.2)

During IIR digital filters design, the main goal is to choose the values of the coefficients bi and a j in such a way that the given filter meets the design assumptions with the minimal error and is a stable filter. The stability of the IIR digital filter depends on the position of the poles of the transfer function 17.2 on the z-plane. If all poles are inside the unit circle (i.e., |z| = 1) then the filter is stable.

Fig. 17.1 The structure of FIR digital filter

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17.3 Nature-Inspired Optimization Methods The general concept of nature-inspired population-based global optimization methods is presented in Algorithm 1 [23]. Algorithm 1 The general concept of nature-inspired population-based global optimization methods 1: Determine the values of all parameters of the algorithm 2: Determine objective function 3: Initialize the initial population of solutions (individuals) 4: Evaluate individuals in the population—calculate their fitness 5: while termination criterion is not satisfied do 6: Make modifications of the population using predetermined operators 7: Note the required information from the population 8: Evaluate modified individuals in the population 9: end while 10: As a result, return the best solution(s) found so far

Algorithm 1 consists of ten steps. In step 1, the values of all parameters which are necessary for the operation of the selected optimization method are defined. In step 2, the objective function that will be optimized during the operation of the optimization algorithm is defined. In step 3, solutions (individuals) of a given problem are randomly generated, thus forming the so-called initial population. In step 4, each individual from the initial population is evaluated using the previously defined objective function. Individuals with a higher value of the objective function (in the case of maximization problems) or individuals with a lower value of the objective function (in the case of minimization problems) will possess a higher chance to be

Fig. 17.2 The structure of IIR digital filter

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selected to the new population. In step 5, the main loop of the optimization algorithm begins. Inside it, in step 6, the population of individuals is modified using the operators present in the algorithm. For example, in the case of the genetic algorithm [9], this will be the crossover, mutation and selection operators. And in the case of the particle swarm optimization algorithm [15, 16, 24], it will be an operator that determines the new value of the particle’s velocity and its new position. In step 7, the information necessary for the operation of a given algorithm is stored. This may include such information as the value of the best individual in the current population. In step 8, the evaluation of individuals from the population is performed using the defined objective function (a process identical to step 4). In step 9, the main loop of the algorithm is completed. At the very end, in step 10, the results obtained are returned.

17.4 Application of Nature-Inspired Optimization Methods to Digital Filters Design In order to test the popularity of particular nature-based optimization methods in the area of digital filter design, these methods were divided into three groups such as Evolutionary Algorithms (EA), Swarm Intelligence Algorithms (SIA), and PhysicalInspired Algorithms (PIA). Representative algorithms were selected from each group in order to take the research mentioned earlier. From the EA group, the following algorithms were selected: Genetic Algorithm (GA) [9], Differential Evolution (DE) [25], Genetic Programming (GP) [26], Evolution Strategy (ES) [27], and Biogeography-Based Optimization (BBO) [28]. The following algorithms were selected from the SIA group: Particle Swarm Optimization (PSO) [24], Ant Colony Optimization (ACO) [29], Bat Algorithm (BA) [30], Firefly Algorithm (FA) [31], Cuckoo Search (CS) [32], Social Spider Optimization (SSO) [33], Krill Herd Algorithm (KHA) [34], Crow Search Algorithm (CSA) [35], Cat Swarm Optimization (CSO) [36], Artificial Bee Colony (ABC) [37], Moth Flame Optimization (MFO) [38], Artificial Fish School Algorithm (AFSA) [39], Elephant Herding Optimization (EHO) [40], Whale Optimization Algorithm (WOA) [41], Salp Swarm Algorithm (SSA) [42], Ant Lion Optimizer (ALO) [43], and Grey Wolf Optimizer (GWO) [44]. From the PIA group, the following algorithms were selected: Harmony Search (HS) [45], Big Bang-Big Crunch (BB-BC) [46], Gravitational Search (GS) [47], Imperialist Competitive (IC) [48], and Fireworks Algorithm (FWA) [49]. The number of papers on the aforementioned algorithms in the field of digital filter design was checked in scientific databases. Such scientific databases as Google Scholar, Springer, IEEE Explore, Science Direct, Web of Science were taken into account. The results obtained are summarized in Table 17.1. From Table 17.1, it can be seen that of the optimization methods based on nature, the genetic algorithm was most often chosen for digital filter design. A total of more than 10,000 published articles were achieved by such algorithms as GA (127733), PSO (57073), DE (21603), GP (20427), ACO (18581), and ABC (12708). In contrast,

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Table 17.1 Number of articles on nature-inspired optimization methods in terms of their application to digital filter design Google Springer IEEE Science Web of Total Algorithm Scholar Explore Direct Science Evolutionary algorithms (EA) GA 105,000 12,793 18,200 2009 DE 16,800 2405 GP 3940 862 ES BBO 1770 281 Swarm intelligence algorithms (SIA) PSO 48,100 5020 15,400 2112 ACO BA 3450 663 FA 5780 988 5680 983 CS 284 60 SSO KHA 540 99 627 153 CSA 884 187 CSO ABC 10,400 1474 MFO 978 194 75 22 AFSA EHO 291 96 WOA 2520 518 SSA 1080 227 561 130 ALO GWO 2620 430 Physical-inspired algorithms (PIA) 4110 642 HS 383 74 BB-BC 3060 521 GS IC 1290 186 FWA 486 107

276 46 19 4 1

9178 1233 1175 333 125

486 115 28 9 3

127,733 21,603 20,427 5148 2180

136 15 6 5 13 0 0 2 2 35 0 0 0 1 0 0 3

3548 1034 268 357 443 26 38 48 78 741 94 1 29 168 11 66 331

269 20 9 12 40 0 0 3 3 58 3 1 2 3 3 0 2

57,073 18,581 4396 7142 7159 370 677 833 1154 12,708 1269 99 418 3210 1321 757 3386

7 0 13 4 0

295 36 259 99 27

8 0 20 7 1

5062 493 3873 1586 621

the fewest publications were recorded for algorithms such as KHA (677), FWA (621), BB-BC (493), EHO (418), SSO (370), and AFSA (99). In the second study, we have tested in which areas papers are most often published in which nature-based optimization methods are used in the aspect of digital filter design. For this purpose, algorithms that have reached a total number of publications greater than 10,000 were selected from Table 17.1. These are algorithms such as GA,

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Table 17.2 Number of papers for selected algorithms in the most published areas of science (industry) GA PSO DE GP ACO ABC Total Area of application EEE CSAI CSTM CSIS TELE CSIA CSHA ISPT ACS ININ EM CSSE EB MCB EC

263 121 93 71 61 52 39 37 36 28 – – – – –

127 84 55 44 28 36 18 – 29 11 18 – – – –

50 41 22 18 13 17 4 – 12 6 6 5 – – –

9 7 6 4 – 5 7 – – – – 2 2 2 1

8 7 3 5 – 2 2 – 3 1 – 1 – – 1

36 12 9 5 5 8 4 – 8 – 9 2 – – –

493 272 188 147 107 120 74 37 88 46 33 10 2 2 2

PSO, DE, GP, ACO, and ABC. For these algorithms, it was checked in the Web of Science database in which areas of science (industry) papers on nature-inspired optimization methods and their applications in the aspect of digital filter design were published most often. The obtained results presenting the number of papers for a given algorithm in the 10 most frequently published areas of science (industry) are summarized in Table 17.2. The designations in Table 17.2 are as follows: Engineering Electrical Electronic (EEE), Computer Science Artificial Intelligence (CSAI), Computer Science Theory Methods (CSTM), Computer Science Information Systems (CSIS), Telecomunication (TELE), Computer Science Interdisciplinary Applications (CSIA), Computer Science Hardware Architecture (CSHA), Imaging Science Photographic Technology (ISPT), Automation Control Systems (ACS), Instruments Instrumentation (ININ), Engineering Multidisciplinary (EM), Computer Science Software Engineering (CSSE), Engineering Biomedical (EB), Mathematical Computational Biology (MCB), and Engineering Civil (EC). From Table 17.2, it can be seen that for the presented nature-inspired optimization methods, there are 6 common areas in which each of these methods has been applied in the aspect of digital filter design. These areas are as follows: Engineering Electrical Electronic (EEE), Computer Science Artificial Intelligence (CSAI), Computer Science Theory Methods (CSTM), Computer Science Information Systems (CSIS), Computer Science Interdisciplinary Applications (CSIA), and Computer Science Hardware Architecture (CSHA). From the analysis, it can be seen that the largest

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number of applications are in the fields of electrical and electronic engineering. A further five areas are related to computer science applications.

17.5 Conclusions The paper present results on nature-based optimization methods and their applications in the area of digital filter design. The results obtained show that the most common nature-based optimization algorithms that are used for digital filter design are such algorithms as genetic algorithm (GA), swarm intelligence algorithm (PSO), differential evolution algorithm (DE), genetic programming algorithm (GP), ant colony optimization algorithm (ACO), and bee colony optimization algorithm (ABC). For each of these algorithms, a total of more than 10,000 articles (in the analyzed databases with scientific publications) related to their application in the field of digital filter design were obtained. In addition, it can also be noted that the aforementioned methods and their application to digital filter design most often occurred in such scientific fields as electrical and electronic engineering and computer science with its various variations.

References 1. Lyons, R.: Introduction to Digital Signal Processing. WKL, Warsaw (2000). (in Polish) 2. Slowik, A.: Application of evolutionary algorithm to design minimal phase digital filters with non-standard amplitude characteristics and finite bit word length. Bull. Polish Acad. Sci.-Tech. Sci. 59(2), 125–135. https://doi.org/10.2478/v10175-011-0016-z 3. Slowik, A., Bialko, M.: Design and optimization of IIR digital filters with non-standard characteristics using particle swarm optimization algorithm. In: Proceedings of 14th IEEE International Conference on Electronics Circuits and Systems, pp. 162 (2007). https://doi.org/10. 1109/ICECS.2007.4510955 4. Slowik, A., Bialko, M.: Design and optimization of IIR digital filters with non-standard characteristics using continuous ant colony optimization algorithm. In: Lecture Notes on Artificial Intelligence, vol. 5138, pp. 395–400, SETN 2008. Springer 5. Slowik, A., Bialko, M.: Design of IIR digital filters with non-standard characteristics using differential evolution algorithm. Bull. Polish Acad. Sci.-Tech. Sci. 55(4), 359–363 (2007) 6. Chen, S., Istepanian, R.H., Luk, B.L.: Digital IIR filter design using adaptive simulated annealing. Dig. Sig. Process. 11(3), 241–251 (2001). July 7. Slowik, A.: Hybridization of evolutionary algorithm with Yule Walker method to design minimal phase digital filters with arbitrary amplitude characteristics. In: Lecture Notes on Artificial Intelligence, vol. 6678, pp. 67–74. Springer (2011) 8. Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. AddisonWesley Publishing Company Inc., Boston (1989) 9. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1992) 10. Slowik, A., Slowik, J.: Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered Pareto set. Int. J. Adv. Manuf. Technol. 37(7–8), 657–669 (2008). https://doi.org/10.1007/s00170-007-1013-0

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11. Slowik, A., Bialko, M.: Design and optimization of combinational digital circuits using modified evolutionary algorithm. In: Lecture Notes in Artificial Intelligence, vol. 3070, pp. 468–473. Springer (2004) 12. Slowik, A., Bialko, M.: Modified version of roulette selection for evolution algorithms—the fan selection. In: Lecture Notes in Artificial Intelligence, vol. 3070, pp. 474–479. Springer (2004) 13. Slowik, A., Bialko, M.: Partitioning of VLSI circuits on subcircuits with minimal number of connections using evolutionary algorithm. In: Lecture Notes in Computer Science, vol. 4029, pp. 470-478, ICAISC 2006. Springer (2006) 14. Slowik, A.: Steering of balance between exploration and exploitation properties of evolutionary algorithms—mix selection. In: Lecture Notes in Artificial Intelligence, vol. 6114, pp. 213–220, ICAISC 2010. Springer (2010) 15. Slowik, A. (Ed.): Swarm Intelligence Algorithms: A Tutorial. CRC Press, Taylor & Francis Group, Boca Raton, USA (2020) 16. Slowik, A. (Ed.): Swarm Intelligence Algorithms: Modifications and Applications. CRC Press, Taylor & Francis Group, Boca Raton, USA (2020) 17. Erba, M., Rossi, R., Liberali, V., Tettamanzi, A.: Digital filter design through simulated evolution. In: Proceedings of ECCTD’01, Espoo, Finland, vol. 2, pp. 137–140, August 2001 18. Karaboga, N.: Digital IIR filter design using differential evolution algorithm. EURASIP J. Appl. Sig. Process. 2005(8), 1269–1276 (2005) 19. Karaboga, N., Cetinkaya, B., Yakhno, T.: Performance comparison of genetic and differential evolution algorithms for digital FIR filter design. In: International Conference on Advances in Information Systems, LNCS, vol. 3261, pp. 482–488 (2004) 20. Serbet, F., Kaya, T., Ozdemir, M.T.: Design of digital IIR filter using particle swarm optimization. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 202–204 (2017) 21. Stubberud, P.: The design of digital IIR filters using a differential evolution optimization algorithm with Taguchi crossover. In: IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) 22. Morikawa, M., Suyama, E.: Design of CSD coefficient FIR filters using diversified ACO. In: 20th International Symposium on Communications and Information Technologies (ISCIT), pp. 129–133 (2021) 23. Slowik, A., Cpalka, K.: Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications. IEEE Trans. Ind. Inform. 18(1), 546–558 (2022). https:// doi.org/10.1109/TII.2021.3067719 24. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995) 25. Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997) 26. Koza, R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992) 27. Rechenberg, I.: Evolution strategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog Verlag (1973) 28. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008) 29. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the European Conference on Artificial Life, pp. 134–142 (1991) 30. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Proceedings of Nature Inspired Cooperative Strategies for Optimization (NICSO), pp. 65–74 (2010) 31. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Proceedings of International Symposium on Stochastic Algorithms, LNCS, vol. 5792, pp. 169–178 (2009) 32. Yang, X.-S., Deb, S.: Cuckoo search via levy flights. In: Proceedings of IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 210–214 (2009)

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33. Cuevas, E., Cienfuegos, M., Zaldivar, D., Perez-Cisneros, M.: A swarm optimization algorithm inspired in the behaviour of the social-spider. Exp. Syst. Appl. 40(16), 6374–6384 (2013) 34. Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012) 35. Askarzadeh, A.: A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput. Struct. 169, 1–12 (2016) 36. Chu, S.-A., Tsai, S.-A., Pan, J.-S.: Cat swarm optimization. In: Proceedings of Pacific Rim International Conference on Artificial Intelligence, LNCS, vol. 4099, pp. 854–858 (2006) 37. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007) 38. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015) 39. Hu, J., Zeng, X., Xiao, J.: Artificial fish school algorithm for function optimization. In: Proceedings of IEEE 2nd International Conference on Information Engineering and Computer Science, pp. 1–4 (2010) 40. Wang, G.G., Deb, S., Coelho, L.D.S.: Elephant herding optimization. In: Proceedings of 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 1–5 (2015) 41. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016) 42. Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H., Mirjalili, S.M.: Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 114, 163–191 (2017) 43. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015) 44. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014) 45. Gao, X.Z., Govindasamy, V., Xu, V., Wang, X., Zenger, K.: Harmony search method: theory and applications. In: Computational Intelligence and Neuroscience, vol. 15 (2015). Article ID: 258491 46. Erol, O.K., Eksin, I.: A new optimization method: Big Bang-Big Crunch. Adv. Eng. Softw. 37(2), 106–111 (2006) 47. Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inform. Sci. 179(13), 2232–2248 (2009) 48. Atashpaz-Gargari, E., Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Proceedings of IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667 (2007) 49. Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Proceedings of ICSI 2010. Tan, Y., Shi, Y., Tan, K.C. (eds.) Advances in Swarm Intelligence. Lecture Notes in Computer Science, vol. 6145, pp. 355–364. Springer, Berlin, Heidelberg (2010)

Chapter 18

The Impact of Urban Factor Market Distortion on the Innovation Efficiency of Industrial Enterprises in Heilongjiang Province Yin Zhao, Yanhua Li, Zelong Ti, and Xiaojia Wang

Abstract Industrial enterprises above the scale are an important force of economic development in the provinces of Heilongjiang. In recent years, the technological innovation ability of Industrial Enterprises above the designated size in Heilongjiang has been improved to a certain extent. However, due to the differences in geographical location, resource endowment, and economic development level, the technological innovation efficiency of industrial enterprises in different regions is significantly different. Studying the influence of factor market distortion on the innovation efficiency of industrial enterprises is conducive to the rational allocation of innovation resources, the implementation of innovation-driven development strategy, and the construction of a resource-saving society. This paper selects 12 industrial enterprises above the designated size in prefecture-level cities of Heilongjiang Province from 2013 to 2020 as the research object. Firstly, it uses the C-D production function method to measure the degree of distortion of the capital and labor factor market in each city in each city of Heilongjiang. Then it constructs the evaluation index system of industrial enterprise innovation efficiency, using the DEA Malmquist index method and dea-ccr method to measure the dynamic innovation efficiency of industrial enterprises in Heilongjiang provinces and industrial enterprises in various cities. It provides a reference for Heilongjiang province to construct innovative industrial enterprises.

Y. Zhao (B) · Y. Li · Z. Ti · X. Wang East University of Heilongjiang, Harbin 150066, China e-mail: [email protected] Y. Zhao North-Eastern Federal University in Yakutsk, Yakutsk 125075, Russia © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_18

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18.1 Introduction After 40 years of reform and opening-up, the Chinese economy has achieved leapfrog development [1]. The Chinese economy has entered a new normal, shifting from high-speed growth to high-quality development [2]. In the context of the current innovation-driven development strategy, achieving high-quality economic development is realizing technological and industrial innovation to achieve sustainable economic development. Under the current economic background, this paper explores the impact mechanism of market distortions on innovation efficiency and the differences in the impact of factor market distortions on innovation efficiency in different regional environments, and how to avoid the inhibiting effect of capital market distortions and labor market distortions on innovation efficiency of regional enterprises according to their development conditions [3]. It is of great theoretical and practical significance to accelerate regional innovation development and build an innovation-oriented country.

18.2 Evaluation of Distortion Degree of Urban Factor Market in Heilongjiang Province 18.2.1 Model Principle and Setting The C-D production function is the Cobb–Douglas production function, named after the American mathematician C. W. Cobb and economist Paul D. H. Douglas. They are to explore the relationship between input and output of the production function model in the form of the general improvement to create the new production function, which is used to predict production and analysis of national and regional industrial enterprise development way of production of an economic mathematical model, referred to as "c-d production function, is the most widely used in the economics of a production function form [4]. It plays a vital role in mathematical economics and econometrics research and application. Referring to the study of Hsieh and Klenow (2009), this paper uses the C-D production function method to determine the degree of factor market distortion in cities in Heilongjiang Province. The model is set as follows: Yit = Ait K ita L itb

(18.1)

After logarithmic linearization transformation of the above equation, the form of the production function obtained is: ln Yit = ln Ait + a ln K it + b ln L it + ε

(18.2)

18 The Impact of Urban Factor Market Distortion on the Innovation …

217

Y represents total output, K represents capital input, L represents labor input, I and t represent cities and years, A and B represent the output elasticity coefficient of capital factor and labor factor, and A means the production technology level. The marginal output of capital and labor factors can be obtained by differentiating K and L in formula (18.1). MP K it = a Ait K ita−1 +

aYit K it

(18.3)

MP L it = b Ait L itb−1 +

bYit L it

(18.4)

Rate is used to represent the price of capital, wage represents the price of labor, and the ratio between marginal output and actual price is the absolute distortion coefficient of the factor market [5]. The formula is as follows: Dist K it = Dist L it

MPkit rate

MP L it wage

Dist = Distka+b Dist L a+b

(18.5) (18.6) (18.7)

If the absolute distortion coefficient is greater than 1, it proves that the marginal output is greater than the actual price, which is harmful; otherwise, if the number is less than 1, it is a forward distortion.

18.2.2 Variable Selection and Data Processing The total output Y is measured by the total industrial output value and deflated by the producer price index (PPI), with 2017 as the base period. The capital input K is measured by the net fixed assets of industrial enterprises in the plan and deflated by the capital price index. The annual average number of employees measures the labor input L and its length [6]. The capital price rate adopts the average yearly benchmark interest rate of enterprise loans for six months to 1 year as a substitute index. The labor price wage is calculated by the average salary of urban unit employees. The data in this chapter comes from the China Statistical Yearbook of Cities and Heilongjiang Statistical Yearbook of 2013–2020 and the Statistical Bulletin of National Economic and Social Development of each prefecture-level city from 2013 to 2020. The benchmark interest rate comes from the statistical data published on the official website of the People’s Bank of China over the years. In 2019 and 2020,

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some regions’ total industrial output value data was missing [7], and enterprises’ annual primary business income was approximately replaced. The calculation of factor market distortion should first estimate the output elasticity of the input of capital and labor factors. To avoid heteroscedasticity, logarithmic processing is carried out. Eviews 9.0 software is used to estimate the model parameters and perform statistical and econometric tests. (1) Descriptive statistics of relevant variables (Table 18.1). (2) Unit root test and regression analysis results: The panel data model for data before regression analysis the stability of the unit root test; otherwise, it’s easy to have a spurious regression phenomenon. In this paper, the variables of the unit root test, any, and LNK stationary test show for the first time a unit root. Therefore, on the first order difference, rejecting the null hypothesis is that LNL passed the test once and did not have a unit root after the show no longer has a unit root. The mixed estimation model was used for regression analysis of variables, and the results are shown in the following Table 18.2: Through the test, all the data obtained are static variables. That is, there is no unit root. Through the unit root test, the level series data of the basic sample estimator is stable, and the next regression can be carried out. The mixed estimation model (OLS) was used for the overall regression of variables, and the results were as follows (Table 18.3). Table 18.1 Descriptive statistical results of related variables Variate

Observd

Average

Maximum

Least

Deviation

Lny

236

16.09604

18.51906

13.55009

1.105936

lnk

236

15.39412

17.44789

13.17568

0.912909

lnl

236

12.54917

14. 18569

11.39976

0.667668

Table 18.2 Statistical results of unit root test Variate

Inspection result

ln

99.3952

Prob.a 0.0078

y

121.148

0.0000

ln

109.138

0.0034

Note

a, b

c

and are respectively significant at 5%, 10% and 1% significance levels

Table 18.3 Regression results of mixed estimation model Variate

Coefficient

Std. error

t-Statistic

Prob

lnk

0.681111

0.099024

11.53948

0.0000

lnl

0.447515

0.072430

6.178576

0.0000

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219

After calculation, the elasticity coefficient of capital output A is 0.681111, and the elasticity coefficient of labor output B is 0.447515. The sum of A and B is close to 1, similar to the assumption of constant return to scale, and conforms to the assumption of a steady return to scale of the selected function. The two factors’ output elasticity coefficient is close to the internationally accepted 0.6 and 0.4. Then the marginal output of capital and labor factors can be obtained according to Eqs. (18.3) and (18.4), and the absolute distortion coefficient of the factor market can be obtained according to Eqs. (18.5), (18.6) and (18.7).

18.2.3 Measurement Results During the study period, each city’s distortion degree of the capital factor market increased and decreased. The distortion degree of the capital factor market increased in 6 cities, namely Harbin, Daqing, Jiamusi, Mudanjiang, Heihe, and Suihua. It decreased in 6 cities: Qiqihar, Jixi, Hegang, Shuangyashan, Yichun and Qitaihe. The degree of distortion of the labor factor market increased in 3 cities, namely Harbin, Mudanjiang and Suihua. It decreased in 9 cities: Qiqihar, Jixi, Hegang, Shuangyashan, Yichun, Qitaihe, Heihe, Daqing and Jiamusi (Tables 18.4 and 18.5). Overall, the prefecture-level gauge of Heilongjiang province ‘s industrial enterprise capital and labor factor has negative impacts [8]. The element market did not achieve optimal resource allocation. Capital market distortions are more significant than the labor market to some degree. Labor utilization is greater than capital usage. Rules on industrial enterprise capital excessive consumption phenomenon. Because of the price, marginal returns are diminishing; when the capital investment increases, it gradually diminishes marginal returns. Increasing factor inputs can create higher Table 18.4 distortion degree of capital factor market in Northeast China City/year

2013

2014

2015

2016

2017

2018

2019

2020

Harbin

28.11

23.94

26.68

28.60

26.54

31.73

47.54

49.22

Qiqihar

24.12

17.48

18.86

20.31

22.72

26.37

33.40

17.11

Jixi

16.94

17.46

18.58

11.45

18.06

14.55

14.69

13.89

Hegang

22.36

17.22

18.55

14.11

10.37

15.65

17.15

17.86

Shuangyashan

22.84

22.79

25.58

8.65

12.57

13.63

18.15

21.43

Taqing

17.20

14.87

15.30

16.56

13.83

14.48

16.33

21.21

Yichun

18.00

15.29

17.19

7.78

6.60

7.46

12.34

14.29

Jiamusi

19.09

21.09

28.44

33.02

23.87

24.52

25.02

26.37

Qitaihe

20.60

15.95

11.45

9.12

11.24

11.11

12.81

12.05

Mudanjiang

23.17

23.35

30.48

35.56

25.99

31.19

59.46

63.39

Heihe

16.23

12.84

14.25

16.09

14.84

18.70

16.51

21.27

Suihua

22.01

22.58

24.92

27.28

33.34

42.88

56.19

49.64

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Table 18.5 Distortion degree of labor factor market in Northeast China City/year

2013

2014

2015

2016

2017

2018

2019

2020

Harbin

2.44

2.11

2.63

2.68

2.80

2.80

2.53

2.82

Qiqihar

3.70

3.46

3.06

3.08

3.27

3.39

3.20

2.04

Jixi

2.44

1.81

2.55

1.79

1.48

1.82

1.51

0.82

Hegang

3.11

1.59

2.63

1.89

1.75

1.95

2.05

1.69

Shuangyashan

6.01

8.26

6.02

2.99

2.25

2.43

2.53

2.25

Taqing

7.24

5.99

6.61

6.78

4.65

4.18

4.32

4.44

Yichun

2.38

1.59

2.15

1.39

0.91

0.90

1.31

1.40

Jiamusi

3.29

2.48

4.56

4.83

3.70

3.71

3.93

3.21

Qitaihe

3.70

3.04

2.47

2.05

1.80

1.83

1.69

1.78

Mudanjiang

3.85

3.96

4.72

4.86

4.83

5.05

2.83

5.72

Heihe

1.32

0.76

1.55

1.60

1.34

1.32

1.21

0.98

Suihua

3.20

3.79

4.67

5.01

5.94

5.11

5.11

4.85

economic efficiency if the factor price is below marginal returns. Traditional investment drives the economic model. In the short term, the economic boosting effect is noticeable. For a long time, it will accelerate the process of economic globalization, but the process of marketization is relatively slow. The two relative operations are not coordinated, further expanding the factor market’s distortion degree.

18.3 Measurement of Innovation Efficiency of Urban Industrial Enterprises in Heilongjiang Province 18.3.1 Selection of Research Methods DEA is a data envelopment analysis method applied to calculate and evaluate the efficiency in the field of a nonparametric approach is A.C Harnes et al., based on the relative efficiency of the evaluation idea of constructing a decision-making unit to calculate the input and output method of relative efficiency, its principle is based on the decision-making unit of input and output unchanged, with the methods of mathematical and statistical planning, the relative effectiveness of the production frontier is evaluated by identifying the relatively effective production frontier and comparing the degree of deviation between the DMUS from the defined production frontier. DEA model provides a relatively objective and scientific method to solve the efficiency problem of multiple inputs and outputs of decision-making units, with substantial flexibility and relatively straightforward economic significance. Commonly used DEA models include CCR and BCC. In this paper, the Malmquist index method is used to measure the overall dynamic innovation efficiency at the provincial level in

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221

Heilongjiang Province. The traditional DEA method measures the static innovation efficiency of prefecture-level cities. Let N DMU, each DMU has M types of input and K types of output, represented by input variables Xi and output variables Yi respectively. Xit > 0 represents the input quantity of the t type of the ith DMU; Yir represents the output of the RTH type of the ith DMU. For each decision unit, the output-oriented DEA-CCR model is as follows: min[θ − ε(e1T s − + e2T s + )] ⎧ n  ⎪ ⎪ λi xi + s − = x0 ⎪ ⎪ ⎪ i=0 ⎪ ⎨  n λi yi − s + = θ y0 ⎪ i=0 ⎪ ⎪ ⎪ λi >= 0, i = 1, 2, . . . n ⎪ ⎪ ⎩ + s >= 0, s − >= 0

(18.8)

where ε is the non-Archimedean infinitesimal, θ is the efficiency value of the DMU. S + and S—are slack variables. Malmquist index is a method to study dynamic efficiency, and the formula is as follows:  M(xt+1 , yt+1 , xt , yt ) =

Dt+1 (yt , xt ) Dt (yt , xt ) ∗ Dt (yt+1 , xt+1 ) Dt+1 (yt+1 , xt+1 )

 (18.9)

Malmquist index reflects the relative efficiency of DMU, which is greater than1, indicating the efficiency level from T period to T + 1 period Rise and fall.

18.3.2 Variable Selection and Data Sources Innovation is a comprehensive activity, an input or output indicator alone; it is hard to carry on the scientific assessment; thus, establishing a set of complete systemic index systems is especially important. Innovation level evaluation is based, is influenced by many factors in the process of assessment, selection of different evaluation indexes of varying influence on the final result will be, In order to ensure the accuracy and reasonableness of the measured efficiency evaluation value, the system construction should follow the scientific principle, systematic principle, operability principle and effectiveness principle. In this paper, the full-time equivalent of R&D personnel is taken as the human input indicator, the internal expenditure of R&D funds and the expenditure of new product development funds are taken as the reflection of capital input, and the number of patent applications and sales revenue of new products is taken as the output indicators. The index system is as follows (Table 18.6). 2013–2020 was the research period to maintain data continuity and statistical consistency. The data were obtained from the China Statistical Yearbook, China

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Table 18.6 Evaluation index system of innovation efficiency of industrial enterprises

Input indicators

Output indicators

Full-time equivalent of R&D personnel R&D

Patent application quantity

Appropriation expenditure New product sales revenue

New product development fund income

Urban Statistical Yearbook, Heilongjiang Statistical Yearbook, the statistical yearbooks of the corresponding years of each city and the statistical bulletin of national economic and social development [9]. The missing data of the full-time equivalent of R&D personnel in some prefecture-level cities are approximately replaced by the number of personnel in the industry by scientific and technological activities, and the accuracy of the results is somewhat reduced. Still, it can also generally illustrate innovation efficiency [10].

18.3.3 Measurement Results Deap2.1 software and the DEA-Malmquist method were used to measure the overall dynamic innovation efficiency of the province and observe its changing trend. The results are as follows (Table 18.7): Overall, from 2013 to 2020, the total factor productivity of industrial enterprises in Heilongjiang Province showed a decreasing trend with an average value of 1.056, and the total factor productivity of Jilin Province showed an increasing trend with an average value of 0.995. The total factor productivity of Liaoning Province showed a decreasing trend with an average value of 1.029. In terms of provinces, the total factor productivity of Heilongjiang Province decreased year by year from 2013 to 2016, from 1.209 to 0.984, and then gradually fluctuated back up. Table 18.7 Dynamic innovation efficiency measurement of three northeast provinces Heilongjiang province 2013–2014

Effch

Techch

Pech

Sech

tfpch

1.237

0.977

1.070

1.157

1.209

2014–2015

1.000

1.104

1.000

1.000

1.104

2015–2016

1.000

0.984

1.000

1.000

0.984

2016–2017

1.000

1.024

1.000

1.000

1.024

2017–2018

0.849

1.236

1.000

0.849

1.049

2018–2019

1.178

0.852

1.000

1.178

1.004

2019–2020

0.787

1.314

0.802

0.982

1.035

means

0.996

1.060

0.978

1.018

1.056

18 The Impact of Urban Factor Market Distortion on the Innovation …

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Table 18.8 static innovation efficiency of industrial enterprises in cities of Heilongjiang Province from 2013 to 2020 City/year

2013 crste

Vrste

Scale

2014 crste

Vrste

Scale

2015 crste

Vrste

Scale

Harbin

0.063

1.000

0.063

0.170

0.659

0.258

0.240

0.908

0.265

Qiqihar

0.480

1.000

0.480

0.545

0.748

0.728

0.752

0.801

0.938

Jixi

0.458

0.759

0.603

0.658

0.685

0.961

0.889

1.000

0.889

Hegang

0.658

1.000

0.658

1.000

1.000

1.000

0.803

1.000

0.803

Shuangyashan

0.441

0.729

0.605

1.000

1.000

1.000

1.000

1.000

1.000

Taqing

0.336

1.000

0.336

1.000

1.000

1.000

1.000

1.000

1.000

Yichun

0.247

0.485

0.509

0.309

0.686

0.450

0.317

0.693

0.457

Jiamusi

0.308

0.394

0.782

0.364

0.377

0.964

0.776

0.798

0.973

Qitaihe

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

1.000

Mudanjiang

0.338

0.362

0.936

0.456

0.886

0.514

0.583

1.000

0.583

Heihe

0.090

0.556

0.162

0.172

0.558

0.309

0.098

0.400

0.244

Suihua

0.310

0.345

0.898

0.345

0.353

0.975

0.817

1.000

0.817

Means

0.394

0.719

0.586

0.585

0.746

0.763

0.690

0.883

0.747

From the perspective of technical efficiency and technological progress, the technical efficiency of Heilongjiang Province is constantly declining, while technological progress is continually increasing [11]. The years of technological efficiency increase in Heilongjiang Province are 2013–2014 and 2018–2019, the years of unchanged are 2014–2017, the years of decrease are 2017–2018 and 2018–2019, and the years of technological progress decrease are 2013–2014 and 2015–2016. From 2018 to 2019, the years added are 2014–2015, 2016–2018, and 2019–2020 (Table 18.8). City/year

2016 crste

Vrste

Scale

2017 crste

Vrste

Scale

2018 crste

Vrste

Scale

Harbin

0.341

0.865

0.395

0.154

1.000

0.154

0.283

1.000

0.283

Qiqihar

0.643

0.758

0.848

0.820

1.000

0.820

1.000

1.000

1.000

Jixi

0.709

0.884

0.802

0.798

0.928

0.860

0.523

0.681

0.769

Hegang

0.836

1.000

0.836

0.496

1.000

0.496

0.751

1.000

0.751

Shuangyashan

0.490

0.610

0.803

0.450

0.761

0.591

0.862

1.000

0.862

Taqing

1.000

1.000

1.000

1.000

1.000

1.000

0.972

1.000

0.972

Yichun

0.312

0.735

0.424

0.250

0.612

0.408

0.183

0.596

0.308

Jiamusi

1.000

1.000

1.000

0.506

0.517

0.979

0.670

0.702

0.953

Qitaihe

0.569

1.000

0.569

1.000

1.000

1.000

0.798

1.000

0.798

Mudanjiang

1.000

1.000

1.000

0.493

0.499

0.988

1.000

1.000

1.000

Heihe

0.219

0.471

0.466

0.132

0.315

0.418

0.219

0.468

0.469

Suihua

0.968

1.000

0.968

0.574

0.617

0.931

0.919

0.921

0.997

(continued)

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(continued) City/year

2016 crste

Vrste

Scale

2017 crste

Vrste

Scale

2018 crste

Vrste

Scale

Means

0.674

0.860

0.759

0.556

0.771

0.720

0.682

0.864

0.760

City/year

2019 crste

Vrste

Scale

2020 Crste

Vrste

Scale

Harbin

0.057

1.000

0.057

0.239

1.000

0.230

Qiqihar

0.155

0.186

0.832

0.324

1.000

0.324

Jixi

0.039

0.362

0.108

0.858

0.908

0.945

Hegang

1.000

1.000

1.000

0.701

1.000

0.701

Shuangyashan

0.390

1.000

0.390

0.529

0.785

0.674

Taqing

0.802

1.000

0.802

0.904

1.000

0.904

Yichun

0.071

0.660

0.107

0.519

0.822

0.631

Jiamusi

0.330

0.696

0.474

0.451

0.569

0.793

Qitaihe

0.054

0.570

0.095

1.000

1.000

1.000

Mudanjiang

0.137

0.165

0.827

0.301

0.343

0.879

Heihe

0.152

1.000

0.152

0.193

0.532

0.363

Suihua

0.252

0.361

0.699

0.451

1.000

0.451

Means

0.287

0.667

0.462

0.539

0.830

0.659

Note CRSTE stands for comprehensive efficiency, VRSTE stands for pure technical efficiency and scale stands for a scale efficiency value

From the perspective of comprehensive innovation efficiency, there are seven cities whose total efficiency value is lower than the average level in 2013, namely Harbin City, Daqing City, Yichun City, Jiamusi City, Mudanjiang City, Heihe City, and Suihua City, indicating that the comprehensive efficiency of these cities is in a low state. The ranking cities higher than the average from small to large is Shuangyashan City, Jixi City, Qiqihar City, Hegang City, and Qitaihe City. In 2014, the total efficiency value of Harbin, Qiqihar, Yichun, Jiamusi, Mudanjiang, Heihe, and Suihua was lower than the average level. Five cities were higher than the average value, among which Hegang, Shuangyashan, Daqing, and Qitaihe had the most comprehensive innovation efficiency, with a value of 1, indicating that DEA was effective. In 2015, there were four cities whose total efficiency value was lower than the average: Harbin, Yichun, Mudanjiang, and Heihe. Eight cities with higher-than-average levels exist, among which Shuangyashan, Daqing, and Qitaihe have the highest innovation efficiency, showing effective DEA. In 2016, there were six cities whose comprehensive efficiency value was lower than the average level, namely Harbin, Qiqihar, Shuangyashan, Qitaihe, Yichun, and Haihe, six cities whose complete innovation efficiency was the highest, among which Daqing, Jiamusi, and Mudanjiang achieved effective DEA. In 2017, there were seven cities whose comprehensive efficiency value was lower than the average level: Harbin, Yichun, Jiamusi, Mudanjiang, Heihe, Shuangyashan, and Hegang. The cities above the average, from small to large, are

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Suihua, Jixi, Qiqihar, Qitaihe, and Daqing. In 2018, the comprehensive efficiency value of Harbin, Jixi, Yichun, Heihe, and Jiamusi was lower than the average level in 5 cities, and the complete innovation efficiency of Qiqihar and Mudanjiang was the highest in 2018. In 2019, innovation efficiency was generally low. Only Hegang, Shuangyashan, Daqing, and Jiamusi were above the average value, while the innovation efficiency of other cities was below the average. In 2020, the comprehensive efficiency value of 8 cities was lower than the average level: Harbin, Qiqihar, Shuangyashan, Yichun, Jiamusi, Mudanjiang, Heihe, and Suihua, and four cities were higher than the average. The ranking from small to large was Hegang, Jixi, Daqing, and Qitaihe. From the perspective of the pure technical efficiency in 2013 in Harbin, Qiqihar city, Hegang, Daqing, qitaihe market value is 1, the technical efficiency optimal, higher than the average pure technical efficiency in each city of cities have, jixi, shuangya shan, yichun, jiamusi, mudanjiang, suihua, heihe city pure technical efficiency is low, This also shows that the influence of technical efficiency partly causes the low efficiency of comprehensive urban innovation. In 2014, 4 cities reached optimal technical efficiency: Hegang, Shuangyashan, Daqing, and Qitaihe. Harbin, Jixi, Yichun, Jiamusi, Heihe, and Suihua were below the average value, and technological innovation efficiency needs to be improved. In 2015, 7 cities achieved optimal technical efficiency with good performance, and four cities were below the average value, namely Qiqihar, Yichun, Jiamusi, and Heihe. Heihe City was below the average value and ranked at the bottom of the province. In 2016, 5 cities reached optimal technical efficiency, and four cities were below the average: Qiqihar, Shuangyashan, Yichun, and Heihe. In 2017, Harbin City, Qiqihar City, Hegang City, Daqing City, and Qitaihe City reached optimal technical efficiency, and the technological efficiency value was 1. The pure technical efficiency value of Jixi is 0.928, which is higher than the average value of other cities and close to the optimal technical efficiency. Shuangyashan City, Yichun City, Jiamusi City, Mudanjiang City, Heihe City, and Suihua City are below the average level, and the efficiency of technological innovation is poor. In 2018, 7 cities reached the optimal technical efficiency, with a technical efficiency value of 1: Harbin, Qiqihar, Hegang, Shuangyashan, Daqing, Qitaihe, Mudanjiang, Jixi, Yichun, Jiamusi, and Heihe below the average value. In 2020, Harbin, Qiqihar, Hegang, Daqing, Qitaihe City, and Suihua City reached optimal technical efficiency with a technical efficiency value of 1, and Jixi City’s pure technical efficiency value was 0.908, which was higher than the average of all cities and close to the optimal technical efficiency. Shuangyashan City, Yichun City, Jiamusi City, Mudanjiang City, and Heihe City are lower than the average of each city, and Mudanjiang City is the smallest and is the lowest in the province. From the perspective of scale efficiency, in 2013, the ranking from largest to smallest is Qitaihe City, Suihua City, Mudanjiang City, Jiamusi City, Hegang City, Shuangyashan City, Jixi City, Yichun City, Qiqihar City, Daqing City, Heihe City, Harbin City. Seven cities were higher than the average, including Qitaihe, Suihua, Mudanjiang, Jiamusi, Hegang, Shuangyashan, and Jixi, while five were lower than the average value. Harbin was the lowest in 2013. In 2014, Hegang, Shuangyashan, Daqing, and Qitaihe had the largest scale efficiency, with a value of 1. Among other

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cities, only Mudanjiang City was a higher-than-average value, and Harbin City had the lowest scale innovation efficiency. In 2015, Shuangyashan, Daqing, and Qitaihe had the largest scale efficiency, while Harbin, Yichun, Mudanjiang, and Heihe were lower than the average value. In 2016, Daqing and Mudanjiang had the highest scale efficiency, with a value of 1, while Harbin, Yichun, Qitaihe, and Heihe were lower than the average value. In 2017, Qitaihe city had the highest scale efficiency, with a value of 1. Jixi City, Hegang City, Shuangyashan City, Daqing City, Jiamusi City, and Mudanjiang City were higher than the average, and Harbin City, Qiqihar City, Yichun City, Heihe City, and Suihua City were lower than the average. In 2018, Qiqihar and Mudanjiang had the highest scale efficiency, with a value of 1, and Harbin, Yichun, Hegang, and Heihe were lower than the average value of all prefectural cities.

18.4 Conclusion This paper adopts the classical function method to measure the factor market’s distortion and the innovation efficiency statistics. The results show that factor market distortions hurt innovation efficiency and significantly inhibit the improvement and development of innovation efficiency Capital market distortions and labor elements market distortions are widespread in our country, and each region has apparent differences. The capital of Heilongjiang province’s level’ s of distortion factor market price is significantly higher than the labor factor market distortions, and innovation efficiency performance in each area is different. In general, the impact of factor market distortion on the innovation efficiency effect is negative; To a certain extent, it inhibits the development of innovation efficiency, which has a particular impact on the economic development of regions and enterprises. The degree of distortion acts on industrial enterprises through the transmission mechanism, and the effect is averse to the transformation and development of regional industries. Acknowledgements This work was supported by the grants Scientific research and innovation team construction project of East University of Heilongjiang (Project No.: HDFKYTD202110); 2019 Heilongjiang Province Philosophy and Social Science Research Planning Project (Project Number: 19GJE285); 2022 key project of the “14th five-year plan” of Educational Science in Heilongjiang Province (Project No.:GJB1422481); Higher Education Teaching Reform Project of Heilongjiang (Project No.:SJGY20210741);2022 China Private Education Association Planning Project (School development category “Research on Teaching Reform and Innovation of Trade Major with Russia under the Background of Cross-border E-commerce” (Project No.: CANFZG22306) Key project of East University of Heilongjiang (Project No.: HDFHX180201);2022 Heilongjiang Province Philosophy and Social Science Research Planning Project (Project Number: 22GJH002);

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References 1. Atkinson, S., Halvorsen, R.: Test of relative and absolute price efficiency in regulated utilities. Rev. Econ. Stat. 40(02), 81–88 (2018) 2. Magee, S.: Factor market distortions, production, distribution, and the dure theory of international trade. Q. J. Econ. 39(4), 623–643 (2017) 3. Dickens, W., Lang, K.: The reemergence of segmented labor market theory. Am. Econ. Rev. 78(2), 129–134 (2019) 4. Wang, H.: The impact of urban factor market distortion on the innovation efficiency of industrial enterprises in Northeast China. Harbin Normal Univ. 42(4), 30–40 (2021) 5. William, T., Dickens, W.: The reemergence of segmented labor market theory. Am. Econ. Rev. 78(2), 129–134 (2016) 6. Lau, L., Yotopoulos, P.: A test for relative efficiency and application to Indian agriculture. Am. Econ. Rev. 61(1), 94–109 (2018) 7. Kornai, J.: The Hungarian reform process: visions, hopes, and reality. J. Econ. Lit. 24(4), 1687–1737 (2019) 8. Tavassoli, S., Carbonara, N.: The role of knowledge variety and intensity for regional innovation. Small Bus. Econ. 43(2), 493–509 (2014) 9. Hsieh, C.T., Klenow, P.J.: Misallocation TFP In China and India. Quart. J. Econ. 124(4), 1403–1448 (2009) 10. Atkinson, S.E., Halvorsen, R.: Parametric efficiency tests, economic of scale, and input demand in U.S. electric power generation. Rev. Econ. Stat. 25(3), 647–662 (1984) 11. Gracia, A.G., Voigt, P., Iturriagagoitia, J.M.Z.: Evaluating the performance of regional innovation systems. In: 5th Triple Helix Conference on “The Capitalization of Knowledge: Cognitive, Economic, Social & Cultural Aspects”. Turin, Italy, pp 18–21 (2005)

Part III

Electronic Commerce Technology and Application

Chapter 19

Optimal Decision of Fresh Agricultural Products’ Supply Chain Under Stochastic Demand and Online Pre-selling Han Xiuping, Yang Guang, and Xu Hang

Abstract This paper discusses the pricing problem of a two-stage dual-channel supply chain system consisting of a single agricultural product supplier and a single online retailer. Research has shown that the key points of Internet distribution are service level and pre-selling discounts, while the demand of traditional channels depends on selling price and freshness. This paper uses the Stackelberg Game approach to study the optimal pricing strategy for products under the centralized and decentralized decisions dominated by suppliers. At the same time, the study calculated the profits of each sector of the supply chain under both decisions and found that the optimal sale price and online service level of fresh produce under the centralized decision are higher than a decentralized decision. The optimized sales price and online service level are getting changed according to the degree of the freshness of the products, but are unaffected by the residual value of farm fresh and the penalty cost. The supply chain gets profit depending on the improvement of the product quality. The optimized sale price and service level will decrease as the presale discount increases, at which point the profits of each sector in the supply chain will be minimized. Therefore, the online discount rate of branded fresh produce is kept at a quite low level.

H. Xiuping (B) Business School, Taizhou University, Taizhou 318000, Zhejiang, China e-mail: [email protected] Harbin University of Commerce Business Administration Postdoctoral Station, Harbin 150028, China Y. Guang · X. Hang School of Business Economics, Harbin Commerce University, HarbinHeilongjiang 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_19

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19.1 Introduction Fresh produce has a short shelf life, is perishable, and can easily be lost in circulation. Due to the asymmetry of information between the supply and demand sides in the traditional supply chain model, the products must go through many links from the field to the table. Each additional link will incur costs and product losses, resulting in fresh produce in the end market, not only high prices, freshness, and nutrition, but also great losses to the entire supply chain. How to reduce this loss has always been a problem that plagues the academic world. The development of the Internet provides an opportunity for the implementation of online pre-selling of fresh produce. Online pre-selling means that online retailers publish pre-sale information of crops to potential consumers online. The customer browses through the orders and places an order online and the supplier then organizes the orders or picks up according to the order and finally delivers the product to the customer. This marketing model helps agricultural suppliers obtain consumer demand in advance, ensures that products are transported directly from farmers to consumers, reduces intermediate links in the supply chain, reduces logistics costs, administrative costs, and wear and tear costs, and improves the economic benefits of the entire supply chain. At present, the online pre-sale model has been successfully applied in the practice of Huaiyuan Pomegranate, Hainan Mango, and American Cherry, but there has been little theoretical research related to it at home or abroad. At first, the research on pre-sale strategy mainly focused on the impact on market demand. Hu and Suo [1, 2] conducted a theoretical analysis of consumer behavior toward fresh products. Prasad et al. [3] and Yu et al. [4] studied the advantages of pre-sale when retailers use pre-sale information to predict current demand. Tang et al. [5], based on the assumption that demand has random disturbance, updated the demand forecast according to the pre-sale information, proving that the enterprise will get more profits and obtain the optimal pre-sale discount rate. Considering the consumer’s risk averseness, Zhao [6] and Weng [7] respectively deduced the optimal pre-sale pricing and inventory strategy under the conditions of no pre-sale, moderate discount and large discount provided by the retailer. Li et al. [8] deduced the optimal strategy for advance sales and on-site sales in the case that there were customers who overestimated the product value during the pre-sale period. Cao [9] made an in-depth analysis of this behavior considered by Li et al. [8] and put forward corresponding preventive measures. Lei Xiao et al. [10] concluded that manufacturers can adopt different pre-sale strategies according to their market strength and consumers’ risk aversion to increasing market demand since manufacturers’ strength is sufficient to affect the market price of raw materials. At present, the pre-sale strategy has become a popular strategy for retailers to sell perishable products, Shao et al. [11] pointed out that the pre-sale of FAP through crowdfunding can promote the connection between production and marketing, reduce logistics costs, and ensure product quality and safety. Dan et al. [12] proposed the “Internet + ” fresh agricultural product supply chain pre-sale model based on consumer crowdfunding given the imbalance of supply and demand in the supply

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chain of fresh agricultural products, and Shao et al. [13] connected community consumers and organized producers through e-commerce platforms and built a joint decision-making model and mechanism of point-to-point vertical integration of fresh agricultural product supply chain, crowdfunding pre-sale, and crowdsourcing production. Hou et al. [14] present a two-warehouse inventory model for noninstantaneous deteriorating items with a price-dependent stochastic demand under advance sales discount and available capacity. Unlike general produce, the ripening of fresh produce is concentrated in a fixed season. Mature products are highly susceptible to deterioration during storage and transportation, making farmers or retailers more willing to sell surplus products at close range after online pre-sales. Offline sales volumes are uncertain due to risks such as uncertainty in output. This paper considers the problem of pre-sale of fresh agricultural product supply chain network with random demand under the line, addresses the problem of unsalable sales of produce due to information asymmetry, reduces the intermediate links in the supply chain, increases revenue for each member of the supply chain, and ensures that consumers have direct access to fresh produce from its origin.

19.2 Model Formation and Basic Assumptions Considering the dual-channel supply chain constructed by suppliers and online retailers. Then, the conceptual model of fresh produce supply chain online pre-selling as shown in Fig. 19.1. The following assumptions are made: Hypothesis 1 Both suppliers and retailers are risk neutral and all information are symmetric on both sides. Suppliers face random market demand and long lead times, and the suppliers can determine the quantity Q of production of products at the beginning of the lead time. Hypothesis 2 In order to further reduce the risk of demand uncertainty, suppliers implement pre-sales through online retailers during the lead times, transforming To send

Advance sale

Agricultural supplier Now the sale

W The order processing

(1-α)p Online retailer

Search and order

Offline customer

Fig. 19.1 Structure of fresh produce supply chain online pre-selling

Online customers

logistics The flow of information

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future random demand into partially deterministic demand. In order to encourage customers to purchase in advance, the price p of the pre-sold product of the online retailer, α is discounted based on the market price, (1−α) p is the price of the pre-sold product becomes. In the pre-sale phase, if there n is a potential consumer in the online market, the online retailer’s service level is s. According to the study of Weng and Parlar [5], the demand function of the advance purchase is set D1 (n, s) = nα + βs, β > 0, to represent the sensitivity of consumers to the level of internet service. Hypothesis 3 In order to compensate the online retailer for the loss caused by the discount, the supplier must first fully meet the demand of the online retailer, and the remaining product can be placed in the retail line of its offline sales. Hypothesis 4 Demand for traditional channels below the supplier’s line is affected by the selling prices and the freshness of the produce, D2 ( p, θ ) = a+λθ −bp+ε. ε is the demand random disturbance factor with probability density f (x) and distribution function F(x) and set ε ∈ [A, B]; θ indicates the freshness of produce, which represents the sensitivity of consumers to product freshness and sales price. Hypothesis 5 The relationship between network service cost and network service level for online retailers is defined as C(s) =

1 2 ηs η > 0 2

For the sake of discussion, the following notation is defined: c Q p ω v h c R

Production costs per unit of product for fresh produce suppliers; Production of fresh produce; Selling price per unit of fresh produce; Wholesale price per unit of fresh produce; Residual value per unit of fresh produce; Out-of-stock costs per unit of fresh produce; represents the overall profit of the supply chain under centralized decisionmaking; expresses the profits of suppliers and retailers under decentralized decisionmaking.

19.3 Model Establishment 19.3.1 Centralized Decision-Making The centralized decision-making indicates that the suppliers of the agricultural product set up their portals for online advance selling and maximize profits across the supply chain by setting the selling price of produce and the level of network services. At present, the problem required to be solved is

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c = (1 − α) p D1 (α, s) + p min{Q − D1 (α, s), D2 ( p, θ )} + v[Q − D1 (α, s) − D2 ( p, θ )]+ 1 − ηs 2 − cQ − h[D2 ( p, θ ) + D1 (α, s) − Q]+ 2

(19.1)

The research method set out in Literature [15] is used. Let z = Q − nα − βs − a − λθ + bp denoting the inventory factor, further collation gives the expected profit of the supply chain as 1 E c = [(1 − α) p − c](nα + βs) − ηs 2 + ( p − c)(a + λθ − bp + z) 2 z z B + p (x − z) f (x)dv + (z − x) f (x)dx − h (x − z) f (x)dx x A

z

A

(19.2) η Proposition 1 When 2bη > β 2 (1 − α)2 + ( (1−F(z)) is established, the optimal p+h−v) f (z) solution of each decision variable under centralized decision-making satisfies 2

⎧ (1 − α)(nαη − β 2 c) + ηM ⎪ ∗ ⎪ p = ⎪ ⎪ ⎪ 2bη − (1 − α)2 β 2 ⎪ ⎪ ⎨ β[(1 − α) p ∗ − c] s∗ = ⎪ η ⎪ ⎪  ∗  ⎪ ⎪ p +h−c ⎪ ⎪ ⎩ z ∗ = F −1 ∗ p +h−v where M = a + λθ + z ∗ + bc +

z∗ A

(19.3)

(x − z ∗ ) f (x)dx.

Proof The hessian matrix of Formula (19.2) is ⎡

⎤ −2b (1 − α) 1 − F(z) ⎦ H = ⎣ (1 − α)β −η 0 1 − F(z) 0 −( p + h − v) f (z) When is established, the matrix H is negatively definite, so the objective function p, s, z is the concave function. The first-order condition on Eq. (19.2) gives Eq. (19.3). Conclusion 1: When the inventory factor remains unchanged in the centralized decision-making, the optimal sales price p ∗ and network service level s ∗ increase with the increase in the product freshness, indicating that the higher the freshness of produce, the higher the selling price, and online retailers are more willing to enhance their network service level for the agricultural products with high freshness, thereby increasing the demand of products in pre-sale period.

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Proof The first-order partial derivative of p ∗ and s ∗ in Formula (19.3) on the product freshness θ may be proved.

19.3.2 Decentralized Decision-Making Under decentralized decision-making, the agricultural supplier is the leader of the Stackelberg Game, and the online retailer is the follower. The agricultural supplier first determines the wholesale price ω and its retail price P. The online retailer determines the service level of online sales after observing the wholesale price and sales price determined by the supplier of the agricultural product. Then, the problem is turned into the solution to the following problem. The supplier’s expected profit is M = ωD1 (α, s) + p min{Q − D1 (α, s), D2 ( p, θ )} + v[Q − D1 (α, s) − D2 ( p, θ )]+ − cQ − h[D1 (α, s) + D2 ( p, θ ) − Q]+ The retailer’s profit is 1 R = [(1 − α) p − ω]D1 (α, s) − ηs 2 2 Let z = Q − nα − βs − a − λθ + bp represents the inventory factor, further collation gives the supplier’s expected profit as z E M = (ω − c)[nα + βs] + ( p − c)(a + λθ − bp + z) + p

(x − z) f (x)dx A

z +v

B (x − z) f (x)dx − h

A

(x − z) f (x)dx

(19.4)

z

Retailer profit is 1 R = [(1 − α) p − ω]D1 (α, s) − ηs 2 2

(19.5)

Proposition 2 When considered, the optimal solution of each decision variable of the supplier satisfies

19 Optimal Decision of Fresh Agricultural Products’ Supply Chain Under …

⎧ (1 − α)(nαη − β 2 c) + 2ηN ⎪ ⎪ p ∗∗ = ⎪ ⎪ ⎪ 4bη − (1 − α)2 β 2 ⎪ ⎪ ⎨ ηnα (1 − α) p ∗∗ + ω∗∗ = ⎪ 2 2β 2 ⎪ ⎪   ⎪ ∗∗ ⎪ p +h−c ⎪ ⎪ ⎩ z ∗∗ = F −1 ∗∗ p +h−v

237

(19.6)

z ∗∗ Here, N = a + λθ + z ∗∗ + bc + A (x − z ∗∗ ) f (x)dx is considered. Similarly, the optimal solution to the network service level of the retailer satisfied s ∗∗ =

β[(1 − α) p ∗∗ − ω] η

(19.7)

Proof Like the proof of Proposition 1. Conclusion 2: When the inventory factor z is constant and the number of potential consumers in the pre-sale stage satisfies ηn ≥ 2b in the decentralized decisionmaking, p ∗ > p ∗∗ , s ∗ > s ∗∗ may be obtained, the optimal selling price of agricultural products in the centralized decision-making is lower than that in decentralized decision-making, and the optimal network service level is higher than that in decentralized decision-making. Proof It can be proved by the differences p ∗ and p ∗∗ , s ∗ and s ∗∗ in Eqs. (19.3) and (19.6), respectively. 1. It is required to satisfy ηn ≥ 2b, indicating that the agricultural product supplier is allowed to open up the pre-sale channels for sales when the number of consumers in the pre-sale phase is sufficient. 2. s ∗ > s ∗∗ , the level of network service of agricultural products in centralized decision-making is higher than that in decentralized decision-making, indicating that online retailer will enhance their investment in the network service level and increase the demand in the pre-sale phase to reduce the impact of uncertain demands from traditional channel consumers in the case of centralized decisionmaking. 3. p ∗ > p ∗∗ The optimal selling price of agricultural products in decision-making is higher than that in the case of decentralized decision-making. This conclusion is exactly opposite to the supply chain for traditional agricultural products. Because consumers are more concerned with the quality of fresh produce than the price under pre-sale network, agricultural suppliers will adopt an agile logistics distribution approach with immediate delivery of cold-chain transportation upon pick-up after fully understanding customer demands in the decision-making to ensure that consumers are allowed to have the freshest products at the earliest.

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19.4 Numerical Examples It supposed that the parameters relating to a fresh agricultural products supply chain are as follows: a = n = 10, Q = 20, β = η = h = λ = 1, c = θ = 2, v = 1, b = 0.5. The random perturbation factor ε follows the uniform distribution [0, 1] and the change in the value of the pre-sale discount rate is demonstrated as follows.

19.4.1 Impacts of Pre-sale Discount Rate on Sales Price and Network Service Level Set the pre-sale discount rate to satisfy α ∈ (0, 1). The values of the remaining parameters are shown above, and the impact of different pre-sale discount rates on optimal selling price and network service level is shown in Figs. 19.2 and 19.3. It may be inferred from Figs. 19.2 and 19.3 that the optimal selling price decreases under centralized and decentralized decision-making, the optimal network service level decreases, and the optimal selling price and the optimal network service level at the time of concentration are higher than those values in the decentralization with the increase in the pre-sale discount rate of α. This indicates that powerful fresh produce suppliers are more willing to set up their portals to pre-sell produce and invest higher funds to improve the level of online services to the optimal level to attract consumers to purchase in advance to reduce the sales pressure on offline channels. While weaker fresh produce suppliers have to sell to third-party online retailers at a certain wholesale price for pre-selling products in advance, online retailers pre-sell produce with their websites, and their network service level is negative when the Fig. 19.2 Effect of pre-sale discount rate on optimal sales price

200

sales price under the centralized decision

100

sales price under the decentralized decision

0 0

0.5

1

α

Fig. 19.3 Effect of pre-sale discount rate on optimal network service level

wholesale price under the decentralized decision

150 centralized decision

100 s

50 0 -50 0

0.5

1

decentralized decision

19 Optimal Decision of Fresh Agricultural Products’ Supply Chain Under …

239

pre-sale discount rate satisfies α > 0.53, delivering a negative impact on the presales volume, and the cost is required to be invested; hence, online retailers will not purchase products from the agricultural suppliers for pre-sales.

19.4.2 The Effect of Pre-sale Discount Rate on the Optimal Profit of Each Member in the Supply Chain Figures 19.4 and 19.5 respectively depict the changes in the pre-sale amounts and optimal profits of all the members under two decisions when the pre-sales discount rate changes. It may be inferred from Figs. 19.4 and 19.5 that when the discount rate for presales is lower, more favorable supply chain members are available, both the optimal sales price and the optimal network service level are raised, and the profits of all members of the supply chain increase; meanwhile, it may be concluded that both the pre-sale discount rate and pre-sale volume under the two types of decision-making are lowered due to that actual pre-order product quantity of potential consumers decreases in pre-sale period, and the consumers are attracted to buying products merely by relying on the online retailer to improve the network service level. In the decentralized decision-making, the profit of online retailers is negative when the pre-sale discount rate is satisfies α > 0.31; hence, online retailers will not purchase agricultural products from agricultural products suppliers for advance selling, the supply chain will break, and it is also the reason why the network pre-sale rate of 150

D1

100

centralized decision

50 decentralized decision

0 0

0.5

1

α Fig. 19.4 Effect of pre-sale discount rate on pre-sales

protif

1000

decentralized supplier

500

decentralized retailer

0 0 -500

0.1

0.2

0.3

0.4

α

Fig. 19.5 Effect of pre-sale discount rate on optimal profit

decentralized the entire chain centralized the entire chain

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most merchants is not greater than 0.3, as it will be more. So, favorable for the supplier and online retailer when the lower the pre-sale rate is. Hence, the discount rate will be lower or even zero when rarer and higher products are subject to pre-selling during advance selling, and occasionally such products will be sold at a premium.

19.5 Conclusion This paper constructs a dual-channel supply chain pricing model for fresh produce under centralized decision-making and decentralized decision-making, respectively, in the context of networked pre-sales. The Stackelberg Game Technique is applied to study the pricing of two types of models and the profits of all the members. It has been shown that the optimal retail price is positively correlated with the optimal network service level, and the service prices are elastic. When the number of consumers in the pre-sale stage is sufficient, the produce supplier may explore pre-sale channels for sales, and the optimal selling price and the level of network service increase as the freshness of the product increases. Both optimal selling prices and network service levels for fresh produce are higher in the centralized decision-making than those in decentralized decision-making. It may be inferred from the numerical examples that the optimal selling price decreases, the optimal network service level decreases, and the profit of all the members of the supply chain decreases with the increase of the discount rate for pre-sales under centralized and decentralized decision-making. While the supply chain will break when the pre-sale discount rate is greater than a certain threshold value in the decentralized decision-making, indicating that a lower or zero discount rate will be offered for rarer and higher brand fresh produce. Acknowledgements This research is supported by the Achievements of Taizhou Philosophy and social science planning project (Grant nos. 22GHB27), 2017 in-station postdoctoral research support program (Grant nos. BSH022).

References 1. Hu, N., Chen, X., Zhang, N.: Influence of service quality of agricultural products E-commerce platform on customer loyalty—the mediating role of customer engagement. Int. J. Smart Bus. Technol. 9(1), 13–28 (2021) 2. Suo, C.X., Ma, Y.J., Wang, P., Xiao, B., Y.Y.: Consumers’ sustainable product preference and green supply chain management analysis. Int. J. u- e-Serv. Sci. Technol. 9(7), 203–212 (2016) 3. Prasad, A., Stecke, K.E., Zhao, X.: Advance selling by a newsvendor retailer. Prod. Oper. Manag. 204, 129–142 (2011) 4. Yu, M., Ahn, H.S., Kapuscinski, R.: Rationing capacity in advance selling to signal quality. Manage. Sci. 61, 560–577 (2015) 5. Tang, C.S., Rajaram, K., Alptekino, L.A.: The benefits of advance booking discount programs: model and analysis. Manage. Sci. 50, 465–478 (2004)

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6. Zhao, X., Stecke, K.E.: Pre-orders for new to-be-released products considering consumer loss aversion. Prod. Oper. Manag. 2(19), 98–215 (2010) 7. Weng, Z.K., Parlar, M.: Integrating early sales with production decisions: analysis and insights. IIE Trans. 1, 1051–1060 (1999) 8. Li, Y., Mi, Y.: Advance selling decisions with overconfident consumers. J. Ind. Manage. Optim. 3(12), 891–905 (2016) 9. Zeng, Y., Qiu, G.S., Huang, S.J.: The exaggeration of product quality and its precautions in the pre-order crowdfunding. J. Manage. Sci. China 7(22), 89–106 (2019) 10. Ma, S., Li, G., Sethi, S.P., Zhao, X.: Advance selling in the presence of market power and risk-averse consumers. Decis. Sci. (2018) 11. Shao, T.W., Lv, X.M.: Price fresh agricultural products by C2B based on F2F. J. Manage. Sci. China 24(11), 146–152 (2016) 12. Dan, B., Zheng, K.W., Shao, B.J.: Research on “Internet +” fresh agricultural products supply chain pre-sale model based on consumer crowd-funding. Rural Econ. 2, 83–88 (2017) 13. Shao, T.W., Lv, X.M.: Joint decision between crowdfunding in preselling and crowdsourcing in production on flesh agricultural products. Syst. Eng. Theory Pract. 6(38), 1502–1511 (2018) 14. Hou, K.L., Hari, M., Srivastava, L.C.: Optimal replenishment policy for non-instantaneous deteriorating items with stochastic demand under advance sales discount and available capacity. Math. Meth. Appl. (2022) 15. Wang, Y., Jiang, L., Shen, Z.J.: Channel performance under consignment contract with revenue sharing. Manage. Sci. 50(1), 34–47 (2004)

Chapter 20

Research on the Impact of Cross-Border Data Flow Restrictions on Digital Service Trade and Its Countermeasures Hui-ying Yang and Xi-yuan Tian

Abstract The improvement of digital transmission speed has promoted the rapid development of digital service trade, but the development of digital service trade is restricted by problems such as cross-border data flow restrictions. Based on the digital service trade restriction index constructed by OECD, the trade gravity model is constructed by selecting the data from 2014 to 2020 to empirically test the impact of cross-border data flow on the export of digital service trade, and the empirical results are robust. The research finds that the cross-border data flow restrictions have an inverted U-shaped impact on the export of digital services trade, and the inhibitory effect of the implementation of data cross-border flow restrictions by exporting countries is stronger than that of importing countries. There is a negative correlation between the flow of bilateral digital service trade and the population of importing countries. The greater the cultural distance, the stronger the inhibitory effect on the development of bilateral digital service trade. Therefore, for the development of digital service trade, it is necessary for all countries to further reduce the restrictions on cross-border data flow, promote the digital trade policy negotiation under the multilateral co-operation mechanism and shorten the cultural distance through cultural exchanges.

20.1 Introduction As the fifth type of production factor, data supports the development of the digital economy. Although the cross-border flow of data can promote the development of digital trade and give birth to new formats of service trade, it also brings challenges to national network security, personal privacy protection and data sovereignty [1]. Since digital service trade is a data-intensive form of trade, restrictive measures on cross-border data flows are bound to have a significant impact on digital service trade H. Yang · X. Tian (B) Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_20

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[2]. Existing research confirms that data flow restriction measures have a significant negative impact on the export probability and export scale of digital services in a country, but as a trade protection policy, data cross-border flow restriction measures are conducive to improving the efficiency of domestic resource allocation to a certain extent [3–5]. In this context, it is necessary to study how restrictions on cross-border data flow affect the development of digital services. What mechanisms or channels are used to restrict cross-border data flows that affect the export of digital services? Do the levels of cross-border data movement restrictions between exporting and importing countries affect bilateral trade flows in digital services to the same extent? These questions need to be answered urgently. The contribution of this paper is: first, to build a new model based on the bilateral trade gravity model to examine the overall impact of cross-border data flow restrictions on digital service exports; second, a qualitative analysis of the impact of cross-border digital movement restrictions on digital services exports is elaborated; third, based on the actual data, testing the inhibition effect of exporting and importing countries and comparing the intensity of the two effects will help to obtain targeted policy enlightenment from them.

20.2 Analysis on the Current Situation of Restrictions on Digital Service Trade and Cross-Border Data Flow 20.2.1 Development Status of Digital Service Trade Digital service trade is a digital trade that eliminates the digitization of trade in goods and can also be considered digital trade in a narrow sense, including the digitization of the traditional service trade industry [6], as well as a new economic model or format spawned after technological iteration, that is, digital industrialization [7]. There are three statistical categories of digital trade, the narrowest scope includes only digital technology services and new economic models or formats spawned by technological upgrading, the narrow scope is to increase the digitization of traditional trade in services on this basis, and the wide scope is expanded from the narrow scope to the digitization of traditional trade in goods. When digital transactions only refer to digital deliverable, it is a narrow-caliber digital trade, that is, digital services trade, in which digitally delivered services correspond to UNCTAD (2018) information and communication services plus digital technology empowerment services, and the scope of accounting specifically includes insurance and pension services, financial services, intellectual property royalties not included elsewhere, telecommunications, computer and information services, research and development services, professional and management consulting services, architecture, engineering, scientific and other technical services, Other categories of services not covered elsewhere, such as commercial services, audio-visual services and related services, health services, educational services and heritage and entertainment services [8]. This paper selects

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US dollars at current prices in billions

10000

0 2011

2012 Canada France India Japan

2013

2014

2015

2016

Year

2017

2018

2019

2020

China Germany Italy Korea, Republic of

Fig. 20.1 Global export scale of digital deliverable trade from 2011 to 2020 (Source United Nations Trade and development database)

the digital delivery service data of the top ten countries in terms of GDP in 2021 and analyzes the development status of digital service trade. From the perspective of trade trends, (as shown in Fig. 20.1), between 2011 and 2020, the total exports of digitally deliverable services’ trade in ten countries have shown an increasing trend. The export volume of digital deliverable services in the USA has been in a leading position, and in 2020, its digital delivery service trade imports and exports were 317.625 billion dollars and 533.093 billion dollars, accounting for 16.83% of the world total and 75.55% of the total service trade of the country. In 2020, the UK’s digital delivery service trade exports were second only to those with the USA, amounting to 286.701 billion dollars, accounting for 9.05% of the world and 83.72% of the country’s total trade in services. In terms of growth, China’s exports grew from 75.007 billion in 2011 to 154.375 billion dollars in 2020, an increase of 2.058 times, the largest increase, accounting for 4.87% of the world’s total in 2020. In terms of annual growth rates, the annual growth rate of trade in digital delivery services in most countries between 2010 and 2020 was mostly positive. However, in 2015, in addition to the USA, the other nine countries experienced negative growth, and from 2019 to 2020, it may be affected by the global spread of COVID-19; the UK, France, Germany, Italy and Japan have all experienced different degrees of decline in growth rates.

20.2.2 Current Situation of Cross-Border Data Flow Restrictions The Organization for Economic Co-operation and Development (OECD) developed the Digital Service Trade Restrictiveness Index (DSTRI) in 2019 to define, classify and quantify the regulatory barriers that affect digital-driven trade in services. Impose

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domestic taxes and fees on digital products and services, and impose discriminatory public procurement restrictions on digital services. DSTRI includes five policy areas: infrastructure and connectivity, electronic transactions, payment systems, intellectual property rights and other barriers affecting trade in digital delivery services. For the measurement of barriers to cross-border data flow restrictions, Zhou N. (2022) scholars propose to use the sum of infrastructure and connectivity indices and other barriers affecting trade in digital delivery services to measure. According to this data, the level of cross-border data flow restrictions in developed countries is generally low and remains below 0.13. Among them, between 2014 and 2021, Canada’s degree of restriction is at the lowest level, and its value is 0. This is followed by the USA and the UK, which has a value of 0.04. The degree of restriction on cross-border data flows in developing countries is at a high level, with the levels of cross-border data flows in China and India being 0.347 and 0.225 in 2021, ranking first and second among the ten countries, respectively. From the perspective of the change in the degree of restrictions on digital trade policies of various countries from 2014 to 2021, the level of cross-border data flow restrictions in developed countries represented by Canada, the USA and the UK has remained unchanged. It should be noted that although Japan’s level of crossborder data flows is very low, it has grown by 0.04. India had the largest growth of 0.084, nearly 4 times that of China, Germany, France, Italy and South Korea. This is consistent with the fact that the development level of digital delivery service trade in developed countries is relatively high and the level of development in developing countries is low, that is, the level of digital trade development between countries is negatively correlated with the level of cross-border data flow restrictions between countries.

20.3 Analysis of the Impact of Cross-Border Digital Flow on Digital Service Trade 20.3.1 Raising the Trade Cost of Enterprises The restrictive measures related to the cross-border flow of data include three aspects, first, data localization measures force multinational companies or digital service exporters to establish new data centers or storage centers in the host country and establish a system to ensure that data does not flow to other countries, the establishment of data centers or systems not only requires financial support, but also needs the technical capabilities of enterprises and finally data localization measures increase the cost of foreign services for consumers in host countries [9]. In the face of strict privacy protection policies, enterprises have to hire professional lawyers or data protection consultants to invest more money in database management or software upgrades, which directly raises compliance costs [10]. Finally, there are content

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review barriers to entry, which will lead to an increase in the cost of corporate supervision, the same content is restricted to different degrees in different countries [11], and the differences in regulatory policies and the uncertainty of digital trade policies will further increase the cost of regulatory transformation caused by differentiated regulatory policies.

20.3.2 Hinder the Improvement of Production Efficiency The improvement of research and development efficiency formed by data elements and artificial intelligence technology means that the efficiency of knowledge creation is improved, and from the perspective of growth economics, knowledge creation is one of the important reasons for the improvement of production efficiency [12]. Cross-border data flow measures will hinder international co-operation between enterprises, and many data elements in one country cannot be combined with mature digital technologies in another country, which is not conducive to the improvement of the production efficiency of one country. On the other hand, the restrictive measures for the cross-border flow of data implemented by exporting countries will not only increase the trade costs of digital trading enterprises in the country, affect the development of digital trade, but also may affect the export country’s enterprises to carry out foreign direct investment (OFDI), the purpose of digital trading enterprises in exporting countries to carry out OFDI is usually to create subsidiaries or research and development centers abroad, the cost of the cross-border data flow will increase, and the scale of OFDI by enterprises in exporting countries will be reduced. Crossborder data movement restrictions prevent existing parent companies from sharing information with offshore subsidiaries or R&D centers, and the reverse technology spillover effects of OFDI will also be reduced, hindering production efficiency.

20.3.3 Reduce the Scale of Export Trade On the one hand, with the proliferation of data resources and the optimization of machine learning algorithms, more effective information and insights can be mined and refined to provide support for the development of new products and services to solve various complex problems, while strict data localization measures make foreign data unobtainable, and enterprises cannot use their mature digital analysis technology to provide innovative products and services for enterprises in other countries, achieve more effective and accurate matching between supply and demand and expand the scale of exports [13]. On the other hand, heavy intermediary responsibility will weaken the ability of enterprises to use their own technological advantages to collect and store massive data and weaken their data resource advantages. In addition, the strict control of cross-border data flow by exporting countries will restrict domestic enterprises from importing emerging digital service products, which is not

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conducive to the diversification of domestic digital service products, and the single market environment makes it difficult for enterprises to develop high-quality products that meet the needs of market segments, so the number of cross-border data flow restrictions is not conducive to the development of new products and the development of new markets.

20.4 Econometric Models and Variable Demarcation 20.4.1 Measurement Model Setting According to the gravitational model, the impact of cross-border data flow restrictions on digital services trade is examined, considering that cross-border data flow restrictions may have an inverted “U”-shaped impact on digital services trade, that is, appropriate data cross-border flow restrictions can protect domestic enterprises and enhance the international competitiveness of domestic enterprises’ exports, and excessive cross-border data flow restrictions will inhibit exports, so the square term of the DSTP is added. In order to avoid the impact of violent fluctuations and heteroscedasticity of the data on the regression results, the variables are logarithmically processed, and the final setting is set as shown in (20.1), a baseline regression model containing the quadratic term of the digital cross-border flow restriction measures. LnExtijt indicates the export volume of digitally deliverable services trade from country i to country j in the t-years; DSTPit in the equation represents the crossborder data flow restriction index of the i country, DSTPjt represents the cross-border data flow restriction index of the j country, Z is the control variable vector, μi and ν j represent the individual fixed effect of the importing and exporting countries, σ t represents the year fixed effect and εijt is the error term. LnEXTi jt = β0 + β1 DSTPit2 + β2 DSTPit + β3 DSTP2jt + β4 DSTP jt + β5 Z + μi + ν j + σt + εi jt

(20.1)

20.4.2 Index Meaning and Data Source Description The Interpreted Variable. The interpreted variable LnEXT is bilateral digital delivery trade export flow. According to the definition of UNCTAD (2015), digital delivery trade is ICT-enabled trade in services and digital delivery services data. It can be obtained from the balance of payments, accounting specifically

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includes insurance and pension services, financial services and intellectual property royalties not included elsewhere, telecommunications, computer and information services, research and development services, professional and management consulting services, construction, engineering, scientific and other technical services, other business services not included elsewhere, audio-visual services and related services, service categories such as health services, educational services and heritage and recreation services. Core Explanatory Variables. DSTPit and DSTPjt are selected to reflect the crossborder data flow restriction index of exporting and importing countries. DSTP2 it and DSTP2 jt are the secondary terms of the cross-border data restriction barrier index. This paper uses the DSTRI developed by OECD to strip out the relevant indicators, namely “infrastructure and connectivity” and “other barrier factors affecting digital service trade”. The two sub-indicators are combined to obtain a comprehensive index to describe the data cross-border flow barrier index. The larger the index is, the more serious the barrier is, and vice versa. Control Variables. The control variables in this paper use the logarithm of the gross domestic product (lgdpi, lgdpj) of exporting country I and importing country j to express their economic scale, respectively. The logarithm of the total population of the target country (lpop) was used to measure the market size of the target country. Its also includes the distance between the two countries (dist), whether the two countries are contiguous (contig), whether they have a common language (comlang_ ~f), whether they have colonial ties (colony), and whether they once belonged to the same country (smctry), so as to control the relevant trade costs. Data Source. Since the time range of OECD DSTRI database is 2014–2020 and considering the availability of digital delivery service trade, the time range of the sample selected in this paper is 2014–2020, including 16 exporting countries and 19 importing countries. The data of digital delivery service trade is obtained from WTO database according to the definition of digital delivery trade by UNCTAD; DSTP data comes from OECD database; GDP data is from UNCTAD database; population data comes from WB database; other control variables are from CEPII database, as shown in Table 20.1.

20.4.3 Estimation Method According to the Hausmann test results, the fixed effect model is suitable for regression in this paper. In addition, considering that the “big N and small T ” data structure in this paper may lead to heteroscedasticity problems, it is decided to use the Poisson estimation (PPML) with high-dimensional fixed effect as the robustness test to effectively deal with the heteroscedasticity problems in the samples.

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Table 20.1 Statistical description of each variable Variable

Sample size

Mean value

Median

Standard deviation

Minimum value

Maximum 11.385

Lext

1554

6.32

6.356

2.076

0

Dstpj

1554

0.088

0.062

0.073

0

0.344

Dstpi

1554

0.087

0.079

0.043

0.04

0.22

Lpop

1554

3.493

3.203

1.566

0.723

7.23

Lgdpi

1554

13.151

13.028

1.533

10.671

16.885

Lgdpj

1554

13.778

13.726

1.432

9.764

16.885

Ldis

1554

7.478

7.334

1.026

4.952

9.715

20.5 Results Inspection and Analysis 20.5.1 Full Sample Regression 20.5.2 Robustness Test PPML is used to test the effect of data cross-border flow barriers on the export scale of digital service trade, in order to eliminate the impact of heteroscedasticity. As shown in Table 20.2, column 5 shows the simple PPML regression, and column 6 shows the regression results with a time fixed effect. The final results show that the regression coefficient of the cross-border data flow restriction of the exporting country is negative in each column, and it is significant in column 6, indicating that no matter before or after the time effect is added, the export and the restrictions on crossborder data flow of importing countries have a certain blocking effect on bilateral trade flow, and the inhibitory effect of exporting countries is more obvious, which is consistent with the conclusion of the regression results, which further confirms that the regression results and model are robust.

20.6 Conclusions and Countermeasures Based on the above analysis, the main conclusions of this paper are as follows: first, the barriers to the cross-border data flow of import and export countries have a negative impact on trade and export, and the relationship between them presents an inverted “U-shaped” relationship. The reason may be that full opening up in the case of immature technical conditions will threaten the national economic security and pose great hidden dangers to the country’s welfare and economic development. Therefore, appropriate protection is conducive to the development of digital service trade, and exporting countries have a greater restrictive effect on trade. Second, there

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Table 20.2 Regression estimation result

Dstpj2 Dstpi2

(1)

(2)

(3)

(4)

(5)

(6)

Lext

Lext

Lext

Lext

Lext

Lext

−2.933

−9.481**

−9.219**

−2.19**

−2.025***

−2.010***

(−0.75)

(−2.55)

(−2.46)

(−2.46)

(−3.08)

(−2.99)

−44.729*** −52.876*** −53.598*** −53.598*** −14.234*** −14.348*** (−4.45)

(−5.58)

(−5.66)

(−5.66)

(−7.14)

(−7.18)

0.018

2.583***

2.364*

2.364*

0.729***

0.711***

(0.01)

(2.06)

(1.85)

(1.85)

(2.92)

(2.74)

9.216***

11.102***

10.859***

10.859***

2.939***

2.918***

(3.91)

(4.99)

(4.86)

(4.86)

(6.61)

(6.48)

−0.328***

−0.327***

−0.320***

−0.320***

−0.073***

−0.073***

(−8.67)

(−9.03)

(−8.71)

(−8.71)

(−5.66)

(−5.46)

1.178***

1.189***

1.183***

1.183***

0.205***

0.205***

(30.76)

(32.30)

(31.56)

(31.56)

(15.86)

(15.33)

0.981***

0.968***

0.963***

0.963***

0.154***

0.154***

(51.82)

(51.21)

(49.91)

(49.91)

(41.39)

(41.39)

−0.717***

−0.717***

−0.717***

−0.115***

−0.115***

(−6.55)

(−6.55)

(−6.55)

(−8.23)

(−8.23)

Comlang_ ~f

0.916***

0.916***

0.916***

0.114***

0.114***

(8.47)

(8.48)

(8.48)

(8.91)

(8.91)

Colony

0.760***

0.754***

0.754***

0.111***

0.110***

(5.83)

(5.80)

(5.80)

(6.67)

(6.60)

0.529***

0.532***

0.532***

0.099***

0.100***

(3.61)

(3.63)

(3.63)

(3.43)

(3.46)

0.000

0.000

(.)

(.)

0.181***

0.000

(2.02)

(.)

0.220***

0.000

(2.46)

(.)

0.220***

0.000

(2.46)

(.)

0.196***

0.000

(2.13)

(.)

0.249***

0.000

(2.71)

(.)

0.276***

0.000

Dstpj Dstpi Lpop Lgdpj Lgdpi Contig

Smctry 2014.year 2015.year 2016.year 2017.year 2018.year 2019.year 2020.year

(continued)

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Table 20.2 (continued) (1) _cons N

(2)

(3)

(4)

(3.00)

(.)

(5)

(6)

−15.163*** −15.110*** −15.111*** −14.920*** −1.936***

−1.921***

(−24.55)

(−24.59)

(−23.57)

(−23.25)

(−10.30)

(−9.70)

1554.000

1554.000

1554.000

1554.000

1554.000

1554.000

R2_a

0.795

F

502.608

Note the statistical value of T in brackets, ***, ** and *, respectively, represent the significant levels of 1, 5 and 10% (the notes in the following table are similar)

is a significant positive relationship between the GDP of importing and exporting countries and bilateral trade flows. Among them, the GDP of importing countries has a greater impact on trade flows. Because the higher the GDP of importing countries represents the strong consumption capacity of importing countries, but the more the population of importing countries, the more unfavorable it is to the expansion of digital service trade [14]. The reason may be that in digital service trade, the population cannot represent the potential digital trade scale, and the more the population, the smaller the per capita GDP, the more unfavorable it is to the development of trade in services. Third, the geographical distance between the two countries will significantly reduce trade exports. Due to the increase of geographical distance, the interconnection cost of telecommunications infrastructure will increase, which will reduce trade exports. Cultural similarity significantly increases trade exports, which may be due to the reduction of communication costs. The countermeasures and suggestions of this paper are as follows: first, moderately reducing digital trade barriers is conducive for enterprises to adopt foreign more advanced digital technology to improve productivity and improve the competitiveness of Chinese enterprises, in order to improve the possibility of domestic digital service enterprises entering the international market [15]. Second, with the in-depth development of regional economic integration, most of the signed regional trade agreements involve digital policy provisions, but there are great differences. Therefore, under the multilateral co-operation mechanism represented by WTO, it is necessary for all countries to conduct policy negotiations on cross-border data flow restrictions and increase policy coordination, which is of great practical significance for the vigorous development of global digital service trade. Third, we should continue to deepen cross-border cultural exchanges, continue to promote the construction process of the “The Belt and Road”, gradually expand the export of content culture, shorten the cultural distance through cultural exchanges and promote foreign trade.

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References 1. Qi, J., Qiang, H.: Cross border data flow restrictions, digital service investment and manufacturing export technology complexity. Ind. Econ. Res. 01, 114–128 (2022) 2. Zhou, N., Yao, T., Huang, N.: Empirical study on the binary marginal impact of data crossborder flow barriers on digital service trade. Int. Econ. Trade Explor. 38(02), 4–21 (2022) 3. Zhou, N., Yao, T.: Empirical study on the impact of restrictive measures on cross-border data flow on the technical complexity of digital trade exports. Guangdong Univ. Finan. Econ. 36(02), 4–15 (2021) 4. Zhou, N., Yao, T.: Measurement and heterogeneity of the impact of cross-border data flow restrictions on digital service imports. Univ. Int. Bus. Econ. 5(02), 1–15 (2021) 5. Cai, Y., Ma, W.: The impact of data elements on high-quality development and data flow constraints. Quant. Econ. Technol. Econ. Res. 38(03), 64–83 (2021) 6. Chen, H.: Whether the signing of “Free Flow of Cross-Border Data” can effectively promote digital trade—an empirical study based on OECD service trade data. Int. Econ. Trade Explor. 36(10), 4–21 (2020) 7. Yue, Y., Li, R.: Comparison of international competitiveness of digital service trade and its enlightenment to China. China’s Circ. Econ. 34(04), 12–20 (2020) 8. Yue, Y., Zhao, J.: Research on the characteristics and influencing factors of digital service export-analysis based on transnational panel data. Shanghai Econ. Res. 08, 106–118 (2020) 9. Zhou, N., Yao, T.: Empirical study on the trade inhibitory effect of restrictive measures in digital service trade. China Soft Sci. 02, 11–21 (2021) 10. Qi, J., Qiang, H.: Does the restrictive measures of digital service trade affect the export of services?—empirical analysis based on digital service industry. World Econ. Res. (09), 37– 52+134–135 (2021) 11. Wang, L.: Connotation measurement and international governance of digital trade barriers. Explor. Int. Econ. Trade 37(11), 85–100 (2021) 12. Zhu, F.: Constraints and promotion path of high-quality development of China’s digital service trade. Acad. Forum 44(03), 113–123 (2021) 13. Wu, S., Zhang, H., Tian, W.: Export characteristics, barriers and conglomeration effects of digital service trade. China Sci. Technol. Forum 03, 72–81 (2022) 14. Jiang, T., Wang, H., Qin, Q.: Export inhibition effect of bilateral digital trade barriers -empirical evidence based on 49 economies. China’s Circ. Econ. 36(07), 62–72 (2022) 15. Peng, Y.: Research on trade regulation of data localization measures. Glob. Legal Rev. 40(02), 178–192 (2018)

Chapter 21

Research on Trade Facilitation of Cross-Border Export B2C E-Commerce Based on Entropy Weight Method Li Dai, Xu Zhang, and Yanhua Li

Abstract In the context of electronic information technology’s continuous development and maturity, trade facilitation is worth studying to promote the stable development of cross-border B2C e-commerce. This paper uses the entropy weight method and the fixed effect model to analyze the influencing factors of cross-border export B2C e-commerce trade facilitation. It constructs the evaluation system of crossborder export B2C e-commerce trade facilitation in China. The fixed effect model is used to conduct empirical research on the influencing factors to explore the influence of selected indicators on trade facilitation. Combined with the entropy weight method and fixed effect model, this paper evaluates the trade facilitation level of cross-border export B2C e-commerce. It provides a reference for promoting the development of cross-border export B2C e-commerce trade.

21.1 Introduction China’s cross-border trade index increased to 56th in the world in 2019. But in the optimization process, there are still various trade facilitation problems. The impact of trade facilitation on cross-border export of B2C e-commerce cannot be underestimated. Promoting trade facilitation and solving technical and institutional obstacles are essential measures to promote the healthy and stable development of China’s cross-border export of B2C e-commerce trade. This paper assigns weights to verify the rationality of the selected indicators and improves the evaluation system of China’s cross-border export B2C e-commerce from the perspective of trade facilitation. By studying the impact of the current level of trade facilitation on all aspects of cross-border export B2C e-commerce, L. Dai (B) · X. Zhang · Y. Li East University of Heilongjiang, Harbin 150066, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_21

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this paper analyzes the correlation between China’s cross-border export B2C ecommerce export and the degree of trade facilitation. It then discusses how to use trade facilitation to promote the stable development of China’s cross-border export B2C e-commerce from the perspective of the correlation between the two, to improve further the possession of Chinese products in the global market and alleviate the economic pressure caused by the epidemic and trade war.

21.2 Research Status First of all, in terms of customs policy, Cheng et al. [1] mentioned that the establishment of a new type of free trade zone and the introduction of policy dividends for cross-border export of B2C e-commerce had created favorable external conditions for relevant platforms and small and medium-sized enterprises engaged in crossborder export of B2C e-commerce to enter the free market quickly. However, in law enforcement, law enforcement personnel use legal loopholes for tax evasion, customs approval of goods, and customs clearance process cumbersome, and other conditions emerge in an endless stream, thus limiting the development of cross-border export B2C e-commerce, trade facilitation process has slowed down [2]. Secondly, regarding financial services, Li [3] pointed out that China’s current cross-border payments are mainly provided by UnionPay, which relies on a solid domestic banking network to carry out overseas credit card consumption, crossborder B2C, and other businesses. However, Cardona and Duch-Brown [4] pointed out that with the opening up of third-party payment institutions, it is essential to standardize financial services and improve payment standards for external monetary payment means, expand exports for cross-border export B2C e-commerce, and improve trade facilitation [5–7]. Finally, in terms of intellectual property, with China’s accession to the World Trade Organization, the relevant intellectual property laws and regulations have gradually improved. However, due to the imperfect development of intellectual property rights in China, domestic enterprises and the public lack sufficient understanding of their intellectual property rights, which leads to severe violations of the legitimate rights of intellectual property rights, and further hinders the independent intellectual property rights of scientific and technological products and high value-added products to go offshore for e-commerce trade, thus affecting the improvement of the level of e-commerce trade facilitation in China [8–11].

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21.3 Model Construction and Variable Introduction 21.3.1 Data Introduction After literature research, it is found that scholars generally follow the evaluation system of trade facilitation of a country proposed by Wilson (2003), which contains four first-level indicators, namely, port efficiency, customs conditions, institutional environment, and e-commerce. However, with the continuous development of Internet information technology and the promotion of regional economic integration, the factors affecting a country’s level of trade facilitation have changed. Liu Zi-Hazel (2020) proposed that financial and e-commerce indicators significantly impact cross-border e-commerce, so this paper selects four first-level hands based on the Global Competitiveness Report and the Global Corruption Index Report and the current rapid development of data networks and the prevalence of financial services. They are shown in Tables 21.1 and 21.2. Based on the export data of cross-border e-commerce from 2015 to 2019 collected by the UN Comtrade database and the General Administration of Customs of the People’s Republic of China, the evaluation index system of trade facilitation is in line with China’s cross-border export of B2C e-commerce is constructed.

21.3.2 Entropy Weighting Method Entropy is a measure of the degree of system disorder. For a particular index, the entropy value can be used to determine the dispersion degree of a specific index. The smaller the information entropy value is, the greater the dispersion degree is, and the more significant the impact on the comprehensive evaluation is. If the importance of a particular index is equal, the index does not play a role in the total review. The basic idea of Wilson et al. (2003) was chosen to calculate and analyze the overall level of trade facilitation with the help of SPSSAU software. yi j =

xi j − xi min xi max − xi min

(21.1)

where is the jth indicator of the ith unit after dimensionless processing, and is the original value of the jth hand of the ith unit? Standardized definition (Tables 21.3 and 21.4). Yi j Yi j = m i=1

yi j

(21.2)

Indicator information entropy value ‘e’ with information utility value ‘d’, then the information entropy value of the jth indicator:

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Table 21.1 Trade facilitation indicators description Level 1

Level 2

Number

Explanation

Port efficiency

Road

S1

1-worst/7-best. (1–7)

Railroad

S2

1-worst/7-best. (1–7)

Port

S3

1-worst/7-best. (1–7)

Aviation

S4

1-worst/7-best. (1–7)

Prevalence of trade barriers

S5

1-highest/7-lowest. (1–7)

Customs procedures burden

S6

1-highest/7-lowest. (1–7)

Intellectual property protection

S7

1-best/7-worst. (1–7)

Government regulatory burden

S8

1-highest/7-lowest. (1–7)

Timeliness of the policy

S9

1-untimely/ 7-timely. (1–7)

Adaptation of legal frameworks to digital business models

S11

1-worst/7-best. (1–7)

Accessibility of financial services

S12

1-hard/7-easy. (1–7))

Network penetration

S13

1-lowest/ 100-highest. (1–100)

Availability of new technologies

S14

1-unavailable/ 7-available. (1–7)

Customs clearance efficiency

Business environment

Degree in financial and IT development

1  Yi j ln Yi j ln(m) i=1

(21.3)

dj = 1 − ej

(21.4)

m

ej = − Information utility value:

In the entropy weight method, the greater the information utility value, the more critical the index, and the greater the importance of evaluation. As shown in Fig. 21.1, the five secondary indicators of the burden of customs procedures, timeliness of policies, adaptability of the legal framework to digital business models, availability of financial services, and network penetration are more critical than other indicators and the greater their impact on trade facilitation levels.

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Table 21.2 China’s cross-border e-commerce exports with 20 closely traded countries, 2015–2019 (in USD billion) Country

2015

2016

2017

2018

2019 1928.37

United States

1249.69

1457.76

1833.13

1989.61

United Kingdom

181.57

210.4

241.59

236.36

289.9

Italy

84.84

124.39

137.96

153.92 213.55

99.64

Mexico

103

152.95

183.03

Sweden

21.64

122.3 23.85

29.93

34.24

39.23

Thailand

116.72

140.54

164.18

178.24

210.16

488.6

584.7

Japan

413.38

610.67

659.82

Spain

66.62

80.55

97.63

103.93

123.57

Singapore

158.33

168.18

191.77

206.62

253.21 345.17

India

177.49

220.73

289.85

318.87

Nigeria

41.76

36.72

51.77

55.99

76.63

Turkey

56.72

63.07

77.19

74.09

79.8

Canada

89.69

103.23

133.65

147.38

169.99

Netherlands

181.22

217.14

285.97

303.29

340.66

Russia

105.94

141.13

182.45

199.11

227.97

France

112.84

113.65

122.36

124.03

154.1

Germany

210.8

246.49

303.02

323.13

367.2

Brazil

83.56

83.06

123.32

139.9

163.44

Pakistan

50.12

65.13

Korea

308.74

354.19

77.75 437.5

70.37

74.56

452.21

511.29

Table 21.3 Presentation of information utility values Level 2

Information utility value

Road

0.029018575

Railroad

0.028786939

Port

0.025742269

Air

0.02102842

Prevalence of trade barriers

0.022816275

The burden of customs procedures

0.050991023

Intellectual property protection

0.039646641

The burden of government regulation

0.031120349

Timeliness of policies

0.045899715

The adaptability of legal frameworks to digital business models

0.032167218

Availability of financial services

0.037097905

Internet penetration

0.035329577

Availability of new technologies

0.025789408

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Table 21.4 Entropy method index weights Trade facilitation evaluation system

Level 1

Weight

Level 2

Weight

Port efficiency

0.302498627

Roads

0.068209

Railroads

0.067664

Ports

0.060508

Air

0.064428

Prevalence of Trade Barriers

0.053631

The burden of Customs Procedures

0.119856

Intellectual Property Protection

0.093191

Customs clearance efficiency

0.198755098

Business environment

0.230437859

The burden of Government 0.07315 Regulation

Financial and information technology development

0.2567448795

Timeliness of policies

0.107889

The adaptability of legal frameworks to digital business models

0.07561

Availability of financial services

0.0872

Internet penetration

0.083044

Availability of new technologies

0.060619

Fig. 21.1 Levels of trade facilitation in 20 trade export destination countries, 2015–2019

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21.3.3 Trade Facilitation Level Comparison Based on Entropy Weight Method According to the principle of the trade facilitation evaluation system, all indicators should be standardized to make them comparable. The entropy weighting method has normalized the data before calculating the weights so that the trade facilitation level can be calculated based on the consequences of the indicators of the existing trade facilitation evaluation system and the trade facilitation index data of 20 countries. This paper calculates the trade facilitation level between China and 20 major trade export destination countries (including developed and developing countries) from 2015 to 2019. To understand the development trend of the trade facilitation level, this chapter summarizes the trade facilitation level of 20 countries from 2015 to 2019. The average value of five years is taken for a more balanced observation of the changes. It can be seen that the degree of trade facilitation in China’s export destination countries declined from 2015 to 2016 but generally showed a slow upward trend. Overall, global trade facilitation is increasing year by year. Among them, the trade facilitation of countries in economically developed regions such as the United States, the United Kingdom, and Singapore is generally higher than that of underdeveloped areas such as Nigeria and Brazil. At the same time, the trade facilitation index of economically developed regions grows faster than that of economically underdeveloped areas.

21.3.4 Application of Fixed Effect Model Based on Entropy Weight Method This paper constructs a model by selecting data related to 20 (countries in five continents with which China has had frequent trade transactions from 2015 to 2019. Many factors affect the trade volume of cross-border export B2C e-commerce, such as the penetration of a country’s financial services level network, trade barriers, the adaptability of the legal framework to digital business models, the degree of people’s mastery, and understanding of online shopping and sea trade. In the above, the thirteen secondary indicators selected to measure the level of trade facilitation have covered these factors. Therefore, in the selection of control variables, only two control variables, namely the level of per capita income and the number population, are selected in this paper to observe the impact of the level of trade facilitation on China’s cross-border export B2C e-commerce. As shown in Table 21.5. EXPTit = α + β1 GNPir + β2 TFIit + β3 POPit + γit

(21.5)

It was found that the data need to be standardized and significantly comparable among the data when conducting the trade facilitation degree model construction.

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Table 21.5 Explanatory variables description Variable symbols

Description

Data source

Explained variables

EXPORT

China’s cross-border export B2C e-commerce to trade export destination countries

China center for E-commerce research (Netscape)

Control variables

GNP

Per capita income level

World bank

Core variables

POP

Population size

World bank

TFI

Level of trade facilitation

Analysis of the results

The results are shown in Tables 21.3, 21.4, 21.5 and 21.6 using descriptive statistics on the original data panel. Table 21.6 is derived from the descriptive statistics of the panel data by Stata software. The table shows that the variation in each data sample is relatively large, and the corresponding standard deviation is slight, so there is no outlier among the data. However, the difference in the order of magnitude between the data in the table is significant, and the data cannot be compared, so logarithmic processing is performed (Tables 21.7 and 21.8). All variables passed the LLC and IPS tests for first-order differential smoothing, so the subsequent ordinary regression was supported. Based on the above, it can be seen that the F-test’s P-value in the fixed effects regression results is 0, so the mixed regression model is excluded. The Hausman test was continued for both regression results (Table 21.9). It is known that P = 0.0000 < 0.05, and the fixed effects model was chosen for the analysis. This was done using the regression results in Tables 21.4 and 21.5, which had Table 21.6 Descriptive statistics for raw panel data Variable

Observation

Standard deviation

Average value

Minimum value

Maximum value

EXPT

180

1155.879

723.4960

30.53897

9920.289

TFI

180

0.1338702

0.5812571

0.3406431

0.8608623

POP

180

210,000,000

118,000,000

4,384,000

131,000,000

GNP

180

15,930.88

20,876.88

1105.188

46,501.33

Table 21.7 Descriptive statistics of the processed data Variable

Observation

Standard deviation

Average value

Minimum value

Maximum value

LnEXPT

180

6.58408705

1.035567

3.316939

9.422043

TFI

180

0.53962571

0.1279602

0.3275458

0.8918419

LnPOP

180

16.98846

1.193704

15.29358

20.09407

LnGNP

180

9.37443

1.208432

7.037978

11.02523

21 Research on Trade Facilitation of Cross-Border Export B2C … Table 21.8 Data smoothness test

Variables

LCC inspection

IPS inspection

Conclusion

LnEXPT

−19.646

−4.009

Stable

0.0000

0.0000

−8.301

−2.997

0.0000

0.0000

−7.832

−2.298

0.0000

0.000

−2.221

−1.859

0.0348

0.000

Variables

Chi-Sq.statistic

P

Conclusion

LnEXPT

248.32

0.0000

Fixed effects model

TFI LnPOP LnGNP

Table 21.9 Hausman test

263

Stable Stable Stable

a goodness-of-fit of 0.8124, which indicated that the fixed effects model had a very matching explanatory power, thus yielding the following coefficient relationships for each variable. lnEXPTit = 5.850001TFIit + 15.29759 ln POPit + 2.629159 ln GNPir − 299.2326

(21.5)

21.4 Experimental Results and Analysis According to the above data, the coefficient of trade facilitation is 5.850001, which is evident at the 1% level, indicating that the degree of trade facilitation has a significant impact on the export volume of cross-border B2C e-commerce and has a significant positive role in promoting. The level of trade facilitation can reflect a country’s government’s emphasis on cross-border trade. The regression data can show that the trade volume of China’s cross-border export of B2C e-commerce will increase with the increase of the level of trade facilitation of the destination country. For every one percent increase in trade facilitation of trade destination countries, the export volume of China’s cross-border export B2C e-commerce to trade destination countries will increase by 5.850001%. Overall, the level of trade facilitation of export destination countries is positively correlated with cross-border export of B2C e-commerce trade; the total population of export destination countries and national per capita income levels positively impact China’s cross-border export of B2C e-commerce. Among the four first-level indicators, business environment, financial, and information technology development

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positively affect cross-border export B2C e-commerce. It shows that cross-border export B2C e-commerce depends mainly on developing finance and information technology and the business environment. The result of finance and information technology will further improve the business environment, and the two complement each other and promote each other.

21.5 Conclusion This paper uses the entropy weight method for weight analysis. It uses the fixed effect model to establish a function to empirically study the trade facilitation of China’s cross-border B2C e-commerce export. The experiment proves that the entropy weight method is more flexible and effective in data processing and is more suitable for studying the trade facilitation of China’s cross-border export of B2C e-commerce. This paper concludes by combining the experimental results with the basic theory: cross-border export B2C e-commerce is most affected by the development of financial services and information technology, followed by intellectual property rights and customs clearance efficiency. In the future, we will gradually improve the level of financial and information technology services, update payment methods and payment platforms, and create an excellent economic environment. Intellectual property rights are subject to policy formulation, lack of protection means and degree, and low national recognition. Therefore, we should continue to improve China’s intellectual property protection policy formulation, enhance the added value of products, and create their brands, cost-effective, high-tech products stationed in the international market. Constantly improve the efficiency of customs clearance while continuously reducing the time of cargo transportation and improving the efficiency of goods running to cross-border buyers. Acknowledgements This work was supported by the grants of the Scientific research and innovation team construction project of East University of Heilongjiang (Project No.: HDFKYTD202110);2019 Higher Education Teaching Reform Research of The Department of Education “Research and Practice on Teaching Mode Reform and Curriculum System Optimization of Application-oriented Foreign Trade Talents Training from the Perspective of Local Characteristic Industries” (item number: SJGY20190542); The undergraduate core course construction project of East University of Heilongjiang(item number: 1810605).

References 1. Cheng, Z.H., Wang, X.Y.: Research on the influencing factors of export trade facilitation of cross-border E-commerce in China. Commercial Econ. Res. 5, 139–143 (2020) 2. Wilson, J.S., Mann, C.L.: Trade facilitation, and economic development: a new approach to quantifying the impact. World Bank Econ. Rev. 17(3), 367–389 (2003)

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3. Li, Y.B.: Research on the impact of trade facilitation on China’s s cross-border E-commerce exports. Institute of International Trade and Economic Cooperation, Ministry of Commerce (2021) 4. Cardona, M., Duch-Brown, N.: Delivery costs and cross-border e-commerce in the EU digital single market. JRC Working Papers on Digital Economy (2016) 5. Harrison, A., Kortuem, S.: Optimized analysis based on the characteristics of cross-border E-commerce logistics business. Int. J. Smart Bus. Technol. 6(2), 1–10 (2018) 6. Shi, G.X.: Research on the influence of trade facilitation on the performance of cross-border E-commerce logistics. Liaoning University of Technology (2021) 7. Xu, X.L.: Study on the influence of ASEAN trade facilitation on China’s s cross-border Ecommerce import and export. Anhui University (2021) 8. Hu, L., Yao, Z.Q.: Total entropy method based on correlation coefficient and its application. J. PLA Univ. Technol. (Nat. Sci. Ed.) 12, 1–4 (2018) 9. Cui, Z., Zhang, X.R., Wei, Q.Z.: Research on the efficiency evaluation of commercial banks based on entropy weight method. Manage. Manage. 7, 1–14 (2022) 10. Shi, Y.R., Yu, J.P., Bi, C.H.: Trade facilitation and enterprise technology upgrading. Int. Econ. Trade Explor. 38(07), 72–85 (2022) 11. Ma, X.Y.: Measurement of trade facilitation index and its international trade effect. Stat. Decis. Making 38(08), 144–148 (2022)

Chapter 22

Research on Pre-sales Services and Pricing Strategies for Fresh Produce e-Commerce Under Different Game Structures Hang Xu, Guang Yang, and Xiuping Han

Abstract This paper addresses service and pricing issues in a fresh produce supply chain system with a single supplier and two competing retailers. E-tailers use a pre-sale strategy, while offline retailers use a sell-now strategy; demand in the online channel depends on the level of service and pre-sale prices of the online retailer, while demand in the traditional retail channel depends on the selling price of the produce and is an exogenous variable. This paper uses the Stackelberg game approach to investigate the optimal service and pricing strategies under three game structures: cooperative, e-tailer dominated and Nash, and presents the profits of each member of the supply chain under the three decisions. It was found that the level of network service provided for production was higher in cooperative games than in non-cooperative games; the level of network service for agricultural products is higher in the Nash game structure than in the e-tailer dominated Stackelberg game; wholesale prices of produce are higher in the Nash game structure than in the e-tailer dominated Stackelberg game; and the optimal selling price of the produce is lower in the cooperative game than in the non-cooperative game.

H. Xu · G. Yang School of Economics, Harbin University of Commerce, Heilongjiang, Harbin, China X. Han (B) Business School, Taizhou University, Taizhou 318000, Zhejiang, China e-mail: [email protected]; [email protected] Harbin University of Commerce Business Administration Postdoctoral Station, Harbin 150028, China Taizhou University, 1139 Shifu Dadao, Taizhou City 31800, Zhejiang Province, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_22

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22.1 Introduction The development of technologies such as e-commerce and cold-chain has led to the creation of e-commerce for fresh produce. Fresh produce e-commerce sales were once called “the last blue ocean in the e-commerce sector” [1], it has reduced the flow of fresh produce, reduced distribution costs and improved transport efficiency. However, due to the low value of produce and the fact that consumers cannot see the product in person when buying it online, consumers are extremely under-estimated to purchase fresh produce online with substantial enthusiasm. As a result, by 2016, several e-commerce platforms had ended up closing down for various reasons. As lychee production in Zengcheng, Guangzhou, has declined in 2019, farmers are working with local e-commerce platforms to reduce losses through pre-sales of their products, in addition to offline cash sales through partner retailers, a hybrid model that allows for comparative behavior that leads consumers to make strategic choices. The strategic choice of consumers is influenced by the price of products and logistics services, but also by the level of service provided by the e-commerce network and its position in the supply chain. Therefore, the question of how to balance the level of network services and their position in the supply chain, the impact of different sales methods on consumer purchasing behavior, and the formulation of balanced decisions to improve business performance is an urgent issue that needs to be addressed. Initially, research on pre-sales strategies focused on the impact on market demand. Hu and Suo provide a theoretical analysis of consumer behavior for fresh produce [2, 3]. Prasad and Yu studied the advantages of pre-selling when retailers use pre-selling information to forecast current demand [4, 5]. Tang updated the demand prediction based on pre-sale information assuming the presence of stochastic perturbations in the demand, proving that firms will make more profit and obtain the optimal pre-sale discount rate [6]. Considering the risk aversion of consumers, Zhao and Weng derived optimal pre-sale pricing and inventory strategies for three scenarios: no pre-sale, moderate discount, and large discount for retailers, respectively [7, 8]. Li derived optimal strategies for pre-sales and live sales in the presence of customers who overestimate the value of the product during pre-sales [9]. Cao provides an in-depth analysis of this behavior considered by Li and suggests corresponding preventive measures [10]. Lei Xiao concluded that depending on the strength of manufacturers sufficient to influence the market price of raw materials, manufacturers can adopt different pre-sales strategies to increase market demand, depending on their market strength and the level of risk aversion of consumers [11]. On the other hand, there is a considerably richer body of research on pre-sales by fresh food retailers based on the characteristics of seasonal perishable goods, Mao constructed a dealer revenue model under a combined pre-sale and buy-back strategy and a single pre-sale strategy to derive the optimal pre-sale price and optimal order quantity [12]. Shao constructed a vertically integrated joint decision-making model for crowd-sourced pre-sales and crowd-sourced production in the fresh produce supply chain in the context of consumer engagement in the consumption experience, and concluded that joint decision-making has a significant synergistic effect

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on expanding demand for online shopping and improving supply chain revenue [13]. Zhou developed an optimal decision model for a retailer’s pre-sales strategy of no presales and allowing customer returns for the coexistence of uncertain market demand and uncertain customer valuations for seasonal products, and solved and analyzed the strategy for both models [14]. Zeng derived optimal strategies for fresh produce supply chains under different domination scenarios and compared them [15]. In view of this, this paper addresses the differences in consumers’ product valuation, portrays consumers’ online shopping behavior under competing sales models, examines the equilibrium pricing of products and corporate profits under two sales models with different channel rights, and analyses the impact of differences in consumers’ product valuation on the design of online companies’ sales models. The innovations in this paper are: constructing profit models based on consumer behavior of fresh produce in the fresh produce supply chain and considering the impact of network service level on market demand; constructions of cooperative, e-tailer dominated, and Nash non-cooperative models in the context of presales; comparison of optimal pre-sale discount rate, network service level, and logistics service level under three decisions.

22.2 Model Formation and Basic Assumptions 22.2.1 Consumer Utility Analysis The marketplace has one e-tailer and one traditional retailer, as well as countless gay and lesbian consumers. Both retailers sell the same fresh produce, and the e-tailer adopts a pre-selling strategy. Consumers in the marketplace are rational and strategic, making purchase decisions based on the utility of the product at the time of pre-sale and at the time of sale, with independence among consumer decisions. Therefore, the utility function of the consumer at the present selling stage is: Us = v − p, where v is the consumer’s valuation of the fresh produce and follows a uniform distribution of v ∼ U [0, 1], p is the selling price of the product and satisfies 0 < p < 1. Fresh produce is perishable and subject to quality loss during transportation, storage, and distribution, so its valuation changes after the pre-sale period, and the customer’s valuation of the product in the pre-sale channel affects the purchase channel choice. Let the estimated value residual rate of value at the pre-sale stage be γ, γ ∈ (0, 1) and utility of the product purchased by the consumer during the pre-sale period is described as U A = γ v − p A + βs, PA is the pre-sale price of the product, s is the level of network services, β is the consumer service sensitivity factor. Consider a supply chain where an e-tailer with pre-sales competes with a tradip A +βs can be deduced, the demand function tional retailer, when U A ≤ Us , v ≥ p−1−γ for the on-sale channel in case of competitive sales is D1 = 1 −

p− p A +βs ; 1−γ

when

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H. Xu et al. p A −βs γ

≤ v ≤

p− p A +βs 1−γ

can be deduced, the demand function for the

p A +βs − p A γ−βs . on-sale channel in case of competitive sales is D2 = p−1−γ All the above-mentioned consumer demands at the time of online pre-sales are satisfied, and the level of online service is positively correlated with the residual value rate and negatively correlated with the pre-sales price, suggesting that the constructed demand function describes consumer demand at the pre-sales stage.

22.2.2 Basic Assumptions Consider a fresh produce supply chain system consisting of a single supplier and two competing retailers, where an e-tailer adopts a pre-sale strategy and an offline retailer adopts a sell-now strategy, the conceptual model of which is illustrated in Fig. 22.1. The following hypotheses are proposed. Hypothesis 1 Both the supplier and the retailer are risk neutral, and all information about both parties is symmetric. Hypothesis 2 To reduce the risk of demand uncertainty, suppliers implement presales through e-tailers in advance. To encourage customers to buy in advance, the price of products pre-sold by e-tailers becomes PA . Hypothesis 3 The relationship between network service cost and network service level for e-tailers is defined as [9]: C(s) = 21 ηs 2 , η > 0, η represents the network service cost factor. Network service costs refer to the platform construction and maintenance costs that online retailers invest in to provide consumers with timely and accurate access to pre-sales information on fresh produce. The cost of network services increases with the level of network services, and the cost of network services is marginal and decreasing.

Fig. 22.1 Supply chain structure of fresh produce for online pre-sales

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For the sake of discussion, the following notations are defined: c Production costs per unit of product for fresh produce suppliers; cm Production costs per unit of product for fresh produce suppliers; ⊓li Profits for nodal companies in the supply chain, the subscripts l = o, r, n represent the cooperative game, the retailer-dominated Stackelberg master– slave game and the Nash game structure; i = c, r, s indicate the entire supply chain, retailers and suppliers, respectively. The decision variables are as follows: pl plA ωl sl

Ready-to-sell selling price per unit of fresh produce; Pre-sale selling price per unit of fresh produce; Wholesale price per unit of fresh produce; Service levels for e-tailers.

22.3 Model Establishment 22.3.1 Service and Pre-sales Strategies in a Cooperative Game The cooperative game in the presence of competition is where agricultural suppliers set up their own portals for online pre-sales and sell products for which there are competitors in the retail market. Suppliers make decisions about the selling price of agricultural products and the level of online services to maximize profits throughout the supply chain. At this point, the question we need to address is ) ( ( ) 1 p o − βs o − η(s o )2 ⊓co = p oA − cm − c 1 − A γ 2

(22.1)

Proposition 1 When 2γ η > β 2 , the optimal solution for each decision variable under the cooperative game satisfies ⎧ β(γ − cm − c) o∗ ⎪ ⎪ ⎨ s = 2γ η − β 2 γ η(γ − cm − c) ⎪ ⎪ ⎩ p o∗ + cm + c A = 2γ η − β 2

(22.2)

The optimal sales volume is D o∗ =

η(γ − cm − c) 2γ η − β 2

(22.3)

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The optimal sales volume is. ⊓co∗ =

η(γ − cm − c)2 ) ( 2 2γ η − β 2

(22.4)

Proof Find the first order partial derivatives of Eq. (22.1) with respect to p oA , s o , respectively, and obtain the result ( ) ⎧ ) ∂⊓co p oA − βs o 1( o ⎪ ⎪ p A − cm − c = 1 − − ⎨ ∂ po γ γ A co ( ) ⎪ β ∂⊓ ⎪ ⎩ p o − cm − c − ηs o = ∂s o γ A

(22.5)

The hessian matrix of Eq. (22.1) for p oA , s o is [ H=

− γ2 β γ

β λ

]

−η

(22.6)

When 2γ η > β 2 is established, the matrix H is negatively definite, so the objective function is concave with respect to p oA , s o . Finding the first order conditions of Eq. (22.1) with respect to p oA , s o yields Eq. (22.2), substituting Eq. (22.2) into Eq. (22.1) gives Eq. (22.4).

22.3.2 Service and Pre-sales Strategies for E-tailer-led Stackelberg Gaming In the non-cooperative game, the e-tailer is the leader of the Stackelberg game and the produce supplier is the follower. The e-tailer first determines the wholesale price and its own retail price. The e-tailer observes the wholesale price and the selling price set by the produce supplier and then determines the level of service to be sold online. At this point, the problem is transformed into solving the following problem. The supplier’s profit is ) ( ( r ) prA − βs r ⊓ = ω −c 1− γ sr

(22.7)

This paper let prA equal ωr and ∆pr , where ∆pr indicates the transfer payment price from the web platform to the supplier, thus it can be deduced that ) ( ( r ) ωr + ∆pr − βs r ⊓ = ω −c 1− γ sr

(22.8)

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The e-tailer’s profit is ⊓ = rr

(

prA

) ( ) p − ωr − ∆pr + βs r ωr + ∆pr − βs r − − ω − cm 1−γ γ r

1 − η(s r )2 2

(22.9)

The profit of offline retailers is ) ( ) ( r p − ωr − ∆pr + βs r r 1 − ⊓rr = p − ω − c m o 1−γ

(22.10)

Proposition 2 When the optimal solution for each decision variable under the cooperative game satisfies ⎧ β(2γ p − γ − cm − c) ⎪ sr ∗ = ⎪ ⎪ ⎪ 4γ (1 − γ )η − β 2 ⎪ ⎪ ⎨ 3γ (1 − γ )η(2γ p − γ − cm − c) prA∗ = + cm + c + γ − γ p ⎪ 4γ (1 − γ )η − β 2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ωr ∗ = γ (1 − γ )η(2γ p − γ − cm − c) + c + γ − γ p 4γ (1 − γ )η − β 2

(22.11)

The supplier’s optimal profit is ⊓r s∗ =

] [ 1 ηγ (1 − γ )(2γ p − γ − cm − c) 2 (1 − p)γ + γ 4γ (1 − γ )η − β 2

(22.12)

The optimal profit for an e-tailer is ⊓rr ∗ =

η(2 pγ − γ − cm − c)2 2[4γ (1 − γ )η − β 2 ]2

(22.13)

The optimal profit for an offline retailer is rγ ∗

⊓0

η(2 pγ − γ − cm − c)( p + pγ − γ − cm − c) 4γ (1 − r )η − β 2 2 γ (1 − γ )η (2 pγ − γ − cm − c)2 + [ ]2 4γ (1 − γ )η − β 2

=

(22.14)

Proof Using reverse induction, the supplier first determines the wholesale price. Firstly, find the first order condition of Eq. (22.8) on to obtain. ωrx =

γ + c − ∆prx + βsxr 2

(22.15)

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Substituting this into Eq. (22.8) gives ⊓rr x

=

(

∆prx

) ( ) γ − c − ∆prx + βsxr 1 − η(sxr )2 − cm γ 2

(22.16)

Find the first order condition of Eq. (22.17) with respect to ∆pr , s r ⎧ r ∂⊓ 2λp − γ − c + cm − 2∆prr x + βs ⎪ ⎪ = ⎨ ∂∆prr 2γ (1 − γ ) x r ( ) ⎪ ∂⊓ β ⎪ ⎩ ∆pr − cm − ηr = ∂s r γ

(22.17)

The hessian matrix of Eq. (22.16) for ∆pr , s r is [ H=

1 − γ (1−γ ) β 2γ (1−γ )

β 2γ (1−γ )

]

−η

(22.18)

When 4γ η(1 − γ ) > β 2 is established, the matrix H is negatively definite, so the objective function is concave with respect to ∆pr , s r . Finding the first order conditions of Eq. (22.17) with respect to ∆pr , s r yields Eq. (22.11).

22.3.3 Nash Gaming’s Service and Pre-sales Strategy When e-retailers and produce suppliers are on equal footing, members of the supply chain play a Nash game structure, where the produce suppliers set wholesale prices and their own retail prices, while the e-retailers set pre-sales and sale prices and service levels for online sales. The supplier’s profit is ) ( ( ) p n − βs n ⊓sn = ωn − c 1 − A γ

(22.19)

This paper let p nA equal ωn and ∆p n , where ∆p n indicates the transfer payment price from the web platform to the supplier, thus it can be deduced that ⊓

sn

) ( ( n ) ωn + ∆p nr − βs n = ω −c 1− γ

(22.20)

The profit of e-tailers is ⊓

rn

=

(

p nA

) ( ) p − p nA + βs n 1 p nA − βs n − η(s n )2 − ω − cm − 1−γ γ 2 n

(22.21)

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The profit of offline retailers is ⊓ron

(

= p −ω n

nr

) ( ) p − ωn − ∆p n + βs n − cm 1 − 1−γ

(22.22)

Proposition 3 When 3γ (1 − γ )η > β 2 , the optimal solution for each decision variable under the cooperative game at a single pre-sale satisfies. ⎧ β(2γ p − γ − cm − c) ⎪ ⎪ s n∗ = ⎪ ⎪ 3γ (1 − γ )η − β 2 ⎪ ⎪ ⎨ 2γ (1 − γ )η(2γ p − γ − cm − c) + cm + c + γ − γ p p n∗ A = ⎪ 3γ (1 − γ )η − β 2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ ωn∗ = γ (1 − γ )η(2γ p − γ − cm − c) + c + γ − γ p 3γ (1 − γ )η − β 2

(22.23)

The supplier’s optimal profit is ⊓

sn ∗

] [ 1 ηγ (1 − γ )(2γ p − γ − cm − c) 2 = (1 − p)γ + γ 3γ (1 − γ )η − β 2

(22.24)

The optimal profit for an e-tailer is ∗

⊓r n =

η(2 pγ − γ − cm − c)2 ]2 [ 2 3γ (1 − γ )η − β 2

(22.25)

The optimal profit for the retailer is ∗

η(1 − γ )(2γ p − γ − cm − c)( p + pγ − γ − cm − c) 3γ (1 − γ )η − β 2 γ (1 − γ )η2 (2 pγ − γ − cm − c)2 + [ ]2 3γ (1 − γ )η − β 2

⊓ron =

(22.26)

22.4 Results This paper compares propositions 1, 2, and 3 to obtain the following results. 1. s o∗ > s n∗ > s r ∗ , on the one hand, in the cooperative game, the online service level of the produce is higher than in the non-cooperative game. This indicates that during the cooperative game period, online retailers will increase their investment in online service levels and increase demand in the pre-sale phase to mitigate the impact of uncertain consumer demand in traditional channels. On the other

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hand, the network service level of agricultural products under the Nash game structure is higher than that under the Stackelberg game dominated by online retailers. This indicates that companies without dominance have a higher level of pre-sales service in the fresh produce supply chain, that is, online and offline retailers are evenly matched and have a better service experience for consumers. 2. ωn∗ > ωr ∗ , Under the Nash game structure, the wholesale price of produce is higher than under the Stackelberg game dominated by online retailers. This indicates that the wholesale prices of the supply chain of fresh agricultural products are elevated for businesses that do not dominate, that is, the cost of obtaining products for retailers increases when online and offline retailers are evenly matched. 3. pr ∗ > p n∗ > p o∗ , in the cooperative game, the optimal selling price of produce is lower than that in the non-cooperative game. Moreover, when online retailers dominate the Stackelberg game, the price of agricultural products sold under the Nash game structure is lower than under the Stackelberg game. When the sales price of agricultural products under the Nash game structure is higher than that under the Stackelberg game dominated by online retailers. The analysis of the regression model, we can see that the adjustment of China’s industrial structure is in line with the law of development of the entire industry. At present, the modern service sector has become the leading industry in China’s economic development, and a tertiary, secondary, and primary industrial structure model has been formed. Economic development, however, has its peculiarities, and there are still many problems in the way of continued adjustment and improvement of the industrial structure.

22.5 Conclusions Based on the fresh agricultural product supply chain system of a single supply chain and two competing retailers, this paper establishes a fresh agricultural product supply chain service and pricing model considering online pre-sale under cooperative and non-cooperative decision-making, respectively, and uses the Stackelberg game method to study the pricing of three different game structure models and the profits of each member. The research results show that the network service level of agricultural products in the cooperative game is higher than that in the non-cooperative game, and the network service level of agricultural products under the Nash game structure is higher than that under the Stackelberg game dominated by online retailers. Wholesale prices of produce under the Nash game structure are higher than under the Stackelberg game dominated by online retailers. The optimal selling price of produce in the cooperative game is lower than in the non-cooperative game. In addition, the size of the sale price of agricultural products under the Nash game structure and the e-tailer dominated Stackelberg game is determined by the residual rate of value at the pre-sale stage, the cost coefficient of the network service, and the consumer service sensitivity coefficient.

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This paper examines the impact of online pre-sales on supply chain service and pricing decisions for the case of a single supplier only. Further issues of pricing and service in fresh-prod supply chains can be considered in the context of sourcing options and two competing supply chains. Acknowledgements This research is supported by 2022 National Social Science Foundation Project (Grant nos. 22BMZ074), Achievements of Taizhou Philosophy and social science planning project (Grant nos. 22GHB27), 2017 in-station postdoctoral research support program (Grant nos. BSH022).

References 1. Why is the last piece of e-commerce blue ocean fresh food e-commerce cannot sell in the vegetable market? http://mt.sohu.com/20160422/n445495167.slitml. Last accessed 21 Sept 2016 2. Hu, N., Chen, X., Zhang, N.: Influence of service quality of agricultural products e-commerce platform on customer loyalty—the mediating role of customer engagement. Int. J. Smart Bus. Technol. 9(1), 13–28 (2021) 3. Suo, C.X., Ma, Y.J., Wang, P., Xiao, B., Yong, Y.: Consumers’ sustainable product preference and green supply chain management analysis. Int. J. u- e- Serv. Sci. Technol. 9(7), 203–212 (2016) 4. Prasad, A., Stecke, K.E., Zhao, X.: Advance selling by a newsvendor retailer. Prod. Oper. Manag. 204, 129–142 (2011) 5. Yu, M., Ahn, H.S., Kapuscinski, R.: Rationing capacity in advance selling to signal quality. Manage. Sci. 61, 560–577 (2015) 6. Tang, C.S., Rajaram, K., Alptekino, L.A.: The benefits of advance booking discount programs: model and analysis. Manage. Sci. 50, 465–478 (2004) 7. Zhao, X., Stecke, K.E.: Pre-orders for new to-be-released products considering consumer loss aversion. Prod. Oper. Manag. 2(19), 98–215 (2010) 8. Weng, Z.K., Parlar, M.: Integrating early sales with production decisions: analysis and insights. IIE Trans. 1, 1051–1060 (1999) 9. Li, Y., Mi, Y.: Advance selling decisions with overconfident consumers. J. Ind. Manage. Optim. 3(12), 891–905 (2016) 10. Zeng, Y., Qiu, G.S., Huang, S.J.: The exaggeration of product quality and its precautions in the pre-order crowdfunding. J. Manage. Sci. China 7(22), 89–106 (2019) 11. Ma, S., Li, G., Sethi, S.P., Zhao, X.: Advance selling in the presence of market power and risk-averse consumers. Decis. Sci. (2018) 12. Mao, Z.F., Liu, B., Li, H.: Joint pre-sale and buy-back of seasonal perishable goods decisionmaking research. J. Manage. Sci. 19(2), 74–84 (2016) 13. Shao, T.W., Lu, X.M.: Joint decision-making of fresh food e-commerce crowdfunding pre-sale and crowdsourcing production. Syst. Eng. Theory Pract. 38(6), 1502–1511 (2018) 14. Zhou, Z.H., Huang, S.Z.: Pre-sale and return strategy considering customer’s strategic behavior under random demand. J. Syst. Manag. 28(2), 277–284 (2019) 15. Zeng, Y.X., Yuan, P., Zhang, Y.W.: Decision-making analysis of fresh e-commerce supply chain with different dominant powers from the perspective of game theory. J. Nanjing Audit Univ. 2019(5), 54–64 (2019)

Chapter 23

Study on Dynamic Pricing of Perishable Goods Based on Consumer Strategic Behavior in Two Purchasing Environments Jian Wang and Wen Hu

Abstract Although e-commerce is a revolution, it has changed the business structure and consumer consumption habits. Considering the development of research on pricing strategies for perishable goods, there is little analysis of the interaction mechanism between consumer strategic behavior and online retailers using pricing strategies. In addition, the previous analysis focused on the pricing of perishable goods, which should be sold as soon as possible to avoid losses caused by low residual value at the end of the sale period, rather than finding out how to balance corporate profits with consumer demand and expectations. Through model and empirical analysis, this paper focused on the impact of online retailers ignoring consumer strategy behavior and the corresponding pricing strategy, and draws relevant management suggestions. In particular, through the analysis of the uncertainty factors brought by the two-stage pricing strategy for both of retailers and consumers, the quantitative analysis is made on the impact of the out of stock risk on the purchase decision of strategic consumers, and conclusions are drawn and statistical tests are conducted. Therefore, we analyze the relationship between the perishable products and their product sales in the online shopping market and the difference of enterprise pricing strategies of consumer strategic purchase behavior.

23.1 Introduction With the rapid growth of all kinds of information on the Internet, the problem of “Information Overload” has emerged. This means that the amount of information that users need to process is growing at a geometric level, and a large number of irrelevant redundant information seriously interferes with users’ selection of relevant useful information. At the same time, recommendations and other technologies have J. Wang (B) · W. Hu Harbin University of Commerce, Harbin, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_23

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emerged to help consumers shopping online. The recommendation technology has helped people solve the problem of information redundancy processing, but it has not solved the problem, the important factor is price in electric shopping. Consumers are still most concerned about the price of goods. With the consumers increasingly experience in purchasing through the pricing strategies of online retailers, which makes them gradually turn to strategic consumption behavior, and it is very common.

23.1.1 Dynamic Pricing of Perishable Goods Dynamic pricing appeared earlier in the revenue management of perishable goods and has been widely used [1]. If the pricing is based on the marginal cost or average cost, the products may be in short supply during the peak demand period; in the period of low demand, supply may exceed demand [2]. Perishable goods have characteristics of short sales period and almost zero residual value at the end of the sale period. Especially, the dynamic pricing strategy of perishable goods sold on the Internet has become one of the important means to ensure online retailers profits. Nowadays, selling goods at sale-platform has brought convenience to dynamic pricing, and the cost of price adjustment is almost zero on the Internet. Retailers can test consumers’ reaction to product prices through the Internet, and adjust prices in real time according to market demand information and product quantity. On the other hand, retailers can also analyze the retention value, age, income, and other characteristics of consumers through the purchase of consumers in Internet, laying the foundation for the implementation of dynamic pricing. The vast and abundant information on Internet has greatly reduced the information asymmetry between retailers and consumers. While manufacturers can dynamically price perishable products through the Internet to increase profits, consumers can also browse products, collect data, find information, download information, compare prices, place orders or change orders, purchase products, and get feedback “anytime and anywhere” without leaving the home. In this way, market information such as product price and supply quantity can be obtained through the Internet, which makes it easier to make purchase decisions, thus becoming more and more “smart”. The implementation of dynamic pricing is like a “double-edged sword” [3]. While increasing the profits of sellers, it will inevitably lead to the occurrence of consumer strategic behavior. In particular, the emergence of the Internet makes it easier for consumers to obtain market related information, further increasing consumer strategic behavior. The profits of business decrease with the increase of the proportion of strategic consumers, and the losses caused by neglect of consumer strategic behavior increase. With the increase of the proportion of strategic consumers.

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23.1.2 The Purpose of the Study Economic theory and information technology have proved that price is one of the most effective tools to affect product market demand. As the engine of revenue management, dynamic pricing plays an important role in revenue management. The product or service is set at different price levels according to the diversity of consumer demand and the difference in consumer recognition of product value at different times. For perishable products, due to the limited life cycle and large inventory costs, manufacturers often choose price reduction promotion to stimulate the demand for products in order to sell all products as soon as possible in the sale period. At this point, the strategic behavior of consumers will undoubtedly have a greater impact on the pricing strategy [4]. If the consumer expects the manufacturer to reduce the price of the product at some time in the future. And they based on the current manufacturer’s inventory and market demand, the consumer will choose to wait. In the face of such waiting behavior of consumers, and intensification of market competition, manufacturers have to further reduce prices and promote sales that should be forming a vicious circle. This paper adds such factors as consumer level, consumer usefulness, consumer risk, and effective consumer behavior to the model, and gives the optimal pricing strategy under two purchase environments: (1) Introduce the concepts of consumer areas such as consumer level, consumer risk, and effective consumer behavior into the strategy model and their interaction; (2) The uncertain factors brought by twostage pricing strategy to online retailers and consumers are analyzed and verified on a large scale real data-set.

23.2 Literature Review The dynamic pricing of online shopping environment is a method to adjust the price of goods. With the change of time, place, type of consumers, and other factors. The purpose of dynamic pricing is to improve the income of online retailers. On the one hand, dynamic pricing can make it possible for online retailers to maximize customer returns; on the other hand, it can improve the return on assets of business [5]. Among the dynamic pricing models of perishable products, there are many models on pricing strategies [6, 7]. Gallego and Wang [8] through a large number of empirical studies, it is found that prices can not only change in the short term, but also become more and more common in reality, such as clothing, food, and other retail industries. Krishnan and Ramachandran [9] studied the pricing problem of a single product under the same consumer retention price. The price of the initial product is very high. As time goes on, the price starts to fall. In fact, demand is one of the most important factors affecting pricing decisions. Generally speaking, demand is a function of time, price, and other factors; Demand is the price of consumption utility and psychological will. Anderson and Wilson

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[1] established demand models using consumer retention prices. These models did not consider the impact of past sales on future demand. Therefore, Akcay et al. [4] studied the pricing problem when the number of price changes is fixed. Mersereau and Zhang [2] summarized the above models. He used dynamic programming to study the following situations: the price can change k times at most, and the time interval between two consecutive price changes is fixed. And then, Parlaktiirk [10] pointed out that economics usually takes price as a distribution mechanism and incorporates it into the budget constraints of consumers, while marketing usually focuses on the relationship between price and quality. Taudes and Rudloff [11] under the condition that the price at each period is an independent random variable, the problem of consumers purchasing a single product at each stage is considered. Herbon and Khmelnitsky [12] expanded the model to consider the situation that consumers face multiple brands when they have price expectations and their own preferences. Chen and Farias [13] considering the impact of pricing strategies on consumers’ purchase decisions under the condition of consumers’ rational expectations. These models show that the dynamic structure model plays a huge role in depicting the purchase behavior of consumers when there is price expectation.

23.3 Theory and Model 23.3.1 A Subsection Sample In this section, firstly, gives a structured definition and formal description of relevant information of the demand function. The formulation of pricing strategies is one of the most important operational aspects of any company in a given market. It is 1% increasing in pricing results in an 8% increase in profit [5]. Research on finding the optimal pricing strategy has prompted researchers to consider reference prices in the development of pricing strategies [9]. Reference prices are widely used in mainstream marketing literature, and are used by consumers to determine whether the observed prices are cost-effective. Consumers’ reactions to gains and losses are asymmetric. At this time, the gain is defined as when the observed price is lower than the reference price, and the loss is defined as when the observed price is higher than the reference price. In this paper, the environment is defined as that consumers face restrictions such as no inventory of online retailers, and consumer strategy behavior is completely effective. The uncertain environment refers to the risk of out of stock and insufficient inventory in the face of the dynamic pricing of retailers, and the strategic behavior of consumers is not completely effective. The reference price model of different consumer groups is different from that of different product categories. The reference price model of perishable goods introduces the “Adaptive Expectation” model to model the reference price using the following model:

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rt = αrt−1 + (1−α) pt−1 α ∈ [0, 1] where pt and r t represent the price and reference price in cycle t respectively, α it is a memory parameter. α usually between 0.15 and 0.4. In order to find an optimal pricing strategy that considers reference prices, we must first model the demand function. Reference prices are widely used in mainstream marketing literature and are used by consumers to determine whether the observed prices are cost-effective. Consumer responses to gains and losses are asymmetric [8]. Gains are defined as when the observed price is lower than the reference price, and losses are defined as when the observed price is higher than the reference price. It has been proved that most customers will compare the current and past prices of products to adjust their purchase decisions. Modeling the demand function, the demand function is modeled as: Dt = a − bpt + βG (rt − pt ) where Dt , pt , rt represents the demand, price, and reference price in t period respectively. a − bpt represents the traditional linear demand. Hypothesis 1 Demand depends not only on product freshness and inventory, but also on sales price and reference price. Hypothesis 2 There is b ≥ βG . The impact of price change on the traditional linear demand part is greater than the reference price part, and the overall “trend” of the demand function is linear.

23.4 Dynamic Pricing of Perishable Products on Consumer Strategic Behavior In this section, the two-stage pricing model is used to analyze the pricing strategy of consumer strategic purchase behavior. The quantitative analysis method is used to show online enterprise profit loss caused by consumer strategy behavior, and the analysis of potential risks caused by pricing strategy.

23.4.1 The Pricing of Consumer Strategic Behavior in Determine Environment Price is an important factor determining market demand, and the demand function of manufacturers can be expressed as Dt . On the monotonic decrease of price p. In fact, here Dt can be regarded as the largest number of consumers willing to pay more

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than or equal to p. The sales of products are divided into two stages. At the beginning of the sales period, sell as the price p1 , and then over time, the remaining products will be sold at the price p2 , generally p1 > p2 . There are two types of consumers. The first type is short-sighted consumers. When their maximum willingness to pay is higher than the price in the first stage, they will buy without waiting. The proportion of such consumption is α. The second type of consumers are strategic consumers, accounting for 1 − α, consumers will have a complete understanding of the product’s future promotion price, promotion time, and other information. At the same time, consumers can also obtain almost the same market information as manufacturers through the network, such as market demand, competitors’ prices, and can accurately predict the prices of manufacturers in the next stage. As there is no limit on quantity, strategic consumers will wait to buy at a lower price to maximize their consumer surplus. Therefore, when the price is p1 , the demand is all the highest willing to pay more than p1 . At the price of p2 , demand consists of two parts: the first part is that the highest willingness to pay is less than p1 , but greater than p2 consumers; The second part is the highest willingness to pay more than p1 , so the profit function of the two stages can be written as: π ( p1 , p2 ) = αp1 D( p1 ) + (1 − α) p2 D( p2 ) It is assumed here that the demand function is linear, that is { D=

a − bp, 0 < p < ab 0, otherwise

The profit of the enterprise can be expressed as: π ( p1 , p2 ) = αp1 (a − bp1 ) + (1 − α) p2 D(a − bp2 ) Thus ∂∂π = (3−α)a , ∂π = (2−α)a , ∂π = bα, obtained from extreme value p1 (4−α)b ∂ p2 (4−α)b ∂ p1 ∂ p2 conditions BC−A2 > 0. The maximum profit is: 2

π ( p1 , p2 ) =

a (4 − α)b

It can be seen from the formula that corporate profits increase with the increase of the proportion of non-strategic consumers, and the proportion of non-strategic consumers increases monotonously. Therefore, in the dynamic pricing strategy for perishable goods that consider consumer strategic behavior, the profit of the enterprise is higher than the optimal price that does not consider the existence of strategic consumers in the market, and the profit of the enterprise decreases as the proportion of strategic consumers entering the market increases. Retailers should effectively

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measure the strategic behavior of consumers when pricing online goods, because this will lead to an increase in the proportion of enterprise losses.

23.4.2 Pricing in Uncertainty Condition with Consumer Strategic Behavior The above mainly assumes that the enterprise can provide unlimited inventory. At this time, strategic consumers can buy goods even in low price periods, and the risk of out of stock is zero. In fact, business can provide limited inventory. When consumers make strategic purchase decisions, they will face risks such as out of stock and insufficient inventory. Generally speaking, there is a probability that they cannot buy products. Therefore, consumers must weigh the expected utility of two-stage purchase. Assume that there are N potential consumers, and N is a certain constant without losing generality. Assume that all consumers are strategic. The consumer’s retention price v is an independent identically distributed random variable with the same distribution function G(v) and density function g(v). Consumers have the lowest price psychological bottom line, and manufacturers can only understand the same probability distribution through the distribution function G(v). In the first stage, consumers are sure to get the product, so as to obtain utility ∆p1 . The second stage will face risks θ to get the product and get the benefit θ ∆p2 . It is assumed that all consumers arriving in the second stage will obtain the product with equal probability. In this way, consumers must weigh their gains at different stages to determine their buying opportunities. The following conditions: 1. Consumers can understand inventory through Internet platform; 2. The dynamic pricing strategy of the enterprise is ( p1 , p2 ). Within the time T, p1 > p2 ; 3. When consumers arrive, they take strategic purchase decisions based on their expected returns.

23.4.3 The Purchase Decision of Strategic Consumers This section first considers the choices of consumers. The pricing strategy ( p1 , p2 ) of a given manufacturer and the probability that the product can be obtained in the second stage θ ; after that, consumers will weigh their expected returns in the two stages. In the second stage, they can obtain more surplus but bear the risk that they may not buy the product. Intuitively, a customer with a higher retention price may be more willing to buy the product in the first stage to avoid the risk of not buying the product. The following assumes that consumers are risk neutral, that is, the utility function of consumers U (x) = x.

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Theorem 1 Given θ is the probability that the consumer will obtain the product in the second stage, then there is v(θ ), when v > v(θ ). When consumers buy at the first stage; and v < v(θ ), consumer will purchase the letter in the second stage v(θ ) about θ strictly increasing. In some cases, there is no difference in consumer strategic behavior ∆p1 = ∆p2 v − p1 = θ (∆v − p2 ) 1 −θ p2 That v = p1−∆θ , 0 ≤ θ ≤ 1, when v > v(q), the consumer adopts the strategy of buying at the high price stage. When v < v(q), the consumer adopts the strategy of buying when the enterprise adopts price adjustment. Obviously, for example, in the second stage, the probability of consumers taking strategic purchase behavior to obtain a commodity θ . Smaller, more consumers will choose to buy at a higher price in the first stage to avoid the risk of out of stock. If the enterprise set the price p1 at the beginning of the sales period to obtain higher profits, when all consumers adopt short-sighted purchase behavior, the risk will be reduced to zero and the enterprise’s income will be maximized. However, under the influence of factors such as purchasing experience and knowing the dynamic pricing strategy of the enterprise in advance, it is inevitable for consumers to adopt strategic purchasing behavior, and only the risk of out of stock v > v(θ ). The strategy adopted when there are irreplaceable factors is the same as short-sighted purchase behavior. To gain balance points, firstly, retailers should try to make consumers purchase at price p1 made the largest profit and accelerated the inventory and capital turnover. Then, in the face of consumers with strategic purchasing behavior, failure to choose an appropriate time to lower the price will lose some potential profits and potential consumers. The pricing strategy of online merchants is analyzed below.

23.4.4 Optimal Dynamic Pricing Decision of E-retailers with Uncertain Demand Although the above obtained the optimal decision of consumers, however, because consumers cannot observe the number of the remaining products of the manufacturer in the second stage, they cannot know the probability of obtaining products in the second stage. However, based on the two-stage prices ( p1 , p2 ) of the manufacturer and the quantity k of products provided by the manufacturer in the first stage, consumers will form an expectation of the probability that the product can be purchased in the second stage. The manufacturer sells its products at price p1 at time [0, T ], and the remaining products at price p2 after time T. The arrival rate of consumers at [0, T ] is subject to the following parameters λ, in which the proportion of consumers with high retention price is a constant α. The type of consumer is personal private information, which is unknown to manufacturers and other consumers. Obviously, the optimal pricing

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strategy of the enterprise is to make Ch , a consumer with a high retention price, buy in the first stage, while Cl , a consumer with a low retention price, waits to buy in the second stage. Therefore, the first step is to start with the manufacturer’s decision in the second stage. If there are still surplus products in the second stage, because the second stage is all consumers with lower reserved prices. The demand function model is: ⎧ ⎨ a − bpt + βG (rt − pt ) pt ≤ rt Dt = a − bpt pt = r t ⎩ a − bpt + β L (rt − pt )t pt ≥ r In addition, the price model of stage t1 is obtained: p1 = C h − (C h − Cl )

θ2 θ1

Therefore, the expected profit function of the enterprise: ⊓=

T Σ



  ( pt − c) a − bpt + βG C h,t + β L Cl,t (rt − pt )θt

t=1

Therefore, e-retailers can maximize profits by setting the price of p2 equal to the pricing strategy of consumers with lower retention prices. Constant βG and β L represents gain and loss parameters, respectively. Consumers usually hate loss, that is, βG ≤ β L . If the price is lower than the reference price (gain), the demand will be greater than the linear demand. If the price is higher than the reference price (loss), the demand will be lower than the traditional linear demand [7, 14]. Probability θ1 as consumers obtaining goods in stage t1 . Probability θ2 as consumers obtaining goods in stage t2 .

23.5 Numerical Experiment This section combines the model with online sales data to analyze the effect of consumer strategic purchase behavior on the pricing strategy of online retailers in two different environments. The stage is the dynamic pricing strategy of strategic consumption behavior under the determined environment.

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23.5.1 The Influence of Consumer Strategic Behavior on Retailers Profits in the Market Because of the existence of strategic consumption behavior in the market. E-retailers must adopt pricing strategies to obtain the optimization of profit π when selling goods online. Therefore, we will discuss the assignment of model parameters in the previous section α = 0.4 and α = 0.75 (see Fig. 23.1). As shown in Fig. 23.1, corporate profits decrease with the increase of the proportion of strategic consumers, and the proportion of consumer strategic behavior decreases monotonously. The strategic purchase behavior of consumers in online perishable goods sales directly affects the pricing strategy of online retailers. It is necessary to consider consumer strategic behavior in enterprise pricing strategy, because the profit margin is higher than the optimal price of consumers who ignore the strategic purchase behavior in the market. Due to the characteristics of Internet sales that historical data such as sales price can be obtained and analyzed, consumers in online markets increasingly adopt strategic purchase behavior to avoid regret about the purchase price. Therefore, the profits of business decrease as the proportion of strategic consumption behavior increases. Therefore, commodity pricing strategy in online shopping environment should effectively measure consumers’ strategic behavior. The reason for the decline in corporate profits is that the proportion of strategic consumers entering the market has increased, and even non-strategic consumers have gradually changed into strategic buying behavior with the accumulation of purchasing experience. The strategy adopted by online sales is mainly two-stage pricing, that is, the price is p1 in the t0 period and p2 in the t1 period. Here, p1 > p2 . E-retailers should enable consumers with high retention prices to purchase in the first stage and meet the needs of consumers with low retention prices in second stage.

Fig. 23.1 A profit function of linear demand ignoring strategy behavior

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23.5.2 Consumer Strategy Behavior Under Out of Stock Risk with Uncertain Environment In the process of selling on the Internet, some information such as stock availability and sales cycle are essential to consumers. Even if they understand inventory and pricing strategies. However, what we have to face is the risk of out of stock when purchasing a certain commodity, which leads to an increase in uncertainty. The purchase opportunity when consumers adopt strategic purchase behavior is an important factor to be measured. Because once out of stock, consumers will face double losses in time and commodity use, and consumers with high retention prices will buy at t1 to avoid out of stock risk; However, online retailers are more potential users for consumers with low retention prices. On the one hand, they adopt strategies to make them obtain more profits in the high price stage t1 , and on the other hand, they adopt the price reduction at t2 to end the sale of perishable products. Stock k and out of stock risk θ draw the purchase intention of strategic consumption behavior under two purchase environments v(θ ). The curve of is shown: Obviously, when the change of inventory k is not obvious, the significant strategic behavior of consumers can be concluded mainly from their weak purchase intention; When the enterprise publishes the inventory and out of stock information, consumers who are willing to buy feel the risk of out of stock and take the strategy of buying as soon as possible with the reduction of inventory. As the quantity of goods provided by the enterprise is not unlimited, which leads to the uncertain environment of purchase, the risk of out of stock in the second price segment of the sales period, namely the low price stage, is recorded as θ , Fig. 23.2 shows the pricing and income curve. The risk of out of stock is θ = 0.25, θ = 0.5 (Fig. 23.3). It can be seen from the image that when consumers face an increase in the probability of out of stock risk in the t2 stage of the enterprise, consumers’ strategic purchase behavior starts to decrease. And with the decrease of commodity inventory, the stronger consumers’ awareness of out of stock risk, the more they give up

Fig. 23.2 Purchase intention under stock k and out of stock risk

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Fig. 23.3 Pricing and profit under different out of stock risks

waiting to take a purchase decision, and the enterprise’s income increases accordingly. p2 − p1 = ∆p, ∆p can reflect the degree of waiting willingness of strategic consumers. Table 23.1 is about the pricing and profit of business. It can be seen from the chart that, because the number of potential consumers is more than the number of products provided by the enterprise, and considering the characteristics of perishable goods, the enterprise should price close to the high reserve price consumer Ch at stage t1 to maximize profits; the remaining commodities at t1 will be sold at a relatively low price at t2 stage. On the other hand, the enterprise make the price as lower as possible isn’t better at stage t2 , which will lead to more consumers with high retention price Ch is waiting for the purchase strategy at t1 , which causing loss of profits; However, if the price of t2 is high, Cl , a consumer with low reserve price will not buy the remaining goods, which will also cause profit loss. In fact, retailers should combine two-stage pricing to achieve maximum profit under the condition that rational expectation equilibrium is achieved. At this time, although the two-stage price strategy is one of the pricing strategies facing strategic consumer purchase behavior, under the optimal price decision of retailers, although there are more products available, in order to reduce consumer waiting strategy, it adopt the optimal pricing decision to choose an appropriate price ( p1 , p2 ). Table 23.1 Pricing and profit statement p2 = 0.04

p2 = 0.08

p2 = 0.1

p2 = 0.14

p2 = 0.18

p1 = 0.2

23.79

24.77

24.12

15.65

1.97

p1 = 0.4

24.58

26.67

26.72

17.34

4.33

p1 = 0.5

25.17

28.23

28.75

20.86

7.81

p1 = 0.8

25.81

30.89

30.67

23.34

12.90

p1 = 1

26.01

31.11

31.83

26.09

13.72

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23.6 Conclusion and Prospect This paper analyzes the pricing strategy of perishable goods sold by online retailers through model and empirical analysis. Through analysis, the corresponding important conclusions are drawn. First of all, as consumers’ online shopping has become one of the important consumption modes, online retailers pricing strategies for the commodity sales process have become an important way for them to obtain high profits. Secondly, as consumers mature in the online market or more consumers perceive the price volatility of perishable goods, the purchase decisions of most consumers have changed, and the proportion of strategic consumers has become the main purchasing group. Third, if online business ignore the increase of strategic consumption behavior, they will inevitably lose their profits in the face of fierce online market competition environment. Finally, we discuss that retailers adopt twostage pricing to optimize the pricing strategy when selling perishable products, so as to avoid potential consumers’ waiting strategy and consumers’ purchase decisions when facing the risk of out of stock. It is proved that as long as the enterprise pricing strategy weighs the psychological expectations of consumers, it can increase profits. In addition, this study supports the analysis results to some extent. First of all, for perishable products such as fresh food and clothing with short sales cycle and low residual value at the end of the sales period, the first stage pricing should enable consumers with high retention price to purchase, because consumers will face increased costs of uncertainty in the second stage. For the same reason, when consumers obtain a large discount factor in the second stage. E-retailers will face more waiting strategy purchase behavior, which will make them face greater risks. Secondly, if online business ignore consumer strategic behavior, it will certainly bring adverse effects. This paper draws conclusions from model and numerical analysis. However, this study has some limitations. The pricing strategy adopted by the enterprise is studied by taking two-stage pricing as an example. When the expected profit of the enterprise is maximum, the enterprise can also adopt multi-stage pricing, and analyze the degree of regret of consumers to adopt the corresponding pricing strategy. In addition, for future empirical research, the research on pricing price difference depends on the type of perishable commodity retailers, such as Internet distribution channels and online offline hybrid retail channels. If possible, it is necessary to analyze a wider range of product categories and give different pricing strategies according to product categories. Finally, consumer reviews are also crucial for online business sales. Therefore, in order to achieve this, it is better to follow the laws of price and Internet sales mode.

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References 1. Anderson, W.: Wait or buy? The strategic consumer: pricing and profit implications. Working paper (2003) 2. Mersereau, A.J., Zhang, D.: Markdown pricing with unknown fraction of strategic customers. Manuf. Serv. Oper. Manag. 14(3), 355–370 (2012) 3. Zhao, N.G., Wang, Q., Cao, P., et al.: Dynamic pricing with reference price effect and pricematching policy in the presence of strategic consumers. J. Oper. Res. Soc. 70(12), 2069–2083 (2019) 4. Akcay, Y., Natarajan, H.P., Xu, S.H.: Joint dynamic pricing of multiple perishable products under consumer choice. Manage. Sci. 56(8), 1345–1361 (2010) 5. Tunuguntla, V., Bsau, P., Rakshitk, K., et al.: Sponsored search advertising and dynamic pricing for perishable products under inventory-linked customer willingness to pay. Eur. J. Oper. Res. 276(1), 119–32 (2019) 6. Zhang, C., Xiao, M., Huang, S., et al.: Optimal pricing of perishable goods in experiential marketing considering customer strategic behavior. J. Syst. Manage. 27(4), 783–790 (2018) 7. Zhou, X., Li, J., Cai, D., et al.: Marketing time strategy of advance selling for perishable goods. Chin. J. Manage. Sci. 25(6), 91–100 (2017) 8. Gallego, G., Wang, R.: Multiproduct price optimization and competition under the nested logit model with product-differentiated price sensitivities. Oper. Res. 62(2), 450–461 (2014) 9. Krishnan, V., Ramachandran, K.: Integrated product architecture and pricing for managing sequential innovation. Manage. Sci. 57(11), 2040–2053 (2011) 10. Parlaktiirk, A.: The value of product variety when selling to strategic consumers. Oper. Manag. 14(3), 371–385 (2012) 11. Taudes, A., Rudloff, C.: Integrating inventory control and a price change in the presence of reference price effects: a two-period model. Math. Methods Oper. Res. 75(1), 29–65 (2012) 12. Herbon, A., Khmelnitsky, E.: Optimal dynamic pricing and ordering of a perishable product under additive effects of price and time on demand. Eur. J. Oper. Res. 260(2), 546–556 (2017) 13. Chen, Y.W., Farias, V.F.: Robust dynamic pricing with strategic customers. Math. Oper. Res. 43(4), 19–22 (2018) 14. Li, R.H., Teng, J.T.: Pricing and lot-sizing decisions for perishable goods when demand depends on selling price, reference price, product freshness, and displayed stocks. Eur. J. Oper. Res. 270(3), 100–108 (2018)

Chapter 24

Challenges and Solutions for Arabic Natural Language Processing in Social Media Sallam AL-Sarayreh , Azza Mohamed , and Khaled Shaalan

Abstract The widespread use of social media has created a wealth of Arabic textual data, presenting challenges for NLP researchers due to the complexity and characteristics of the Arabic content. Despite the ambiguity of unstructured data and the peculiarity of the Arabic language and its writing style, there have been numerous studies in the field. These studies have achieved progress in areas such as sentiment analysis, topic classification, and named entity recognition. Hence, this systematic review aimed to review studies published on Arabic NLP that are focused on social media to offer a thorough understanding of what are the issues and discuss the main challenges along with solutions across various applications. The results indicated solutions. One solution is to increase annotated data for training NLP models and utilize transfer learning techniques. Another solution is to improve the text preprocessing techniques to improve the performance of the Arabic NLP computational model. Arabic NLP models can be improved with further research to better understand the rich and diverse Arabic content on social media, having significant implications for a variety of applications.

24.1 Introduction 24.1.1 Overview Social media platforms have become an integral part of everyday life, with billions of users accessing online content every second. Platforms like Facebook, Twitter, and Instagram have become sources of news and communication, with political parties S. AL-Sarayreh · K. Shaalan (B) Faculty of Engineering and IT, The British University in Dubai, 345015 Dubai, UAE e-mail: [email protected] A. Mohamed Khawarizmi International College, Al Ain, UAE e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_24

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and businesses using them to reach out to customers and supporters. The unstructured nature of social media data, however, presents challenges in terms of drawing conclusions and understanding the impact of social interactions on information transmission [26]. With the increasing amount of Arabic content online, it is crucial to develop effective methods for processing and analyzing Arabic text in social media [3]. Previous research on text processing has primarily focused on English, with limited studies on Arabic natural language processing (NLP) [11, 21]. The challenge of processing Arabic text used in social media is twofold, with the need to address the growth of the Arabic language and the complexities of processing informal language in social media. Hence, this study aims at highlighting the main challenges in processing Arabic text in social media and presenting research that has tackled any of these issues in the past. Starting with NLP preprocessing techniques, the study explores suggested machine learning methods that can handle language special cases, leading to the implementation of models that suits sentiment analysis and fake news detection. The study also discusses the need for more research and applications in computational linguistics to address the challenges of processing Arabic text in social media, which is crucial for some NLP applications such as information retrieval, email spam detection, and web page content screening. The goal of this study is to present innovative solutions for processing Arabic text in social media, which have significant implications for a range of fields from marketing and politics to public opinion [6, 10, 13, 22].

24.1.2 Background Arabic is a widely spoken language, ranking among the top five in the world, and is used in the Muslim world for religious purposes, with the Qur’an written in classic Arabic for over 1500 years [4]. It is a member of the Semitic language family, with a complex syntax and several dialects, including classical, standard, and colloquial forms. Sentences in Arabic are written from right to left with 28 different alphabets, and it has a rich morphology, with a vast variety of synonyms [6, 23]. Arabic NLP faces difficulties due to the inflected nature of the language, its deeprooted morphology, and a wide range of synonyms [5, 12]. Despite these challenges, NLP research in Arabic focuses on informal Arabic, with modern standard Arabic being one of the six official languages of the United Nations. The goal of NLP is to make human–machine communication easier, by analyzing linguistic structures of sentences to determine their significance in automated settings [2, 15].

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24.2 Problem Identification and Analysis The purpose of this systematic study is to identify the issues and barriers connected with Arabic text processing in general, and specifically in social media, as well as the available applications and solutions in place to address these challenges. The study evaluates various publications to gather information that supports its goals and presents a comprehensive picture of the research. The challenges faced in processing Arabic text are due to several factors, including the ambiguity of the Arabic script, its diglossia, and lack of capitalization [5, 12, 17, 24]. In addition to the complexity of the designated letters, the morphology of the words as well as the letter shifts based on the position in the word that make reading and understanding Arabic difficult [11]. Moreover, the inconsistent broken plural and the erroneous normalization/transliteration of letters pose a challenge in Arabic NLP, which limits its effectiveness in information access applications [8]. The majority of modern written Arabic texts lack diacritics, which adds to the language’s ambiguity because a single short vowel is optional [15, 16]. The various dialects of Arabic are often treated separately, requiring methods and techniques that can handle all dialects [6]. The challenges of tokenization, stemming, and morphological analysis for Arabic text are also a concern for researchers and developers [11, 20]. Finally, the lack of academic standards for spelling or language and noun ambiguity, where words can be adjectives, nouns, or proper nouns, present further challenges for Arabic NLP [15]. The challenges associated with Arabic text processing in social media are also significant [5]. There is a lack of standard or coordinated work on Arabic text preparation for social media, with a need for tools and frameworks that can generate fully detailed datasets [11]. The text may contain non-Arabic or ill-formed words, such as misspellings, repeated letters, or emotively stated words. Furthermore, it is necessary to deal with noisy words and to restore the text to its normal form to deal with the dialect version of the language. Arabizi text, which is a transliteration of spoken Arabic written with Latin letters and numerals to represent Arabic letters, which is widely used on chat and social media platforms [6]. The sentiment in evaluations is dependent on word order, and word relationships play a significant role in identifying the overall sentiment [10].

24.3 Literature Review Preprocessing techniques are essential in the analysis of Arabic text extracted from web content, especially social media platforms, due to the unique nature of the Arabic language, and the complex or the unstructured nature of Arabic text on social media [19, 25]. The nature of the Arabic language raises the need for preprocessing techniques to be employed to improve the performance and accuracy of the classifier results. Techniques used include text normalization, tokenization, stop words

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removal, and standardization of multiple variation of the same word through Arabic stemming (light and heavy) and lemmatization. Studies have shown that the use of these techniques can significantly improve the accuracy of classifiers such as Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). For example, the accuracy of NB increased from 65 to 93% after preprocessing. Another study compared the performance of several commonly used machine learning algorithms on Arabic text, with a focus on NLP. The study found that SVM performed the best, with a high level of accuracy in categorizing Arabic text, followed by NB. The most accurate model was found to be an unsupervised learning model using K-means [11]. The study also introduced a morphology algorithm that generates a word vocabulary based on Arabic morphology templates, which was successful in revealing information and producing novel information extraction, topic classification, and sentiment analysis approaches and algorithms. The importance of feature selection techniques has also been discussed in relation to improving Arabic named entity recognition from social media [18, 23, 25]. One study found that the application of particle swarm optimization (PSO) significantly improved the system’s overall performance [9, 15]. However, the challenge of dealing with fake news on social media platforms remains a problem, reducing trust in the data presented. Transformer models in NLP, including BERT, GigaB-ERTv4, Arabert, Arabic-Bert, and others, have been introduced as a solution. These models have been trained on a large amount of data and have been shown to be more effective in NLP tasks for Arabic text compared to more conventional methods such as tokenization and stemming [15]. One of the challenges in Arabic word extraction is the problem with root-based methods. One study has addressed this issue by producing new rules for stemming the broken plural rule (BPR) algorithm, which deals with irregularly broken plural difficulties based on Arabic original grammar. The BPR algorithm was used to improve the ISRI stemmer, solving a number of irregularly broken plural difficulties and ultimately helping to improve the effectiveness of an Arabic stemmer based on roots [7]. In conclusion, the analysis of Arabic text from social media platforms is crucial and requires preprocessing techniques to improve the accuracy of classifiers. Studies have shown the significance of techniques such as text tokenization, stop words removal, Arabic stemming, and lemmatization, as well as the importance of feature selection techniques and the use of advanced NLP models such as transformer models. The challenge of dealing with fake news on social media remains a problem, but NLP models trained on large amounts of data have been introduced as a solution. The issue with root-based methods in Arabic word extraction has also been addressed through the production of new rules for the BPR algorithm [7, 10].

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24.4 Methodology 24.4.1 Criteria for Acceptance and Removal For this systematic literature review study, only English language papers published after 2020 that relate to the keywords of “social media,” “Arabic language neutral processing,” and “Arabic text processing” are considered. The sources used must come from reputable journals and have a direct connection to the research keywords. In order to give precedence in the review process, papers that provide real-world examples and solutions are prioritized. To guarantee a comprehensive analysis and evaluation of the literature were conducted which resulted in 20 papers have been reviewed.

24.4.2 Data Sources and Search Strategies To identify the research gap, bibliometric literature was analyzed. The goal of the study was to evaluate the challenges associated with processing Arabic in digital content, particularly in social media, and to review prior research in the field that demonstrates approaches and resources for overcoming some of these problems. In order to validate the study issue, this analysis focused on recent studies that should have the keywords in the title or abstract and made reference to the repeated of keywords in the 733 articles gathered by the research string listed in Table 24.1. The resultant graphs are shown in Fig. 24.1. The VOSViewer software was used to assess the metadata produced by SCOPUS, ScienceDirect, and Springer in relation to the purchased papers. Total keywords were 5049 retrieved based on 10 occurrences for these keywords in Fig. 24.1 that demonstrates the “social media” and related topics are the subjects of the bulk of studies. The second highlighted term is “machine learning algorithms,” and the final item is “sentiment analysis,” which is a technique for analyzing text and extracting useful figures. Based on this analysis shows the intersection of the research parts. Table 24.1 To find the publications for this research, conduct a web search Research string TITLE-ABS-KEY(“arabic natural language processing”) OR (“arabic text processing”) OR (“arabic text in social media”) OR (“social media content”) AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (PUBSTAGE, “final”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (SUBJAREA, “COMP”)) AND (LIMIT-TO (PUBYEAR, 2023) OR LIMIT-TO (PUBYEAR, 2022) OR LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020)) AND (LIMIT-TO (LANGUAGE, “English”))

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Fig. 24.1 10 keyword co-occurrences for 733 research papers

On the other hand, even though our research keywords are specific to the Arabic language, we have observed limitations in the resources related to Arabic text as most of the research is on the general elements of text preprocessing and categorization, which represents the gap in Arabic NLP research. We have changed the occurrences parameter from 10 to 5, seeking to obtain a correlation between the interaction of Arabic text analysis keywords with the other themes, in order to do an in-depth analysis of the extracted research, and as a result, there is an association between Arabic NLP and social media keywords, machine learning techniques that have been discussed in our research, as well as sentiment analysis. The research PRISMA was conducted to identify relevant records. A total of 1823 records were identified from all databases, including 789 from SCOPUS, 699 from Springer, and 335 from Science Direct. During the screening process, 288 duplicate records were removed, and 912 records were removed for other reasons, such as not being related to the topic or being written in a language other than English. After the title and abstract screening, 623 records were selected for further analysis, with 520 being excluded for not being a journal or not being relevant to the study. After a full-text study, 103 reports were selected for eligibility assessment, with 69 not being retrieved and not being relevant to the study. After assessing eligibility, 34 reports were included in the study, with 14 being excluded for having poor paper quality. The final number of studies included in the review was 20.

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24.4.3 Quality Assessment The quality evaluation is a crucial consideration in addition to the inclusion and exclusion criteria [1]. A method of evaluating the content of the research articles that were maintained for further investigation (N = 20) was developed, and it was included in a quality evaluation checklist. The checklist was created using inputs from others [14]. Each question was graded on a three-point scale, with “1” indicating “Yes,” “0” indicating “No,” and “0.5” indicating a half point. As a result, each study might receive a score ranging from 0 to 7, with the greater the overall score, the better the study’s ability to address the research objectives. Quality assessment checklist • • • • • • •

Is the context and domain of the study clearly defined? Are the topics addressed in the study clearly stated? Are the procedures for collecting data adequately explained? Does the study deepen our knowledge of the topic? Is the nation and journal considered to be of a higher quality than Q2? Is the study relevant to computer science? Was the review of relevant literature comprehensive enough and all relevant studies included?

As a result, all the studies have successfully completed the quality assessment with a score of 100%, indicating that they are qualified to be used in continuing research. Papers of lower quality have been eliminated.

24.5 Results Social media has become a prevalent source of news for many individuals due to its ease of use and ability to create, share, and publish information about any subject [15]. The vast amounts of data generated from social media have a significant impact on organizations and businesses, providing valuable insights into community feedback and requirements. However, social media also has its drawbacks, such as the spread of false and misleading news, which can harm society [15]. This research focuses on the challenges of processing Arabic text, which has received little attention in comparison to other languages. The Arabic language’s distinctive qualities, as well as concerns with Arabic content published on social media sites, provide various processing challenges. These include ambiguity, diglossia, the absence of capitalization in the Arabic script, irregular broken plurals, and the presence of several Arabic dialects [11]. Moreover, non-Arabic terms, orthographic characteristics such as misspellings, and emotionally charged words further complicate the translation of Arabic text for social media use. Word relationships play an important role in determining the sentiment of the text [10].

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To address these challenges, preprocessing and normalization techniques such as tokenization, stemming, and lemmatization have been applied to Arabic text. Tokenization removes stop words and unrelated text, while stemming links inflected words to their roots and lemmatization reduces the data dimension by regrouping semantically related words [15]. Several Arabic text classification techniques have been evaluated [27], with the top three algorithms, Naive Bayes (NB) classifier, Support Vector Machine (SVM), and Decision Tree (DT), producing high accuracy results. Toolkits have been developed to cover all processes of Arabic text preparation and classification, including morphological algorithms, stemming, tokenization, and normalization. A new Broken Plural Rule (BPR) algorithm has been developed to address words that follow a broken plural rule, increasing the effectiveness of the Arabic stemmer. Additionally, models have been put in place to address specific issues related to analyzing Arabic text processing in social media and fake news [15]. Recently, the Multilingual Offensive Language Detection (MOLD) project has been investigated to manage multilingualism through joint-multilingual and translationbased strategies [11]. The joint-multilingual strategy involves developing a single classification scheme for various languages, while the translation-based strategy categorizes texts after translation into a universal language.

24.6 Discussion and Conclusion The rise of social media has created a vast amount of data that can be collected, but the nature of this data is unstructured and often ambiguous, making it difficult to process. The study of social computing faces new challenges in understanding the impact of social interactions on the transmission of information. This results in a large amount of textual material that can significantly influence global events and public opinion. To maximize the benefits of data from these platforms, significant efforts have been made to improve text processing techniques. However, most of these efforts have focused on English text, neglecting the complexities of Arabic text. Arabic text is structurally different from English, and existing techniques that rely on English language processing methods may not be adequate. As a result, there is a growing need for studies and applications that specifically address the challenges of Arabic text processing. In this study, we focus on two primary challenges in processing Arabic text: the diversity of the Arabic language and the difficulties in understanding Arabic text used in social media. We also discuss existing applications and the valuable contributions they have made to address these challenges. Our approach was to first use NLP preprocessing techniques and machine learning models to analyze the meaning of Arabic text, with a focus on sentiment analysis and fake news detection. Despite the progress made in Arabic text processing, further research is still required. Our ultimate goal is to shed the light on the need to develop a model that can standardize various Arabic text structures into a unified language. This will have a significant impact on digital transformation, which is becoming increasingly

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important in today’s world. We encourage researchers to tackle this research, as it will play a crucial role in shaping the future of digital technologies. Furthermore, the development of more sophisticated text processing techniques could lead to more accurate sentiment analysis, which could help businesses and organizations better understand public opinion on certain issues. Additionally, advancements in Arabic text processing could help combat the spread of fake news and misinformation on social media, which can have serious consequences for society. Furthermore, the ability to process and analyze large amounts of social media data could lead to new insights into human behavior and the impact of social interactions on global events. These advancements in Arabic text processing could have far-reaching implications for both the business and academic worlds. Acknowledgements This work is a part of a project undertaken at the British University in Dubai.

References 1. Al-Emran, M., Mezhuyev, V., Kamaludin, A.: Technology acceptance model in m-learning context: a systematic review. Comput. Educ. 125(June), 389–412 (2018) 2. Aljedaani, W., Abuhaimed, I., Rustam, F., Mkaouer, M.W., Ouni, A., Jenhani, I.: Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study. Soc. Netw. Anal. Min. 12(1) (2022) 3. Alkhatib, M., el Barachi, M., Shaalan, K.: An Arabic social media based framework for incidents and events monitoring in smart cities. J. Clean. Prod. 220 (2019) 4. Alkhatib, M., Monem, A.A., Shaalan, K.: A rich Arabic WordNet resource for Al-Hadith Al-Shareef. Procedia Comput. Sci. (2017) 5. Alkhatib, M., Shaalan, K.: The key challenges for Arabic machine translation. In: Studies in Computational Intelligence (2018) 6. Alruily, M.: Classification of Arabic tweets : a review (2021) 7. Alshalabi, H., Tiun, S., Omar, N., abdulwahab Anaam, E., Saif, Y.: BPR algorithm: new broken plural rules for an Arabic stemmer. Egypt. Inform. J. 23(3), 363–371 (2022) 8. Alshalabi, H., Tiun, S., Omar, N., AL-Aswadi, F.N., Ali Alezabi, K.: Arabic light-based stemmer using new rules. J. King Saud Univ.—Comput. Inf. Sci. 34(9), 6635–6642 (2021) 9. Al-Zoghby, A., Shaalan, K.: Semantic search for Arabic. In: International Florida Artificial Intelligence Research Society Conference (FLAIRS). USA [online] (2015). Available at: http:/ /www.aaai.org/ocs/index.php/FLAIRS/FLAIRS15/paper/view/10360 10. Diwali, A., Dashtipour, K., Saeedi, K., Gogate, M., Cambria, E., Hussain, A.: Arabic sentiment analysis using dependency-based rules and deep neural networks. Appl. Soft Comput. 127, 109377 (2022) 11. El-Alami, F.Z., El Alaoui, S.O., Nahnahi, N.E.: A multilingual offensive language detection method based on transfer learning from transformer fine-tuning model. J. King Saud Univ.— Comput. Inf. Sci. 34(8), 6048–6056 (2022) 12. Farghaly, A., Shaalan, K.: Arabic natural language processing: challenges and solutions. ACM Trans. Asian Lang. Inf. Process. (TALIP) 8(4), 1–22 (2009) 13. Hamza, A., En-Nahnahi, N., El Mahdaouy, A., El Alaoui Ouatik, S.: Embedding Arabic questions by feature-level fusion of word representations for questions classification: it is worth doing? J. King Saud Univ.—Comput. Inf. Sci. 34(9), 6583–6594 (2022) 14. Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering—a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009)

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15. Nassif, A.B., Elnagar, A., Elgendy, O., Afadar, Y.: Arabic fake news detection based on deep contextualized embedding models. Neural Comput. Appl. 34(18), 16019–16032 (2022) 16. Nassif, A.B., Shahin, I., Attili, I., Azzeh, M., Shaalan, K.: Speech recognition using deep neural networks: a systematic review. IEEE Access, vol. 7 (2019) 17. Othman, E., Shaalan, K., Rafea, A.: Towards resolving ambiguity in understanding Arabic sentence. In: International Conference on Arabic Language Resources and Tools. NEMLAR, Cairo, Egypt, pp. 118–122 (2004) 18. Oudah, M.A.I., Shaalan, K.: NERA 2.0: improving coverage and performance of rule-based named entity recognition for Arabic. Natural Lang. Eng. (2016) 19. Oudah, M., Shaalan, K.: A pipeline Arabic named entity recognition using a hybrid approach. In: The International Conference on Computational Linguistics (COLING). Mumbai, India [online] (2012). Available at: http://aclweb.org/anthology-new/C/C12/C12-1132.pdf 20. Rafea, A., Shaalan, K.: Lexical analysis of inflected Arabic words using exhaustive search of an augmented transition network. Softw. Pract. Exp. 23(6), 567–588 (1993) 21. Ray, S., Shaalan, K.: A review and future perspectives of Arabic question answering systems. IEEE Trans. Knowl. Data Eng. (2016) 22. Salloum, S.A., Al-Emran, M., Abdallah, S., Shaalan, K.: Analyzing the Arab gulf newspapers using text mining techniques. In: Advances in Intelligent Systems and Computing (2018) 23. Shaalan, K.: A survey of Arabic named entity recognition and classification. Comput. Linguis. 40(2), 469–510 (2014) 24. Shaalan, K., Magdy, M., Samy, D.: Towards resolving morphological ambiguity in Arabic intelligent language tutoring framework. In: The Seventh International Conference on Language Resources and Evaluation (LREC{\textquoteright}10) Workshop on Supporting eLearning with Language Resources and Semantic Data. Valletta, Malta: LREC [online] (2010). Available at: http://www.lrec-conf.org/proceedings/lrec2010/workshops/W15.pdf 25. Shaalan, K., Oudah, M.: A hybrid approach to Arabic named entity recognition. J. Inf. Sci. 40(1), 67–87 (2014) 26. Toraman, C., Sahinuç, ¸ F., Yilmaz, E.H., Akkaya, I.B.: Understanding social engagements: a comparative analysis of user and text features in Twitter. Soc. Netw. Anal. Min. 12(1) (2022) 27. Wahdan, A., Hantoobi, S., Salloum, S.A., Shaalan, K.: A systematic review of text classification research based on deep learning models in Arabic language. Int. J. Electr. Comput. Eng. 10(6) (2020)

Part IV

Image Analysis and Processing

Chapter 25

MSAN: Multi-stage Human Pose Estimation Universal Network Based on Attention Mechanism Yuru Zhang, Jiayuan Zhao, Xiaodong Su, Shizhou Li, Yurong Zhang, and Hongyan Xu

Abstract The task of human pose estimation aims to detect and locate all human key points in an image. Many existing methods based on Convolutional Neural Networks (CNNs) can obtain the appearance features of key points very well and detect the visible key points. However, due to their limitations, it is difficult to model the relationship between key points. In this paper, we design a network that implicitly models joint relationships, called a multi-stage attention network (MSAN), to model the constraint relationship between joints and improve the recognition ability of complex key points such as “knee” and “ankle”. Specifically, the proposed method uses the channel spatial attention network to obtain key features from the backbone network and suppress redundant features. In order to better model the constraint relationship between key points, this paper uses a multi-stage self-attention network to implicitly model the joint constraints and iteratively calculate the correlation between key point features to explicitly learn the constraint relationship between key points. In order to make the model better at recognizing complex key points, we use focus loss to make the model pay more attention to complex key points. We use HRNet and ResNet as the backbone networks, respectively, and prove the effectiveness of the method in this paper on the benchmark datasets MPII and COCO.

Y. Zhang · J. Zhao (B) · X. Su · S. Li · Y. Zhang · H. Xu Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_25

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25.1 Introduction Two-dimensional human pose estimation tasks have been widely used in human– computer interaction [1], behavior recognition [2], and other fields. According to the current work, although convolutional neural networks perform well in pose estimation tasks, convolutional neural networks are sensitive to local information and to obtain global contextual information, they can only rely on larger convolutional kernels or deeper layers. At present, numerous single-scale networks are mostly influenced by Newell, Xiao [3, 4] and others and have a classical encoder–decoder structure. The input images are extracted by downsampling operations such as convolution and pooling, and the resolution is recovered by upsampling. Although this structure achieves excellent performance, it is difficult to improve the recognition accuracy for complex key points. The main reason for this is that single-scale networks lack contextual information in the input space. To solve this problem, some scholars [5, 6] proposed a multi-scale structure to fuse features from different scales to obtain a shared feature expression. Although the multi-scale structure has achieved good results, there are still some problems. For example, in the actual task scene, it is affected by the variability of the human body and external factors, and it is difficult to accurately identify complex key points such as “knee” and “ankle”. In the MPII dataset, the [email protected] accuracy of HRNet for simple key points such as “head” and “shoulder” reaches 96.2 and 95.0%, respectively, which is almost saturated. However, the accuracy of [email protected] for complex key points such as “knee” and “ankle” only reaches 84.3 and 80.6%, which is often unsatisfactory. Although HR-ARNet [7] improves the HRNet [6] model, its framework is shown in Fig. 25.1, and the attention mechanism is introduced into the model [8, 9]. However, according to our research, as shown in Fig. 25.2, the single-layer constraint calculation will make the model’s attention range too large and lack of control over specific areas, which will lead to unclear relationships between learned joints. According to the above research, we designed a joint constraint implicit modeling network using self-attention mechanism, multi-stage human pose estimation general network (MASN), to implicitly model the relationship between joints. The model k×1

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Fig. 25.2 Single-stage attention

consists of three different attention mechanisms [8, 10], and the model architecture diagram is shown in Fig. 25.3. The proposed method first uses the channel spatial attention network to screen features, focus on important features, and suppress redundant features. Then, through multi-stage constraint modeling, the correlation between key point features is calculated, and the features are gradually refined to obtain the predicted key points. In this paper, the ResNet network [11] and the HRNet network [6] are selected as the backbone network, respectively, and the effectiveness of the model in this paper is proved on the public MPII [12] and COCO [13] datasets. The experimental results show that the network in this paper can improve the recognition accuracy of existing pose estimation models.

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25.2 Related Works 25.2.1 Human Pose Estimation Early human pose estimation methods are mostly based on template matching and graph structure model to solve this problem [14–17]. The appearance features of limbs are extracted manually, and the set graph structure model or template is used for feature matching. However, such an approach does not work well because the pose space is too large.

25.2.2 Human Pose Estimation Based on Convolutional Neural Network The first human pose estimation algorithm based on deep learning is DeepPose [18]. Toshev et al. from Google draw on the good performance of Deep Neural Networks (DNNs) in image classification and other tasks. This paper carried out the research on human pose estimation using DNNs and transformed the original manual feature extraction and template matching problem into the automatic feature extraction and key point coordinate regression problem. In 2014, Tompson et al. [19] from New York University developed the first human pose estimation model based on heat map, proposed a new hybrid architecture composed of DCNNs and MRF (Markov Random Field), and proposed the method of heat map rendering labels. Became the most popular algorithm at the time. Newell et al. [3] designed stacked hourglass networks that can process and merge features at all scales to best capture various spatial relationships related to the body. Moreover, a repeated bottom-up and top-down intermediate supervision mechanism is used to improve the model performance. Successive steps of pooling and upsampling are taken in order to generate the final prediction set. Since then, many methods based on multi-scale

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feature fusion have appeared. In 2017, Chen et al. [20] proposed Cascaded Pyramid Network (CPN), which combines feature pyramid structure and multi-scale fusion to explicitly deal with “difficult” key points through multi-scale information. Finally, the final feature map is obtained by upsampling and concatenating the information of different scales. Later, Wang et al. proposed Pyramid Residual Module (PRMs) [5], which extended the residual module to the pyramid network to enhance the scale invariance of DCNNs. Simple baseline [4] inherits the hourglass structure, but uses ResNet [11] to replace the original symmetric structure, providing a new feature extraction network for human pose estimation tasks. With the above method is different, China university of science and technology of Sun [6] and others, think from low to high resolution recovery process will lose some characteristic information; therefore, put forward the whole HRNet in high-resolution characteristic of network, the network is composed of parallel high-resolution to low-resolution subnet, and then repeat the multi-scale fusion between multiple child networks. Through the information at different scales, the high-resolution representation is improved, so that the high-resolution feature map also has rich pose estimation. In this way, high-resolution features are maintained throughout the process, and multiple scale feature fusion can be repeated to generate more accurate point heat maps. Due to such dense multi-scale feature fusion, the convolutional neural network learns shared feature representations, which also enables the architecture to achieve state-of-the-art results. In addition, Chen et al. [20] used the OHKM loss function to replace the ordinary MSE loss function, which improved the recognition accuracy of the model from the perspective of loss function to pay extra attention to some complex key points.

25.2.3 Attention Mechanism Attention mechanisms are widely used in various domains in computer vision tasks, such as image segmentation [21] and object detection [22]. Sanghyun et al. [8] designed the channel spatial attention network to refine the key information in the feature map. Spatial connectivity is more conducive to extracting effective features of the target. With the proposal of vision transformer [23], it provides a different idea for the task of human pose estimation. The main idea of transformer is to calculate the attention and self-attention between inputs, so that the global receptive field can be obtained. PoseFormer [24] is the first model to use pure transformer architecture for pose estimation, but since pure transformer structure feature extraction capability is not strong and requires a large amount of training data, most works use a combination of CNN and transformer architecture. TokenPose [25] uses CNN for feature extraction, embedding each key point explicitly with a token to simultaneously learn constraints and appearance cues from images. HR-ARNet [7] proposes an attention refinement network, which uses the attention mechanism to refine the features extracted by HRNet and uses a single-stage self-attention mechanism to find prolonged-distance

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joints. However, using a single-stage approach makes each key point less focused. Inspired by TransPose [26], MSAN repeatedly performs attention computation on the features extracted by CNN to refine the target key points.

25.3 Method In this paper, we design a plug-and-play network for implicitly modeling human key point constraint relationships. This chapter will first describe the overall architecture of the model, then carry out mathematical analysis of the attention calculation, and finally combine the methods of related loss functions to achieve the purpose of complex key point identification.

25.3.1 Model Architecture Inspired by HR-ARNet [7] and Transpose [26], this paper proposes MSAN, a general network for multi-stage human pose estimation based on attention mechanism. As shown in Fig. 25.3, the overall network consists of three stages: feature extraction stage, refinement stage, and implicit modeling stage, which will be introduced in turn next.

25.3.2 Backbone Network Currently, there are numerous feature extraction networks with promising results for human pose estimation tasks. In order to prove the effectiveness of the method in this paper, we propose our method based on HRNet [6], which is the most accurate current backbone network, and use the classic feature extraction network ResNet [11] as the backbone network to prove the robustness of our method. The HRNet proposed by Sun et al. [6] maintains the high-resolution features of the input image during the forward propagation of the entire network and simultaneously performs high- and low-resolution feature fusion at different stages, thereby achieving excellent results on COCO and MPII datasets. As shown in Fig. 25.4, in the last layer of HRNet, the information from the sub-networks of the previous layers is fused to generate the output, and the high-resolution information is retained throughout the process. Since the parallel network represents semantic information from different scales in the previous network, there is information redundancy in direct aggregation. Therefore, in this paper, channel spatial attention networks are used to focus on key information, suppress redundant information, and enhance feature fusion of the network.

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25.3.3 Channel Spatial Attention Network For HRNet, a high-resolution multi-scale network, it is easy to fuse redundant information in its output phase. At the same time, due to the original fusion method of superimposing after resizing, the semantic gaps at different depth conditions are neglected, which cause some global contextual information to be lost. In this paper, channel spatial attention networks are used to reinforce critical information and suppress redundant information. In the following, we will explain its principle. Channel Attention Network. The channel attention network adaptively rescales the weights of each channel, which can be viewed as an object selection process, showing that the importance of different channels is modeled and its structure is shown in Fig. 25.5a. We assume that the feature map is F ∈ R H ×W ×C , H × W represents the resolution of the feature map and C represents the number of feature maps. In channel attention network [8], we consider each channel to represent different features and imply that some features are significant and some features can be ignored. Previous studies have shown that average pooling and max pooling operations can filter out vital information. As shown in Fig. 25.5a, the module first performs Global Average Pooling (GAP) and Global Maximum Pooling (GMP) on the input feature map and then passes through the perceptron network to enhance its representation ability. The channel attention network output weights Att C(F) ∈ R 1×1×C can be expressed as:     1×1×C  1×1×C + Conv Fmax Att C(F) = σ Conv Favg

(25.1)

where Favg and Fmax represent the feature map after passing through the pooling layer, and Conv represents the convolution layer with a 1 × 1 convolution kernel. Spatial Attention Network. Channel attention networks differ from spatial attention networks in that they focus on the location of key information. To achieve this, we first obtain Favg ∈ R H ×W ×1 and Fmax ∈ R H ×W ×1 through average pooling and maximum

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pooling of a channel dimension, respectively. In CBAM [8], a convolutional network with 7 × 7 convolution kernels is adopted to fuse it. However, it is shown by HRARNet [7] that in the task of human pose estimation, a larger receptive field should be concerned. Inspired by Wang and Peng [7, 27], this paper uses X pooling and Y pooling to expand the receptive field. As shown in Fig. 25.5b, the network has two sub-branches using convolution operations with different scales, which are fused to form dense connections within the k ×k region. It is shown by the work of HR-ARNet [7] that k is 11, which is the best and more suitable for receiving remote information. The spatial attention network can be expressed as:    H ×W ×1 H ×W ×1 Att S(F) = sigmoid GCC Favg , Fmax

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Attention Fusion. The work of CBAM [8] shows that the effect of channel attention network mechanism followed by spatial attention mechanism is better than other effects. The feature screening network is shown in Fig. 25.5c. After features, the output of the filter graph Fout ∈ R H ×W ×C can be represented as: Fout = Att S( Att C(Fin ) ⊗ Fin ) ⊗ ( Att C(Fin ) ⊗ Fin )

(25.3)

25.3.4 Multi-stage Self-attentive Network In the human pose estimation task, the convolutional operation only deals with the local information of the convolutional kernel size, and to obtain the relationship between key points at a distance, the perceptual field needs to be expanded. Most of the current methods to expand the perceptual field based on convolutional neural networks are to increase the depth of the convolutional neural network or to increase the convolutional kernel [28], but this undoubtedly brings a larger computational effort. To solve this problem, we use self-attention algorithm to model critical remote constraint relationships. Let the CNN backbone network output feature map F ∈ R H ×W ×C , which is first mapped to feature map Fforward ∈ R H ×W ×d by 1 × 1 convolution to improve the network’s ability to characterize the features. If we directly utilize the feature map F to calculate the similarity, it directly reflects the semantic similarity, which will produce some limitations in calculating the attention weights. The mapping to the new space increases the diversity of similarity computation between inputs, not only in terms of semantic similarity, but also in terms of reinforcing contextual attention. After that, the feature map is expanded into a sequence of X ∈ R L×d , where L = H × W , through N-layer attention networks and feedforward networks (FFNs). The overall architecture of the multi-stage joint modeling network is illustrated in Fig. 25.6, and we next analyze its principles.

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For the previous stage feature maps, first map it to queries Q ∈ R L×d , keys K ∈ R L×d , and values V ∈ R L×d by three matrices Wq , Wk , and Wv ∈ R d×d . After that, the attention fraction matrix A ∈ R N ×N is computed as follows:  A = softmax

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where Wq , Wk , and Wv , the parameters in the three matrices are learnable. The self-attentive mechanism models the long-range joint relationships by calculating the correlation between vectors, which well explains the correlation between feature maps. And by stacking N layers, the response locations can be gradually refined. The FNN part of the network is vital, and in the internal structure computed by the self-attentive mechanism, we perform all linear transformations. The linear transformations are not as strong as the nonlinear changes, so the attention output may not be as expressive as this representation, despite the fact that a new representation of each feature is learned using the self-attentive mechanism. Using the activation function, we expect the larger values to be reinforced and the smaller values to be suppressed, resulting in a better representation of the relevant parts. Also in the fully connected layer, the process of mapping the data to a high-dimensional space and then to a low-dimensional space allows more abstract features to be learned and also prevents overfitting.

25.3.5 Loss Function This paper is a heat map-based pose estimation method, in which the network generates a probability map for each key point during the network’s prediction of the picture, there are N probability maps, and N is the number of joints. Suppose Hˆ k is the model which predicts the kth key points’ probability map, rendered by a Gaussian function: Hk ∼ N (z k , σ ), where z k = (xk , yk ) represents the position of the kth key points in the input image, and the loss value is calculated by the mean square error:

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It can be seen that if the loss value is calculated through all key points, it may cause the network to focus on “easy” key points, such as “head”, and ignore “hard” key points, such as “knee”. Inspired by HR-ARNet [7], this paper uses focus loss to make the model more focused on “hard” key points, and the expression is as follows: L FL =

M 2 1    Hk − Hˆ k  2 k=1

(25.6)

Using focus loss causes the model to focus on the top M points with the largest loss values, and based on the work of HR-ARNet, setting M = 4 will achieve good results.

25.4 Experiments 25.4.1 Datasets and Evaluation Indicators This paper evaluates our method using the challenging MPII dataset [12] and COCO dataset [13]. All models are implemented with the PyTorch framework, and the experimental graphics card is an Nvidia GeForce RTX 2080 Ti. MPII Dataset. The MPII dataset consists of approximately 25 K images and over 400 K tags with 16 key points. The dataset contains 410 human activities such as “cycling”, “skiing”. There are more than 33 K invisible key points and a large number of images with complex backgrounds, which are ideal for evaluating this work. Following previous works [5, 6, 29, 30], the input image is cropped from the original image to 256 × 256 according to the scale parameter of the center in the annotation. COCO Dataset. In the COCO dataset, we resize the resolution of the input images to 256 × 192. The COCO dataset is divided into train/val/test-dev sets, which contain 57 K, 5 K, and 20 K images, respectively, and the experiments in this paper are trained on the train set and validated on the val set. Evaluation Index. The MPII dataset evaluation metric is PCKh (head-normalized probability of correct key points) [12], which is calculated as follows: J PCKh@α =

i=1

f ( pi )@α J

(25.7)

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where J is the number of key points, PCKh@α is the proportion of correct key points predicted when the head threshold is α, and f ( pi ) represents the key points’ similarity and is calculated as follows:

f ( pi )@α =

 pre gt  1, if d  pi , pi  ≤ α · 0.6 · L pre gt 0, if d pi , pi ≤ α · 0.6 · L

pre

(25.8)

gt

where pi  is the predicted value of the key point pi , pi is the true value of the key pre gt  point pi , d pi , pi represents the Euclidean distance between the predicted value and the true value, and α is the head normalization threshold. On the COCO dataset, we use average precision (AP) as the evaluation metric. AP is calculated based on key points’ similarity (OKS), and OKS is calculated as follows: OKS =

i

e−di /2s ki δ(vi > 0) i δ(vi > 0) 2

2 2

(25.9)

where di is the Euclidean distance between the network’s predicted value and the ground truth, vi is the visibility of the key point, s is the pixel area of the object, and ki represents the key point influence factor. Other parameters are expressed as follows: AP50 (OKS = 0.5 for AP), APM (medium scale target), APL (large scale target), AP (OKS = 0.5, 0.55, to 0.95 for average precision), AR (OKS = 0.5, 0.55 to 0.95 for average recall).

25.4.2 Results We reproduce the different models on an Nvidia GeForce RTX 2080 Ti graphics card for comparison with our method. The experimental results on the MPII dataset are shown in Table 25.1. In this paper, we use HRNet [6] as the backbone network for design and the classical ResNet [11] as the backbone network to prove the compatibility of our method. The results demonstrate that this method can enhance the accuracy of different feature extraction networks and improve the recognition ability of “hard” key points. Based on the original HRNet, this method improves 1.2%, and for ResNet, this Table 25.1 MPII experimental results Model

Head

Shoulder

Elbow

Wrist

Hip

Knee

Ankle

[email protected]

HRNet

96.2

95.0

89.1

83.6

88.7

84.3

80.7

89.0

HRNet+MSAN

96.7

95.6

90.0

86.1

89.2

86.4

82.8

90.2

ResNet

84.3

80.6

70.6

64.4

66.0

60.4

54.3

70.0

ResNet+MSAN

95.2

93.8

86.0

81.4

86.5

81.5

77.5

86.9

25 MSAN: Multi-stage Human Pose Estimation Universal Network Based …

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method improves 16.9%. It is worth mentioning that in the experiments with HRNet as the backbone network, the accuracy of key points such as “knee” and “ankle” is enhanced by 2.1%. In the experiment with ResNet as the backbone network, the recognition accuracy of “knee” key points is improved by 21.1% and that of “ankle” key points is improved by “23.2%”. It can be proved that this method is effective and robust for different feature extraction networks. The results in Table 25.2 demonstrate that the accuracy of the model with HRNet as the backbone network is higher than that of other models. In particular, when ResNet is the backbone network, our model has comparable accuracy with other excellent models. The experimental results on COCO dataset are shown in Table 25.3. The results show that when using ResNet as the backbone network, its AP value is improved by 33.5%, and when using HRNet as the backbone network, its AP value is improved by 1.5%. In the comparison experiments, it can be found that when using the classical ResNet as the backbone network, its accuracy can be comparable to the accuracy of other excellent models, which again proves the effectiveness of the method. The generalization ability of the network in this paper can be proved through the experiments of COCO dataset. We visualized each stage in the multi-stage attention mechanism, and the results are illustrated in Fig. 25.7. Also, we show a visual comparison of the images predicted by applying MSAN to two different backbone networks, as shown in Fig. 25.8. Figure 25.7a shows the effect plot for HRNet and Fig. 25.7b shows the effect plot Table 25.2 MPII comparative experiment Model

Head

Shoulder

Elbow

Wrist

Hip

Knee

Ankle

[email protected]

HRNet

96.2

95.0

89.1

83.6

88.7

84.3

80.7

89.0

HRNet+MSAN

96.7

95.6

90.0

86.1

89.2

86.4

82.8

90.2

ResNet

84.3

80.6

70.6

64.4

66.0

60.4

54.3

70.0

ResNet+MSAN

95.2

93.8

86.0

81.4

86.5

81.5

77.5

86.9

Wei et al. [31]

97.8

95.2

89.3

84.4

88.4

83.4

78.0

88.5

Tang et al. [32]

94.1

90.2

83.4

77.3

82.6

75.7

68.6

82.4

Newell et al. [3]

96.6

94.3

92.3

85.3

91.3

85.6

81.5

88.8

Table 25.3 COCO comparative experiment Model

AP

AP50

AP75

APM

APL

AR

ResNet+MSAN

0.705

0.914

0.782

0.673

0.754

0.739

ResNet

0.370

0.709

0.331

0.329

0.432

0.438

HRNet

0.741

0.925

0.811

0.709

0.791

0.771

HRNet+MSAN

0.756

0.935

0.813

0.714

0.797

0.788

Newell et al. [3]

0.669

–

–

–

–

–

Simple baseline [4]

0.720

0.893

0.798

0.687

0.789

0.778

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Att. layer0 Att. layer1 Att. layer2 Att. layer3 Ank.(r) Kne.(r) Hip.(r) Hip.(l) Kne.(l) Ank.(l) Pel.

Tho.

Neck

Head

Wri.(r) Elb.(r) Sho.(r) Sho.(l) Elb.(l) Wri.(l)

a HRNet attention map Att. layer0 Att. layer1 Att. layer2 Att. layer3

Ank.(r) Kne.(r) Hip.(r) Hip.(l) Kne.(l) Ank.(l) Pel. Tho. Neck Head Wri.(r) Elb.(r) Sho.(r) Sho.(l) Elb.(l) Wri.(l)

b ResNet attention map Fig. 25.7 a HRNet attention map, b ResNet attention map

for ResNet. From the figures, we can see that the joint-dependent preferences are different for different CNN architectures. However, with the increase of attention layers, the attention points of both models are gradually refined. Even for the invisible points, the models can still make inferences about the invisible points by other joint cues. It is likely that this attention approach to find the relationship between upper and lower frames can be applied to 3D pose estimation and action recognition work in future work.

25 MSAN: Multi-stage Human Pose Estimation Universal Network Based …

Input

HRNet

ResNet

Input

HRNet

ResNet

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Fig. 25.8 Visual comparison chart

25.5 Conclusion In this paper, we propose a multi-stage attention network (MSAN) to implicitly model the constraint relationship between distant key points. By iteratively modeling the constraint relationship between key points, the model’s ability to identify complex key points is improved. In this paper, attention refinement mechanism is used to strengthen key features and suppress redundant information. In order to better construct the relationship between key points, this paper uses multi-stage joint constraint modeling, which explicitly learns the constraint relationship between key points by repeatedly calculating the correlation between joint features and implicitly models the high-order relationship between joints. In this paper, the focus loss is used to make the model pay more attention to the complex identification key points, so as to improve the recognition ability of the model for complex key points. The experimental results show that the proposed method can be well integrated into any human pose estimation feature extraction network, plug and play, can improve the network recognition accuracy, and has generalization ability. Acknowledgements This work is supported by Heilongjiang Provincial Natural Science Foundation of China (No. LH2022F035) and Harbin University of Commerce Graduate Innovative Research Project (NO. YJSCX2022-743HSD).

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References 1. Xia, Y.: Privacy-preserving pose estimation for human-robot interaction (2020) 2. Kim, U.H.: A real-time vision framework for pedestrian behavior recognition and intention prediction at intersections using 3D pose estimation (2020) 3. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Computer Vision—ECCV 2016. Springer International Publishing, pp. 483–499 (2016) 4. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018) 5. Yang, W.: Learning feature pyramids for human pose estimation. In: 2017 IEEE International Conference on Computer Vision (ICCV) (2017) 6. Sun, K.: Deep high-resolution representation learning for human pose estimation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019) 7. Wang, X., Tong, J., Wang, R.: Attention refined network for human pose estimation. Neural Process. Lett. 53(4), 2853–2872 (2021) 8. Woo, S.: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV) (2014) 9. Zhang, H.: Self-attention generative adversarial networks. In: International Conference on Machine Learning (2019) 10. Vaswani, A.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017) 11. He, K.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 12. Andriluka, M.: 2d human pose estimation: New Benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014) 13. Lin, T.Y.: Microsoft COCO: common objects in context. Springer International Publishing (2014) 14. Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vision 61(1), 55–79 (2005) 15. Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR 2011 (2011) 16. Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: CVPR 2011. IEEE (2011) 17. Dantone, M.: Human pose estimation using body parts dependent joint regressors. In: IEEE Conference on Computer Vision & Pattern Recognition (2013) 18. Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. IEEE (2013) 19. Tompson, J.: Joint training of a convolutional network and a graphical model for human pose estimation. Eprint Arxiv. pp. 1799–1807 (2014) 20. Chen, Y.: Cascaded pyramid network for multi-person pose estimation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018) 21. Su, X.: Small-scale image semantic segmentation method based on multilevel superposition and enhancement fusion. In: 2021 33rd Chinese Control and Decision Conference (CCDC) (2021) 22. Su, X.: Multi-scale object detection algorithm based on faster R-CNN. In: International Conference on Business Intelligence and Information Technology (2021) 23. Dosovitskiy, A.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020) 24. Zheng, C.: 3d human pose estimation with spatial and temporal transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021) 25. Li, Y.: Learning keypoint tokens for human pose estimation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021) 26. Yang, S.: Keypoint localization via transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)

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27. Peng, C.: Large kernel matters--improve semantic segmentation by global convolutional network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017) 28. Ding, X.: Scaling up your kernels to 31x31: revisiting large kernel design in CNNs. arXiv e-prints (2022) 29. Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition (2018) 30. Zhang, H.: Human pose estimation with spatial contextual information (2019) 31. Wei, S.-E.: Convolutional pose machines. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) 32. Pishchulin, L.: DeepCut: joint subset partition and labeling for multi person pose estimation. In: Computer Vision & Pattern Recognition (2016)

Chapter 26

An Image Matting Algorithm Based on Inception-ResNet-V2 Network Guilin Yao and Ruiguo Huang

Abstract In the era of deep neural networks, deep learning has been introduced as a research technology in almost every field. Image matting, as a part of image processing, has been constantly updated and iterated in recent years through deep learning-based matting techniques, and has achieved remarkable results. Image matting is performed by training a neural network model, and the matting effect is improved by deepening the number of layers of the network. In this paper, we adopt a lighter network structure Inception Resnet v2 compared with the DIM encoding network, which can significantly lessen the number of model parameters and save the training and computation time. At the same time, the network incorporates the residual network, which allows for more accurate training results with less computer performance. Compared with the VGG16 network structure, the Inception Resnet v2 network can accelerate the training process of the model while maintaining a better matting effect.

26.1 Introduction Image matting is the process of extracting the foreground objects of interest from a given image. An image can be simply viewed as consisting of two parts, the foreground and the background, and matting is simply the process of separating the foreground from the background of a given image. Image matting is a fundamental problem in computer vision and it can be used for many different things in many fields [1]. Matting is of great importance as a problem of accurate estimation of foreground in images and video sequences. It is a core technology in the field of image editing and production, and efficient natural image processing techniques can lead G. Yao (B) · R. Huang Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_26

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to a significant improvement in the current workflows. He needs a way to be used in an unconstrained way to deal with real-life image imaging problems. To address the issue of adapting to environmental constraints, researchers have proposed natural image matting. Since it is not restricted to specific backgrounds and eliminates the tedious process of setting up solid color curtains, the use of natural image matting techniques is no longer limited to the studio and can be performed in any natural scene [2]. At the same time, the lack of a priori knowledge of the issue with natural image matting makes it difficult to estimate the transparency mask. The natural image matting technique solves the shortcomings of the traditional chromakeyed matting cloth with a solid color curtain, which has greatly expanded its application areas. Therefore, an in-depth study of natural images is of great significance, both theoretically and practically. Natural image matting models the observed image as a convex blend of the foreground and background. In this model, for any pixel i in a color image, its observed pixel color Ii is a convex blend of the foreground color Fi and the background color Bi [3]. Ii = α ∗ Fi + Bi ∗ (1 − α)

(26.1)

This mathematical expression was first published in the article “Blue Screen Matting” and was proposed by two people, Smith Alvy Ray and James F. Blinn, who can also be considered a pioneer in graphics and the author of the HSV color space. The difficulty point for the matting problem is that each pixel has 7 unknown values and only 3 known values. Only the RGB color of Ii is known, while αi , Fi and Bi are unknown, and the number of unknowns in the equation is more than the quantity of equations, so this is a great limitation for image matting. Natural image matting mainly includes two categories of matting algorithms based on traditional machine learning and deep learning-based matting algorithms. In today’s rapid development of deep learning, as deep learning-based algorithmic products are widely employed in various industries like medicine, finance, and computers, many algorithms for building models with neural networks have emerged in this field of image matting. The majority of these algorithms use an end-to-end training approach to train the model by improving computer performance, extracting feature information from the image, and using the trained neural network for alpha map prediction of natural images. The outcomes of the experiment demonstrate that the deep neural network-based matting algorithm has better prediction accuracy.

26.2 Related Work The deep learning-based image matting technique educates a mapping from the input image to alpha by training a large number of image datasets. DCNN-Matting [4] method proposes a convolutional network combining Closed-form and KNN matting but requires the initial results of both methods as the input to the network, which is

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a tedious computational process. The AlphaGAN [5] algorithm designs a generative adversarial network to perform image matting, which reduces the phenomenon of overly smooth object boundaries compared to deep learning methods that directly regress alpha values. The Learning-based Sampling [6] approach borrows from the traditional sampling-based matting approach by modelling foreground pixel sampling, background pixel sampling, and alpha inference all using neural networks. Xu et al. [7] used their constructed image dataset to train an encoder-decoder network to create the alpha matte of the image, afterwards utilizing a small residual network to take the generated alpha matte and trap as input to obtain a more accurate alpha matte, and showed through experimental results that a more accurate matting effect could be obtained using this model structure. In this paper, the model adopts a network structure like that of encoder-decoder networks. In the encoding network phase, Inception-ResNet-v2 is used as the encoding network of the model instead of VGG16 for model training, which is a network model structure proposed by the Visual Geometry Group (Oxford University) [8]. Five convolutional layers, three fully connected layers and softmax output layers, with a maximum pooling layer separating the network layers and ReLU as the activation function for all hidden layers, which make up the network structure in the encoding phase. Compared with other network models, VGG16 has a deeper network layer, which can effectively extract image feature information and has a good classification effect, but in the process of model training, many parameters will be generated for calculation, so using VGG16 for model training has certain requirements on computational performance. The Inception deep convolutional structure was first used in the paper [9], which was first referred to as the Inception-v1 network, and then the batch normalization layer was the first method that the Inception architecture was improved (Inception-v2) by Ioffe et al. [10], and further improvements to the architecture were made in the third iteration by the additional idea of factorization, which is Inception-v3 [10], with a large difference in the input part from v1 to v3. The design aims to use parallel structure, and asymmetric convolutional kernels, which can reduce the computational effort while making certain that the information loss is minimal. Dimensionality reduction is performed in the network structure by using 1*1 convolutional kernels and also increasing the nonlinearity. It is shown through experiments that the training results of models from v1 to v3 networks in areas such as image classification are indeed getting better. In the 2015 ILSVRC challenge, the introduction of residual connections, as well as more classically styled construction, brought quite superior performance with similar performance to the Inception-v3 network, so Christian et al. considered whether using the Inception architecture while maintaining residual connections could yield better performance results, and therefore a new network model structure Inception-v4 and Inception-ResNet networks combining the Inception network with residual networks was proposed in the paper [11]. It is shown experimentally that the new network model is significantly better than the Inception-v1 to v3 models in terms of training effectiveness and training speed. In our method, the Inception-ResNet-v2 network model is chosen as the encoding network. Compared with the Inception-v4 network, Inception-ResNet incorporates residual connectivity to add shallow features to meet

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Fig. 26.1 Error evolution ranking of the four models of the Inception network [11]

the goal of feature reuse, higher-level features are implemented through a different branch, while also avoiding the gradient dispersion problem of deeper nets. Also adding residual connections for training will significantly accelerate the training of the Inception network. Meanwhile, according to the model training experiments by Christian et al. [11], performance comparison results for different networks are shown in Fig. 26.1. Through the error comparison of model training, we can see that the convergence effect of the Inception network after adding residual connections is better than other models in terms of model accuracy, so we choose to use Inception-ResNet-v2 as the encoding network for feature extraction of images.

26.3 Matting Model Design 26.3.1 Image Composition In this paper, we use the dataset constructed in DIM [7], proposed by Xu et al. in which the test set has 50 foreground photos, whereas the training set has 431 foreground images. Xu et al. construct a synthetic training set data of 43,100 sizes by extracting the foreground objects of the images in the training set and synthesizing the foreground objects with layer masks of 100 different background images, using the same method used to combine the images in the test set with 20 different layer masks of background images to create a test set Composition-1k with a size of 1000

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Fig. 26.2 Synthesis process of the competition-1k image dataset

images, which overcomes the problem of overfitting the data due to the small data set and thus cannot be generalized to real scenes. At the same time, it can increase the diversity of training data with a small dataset, improve the generalization capabilities of the model significantly, and make the training result of the model can get a better matting effect in natural scenes. The synthesis process of its data set is shown in Fig. 26.2.

26.3.2 Trimap Generate Trimap is a relatively rough division of a given image into three parts: foreground, background and unknown area. When we obtain the trimap of an image, we usually use the operation of dilation and erosion for processing, which is commonly used in matting techniques. In the extraction process, the dilation operation can merge all the background points that come into contact with the object and expand the boundary outward and if all of the original image’s pixels corresponding to the convolution kernel have values of 1, the convolution kernel will roll along the image, then the center element remains the same, otherwise, it becomes zero; while the erosion

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Fig. 26.3 The process of trimap image generation

operation can eliminate the boundary points, making the boundary shrink inward and the value of the original image’s pixels corresponding to the convolution kernel is 1, provided that there are at least one of those pixels, the center pixel value is 1. By obtaining the trimap image, we can better perform edge detection on the image, and after feeding the trimap image into the network for training, it allows us to get a more accurate alpha matte. Trimap image generation is shown in Fig. 26.3. In Fig. 26.3, we first corrode the green area of the alpha image to get the corrosive image, and then expand the green area of the alpha image to get the dilation image, and then subtract the dilation image from the erosion image to get the unknown area, and finally combine the unknown area with the erosion green area to get the trimap image. In the process of erosion and dilation, setting different dilation and erosion rate can get different sizes of the unknown region. In our method, the erosion and dilation kernel is a random value between 1 and 5, and the number of iterations is a random value between 1 and 10.

26.3.3 Overall Network Model After considering the Inception ResNet network characteristics, we designed a DIMlike encoder and decoder network structure by replacing the encoding network with Inception ResNet v2. The designed model structure is shown in Fig. 26.4.

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Fig. 26.4 Network model structure

In the encoding phase in the network model structure, we used five Inceptionresnet-A modules, ten Inception-ResNet-B modules, and five Inception-ResNet-C modules, while Inception modules can obtain sparse or non-sparse features on the same layer, ResNet’s structure not only speeds up training but also enhances performance and eliminates gradient dispersion. Combining the two and improving them makes the network further reduce the error rate and converge better for model training in areas such as image recognition. It should be noted here that the 1*1 convolution kernel in the network structure not only achieves the role of dimensionality reduction, but also the convolution layer is followed by the excitation layer, and the nonlinear excitation is connected to the learning representation of the preceding layer via the 1*1 convolution, which improves the network’s capacity for expression and can significantly increase the nonlinear characteristics of the network while maintaining the same feature map scale. In this way, it is possible to deepen the network level to improve the ability of the real image of the network. Throughout the Inception network, a separable convolution is added, and through this ingenious design, the feature maps obtained by the convolution of different senses can be connected, while the role of the Reduction layer in the network is to turn large feature maps into small ones, and the number of channels is increased to play a pooling or down-sampling role. So that there is no serious loss of information and representation bottleneck while not allowing the computation to explode. The above network layer settings can greatly reduce the amount of parameter computation and accelerate the training speed of the model. The BN layer is also added to the network system of Inception. In the model training, the network has to perform a large number of parameter updates at each iteration, making the whole network suffer from instability and slow convergence during the learning process. If the BN layer is added between the convolutional layers and normalized, it can successfully address the issues of network instability and slow

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convergence. The specific formula of the BN layer is as follows [12]: m 1 Σ xi , m i=1

(26.2)

m 1 Σ (xi − μ B )2 , m i=1

(26.3)

μB ←

σ B2 ←

(xi − μ B ) , xˆi ← / σ B2 + ε

(26.4)

yi ← γ xˆi + β,

(26.5)

In the formula, xi is the input data; m is the count of data in the batch input network, i.e., batch size; μ B and σ B2 are the input data’s mean and variance; xˆi is the normalized data; yi is the result of BN layer; γ and β are the two learnable parameters set. Of course, the disadvantage of adding the BN layer is that it increases the quantity of participants and increases the storage capacity of the network. In order to reduce the storage consumption caused by the BN layer, Inception ResNet only uses it in the stem module instead of in each Inception block as before. In our designed network structure, Inception ResNet v2 serves as the backbone network for the encoding network, and the input image is corroded and inflated to obtain the trimap image, and then the trimap is utilized as the network’s input for image feature extraction. During the image feature extraction procedure, the BN layer is added to the stem module, and after the convolutional layer, the BN layer is added to produce a more stable distribution of the data and to expedite the network model’s training in order to avoid overfitting. Then, we use separable convolution in the Inception module to reduce the matrix operations in the model training and reduce the number of parameters to improve the model performance. Finally, the image feature information is passed through the Dropout layer, which we set to 0.6 to obtain an image with 512 features. The decoding stage adopts the same network model structure as DIM, using the inverse pooling layer, reverse maximum pooling operation and inverse convolution layer to up-sample the feature map, including six convolutional layers, five nonpooling layers and alpha prediction layer, after the alpha prediction layer we get a relatively coarse alpha matte, then we input the alpha matte with the supplied image to the later convolutional layers and the final alpha prediction layer to further sharpen the alpha map to get the final alpha matte.

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26.3.4 Model Training The model is educated utilizing a loss function to determine the difference between the alpha matte and the Ground truth, using the loss function shown in Eq. (26.9). Lα =

/( )2 α p − αg + ε2 , α p , αg ∈ (0, 1)

(26.6)

In the above formula, α p is the predicted alpha map and αg is GroundTruth, and the accuracy of the resulting alpha matte is expressed by the difference between them, while ε = 1 × 10−6 in the formula. For training, the excitation layer uses ReLU as the activation function, the batch size is 16, the number of iterations is 20, tand he learning rate λ = 1 × 10−4 , and the Adam optimizer is used for gradient descent to optimize the parameters.

26.4 Experimental Results In order to compare the effectiveness of our approach, we compare several traditional image matting techniques based on machine learning and some depth-based matting methods with this and evaluate our method by image matting performance, model metrics evaluation, and model training time. First, we compare the alpha matte obtained from the trained model with the existing traditional image matting techniques, deep image matting techniques and Ground Truth, as shown in Fig. 26.5. The visualization results show that the Inception ResNet network-based image matting model can achieve a more accurate matting effect on the test set due to the inclusion of the BN layer, separable convolution and 1*1 convolution kernel in the encoding network structure, which can efficiently extract the image’s features, and then the subsequent convolution layer will sharpen the alpha matte generated in the encoding and decoding stage so that an accurate matting effect can be obtained in the alpha matte after this stage. In our algorithm, we randomly discard some feature information to get 512-size data at the output of the Inception ResNet network after the Dropout layer to fit the input of the decoding network, so the algorithm cannot achieve a more accurate keying result. But even if we lose some feature information, we can still get a good matting result, and we can also improve the model’s ability and accuracy to handle real scenes by continuing to deepen the network. For model evaluation, we used the commonly used metrics SAD and MSE based on those proposed by Rhemann et al. [16] as another criterion for model evaluation, and the quantitative results under these two metrics are shown in Table 26.1, from the results indicate that our designed network model outperforms other model algorithms, and the convergence of the network was significantly faster in the experiment and improved in both metrics.

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Fig. 26.5 Comparison of the prediction results of several methods on competition-1k

Table 26.1 Comparison of the outcomes of various techniques on the competition-1k test set

Method

SAD

MSE

Shared matting [13]

131.7

0.070

KNN matting [14]

175.4

0.103

Comprehensive sampling [15]

80.4

0.032

158.1

0.085

DIM [7]

64.2

0.024

Ours

62.3

0.023

DCNN matting [4]

In our research, we use the GPU3060Ti for model training, we use the average computation time of several methods on the same image to evaluate the model’s training period, the experimental results are shown in Table 26.2, because the network in the DIM structure model is deeper, and at the same time need to generate a large amount of parameter computation, so the period that the whole model is relatively long, while the Inception-ResNet-based network model not only uses 1*1 convolutional kernel to cut down on the process’s amount of variables but also adds residual connections to accelerate the model’s training procedure, so the training time of the

26 An Image Matting Algorithm Based on Inception-ResNet-V2 Network Table 26.2 Comparison of the execution times of various techniques on the same image

Method

333

Time/s

Shared matting [13]

11.673

KNN matting [14]

16.330

Comprehensive sampling [15]

10.910

DCNN matting [4]

4.390

DIM [7]

1.500

Ours

1.080

model is reduced by one-third compared to DIM. Therefore, the algorithm is more advantageous in terms of the model’s capacity for computing.

26.5 Conclusion In this paper, we propose an Inception-ResNet-V2-based matting algorithm, which adopts an encoder and decoder network structure, uses the Inception module to accelerate the model training by adding residual connections and effectively extracts the image feature information by using separable convolution. The encoder network based on the Inception ResNet network can achieve a more accurate matting effect than traditional image matting algorithms and most deep learning-based algorithms. It is undeniable that adding residual connectivity to the Inception-based network architecture effectively speeds up the training of the model. In future research experiments, we consider training the model on a more lightweight network model structure to reduce the number of parameters computed during model training and to ensure the same or even higher matting results while utilizing smaller computer performance. Acknowledgements This work is supported by the Heilongjiang Provincial Natural Science Foundation of China (No. LH2022F035).

References 1. Sun, W.: Research on natural image keying algorithm guided by visual perception features. Beijing Jiaotong University (2015) 2. Liang, C., Huang, H., Cai, Z.: A review of natural image keying techniques. Comput. Appl. Res. 38(05), 1294–1301 (2021) 3. Chuang, Y.Y., Curless, B., Salesin, D.H.: A Bayesian approach to digital matting. CVPR (2001) 4. Donghyeon, C., Yu-Wing, T., In-So, K.: Natural image matting using deep convolutional neural networks. In: Computer Vision—ECCV 2016—14th European Conference, Amsterdam, The Netherlands, 11–14 Oct 2016, Proceedings, Part II, pp. 626–643 (2016) 5. Lutz, S., Amplianitis, K., Smolic, A.: AlphaGAN: generative adversarial networks for natural image matting. In: British Machine Vision Conference 2018. arXiv (2018)

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6. Jingwei, T., Yagiz, A., Cengiz, O.: Learning-based sampling for natural image matting. In: CVPR (2019) 7. Xu, N., Price, B., Cohen, S.: Deep image matting. In: IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society. 2017, pp. 311–320 (2017) 8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014) 9. Szegedy, C., Liu, W., Jia, Y.: Going deeper with convolutions. IEEE Comput. Soc. (2014) 10. Szegedy, C., Vanhoucke, V., Ioffe, S.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016, pp. 2818–2826 (2016) 11. Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, Inception-Resnet and the impact of residual connections on learning (2017) 12. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. JMLR.org. JMLR.org (2015) 13. Eduardo, S., Lopes, G., Manuel, M.: Shared sampling for real-time alpha matting. Comput. Graph. Forum. 29(2), 575–584 (2010) 14. Qifeng, C., Dingzeyu, L., Chi-Keung, T.: KNN matting. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 869–876(2012) 15. Ehsan, S., Deepu, R., Brian, L.: Improving image matting using comprehensive sampling sets. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013, pp. 636–643 (2013) 16. Rhemann, C., Rother, C., Wang, J.: A perceptually motivated online benchmark for image matting. In: IEEE Conference on Computer Vision & Pattern Recognition. IEEE (2009)

Chapter 27

An Improved Image Colorization Algorithm Based on Pix2Pix Haitao Xin and Zixuan Zhang

Abstract Image colorization, a challenging task in computer vision, purposes to generate a color image by using a given re-input. In this paper, we propose PR-UNet by using U-Net as a Ga generator of Pix2Pix to solve the problems which are low color quality by the current image colorization algorithm. Our work mainly includes the following aspects: firstly, we use residual network (ResNet) as the encoder of origin U-Net; secondly, partial convolution is used taking place of standard convolution to eliminate invalid information caused by zero padding operation; thirdly, instance normalization is used to enhance image coloring to further improve image colorization quality; fourthly, in order to match the encoder, we also design PRU-Net as a new decoder. The experimental results show that our method has great advantages in coloring effect.

27.1 Introduction Image colorization is not only the most important branch of computer vision research, but also a digital image processing technology which aims to improve their aesthetic and perceptual quality by assessing RGB dye for grayscale pictures or video structures Due to the rapid development of multimedia information technology, this task is widely used in painting creation [1], old black and white photo repair [2–4], black and white film rendering, [5] and other fields. The early image colorization models were plain networks which utilize stacked convolutional layers. These models have simple architectures which make them easy to implement. However, the disadvantages of these models are obvious. Due to the simple model structures, it is difficult to design deep networks, which limit their performance. Zhang et al. [6] solved the image coloring task from the perspective of H. Xin (B) · Z. Zhang Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_27

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image classification by using VGG, the network takes grayscale images as input and predicts 313 “ab” pairs, which provide a new solution for image coloring task. Zhao et al. [7] proposed to use objects composed of pixels to guide image coloring with the use of pixelated semantic embedding and generator, more realistic and finer results are produced in this way. Joshi et al. [8] come out with a deep convolutional neural network. It is combined with Inception-Resnet-V2 which uses back-propagation to recognize the pattern in RGB and grayscale values to train sets of sample images and also achieved relatively good results. Generative adversarial networks (GANs) [9] were first proposed by Ian Goodflow et al. in the University of Montreal in 2014. The generation of GAN has introduced a new model training idea to the field of deep learning. Due to its powerful image generation ability, it has become a mainstream way to image colorization and any other image related tasks. Ci et al. [10] come out with an end-to-end deep conditional GAN (CGAN). It can be used to colorize artificial video line arts. Its generator adopts the architecture of U-Net [11] and utilizes LeakyReLU as an activation function, while the discriminator, it employed the architecture of SRGAN [12]. Isola et al. [13] improved the traditional GAN, extracted image feature information using UNet, and used L1 distance loss and patchGAN to produce coloring results closer to real images. This method overcomes the shortcoming of human intervention. Xu et al. [14] combined the advantages of DenseNet and U-Net structures to design a new generator and also designed a new loss function to optimize the face images, which can get more natural results on the face image. In this paper, we continue to use the powerful GAN structure to tackle the issues of image colorization. For the problem of low-quality of image coloring by the current image colorization algorithm, we improve the original U-Net [11] network structure and use the residual structure to replace the simple convolution stack to enhance the capacity of network feature extraction; partial convolution-based padding [15] is used to avoid invalid information brought by zero padding, we also use the instance normalization [16] which is more suitable for the image coloring task. Experiments show that our method can achieve a better image coloring effect, make the visual display effect of the image significantly improved, and has great advantages in network volume.

27.2 Related Algorithm Theory 27.2.1 Lab Color Space The RGB color system uses the three primary colors of R, G, and B to represent any color by mixing in different proportions, it cannot intuitively measure hue, saturation, and brightness. Moreover, there is a certain correlation between the three components and they are proportional in most cases, mainly in the natural scene, if a certain channel is large the other channel values of the pixel are also large.

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Unlike the RGB color space, the channels of the Lab color space have minimal correlation, which is more suitable for solving image-related tasks in the domain of computer vision. The full name of Lab is CIELab, where CIE refers to International Commission on Illumination. The Lab space is made of one brightness channel, one green to red color channel and a blue to the yellow color channel. For those channels, “L” represents the brightness, “A” represents the color channel from green to red, and “b” represents the color channel from blue to yellow.

27.2.2 U-Net U-Net [11] was originally used to solve image semantic segmentation, but as an end-to-end network structure, it can better adapt to a small training set, so it is often applied to other image processing tasks. In addition, U-Net is also a feature pyramid structure, which consists of an encoder, decoder, and horizontal connections: the encoder can extract different levels of features (such as texture, shape, semantics, etc.) through multiple down sampling, the decoder applies the horizontal connection and combines the features of each level from the encoder to decode the image, the horizontal connection effectively compensates for the lack of spatial information in the course of down sampling and increases the interaction and fusion of features at different levels, thereby improving the learning ability of U-Net. For image processing tasks, the superiority of the results is largely related to the structure of the network. Since the original U-Net is constructed by stacking standard convolutions, the feature extraction ability is still insufficient. Besides, there are a large amount of zero padding operations in standard convolution, which will add invalid information and lead to inaccurate image edge prediction. In view of the above problems, we design PR-U-Net, which we will introduce in detail in Chap. 3.

27.2.3 Instance Normalization For image colorization tasks, the information on each pixel of each sample is very important. Ulyanov D et al. proposed instance normalization (IN) [17], a normalization algorithm more suitable for scenes with higher requirements for a single pixel. According to the principle that the image has similar chrominance information between adjacent pixels with similar brightness, the result of image coloring depends on the brightness information of each image. IN considers all elements of a single sample and a single channel when calculating normalized statistics. Therefore, IN is more suitable for image colorization, in addition, it can also accelerate the convergence speed of the model.

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27.2.4 Partial Convolution-Based Padding Liu et al. [18] found in the experiment that zero padding is to add irrelevant data to the input image/features, which will make the edge of the output feature map produce the strongest activation response, which may influence the quality of the model from the opposite side. Based on the above problems, partial convolution-based padding as a new filling method is proposed, which has better robustness. During the convolution operation, the convolution results are re-weighted near image borders based on the ratios between the padded area and the convolution sliding window area. The mathematical expression is as follows: p0

x(i, j) = W T X (i, j )r(i, j) + b

r(i, j)

(27.1)

   p1  1(i, j)  1 =  p0  1(i, j) 

(27.2)

1

p0

where x(i, j) is the convolution results, W is the filter weight matrix, X (i, j) denotes p1

the zero padded result of the input image/feature X (i, j) , b is the filter bias term, 1(i, j) p0

denotes the zero padded result of 1i, j , and 1(i, j) denotes the one padded result of 1i, j .

27.3 Network Structure The PR-U-Net structure designed is shown in Fig. 27.1. The PR-U-Net encoder constructed contains five down-sampling layers to capture the context features in the image, and the decoder contains five up-sampling layers to predict the color information of the image. For improving the feature extraction capability of the encoder, five down-sampling layers are composed of the ResNet-18 feature extraction part of the torch vision library, which are conv1, conv2_x, conv3_x, conv_4x, and conv5_x in sequence. Compared with the original U-Net [11], the encoder constructed in this paper is no longer a traditional stack of standard convolution layer and maximum pooling layer, but by introducing short-circuit connection to supplement the feature information lost in the process of image coding, thus achieving better performance. In addition, on the one hand, we replace all the standard convolution in ResNet-18 with partial convolution to improve the accuracy of image edge information prediction. On the other hand, instance norm instead of batch norm can not only accelerate the model convergence, but also maintain the independence between each image. Each up-sampling layer decreases the resolution of the input features by 50%, and through the coding layer, the image size is finally compressed 32 times and the number of channels is 512. For decoders, the first four up-sampling layers have the same structure. The features transferred through long jump connections in the down-sampling

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layer are unsampled by a transpose convolution with a convolution kernel size of 2 and a step of 2, and then spliced with the upper-level features in the channel dimension. Finally, the interweave features are convolved with a step size of 3 and a fill pixel of 1. The activation function is LeakyReLU. The last up-sampling layer is to up-sample the output features of the previous layer through interpolation, and then predict the color information of the image through two partial convolution kernels of 3 and filled pixel of 1 and a point-by-point partial convolution, where the activation function is Tanh. The specific network parameters are given in Table 27.1.

Fig. 27.1 Structure of PR-U-Net

Table 27.1 Architecture of the decoder and the following structure Layer ID

Type

Act

K

S

P

In

Out

1

CT

–

2

2

0

512

256

1

PC

LeakyReLU

3

1

1

512

256

2

CT

–

2

2

0

256

256

2

PC

LeakyReLU

3

1

1

384

256

3

CT

–

2

2

0

256

128

3

PC

LeakyReLU

3

1

1

192

128

4

CT

–

2

2

0

128

64

4

PC

LeakyReLU

3

1

1

128

64

5

Up

–

–

–

–

64

64

5

PC

–

3

1

1

64

32

5

PC

–

3

1

1

32

64

5

PC

Tanh

1

1

0

64

2

“Act” denotes activation type, “K” for kernel size, “S” for stride, “P” for padding, “In” for input channel number, and “Out” for output channel number. “CT” denotes transposed convolution, “PC” denotes partial convolution, and “Up” means up-sampling. Please refer to Fig. 27.1c in the paper for details of the decoder.

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27.4 Experiments 27.4.1 Setups and Dataset In this paper, all the grayscale image automatic coloring experiments are completed on the same computer, and the computer hardware configuration is as follows: Linux operating system, AMD EPYC 7601 processor (32G), and NVIDIA GeForce RTX 3080 graphics card (10G). The computer software environment is as follows: Python3.8.10 and Pytorch1.10.0, CUDA11.1. The dataset used in this paper is from Places365-Standard, which contains a total of 365 unique scene categories. For each scene class, the training set has 5000 images and the test set has 100 images. We pick out five scene categories: “valley”, “sky”, “desert road”, “mountain”, and “mountain path” for automatic grayscale image colorization experiments.

27.4.2 Experimental Process and Evaluation Index Firstly, convert the original image from RGB to Lab space, where the L channel is the input information of the network and the a and b color channels are the target images of the network. Secondly, build the network for the image colorization task, where its generator is PR-U-Net and the discriminator, loss function, and basic parameters setting are consistent with Pix2Pix [13]. Thirdly, perform adversarial training for the training process, the generator and the discriminator are trained once in each iteration. Finally, get the prediction result and convert Lab to RGB for display. It is mentioning that, the algorithm will be compared with the methods of Zhang [3] and Pix2Pix [13], and the input image resolution of these methods is 256 × 256 (Fig. 27.2). Different models will produce different coloring effects, we will provide the visual effect diagram of each algorithm. It is worth noting that the result of image coloring need not be similar to the ground truth, just reasonable. Besides, in order to objectively quantify the visual image feature, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [18] are also used to evaluate the test images. Among them, PSNR focuses on evaluating the integrity and similarity of the content and the results with higher scores indicate that it is closer to the reference image according to the picture subject, while SSIM mainly calculates the structural similarity between the artificial picture and the real photos, the results with higher scores indicate that it is closer to the reference picture according to image frame and texture. Finally, PSNR and SSIM mean the average value over the entire test set.

27 An Improved Image Colorization Algorithm Based on Pix2Pix Fig. 27.2 Experimental model flow chart

341

Original data set and preprocessing

Building network Train Test

Adversarial training

Predict results and postprocessing

27.4.3 Experimental Results and Analysis Image Colorization Comparison: Figure 27.3 shows the coloring effect of different methods on some grayscale images. It is obvious that our method has great advantages in coloring effect. Although Zhang [3] and Pix2Pix can realize image colorization but can easily predict multiple colors on some same objects, and their effect is limited, such as Fig. 27.3a and d, these two methods have a large number of false coloring areas in the road and some tree scenes, respectively, which is unreasonable. Zhang’s method adopts the idea of classification. When the number of images is small, it is easy to lead to poor pixel classification effect, that is, false coloring, Pix2Pix, as introduced in Sect. 21.2.2 of the summary; the original U-Net [11] as a generator still has insufficient feature extraction ability, so the image coloring quality is not high. Because the generator with stronger feature extraction ability is used in this method, which can extract more information from grayscale images no matter high or low level, the color prediction on sky, road, forest, and other elements is truer and more accurate, and the image coloring quality is higher. Table 27.2 lists the PSNR and SSIM values of different methods in the image coloring task. From the table, we can clearly notice that the image coloring algorithm in this paper is better than Zhang algorithm and Pix2Pix, which is consistent with the subjective picture quality evaluation results and further shows that our method has better color prediction ability. Efficacy of Instance Norm: To validate the efficacy of instance norm, we conduct ablation study: (1) replace instance norm with batch norm in PR-U-Net (w/batch norm), (2) ours (w/instance norm). Figure 27.4 shows the coloring effect of different normalization methods. As we can see, batch norm causes the same object to have multiple colors (see the sky, mountain, and reed), this phenomenon is unreasonable. Our results are more accurate

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Fig. 27.3 Qualitative comparison outcome of image colorization with different way

Table 27.2 Quantitative comparison of image colorization with different methods

Method

Zhang [4]

Pix2Pix [17]

Ours

PSNR

41.72

43.20

44.84

SSIM

0.9945

0.9951

0.9975

and reasonable by using instance Norm. Table 27.3 indicates that using batch norm is superior to instance norm in PSNR, but SSIM is not. Since the final effect of coloring is difficult to be accurately measured mathematically, the final method needs to refer to the qualitative results more. Size Comparison of Different Methods: Table 27.4 gives the model parameters of different methods, indicating that our method has advantages in model volume, also further showing that we designed PR-U-Net with a less redundant structure. This is mainly because the residual structure is introduced into the PR-U-Net, which not only enhances the model feature extraction ability but also reduces the number of model parameters.

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Fig. 27.4 Qualitative comparison of the efficacy of instance norm

Table 27.3 Quantitative comparison about efficacy of instance norm

Method

w/Batch norm

w/Instance norm

PSNR

45.48

44.84

SSIM

Table 27.4 Performance comparison of different models

0.9971

0.9975

Method

Zhang [4]

Pix2Pix [17]

Ours

Parameters

32.24M

54.41M

15.03M

27.5 Conclusion In this paper, we design a new decoder which is PR-U-Net. By using the residual network structure instead of the simple stacking of standard convolutions in the original U-Net, we can obtain higher quality image features in the process of feature extraction, using partial convolution instead of standard convolution effectively further improves the picture quality. In the aspect of normalization, the instance normalization is more suitable for image tasks and further improves the quality of image colorization. Although we already achieve some seasonable results by using our new decode method, there still are some limitations. Our method could only handle five scene categories, the superiority of the method needs to be further verified in other categories. Finally, we hope of exploring further methods in PR-U-Net for picture generation or translation tasks in future research. Acknowledgements This work was supported by the project called Design and Implementation of a Decoration System Based on the Hadoop Platform.

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References 1. Sangkloy, P., Lu, J., Fang, C.: Scribbler: controlling deep image synthesis with sketch and color. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5400–5409 (2017) 2. Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Trans. Graph. 35(4), 110 (2016) 3. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Proceedings of European Conference on Computer Vision. Springer, Heidelberg, pp. 649–666 (2016) 4. Deshpande, A., Lu, J.J., Yeh, M.C.: Learning diverse image colorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Press, Los Alamitos, pp. 2877–2885 (2017) 5. Joshi, M.R., Nkenyereye, L., Joshi, G.P.: Auto-colorization of historical images using deep convolutional neural networks (2020) 6. Zhao, J., Han, J., Shao, L., et al.: Pixelated semantic colorization. Int. J. Comput. Vis. 128(4), 818–834 (2020) 7. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. Eur. Conf. Comput. Vis. 2(5), 99–110 (2016) 8. Joshi, M.R., Nkenyereye, L., Joshi, G.P., et al.: Auto-colorization of historical images using deep convolutional neural networks (2020) 9. Goodfellow, I., Pouget-Abadie, J., Mirza, M.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014) 10. Ci, X., Ma, Z., Wang, H., Li, Z.: User-guided deep anime line art colorization with conditional adversarial networks. In: 26th ACM International Conference on Multimedia, pp. 1536–1544 (2018) 11. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and ComputerAssisted Intervention. Springer, pp. 234–241 (2015) 12. Isola, P., Zhu, J.Y., Zhou, T.H., et al.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Press, Los Alamitos, pp. 5967–5976 (2017) 13. Xu, J., Lu, K., Shi, X.: A dense net generative adversarial network for near-infrared face image colorization. Sign. Process. 2021(11), 108007 (2021) 14. Liu, G., Shih, K.J., Wang, T.C.: Partial convolution based padding. 1811.11718 (2018) 15. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016) 16. Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. 1607.08022 (2016) 17. Guo, D., Liu, H., Zhao, H.: Spiral generative network for image extrapolation. In: European Conference on Computer Vision. Springer, Cham, pp. 701–717 (2020) 18. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and ComputerAssisted Intervention. Springer, Cham, pp. 234–241 (2015)

Chapter 28

Quality Evaluation Algorithm of Human Video Motion Image Segmentation Based on Visual Perception Qingwei Wang, Xinyu Wang, Zhifeng Lv, and Dahai Tan

Abstract In view of the problems of the existing human video motion image segmentation quality (SQ) evaluation method with low evaluation accuracy and poor anti-interference during the evaluation process, a human video motion image SQ evaluation algorithm based on visual perception was proposed. Firstly, the model of edge contour feature detection and pixel reconstruction is constructed to realize multi-level feature decomposition and edge pixel features. Secondly, the visual feature reconstruction model is established, and the image is segmented by the method of spatial region reconstruction. The spatial region reconstruction in the segmentation process is realized through similarity information fusion, and the number of fuzzy features of the image is extracted. Finally, pixel space fusion matching technology is adopted to realize the visual perception of the image, obtain its sparse segmentation model, and realize the evaluation of the SQ of human body motion video images based on template matching. The results show that this method is more accurate in evaluating the SQ of human video motion images, and the output evaluation results have a higher signal-to-noise ratio and good image SQ detection performance.

28.1 Introduction Analyzing and extracting features of human motion video (HMV) is a visual image processing method that establishes a segmentation model of human video motion images, which helps to improve the ability to recognize and reconstruct the movement characteristics of human movement [1]. The feature reconstruction of human motion is based on the segmentation of human video motion images. Image segmentation is the premise and foundation of various subsequent processes such as target tracking and behavioral understanding. Images can be represented as a collection of physically meaningful connected regions and correctly classified. Only after the Q. Wang · X. Wang · Z. Lv · D. Tan (B) Harbin Huade University, Harbin 150025, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_28

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image is successfully segmented [2], the target features are effectively extracted and recognized, can the key characteristics number of the human video motion image be extracted by the 3D reconstruction method of features, and then the pixel tracking fusion is used to realize the human video motion recognition (HVMI). Therefore, the quality of human video motion is an important factor that determines the quality of HVMI. Assessing the quality of HVMI segmentation is to improve the quality of image segmentation. Related research on HVMI segmentation evaluation methods has also received great attention [3]. Some scholars have put forward some mature research results in motion image segmentation quality (SQ) evaluation, and have achieved certain results in improving the quality of moving image segmentation. In Literature [4], a remote sensing image SQ evaluation algorithm based on spectrum and shape is designed. The algorithm combines the superpixel method to segment the image and then merges the adjacent areas of the image according to the merging criterion to generate images of different scales. On this basis, different regions are indexed using the scale set structure to obtain the relationship between the regions. Then calculate the homogeneity and heterogeneity of the image area according to the shape compactness and smoothness formulas to achieve the SQ evaluation. In Literature [5], a gray evaluation algorithm for image segmentation based on combination weighting was designed. This algorithm combines the Delphi method, mandatory determination method and entropy weight method to form a subjective and objective combination weighting method. The image SQ is evaluated from three aspects: the probability edge index, the consistency error, and the amount of transformed information. However, the algorithm involves many evaluation methods, resulting in a large evaluation process and poor real-time performance. In Literature [6], an image segmentation evaluation with dynamic particle swarm optimization was proposed. The algorithm enhances its performance of the algorithm by adjusting the inertia coefficient and the learning factor. Based on the calculation of the particle swarm fitness variance, the algorithm was switched to the K-means algorithm, and the output result was used as the clustering center. The clustering center was updated through multiple iterations until all the image segmentation results were detected and evaluated. However, the signal-to-noise output of the algorithm is low. According to the above problems, this paper represents an algorithm for evaluating the quality of HVMI segmentation with visual perception. The idea is as follows: First, construct edge contour feature (ECF) detection and pixel reconstruction (PR) models of HVMIs, and perform multi-level feature decomposition (FD) and edge pixel feature (EPF) separation of HVMIs. Then combined with the visual feature reconstruction model of the HVMI, the fuzzy characteristics number of the HVMI is extracted, and then the image pixel space fusion matching technology is used to realize the visual perception and SQ evaluation of the human moving video image. The results of simulation experiments prove the superior performance of the algorithm in improving the quality of human video motion image SQ assessment.

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28.2 Feature Detection and PR of HVMI 28.2.1 Detection of ECF To realize the evaluation of HVMI segmentation with visual perception, a pixel space fusion model of HVMIs is constructed, and feature matching methods are used to detect HVMI features [7–9]. Firstly, the spatial sparseness feature reconstruction of the HVMI is performed. A template matching model of the HVMI segmentation is constructed, as shown in Fig. 28.1. Supposedly the gray pixel set of an HVMI is (i, j)(i, j) which is used as the center of the pixel [10], and a feature segmentation is constructed using the sharp template block [11]. For the gray value I swk of the image collected in k-th sub-band, the image gradient feature component is. P=

r

∞

k=1 Iswk (i,

j)

(28.1)

c

where c is the number of columns of the HVMI, and r is the motion blur feature amount. Combined with the PR method, the feature set distribution of HMV is obtained, and the visual perception and 3D reconstruction of the human video motion image are obtained. In combination with the spatial region reconstruction method, feature segmentation and detection of HMV are performed, and gray pixel feature reconstruction of HMV is performed [12, 13]. The monocular vision tracking method is used to perform noise reduction processing on low-resolution HMV images to construct a model of HMV. The output feature quantities are: G =  f, d × P + R

(28.2) L

Fig. 28.1 Template matching model of HVMI segmentation

PNL

P1

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where  f, d refers to the pixel feature points of HVMIs at the direction d, and R refers to the sparse residual. On this basis, visual perception human motion is performed to realize ECF detection of human video motion images [14, 15].

28.2.2 Pixel PR Model Construct ECF detection and PR models of HVMIs, perform multi-level feature of HVMIs, and establish visual feature reconstruction models of HVMIs. The distribution of visual features of HVMIs is:  a   D= Ai + G (28.3) i=1

where a is feature series, a = 1, 2, . . . , i, · · · , and Ai means EPF. The HVMI edge segmentation model is constructed. The fuzzy closeness function of the human moving video image [16] is obtained as follows: F = D + Nδ

(28.4)

where N is the total layer where the image is decomposed, and δ is the estimated image threshold. The threshold is modified to obtain a new HVMI threshold:  δnew = δ · exp

1 −1 N

 (28.5)

Consider gray-scale pixel levels of HVMIs g, and g = 16, 32, 64. The grayinvariant moment is used to construct a distribution model of human video motion. The PR model of the visual perception image of human video motion is [17]: W =

 

 δnew g × E

(28.6)

g

where E refers to the optimal minimum solution for visual human video motion features. Then, the segmentation processing of the HVMI can be performed under the action of the average value of the pixels [18].

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28.3 Quality Evaluation of HVMI Segmentation 28.3.1 Blur Feature Extraction of HVMIs Based on the above-mentioned ECF detection and PR model of the HVMI, the EPF separation of the HVMI is performed to evaluate the SQ of the HVMI. This paper proposes an algorithm for evaluating the quality of HVMI segmentation based on visual perception. Feature segmentation and detection of HVMIs are combined with spatial region reconstruction methods [19]. A visual perception model of HMV images is constructed, and the visual perception function expression is: V = N (g ⊗ β)

(28.7)

where β refers to the sense factor, and ⊗ is the convolution operator. A vector set fusion process was performed on the human video motion images to construct a model of ECF decomposition of human video motion. Assume the best distinguishing feature value of human video motion is s. The similarity reconstruction method is used to reconstruct and reconstruct the features of human video motion, as shown in Equation. M=

w ×s+t V

(28.8)

where t is the spatial pixel gain, and w is the blur feature components of human video motion. The image enhancement method was used to reconstruct the gray histogram of the HVMI. The reconstruction of the HVMI was performed. T =

R × Mφ η

(28.9)

where η refers to the edge segmentation function, φ refers to the angle function of HMV image segmentation, and R refers to the template matching coefficient. Based on this, the gray pixel feature decomposition method and sparse representation method are adopted to realize the fuzzy feature extraction of HVMIs. Optimized segmentation based on feature extraction results to obtain p × q 2 × 2 sub-blocks B pq . The fuzzy feature extraction model of the HVMI in the gradient direction is expressed as:  A=

 Tx − λ × B pq u

(28.10)

where u is the number of edge pixels of HVMI, x refers to gray pixel values of human video motion in sharpened areas, and λ is image gray shift. Through the above process, the fuzzy feature extraction of the HVMI is implemented, and image segmentation is performed.

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28.3.2 Visual Perception of Image Segmentation Set δ 2 as local variance of HVMIs, and set μ as the optimization coefficient of HVMI. The gradient descent method is used to perform region block segmentation of human video motion [20]. The threshold δ y at the y frame of the HVMI is obtained. Based on this, the template matching of HVMIs is performed with representation method to obtain the global random noise r , facilitation effect error e, and edge error h of the HVMIs. Take these three terms as three principal components, weight these three principal components, and judge the evaluation of image SQ according to multivariate regression analysis. The results of multivariate regression analysis are as follows: Z=

∂1r + ∂2 e + ∂3 h δy

(28.11)

where ∂1 , ∂2, and ∂3 are weighting factors of different weights. The image segmentation quality is judged based on multivariate regression analysis. The larger the value of the result of multivariate regression analysis, the higher the quality of segmentation. In summary, the image pixel space fusion and matching technology is used to realize the visual perception and segmentation of HMV images. In summary, the evaluation process of the HVMI SQ evaluation algorithm is as follows: (1) Reconstruct the spatial sparse features of HVMIs, and combine the template matching model and Atanassov extension method to achieve the feature point matching of the image. (2) The distribution of feature pixel sets is obtained by using the sharpened template block combination method, and feature segmentation and are realized through visual perception technology and information fusion tracking. (3) Decompose the multi-level features of the human video motion image, mark the key feature points, and use the gray-invariant moment feature decomposition method to build a multi-dimensional histogram distribution model to achieve PR. (4) Construct a visual perception model of the image. To extract fuzzy image features, and the global random noise r , fastening effect error e, and edge error h of the image are weighted.

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28.4 Experiments Analysis 28.4.1 Data Sets Source data of human video motion images is the MNIST image data set; training sample set of human video motion images is 600; edge contour pixel distribution set of human video motion images is 400 × 400.The ECF decomposition coefficient is 0.34. In order to ensure the effectiveness and persuasibility of the experiment, the proposed algorithm for evaluating the quality of human video motion image segmentation based on visual perception is compared with the spectral and shape-based SQ evaluation algorithm in Literature [4], the gray evaluation algorithm based on combined weighting for image segmentation in Literature [5] is compared with the image segmentation evaluation and K-means clustering in Literature [6].

28.4.2 Evaluation Criteria Evaluation error rate: According to the evaluation error rate, the evaluation accuracy of the image quality evaluation algorithm can be detected. The calculation process is: Evaluation error rate =

Number of times of evaluation error Total evaluation times

(28.12)

Signal-to-noise ratio (SNR): SNR is the ratio of valid information to noise in the output result. The calculation process is as follows:  SNR = 10lg

Ps Pn

 (28.13)

where Ps and Pn are effective signal and noise power values. Time-consuming evaluation process: The time-consuming evaluation process can reflect the evaluation efficiency of the image SQ evaluation.

28.4.3 Experimental Results Firstly, the moving image of human body video is segmented. The human video motion image to be segmented is shown in Fig. 28.2. The HVMI of Fig. 28.2 is used as an experimental sample for information fusion, and an ECF detection and PR of the HVMI is constructed. Human video motion image

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Fig. 28.2 Human video motion image to be segmented

segmentation is performed using a typical segmentation. As shown in Fig. 28.3. The segmentation process effectively achieves the goal of dividing the image into several specific, unique regions. Base on this analysis, the segmentation results of the proposed algorithm for evaluating the quality of human video motion image segmentation based on visual perception and algorithms in Literature [4], Literature [5], and Literature [6] are used to segment the results to evaluate and compare the evaluation errors of different evaluation algorithms. It is shown in Fig. 28.4. According to Fig. 28.4, the evaluation error rate of different image SQ evaluation algorithms also continuously changes, and the overall trend shows a downward trend. The proposed algorithm for evaluating the quality of HVMI segmentation based on visual perception not only has a large decrease in the evaluation error rate, but also the evaluation error rate is always lower than the other three algorithms. This proves that the proposed algorithm can effectively evaluate the SQ of HVMIs. To test the effectiveness for evaluating the quality of HVMI segmentation based on visual perception. It is shown in Fig. 28.5 and Table 28.1. It can be seen from Table 28.1 that with the number of iterations increases, the SNR of the different image SQ evaluation is constantly changing, but, the peak value of the proposed algorithm for evaluating the quality of HVMI segmentation based on visual perception always remains the highest, which proves that the output of the evaluation algorithm has the most effective information. It can be seen from Fig. 28.5 that with the continuous the number of iterations increases, the total time spent in the evaluation process of different image SQ evaluation algorithms is constantly changing, and this change is not fixed. However,

Fig. 28.3 Segmentation results of human video motion image

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30

Literature [4]algorithm

25

Literature [6]algorithm

Evaluation error/%

Literature [5]algorithm

The proposed algorithm

20 15 10 0

100

200 300 400 Number of iterations

500

Fig. 28.4 Comparison of error rates of different evaluation algorithms

Fig. 28.5 Comparison of evaluation process time of different evaluation algorithms

the total time consumption of the proposed algorithm evaluation process is slightly higher than the algorithm in Literature [4] only when the number of iterations is 500, and the overall time consumption is the least.

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Table 28.1 Comparison of SNR of output results of different evaluation algorithms Number of iterations

The proposed algorithm

Literature [4] algorithm

Literature [5] algorithm

Literature [6] algorithm

100

55.75

37.58

24.67

30.56

200

63.63

42.93

28.46

34.89

300

69.56

39.75

29.56

35.87

400

72.68

50.90

32.34

36.54

500

73.27

56.67

35.22

38.65

28.5 Conclusions The segmentation model of HVMIs can realize the recognition and reconstruction of the motion feature, and the evaluation of the segmentation results of HVMIs can effectively improve the SQ. This paper designs a SQ evaluation algorithm of human video based on visual perception. HVMI features are detected by feature matching method, and ECF detection and PR models of HVMIs are constructed. The gray-invariant moment is used to construct a multi-dimensional histogram distribution model of human video motion. The similarity information fusion model is used to perform visual perception and spatial region reconstruction in the segmentation process of HVMIs. The fuzzy feature amount of human video motion image is extracted, and the image pixel space fusion matching technology realize visual perception and SQ evaluation of HMV. It is known from experimental research that the performance of the segmentation evaluation of HVMIs using this algorithm is better and has obvious application advantages. However, in the process of using this algorithm to evaluate the quality of image segmentation, the energy consumption is large. Therefore, in the future research stage, the algorithm will be further optimized in terms of reducing the evaluation energy consumption.

References 1. Wang, K.F.: Application of sports video image analysis based on fuzzy clustering algorithm. Modern Electron. Technol. 40(9), 47–50 (2017) 2. Ning, C.: Design and research of motion video image analysis system in sports training. Multim. Tools Appl. 1, 1–19 (2019) 3. Yue, G.G., Hou, C.P., Gu, K.: Combining local and global measures for DIBR-synthesized image quality evaluation. IEEE Trans. Image Process. 3(21), 56–62 (2018) 4. Wei, X.W., Zhang, X.F., Xue, Y.: Quality evaluation method of remote sensing image segmentation based on spectrum and shape. J. Earth Inform. Sci. 20(10), 121–131 (2018) 5. Xue, J.J., He, X.S., Feng, Y.: Grey evaluation model of image segmentation based on combined weighting. Progr. Laser Optoelectr. 55(06), 154–160 (2018) 6. Li, L.J., Zhang, X.G.: Image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering. Modern Electron. Technol. 41(10), 164–168 (2018) 7. Guo, R.F.: Super-resolution image reconstruction method of acute motion based on orthogonal matching tracking algorithm. Sci. Technol. Eng. 17(30), 69–73 (2017)

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8. Zhang, M.K., Liang, J., Liu, L.J.: Denoising method based on Sr300 body sensor scanning point cloud. J. Central South Univ. 49(09), 2225–2231 (2018) 9. Hemalatha, R.J., Vijaybaskar, V., Thamizhvani, T.R.: Performance evaluation of contour based segmentation methods for ultrasound images. Adv. Multim. 2018(3), 1–8 (2018) 10. Gao, H., Tang, Y.W., Jing, L.H.: A novel unsupervised segmentation quality evaluation method for remote sensing images. Sensors 17(10), 34–40 (2018) 11. Wu, M.T., Li, W.H., Gong, W.G.: Double frame convolutional neural network for blind restoration of motion blurred images. J. Comput. Aided Des. Graph. 30(12), 137–144 (2018) 12. Cabazos-Marín, A.R., Álvarez-Borrego, J.: Automatic focus and fusion image algorithm using nonlinear correlation: image quality evaluation. Optik 164, 224–242 (2018) 13. Ziółko, B., Emms, D., Ziółko, M.: fuzzy evaluations of image segmentations. IEEE Trans. Fuzzy Syst. 26(4), 1789–1799 (2018) 14. Shan, P.F.: Image segmentation method based on K-mean algorithm. EURASIP J. Image Video Process. 1, 1–9 (2018) 15. Liao, T., Chen, J., Xu, Y.: Quality evaluation-based iterative seam estimation for image stitching. SIViP 13(6), 1199–1206 (2019) 16. Selwyn, E.J., Florinabel, D.J.: Performance evaluation of frequency transform based block classification of compound image segmentation techniques. J. Inst. Eng. 99(2), 157–165 (2018) 17. Li, Y.X., Pu, Y.Y., Xu, D., et al.: Image aesthetic quality evaluation using convolution neural network embedded learning. Optoelectron. Lett. 13(6), 471–475 (2017) 18. Li, D., Zhang, G., Shi, S.: Moving object detection based on background subtraction and image sequence difference. Telecommun. Sci. S1, 182–185 (2017) 19. Gu, Y., Zhou, Y., Ren, G., et al.: Image stitching with optimal suture and multi-resolution fusion. Chinese J. Image Graphics 22(6), 842–851 (2017) 20. Li, G.J., Li, X.K.: Moving object real-time detection algorithm based on sparse optical flow field segmentation. Comput. Engi. Des. 38(11), 3029–3035 (2017)

Chapter 29

Target Tracking Method for Human Motion Image Based on Kalman Filtering Algorithm Qingwei Wang, Xinyu Wang, Dahai Tan, and Zhifeng Lv

Abstract To improve the target tracking effect of the human motion image, a target tracking method of human motion image based on Kalman filtering algorithm is proposed. Using the prediction function of Kalman filter, the human motion image features are collected, the image motion features are analyzed, and the human motion image feature tracking algorithm and evaluation index are constructed, the target tracking of human motion image is realized. The results show that the target tracking method of human motion image based on Kalman filtering algorithm has practicability in the process of practical application.

29.1 Introduction Image tracking technology has been widely used in all life and has gradually become a hot issue in computer vision. In computer vision, the edge of a moving object is an important information characteristic as well as its edge [1]. The research on feature extraction and tracking algorithm of human moving target is mainly analyzed and studied from two aspects: feature extraction and motion tracking. Some will effectively combine the first three basic methods to extract the moving foreground image [2]. For example, the extraction method based on the combination of inter frame difference method and background difference method. These methods can extract motion features, but they have corresponding defects and disadvantages. In this case, how to effectively track and mark motion image targets in dithering state has become the primary task to be solved in this field, which has attracted extensive attention of many experts and scholars [3]. Accurate tracking of motion image targets can reduce the search range of targets and improve the effect of motion image processing. In the dithering state, the contour of the motion image fluctuates. The traditional tracking and marking method mainly extracts several set contour trace Q. Wang · X. Wang · D. Tan · Z. Lv (B) Harbin Huade University, Harbin 150025, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_29

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points for tracking and marking, ignoring the impact of a large number of fluctuations on the tracking mark, resulting in the problems of low efficiency and large error of motion image target tracking [4]. A target tracking method of human motion image based on Kalman filtering algorithm is proposed.

29.2 Target Tracking Method of Human Motion Image 29.2.1 Human Motion Image Feature Acquisition The region based human tracking algorithm first needs to determine a template containing human targets, which can be determined automatically or manually, and then track the motion tracking algorithm of human targets. At present, there have been many researches on region-based tracking. An adaptive background template is established, and then the moving target is extracted by the background difference method to solve the problem of occlusion or shadow interference when tracking multiple human targets [5]. The automatic tracking method of human moving target first collects the moving target image through the camera and transmits it to the control computer. After target detection, the position of the image where the moving target is located is calculated, and the deviation and direction between the position and the image setting position or area are obtained, and then the deviation is converted into the control output through the controller; control the turntable on the remote control weapon station to drive the camera on it to the set position [6]. Then, due to the movement of the tracking target, the camera continues to collect the image and repeats the above image detection and automatic tracking process, so as to always lock the moving target within the camera setting range. The principal block diagram of automatic motion tracking method is shown in Fig. 29.1. Kalman filter is a linear minimum error estimation algorithm for the state sequence of the dynamic method, which is generally used in a linear method. Although the camera in this paper belongs to a nonlinear method when moving relative to the target, due to the short acquisition time interval of the image sequence k, the motion of the target in the image in the unit time interval can be regarded as uniform motion, and the target motion parameters can be estimated by Kalman filter. In order to simplify the model, assuming that the camera target tracking method [7] is a linear discrete state, the camera observation method can be described as a state equation L = φ(k|k−1) xk−1 + hmWk−1 .

(29.1)

Z = Hk xk + dVk − k

(29.2)

Observation formula:

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Set position (x, y)

Automatic human target tracking

Controller

Target recognition

Pan tilt actuator

Image segmentation

Camera plane

Target image

Human target

Fig. 29.1 Principal block diagram of automatic human target tracking

where x is the n of the method state φ(k|k−1) , one-dimensional state vector, φ(k|k−1) is m × one-dimensional observation vector, where Hk is dimensional state transition matrix, h is m-dimensional observation coefficient matrix, Vk is k-dimensional random interference noise vector, d is observation noise vector. This paper uses the position and speed of the target in the image at a certain time to represent the motion state of the target sk . To simplify the computational complexity of the algorithm, two Kalman filters are designed to describe the changes of the position and velocity of the target in the x-axis and y-axis directions, respectively. The following only discusses the implementation process of Kalman filter that is vk , and the same is true in the y-axis direction. The target motion formula is 

xsk+1 = xsk + xvk t .. xvk+1 = xvk + xak t

(29.3)

The current mainstream human image motion tracking method is based on the image pixel frame difference method to complete the extraction of human dynamic

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features and effective tracking. The specific methods are as follows: firstly, calculate the background image frame difference results. cI = P

n 

φ(k|k−1) /

n 

xsk+1 −

n 

xvk+1 /

n 

Hk dVk

(29.4)

where P is the pixel points calculated based on the frame difference of effective features between adjacent frames of the image, h(t) is the gray feature, Pk−1 is the sum of pixel features, and the human body tracking dynamic feature model is used to complete the dynamic tracking of human body image sequence. The method is as follows: s(x, y) = n

Pk−1 + c I × k. h(t)

(29.5)

Kalman filter is used to predict the moving target, and the motion parameters of the target are used to continuously modify the estimated value of the motion state of the target, so as to improve the possibility of successful matching between the search window and the target [8]. To better track the target, the position prediction of the moving target is to predict the next position of the target according to the motion estimation of the previous position of the target and the current position of the target.

29.2.2 Target Feature Recognition Algorithm of Human Motion Image In recent years, Kalman filtering has been more and more used in target tracking because of its superior tracking performance. Target tracking is generally carried out in noisy environment, so filtering is an indispensable and important link [9]. Because moving target tracking generally tracks the target in a complex environment, the filtering in the general sense cannot meet the requirements and the criteria of Kalman filtering. It has the following outstanding advantages in target tracking: (1) The Kalman filtering and prediction gain sequence based on target maneuver and measurement noise model can be automatically selected. (2) Kalman filtering and prediction gain sequence can automatically adapt to the changes of detection process [10]. (3) Kalman filtering and prediction can easily measure the estimation accuracy through covariance matrix. At the same time, in multitarget tracking, this measurement tool can also be used to track the formation of the gate and determine the size of the threshold. (4) Through the change of residual vector D (k) in Kalman filtering and prediction, we can judge whether the original assumed target model is consistent with the motion characteristics of the actual target.

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(5) In dense multiecho environment, the influence of false correlation can be partially compensated by the use of Kalman filtering and prediction method [11]. The influence of uncertainty-related errors can be reflected by increasing the covariance matrix. Now a lot of work has been done to improve the standard Kalman filter, such as extending the Kalman filter to colored noise by using the methods of state amplification and pseudo quantity measurement [12]. In the tracking of moving target, the information of the tracked target is collected during initialization, and then matched based on the characteristics of shape, texture, color, speed, position, and so on, so as to obtain the moving parameters and other information of the target. According to the shape of the target itself, it can be classified as follows (Fig. 29.2). The target changes can be divided into rigid and non-rigid targets. Rigid targets such as vehicles and aircraft with rigid structures will not deform during tracking. While, non-rigid targets such as pedestrians and animals have non-rigid structures, so the shape and structure often change in the process of tracking. According to the number of targets in the tracking process of the algorithm, it can be divided into single target and multitarget. Single target is the basis of multitarget. Multitarget needs to estimate the state of multiple targets at the same time and deal with the problem of mutual occlusion between targets [13]. The difficulties in practical application will be more complex. The change of human will produce self-occlusion. At the same time, the change of illumination will also lead to the change of regional color characteristics during human motion. If the regional color feature model is established only at the beginning of tracking, the above situation will lead to the reduction of motion tracking accuracy and the failure of motion tracking [14]. Based on this situation, we adopt the adaptive regional color feature model in the process of motion tracking. The so-called Motion relationship between equipment and target

Equipment static target static, equipment static target moving, equipment moving target static, equipment moving target moving

Number of targets

Single target tracking, multi-target tracking

Number of equipment

Monocular tracking

Target structure

Rigid target, non rigid target

Imaging band

Visible light sequence, infrared sequence

Application occasion

Video target, laser target, wireless sensor target

Classification of target tracking

Fig. 29.2 Classification of motion image target tracking

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adaptive regional color feature model is that we constantly update the target regional color feature model in the process of tracking. The updating process of adaptive feature model is: after the moving target is accurately located, judge whether the moving target encounters large occlusion. If the moving target does not encounter large occlusion, update the moving target area feature model. In the updated feature model, the moving target area feature model of the previous frame accounts for a large proportion [15]. The regional color feature model of the current target’s  precise k = 1, position accounts for a small proportion, and the target feature model is and δ satisfies the formula:  2    Pk−1   qu = s(x, y) k  (29.6)  h(t) − 1 δ. Suppose the center of the target area in the current frame is  , h d pixels in the area are represented by n, and the number of eigenvalues bin is m. It is known that the sampling data of N-point feature space is l, the window radius of kernel function is r, and the estimated value of kernel function density at point is: fˆ(x) =

 1 qu K (X k − Yk )/( m/lr − 1). d n−h

(29.7)

Kalman is a linear recursive filter, which makes the optimal estimation of the next state based on the previous state sequence of the method. The prediction is unbiased, stable, and optimal. The state equation and observation equation of Kalman filter algorithm are as follows X k = jk,k−1 X k−1 + qu Wk−1

(29.8)

Yk = Hk X k + E N VK − r

(29.9)

where jk,k−1 is the k of the method state k ∗ k vector; X k−1 is the n of the observed method state vector; Wk−1 is k-dimensional state transition matrix; E N is time N, and the observation matrix Vk is time K. The random interference of one-dimensional state is the random vector of white noise. Single-region motion tracking and multiregion tracking adopt the same similarity algorithm. Therefore, this paper sets the similarity threshold according to the test data samples of single region tracking algorithm Hk . The setting of affects the tracking accuracy, and its selection will occur in two cases: the actual tracking target has been lost, but the tracking is successful [16]. The actual tracking is successful, but the tracking is considered to have failed. We define the misjudgment rate of case as e and the misjudgment rate of case r, as shown in equation.

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⎧ n1 ⎪ ⎨ e1 = X k n n2 ⎪ ⎩ e2 = Yk n

363

(29.10)

where n is the total number of frames of the video image sequence, n is the number of frames in case n 1 , and n 2 is the number of frames in case. For all pixels in the target area in the initial frame image, the probability of each eigenvalue is calculated, which is called target model [17]. The calculation of each eigenvalue of the feature space in the candidate region where the target may exist in each subsequent frame image is called candidate model. Kalman filter algorithm is based on the observation value in the process of the method and uses the method of solving the matrix differential variance to estimate the value of the method state variable [18]. Assuming that the continuous method equation is 

X˙ (t) = F(t)X (t) + G(t)W (t) Z (t) = H (t)X (t) + V (t)

(29.11)

where F(t) is the state vector of the method; X (t) is the t-dimensional observation vector of the method; W (t) is the t-dimensional method excitation noise sequence; G(t) is the t-dimensional observation noise sequence, V (t) is the t-dimensional state transition matrix of the method.

29.2.3 Realization of Object Tracking in Human Motion Image In image tracking, real-time continuous long-distance tracking of moving targets is a very difficult task. Moving targets, whether rigid or non-rigid, may deform at any time in the tracking process. In addition, occlusion will occur in the tracking process. Therefore, it is generally not feasible to use template matching method to track targets simply by taking the target image as the mode, unless the processing speed is very fast and the time interval between adjacent frames is very short, so that the target mode is almost unchanged and no other occlusion occurs [19]. Therefore, how to select the characteristic information of the target and simplify the operation on the premise of reliability is the key of target tracking. In the method of dynamic tracking, whether the target is rigid or non-rigid, single or multitarget, the target fixed by the camera and the target moving by the camera, the target in complex scene and the target in simple scene, one or more features that can uniquely represent the target should be selected according to the target and its environment. Then, the target position that best matches the selected feature is searched in the subsequent images [20]. Due to the complex background and various situations in practical application, the video image background is moving due to the movement of the camera taking the video image at the same time. In this case, the complexity of the

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target location Target feature matching Target location prediction Target feature extraction Input video

Fig. 29.3 Basic composition block diagram of moving target tracking

tracking method of detecting moving targets increases, and errors will also occur in the background alignment process stage, resulting in the decline of tracking accuracy in the tracking stage. In view of the above analysis, the current general video image sequence tracking algorithms assume the existence of moving objects in the tracking image sequence, and there is no need to detect the existence of moving targets. The existing tracking algorithms generally adopt the circular algorithm of moving target prediction, moving target accurate positioning and updating the prediction model to track the video image sequence. The composition block diagram of motion tracking is shown in the Fig. 29.3. In the particle swarm optimization algorithm, each particle continuously optimizes the current particle according to the best experience and global best of the current particle, so that each particle approaches a better estimation point, and a random quantity is added in the process of approaching, which increases the diversity of particles without particle dilution. In particle swarm optimization algorithm, because the solution of each particle is optimized, it is distributed near the optimal solution. In particle filter, the solution process of the optimal solution is obtained by estimating the posterior probability density through a large number of random experiments, so it will consume a lot of computing time. To determine whether resampling processing is required according to the weights of all current particles or not, so we have to estimate the current state according to the position of each particle and its weight. Then, determine whether it is the end frame or not. If so, end the tracking, otherwise enter the next frame to continue the tracking as shown in Fig. 29.4. Because multiregion association can make multiple regions in the target affect each other and increase tracking stability, the multiregion association idea is adopted, and a multiregion association target tracking model based on Kalman filter is proposed to solve the problem of single-target occlusion and tracking drift. In multiregion feature matching, the target is divided into multiple regions and the distance factor between adjacent regions is introduced. The global multiregion similarity measurement function is constructed, so that when occlusion or tracking offset occurs in a region or some regions in the process of target tracking, other regions can correct the position of these regions and enhance the tracking stability as shown in Fig. 29.5.

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End state estimation

End frame no

Extracting color features

Resampling Multi feature adaptive fusion

Initialize target area

yes

state transition

no Neff 30% means that the number of days with an error rate greater than 30% in 80 days of the classification model in the three months (January, February, and December) is 22 days. Due to the different prediction effects in different time periods, the error rate of the model reference comparison in each time period is different; for example, the error rate in Jan. Feb. and Dec. is greater than 30%, while that in Jul. and Aug. is greater than 15%. From table, we can see that except for MaxPE in April and May and MaxAE in September and October, each comparison item is that the classification model is better than the annual model, especially the MAPE of the classification model is much smaller than the annual model. In both the classification model and the annual model, the prediction error rate of the peak tourism season is lower than that of the Table 33.3 Comparison of prediction results of classification model and annual model Model

Comparison item MAE

Jan. Feb. Dec. Mar. Jun. Nov.

MaxAE

MAPE

MaxPE (%)

Classification

246

2239

21.35

96.04

22(80) > 30%

Annual

560

3178

27.14

127.97

26(80) > 30%

Classification

1272

4262

17.33

69.89

23(88) > 25%

Annual

1326

5539

21.03

84.72

28(88) > 25%

Apr. May

Classification

977

3509

13.03

29.09

14(55) > 20%

Annual

1259

5371

16.29

48.35

17(55) > 20%

Jul. Aug.

Classification

880

1878

9.09

31.62

10(62) > 15%

Annual

1220

3282

12.04

30.92

17(62) > 15%

Sept. Oct. Holiday

Classification

1024

4355

12.38

38.10

17(51) > 15%

Annual

1212

3954

15.87

42.17

21(51) > 15%

Classification

1291

4194

10.65

66.96

3(29) > 15%

Annual

1651

4464

12.68

64.84

8(29) > 15%

33 The Daily Tourist Predicting Based on Classification Model

413

tourism off-season. The reason is not only that the jump volatility of the off-season tourism data is greater, but also that there are too few people in the tourism off-season. Even if the error of the predicted number of people is very small, the error rate is very large. It can also be seen from the first four comparison items of the models in January, February, and December. Although Mae and MaxAE are much lower than other models, MAPE and MaxPE are significantly higher than other models. That is the reason we set different error rates for comparison; we think the tourism off-season brings less management challenges to the scenic area.

33.4 Conclusion The prediction of daily tourist flow demand is becoming more and more important with the tourism boom. According to the distribution characteristics of tourist flow in a scenic area from 2016 to 2020, this paper establishes six classification models, determines the input items of each model through the correlation coefficient, and uses virtual variables to characterize the weather, holidays, and weekends. Compared with the whole year model, the prediction results and evaluation indexes of the classification model are better, and the main reason is that the classification model greatly reduces the jump volatility of data. In short, the classification model established according to the data distribution characteristics in this paper not only provides a new perspective for the prediction method of tourist flow demand, but also the scenic spot tourism management department and tourism-related enterprises can make arrangements for materials, personnel, and transportation according to the tourist flow prediction, which is helpful to improve the service quality of the scenic area, and it also has a great significance to reasonably allocate tourism resources and improve tourism revenue. Although the classification model can better predict the daily tourist flow, it still needs to be improved. For example, the prediction error of MaxAE and MaxPE in Table 33.3 is still large, and the prediction effect in the off-season is not good. Therefore, the next step is to collect and analyze more data which related to tourist flow, such as scenic area traffic and hotel data. At the same time, the data processing method may also need to be further optimized.

References 1. Council, W.T.T.: Travel & tourism economic impact 2015 China. https://wttc.org/. Last accessed 21 Mar 2022 2. Hu, C., Chen, M., Chen, S.: Forecasting in short-term planning and management for a casino buffet restaurant. J. Travel Tour. Mark. 16(2), 79–98 (2004) 3. Song, H., Turner, L.: Tourism demand forecasting. In: Dwyer, L., Forsyth, P. (eds.) International Handbook on the Economics of Tourism. Edward Elgar, Cheltenham (2006)

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4. Chu, F.: Forecasting tourism demand with ARMA-based methods. Tour. Manage. 30(5), 740– 751 (2009) 5. Cho, V.: Tourism forecasting and its relationship with leading economic indicators. J. Hospitality Tour. Res. 25(4), 399–420 (2001) 6. Goh, C., Law, R.: Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention. Tour. Manage. 23(5), 499–510 (2002) 7. Chan, F., Lim, C., McAleer, M.: Modelling multivariate international tourism demand and volatility. Tour. Manage. 26(3), 459–471 (2005) 8. Kulendran, N., Witt, S.: Forecasting the demand for international business tourism. J. Travel Res. 41(3), 265–271 (2003) 9. Witt, S.F., Song, H., Wanhill, S.: Forecasting tourism-generated employment: the case of Denmark. Tour. Econ. 10(2), 167–176 (2004) 10. Papatheodorou. A.: The demand for international tourism in the Mediterranean region. Appl. Econ. 31(5), 619–630 (1999) 11. Kon, S.C., Turner, L.W.: Neural network forecasting of tourism demand. Tour. Econ. 11(3), 301–328 (2005) 12. Law, R.: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tour. Manage. 21(4), 331–340 (2000) 13. Pai, P.F., Hong, W.C.: The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. Int. J. Manage. 23(2), 375–385 (2006) 14. Qi, E., Shen, J., Dou, R.: A forecasting model for short term tourist arrival based on the empirical mode decomposition and support vector regression. Springer, Berlin, Heidelberg, pp. 1009–1021 (2014) 15. Wang, L., Wu, B., Zhu, Q.: Forecasting monthly tourism demand using enhanced backpropagation neural network. Neural Process. Lett. 52(3), 1–30 (2020) 16. Xie, G., Qian, Y., Wang, S.: Forecasting Chinese cruise tourism demand with big data: an optimized machine learning approach. Tour. Manage. 82, 104208 (2021) 17. Nor, M.E., Nurul, A., Rusiman, M.S.: A hybrid approach on tourism demand forecasting. J. Phys: Conf. Ser. 995, 012034 (2018) 18. Colladon, A.F., Guardabascio, B., Innarella, R.: Using social network and semantic analysis to analyze online travel forums and forecast tourism demand. Decis. Support Syst. 123, 113075 (2019) 19. Reina, M.: Forecasting using big data: the case of Spanish tourism demand. In: International Conference on Time Series and Forecasting. ITISE (2019)

Chapter 34

Information Asymmetry Simulation of Comprehensive Transparency with Meta Model in a Trader Behavior Environment Haoyang Du

Abstract The level of corporate comprehensive information transparency is of great significance to China’s capital market, and more transparent information disclosure can attract more attention from market participants. This paper is based on the annual data of A-share listed superior companies in China from 2011 to 2021 using highfrequency data. We set up the information asymmetry meta-model based on the agent-based model from five aspects: accounting information transparency, company inner information transparency, stock price information transparency, trading risk preferences, and trading strategies. Based on GH model and LSB model in market microstructure theory, this paper decomposes the adverse selection cost as an alternative to of the information asymmetry index, and studies the influence mechanism of companies’ comprehensive information transparency and analysts’ prediction on information asymmetry. The simulation results show that indicating the metamodel constructed in this paper is effective, company comprehensive information transparency will be able to reduce information asymmetry definitely, but traders’ behaviour in different sample intervals shows different characteristics, this shows that the company comprehensive information transparency is not reduced through the trade behaviour or activities. To some extent, this shows that traders have not played their role.

Present Address: H. Du (B) Henan University of Economics and Law, Zhengzhou 450046, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_34

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34.1 Introduction Information asymmetry exists in the capital market, which is an information market essentially [1]. The company’s information disclosure strategy is determined by a variety of factors, including the market environment, corporate governance mechanism, company development, and the interests of internal control products [2]. This makes horizontal differences in the level of information disclosure even under the basic framework of mandatory disclosure, thereby showing different levels of transparency. Therefore, promoting listed companies to enhance information disclosure and transparency will help investors’ decision-making and effective allocation of resources. In more mature Western markets, some studies have shown that investors can identify companies with different transparency and that higher levels of corporate disclosure can reduce the degree of information asymmetry in the market, thus reducing the information asymmetry component of capital costs [3]. However, in the Chinese market with different environments, do investors behave differently towards companies with different transparency? There is no consistent definition of what information a company should disclose. Most of the existing studies use the information disclosure evaluation results of Shenzhen Stock Exchange. Although the information disclosure evaluation results can reflect the company’s information transparency to a large extent, they are not comprehensive enough. Information asymmetry plays a crucial role in capital markets, and a large number of models and empirical studies link information asymmetry to capital cost and efficiency of resource allocation [4]. Although information asymmetry plays a central role in the capital market, fewer studies directly examine how corporate information transparency affects information asymmetry, especially the influence mechanism of trader behaviour on information asymmetry in the capital market. In addition, while public and private information can improve the overall predictive power of the market, private information also expands the difference in predictive power between informed and uninformed traders. It can be seen that the differences in the information transparency of companies will make the behavior of traders on information asymmetry present different characteristics [5]. This is also the basic starting point of the present study. The meta-model is simply the “model” and is the integration of a set of models. Meta-models can be applied increasingly by effectively describing the behaviours of various heterogeneous subjects in a complex system and the complex macroscopic phenomena emerging under their interactive influence. The meta-model is a further upgrade and collection of agent models to observe the results generated by describing and defining the influence ways and norms between the various groups. For the financial market, the market is the place and medium for traders to engage in trading activities, and its performance is the result of the collective decision-making behavior of all traders [6]. The market also carries earnings and risk transfers based on the system characteristics and market environment of China; this paper believes that in the financial market, corporate comprehensive information transparency should include at least three dimensions: accounting information transparency, stock price information transparency, and internal information transparency. Trader behavior

34 Information Asymmetry Simulation of Comprehensive Transparency …

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should include at least two dimensions: trade behaviour or preference and trader strategy. Therefore, it is necessary to set the model from five dimensions of the company’s comprehensive information transparency and trader behavior and observe the results according to the process of quantitative analysis, simulation, and empirical research, and the above constitutes the whole process of meta-model analysis. The meta-model established by the above elements can be regarded as the analogue alternative of the information asymmetry index in the market. Every component of the market has a characteristic risk and uncertainty. Based on this, the paper draws on relevant measurement methods, selects relevant indicators to measure information transparency, and calculates the comprehensive information transparency of the company through principal component analysis.

34.2 Model Variable Definition 34.2.1 Sample Selection and Data Sources This paper selects Shanghai and Shenzhen A-share listed companies from 2011 to 2021 as the research sample. The corresponding data features were extracted and used as the reference data for the model. The samples are selected according to the following criteria: the companies with ST and ST * in the sample are excluded because they are not universal; the companies in the financial industry are excluded because the accounting standards the of the financial industry are quite different from those of other industries, and the relevant indicators are not comparable between the financial industry and non-financial industry; the samples with missing data are excluded because they may affect the effectiveness of the results.

34.2.2 Variable Definition and Model Building Information asymmetry is the main consideration of the explanatory variable in this paper, there are many indicators of information asymmetry in the existing literature, among them the use of market micro-structure theory in the cost of reverse selection as a measure of information asymmetry indicators is widely recognized by academia, He et al. made a very valuable research [7]. In this paper, the reverse selection cost in the market micro-structure theory is chosen as a measure of information asymmetry. Based on the existing research results and the institutional background of China’s trading mechanism, the LSB model (Lin, Sanger, and Booth, 1995) [8] and GH model (Glosten and Harris, 1988) [9] are widely used and recognized in the market micro-structure theory and are selected for analysis.

418

H. Du

The LSB model studies the relationship between the spread and the transaction size while breaking the bid-sell spread. The model is as follows: Mt+1 − Mt = λZ t + et+1

(34.1)

Z t+1 = θ Z t + ηt

(34.2)

Among them, M t is the midpoint value at time t, Z t = Pt −M t , Pt is the transaction price at time t. λ is the reverse selection component of the effective spread. δ = (θ + 1)/2 is the instruction duration parameter, et+1 , ηt+1 is the random error term. The various parameters of the equation are estimated using the generalized matrix method (GMM). Therefore, according to the LSB model and drawing on the study of Qu et al. [10], the measurement model is estimated as follows: ΔMt+1 = α + λZ t + et+1

(34.3)

in which, ΔM t+1 = M t+1 −M t , et+1 is the deviation. The parameter λ is the reverse selection component (hereinafter referred to λ as LSB), which indicates the proportion of the information cost in the effective price difference, and the other variables are defined as the same as above. The GH model decomposes the bid-ask spread into temporary components and persistent components and represents the cost of instruction processing and reverse selection, respectively, in the instruction-driven market. Based on the analysis of He et al. [7], the following equation is mainly estimated to achieve: ΔPt = μ + c0 ΔQ t + c1 Δ(Vt Q t ) + z 0 Q t + z 1 Vt Q t + u t

(34.4)

where Pt is stock prices, Vt is trading order size, Q t is indicator variable for the transaction driver (when the buyer drives the transaction Q t = 1), while the seller drives Q t = −1, subscript t is trading time. In GH model, the reverse selection cost is z 0 + z 1 Vt (the after analysis for short is λGH), the instruction processing cost is c0 + c1 Vt , implied (valid) price spread is 2(c0 + c1 Vt ) + (z 0 + z 1 Vt ).

34.3 Model Parameter Definition In the research process of subject-based modelling, the compression of model parameter space and the calibration of parameters are key problems. It is precisely because of the large degree of freedom of the model in the subject modelling, the choice of parameters is more subjective, so it will also have significantly different effects

34 Information Asymmetry Simulation of Comprehensive Transparency …

419

on the results. The meta-model of this paper involves five dimensions of parameters: corporate accounting information transparency parameter, stock price information transparency parameter, internal information transparency parameter, trading risk preferences parameter, and trading strategies parameter [11]. Taking the actual data of the Shanghai and Shenzhen A-share index as a sample, combined with the actual operating characteristics of the market, the parameter calibration and sensitivity analysis of qualitative analysis and quantitative analysis are adopted. In the specific implementation, different setting methods can be adopted according to the different parameter attributes. For parameters such as accounting information and internal information, the sample data estimation results are used as the corresponding parameter values of the meta-model; for the parameters of stock price information or transaction cost, the actual relevant interest rate and the tax rate of the Chinese market are used as the main reference; for trading strategy parameters behavioural or preference parameters, they are mainly based on the parameter setting mode of existing relevant documents.

34.3.1 Explanatory Variable There is no clear definition of corporate information transparency, which largely depends on the research problems and research goals. In practice, the meaning of transparency is relatively broad and vague. Bushman et al. [2] believe that corporate information transparency is the extent to which external investors can obtain internal information about the company. The vague definition of concepts has led to the diversification [12] of transparency measures taken by existing studies. Existing studies use a wide variety of approaches to measuring transparency. It can be roughly divided into two categories: the first type is the transparency measure based on the amount of information disclosure, which is divided into the number of compulsory disclosure and voluntary disclosure. This method mainly adopts the method of establishing an information disclosure index. It can be roughly divided into two categories: firstly, the category is the transparency measure method based on the number of information disclosure, which is divided into the number of compulsory disclosure and the voluntary disclosure quantity. This category of method mainly adopts the method of establishing the information disclosure index. Secondly, the category is transparency measures based on the quality of disclosure. Such methods include directly using the evaluation results of the relevant organization. Based on the above analysis and based on the characteristics of China’s system and market environment, this paper believes that corporate comprehensive information transparency should include at least three dimensions: accounting information transparency, stock price information transparency, and internal information transparency. The first dimension is the transparency of accounting information. This paper mainly draws on Bhattacharya [13] and Francis [14] research, to select the surplus

420

H. Du

radical degree (EA) and earnings smoothness (ES) these two indicators Surplus Radicity CalMethod EAi,t = ACCi,t ) ( = ΔCAi,t − ΔCLi,t − ΔCASHi,t + ΔSTDi,t − DEPi,t + ΔTPi,t /TAi,t (34.5) among which ACCi,t is Aced for the listed company, measure the surplus radical, ΔCAi,t is current assets increase amount, ΔCLi,t is the increase in the current liabilities, ΔCASHi,t is monetary funds increasing, ΔSTDi,t is the increase in long-term liabilities due within one year, DEPi,t is depreciation and amortization expenses, and ΔTP is the increase in income tax payable. The second dimension is the transparency of stock price information. Drawing on the research of Durnev et al. [15], the synchronization of the stock price information that can show the characteristics of the stock price information is used for the transparency of the stock price information. If corporate stock earnings are highly correlated to market and industry factors, fewer stock earnings may contain corporate trait information. If stock earnings changes are out of sync with the market and industry earnings, this means that stock prices contain more information about corporate traits. The following regression models are constructed ri,t = αi + β1,i rind,t + β2,i rm,t + εi,t .

(34.6)

Among them, r i,t is the weekly income of the company i, r ind,t is the industry valueweighted income after classification according to the industry, and r m,t is the market income weighted by )market ] value. Define stock price information transparency: [( STran = ln 1 − Ri2 /Ri2 , Ri2 represents goodness of fit. The larger the Ri2 , the more the earnings of a single stock synchronized with industry and market indices. Stock prices contain less corporate trait information, and the lower the transparency value of stock price information. The third dimension is internal information transparency, which reflects internal information transparency related to the company, based on the autocorrelation of stock earnings based on trading volume. Llorente et al. proved that the more opaque the internal information, the easier it leads to autocorrelation in stock earnings [16]. Internal information transparency is the coefficient C 2 of the following time series regression (multiplied by-1) the abbreviated form of Insdt below: Ri,t+1 = Ai + C1,i Ri,t + C2,i Ri,t Vi,t + λi,t

(34.7)

For each company i, regression analysis to weekly stock volume V i,t by weekly earnings Ri,t , weekly volume V i,t is defined as trend removal volume, definition is as follows:

34 Information Asymmetry Simulation of Comprehensive Transparency …

( Vi,t = log

VOLi,t Ni,t

) −

421

) ( 20 VOLi,t− j 1 Σ log 20 j=1 Ni,t− j

where VOL is the stock trading data, N is the number of shares issued outside. The fourth and fifth dimensions are trader behavioral preferences and strategies. There are many characteristics of traders’ behavior activities in the existing literature, such as trader behavior type, behavior divergence, etc., this paper mainly studies how corporate transparency affects information asymmetry through traders’ behavior preference. The most direct embodiment of the corporate information transparency to the trader behavior activity is the trader behavior strategy, which describes the accuracy of the trader type, which will directly affect the investors’ consistent prediction of the future earnings of the listed company. Traders can be divided into n different types according to their different trading strategies and behavioral preferences. Based on this, this paper mainly refers to the method of Liu and Peng [17] and defines trader behavior accuracy as: | (| | | ) | | Accuracyi jt = −| E jt − Eˆ i jt |/ | E jt | + 0.5 .

(34.8)

Among them, i represents the forecasting agency, j represents the forecasting enterprise, and t represents the year of trader behavior. E jt is the actual earnings per share, and Eˆ i jt is the forecast of earnings per share. The denominator of accuracy plus 0.5 was used to reduce the outliers generated by hourly EPS in that year. Larger accuracy represents higher behavioral accuracy.

34.3.2 Control Variable This paper mainly selects these control variables: stock price (Price), earnings volatility (Vola), turnover rate (Turnover), trading volume (Vol), and market value of tradable shares (Liqval), and also controls the number of traders with different behavior preferences (Analyst). The specific variable definitions are given in Table 34.1.

34.3.3 Model Building Meanwhile, in order to test how the company’s comprehensive information transparency affects the information asymmetry [18], and study the role of the trader behavior, the following two models are constructed: InfoAsyi,t = α0 + β1 Comtrani,t /Accuracyi,t

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H. Du

Table1 Variable definition Variable symbol

Variable meaning

Computational method

λLSB

Information asymmetry index 1

The reverse selection cost of splitting the price and sale spread according to the LSB model

λGH

Information asymmetry index 2

The reverse selection cost of splitting the bid-selling spread according to the GH model

Comtran

Company comprehensive information transparency

(ATran1), (ATran2), (STran), (Insdt), (Tadsdt), (Stasdt) Comprehensive information transparency was extracted by performing the principal component analysis

Accuracy

Trader behavior

The estimates were calculated according to the method of Huberts and Fuller (1995)

Price

Share price

The annual average of the stock’s daily trading price

Vola

Profit fluctuation ratio

The annual average value of the daily stock yield standard deviation

Turnover

Turnover rate

The average value of the daily stock turnover rate

Vol

Trading volume

The average value of the daily stock trading volume

Liqval

The market value of tradable shares

The average market value of the outstanding shares

Analyst

Number of traders with different behavioral preferences

Each company has different behavioral preference number of traders for each year

+ β2 Analyst + β3 Pricei,t + β4 Turnoveri,t + β5 Volai,t + β6 Voli,t + β7 Liqvali,t + εi,t

(34.9)

Accuracyi,t = α0 + β1 Comtrani,t + β2 Pricei,t + β3 Turnoveri,t + β4 Volai,t + β5 Voli,t + β6 Liqvali,t + εi,t

(34.10)

Model (34.9) is not only used to test the influence of the company’s comprehensive information transparency and trader behavior on information asymmetry but also used to analyze the influence of the two on information asymmetry comprehensively. In InfoAsy regression, LSB and GH are respectively defined in Table 34.1; the model (34.10) is used to test the influence of comprehensive information transparency on the behavior of traders. The specific meanings and calculation methods of other variables are given in Table 34.1.

34 Information Asymmetry Simulation of Comprehensive Transparency …

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34.4 Empirical Analysis Firstly, comprehensive information transparency is extracted from five different dimensions, including accounting information transparency (ATran1, ATran2), stock price information transparency (STran) and internal information transparency (Insdt), trader behavior preference (Tadsdt) and trader strategy (Stasdt). Analyzing the principal component of the transparency metrics at five latitudes, extracting the first principal component Comtran which can explain 69.76% of the information on all variables, 70.58 and 70.72% with ATran1 and ATran2,3.89% with STran, and 1.54% with Insdt. Secondly, descriptive statistical analysis and empirical test were conducted on the main variables in this paper.

34.4.1 Descriptive Statistical Analysis As can be seen from Table 34.2, the mean values of λGH and λLSB of the two information asymmetry indicators were 8.749 and 3.607, respectively, and the mean and median values of the integrated information transparency (Comtran) were 0.001 and -0.016, The mean and median values of trader behavior (Accuracy) are -0.168 and -0.096, which indicates that trader behavior is still high. On average, the average number of traders with different behavioral preferences (Analysts) in each company is 25.31, the median is 22, the maximum value is 65, and the standard deviation is 11.02, indicating that the number of traders in different companies fluctuates. Descriptive statistical results of the other variables will not be repeated. To test the difference of information asymmetry between different information transparency, the transparency of different latitudes and comprehensive information transparency were divided into two groups: low transparency and high transparency Table 34.2 Descriptive statistical results of the variables Variable

Mean value

Median

Minimum

p25

p75

Maximum

Standard deviation

λGH (10−4 )

8.749

7.292

1.383

5.224

10.521

43.254

5.333

λLSB

(10−4 )

3.607

3.093

0.275

1.853

4.738

18.963

2.348

Comtran

0.001

−0.016

−2.009

−0.926

0.922

2.058

1.162

Accuracy

−0.168

−0.096

−3.010

−0.190

−0.053

−0.001

0.217

Price

10.582

7.215

0.873

4.542

12.340

225.119

10.994

Turnover

5.950

4.967

0.125

2.747

7.966

31.265

4.221

Vola (%)

47.445

44.832

6.620

35.486

56.098

314.263

17.054

(106 )

1163

674

96

272

1628

4496

1222

Liqval (106 )

2239

1104

226

485

2745

10,363

2684

Analyst

25.31

22

4

15

32

65

11.02

Vol

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H. Du

according to the median. T-test and Wiloxon rank sum test were used for the mean and median of the high and low two groups, Table 34.3 reports the results of differential tests of information asymmetry between different transparency. As can be seen from Table 34.3, after the information transparency grouping based on different latitudes, the information asymmetry index λGH is significantly lower compared with the low transparency group, which is statistically significant. When λLSB was used as an indicator of information asymmetry, it generally agreed with the analysis of λGH. The results by grouping comprehensive information transparency did not change.

34.4.2 Empirical Analysis Using models (34.9) and (34.10) constructed above, this section, respectively, examines the influence of the company’s comprehensive information transparency and traders’ behavior on information asymmetry, and further tests the influence of traders’ behavior on information asymmetry. The results are given in Table 34.4. The t-value is given in parentheses. To ensure the purity of the analysis, the number of different behavioral preferences (Analyst) variables were not controlled in this separate analysis, and they were included in the model only for the comprehensive impact of the two on information asymmetry. According to the regression results (1) (2) in Table 34.4, the company’s integrated information transparency shows a significant negative correlation with the information asymmetry λGH and λLSB, which shows that the improvement of the integrated information transparency can indeed reduce the information asymmetry. From the regression results (3) (4), the higher the trader behavior, the higher the information asymmetry degree. However, accuracy was not statistically significant with λGH and statistically significantly positively correlated with λLSB. This also coincides with the correlation analysis, the information produced by traders cannot be used to infer the direction of the trader’s behavior on information asymmetry. The above is a separate analysis of the company’s comprehensive information transparency and traders’ behavior on information asymmetry. Integrating two factors into the model simultaneously, the results are given in Table 34.5.

34.4.3 Robustness Test To ensure the robustness of the test model, on the one hand, the GKN (1997) model is decomposed to measure the information asymmetry, represented by λGKN; on the other hand, this paper divides the comprehensive information transparency into two groups according to the median. When Comtran is greater than the median, the virtual variable Comtran_G is equal to 1, otherwise, 0. λGKN and λLSB were used

0.855

0.866

0.607

0.629

0.394

GATran2

Comtran

0.129*

0.037*

0.022

0.047*

0.035*

0.103*

0.048*

0.049*

0.049*

0.055*

0.085*

7.532

2.695

1.313

4.161

2.768

10.954

1.567

1.688

2.393

2.756

4.635

1.021

0.343

0.554

0.564

0.557

0.603

0.653

0.765

1.002

1.003

1.021

0.310

0.536

0.523

0.533

0.486

0.633

0.722

0.958

0.960

0.944

0.952

High transparency

0.032*

0.019

0.042*

0.025*

0.117*

0.042*

0.044*

0.044*

0.043*

0.077*

0.069*

Discrepancy

2.430

1.333

3.580

2.182

10.190

1.623

1.737

2.852

3.140

5.810

4.691

Wilcoxon rank test

Note *indicates significance at atleast 5%. GSTran, Ginsdt, GATran, Comtran, and Trastd, represent the test results grouped by share price information transparency, internal information transparency, accounting information transparency, integrated information transparency, trader behavior, and trader-median strategy, respectively.

0.358

0.601

0.593

0.636

0.640

Ginsdt

GATran1

GSTran

1.117

1.121

0.567

0.875

Trastd

1.082

1.104

0.670

0.915

Comtran

λLSB

1.172

1.170

GATran1

GATran2

1.211

1.189

GSTran

T-test

Median Discrepancy

Low transparency

High transparency

Mean value

Low transparency

Ginsdt

λGH

Group basis

Table 34.3 Differential tests of information asymmetry between different transparency groups

34 Information Asymmetry Simulation of Comprehensive Transparency … 425

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H. Du

Table 34.4 Preliminary regression results

Comtran

(1)

(2)

(3)

(4)

(5)

λGH

λLSB

λGH

λLSB

Accuracy

−0.134**

−0.192***

(−2.56)

(−3.87)

0.126*** (4.62) 0.513

Accuracy

0.704***

(1.57)

(3.79)

−0.231***

−0.119***

−0.120***

−0.248***

0.0081***

(−12.09)

(−13.40)

(−9.68)

(−7.80)

(4.08)

−0.279***

−0.165***

−0.329***

−0.158***

0.0120

(−12.12)

(−12.59)

(−13.90)

(−5.41)

(1.06)

Vola

−0.159***

−0.129***

−0.258***

−0.208***

−0.0406***

(−4.57)

(−8.92)

(−5.12)

(−6.20)

(−10.12)

Vol(10−3 )

0.423***

−0.681***

0.394***

−0.423***

−0.018

(3.67)

(−1.12)

Price Turnover

(−8.55)

(3.14)

(−5.75)

−0.876***

−0.125

−0.824***

−0.268**

0.0369

(−2.69)

(−0.98)

(−4.34)

(−2.66)

(1.56)

_cons

14.65***

9.789***

12.34***

7.291***

0.056***

(56.21)

(52.45)

(38.76)

(35.36)

(6.32)

adj. R2

0.209

0.321

0.256

0.290

0.123

389.6

99.43

67.32

43.68

Liqval(10−4 )

F Note

221.0 *p

< 0.10,

** p

< 0.05,

*** p

< 0.01, and t-values are given in parentheses

for information asymmetry indexes. Table 34.6 reports the robustness regression results.

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Table 34.5 Regression results of information transparency and trader behavior to information asymmetry 2014–2016 (1)

2018–2021 (2)

(3)

All (4)

(5)

(6)

λGH

λLSB

λGH

λLSB

λGH

λLSB

Comtran

−0.187

−0.0378

−0.937

−0.515

−0.295***

−0.196***

(−1.37)

(−0.66)

(−1.28)

(−0.99)

(−3.75)

(−3.80)

Accuracy

−4.773*** −1.398**

0.619***

0.748***

0.566

0.691***

(−3.17)

(−2.35)

(2.39)

(3.86)

(1.46)

(3.43)

Analyst

−0.540

−0.635***

−1.290***

−1.027***

−1.098***

−1.689***

(−1.56)

(−4.57)

(−8.79)

(−10.21)

(−11.02)

(−20.60)

Price

−0.167*** −0.105*** −0.0345*** −0.0096*** −0.0765*** −0.0341*** (−3.05)

(−7.09)

(−8.91)

Turnover

−0.787*** −0.255*** −0.279***

−0.056***

−0.392***

−0.190***

(−9.10)

(−7.97)

(−10.14)

(−4.95)

(−15.26)

(−13.02)

Vola

0.0334**

0.0205***

0.0045

0.0301***

−0.029***

−0.0901*

(2.25)

(3.42)

(1.13)

(7.23)

(−5.12)

Vol(10−3 )

3.41***

−0.482*** 0.561***

(−3.03)

(7.67)

adj. F

R2

(−5.76)

−0.0876*** 0.542***

(−1.82) −0.259***

(−4.84)

(7.18)

(−5.67)

(5.87)

(−5.78)

−1.10*

−0.679***

−0.129***

−0.788***

−0.321*

(−4.42)

(−1.67)

(−4.19)

(−3.11)

(−4.38)

(−1.95)

15.60***

9.881***

12.23***

6.093***

10.92***

10.22***

(19.46)

(28.17)

(20.15)

(18.03)

(32.31)

(45.12)

Liqval(10−4 ) −9.24*** _cons

(−5.32)

0.344

0.316

0.208

0.209

0.345

0.376

27.25

44.68

46.02

45.21

89.38

107.8

Note * p < 0.10, ** p < 0.05, *** p < 0.01, t-values are given in parentheses

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Table 34.6 Robustness test 2014–2016 (1)

2018–2021 (2)

(3)

All (4)

(5)

(6)

λGKN

λLSB

λGKN

λLSB

λGKN

λLSB

−0.0661

−0.0641

−0.290*

−0.087

−0.315**

−0.189***

(−0.21)

(−0.49)

(−1.79)

(−1.20)

(−1.98)

(−3.59)

Accuracy

−4.901***

−1.412**

0.723***

0.689***

0.467

0.678***

(−3.26)

(−2.38)

(3.69)

(3.97)

(1.34)

(3.58)

Analyst

−0.500

−0.631*** −1.432***

−1.028***

−1.869***

−1.604***

(−1.44)

(−4.55)

(−10.14)

(−12.09)

(−23.16)

Price

−0.169*** −0.106*** −0.0243*** −0.0951*** −0.0185*** −0.0376***

Comtran_G

(−9.08)

(−3.07)

(−5.35)

(−4.27)

(−2.97)

(−6.09)

(−6.94)

Turnover

−0.786***

−0.255***

−0.289***

−0.0526***

−0.396***

−0.156***

(−9.07)

(−7.96)

(−13.98)

(−4.96)

(−14.28)

(−12.30)

Vola

0.0327**

0.0204***

0.0032

0.0295***

−0.0279*** −0.0520*

(2.21)

(3.41)

(1.60)

(7.89)

(−5.43)

(−1.68)

Vol(10−3 )

3.40***

−0.483*** 0.460***

−0.095***

0.513***

−0.209***

(7.63)

(−4.86)

(6.09)

(−5.01)

(5.89)

(−6.18)

Liqval(10−4 )

−9.20***

−1.09*

−0.509***

−0.189***

−0.789***

−0.132*

(−4.40)

(−1.66)

(−3.12)

(−3.45)

(−3.98)

(−1.903)

_cons

15.59***

9.908***

10.12***

5.909***

16.53***

10.23***

(19.11)

(27.86)

(22.65)

(17.63)

(32.45)

(40.12)

adj. R2

0.343

0.316

0.290

0.215

0.306

0.406

44.47

52.45

48.09

89.54

106.2

F Note

26.86 *p

< 0.10,

** p

< 0.05,

*** p

< 0.01, and t-values are given in parentheses

34.5 Conclusion In China’s listed companies, information transparency is mostly at a good level, but the quality of information disclosure still needs to be improved, to provide a more transparent market environment for investors [19]. This paper uses A-share listed companies in Shanghai and Shenzhen from 2011 to 2021 as research samples to analyze the relationship between the comprehensive information transparency and trader behavior and information asymmetry. Heuristic method was also used to assign the model parameters and perform sensitivity analysis. The results prove that the meta-model constructed in this paper is valid. This paper focuses on the influence of comprehensive information transparency on information asymmetry, and further studies the traders as an important part of the market, the study found that comprehensive information transparency can significantly reduce information asymmetry, although the trader behavior in the process of

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transparency is not obvious, the sensitivity of traders’ behavior to market parameters than the market value to parameters. Corporate information transparency has always been a hot topic in the field of accounting research which is of great significance to China’s capital market. Highquality information disclosure can greatly reduce the degree of information asymmetry, which can effectively guide investors to make investment decisions, so as to improve the efficiency of the capital market. More transparent information disclosure can attract more attention from market participants and improve the accuracy of traders’ behavior. However, the higher the trader behavior preference value, the higher the information asymmetry, so it is not obvious in improving the information asymmetry.

References 1. Bergh, D.D., Ketchen, D.J., Jr., Orlandi, I., et al.: Information asymmetry in management research: past accomplishments and future opportunities. J. Manag. 45(1), 122–158 (2019) 2. Bushman, R.M., Smith, A.J.: Financial accounting information and corporate governance. J. Account. Econ. 32(1–3), 237–333 (2001) 3. Tiberto, B.P., Moraes, C.O., Correa, P.P.: Does transparency of central banks communication affect credit market? Empirical evidence for advanced and emerging markets. N. Am. J. Econ. Finan. 53, 101207 (2020) 4. Grassmann, M., Fuhrmann, S., Guenther, T.W.: Assurance quality, disclosed connectivity of the capitals, and information asymmetry–an interaction analysis for the case of integrated reporting. Meditari Accountancy Res. (2021) 5. Jacoby, G., Liu, M., Wang, Y.: Corporate governance, external control, and environmental information transparency: evidence from emerging markets. J. Int. Finan. Markets. Inst. Money 58, 269–283 (2019) 6. Ryu, D., Yang, H., Yu, J.: Insider trading and information asymmetry: evidence from the Korea exchange. Emerg. Mark. Rev. 51, 100847 (2022) 7. He, C., Lu, Z.: Research on revere-selection cost in Chinese stock market. J. Econ. Res. 2, 68–80 (2009) 8. Lin, J.C., Sanger, G.C., Booth, G.G.: Trade size and components of the bid-ask spread. Rev. Finan. Stud. 8(4), 1153–1183 (1995) 9. Glosten, L.R., Harris, L.E.: Estimating the components of the bid/ask spread. J. Finan. Econ. 21(1), 123–142 (1988) 10. Qu, W., Xie, Y.L., Gao, J.X.: Information asymmetry, liquidity and equity structure-an empirical research based on Shenzhen securities market. J. Nankai Manage. Rev. 14(1), 44–53 (2011) 11. Murali, K.: Market access and issues of data gaps and transparency and information asymmetry: a case of RCEP negotiations. Available at SSRN. 3555910 (2020) 12. Chod, J., Lyandres, E.: A theory of ICOs: diversification, agency, and information asymmetry. Manage. Sci. 67(10), 5969–5989 (2021) 13. Bhattacharya, U., Daouk, H., Welker, M.: The World price of earnings opacity. Account. Rev. 78(3), 641–678 (2003) 14. Francis, J.: Cost of equity and earning attributes. Account. Rev. 79(4), 967–1010 (2004) 15. Durnev, A., Errunza, V., Molchanov, A.: Property rights protection, corporate transparency, and growth. J. Int. Bus. Stud. 40(9), 1533–1562 (2009) 16. Llorente, G.: Dynamic volume-return relation of individual stocks. Rev. Finan. Stud. 15(4), 1005–1047 (2002)

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17. Liu, S., Peng, X.: Fair information disclosure and trader behavior-empirical evidence from Chinese listed journal of companies. Secur. Market GuideRep. 3, 33–38 (2012) 18. Naqvi, S.K., Shahzad, F., Rehman, I.U.: Corporate social responsibility performance and information asymmetry: the moderating role of analyst coverage. Corp. Soc. Responsib. Environ. Manag. 28(6), 1549–1563 (2021) 19. Kucherova, H., Didenko, A., Kravets, O.: Scenario forecasting information transparency of subjects under uncertainty and development of the knowledge economy. In: CEUR Workshop Proceedings (2020)

Chapter 35

Implementation of Business Data Sharing Based on Blockchain and Improvement of Consensus Algorithm Biying Zhang, Bowen Zhang, and Lei Zhang

Abstract Due to different data categories, there may be great differences between data, and it is difficult to have a unified sharing model, so most of the research is on the sharing mechanism or sharing model of some specific data using blockchain. However, in the field of commercial data sharing, few people pay attention to commercial data sharing. At the same time, the PoW algorithm has very high security, but there are some problems at the same time, such as low efficiency, and the emergence of ASIC mining machines dedicated to mining, which wastes a lot of resources. Therefore, this paper improves the data-sharing model and implements it on the consortium blockchain. At the same time, based on the existing proof of learning (PoLe) consensus algorithm, the pre-training of the next task is carried out in the verification and reward process, and the block speed is increased under the premise of ensuring the security of the original algorithm.

35.1 Introduction In recent years, big data has become the focus of the pursuit and development of various Internet companies and other industries. Combined with the concepts of autonomous driving, artificial intelligence, blockchain, distributed computing, industrialization 4.0, and so on has formed an information-crazy flow sweeping the streets. The era of “big data” was first proposed by McKinsey Global Institute (MGI) [1], a world-renowned consulting company. Nowadays, big data has penetrated into our various daily lives and penetrated into people’s clothing, food, housing and transportation. At the same time, the massive data generated by people’s daily life has a quite high value, and its analysis and application can improve the quality of people’s lives and the production efficiency of enterprises. Big data has the following characteristics [2]: (1) volume, (2) velocity, (3) variety, and (4) value. B. Zhang (B) · B. Zhang · L. Zhang Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_35

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Business data, as a data form of big data, has its unique characteristics, but it still has the problem of data island. For the business data between business alliances, because it is limited by business secrets and data security, there is little communication and exchange between enterprises, which leads to a huge waste of resources in the industry. How to maintain enterprise secrets while simultaneously sharing and controlling data access through data networking? Reducing repeated resource investment and ineffective competition at low levels has become an urgent problem to be solved in the industry. In summary, the main contributions of this paper are summarized as follows. (1) Based on the existing commercial data-sharing model, it is improved and implemented on the Hyperledger consortium chain. (2) The learning-based consensus algorithm (Pole) is improved to the proof of learning of preliminary train (Pole-Pre-Train) consensus algorithm. Pole consensus algorithm spends a certain amount of time in verifying the legitimacy of blocks, and Pole-Pre-Train uses this time to pre-train the next task to improve the consensus speed of the blockchain.

35.2 Related Research Lucas et al. [3] designed a blockchain-based lightweight storage and sharing scheme for the disadvantages of traditional smart grids, such as privacy exposure. Manzoor et al. [4] proposed a blockchain-based IoT data-sharing market to solve the scalability and trust problems of IoT data sharing. Saxena et al. [5] used blockchain technology to achieve a secure data-sharing scheme for smart home network security in the Internet of Things (IoT). Jiang et al. [6] proposed a blockchain-based data-sharing scheme for vehicular social networks (VNS) aiming at the security and privacy of data sharing in vehicular ad hoc networks (VANETs). Jeong et al. [7] proposed a blockchain-based vehicle data market platform model and data-sharing scheme for the “connected car” problem. Lucas et al. [3] combined blockchain technology with scheduling coordination and disaster recovery in the power grid to realize the functions of disaster recovery and data-sharing coordination. Chi et al. [8] aiming at the security and efficiency of data sharing in the industrial Internet of Things, proposed a secure and efficient data-sharing framework based on identity authentication and Hyperledger. Kakkar [9] combined the fifth generation (5G) communication network with the blockchain-based scheme to propose a safe and reliable data-sharing scheme for autonomous vehicles. Chenli [10] collects and stores valid shared records in the blockchain by tracking and recording the history of shared data. Lee [11] can ensure the correctness of medical data and the privacy of patients by using blockchain, while the integrity of specific patients can be realized through smart contracts, effectively eliminating the problem of patient identity leakage. Christine [12] proposed a blockchain-based energy trading model. Rahul et al. [13] proposed a secure transaction model based on blockchain.

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35.3 Data-Sharing Model As shown in Fig. 35.1, the whole shared system consists of two parts: (1) the data transaction part, including the data link, data search, and transaction process; (2) the PoLe-Pre-Train consensus algorithm. The data transaction part is divided into ➀ data link, ➁ data search, and ➂ transaction process. ➀ Datalink: Firstly, the generation of the user’s private key randomly generates a 256-bit random number (0, 1), which is displayed in hexadecimal. The public key is generated by the elliptic curve encryption algorithm of the private key. Users upload their own metadata. The metadata is specified as follows: (1) The

Fig. 35.1 Data-sharing model

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hash value of the specific data is obtained by using SHA256 for the summary. (2) Data types. (3) Data description. (4) Data size. (5) Who owns the data? (6) Other. ➁ Data search: You can search for data by keyword. ➂ Transaction process: The user queries the required data and initiates the smart contract of the transaction. The seller encrypts the data with the buyer’s public key, and the buyer decrypts the data with his private key to obtain the original data.

35.4 PoLe-Pre-Train This algorithm uses the time wasted in the process of verifying the legitimacy of blocks and rewarding in the PoLe consensus algorithm to pre-train the next task. The publisher structure is obtained according to the task name (task name is unique) as the key. This structure stores error information (err), pre-consensus model (preModel), pre-consensus model accuracy (preMAcc), final model (ENDModel), final model accuracy (ENDModelAcc), public key written to preModel node (PubKeyMod), and public key written to final model node (ENDKeyMod). Four flag bits of the whole consensus algorithm are specified. ➀ Flag = train: This flag bit represents that the node is in the training state. ➁ Flag = reward: This bit indicates a reward state. ➂ Flag = verify: This bit indicates that the state is in the verified transition. ➃ Flag = END: This bit indicates the end of the reward algorithm.

35.4.1 Pre-train Algorithm 4.1 Pre-train Input: Publisher;taskArr 1: task = taskArr [1] 2: while True do 3:

if ! flag = end then

4:

train model

5:

Publisher (preModel, PubKeyMod) ← TX (Model, Pubkey)

6: 7: 8:

else Train() end if

9: end while

TaskArr is the task list, where the first element, taskArr [9], is the task with the highest reward.

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Algorithm flow: Take the first training task as an example. Take the next training task from the task list (line 1). Determine if the node is in a reward or validation state, and if so, pre-trains the next task and writes it to the publisher struct (lines 2–5). If flag = end then the reward algorithm is finished and the train () algorithm is executed (lines 6–8).

35.4.2 Train The training process of this algorithm is to take out the pre-trained model for training. Finally, the corresponding flag bit is set when executing reward and verify. Algorithm flow: Get the task accuracy and the maximum task time, and change the flag to train. Get the current time and the pre-trained model, determine whether the maximum training time is reached, if the maximum training time is reached, determine whether the final model exists, if not, write the model and model accuracy, then execute the reward function. It determines whether the training accuracy reaches the highest accuracy. If the highest accuracy requirement is reached, the model, model accuracy, and node public key are written into the final model. If the test set is received, the training is stopped, and if the final model does not exist at this time, the final model and the model accuracy are written. If the final model exists, the training is stopped.

35.4.3 Reword Algorithm 4.3 Reword Input: Publisher;taskArr 1: tAcc = taskArr[0.tReqAcc] 2: PubKeyMod = Publisher.PubKerMod 3: ENDKeyMod = Publisher.ENDKeyMod 4: ENDModektAcc = Publisher.EndModelAcc 5: if ENDModelAcc ≥ tAcc then 6:

PubKeyMod(ENDKeyMod) ← TX(0.01 × reword)

7:

ENDKeyMod ← TX(0.99 × reword)

8: else 9:

ENDKeyMod ← TX(0.5 × reword)

10: end if 11: taskArr.pop 12: flag = end

(1) If the model that meets the accuracy requirement is trained within the specified time, the node of the pre-training model will be given 1% reword, and the winning node will get 99% reword (lines 5–7).

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(2) If no one reaches the required accuracy within the specified time, 50% reword will be issued to the model with the highest accuracy (lines 8–10).

35.4.4 Security Analysis (1) Analysis of Sybil Attack Scenario: Sybil Attack, which is to gain control of the blockchain by forging multiple ids to take control of the accounting rights vote. Analysis: PoLe-Pre-Train and PoLe consensus algorithms both rely on computing power to vote, that is, train machine learning models to obtain accounting rights. So, using a witch attack is expensive. (2) Other Attack Analysis Scenario 1: The model trainer gets a reward by training the test set prepared in advance and reducing the number of model training. Analysis: 20% of the entire dataset is randomly selected as the test set of the model, and the rest is the training set. And once the test set is released, other uploaded models are rejected. Scenario 2: The model trainer uses the dataset in advance to train the model in order to get the reward one step earlier. Analysis: The dataset is encrypted and on-chain. The hash value of the previous block is used for encryption to ensure that the data set will not be leaked in advance. In general, the idea of the PoLe consensus algorithm is to use the wasted computing power of a proof-of-work-based consensus algorithm to train a machine learning model. On the basis of the PoLe consensus algorithm, the PoLe-Pre-Train consensus algorithm uses its verification and reward time to pre-train the next model, so as to improve the blockchain consensus speed.

35.5 Experiment 35.5.1 Experimental Environment Machines used: AMD Ryzen 7 4800H 2.90 ghz, RAM 16.0 GB, VMware Workstation Pro is used to create VMS, and Hyperledger Fabric Consortium Blockchain are set up on the VMS.

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Hyperledger Fabric is specifically contains four peer nodes, one Ordered node, and one CLI client. There are two orgs (organizations), and one org has two peer nodes. The Hyperledger Fabric Consortium has no transaction fees or gas concepts, and its smart contracts run in docker containers. Start the Fabric network, install and launch the Hyperledger Fabric performance monitoring tool Hyperledger Explorer, and install the Hyperledger Fabric performance testing tool Tape. Tape can be used to stress test a smart contract.

35.5.2 Training Script The dataset is MNIST and the number of training sets: 55,000. The number of validation sets: 5000. Number of test sets: 10,000. Dimension description of each layer (excluding batch): input layer (28 × 28 × 1), the output of convolutional layer 1 (28 × 28 × 32) (32 filters), the output of pooling layer 1 (14 × 14 × 32), the output of convolutional layer 2 (14 × 14 × 64) (64 filters), the output of pooling layer 2 (7 × 7 × 64), the output of fully connected layer 1 (1 × 1024), and the output of fully connected layer 2 with softmax (1 × 10).

35.5.3 Experiment Content For this experiment, each node is trained repeatedly on the MNIST dataset (which is not actually allowed). The maximum training time is set to 320 s based on the data from the simulation training. A task list of 30 tasks (taskArr) is created based on the task information entered in advance. Among them, pubTask is all trained on the MNIST dataset, tModel is a machine learning training script, tTimeMax is set to 320 s, tReqAcc is set to 90%, and reword is successively increased from 1 to 30. Business Data Sharing. Start Hyperledger Fabric and Hyperledger Explorer (Figs. 35.2 and 35.3). Data link.

Fig. 35.2 Hyperledger fabric

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Fig. 35.3 Hyperledger explorer

Fig. 35.4 Data link

CRD is commodity review data (Fig. 35.4). Data trading (Fig. 35.5). Query (Fig. 35.6). PoLe-Pre-Train For 30 training tasks, the block time of PoLe and PoLe-Pre-Train is shown in Fig. 35.7, and the block time statistics are given in Table 35.1. Theoretically, PoLePre-Train uses the time wasted by the PoLe consensus algorithm to pre-train the next task. Since the time consumed in the verification and reward stages is not much, the block generation time should be slightly less than the block generation time of PoLe. Experiments show that the average block generation time of PoLe-Pre-Train is slightly less than that of PoLe, which is in line with the experimental expectation.

Fig. 35.5 Data trading

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Fig. 35.6 Query

Fig. 35.7 Block generation time of PoLe and PoLe-Pre-Train

Table 35.1 Block generation time statistics

Algorithm

Average

Max

Min

PoLe

278.51

320

208.94

PoLe-Pre-Train

261.40

320

179.96

35.6 Conclusion The PoLe consensus algorithm uses the computing power wasted by the PoW consensus algorithm to train the machine learning model, but it has some problems. This paper improves the commercial data-sharing model and implements the PoLe consensus algorithm on the Hyperledger consortium blockchain, and improves it. Experiments show that PoLe-Pre-Train improves the block generation speed to a certain extent on the basis of ensuring the security of the original algorithm.

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Acknowledgements This work was supported by the Science and Research Project of Harbin University of Commerce (2019DS032).

References 1. Mei, Y.: Data mining and application in the era of big data. Netw. Sec. Technol. Appl. 06, 51–52 (2021) 2. Emmanuel, I., Stanier, C.: Defining big data. In: Proceedings of the International Conference on Big Data and Advanced Wireless Technologies, pp. 1–6 (2016) 3. Lucas, A., Geneiatakis, D., Soupionis, Y.: Blockchain technology applied to energy demand response service tracking and data sharing. Energies 14(7), 1881 (2021) 4. Manzoor, A., Braeken, A., Kanhere, S.S.: Proxy re-encryption enabled secure and anonymous IoT data sharing platform based on blockchain. J. Netw. Comput. Appl. 176, 102917 (2021) 5. Saxena, U., Sodhi, J.S., Tanwar, R.: Augmenting smart home network security using blockchain technology. Int. J. Electron. Secur. Digit. Forensics 12(1), 99–117 (2020) 6. Jiang, Y., Shen, X., Zheng, S.: An effective data sharing scheme based on blockchain in vehicular social networks. Electronics 10(2), 114 (2021) 7. Jeong, B.G., Youn, T.Y., Jho, N.S.: Blockchain-based data sharing and trading model for the connected car. Sensors 20(11), 3141 (2020) 8. Chi, J., Li, Y., Huang, J.: A secure and efficient data sharing scheme based on blockchain in industrial internet of things. J. Netw. Comput. Appl. 167, 102710 (2020) 9. Kakkar, R., Gupta, R., Agrawal, S.: Blockchain-based secure and trusted data sharing scheme for autonomous vehicle underlying 5G. J. Inform. Sec. Appl. 67, 103179 (2022) 10. Chenli, C., Tang, W., Gomulka, F.: ProvNet: networked bi-directional blockchain for data sharing with verifiable provenance. J. Parallel Distrib. Comput. 166, 32–44 (2022) 11. Lee, J.S., Chew, C.J., Liu, J.Y.: Medical blockchain: data sharing and privacy preserving of EHR based on smart contract. J. Inform. Sec. Appl. 65, 103117 (2022) 12. Christine, S.: Research on distributed energy transaction based on blockchain. Int. J. Smart Bus. Technol. 7(2), 15–26 (2019) 13. Rahul, J., Anurag, S.P.: BLAST: blockchain algorithm for secure transaction. Int. J. Sec. Appl. NADIA 13(4), 59–66 (2019)

Chapter 36

Knowledge Structure and Research Progress in Mental Workload (MWL) Using CiteSpace Based on Bibliometric Analysis Yingying Wei , Huimei Qu, Song Wang, and Changdong Xu

Abstract As a key topic of human–machine interface (HCI), improving mental load (MWL) level has important practical significance. This paper conducted a bibliometric analysis of 2165 mental workload-related articles and their references published from 2010 to 2021. The most influential MWL research publications/citations and their findings were identified. Through co-citation analysis, this study identified and explained the main research topics, and used keywords co-occurrence analysis to further identify knowledge groups and future research directions. Compared with the existing literature review studies, this research uses information bibliometric methods to conduct an objective visual analysis of the literature review. In this process, the core documents with high influence have been identified and the visualization map of MWL knowledge clustering is constructed, which is of great significance for future research.

36.1 Introduction With the development of HCI, the role of humans tends to be the decision-maker and supervisor of the system, often in highly concentrated mental work. Therefore, mental workload (MWL) has become the most widely used concept in ergonomic research and practical applications. Mental workload depends on the task requirements and the resource supply. Studies have shown that a high MWL can lead to fatigue, decreased

Y. Wei (B) · H. Qu · S. Wang · C. Xu College of Management, East University of Heilongjiang, Harbin, China e-mail: [email protected] Y. Wei College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_36

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flexibility, frustration, and increased error rates. The low MWL can lead to drowsiness, disgust, and decreased human performance [1]. Therefore, the measurement, evaluation, and prediction of MWL are important topics in ergonomics. In the past ten years, the rapid growth in the number of literary studies has proven the extensive development of MWL knowledge systems. To improve safety and work efficiency, MWL has been discussed in many areas, including air traffic control [2–5], nuclear power plant monitoring room, military action, driving operation and surgery, etc. In 2020, there were 297 articles published in the whole year, an increase of nearly four times compared with 79 articles in 2010. In the past 11 years, the United States has been the main contributor to MWL research, publishing a total of 590 articles (27%), followed by China (21%), Germany (8%), Japan (7%), and France (7%). From 2010 to 2021, the proportion of MWL’s annual publication volume and the publication volume of the world’s top ten leading countries is shown in Fig. 36.1. So far, the journals and articles included in the research are usually predetermined by the researchers, regardless of the impact of the research on the literature [6]. The dynamic nature of the research process makes scientists and policymakers face enormous challenges. Understanding the evolution of research frontiers is particularly important for scientists, analysts, and makers to identify new trends and sudden

Fig. 36.1 Enhancement of mental workload: a Annual number of papers published by MWL from 2010 to 2021, b Percentage of papers issued by the top ten countries

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changes in scientific development. However, the literature review is a useful means of identifying the content of the research field and helps to identify the most popular research fields in the article within a certain period. Potential changes in themes can be captured through this change of popular words and can provide theoretical guidance for future research [7]. A comprehensive literature review of the dimensionality and accuracy of MWL studies can be conducted by drawing knowledge maps using bibliometric analysis software. Therefore, we used bibliometric research methods in this study to visualize the knowledge background, research state, and knowledge structure state of MWLrelated literature, identifying the most prolific publications and research areas related to the booming MWL research.

36.2 Mental Workload Theory During the design and evaluation of complex information systems in society, MWL evaluation is very important. The complexity and diversity of the information presented in the system put forward higher requirements for the operator. The human brain has limited attention capacity, which causes these attention resources to be preempted by multiple channels [8]. Just as physical workload represents the task’s demand for muscle power, MWL represents the task’s demand for limited brain information processing capabilities [9]. Figure 36.2 shows the impact of MWL in the process of human–computer interaction, that is, MWL is affected by internal factors (including personal ability, training, experience, pressure, and personality, etc.), external factors (including several tasks, task complexity, task constraints, time pressure, etc.), and environmental factors (lighting, swing, noise, ventilation, etc.). There will be a phenomenon of underload or overloading mental effort [10]. Therefore, engineering psychologists and designers are interested in predicting when demand will exceed supply and performance will decline, and what remedial measures will be used when the overload occurs. The evaluation or measurement of MWL not only runs through the entire design life cycle to improve the design of systems and tasks but also applies to work performance evaluation during the operation of existing systems. Although the MWL is qualitative, researchers are still trying to find measurable standards on a multidimensional basis to quantify this phenomenon. Researchers usually use subjective rating evaluation, objective performance measurement, and physiological measurement to evaluate and predict the MWL of operators.

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Fig. 36.2 Basic principles affecting MWL and its evaluation methods

36.3 Materials and Methods 36.3.1 Bibliometric Study of MWL This study mainly uses literature co-citation analysis (DCA) and burst detection analysis (BDA) [11], and used ISI web of science (WoS) as the data source. The specific databases used are Science Citation Index Expanded (SCIE), Conference Proceedings Citation Index-Science (CPCI-S), Emerging Sources Citation Index (ESCI), and Social Sciences Citation Index (SSCI). The data has selected the topic = “mental workload” from January 1, 2010 to December 31, 2021. A total of 2165 complete records of articles are collected.

36.3.2 Web of Science (WoS)-Based Investigation As shown in Fig. 36.3, the top 20 journals with academic achievements in research are related to MWL, as well as the number and percentage of published articles. Among them, Human Factors published the most articles (107), followed by Ergonomics (102), Applied Ergonomics (62), Lecture Notes in Computer Science (61), Lecture

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Fig. 36.3 Top 20 journals with academic achievements in research related to MWL

Notes in Artificial Intelligence (57), International Journal of Industrial Ergonomics (50) et al. Most journals mainly focus on human factors ergonomics related workload, emotion, stress level and other measurement methods, as well as the use of artificial intelligence methods to evaluate and identify psychological workload [12–14], which shows that research related to MWL is mainly focused on personnel performance.

36.4 Results and Discussion 36.4.1 Document Co-citation Analysis (DCA) The citation frequency of a particular article can be used to measure its impact on the research field. The higher the common citation frequency of the document, the greater the value of the document. Therefore, DCA is an effective method to discover the knowledge structure of the proposed research field [11]. Cite Space was used to conduct document co-citation analysis (Time slicing: 2010–2021; Note types: Reference; Selection criteria: g-index k = 25; Pruning: Pathfinder & Pruning sliced networks) and drew the visualization network described in Fig. 36.4, including

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Fig. 36.4 MWL document co-citation visualization network

683 nodes and 1610 links. The node link indicates the common citation relationship between the cited documents. The color of the nodal ring changes from cool to warm in chronological order. The thickness of an annual ring represents the importance of a document, and its thickness is proportional to the number of references to a particular time zone. The most influential knowledge areas include physiological measurement, the impact of stress and task complexity, multi-index evaluation methods, and the improvement of human–computer interaction. On the one hand, Ayaz [15] mainly compares and evaluates the operator’s MWL when performing standardized tasks and complex cognitive tasks. Young are more inclined to focus on the quantitative evaluation research of the workload “red line” in complex safety systems, such as transportation, control, etc. [16]. On the other hand, with the rapid development of neuroengineering and brain-computer interface (BCI), physiological measurement methods have become the most prominent research field in MWL-related research [17]. They mainly studied the neurophysiological findings related to human work performance and behavior measurement of operators during operational tasks, and how these sensitivity indicators are related to the concept of mental fatigue or situational awareness [18].

36.4.2 Keyword Co-occurrence Analysis Keywords reflect the important core information of the article. Keyword cooccurrence analysis helps identify the most popular words used over time. The changes in these popular words can capture the changes in potential research topics and field hotspots [19, 20]. Keyword co-occurrence analysis was performed on

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Cite Space (Time slicing: 2010–2021; Note types: Keyword; Selection criteria: g-index k = 25; Pruning: Pathfinder & Pruning sliced networks & pruning the merged network) and plotted in Fig. 36.5. The visual network described contains 525 nodes and 2171 links. Each node represents a keyword, and the thickness of the annual ring represents the frequency of occurrence of the keyword. Most of the keywords that appear frequently are close to each other. Among them, co-occurrence frequency and centrality are two important indicators of keyword co-occurrence analysis. Co-occurrence frequency mainly reflects the citation counts, and centrality mainly reflects the closeness between link nodes. Nodes with intermediate centrality exceeding 0.1 are called position key nodes and have an important pivotal role. Centrality mainly reflects the closeness between link nodes. Nodes with an intermediary centrality exceeding 0.1 are called position key nodes and have an important pivotal role. The keywords most frequently appearing for MWL based on frequency are “performance (501)”, “task (248)”, “EEG (198)”, “stress (174)”, and “attention (133)”, etc. These keywords show the main research hotspots in the MWL field. The keywords with centrality > 0.1 are “adaptive automation (0.13)”, “physiological response (0.12)”, “efficiency (0.12)”, “activation (0.12)”, “communication (0.11)”, “cognitive performance (0.11)”, “human performance (0.11)”, “association (0.1)”, “decision-making (0.1)”, “safety (0.1)”, and “impact (0.1)”, which show the difference from other nodes or the importance of the network structure between clusters.

Fig. 36.5 Knowledge mapping of the keyword co-occurrence network on MWL-related studies

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The keyword “performance” is prominent in the MWL study, with a frequency of 501. Due to the constant emphasis on work safety, operators are required to exert more stable performance. In recent years, performance failures may lead to catastrophic losses, for example, in the case of nuclear power plant monitoring, military command and control and air traffic control.

36.4.3 Hotspots and Emerging Trends of MWL The dynamic nature of the research frontier makes people who pursue the rapid development of science face great challenges. Understanding the dynamic mechanism of the evolution of the research frontier is particularly important for researchers to identify new trends and mutations in scientific development. According to Whitaker [19], the keywords with a high quoted burst rate are milestones in finding future trends. In the development of the field, burst detection is an effective and reliable text mining method, which can identify burst words as the prediction of the frontier theme and emerging trends. Table 36.1 shows the top 20 keywords with the strongest burst. “Deep learning” has been ranked first since 2019 with 5.22 and is the strongest theme for future development in 2010–2021. Among them, “Deep learning”, “Convolutional neural network”, “Eye tracking”, and “Electroencephalography” are four major emerging topics. It represents a new Table 36.1 Top 20 keywords with the strongest burst No. Keywords Begin 1 Management 2010 2 Adaptive automation 2011 3 Training 2011 4 Time pressure 2011 5 Simulator training 2012 6 Physiological index 2013 7 Psychophysiology 2014 8 Human-computer interaction 2014 9 Cognitive control 2016 10 Simulation 2016 11 Cognitive ergonomics 2016 12 Prediction 2016 13 Recognition 2017 14 Reaction time 2017 15 Display 2018 16 Signal 2018 17 Deep learning 2019 18 Convolutional neural network 2019 19 Eye tracking 2019 20 Electroencephalography 2019

End 2013 2014 2016 2012 2014 2015 2017 2015 2017 2019 2018 2017 2019 2018 2019 2021 2021 2021 2021 2021

2010 - 2021

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trend of development and a frontier field worthy of attention, and constantly affects the development of the field of MWL. Deep learning is a robust artificial intelligence algorithm that imitates the human brain’s information processing patterns and distributed communication node structure for data screening, diagnosis, and prediction. At present, several deep learning architectures have used physiological signals to predict workload levels. Eye tracking is a technique that typically uses infrared imaging to track where a user is looking on a display screen. It can be used to track approximate gaze positions and is often used as a means of understanding visual attention distribution. Eye-tracking technology has been proven to be able to reflect changes in MWL. The most commonly used eye-tracking indicators in workload assessment are pupil diameter, blink frequency and fixed time, etc. Electroencephalogram (EEG) is an experimental method of recording brain activity. Brain activity is also an important indicator of MWL evaluation. It has been proved that the relevant frequency bands of EEG signals are sensitive to the changes of MWL, including delta δ (1–3 Hz), theta 8 (4–7 Hz), alpha α (8–12 Hz), beta β (13–30 Hz), and gamma γ (31–80 Hz).

36.5 Conclusions This paper focuses on identifying the most influential references in MWL research. Different from the existing subjective literature review in the field of HCI, our research uses the objective quantitative analysis method of bibliometrics as the evaluation standard of key articles, which provides innovative insights and references for future researchers to promote the development of MWL research. In this process, the highly influential articles identified by literature co-citation analysis represent the core literature and knowledge structure of MWL. These key references play an important role in promoting the MWL literature. Through keyword co-occurrence and burstiness analysis during 2010–2021, we also identified some important trends and opportunities for MWL research. More research work will be devoted to the use of EEG, eye tracking, and other physiological signal technologies in the study of MWL and their deep learning applications. In general, through information visualization, researchers can identify the research status and hotspots of MWL, explore the latest trend of future research in this field, greatly enhance the understanding of MWL, and look forward to more in-depth research in this area. Acknowledgements This work was supported by the grants of Heilongjiang Province Higher Education Teaching Reform General Project (Project No. SJGY20210740, SJGY20200579), Key Topic of the 14th Five-Year Plan for Heilongjiang Province Education Science (Project No. GJB1422479), East University of Heilongjiang Scientific Research and Innovation Team Building Project (Project No. HDFKYTD202108).

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References 1. Wilson, G.F.: Operator Functional State Assessment for Adaptive Automation Implementation, vol. 5797, pp. 100–104 (2005) 2. Park, S., Jeong, S., Myung, R.: Modeling of multiple sources of workload and time pressure effect with ACT-R. Int. J. Ind. Ergon. 63, 37–48 (2018) 3. Sauvet, F.: In-flight automatic detection of vigilance states using a single EEG channel. IEEE Trans. Biomed. Eng. 61(12), 2840–2847 (2014) 4. Qiao, H.: Exploring the peak-end effects in air traffic controllers’ mental workload ratings. Hum. Factors 64, 1292 (2021) 5. Bernard, F.: Physical and cognitive dimensions: towards a necessary consideration in aviation maintainability. Arch. Des Maladies Professionnelles Et De L Environnement. 82(2), 170–183 (2021) 6. Koons, G., Schenke-Layland, K., Mikos, A.: Why, when, who, what, how, and where for trainees writing literature review articles. Ann. Biomed. Eng. 47(11), 2334–2340 (2019) 7. Sebastian, Y., Siew, E.G., Orimaye, S.O.: Emerging approaches in literature-based discovery: techniques and performance review. Knowl. Eng. Rev. 81(8), 1–35 (2017) 8. Wickens, C.D.: Multiple resources and mental workload. Hum. Factors 50(3), 449–455 (2008) 9. Eggemeier, F.T., et al.: Workload assessment in multi-task environments. In: Multiple Task Performance, pp. 207–216 (1991) 10. Parasuraman, R., Sheridan, T.B., Wickens, C.D.: Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. J. Cogn. Eng. Decis. Making 2(2), 140–160 (2008) 11. Chen, C.: The thematic and citation landscape of data and knowledge engineering (1985–2007). Data Knowl. Eng. 67, 234–259 (2008) 12. Lim, W.L.: EEG-based mental workload and stress monitoring of crew members in maritime virtual simulator. In: Gavrilova, M.L., Tan, C.J.K., Sourin, A. (eds.) Transactions on Computational Science Xxxii: Special Issue on Cybersecurity and Biometrics, pp. 15–28. Springer International Publishing Ag, Cham (2018) 13. Li, S., Zhang, T., Zhang, W., Liu, N., Lyu, G.: Effects of speech-based intervention with positive comments on reduction of driver’s anger state and perceived workload, and improvement of driving performance. Appl. Ergon. 86, 103098 (2020). https://doi.org/10.1016/j.apergo.2020. 103098. Epub 2020 Mar 13. PMID: 32174447 14. Wilson, M.D.: Understanding fatigue in a naval submarine: applying biomathematical models and workload measurement in an intensive longitudinal design. Appl. Ergon. 94, 103412– 103412 (2021) 15. Ayaz, H.: Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59(1), 36–47 (2012) 16. Young, M.S.: State of science: mental workload in ergonomics. Ergonomics 58(1), 1–17 (2015) 17. Parasuraman, R.: Neuroergonomics: brain, cognition, and performance at work. Curr. Directions Psychol. Sci. 20(3), 181–186 (2011) 18. Charles, R.L., Nixon, J.: Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221–232 (2019) 19. Whittaker, J.: Creativity and conformity in science: titles, keywords and co-word analysis. Social Stud. Sci. 19, 473–496 (1989) 20. Synnestvedt, M., Chen, C., Holmes, J.: CiteSpace II: visualization and knowledge discovery in bibliographic databases. In: AMIA. Annual Symposium Proceedings/AMIA Symposium. AMIA Symposium, pp. 724–728 (2005)

Chapter 37

Predicting Social Events Using Analyses of Arabic Dailies Renata Avros, Dan Lemberg, Elena V. Ravve , and Zeev Volkovich

Abstract The research is devoted to a novel method for a real-time prediction of significant discontinuities in social states using the automatic analyses of Arabic dailies’ overall logical structures. The paper introduces the novel, named the superfrequent N-gram approach and the Regression Mean Rank Dependency characteristic, and presents their arrangement with a new model. It makes it possible to reliably forecast social changes based on the dailies’ semantic content’s high-level repercussions. An evaluation of the approach on four prominent Arabic dailies demonstrates its high ability to reflect changes in the social state and particularly to reveal significant events of the “Arab Spring” even though they took place in countries other than the newspaper’s hosting land. The study shows that the resulting N-gram models, constructed for the different dailies, correspond, in general, to different sizes of N and are capable together of providing a more vital prediction tool. The methodology consistently succeeds in predicting substantial changes in the social state in an online simulation fashion.

37.1 Introduction The so-called mediated culture phenomenon is an outstanding aspect of the modern age. Many kinds of mass media mirror the people’s intentions and comprehend specific themes via the massive unstructured documents amounts, distributed in the R. Avros · D. Lemberg · E. V. Ravve · Z. Volkovich (B) Braude College, Karmiel, Israel e-mail: [email protected] R. Avros e-mail: [email protected] D. Lemberg e-mail: [email protected] E. V. Ravve e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_37

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virtual space. Monitoring such activity is crucial for each multimedia system and turns it into a proper forecasting instrument in various fields like election results, cf. [1, 2], crime activity, cf. [3, 4], social events, cf. [5–7] asf. As usual, intelligent summarizations provided for this purpose are created, lacking any assay of the linguistic matter of the documents since the handled corpus is typically associated with a collectively created resource, created by informal language with genuine content. For example, methodologies intended to predict stock market prices often employ text mining methods cf. [8] and use data from many sources. For this reason, the linguistic information contained in a studied text is typically disregarded. In this paper, we suggest a new approach for the online prediction of significant changes in the social state through alterations in the linguistic content with a case study of an Arabic daily. A method of modeling traditional Arabic media was previously proposed in [9] and then applied to studying the English literary writing style, cf. [10], based on a dynamic replica of the human writing process. The proposed methodology presents each text issue as a histogram of the suitably chosen N-grams occurrences inside the famous Vector Space Model [11]. The critical notation pioneered in this way is the Mean Rank Dependency, designed as the mean Spearman’s correlation among the current histogram issue and several previously published ones. This measure reveals a time series display of the publishing process. If two or more predictor variables are highly correlated, then the estimation of the regression coefficients can be unstable. A probable source of this instability is the inherent dependency between the precursors’ media issues. An analogous problem, named the multicollinearity phenomenon, occurs in the multiple regression methodology. Many methods have been recommended to deal with this problem. Among others, the Ridge regression, the shrinkage estimators, the LASSO method, and approaches based on the Principal Component Analysis can be mentioned, cf. [12]. The most famous methodology, the Principal Component Regression (a regression model based on the Principal Component Analysis), is used in this paper. The dailies under consideration are accumulated, and their new time series version is composed, resting upon the super-frequent N-grams model introduced in the current paper and the Regression Mean Rank Dependency. Instead of directly evaluating the mean association with precursors, the leading components of their vector histograms are generated via the Principal Component Analysis. The multivariate linear regression estimates the current issue histogram based on this subset. Finally, Spearman’s correlation between the actual histogram of the current issue and its regression prediction provides the Regression Mean Rank Dependency value. The correlation p-values is treated as a dissimilarity measure with attention to evaluating the constructed indicator’s goodness. A value of the Regression Mean Rank Dependency close to one would indicate a similarity between the style of the current issue and its precursors. On the other hand, a poor connection that is close to zero can specify a change in linguistic content. Small, i.e., close to zero p-values signify a tight connection.

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So, sufficiently high values, which are suggested to be outliers in a sequence of p-values, hint at a possible style alteration. The current p-value is examined to be an outlier (anomaly) employing the modified Thompson Tau test, cf. [13], within the time series of the p-values constructed for the previously published issues. It aspires to identify a significant change in the linguistic content related to a discontinuity in the social state. Thus, a change in the social state is recognized through an anomaly in the p-value behavior within streaming data. The Arabic language is one of the Semitic languages, which about 300 million people speak (according to the Egyptian Demographic Center, 2000). The Arabic alphabet is composed of 28 letters. There is no difference between upper and lower case or written and printed characters. Some are connected to neighboring ones on both sides, whereas others are connected only on the right. Subsequently, the Arabic lettering materializes as a “ligature”. It is one of the many causes of a very complex Arabic morphology, especially in comparison with European languages like English or Russian, and one of the reasons challenging to recognize Arabic handwritten texts (see, e.g., [14]). In order to treat this problem, we propose a new version of the character N-grams named the super-frequent N-grams model for Arabic. Recall that a character N-gram is a connecting sequence of N characters from a text, appearing in a sliding window of length N. The N-gram methodology is standard in text retrieval, particularly in Arabic text mining, cf. [15–17]. A straightforward construction of an N-grams model could lead to challenging classification tasks because of a great variety of stylistic patterns and characters. One of the ways to overcome this difficulty is a subsequent text normalization, cf. [18, 19]. It is done in the proposed super-frequent N-grams model by considering only N-grams, formed by the most frequent letters. In the framework of the proposed method, no relevant training data are generally available. For this reason, all calculations are provided for numerous different Ngrams sizes, accompanied by configurations of other model parameters. Thus, de facto, we use the ensemble methodology to ensure the approach’s robustness and predictors, planning to build a composed forecaster outperforming single ones. Our research is evaluated, using simulated real-time tracking, on editorial texts, published in the most widely circulating Egyptian daily newspaper “Al-Ahraam” in the Arab Spring period. This newspaper is commonly respected as a prominent Arabic writing style source. It is maintained by the government and is considered a newspaper of record for Egypt. The rest of the paper is structured in the following way. Section 37.2 presents the novel time series patterning of newspapers based on a dynamic replica of the human writing process. Section 37.3 introduces the proposed approach. Section 37.4 provides the partial experimental study results. Section 37.5 is devoted to discussion and conclusions.

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37.2 Time Series Newspapers Patterning This section describes a novel time series representation of newspapers. First of all, we introduce the notion of the Regression Mean Rank Dependency.

37.2.1 Regression Mean Rank Dependency Let us consider a series of m sequential issues of a newspaper D = {D1 , D2 , . . . , Dm } and take a vocabulary of terms Term_D = {t1 , t2 , . . . , tn } aiming to represent an issue, say Di , within the Vector Space Model, as a histogram h(Di ) of term frequencies; namely the number of times each term appears in Di . The Mean Rank Dependence quantifying the mean relationship between the current issue Di and the group of its T “precursors.” { } Δi,T = D j , j = i − T , . . . , j = i − 1 is defined as ( ) 1 ZVT Di , Δi,T , η = T

Σ

( ( )) η h(Di ), h D j , i = T + 1, . . . , m,

D j ∈Δi,T

where η is a similarity measure calculated between the histograms. The characteristic ZVT was introduced with specific configurations in [9, 10] to reflect “a posteriori” the significant global changes in the appropriate social state via the linguistic behavior of the Arabic media and to analyze the writing style evolution. We aim to solidify and generate the system and, specifically, to improve its ability to respond to changes in the issues’ style in a stable manner. To this end, we use the Principal Component Regression (PCR) methodology. Principal Component Analysis is a well-known dimensionality reduction mechanism seeking an array of new uncorrelated variables, called principal components, whose number is smaller than the quantity of the primary variables. The components are arranged concerning their variations such that the first few leading components cover most of the total data variation in the Singular Value Decomposition of the general covariance (or correlation) matrix cf. [12]. Note that the feature reduction procedures in studying Arabic texts are also considered in the literature (see e.g., [20]). Linear regression is a methodology for the evaluation of a relationship between } { predictors X = X1 , . . . , X p , and a response variable Y. A relationship is explained through linear function-dependent parameters, estimated on the samples

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Y = β1 X1 + · · · + β p X p + ε, } { where β = β1 , . . . , β p denotes an array of real numbers, named the regression coefficients, and ε is the vector of uncorrelated random errors with a zero mean and the same variation. If two or more predictors are significantly correlated, this estimation is erratic and very sensitive to minor sample changes (multicollinearity phenomenon). In the ideal multicollinearity case, the matrix XT X involved in the calculation of the regression coefficients is singular and, therefore, non-invertible. In practice, ill-conditioned matrixes frequently appear. PCR is one of the methods applied to disable the multicollinearity problem, where regression is raised, resting not upon the dependent predictors themselves but on the leading principal components. Our model is constructed to detect and forecast changes in the linguistic content of a newspaper. Thus, we predict the current issue (more precisely, its histogram) through PCR, based on the histograms of its T precursors, and compare the result with the issue’s histogram itself. Agreement between these values indicates the stability of the style at the current point. Otherwise, it can be concluded that the linguistic content significantly changes. More formally, we apply the methodology as follows: • Construct the response variable as Y = h(Di ) as a histogram of the current issue Di for i > T . • Compose the predictors set Xi,T = {X1 , . . . , XT } from the histograms { ( ) } X j+T +1−i = h D j , j = i − T , . . . , i − 1, D j ∈ Δi,T • Choose the total percent of the explained variance or number of leading components. ( ) { } • Create the set of the leading components Wq Xi,T = W1 , . . . , Wq of Xi,T . • Construct a regression Y = β1 W1 + · · · + β p Wq Δ

• Calculate the regression prediction h(Di ) of h(D( i ) using this) equation. • The Regression Mean Rank Dependence RZVT Di , Δi,T , η is defined as ) ( ( ) RZVT Di , Δi,T , η = η h(Di ), h(Di ) , Δ

where η is a similarity measure expressing the histograms’ resemblance. The selection of a similarity measure η is essential in the proposed approach. Consistent with our perception, the writing style of each issue under consideration is outlined by the appropriate histogram of terms. For this reason, a similarity measure has to be appropriately chosen to reflect the relationship between styles. It appears natural to operate in this connection with a similarity measure whose significance can

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be estimated by a test statistic in a hypothesis test constructed to establish whether two variables may be regarded as statistically independent. Formally speaking, let us take two sets of random variables, X, and Y , and test the null hypothesis: H0 : There is no relationship between X and Y (they are independent) against the alternative. H1 : There is a relationship between X and Y (they are dependent). A test is usually specified by a test statistic S, whose value is evaluated using the drawn samples. The decision is made by associating the test’s threshold value, called the significance level of the test ((α), traditionally set as 0.05), and the p-value of the test statistic, which can be interpreted as the probability of the current samples occurring if the null hypothesis is correct. The p-value can be comprehended as a random variable attained from the drawn samples. A rise in the p-value points to the acceptance of the null hypothesis that testifies to a weakening relationship. In this paper, we consider the following known association measures: F1 =

{

} { } f i(1) , i = 1, . . . , m , F2 = f i(2) , i = 1, . . . , m

• The Spearman’s Rank Correlation Coefficient (the Spearman’s ρ) being a variant of the usual Pearson’s correlation coefficient calculated for the data transformed to rankings. The Pearson’s correlation evaluates linear relationships, while the Spearman’s correlation evaluates monotonic relationships because this measure estimates correspondence between two arrangements of the same elements:

ρ(F1 , F2 ) = 1 −

6

( ))2 Σ m ( ( (1) ) (2) R f − R f i=1 i i ( ) . 2 m m −1

) ( • A function R maps the sets F j , j = 1, 2 onto (1, . . . , n) such that R f i( j) is the ( j)

rank (position) of f i in the arranged array F j . The significance of this measure is also evaluated using the statistic / S=r

m−2 , 1 − ρ2

which is approximately distributed according to the Student’s distribution with m − 2 degrees of freedom under the null hypothesis of ρ = 0. This similarity has been successively applied to visual word histogram relationship evolution, and for clustering genomes within the compositional spectra approach. • The Kendall’s rank correlation coefficient (Kendall’s Tau τ ) is a statistic constructed to evaluate the ordinal association between two variables defined

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( ) as follows. Let us take two observations (X i , Yi ) and X j , Y j . They are concordant if they are in the same order concerning each variable; that is, if (X i < X j and Yi < Y j ). If (X i < X j and Yi < Y j ) or (X i > X j and Yi > Y j ), then the observations are discordant. The Kendall’s Tau τ is given as τ=

Nc − Nd , 1 m(m − 1) 2

where Nc is the number of concordant and Nd is the number of the discordant pairs. Under the null hypothesis, the sampling distribution of τ can be approximated by the zero mean normal distribution having the variance σ2 =

2(2m + 5) . 9m(m − 1)

37.2.2 Modified Thompson Tau Test The Modified Thompson Tau test, cf. [13], verifies if outliers exist in a sample of scalar quantities. This method is based on the sample’s standard deviation and the sample average. It supplies a statistically determined rejection zone to decide if a data point is an outlier. One potential outlier is tested at a time using a version of the Student t − test. Generally speaking, the Tau test disregards outliers, which are more than two standard deviations away from the mean. Let X be a vector of size n. Denote by X the average of X and by σ (X ) the standard deviation of X . The rejection threshold is given as tα/2,n−2 (n − 1) rej = / ( ), 2 n n − 2 + tα/2,n−2 where tα/2,n−2 is the critical value of the Student distribution corresponding to the significance level α and degree of freedom n − 2. For the following value | | |x − X | δ(x) = σ (X ) settles if a point x is an outlier by the comparison if δ(x) > rej. If it is true, then x is recognized as an outlier; else (if δ(x) ≤ rej), x is not considered an outlier.

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37.3 Methodology This section presents the proposed approach to tracking daily style fluctuations via the model described earlier. Above all, a value of the delay parameter T jointly with a similarity measure η has to be set. We assume that a vocabulary of the terms Term_ D is given. The suggested dictionary construction method is discussed later. When each issue Di , i > T is represented as a histogram of Term_D, it becomes possible to calculate the appropriate values of RZVT according to explained in Sect. 2.1 by RSi = RZVT (Di , Δi,T , η) together with their p-values pi . The suggested dictionary construction method is discussed later. When each issue Di , i > T is represented as a histogram of Term_D occurrences, it becomes possible to calculate the appropriate values of RZVT according to explained in Sect. 2.1 by ( ) RSi = RZVT Di , Δi,T , η together with their p-values pi . Algorithm 1 Detection phase Input: • • • • •

T —Delay parameter. η—Similarity measure. {L—Sliding window size. } p j , j = {i − L , . . . , i − 1 —Precalculated set of the previous p-values. } Δi,T ,0 = D j , j = i − T , . . . , j = i —A collection including the current issue Di together with its T predecessors. • Term_D—Current dictionary. • αT h —Significance level of the Modified Thompson Tau test. α0 —Significance level of p-value. Output: • p-value pi . • Flag = 1 if Di is recognized as a change point, and Flag = 0 otherwise. Procedure: 1. Calculate histograms of Δi,T,0 using( the current)dictionary Term_D. 2. Calculate the attitude RSi = RZVT Di , Δi,T , η and its p-value pi . 3. Perform the Modified Thompson Tau test for the set { pi−L+1 , . . . , pi } at the significance level αT h . 4. If pi is identified as a potential outlier, then check if pi > α0 .

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5. If this is true, then Di is recognized as a change point, and pi and Flag = 1 are returned, otherwise pi and Flag = 0 are returned. As explained in Sect. 2.1, the central assumption is that if the style of a newspaper does not significantly change, then the association between each current issue and its regression prediction is sufficiently high, and the corresponding p-value is sufficiently small. The values of the constructed feature chronologically, considered across the time axis of “i,” form the desired time series. In turn, an outlier, appearing at the end of the evaluated p-values sequence, can indicate a considerable modification of the style since RZVT decreases at this point are caused by possible forthcoming transformations in the social state. We use the modified Thompson Tau test (see Sect. 2.2) to recognize the desired anomalies. In order to illustrate these matters, let us consider an example of an Egyptian newspaper, “Al-Ahraam.” Figure 37.1 represents an example of graphs of RZVT (in the top panel) and the corresponding p-value (in the bottom panel), constructed based on the newspaper’s issues, published during a crucial period of the Arab Spring in Egypt. This figure exhibits that the indicator RZVT falls before an expected change in the social situation. Sequential issues located within a sliding window with the length L are mutually tested, aiming to check if the style of the last item is considerably different (i.e., an outlier) from most of the others in the investigated period. The style might stabilize after this point at another level of RZVT . This fact is also reflected more explicitly by the behavior of the corresponding p-value, sharply jumping at the appropriate position. Such p-value outliers are recognized in the detection step of our

Fig. 37.1 Example of RZVT and p-value graphs

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method. When a point is recognized as a potential outlier, the corresponding p-value is compared to a given threshold, say 0.01, aiming to be convinced that the p-value is sufficiently significant, and only after the point is accepted as an outlier. As was mentioned earlier, we construct the dictionaries in the framework of the Vector Space Model, which provides an algebraic representation of a text and suggests that the connotation of a document can be drawn from its constituent terms. A text is represented as vectors of term occurrences, and each single independently treated term links to a dimension in the model space. An additional assessment of the p-value is provided to exclude the possible untruthful random outliers, which can occur for the small magnitudes of the p-values. This work proposes a new method for enriching the model based on the so-called super-frequent N-grams. The suggestion is that the style of the media is stable within an initial period containing L > T opening issues. Afterward, successive issues are considered in a sliding window with size L. Algorithm 2 Detection phase Input: • L—Sliding window size. • N —N-grams size. • f —Fraction of the occurrences of the chosen super-frequent N-grams in the total occurrences. • Nmin —Minimal size of the constructed dictionary. Procedure:

{ } 1. Concatenate all issues in the first sliding window D j , j = 1, . . . , j = L and obtain a text D. 2. Omit all superfluous characters in D. 3. Count occurrences of all characters in D. 4. Construct the list D N of the top N most frequently occurring characters. 5. Construct the set D N of N-grams including at least N − 1 characters belonging to D N . 6. Sort the elements of D N in descending order according to their occurrences. 7. Choose the subset D N , f ⊂ D N of the top most frequently occurring elements covering the f fraction of the total occurrences of D N . 8. If the size of D N , f is less than Nmin then take D N , f as the set of the Nmin most frequently occurring elements of D N . 9. If the size of D N is less then Nmin print (“A dictionary cannot be Constructed”) and Stop. 10. Return Term_D = D N , f . At the preprocessing step, all characters that are not the language letters are omitted. All single characters are sorted in descending order according to their occurrence in the corpus. A dictionary Term_D is constructed using a certain fraction of

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the most frequent N-grams, including at least the N − 1 most popular characters, resting on L preliminary issues (see, Algorithm 2). An example of the super-frequent N-grams portions among the most frequent regular N-grams once their numbers grow from one to fifty is given in Fig. 37.2 for N = 2, 3, and 4. As it can be seen, the super-frequent N-grams form a compact and dense subset within the regular N-grams. Selecting an appropriate fraction of the most common super-frequent N-grams strengthens these properties, stabilizes the classification process, and makes it possible to estimate the number of the N-grams, involved in the model design more accurately. It is especially evident in the N) ( case of longer grams leading to sparser representations. Two graphs of RZVT Di , Δi,T , η , which are calculated for the example mentioned earlier for N = 4, are given in Fig. 37.3. The one, marked in blue, is constructed employing the super-frequent N-grams approach, while the second graph, marked in red, is built via but using the ( the same technique ) regular N-grams. The related descent of RZVT Di , Δi,T , η is higher in the first case, namely the first method more explicitly distinguishes the style changes. The p-values are 0.38 and 0.002 correspondingly, with dictionaries having sizes of 30 (the minimal possible amount in this example) and 169. Nevertheless, once single words or characters are merged into N-gram words or characters, the frequency of tokens generally fits Zipf’s Law approximately with the slope close to (−1). The distribution of the most common 3-grams in Arabic texts, belonging to the same category also follows this law, cf. [16]. Zipf’s Law entails that a N-gram-based classification procedure should not be very responsive concerning distribution truncation at a particular rank. In other words, the conclusion can be made using a relatively limited number, say N min of the most superfrequent N-grams. All procedures are performed for various sizes of N, conveyed by specific arrangements of other factors. We use the ensemble methodology and combine several parallel predictors, as mentioned earlier.

37.4 Findings In this section, we present and discuss results obtained via application of the proposed methodology to issues of the Arabic newspaper “Al-Ahraam” mentioned earlier. In our experiments, we study the prediction process by a simulation procedure, where sequential issues of a newspaper are considered within a sliding window to forecast the following issue histogram.

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Fig. 37.2 Fractions of the super-frequent N-grams

) ( Fig. 37.3 Graphs of RZVT Di , Δi,T , η

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37.4.1 Parameters Selection All calculations are provided for the N-gram sizes of N = 2, 3, and 4, accompanied by the values 0.2 and 0.3 of the fraction of the most common super-frequent Ngrams and with the minimal size of the dictionary N min = 20. We set the following significance levels. • Significance level of the Modified Thompson Tau test α = 0.01. • Significance level of outlier p-value α 0 = 0.01. For large values of the delay parameter T, the resulting RZVT is expected to be smoother. However, the necessary information could be lost. It should also be noted that, on the other hand, an increase in parameter T would improve the model-based forecast. A balance point between these contrary factors could provide a reasonable estimation of T. We choose T = 10 as such a poise point, with the appropriate size of the sliding window as L = 2T. The experiments are delivered for both similarity measures, discussed earlier: the Spearman’s (the Spearman’s ρ) and the Kendall’s (the Kendall’s tau τ ) rank correlation coefficients. The total percent of the explained variance in the used PCR model is 80%. The studied dataset consists of 909 issues of the “Al-Ahraam,” published in the following periods: 1.1.2010–31.12.2011 and 1.1.2014–30.6.2014. Figures 37.4 and 37.5, demonstrating results obtained for the Spearman’s and the Kendall’s similarities, present the locations of the detected change points with the corresponding clarifications given in Tables 37.1 and 37.2. Figure 37.6 demonstrates the principal components numbers found for different sizes of N. The figures’ top, middle, and bottom panels exhibit results for each size of the considered N-grams, summarized across the fraction values. The first row in the tables contains the number of found checkpoints in the database; the following three rows give the corresponding date, while the last one provides the mean probability of the appearance. Let us consider the alteration in the social states connected to the presented positions: • 14.2.2010. This change point is close to 24.2.2010, when Mohamed El-Baradei, with several other opposition leaders, founded a new apolitical movement called the “National Association for Change”. The common goal was to form a broad opposition to establish changes in the National Assembly.1 • 18.6.2010 and 18.7.2010. On 25.6.2010, Mohamed El-Baradei headed a multitudinous demonstration in Alexandria against suspected manipulations of the police and visited Saeed’s family to express condolences. The checkpoints indicate the demonstrations over Saeed’s death occurred in Cairo’s Tahrir Square and front of the Egyptian Embassy in London.2 • 7.1.2011. This point indicates 25.1.2011 (“Day of Revolt”) when protests against.

1 2

http://news.bbc.co.uk/2/hi/middle_east/8534365.stm. http://edition.cnn.com/2010/WORLD/africa/06/25/egypt.police.beating/index.html.

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Fig. 37.4 Change points locations found for different sizes of the N-grams using the Spearman’s similarity

• Plentiful demonstrations exploded throughout the country, demanding the resignation of President Hosni Mubarak. This event is correctly predicted by our method. The beginning of the regime transformation is indicated by a significant change in the linguistic content of the newspaper. Before this date, the media loyal to the government covered the events from the authority viewpoint and did not suggest any alteration in the social state.3 • 28.2.2011. This date is close to 2.3.2011 when the constitutional referendum was set on 19.3.2011.4 • (6, 21, and 29) in 6.2011. These change points are strictly associated with a social explosion that appeared in the summer of 2011. Thousands of black-dressed Egyptians took part in marches on June 6 to honor Khaled Saeed. The protests 3 https://www.bbc.com/news/av/world-middle-east-12282585/three-reported-dead-after-egypt-sday-of-revolt. 4 https://www.thestar.com/news/world/2011/03/20/constitutional_amendments_approved_in_e gypt_referendum.html.

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Fig. 37.5 Change points locations, found for different sizes of the N-grams using the Kendall’s similarity

continued, and on June 28, a bloody clash in central Cairo between demonstrators and security forces resulted in numerous victims. Further, the “Friday of Retribution” happened on July 1, when crowds in Suez, Alexandria, and Tahrir Square in Cairo expressed deep disappointment with the governing of the Supreme Council of the Armed Forces. Similar rallies, named the “Friday of Determination” and the “March of the Million,” occurred on July 8 in Suez, Alexandria, Cairo, and other cities.5 • 23.2.2014. Many commentators agree that after the July overthrow (there is no data about this period in the considered newspaper collection) of the elected PresidentIslamist Mohammed Mursi and the following repressions against Islamists and liberals, the military authorities decided to return to the era of autocrat Hosni Mubarak. On 1.3.2014, the Provisional Government of Egypt was sworn in. The

5

https://www.theatlantic.com/photo/2013/07/millions-march-in-egyptian-protests/100543/.

2010

3

10

2010

2

14

0.02

Year

Month

Day

Prob.

0.01

69

45

Num.

0.01

22

6

2010

173

0.01

18

7

2010

199

0.01

8

11

2011

312

0.1

24

12

2011

358

0.03

7

1

2011

372

0.01

28

2

2011

424

0.02

6

6

2011

522

Table 37.1 Change points detected for “Al-Ahraam” newspaper using the Spearman’s similarity

0.01

21

6

2011

537

0.06

29

6

2011

545

0.02

3

7

2011

549

0.02

1

10

2011

639

0.05

23

2

2014

784

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2010

3

10

2010

2

14

0.02

Year

Month

Day

Prob.

0.01

69

45

Num.

0.01

22

6

2010

173

0.01

18

7

2010

199

0.01

8

11

2011

312

0.1

24

12

2011

358

0.03

7

1

2011

372

0.01

28

2

2011

424

Table 37.2 Change points detected for “Al-Ahraam” newspaper using the Kendall’s similarity

0.02

6

6

2011

522

0.01

21

6

2011

537

0.06

29

6

2011

545

0.02

3

7

2011

549

0.02

1

10

2011

639

0.05

23

2

2014

784

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Fig. 37.6 Number of the leading components found for different sizes of the N-grams using the Spearman’s similarity for Al-Ahraam

date 2.1.2014 was the nomination date of the new editor and editor-in-chief of AlAhraam. As said by Reuters I, “Egypt’s government resigned on Monday (24.2, close the found change point), paving the way for the army chief Field Marshal Abdel Fattah al-Sisi to declare his candidacy for president of a strategic US ally gripped by political strife.” • On 24.3.2011, an Egyptian court condemned 529 supporters of the Muslim Brotherhood to death. 6 It is curious to track the number of leading components in predicting. If N = 2, then the component number always equals 1; i.e., the predecessors’ histograms, and the issues themselves are permanently highly associated. This relationship is blurred with increases in N, so the component number becomes 3 in almost half of the cases

6

http://uk.reuters.com/article/uk-egypt-politics-idUKBREA1N0KM20140224.

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Table 37.3 Cumulative distribution of the leading components found for N = 4 Value

1

2

3

4

5

Frequency

0.34

23.60

50.79

24.38

0.90

(see, Table 37.3). Figure 37.6 demonstrates the very diverse behavior of this quantity for different sizes of N, calculated for the fraction f = 0.3. Thus, the method is more sensitive with greater values of N, where slighter style fluctuations can be detected. This fact can also be understood from Fig. 37.4, where only the most essential two changes were found for N = 2, while for N = 4, more minor effects are likewise discovered. The results obtained using the Kendall’s similarity are given. Generally speaking, they indicate very comparable discontinuities with slightly different probabilities. Interestingly, this indicator reveals an additional fluctuation on 8.11.2010, related to the Egyptian parliamentary elections, held in two rounds on November 28 and December 5, 2010.

37.5 Discussions and Conclusions This paper proposes a novel method for online real-time prediction of significant discontinuities in social states using the linguistic development of Arabic dailies by introducing the novel Regression Mean Rank Dependency characteristic. The method describes its combination with a new super-frequent N-grams model, making it possible to forecast social changes reliably using the daily stylistic content. The proposed super-frequent N-grams method demonstrates its ability to provide highly beneficial linguistic models for linguistically complex languages. An evaluation of the approach demonstrates its ability to reflect changes in the social state, predominantly to reveal particular events of the “Arab Spring.” The study shows that the resulting N-grams models correspond, in general, to different sizes of N. Apparently, newspapers are affected by varieties in vocabulary (in the domain of the language of mass media) due to lexical borrowing and the policies of editorial boards depending on the state authorities, as well as different types of target readers. Possible applications for resources in other languages, such as Hebrew or Persian, appear to be very attractive.

References 1. Gerber, M.: Predicting crime using Twitter and kernel density estimation. Decis. Supp. Syst. (Elsevier) 61, 115–125 (2014). https://doi.org/10.1016/j.dss.2014.02.003 2. Sallam, R.M., Mousa, H.M., Hussein, M.: Article: improving Arabic text categorization using normalization and stemming techniques. Int. J. Comput. Appl. 135(2), 38–43 (2016)

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3. Harrag, F., Al-Qawasmah, E.: Improving Arabic text categorization using neural network with SVD. JDIM 8(2), 125–135 (2010) 4. Yu, S., Kak, S.: A survey of prediction using social media. CoRR abs/1203.1647 (2012) 5. Kendall, M., Gibbons, J.: Rank Correlation Methods. Edward Arnold (1990) 6. Khreisat, L.: A machine learning approach for Arabic text classification using N-gram frequency statistics. J. Informet. 3(1), 72–77 (2009) 7. Leiter, D., Murr, A., Ramrez, E.R., Stegmaier, M.: Social networks and citizen election forecasting: the more friends the better. Int. J. Forecast. 34(2), 235–248 (2018) 8. Kalyanam, J., Quezada, M., Poblete, B., Lanckriet, G.: Prediction and characterization of high-activity events in social media triggered by real-world news. PLoS ONE 11(12), 1–13 (2016) 9. Wang, X., Brown, D.E., Gerber, M.S.: Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information. In: Zeng, D., Zhou, L., Cukic, B., Wang, G.A., Yang, C.C. (eds.) ISI, pp. 36–41. IEEE (2012) 10. Amelin, K.S., Granichin, O.N., Kizhaeva, N., Volkovich, Z.: Patterning of writing style evolution by means of dynamic similarity. Pattern Recogn. 77, 45–64 (2018) 11. Sawaf, H., Zaplo, J., Ney, H.: Statistical classification methods for Arabic news articles. In: Arabic Natural Language Processing in ACL2001 (2001) 12. Kalampokis, E., Tambouris, E., Tarabanis, K.: Understanding the predictive power of social media. Internet Res. 23(5), 544–559 (2013) 13. Volkovich, Z., Granichin, O., Redkin, O., Bernikova, O.: Modeling and visualization of media in Arabic. J. Informet. 10(2), 439–453 (2016) 14. Franch, F.: (wisdom of the crowds)2: 2010 UK election prediction with social media. J. Inform. Technol. Polit. 10(1), 57–71 (2013) 15. Al-Thubaity, A., Alhoshan, M., Hazzaa, I.: Using word N-grams as features in Arabic text classification. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 35–43. Springer International Publishing, Cham (2015) 16. Korolov, R., Lu, D., Wang, J., Zhou, G., Bonial, C., Voss, C., Kaplan, L., Wallace, W., Han, J., Ji, H.: On predicting social unrest using social media, pp. 89–95. Institute of Electrical and Electronics Engineers Inc., United States (2016) 17. Thompson, R.: A note on restricted maximum likelihood estimation with an alternative outlier model. J. Royal Stat. Soc. Ser. B (Methodological) 47(1), 53–55 (1985) 18. Ionescu, R., Popescu, M.: PQ kernel: a rank correlation kernel for visual word histograms. Pattern Recogn. Lett. 55, 51–57 (2015) 19. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975) 20. Ayedh, A., Tan, G., Rajeh, H.: The impact of feature reduction techniques on Arabic document classification. Int. J. Database Theor. Appl. 9, 67–80 (2016) 21. Bolshoy, A., Volkovich, Z., Kirzhner, V., Barzily, Z.: Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer Science & Business Media (2010) 22. Finkelstein, B., Kuncan, K.: The study of handwriting recognition algorithms based on neural networks. Int. J. Hybrid Inform. Technol. 14, 69–80 (2021) 23. Jolliffe, I.: Principal Component Analysis. Springer (1986) 24. Kerr, N.L., Tindale, R.S.: Group-based forecasting? A social psychological analysis. Int. J. Forecast. 27(1), 14–40 (2011). https://doi.org/10.1016/j.ijforecast.2010.02.001. Group-Based Judgmental Forecasting

Chapter 38

Barrage Sentiment Analysis Based on Snow NLP—An Example of Liu’s Fitness Video Lixia Zhang and Yuxuan Zhang

Abstract In the era of big data, netizens can express their views on various video platforms. Judging users’ attitudes to the videos they see through the barrage reviews has become a hot spot in data mining research. This paper takes Liu’s fitness video as the research object, uses Python to crawl the data, pre-process the data, extract effective commentary information, draw the word cloud for data visualization, and use Snow NLP for sentiment analysis, so as to help users obtain valuable information more efficiently and conveniently.

38.1 Introduction In recent years, due to the impact of the epidemic, more and more people are choosing to conduct educational and entertainment activities at home, resulting in a surge in the number of people online on major video sites. The report details bilibili’s efforts in the areas of responsible governance, product responsibility, social care, and environmental protection. In the first quarter of 2022, the average monthly active users of bilibili increased by 31% year-on-year to reach 294 million, while the average monthly active mobile users increased by 33% year-on-year to reach 276 million. Specifically, the average daily video playback on bilibili approached 3 billion times in the quarter, an 84% year-on-year improvement; the total average daily video playback time also increased 52% year-on-year in the quarter, reflecting the high growth in overall community traffic; and the average monthly number of community interactions reached 12.3 billion times, an 87% year-on-year increase. With the development of the field of big data and artificial intelligence, and the emergence of various new media, there is a large amount of data available to support the use of deep learning, L. Zhang · Y. Zhang (B) Harbin University of Commerce, Harbin 150028, China e-mail: [email protected] Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin 150028, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_38

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and accurate determination of the meaning of video pop-up words is crucial for video creators to analyze video quality and make decisions for improvement.

38.2 Related Work 38.2.1 Chinese Bullet Screen A barrage (or danmaku in Japanese) is a commentary subtitle that pops up when watching a video on the Internet. In English it is called “Bullet Hell” or “Bullet Curtain”. Originally a military term, the term was first brought to the ACGN community in the rise of Shooting Game (STG) in Japan, and because the commentary function on the Japanese pop-up video sharing site niconico player resembled a horizontal pop-up shooting game, the commentary function was later named pop-up in China. It was first introduced in China by AcFun and later by bilibili. As a tool for instant interaction, pop-ups visualize what the audience is thinking at the moment with short text symbols and a scrolling presentation. Compared to the traditional player commenting system, which was independent of the player, its introduction caused an alienation of the traditional viewing relationship: the audience changed from passive viewers to active participants [1]. The use of pop-ups in fact presents a high degree of autonomy, with users gradually becoming active participants in the modern media revolution by using them to express their existential demands in their deeper selves [2]. The interactivity of pop-ups is mainly reflected in the realtime interaction with the video content [3], which is more conducive to enhancing the user’s sense of involvement and making the user’s active cycle longer. Compared with traditional comments, the real-time synchronization and anonymity of pop-ups can motivate video viewers to participate more in interaction, alleviating the sense of isolation and enhancing the sense of participation and belonging [4].

38.2.2 Sentiment Analysis Based on the Sentiment Dictionary Text sentiment analysis is different from text mining and text classification, as the sentiment contained in text is abstract in nature and difficult to process directly based on literal information. The main tasks of sentiment analysis include: sentiment information extraction, sentiment tendency analysis, sentiment retrieval, and summarization. The task of sentiment analysis is to identify the subjective viewpoint of a given text and to determine the positive and negative sentiment of the text, which mainly includes lexicon and rule-based methods and machine learningbased methods. The sentiment analysis method based on sentiment dictionaries uses sentiment dictionaries to obtain the sentiment values of sentiment words in a document, and then determines the overall sentiment tendency of the document through

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weighted calculations [5]. The execution process of sentiment dictionary is generally to input the text first, through the pre-processing of the data followed by word separation operation, then the different types and degrees of words in the sentiment dictionary are put into the model for training, and finally the sentiment type is output according to the sentiment judgment. Currently, there are some representative sentiment lexicons: General Inquirer lexicon, Sentiment lexicon, MPQA subjectivity lexicon, Emotion lexicon, etc. [6]. In China, Gao et al. [7] added a phrase lexicon, a negation lexicon and an adverb lexicon for sentiment analysis of user comments on the basis of the general lexicon, effectively improving the accuracy of fine-grained sentiment classification. Xu et al. [8] effectively achieved sentiment classification of text by constructing an extended sentiment lexicon containing basic sentiment words, scene sentiment words, and multi-sense sentiment words. Cai et al. [9] addressed the problem of the existence of multiple meanings of sentiment words by constructing a domain-specific sentiment lexicon, and showed through experiments that better performance could be obtained by overlaying two classifiers, SVM and GBDT, together. Li [10] proposed a dynamic sentiment lexicon construction method based on BiLSTM, improving the Continuous Bag of Words (CBOW) model to Emotional CBOW (ECBOW) model to implement a dynamic sentiment lexicon. Zhang et al. [11] based on the constructed degree adverb lexicon, network word lexicon, negation word lexicon, and other Bravo-Marques et al. [12]. Proposed a sentiment classifier that trains incremental words from timevarying distributed word vectors to automatically extract constantly updated sentiment words from Twitter streams to obtain a time-varying sentiment lexicon based on incremental word vectors. Overall, the classification method based on the emotion dictionary can reflect the unstructured features of the text, and the classification effect is more satisfactory when the coverage of emotion words in the dictionary and the annotation accuracy are high [13].

38.3 Data Analysis 38.3.1 Data Crawling and Pre-processing Data acquisition refers to the use of a device that automatically collects data from various data sources into one device. This paper is based on the Beep Animation website and acquires the corresponding data through Python’s crawling technology, broken down into: initiating a request, obtaining an address, analyzing a web page, extracting data, and storing data. Data crawling is done by crawling technology to initiate a request to a specified website, and by accessing the URL address to obtain the data returned from the web server side and parse it. The URL of the page is wrapped into a request using requests to access the server side and obtain the source code of the web page. Choose the Beautiful Soup library to parse the response and

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Fig. 38.1 Part of the pop-ups of “Compendium of Materia Medica” on Bilibili

remove the JS script tags, CSS code, HTML tags, and other content from the source code. Use regular expressions and Beautiful Soup to locate and extract data from the parsed web page. The final output is stored in a .csv format file. The dataset in this paper was selected from the top five official video pop-ups played on Bilibili Liu, crawled nearly 50,000 pieces of data, due to space limitations, some of the pop-up content crawled is shown in Fig. 38.1. Dirty data can affect the subsequent text analysis, and data pre-processing is necessary for data analysis. Data cleaning means removing duplicate information and error information from the information. Data cleaning mainly includes removing error data, handling duplicate data, handling null values, detecting outlier points, and handling abnormal data [14]. In this paper, we use the time module to unify the time format in the collected raw data, clean-list all elements into strings, and comment content into strings, and the processed data is shown in Table 38.1.

38.3.2 Sentiment Analysis of Pop-Up Comments SnowNLP is a class library written in python that facilitates the processing of Chinese text content and comes with a number of trained dictionaries. The module in snowNLP that supports sentiment analysis is in the sentiment folder and its core code is __init__.py. The classify function and train function are the two core functions, where the train function is used to train a sentiment classifier, and the classify function is used for prediction. The handle function is used in both functions, and its main task is to classify and deactivate the input text. Its simple classification of text into two categories, positive and negative, returns a probability of sentiment, the closer to 1 for positive and the closer to 0 for negative. The prediction process is formulated as in (38.1), let U = {W, C} be the sample data set, where W = {w1 , …, wn } is the set of indicator variables for the sample data, C is the class variable for credit assessment, and the classification results are: {c1 = 0 for good credit and c2 = 1 for bad credit}, and wi is the value taken for the attribute W i . The sample wi = (w1 , …, wn ) belong to ci , i = 1, 2 with probability, where P(cj ) is the prior probability of

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Table 38.1 Partial pre-processed data Time

Content

2022/5/17 23:43

Ha Ha Ha Ha Ha https://www.bilibili.com/ video/BV1Pa411v7vg

Video address

2022/5/17 00:42

Why is it so long

2022/5/17 11:10

Cute screen

2022/5/16 11:58

Feels a little fussy

2022/5/15 20:07

Tired

2022/5/15 23:12

So cute

2022/5/15 17:58

Why is there no pop-ups?

2022/5/15 13:33

Where dreams begin

2022/5/15 14:08

What is this song’s name?

2022/5/16 20:17

Go for it!

2022/5/16 19:50

I’ll just watch from the bed

2022/5/15 19:51

I’m dying

2022/5/09 20:30

Doesn’t your knee hurt?

2022/3/28 13:22

Nice

Pop-up address http://comment.bilibili.com/ 574147025.xml

class cj ; P(w1 , …, wn |cj ) is the likelihood of the class cj with respect to wi . P(c1 |w1 , . . . , wn ) =

P(w1 , . . . , wn |c1 )P(c1 ) P(w1 , . . . , wn |c1 )P(c1 ) + P(w1 , . . . , wn |c2 )P(c2 ) (38.1)

In this paper, a sentiment score of less than 0.5 is considered negative, more than 0.5 is considered positive and equal to 0.5 is considered neutral. Some results are shown in Table 38.2.

476 Table 38.2 Partial results of pop-up emotional tendency analysis

L. Zhang and Y. Zhang

Time

Content

2022/5/17 23:43

Ha Ha Ha Ha Ha 0.990223

Positive

2022/5/17 00:42

Why is it so long 0.483870

Negative

2022/5/17 11:10

Cute screen

0.991495

Positive

2022/5/16 11:58

Feels a little fussy

0.370989

Negative

2022/5/15 20:07

Tired

0.289741

Negative

2022/5/15 23:12

So cute

0.961758

Positive

2022/5/15 17:58

Why is there no pop-ups?

0.500000

Neutral

2022/5/15 13:33

Where dreams begin

0.632690

Positive

2022/5/15 14:08

What is this song’s name?

0.500000

Neutral

2022/5/16 20:17

Go for it!

0.904761

Positive

2022/5/16 19:50

I’ll just watch from the bed

0.492916

Negative

2022/5/15 19:51

I’m dying

0.135994

Negative

2022/5/09 20:30

Doesn’t your knee hurt?

0.011495

Negative

2022/3/28 13:22

Nice

0.800000

Positive

Sentiment score

Analysis results

38.3.3 Data Visualization In the age of data, Python is a great choice for unlocking the value of data through data analysis, and it includes major data analysis libraries such as NumPy, Pandas, Matplotlib, and others. The most used libraries are word cloud, a word cloud visualization library, and word cloud by pyecharts. Word clouds are a typical text visualization technique. Word clouds visually highlight “keywords” that appear more frequently in text, resulting in a “keyword cloud” or “keyword rendering”. Data visualization is the primary means of data exploration. The goal of data visualization is to communicate information clearly and effectively to the user through the visual presentation of the chosen method. Effective visualization helps to analyze and reason about data and evidence. This makes complex data easier to access, understand, and use.

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Fig. 38.2 Percentage of emotional disposition

Based on Matplotlib and other libraries to visualize the results of the pop-up comment data, the pop-up sentiment tendency data, and pie charts were obtained as shown in Fig. 38.2. After removing some of the dirty data and neutral data, it was concluded that over half of the people reacted positively to Liu’s fitness exercise, and the emotions were mainly positive, with 88.65% of the emotions of praise, amazement, and joy. These positive emotions are the majority of netizens lamenting that their physical strength has become better and their bodies healthier through physical exercise. However, there were also a small number of negative emotions appearing, with negative emotions accounting for less than one-fifth. And after manual review, it was concluded that the negative part of the emotions (such as disgust, sadness, fear.) were more mainly complaints about the discomfort generated by the body after excessive exercise, favoring the gym exercise as too strenuous rather than against the video and Liu itself. Statistics on the gender of the fans who sent the pop-ups, it is concluded that women account for a higher percentage, accounting for 86%, more reflecting the trend of women focusing on health since the new era. The age group of fans, on the other hand, is mainly distributed between 24 and 40 years old, while people under 18 and over 51 years old also occupy a certain proportion. The combination of the two is not difficult to find that Liu’s fitness exercises are more attractive to young people, especially young women. They have a new pursuit of a healthy life, because of the impact of the new crown pneumonia epidemic, travel, recreation, and other usual leisure activities are greatly reduced, netizens have the need for exercise in the home process, which is actually a reflection of the optimistic and positive side of the social population. And according to the data of “2021 China Sports and Fitness Industry Report”, 32.6% of male users and 67.4% of female users are involved in sports and fitness in China. The number of female users is about twice that of men. This happens to overlap with the characteristics of Liu’s more female fans. And word cloud maps were generated using WordCloud as shown in Fig. 38.3. The background picture is a photo of Liu and his daughter “Little Puff” together. Liu’s fitness exercises have exploded on the Internet, according to new data, since April 7, Liu’s fans have continued to soar, rising by more than 1.5 million a week, with a 45.9% increase in the number of fans. The analysis of the pop-up data shows that the enthusiasm for fitness has not been dampened by the epidemic, with nearly 60% of viewers watching the video daily to clock in on fitness. The masses are

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Fig. 38.3 Video pop-up word cloud map

awakening to the pursuit of a healthy lifestyle and the awareness of “sports for life”, cultivating good exercise habits.

38.4 Summary New technologies and methods for natural language processing are emerging, and sentiment analysis, as one of the hot spots in natural language processing in recent years, plays a crucial role in spot analysis and public opinion monitoring. With the emergence of major live video websites in recent years, pop-up texts have started to enter the field of NLP. However, the overall development of pop-up culture is relatively short, and there is still a lack of corresponding effective and rational management and research. This paper makes a preliminary exploration of the sentiment analysis of pop-up comments, and proves the effectiveness of this paper’s method through experiments, but there are some shortcomings in the experiments, for example, in terms of pop-up comment texts, the corpus is small, which is not enough to better show the stability and scalability of the model; this paper’s research is mainly based on the sentiment classification of binary classification, and on this basis, the positive and negative sentiment values per unit of time are calculated, and the sentiment analysis of multiple classification can multi-category sentiment analysis can be used as a further research direction for this study. In terms of information extraction, the data mainly comes from online video pop-up comments, and the extracted comments have more noise, which affects the syntactic semantic learning between sentences. In the future, other analysis tools such as word2vec can be used to cross-analyze and filter the pop-up comments, and more datasets can be introduced for training analysis. Acknowledgements Fund Project: Joint Guidance Project of Heilongjiang Natural Fund in 2021 (LH2021F036)

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References 1. Wang, R., Liu, R.Y., Jiao, L.B., Xu, J.Y.: Towards popular pop-ups: media functions and their realization. 2019(05), 44–54 (2019) 2. Zhao, Y.Z., Jiang, L.L., Liu, Y.L.: Pop-ups as “location”: users’ virtual space practices. 2022(04), 32–41 (2022) 3. Gao, P.L.: Exploration of pop-up video characteristics and audience interaction behavior. Commun. Copyright 2018(11), 113–114 (2018) 4. Zhang, L., Wang, R.J.: A comparative study of online educational video users’ commenting behavior—taking bilibili website video commenting as an example. Mod. Intell. 40(02), 62–71 (2020) 5. Wang, Y.J., Zhu, J.Q., Wang, Z.M., Bai, F.B.: Bow and arrow. A review of natural language processing applications in the field of text sentiment analysis. Comput. Appl. 42(04), 1011– 1020 (2022) 6. Yang, L.G., Zhu, J., Tang, S.P.: A review of text sentiment analysis. Comput. Appl. 33(06), 1574–1578+1607 (2013) 7. Gao, H.L., Zhang, J.: Sentiment analysis and visualization of hotel reviews based on sentiment dictionary. Comput. Eng. Softw. 42(1), 45–47+66 (2021) 8. Xu, G., Yu, Z., Yao, H.: Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access 7, 43749–43762 (2019) 9. Cai, Y., Yang, K., Huang, D.P.: A hybrid model for opinion mining based on domain sentiment dictionary. Int. J. Mach. Learn. Cybern. 10, 2131–2142 (2019) 10. Li, Y.S.: Research on Construction Method of Dynamic Sentiment Dictionary Based on TwoWay LSTM and Text Sentiment Analysis, pp. 20–32. Zhengzhou University, Zhengzhou (2019) 11. Zhang, S.X., Wei, Z.L., Wang, Y.: Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Futur. Gener. Comput. Syst. 81, 395–403 (2018) 12. Bravo-Marques, F., Khanchandani, A.: Incremental word vectors for time-evolving sentiment lexicon induction. Cogn. Comput. 14, 425–441 (2021) 13. Li, R., Lin, Z., Lin, H., Wang, W.P., Meng, D.: A review of text sentiment analysis. Comput. Res. Dev. 55(01), 30–52 (2018) 14. Yang, Z.: Visual analysis of recruitment information based on python language. Comput. Netw. 46(02), 61–64 (2020)

Chapter 39

Statistical Analysis of COVID-19 Pandemic Lockdown on Rural Undergraduate Students Mohamed Hamdi, Nakka Marline Joys , Debnath Bhattacharyya , and N. Thirupathi Rao

Abstract Due to the COVID-19 pandemic, online learning, or e-learning, has become a vital aspect of education. However, India’s intention to move education to the online mode is controversial considering the country’s technology and human preparation in rural areas. This work aims to examine the usefulness and challenges of e-learning in rural regions during the pandemic of COVID-19. The study used a descriptive survey methodology. The data-gathering instrument is a self-designed questionnaire. The study’s findings indicate that e-learning is a significant problem for rural schools due to a need for e-learning resources and competent ICT teachers.

39.1 Introduction The new coronavirus pandemic has touched every sector of human existence [1] (COVID-19). This global health disaster has upended every facet of human life. Education is one area where its effects are most noticeable. Governments in numerous M. Hamdi University of Carthage (Tunisia), Cytekia (Tunisia), Sidi Bou Said, Av. de la République, 1054 Carthage, Tunisia e-mail: [email protected] N. M. Joys (B) Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Sangivalasa, Bheemunipatnam, AP 531162, India e-mail: [email protected] D. Bhattacharyya Department of Computer Science and Engineering, Koneru Laksmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur 522502, India e-mail: [email protected] N. Thirupathi Rao Department of Computer Science and Engineering, Vignan’s Institute of Information Technology (A), Visakhapatnam, AP 530049, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_39

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nations have been compelled to shut down all educational institutions [2] because of the COVID-19 outbreak. The epidemic has spurred educational growth and innovation. Due to the COVID-19 epidemic, online learning or e-learning has become an essential aspect of education. SWAYAM, e-pathsala, and other online learning platforms, available to both teachers and students, have been launched and recommended by the Indian government [3] and relevant educational authorities/bodies. But in India, where most of the population lives in rural regions, the quick transition to online education is a serious difficulty. According to India’s lack of technological and manpower preparedness in rural schools, the country’s choice to shift education online is doubtful. One year at the end of 2013, a new coronavirus disease (COVID-19) [4] was found in Wuhan in China. Because COVID-19 spread so quickly around the world, the WHO called On 11 March 2020, it will become a “pandemic”. COVID-19, 2020; Pelmin, 2020, according to the WHO. Lockdowns, social and physical segregation, avoiding face-to-face teaching and learning, and immigration restrictions are all being utilized by governments worldwide to attempt to halt the spread of this highly contagious and potentially deadly disease [2]. Around the world, a lot of schools have been shut down, which means that almost 600 million kids who are in school are affected [5]. As of 2020, UNESCO says that India has more than 320 million students who have been affected. 34 million of them are students at postsecondary institutions. Is the study important? We can learn a lot from this study about how lockdown affects the education of college students who live in a rural area where education isn’t important and most students work on their own farms. There are some practical issues that rural students face, especially when it comes to e-learning. This study can help educators think about how to teach and learn during pandemics or other crises, especially for rural children. As a result of this study, students and teachers who want to learn more about the technology that is available in rural India will also find it useful. The findings indicated that students who are unable to pay for their e-learning should be provided with actual items to enable them to continue. Otherwise, there will be a significant disparity in the educational attainment of economically qualified individuals and those who are not. This investigation also found that there was a big difference between what the government informed was shown in Fig. 39.1.

39.1.1 Objectives • To assess the efficacy of e-learning in rural areas during the COVID-19 pandemic.

39 Statistical Analysis of COVID-19 Pandemic Lockdown on Rural …

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Teaching methods are being modified. Methods of instruction are delivered.

Technical ability of teachers

satisfied with earlier schooling partnership with educational institutions linguistic origins Marital Status capable of aiding with home-schooling lockdown-induced stress alternative child care adjusting the workload modifying the quantity of work

Fig. 39.1 Parents were happy with the school during the lockdown

• To identify the challenges associated with e-learning in rural areas during the COVID-19 pandemic.

39.2 Literature Review There is little technological equipment. When considering digital learning, it is critical to consider how each student can obtain the appropriate equipment for accessing digital information. In rural India, few individuals own laptops or computers, and phone displays do not allow extended study periods. Additionally, data packs and associated costs can be a significant issue for professors and students, particularly in live classes. Many kids either do not have computers or smartphones or have them for a limited period. As a result, education is constrained by a scarcity of technology instruments.

39.2.1 Inadequate Familiarity with Digital Technology While Smart Classrooms and Digital Learning have grown in popularity in urban schools, many rural nations continue to teach using traditional techniques. As a

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result, the transition from traditional to digital modes of instruction will be lengthy [9, 10]. Teachers and students want to learn how to use digital technology and feel more comfortable teaching and learning with it. They also want more easy to use platforms. Online learning doesn’t seem to be able to make students feel as serious as they would if the teacher were there in person. Another thing that made it hard for them to learn was that their parents said: “Distance learning is hard when the teachers aren’t ready”. Teachers, too, seemed to find this new way of learning a little odd. They had to move from textbooks and blackboards to computers in a way that was completely new. It has been a long time since our educational system has changed, and this shows how unprepared the institution is for new ways of learning. This means that our children’s education is in danger, and their parents are right to be worried. Without physical classes, there is no way for people to learn from each other.

39.3 Methodology 39.3.1 Method of Study The study used a descriptive survey method. The researcher employed a self-designed questionnaire to investigate e-learning in government schools. The questionnaires are distributed by e-mail to the selected sample. The obtained data is next computed and analysed from Fig. 39.2. Fig. 39.2 The product’s function or other aspects influence user engagement with the tools

Tools Usage

Google Classroom

Google Docs

Class Link

Google Drive

Youtube

Canvas

Meet

Google Forms

Clever

Zoom

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Students enrolled in postsecondary education was clearly shown in Fig. 39.2, who were at least 16 years old were the intended audience. These individuals are undergraduate students of Government College Sanghivalasa in the Andhra Pradesh district of Thagarapuvalasa. A poll was performed online and offline between 12 and 15 November 2020. To ascertain students’ interest in online education, a link to a “Google form” questionnaire was distributed via WhatsApp as part of a series of online quizzes. To get the data, we kept track of online class attendance during the lockdown and in-person class attendance when the universities reopened again. This study looked at 265 people, 93 of whom were men, and 172 of whom were women. All of these people live in the same town and area. The percentage distribution was used to determine how much knowledge the study participants learned. It was also compared to data from the following year.

39.3.2 Design and Analysis 39.3.2.1

Intersectionality and Rural Residents’ Educational Disadvantage

The fact that this wave of educational reforms occurred abruptly rather than gradually is logical, especially given the magnitude of the shift. Parental concerns have emerged as a result of the challenges associated with distance learning for both themselves and their children. Online education requires computer and Internet access at specific times throughout the day. Any technical issues with the criteria may prevent the child from participating in class. As one parent stated, “since sessions are scheduled at specified times, we must maintain constant Internet access, which is challenging when we have more than two children at home who attend the same school and take classes at the same time”. Additionally, parents reported that they have been required to be more active than ever in their children’s homework, including explaining worksheets and other tasks. Pupils may be hesitant to ask questions on-screen, or the Internet may be too sluggish, resulting in all enquiries being sent to parents, some of whom have their own job to do from home, as seen in Fig. 39.3. Fig. 39.3 Disadvantage of online learning in rural areas

Adhering to a rigorous schedule Participating in creative activities Difficulty in keeping youngsters occupied

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Inequality and Injustice Are Impacted

In reaction to the COVID-19 outbreak, the vast majority of countries have enacted lockdown and social isolation measures, resulting in the closure of schools, training institutes, and institutions of higher education. The way instructors deliver highquality education is changing drastically, thanks to the proliferation of online learning environments. Although online education, remote education, and continuing education have all emerged as viable methods for fighting this unprecedented international pandemic, instructors and students continue to encounter several obstacles. For both learners and instructors, the transition from traditional face-to-face education to online education may be an entirely different experience, and one to which they must become used due to the lack of acceptable alternatives. The educational system and instructors have welcomed “Education in an Emergency” through a variety of online platforms, and they are now being compelled to adapt to a system for which they are unprepared as a result of this acceptance. Implementing a suitable and relevant pedagogy in online education may be dependent on instructors’ and students’ prior experience with and exposure to information and communication technologies (ICT). To far, a variety of online systems have been used, including unified communication and collaboration platforms such as Microsoft Teams, Google Classroom, Canvas, and Blackboard, among others. These platforms make it possible for instructors to create instructional courses, training programmes, and skill development programmes for students. They use features such as office chat, video conferencing, and file storage in order to create courses that are well-structured and easy to navigate. Media formats such as Word, PDF, Excel, as well as audio and video files are often supported by these systems. Additionally, these allow for the tracking and assessment of student learning through quizzes and the use of rubrics to grade projects that have been submitted. To put it simply, the flipped classroom is an effective method of disseminating pre-class resources such as articles, pre-recorded videos, and YouTube links prior to class. Through interaction with instructors and peers, students’ time in the online classroom is put to use to increase their knowledge. This is a very effective strategy for developing problem-solving abilities, critical thinking skills, and the ability to study on one’s own initiative. Virtual classrooms are quickly using videoconferencing technology and flexible cloud-based learning management software to become more effective.

39.3.2.3

Distance Education and the Student-Parent-School Relationship

Education systems should try to give parents more information and help on how to help their kids grow up by working more closely with schools and parents. Instructors also need help with how to use technology in their teaching and how to help students deal with some of the problems that come with this type of learning environment. Teachers need help learning how to use digital resources for pedagogical practice and learning how to teach in a way that works best in this environment.

39 Statistical Analysis of COVID-19 Pandemic Lockdown on Rural … Fig. 39.4 The relationship between students’ reading performance and parental and teacher support for children

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Help and support from parents Instructing on reading stimulation Assistance to educators Instructor-led training Teacher evaluations

Students’ attitudes about learning are influenced by the help they get from their teachers and their parents. In general, effective policy involvement can encourage and influence many types of help, but this is especially true in the unique circumstances of the COVID-19 epidemic. As a result, it is important to know which kinds of help teachers and families should use to keep kids’ digital learning processes safe. Children learn better when they work on things with their parents. This is because they spend more time together as a result. In these kinds of situations, parents can be a source of comfort and help their children deal with their stress and pain. They can also have conversations with their kids to help them deal with their fears. During times of uncertainty, it has been suggested that parents be taught how to help their children cope with their feelings. Using an online system that has rules for parents to follow when they help their kids could help strengthen the relationship between the two of them as shown in Fig. 39.4.

39.3.2.4

Online Continuing Education Pedagogy

In response to the COVID-19 outbreak, most countries have enacted lockdown and social isolation measures, closing schools, training centres, and universities. Educators are delivering great education in new ways, including online. To fight this extraordinary global pandemic, instructors and students have found solutions such as online, remote, and ongoing education. Changing from traditional face-to-face education to online education may be a difficult adjustment for both students and educators. The educational system and teachers are compelled to adapt to a system for which they are unprepared. During this epidemic, e-learning technologies were vital in supporting schools and institutions in enhancing student learning. Adapting to new developments requires

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monitoring and assisting staff and students. Learners with a fixed mindset struggle to adapt and change their behaviour, but learners with a growth mindset easily adjust. There is no such thing as a universal online learning system. Each topic has its own set of prerequisites. Diverse sectors and age groups demand diverse online teaching approaches. Schools worldwide are closed because to the COVID-19 epidemic, affecting students, parents, and instructors. While governments, frontline workers, and public health authorities battle to stop the spread, educational institutions endeavour to ensure that all children receive a decent education. Many students have suffered psychological and emotional turmoil at home, limiting their ability to work productively. The most effective approaches for online home schooling are currently being studied. The selection of an acceptable and relevant pedagogy in online education may be dependent on instructors’ and students’ prior exposure to ICT. Many online systems have been used, including Microsoft Teams, Google Classroom, Canvas, and Blackboard. Educators may use these platforms to create courses and training programmes. Courses are organized and easy to use with resources like office chat, video conferencing, and file storage. They normally allow the interchange of Word, PDF, Excel, audio, and video files. These also track and assess student learning through quizzes and rubric-based project grading. The flipped classroom is an easy way to distribute pre-class information including articles, videos, and YouTube links. Students use online classroom time to connect with lecturers and peers to increase their knowledge. Useful for improving problem-solving skills, critical thinking, and self-directed learning. Learning management systems and videoconferencing are increasingly becoming virtual classroom staples.

39.3.2.5

During the Pandemic Lockdown of COVID-19, We Looked at How Online Education Worked

Data showed that people used a lot of different electronic devices to study online. There were a lot of people who used their phones the most, with laptops (42.8%) and tablets (8.6%) coming in second and third. It was the personal computer that was used the least (7.6%). People spent between 44.7 and 48.8 of their time online learning. People spent between 6 and 14-h online learning. Overall, the score ranged from 5.1 to 2.4. There was a score of 3.6–2.6 for the practical parts and 5.1–2.4 for the whole thing. In general, 56.9% of participants (n = 792) gave the online learning 1–5 out of 10 points. In practical lessons, 78% of participants gave the online learning 1–5 out of 10 points, as well. People used online classes and PDFs to find the most study materials. Then they looked for online e-books and videos on YouTube. To get to the online classes, a lot of different online tools were used at one time. By how many people used them, the online tools were ranked at the top. Most people used Zoom. WhatsApp and Google Classroom came in a close second. It was used a lot: Microsoft Teams, Skype, and Google Meet were all used at the meeting. They weren’t the most popular tools. But they were still very useful.

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Fig. 39.5 Showing the top 10 providers of the online platforms during COVID-19 pandemic

39.3.2.6

COVID-19: Learning at Home and How Parents Can Help Their Kids Learn

Since the beginning of the COVID-19 epidemic, several nations have been obliged to shut schools in order to stem the virus’s spread. From 2020 to 2021, the epidemic touched more than 56 million schoolchildren in the United States. As a result of the reshaping of education, several institutions have sought to reach students remotely using online learning tools and digital platforms. Online learning might be part of a long-term answer. The usefulness of digital learning, on the other hand, is debatable. The amount of student engagement on digital learning tools is a good indicator of the chance that a learning experience will be effective, since it reflects students’ involvement and collaboration with their peers and instructors. Understanding how students utilize digital learning tools may help improve online learning effectiveness and product engagement. We attempt to identify the condition of digital learning and investigate what elements may influence (Fig. 39.5).

39.4 Conclusion After the COVID-19 outbreak, several academics shared their results on teaching and learning in various ways. Many institutions, colleges, and universities have eliminated face-to-face instruction. There is a chance that the 2020 academic year, or more, will be lost soon. Alternative instructional and assessment approaches must be developed and adopted promptly. The COVID-19 outbreak has given us an opportunity to promote digital learning. The COVID-19 closure has forced several lessons to be postponed or cancelled. Students’ academic achievement was evaluated during

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the epidemic. It doesn’t matter whether many pupils get their information online. Many of them struggle with online learning.

References 1. Abidah, A., Hidaayatullaah, H.N., Simamora, R.M., Fehabutar, D., Mutakinati, L.: The impact of Covid-19 to Indonesian education and its relation to the philosophy of “MerdekaBelajar”. SiPoSE Stud. Philos. Sci. Educ. 1(1), 38–49 (2020). [Google Scholar] 2. Gonzalez, T., de la Rubia, M.A., Hincz, K.P., Comas-Lopez, M., Subirats, L., Fort, S., Sacha, G.M.: Influence of COVID-19 confinement in students’ performance in higher education (2020). arXiv preprint arXiv:2004.09545 3. Goyal, S.: Impact of coronavirus on education in India (2020). https://www.jagranjosh.com/art icles/dmrc-result-2020-released-delhimetrorailcom-check-cut-off-marks-1587122899-1?itm 4. India Today: Effect of Covid-19 on campus: major steps being taken by colleges to keep education going (2020). https://www.indiatoday.in/education-today/featurephilia/story/effectof-covid-19-on-campus-steps-taken-by-colleges-1668156-2020-04-17 5. Kumar, D.N.S.: Impact of Covid-19 on higher education. Higher Education Digest (2020). https://www.highereducationdigest.com/impact-of-covid-19-on-higher-education/ 6. Manzoor, A.: Online teaching and challenges of COVID-19 for inclusion of persons with disabilities in higher education (2020). https://dailytimes.com.pk/595888/online-teaching-andchallenges-of-covid-19-for-inclusion-of-pwds-in-higher-education/ 7. Ministry of Health and Family Welfare. https://www.mohfw.gov.in/COVID-19 INDIA as on 25 May 2020, 08:00 IST (GMT+5:30) 8. Pelmin, M.: Readings on coronavirus disease (COVID-19) and the higher education institution (HEIs) emergency preparedness in the Philippines (2020). Available at SSRN 3573896. https:/ /ssrn.com/abstract=3573896 9. Mishra, L., Gupta, T., Shree, A.: Online teaching-learning in higher education during lockdown period of COVID-19 pandemic. Int. J. Educ. Res. Open 1, 100012 (2020). ISSN 2666-3740. https://doi.org/10.1016/j.ijedro.2020.100012. https://www.sciencedirect.com/sci ence/article/pii/S2666374020300121 10. Kapasia, N., Paul, P., Roy, A., Saha, J., Zaveri, A., Mallick, R., Barman, B., Das, P., Chouhan, P.: Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Child. Youth Serv. Rev. 116, 105194 (2020). https://doi.org/10.1016/j.childyouth.2020.105194

Chapter 40

Detection of COVID-19 Using SIS-CODE Algorithm Daegeon Kim and Sudeepthi Govathoti

Abstract The COVID-19 pathological test can be carried out with a sample taken from the nose, the back of the throat, or both, using a special swab. Currently, laboratories are checking through a process called Polymerase Chain Reaction (PCR) on a collected sample from the suspected person. PCR is used to find genetic material on the virus. PCR amplifies the genetic material such that the virus can be detected easily. In general, the PCR process is used to match the genetic material with the virus that causes COVID-19 is unique. Blood tests are also can be done to detect if a person has previously been exposed to the virus or not. This blood test is useful for knowing if a person may have developed immunity. In our paper, we propose a unique technique which will work on the microscopic image of Coronavirus affected tissue and detects the tissue is affected or damaged by the Coronavirus or not. In this research, we use the microscopic image(s) of a smear of epithelial throat/bronchial tract tissue. Firstly, image smoothing is conducted, and then Region of Interest (ROI) is detected. Then that 24-bit colour image (ROI) is converted to grey to bi-colour images using edge detection by setting a threshold. At last, the contours is detected and counted. In our technique, the resultant contours are the affected areas, and the count is the number of possible affected areas, an approximation. Thus, the infection depth can be guessed. Our technique is not only detecting the infection but also identifies the approximate number of attacked spots in a specific tissue area. Thus, the proposed technique identifies the person has COVID-19 or Coronavirus disease.

D. Kim Department of Architecture Engineering, Dongseo University, Busan, Republic of Korea e-mail: [email protected] S. Govathoti (B) Department of Computer Science and Engineering, GITAM School of Technology, GITAM Deemed-to-be-University, Hyderabad, Telangana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_40

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40.1 Introduction At first, the people in China in the area of Wuhan had experienced a new disease, and many people used to suffer from that disease, and this happened in December 2019. This disease is later identified to be Coronavirus. Several thousands of people had suffered a lot with this disease in China. From there, slowly started spreading to other nations around the world. Today almost all the nations in the world are suffering from this disease of Coronavirus. Nearly 35 lakhs of patients are being identified around the world, and the population in developed countries is affecting more and suffering a lot with this disease. The name of this disease known as corona had given based on the spikes observed on the surface of the virus with the shape of the crown [1, 2]. Earlier the people used to call it as 2019-nCoV and now later on the named was remodified as 2019 novel Coronavirus. The World Health Organization had given the official name of this disease as COVID-19 in February 2020. The Coronavirus is one of the diseases that some of the symptoms of this virus are matching with the other two are MERS and SARS. These virus symptoms mainly affect the respiratory system of the human beings with cold and sometimes leads to the serious problems of pneumonia and death in some special cases like the patients with severe other health problems like diabetes, kidney problems, heart problems, etc. This virus mainly originates in animals like the bats, camels, etc. The common symptoms to be observed in human being affected with this disease are cold, dry cough and high fever. The other people also experienced some other symptoms like the diarrhoea, difficulty in breathing, headaches, etc. The recent symptoms observed are the patients cannot smell or taste the item that is the patients are losing the feeling of taste and smell. The major symptom can be observed in COVID-19 patients is that the shortness of breathing and this symptom is happening in early 5 days of the virus attacked with the person [3]. In recent days, the World Health Organization had declared that around 85% of the cases being noted or being identified are mild cases. The patients with a mild attack can feel the cold, dry cough and fever. The majority of the doctors suggest for this kind of patients are to take a large number of liquid fluids and isolate themselves from meeting with others such that to reduce the spread of this virus. Maintaining the physical distance between the patients or the common people was the best possible option or the solution for the time being for not getting affected or not spreading to others in the home or the workplace or the society. Around 15% of the cases are serious cases, and patients with these severe cases are required to be joined in the hospitals for ventilators and other oxygen supply. This Coronavirus is the family of viruses like the SARS and MERS [4]. Somehow due to several reasons, the researchers had failed to identify the vaccine for both SARS and MERS. Today the scientists say that the major difference between the SARS and the Coronavirus was only one mutation. It would be different in a way if the scientists had invented the vaccine for SARS. Somehow the whole world might be saved from this pandemic Coronavirus [5].

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Severe Acute Respiratory Syndrome, SARS, is a respiratory tract infection, caused by virus, and it spreads into the lungs very fast, which leads to severe injury in lungs. It may affect several other organs also depending on the immunity of the body. SARS Coronavirus starts its manifestation on epithelial cells (tissue) of the respiratory tract, generally. So, detection of infection can be done with the sample collected from those affected areas. As per the Centres for Disease Control and Prevention (CDC), Laboratory Testing for the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection can be classified [6]: Molecular tests is used for detection of active infection and serology tests is used to identify the previous infection by detecting antibodies to MERS-CoV. Serology tests are for surveillance or investigational purposes and not for diagnostic purposes. WHO also published its guideline of Laboratory Tests to detect 2019-nCoV (Novel Coronavirus Infection) in humans [7]. Medically, all the testing labs have to follow the published guideline of WHO. All these pathological tests currently in practice are conducted manually and time-taking as well. Cost also is high in third world countries, like India and its other neighbouring countries where poverty level stands near about 50%, more or less with high population density. WHO already declared the COVID-19 is pandemic and will be too difficult if it will enter into community spreading, known as Stage-3. Thus, a fast, accurate and economic testing is most desirable now as the condition of this devastating Coronavirus infection.

40.2 Literature Review This infectious disease is not very much new on the globe, but it was restricted to North and South America, Europe and Asia mainly. But now the situation is different, most of the countries are affected by this infection with the highest outbreak in China, Europe and America, expectedly, as per the previous studies regarding the nature of SARS or MARS CoV viruses. Zaki S. R., Goldsmith C. S., in 2005, explained in their book chapter [8] that SARS was identified during the outbreak of infection in the form of pneumonia that was first observed in late 2002 in Guangdong, China, and then pandemic in February 2003 where Asian countries, European countries, North American countries and South American countries, almost total 24 countries affected. The disease symptom was similar to influenza with high fever, cough, headache, and in worst cases, death. At that time transmission of infection like today’s transmission media person-to-person, international or intra country group travel with an infected patient initiated the worldwide spread of the disease, and by the time the peak outbreak happened, 8098 (approx.) cases within 774 deaths were recorded [8]. At that with the knowledge of WHO, 11 laboratories established worldwide to identify the causal agent. It is during the investigation of the specimen of a SARS patients and identified the virus as belonging to the family Coronaviridae or not [8]. From

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that time the focus shifted to the newly recognized Coronavirus (SARS-CoV). The chapter presented the morphologic characteristics of SARS-CoV grown in tissue, electron microscopic findings and cellular localization of the virus in tissues from human patients. The specimen is shown in Fig. 40.1 [8]. In April 2007, Jiang Gu and Christine Korteweg observed severe acute respiratory infection in the respiratory tract of a patient. Coronavirus attacked the epithelial tissue of the entire respiratory tract and badly damages it, mainly. Other organs also infected in the course of the illness, including the heavy injury of mucosal cells and lungs [9]. The studies produced a basic understanding of the COVID-19 infection, today. Fig. 40.1 Histopathology image of SARS in the affected patient. a Low power photomicrograph of lung showing interstitial pneumonia and intra-alveolar oedema. b Higher power photomicrograph showing diffuse alveolar damage with prominent hyaline membranes. c Multinucleated syncytial giant cells are seen in some cases of SARS [8]

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SARS first observed at Guangdong Province, on November 2002. During the next 3 months, it spread very fast almost all over the world; many countries were affected and resulting in pandemic declared by WHO, the first in the twenty-first century. August 2003, 8096 approximately, SARS cases had been reported at that time, with a fatality rate of ∼10% as per WHO. Hafeez et al. [1] had discussed in detail about the various symptoms of the virus, the methods to identify the virus and precautions to be considered for protection from the spread of the virus had discussed in detail. Tripathy et al. [2] had discussed the outbreak of the virus and the situations converted afterwards when the outbreak of the virus had happened. The mobility of the patients and the public during the outbreak had discussed in detail by this author. Zhong et al. [3] had discussed the early prediction and outbreak of the virus at china with the help of a mathematical model. The authors had presented the model by using a simple mathematical model, and the results were displayed in the results section. Ahn and Jang [4] had discussed the situations and the symptoms were observed in the spread of the virus especially in the area of the Middle East and Asia. Gennaro et al. [5] had discussed the main outbreak of the virus, future scenarios, preventive measures to be followed for protecting from the virus, and various other aspects related to the virus had discussed in detail.

40.3 Proposed Method So far, Biochemists, Medical Practitioners and Pathologists have used and are using the PCR process or visual process or clinical process to detect the presence of Coronavirus within the patient body. Here we are proposing a computer vision-based analysis and detection technique related to today’s artificial intelligence and machine learning framework. Electron microscopic image of the tissue of the specimen is required to detect the attack of Coronavirus in our technique. The image will be passed through our 2 algorithms, and finally, the output of the last algorithm will be the decision. We will use SIS-CODE technique to detect Coronavirus attack. For a better understanding of the performance of the current model, we had tested for various images which include both virus affected case and the virus not affected cases of images.

40.3.1 Specimen Image Smoothing (SIS) Algorithm Input: Microscopic Image of tissue of Specimen Output: Transformed Grey Image Step 1: Open Image File, ‘in’ (Fig. 40.2)

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Fig. 40.2 Specimen image severely affected by Coronavirus [10]. An electron microscopic image of a thin section of MERS-CoV, showing the spherical particles [10]

Fig. 40.3 ROI with many black rounded areas (manifestation of Coronavirus)

Step 2: Read Image data and store into Memory Step 3: Identify Region of Interest (ROI) in the form of Coordinates Step 4: Extract ROI from Original Image data Step 5: Smooth the ROI using 5 × 5 kernel using a Threshold value, t Step 6: Write the smoothen (transformed) Image data into Image File, ‘out’ (Fig. 40.3) Step 7: Stop.

40.3.2 Coronavirus Detection (CODE) Algorithm Input: ROI Image (‘out’ from SIS Algorithm) Output: Non zero positive value (number of manifestation in ROI or 0 if no infection)

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Step 1: Open ROI Image Step 2: Read the Image data and store to Memory Step 3: Convert the image data to Grey (0–255) then to binary (0–1) format Step 4: Converted binary image data again divided on a threshold value, t Step 5: Contour detection and count number of contours by edge detection Step 6: Print number of Contour count, K, maybe 0 or a positive integer Step 7: Stop. The developed algorithms had been tested for two sets of input images and results are displayed in the following images. The original image can be observed in Fig. 40.4a. Thereafter the converted image can be observed at Fig. 40.4b. The converted monochrome image can be observed in Fig. 40.4c. The detection of contour for the input image can be observed clearly in Fig. 40.4d. The total process of identifying the contours on the input images can be observed in Fig. 40.4 and its subpart number of Fig. 40.4a–d. The second case or the second input image has been taken as input for the testing of the current model. The input image considered in this test was the smooth tissue image, and we will try to identify the result of the current algorithm model such that to predict the performance of the currently developed model. From the above two cases that the analysis had been done for better understanding the performance of the currently developed SIS-CODE algorithms, the developed algorithms are working perfectly for the identification of contour on the images and also the model can able to give the prediction of ruptured images and the smooth images difference also observed. The current algorithms are working perfectly for the case of identification of contour on the images which will be supplying as input images to the current models. For a better understanding of the performance of this model, histograms also generated for both the images and the results, and further discussions had presented in the results section.

40.4 Results In this research, we tested our algorithms with images available in CDC and WHO Websites [6, 7]. 9 sets of images are tested successfully. The algorithms are working on 24-bit colour images as well, as it is converting 24-bit colour image to grey image first. Algorithms are working almost accurately, as the count of affected regions of tissue is matching accurately, which is tested manually for all images.

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40.4.1 Discussions For example, we have shown the output of SIS Algorithm with Figs. 40.2 and 40.3. Same output image has used in CODE Algorithm for decision making. Figures 40.4

(a). Original Image

(b). Converted Image

(c). Converted monochrome, binary Image Fig. 40.4 a–d SIS-CODE algorithms to detect damaged tissue

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(d). Contour Detection, Count, K = 42 (Approx), K area affected badly Fig. 40.4 (continued)

and 40.5 are showing the different stages of execution of CODE algorithm. Here input images are taken from the CDC Website [6]. Algorithms are implemented in Ubuntu 18 (8 GB RAM) with Python 3 and OpenCV. Average execution time for processing one sample is 10 s roughly. Algorithms are suitable for any Image size. Damaged tissue Histogram, Fig. 40.6, clearly showing more black spots is there in the image, which is damaged severely. On the other hand, Table 40.1 shows the comparison of performance with popular and widely used PCR Process and proposed SIS-CODE technique. Our proposed method can be improved if we will get well established Pathological Database. Thus, more sample images are required to check the performance. Shortly

Fig. 40.5 Damaged tissue histogram of binary image (value varies between 30 and 217)

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Fig. 40.6 Histogram of non-affected tissue, all near to 217 which is white

Table 40.1 Accuracy of the proposed model Test

Accuracy

Time

PCR

100% almost

2–5 days

Proposed SIS-CODE

98% (estimation) if the testing sample is huge

Immediate

we hope, the database will be available to check our algorithm. Our Algorithm is working fine with the available data so far we collected. 2% false recognition (statistical error) always will be there as the data is transformed and processed for detection.

40.5 Conclusions The main focus of our research is to detect the COVID-19 infection in the patient pathological sample. Our interest is to make the process automatic, computerized, untouched test and to get the immediate result. To detect we propose two new innovative algorithms using image recognition. In the discussion section, we have discussed the analysis of the algorithms and results. The result we have got is convincing, and more importantly, the level of infection also can be detected. This research is under the category of computer vision.

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References 1. Hafeez, A., Ahmad, S., Ali Siddiqui, S., Ahmad, M., Mishra, S.: A review of COVID-19 (coronavirus disease-2019) diagnosis, treatments and prevention. Eur. J. Med. Oncol. 4(2), 116–125 2. Tripathy, A.K., Mohapatra, A.G., Mohanty, S.P., Kougianos, E., Joshi, A.M., Das, G.: EasyBand: a wearable for safety-aware mobility during pandemic outbreak. IEEE Consum. Electron. Mag. 1 (2020) 3. Zhong, L., Mu, L., Li, J., Wang, J., Yin, Z., Liu, D.: Early prediction of the 2019 novel coronavirus outbreak in the mainland China based on simple mathematical model. IEEE Access 8, 51761–51769 (2020) 4. Ahn, I., Jang, J.-H.: Comparative study of Middle East respiratory syndrome Coronavirus using bioinformatics techniques. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2020) 5. Di Gennaro, F., Pizzol, D., Marotta, C., Antunes, M., Racalbuto, V., Veronese, N., Smith, L.: Coronavirus diseases (COVID-19) current status and future perspectives: a narrative review. Int. J. Environ. Res. Public Health 17, 2–11 (2020) 6. https://www.cdc.gov/coronavirus/mers/lab/lab-testing.html. Last accessed on 29 Mar. 2020 7. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-uidance/labora tory-guidance. Last accessed on 29 Mar. 2020 8. Zaki, S.R., Goldsmith, C.S.: SARS coronavirus infection: pathology and pathogenesis of an emerging virus disease, pp. 87–99 (2005). (Published in the book: Schmidt, A., Weber, O., Wolff, M.H. (eds.): Coronaviruses With Special Emphasis on First Insights Concerning SARS) https://doi.org/10.1007/3-7643-7339-3_4 9. Gu, J., Korteweg, C.: Pathology and pathogenesis of severe acute respiratory syndrome. Am. J. Pathol. 170(4), 1136–1147 (2007). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1829448/ 10. https://www.cdc.gov/coronavirus/mers/photos.html. Last accessed on 29 Mar. 2020

Chapter 41

Nanotechnology and the Application of Information Technology for Sustainable Innovation in Agriculture Pushan Kumar Dutta, Ujan Banerjee, Banani Manna, Kabyashree Hazarika, Bimalangshu Das, and Sudipta Roy

Abstract The field of nanomaterials has been constantly explored finding solutions to enhance the overall crop quality and production scale. Those nanoparticles with tiny size to broad surface area (1–100 nm) have plenty potential functions specifically in the field of agriculture. These days, sustainable agriculture is required. The evolution of nanomaterials has appeared as promising agents for the plant growth, fertilizers, and pesticides. This review provides basic data regarding CNTs, as well as their various properties, with a focus on their applications in the agriculture domain. Furthermore, the mechanisms of the uptake and translocation of CNTs in plants and their defense mechanisms against environmental stresses, and uses in pesticide, antimicrobial activity, development of robust nanosensors and targeted drug delivery systems are mentioned. Mainstream farming methods are now becoming progressively inadequate as the requirements on the key ecological increase. New technology adoption is essential if output is to be enlarged to encounter demand for food, fodder, and fiber. Nanotechnology guarantees an advancement in improving nutrient use efficiency through nano-formulation of fertilizers, having broken yield and nutritional quality barriers through bio-nanotechnology, pest and disease monitoring and control, and understanding the molecular pathways of host-parasite interactions.

P. K. Dutta (B) Amity School of Engineering and Technology, Amity University Kolkata, Kolkata, West Bengal, India e-mail: [email protected] U. Banerjee · B. Manna · K. Hazarika · B. Das Amity Institute of Biotechnology, Amity University Kolkata, Kolkata, West Bengal, India S. Roy Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai 410206, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_41

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41.1 Introduction Information science in nanotechnology involves the study and application of information technology to the field of nanotechnology. It includes the development of data management systems, computational simulations, and modeling of nanoscale systems and devices. The goal is to support the design, fabrication, and characterization of nanoscale materials and devices. Information science in nanotechnology also includes the study of data privacy and security for nanoscale systems and devices. Nanotechnology has a wide range of applications in various fields such as medicine, electronics, energy, environmental science, and materials science. Some examples include Medical: Nanoparticles can be used for targeted drug delivery, imaging, and diagnostics. Electronics: Nanoscale transistors and other components are used in computer processors, memory devices, and other electronic devices. Energy: Nanotechnology is being used to improve solar cells, batteries, and fuel cells. Environmental Science: Nanotechnology is being used to develop more efficient catalysts for chemical reactions, sensors for detecting pollutants, and methods for cleaning up contaminated sites. Materials Science: Nanotechnology is being used to create new materials with improved strength, electrical conductivity, and other properties. These are just a few examples, the potential applications of nanotechnology are still being explored and new ones are being discovered. While nanotechnology offers many potential benefits, there are also some potential negative aspects to consider. Some of the main concerns include Environmental and health risks: Because nanoparticles are so small, they can easily penetrate living cells and tissues, which raises concerns about their potential toxicity. Some studies have suggested that certain types of nanoparticles may be harmful to human health or the environment. Economic and social impacts: The development and commercialization of nanotechnology could have a significant impact on the economy and society. There are concerns that it could lead to job losses and income inequality, as well as the concentration of economic power in the hands of a few large companies. Ethical and legal issues: The unique properties of nanoparticles raise several ethical and legal questions, such as the potential use of nanotechnology in weapons, surveillance, or other controversial applications. Lack of regulation: As nanotechnology is a relatively new field, there are currently few regulations in place to govern its development and use. This lack of oversight could lead to unintended consequences if proper precautions are not taken. It is crucial to note that these worries are not distinctive to nanobiotechnology, but pertain to any new technology. Nanofertilizers and information technology go hand in hand in modern agriculture. Nanofertilizers are tiny particles of fertilizers that are much smaller in size than conventional fertilizers. These particles can penetrate deeper into the soil and provide better nutrient uptake by crops. This results in more efficient and effective use of fertilizer, reducing waste and increasing crop yields. Information technology plays a crucial role in the development and distribution of nanofertilizers. Advanced sensors, remote monitoring systems, and data analysis tools can provide valuable insights into soil health, crop growth, and fertilizer usage. This information

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can be used to develop more targeted and effective fertilizer management strategies, reducing the risk of fertilizer runoff and environmental damage. Additionally, information technology can help with the efficient distribution of nanofertilizers. Precision agriculture techniques can be used to precisely apply fertilizers, reducing waste and ensuring that crops receive the optimal amount of nutrients. This leads to higher yields, improved soil health, and lower costs for farmers. Overall, the combination of nanofertilizers and information technology is leading to a revolution in agriculture, improving productivity and sustainability while reducing waste and environmental impact. As the field of nanotechnology changes over time, active research and regulatory policies are being taken to address these concerns and make sure the safe and responsible development of this technology. The agriculture sector is currently dealing with immense issues such as climate change, infectious crop diseases, soil nutrient deficit, crop yield reduction, lack of awareness of genetically modified crops, and a shortage of labor, all of which endanger agriculture’s sustainability. Climate change has exacerbated the magnitude of abiotic stresses like drought and extreme heat, causing significant losses in important cereal crops like wheat, maize, and barely. Moreover, biotic stresses such as pests and diseases reduced agricultural production by approximately 20–30% annually, which is viewed as the most saddening obstacle to achieving global food security [1]. Only a few food crops, such as maize, rice, potato, papaya, squash, and apple, have genetic engineering varieties that have used trying to cut biotechnology tools to introduce, completely remove, or rearrange specific genes.

41.2 Application of Nanotechnology in the Field of Information Science in Agriculture Nanotechnology has the potential to revolutionize agriculture and allied sciences by providing new tools for enhancing crop productivity, improving soil quality, and reducing environmental impact. Some of the specific applications of nanotechnology in agriculture and allied sciences include Development of nanofertilizers—nanoparticle-based fertilizers that can provide targeted delivery of essential nutrients to crops, reducing waste and increasing efficiency. Nanopesticides—using nanoparticles to deliver pesticide active ingredients to crops, leading to improved efficiency and reduced environmental impact. Development of nanosensors for precision agriculture—using nanotechnology-based sensors for monitoring soil moisture, pH, and nutrient levels, to optimize crop growth and reduce waste.

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Nanocomposite-based food packaging—using nanotechnology to develop new, more effective food packaging materials that can extend the shelf life of food and reduce food waste. Development of nanomaterials for soil remediation—using nanotechnology to remove contaminants from contaminated soil and improve soil quality. These are some of the ways in which nanotechnology is being applied in agriculture and allied sciences to improve crop yields, reduce waste, and promote sustainable agriculture. Nanotechnology has the potential to enhance agriculture in a number of ways, some examples include • Improving crop yields: Nanotechnology can be used to develop new types of fertilizers, pesticides, and plant growth regulators that are more effective and efficient at promoting plant growth and protecting crops from pests and diseases. • Improving food quality: Nanotechnology can be used to create new food packaging materials that extend the shelf life of perishable foods and can also be used to enhance the nutritional content of food. • Increasing crop resistance: Nanotechnology can be used to develop new crop varieties that are more resistant to environmental stresses such as drought, heat, and salinity. • Improving soil health: Nanotechnology can be used to develop new soil amendments that can improve the fertility and water-holding capacity of soil, which can help to increase crop yields. • Precision farming: Nanotechnology can be used to develop new sensors and imaging technologies that can help farmers to monitor and manage their crops, which can lead to more efficient use of resources and higher yields more precisely. It’s important to note that while nanotechnology has the potential to enhance agriculture, it’s also important to consider the potential risks and negative impacts, and research is ongoing to find the best and most sustainable ways to implement these technologies in the field. Nanotechnology has the potential to turn conventional farming into ‘precision farming,’ making it a promising foundation for sustainable agriculture. Precision farming is a balanced approach to boosting agricultural production by tracking environmental factors and taking carefully calculated adjustments in response to each environmental situation [2]. With revolution and severe measures like the use of genetically modified crops and agrochemicals, agriculture has changed from being an unappreciative occupation to one that is exceedingly complex. Nanotechnology has the potential to increase crop yields while balancing ecological stability, environmental sustainability, and economic strength [2]. In this review, we offer a broad overview of the applications of NTs in the agriculture field. We aimed to provide upgraded data and an entire mechanistic understanding of the new paradigms of the applications and progress of NTs in agriculture by working their roles in promoting seed germination and plant growth, antifungal activity, gene delivery, and as nanosensor, toward panning an additional robust and more sustainable agriculture system for the future. Moreover, we provide the mechanism by

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which NTs can promote sustainable agriculture through their uptake, translocation, accumulation, and defense mechanisms against environmental obstructions. Finally, NT-related challenges and future views in the agriculture field are highlighted. Agriculture scientists should consider nanotechnology because it offers potential solutions to some of the challenges facing agriculture such as • • • • •

Improving crop yields and quality Increasing the efficiency of fertilizers and pesticides Providing more precise and targeted delivery of nutrients and other substances Developing new materials for agriculture Improving food safety and preservation.

By incorporating nanotechnology, agriculture scientists may be able to address some of these challenges and improve the overall sustainability and productivity of agriculture.

41.3 Implementation of Nanoparticles in Agriculture 41.3.1 Nanobiosensors in Agriculture Nanobiosensors are a type of sensor that use nanotechnology to detect and measure biological molecules or processes. In agriculture, nanobiosensors can be used in a variety of ways, such as 1. Soil analysis: Nanobiosensors can be used to detect and measure the levels of nutrients, minerals, and contaminants in soil. This can help farmers to optimize their use of fertilizers and other inputs and can also help to identify and address potential soil health issues. 2. Plant disease detection: Nanobiosensors can be used to detect the presence of plant pathogens, such as viruses and bacteria, which can help farmers to quickly identify and address disease outbreaks. 3. Pest detection: Nanobiosensors can be used to detect the presence of pests, such as insects and nematodes, which can help farmers to more effectively manage pest populations. 4. Crop monitoring: Nanobiosensors can be used to monitor the growth and health of crops, such as measuring the amount of water in soil, photosynthesis, and stress level of the plant. Livestock monitoring: Nanobiosensors can be used to monitor the health of livestock, such as detecting diseases or monitoring the feeding and activity levels of animals; this can help farmers to optimize the care and management of their animals. Overall, the use of nanobiosensors in agriculture has the potential to revolutionize the way farmers monitor and manage their crops and livestock, leading to more efficient use of resources and higher yields.

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41.3.2 Encapsulation of Nanotechnology in Agriculture The process refers to the mode of enclosing or trapping a material within a nanoparticle or nanocapsule. In agriculture, encapsulation can be used in a variety of ways, such as Controlled release of fertilizers and pesticides: By encapsulating these materials within nanoparticles, they can be released at a controlled rate, which can help to optimize their effectiveness and reduce the amount of waste. Protection of beneficial microorganisms: By encapsulating beneficial microorganisms, such as bacteria or fungi, within nanoparticles, they can be protected from environmental stresses, such as heat, light, or drought, and can be more effectively delivered to the target crop or soil. Protection of plant growth-promoting substances: By encapsulating plant growth-promoting substances, such as hormones or enzymes, within nanoparticles, they can be protected from degradation and can be more effectively delivered to the target plant. Protection of seed: By encapsulating seed with nanoparticles, it can be protected from pests, pathogens, and environmental stress, and can improve germination rate and seedling growth. Protection of food: By encapsulating food with nanoparticles, it can be protected from oxidation, moisture loss, and other factors that can cause spoilage, which can help to extend the shelf life of perishable foods. Overall, encapsulation can be an effective way to optimize the delivery and effectiveness of various materials used in agriculture and can also help to protect beneficial microorganisms and substances, leading to more efficient use of resources and higher yields.

41.3.3 Nanoherbicides Growth and Monitoring Nanoherbicides are a type of herbicide that use nanotechnology to target and control weeds. They are designed to be more effective and selective than traditional herbicides and have several potential benefits for agriculture: Increased selectivity: Nanoherbicides can be designed to target specific types of weeds, which can help to reduce the risk of damage to non-target crops or plants. Increased efficacy: Nanoherbicides can be designed to be more effective at controlling weeds, which can help to reduce the need for multiple applications or the use of other control methods. Reduced toxicity: Nanoherbicides can be designed to be less toxic to humans, animals, and the environment than traditional herbicides. Reduced drift: Nanoherbicides can be designed to be less prone to drift, which can help to reduce the risk of damage to non-target areas or plants. Enhanced penetration: Nanoherbicides can be designed to penetrate the weeds’ cuticle and reach the meristematic tissue, which can help to control even the most resistant weeds. Overall, nanoherbicides have the potential to revolutionize weed control in agriculture, leading to more efficient and effective weed management, and less impact on the environment. However, it’s important to note that nanoherbicides are still in the early

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stages of development, and more research is needed to understand their long-term safety and efficacy.

41.3.4 Nanoparticles in Sustained Agriculture Monitoring Several nanoparticles are utilized in agriculture on a commercial scale. The following list contains prevalent nanoparticles: • Polymeric Nanoparticles: Polymeric nanoparticles are employed in the agricultural industry to administer agrochemicals slowly and precisely. Polymeric nanoparticles have several benefits, including greater biocompatibility and less effect on creatures that aren’t intended to be affected. Polyethylene glycol, poly(epsilon-caprolactone), poly(lactide-co-glycolides), and poly(-glutamic acid) are a few of the polymeric nanomaterials employed in agriculture [4]. • Silver Nanoparticles: The antibacterial properties of silver nanoparticles against a variety of phytopathogens are widely exploited. Silver nanoparticles have been found to promote plant development, according to scientists. • Nano-aluminosilicates: Nano-aluminosilicate formulations are a popular choice among chemical manufacturers for pesticides. • Titanium dioxide Nanoparticles: These biocompatible nanoparticles are employed as a water disinfectant. • Carbon Nanomaterial: For enhanced seed germination, carbon nanoparticles like graphene, graphene oxide, carbon dots, and fullerenes are employed. Carbon nanotubes (CNTs), one of the many types of carbon-based nanomaterials (CBNs), have drawn more attention in agricultural applications due to their effects on controlling plant growth, ability to pass through plant cell walls, agricultural smart delivery, nanotransport, and as a medium for biosensors [5, 6].

41.4 Application of Metal-Based Nanomaterials Sustainable agriculture is crucial at this stage. It might be interpreted as giving an ecosystem a favorable long-term strategy. The impact of various techniques on soil qualities, which are crucial to sustainability, must be demonstrated by long-term trials, which will also give crucial information on this goal. Several researchers have documented the effects of several NPs on plant development and phytotoxicity, including those of magnetite (Fe3 O4 ) nanoparticles, alumina, zinc, and zinc oxide on seed germination and root growth of five higher plant species, including radish, lettuce, maize, and cucumber, silver nanoparticles and seedling growth in wheat, sulfur nanoparticles on tomato, zinc oxide in mung-bean, and nanoparticles of AlO, wheat growth and yield can be stimulated by silver nanoparticles [6]. Wheat growth and production were very favorably influenced by the 25 ppm SNPs that were sprayed to the soil [7].

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Despite only being needed in minimal levels by plants, zinc has been regarded an important mineral for metabolic processes in plants. It was discovered that zinc plays a significant part in controlling reactive oxygen species and safeguarding plant cells from oxidative stressors. When tryptophan is converted to auxin or indoleacetic acid (IAA), as well as throughout the metabolic processes necessary for the production of carbohydrates and chlorophyll, zinc plays a crucial role. Zn deficiency may have an impact on agricultural output and produce quality [6]. For agricultural and public health, the emergence of pesticide resistance in pest insects has become a growing issue.

41.4.1 Plant Pest Management and Nano-insecticidal Potential The use of nanomaterials as a substitute for traditional plant pathogen management methods has recently been given some thought. Due to its high output loss, protracted fungal survival in soil, and development of resistance races, fusarium wilt is a damaging disease for tomatoes and lettuce in many regions. However, the emergence and evolution of novel pathogenic races remain a challenge, and pharmacological treatment is both costly and occasionally ineffective. By employing aqueous extracts of Punica granatum peels, Olea europaea leaves, and Chamaemelum nobile flowers, copper oxide (CuO), zinc oxide (ZnO), magnesium hydroxide (MgOH), and magnesium oxide (MgO) were effectively synthesized as nanomaterials [8]. There are numerous applications for nanotechnology in agriculture, including molecular farming through the use of nano-vectors, which aims to replace viral vectors and insecticides and fertilizers with efficient effects on plant development [9]. For this with the synthesis of CuONPs, MgOHNPs and MgONPs, and ZnONPs, researchers have devised a variety of methods, including chemical route, precipitation, hydrolysis in polar organic solvents, water-in-oil microemulsion, hydrothermal route, and microwave synthesis [10, 11].

41.4.2 Nanomaterials as Antimicrobials Silver nanoparticles are one of several nanomaterials that are utilized as antibacterial agents in food packaging. Titanium dioxide (TiO2 ), zinc oxide (ZnO), silicon oxide (SiO2 ), magnesium oxide (MgO), gold, and silver are some more nanoparticles now in use. Each one of them has distinct qualities and roles; for instance, zinc nanocrystal exhibits antibacterial and antifungal activity. Silver, silver zeolite, and other forms of silver were utilized by NASA and the Russian Space Station to cleanse and sterilize water. Gold has exhibited excellent antifungal and antibacterial activities against 150 distinct microorganisms, high-temperature stability, low volatility,

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and low flammability. The direct use of silver as a disinfectant in commercial water was approved by the FDA in 2009 due to its effectiveness in preventing germs. E coli, L. monocytogenes, and Staphylococcus aureus had an antibacterial impact on these, and nano-silver particles treated with cellulose acetate phthalate also produced comparable results [11]. A few nanoparticles have demonstrated antifungal properties. These fungi comprise yeast, Aspergillus niger, and Candida albicans. Staphylococcus aureus with methicillin resistance has also been shown to be susceptible to AgNPs [12]. Antimicrobial properties have also been discovered in nanoparticles other than silver, such as titanium oxide (TiO2 ). Its antibacterial properties under UV light were clear. According to reports, packing materials made of zinc oxide offer antibacterial properties. Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli prevalent strains have been successfully eradicated by zinc oxide nanoparticles created using Punica granatum peel aqueous extract [13].

41.4.3 Nanofertilizers In traditional agriculture, more fertilizer is administered than the plant needs, either directly to the soil or as a spray on the leaves. This is because relatively little fertilizer really reaches its intended site due to chemical leaching, evaporation, drift, hydrolysis, runoff, and photolytic or microbiological decomposition [9]. This overuse of chemical fertilizer degrades the soil’s nutritional balance and contaminates nearby water sources when hazardous substances seep into the water. Nanofertilizer is a type of fertilizer that uses nanoparticles to deliver nutrients directly to plant roots. The small size of the nanoparticles allows them to penetrate deep into the soil and provide targeted, efficient delivery of essential nutrients to plants, leading to improved plant growth and yield. Nanofertilizers are seen as a promising solution for addressing issues of fertilizer waste and overuse. By enhancing the availability of fertilizer nutrients in the soil and nutrient absorption by plants, nanomaterials can boost agricultural output. By directly attacking phytopathogens through several processes, including the generation of reactive oxygen species, these substances help prevent crop illnesses [6, 14]. These substances also indirectly improve crop yield by enhancing plant defense mechanisms and crop nutrition. The damaging effects of conventional agricultural techniques may be lessened by the effective use of nanomaterials. Nanofertilizers have been shown in recent laboratory studies to increase agricultural output by speeding up seed germination, seedling development, and photosynthetic activity. The primary nutrient entry points for plant systems and the surfaces of the leaf and root are stimulated by nanomaterials. Through the development of new pores, the exploitation of endocytosis or ion channels, or the facilitation of complexation with molecular transporters or root exudates, nanofertilizers can improve the plant’s ability to absorb nutrients through these pores [15]. Exploration is also being done on nanobiosensors that respond to certain root exudates. The myriad ethical and safety concerns with these relatively new procedures must be carefully considered before use.

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41.4.4 Robust Biosensor Networks Because of their high sensitivity and low detection limit, CNT-based biosensors are advantageous for efficient and sustainable agriculture. CNT-based nanosensors have been used in agriculture to identify pests and detect soil moisture, pesticide residues, proteins, and toxic chemicals [1]. Based on chirality-dependent fluorescence in the near-infrared range, SWCNTs can be a viable tool for biosensing applications (NIR). Three-week-old spinach plants were treated with SWCNTs coated with polyvinyl alcohol (PVA) and Bombolitin II to transform them into nitroaromatic detectors (hereafter referred to as Bombolitin). Using a needleless syringe, PVA-SWCNTs (PSWCNTs) and Bombolitin-SWCNTs (B-SWCNTs) were introduced into two parts of a single leaf’s lamina that were divided by the mid-vein and were lodged in the parenchyma tissues [16, 17]. To identify H2 O2 in Arabidopsis thaliana leaves, an NIR fluorescence sensor based on SWCNTs was devised. H2 O2 quenched the sensor’s NIR fluorescence response, and no other stress-related signaling substances caused the same response. In vivo and distant NIR imaging of plant conditions in response to various stimuli, such as a pathogen-related peptide, high light, and ultraviolet-B light, were both made possible by this H2 O2 sensor. Leaf damage, however, could not be picked up by this device [18]. Currently, ethylene-based plant volatile organic chemical sensors are commercially available for use in agricultural applications. They haven’t yet been directly interfaced with crops for the purpose of monitoring plant signaling molecules, though.

41.4.5 Nanotechnology in Diagnosis and Management of Plant Disease Nanotechnology can be used in the diagnosis and management of plant diseases in several ways: • Diagnosis: Nanotechnology can be used to develop diagnostic tools, such as biosensors and diagnostic imaging techniques, that can detect the presence of plant pathogens with high sensitivity and specificity. These tools can be used to quickly and accurately diagnose plant diseases, which can help to prevent their spread and minimize crop losses. • Detection: Nanotechnology can be used to develop tools for early detection of plant pathogens, such as PCR-based methods and biosensors that can detect the presence of pathogens even at very low concentrations. This can help to identify the onset of a disease outbreak early, which can allow for more effective disease management. • Treatment: Nanotechnology can be used to develop new treatment options for plant diseases, such as nanoparticle-based delivery systems for pesticides and fungicides, and nanoencapsulation of beneficial microorganisms. These methods

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can help to optimize the delivery and effectiveness of these treatments and can also help to reduce their toxicity and environmental impact. • Crop protection: Nanotechnology can be used to create protective coatings, such as nano-formulations of essential oils that can protect the crops from pathogens and pests; this can lead to increased crop yields and a reduced need for chemical pesticides. Overall, the use of nanotechnology in the diagnosis and management of plant diseases has the potential to revolutionize the way we detect, treat, and prevent plant diseases, leading to more efficient use of resources and higher crop yields. Nanofood refers to food products that contain nanoparticles or have been processed using nanotechnology. Nanotechnology is used in food production to improve food safety, quality, and nutritional value, as well as extend shelf life. Nanoparticles can be used to encapsulate flavors, vitamins, and minerals to enhance their stability and delivery. However, the safety and regulation of nanofood is still a topic of debate and ongoing research in the food industry.

41.4.6 Innovative Drug Administration System Nanocarrier-based nutrient delivery could be a promising technique for plant improvement. Smart delivery systems of agrochemicals and organic molecules as well as transport of DNA molecules or oligonucleotides into plant cells are potential applications of nanobiotechnology, based on the penetration ability of carbon nanomaterials through cell walls and membranes of plant cells. By employing a lipid exchange envelope and penetration model, NMs were improved for the selective delivery of a biological material to specific plant organelles via passive delivery [19]. These nanoparticles, passively penetrating through the chloroplast membrane via diffusion, were ready to influence photosynthetic activity by supplying electrons into the photosynthetic electron transport chain. The compounds that were adsorbed by the plants via the MWCNTs could be liberate within the plant to effectively provide routes for the delivery of genetic material or drugs to specific sites of intact plants. Apart from all these agricultural applications, CNTs are also being investigated as molecular transporters also in animal cells for medical purpose. In parallel, a lot of attention is being paid to analysis and development of techniques for directed modifications of CNTs to prevent cytotoxicity.

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41.4.7 Nanotechnology in Food Technology Nanotechnology in food science and technology involves the use of nanoparticles, nanoscale materials and processes, to modify, enhance, and improve food and foodrelated products. The application of nanotechnology in food includes • Food packaging: Nanoparticles can be used to create smarter food packaging that helps extend the shelf life of products and protects against contamination. • Food preservation: Nanoparticles can be used to slow down spoilage and extend the shelf life of food products. • Food fortification: Nanoparticles can be used to encapsulate vitamins and minerals, making it easier to add nutrients to food products. • Flavor and aroma enhancement: Nanoparticles can be used to deliver and preserve flavors, leading to improved taste and aroma in food products. Improved sensing and detection: Nanotechnology can be used to develop faster and more sensitive food sensors, improving food safety and quality control. While nanotechnology has the potential to offer significant benefits in the food industry, it is also important to carefully consider the potential risks and to ensure that nanotechnology is used in a responsible manner.

41.4.8 Nanopesticides in Agriculture Nanopesticides are pesticides that use nanotechnology to deliver active ingredients at the nanoscale level. They can be formulated as nanoparticles, nanocomposites, or nanoemulsions and have several potential benefits for agriculture: • Increased efficacy: Nanopesticides can be designed to target specific pests or pathogens, which can help to increase their effectiveness and reduce the need for multiple applications or the use of other control methods. Reduced toxicity: Nanopesticides can be designed to be less toxic to humans, animals, and the environment than traditional pesticides. • Increased penetration: Nanopesticides can be designed to penetrate the cuticle and reach the target pest or pathogen, which can help to control even the most resistant pests or pathogens. Increased shelf-life: Nanopesticides can be designed to have a longer shelf life than traditional pesticides, which can help to reduce the need for frequent reapplication. • Reduced drift: Nanopesticides can be designed to be less prone to drift, which can help to reduce the risk of damage to non-target areas or plants. Overall, nanopesticides have the potential to revolutionize pest and disease control in agriculture, leading to more efficient and effective control of pests and pathogens, and less impact on the environment. However, it’s important to note that nanopesticides are still in the early stages of development, and more research is needed to understand their long-term safety and efficacy, as well as to consider the potential risks of

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nanoparticles in the environment. Nanomaterials can be used in conjunction with genetic transformation techniques to improve the efficiency and effectiveness of crop improvement.

41.4.9 Delivery of Genes Nanomaterials can be used to deliver genetic material, such as DNA or RNA, into plant cells. This can be done using nanoparticles, such as liposomes, polymers, and dendrimers, which can protect the genetic material and enhance its uptake by the cells. This can help to improve the efficiency of genetic transformation, leading to more successful transformation events and higher expression of the transgene. Stabilization of genetic material: Nanomaterials can be used to stabilize genetic material, such as DNA or RNA, during the transformation process. This can be done using nanoparticles, such as gold nanoparticles, which can protect the genetic material from degradation and help to maintain its integrity.

41.4.10 Targeting Specific Cells Nanomaterials can be used to target specific cells or tissues for genetic transformation. This can be done using nanoparticles, such as quantum dots, which can be conjugated with targeting moieties, such as antibodies or peptides that can bind to specific receptors on the cell surface. This can help to increase the specificity of the transformation process and reduce the risk of off-target effects. Overall, the use of nanomaterials in conjunction with genetic transformation techniques can help to improve the efficiency and effectiveness of crop improvement, leading to more successful transformation events and higher expression of the transgene. However, it’s important to note that the use of nanomaterials in genetic transformation is still in the early stages of development, and more research is needed to understand their long-term safety and efficacy. Iron nanomaterials have been explored to clean contaminated soils. They can be used in a process called “nanoparticleenhanced phytoremediation” or “phytoremediation assisted by nanotechnology” (PAN) in which plants are used in conjunction with iron nanoparticles to remove contaminants from soil. Iron nanoparticles can be applied to the soil, where they react with contaminants such as heavy metals, and convert them into less toxic forms. The plants can then uptake these contaminants through their roots and accumulate them in their tissues. This can help to reduce the overall concentration of contaminants in the soil and make the soil safe for agriculture or other uses. Iron nanoparticles have been shown to be effective at removing a wide range of contaminants, including heavy metals such as lead, cadmium, and copper, as well as organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs). Iron nanoparticles have several advantages over other methods of soil cleaning, such

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as being relatively low cost, easy to produce and apply, and have a low environmental impact. However, more research is needed to understand their long-term effects on the soil, plants, and other organisms, and to develop effective protocols for their use in soil cleaning. It’s worth noting that although iron nanoparticles are bio-degradable and are seen as a safer alternative to traditional chemical methods, the long-term effects of their use in the environment are still not fully understood. • Nanotechnology has applications in various fields of animal science, including: • Drug delivery: Nanoparticles can be used to deliver drugs in a targeted and controlled manner to treat diseases in animals. • Diagnostics: Nanotechnology can be used to develop rapid, sensitive, and specific diagnostic tools for diseases in animals. • Tissue engineering: Nanoscale materials can be used to create artificial tissues for repairing damaged tissues or organs in animals. • Feed additives: Nanoparticles can be used as feed additives to enhance the nutrition and health of livestock. • Biosensors: Nanotechnology can be used to develop biosensors for monitoring various parameters related to animal health and welfare, such as stress levels, hydration status, and food intake.

41.4.11 Health and Environmental Concerns Related to Nanoparticles Include • Toxicity: Some nanoparticles have been shown to be toxic to living organisms, including humans and animals, and may cause damage to cells and organs. • Bioaccumulation: Nanoparticles have the potential to accumulate in the environment and in living organisms, leading to long-term exposure and potential harm. • Environmental pollution: Nanoparticles may be released into the environment during their production, use, and disposal, leading to potential contamination of water and soil. • Human exposure: Nanoparticles can enter the human body through inhalation, ingestion, and skin contact, leading to potential health effects. • Regulatory gaps: Current regulations and standards may not adequately address the potential health and environmental impacts of nanoparticles. It’s important to carefully evaluate and mitigate the potential health and environmental risks associated with nanoparticles and to continually monitor and assess their impacts as the field continues to advance. • Biosensors to detect nutrients and contaminants. Biosensors can be used to evaluate.

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• Food safety: Biosensors can be used to detect contaminants quickly and accurately such as bacteria, viruses, and toxins in food, enabling rapid response to potential outbreaks. • Nutrient analysis: Biosensors can be used to determine the nutrient content of food, including vitamins, minerals, and amino acids, enabling better monitoring of dietary intake. • Environmental monitoring: Biosensors can be used to detect contaminants in the environment, such as heavy metals, pesticides, and microplastics, allowing for early detection and remediation of potential environmental hazards. • Medical diagnosis: Biosensors can be used in medical settings to detect and monitor various biomarkers, including glucose, lactate, and cholesterol, enabling real-time monitoring of patient health. • Agricultural monitoring: Biosensors can be used in agriculture to monitor soil health, including nutrient levels and pH, enabling better management of crop yields and soil quality.

41.5 A Comparative Study of Approaches to Merge Nanomaterials to Various Agricultural Sectors 41.5.1 Constraints in the Field of Nanotechnology Nanotechnology is a rapidly growing science discipline with applications almost in every sector. Despite its possibilities, the mass production and preparation of nanoparticles has had unanticipated adverse effects on humans and the environment [20]. Bandyopadhyay et al. [21] discovered that nanoparticles have an effect on humans when they approach the food network and aquifers. According to widespread accounts, nanobiotechnology can assist in reducing poverty and other troubles. The dimension of the particles makes breathing difficult because they pierce the lungs [22]. As according to Buzea, asbestos nanoparticles and carbon nanotubes have one massive effect on lung diseases. According to [23], the reliability and decomposition of inorganic nanoparticles is a subject of debate because of environmental and residual concerns. Nanoparticles interact with zones that are not designed for them, starting to cause health and environmental issues [24]. Various committees and unions, such as the European Union and the Royal Commission on Environmental Pollution, have been set up in various countries to evaluate the dangers of nanotechnological breakthroughs. Nanoparticle-related hazards are difficult to detect [25]. Nanotechnology danger and its impact on the environment and people are difficult to quantify [26] (Table 41.1). Implementing the following strategies can help us increase agricultural output: • Nanocapsules for herbicide delivery and pest management. • The application of nanosensors to detect underwater toxins.

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Table 41.1 Analogy of various approaches of agricultural sectors in incorporating nanomaterials Type of nano-products

Components

Strengths

Fertilizers/ pesticide

Ammonium charged zeolites, graphene oxide films, nano-calcite, nano SiO2 , MgO

Increased surface mass ratio due to reduced dimensions. Facilitation of complexation with molecular transporters, increases Absorptivity, respond well to root exudates, better uptake of micro-nutrients, reduces nitrogen loss

Antimicrobials/ disinfectants

Titanium dioxide (TiO2 ), Zinc oxide (ZnO), Silicon oxide (SiO2 ), Magnesium oxide (MgO), Gold, Silver

High temperature, stability, low volatility, and low flammability, food grade compatibility

Biosensors

Carbon nanotubes (CNTs), quantum dots (QDs), gold, silica, silver (Ag), graphene

Better assembly of bio-recognition element, the high surface area, high electronic conductivity for ease of detection

Drug delivery

Mesoporous silicon-based Bio-degradable, low batch-to-batch materials, solid-lipidic variability, nontoxic, eco-compatible water nano-particles, soluble, stimuli sensitive nanoemulsions, dendrimers, nano-crystals, hydrogels

• Microbioplastics with low environmental and economic impact could also be used in heavy metal detoxification and reprocessing. • Smart particles could be useful for monitoring systems and purification. • Nano-structured metals can be used to deteriorate hazardous organic wastes at room temp.

41.6 Challenges Encountered by Nanomaterials Associated with Agriculture The toxicity concern stands as a major stumbling block in the way of incorporating nanoparticles in the field of agriculture, specifically showing its detrimental effects in seed germination and root growth [27]. The discarding of nanoparticles is also a task to be taken care of as the unplanned incineration of the nanoparticles might pollute the water bodies and enter the ecosystem. Majorly nanoscale zinc and zinc oxide concentrations are experimentally observed to delay and damage the germination process of seedlings during incubation. Nanomaterials enter the food chain when they are employed for or by directly inserting nanoparticles into food as nanoemulsions or nanocapsules. The use of nanoparticles at a fixed concentration and the length of exposure period have different toxic consequences. They affect the CNS, GI tract inflammation, Parkinson’s syndrome, Alzheimer’s disease, DNA impairment, and adversely affect the lungs, kidneys, and other vital organs by inducing

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oxidative stress to human cells and traveling from the lung to the blood. The nanoparticles from the agriculture products interact directly with the human physiological systems instigating some harmful physiochemical reactions such as generating reactive oxidative species (ROS) that act as the main culprit leading to autophagy of cells due to mitochondrial and DNA damage [28]. The interaction of the nanoparticles with the external ecosystem depends largely on their respective concentrations and their exposure patterns to the varied environmental factors. The raw nanomaterials derived particles are not directly consumed as food products but have utilization as food color pigments and additives. The prospects of nanowire derived materials in the food and beverage industry is very widespread. With that comes the issue of consumer acceptance. Any new bio-derived synthetic material naturally triggers worries and confusion among the public specially when it is related to the food sector [29]. Starting with the creation of public awareness to proper risk and toxicity assessment by the authorities and regulation by the legislation, the successful marketing and distribution of such nano-based materials should be feasible.

41.7 Conclusion Nanotechnology’s utility in the broad array of food technology, spanning agriculture to food processing, packaging, and nutritional supplements, is currently being researched, evaluated, and in some cases deployed. They possess special mechanical, physical, and chemical characteristics. As a source of sustainable raw materials, agricultural waste materials have grown in favor recently. One of the finest instances of evolution taking place on an ecological time scale is insecticide resistance. Because it helps us understand how evolutionary processes work in real time, the study of pesticide resistance is crucial. Insecticide resistance in pest species has become a bigger issue for both agriculture and public health. Plant extract-based green approaches for manufacturing nanoparticles have the advantages of being quick, easy, environmentally benign, and simple. Nanomaterials produced using environmentally friendly and sustainable ways may boost agriculture’s potential for strengthening the fertilization procedure, plant growth regulators, pesticides’ active component delivery to desired target locations, wastewater treatment, also promoting nutrient uptake in plants. Additionally, they lessen the quantity of hazardous substances that damage the environment. Consequently, this technique aids in lowering environmental pollution. Innovation in sustainable agriculture aims to address the challenges of producing sufficient food while minimizing environmental impact, and it involves • Precision agriculture: using technology to optimize crop management, including fertilization, pest control, and irrigation, reducing inputs and increasing efficiency. • Agroecology: using principles of ecology to design and manage agricultural systems, including crop diversity, natural pest control, and soil management, promoting resilience and sustainability.

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• Alternative protein production: exploring new sources of protein, such as insects, algae, and plant-based products, to reduce reliance on traditional livestock-based systems. • Sustainable intensification: improving productivity in agricultural systems while reducing environmental impact, including through integrated pest management, conservation agriculture, and agroforestry. • Circular agriculture: designing agricultural systems that mimic natural processes and close nutrient cycles, reducing waste and increasing resource efficiency. • These innovations are helping to improve food security, reduce environmental impacts, and promote sustainable agriculture practices globally.

References 1. Safdar, M., Kim, W., Park, S., et al.: Engineering plants with carbon nanotubes: a sustainable agriculture approach. J. Nanobiotechnol. 20, 275 (2022). https://doi.org/10.1186/s12951-02201483-w 2. Abhilash, P.C., Tripathi, V., Edrisi, S.A., et al.: Sustainability of crop production from polluted lands. Energ. Ecol. Environ. 1, 54–65 (2016). https://doi.org/10.1007/s40974-016-0007-x 3. Acharya, A., Pal, P.K.: Agriculture nanotechnology: translating research outcome to field applications by influencing environmental sustainability. NanoImpact 19, 100232 (2020). https:// doi.org/10.1016/j.impact.2020.100232 4. Prasad, R., et al.: Nanotechnology in sustainable agriculture: recent developments, challenges, and perspectives. Front. Microbiol. 8, 1014 (2017). https://doi.org/10.3389/fmicb.2017.01014 5. Pandey, G.: Challenges and future prospects of agri-nanotechnology for sustainable agriculture in India. Environ. Technol. Innov. 11, 299–307 (2018). https://doi.org/10.1016/j.eti.2018. 06.012 6. Ghidan, A.Y., Antary, T.M.A.: Applications of nanotechnology in agriculture. In: Applications of Nanobiotechnology. IntechOpen, London, United Kingdom (2019). [Online]. https://www. intechopen.com/chapters/68970; https://doi.org/10.5772/intechopen.88390 7. Ghidan, A.Y., Al-Antary, T.M., Awwad, A.M., Ghidan, O.Y., Al Araj, S.E., Ateyyat, M.A.: Comparison of different green synthesized nanomaterials on green peach aphid as aphicidal potential. Fresen. Environ. Bull. 27(10), 7009–7016 (2018a) 8. Ghidan, A.Y., Al-Antary, T.M., Awwad, A.M., Ayad, J.Y.: Physiological effect of some nanomaterials on pepper (Capsicum annuum L.) plants. Fresen. Environ. Bull. 27(11), 7872–7878 (2018b) 9. Al-Antary, T., Al-Momany, A.: Pests of Garden and Home, 1st edn., p. 360. Dar Al-Arabiah for Publications and Distributions, Cairo (2013) 10. Al-Momany, A., Al-Antary, T.: Pests of Garden and Home, 2nd edn., p. 518. Publications of University of Jordan, Amman (2008) 11. Al-Antary, T., Khader, B.: Toxicity of four insecticides on longevity and fecundity of three populations of the green peach aphid Myzus persicae (Aphididae: Homoptera) for three generations. Jordan J. Agric. Sci. 9(1), 52–62 (2013) 12. Vermeiren, L., Devlieghere, F., Debevere, J.: Effectiveness of some recent antimicrobial packaging concepts. Food Addit. Contam. Part A 19(Suppl), 163–171 (2002) 13. Kim, B., Kim, D., Cho, D., Cho, S.: Bactericidal effect of TiO2 photocatalyst on selected food-borne pathogenic bacteria. Chemosphere 52(1), 277–281 (2003) 14. Al-Momany, A., Al-Antary, T.: Pests of Garden and Home, 2nd ed., p. 518. Ublications of University of Jordan, Amman

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15. Awwad, A., Ibrahim, Q.: Optical and X-ray diffraction characterization of biosynthesis copper oxide nanoparticles using carob leaf extract. Arab. J. Phys. Chem. 2, 20–24 (2015) 16. Das, B., Ahmed, N., Singh, P.: Prunus diversity-early and present development: a review. Int. J. Biodivers. Conserv. 3, 721–734 (2011) 17. Michael, C., Utama, D.N.: Social media based decision support model to solve Indonesian waste management problem: An improved version. Int. J. Emerg. Technol. Adv. Eng. 11(10), 1–12 (2021) 18. Savvides, A., Ali, S., Tester, M., Fotopoulos, V.: Chemical priming of plants against multiple abiotic stresses: mission possible? Trends Plant Sci. 21, 329–340 (2016) 19. Kah, M., Tufenkji, N., White, J.C.: Nano-enabled strategies to enhance crop nutrition and protection. Nat. Nanotechnol. 14, 532–540 (2019) 20. Choudhary, D., Malasri, S.: Machine learning techniques for estimating amount of coolant required in shipping of temperature sensitive products. Int. J. Emerg. Technol. Adv. Eng. 10(10), 67–70 (2020) 21. Bandyopadhyay, S., Jose, R.P.V., Jorge, L.G.T.: Advanced analytical techniques for the measurement of nanomaterials in food and agricultural samples: a review. Environ. Eng. Sci. 30, 118–125 (2013) 22. Mukhopadhyay, S.S.: Nanotechnology in agriculture: prospects and constraints. Nano-technol. Sci. Appl. 7, 63–71 (2014) 23. Baharun, N., Razi, N.F.M., Masrom, S., Yusri, N.A.M., Rahman, A.S.A.: Auto Modelling for Machine Learning: A Comparison Implementation between Rapid Miner and Python. Int. J. Emerg. Technol. Adv. Eng. 12(5), 15–27 (2022) 24. Bonne, P.A.C., Beerendonk, E.F., VanderHoek, J.P., Hofman, J.A.M.H.: Retention of herbicides and pesticides in relation to aging RO membranes. Desalination 132, 189–193 (2000) 25. Claudia, P., Mauro, V., Emilio, R.: In: Proceedings of Workshop on “Nanotechnology for the Agricultural Sector: From Research to the Field” (2012). Available online at https://ec.europa. eu/jrc 26. Dhawan, A., Sharma, V., Parmar, D.: Nanomaterials: a challenge for toxicologists. Nanotoxicology 3, 1–9 (2009) 27. Nel, A., Xia, T., Madler, I., Li, N.: Toxic potential of materials at the nano level. Science 311, 622–627 (2006) 28. He, X., Deng, H., Hwang, H.M.: The current application of nanotechnology in food and agriculture. J. Food Drug. Anal. 27(1), 1–21 (2019). https://doi.org/10.1016/j.jfda.2018.12.002. Epub 2018 Dec 24. PMID: 30648562; PMCID: PMC9298627 29. Dasgupta, N., Ranjan, S.: Nanotechnology in food sector. In: An Introduction to Food Grade Nanoemulsions. Environmental Chemistry for a Sustainable World. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6986-4_1

Chapter 42

Implementing Solar Panel Surface Dust Cleaning Innovation Using a Solar Innovation Framework Model P. K. Dutta, Sujatra Dey, Sayani Majumder, Pritha Sen, and Sudipta Roy

Abstract The accumulation of dust on the surface of the solar modules decreases the amount of sunlight that hits the solar cells beneath, lowering the solar panel’s efficiency. They must always be scrubbed on a regular basis, usually with water, to function properly. Because of the abundance of sunlight, the Middle East region is ideal for harvesting solar energy, but there is also a lot of sand and dust. Cleaning becomes difficult and expensive in this area due to water scarcity. The cleaning procedure is performed by using a compressed air spray and is hereafter rubbed with a foam surface rolling material with a poly wool synthetic surface remover. An electronically controlled mechanical assembly is used to fix the rollers and make them move along the surface of the solar panels. In this study, we suggest a nanoparticlebased technology that does not allow dust to make the surface opaque, thus enabling it to be easily removed with microfiber without affecting effectiveness of the panel power efficiency.

P. K. Dutta (B) Amity School of Engineering and Technology, Amity University Kolkata, Kolkata, West Bengal, India e-mail: [email protected] S. Dey · S. Majumder · P. Sen Amity Institute of Biotechnology, Amity University Kolkata, Kolkata, West Bengal, India e-mail: [email protected] S. Majumder e-mail: [email protected] P. Sen e-mail: [email protected] S. Roy Artificial Intelligence and Data Science, Jio Institute, Navi Mumbai 410206, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_42

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42.1 Introduction A solar innovation framework refers to a set of policies, practices, and technologies that promote the development and adoption of solar energy. It aims to increase the efficiency and cost-effectiveness of solar energy systems while reducing their environmental impact. Dust on solar panels can reduce their efficiency and overall performance by blocking sunlight from reaching the photovoltaic cells. To combat this, solar panel manufacturers have implemented several technologies such as selfcleaning coatings and panel inclinations to minimize the amount of dust buildup on panels. Additionally, regular cleaning schedules can help keep panels operating at optimal performance. Cleaning solar panels is important because accumulated dust, dirt, and other debris can significantly reduce the panels’ efficiency by blocking sunlight. Regular cleaning helps maintain the performance of the panels and ensures they are producing the maximum amount of energy possible. Solar innovation is necessary because it drives the development of new technologies and practices that make solar energy more efficient, cost-effective, and accessible. This can include advancements in panel design, energy storage, and energy management systems, as well as improvements in the manufacturing process and materials used. By fostering innovation, we can ensure that solar energy continues to play a key role in meeting our growing energy needs and mitigating the impacts of climate change. The theory considers various factors that influence the adoption of an innovation, such as the characteristics of the innovation itself, the characteristics of the adopters, and the communication channels through which information about the innovation is spread. By taking these factors into account, DOI can help decisionmakers predict the rate and pattern of adoption for a new solar technology and assess its potential for widespread diffusion in the market. This information can be useful for companies, investors, and policymakers as they evaluate the potential of new solar innovations and make decisions about investment, research and development, and market entry. The following factors can affect the accumulation of dust on solar panels: 1. Location: Dust accumulation is higher in dry and arid regions compared to humid regions. 2. Climate: Wind and rain help to remove dust from solar panels, whereas hot and dry conditions can increase dust buildup. 3. Air pollution: High levels of air pollution can contribute to increased dust accumulation on solar panels. 4. Panel orientation: Panels facing upward are more susceptible to dust buildup compared to panels facing downward. 5. Panel texture: Rougher panel surfaces can trap more dust compared to smoother surfaces. 6. Panel tilt angle: Panels with a steeper tilt angle can shed dust more effectively compared to panels with a flatter tilt angle. 7. Panel cleaning frequency: Regular cleaning of panels can help reduce the buildup of dust.

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42.2 Theories Addressed in Solar Innovation for Solar Panel Cleaning There are several theories and technologies being developed to clean solar panels and improve their efficiency. Some of these include: Self-Cleaning Solar Panels: A surface coating is applied to the panels to make them hydrophilic, causing water to bead up and roll off, taking dirt and debris with it. Robotic Cleaning Systems: Robots equipped with brushes, water jets, or other cleaning tools are used to clean the panels. Drying Agents: A misting of water with a surfactant (such as soap) is applied to the panels to loosen dirt and grime, followed by a blast of air to dry the panels. Ultraviolet Light: UV light is used to break down organic matter, such as algae and moss, on the panels. Water Harvesting: Collecting rainwater to use for cleaning the panels, instead of using potable water. The effectiveness of these theories and technologies is still being researched, and the ideal solution will depend on several factors, such as the location and climate of the installation, the type of panels being used, and the level of dirt and debris buildup. Cleaning solar panels is an important part of maintaining and maximizing their efficiency. Over time, dirt, dust, and other debris can accumulate on the surface of the panels, which can decrease their ability to convert sunlight into electricity. There are a few different methods that can be used to clean solar panels, including Rinsing with water: This is the simplest and most common method of cleaning solar panels. By using a hose or a pressure washer, you can rinse away dirt and debris from the surface of the panels. Scraping: For tough, caked-on debris, you may need to use a scraping tool to remove it from the surface of the panels. A soft-bristled brush or a squeegee can also be used to remove debris. For particularly dirty panels, you may want to use a cleaning solution. There are a variety of commercial solar panel cleaning solutions available, or you can make your own by mixing water with a small amount of mild detergent. It’s important to note that cleaning solar panels should be done carefully, as not to damage the panels by scratching them or using abrasive materials. And also the cleaning process must be done when the solar panel is cool, avoid cleaning it under direct sun exposure. It’s a common mode of work for rugged panels to collect debris as dust which settles over the embedded solar panels that may reduce the power output. Overall, regularly cleaning your solar panels can help keep them working at peak efficiency and prolong their lifespan. Solar photovoltaic (PV) panels require minimal maintenance and are designed to operate at maximum operational capacity for electric power generation. Solar panels can be cleaned in a variety of ways, ranging from mechanical washing to fully automated technologies. While rainwater can wash it away some of the grime that gathers on panels over time, it can also cause dirt to accumulate at the bottom of the panels and is insufficient to remove heavy pollution [1]. Dust accumulation can affect a

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variety of things depending on where it occurs. Some examples include: Dust can accumulate inside electronic devices such as computers and televisions, which can cause overheating and potentially damage the components. HVAC systems: Dust can accumulate in heating, ventilation, and air conditioning systems, reducing their efficiency, and potentially causing indoor air pollution. Dust can accumulate in industrial equipment, causing wear and tear on moving parts and reducing their efficiency. Dust can accumulate on solar panels, reducing the amount of sunlight that reaches the cells and reducing the panels’ overall efficiency. Dust and dirt on photovoltaic arrays can end up causing energy losses of up to 7% per year in some parts of the US and up to 50% in the Middle East. So, how should soiling be excluded from solar panels? Companies such as Italy’s Washpanel are using robotics technology to create automatic and semi-automatic solar panel cleaning robots. It provides moderately portable robots for parking garage, greenhouse, and shed roof panels [2]. We also offer fixed roof robots for dusty environments where regular scrubbing is required. High-pressure washing can damage the modules, so Eccopia regularly uses solarpowered autonomous robots to clean PV modules using soft fibers and a stream of air instead of water. Solar maintenance companies such as the America Bland Company and Premier Solar Cleaning have revealed that using distilled water with a rolling or car brush allows them to clean panels without the use of soap, unlike rain water which leaves a coating of the dirt that not only shades panels but also attracts dirt. Polywater, a lubricant manufacturer, makes a Solar Panel Wash to help water lift grime without leaving a film behind. Robots, waterless vibration, and special coating solutions are effective and creative; however, in many circumstances, these solutions can be quite costly and inefficient. This is especially important for installations, whether residential or commercial, as well as unique structures and installations such as agrivoltaics. When it comes to cleaning solar panels, there are a few tools that will come in handy [4]. On the one hand, there are numerous rotating brushes that brush the soil from the panel. Any basic cleaning tool, such as those used on car windshields, could also be useful. Cleaning the PV panels by hand is a good old technique! Robots, waterless vibration, and special coating solutions are effective and creative; however, in many circumstances, these solutions can be quite costly and inefficient and many research ideas have pointed to the use of different research approaches for this purpose [5]. The National Renewable Energy Laboratory (NREL) in the USA is also working on a method to monitor the amount of electricity panels produce each day to detect when soiling has begun to degrade output. The accumulation of dust on the surface of the solar modules decreases the amount of sunlight that hits the solar cells beneath, lowering the solar panel’s efficiency [6]. To work correctly, they must be scrubbed on a regular basis, usually with water. Because of the abundance of sunlight, the Middle East region is ideal for harvesting solar energy, but there is also a lot of sand and dust. Cleaning becomes difficult, complicated, and expensive in this area due to water scarcity. Unlike other designs, this system is straightforward, user-friendly [7], long-lasting, breathable, precise, and credible. Because it is completely autonomous, it eliminates the need for a large workforce.

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42.3 Determinant Factors that Affect the Solar Panel Cleaning Mechanism The site of the components involves determining what and how frequently the panels must be cleaned to maintain efficiency [8]. It ought to be cleaned once or twice a year at the quite basic level. But even so, in certain cases, more regular cleaning may be beneficial. Darwish et al. demonstrated in one’s study [9] that particulate properties such as surface chemical structure, particle density, as well as size distribution have a significant influence on the output energy of solar modules. Those who did not, however, account for wind effects in their laboratory experiments. Gentle wind gusts have a significant impact on the amount of dust that settles on flat plates in normal conditions. 1. Polluted areas—Installation of PV modules in polluted areas, especially near factories, highways, and airports, accumulates a large amount of atmospheric dirt and oil. 2. Wooded areas—Installation near many trees increases the chances of leaves falling on the panel, preventing it from gaining sunlight. Trees also attract birds, which can coat the solar cells and lead to a build-up of droppings that causes surface damage from acid, reducing efficiency. 3. Desert sand build-up on PV panels is more prevalent in locations with dry, dusty climates such as the Middle East and the southwestern USA, and can scratch surfaces and block light. Wildfire ash from places like California and Australia can quickly fall into large clumps onto slabs. The cleaning of photovoltaic (PV) cell surfaces can impact their performance and longevity. In a dry condition, the cleaning process typically involves using physical methods such as brushing or blowing dust and debris from the surface. In wet conditions, cleaning may involve washing the surface with water and possibly using cleaning agents to remove dirt and grime. For a dry cleaning framework analysis, factors to consider include the type of debris on the surface, the type of brush or blower to be used, and the method of application. For a wet cleaning framework analysis, factors to consider include the type of dirt or grime, the type of cleaning agent to be used, the method of application, and the drying process. It is important to note that the type of PV cell material may also impact the cleaning method and choice of cleaning agents used, as some materials may be sensitive to certain cleaning agents or techniques. Additionally, care should be taken to ensure that the cleaning process does not damage the surface of the PV cell and negatively impact its performance.

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42.3.1 Dry Cleaning of Solar Panels There are several methods used for cleaning solar panels in dry mode, meaning without the use of water. These include 1. Dry Brushing: A soft-bristled brush is used to gently remove dirt, dust, and other debris from the surface of the panels. 2. Dry Wiping: A clean, dry cloth is used to wipe the surface of the panels to remove dirt and debris. 3. Dry Ice Blasting: Dry ice (frozen carbon dioxide) is blasted onto the panels using a specialized machine. The dry ice sublimates on contact with the panels, removing dirt and debris without leaving any residue. 4. Compressed Air: A blast of compressed air is used to remove dirt and debris from the surface of the panels. 5. Vacuum Cleaning: A specialized vacuum is used to suck dirt, dust, and other debris off the surface of the panels. These dry cleaning methods are suitable for small-scale cleaning jobs, or for removing light dirt and debris buildup. For more extensive cleaning, or for removing stubborn buildup, a combination of dry cleaning and wet cleaning methods may be necessary. Solar panels can be cleaned using a variety of techniques, including fully automated systems and manual washing. Rainwater can wash away some of the dirt that builds up on solar panel surfaces over time, but it can also cause dirt to gather at the bottom of the panels and is insufficient to eliminate serious pollution. Robotics technology enables businesses like Italy’s Washpanel to manufacture automatic and semi-automatic robots that are specifically made for cleaning solar panels. For panels put on locations like carports, greenhouses, and shed roofs, it supplies transportable semi-automatic robots. Additionally, it provides fixed roof robots for substantial installations in dusty settings that need regular cleaning.

42.3.2 Soap-Less Brushes and Sponges Solar maintenance companies such as the US-based Bland Company and Premier Solar Cleaning have unearthed that the use of deionized water with a rolling or vehicle-mounted brush can effectively clean solar panels instead of soap, which leaves a residue that not only shades panels but also attracts dirt. A Solar Panel Wash from lubricant producer Polywater helps water lift off grime without leaving a film behind. To get rid of filth, SunSystem Technology mixes diluted vinegar and hydrogen peroxide. Furthermore, without the use of any cleaning solutions, homeowners can manually clean their solar panels with a garden hose and a soft sponge. Researchers at NASA-funded projects in the USA and Heriot-Watt University in Scotland have developed methods for vibrating solar panels to shake surface dust

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Fig. 42.1 Diagrammatic representation of dust accumulation and analysis of solar panel

loose. Dust accumulation on solar panels can reduce their efficiency by blocking sunlight. Dry cleaning can be an effective way to remove the dust and keep the panels working efficiently. However, it’s important to use a gentle cleaning method that won’t damage the panels, such as soft brushes, air blowers, or specialized cleaning solutions. It’s also recommended to have the panels inspected and cleaned regularly to maintain their performance (Fig. 42.1).

42.3.3 Parameters Affecting Dust Accumulation Dust particle characteristics such as particle size, dispersion, and distribution morphology, as well as dust chemical composition, all have a significant impact on the dust accumulation process. The chemical and physical properties of accumulated dust vary depending on its origin. Ismagilov [10] probed the multiple sources of dust activities in a recent comprehensive study. According to review of the literature, a few optical techniques such as electron microscopy and scanning probe microscopy have been used to determine and analyze some of these properties. According to Liu et al. [11], the grain size of the accumulated dust particles ranged from 4 to 8 m. Furthermore, quartz was the most abundant component of dust, followed by calcite and albite. It should be mentioned that the chemical structure and particle size of dust vary by location. The chemical makeup of dust, in addition to particle diameter, dispersion, and distribution morphology, plays an important role in dust adhesion between particles and to the surface. Several studies have been conducted in this regard to investigate this same major chemical component of accumulated dust particles and their impact on the performance of PV panels [12]. Moisture is one of the effective parameters that increases the accumulation of dust particles on the surface of photovoltaic panels. In general, as the absolute humidity

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on a site rises, so does the amount of dust accumulation [13]. This is because moisture condenses on the surface of photovoltaic panels, forming capillary bridges between the dust particles and the surface. This creates a capillary force, which increases the adhesion between the dust particles and the surface of the panels and thus accelerates the dust deposition rate. According to Gholami et al. [14], the amount of atmospheric moisture has a significant influence on the adhesion force between dust particles and the surface. Said et al. [15] discovered that the bonding strength between the dust particles and the surface of sand and dirt on the surface of photovoltaic panels is significantly greater than the adhesion force between the particles. In general, dust particles absorb some of the water vapor in the air and form a muddy layer on the surface of the panels when there is moisture in the air. As the layer dries in the sun, it forms a cement-like mixture that is much more difficult to remove from the surface. Aside from dust properties, the physical and chemical properties of the cover glass surface have a significant impact on dust accumulation rate and particle adhesion force to the surface. The studies in this area are primarily concerned with investigating the effects of various nanocoatings on the glass cover of panels. Rain is one of the organic ways to remove dust particles from the surface of photovoltaic panels. In accordance with a review of recent studies, turning solar panels to a vertical position during the night and rainy days could be a practical method of washing dust particles away from the panels. However, rotating large arrays of solar panels is difficult, consumes a noticeable amount of energy, and increases maintenance costs. Wind can have differential influences on the soiling process on the panels depending on its direction and velocity. On the one hand, wind can blow away the settled dust particles, lowering the dust concentration. Wind, on the other hand, could lift dust and dirt from deserted areas and transport them to a site, increasing the dust level in the area. For instance O’Hara et al. [16] reported an increase in dust concentration levels on the surface in the Libyan desert due to an increase in the monthly average wind speed. Light wind swept away some of the dust particles, according to Vedulla et al. [17], for the surfaces facing the wind. Gravity is regarded as one of the most natural methods of removing dust from surfaces. Aldawoud et al. [18] demonstrated that increasing the tilt angle on both coated and uncoated surfaces increases gravity impacts on dust particles and reduces the amount of settled dust. However, because of the decrease in adhesion force, gravity force has a greater impact on coated samples. Furthermore, they stated that changing the mounting angle alters the amount of radiation received by the panel and may have a significant impact on solar panel performance. As a result, determining the best mounting angle on a site is critical.

42.4 Methodology in Solar Panel Cleaning Process An anti-dirt solar panel coating has been created by researchers at the International Advanced Research Centre [6] for Powder Metallurgy and New Materials (ARCI) division of India’s Department of Science and Technology [19]. In India, a trifecta of high temperatures, high humidity, and severe pollution reduces the efficiency of PV

42 Implementing Solar Panel Surface Dust Cleaning Innovation Using … Table 42.1 Configuration of the different configurable parts of solar panel

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S. No

Components

Parameters

Calculation values

1

Shaft

Bending moment

M t = 334.22 × 103 N mm

Diameter

D = 32.40

2

Spur Gear

Gear ratio

i=3

Initial torque

M t = 490.08 N m

Bending stress

σ b = 111.23 N/mm2

Compressible stress

σ c = 852.6 N/mm2

Centre distance

155 mm

Module

m = 5 mm

Pitch line velocity

v = 5.34 m/s

Induced contact stress

σ c = 756.9 N/mm2

panels. The nanoparticle-based technology is highly clear and repels dust, allowing it to be easily removed with water without affecting the effectiveness of the panel (Table 42.1). Hydrophobic solutions, also known as water repellent solutions, can be used to clean solar panels by creating a barrier between the panel and dirt, dust, and other contaminants. The solution creates a hydrophobic (water-repelling) surface that causes water and debris to roll off the panel instead of sticking to it. This helps to keep the panels clean and free of debris, which can improve their efficiency. The application of hydrophobic solutions can be done by spraying or brushing the solution onto the panel, or by adding the solution to the washing water. The solution can also be formulated with other cleaning agents to enhance its cleaning power. It is important to note that not all hydrophobic solutions are suitable for use on solar panels, as some can cause damage to the surface or reduce the panel’s efficiency. Therefore, it is recommended to use a hydrophobic solution specifically designed for cleaning solar panels and to follow the manufacturer’s instructions for use (Fig. 42.2).

42.4.1 Silica Sol: Making of Silica Sol: Different Processes A three-dimensional network structure is created by the wet chemical process known as sol–gel, which starts with the production of an inorganic colloidal suspension (sol) and ends with the gelation of the sol in a continuous liquid phase (gel). There are two main ways to make silica nanoparticles: the Stöber synthesis and the microemulsion method. The Stöber method [20] was first developed to create homogeneous silica particles through the hydrolysis and condensation of ethoxy-silanes in low molecular weight alcohols under the influence of ammonia. In Stöber synthesis [20],

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Tetraethyl orthosilicate (TEOS) condensation occurs in ethanol, water mixtures under alkaline conditions at room temperature in this straightforward one-step production procedure. The Stöber synthesis has the advantage of having the ability to transfer the nanoparticles into aqueous solutions with ease and being easily scaled up for commercial purposes. Reverse microemulsion, flame synthesis, and the commonly used sol–gel technique are some more ways to make silica nanoparticles. Surfactant molecules dissolved in organic solvents create spherical micelles in a reverse microemulsion. To use a reverse microemulsion approach, narrow size distribution silica-coated gold (or silver) nanoparticles are created without the use of a silane coupling agent or polymer as a surface primer. This method enables precise influence of the silica shell thickness with nanoscale accuracy. For decades, industry has primarily used flame synthesis to produce silica particles. It is a continuous process that generates large amounts of particles with good control over particle size and crystallinity without the need for additional treatments such as calcination. For the creation of different nanostructures, particularly metal oxide nanoparticles, the sol–gel procedure is a more chemical (wet chemical) method. In this procedure, the molecular precursor (often metal alkoxide) is dissolved in water or alcohol and heated and stirred until it gels. The sol–gel procedure is frequently used to create silica, glass, and ceramic materials for deterioration because it can produce pure, homogenous results under mild circumstances (Fig. 42.3).

42.4.2 Properties of Silica Sol Among silica nanoparticles’ benefits include effective reinforcement with superior mechanical strength, heat stability, decreased shrinkage, thermal expansion, and residual stress, better abrasion resistance, and improved optical and electric properties. The careful manipulation of the particle size, crystallinity, porosity, and form makes it possible to use silica nanoparticles for a variety of applications. Due to the advancement in different methods for the production of silica sol, and it’s various advantageous properties, nanoparticles of silica is an appropriate material for the production of hydrophobic sheet, which is used to cover the surface of the solar panels. For the SMD-50 silicon polycrystalline solar panel, the useful power generation for each month was calculated (Fig. 42.4). The hydrophobic layer of silica sol and nano-SiO2 (SS-SiO2 ) is attached to the solar panel. It lowers the surface energy and increase the surface roughness of coating, which in turn lowers the adhesion between the dust particles and the surface. It is even effective in cleaning the bird droppings, as the hydrophobic film [21] will not let the dropping to stick to the surface, as a result with the help of the blower or natural wind, the dropping will fall off from the panel. High transmittance can also be obtained by these coatings which in turn increases the efficiency [22]. A solar power blower system is attached to the top end of the panel, which cleans the loosened dust particles (Fig. 42.5).

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Fig. 42.3 Proposed methodology of flow and execution of manufacturing silica gel (such as HCl) or bases (such as NH3 ) are used to hydrolyze and condense metal alkoxides (Si(OR)4 ) such as tetraethyl orthosilicate (TEOS, Si(OC2 H5 )4 ) or inorganic salts such as sodium silicate (Na2 SiO3 )

Fig. 42.4 Spherical silica property analysis in nanoparticle coating process

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Fig. 42.5 Solar panel orientation

42.5 Conclusions The coating’s longevity is approximated to be 4–5 years. To reinstate the anti-dust effect, low-cost materials can be re-coated. The linear actuator’s working principle can be used to create a cleaning system. Various types of batteries can be employed to power an electric motor with a rated voltage of 12 V. Lithium-ion batteries are in high demand, based on case experience over the last five years; the battery can be powered by a solar battery via a charge controller. The comparison of the electricity produced by a typical PV panel before and after cleaning tends to result in a 16.7% improvement in maximum produced power. As shown, the system is supported by a smart control system that allows for full automatic operation, needing less workforce. Second, as opposed to the Venturi method, no additional bulky equipment is required.

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Third, the system does not necessitate a lot of power, easy preparation process, largearea fabrication and no limit to base materials makes our solution economical and sustainable.

References 1. Adinoyi, M.J., Said, S.A.: Effect of dust accumulation on the power outputs of solar photovoltaic modules. Renew. Energ. 60, 633–636 (2013) 2. Syafiq, A., Pandey, A.K., Adzman, N.N., Abd Rahim, N.: Advances in approaches and methods for self-cleaning of solar photovoltaic panels. Sol. Energ. 162, 597–619 (2018) 3. Pickerel, K.: Dirty solar panels? Pro tip: never use soap to clean. Solar Power World, 26 Oct. 2021. https://www.solarpowerworldonline.com/2020/05/pro-tip-never-use-soap-toclean-dirty-solar-panels/ 4. Myyas, R.N., Al-Dabbasa, M., Tostado-Véliz, M., Jurado, F.: A novel solar panel cleaning mechanism to improve performance and harvesting rainwater. Solar Energ. 237, 19–28 (2022). ISSN 0038-092X. https://doi.org/10.1016/J.Solener.2022.03.068 5. Mani, M., Pillai, R.: Impact of dust on solar photovoltaic (PV) performance: research status, challenges and recommendations. Renew. Sustain. Energ. Rev. 14(9), 3124–3131 (2010). ISSN 1364-0321, https://doi.org/10.1016/J.Rser.2010.07.065 6. Jaiganesh, K., Reddy, K.B.S., Shobhitha, B.K.D., Goud, B.D.: Enhancing the efficiency of rooftop solar photovoltaic panel with simple cleaning mechanism. Mater. Today Proc. 51(1), 411–415 (2022). ISSN 2214-7853, https://doi.org/10.1016/J.Matpr.2021.05.565 7. Zatsarinnaya, J.N., Amirov, D.I., Zemskova, L.V.: Analysis of the environmental factors influence on the efficiency of photovoltaic systems. IOP Conf. Ser. Mater. Sci. Eng. 552(1), 012033 (2019, June). IOP Publishing 8. Sunpower Cleans Up Solar With Acquisition of Greenbotics. Available at https://www. greentechmedia.com/articles/read/sunpower-cleans-up-solar-with-acquisiton-of-greenbotics. Accessed: 20.01.2020 9. Darwish, Z.A., Sopian, K., Fudholi, A.: Reduced output of photovoltaic modules due to different types of dust particles. J. Clean. Prod. 280, 124317 (2021) 10. Ismagilov, F.R., Vavilov, V.E., Nurgalieva, R.A.: Method for automated cleaning of solar panels. Patent RF No. 2018104009 (2018) 11. Liu, L., Qian, H., Sun, E., Li, B., Zhang, Z., Miao, B., Li, Z.: Power reduction mechanism of dust-deposited photovoltaic modules: an experimental study. J. Clean. Prod. 378, 134518 (2022). ISSN 0959-6526, https://doi.org/10.1016/J.Jclepro.2022.134518 12. Adila, A.S., Husam, A., Husi, G.: Towards the self-powered inter-net of things (Iot) by energy harvesting: trends and technologies for green Iot. In: 2018 2nd international symposium on small-scale intelligent manufacturing systems (SIMS), pp. 1–5. IEEE (2018) 13. Lu, H., Zhao, W.: CFD prediction of dust pollution and impact on an isolated ground-mounted solar photovoltaic system. Renew. Energ. 131, 829–840 (2019) 14. Gholami, A., Mohammad, A.M.E.R., Zandi, M., Ghoachani, R.G.: Dust accumulation on photovoltaic modules: a review on the effective parameters. Sigma J. Eng. Nat. Sci. 39(1), 45–57 (2021) 15. Said, S.A., Hassan, G., Walwil, H.M., Al-Aqeeli, N.: The effect of environmental factors and dust accumulation on photovoltaic modules and dust-accumulation mitigation strategies. Renew. Sustain. Energ. Rev. 82, 743–760 (2018) 16. O’Hara, S.L., Clarke, M.L., Elatrash, M.S.: Field measurements of desert dust deposition in Libya. Atmos. Environ. 40(21), 3881–3897 (2006) 17. Vedulla, G., Geetha, A., Senthil, R.: Review of strategies to mitigate dust deposition on solar photovoltaic systems. Energies 16(1), 109 (2022)

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Chapter 43

Quality of Service Analysis in a Fog Computing Network with Breakdown and Vacation Interruption Hibat Eallah Mohtadi, Mohamed Hanini, and Abdelkrim Haqiq

Abstract Fog computing was developed to expand cloud computing’s computation, storage, and networking capabilities to the network’s edge, therefore reducing latency and improving the quality of service. In fact, it produces a little cloud at the network’s edge by utilizing a huge number of community and geo-distributed network equipments called as fog nodes (FN), such as routers, switches, and access points. Fog computing raises concerns about offloading tasks for remote processing, such as the loss of a node in a computer network, whether due to unforeseeable causes such as system failure, breakdowns, or scheduled outages such as vacations taken by the fog node. These concerns can result in reduced computer network performance in a fog environment or loss of redundancy. This work provides an analytical model based on queening theory for investigating the quality of service in a fog computing network where the fog nodes could break down or go on vacation.

43.1 Introduction Most of the existing works discuss the decision of task offloading and direction depending on the objectives that make the metrics of optimization, for example, energy consumption and latency or power consumption [1]. To reduce the latency and boot the system efficiency in a fog computing environment, the task offloading Hibat Eallah Mohtadi, Mohamed Hanini and Abdelkrim Haqiq authors are contributed equally to this work. H. E. Mohtadi (B) · M. Hanini · A. Haqiq Faculty of Sciences and Techniques, Computer, Networks, Mobility and Modeling laboratory: IR2M, Hassan First University of Settat, Settat26000, Morocco e-mail: [email protected] M. Hanini e-mail: [email protected] A. Haqiq e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_43

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is one of the fundamental concepts that many works had presented. Work in [2] presented a mathematical models to examine the performance parameters such as response time, the number of tasks, and waiting time. It introduces a parallel offloading mechanism that combines the mobility of vehicles and its effect on the variation of the computation capabilities to form an algorithm based on Markov chain theory. The authors analyzed the performances taking into consideration the service time. The task offloading in [3] is formulated using SMDP, and the formulation gives an optimal task offloading scheme taking into account the departure of vehicles in charge of the processing of a tasks. In [4], the authors had set up a method using reinforcement learning model and based on Markov decision process for offloading decision. Their method takes into account some metrics like the number of vehicles parked in a parking and the arrival rate of tasks and moving vehicles. Moreover, deep learning neural networks could allow analyzing the mobility of vehicles and predict the proper direction to offload the tasks. The authors of [5] presented a fuzzy reinforcement learning approach based on a greedy heuristic. The proposition satisfies the energy consumption minimization including communication and computation costs. Indeed, the approach aims to reduce the response time by making the right selection of the mobile fog nodes, which are vehicles. The work compared three kinds of scheduling algorithms, namely fuzzy, rate monotonic scheduling(RMS) algorithm, and first come first serve scheduling. They find out that the proposed algorithm had effectively perform well in terms of reducing the energy consumption and the service time of tasks. In [6], the authors proposed an approach relying on two algorithms, first the partitioning algorithm and second scheduling algorithm. They modeled the system as a multi-agent system MAS based on fuzzy logic that provides a negotiation between agents to find out the efficient scheduling in fog cloud computing environment. The work adopts genetic algorithm to provide the multi-objective optimization to workflow scheduling. Researches in [7–10] address general service times for cloud server farms by using M/G/1, M/G/m, and M/G/c/k queuing models to derive performance indicators such as energy usage, infrastructure cost, and mean response time. However, the primary problem of all the studied articles is that they ignore the breakdown of computing nodes, which are a typical event in IoT networks. This paper differs from newly in-print papers in that the current study : • Takes into account system breakdowns and server vacation interruptions; • Presents efficient numerical techniques to determine the transient queue-size distribution in very little computational time; • Proposes a repair process that begins immediately after the breakdown. The current study has two goals. The first step is to create a mathematical model of a multi-fog node network architecture that includes network difficulties such as breakdown and vacation. The second step is to offer an efficient numerical approach for determining the queue size and mean waiting time in order to evaluate the system

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under those constraints. To the best of our knowledge, the key aspects of our paper have not been addressed in previous works. The rest of the paper is organized as follows. In Sect. 43.1, the related works are reviewed. Then, the proposed analytical model is described in Sect. 43.2. The analytical resolution of the proposed model is discussed in Sect. 43.3. Finally, Sect. 43.4 concludes this study.

43.2 Model Description The queue model of the fog computing nodes In this work, the fog network is composed of K parallel nodes that are working simultaneously, each node is modeled as a M/M/1 queue where the tasks arrived in the system in Poisson process, and they are processed exponentially. Let λ j be the arrival rate to jth fog node, we assume that we have the same arrival rate either the system is on vacation, busy or in repair process. When the queue length is less than L the service works with faster service rate μ1 and when the queue length is larger than L it is processed with other service rate μ2 slower which is obvious because the system will be so busy. We assume that μ1 < μ2 (Fig. 43.1). The queue model of the fog computing nodes including the vacation and the breakdown To take into account the breakdown and server vacation, we will study the state of a single node, and then we will deduce the performances of the whole system. Each fog node is characterized as M/M/1 queue with heterogeneous arrival and departure with server vacations and breakdowns. The system may breakdown randomly with b is the breakdown rate, and we also denote by ν the vacation rate. When the system is either empty or the server itself breaks down, we term it as the server vacation.

Fig. 43.1 Queuing system model

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Once the system breaks down, it repairs start immediately, and r is the repair rate. The node state can be described by a pair of values (I, N ), where I denotes the state of the system, and N is the total number of tasks in the system. The transient-state probabilities are defined as follows: q0,n = Prob{I = 0, N = n}; n = 0, 1, 2, . . . , q1,n = Prob{I = 1, N = n}; n = 1, 2, . . . , q2,n = Prob{I = 2, N = n}; n = 1, 2, . . . , where {(I, N ) | I = 0, 1, 2; N = 0, 1, 2, . . .} ⎧ ⎪ ⎨0; I ≡ 1; ⎪ ⎩ 2;

server is on the vacation server is in a busy state server is in repair process

N ≡ Number of tasks in the buffer the fog computing node Case 0: When the system is on vacation. λ j q0,0 = μ1 q1,1 

 v + λ j q0,i = λ j q0,i−1 ; i ≥ 1

(43.1)

(43.2)

Case 1: When the system is busy.   

 λ j + μ1 + b q1,1 = νq0,1 + μ1 q1,2 + rq2,1

(43.3)

 λ j + μ1 + b q1,i = λ j q1,i−1 + νq0,i + μ1 q1,i+1 + rq 2,i ; 2 ≤ i ≤ L

(43.4)

 λ j + μ1 + b q1, L = λ j q1, L−1 + νq0, L + rq 2, L + μ2 q1, L+1 ; i = L

(43.5)

  λ j + μ2 + b q1,i = λ j q1,i−1 + νq0,i + μ2 q1,i+1

(43.6)

Case 2: When the system is in repair.



  λ j + r q2,1 = bq 1,1

(43.7)

 λ j + r q2,i = bq 1,i + λb q2,i−1 ; i ≥ 2

(43.8)

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43.3 Model Resolution In this section, we employ the generation functions, to determine the transient-state probabilities of the computing network from the system. For this purpose we determine: F0 (z) =

∞ Σ

q0i z i

i=0

F1 (z) =

∞ Σ

q1i z i =

i=1

F2 (z) =

∞ Σ i=0

L Σ i=1

q2i z i =

L Σ

∞ Σ

q1i z i +

q1i z i = F11 + F12

i=L+1 ∞ Σ

q2i z i +

i=1

q2i z i = F21 + F22

i=L+1

Substituting the value of q11 from Eqs. (43.1), (43.2) we get q0i = ρ0i q00

(43.9)

λ

j With ρ0 = λ j +ν Then we obtain,

F 0 (z) =

1 q00 1 − ρ0 z

(43.10)

Multiplying Eq. (43.4) by z i and we sum for i = 2, 3, . . . , L − 1, similarly multiplying Eq. (43.5) by z L , multiplying Eq. (43.6) by z i and sum for i = L + 1, L + 2, . . . and adding the results we obtain 

   λ j + μ1 + b F1 (z) − λ j + μ1 + b q11 z − (μ1 − μ2 ) F12 (z) μ1 F1 (z) − μ1 q11 − μ1 q12 z = λ j z F1 (z) + v F0 (z) − vq00 − vq01 z + (43.11) z

μ2 μ1 − F12 (z) + r F2 (z) − rq21 z + z z

Multiplying Eq. (43.8) by z i and we sum for i = 2, 3, . . .    ( λ j + r F2 (z) − λ j z F2 (z) = bF1 (z) − bzq11 + λ j + r zq21

From Eqs. (43.7), (43.12) can be written as

(43.12)

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F2 (z) =

b F 1 (z) λj + r − λjz

(43.13)

Substituting the value of F2 (z) from Eq. (43.13) into Eq. (43.11) we get

  z H (z) − H (z)  F1 (z) = z λ j + μ1 + b q11 − vq01 − μ1 q12 − rq 2,1  z λj + r − λjz  v − vq 00 + μ1 q11 + q00 (43.14) 1 − ρ0 z

1 + (μ1 − μ2 ) 1 − F12 (z) z where, H (z) = λ2j z 2 − λ2j z − bλ j z − μ1 λ j z − λ j r z + μ1 r + μ1 λ j . From Eq. (43.14) we obtain     λjz λj + r − λjz (μ1 − μ2 ) λ j + r − λ j z q00 + F12 (z) μ1 q12 = H (z) (1 − ρ0 z) H (z)     λjz λj + r − λjz (μ1 − μ2 ) λ j + r − λ j z F1 (z) = q00 + F12 (z) ⟨1 − ρ0 z) H (z) H (z)

(43.15)

(43.16)

In order that the steady state queue length distribution exists both roots of the equation H (z) = 0 must be greater than 1, which means that if and only if H (1) < 0 and H , (z) < 0. We obtain the stability condition

bλ j μ1 r

μ1r ≥ bλ j + λ j r λ + μ1j < 1

Using the Eqs. (43.16), (43.13), and (43.10) in the generating function for the queue length distribution is giving by: F(z) = F0 (z) + F1 (z) + F2 (z)

(43.17)

  H (z) + λ j z λ j + r + b − λ j z F(z) = q00 (1 − ρ0 z) H (z)   (μ1 − μ2 ) λb + r − λ j z + b + F12 (z) H (z)

(43.18)

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When z = 1 using the factors of H (z) only in the numerator we get   μ1 r + λ j (1 − α)(1 − β) (1 − ρ0 ) − (μ1 − μ2 ) (1 − ρ0 ) (r + b)F12 (1) q00 = μ1r − bλ j − r λ j + λ j (r + b) (43.19) Using the value of q00 into (43.18), we obtain F(z)

   μ1 r + λ j (1 − α)(1 − β) (1 − ρ0 ) − (μ1 − μ2 ) (r + b) (1 − ρ0 ) F12 (1) = R(z) (1 − ρ0 z) H (z)   (μ1 − μ2 ) λ j + r − λ j z + b + F12 (z) H (z)

(43.20)

With R(z) =

H (z)+λ j z (λ j +r +b−λ j z ) μ1 r −bλ j −λ j r+λ j (r+b) q

The mean number of tasks in the queue L j at jth fog node can be found by computing F , (1) from (43.20): α β ρ0 + + (1 − α) (1 − β) (1 − ρ0 )     λ j λ j − b − μ1 + λ j r + b − λ j − r λ j + μ1r − bλ j − λ j r + λ j (r + b) Σ∞ q1i (μ1 − μ2 ) (r + b) (1 − ρ0 ) i=L+1   − μ1 r + λ j ⎡ ⎤ λ j (λ j −b−μ1 )+λ j (r+b−λ j )−r λ j (1−α)(1−β)(1−ρ μ r−bλ −λ r +λ (r +b) ) } 0 { 1 j j j ⎦ ×⎣ 0 )+β(1−α)(1−ρ0 )+ρ0 (1−α)(1−β)} + {α(1−β)(1−ρ(1−α) 2 (1−β)2 (1−ρ )2

L jq =

(43.21)

0

The mean number of tasks in the system at jth fog node is given by: L js = L jq +

λj λj + μ1 μ2

(43.22)

The mean waiting time of the tasks in the queue at jth fog node : q

Wj = q

Lj λj

(43.23)

The mean waiting time of the tasks in the system at jth fog node: Wjs = L js +

1 1 + μ1 μ2

(43.24)

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Hence, the mean response time of the whole system Wtotal is given by Wtotal =

K Σ

Wjs

(43.25)

j=1

And the mean number of tasks of the whole system L total is given by L total =

K Σ

L js

(43.26)

j=1

43.4 Conclusion The problem of breakdown and vacation of nodes in a fog computing network is a concern that can negatively affect the performance of the network. To take into account this problem, we propose in this paper an analytical model for load balancing, offloading, and scheduling in such distributed systems. The model addresses the breakdown and server vacation that may occur during tasks service. We investigate and suggest that the repair procedure begins promptly. The proposed model provides network performance metrics, namely the mean waiting time and the mean number of tasks in the system, which may be used to evaluate the quality of service in the fog computing network. The numerical validation and simulation of the proposed model are the perspectives for this work.

References 1. Kumari, N., Yadav, A., Jana, P.K.: Task offloading in fog computing: a survey of algorithms and optimization techniques. Comput. Netw. 214, 109137 (2022) 2. Xie, J., Jia, Y., Chen, Z., Nan, Z., Liang, L.: Efficient task completion for parallel offloading in vehicular fog computing. China Commun. 16(11), 42–55 (2019) 3. Wu, Q., Ge, H., Liu, H., Fan, Q., Li, Z., Wang, Z.: A task offloading scheme in vehicular fog and cloud computing system. IEEE Access 8, 1173–1184 (2019) 4. Hamdi, A.M.A., Hussain, F.K., Hussain, O.K.: Task offloading in vehicular fog computing: state-of-the-art and open issues. Future Gener. Comput. Syst. (2022) 5. Vemireddy, S., Rout, R.R.: Fuzzy reinforcement learning for energy efficient task offloading in vehicular fog computing. Comput. Netw. 199, 108463 (2021) 6. Mokni, M., Yassa, S., Hajlaoui, J.E., Omri, M.N., Chelouah, R.: Multi-objective fuzzy approach to scheduling and offloading workflow tasks in fog-cloud computing. Simul. Model. Prac. Theor. 123, 102687 (2023) 7. Alam, M.G.R., Hassan, M.M., Uddin, M.Z., Almogren, A., Fortino, G.: Autonomic computation offloading in mobile edge for IoT applications. Future Gener. Comput. Syst. 90, 149–157 (2019)

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8. Khazaei, H., Misic, J., Misic, V.B.: Modelling of cloud computing centers using m/g/m queues. In: 2011 31st International Conference on Distributed Computing Systems Workshops, pp. 87– 92 (2011). IEEE 9. Outamazirt, A., Barkaoui, K., Aïssani, D.: Maximizing profit in cloud computing using m/g/c/k queuing model. In: 2018 International Symposium on Programming and Systems (ISPS), pp. 1–6 (2018) 10. Xiao, H., Zhang, Z., Zhou, Z.: GWS—a collaborative load-balancing algorithm for internetof-things. Sensors 18(8), 2479 (2018)

Chapter 44

The Adaptation Process of Multicultural Married Migrant Women’s University Life Culture Based on Grounded Theory Chungyun Kim

Abstract The purpose of this study was to explore the meaning of multicultural married immigrant women’s adaptation process to college life using grounded theory research methods. In other words, it is to systematically explore the motivation of multicultural marriage migrant women to go to college, what kind of learning experience they have through the college life process after entering college, and what are their expectations for learning and life after college life adaptation process. Accordingly, in this study, an in-depth analysis based on grounded theory was conducted using 1:1 face-to-face interview data related to the process of adaptation to college life and culture with 10 multicultural marriage migrant women participating in higher education. As a result, a total of 68 concepts, 26 subcategories, and 12 categories were derived. In addition, a learning experience process model that changes into 4 stages ‘anxiety stage, challenge stage, adaptation stage, and transition stage’ and a core category called ‘transitional learning experience process through participation in higher education’ were found. It was discovered that the research participants were adjusting to college life and culture while also going through transitional learning through difficulties and endeavors, escaping the stress of real life, and joining college through higher education. These results suggest that multicultural marriage migrant women can be basic data to provide qualitative support in terms of higher education so that they can live life while playing a professional role in Korean society through higher education.

44.1 Introduction Korea has almost doubled in 10 years after the number of foreigners exceeded 1 million in 2007 [1]. As a result, social policy interest in the multicultural population residing in Korea has also increased, and the ‘3rd Basic Plan for Foreigners Policy C. Kim (B) Free Major Department, Gangnam University, Yongin, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_44

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(2018–2022)’ and ‘3rd Basic Plan for Multicultural Family Policy (2018–2022)’ have been developed for them. Departmental policy support is being provided. However, the policies so far are largely composed of support for the initial stay in the country for multicultural people, support for language and social adaptation for initial settlement, and family support [2]. This multicultural family policy for multicultural people has made a significant contribution because it aims to give children of multicultural families equal educational opportunities, including Korean language learning, education to understand Korean society, and equal educational opportunities for foreigners and marriage immigrant women to integrate into Korean society at a young age. However, it has been pointed out as a limitation that almost all of the education is focused only on supporting elementary and middle school-age children’s school education [3].

44.2 Theoretical Background ‘Multiculturalism’ implicitly reflects the new social trend and phenomenon in Korea. In other words, it is establishing itself as an everyday term, referring to the increasing number of international marriages, marriage of migrant women through immigration to Korea, and multicultural families. In particular, since the early 2000s, foreign marriage immigrants, migrant workers, and North Korean defectors have entered the country rapidly, leading to a rapid entry into a multicultural society. In 2018, there were 150,000 multicultural families, with women accounting for the absolute majority at 83.2%, followed by Chinese, Vietnamese, Japanese, and Filipinos, with the most nationalities from Southeast Asia, and it is gradually becoming more diverse [4]. As the number of immigrant women through marriage increases rapidly, support is needed to develop their capabilities through policies that enable them to engage in professional occupational activities through a stable settlement of Korean culture and self-development through learning. This is related to discussions on the benefits of pursuing a bachelor’s degree through higher education and, in particular, the fundamental life changes and implications for multicultural married immigrant women who will become active participants in our society as independent citizens as a result of their learning and cultural adaptation to college life. The process of adapting to the college life culture of multicultural married immigrant women is an art of the effort necessary to design and prepare for a better future life while living in a strange and fearful environment and maintaining a healthy life in Korea. This suggests that it can become one of the main target learner groups for higher education in a situation where university admission resources are gradually decreasing due to the low birth rate and aging population. Therefore, multicultural marriage migrant women do not exist as mere beneficiaries of relevant policies, but it is necessary to confirm their interest and demand for higher education and prepare to expand support for this. However, some previous studies [5–7] reported information on academic demands, difficulties, and psychological decline related to participation in higher education by foreigners in Korea and foreign students. Due to insufficient research,

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there is a lack of practical and theoretical discussions on support from universities for them. Accordingly, in this study, it is necessary to discuss the support plan in higher education based on the in-depth exploration of the changes and meanings of life in the process of adapting to the college life culture of multicultural marriage migrant women. Although the number of migrant women in multicultural marriages is gradually rising, the scope of educational support and related research for them has been very constrained, with a primary focus on elementary and secondary education. Examples of this include the Korean language curriculum, liberal arts courses only open to foreigners, and mentoring program operations. On the other hand, studies on home life adaptation and social adaptation for multicultural families focused mainly on language and communication, family relationships between husband and wife and their children, and short-term employment preparation education to secure jobs for married immigrant women. In other words, in previous studies, problems such as language, economy, family, and culture of multicultural marriage migrant women were raised. It was revealed that these problems lead to an increase in marital conflict and family dissolution. However, as an alternative to solve this problem, research on higher education for the purpose of professional learning and career development for multicultural marriage migrant women is needed, but such research is very lacking. Therefore, a study that analyzes the adaptation process of multicultural marriage immigrant women receiving higher education will be very important basic research to help them settle down in Korea by raising the quality of human resources. This is a differentiating characteristic from previous studies. As migrant women from multicultural marriages are new higher education consumers in Korean society, the purpose of this study is to explore the role of universities in fostering the development of these women’s talents in their studies and careers. The detailed goal is for the researcher to directly conduct interviews and transcription, and use the data to conduct the following 4 analyzes based on the grounded theory. To put it another way, open coding comes first, followed by a paradigm model through axial coding, selective coding, and process analysis. While receiving education, make a detailed and practical analysis of the process of adapting to college life and culture. Therefore, the goal of conducting this analysis is to increase the amount of multicultural marriage migrant women’s prospects for higher education while also laying the groundwork for new roles that colleges can play in assisting them.

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44.3 Research Methodology 44.3.1 Research Design In this study, a sample was taken from among multicultural married immigrant women enrolled in a 4-year university, who completed at least 4 semesters out of a total of 8 semesters according to the purpose of the study. In other words, a ‘standard sampling’ method can represent those who have experienced a change in life and discovered meaning while adapting to the college life culture among multicultural marriage migrant women, and a sample and interview of research subjects until research data are saturated. Study subjects were selected based on ‘snowball sampling’. Accordingly, the subjects of the study were interviewed by two people currently enrolled in a four-year university, recommendations were received from each student, and the final study subjects were selected from up to 10 by snowball sampling. According to the 2021 National Survey on Multicultural Families, which targeted 15,578 multicultural families across the country, 9 out of 10 multicultural families with school-aged children are experiencing difficulties raising children. In addition, while social discrimination against multicultural families and conflicts between husband and wife is gradually decreasing, it is revealed that the issue of children’s education is emerging as the biggest concern. In particular, more than 90% of married migrant women with children aged 6 or older complained of difficulties in learning and career while raising children [8]. However, the higher the mother’s educational level, the more her child recognizes the importance of academic ability beyond college graduation, recognizes that entering a good university is an advantageous strategy, and thus appreciates the socioeconomic value of academic achievement [9]. In order to prevent multicultural marriage migrant women from dropping out of university courses, it is necessary to have a policy that can motivate them to enroll in higher education. Additionally, research subjects that can adequately represent marriage migrant women who have gone through the process of acclimating to college life culture must be chosen. Based on grounded theory, a qualitative research method, the study was designed through 1:1 interviews.

44.3.2 Respondents of the Study In order to secure the quality level of the study, research participants were selected who satisfied the following conditions. First, a migrant married woman, who was born outside of Korea and came to Korea through marriage, has one or more children, is married, and is also enrolled in university. Second, students must have experienced

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college life and culture for more than 4 semesters while enrolled in an open university or cyber university. Then, depending on the appropriateness and sufficiency of collecting research data using the ‘standard sampling’ method and ‘snowball sampling’ can represent individuals who have experienced the phenomenon, until data are saturated [10]. This was done by continuously determining the number of study participants. Accordingly, one researcher interviewed, recorded, and transcribed 10 married immigrant women residing nationwide, including metropolitan and non-metropolitan areas.

44.3.3 Reliability of Data Unlike quantitative research, the reliability of data is important in qualitative research, so reliability of data was secured as follows. In this study, the data are evaluated by applying the four criteria of trustworthiness, applicability, consistency, and neutrality [11] in order to secure the reliability value of the four qualitative studies. First, in order to secure ‘authenticity’, the purpose of the research is explained to the research participants before the 1:1 interview, and the contents validity of the expert is verified by e-mail. In addition, before the face-to-face interview, the research was fully explained one more time, and the research participants’ consent to participate in the interview was obtained. Second, to ensure ‘relevance’, the findings of this study were gathered through the transcription of research participants’ words, deeds, and even the interview environment, as well as various domestic and international data pertaining to the process of acclimating to university life and culture for multicultural married immigrant women. Third, in order to secure ‘consistency’, data collected through the two-step coding process are repeatedly compared and analyzed, and similarities and differences between data are found and described by continuously comparing phenomena, concepts, and categories. Moreover, three doctors who majored in higher education and lifelong education review the entire research process and research results. Fourth, in order to secure ‘neutrality’, the situation and atmosphere collected during the interview should be viewed objectively without prejudice, and research results should not be drawn in a specific direction.

44.3.4 Reliability and Validity of the Study In this study, reliability and validity are secured based on the strategy to be applied in grounded theory [12]. First, by utilizing the ‘triangular verification method’, based on various data such as previous research, interview data, and memos, efforts are made to discover various

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categories and concepts in the process of adaptation of multicultural marriage migrant women to college life and culture. Second, by selecting two people from among the research participants and examine whether the subject of the research results accurately represents the experiences and opinions of the research participants. Third, using the ‘peer-review method’, fellow researchers with experience in qualitative research were asked to discuss the method of interpretation of this researcher’s research, its relevance to the purpose and goal of the research, and whether or not the research problem was properly solved to enable faithful research to proceed. Fourth, in order to ‘ensure researchers’ sensitivity’, study coaching and career coaching for multicultural married immigrant women are continuously conducted, and foreign students are in charge of classes to sympathize with their difficulties in adapting to college life and culture. In addition, while having a close relationship with the community of multicultural marriage migrant women, they share their anxiety and worries and try to fill in the missing parts by reading domestic and foreign papers and books.

44.4 Results and Discussion This study shows that the desire to enter higher education of multicultural marriage migrant women who are receiving higher education is gradually increasing, but in contrast, the system for their adaptation to college life culture is still insufficient at each university. However, the purpose of this study is based on grounded theory, based on the specific experience of going through a life transition that can dream of starting over in Korea as a result of the creation of various circumstances and conditions, and based on the efforts and encouraging self-suggestions of multicultural marriage migrant women. The examined materials can be displayed as academic accomplishments. The results of this study were presented as a paradigm model composed of axial coding (see Fig. 44.1) and process analysis (see Fig. 44.2).

44.4.1 Causal Condition The causal conditions of this study were ‘face of discrimination in society’ and ‘encouragement of participation in higher education’. In other words, as the period of residence in Korea increases and the children born grow up, they want to overcome poverty in their children’s education and family, but as more people live in rural areas than in large cities, they feel discrimination in society, and accordingly, they are experiencing difficulties in acquiring licenses and professionalism. They were being encouraged to enter universities in order to acquire qualifications and become specialized.

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Fig. 44.1 Paradigm model of axial coding for multicultural married immigrant women’s adaptation process to university life culture

Fig. 44.2 Process analysis on the adaptation process of college life and culture of multicultural married immigrant women

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After getting married in Korea and having children, there are few institutions where I can receive professional education in the area where I live. My Korean isn’t perfect, but I didn’t want to be discriminated against because of my language and academic background (Participant 2). I want to use the English I can speak well in Korean society. However, if I receive professional education, I think I will be able to obtain a teaching license and be recognized as a professional teacher (Participant 5).

44.4.2 Contextual Condition In this study, the contextual conditions were ‘pressure to acquire a bachelor’s degree’ and ‘learner’s indecisive attitude’. In this study, the research participants, as foreigners, are multicultural women in Korean society and mothers with children, leading home life and learning at the same time. It was under pressure as a human resource that could work and relieve economic hardship. On the other hand, learners were often unfamiliar with the Korean language and college life culture while handling various social roles. As a result, there are many shortcomings in communication with other learners and self-directed learning, so they do not adapt well to the culture of college life and have a passive attitude of only taking classes passively. You will show an indecisive attitude that requires you to drop out of the course or certification course. I feel like I’m under unspoken pressure to get a bachelor’s degree once I’ve entered college. However, there are times when I have to live at home, raise my children, and earn money, so I have a lot of pressure to do well in college (Participant 3). It is said that there are many foreign students in universities these days, but when you go to university, it is not easy to meet multicultural female learners like me who are married in Korea, have children, and are living in college. So, I made a lot of effort to adapt to the college life culture, but when I was doing activities such as submitting reports and giving presentations, I thought a lot about whether I should stop or continue (Participant 7).

44.4.3 Central Phenomenon In this study, the tendency of multicultural marriage migrant women to take on ‘self-directed challenges to live a new life’ was revealed as a central phenomenon. I thought I was good at English, but it is difficult to study a subject that can teach English. So, while preparing and reviewing thoroughly in class, I am taking on the challenge to become a professional teacher by earning good grades by doing my best in assignments and exams, obtaining a license to become an English teacher that I want to be (Participant 8).

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As a multicultural woman, I need various things to take on a self-directed challenge in Korea. So, I am always trying to figure out the system that I can get from school or community and use it to adapt well to the college life culture and graduate well (Participant 4).

44.4.4 Interventional Condition In this study, ‘enjoyment and positive expectations for new learning’ and ‘preparation of opportunities for adaptation through expansion of interpersonal relationships’ were found as interventional conditions. In particular, the field of university learning has become largely established in the existing life, and as they experience new professors, learners, and learning systems, they have the pleasure of lifelong learning and positive expectations that they can play a social role in Korean society through this. In addition, as human relationships expanded through learning communities, tutoring, and clubs, multicultural marriage migrant women met various people who could not meet in the local community or village where they lived and gained a broader perspectives. As I was living in college, I became excited while studying new things and having various new experiences, different from the monotonous life I had before. I can get a license through new studies, get good grades, and get a scholarship, so everything is fun. I am looking forward to it because I think my life in Korea will be able to develop further (Participant 6). When I was living as a full-time housewife at home, there was no place to relieve stress when there were conflicts with my children, husband, and in-laws. However, as I started my college life, new people, seniors, and professors explained difficult technical terms to me, and I studied with other foreign students, so my personal relationships expanded and my college life was enjoyable. I am of a similar age, but I am living my married life and living in Korea first. It is also very rewarding to help other international students who are having a hard time studying in Korea for the first time. The professors know my situation well, and when I ask questions, they kindly and meticulously teach me, so I feel reassured that there are many people who support me (Participant 1).

44.4.5 Action/Interaction In this study, ‘continuous and active efforts’ and ‘self-reflection and integration’ were taken as new intentional strategies for the central phenomenon of self-directed challenge to live a new life. Multicultural married immigrant women gained family support for their identity after entering college through adaptation to the college life culture of Korea, and they were recognized as a person who could play a key role in college life through collaboration and communication. I did. In addition, while living

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in college, I learned how to educate my children, and while acquiring knowledge, skills, and attitudes to perform tasks in a stable job, I continued to reflect on myself as a lifelong learner while studying in-depth studies and learn various roles. I was able to integrate my own image of doing it effectively. I have a new perspective and perspective on college life and lifelong learning that I have known so far. Also, while raising children while living in college, I learned how to raise and educate children well (Participant 10). I met a lot of people of different nationalities, ages, and experiences while actively living my college life and participating in various learning activities. Before college, I was full of thoughts of earning money for myself and my family, but after meeting various people, I realized that I could contribute to them in Korean society, so I studied harder (Participant 9).

44.4.6 Result In this study, it is described as ‘expectation for professional occupation’ and ‘learning to become a leader in independent life in Korean society’. Multicultural married immigrant women gradually adapt to the culture of college life, acquire licenses and bachelor degrees, have expectations for professional occupations after graduating from college, and become leaders who live independent lives in Korean society, not as foreigners. It shows that they are making progress in learning while overcoming discrimination and difficulties in order to become successful. In order to work as a social worker, I am diligently learning the theories of social welfare one by one. I already feel good when I imagine and plan to work as a social worker later. Until now, I have received help from Korean society, but now I am looking forward to being able to become a subject that can help (Participant 2). I wanted to listen to my acquaintances when they expressed their concerns and help them solve their worries and difficulties. So, I am currently studying counseling, and I started learning to help others, but rather, my self-esteem has increased and I am very happy with the thought that I can become a leader who can live independently in Korean society (Participant 3). In order to adapt to the college life culture, I felt that it was difficult to communicate in Korean as a foreigner and to adapt to the society and university in Korean. Through college life like this, I can realize my dreams, prepare an economic foundation to build a stable family, and learn to have a professional job. I came to feel that learning to be a confident parent to my children is inseparable from my life (Participant 1). First of all, there is not a specific department for multicultural married immigrant women who face prejudice because of their academic background in Korean society before enrolling in college. Additionally, when these women want to enroll in college, they have trouble getting the information they need about things like preparing their admissions materials, learning about their departments, and college life. In addition, in situation where documents translated into multiple languages are not properly

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prepared, students experience various difficulties in adapting at the beginning of college life. Second, even after entering college, they face difficulties related to basic academic affairs, such as academic affairs schedules, academic terminology, academic information system use, and homepage access. In addition, various aspects of academic support at the university, such as taking exams, writing reports, participating in group activities such as scholarships, mentoring, and tutoring, interacting with seniors and juniors, and using facilities. In addition, they have a strong internal motivation to pursue professional career activities through obtaining a bachelor’s degree through higher education, but on the other hand, they have a dual attitude, showing passiveness about dropping out of college life while facing various difficulties. Third, as they gradually feel the fundamental pleasure of various learning activities and academic research, they have joy and positive expectations, and as the relationship between seniors and juniors gradually improves, they take up self-directed challenges toward a new life, and marriage migrant women are successful in college life culture will adapt to. Fourth, in the process of adapting to college life and culture, students face various difficulties and problems, but as they develop their learning and career competencies, they make continuous and active challenges and efforts, resulting in self-reflection and integration. Fifth, through the process of adapting to college life and culture, multicultural married immigrant women have expectations for professional–vocational activities, develop academic self-efficacy, and face an independent life transition while expecting a second life in Korea.

44.5 Conclusion Based on the results of this study, the following implications of higher education for multicultural married immigrant women can be effectively applied and utilized in Korean society. First, it is necessary to actively support policies related to easier entry into higher education for multicultural marriage migrant women. This acknowledges the potential and potential of students from diverse backgrounds and enables them to be developed as new vital human resources in Korean society. In addition, their children also generate a new talent pool that can play a significant role in Korean society. Second, continuous management and support for multicultural marriage migrant women receiving higher education should be strengthened. It is not just a short-term employment education or training, but various social supports should be provided so that they can be active in a stable and professional field in Korean society. Third, career and employment support is needed for multicultural married immigrant women who have achieved a change in their lives while actively adapting to the university life culture. This is because obtaining a bachelor’s degree after completing

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a higher education, such as a university, means that there is a high level of competence to solve and deal with various problems beyond simply completing the regular curriculum [13]. Therefore, it is necessary to prepare a system that can develop as an important human resource in Korean society by linking jobs that enable people to advance into Korean society with professional skills or competencies or linking them with local community organizations or companies. The fact that their learning experiences are related to their future life through the process of adapting to college life of multicultural marriage migrant women can be seen as an active dedication to learn to become an independent life leader in Korean society. They were pursuing a stable and independent life in Korean society with professionalism and qualifications in a situation where they were not economically well-off [14]. Universities can be seen as playing an important role in this process of adapting to college life so that multicultural married immigrant women can have a sense of belonging as Koreans and become a major factor in social development. Therefore, universities should actively support career and vocational education as well as higher education for multicultural married immigrant women to improve their social integration and economic power in Korean society. In higher education for multicultural married immigrant women, universities should play a central role in supporting community education to provide higher education opportunities. In addition, support should be provided according to customized needs so that students can acquire a bachelor’s degree by continuously adapting to college life without dropping out of college life. In addition, it is necessary to vitalize lifelong education institutions so that multicultural marriage migrant women can continue to live as independent leaders in society and to work with multicultural community organizations to increase their efficiency. Through this study, mutually beneficial multicultural education in which all members of Korean society can respect and understand each other’s diverse cultures and overcome and accept heterogeneous differences between cultures is implemented not only in elementary schools, middle schools, and high schools, but also in universities. We need to create a culture where differences can be acknowledged and cultures shared and enjoyed. Through this, a community society that does not discriminate against culture and gets along together will be created. In this study, not a quantitative study using statistics, but a qualitative study through in-depth interviews, grounded theory was used to analyze the process of adapting to college life in Korea, where academic background and professionalism are valued by multicultural marriage migrant women. The expected learning and meaning of life and the meaning of what one expects from one’s life, family, and society after graduation were derived. Since this study was conducted centering on learning in the process of adapting to college life, further research should be conducted to help multicultural married immigrant women’s lifelong learning from more diverse angles in the future.

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References 1. Ministry of Justice Immigration, Foreign Policy Statistics (2022) 2. Ministry of Women and Family, The 3rd Multicultural Family Policy Basic Plan (2018–2022) (2018) 3. Yang, G., Kim, S., Kim, J.: A study on the comprehensive development plan for multicultural education. Ministry of Education (2017) 4. Ministry of Justice, Statistical research report of statistical yearbook on immigration and foreign policy (2021) 5. Park, M.E., Oh, H.H., Oh, H.S.: Analysis of university immersion profile of international students: a human-centered approach. Compr. Educ. Res. 20(1), 207–230 (2022). https://doi. org/10.31352/JER.20.1.207 6. Park, M.E., Hong, H.M.: Holistic education plan for active academic participation of international students in Korea. Holistic Convergence Educ. Res. 20(1), 207–230 (2022) 7. Choi, J.Y.: Social support and adaptation to college life of international students. 14(1), 39–70 (2022) 8. Ministry of Women and Family, 2021 national multicultural family survey. Ministry of Women and Family (2022) 9. Bowles, S., Gintis, H.: The inheritance of inequality. J. Econ. Perspect. 16(3), 3–30 (2022) 10. Morese, M., Field, A.: Qualitative Research Methods for Health Professionals. Sage Publications, Thousand Oaks (1995) 11. Lincoln, Y.S., Guba, E.G.: Naturalistic Inquiry. SAGE (1985) 12. Merriam, S.B.: Qualitative Research: A Guide to Design and Implementation. Jossey-Bass, San Francisco, CA 13. Gutierrez, L., Alvarez, A.R., Nemon, H., Lewis, E.A.: Multicultural community organizing: a strategy for change. Soc. Work 41, 501–508 (1996) 14. Guibernau, M., Rex, J.: The Ethnicity Reader. Polity Press, Cambridge

Chapter 45

Statistical Data Analysis Method for Factors Affecting Geriatric Nursing Performance of Tertiary Hospital Inok Kim and Hyunli Kim

Abstract This study is a descriptive survey to explore the relationship between patient-centered nursing culture, role conflict, and geriatric nursing performance of nurses in tertiary institutions, as well as the factors influencing geriatric nursing performance. The subjects of this study were 178 nurses with more than one year of nursing experience for the elderly, and data were collected by distributing questionnaires. For data analysis, T-test, Anova, Scheffe test, Pearson’s correlation coefficient, and Hierarchical regression analysis were used. As a result, there were significant differences in patient-centered nursing according to the working department and there were also significant differences in geriatric nursing performance according to the working department. Patient-centered nursing culture and the geriatric nursing performance had a positive correlation, and patient-centered nursing culture and role conflict had a negative correlation. Factors affecting geriatric nursing performance by nurses in tertiary hospitals were identified as work departments, patient-centered nursing culture, and role conflict. The results of this study confirmed that patientcentered nursing culture was a major factor for geriatric nursing performance, and it was found that a strategy to improve patient-centered nursing culture was needed to improve geriatric nursing performance. Therefore, efforts will be needed to create and maintain a working environment and culture to improve patient-centered nursing.

I. Kim Chungnam National University Hospital and Master, College of Nursing, Chungnam National University, Daejeon, Korea e-mail: [email protected] H. Kim (B) College of Nursing, Chungnam National University, Daejeon, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 A. E. Hassanien et al. (eds.), Business Intelligence and Information Technology, Smart Innovation, Systems and Technologies 358, https://doi.org/10.1007/978-981-99-3416-4_45

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45.1 Introduction The elderly population in Korea is about 17.5% of the total population as of 2022 and is expected to enter a post-aged society by 2025 [1]. Tertiary hospitals had the highest share of hospitalization days in the 75–79-year-old age group, while general hospitals had the highest percentage of hospitalization days in the 70–74-year-old age group [2]. In addition, in 2018, medical expenses for the elderly accounted for a high proportion of the population with 21.4% of tertiary hospitals and 22.3% of general hospitals [3]. As the proportion of elderly patients is also increasing in tertiary hospitals focused on acute treatment, attention and efforts are needed for nurses who consider the characteristics of the elderly in the hospital environment. Tertiary hospitals are defined as “general hospitals specializing in medical practices with high difficulty for severe diseases” and have been introduced since 2011 for the purpose of providing medical services for severe diseases and are selected every three years [4]. Currently, there are many studies on general hospitals, because it has not been long since Korea distinguished tertiary hospitals and general hospitals. As the rate of increase in the elderly population accelerates and the proportion of the elderly in tertiary hospitals is increasing, research on the elderly in tertiary hospitals is expected to increase in the future. Elderly patients admitted to general hospitals often have complex diseases, so patient-centered nursing is required considering the individual characteristics and preferences of the elderly. In order to develop and apply nursing interventions for the implementation of patient-centered nursing, it is necessary to create a physical and emotional hospital environment, and medical institutions need to make efforts to create and maintain a culture for patient-centered nursing [5]. Patient-centered nursing culture includes factors for the organizational culture of hospitals and nursing units to implement patient-centered nursing and factors for the nursing work environment. Since organizational culture continuously promotes behavioral change for organizational members [6], it is necessary to establish a patient-centered nursing culture in order to implement patient-centered nursing in the nursing field. When patient-centeredness is formalized as a culture of the nursing organization, it induces behavioral changes that direct nurses toward patientcenteredness [7]. For nurses, the establishment of this nursing culture can be a factor influencing nursing performance [8]. Therefore, it is judged that the patient-centered nursing culture, a concept that combines patient-centered nursing and organizational culture, can be considered a major variable in geriatric nursing performance. Nurses are expected to provide high-quality care in a rapidly changing medical environment, yet they can face many conflicts if they do not quickly address the nursing demands of patients or execute activities with several departments. According to recent research on nurses at tertiary institutions, role conflict was greater than usual, with role conflict being strongest in environmental disability variables relating to personnel and facilities [9]. From these results, it can be seen that hospital nurses have experienced and recognized many conflicts related to the nursing work environment.

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In hospitals with diverse organizational cultures, increased role conflict among nurses can lead to increased work stress, tension, and dissatisfaction, resulting in lower job satisfaction and organizational commitment, and negative consequences such as inadequate role performance and reduced job performance [10]. As such, role conflict is judged to be a factor that reduces the nursing performance of nurses caring for acute elderly patients, but in previous studies, studies that analyzed role conflict as a factor related to the geriatric nursing performance of nurses in tertiary hospitals were insufficient. Recently, studies on geriatric nursing performance are being conducted at various clinical sites, but there have been many studies related to nursing individual characteristics such as knowledge and attitude [11], empathy [12, 13], resilience and elderly nursing stress [14], and studies on the relationship between patient-centered nursing culture (PCNC), role conflict, and geriatric nursing performance were insufficient. Therefore, this study attempted to prepare basic data to improve nursing for elderly patients by investigating patient-centered nursing culture and role conflict as variables related to the geriatric nursing performance of nurses in tertiary hospitals. Research purpose 1. It identifies the score of the patient-centered nursing culture, role conflict, and geriatric nursing performance. 2. It identifies differences in patient-centered nursing culture, role conflict, and geriatric nursing performance according to general characteristics. 3. It identifies the relationship between patient-centered nursing culture, role conflict, and geriatric nursing performance. 4. It identifies factors affecting geriatric nursing performance.

45.2 Method 45.2.1 Study Design This study is a descriptive survey to explore the relationship between patient-centered nursing culture, role conflict, and geriatric nursing performance of nurses in tertiary institutions, as well as the factors influencing geriatric nursing performance.

45.2.2 Sample The subjects of this study are nurses with more than one year of clinical experience in nursing for the elderly among nurses at the headquarters and branch of a local tertiary hospital. The exclusion criteria for the study are obstetrics and gynecology, pediatrics, operating room nurses, and nursing managers above. Using the G-power program 3.1.9.4, a statistical power was set to 0.90 for multiple regression analysis, a

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significance level of 0.05, an effect size of 0.15, and the minimum number of samples calculated with 15 independent variables was 171. A total of 200 questionnaires were distributed considering the dropout rate of 15%, but a total of 178 data were finally analyzed excluding missing values.

45.2.3 Survey Instruments 45.2.3.1

Patient-Centered Nursing Culture Scale (PCNCS)

The patient-centered nursing culture scale (PCNCS) developed by Shin and Yoon [7] was used after obtaining approval from the author. The higher the score of the tool, the higher the awareness of the patient-centered nursing culture, and the reliability at the time of tool development is Cronbach’s α 0.96 [7].

45.2.3.2

Role Conflict

A tool modified by Hwang [15] on Pareek’s scale [16] was used. The higher the score of the tool, the higher the role conflict, and the reliability of the tool is Cronbach’s α 0.89 in the study of Hwang [15].

45.2.3.3

Geriatric Nursing Performance

Older Patients in Acute Care Survey (OPACS) is to measure nurses’ geriatric nursing performance, attitude, and knowledge of elderly patients in an acute treatment environment. After translating the OPACSUS tool by Kim et al. [17], the Korean version of Older Patients in Acute Care Survey was completed. In this study, the results were analyzed with 11 questions in the area of acute geriatric nursing performance among the Korean version of Older Patients in Acute Care Survey. The reliability of the tool was Cronbach’s α 0.85 in the study of Kim et al. [17].

45.2.4 Data Collection A questionnaire containing the survey instruments (patient-centered nursing culture scale, role conflict, geriatric nursing performance) of this study was distributed to 200 research subjects, and 178 copies of data were collected excluding missing values. The data collection period for this study is from October 29, 2021, to November 17, 2021. In order to collect the data of this study, the researcher explained the contents and purpose of the study to the nursing department of each hospital and visited the hospital after obtaining cooperation approval. The purpose and content of the study,

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the anonymity and confidentiality of the study subject, and the explanation that it can be stopped at any time during the study were specified in the consent form. The completed questionnaire was visited by the person in charge of the research and collected the questionnaire.

45.2.5 Ethical Consideration This study was conducted after obtaining approval from the Institutional Review Board (IRB) (202109-SB-202-01). The questionnaire was distributed to nurses who read the consent form, understood the study, and finally agreed to the study consent form for participation.

45.2.6 Data Analysis The collected data were analyzed using the SPSS Statistics 28.0 program. 1. Patient-centered nursing culture, role conflict, and geriatric nursing performance were analyzed using mean and standard deviation. 2. Differences in patient-centered nursing culture, role conflict, and geriatric nursing performance according to general characteristics were analyzed using t-test and ANOVA, and Scheffe test. 3. The correlation between patient-centered nursing culture, role conflict, and geriatric nursing performance was analyzed as Pearson’s Correlation Coefficient. 4. Factors affecting geriatric nursing performance were analyzed by hierarchical regression analysis.

45.3 Result 45.3.1 Reliability In this study, the reliability of the patient-centered nursing culture scale (PCNCS) was Cronbach’s α 0.95, and the reliability of the role conflict tool was Cronbach’s α 0.93. The reliability of the geriatric nursing performance was Cronbach’s α 0.69.

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45.3.2 Analysis Women accounted for the majority with 170 (95.5%), with an average age of 28.29 ± 3.37 years old, and 92 (51.7%) aged 26–29. By size of the hospital, 92 nurses (51.7%), 86 branch nurses (48.3%), 133 unmarried (74.7%), and 120 (67.4%) had no religion. There were 57 (32.0%) surgical wards, 47 (26.4%) internal medicine wards, 50 (28.1%) intensive care units, and 24 (13.5%) comprehensive nursing care service wards. The total working experience averaged 64.08 ± 40.14 months, with 60–120 months being the most common with 72 (40.4%). The current department’s work experience was 24.14 ± 21.19 months, and 106 people (59.6%) were the most common among those over 12 months and under 36 months. The final educational background included 20 professional bachelor’s degrees (11.2%), 147 bachelor’s degrees (82.6%), 11 master’s degrees or higher (6.2%), and 73 (41.0%) turnover experience. Score of Patient-centered nursing culture, Role conflict, and Geriatric nursing performance The average of the patient-centered nursing culture was 3.68 ± 0.41 out of 5, the highest in the area of supportive teamwork by sub-factors, and the lowest in the area of the nursing workplace environment at 3.26 ± 0.70. Role conflicts averaged 2.65 ± 0.65, the highest at 3.22 ± 0.93 in the Resource inadequacy area, and the lowest at 1.99 ± 0.77 in the role ambiguity area. The geriatric nursing performance had an average score of 3.43 ± 0.59 (Table 45.1).

Table 45.1 Score of patient-centered nursing culture, role conflict, and geriatric nursing performance (N = 178) Variables

M ± SD

Variables

M ± SD

Patient-centered nursing culture

3.68 ± 0.41

Role conflict

2.65 ± 0.65

1. Top management leadership

3.52 ± 0.60

1. Role isolation

2.19 ± 0.73

2. Policy and procedure

3.71 ± 0.53

2. Role expectation

2.49 ± 0.86

3. Education and training

3.43 ± 0.70

3. Person-role

2.66 ± 0.84

4. Middle management leadership

3.85 ± 0.72

4. Role ambiguity

1.99 ± 0.77

5. Supportive teamwork

3.92 ± 0.52

5. Inter-role distance

2.67 ± 0.94

6. Nursing workplace environment

3.26 ± 0.70

6. Role overload

3.04 ± 0.97

7. Professional competence

3.66 ± 0.56

7. Resource inadequacy

3.22 ± 0.93

8. Patient-centered nursing activity

3.81 ± 0.44

Geriatric nursing performance

3.43 ± 0.59

9. Values of nurse

3.85 ± 0.52

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Differences in Patient-centered nursing culture, Role conflict, and Geriatric nursing performance according to general characteristics Patient-centered nursing culture had significant differences according to the subject’s working department. The comprehensive nursing care service wards had a higher patient-centered nursing culture than the internal medicine ward and the intensive care unit (F = 4.34, p = 0.005). The role conflict differed according to the subject’s total work experience (F = 2.85, p = 0.039), as a result of the post-hoc analysis, it was not statistically significant. The geriatric nursing performance differed according to the working department of the subject, and the surgical ward and the comprehensive nursing care service wards had higher geriatric nursing performance than the intensive care unit and the internal medicine ward (F = 8.42, p =