LISS 2022: 12th International Conference on Logistics, Informatics and Service Sciences (Lecture Notes in Operations Research) 9819926246, 9789819926244
This book aims to provide new research methods, theories, and applications from various areas of management and engineer
141 41 13MB
English Pages 562 [542] Year 2023
Contents
A Framework for Modelling Enterprise Technological Innovation Knowledge System Using Semantic Ontology Representation
1 Introduction
2 Literature Review
3 Enterprises Technological Innovation Ontology Modeling
3.1 Ontology Planning
3.2 Resource Collection
3.3 Ontology Analysis
3.4 Ontology Design
3.5 Ontology Maintenance
4 The Enterprises Technological Innovation Domain Ontology Visualization
4.1 Visualization of Hierarchical Relationships
4.2 Visualization of Conceptual Relationship
5 Conclusion
References
Exploring Effects of Patients’ Regulatory Focus on Their Compliance After Misdiagnosis
1 Introduction
2 Research Model and Hypotheses
3 Methods
4 Results
5 Discussion
References
Understanding the Antecedents of Patient Self-management Behavior in Online Health Communities: An Application of the UTAUT Model
1 Introduction
2 Literature Review
2.1 UTAUT Model
2.2 Self-management Behavior
3 Hypotheses Development
3.1 Performance Expectancy
3.2 Effort Expectancy
3.3 Social Influence
3.4 Continuous Intention and Self-management Behavior
4 Method
4.1 Research Setting
4.2 Pretesting
4.3 Data Collection
5 Results
6 Discussion
6.1 Implications for Theory and Practice
6.2 Limitations
7 Conclusion
References
Translational Medicine Informatics Services from the Bedside Over QL4POMR
1 Introduction
2 Extending Ql4POMR with TMI Services
3 Additional TMI Connectivity Through Opentargets
4 Conclusion
References
Exploring Online Physician–Patient Interactions Through Information Sharing with Agent-Based Modeling
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Network Initialization
3.2 Physician and Patient Agents
3.3 Physician–Patient Interaction
3.4 Network Updating
3.5 NetLogo Simulation
4 Results
5 Discussion
6 Conclusions
References
Information System Function-Data Architecture Planning Based on Subspace Partition
1 Introduction
2 Related Work
2.1 Heterogeneous Information Network
3 Concept Preparation
3.1 Function—Data Network
3.2 Homogeneous Entity Link Degree
3.3 Information System Subspace Density
4 Steps of Information System Function-Data Architecture Planning Method
4.1 Homogeneous Entity Clustering
4.2 Function-Data Subspace Partition
4.3 Function—Data Subspace Adjustment
5 Experiment and Result Analysis
5.1 Experimental Data
5.2 Information System Structure Planning Experiment and Result Analysis
6 Conclusion
References
Scenario Construction Model of Railway Traffic Accidents Based on Similarity Theory
1 Introduction
2 Literature Review
2.1 Emergency Management System
2.2 Scenario Construction
2.3 Smilarity Theory
3 Scenario Analysis of Railway Accidents
3.1 Characteristics of Railway Accidents
3.2 Government Emergency Management Process
3.3 Scenario Construction
4 Scenario Construction Model
4.1 Scenario Elements Analysis
4.2 Feature Attribute Extraction
4.3 Similarity Calculation
4.4 Government Decistions
5 Model Validation
6 Conclusion
References
Research on Operational Performance Evaluation of Listed Coal Companies in China Under the New Normal of Economy
1 Introduction
2 Principal Component Analysis of Operating Performance Evaluation of Coal Listed Companies
2.1 Selection and Data Sources
2.2 Determination of Operational Performance Evaluation Index System
2.3 Process Using Principal Component Analysis
3 Cluster Analysis of Operational Performance of Listed Coal Companies
4 Conclusion
References
The Effect of Green Mergers and Acquisitions on the Performance of Heavily Polluting Enterprises: A Case Study of Gezhouba Group in China
1 Introduction
2 Theoretical Basis and Literature Review
2.1 Theoretical Basis
2.2 Literature Review
2.3 Theoretical Framework
3 Research Methods and Data
3.1 Research Methods
3.2 Case Selection
3.3 Data Collection and Analysis
4 Case Study
4.1 Motivation for Mergers and Acquisitions
4.2 Process Mechanism
4.3 Merger and Acquisition Results
5 Research Findings
5.1 Conclusions
5.2 Limitations
References
Carbon Emissions Reduction in Vehicle Routing Problems with Split Deliveries and Pickups
1 Introduction
2 Problem Description
3 Three-Stage Approach
3.1 First Stage-Clustering First
3.2 Second Stage-Routing Criterions
3.3 Third Stage-Total Costs Calculation
4 Experiments
4.1 Execution 1 on the Modified Constructed Dataset
4.2 Execution 2 on the Modified Constructed Dataset
5 Conclusion
References
Study on the Whole Process Coping Strategy of Supply Chain Disruption Risk Based on NVIVO
1 Introduction
2 Research Design
2.1 Research Methods
2.2 Data Collection
3 Coding Analysis
3.1 Open Coding-Categorization of Concepts
3.2 Axial Coding-Determination of the Main Category
3.3 Selective Coding-Definition Core Category
4 Research Discussion
4.1 The Strategies of Beforehand Prophylaxis
4.2 The Strategies of Processing Control
4.3 The Strategies of Afterward Treatment
5 Conclusions and Future Study
5.1 Conclusions
5.2 Future Study
References
Service Quality Evaluation of New Retail Fresh E-commerce Based on AHP-Entropy TOPSIS Model
1 Introduction
2 Literature Review
2.1 The Evaluation of Fresh E-commerce Service Quality
2.2 The Improvement of TOPSIS Method
3 Construction of Service Quality Evaluation Index System for New Retail Fresh E-commerce
3.1 Construction of Initial Evaluation Index System
3.2 Optimization of Evaluation Index System
4 Topsis Evaluation Model Based on AHP-Entropy Weight Method
4.1 Determination of Weight Method
4.2 Determination of Comprehensive Evaluation Method
4.3 Decision Process of AHP-Entropy TOPSIS Model
5 Empirical Analysis
5.1 Instance Selection and Data Collection
5.2 Decision-Making Process
5.3 Service Quality Improvement Strategy
6 Conclusion
References
Research on Book Location Algorithm of Library Based on Improved LANDMARC Algorithm
1 Introduction
2 Library Book Location Algorithm Based on LANDMARC Improved Algorithm
3 Simulation Experiment and Result Analysis
3.1 Environment Construction
3.2 Environment Construction
4 Conclusion
References
Design of Supply Chain Intellectual Property Securitization Service Agreement Based on Moral Hazard
1 Introduction
2 Problem Description and Model Hypothesis
2.1 Hypothesis 1
2.2 Hypothesis 2
2.3 Hypothesis 3
2.4 Hypothesis 4
3 The Design of Incentive and Punishment Contracts Considering the Moral Hazard of Original Stakeholders
4 Numerical Analysis
5 Conclusion
References
Research on Emergency Logistics Decision Platform Based on Knowledge Graph
1 Introduction
2 Research Status of Knowledge Engineering in the Field of Emergency Logistics
3 Knowledge Representation and Graph Construction in the Field of Emergency Logistics
3.1 Knowledge Representation in the Field of Emergency Logistics
3.2 Domain Knowledge Graph Construction
3.3 Intelligent Decision-Making Framework for Emergency Logistics Based on Knowledge Graph
4 Conclusion
References
Research on the Impact of Digital Inclusive Finance on People’s Well-Being
1 Introduction
2 Analysis of Mechanisms
2.1 Impact of Digital Inclusive Finance on Economic Development
2.2 Impact of Digital Inclusive Finance on people’s Lives
2.3 Impact of Digital Inclusive Finance on Social Security
2.4 Impact of Digital Inclusive Finance on the Ecosystem
3 Study Design
3.1 People’s Well-Being and Standard of Living
3.2 Selection of Variables and Data Sources
3.3 Model Construction
4 Empirical Analysis
4.1 Empirical Results
4.2 Amendments and Tests
4.3 Regression Results
4.4 Cluster Analysis
5 Conclusions and Recommendations
References
Study on the Evaluation of Employment Quality in China’s Provinces Based on Principal Tensor Analysis
1 Introduction
2 Research Method
2.1 PTAk Model of k-order Tensor (K > 2)
2.2 PTA3 Model Algorithm
3 China’s Interprovincial Employment Quality Measurement and Its Visualization
3.1 Employment Quality Measure
3.2 Expansion and Visualization of Principal Tensors
4 Conclusion
References
Study on Low-Carbon Emissions in Vehicle Routing Problems with Split Deliveries and Pickups
1 Introduction
2 Problem Description
3 Proposed Two-Stage Approach
3.1 First Stage-Distance Minimizing
3.2 Second Stage-Cost Minimizing
4 Experiments
4.1 Execution on the Constructed Dataset 1
4.2 Execution “Pickuping After Delivering” on the Constructed Dataset 1
4.3 Execution on the Constructed Dataset 2
5 Conclusion
References
An Improved Contention-Based MAC Protocol Based on IEEE 802.11 Distributed Coordination Function
1 Introduction
2 System Model
2.1 Network Structure
2.2 DCF* Protocol
3 Performance Analysis Model
3.1 Maintenance Delay
3.2 Contention Delay
4 Numerical Simulation and Result
4.1 Analysis of the Backoff Parameter
4.2 Analysis of the Maintenance Times
4.3 Analysis of the Number of Access Network Nodes
5 Conclusion
References
Method of Building Enterprise Business Capability Based on the Variable-Scale Data Analysis Theory
1 Introduction
2 Related Works
2.1 Digital Transformation
2.2 Enterprise Business Capability Platform
2.3 Variable-Scale Data Analysis Theory
3 Enterprise Business Capability Construction Method of Composable Enterprise
3.1 Scale Space Model of Composable Business Platform
3.2 Enterprise Business Capability Construction Method Based on the Variable-Scale Data Analysis
4 Experiment Results and Discussion
5 Conclusions
References
Design of Coding Domain Non-orthogonal Demodulation Based on OFDM System
1 Introduction
2 System Modle and Algorithm Design
2.1 Traditional OFDM System
2.2 Across-Slot SIC Based on OFDM Description
2.3 Model and Scheme Design of Across-Slot SIC Based on OFDM
3 Simulation and Results Analysis
3.1 Results and Analysis When Using Traditional Frame Format
3.2 Improved Method and Results for Quasi-Synchronous Scenario
3.3 Performance Contrast and Analysis
4 Conclusion
References
Coordination Mechanisms for a Three-Echelon Fast-Moving Consumer Goods Supply Chain Considering Supply and Demand Effects
1 Introduction
2 The Model
2.1 The Retailer's Cost Function
2.2 The Distributor’s Cost Function
2.3 The Supplier’s Cost Function
3 Analysis
4 The Coordination Mechanism
5 Conclusion
References
Research on Knowledge Graph Platform of Logistics Industry Based on Big Data
1 Introduction
2 Definition of Knowledge Graph
3 Key Technologies and Processes for Knowledge Graph Realization in Logistics Industry
3.1 Knowledge Acquisition Link
3.2 Knowledge Processing Link
3.3 Knowledge Application
3.4 Knowledge Reasoning
4 Architecture Design of a Knowledge Graph Platform for the Logistics Industry Based on Big Data
5 Application of Logistics Industry Knowledge Graph Platform
5.1 Knowledge Graph Relationship Model for the Logistics Industry
5.2 Knowledge Graph Visualization
5.3 Function Realization of Knowledge Graph Platform in the Logistics Industry
6 Conclusion
References
A Study on Market Development System and Competitiveness Evaluation of Energy Internet
1 Introduction
2 Energy Internet Market Development System and Market Competitiveness Evaluation
2.1 Constructing the Energy Internet Market Development System
2.2 Market Competitiveness Evaluation Based on Multi-level Fuzzy Comprehensive Evaluation Method
3 Conclusion
References
Judgment Model of Pollution Source Excessive Emission Based on LightGBM
1 Introduction
2 Analysis of Multi-Feature Model of Pollution Source Excessive Emission
2.1 LightGBM Model
2.2 Improved Multi-feature Model
3 Experiment Analysis
3.1 Dataset
3.2 Experiment Environment
3.3 Results
4 Conclusion
References
Spatial–temporal Evolution of Green Patent Cooperation Network in BTH Region
1 Introduction
2 Data Sources and Research Methods
2.1 Data Sources
2.2 Network Construction and Measurement Index
3 Types of Green Patent Cooperation Nodes
4 Temporal Evolution Characteristics
5 Characteristics of Core Nodes
5.1 Evolution Analysis of Degree Centrality
5.2 Evolution Analysis of Betweenness Centrality
6 Cross-Regional Cooperation Character-istics
7 Conclusion
References
A Scheduling Delay Optimization Scheme for Centralized Ad Hoc Networks
1 Introduction
2 Network Structure
3 TDMA-CDMA Scheduling Scheme
3.1 Schedule Tree Construction
3.2 Frame Structure
3.3 Scheduling Process
3.4 Network Resource Allocation Algorithm
4 Schedule Delay Model
5 Numerical Simulation
5.1 The Performance of TDMA-CDMA
5.2 Influence of ps: [/EMC pdfmark [/objdef Equ /Subtype /Span /ActualText (r Subscript t) /StPNE pdfmark [/StBMC pdfmarkto.ps: [/EMC pdfmark [/Artifact /BDC pdfmark rtps: [/EMC pdfmark [/StBMC pdfmark ps: [/EMC pdfmark [/StPop pdfmark [/StBMC pdfmark
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6 Conclusion
References
A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns
1 Introduction
1.1 Research Background
1.2 Literature Review
2 Research Outlines
2.1 Research Design
2.2 Pharmacy Overview
3 Scheduling Optimization Model
3.1 Pharmacist Work Experience Weight
3.2 Scheduling Model
3.3 Case Analysis
3.4 Robustness Analysis
4 Extentions
4.1 Optimization of Work Content
4.2 Extention with Overtime Working
5 Summary
References
Research on Book Publishing Enabled by Digital Twin and NFT
1 Introduction
2 The Dilemma Existing in the Development Process of Publishing Integration
2.1 Paper Books Face the Dilemma of “Losing Their Charm”
2.2 Electronic Book Piracy is Rampant
2.3 Profit Distribution of Recoverable Copyright
3 The NFT and Digital Twins Make the “Digital Scarcity” of Books Possible
3.1 Deconstructing the Digital Twin of the Recombinant—Entity Books from a Unique Perspective
3.2 Create Digital Scarcity—NFT Continues the Value Chain and Application Scenarios of Books
4 Reality and Potential Applications of NFT and Digital Twin in the Field of Book Publishing
4.1 Books: From “Collected Physical Books” to “Collected NFT Books”
4.2 Users: Digital Books Become an Asset, No Longer Limited by the Platform
4.3 Author: Digital Rights Protection and Multi-author Rights Confirmation
4.4 Publishing House: Visualize Readers on the Basis of Copyright Protection, and Distribute Publishing on Demand
5 Conclusion
References
Key Technology of Cloud Service for the Aggregation of Satellite, Aerial and Terrestrial Multi-source Spatiotemporal Information
1 Introduction
2 Study Status of Massive Spatiotemporal Data Model and Cloud Service Architecture
2.1 Massive Spatiotemporal Data Models
2.2 Cloud Service Architecture for Massive Spatiotemporal Data Model
2.3 Indexing Technology for Multi-source Spatiotemporal Data Query
3 Improvement of Spatiotemporal Data Read–Write Framework
4 Study on Improved Spatiotemporal Indexing Method
5 Conclusion
References
The Dilemma and Reflection of Short Video Infringement from the Perspective of Game Theory: Publicity and Protection of Film and Television Works
1 Introduction
2 Infringement Mode of Short Video—Subject and Performance of Copyright Game of Film and Television Works
2.1 Game Between Copyright Holders and Copyright Users
2.2 Game Between Short Video Platforms and Copyright Holders
3 Reflections on Short Video Tort—The Cause of Non-Cooperative Game
3.1 Network Audio-Visual Publishing Products Have Not Been Included in the Scope of Management
3.2 “Rational Utilization” of Second Creation of Short Video
4 Publicity and Protection of Film and Television Works—Formulating Reasonable Game Rules
4.1 Improve the Copyright Protection Mechanism
4.2 Establish a Mutually Beneficial Business Model
4.3 Use Technology to Strengthen Copyright Protection
5 Conclusion
References
The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data
1 Introduction
2 Related Research
2.1 Weibo Emotion Analysis
2.2 Analysis of Weibo Public Opinion Evolution
2.3 Research on the Evolution of Public Opinion and Emotion in Public Health Emergencies
3 Public Opinion Emotion Evolution Model for Public Health Emergencies
3.1 Word2vec Model
3.2 LSTM Model and Emotion Classification
3.3 Model Flow Chart Construction
4 Emotional Evolution Analysis of COVID-19 Event
4.1 The Experimental Data
4.2 Selection of Evaluation Indicators
4.3 Experimental Process and Results
5 Public Opinion Evolution Analysis
5.1 Evolutionary Stage Division
5.2 Proportion Analysis of Emotional Tendency in Different Stages
5.3 Analysis of Age Characteristics at Different Stages and the Proportion of Emotional Tendencies in Each Age Stage
5.4 Distribution Characteristics of Domestic Provinces at Different Stages
6 Conclusion
References
Research on the Impact of Digital Economy Development on Enterprise Innovation from the Perspective of Financing Constraints
1 Introduction
2 Data and Method
2.1 Data
2.2 Variables
2.3 Method and Design
3 Results and Discussion
3.1 Main Regression Result
3.2 Mediation Test
3.3 Heterogeneity Analysis
3.4 Robustness Check
4 Conclusion
References
How to Improve Innovation Ability of Graduate Students Through Science and Technology Competition?
1 Introduction
2 Related Work
2.1 Cultivation Mode
2.2 Innovative Ability
3 Strategies About How to Improving Innovation Ability of Graduate Student
3.1 A Ladder-Type Path to Improve Innovation Ability of Graduate Students
3.2 Taking Science and Technology Competition as the “Second Classroom”
4 Conclusion
References
Analyses and Suggestions on the Reform of Land Requisition
1 The Basic Land Regimes Between China and the US
2 Range of Requisition
3 Compensation of Land Requisition
4 The Advantages and Disadvantages of China’s Land Requisition Regime
5 Establish the Quota of Land Converted Trading Mechanism by Drawing Lessons from the Us System of Land Development Rights
References
The Market Effectiveness of China's Shipping Derivatives Under the Background of Financial Support for Shipping Logistics
1 Introduction
2 Literature Review
3 Research Method
3.1 Information Effectiveness
3.2 Functional Effectiveness
4 Empirical Analysis
4.1 Information Effectiveness Text
4.2 Functional Effectiveness Test
5 Conclusions and Recommendations
References
Service Supply Chain Optimization of Community Elderly Care-A Case Study in Beijing
1 Introduction
1.1 Background of Study and questions raised
1.2 Purpose and Significance of Study
1.3 Methodology
2 Literature Review
2.1 Domestic and International Research on Service Supply Chain Theory and Elderly Service
2.2 Kano Model and AHP Model for Elderly Care Services
2.3 Innovation
3 Field Research and Model Data Analysis
3.1 Community D Elderly Care Survey
3.2 Community D Elderly Care Service Supply Chain Framework
3.3 Results of the Questionnaire Survey
3.4 Satisfaction Analysis Based on Kano Model
3.5 Weighting Analysis Based on AHP Model
4 Improvement and Suggestions
4.1 Recommendations for Government Policies
4.2 Recommendations from the Perspective of Service Supply Chain
5 Research Conclusions and Prospects
5.1 Conclusion
5.2 Future Research
References
Production Channel Strategies of an Automotive Supply Chain Under Government Intervention
1 Introduction
2 Literature Review
3 Problem Definition and Assumptions
4 Model Formulation
4.1 Case C
4.2 Case E
4.3 Case F
4.4 Case T
5 Decision Analysis
6 Numerical Analysis
6.1 Sensitivity Analysis
6.2 Impact of Credit Ratio and Tariff on the Profits and Social Welfare
6.3 Impact of Credit Price and Tariff on the Profits and Social Welfare
7 Conclusions
8 Appendix
8.1 Proof of Demand Function
8.2 Proof of Results in Case C
8.3 Proof of Results in Case E
8.4 Proof of Results in Case F
8.5 Proof of Results in Case T
References
Analysis of Reading Promotion in the Digital Twin Library
1 The Predicament of Reading Promotion in Public Libraries
1.1 Insufficient Content Supply
1.2 Limited Reading Space Conditions
1.3 Lack of Professional Reading Instruction
2 An Overview of the Digital Twin Library
2.1 The Concept of Digital Twin Technology
2.2 The Concept of Digital Twin Library
3 The Digital Twin Library Reading Scene Application
3.1 Dynamic Monitoring to Meet the Individual Needs of Users
3.2 Optimize Allocation and Integrate Supporting Resources Reasonably
3.3 Intelligent Interaction to Create a System and Service System
4 The Reading Promotion Path of the Digital Twin Library
4.1 The Digital Twin Library Provides High Quality Content
4.2 The Digital Twin Library Optimizes the Allocation of Reading Resources
4.3 The Digital Twin Library Improves Reading Space Conditions
4.4 The Digital Twin Library Guarantees the Basic Reading Needs of Grassroots Groups
5 Conclusion
References
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Lecture Notes in Operations Research
Xiaopu Shang · Xiaowen Fu · Yixuan Ma · Daqing Gong · Juliang Zhang Editors
LISS 2022 12th International Conference on Logistics, Informatics and Service Sciences
Lecture Notes in Operations Research Editorial Board Ana Paula Barbosa-Povoa, University of Lisbon, Lisboa, Portugal Adiel Teixeira de Almeida , Federal University of Pernambuco, Recife, Brazil Noah Gans, The Wharton School, University of Pennsylvania, Philadelphia, USA Jatinder N. D. Gupta, University of Alabama in Huntsville, Huntsville, USA Gregory R. Heim, Mays Business School, Texas A&M University, College Station, USA Guowei Hua, Beijing Jiaotong University, Beijing, China Alf Kimms, University of Duisburg-Essen, Duisburg, Germany Xiang Li, Beijing University of Chemical Technology, Beijing, China Hatem Masri, University of Bahrain, Sakhir, Bahrain Stefan Nickel, Karlsruhe Institute of Technology, Karlsruhe, Germany Robin Qiu, Pennsylvania State University, Malvern, USA Ravi Shankar, Indian Institute of Technology, New Delhi, India Roman Slowi´nski, Pozna´n University of Technology, Poznan, Poland Christopher S. Tang, Anderson School, University of California Los Angeles, Los Angeles, USA Yuzhe Wu, Zhejiang University, Hangzhou, China Joe Zhu, Foisie Business School, Worcester Polytechnic Institute, Worcester, USA Constantin Zopounidis, Technical University of Crete, Chania, Greece
Lecture Notes in Operations Research is an interdisciplinary book series which provides a platform for the cutting-edge research and developments in both operations research and operations management field. The purview of this series is global, encompassing all nations and areas of the world. It comprises for instance, mathematical optimization, mathematical modeling, statistical analysis, queueing theory and other stochastic-process models, Markov decision processes, econometric methods, data envelopment analysis, decision analysis, supply chain management, transportation logistics, process design, operations strategy, facilities planning, production planning and inventory control. LNOR publishes edited conference proceedings, contributed volumes that present firsthand information on the latest research results and pioneering innovations as well as new perspectives on classical fields. The target audience of LNOR consists of students, researchers as well as industry professionals.
Xiaopu Shang · Xiaowen Fu · Yixuan Ma · Daqing Gong · Juliang Zhang Editors
LISS 2022 12th International Conference on Logistics, Informatics and Service Sciences
Editors Xiaopu Shang School of Economics and Management Beijing Jiaotong University Beijing, China Yixuan Ma School of Software Engineering Beijing Jiaotong University Beijing, China
Xiaowen Fu Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong Daqing Gong School of Economics and Management Beijing Jiaotong University Beijing, China
Juliang Zhang School of Economics and Management Beijing Jiaotong University Beijing, China
ISSN 2731-040X ISSN 2731-0418 (electronic) Lecture Notes in Operations Research ISBN 978-981-99-2624-4 ISBN 978-981-99-2625-1 (eBook) https://doi.org/10.1007/978-981-99-2625-1 © 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 solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, 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 Paper in this product is recyclable.
Contents
A Framework for Modelling Enterprise Technological Innovation Knowledge System Using Semantic Ontology Representation . . . . . . . . . . Qianqian Zhang and Guining Geng
1
Exploring Effects of Patients’ Regulatory Focus on Their Compliance After Misdiagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xinyi Lu
17
Understanding the Antecedents of Patient Self-management Behavior in Online Health Communities: An Application of the UTAUT Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pei Wu and Runtong Zhang
31
Translational Medicine Informatics Services from the Bedside Over QL4POMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer, and Peter Sertic
45
Exploring Online Physician–Patient Interactions Through Information Sharing with Agent-Based Modeling . . . . . . . . . . . . . . . . . . . . . Donghua Chen
59
Information System Function-Data Architecture Planning Based on Subspace Partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuwen Huo, Xuedong Gao, and Ai Wang
75
Scenario Construction Model of Railway Traffic Accidents Based on Similarity Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dan Chang, Lei Huang, and Daqing Gong
89
Research on Operational Performance Evaluation of Listed Coal Companies in China Under the New Normal of Economy . . . . . . . . . . . . . 103 Qixin Bo and Xuedong Gao
v
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Contents
The Effect of Green Mergers and Acquisitions on the Performance of Heavily Polluting Enterprises: A Case Study of Gezhouba Group in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Xizhe Zhang, Sen Wang, and Yan Huang Carbon Emissions Reduction in Vehicle Routing Problems with Split Deliveries and Pickups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Cheng Jin, Lijun Lu, and Jianing Min Study on the Whole Process Coping Strategy of Supply Chain Disruption Risk Based on NVIVO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Mengmeng Miao, Hongmei Shan, Zihao Li, and Qian Zhang Service Quality Evaluation of New Retail Fresh E-commerce Based on AHP-Entropy TOPSIS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Zhen Li and Yuping Xing Research on Book Location Algorithm of Library Based on Improved LANDMARC Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Yue Li, Shuihai Dou, Yanping Du, Zhaohua Wang, Xianyang Su, and Lizhi Peng Design of Supply Chain Intellectual Property Securitization Service Agreement Based on Moral Hazard . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Cheng Liu, Qiuyuan Lei, Xinzhong Bao, and Wenjing Xie Research on Emergency Logistics Decision Platform Based on Knowledge Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Liyan He, Juntao Li, Meijuan Zhao, and Ruiping Yuan Research on the Impact of Digital Inclusive Finance on People’s Well-Being . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Qi Wu, Jiaqi Feng, and Shouheng Sun Study on the Evaluation of Employment Quality in China’s Provinces Based on Principal Tensor Analysis . . . . . . . . . . . . . . . . . . . . . . . . 227 Yingxue Pan and Xuedong Gao Study on Low-Carbon Emissions in Vehicle Routing Problems with Split Deliveries and Pickups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Cheng Jin, Lijun Lu, and Jianing Min An Improved Contention-Based MAC Protocol Based on IEEE 802.11 Distributed Coordination Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Wenpeng Li and Xu Li Method of Building Enterprise Business Capability Based on the Variable-Scale Data Analysis Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Ai Wang and Xuedong Gao
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Design of Coding Domain Non-orthogonal Demodulation Based on OFDM System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 He Liu and Xu Li Coordination Mechanisms for a Three-Echelon Fast-Moving Consumer Goods Supply Chain Considering Supply and Demand Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Yi Wang, Bingkun Dong, Yefei Yang, and Shaobin Wei Research on Knowledge Graph Platform of Logistics Industry Based on Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Fan Yang, Juntao Li, Ruiping Yuan, Fan Wang, and Huanli Zhao A Study on Market Development System and Competitiveness Evaluation of Energy Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Weizheng Kong, Suxiu Li, and Xingtong Chen Judgment Model of Pollution Source Excessive Emission Based on LightGBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Wenhao Ou, Xintong Zhou, Zhenduo Qiao, Liang Shan, Zhenyu Wang, and Jiayi Chen Spatial–temporal Evolution of Green Patent Cooperation Network in BTH Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Mingxuan Yang and Shuo Zhang A Scheduling Delay Optimization Scheme for Centralized Ad Hoc Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 Jiale Li and Ying Liu A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Yuyao Feng and Xiang Jie Research on Book Publishing Enabled by Digital Twin and NFT . . . . . . . 379 Kehan Li and Liang Wang Key Technology of Cloud Service for the Aggregation of Satellite, Aerial and Terrestrial Multi-source Spatiotemporal Information . . . . . . . 393 WenHao Ou, Peng Luo, LinLin Liu, Liang Shan, JiaYi Chen, and Zhenyu Wang The Dilemma and Reflection of Short Video Infringement from the Perspective of Game Theory: Publicity and Protection of Film and Television Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405 Anyi He and Liang Wang The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data . . . . . . . . . . . . . . . . . . . . 415 Jiazheng Sun, Xiaodong Zhang, and Shaojuan Lei
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Research on the Impact of Digital Economy Development on Enterprise Innovation from the Perspective of Financing Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435 Junyue Zhou and Qi Qiu How to Improve Innovation Ability of Graduate Students Through Science and Technology Competition? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 Dandan Li, Yan Huang, and Shiyin Yan Analyses and Suggestions on the Reform of Land Requisition . . . . . . . . . . 461 Qianxiang Yu The Market Effectiveness of China’s Shipping Derivatives Under the Background of Financial Support for Shipping Logistics . . . . . . . . . . . 473 Siyuan Wang and Xiaojie Liang Service Supply Chain Optimization of Community Elderly Care-A Case Study in Beijing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Yongkang Liu, Xufan Zhang, and Yi Zhang Production Channel Strategies of an Automotive Supply Chain Under Government Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 Li Shen, Luhong Chang, and Xiaohui Yu Analysis of Reading Promotion in the Digital Twin Library . . . . . . . . . . . . 543 Xiaoyu Liu and Yuqing Ma
A Framework for Modelling Enterprise Technological Innovation Knowledge System Using Semantic Ontology Representation Qianqian Zhang and Guining Geng
Abstract With the rapid development of the semantic web, ontology engineering has become an important research field of the semantic web application. The domain ontology can realize the knowledge organization and representation. Presently, the research of domain ontology is mainly concentrated in the fields of medicine, geography, agriculture and biology. In terms of enterprise technological innovation ontology establishment and knowledge acquisition, the related work has not been carried out. Since there is no mature domain ontology in the field of enterprise technological innovation, this paper uses scientometric analysis to locate relevant literature and extracts key terms to form the basic domain terms set. A domain ontology related to construction aspects of enterprises technological innovation was established based on the basic term set and combined with the Seven-step ontology methodology, which provides support for semantic knowledge mining and acquisition. Keywords Enterprise technological innovation · Knowledge base · Ontology · Scientometric
1 Introduction With the accelerating pace of economic globalization, the market has stepped into the information age from the era of industrialization. The market demand has changed rapidly, and the market competition has become extremely fierce. In this context, the technological innovation capability is increasingly becoming the internal driving force and primary source of enterprise development. However, it is difficult to manage the knowledge of enterprise technology innovation because of the distributed and Q. Zhang (B) School of Information, Beijing Wuzi University, Beijing, China e-mail: [email protected] G. Geng 360 Digital Security Technology Group Co., Ltd, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_1
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heterogeneous characteristics. With the rapid development of the semantic web, ontology construction has become an important research field of the semantic web application. An ontology defines the basic terms and relations comprising the vocabulary of a topic area and the rules for combining terms and relations to define extensions to the vocabulary [1]. Therefore, it is a feasible approach to construct the ontology to realize the knowledge system organization of enterprise technology innovation and the knowledge sharing and representation. Given that there is no more mature domain ontology in the field of enterprise technological innovation, this paper constructs the domain ontology through the background of enterprise technology innovation and enterprise management literature and combines the domain ontology construction methodology to provide support for knowledge sharing and acquisition at the semantic level. The remainder of this paper is organized as follows. The second section presents the review of related researches. The third section provides the implementation process of the proposed modelling for enterprise technological innovation. Section four presents the structure of the domain ontology with protégé software. In the final section, we conclude the work and presents the limitation and future works.
2 Literature Review Ontology is a complex, multidisciplinary field that draws on knowledge in information organization, natural language processing, information extraction, artificial intelligence, knowledge representation and acquisition. It is a reference model that defines the application domain and aims to improve information consistency, reusability, system interoperability and knowledge sharing. Ontology has enormous implications for organizations dealing with massive amounts of distributed and heterogeneous information through the knowledge shared between people and application domains. It has been widely used in the field of describing complex relational semantics and conceptual modelling. The work of ontology construction has made significant progress and has prospects for development in many areas. For example, Sun et al. [2] constructed an ontology for the software testing domain. Neha and Niladri [3] proposed a scheme for designing an ontology for the agriculture domain. Qi et al. [4] constructed a food safety domain ontology based on the automatic extraction of the semantic hierarchical relations method. Ji et al. [5] established the hierarchical system of ocean flow field domain knowledge. Zhang et al. [6] constructed the ontology database for the Chinese medical domain. Chin and Chang [7] built the Information and Communication Technologies (ICT) Education ontology which provides a central repository of classified knowledge about ICT education. At present, the relevant research has not been fully carried out for the application of ontology in the field of enterprise technological innovation. Only a few studies have begun to attempt the application of ontology in the field of enterprises. For example, ontological modeling of economic processes related to innovative activity [8], model the enterprise risk knowledge management [9], developing a knowledge-based system
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for innovative product creation processes [10], construct a frame-based ontology model expressing the generic innovation designing [11], and a business concept ontology for monitoring the performance and competence of enterprise [12]. The most concerning issue in ontology engineering is the construction methodology and process. Ontology can be built from scratch or by reusing other available ontologies. The ontology construction methodologies can be divided into two categories: thesaurus and ontology engineering. The thesaurus is a domain concept description based on subject domain knowledge. It has a relatively complete organizational system and can be an essential resource for reusing existing domain knowledge. There are many kinds of ontologies converted from the thesaurus, such as the agricultural ontology developed from the AGROVOC thesaurus [13]; the Geoinformatics field ontology was built based on dictionary of geographical information systems (GIS) [14]. The ontology engineering method explores the idea of ontology development from the perspective of knowledge engineering. It emphasizes the whole process of ontology planning, establishment and maintenance guided by the idea of engineering development. The typical approaches include the TOVE, ENTERPRISE, METHONTOLOGY and Seven-step method, as shown in Table 1. However, the ontology-based semantic organization method has no authoritative methodology and lacks standardized management and restriction. The above methods summarise the specific project development process and experience, which is difficult to apply to the whole field. Therefore, one or more methods should be used flexibly according to the domain knowledge and specific engineering characteristic in knowledge ontology development.
3 Enterprises Technological Innovation Ontology Modeling Enterprise technological innovation involves knowledge in multidisciplinary fields such as enterprise management and technological innovation. Up to now, there is no research to realize knowledge sharing in this field. It is necessary to abstract and generalises the model to construct a unified knowledge representation model in the enterprise technological innovation field. Compared with other ontology construction methods, the seven-step method is more mature than other methods. Therefore, this paper uses the idea of the seven-step method to design the seed ontology in the field of enterprise technology innovation. The construction process is shown in Fig. 1.
3.1 Ontology Planning Before constructing the domain ontology, it is necessary to analyze the requirements first. The issues that should be clarified in requirements analysis include: the application field and scope of ontology, the goal of ontology construction, the target users
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Table 1 Three representative ontology construction methods Methodology
Ontology construction process Features
Tove
1. Identify application areas and expected solutions 2. Specify the term 3. Use terms to express definite problems 4. Specify axioms and definitions for terms 5. Assess the integrity of the ontology
Emphasize the evaluation of ontology integrity
Enterprises
1. Define purpose and scope 2. Knowledge acquisition and conceptualization 3. Knowledge coding and formalization 4. Ontology evaluation 5. Records of ontology
Distinguishes the informal and formal stages of ontology construction
Methodology
1. Requirement statements Maintenance of ontology life cycle phases 2. Domain knowledge conceptualization 3. Formalization of conceptual models 4. Formal model implementation 5. Ontology maintenance 6. Knowledge acquisition 7. Ontology records 8. Ontology evaluation
Seven-step method
1. Determine the domain ontology 2. Examine the possibility of reusing existing ontologies 3. List important terms of the ontology 4. Define classes and the hierarchy 5. Define the attributes of the class 6. define attributes 7. Create instance
Based on ontology development tool of Protégé
of ontology, the reusability of ontology, etc. Then formulate an ontology construction plan based on the results of the demand analysis, including methods, ontology description language, modeling tools, etc. The domain ontology constructed in this study is mainly used in the enterprise’s technological innovation. The goal is to discover the influencing factors of enterprise technological innovation and modeled with protégé compilation software.
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Fig. 1 The process of domain ontology construction
3.2 Resource Collection In the stage of resource collection, extract important concepts by analyzing topic knowledge and record the topic concepts after identifying the target topic. In the enterprise’s technological innovation domain, the sources of the basic terminology set in this paper are divided into two categories: existing terminology resources and related literature. The existing terminology resources mainly include the Oslo Manual and the Thesaurus of Chinese Classification. The related literature mainly uses scientometric analysis to obtain key term sets. The specific process is shown in Fig. 2. (1) Reuse the existing ontology The field of enterprise technological innovation involves many disciplines such as business economics and business management. It has an extensive vocabulary and lacks an authoritative thesaurus. Therefore, reusing the existing ontology and terminology can improve the efficiency of domain ontology construction. Enterprise Ontology and TOVE Ontology are the Deductive Enterprise Model (DEM) based on the Generic Enterprise Model (GEM), which is the most commonly used enterprise domain ontology template. Enterprise Ontology has established four first-class categories for enterprise domain ontology, including activities and processes, organizations, strategies and markets, which cover the enterprise’s daily business activities and resources and the strategic level. TOVE includes four classes, enterprise design ontology, engineering ontology, plan ontology and service ontology, which reflect the mobility and sequence dependence of the enterprise’s internal activities. The concept and hierarchical relationship proposed by the Enterprise and TOVE ontology can be inherited in this research. Furthermore, the Oslo Manual is an important document in internal innovation measurement [16]. It was proposed and published by the Organization for Economic Co-operation and Development (OECD). It illustrates the significance of innovation measurement and establishes the conceptual framework and theoretical model of innovation investigation, which has reference significance for the construction of enterprise technological innovation. Therefore, important
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Fig. 2 The process of resource collection
concepts about the field of technological innovation of enterprises can be extracted and recorded. The Thesaurus of Chinese Classification is a terminology compiling tool developed by the library and information field according to international standards, which covers multiple disciplinary subjects and is suitable for establishing large-scale domain ontology [17]. This paper uses the F27 category content in the Chinese Classification Thesaurus economic category to enrich the domain ontology. (2) Term extraction based on relevant literature The definition of bibliometrics was first proposed by Mulchenko [18], a quantitative study of the development of scientific literature. It can also be considered a technology based on large-scale academic data sets, mainly reflecting the overview of the development of the research field, citation process, mapping knowledge structure and the evolution of domain knowledge. This paper determined the related literature and extracted terms by the scientometric analysis, the process includes literature retrieval, bibliometric analysis and keyword list. First of all, determine the database and the keywords used for retrieval. This section mainly uses the ISI Web of Science, and the search field is “TI = (enterprise technology innovation* OR enterprise technique innovation* OR technological innovation* OR technology innovation* OR technologic innovation capability* OR innovation capacity* OR technology innovation ability)”. There is a total of 2944 related literature retrieved from 1990 to 2022. The most important literature were filtered by the scientometric analysis such as co-citation analysis, literature citation frequency analysis, literature association
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Fig. 3 Document co-citation analysis
analysis, author co-citation analysis, etc. Figure 3 is the visual analysis of the literature co-citations by CiteSpace software. The size of the nodes represents the cited times, and the colour of the connecting line represents the time of the first co-citation. The key literature with the highest number of co-citations can be found through the visual co-citation analysis. Citation frequency and betweenness centrality are important indicators for selecting key literature. Therefore, there are 76 articles selected by a citation frequency greater than 10 or betweenness centrality greater than 0.15, as shown in Fig. 4. These articles become the first batch of core documents screened out in this study and serve as the theoretical source for the follow-up summary of the literature research on keywords of enterprise technological innovation. These core documents are served as the theoretical source for key terms extraction. This research also supplemented the Chinese authoritative research by searching the CNKI Chinese knowledge database. The scientometric analysis selected a total of 10,614 relevant documents from 1990 to 2022, and 99 papers were selected as the core papers in this research. After determining the key literature, the next step is to conduct a preliminary exploratory collection of key terms from the selected literature. Through the identified 177 key documents, the terminology used in each paper on the mode, behaviour and influencing factors of technological innovation activities of enterprises was collected and recorded. Table 2 shows the collection of documents and key terms used as the thesaurus for follow-up research.
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Fig. 4 The list of literature cited and betweenness centrality
3.3 Ontology Analysis (1) Domain ontology framework design This research inherited The Enterprise Ontology and TOVE Ontology projects and takes the innovation resources, organizational innovation, marketing innovation, strategic innovation and product innovation activities as the first classes of the enterprise technology innovation ontology. In addition, the innovation output and protection measures are also the key factors affecting the technological innovation capability of enterprises and should be included in this domain ontology framework. Therefore, the framework of the ontology of enterprise technological innovation is determined, as shown in Fig. 5.
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(2) Design of classes, attributes and instances According to the designed framework of enterprise technology innovation domain ontology, the term set is divided into subclasses of various levels, attributes of classes and instances. This paper uses a top-down approach, starting with the largest concept and progressively refining the upper-level concepts by adding subclasses. For example, the strategic innovation class includes four subclasses of business Table 2 Collection of parts of articles and key terms Articles
Authors
Time
Key terms
The quadratic relative evaluation method of enterprise technology innovation ability
Lu et al.
2002
Basic conditions, human factors, financial factors, innovation input, innovation output, product innovation, intellectual property rights, promoting social progress, improving the quality of workers, environmental protection, management status, management system and mechanism, management plan implementation
The mechanism and empirical Huang et al. study of mutual promotion between environmental regulation and enterprise technological innovation
2010
The proportion of industrial added value, total labour productivity, energy consumption output value of products, comprehensively utilizing output value of new products in total output value
Comparative study on the impact of different types of environmental regulations on enterprise technological innovation
Zhang et al.
2016
Number of patents, number of scientific and technological personnel, funds for scientific and technological activities, actual utilization of foreign direct investment, environmental input, cost-based environmental regulation, investment-based environmental regulation
An empirical study on the impact of Ownership structure on enterprise technological innovation
Dewei Yang
2011
Ownership structure, ultimate ownership, private ownership, foreign ownership (continued)
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Table 2 (continued) Time
Key terms
Dan Chen & Huili Zhang An empirical study on the relationship between technological innovation capability and growth of smes
2011
Three-year growth rate of enterprise profitability, sales profit growth rate, three-year growth rate of return on equity, three-year growth rate of net profit, three-year growth rate of average return on assets, three-year expansion capacity of enterprise, three-year growth rate of total assets, three-year growth rate of net assets and three-year growth rate of main business income
Research on the synergy degree of technological innovation, institutional innovation and sustainable growth of enterprises
2008
The proportion of technological innovation input in product sales revenue, the proportion of R&D personnel in the total number of employees, the number of patents and new products Enterprise culture of innovation, innovative spirit, and indicators of enterprise growth: profit margin on total assets, proportion of new product sales revenue in total revenue, relative market share, enterprise popularity
Articles
Authors
Xu et al.
decision-making, institutional change, enterprise planning and economic evaluation, expanding at different levels of subordinates. Attributes describe class concepts, including the Object Property and Datatype Property. Object Property constrains the relationship between instances of two classes. The domain is a class, and the range is an instance of a class. The Datatype Property constrains the relationship between the instance of the data type attribute and the RDF literal or an XML Schema data type. The domain is an instance of a class, and the range is the Boolean, String and Float type. The instance is an individual, the basic elements contained in the ontology class and the most basic object in the ontology. (3) Design of relationship The structure of ontology can be expressed as Eq. (1). O = (C, ≤ c, R, σ, ≤ r )
(1)
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Fig. 5 The framework of enterprise technological innovation ontology
Consisting of • Two disjoint sets C and R, whose elements are called concept identifiers and relation identifiers respectively. • A partial order ≤ c on C is called concept hierarchy or taxonomy. • A function σ represents a special kind of relationship C1 · C2 . . . · Cn−1 → Cn . • A partial order ≤ r on R is called relation hierarchy, where r1 ≤ R r2 implies |σ (r1 )| = |σ (r2 )| and πi (σ (r1 )) ≤ C πi (σ (r2 )) for each 1 ≤ i ≤ |σ (r1 )|. For the two concepts Ci and Cj in domain ontology, if Ci is defined as the Equivalent Class of Cj, the semantic concept of Ci and Cj are equal, recorded as Ci=C j; If Cj is defined as SubClass Of Ci, the semantic concept of Ci contains Cj and marked as SC j ⊇ SCi . For two conceptual sets SCi and SCj, if for any concept Cj in SCj, there is a concept Ci in SCi that satisfies Ci = C j or Ci ⊇ C j , hence the semantic of SCi contains SCj and recorded as SCi ⊇ SC j ; If there is SCi ⊇ SC j and SC j ⊇ SCi , the semantic of SCi is equal to the semantic of SCj and marked as SCi = SC j . There are two main types of relationships: vertical and horizontal. The vertical relationship includes the kind-of relationship, part-of relationship, attribute-of relationship and instance-of relationship. The horizontal relationship includes synonymy-of relationship and relevant-of relationship. The relationship between classes is shown in Table 3.
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Table 3 The relationship between the classes of enterprises’ technological innovation Relationship type Vertical relationship
Horizontal relationship
Relationship
Description
Kind-of
The relationship between a parent class and a child class
Part-of
Distinguishes the informal and formal stages of ontology construction
Attribute-of
Relationships between attributes and classes
Instance-of
Relationships between instances and concepts
Synonymy-of
The relationship between two classes that have the same meaning
Relevant-of
The relationship between two related classes
3.4 Ontology Design First, normalize the built ontology and use protégé to compile the software implementation. The ontology is then recorded and preserved for subsequent revision and development. As the latest Semantic Web Ontology language of W3C, OWL provides rich class axioms, and its functions are superior to XML, XML Schema and RDF. Therefore, the OWL was used to store ontologies.
3.5 Ontology Maintenance Due to the change in the definition of existing knowledge concepts and the addition of new concepts, it is necessary to adjust the existing ontology, which can be realized by the automatic extension method of the ontology in the future.
4 The Enterprises Technological Innovation Domain Ontology Visualization Protégé is an open-source ontology editor developed based on Java, which supports multiple inheritances and has strong extensibility, and can convert protégé into various forms of text representation. Therefore, this paper uses protégé as an ontology development tool and knowledge visualization in data modelling and enterprise technology innovation.
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Fig. 6 Protection measures class and subclasses visualization
4.1 Visualization of Hierarchical Relationships The constructed enterprise technology innovation domain ontology is shown in Fig. 6. The Protégé compilation software is used for ontology construction, and its Jambalaya tool is used to visualize the results of the constructed enterprise technological innovation domain ontology.
4.2 Visualization of Conceptual Relationship The concept of domain ontology in enterprise technological innovation includes nonhierarchical and hyponymous relationships. Figure 7 shows part of the relationship of enterprise’s technological innovation domain ontology. For example, the first class of strategic innovation includes four subcategories: operation decision, system reform, enterprise plan and economic evaluation, the relationships between the parent class of strategic innovation and child classes are kind-of relationship. The construction of ontology relationships helps researchers to discover the measurement of relationships between domain concepts and the neglected knowledge.
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Fig. 7 Visualization of conceptual relationship
5 Conclusion This paper utilizes the bibliometric method to analyze the domain-related literature, extracts the key terms of the factors affecting the enterprise technological innovation and forms the basic domain terms set. The acquisition of ontology terms is the key to establishing domain ontology. Through the research on the construction methods of ontology, this paper initially uses the classical seven-step method to construct the enterprise technological innovation domain ontology. The domain ontology reflects the complex knowledge structure and lays the foundation for the realization of knowledge sharing, knowledge fusion and knowledge reuse in the field of enterprise technology innovation. However, the knowledge contained in the domain ontology needs to be refined and supplemented. Hence, it is necessary to continuously optimize the domain ontology according to the application’s feedback information and mining effect. The future work needs to solve several problems, firstly using the intelligence information acquisition methods and text analysis techniques to optimize the knowledge in the field with the domain ontology semantic analysis feedback. Secondly, based on the construction of enterprise technological innovation domain ontology, further text mining analysis is carried out to realize the combination of ontology and text mining. Acknowledgements This research was supported by the Youth Research Fund Project of Beijing Wuzi University (2022XJQN19).
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Exploring Effects of Patients’ Regulatory Focus on Their Compliance After Misdiagnosis Xinyi Lu
Abstract Patients may not be able to correctly treat misdiagnosis, which may influence treatments. Patients with different regulatory focuses may view the misdiagnosis from different perspectives, so this study aims to explore the effects of patients’ regulatory focus on their compliance when there is a misdiagnosis, by establishing a research model. An online survey involving 345 valid responses was conducted to collect data, and partial least squares-structural equation modelling was adopted to analyze data and test hypotheses. Results indicate that after misdiagnosis, promotion focus had positive effects on patients’ trust in physicians and psychological safety, while had a negative effect on patients’ ego depletion. Prevention focus had positive effects on patients’ go depletion, and had negative effects on patients’ trust in physicians and psychological safety. Patients’ trust in physicians and psychological safety had positive effects on their compliance, but ego depletion had a negative effect on compliance. Findings suggest that: (1) hospitals and physicians should focus more on patients with a prevention focus; (2) physicians should recognize their mistakes accurately and make efforts to prove their professionalism to regain trust; (3) physicians can create a safe treatment environment to increase patients’ psychological safety, and help regulate patients to improve compliance. Keywords Regulatory focus theory · Patient compliance · Trust in physicians · Ego depletion · Psychological safety
1 Introduction Not all of physicians who have ever misdiagnosed patients are quacks or unprofessional, but patients may be unable to treat the misdiagnosis correctly and be likely to be overwhelmed by negative emotions, such as being in bad mood, getting angry with their physicians, and even refusing treatments or giving up complying with medical regimens and treatments, which may have an adverse effect on patients’ X. Lu (B) School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_2
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timely treatments. Patient compliance positively affects health outcomes and rehabilitation [1, 2] since the effectiveness of medical regimens and treatments is associated with self-management and self-monitor that are two main elements related to patient compliance [3]. Given that the proportion of chronic diseases has increased year by year [4], patient compliance is increasingly important [5], including the situation of misdiagnosis. For example, keeping a healthy lifestyle such as a balanced diet is conducive to diabetes management [6], and self-management behaviors such as taking antihypertensive medications regularly are beneficial for controlling hypertension [7]. Low compliance or noncompliance tends to cause serious consequences from three aspects: (1) Low compliance or noncompliance is likely to cause poor health status, and the morbidity and mortality may increase; (2) Low compliance or noncompliance to medical regimens and treatments, to some degree, is the waste of medical productivity and resources; (3) Sometimes, patients do not take medicine following prescriptions in clinical practice, which has an adverse effect on confirming the usability and effectiveness of drugs [4, 8]. Patient compliance has been widely discussed in previous studies, but related studies from the perspective of psychology are limited. Therefore, this study attempts to explore how patient compliance may change after misdiagnosis from psychological perspectives. Regulatory focus theory proposed by Higgins [9] brings significant effects to the pleasure principle, consisting of promotion focus and prevention focus. Promotion focus pays attention to accomplishments, aspirations, personal development, and its strategic inclination is making progress and approaching the desired end-state. By contrast, prevention focus pays attention to duties, obligations, avoiding failures and mistakes, and its strategic inclination is being prudent and avoiding mismatches to the desired end-state [9, 10]. Individuals with a promotion focus desire to pursue success and are sensitive to positive outcomes [11], while individuals with a prevention focus tend to avoid losses and approach safety [12] and concentrate on negative outcomes [11].Therefore, this study intends to identify how regulatory focus can affect patient compliance after misdiagnosis. Trust plays a central role in healthcare system [13], and it is the foundation of effective relationships [14]. As a type of interpersonal trust, patients’ trust in physicians is defined as the degree of patients’ confidence in physicians’ performances, which is divided into two dimensions: cognition-based trust and affected-based trust whose basics are knowledge and emotion, respectively [15, 16]. Patients are willing to follow their physicians’ advice if they consider their physicians professional based on their cognition. As for affect-based trust, patients may feel safe and comfortable and be willing to show their vulnerability when they perceive emotional connections with their physicians [17]. Trust in physicians can help improve physician–patient communication, promote patients to comply with medical regimens and treatments, thereby improving health outcomes [18]. For example, trust in physicians is considered important in deciding whether to enroll in a new treatment for cancer or not. If patients distrust their physicians, the physician–patient relationship may be short [19]. Therefore, trust in physician is regarded as an important element in diagnosis, treatment, and healing, as well as a necessary part in an effective healthcare system [20, 21].
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Ego depletion is a temporary state of mental fatigue when the ability of selfregulation is temporarily insufficient because the amount of available resources for self-control is limited [22, 23]. In such a situation, individuals are unable to focus, and their willpower and volitional action capacity will be diminished [24]. Because of the weakened ability of conscious, deliberate, and complex thinking in ego depletion, there are a lot of cognitive errors in the process of dealing with problems. In addition, the process of regulating emotions may be influenced by ego depletion [23]. Several activities such as regulating negative emotions consume more resources than routine activities, resulting in high ego depletion [24]. Therefore, when patients experienced misdiagnosis, they need more resources to regulate themselves than in daily life. Psychological safety is defined by Edmondson [25] as a perception that individuals can freely share information, exchange views with others and express themselves in a safe environment without worrying about negative consequences and interpersonal risks [26, 27]. The physician–patient relationship is a special partnership, patients cooperate with physicians to complete treatment missions. Psychological safety can help individuals overcome negative feelings when they experience something unexpected, such as misdiagnosis [26]. With a high level of psychological safety, patients are willing to trust their physicians, tell physicians their conditions and follow physicians’ advice. Psychological safety is characterized by uncertainty and change, so it is a dynamic parameter [26], and different individuals can perceive different degrees of psychological safety sense. Accordingly, this study aims to examine the effect of regulatory focus on patient compliance through the mediations of patients’ trust in their physicians, ego depletion, and psychological safety after misdiagnosis.
2 Research Model and Hypotheses This study established a research model (Fig. 1) including two independent variables (promotion focus and prevention focus), three mediators (patients’ trust in physicians, ego depletion, and psychological safety), and one dependent variable (patient compliance). Patients generally regard their physicians’ professional performances as the fundamental behavior, and physicians’ integrity and ability are taken for granted and may
Fig. 1 Research model
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not increase their trust in physicians. However, physicians acting across the red lines, such as misdiagnosing patients, may make their patients be in a situation with threat and then erode their patients’ trust [28]. Patients with a prevention focus are likely to pay attention to physicians’ negative behaviors, which may erode their trust in physicians regardless physicians’ past positive behaviors. Meanwhile, patients with a promotion focus concentrate on their growth and development needs and evaluate their physicians from comprehensive perspectives, so they may still trust their physicians if they consider their physicians professional even though they have experienced misdiagnosis. Therefore, this study derived the following hypotheses: H1: After misdiagnosis, promotion focus has a positive effect on patients’ trust in physicians. H2: After misdiagnosis, prevention focus has a negative effect on patients’ trust in physicians. Patients may be overwhelmed by negative emotions, such as being in bad mood, getting angry with their physicians, and even refusing treatments, if they have been misdiagnosed. Therefore, these patients need more internal resources that are limited, such as energy, to regulate themselves [29], resulting in ego depletion [24]. Patients with different regulatory focuses have different sensitivities to misdiagnoses. Specifically, patients at a promotion focus are engaged in innovative activities [30] and pay attention to a single end-state [31], thereby consuming a small number of resources and causing low ego depletion. By contrast, patients with a prevention focus concentrate on various end-states to avoid negative consequences [24], which needs more attention to deal with details and process information and consumes a lot of resources. Moreover, activities motivated by prevention focus deplete more energy than that motivated by promotion focus [32]. This situation led to the following hypotheses: H3: After misdiagnosis, promotion focus has a negative effect on patients’ ego depletion. H4: After misdiagnosis, prevention focus has a positive effect on patients’ ego depletion. Previous work proposes that regulatory focuses have a certain relevance to safety behaviors [33]. Given that individuals’ behavior is always motivated by mentality, promotion focus and prevention focus are associated with psychological safety. Patients with a prevention focus are sensitive to security needs and make efforts to avoid negative outcomes [33]. When they are in a threatening situation such as being misdiagnosed, they are likely to view this situation from the perspective of loss, and then their psychological safety may be declined [10]. By contrast, patients with a promotion focus view the consequences of misdiagnosis in the long run, so their psychological safety may be strong. The above discussion led us to propose the following hypotheses: H5: After misdiagnosis, promotion focus has a positive effect on patients’ psychological safety. H6: After misdiagnosis, prevention focus has a negative effect on patients’ psychological safety. Self-efficacy refers to the belief in individuals’ capability related to a specific behavior [34, 35]. Misdiagnosis may make patients be overwhelmed by anxiety, and
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erode their self-efficacy [34]. Then patients may be less confident to care themselves well. Constructing trust with patients is beneficial for physicians to help improve patients’ self-efficacy [36]. If patients highly trust their physicians, their sense of anxiety may be decreased and their self-efficacy may be enhanced, thereby promoting patients to overcome psychological barriers and comply with physicians [37]. Consequently, this study suggested the following hypothesis: H7: After misdiagnosis, patients’ trust in physicians has a positive effect on their compliance. Patients complying with medical regimens and treatments after misdiagnosis requires more limited resources to regulate themselves than before, thereby possibly decreasing their ability to self-control [38]. With such a situation of ego depletion, patients may fail to carry out deliberation and rule-based analyses, ultimately resulting in low compliance [22]. Given the unsuccessful self-regulation and low compliance, patients may be unable to engage in some activities, such as keeping a balanced diet and giving up smoking [39]. Accordingly, this study proposed the following hypothesis: H8: After misdiagnosis, patients’ ego depletion has a negative effect on their compliance. As a special cooperative relationship, physician–patient relationship takes psychological safety as an important factor to complete treatment missions. With psychological safety, patients are willing to trust their physicians, tell physicians their healthrelated conditions and maintain a healthy lifestyle following physicians’ advice. When patients have experienced misdiagnosis, they may be overwhelmed by negative emotions, and sometimes be full of defenses against the outside world. Psychological safety can help patients overcome these negative feelings [26]. Patients with a higher level of psychological safety are more likely to consider the treatment environment safe, thereby being more possible to comply with their physicians [40]. Accordingly, this study suggested the following hypothesis: H9: After misdiagnosis, patients’ psychological safety has a positive effect on their compliance.
3 Methods A. Instrument Development This study used previously validated multiple-item scales to measure variables of the research model (see Fig. 1). All items were measured by a seven-point Likert type response format that ranges from “strongly disagree” to “strongly agree”. Promotion and prevention focus were measured by two different nine-item scales from a previous study [41]. A 15-item scale from Salmon et al. [42] was adopted to measure patients’ ego depletion after misdiagnosis. The sense of psychological safety was measured using a three-item scale from Chikoko et al. [43]. Patient compliance was measured by a seven-item scale from Laugesen et al. [1].
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Before the formal investigation, the instrument was translated into Chinese. First, three native Chinese speakers who held at least a master’ degree in English and were skilled in scientific research translation were recruited to translate the questionnaire into Chinese, considering the cross-cultural adaptation[44]. Second, individuals with different backgrounds, ages, genders, and educational levels were invited to read the initially translated version and propose recommendations to improve the instrument in the Chinese cultural context. The last step was translating the Chinese version into English and comparing these two versions to ensure the consistency. B. Data Collection and Respondent Profile Our subjects were individuals who had experienced misdiagnosis before, and an anonymous investigation was conducted online in November 2020. We received 409 responses, and 345 (84.35%) of them were valid. As shown in Table 1, over half of the sample were 20–40 years old, female, and held, at least, a bachelor’s degree. Our investigation channel was the Internet whose users are characterized young, female, and highly-educated [45], so the sample could be used to analyze the research model.
Table 1 Sample demographics
Demographic characteristics Age
Gender Living area Education level
Value, n (%)
< 20
18 (5.22)
20–29
115 (33.33)
30–39
121 (35.07)
40–49
47 (13.62)
50–59
33 (9.57)
60 and above
11 (3.19)
Male
148 (42.90)
Female
197 (57.10)
Urban
203 (58.84)
Rural
142 (41.16)
Junior middle school
43 (12.46)
High school
65 (18.84)
Junior college
38 (11.01)
Bachelor’s degree
156 (45.22)
Master’s degree
39 (11.30)
Ph.D
4 (1.16)
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4 Results A. Data Analysis Given the different background and participants of this study from previous works, this study evaluated the reliability and validity of scales. As shown in Table 2, all Cronbach’s alpha values of promotion focus, prevention focus, trust in physicians, ego depletion, psychological safety, and patient compliance were above the cut-off value of 0.700, so the scales had good reliability [46]. The Kaiser–Meyer–Olkin (KMO) value was 0.959, so that data could be used for factor analysis. This study adopted confirmatory factor analysis (CFA) to evaluate the validity of scales. Table 2 presents the composite reliability(CR) and average variance extracted(AVE) of constructs. Since all values of CR were greater than 0.700, and all values of AVE were greater than 0.500, the convergent validity was acceptable. In addition, as shown in Table 3, each construct’ SqrtAVE was greater than the correlation coefficients between other constructs and itself, which means good discriminant validity [47, 48]. Table 2 Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) Construct
Cronbach’s alpha
CR
AVE
Sqrt AVE
Promotion focus
0.892
0.913
0.537
0.833
Prevention focus
0.937
0.947
0.665
0.815
Trust in physicians
0.899
0.919
0.541
0.736
Ego depletion
0.965
0.968
0.672
0.819
Psychology safety
0.768
0.865
0.682
0.826
Patient compliance
0.939
0.953
0.803
0.896
Table 3 Inter-construct correlations Construct
PROF
PREF
TP
ED
PS
a
0.833
PREF b
−0.227
0.815
c
0.514
−0.321
0.736
ED d
−0.308
0.444
−0.396
0.819
PS e
0.367
−0.327
0.458
−0.496
0.826
f
0.614
−0.260
0.598
−0.431
0.434
PROF TP
PC
PROF = Promotion Focus; PREF = Prevention Focus; TP = Trust in Physicians; TP = Trust in Physicians; PS = Psychology Safety; f PC = Patient Compliance.
PC
0.896
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B. Hypotheses Testing This study adopted partial least squares-structural equation modelling (PLS-SEM) to test hypotheses and analyze the research model [1], and used SmartPLS to analyze data. The magnitude and significance of path coefficients are shown in Table 4, which indicates that all hypotheses H1-H9 were supported. In addition, this study used Cohen ƒ2 [49](see Table 5) to examine the effect sizes of relationships. Results reveal that, after misdiagnosis, promotion focus had a positive effect on patients’ trust in physicians, with medium effect size, had a negative effect on patients’ ego depletion, with small effect size, and had a positive effect on patients’ psychology safety, with small effect size; Prevention focus had negative effects on patients’ trust in physicians and psychology safety, with small effects, had a positive effect on patients’ ego depletion, with medium effect size; Patients’ trust in physicians and psychology safety had positive effects on their compliance, with medium and small effects, respectively; Patients’ ego depletion had a negative effect on their compliance, with insignificant effect size.
Table 4 Results of hypotheses testing Hypotheses
Path coefficient
t-value
p-value
H1: After misdiagnosis, promotion focus has a positive effect on patients’ trust in physicians
0.479
11.005
< 0.001
H2: After misdiagnosis, prevention focus has a negative effect on patients’ trust in physicians
−0.211
3.972
< 0.001
H3: After misdiagnosis, promotion focus has a negative effect on patients’ ego depletion
−0.220
4.261
< 0.001
H4: After misdiagnosis, prevention focus has a positive effect on patients’ ego depletion
0.391
7.352
< 0.001
H5: After misdiagnosis, promotion focus has a positive effect on patients’ psychological safety
0.308
6.371
< 0.001
H6: After misdiagnosis, prevention focus has a negative effect on patients’ psychological safety
−0.262
4.666
< 0.001
0.477
10.624
< 0.001
−0.184
3.784
< 0.001
0.120
2.643
0.008
H7: After misdiagnosis, patients’ trust in physicians has a positive effect on their compliance H8: After misdiagnosis, patients’ ego depletion has a negative effect on their compliance H9: After misdiagnosis, patients’ psychological safety has a positive effect on their compliance
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Table 5 Partial least squares effect size analysis Variables
R2 In
Out
ΔR2
Cohen ƒ2
Effect size
Patient compliance Trust in physicians
0.422
0.255
0.167
0.289
Medium
Ego depletion
0.422
0.412
0.010
0.017
Insignificant
Psychology safety
0.422
0.398
0.024
0.042
Small
Promotion focus
0.323
0.113
0.21
0.310
Medium
Prevention focus
0.323
0.282
0.041
0.061
Small
Promotion focus
0.250
0.205
0.045
0.060
Small
Prevention focus
0.250
0.109
0.141
0.188
Medium
Promotion focus
0.201
0.114
0.087
0.109
Small
Prevention focus
0.201
0.139
0.062
0.078
Small
Trust in physicians
Ego depletion
Psychology safety
5 Discussion A. Principal Results This study examines the changes in compliance of patients with different regulatory focuses once they experienced misdiagnosis, and it has contributions to future study from theoretical and practical perspectives. First, we constructed a research model to clarify the mechanisms through which regulatory focus affects patient compliance after misdiagnosis, which enriches theoretical works on physician–patient relationship, patient compliance, and behavioral psychology. This study also enriches the application of regulatory focus theory in the field of healthcare. Second, patients’ trust in their physicians has a positive effect on their compliance although they have been misdiagnosed by their physicians. Patients with a promotion focus are likely to trust their physicians again if they regard the misdiagnosis as an accident. Therefore, hospitals and physicians are required to improve patients’ trust in the physician–patient relationship and make the trust be less likely to be decreased although patients were once misdiagnosed. In addition, physicians should recognize their mistakes accurately and make their efforts to prove their professionalism to regain patients’ trust. Meanwhile, the trust of patients with a prevention focus in their physicians will be decreased. Therefore, it suggested that physicians pay more attention to patients with a prevention focus than to patients with a promotion focus. When physicians have misdiagnosed patients, they should first identify which type of focus regulates their patients, and then try harder to regain the trust of patients at a prevention focus.
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Third, ego depletion is another factor that affect patient compliance. When the ego is depleted seriously, patients may not adhere to medical regimens and treatments. After misdiagnosis, it is important to help patients control and regulate themselves to reduce the consumption of available resources for self-control and increase the speed of resources’ recovery. Hence, the rest of available resources can be used in supporting compliance behavior. Although the ego depletion level of patients who have experienced misdiagnosis may be high, physicians can help improve patient compliance by proposing health-related options for patients. This finding suggests that physicians should provide resources for patients to help them make decisions when patients’ abilities to carry out medical activities are limited. Moreover, prevention focus has a positive effect on patients’ ego depletion, while promotion focus has a negative effect on patients’ ego depletion, thereby requiring physicians to pay more attention to patients with a prevention focus than to patients with a promotion focus and provide much favor for patients’ self-regulation, given that promotion focus can help decrease the ego depletion to a certain extent. Fourth, patients’ psychology can help improve their compliance to treatment and physicians’ advice. In terms of regulatory focus, promotion focus has a positive effect on patients’ psychological safety, while prevention focus has a negative effect on patients’ psychological safety. Promotion focus can regulate patients’ psychological safety, thus, the psychological safety of patients with a promotion focus can be enhanced even if they have been misdiagnosed. By contrast, prevention focus may decrease patients’ psychological safety when they have experienced misdiagnosis. Accordingly, hospitals and physicians should focus more on patients with a prevention focus than on those with a promotion focus when there is a misdiagnosis. B. Limitations and Future research This study has several limitations and prospects. First, this study considered patients’ trust in physicians, ego depletion, and psychological safety as mediators, and other mediators can be further examined in future studies. Second, the situation of healthcare is special and complex in China and is different from other countries and regions. Therefore, similarities and differences between China and other countries/regions can be discussed in further study. Third, this study was conducted statically because all concepts and relationships were only measured once. Fourth, all conclusions in this study were proposed based on relationships between the questionnaire and responses in the absence of external validation. C. Conclusion This study reveals that promotion focus and prevention focus affect patient compliance through the mediations of patients’ trust in their physicians, ego depletion, and psychological safety when there is a misdiagnosis. Findings suggest that (1) hospitals and physicians should take measures to moderate the relationships with misdiagnosed patients from the perspectives of patients’ trust, ego depletion, and psychological safety; (2) Patients with a prevention focus should be paid more attention than those with a promotion focus; (3) When physicians misdiagnosed patients,
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they should recognize their mistakes accurately, make efforts to prove their professionalism, and help control and regulate their patients; (4) Hospitals and physicians should create a safe treatment environment to encourage patients to cooperate with treatments.
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Understanding the Antecedents of Patient Self-management Behavior in Online Health Communities: An Application of the UTAUT Model Pei Wu and Runtong Zhang
Abstract Online health communities have emerged with the development of information and communication technology. Patients with chronic diseases can consult doctors in OHCs to achieve more efficient self-management. This study aims to explore the effects of OHCs acceptance on patient self-management based on the Unified Theory of Acceptance and Use of Technology. We conducted an online questionnaire survey of patients who have participated in OHCs and used partial least squares to analyze the data collected. The results show that performance expectancy and social influence positively affect patients’ continuous intention and health selfmanagement behavior in OHCs. Moreover, the continuous intention has positively affected self-management behavior. However, effort expectancy has no significant positive effects on continuous intention and self-management behavior. Finally, the implications of theoretical and practical implications are discussed. Keywords Performance expectancy · Effort expectancy · Social influence · Patient self-management behavior · UTAUT model · Online health communities
1 Introduction Recently, the development of information and communication technology (ICT) facilitated the emergence of OHCs, which is the application of electronic commerce (e-commerce) in the healthcare field [1]. OHCs are online platforms for users to seek and provide healthcare information and services and these online platforms are affecting healthcare services [2]. In OHCs, doctors can provide professional healthcare consultations and valuable information for patients, and patients can seek healthcare information and emotional support from doctors and other similar patients, P. Wu · R. Zhang (B) School of Economics and Management, Beijing Jiaotong University, Beijing, China e-mail: [email protected] P. Wu e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_3
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remotely [3, 4]. The continuous communication between patients with chronic diseases and doctors affects effective self-management [5, 6]. The self-management of chronic diseases mainly occurs in nonhospital settings [7, 8], which is a challenge for patients to frequently face-to-face communicate with doctors. Patients with chronic diseases are willing to consider OHCs for support to self-manage health [9], as OHCs can provide online health services anytime and anywhere, not limited by time and space. In addition, patients with chronic diseases can continuously obtain healthcare information and emotional support from doctors and similar patients to achieve self-management through OHCs [10]. Therefore, we suggest that using OHCs can be a significant supportive tool for the self-management of patients with chronic diseases. As more patients use OHCs, existing studies have examined the influence of OHCs usage on patients’ compliance. For example, one empirical study found the relationship between online health information and patients’ compliance [11]; another study explored the effects of patient-centered communication on the perceived quality of care [12]. In addition, existing studies have explored the patients’ acceptance of doctor-patient interaction in OHCs [13]. However, these studies mainly focus on the use of OHCs, which do not focus on the effects of OHCs acceptance on patient self-management. To narrow the research gap, this study focuses on doctor-centered OHCs, in which doctors can create and maintain information and consulting services [12]. Through this form of OHCs, patients can communicate with appropriate doctors, and learn about health information from patients who have the same disease and doctors. OHCs are suitable for patients with chronic diseases to conduct self-management because the doctor-patient collaboration and the combination of doctors’ medical expertise and patients’ experiences support better self-management [14]. The purpose of this study is to discuss the impacts of OHCs acceptance on the self-management of patients with chronic disease, which helps to understand the value of OHCs and provides future research directions in the information systems and healthcare intersection fields. To achieve the objective of this study, we investigate the effects of performance expectancy, effect expectancy, and social influence on patients’ continuous intention of using OHCs and health self-management behavior based on the UTATU model. In addition, we attempt to explore the relationship between continuous intention and self-management behavior. We conducted an online questionnaire survey from patients who have participated in OHCs to test the proposed research model. Performance expectancy and social influence were found to positively influence patients’ continuous intentions and self-management behaviors. However, effort expectancy did not have a significant positive effect on continuous intention and self-management behavior. In addition, continuous intention positively affect patient self-management behavior.
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2 Literature Review 2.1 UTAUT Model UTAUT was developed based on rational behavior theory, technology acceptance model (TAM), planned behavior theory (PBT), the combination of PBT and TAM, motivation theory, innovation diffusion theory, social cognition theory, and performance expectancy usage theory model [15]. UTAUT has been tested by researchers in various fields and proven to be superior to the eight theoretical models [15–17]. UTAUT is also an effective tool for scholars to evaluate individuals’ acceptance and use of new technology, especially medical and health information technologies [18]; it is the most suitable model among technology acceptance models [19]. Prior studies have explored the effect of motivation and social influence on users’ purchasing intentions in virtual communities based on UTAUT [20]. Some researchers borrowed UTAUT to discuss the acceptance of online technologies by British citizens; they found the value of online technology for academic interaction [21]. In the health field, prior studies have proven the applicability of UTAUT in exploring the effect of mobile health services on the elderly in developing countries [22]. UTAUT model was used to explain the effect of the interaction between doctors and patients on patients’ behavioral intentions in OHCs [13]. Prior studies also focused on the extended UTAUT model to examine the factors affecting the adoption of innovative health services in Bangladesh, which observed that effort expectancy did not have a significant positive influence on behavioral intention. The successful implementation of OHCs as innovative virtual health platforms is significant for individuals’ acceptance and continued behavior. Based on the UTAUT model, this study aims to analyze the effects of performance expectancy, effect expectancy, and social influence on patients’ continuous intention of using OHCs and self-management behavior.
2.2 Self-management Behavior Self-management refers to patients’ involvement in the management of their own healthcare [23] and patients with the ability to cope with the inherent symptoms, treatment, physical and psychosocial consequences, and lifestyle changes [24]. Selfmanagement behavior is used to improve the health of patients with chronic diseases [25]. However, due to patients’ low compliance with doctors in OHCs, poor mental health, and low quality of life, effective self-management is usually difficult [26]. Successful self-management interventions enable chronic patients to make informed decisions about their healthcare and enhance their self-efficacy, thereby improving their health conditions [23]. Although prior studies have explored the use of OHCs, the facilitators of patient self-management behavior in OHCs need to be examined in further studies.
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OHCs can provide virtual health services to patients whenever and wherever possible [2]. Patients can search for professional health information, actively participate in the decision-making of self-management, obtain various suggestions about diagnosis and treatment, and acquire emotional support during the diagnosis and treatment processes. Thus, OHCs as engaging tools in supporting behavior change can better help patients understand their illnesses, and may help them cooperate with healthcare professionals, enhancing self-management. In addition, OHCs can easily encourage patients’ self-management by supporting their feedback on diagnosis, promoting two-way communication, and exchanging information between patients and doctors. Moreover, patients with chronic diseases obtain health information and personalized alerts. Especially when they need to take action, OHCs can effectively enhance patients’ ability to self-manage diseases [27]. Although people are believed to potentially significantly benefit from OHCs, few prior studies have explored the effect of using OHCs on patients’ self-management. Thus, considering the dissimilar attitudes and usage of OHCs by patients, this study attempts to explore factors that affect patients’ continuous intention of using OHCs and self-management behavior.
3 Hypotheses Development 3.1 Performance Expectancy Performance expectancy refers to individuals’ judgment on performance or benefits brought by new products and services, and the results of this judgment affect users’ intentions and behaviors [28]. If people judge that new products or services can improve performance, then they will have positive attitudes to accept and use new products or services [29–31]. Performance expectancy is an important factor in individuals’ use of new technologies. Performance expectancy positively affects intentions, and experiences have shown that higher performance expectancy indicates a greater likelihood that people would accept and use new products and services [32]. As OHCs are emerging platforms for health services, which can improve patients’ health outcomes to a certain extent [33], patients’ acceptance and adoption of OHCs may be affected by performance expectancy. The effects of performance expectancy on continued intention to use emerging technologies were observed by prior studies. For example, performance expectancy positively affects the elderly users’ intention to accept mobile health services in developing countries [22]; the effects of performance expectancy on users’ satisfaction with mobile food ordering applications [28]. In offline treatments, serious problems for patients include inconvenient transportation, remote geographical location, and long queue time for treatments. The flexibility of OHCs provides some convenience for doctor-patient interactions. Patients can find doctors who are suitable for their diseases through online health information, regardless of time and geographical restrictions [34].
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3.2 Effort Expectancy Effort expectancy refers to peoples’ ability to accept and use new products or services with little effort and time; thus, peoples’ perceived ease of use [27, 35, 36]. Perceived ease of use is an important part of the technology acceptance model and innovation diffusion theory [37–39]. In the UTAUT model, effort expectancy integrates the perceived ease of use in the technology acceptance model and the innovation diffusion theory. Effort expectancy positively affects individuals’ intentions to use new technologies [22, 40]. Effort expectancy is related to the degree of ease related to new technology usage [15]. People usually feel that using emerging technologies is convenient and easy [35]. OHCs are the application of ICT in healthcare. If the effort of using OHCs is expected to be simple, then people become satisfied with them. Prior studies have also observed that effort expectancy significantly affects students’ satisfaction with online learning courses [41]. Whether patients participate in OHCs for health selfmanagement may be influenced by expectancy efforts. Effort expectancy is suitable for studying the continuous intention of OHCs.
3.3 Social Influence Social influence is a significant factor that determines whether people accept and use new technologies [28]. Social influence refers to the degree to which people consider that their behavior in using new technologies is influenced by others [15]. As emerging communities, OHCs are not fully accepted by most patients. Patients who have used OHCs may recommend them to those who have not used OHCs. Thus, the former’s attitudes and evaluations of OHCs are essential to the latter [35]. Moreover, patients use OHCs to obtain health information and enhance their reputation, thereby acquiring social recognition in OHCs due to their adoption of emerging technologies [41, 42]. Existing research on online health has proven the important role of social influence on behaviors [13]. People are susceptible to others’ attitudes and evaluations of things. For example, people learn about the social value of emerging technologies from others’ reviews, thereby affecting their continuous intention [43]. The frequency of interaction among users significantly affects their satisfaction with online service quality [44]. In addition, social influence is significant for attitude to new technologies [45]. Selfmanagement is behavior towards physical and mental health in nonhospital environments. Based on existing studies, OHCs can provide patients with self-management by participating in OHCs. H1: Performance expectancy positively affects patient continuous intention of using OHCs. H2: Effort expectancy positively affects patient continuous intention of using OHCs.
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Fig. 1 Research model
H3: Social influence positively affects patient continuous intention of using OHCs. H4: Performance expectancy positively affects patient health self-management behavior in OHCs. H5: Social influence positively affects patient self-management in OHCs. H6: Effort expectancy positively affects patient self-management in OHCs.
3.4 Continuous Intention and Self-management Behavior People who are satisfied with their experiences continue to exhibit their behavior and become dependent on their behavior [46]. Continuous intention refers to online communities providing pleasant usage experiences to patients who are willing to continue to self-manage their health, which is important for patients to obtain a higher quality of healthcare services and lower health costs [47]. If OHCs can satisfy patients’ expectations and satisfaction, their compliance will be positively influenced [48]. Patients are willing to take action for health self-management. Thus, the following hypothesis is proposed (Fig. 1): H7: Continuous intention positively affects self-management behavior in OHCs.
4 Method 4.1 Research Setting An online questionnaire survey in China was conducted to test the research model and hypothesis. The target of the investigation was patients who had used OHCs. To ensure the validity of the scale presented in this study, all items selected were adapted from existing studies and modified based on the Chinese background. The research model includes six constructs, and each item was measured using a sevenpoint Likert scale. Scales for performance expectancy, effort expectancy, and social influence were measured according to Venkatesh et al. [17]. Items for continuous intention were adapted from Bhatttacherjee [48]. Patients’ self-management behavior to use OHCs was tested from items suggested by Hsu et al. [49]. To investigate the
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demographic characteristics of responses, the questionnaire also introduced gender, age, educational background, and frequency of using OHCs as control variables.
4.2 Pretesting All items were stated in English, and the researchers used the back-translation method to translate the English items into Chinese. Thus, certain terms were slightly changed in the Chinese version to effectively reflect the contents of the original English questionnaire. A pilot test was conducted to ensure the authenticity and validity of the survey questionnaire. Five professors who had studied online services and five patients who had used OHCs for self-management discussed the six constructs in the research model to evaluate the content validity. Moreover, 10 interviewees were selected to detect the clarity of language. Finally, according to the pretest results, the questionnaire was revised and included 30 items.
4.3 Data Collection To test the hypothesis, an online survey of patients who used OHCs was conducted in China. The online survey was conducted over two months. Valid data were filtered according to the following two criteria: the data in the questionnaire are complete and the questionnaire is responded to in more than 60 s [45]. The two criteria are set because the completion time of the online survey is faster than that of the offline survey, and the online survey is prone to incomplete data. A total of 549 questionnaires were distributed. After removing the outlier respondents, 412 valid questionnaires were returned for further analysis. The validity rate was 75% (412/549).
5 Results Table 1 shows that more than half of the participants are female, and more than three-quarters of the participants are young people. The smallest percentage was the group of those aged sixty and above. In addition, the vast majority of participants had bachelor’s degrees or above, and more than half of the participants indicated that they would use OHCs when they were ill. In summary, the respondent’s gender, age, education level, and frequency of use of OHCs are consistent with the existing research and actual conditions. Thus, the respondent’s sampling level is reasonable. The structural equation model comprises two stages, namely, the measurement model and structural models [50]. The two-stage analysis aims to evaluate the reliability and validity of all constructs before assessing all hypotheses. As shown in Table 2, composite reliability (CR) and Cronbach’s alpha values of all constructs in
38 Table 1 Demographic statistics (n = 412)
P. Wu and R. Zhang
Frequency
Percentage (%)
Male
180
43.69
Female
232
56.31
18–25
68
16.5
26–40
306
74.3
Demographic characteristics Gender
Age (years)
Above 40
38
9.22
Education 6
1.46
Junior college
362
87.86
Master (above)
44
10.68
Almost everyday
196
47.57
Use when needed
213
51.7
High school(below)
Frequency of using OHCs
Rarely use
3
0.73
the research model are higher than the threshold value of 0.7 [51]. Moreover, the average variance extracted (AVE) value of each construct exceeded the threshold value of 0.5 [52]. Thus, all constructs in the research model have good convergent validity and internal reliability. Table 3 shows that the square roots of AVE of the constructs on the diagonal are higher than their corresponding correlation coefficient with other constructs, which indicates that the research model has acceptable discriminant validity without concern regarding multicollinearity. The structural model for the research model was estimated with AMOS17.0. Table 4 shows that all model-fit indexes of the research model are acceptable. The ratio between χ 2 and the degrees of freedom (χ 2 /d f ) is 1.248. The goodness of fit index (GFI) is 0.924, and the adjusted GFI (AGFI) is 0.892. The normed fit index (NFI) is 0.874, and the comparative fit index (CFI) is 0.971. The root means square error of approximation (RMSEA) is 0.025. The Tucker-Lewis index (TLI) is 0.961, and Table 2 Results of convergent validity testing
Constructs
AVE
CR
Cronbach’s α
Performance expectancy
0.706
0.969
0.815
Effort expectancy
0.697
0.919
0.707
Social influence
0.703
0.959
0.777
Continuous intention
0.757
0.872
0.718
Self-management behavior
0.648
0.928
0.831
AVE = Average variance extracted; CR = Composite reliability
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Table 3 Correlation matrix and square roots of AVE Constructs
PE
PE
0.840a
EE
SI
COI
EE
0.047
0.835
SI
0.103
0.189
0.839
COI
0.181
0.261
0.284
0.870
SMB
0.318
0.295
0.322
0.341
SMB
0.805
PE = Performance Expectancy; EE = Effort Expectancy; SI = Social Influence; COI = Continuous Intention; SMB = Self-Management Behavior a The leading diagonal in bold font shows the square root of the AVE of each construct
Table 4 Goodness-of-fit assessments
Goodness-of-fit measures
χ 2 /df
GFI
Goodness-of-fit ranges
1~3
> 0.900
> 0.900
SEM model
1.248
0.924
0.961
TLI
Goodness-of-fit measures
CFI
IFI
RMSEA
Goodness-of-fit ranges
> 0.900
> 0.900
< 0.050
SEM model
0.971
0.972
0.025
the incremental fit index (IFI) is 0.972. The results indicate a good fit between the research model and data. Table 5 presents the results of hypothesis testing, and Fig. 2 shows the hypothesized model and standard path coefficients. Performance expectancy, effort expectancy, and social influence have different effects on the continuous intention of using OHCs. The results indicate that the standardized path coefficients of performance expectancy and social influence are positive and significant. The results also show positive effects of performance expectancy and social influence on patients’ self-management in OHCs. However, this study did not find a significant influence of effort expectancy on continuous intention, and the positive effect of effort expectancy on patients’ self-management behavior is not found. Moreover, the results indicate that continuous intention positively influences self-management behavior.
6 Discussion OHC provides an opportunity for the cross-development of ICT and healthcare, which is beneficial for patients’ healthcare [14]. Patients with chronic diseases can manage their healthcare and slow down the progression of diseases through OHCs. This study explored the effects of performance expectancy, and effort expectancy, social influence on patients’ continuous intention and self-management behavior in OHCs. According to the analysis of path coefficients, performance expectancy and
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Table 5 Hypothesis testing Std. Beta
Std. Error
H1
0.523***
0.077
T-values
H2
0.116 ns
0.184
0.631
Not supported
H3
0.441***
0.051
8.647
Supported
H4
0.457***
0.052
8.788
Supported
6.792
Results Supported
H5
0.473***
0.038
12.447
H6
0.046 ns
0.357
0.129
Not supported
Supported
H7
0.608***
0.062
9.806
Supported
Fig. 2 Results of structural model
social influence have significant relationships with patients’ continuous intention of using OHCs. Moreover, continuous intention significantly positively affects patients’ self-management behavior in OHCs. However, a significant positive effect was not found between effort expectancy and continuous intention. A possible reason for this result is that patients believe that OHCs will bring difficulties to their health outcomes and the stereotypes of OHCs are distrustful.
6.1 Implications for Theory and Practice This study has important theoretical contributions to the development of OHCs in China. First, based on the UTAUT model, this study extended the UTAUT model to the scenario of OHCs and explored the effects of performance expectancy, effort expectancy, and social influence on patients’ continuous intention of using OHCs and self-management behavior. As users of OHCs, patients are different from physicians who have professional health knowledge. The participation of physicians can bring better self-management for patients with chronic diseases [14]. Second, this pioneering study enriches the current research on patient self-management behavior through OHCs and expands the application of OHCs. Third, this study provides literature support for encouraging patients’ participation in OHCs.
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Besides this, this study provides some practical implications. First, this study provides a novel means for patients’ self-management behavior through OHCs. Second, the results of this study suggest that the managers of OHCs should give patients publicity, which has effects on their continuous intention and selfmanagement behavior. Third, managers of OHCs should consider increasing patient participation and consultation experiences by focusing on the functions, such as personalized health services, doctor-patient responsiveness, and connectivity at any time. For example, if patients can acquire high-level personalized health services, then they become willing to self-management behavior according to health information in OHCs.
6.2 Limitations This study has some limitations. First, owing to its cross-sectional nature, this study could not explain whether patients’ continuous intention and self-management behavior over time. In the future, longitudinal studies are required to explore the change. Second, though performance expectancy, effort expectancy, and social influence are included in our research model, other factors, such as habits, online services quality, and price, should be considered in the future. Third, this study does not consider the effects of professional health information on self-management behavior. The quality of professional information would depend on how honest and complete patients are in reporting about their health conditions. Future research may consider the evaluation of the quality of health information in OHCs to help chronic patient self-management.
7 Conclusion This study explains the effects of performance expectancy, effort expectancy, and social influence on the continuous intention of using OHCs and self-management behavior. Prior literature was reviewed and found that few studies have explored chronic patients’ self-management behavior in OHCs and the UTAUT model was mostly used to explore the adoption and usage of emerging information technologies. Owing to the particularity of health services and online environments, OHCs provide health services to patients for managing their health status. The data were collected from patients who have used OHCs and were analyzed by the structural equation model. Our result found that performance expectancy and social influence positively affect patients’ continuous intentions and self-management behaviors. However, effort expectancy did not have a significant positive effect on continuous intention and self-management behavior. In addition, continuous intention positively affect patient self-management behavior. The results enrich the current research on
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patient self-management behavior through OHCs and expands the application of OHCs. Acknowledgements This work was supported by the Fundamental Research Funds for the Central University (B22YJS00020); National Social Science Foundation of China (18ZDA086); the National Natural Science Foundation of China (62173025). This work was supported by the Beijing Logistics Informatics Research Base.
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Translational Medicine Informatics Services from the Bedside Over QL4POMR Sabah Mohammed, Jinan Fiaidhi, Darien Sawyer, and Peter Sertic
Abstract Translational medicine informatics (TMI) requires data integration on a massive scale to promote medical discovery. To accomplish this, the integration of data about drugs, diseases, targets, and genetics needs to happen at the physician point of care level via electronic health records and other care applications, but the legacy REST APIs and unstructured nature of current healthcare data poses a great challenge for this integration. Previously we have developed QL4POMR as an updated representation of the electronic healthcare record that uses GraphQL and the problem oriented medical record (POMR) to provide better structure and interoperability to healthcare data. We have now also developed a data layer using Gatsby and GraphQL gateways to pull medical data from outside sources into the QL4POMR ecosystem. In this paper, we outline how we leverage the QL4POMR data layer to apply TMI at the physician level EHR using information from trusted sources like DrugBank and OpenTargets. The TMI processes described can be applied to develop a wide range of microservices for physicians and patients.
Resrach supported by the 1st Author NSERC DDG 2021 grant and the 1st and 4th authors MITACS Accelerate 2021 grant. This is a submission to 12th International Conference on Logistics, Informatics and Service Sciences (LISS2022), July 2022, Beijing Jiaotong University, China. S. Mohammed (B) · J. Fiaidhi · D. Sawyer · P. Sertic Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada e-mail: [email protected] J. Fiaidhi e-mail: [email protected] D. Sawyer e-mail: [email protected] P. Sertic e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_4
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1 Introduction Translational Medicine Informatics (TMI) is the de-facto of medical research for cohort clinical discovery [1]. It is ultimately requires data integration on a massive scale. In order to accelerate medical discovery, the integration of drugs data and other biomedical data needs to happen at the physician point of care level and possibly through the electronic health records (EHRs). The lack of real time access to biomedical information at the bedside affects the overall processes of diagnosis, prognosis and patient safety. Moreover, unlocking patient data from the bedside will provide tremendous amounts of data to be used by the different laboratories at the bench side including the physician assessments data, treatment plan as well as the data from the diagnostic and monitoring devices. The traditional TMI research focuses on translating the achievements of the basic science from the bench side to be used at the bedside and clinical practice. TMI research needs to bridge the gap between the bench side basic science and the bedside clinical practice. Obviously the TMI research trend showing great unbalanced flow that favors the flow from the bench side to bedside [2]. Figure 1 illustrates the TMI research imbalance dilemma. Solving the TMI dilemma needs to overcome the unilateral concept that is focused on bench side expertise only where it is missing the crucial feed-back from bedside which is as equally important as bench side [3]. Thus, TMI research workflow needs to evolve into a “two-way bridge”. However paving such two way road requires data integration with flexible protocols that provide seamless integration between the contexts of the two worlds. The legacy integration protocols used in healthcare at both sides are those built around the REST (REpresentational State Transfer) API and the Simple Object Access Protocol [4]. The uses of REST and SOAP protocol are aging as they have been the technology used for the last 20 years as methods for accessing web services. Major venders like Facebook and many software developers really struggle to build modern apps on top of that REST API technology [5]. Imagine using REST for an application that requires large number of different pieces of data that you need to be accessed and processed from a dozen different places in the cloud, whether they are from databases or microservices or other data that can be collected Fig. 1 TMI research imbalance dilemma
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via 3rd party APIs. REST will be a bottleneck for such applications as it can manage point-to-point integration and anything beyond this becomes a challenge with huge performance tag. One of the most common problems with REST is that of over- and underfetching [6]. This happens because the only way for a client to download data is by hitting endpoints that return fixed data structures. It’s very difficult to design the API in a way that it’s able to provide clients with their exact data needs. However, the current industry solution is pointing to the use GraphQL API instead of REST where we can put a more extensible data graph in the middle of the connections that ease the multi ways of communicating and integrating services [6]. Moreover, REST does not allow for rapid iterations on the frontend as every UI change requires a change on the backend to account for the new data needs. GraphQL, on the other hand, solves this problem by allowing changes on the client side to fetch new data from the server without the need of any extra work on the server. Additionally, GraphQL is a schema-first API that uses a strong type system to define the capabilities of an API. This schema serves as the contract between the client and the server to define how a client can access the data. Once the schema is defined, the teams working on frontend and backend can do their work without further communication since they both are aware of the definite structure of the data that’s sent over the network. In this paper we are describing our efforts to integrate the problem oriented medical record (called QL4POMR) that has been defined around the notion of SOAP note (Subjective, Objective, Assessment and Planning) to describe clinical cases at the bedside. Actually, QL4POMR uses the GraphQL API not only to describe clinical cases using SOAP at the bedside [7] but also to connect with HL7 FHIR1 (HL7 Standard Electronic Healthcare Record) and the HL7 IPS2 (Hl7 International Patient Summary) [8] where querying requires merging the SOAP bedside schema with the HL7 Resources and IPS Schemas. In this article we are describing an extension to QL4POMR to provide a flexible connectivity with the biomedical information available from the bench and laboratories. The extension utilizes schema stitching facilities available at a Gatsby3 data layer built around QL4POMR to connect the SOAP QL4POMR querying to query other biomedical data sources and services like the drug information (e.g. Drug Bank4 ) and other transitional medical informatics data APIs available at the OpenTargets.5 Figure 2 illustrates the original QL4POMR framework where the interactions with external resources like the HL7 FHIR and the HL7 IPS require merging all the external sources data types within the QL4POMR SOAP schema so when queries come in to fetch or change data at the FHIR patient record or at the IPS patient summary, they are validated and executed against the merged schema. Schema stitching helped our Gatsby data layer to connect to multiple microservices that each exposes a GraphQL endpoint like OpenTargets. Schema Stitching also help us to creates a proxy schema on top of other available subschemas, 1
https://www.hl7.org/fhir/. http://hl7.org/fhir/uv/ips/. 3 https://www.gatsbyjs.com/. 4 https://www.drugbank.com/. 5 https://www.opentargets.org/. 2
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Fig. 2 The original QL4POMR framework
so the parts of that schema are executed using GraphQLJS internally which helps in connecting to non-GraphQL services like DrugBank. Our extended QL4POMR with the new flexible connectivity is used for translating basic medical research on drug interaction for the use with the clinical research to improve human health and reduce risks. The general objective of our improved QL4POMR TMI research is to bridge the gaps between often-distinct areas of expertise like clinical practice at the bed-side and pharmaceutical research obtained at the bench-side by making the transitions faster, less expensive, and more effective through the use of modern GraphQL API that offers opportunity for higher interoperability, integration and better clinical insights. This paper uses the schema stitching as defined by the graphql-tools6 to create a single GraphQL gateway schema from multiple underlying GraphQL services such as the clinician patient SOAP case description and other sources including the patient FHIR record and health and biomedical data sources like the Drug Bank and the OpenTarget. This article is to describe extending the original QL4POMR to study the relationship between patient case assessment and treatment plan including prescribed medications and the possible medications interactions with the patient medication history as well as to provide the genetic associations of these newly prescribed drugs and their side effects.
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https://www.graphql-tools.com/.
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2 Extending Ql4POMR with TMI Services The original QL4POMR created its own GraphQL server by creating schemas and resolvers for SOAP, FHIR and IPS. Although the QL4POMR provided a good ecosystem to describe patient cases based on the notable problem-oriented medical record [9] and the translation into the standard HL7 FHIR medical record, it fall short in expanding the connectivity of some important attributes of the patient case to relevant biomedical data such as drugs and diseases. To incorporate the capability of easily linking to external services and data sources, we will need to empower the QL4POMR with a data layer that can stitch the external service/data schema with the QL4POMR SOAP Schema. For this we will need an upper API to pull and query external resources like the Gatsby API.7 The Gatsby data layer encompasses both Gatsby’s internal GraphQL API and the data source plug-in, which together collects data and define a GraphQL schema that traverses that data. Moreover, we will need schema stitching APIs to create a single GraphQL gateway schema from multiple underlying GraphQL/REST services. Unlike schema merging, which simply combines local schema instances, stitching builds a combined proxy layer that delegates requests through to underlying service APIs [10]. It is important to note that stitching is comparable to Apollo Federation [11]. The type of schema sttiching involves remote schema stitching. In the normal GraphQL schema stitching we take two separate GraphQL schemas and combining them into one. This assumes that both schemas are on the same server. The graphQL remote schema stitching is exactly the same, except the schemas we’re stitching together aren’t just coming from different directories on the same server; they’re coming from different servers or different microservices. For this we will need to import the following methods from graphql/ tools repository: import { introspectSchema, makeRemoteExecutableSchema, mergeSchemas, } from 'graphql-tools' import { HttpLink } from 'apollo-link-http' import fetch from 'node-fetch'
Figure 3 illustrates the overall architecture of our TMI ecosystem that empowers our original QL4POMR with connectivity to external resources. Whether this data comes from the surrounding file system or from a REST or other GraphQL, Gatsby’s internal GraphQL API facilitates the single-file co-location of data requirements, extraction, schema stitching and data rendering. The Gatsby v2.1.0 API, however, introduced a new feature called useStaticQuery to provide the ability to use a React Hooks to query with GraphQL at build time. It allows the React components to retrieve data via a GraphQL query that will be parsed, evaluated, and 7
https://www.gatsbyjs.com/docs/reference/config-files/gatsby-node/.
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Fig. 3 Empowering QL4POMR with TMI connectivity
injected into the component. Our extend QL4POMR ecosystem with the data layer construct a query by including the relevant fields that can seek additional clinical associations (e.g. disease and target drugs). The TMI connectivity can take many levels (direct or indirect). Direct TMI connectivity may identify disease to drug relations, however the indirect TMI may identify relations that can have multilevel such as Disease to Drug to Target Drug to Genes then to a Gene Function. QL4POMR TMI Connectivity through Drug Bank. Modern clinical practice as well as pharmaceutically research targets protein families (e.g. enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors) and their interaction with drug compounds as well as the interaction between drug to drug (DDI) [12]. This translational knowledge requires the integration to pharmaceutical and genetic spaces. This linkage can be done by accessing drug repositories like DrugBank, KEGG and ChEMBL as well as to biomedical repository like the OpenTarget. In this section we are describing how QL4POMR can be extended to query the DrugBank repository for DDI interaction through our TMI ecosystem layers. The execution of the DDI microservice with the QL4POMR system is described using the createDischargeSum use-case and can be seen in Fig. 4. The client requests to create a new patient discharge summary by passing in required parameters such as planID and the newly prescribed medication. The CRUD API creates a new discharge summary, then searches for a plan node matching the given plan ID. Once found, the system links the discharge summary to the plan node. The system then retrieves the medical history of the patient by finding a soap node containing the same plan ID. Once the soap node is located,
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Fig. 4 Sequence Diagram of createDischargeSum mutation showing the communication between the client, CRUD API, the DDI_Microservice and the DrugBank website
the corresponding subjective node, followed by the patient medical history, can also be found and retrieved. The previously prescribed medication stored in the SOAP schema is then recorded and passed to the DDI microservice alongside the newly prescribed medication provided by the initial createDischargeSum query parameters. Figure 5 shows the previously prescribed medication and planID information required for the CRUD API. The DDI data obtained by drug bank is retrieved using a backend JSON API, which allows us to enter a specific URL to trigger the API GET call for a specific drug. The URL contains three key parameters, Start, Length, and drugID [13]. The DDI microservice then extracts the DDI data string and sends it back to the CRUD API. The QL4POMR system then assigns the DDI information to the patientEducation node located in the dischargeSummary and saves the new plan and dischargeSummary nodes according to the SOAP schema.
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Fig. 5 Reading a patientMedicalHistory and plan node via CRUD to display the previously prescribed medications and the planID
3 Additional TMI Connectivity Through Opentargets OpenTargets is a large repository of medical information pertaining to drugs, genetic targets for diseases, general disease information, and compiled genetics information from studies to aid in discovering new variants for diseases and genes. This data source is conveniently accessible through GraphQL endpoints: one for drug, target, and disease information, and a second endpoint dedicated to genetics information. We focus on integrating informatics from the first endpoint into the physician level QL4POMR electronic healthcare record (EHR). Since the endpoint to access OpenTargets is built with GraphQL, all data types and resolvers are defined and we do not need to make any changes to how the data is queried. To integrate the OpenTargets API into QL4POMR, we stitch their schemas with our CRUD API, which is used to read and manipulate the EHR SOAP nodes, into one interface. Schema stitching is a useful technique for combining microservice APIs with our QL4POMR APIs into one interface, while also being able to extend existing data types to include fields and queries from other services without having to alter any source code. For example, we can extend the assessment type of our CRUD API to include a field for disease information that is populated by the results of an OpenTargets API query. Stitching schemas in this way requires 3 components/processes that rely mainly on the loadSchema() and mergeSchemas() functions from the graphql-tools library. The first process is to load the schemas from where they are accessible, in our case we use URL endpoints:
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const openTargetsSchema = await loadSchema(openTargetsEndpoint, { loaders: [new UrlLoader()], }); const crudSchema = await loadSchema(crudEndpont, { loaders: [new UrlLoader()], }) const gatsbySchema = await loadSchema(gatsbyEndpoint, { loaders: [new UrlLoader()], })
Gatsby creates a grpahQL endpoint to access data stored in the data layer, so this is how we can load the generated Gatsby data layer schema and access what we need, just as we do with OpenTargets. Next, we define extended schema to connect the useful features of different microservices into the types and fields of a main schema like CRUD, creating a seamless integration of services: const extendSchemaDefs = ` extend type Assessment { diseaseInfo: Disease } extend type DischargeSummary { drugInfo: Drug }`
Here we are extending two data types from CRUD to now have fields for additional disease and drug information, but when queried, they return the Disease and Drug types from OpenTargets. This same method of extending schema can be used to combine data types from Gatsby, DrugBank, etc. The last component of schema stitching is to merge the original and extended schemas while also telling the extended types how to get the information from the different microservices:
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const schema = mergeSchemas({ schemas: [ openTargetsSchema, crudSchema, gatsbySchema, ], typeDefs: [extendSchemaDefs], resolvers: { Assessment: { disease(parent, args, context, info) { return delegateToSchema({ schema: openTargetsSchema, operation: 'query', fieldName: 'disease', args: { efoId: parent.efoId, } }); } }, DischargeSummary: { drugInfo(parent, args, context, info) { return delegateToSchema({ schema: openTargetsSchema, operation: 'query', fieldName: 'drug', args: { chemblId: parent.chEMBL } }); }, } } })
By giving the schemas we want to merge to the schemas option and the extended types to the typeDefs option, mergeSchemas() makes quick work of combining these APIs into one interface, and the resolvers option is where we tell the extended schema fields how to return the proper information. Using delegateToSchema(), we can easily pass on arguments from the parent node such as Assessment, to the original schema such as OpenTargets, and return the result to the newly extended field. Being able to integrate OpenTargets information into the physician level CRUD API allows for seamless integration with trusted repositories for drugs, targets, diseases, and genetics. The TMI integration we have built as an example is the ability to automatically populate an assessment node, which would contain diagnosis information for a patient, with all information for that disease from OpenTargets, all while using a single CRUD query to pull the information from the two sources. In order to do this, the disease query in OpenTragets requires an ID (efoId) from the Experimental Factor
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Fig. 6 Reading an assessment node for a main diagnosis via CRUD and its children diseases through ontology via OpenTargets
Ontology (EFO)8 where such ID (e.g. the EFO ID for “coronary artery disease” is EFO_0001645) can be used to fetch related clinical and biomedical associations. The efoId is typically present in the SOAP assessment node and we can pass that ID as the argument to the OpenTargets query, and the response is shown. This allows the user to query any disease information in OpenTargets associated with a diagnosis. For example, as show in Fig. 6, a user wants to read a diagnosis for a patient while simultaneously seeing what children diseases are part of the main diagnosis through ontology. Being able to integrate OpenTargets information into the physician level CRUD API allows for seamless integration with trusted repositories for drugs, targets, diseases, and genetics. The TMI integration we have built as an example is the ability to automatically populate an assessment node, which would contain diagnosis information for a patient, with all information for that disease from OpenTargets, all while using a single CRUD query to pull the information from the two sources. In order to do this, the disease query in OpenTragets requires an ID (efoId) from the Experimental Factor Ontology (EFO)9 where such ID (e.g. the EFO ID for “coronary artery disease” is EFO_0001645) can be used to fetch related clinical and biomedical associations. The efoId is typically present in the SOAP assessment node and we can pass that ID as the argument to the OpenTargets query, and the response is shown. This allows the user to query any disease information in OpenTargets associated with a diagnosis.
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https://www.ebi.ac.uk/efo/. https://www.ebi.ac.uk/efo/.
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Fig. 7 OpenTargets seven venues of bench side data
Future applications of this TMI via OpenTargets is to fully integrate all the seven data types into our QL4POMR SOAP to pull relevant information where necessary, such as drug information for medications in a plan node, genetics information for relevant lab tests in an objective node, etc. Figure 7 list the seven biomedical venues that OpenTargets provides. Empowering QL4POMR to connect to these biomedical data from the bench side will enable clinician to bridge the gap between what they describe at the bedside in SOAP and save at the FHIR electronic record with the bench biomedical data. This will enable the patient information to be available at the bench side and visa vera.
4 Conclusion GraphQL gives us the opportunity to develop healthcare applications with highly structured and interoperable data as well as the flexibility of connecting to a variety of supporting services and data sources. These outside data sources must first be brought into a data layer that can act as a server for sending the connected data to clients. In this paper, we have described how we use our QL4POMR platform and data layer to bring in outside data sources to a GraphQL server using Gatsby and GraphQL gateways. We also described how the data layer can be leveraged to implement TMI at the physician point of care level via the EHR, and exhibited useful examples of TMI through microservices like an automated drug-to-drug interaction checker or pulling relevant drug and disease information from a large trusted repository. The processes through which TMI is achieved in this paper can be extended to develop a wide range of TMI based microservices and machine learning implementations within the QL4POMR ecosystem. Acknowledgements The authors acknowledge the financial support to this research project from MTACS Accelerates Grant (IT22305-2020) and the first author NSERC DDG Grant (DDG-202100014). This paper is presented to the 12th International Conference on Logistics, Informatics and Service Sciences (LISS2022), July 2022, Beijing Jiaotong University, China.
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References 1. Brito, G., Valente, M.T.: Rest vs graphql: a controlled experiment. In: 2020 IEEE International Conference on Software Architecture (ICSA), pp. 81–91. IEEE (2020) 2. Keramaris, N.C., Kanakaris, N.K., Tzioupis, C., Kontakis, G., Giannoudis, P.V.: Translational research: from benchside to bedside. Injury 39(6), 643–650 (2008) 3. Cohrs, R.J., Martin, T., Ghahramani, P., Bidaut, L., Higgins, P.J., Shahzad, A.: Translational medicine definition by the European society for translational medicine. New Horiz. Transl. Med. 2(3), 86–88 (2015) 4. Sanjana, G.B., NS, G.R.S.: High resilient messaging service for microservice architecture. Int. J. Appl. Eng. Res. 16(5), 357–361 (2021) 5. Poor, H.: An Introduction to Signal Detection and Estimation. Springer, New York (1985) 6. Mukhiya, S.K., Rabbi, F., Pun, V.K.I., Rutle, A., Lamo, Y.: A GraphQL approach to healthcare information exchange with HL7 FHIR. Proced Comput. Sci. 160, 338–345 (2019) 7. Mohammed, S., Fiaidhi, J., Sawyer, D.: GraphQL patient case presentation using the problem oriented medical record schema. In: 4th Special Session on HealthCare Data 4th Special Session on HealthCare Data, 2021 IEEE Big Data conference, 15–18 Dec 2021 8. Mohammed, S., Fiaidhi, J., Sawyer, D.: QL4POMR interface as a graph-based clinical diagnosis web service. In: 11th IEEE International Conference on Logistics, Informatics and Service Sciences (LISS2021). http://icir.bjtu.edu.cn/liss2021/news/892.jhtml 9. Cillessen, F.H.J.M., de Vries Robbé, P.F.: Modeling problem-oriented clinical notes. Methods Inf. Med. 51(06), 507–515 (2012) 10. Burk, N.: GraphQL Schema Stitching explained: Schema Delegation, Prisma Blog Article, 12 December 2017. https://www.prisma.io/blog/graphql-schema-stitching-explained-schema-del egation-4c6caf468405 11. Mohammed, S., Fiaidhi, J.: Establishment of a mindmap for medical e-Diagnosis as a service for graph-based learning and analytics. Neural Comput. Appl. 1–12 (2021) 12. Gönen, M.: Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization. Bioinformatics 28(18), 2304–2310 (2012) 13. Chui, C.: How you can crawl the list of drug-drug interactions (DDI) from the DrugBank with Java. Medium, 24-Sep-2021. https://medium.com/geekculture/how-you-can-crawl-thelist-of-drug-drug-interactions-ddi-from-the-drugbank-with-java-9280b3f45b95. Accessed 05 Nov 2021
Exploring Online Physician–Patient Interactions Through Information Sharing with Agent-Based Modeling Donghua Chen
Abstract Online health communities enhance physician–patient interactions through various social connections. Health-related posts have a significant influence on patients who are suffering from various symptoms. However, misinformation from the posts may impact patients’ decision-making. This paper proposes an agentbased model to explore the physician–patient interactions through health information sharing in online health communities. First, we introduce two agent types, namely influential users and ordinary users for a general online health community. Network parameters like numbers of users, followers, and fans as well as posting probability are considered in our model. Physician and patient agents are used to measure users’ average posts and support degrees for specific topics. Physician–patient interaction with factors in posting actions, social support, and random behaviors are also simulated. Finally, we run the above model on NetLogo. Taking the haodf.com website, a famous Chinese online health community, as an example, we examined the changes of the physician–patient interaction in different model parameter settings. The results demonstrate the feasibility of the NetLogo-based model in understanding the relationships between physicians and patients, and improving their health decision-making abilities in simulation. Keywords Online health community · Physician–patient interaction · Agent-based modeling · Decision-making
1 Introduction Online health communities reduce the gap between physicians and patients by providing medical knowledge-based support and enhancing their user interactions. Multiple stakeholders in the digital health environments include physicians and patients. Online patients often seek relevant health information from the online D. Chen (B) School of Information Technology and Management, University of International Business and Economics, Beijing 100029, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_5
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services provided by physicians, thus providing physician–patient communication channels. Such interactions are similar to user interactions on general-purpose social network platforms such as Weibo and Twitter. However, an online health community is a more professional and trusted environment that focuses on building trust between physicians and patients based on their reputation. Clinical suggestions in the health-related posts provided by physicians may affect online patients’ decisionmaking significantly. System science methods that covers the research of system dynamics, network analysis, and agent-based modeling have played an essential role in understanding the complexity of public health systems [1]. Agent-based models can simulate their interactions with multiple agents, such as patient and physician agents in the simulation. Existing research includes discrete event simulation and agent-based simulation, which people are optimistic about in the healthcare sector [2]. The analysis of social interaction complexity between physicians and patients in online health communities can guide the better design of the communities to support effective patient decision-making. Various methods have been conducted to contribute to studying physician– patient interactions. Traditional empirical research introduces hypotheses to examine the given mathematical model. Some researchers leverage data-driven methods to explore user behaviors between physicians and patients on the Internet. Few studies focus on studying the complexity of involving all parties of the online health community using agent-based modeling. We fill this gap by proposing a physician–patient interaction model based on sharing of health-related posts on an online health community. Furthermore, we take the haodf.com website, a popular Chinse online health community, as an example to examine the model based on NetLogo, and discuss its implications for future studies on agent-based modeling for online health communities. NetLogo with agent-based models can simulate real-world processes that can inform medical and health interventions [3]. Social sharing characteristics are considered in the process of modeling in this study. The remaining sections are organized as follows. Section 2 presents related work about NetLogo simulation in the healthcare sector. Then, Sect. 3 illustrates our proposed NetLogo model for physician–patient interaction in online health communities. Section 4 summarizes our simulation results. Finally, we discuss and conclude our work in Sects. 5 and 6, respectively.
2 Related Work Agent-based modeling can help us understand complex health behaviors by simulating entity actions within a system [4]. Pardo et al. [5] characterized and programed three agent types, including technological, organizational, and human, as well as their interactions and environment to facilitate the process of information technologies in healthcare systems. Yousefi and Ferreira [6] simulated the process of allocating resources based on their observations by utilizing all resources in a hospital’s emergency department.
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The impact of government communication during a public health crisis is difficult to understand due to multiple stakeholders involved in their interactions [7]. Public health decision-makers usually encounter the complexity of public health when dealing with biological incidents [8]. Galeano et al. [9] simulated the waiting time of patients in the queues in the outpatient department of the Hospital de Clinicas to identify inefficient processes in the chain order to make rational decisions to improve the service. Agent-based modeling is also proven to be flexible in applying in simulating behaviors of the patients who leave a hospital for decision-makers to assess control strategy impact [10]. Taboada et al. [11] proposed a simulator to enhance decision support for aiding the heads of a hospital emergency department to make the best-informed decisions with an initial simulation using NetLogo. Agent-based modeling is also suitable for studying vaccination programs in public. Li et al. [12] analyzed human behaviors in disease modeling for the impact of fearfulness in people that receive complete information and incomplete information on the vaccination program. Shafqat et al. [13] simulated an context-aware healthcare community cloud based on patients’ current medical conditions. Sulis et al. [14] adopted NetLogo to evaluate the best vaccination criteria by adding genetic algorithms, which is promising in defining vaccine rates by population types over time and agent-oriented methods in public health policies. NetLogo as a multi-agent simulation platform has been applied in studying user behaviors to search for useful online health information [15]. Li et al. [16] used a social network theory and NetLogo to simulate the temporal and spatial evolution of online public sentiments on emergencies. Zhu et al. [17] explored the value cocreation behaviors in different online health communities with a multi-agent conceptual model, indicating the importance of improving user mindsets and knowledge reserves. Liu et al. used NetLogo and the SIR propagation model to analyze rumor propagation in social platforms like Twitter [18]. Carrier et al. [19] also leveraged the NetLogo platform to explain trends and relationships between factors that affect the adoptions of Continuous Glucose Monitoring by independent decision-makers. Tulang [20] adopted an agent-based model to examine the factors that influenced the learning experiences in learning communities during the COVID-19 pandemic.
3 Materials and Methods We define a social interaction model to simulate physician–patient interactions in online health communities.
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3.1 Network Initialization We first propose an agent network that contains two agent types, namely influential users and ordinary users. The influential user agent represents the users that most people trust, usually referring to physicians and the patients with many shared posts in an online health community. Ordinary user agents represent general users that only seek health information provided by physicians in the community. Ordinary users may become influential users over time. A user sharing lots of valuable posts tends to attract ordinary users, thus becoming an influential user at some time. Figure 1 shows network initialization with ten influential users in a one-hundred agent network. We initialize the agent network by using several parameters shown in Table 1. First, we determine the total number of users in the network. Then, we create users’ follower relationships between an agent and the other agents randomly in a radius of distance. The max-followers-num and max-fans-num parameters are also set in the process. A number of influential users are assigned initially. Each user in the community has a probability of posting each day. The rate-influence-offline parameter is used to estimate the impact of offline information on online users. There are also some random factors defined by the rate-random-people-to-follow parameter to allow a proportion of existing users to follow other users randomly in the network. Fig. 1 Network initialization with 10 influential users in a NetLogo model with 100 agents
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Table 1 Parameters of network initialization Parameter name
Parameter description
number-of-users
Number of users at the beginning of simulation
init-radius-dist
Radius distance in network to create relationships among followers and fans
max-followers-num
Maximum number of users that a user follows at the beginning
max-fans-num
Maximum number of a user followed by other users
init-number-of-influential-users
Number of influential users at the beginning of simulation
number-to-become-influential
Total number of posts for a user to turn into an influential user
post-probability
Probability of number of posts per user each day
rate-influence-offline
Rate of users to be influenced offline
rate-random-people-to-follow
Rate of users to follow by some random users
3.2 Physician and Patient Agents Physician and patient agents have their variables in our simulation, namely user-type, post-rate, total-posts, new-posts, support, days, avg-posts, and avg-support variables, as shown in Table 2. Agent variables changes over steps in simulation. The use-type variable represents different user type values, namely 0 and 1 for ordinary users and influential users, respectively. Each agent can post in a random probability based on a cardinality of the pre-defined posting probability. The total-post variable represents the total number of posts per user. The support variable represents average support degree of a user given a topic. The days variable represents the total number of days since the agent joins the network. Finally, the avg-posts and avg-support variables measure the mean value of the number of posts and support degree, respectively. Table 2 Summary of Agent variables and their meanings
Agent variable
Meaning
user-type
User type, 0 for ordinary users and 1 for influential users
post-rate
Posting rate of each user per day based on ordinary-post-rate
total-post
Total number of posts per user
new-posts
Number of posts added each day by a user
support
Total support degree for a specific topic
days
Number of days passed since simulation
avg-posts
Average number of posts
avg-support
Average support degree
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3.3 Physician–Patient Interaction To model the interaction between influential users and ordinary users, we consider several factors that may be involved in the interaction, including posting behaviors, support change, platform interference, and random behaviors. (1) Posting behaviors First, each agent in the network is designed to send some posts with the postprobability variable in a day. Supposing that t is the current simulation step, the agent variables with a specific posting probability each day over time can be defined as in: daily_user _uset = daily_user _uset−1 + 1 new_ postst = (randor m_ f loat post_ratet−1 ) ∗ hot_ postt−1 total_ postst = total_ postst−1 + new_ postst−1 { suppor tt =
suppor tt−1 + (random_ f loat1), user _t ype = 0 suppor tt−1 + (random_ f loat3), user _t ype = 1
The (random_float post_ratet-1 ) represents that post_ratet-1 is set in between 0 and 1 randomly. Then, if the post-probability variable is larger than a specific threshold, the support variable decreases as in suppor tt = suppor tt−1 − (random_ f loat 1) new_ postst = 0 Finally, each user updates the following variables, as defined in: dayst = dayst−1 + 1 avg_ postt =
total_ postt dayst
(2) Topic support degree When a user reads some posts from his/her following users, the user’s support degree increases by a random value. We model the process as follows. If a follower of the user belongs to influential users and the number of posts of the user is larger than zero, then
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new_suppor tt = new_suppor tt−1 + (random_ f loat2) Meanwhile, if a user belongs to ordinary users and the number of posts of the user is larger than zero, then we have new_suppor t_nor malt = new_suppor t_nor malt−1 + (random_ f loat1)/2 If the new-support variable is larger than zero, then new_suppor tt = 5i f new_suppor tt−1 > 5 supor tt = suppor tt−1 + (random_ f loat2) ∗ new_suppor tt−1 If the new-support-ordinary variable is larger than zero, then new_suppor t_nor malt = 5i f new_suppor t_nor malt−1 > 5 suppor tt = suppor tt−1 + (random_ f loat1) ∗ new_suppor t_nor malt−1 (3) Random following behaviors A social network can have some random behaviors. For example, some random users may follow other random users. To model the case, we select some random users from all users in the network to create relationships between users. Besides, offline context may also impact online users’ behaviors by their opinions. We simulate the case as follows. First, we choose some random ordinary users in the network using the rate-influence-offline parameters to update their support values by suppor tt = suppor tt−1 + (random_ f loat 1) Finally, each agent in the model has their support degree over time, providing a measure of network status for supporting the given topics.
3.4 Network Updating Except for physician–patient interaction, the network updating mechanism is introduced. The mechanism includes user-agent transition, user number change, and platform interference. (1) User agent transition An ordinary user will eventually become an influential user when the ordinary user’s support reaches the value of the pre-defined number-to-become-influential parameters and the support degree value is larger than five. Meanwhile, the post-rate variable
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of each user per day will be set as in: post_ratet = 1 + (random_ f loatin f lu_ post_ratet ) (2) User number change The network is considered as a system that interacts with the external environment. Therefore, new users may join the network while others may leave it. There are several parameters that simulate the change of users in the network. They are new-usersby-day, relation-by-day, dying-users-by-day, ordinary-post-rate, and influ-post-rate parameters. The new-users-by-day parameter indicates the number of new users added to the network each day. The relation-by-day parameter indicates a maximum number of random relationships created between users. The post-rate of each user is defined in: post_rate = 0.1 + (random_ f loatnor mal_ post_rate) The dying-users-by-day parameter indicates the daily number of users removed from the network. The ordinary-post-rate and influ-post-rate parameters represents the post probability of each new ordinary users and influential users, respectively. (3) Platform interference User interactions in an online health community sometimes are influenced by platform moderators and other decision-makers. A hot-topic list model is introduced in our model. The daily hot-topic list includes a list of topics with different possibilities to be seen by a user in simulation. Each day a hot-topic list is generated randomly. Each user sees each topic with different possibilities in the hot-topic list in the network and then changes their support. We introduce several model parameters, namely hot-topic-num, consider-hottopic, rate-relevant-topic, and see-hot-topic-probability. The hot-topic-num parameter represents the number of topics in the hot-topic list on the platform. The considerhot-topic parameter suggests if the model considers the use of a hot topic list, the rate-relevant-topic parameter indicates the probability of a topic relevant to user interests. The see-hot-topic-probability parameter determines the probability of a user seeing the topic and being influenced. Then we have the hot-post variable defined as in: hot_ postt = hot_topics_num ∗ rate_r elevant_topic Finally, the new-post of each user each day also considers the hot_topic variable as in: new_ postt = (random_ f loat post_ratet ) + hot_ postt
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3.5 NetLogo Simulation We leverage NetLogo to create our proposed physician–patient interaction model to examine its model changes over parameter. NetLogo is a multi-agent programmable modeling software well suited for modeling complex systems developing over time. To estimate the change in the model during the simulation, we define the following variables to monitor network change. The variables include average support degree, average number of daily posts, average number of followers, daily user use, and the total number of users. We will monitor the change of the above variables related to influential users and ordinary users, respectively. Then, we evaluate the changes in the proposed model and their implications on our research objective.
4 Results We first leverage the data from the haodf.com website to estimate the initializing parameters of the social physician–patient interaction model. Table 3 shows a descriptive study of important information about physicians and patients collected from the website. In the table, the mean physician score value for the website is 3.95 (±0.4685). The average number of patients following a physician in the community is about 3052, while the maximum number of patients is about 40 thousand. The average number of posts provided by a physician is about 32. For each user’s posts, the average numbers of paid, audio and video posts are 2.132, 1.514, and 2.555, respectively. Based on the results in Table 3, we initialize the model network with the parameter values that approximately illustrates the characteristics of the haodf.com website, as shown in Table 4. NetLogo is a multi-agent simulation platform that supports modeling of complex systems. A screenshot of our proposed NetLogo model is shown in Fig. 2. The NetLogo model has several parts. First, there are two buttons, setup and go. The setup button initializes the model at the beginning, and the go button starts the model. Then Table 3 Descriptive study of physician-related information in the haodf website (N = 2659) Variable Physician score
Mean
Min
Q1
Median
Q3
Max
3.96
2.9
3.6
3.9
4.3
5
3052.20
14
684
1584
3630
40,004
No. gifts
234.72
0
33
86
244
5708
No. posts
32.12
1
5
13
20
5100
No. paid posts
2.13
0
0
0
0
629
No. audio posts
1.51
0
0
0
0
528
No. video posts
2.56
0
0
0
0
592
No. patients
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Table 4 Estimated parameter values of the model from real-life hanodf.com Parameter type
Parameter
Value
Source
Initialization
number-of-users
1500
Haodf
init-radius-dist
10
Estimated
max-followers-num
10
Haodf
max-fans-num
2
Haodf
init-number-of-influential-users
5
Haodf
number-to-become-influential
10
Estimated
post-probability
0.2
Haodf
ordinary-post-rate
2
Haodf
influ-post-rate
5
Haodf
rate-influence-offline
0.01
Estimated
rate-random-people-to-follow
100
Estimated
hot-topic-num
16
Haodf
Rate-relevant-topic
0.1
Estimated
See-hot-topic-probability
0.9
Estimated
New-user-by-day
50
Estimated
Dying-user-by-day
30
Estimated
Relation-by-day
20
Estimated
Posting action
Random behaviors Hot-topic list
User change
the parameters about creating networks, posting actions, random behaviors, and a hot-topic list, and network user changes are pre-defined before running simulation. A series of monitors and charts examine model changes over time. Figure 3a illustrates the change of numbers of influential and ordinary users over time. The sub-figure shows both numbers of influential users and ordinary users grow gradually, respectively. The cardinal number of influential users is much larger than that of ordinary users. With the increasing number of the influential users, the
Fig. 2 Our physician–patient interaction model based on NetLogo
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Fig. 3 Changes of numbers of users and average support degree for ordinary users and influential users, respectively
(a) User numbers
(b) Support degree
average support degree for each user in the model tends to keep low in a period, and then the numbers of ordinary users dramatically increase while the average support degree of the number of influential users tends to increase slowly all time. Regarding daily user use, Fig. 4 shows that daily user use tends to go up over the total number of users in the network. However, Fig. 5 shows the average number of followers per influential user first dramatically grow and then decrease a bit before it continues to grow. The followers of ordinary users keep approximately zero since patients in the online health community are not allowed to be followed by other users. As shown in Fig. 5b, the average number of daily posts per influential users and ordinary users is relatively small, respectively.
5 Discussion Our study proposes a physician–patient interaction model for an online health community based on NetLogo. Taking haodf.com as an example, we examine the experimental results from the established model. The proposed model is feasible to simulate social physician–patient interactions and produces convincing results compared to the actual world. Our study is similar to various social network models but with considerations of posting probability, platform interference, offline influence, and hot-topic-list in the online health communities. Existing models always suffer from a lack of complexity in real life, making them hard to apply in simulating the actual case. For example, Alelrod’s study [21] inspired a NetLogo model to simulate how people become similar through interaction. The model indicates that the general feature of culture
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Fig. 4 Change of all users and daily user use in the model
(a) Number of all users
(b) Daily user use
Fig. 5 Change of average numbers of followers and daily posts per user
(a) Average number of followers per user
(b) Average number of daily posts
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is that people can learn from each other. Alelrod proposed two assumptions; that is, people are like to interact with similar people, and people will become similar after their interaction. Thus, the model used culture and similarity properties to illustrate their change over time, which shows great limitations when trying to understanding complicated interactions between agents, such as asymmetric interaction analysis. Another model [22] investigated the influence of public speech on social decisions, which are determined by opinion changes from the agent network. The public speaker tries to influence their listeners’ opinion. In this model, some agents commit unchanged opinions, while others may change their opinions after interacting with the speaker. The model also has limitations in the complexity of agent commitment about their opinions, since some agents do not always change their minds after interacting with the speaker. We can also consider other existing NetLogo models in the future studies. The models include the word-of-mouth model, the Ising model, the social-natural model, the social network sharing model, and the social content conception model. The word-of-mouth model [23] identifies two facets in the transmission of information, seeking, and spreading. Information seeking considers two levels of knowledge, namely awareness and expert knowledge. The Ising network [24] studied how social decisions, known as magnetization, change over time. In the model, agents have three types of opinions and adjust their opinion over time based on their current opinion. The social-natural model [25] aims to identify social behaviors relevant to the resiliency of both culture and environment utilizing comparison between two different social systems. This model incorporates features from other NetLogo models, including diffusion on a directed network, cooperation, feeding, and wolf/ sheep predation. The social network sharing model [26] simulates the spread of an image via a social network with computers and links. When one computer shares an image, all computers connected to the computer will see the image, which is similar to the virus on a network. Another model called social conception of content model [27] simulates the influence of content delivery and offensive content from one individual to another. This study has limitations. First, our study merely focused on physician–patient interactions though we can use the model for analysis of traditional social platforms. Second, we cannot identify all factors in our established model. Therefore, our model may not suitable for those cases with other factors that we did not consider. Third, we estimate model parameters from an existing online community. Our work cannot be suitable for specific cases, but the established NetLogo model with multiple adjustable parameters can be used further by other researchers with different parameter value sets.
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6 Conclusions This study introduces a NetLogo-based physician–patient interaction model to explore complex relationships among users through information sharing in an online health community. We illustrate the modeling process by initializing the agent network, designing physician and patient agents, simulating physician–patient interactions, and defining a network updating mechanism. Compared to existing models, we considered the influence of user-agent transition, user number change, platform interference, and the use of a hot-topic list on support degree. We took haodf.com as an example to examine the model feasibility. Results showed that our model with multiple factors has the advantages of illustrating the complicated relationships between physicians and patients over time. With adjustable parameters, researchers can examine model changes over parameter settings from various cases of online health communities. Acknowledgements This work was supported in part by the Fundamental Research Funds for the Central Universities in UIBE with grant numbers 20QD22 and CXTD12-04, and National Natural Science Foundation of China with grant number 62102087.
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12. Li, T.S., Labadin, J., Piau, P., et al.: The effect of vaccination decision in disease modelling through simulation. In: 2015 9th International Conference on IT in Asia (CITA), pp. 1–6, IEEE (2015) 13. Wilensky, U., Rand, W.: An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press (2015) 14. Sulis, E., Terna, P.: An agent-based decision support for a vaccination campaign. J. Med. Syst. 45(11), 1–7 (2021) 15. Eysenbach, G., Köhler, C.: How do consumers search for and appraise health information on the world wide web? qualitative study using focus groups, usability tests, and in-depth interviews. BMJ 324(7337), 573–577 (2002) 16. Li, S., Liu, Z., Li, Y.: Temporal and spatial evolution of online public sentiment on emergencies. Inform. Proc. Manag. 57(2), 102177 (2022) 17. Zhu, P., Shen, J., Xu, M.: The agent-based value co-creation behaviors in online health communities and its influencing factors. In IOP Conf. Ser. Mater. Sci. Eng. 490(6), 062051 (2019) 18. Liu, D., Chen, X.: Rumor propagation in online social networks like Twitter—a simulation study. In: 2011 Third International Conference on Multimedia Information Networking and Security, pp. 278–282, IEEE (2011) 19. Carrier, J.M., Huguenor, T.W., Sener, O., et al.: Modeling the adoption patterns of new healthcare technology with respect to continuous glucose monitoring. In: 2008 IEEE Systems and Information Engineering Design Symposium, pp. 249–254, IEEE (2008) 20. Tulang, A.B.: Online learning communities amid the COVID-19 pandemic: an agent-based model. Turk. J. Comp. Math. Edu. 12(10), 6294–6302 (2021) 21. Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resol. 41(2), 203–226 (1997) 22. Diermeierm, D.: Social Consensus—Network. http://ccl.northwestern.edu/netlogo/models/ community/Social%20Consensus%20-%20Network (2015) 23. Thiriot, S.: Word-of-mouth dynamics with information seeking: information is not (only) epidemics. Phys. A 492, 418–430 (2018) 24. Diermeierm, D.: Ising Network. http://ccl.northwestern.edu/netlogo/models/community/ Ising%20-%20Network (2015) 25. Hatlestad, K.: The Socio-Natural Model. http://ccl.northwestern.edu/netlogo/models/commun ity/The%20Socio-Natural%20Model (2015) 26. Dana-Farley, J.: Social Network Sharing. http://modelingcommons.org/browse/one_model/ 4448#model_tabs_browse_info (2015) 27. Drory, N.: Model 2—Social Conception of content. http://ccl.northwestern.edu/netlogo/mod els/community/Social%20Consensus%20-%20Network (2017)
Information System Function-Data Architecture Planning Based on Subspace Partition Yuwen Huo, Xuedong Gao, and Ai Wang
Abstract Under the background of big data, the development of enterprise business and the growth of data lead to the characteristics of enterprise information system with complex business functions, large amount of data, and sparse connection between functions and data. Therefore, dividing the information system structure based on the link between functions and data can effectively improve the construction and operation efficiency of enterprise information systems and facilitate system maintenance and management. However, the traditional information system planning method only focuses on the planning of the functions, and cannot plan a reasonable data structure at the same time. In order to solve the above problems, this study proposes A Information System Function-Data Architecture Planning Method that can simultaneously complete the information system function-data system planning. It takes the Function-Data Subspace Partition Algorithm as the core, and realizes the information system structure partition from the perspective of function and data at the same time. Keywords system structure partition · Two-dimensional clustering · Subspace partition
1 Introduction Since the 1970s, enterprise information system planning [1] has attracted the attention of researchers and enterprises, and has been widely used in many fields such as industry, agriculture, and service industries. Among them, the Business System Y. Huo · X. Gao (B) · A. Wang University of Science and Technology Beijing, Beijing, China e-mail: [email protected] Y. Huo e-mail: [email protected] A. Wang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_6
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Planning (BSP) method [2] proposed by the International Business Machines Corporation (IBM) uses the process-oriented management idea to help enterprises plan the information system structure, and is the most influential enterprise system structure partition method at this stage. However, the BSP method only focuses on the partition of the functions of the enterprise information system, while ignoring the partition of the data. It cannot provide data organization solutions for enterprises, and cannot fully meet the basic needs of enterprises for structured data organization in the era of big data [3]. Therefore, this study proposes a Information System Function-Data Architecture Planning method (ISFDAP), which is structured to meet the structural partition requirements of information systems in the era of big data. This method takes the Function-Data Subspace Partition Algorithm (FDSP) as the core, and realizes the partition of information system structure from the perspective of function and data at the same time, and organizes the functions with similar data usage and the data they use in the same subsystem, that is, information system structure partition plan. The main contributions of this study include the following two aspects. First, the ISFDAP method is proposed, which structurally solves the problems of enterprise information system functions and data organization under the background of big data, as well as improves the efficiency of enterprise business processing. Second, the two-dimensional FDSP algorithm is proposed, with the concepts of Link Shared Degree and subspace density are introduced, and the traditional clustering method is extended from one-dimensional to two-dimensional.
2 Related Work 2.1 Heterogeneous Information Network In 2009, Sun et al. [4] proposed the concept of Heterogeneous Information Network (HIN), which clearly distinguishes the object types and relationship types in the network. That is, given the set of object type A = {A} and the set of relational type R = {R}, information network [5] is defined as a directed graph G = (V , E), which has an object type mapping function ϕ : V → A and a link-type mapping function φ : E → R, where each object v ∈ V belongs to a specific object type, denoted by ϕ(v) ∈ A, each link e ∈ E belongs to a specific relation type φ(e) ∈ R. Besides, if two links belong to the same relationship type, the two links have the same start object type and end object type. For a network, while the number of object type |A| > 1 or relationship type |R| > 1, has described the network as a heterogeneous information network, otherwise known as homogenous information network.
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In order to better understand the object types and relationship types in the network, Han [6] put forward the concept of network schema in 2009 to represent the metalayer features in the network. A network schema is the the meta template of a heterogeneous information network G = (V, E), with an object type mapping ϕ : V → A, a link mapping φ : E → R, , denoted as TG = (A, R). Based on HIN, Sun et al. [7] proposed the concept of meta-path in 2011 to describe the relationship between objects in HIN with its rich semantic information. The proposed meta-path provides a basis for similarity measurement and clustering among objects in HIN. Subsequently, in order to solve the problems that the recommendation accuracy was limited due to the inability to measure the weight of different meta-paths and the difficulty in effectively combining the information of multiple meta-paths, Shi et al. [8] proposed the concept of weighted heterogeneous information network and weighted meta-path in 2015, providing a new idea for clustering and recommendation of HIN. In 2016, Huang et al. [9] proposed the concept of meta-structure, which is used to capture the complex semantic relations between two HIN objects, further extending the concept of meta-path, which is essentially a special case of meta-structure [10].
3 Concept Preparation 3.1 Function—Data Network Through the investigation and analysis of the information system that the enterprise is ready to develop and construct, it is found that the enterprise information system consists of m function entities Fi (i = 1, 2, . . . , m) and n data entities D j ( j = 1, 2, . . . , n). The function entity Fi and the data entity D j are closely linked through the relationship of creating/being created and using/being used to realize the business operation of the enterprise information system. This study realizes the relationship analysis between function entities and data entities of information system by establishing function-data network. The functiondata network G = (V , E) is a two-dimensional heterogeneous information network [11] composed of two types of elements: function and data, and the function and data are closely related through the two relations of create and use. Among them, V = {Fi , i ∈ [1, m]} ∪ {D j , j ∈ [1, n]}, E = {< Fi , D j >, i ∈ [1, m], j ∈ [1, n]}, and A = {“Function”, “Data”}, R = {“Create”, “Use”}. The network mode T G = (A, R) can be used to represent the meta-layer features in the network, and the network mode of the function-data network is shown in the figure below (Fig. 1). The function—data network of information system can be stored and represented by U/C matrix [12]. U/C matrix is a two-dimensional matrix composed of information system function entities, data entities and the use/create relationship between them. The rows of the matrix represent function entities, and the columns represent data entities. The use/create relationship between the two is represented by U(use)/
78 Fig. 1 Enterprise information system function-data network mode
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Function (F)
Create (C)
Data (D)
Function (F)
Use (U)
Data (D)
Fig. 2 Information system function—data network example
Fig. 3 Information system function—data network U/C matrix representation
C(create) and placed in the corresponding position of the matrix. Taking the information system function-data network shown in Fig. 2 as an example, it can be expressed as the U/C matrix shown in Fig. 3.
3.2 Homogeneous Entity Link Degree In this study, two entities are called homogeneous entities, which means that they are both function entities or data entities. Definition 1 (Link Shared Degree) Assuming any two homogeneous entities Oi and O j , the sets of adjacent nodes are Si and S j respectively. The Link Shared Degree between the two is defined as the minimum value of the ratio between the number of adjacent nodes intersected by the two and the number of the two’s adjacent nodes respectively. The formula is as follows:
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( LS(Oi , Oj ) = Min
|Si ∩ S j | |Si ∩ S j | , |Si | |S j |
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) (1)
Among them, LS(Oi , Oj ) is the Link Shared Degree of entities Oi and Oj , the value range is [0, 1]; |Si ∩ S j | represents the size of the intersection of Si and S j , and |Si |, |S j | represent the size of Si and S j respectively. Definition 2 (Link Shared Degree of Homogeneous Entity Sets) Suppose that any two entity sets O S k , O S r , the sets O S k and O S r respectively contain several entities Oik (i = 1, 2, . . . , m), O rj ( j = 1, 2, . . . , n), the Link Shared Degree of Homogeneous Entity Sets is defined as the minimum value of the Link Shared Degree between entities Oik (i ∈ [1, m]) and O rj ( j ∈ [1, n]). The formula is as follows: L SS(O S k , O S r ) = Min({LS(Oik , O rj )|Oik ∈ O S k , O rj ∈ O S r })
(2)
Among them, L SS(O S k , O S r ) represents the Link Shared Degree of Homogeneous Entity Sets O S k and O S r the value range is [0, 1]; LS(Oik , O rj ) represents the Link Shared Degree of entities Oik and O rj . Example 1: According to Definition 2, calculate the Link Shared Degree of Homogeneous Entity Sets O S k1 = {F1 , F2 } and O S k2 = {F3 , F4 } in the information system function-data network shown in Fig. 2. According to Definition 2, the calculation steps are as follows: (1) Step 1: As can be seen from Fig. 2, F1 adjacent node set S1 = {D1 , D2 }, F2 adjacent node set S2 = {D3 , D6 }, F3 adjacent node set S3 = {D1 , D2 , D4 }, F4 adjacent node set S4 = {D5 , D6 }; (2) Step 2: For function entities F1 and F3 , S1 ∩ S3 = {D1 , D2 }, |S 1 ∩ S3 | = 2, |S 1 | = 2, |S 3 | = 3; (3) Step 3: Calculated by using (1), LS(F1 , F3 ) = 2/3; (4) Step 4: The same can be obtained, LS(F1 , F4 ) = 0, LS(F2 , F3 ) = 0, LS(F2 , F4 ) = 1/2; (5) Step 5: Calculated by using (2), the Link Shared L SS(O S k1 , O S k2 ) is Degree of Homogeneous Entity Sets Min(L S(F1 , F3 ), L S(F1 , F4 ), L S(F2 , F3 ), L S(F2 , F4 )) = 0.
3.3 Information System Subspace Density Definition 3 (Information System Subspace) For information system I S, the set composed of all function entities and data entities is denoted as V , and the set composed of all links between entities is denoted as E. If there is V ' ⊂ V and V ' /= ∅, the set formed by the link in E of the two endpoints at V ' is denoted as E ' , and the information system composed of the entity V ' and the link E ' is called the Information System Subspace, denoted as ISS. Definition 4 (Information System Subspace Density) For the information system ' ' subspace I SS containing p function entities Fi (i ∈ [1, p]), q data entities D j ( j ∈
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[1, q]) and the link E i j between them, the Information System Subspace Density is ' defined as the ratio of the number of links E i j in the I SS to the subspace size, the formula is as follows: ( ) Ep Eq count E i' j i=1 j=1 (3) S D(I SS) = p×q Among them, S D(I SS) represents the Information System Subspace Density ' ' of I SS, the value range is [0,1]; count(E i j ) represents whether the link E i j exists ' ' between the function entity Fi and the data entity D j , the value is 0 or 1; p and q represent the number of function entities and data entities in the I SS, respectively, and p × q represents the size of the I SS. Example 2: In the information system with the structure shown in Fig. 2, according to Definition 4, calculate the Information System Subspace Density of the I SS composed of the function entity set O S k = {F1 , F3 }, the data entity set O S r = {D1 , D2 , D4 } and the links between them. According to Definition 4, the calculation steps are as follows: '
'
'
'
'
(1) Step 1: As shown in the figure, there are links E 11 , E 12 ,E 31 , E 32 , E 34 ’in I SS; ' ' ' (2) Step 2: the number of link E i j in the I SS is count (E 11 ) + count (E 12 ) + ' ' ' count (E 31 ) + count (E 32 ) + count (E 34 ) = 5; (3) Step 3: The subspace size is |O S k | × |O S r |, and |O S k |=2, |O S r |=3; (4) Step 4: Calculated by using (3), the Information System Subspace Density of the I SS is: S D(I SS) = 5/(2 × 3) ≈ 0.83
4 Steps of Information System Function-Data Architecture Planning Method As shown in Fig. 4, the ISFDAP Method proposed in this study includes three stages, that is, Homogeneous Entity Clustering stage, Function-Data Subspace Partition stage, and Function—Data Subspace Adjustment stage. Among them, the FunctionData Subspace Partition stage is the core of the planning method. According to the different aggregation objects, this stage can be divided into two steps, that is, Function—Data Heterogeneous Entity Aggregation step and Function—Data Subspace Aggregation step.
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Fig. 4 Information system function-data architecture planning
4.1 Homogeneous Entity Clustering Based on the idea that similar entities often have similar links, this stage clusters the function/data entities with similar links into one category by calculating the Link Shared Degree between entities, and realizes one-dimensional clustering of two types of homogeneous entities, function entities and data entities. The input, output and steps of this stage are as follows. (1) Input: information system U/C matrix, homogeneous entity similarity threshold α. (2) Output: function /data entity sets. (3) Steps: (a) Step1: According to the U/C matrix, construct the initial function/data entity sets, denoted as X ik , (i = 1, 2, . . . , n); (b) Step2: Calculate L SS(X 01 , X 20 ) by using (2). If L SS(X 01 , X 20 ) ≥ α, merge X 10 and X 20 , and the merged set denoted as X 11 ; if L SS(X 01 , X 20 ) < α, denote X 10 and X 20 as sets X 11 and X 21 respectively. Denote the number of sets as c; (c) Step3: Calculate L SS(X 03 , X 1j )( j = 1, 2, . . . , c) by using (2). IfL SS(X 03 , X 1j ) ≥ α, merge X 30 andX 1j , and the merged set still denoted asX 1j ; if for any j ∈ [1, c],L SS(X 03 , X 1j ) < α, then take X 30 as a new set, 1 , the number of setsc = c + 1; denoted asX c+1 (d) Step4: For i = 4, 5, . . . , n, perform the operation of step 3 in sequence; (e) Step5: Consider X 1j ( j = 1, 2, . . . , c) as a new object to be clustered, and repeat the operations from step 2 to step 5 until the formed set no longer changes.
4.2 Function-Data Subspace Partition The function entities and data entities in the information system are linked by the relationship of create and use, and the closely linked function entity sets and data entity sets together constitute the information system subspace, that is, the two-dimensional function-data subspace. The partition of information system is essentially to divide
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the information system into multiple information system subspaces whose subspace density is greater than a given threshold. (1) Function—Data Heterogeneous Entity Aggregation Based on the principle of “the subset of frequent itemsets must be frequent” [13], in order to obtain the subspace that constitutes the final information system partition scheme, the function entity set and data entity set generated in the first stage can be combined and merged to obtain several two-dimensional functions—data entity sets. Therefore, for a given threshold β, this stage merges the one-dimensional function entity set and data entity set with link relationship and the subspace density greater than the threshold β into a two-dimensional function-data entity set to obtain the function-data initial subspace. (2) Function—Data Subspace Aggregation The partition of information system considers the close relationship between function entities and data entities within a subsystem, and also needs to consider reducing the degree of overlap between subsystems. Therefore, this study introduces the principle of priority with the highest density and the least intersect, that is, for all subspaces of the information system, the two subspaces with the highest subspace density after the merger are preferentially aggregated. In addition, if there are multiple merging schemes with the highest and equal subspace density after merging, the two subspaces with the smallest degree of overlap with other subspaces in the scheme are preferentially aggregated. At this stage, the idea of the Co-clustering [14] and the Hierarchical clustering [15] are adopted. Based on the merging principle of priority with the highest density and the least intersect, aggregate the function-data initial subspace which the subspace density after merging is greater than the threshold β, then the function-data subspace aggregation result with strong cohesion within subspaces and relatively independent is obtained. The input, output and steps of this stage are as follows. (1) Input: the function-data initial subspace, information system U/C matrix, threshold β of subspace density. (2) Output: the function-data subspace. (3) Steps: (a) Step1: Denoted function-data initial subspaces as Si , (i = 1, 2, . . . , k); (b) Step2: Calculate S D(I SS) by using (3), a k-order subspace density matrix S D i j is established, and the matrix element ai j represents the subspace density of the subspace obtained by merging Si and S j ; (c) Step3: If there is an element ai j ≥ β in the density matrix S D i j , according to the principle of the highest density priority, merge the two subspaces corresponding to the maximum value of the matrix element (ai j )max ; if there are multiple maximum matrix elements, the two subspaces with the least intersection with other subspaces are merged according to the principle of least intersect priority. The number of merged subspaces is denoted as c, ' and the merged subspace is denoted as Sc .
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(d) Step4: For the two subspaces that have been merged, delete their corresponding rows and columns from the matrix, and repeat step 3 until any element ai j < threshold β in the density matrix S D i j ; (e) Step5: The remaining (k − 2c) function-data initial subspaces Si of the matrix S D i j that have not been merged are denoted as new subspaces S 'j ( j = c + 1, c + 2, . . . , c + k − 2c); (f) Step6: Consider the subspace S 'j ( j = 1, 2, . . . , c + k − 2c) as the new object to be aggregated, and repeat the operations from steps 1 to 5 until the formed subspace no longer changes.
4.3 Function—Data Subspace Adjustment After dividing the information system function-data subspace according to the above steps, there may be two situations that lead to the incomplete partition of the information system structure. First, there are isolated function entities and data entities, causing system functions/data to be missing; second, the creating relationship C between functions and data does not belong to any subspace, resulting in the problem that the data between systems cannot be generated and operated normally. Therefore, it is necessary to divide these isolated function/data entities and incomplete functiondata creating relationships into corresponding subspaces, and the adjusted subspace partition result is the information system function-data architecture partition scheme. This study proposes two function-data subspace adjustment principles. First, the adjustment principle of isolated function/data entity. For an isolated function/data entity, if there is a data/function entity that has a creating relationship C with it, divide it into the subspace where the corresponding data/function entity is located; otherwise, managers can formulate adjustment plans according to specific management scenarios. Second, the adjustment principle of incomplete function-data creating relationship. For the creating relationship C between functions and data that do not belong to any subspace, the corresponding data entities are divided into the subspace where the function entities that have the creating relationship are located.
5 Experiment and Result Analysis 5.1 Experimental Data The experimental data of the information system used in this study is from [16]. The information system I S contains 19 function entities and 16 data entities. The U/C matrix of the information system I S is shown in Fig. 5.
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Fig. 5 The U/C matrix of the information system IS
The correctness of the U/C matrix of the information system was checked through the completeness check, the consistency check and the non-redundancy check, which ensures the rationality of the information system structure in this study.
5.2 Information System Structure Planning Experiment and Result Analysis (1) Homogeneous Entity Clustering According to the algorithm principle and steps, the function entities and data entities in the U/C matrix were clustered respectively, and 12 function entity sets and 12 data entity sets were obtained (Link Shared Degree threshold α = 0.65). According to the clustering results of function entities/data entities, the rows and columns of the U/C matrix were adjusted so that the function/data entities of the same class were located in adjacent rows/columns of the matrix (Tables 1 and 2). (2) Function-Data Subspace Partition According to the algorithm principle and steps of “Function—Data Heterogeneous Entity Aggregation” in the first step of this stage, 12 function entity sets and 12 data entity sets generated in the first stage were aggregated to obtain 19 functiondata initial subspaces. As shown in Fig. 6, the distribution of function-data initial subspace is discrete, and the subspace overlap is high. Therefore, according to the algorithm principle and steps of the second step “Function—Data Subspace Aggregation” in this stage, 19 function-data initial subspaces were aggregated into 8 subspaces. As shown in Fig. 7, the aggregated subspace has strong internal cohesion and relatively independent (threshold β = 0.8).
Information System Function-Data Architecture Planning Based … Table 1 Function entity set
Table 2 Data entity set
Function entity set number
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Function entity set
F_Clus 1
F1 , F2
F_Clus 2
F3
F_Clus 3
F4 , F13
F_Clus 4
F5 , F6
F_Clus 5
F7
F_Clus 6
F8
F_Clus 7
F9 , F11
F_Clus 8
F10
F_Clus 9
F12 , F14 , F15
F_Clus 10
F16
F_Clus 11
F17
F_Clus 12
F18 , F19
Data entity set number
Data entity set
D_Clus 1
D1 , D2 , D15
D_Clus 2
D3
D_Clus 3
D4 , D5
D_Clus 4
D6
D_Clus 5
D7
D_Clus 6
D8 , D9
D_Clus 7
D10
D_Clus 8
D11
D_Clus 9
D12
D_Clus 10
D13
D_Clus 11
D14
D_Clus 12
D16
(3) Function—Data Subspace Adjustment Observing the above subspace aggregation results, it is found that outside the function-data subspace, there are still discrete function entities and data entities, and there is a case where the creating relationship C between functions and data does not belong to any subspace. According to the adjustment principle of the third stage of the method, these isolated function/data entities and incomplete function-data creating relationships were divided into corresponding subspaces, and the information system function-data architecture partition scheme was obtained. Combined with the actual meaning of the function entities and data entities that constitute each subsystem, the eight subsystems in Fig. 8 are, from top to bottom, the Business Planning System, Product Sales System, Product Design System, Inventory
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Fig. 6 The function-data initial subspace
Fig. 7 The function-data subspace after aggregation
Management System, Workshop Scheduling System, Manufacturing System, Order Management System and Human Resources Management System.
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Fig. 8 Function-data architecture partition scheme of IS
6 Conclusion Based on the requirements of enterprise information system structure partition in the era of big data, this study proposes the ISFDAP method. The method takes the Function-Data Subspace Partition algorithm as the core, adopts the idea of Hierarchical clustering and Co-clustering, based on the creating and use relationship between functions and data, and divides the enterprise information system from the two dimensions of function and data at the same time, then obtains the information system function—data architecture planning scheme. The method specifically includes three stages: homogeneous entity clustering, function-data subspace partition, and function-data subspace adjustment. Experiments show that the ISFDAP method proposed in this study can effectively aggregate the discrete original function-data U/C matrix into multiple sub-matrices with strong cohesion according to the creating and use relationship between functions and data. And the enterprise information system function—data architecture planning scheme generated by the aggregation results conforms to the actual application situation. In the next step, this study will consider extending this method from the two-dimensional space of “function-data” to a higher-dimensional space, such as the three-dimensional space of “function-data-knowledge”, to meet the more complex and refined enterprise information system structure partition needs of managers.
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Scenario Construction Model of Railway Traffic Accidents Based on Similarity Theory Dan Chang, Lei Huang, and Daqing Gong
Abstract Scenario construction has received extensive attention in the field of the emergency management of railway traffic. In this paper, based on government documents and regulations, a scenario construction method is used to establish a similarity calculation model based on doc2vec, which solves the problem of quickly finding similar events as well as response strategies by dividing the scenario elements. The railway accidents were used as real-life examples to verify the result of the model. Finally, the historical scenarios are ranked according to the comprehensive similarity, which can effectively provide timely decision support for the related governments to respond the railway emergencies and accidents. Keywords Scenario similarity · Emergency management · Railway accidents
1 Introduction Although Chinese railway industry has grown quickly in recent years, significant loss of life and property is still caused by safety issues. Thus the academic community has paid close attention to scenario construction in the fields of safety management and emergency management. Case reasoning, emergency decision support systems, knowledge management, and other methods are all useful in supporting emergency decision making for railway accidents. However, the common problem that limits the effectiveness of these supporting methods is how to reasonably use documented materials and historical data as an effective empirical guidance for new accidents in order to form reasonable D. Chang · L. Huang (B) · D. Gong School of Economics and Management, Beijing Jiaotong University, Beijing, China e-mail: [email protected] D. Chang e-mail: [email protected] D. Gong e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_7
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response strategies. In addition, within a relatively short period of time, it is challenging to locate identical accidents and related accidents for comparison. Therefore, how to effectively use the historical data and documentations to develop a realistic and useful response strategy and offer appropriate experience counseling for future accidents is one of the issues that has to be solved. Based on this background, this paper investigates the scenario construction model for railway traffic accidents to address the complexity of railway accidents and the scarcity of similar cases. The innovation of this paper is to use an improved natural language processing algorithm, doc2vec, to solve the problem of sparse railroad accident cases. In addition, the improved similarity calculation model of document segmentation comparison is proposed for the case of few global similarity of accident documents. The second section of the article is a literature review on railway scenario construction methods. The third section introduces the current situation of railway accidents and emergency management in China and the scenario construction theory used in this article. The fourth section describes the scenario modeling process and derives the model results from railway incident reports. The fifth section uses examples to validate the model. Finally, the paper presents a conclusion and an outlook for future research.
2 Literature Review 2.1 Emergency Management System Most of the research on the emergency management system of existing railway accidents is theoretical research by abstracting reality cases into the modelized process. For instance, Alvear et al. [2] constructed a traffic evacuation emergency scenario model by contextualizing road information at different stages for traffic jam events. Prakash et al. [11] constructed a flood dam failure disaster event scenario model to study the factors affecting flood dam failure. Some other scholars focus on introducing mathematical models into the decision-making process. For example, Sun et al. [16] implemented a probabilistic approximation of fuzzy rough sets over space. Rodríguez-Sanz et al. [14] proposed method for historical cases and data for traffic forecasting. The purpose of the railroad safety emergency system is to provide operators with decision support during real-time events or emergencies [4]. However, due to the limitations of structured characterization of incidents and real-time evolutionary analysis of specific processes, more and more scholars are focusing on the intelligent system construction and knowledge management of emergencies and accidents.
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2.2 Scenario Construction Scholars have gradually introduced the concept of scenario construction in knowledge management and emergency management. Raman [13] designed a webbased knowledge management system using Wiki technology. Amailef and Lu [3] constructed a case retrieval process that makes it easier to propose new solutions based on past disaster events by increasing efficiency. Qian et al. [12] developed a multi-dimensional “case-scenario-unit” model to study the evolutionary process and inference relationships between single and multiple scenarios. Subsequent studies have explored emergency management intelligence from the perspective of heterogeneous information fusion, intelligence workflow, and multi-source intelligence integration [1, 7, 10]. Most of these studies focus on introducing mathematical models in the decision-making process, incorporating historical case information, and determining the best emergency decision options based on database technologies such as knowledge bases and ontologies. While most of these studies are based on database technologies, TF-IDF algorithms and the SVM (Support Vector Machines) method have also been applied to scenario construction with the emergence of algorithms such as NLP (Natural Language Processing) and deep learning [18, 19].
2.3 Smilarity Theory Similarity computation is an important component of scenario construction, and scholars work to refine and compare models to get the optimal solution for a given task. The disadvantage of TF-IDF described before is that it needs word frequency to measure the importance of a word. However, in certain text, sometimes important words may not appear enough, and if the contextual structure of words is to be represented, other similarity computation methods like word2vec are brought out. Le and Mikolov [9] proposed doc2vec based on word2vec. Caselles-Dupré et al. [6] found that hyperparameter modifications may make word2vec-based models perform better. Botev et al. [5] developed WISDM (Word Importance-based Similarity of Documents Metric) model. Sun et al. [17] proposed a framework for computing sentence similarity based on contextual definitions. Among those models, scholars have compared the performance of different vector space models and similarity algorithms in the domain [8, 15]. In conclusion, the model building and management methodologies investigated by the predecessors can serve as theoretical guides for scenario construction, while the updates to the deep learning and NLP algorithms can expedite similarity matching and make scenario construction more reasonable. Owing to the need for real-time decision-making in railroad accident emergency management, scholars usually innovate in management methods, algorithm applications, but ignore the scarcity of railroad accidents and the use of railroad accident text materials. In this paper, a similarity calculation model based on doc2vec of gensim is selected after taking into account the model and the data source of the case text.
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3 Scenario Analysis of Railway Accidents Broadly speaking, emergencies do not lead to accidents if they are handled in a timely and reasonable manner. Scenario construction theory is mainly applied to the field of emergency management. Emergency management refers to the response mechanism proposed for accidents, disasters, emergencies, and even for potential risk. Accidents that occurred in the past, regardless of whether future events lead to accidents eventually, can be used as a reference for response strategies. Therefore, before building the scenario construction model, it is important to sort out the characteristics of railway accidents and analyze the current emergency management process of the Chinese government and related departments for railway accidents to ensure that the model is reasonable and realistic.
3.1 Characteristics of Railway Accidents • Various Causes. There are numerous reasons why train accidents occur. The highspeed railway system is frequently affected by natural disasters, human mistake, and other environmental issues in addition to extreme conditions that include high speed, high density and high intensity. Although some of the causes of these accidents are unavoidable, not every one of them will eventually lead to an accident (Table 1). • Geographical limitations. Railway accidents are bound to occur on or near the tracks, and the geographical limitations of railway accidents are important features that distinguish it from road traffic accidents and other means of transportation. Although there are still railroads running through the city’s downtown, the tracks are usually set in the suburbs according to the current urban construction and planning. Moreover, accidents often occur in mountainous areas and other areas. • Significant risk. Despite the fact that there are thousands of railway problems or emergencies annually in China, there aren’t many train accidents that end in significant accidents. Due to this circumstance, there isn’t much historical experience to draw from when dealing with current accidents. However, when a significant railway disaster occurs, the harm is significantly more than situations of regular road traffic accidents that can be comparable in terms of the scope and severity.
3.2 Government Emergency Management Process Emergency management includes four parts: prevention, response, disposal and recovery, which correspond to before, during and after the event. This paper mainly focuses on the relevant measures after the occurrence of railway emergencies.
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Table 1 Railway accidents causes Table head
Cause of the accidents
External environment
Foreign object intrusion, pedestrians illegally crossing the road, motor vehicles grabbing over the railway crossing, illegal construction along the line damage to railway equipment and facilities, up across the bridge and the public-rail parallel section of the motor vehicle accidentally intrusion and other accidents
On-site Safety management
Weak links still exist in the safety management of grass-roots units, the basic operating system, the implementation of operational standards still need to be further strengthened. Some local railway equipment and facilities lost maintenance and repair, on-site operations out of control, shunting off the squeeze and other problems occur
Facilities and equipment
Rolling stock, communication signals, contact networks and other equipment failure, inadequate maintenance, and repair quality
Natural disasters
Heavy rainfall, typhoons, haze, freezing and other severe weather-induced equipment signal interruptions, train shutdowns, etc
Usually, after the occurrence of the emergency, the train driver or running car driver should immediately stop and take emergency disposal measures. For those who cannot be disposed of, they should immediately report to the neighboring railway station degree officer for disposal. Accidents scene of the railway transport enterprise staff or other personnel should immediately report the neighboring railway station, train dispatcher or public security organs bit and personnel received the report, should immediately report the accident situation to the accident occurred railway management agencies. Railway management agencies receive reports of accidents, should verify the situation as soon as possible, and immediately report to the State Council railway authorities. Particularly, authorities should immediately report the significant and major accidents to the State Council and notify the production safety supervision and management and other relevant departments. The competent railway authorities of the State Council, railway management agencies, local people’s governments above the county level where the accidents occurred or railway transport enterprises shall start the appropriate emergency plan. If necessary, on-site emergency rescue agencies could be established. In this process, on the one hand, the subjects of emergency management are the government, enterprises and other public organizations, of which the responsible subject is the government, which plays a leading role. On the other hand, although the government is the responsible body for emergency management, it is impossible to achieve good results in emergency response without the power of the whole society.
3.3 Scenario Construction Scenario construction is a method of generating future scenarios by means of hypothesis, prediction and simulation, and analyzing the impact of the scenarios
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on the objectives. Generally, the common process of scenario construction could be summarized as follows. First, scenario analysis is performed for specific scenarios to provide the basis for subsequent tasks. Scenario element analysis is the process of qualitative analysis of the historical intelligence resource collection of the decision target problem, which is composed of thematic libraries, case libraries, and knowledge bases. The basic components of scenario construction include causing scenario, bearing scenarios and response scenario. Each of the components corresponds to the analysis of the current scenario, the task mapping and the final competency assessment work. Secondly, as for the task mapping and assessment, scholars develop models that using NLP methods. In the realization of feature attribute extraction of emergent events. Firstly, a corpus of specific types of emergent events is established as the evaluation basis for extracting feature values. Secondly, the data from the corpus is divided and filtered using tools such as word splitters to build dictionaries. Then, the original documents are transformed into vectors according to the dictionary, and the corresponding documents are constructed into a document set, and the similarity of the documents is calculated using cosine similarity. If A and B represent two vectors of text, Ai and Bi are words in the text set the similarity of A and B is: ∑n Ai × Bi A•B / similarit y( A, B) = = ∑ i=1 /∑ n n ||A|| × ||B|| 2 A × i=1
i
i=1
(1) Bi2
The calculation of cosine similarity can transform the text into computable numerical values. Achieving mathematical transformation of text facilitates people to handle and understand textual materials which are widely utilized in the management of government departments. In summary, based on the characteristics of railway accidents we can know the matching of historical railroad accidents need to pay attention to the differences in the causes of railroad accidents. Besides, reasonable management for the government is a vital part of the response to railway accidents. Based on these facts, the use of scenario construction can be effective for the application of government text materials and accelerate the decision making of emergency management process.
4 Scenario Construction Model Scenario construction models are built by combining textual sources such as railway accident reports in this section. 11 accident investigation reports are selected as the similarity calculation text of the model as well as the validation text, which will be introduced later in this paper. All the accident reports are from the official websites national railway administration (http://www.nra.gov.cn/), supplemented by the use of news websites.
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Fig. 1 Scenario construction process
According to Fig. 1, the information especially text material in government documentation, accident reports and news reports is the sources of data before scenario analysis. In the process of building scenario construction, data processing must be conducted when determining the scenario element. The scenario element analysis results provide the basic words for generating the corpus. After word segmentation, the dictionary of the model is generated. By converting text into vectors, the document set are set to be numerical data, which could be computing. Finally, the results of the similarity calculation can be derived from which historical accident has the highest similarity to the target accident. In this way, the government can get materials to support its decisions. These materials can likewise be a source of new data.
4.1 Scenario Elements Analysis The premise of scenario element analysis is the existence of available emergency intelligence resources, especially typical cases. Therefore, the analysis text used in this paper selects the investigation report of railway accidents as the base data for scenario element analysis. Scenario element analysis is conducted by collating three data sources: government policies, railway traffic accident reports, and news reports. The division of the components of the accident reports is evident in these texts. Moreover, each section contains the classification obtained from the previous scenario element analysis.
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Table 2 Scenario element analysis results Core categories Causing scenario
Bearing scenarios
Response scenario
Main categories
Categories
Causing process
Trigger conditions, evolutionary process
Causing action
Management methods
Main Cause
Direct cause
Fires, collision, derailments
Indirect cause
Traffic accidents, natural disasters, signal failure
External environment
Foreign object intrusion, environmental damage,
Internal environment
Railway track damage, bridge damage
Personnel casualties
Number of people injured, number of people killed, number of people moved
Economic losses
Direct economic loss, indirect economic loss
Emergency organizations
National rescue team, provincial and regional rescue team
Rescue methods
Emergency aid, emergency management
Resource allocation
Firefighting, fire engines, ambulances, medical care, technical rescue, other rescue resources
Therefore, according to the five parts of the accident reports, we set the corresponding accident text material subsections. (1) The basic situation (including accident overview, emergency response and simple investigation of the accident); (2) The casualties and direct economic losses caused by the accident; (3) The causes of the accident; (4) The identification and rules of the accident; (5) The prevention and work requirements after the accident happened. According to the characteristics and current situation of railway accidents and government emergency process analyzed in Sect. 3, the elements of the scenario are summarized as shown in Table 2. And the above subparagraphs all exist within the text corresponding to the scenario elements.
4.2 Feature Attribute Extraction In order to make the decision target contingencies and the constructed scenario elements to perform similarity test, so as to verify the feasibility of the intelligence perception method. In this paper, a railway contingency scenario construction model is constructed, and the feature words of specific contingencies are extracted in the model building.
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Firstly, the case text use jieba to divide words, deactivated words are filtered, and then the corpus is built. The documents to be compared are loaded, and the documents to be compared are converted into sparse vectors by doc2bow, and the sparse vectors are further processed. In addition, this paper builds different corpora with dictionaries for the five parts contained in the accident investigation documents.
4.3 Similarity Calculation We use the functions of models, corpora, and similarities of the gensim library in this paper. Similarity calculation is performed on the documents. The dictionaries are generated using corpora’s built-in functions to build training and testing models. After representing the documents as sparse vectors, the similarity is calculated using the built-in cosine method of similarities. The “Beijing-Guangzhou railway T179 passenger train derailment railway traffic accident” was randomly selected as the target document, and 9 other documents from 2019–2021 were selected for similarity matching (Table 2), and the similarity results are shown in Table 3 (Table 4). According to the formula of cosine similarity, the closer the cosine value is to 1, the closer the angle is to 0 degrees, which means the more similar the two texts are. By calculation result, we can get the following information. For example, the overview of the accident reports (column A), the highest similarity with the target document (accident 1) is accident 3 in Fig. 2a. This is due to the fact that both accident 1 and accident 3 are passenger train accidents, and Table 3 Accidents list ID
Accidents
1
Beijing-Guangzhou railway T179 passenger train derailment railway traffic accidenta
2
Chinalco Logistics Group Central International Dry Port Limited. train derailment traffic accident
3
Jinzhou-Chengde Railway passenger train derailment accident
4
Lanzhou-Xinjiang railway K596 passenger train collided with the operator accident
5
Chengdu-Chongqing railway 87,031 freight train derailment accident
6
Bengxi-huanren railway freight train derailment accident
7
Wulumuqi-Jiangjunmiao railway freight train derailment accident
8
Haolebaoji-Ji’an railway freight train derailment accident
9
Litang-Zhanjiang railway No.43031 freight train derailment accident
10
Litang-Zhanjiang railway No.X9547 freight train fire accident
11
ShanxiLinfen “8–29” major collapse accident (non-railway accident)b
a Accident b Accident
1 is the target text 11 is the verified text
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Table 4 Similarity calculation results A
B
C
D
E
Overview
Damage
Cause
Rules
Aftercare
2
[0,0.5793]
[0,0.7121]
[0,0.3904]
[0,0.6724]
[0,0.7194]
3
[0,0.7378]
[0,0.6587]
[0,0.3815]
[0,0.7064]
[0,0.7709]
4
[0,0.5195]
[0,0.6172]
[0,0.5268]
[0,0.6647]
[0,0.6028]
5
[0,0.6699]
[0,0.7412]
[0,0.5850]
[0,0.8660]
[0,0.6532]
6
[0,0.7044]
[0,0.7508]
[0,0.4529]
[0,0.4591]
[0,0.5367]
7
[0,0.6933]
[0,0.7229]
[0,0.3554]
[0,0.6588]
[0,0.5829]
8
[0,0.5652]
[0,0.5258]
[0,0.4954]
[0,0.6157]
[0,0.5418]
9
[0,0.6602]
[0,0.8350]
[0,0.4195]
[0,0.8250]
[0,0.7190]
10
[0,0.6585]
[0,0.5725]
[0,0.3761]
[0,0.6104]
[0,0.4934]
11
[0,0.4292]
[0,0.4524]
[0,0.3631]
[0,0.1506]
[0,0.4909]
ID
both are derailments rather than collisions, which have higher similarity in terms of emergency response. In Fig. 2b, for the accident casualties and losses (as shown in the column B), accident 9 has the highest similarity with accident 1, which is due to the fact that the number of casualties and direct economic losses of other accidents are different, but the direct economic losses of accident 9 and accident 1 are the closest though. In terms of accident causes, since both accident 5 and accident 1 were due to rock weathering caused by continuous heavy rainfall, which in turn led to road collapse. However, since accident 5 was a derailment and accident 1 was a collision with a landslide, the differences in specific causes resulted in a low degree of similarity. In terms of accident causes (column C), the overall similarity is low, precisely because of the low number of railway accidents and the generally high independence and low similarity of causes. However, the results could still show the similarity of accident 5 is higher than others as shown in Fig. 2c. As it is shown in the rules part (column D) and Fig. 2d, the similarity between accident 5, accident 9 and accident 1 are high, which is due to the fact that according to the relevant provisions of the Regulations on Emergency Rescue and Investigation of Railway Traffic Accidents and Rules for Investigation and Treatment of Railway Traffic Accidents, these issues are natural disasters under severe meteorological and special geological conditions, and the relevant agencies of railway management are designated as non-liability. In the section of event prevention and aftercare (column E), accident 9 has the highest similarity in Fig. 2e. And the documents of both accident 9 and accident 1 contain the contents of establishing awareness of responsibility for safety production, implementing basic rules and regulations for safety production, preventing and resolving major safety risks, ensuring the safety of line opening and operation, strengthening the investigation of hidden dangers of railway flood prevention, and strengthening joint prevention and control of road protection.
Scenario Construction Model of Railway Traffic Accidents Based … Fig. 2 Similarity calculation results
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4.4 Government Decistions Based on the similarity results and analysis above, the government can use the subsection of the documents for similarity matching when it encounters a new breaking news event, as long as it has any text material of this breaking news event. For example, in the target accident of this paper, if only the cause of the accident is known, the matching accident with the highest similarity can be searched by the cause of the accident. And the accident aftercare measures of the specific historical accident with the highest similarity can provide a reference for it. To be more specific, the accident 1 is due to erosion caused by natural disasters. Accident 5 is also due to rock weathering caused by continuous heavy rainfall. Therefore, the government can choose to make sure to check the weather prediction in advance, maintain real-time signal detection along the railway tracks and as stated in the reports of accident 5.
5 Model Validation The words and sentences that may exist in accident reports are similar. Therefore, in order to test whether the scenario construction model in this paper is focused on railroad accidents or not, the validation was performed using non-railway accident texts. The result is shown in Fig. 3. In Fig. 3, due to the fact that accident 11 (Shanxi Linfen “8–29” major collapse accident) is not a railway accident, the similarity is low in the result of the model similarity calculation. This means that railway accidents caused by emergencies are different from other accidents in terms of sensitivity to the scenario similarity model, which not only means that intelligence perception of emergencies can be achieved by scenario similarity calculation, but also means that railway accidents are special. Fig. 3 Scatterplot of similarity
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It is worth noting that in terms of column D, the similarity of the validation document accident 11 is very low, which is due to the fact that railway-related accidents are based on railway-related laws and regulations, which are professional in nature. In terms of accident causes, the similarity of model output is more concentrated but low, which is due to the fact that the causes of collapse accidents are natural disasters, which are consistent with the causes of matching accidents and target accidents.
6 Conclusion In conclusion, this paper, through the literature, reflects the important role of scenario construction in the emergency management system of railway accidents, and through the combing of word vector technology, clarifies the doc2vec method on which this paper is based. Secondly, through the scenario analysis of railway accidents, we provide the theoretical and practical basis for the scenario similarity model approach in the later paper. The comparison of accident segment similarity is realized by matching the similarity of target documents. The result of the similarity calculation illustrate that despite the diverse and independent causes of railway emergencies and accidents, historical data are still highly informative for the target events. The similarity comparison of different event segmented texts can provide decision support for similar events. However, the similarity matching model established in this paper still needs some work to be improved and extended due to the limitation of data sources of the corpus caused by the low frequency of railway accidents. Research issues on the dynamic evolution of railway emergencies and accidents are to be further discussed in the future. Acknowledgements This paper is supported by Beijing Logistics Informatics Research Base of the International Center for Informatics Research (ICIR).
References 1. Alamdar, F., et al.: Towards multi-agency sensor information integration for disaster management. Comput. Environ. Urban Syst. 56, 68–85 (2016) 2. Alvear, D., et al.: Decision support system for emergency management: Road tunnels. Tunn. Undergr. Space Technol. 34, 13–21 (2013) 3. Amailef, K., Lu, J.: Ontology-supported case-based reasoning approach for intelligent MGovernMent emergency response services. Decis. Support Syst. 55(1), 79–97 (2013) 4. Balboa, A., et al.: Intelligent emergency management system for railway transport. Transp. Res. Procedia 58, 193–200 (2021) 5. Botev, V., et al.: Word importance-based similarity of documents metric (WISDM): fast and scalable document similarity metric for analysis of scientific documents. In: Proceedings of the 6th International Workshop on Mining Scientific Publications. https://doi.org/10.1145/312 7526.3127530 (2017)
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6. Caselles-Dupré, H., et al.: Word2vec applied to recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems. https://doi.org/10.1145/3240323.3240377 (2018) 7. Erskine, M.A., Pepper, W.: Enhancing emergency response management using emergency description information technology (EDIT). E-Health Telemed 1264–1278 (2016) 8. Gómez, J., Vázquez, P.: An empirical evaluation of document embeddings and similarity metrics for scientific articles. Appl. Sci. 12(11), 5664 (2022) 9. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International conference on machine learning. PMLR, pp. 1188–1196 (2014) 10. Pereira, V.A., et al.: Multi-criteria fusion of heterogeneous information for improving situation awareness on emergency management systems. Lect. Notes Comput. Sci. 3–14 (2015) 11. Prakash, M., et al.: Modelling the impact of dam failure scenarios on flood Inundation using SPH. Appl. Math. Model. 38(23), 5515–5534 (2014). https://doi.org/10.1016/j.apm.2014. 03.011 12. Qian, J., et al.: A cross-reasoning method for scenario awareness and decision-making support in earthquake emergency response. Web-Age Inf. Manage. 106–118 (2014) 13. Raman, M.: Claremont colleges emergency preparedness: An action research initiative. Syst Pract Action Res 19(3), 253–271 (2006) 14. Rodríguez-Sanz, Á., et al.: A data-driven approach for dynamic and adaptive aircraft trajectory prediction. Transp. Res. Proced. 58, 5–12 (2021) 15. Shahmirzadi, O., et al.: Text similarity in vector space models: a comparative study. SSRN Electr. J. https://doi.org/10.2139/ssrn.3259971 (2018) 16. Sun, B., et al.: Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision-making. Expert. Syst. 32(4), 507–521 (2015) 17. Sun, X., et al.: Sentence similarity based on contexts. Trans. Assoc. Comput. Linguis. 10, 573–588 (2022) 18. Xu, X., Li, Y.: Research on multi-granularity response model of emergency based on scene similarity. Inf. Sci. 39(2), 18–23 (2021). https://doi.org/10.13833/j.issn.1007-7634.2021. 02.003 19. Yang, F., et al.: The method for intelligence awareness in an emergency based on scenario similarity. J. China Soc. Sci. Tech. Inf. 38(5), 525–533 (2019)
Research on Operational Performance Evaluation of Listed Coal Companies in China Under the New Normal of Economy Qixin Bo and Xuedong Gao
Abstract Coal is not only the basic energy but also the non-renewable energy, which plays an irreplaceable role in promoting the development of national economy. In recent years, China’s coal listed companies have been faced with increasingly severe internal and external business environment due to the influence of domestic traditional energy restriction policies and supply–demand contradictions. China’s coal industry has been in business difficulties since 2012, and entered an unprecedented “winter period” in 2015. Under the new normal of economy, the scientific and effective evaluation of their business performance is directly related to the quality and even the survival of such companies’ periodic business decisions. By selecting coal listed companies as the research object, this paper constructed an operating performance evaluation index system including 12 financial indicators from five angles. Principal component analysis and cluster analysis were used to comprehensively evaluate the operating performance of 16 coal listed companies from 2015 to 2017. The paper compares the solvency, profitability, operation and development capacity of 16 sample listed companies in the same period, thus providing reference for companies to formulate development policies and investors to invest rationally. Keywords Listed coal companies · Operating performance · Principal component analysis · Cluster analysis
Q. Bo · X. Gao (B) School of Economics and Management, University of Science and Technology Beijing, Beijing, China e-mail: [email protected] Q. Bo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_8
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1 Introduction Coal is the pillar of China’s traditional energy industry and plays an irreplaceable role in promoting China’s economic development. Similarly, the profitability and income of coal enterprises as a key factor of China’s industrial GDP growth has been widely concerned. Before 2012, China’s coal industry was in a period of prosperity and development, with the coal price rising all the way, continuous achievements in performance and strong development in all aspects. However, since the second half of 2012, China’s coal industry has shrunk as a whole, and the coal price has fallen all the way. Coupled with the impact of the international coal market and economic crisis, China’s coal industry began to decline during the boom period. In July 2014, aiming at the current domestic economic downturn and depressed coal market situation, the state proposed to control the production of enterprises with over-production control and enterprises with seriously over-production line [1]. In March 2015, in view of the non-standard operation of China’s coal market and the continuous decline of enterprise performance, the state put forward the “four strict” governance principles, China’s coal industry has entered an unprecedented “winter period”. With overcapacity, inventory intensification, demand reduction and environmental oppression, new difficulties have emerged in the operating performance of coal enterprises [2]. In 2016, China began to introduce various policies to support coal enterprises to get out of difficulties. However, China’s energy resource endowment, which is short of oil and gas and rich in coal, determines that coal will remain an important energy pattern in China [3]. At the same time, the state began to carry out macro-control, supply-side structural reform, coal enterprises “exit mechanism”, “overcapacity” and other policies issued one by one. In view of the current situation of the coal market, the State-owned Assets Supervision and Administration Commission (SASAC) also set the annual capacity reduction target of 24.93 million tons in the 2017 meeting [4]. During the boom period of the coal industry, various development indicators of the enterprise will increase accordingly, and some of the shortcomings of the enterprise’s operation and strategic planning are easily covered up. When the coal industry is in a downturn, the business performance of enterprises is not ideal, and the development and business index values show differences, and the deficiencies of enterprises in the business process will be well reflected [5]. Therefore, in the new normal of economic development in China, green and high-efficiency energy is widely used. Based on the research on the operating performance of listed coal companies in the industry downturn, it is conducive to scientifically evaluate the problems existing in coal companies, and explore their operating conditions and future. Development situation, guide the healthy growth of the enterprise, optimize the development strategy, and plan the investment rationally. At the same time, it can also provide a reference for the future development of coal enterprises that are currently in deep trouble.
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Foreign research on financial performance evaluation started earlier and was applied in the management of corporate financial performance. Pearson K. proposed principal component analysis, which simplified multiple indicators into a few core indicators [6]. Scholars such as Serpil Canbas and Altan Cabuk use principal component analysis to study foreign research on financial performance evaluation [7]. In terms of the research on the financial performance evaluation index system of listed companies, Zuo Yuanli took Jiangsu manufacturing listed companies as an example, used factor analysis method to build a multidimensional comprehensive performance evaluation model, and gave relevant suggestions on the comprehensive performance level of Jiangsu manufacturing listed companies [8]. Zhao Xiaoge and Jiang Xin conducted performance research on the financial data of 40 listed pharmaceutical manufacturing companies in China based on factor analysis [9]. There are many researches on the operating performance of coal listed companies in China, but few researches on the operating performance evaluation of coal listed companies under the new normal of economy and in the downturn of the industry. Zhang Shuwu and Jiao Jing analyzed the relationship between performance changes of listed coal companies and the trend of financial indicators [10]. Li Baiji and Zhou Nan established a DEA model to evaluate the operating performance of Listed coal companies in China [11]. Lin Yingcheng and Gao Tianhui empirically studied the operating performance of China’s listed coal enterprises in 2012 based on the financial data of 41 listed coal enterprises in China [12]. This study chooses to use principal component analysis and cluster analysis to find the key factors that affect the business performance of listed coal companies in the downturn of the industry, and to evaluate their performance.
2 Principal Component Analysis of Operating Performance Evaluation of Coal Listed Companies 2.1 Selection and Data Sources This paper selects the listed companies of coal mining and selection in China from 2015 to 2017 as a sample for research. At the same time, in order to ensure the validity of the data and ensure the representativeness, universality and correctness of the empirical analysis, when screening the selected samples, the selected sample companies are all listed before December 31, 2014, and continue to operate during the research years. Excluding ST, *ST companies and companies with incomplete data, 16 listed coal companies were finally selected as the research objects. The 16 selected listed companies and their stock codes are Datong Coal (601001), Shaanxi Media (601225), China Coal Energy (601898), Lu’an Environmental Energy (601699), Pingmei (601666), Yanzhou Coal Industry (600188), Hengyuan Coal and Electricity (600971), Yangquan Coal Industry (600348), Panjiang Co., Ltd. (600395), Lanhua Technology (600123), Shanghai Energy (600508), Haohua Energy (601101), Yongtai
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Energy (600157), Jinrui Mining (600714), Jizhong Energy (000937), Xishan Coal and Electricity (000983). The research sample data comes from the annual reports of listed companies published by www.cninfo.com.cn, and SPSS 22.0 software is used for data processing and statistical analysis.
2.2 Determination of Operational Performance Evaluation Index System In this paper, principal component analysis is selected as the method for evaluating the operating performance of listed coal companies. The selection of evaluation indicators is an important issue in performance evaluation research. Similar methods to study the same problem will produce different conclusions due to the selection of indicators. The most reasonable and direct reflection of measuring the company’s operating performance is financial indicators. According to the basic principles of financial performance evaluation of listed companies—the principles of relevance, comprehensiveness, importance and operability, the coal enterprise’s own financial characteristics are integrated, combined with the current situation. There are literature studies to establish evaluation indicators from five aspects: profitability, solvency, operating ability, development ability, and equity expansion ability. A total of 12 indicators are selected, namely: main business profit margin, net asset income ratio, earnings per share, asset-liability ratio, quick ratio, current ratio, total asset turnover, fixed asset turnover, inventory turnover, main business income growth rate, net assets per share, and provident fund per share. These indicators reflect the operating performance of listed companies and are directly related to the research purpose of this paper, but these indicators are highly correlated, so this paper will carry out principal component analysis on them to achieve the effect of dimensionality reduction. The selection, calculation methods and types of specific indicators are shown in Table 1. The data in this paper comes from the annual report information of listed companies published by CNINFO. The research sample data from 2015 to 2017 is selected, and the weighted average of the selected data is used as the analysis basis. The later the data is disclosed, the more weight is given to the data of the corresponding year, and the weights of 2017, 2016 and 2015 are 3, 2 and 1 respectively. Then the weighted average of the original data was calculated. The new data after weighted average is forwarded and normalized. Among the selected indicators, only X 4 , X 5 , X 6 are moderate indicators, and the rest are positive indicators. The forwarding and normalization formula is as formula (1). Y=
1 |X − N |
(1)
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Table 1 Operational performance evaluation index system of listed coal companies No.
Index
Index calculation formula
Index type
1
Main business profit margin (X 1 )
Net profit/main business income
Positive indicator
2
Net asset income Net profit/average shareholders’ equity ratio (X 2 )
Positive indicator
3
Earnings per share (X 3 )
Profit after tax/total share capital
Positive indicator
4
Asset-liability ratio (X 4 )
Total liabilities/total assets
Moderation indicator
5
Quick ratio (X 5 ) Liquid assets/current liabilities
Moderation indicator
6
Current ratio (X 6 )
Current assets/current liabilities
Moderation indicator
7
Total asset turnover (X 7 )
Operating income/total average assets
Positive indicator
8
Fixed asset turnover (X 8 )
Sales income/fixed value net production
Positive indicator
9
Inventory turnover (X 9 )
Operating cost/average inventory balance
Positive indicator
10
Main business income growth rate (X 10 )
(Main business income in the current period—main business income in the previous period)/main business income in the previous period
Positive indicator
11
Net assets per share (X 11 )
Shareholders’ equity/total shares
Positive indicator
12
Provident fund per share (X 12 )
Provident fund/total shares
Positive indicator
Among them, Y is the new sample value after convergence; X is the original sample value; N is the moderate value. The moderate value of X 4 is 0.5, and the moderate value of X 5 and X 6 is 1. '
Xi j = '
X i j − min X i j max X i j − min X i j
(2)
Among them, X i j is the standardized new sample value; X i j is the original sample value; min X i j is the minimum value of the sample; max X i j is the maximum value of the sample.
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Table 2 KMO and Bartlett test
Kaiser–Meyer–Olkin
Measuring sampling suitability
0.519
Bartlett’s test for sphericity
Approximate Chi-square
132.929
Degrees of freedom
66
Salience
0.000
2.3 Process Using Principal Component Analysis 2.3.1
Analysis Feasibility and Significance Test
Using SPSS 22.0 to test the feasibility of principal component analysis, after analysis, the information represented by the 12 evaluation indicators of the operating performance of listed coal companies has a large overlap. The KMO and Bartlett tests are carried out on the above 12 evaluation indicators. When the KMO value is greater than 0.5, it is suitable for factor analysis. The output results of the above indicators KMO and Bartlett test are shown in Table 2. The value is 0.519, and the significance of Bartlett’s test of sphericity is 0.000. With reference to the KMO standard and test output results of whether it is suitable for principal component analysis given by Kaiser, it can be seen that the sample data is sufficient, the correlation coefficient matrix R is a non-unit matrix, and the sample data is suitable for principal component analysis.
2.3.2
Extraction and Interpretation of Common Factors
➀ Eigenvalue, eigenvalue contribution rate and cumulative contribution rate In this paper, the principal component analysis method is used to calculate the eigenvalue, contribution rate and cumulative contribution rate of the correlation coefficient matrix R. Four of the factors have eigenvalues greater than 1, and the cumulative contribution rate of the first four factors reaches 78.377%, which can explain the total variance well and has a good description of the business performance of the research sample. Therefore, four common factors are extracted according to the eigenvalues and contribution rates of the common factors, that is to say, the extracted four principal component factors can give a more comprehensive description and analysis of the operating performance of the 16 listed coal companies. ➁ Gravel diagram In this paper, the importance of these 4 factors is visualized through the gravel diagram. A gravel plot displays the eigenvalues associated with a factor and the number of factors in descending order, which provides a visual assessment of which factors account for most of the variability in the data. The gravel diagram is a steeper curve, then a curve, and finally a flatter line. Therefore, those factors before the
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Fig. 1 Factor gravel plot
steeper curve and the first point where the trend of the curved line begins should be retained. After factor analysis for 12 different indicators, as shown in Fig. 1, 4 of these factors account for most of the variability. The line starts to straighten after factor 4, and the variability that the remaining factors account for only a small fraction may not matter. Therefore, 4 factors were chosen to explain the overall 12 indicators. ➂ Rotated factor loading matrix In order to better explain the actual meaning of the obtained common factors, it can be seen from the factor loading matrix that all variables in the first factor have large positive loadings on the common factors, so the maximum variance method is used to rotate the factors, It can be seen from the rotated factor matrix that each variable produces significantly different loadings on different factors. Use SPSS 22.0 to calculate the rotation factor matrix (Table 3). It can be seen from Table 3 that: The indicators corresponding to the first common factor have high load values on Earnings Per Share (X 3 ), Total Asset Turnover (X 7 ), Fixed Asset Turnover (X 8 ), Inventory Turnover (X 9 ), and Net Assets Per Share (X 11 ), which are 0.824, 0.860, 0.916, 0.630 and 0.664 respectively. These five indicators measure the company’s operating results, asset operating efficiency, asset management, etc., and mainly reflect the operating capacity of the company, so it is named as the operating factor (F1 ). The indicators Asset-liability Ratio (X 4 ), Quick Ratio (X 5 ), and Current Ratio (X 6 ) corresponding to the second common factor have high load values, which are 0.835, 0.934, 0.936 respectively. These three indicators measure the company’s ability to repay debt, so they are named debt repayment factor (F2 ).
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Table 3 Factor matrix Factor
Index
1
2
3
4
Total asset turnover (X 7 )
0.823
0.262
0.241
−0.097
Fixed asset turnover (X 8 )
0.736
0.400
0.202
−0.311
Current ratio (X 6 )
−0.715
0.533
0.289
−0.233
Quick ratio (X 5 )
−0.235
−0.698
0.557
0.275
Inventory turnover (X 9 )
0.691
0.310
−0.043
0.151
Earnings per share (X 3 )
0.673
0.607
−0.095
−0.101 −0.416
Net assets per share (X 11 ) Main business profit margin (X 1 ) Net asset income ratio (X 2 )
0.611
−0.085
0.349
−0.254
0.715
−0.209
0.395
0.264
0.597
−0.496
0.245
Provident fund per share (X 12 )
0.229
−0.328
0.669
0.322
Asset-liability ratio (X 4 )
0.486
−0.485
−0.517
0.041
Main business income growth rate (X 10 )
0.201
0.118
0.542
0.685
The Main Business Profit Margin (X 1 ) and Net Asset Income Ratio (X 2 ) of the indicators corresponding to the third common factor have relatively high load values, which are 0.796 and 0.785 respectively. These two indicators measure the ability of an enterprise to obtain profits, so they are named as profitability factor (F3 ). The indicators corresponding to the fourth common factor, the Main Business Income Growth Rate (X 10 ), and the Provident Fund Per Share (X 12 ) have higher load values, which are 0.882 and 0.736 respectively. These two indicators measure the ability of an enterprise to grow and develop, so they are named growth factor (F4 ). Therefore, these four factors can better reflect the operating performance of listed coal companies.
2.3.3
Factor Composite Score and Company Ranking
The sample information is synthesized according to the contribution rate of the common factor and the rotated factor value to reflect the operating performance of listed coal companies during the industry downturn. The extracted four public factors basically reflect all the information on the operating performance of listed coal companies. According to the factor score matrix, use the following formula to calculate the score coefficient matrix of 4 common factors: F1 = −0.059X 1 + 0.028X 2 + 0.259X 3 − 0.069X 4 + 0.062X 5 + 0.055X 6 + 0.270X 7 + 0.331X 8 + 0.146X 9 − 0.058X 10 + 0.267X 11 − 0.016X 12
(3)
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F2 = 0.06X 1 − 0.094X 2 + 0.039X 3 − 0.344X 4 + 0.364X 5 + 0.364X 6 + 0.032X 7 + 0.112X 8 − 0.073X 9 + 0.007X 10 + 0.081X 11 + 0.013X 12
(4)
F3 = 0.428X 1 + 0.419X 2 + 0.143X 3 + 0.051X 4 − 0.042X 5 − 0.052X 6 − 0.05X 7 − 0.096X 8 + 0.16X 9 + 0.166X 10 − 0.316X 11 − 0.152X 12
(5)
F4 = 0.127X 1 − 0.052X 2 − 0.086X 3 − 0.146X 4 − 0.076X 5 − 0.069X 6 + 0.063X 7 − 0.088X 8 + 0.094X 9 + 0.629X 10 − 0.086X 11 + 0.474X 12
(6)
Calculate the value of each common factor, and then combine their respective variance contribution rates ηi (i = 1, 2, 3, 4) to calculate the comprehensive evaluation score of each sample company selected in this study, and the calculation of the comprehensive evaluation score Y is as follows: Y =
η1 F1 + η2 F2 + η3 F3 + η4 F4 η1 + η2 + η3 + η4
(7)
According to the above methods and relevant information, the selected 16 listed coal companies are comprehensively evaluated. The higher the score, the better the relative performance, and the lower the score, the worse the relative performance. The specific comprehensive score and its score ranking are detailed. See Table 4. Because the calculated score is calculated on normalized data, the positive and negative nature of the data reflects the company’s position relative to its average, with zero as the average. At the same time, the scores of the 4 factors and their score rankings obtained after factor analysis of the 12 evaluation indicators of its business performance are shown in Table 4.
2.3.4
Comprehensive Evaluation of Operating Performance of Sample Enterprises
➀ Analysis of operating factor (F1 ) Operational capability analysis is the basis and supplement to the analysis of corporate profitability and solvency. Among the listed coal companies, Yanzhou Coal Industry, Yangquan Coal Industry, and Panjiang Co., Ltd. performed well in terms of fixed asset turnover and total asset turnover. The research found that in 2014, Yanzhou Coal began to expand its financial leasing and investment business. In 2015, it added non-coal trading and mining equipment production business. In 2017, it acquired all
10 15
4 16
13 7
3.469127
2.833473
5.575942
4.718675
9.635737
6.082104
5.810328
5.645804
1.926340
3.267595
5.103818
3.091541
4.939047
3.425467
3.539405
3.084235
Jizhong Energy
Xishan Coal and Electricity
Lanhua Technology
Yongtai Energy
Yanzhou Coal Industry
Yangquan Coal Industry
Panjiang Co., Ltd.
Shanghai Energy
Jinrui Mining
Hengyuan Coal and Electricity
Datong Coal
Haohua Energy
Shaanxi Media
Pingmei
Lu’an Environmental Energy
China Coal Energy
14
9
11
6
12
3
2
1
8
5
Rank
F1
Company
15 14
−0.928301 −0.836040
0.513274
5
8
11
−0.111429 0.295231
2 13
0.659153
10
9
1
−0.748866
0.216932
0.218653
3.804675
4
12
−0.676745
0.525687
6 16
0.375602
3
7
Rank
−1.498444
0.532440
0.368641
F2
9 10 14
−0.631965 −1.606996
5
8
−0.513850
1.838012
0.193960
15
−4.830593
13
−1.542971 3
12
−1.454617 3.264269
1
4
6
3.747443
1.956174
1.805607
2
16
−7.172915 3.376166
7 11
0.712506
Rank
−1.195858
F3 0.894790
5.034874
1.685376
1.055473
−0.367419
0.988178
8.068756
−17.87102
14.47358
−2.595667
2.470421
5.869439
4.400865
3.827429
2.672532
−19.29630
F4
Table 4 Evaluation of various factors, comprehensive scores and rankings of the 16 selected listed coal companies
9
10
13
11
2
15
1
14
8
3
5
6
7
16
12
4
Rank
1.036188
2.257306
1.159912
1.352392
0.992007
2.026305
2.588879
−1.999501
4.167789
1.044974
2.207690
3.469729
2.938518
4.121463
2.313740
−2.493622
Y
11
10
14
9
5
15
1
12
8
3
4
2
6
16
13
7
Rank
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the shares of United Coal held by Rio Tinto. Yanzhou Coal has formed a real industry, financial industry and logistics trade “three-in-one” industrial system structure, even in the downturn of the coal industry can still achieve profitability. Yangquan Coal has a relatively high capacity utilization rate, reaching 117.8% in 2016, and sold a certain amount of inventory during the study period. In the face of the downturn in the coal market, Panjiang Co., Ltd. has transformed a single industrial structure model, strengthened and consolidated its main coal business, and developed in multiple directions in the direction of logistics, investment, and oil, and achieved a major strategic transformation, ensuring that the company can survive the market. Profitable despite the boom. And China Coal Energy, Xishan Coal and Electricity, Jinrui Mining are poor. The study found that the industrial structure of China Coal Energy is relatively simple, and the operating capacity has declined in the sluggish market conditions of the coal industry. The decline in the turnover capacity of Xishan coal power assets affects the efficiency of capital use, resulting in a decline in operating capacity. ➁ Analysis of debt repayment factor (F2 ) Debt solvency factor not only reflects the short-term liquidity of the enterprise, but also reflects the long-term solvency. Among the listed coal companies, Jinrui Mining, Haohua Energy, and Xishan Coal and Electricity have outstanding performance. However, Panjiang, Yangquan Coal and Yongtai Energy performed poorly. The research found that the expansion of Yangquan Coal Industry and Yongtai Energy is mainly based on borrowing rather than the accumulation of owners’ equity, so the risk of debt repayment is relatively high. ➂ Analysis of profitability factor (F3 ) The profitability factor mainly reflects the asset management status and financial structure of the enterprise. Among the listed coal companies, Zhongpanjiang Co., Ltd., Yongtai Energy, and Hengyuan Coal and Electric Power have outstanding performance. The performance of China Coal Energy, Datong Coal, and Lanhua Science and Technology was poor. After research, it was found that China Coal Energy’s development focus is on the production of mining machinery. In recent years, the coal market has been weak and the coal market has oversupplied. Coal mining companies are cautiously building new mining areas, which has caused serious setbacks to the development of China Coal Energy’s key businesses. Datong Coal has a single industrial structure, and the company’s revenue is mainly from coal sales. In the case of a weak market, coal products lack competitiveness, resulting in excess production capacity and a sharp decline in profitability. Datong Coal’s coal business revenue accounts for more than 96% of its sales revenue. Facing the clifflike decline in coal prices, its economic revenue has dropped sharply. Although the
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company is also committed to expanding non-coal business, the development speed is slow and it still has not formed a scale. ➃ Analysis of Growth factor (F4 ) The growth factor mainly reflects the growth rate of an enterprise and its future production and operation strength. Among the listed coal companies, Hengyuan Coal Power, Yanzhou Coal Industry and Panjiang Co., Ltd. performed outstandingly. The performance of Pingmei Coal, Datong Coal, and Lanhua Science and Technology was relatively poor. The study found that Hengyuan Coal and Power was originally engaged in coal mining and washing business. After acquiring the coal, transportation and sales companies of Wanbei Group, the industry was re-integrated to form a selfproduced and self-sold industrial chain, while avoiding the competitive relationship with Wanbei Group in the early stage further consolidated the market. Yanzhou Coal has increased its non-coal business since 2014, and its non-main business accounts for an increasing proportion, which has contributed more than 50% to corporate profits. At the same time, Yanzhou Coal’s ability to resist the recession risk of entity business has been significantly enhanced by the company’s strong capital operation capability. In the slump in the coal market, Pingmei Co., Ltd. has reduced its corporate investment year by year and its financing capacity has weakened.
3 Cluster Analysis of Operational Performance of Listed Coal Companies Cluster analysis is a general term for multivariate statistical analysis techniques that classify research objects according to their characteristics. Based on the results of the above principal component analysis, this paper conducts a cluster analysis on the operating performance of these companies, with the purpose of classifying the operating performance of the selected 16 listed coal companies during the downturn. This paper uses SPSS 22.0 software to perform hierarchical clustering analysis on the 16 groups of valid sample data after processing. In the analysis of specific enterprises, the scientific grouping method is used to analyze the status quo of enterprise performance evaluation in more detail. It can be seen from the tree structure diagram in Fig. 2 that it is reasonable to divide the sample listed companies into four categories. As can be seen from Table 5, Xishan Coal Power, China Coal Energy, Lu’an Environmental Energy, Pingmei Co., Ltd., Shanghai Energy, Jizhong Energy, Haohua Energy, and Jinrui Mining are classified as the first category. This category performs better in terms of solvency and growth ability, while it performs worse in terms of operating ability and profitability. Although the corporate debt default risk is small, the overall ranking is poor. Hengyuan Coal and Power is classified into the second category, with poor performance in operating capacity and solvency, but better profitability and growth capacity, and the highest overall ranking. Yongtai Energy, Yangquan Coal Industry, Panjiang Co., Ltd., Shaanxi Coal Industry, and Yanzhou Coal Industry are classified into the third category. Listed companies of this type
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Fig. 2 Tree structure diagram of the operating performance of listed coal companies
have poor solvency, but their operating ability, profitability, and growth ability are all good. The ranking is better, such enterprises need to pay attention to investment risks and resist the debt crisis. Lanhua Kechuang and Datong Coal are classified into the fourth category. These listed companies have average operating capacity and solvency, poor profitability, and the worst overall ranking. Such companies need to adjust their development strategies to cope with the changing market environment. Table 5 The results of cluster analysis of the operating performance of the selected 16 listed coal companies Category
Company
Advantage
Disadvantage
1
Xishan Coal and Electricity, China Coal Energy, Lu’an Environmental Energy, Pingmei, Shanghai Energy, Jizhong Energy, Haohua Energy, Jinrui Mining
Debt paying ability, growth ability
Operation ability, profitability
2
Hengyuan Coal and Electricity
Profitability, growth ability
Operating capacity, debt paying ability
3
Yongtai Energy, Yangquan Coal Industry, Operation Panjiang Co., Ltd., Shaanxi Media, Yanzhou Coal ability, Industry profitability, growth ability
Debt paying ability
4
Lanhua Technology, Datong Coal
Profitability
Operation ability, debt paying ability
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4 Conclusion This paper comprehensively uses principal component analysis and cluster analysis to evaluate the operating performance of my country’s listed coal companies in the downturn of the industry under the new economic normal. The main conclusions are as follows: First, the profitability and growth capacity of coal listed companies play a decisive role in enterprise business performance. Therefore, investors should pay primary attention to the four indicators of profit rate of main business, return on net assets, net assets per share and common reserve per share when making investment. Second, under the new normal of economy, the continuous rise of new energy has a certain impact on traditional coal. Improving the single operation mode, expanding and improving the business scope, adopting a variety of marketing strategy combination, or merging and restructuring will help improve the business performance of coal enterprises. Third, it can be seen from the cluster analysis results that listed coal companies can be divided into four categories, each of which has advantages and disadvantages. Companies can take corresponding measures according to the actual situation to carry out resource integration or innovative transformation and development, and actively cooperate with the national reform to achieve balanced and efficient development.
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The Effect of Green Mergers and Acquisitions on the Performance of Heavily Polluting Enterprises: A Case Study of Gezhouba Group in China Xizhe Zhang, Sen Wang, and Yan Huang
Abstract This paper adopted a case study approach to investigate the green mergers and acquisitions of a heavily polluting enterprises in order to understand the resource integration path after its acquisitions, and to reveal the heterogeneous impact mechanism of green mergers and acquisitions on enterprise performance. This paper introduces resource dependency theory and sustainable development theory to explore the motivation and process of enterprise mergers and acquisitions, and uses synergy theory to explore the results of enterprise mergers and acquisitions. The re-search findings indicated that the green mergers and acquisitions behavior of enterprises comes from the dual drive of external pressure and internal motivation. External pressure comes from the constraints of the external macro environment such as politics, economy, society and technology. The internal motivation comes from the green transformation and upgrading needs of enterprises. After green mergers and acquisitions, enterprise quickly acquired green technology, technical personnel, environmental protection equipment, market customers and management experience in the field of water treatment. It integrates resources by updating the concept of green development, strengthening the research and development of green resources, improving the management and operation of green production, and integrating the water industry chain. And the integrated resources were applied inside and outside the company. It optimizes traditional business production internally, provides environmental protection services externally, and finds new sources of profit for enterprises. The enterprise’s market performance, financial performance and environmental performance all have been improved after its mergers and acquisitions. Although this study focused on just one Chinese heavily polluting enterprise, it should still provide some insight
X. Zhang (B) RDFZ Chaoyang Branch School, Beijing, China e-mail: [email protected] S. Wang · Y. Huang Management College, Beijing Union University, Beijing, China e-mail: [email protected] Y. Huang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_9
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for the better understanding of green mergers and acquisitions practices of heavily polluting enterprise s in general. Keywords Green mergers and acquisitions · Resource integration path · Enterprise performance · Case study
1 Introduction With the increasingly serious resource consumption and ecological problems, economic development can no longer be separated from environmental issues. In order to balance the increasingly serious contradiction between economic development and environmental protection, the Chinese government has established environmental protection as a basic national policy. A series of policies have been promulgated successively, and the implementation of specific measures such as tax reduction and fee reduction, credit incentives and financial subsidies has guided industrial enterprises such as heavily polluting industries to optimize their industrial structure and achieve green development. The public’s awareness of environmental protection is also gradually improving, and informal environmental regulation led by media reports and social supervision are also playing an important role in promoting enterprises to achieve green transformation and upgrading. From the perspective of enterprises themselves, heavy industry enterprises have problems such as high pollution and high energy consumption, which not only cause environmental pollution, but also hinder the sustainable development of enterprises. Under the background of internal and external troubles, the original production mode of enterprises at the expense of polluting the environment is gradually being phased out, and green development has become the only way for enterprises. The construction cement industry plays a pivotal role in stimulating economic growth and promoting social development. However, in recent years, although the output value of construction cement industry has maintained an overall growth in China, the growth rate has been declining year by year. This shows that the construction cement industry has experienced overcapacity, and it is urgent to optimize the industry structure and carry out industry transformation and upgrading. At the same time, the introduction of many environmental protection policies is also forcing the industry to eliminate lagging production capacity and promote the green transformation of the industry. Construction enterprises with backward technology, sub-standard production and serious pollution will be eliminated from the market. Safety, green and recycling will become the key development directions of the future construction industry. Such an industry background not only puts forward higher requirements for the technical resources owned by enterprises, but also forces enterprises to take environmental protection and green development into consideration in production and operation. The green transformation and upgrading of construction enterprises has become the general trend.
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There are two main approaches for the green and sustainable development of enterprises [1]: one is to increase internal environmental protection investment, and to develop products or processes that are conducive to reducing environmental pollution and improve energy saving and emission reduction; The other is to quickly acquire green resources from other enterprises through mergers and acquisitions. Compared with the uncertain factors such as long R&D cycle and difficulty in breaking through technical bottlenecks in increasing internal environmental protection investment, green mergers and acquisitions has more advantages [2]. It can not only help enterprises reduce the loss of potential resources, but also rapidly improve the quality of assets, respond to the needs of all stakeholders, drive enterprises to achieve green transformation and upgrading in an innovative form, and obtain green competitiveness [3]. According to statistics, the average proportion of green mergers and acquisitions of Chinese listed companies increased from 15% in 2006–2012 to 25% in 2012–2018, due to the improvement of China’s green industry policy since 2012. Moreover, more than 30% of the enterprises in the heavily polluting industries have chosen to carry out green mergers and acquisitions [4]. It can be seen that with the deepening of the concept of green development and the need for enterprise transformation and upgrading, green mergers and acquisitions, a market economic behavior, has been favored by more and more enterprises, especially for heavy polluting enterprises such as the construction cement industry. It is an important way to quickly realize the green development of enterprises. Although many heavily polluting enterprises choose to carry out mergers and acquisitions, their effects are significantly heterogeneous. Some studies have found that this heterogeneity may be caused by internal and external factors such as corporate green mergers and acquisitions motivation, local policies, and executive characteristics etc. [5]. The green mergers and acquisitions behavior of enterprises and how to integrate resources is the most key point in the entire green mergers and acquisitions process, and will also directly affect the green development effect of enterprises. However, there are few studies on how to integrate resources after green mergers and acquisitions, and how the path of resource integration will affect the performance of enterprises. Therefore, this paper focuses on the process of enterprise green mergers and acquisitions through case studies. This paper starts with the green thinking of executives, technical resources, management mode and the layout of the industrial chain, etc., and tries to study the resource integration path after green mergers and acquisitions of enterprises, and then reveals the heterogeneous impact mechanism of green mergers and acquisitions on enterprise performance.
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2 Theoretical Basis and Literature Review 2.1 Theoretical Basis The theoretical foundations on which this paper is based include resource dependency theory, sustainable development theory and synergy theory. Resource dependence theory believes that an organization needs to have some resources that it does not have in order to survive, so it needs to exchange resources with other organizations in the environment. These resources include personnel, funds, customers, technologies and material inputs and so on. However, in the process of acquisition, the organization will gradually become dependent on the environment that owns the resources. The dependence of enterprises on key and important resources will deeply affect the behavior of enterprises. The more external resources that play an important role in enterprises, the more incentives enterprises have to establish re-source dependence with them [6]. With the increasingly fierce market competition, the important role of resources in the development of enterprises has become more prominent. According to the theory of resource dependence, if an enterprise wants to occupy an advantageous position in the competition and reduce the risks caused by the uncertainty of the external environment, it must take active strategic measures to enhance its control over important and scarce resources. Enterprises can reduce their dependence on environmental resources from five aspects: mergers and acquisitions, joint ventures, board of directors, corporate governance, and executive succession. Among them, mergers and acquisitions, as one of the mechanisms to reduce the inter-dependence among enterprises, is a strategy adopted by many enterprises. According to the theory of resource dependence, green mergers and acquisitions in heavily pol-luting industries are to a certain extent to reduce their dependence on cleaner production resources. The theory of sustainable development was first proposed by the United Nations Commission on World and Environmental Development in 1987. Sustainable development theory mainly includes three levels of sustainable development: economic sustainable development, ecological sustainable development and social sustainable development [7]. Economic Sustainable development requires a country to pay attention to the quality of economic growth while vigorously developing its economy, and to balance the relationship between the quantity of economic growth and the quality of economic growth. Ecological sustainable development refers to developing the economy to the greatest extent within the acceptable range of nature, such as solving the root cause of the environment by changing the production mode. Social sustainable development refers to the coordinated development of economy and ecology to achieve the sustainability of the whole society. The Chinese government has successively promulgated relevant policies, emphasizing the importance of protecting the environment along with social and economic development. Enterprises only pursue short-term economic benefits and cannot make them develop for a long time. They should pay attention to their future long-term economic benefits and overall performance. Especially for high-polluting enterprises, after the implementation of mergers
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and acquisitions, they should pay attention to technological innovation and product innovation while pursuing operating profits, and maximize the use efficiency of resources, so as to improve environmental performance and financial performance, and achieve sustainable development of the enterprise. Synergy refers to the integration of two companies through mergers and acquisitions, so that the overall level of the company’s social status, business performance, and competitive strength is higher than the performance before integration. Bao et al. [8] pointed out that if an enterprise wants to gain innovation advantages, it needs to pay more attention to synergy effect. The synergy effect is based on the integration of various resources. That is to say, through integration methods such as mergers and acquisitions, enterprises can obtain heterogeneous resources including technology, finance, human resources, information systems, and even corporate structure and corporate culture. Through resource integration, enterprises can absorb high-quality re-sources, optimize the industrial structure, and achieve synergies. After green mergers and acquisitions, enterprises can not only use technological advantages to improve production efficiency, gain green competitiveness, and seize market share, but also absorb advanced green management concepts, which will help enterprises further achieve green independent innovation and fundamentally help enterprises achieve green transformation. Lower switching costs are also one of the advantages of green mergers and acquisitions. Through this approach, enterprises can not only obtain what they need at a lower cost, but also promote their diversified development and reduce their financing costs through cross-industry mergers and acquisitions.
2.2 Literature Review As an important part of the green management activities of enterprises, green mergers and acquisitions will have different motivations under the influence of different factors, which will lead to different effects. According to enterprise’s response to environmental rules, green mergers and acquisitions motivation can be divided into passive response and active response. Green mergers and acquisitions has the advantages of strong target, low time cost and strong certainty, which can help enterprises obtain green resources in a fast, efficient and targeted manner. From the perspective of active response, in order to achieve sustainable development, heavily polluting enterprises will actively seek to renew the industrial structure at the least cost. Green mergers and acquisitions have become the preferred way to achieve this goal. Green mergers and acquisitions can help heavily polluting companies seize green resources and gain a favorable competitive position first. In addition, green mergers and acquisitions can promote enterprises to increase environmental protection in-vestment, and fundamentally promote enterprises to achieve green transformation and upgrading [9]. From the perspective of passive response, green mergers and acquisitions is one of the external manifestations of corporate legitimacy. Legitimacy refers to a
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form manifested by an enterprise operating in accordance with the values recognized by the society. An organization has legitimacy when its value system agrees with the social system. When government departments emphasize the importance of ecological protection and pollution control, in order to gain legitimacy, heavily polluting companies will actively take measures that can promote the green development of them. For example, green mergers and acquisitions have become one of the effective ways. Cornaggia and Li [10] found that green transformation activities through green mergers and acquisitions can make it easier for enterprises to obtain the resources required for survival and change, enhance risk tolerance and improve their organization-al legitimacy. Mergers and acquisitions are an important part of enterprise economic activities, and more and more enterprises hope to enhance enterprise value through them. However, not all enterprises can improve enterprise performance through mergers and acquisitions. The reason is the influence of various factors inside and outside the enterprise. From the perspective of internal enterprises, Wang et al. [9] believes that the level of corporate governance will positively promote the market performance and financial performance of mergers and acquisitions. Wang and Yang [11] found that executives’ overconfidence has a negative effect on mergers and acquisitions performance. From the perspective of external factors of enterprises, Creyer and Ross [12] believed that the uncertainty of economic policy can improve the ability of enterprises to integrate mergers and acquisitions, thereby promoting the improvement of enterprise mergers and acquisitions performance. Wu and Fan’s [13] research based on institutional theory found that the greater the distance between the formal institutional environment between the two countries, the less conducive to the structural integration of enterprises after mergers and acquisitions. However, this masking effect indirectly promotes the performance of mergers and acquisitions. To sum up, the existing literature on the behavior of green mergers and acquisitions is mostly based on its motivation and influencing factors, and explores the heterogeneity of green mergers and acquisitions behavior under different conditions. Regarding the impact of green mergers and acquisitions, some scholars agree that green mergers and acquisitions will improve the performance of heavily polluting enterprises, but some scholars point out that green mergers and acquisitions may also have negative effects on enterprises. However, existing research perspectives are often limited to considering a single performance. In addition, the existing literature has little research on how green mergers and acquisitions affect enterprise performance and the post-merger resource integration process.
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Fig. 1 Theoretical framework
2.3 Theoretical Framework By selecting the construction cement industry as the research object, this paper attempts to analyze the impact of green mergers and acquisitions on enterprise performance and the mechanism of resource integration paths after mergers and acquisitions. Based on the perspective of resource integration, an analysis framework of motivation-process-result is constructed, as shown in Fig. 1, to analyze the process mechanism of China Gezhouba Group Co., LTD’s (referred to as Gezhouba Group) acquisition of Kaidan Water International Group (Hong Kong) Co., Ltd. (referred to as Kaidan Water).
3 Research Methods and Data 3.1 Research Methods When studying the mechanism of motivation-process-result, this paper selects the case analysis method based on the following reasons. First, the purpose of this research is to explore how heavily polluting enterprises integrate resources and improve enterprise performance in green mergers and acquisitions, which is a typical How type of question, suitable for case study methods [14]; Second, This research is at an early stage in the field, and existing theories do not yet provide a good explanation. The re-search content of this paper is a procedural problem, which is suitable for exploration and induction with a single case. Because inductive research is especially useful for developing theory in this context.
3.2 Case Selection This paper selects Gezhouba Group as a research case, mainly based on the following reasons. First of all, Gezhouba Group is a typical construction enterprise whose industry is a typical heavy polluting industry. Second, the main purpose of Gezhouba Group’s acquisition of Kaidan Water is to obtain its green resources in top technology, market, equipment, talents, etc. This event fits the definition of green mergers
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and acquisitions. Third, Gezhouba Group has successfully built the environmental protection industry into the company’s second largest source of profit growth after the construction sector. It has successfully transformed through green mergers and acquisitions, and its rapid development have strong reference for the future environmental protection transformation and development of many enterprises. Fourth, as a listed company, Gezhouba Group actively fulfills its obligation of in-formation disclosure. The relevant information disclosure of its acquisition of Kaidan Water is relatively comprehensive, which is helpful for rational exploration of the background, motivation and subsequent integration paths of its mergers and acquisitions.
3.3 Data Collection and Analysis This study extensively collects primary and secondary data at the mergers and acquisitions stage to form an evidence triangle. First of all, the research team’s follow-up investigation on Gezhouba Group’s acquisition of Kaidan Water lasted for nearly two years. During this period, it conducted formal and informal interviews with Gezhouba Group’s merger and acquisition department and other relevant departments. Interviews take a variety of forms, ranging from intensive in-depth interviews with merger and acquisition heads, open-ended discussions between the research and merger and acquisition teams, and topical interviews on specific topics such as resource integration. In addition, members of the research team will meet informally with members of the merger and acquisition department or communicate via internet communication tools, telephone, etc. Secondly, the research team also collected data from the following sources. (1) Gezhouba Group’s listing announcement materials, including annual reports, information disclosure reports, and a series of announcements on mergers and acquisitions. (2) The official website of the enterprise, enterprise publicity documents, relevant news reports, public speeches and documents of the enterprise’s senior leaders, etc. (3) Gezhouba Group’s internal archives, including the Gezhouba Group social responsibility report, the internal journal of Gezhouba Group, the preliminary investigation report and the review summary report of the mergers and acquisitions project, etc.
4 Case Study 4.1 Motivation for Mergers and Acquisitions Gezhouba Group’s green mergers and acquisitions are driven by both external pressure and internal motivation. External pressure comes from the constraints of the external macro environment such as politics, economy, society and technology.
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External pressure comes from the constraints of the external macro environment such as politics, economy, society and technology. First, from a political point of view, the promulgation of the new environmental protection law has prompted construction enterprises to improve their production and operation methods. At the same time, the promulgation of policies and measures such as the Ten Measures for Water and Ten Measures for Atmosphere has created a better external policy environment for the development of the water industry. Gezhouba Group’s acquisition of Kaidan Water not only responds to the implementation of environmental protection policies, but also optimizes its industrial layout. Second, from an economic perspective, the Chinese domestic market for the construction business is shrinking. Gezhouba Group has become more difficult to operate and needs to cultivate new profit growth points. Gezhouba Group’s acquisition of Kaidan Water can not only expand the scope of green industries, but also successfully enter new industries and gain new profit growth points. Third, from a social perspective, the rapid growth of Chinese economy and the improvement of people’s living standards have put forward higher requirements for the quality of the living environment. Gezhouba Group already has water conservancy engineering business, and the acquisition of Kaidan Water can not only strengthen its water affairs strength, but also help convey its social responsibility to the public. Fourth, from the technical point of view, the environmental protection industry has high technical requirements. To achieve clean production, a large number of green production technologies need to be used. One of the major motivations for Gezhouba Group’s acquisition of Kaidan Water is to acquire green technology by taking advantage of its high platform. The internal driving force of Gezhouba Group’s green mergers and acquisitions comes from giving full play to its own advantages, avoiding disadvantages and seizing opportunities, so as to effectively solve the threats it faces. In terms of advantages, Gezhouba Group has accumulated excellent project management experience, and has a professional management system and operation team, which is conducive to its rapid completion of business integration after mergers and acquisitions. Gezhouba Group has obvious advantages in terms of brand, capital and business integration when it acquires Kaidan Water. In terms of disadvantages, Gezhouba Group’s main business is mainly cement production, construction projects, etc., with a single industrial structure and serious pollution. With the slowdown in the development of the traditional construction industry, Gezhouba Group’s profit model is limited, and it is urgent to expand new businesses. Gezhouba Group entered the water industry through the acquisition of Kaidan Water, which can avoid its own disadvantages to a certain extent. In terms of opportunities, environmental protection, as one of the industries strongly supported by China, has broad prospects for development. Gezhouba Group’s acquisition of Kaidan Water can open up a channel for learning from outstanding foreign environmental protection water companies and enter the environmental protection industry quickly and with high quality. In terms of threats, the development of the traditional construction industry is no longer suitable for the development of today’s market economy. Gezhouba Group’s acquisition of Kaidan Water can realize the extension of its entire industry chain, and incorporate the
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concept of big environmental protection into its overall layout, which will help it resist the risks brought by the traditional operation mode.
4.2 Process Mechanism Firstly, in terms of resource acquisition, it quickly carried out resource acquisition, resource integration and resource application. First of all, in terms of resource acquisition, it quickly acquired green technology, technical personnel, environmental protection equipment, market customers and management experience in the field of water treatment. Kaidan Water has a large number of international top water treatment technologies and talents with rich project treatment experience and technical capabilities in water treatment. Through mergers and acquisitions, Gezhouba Group quickly accumulated the ad-vantages of water treatment talents. The parent company of Kaidan Water has water treatment projects in more than 50 countries or regions around the world. Through this platform, Gezhouba Group opens a window to the international water market. Secondly, in terms of resource integration, Gezhouba Group invested a lot of time in resource integration, including updating the concept of green development, strengthening the research and development of green resources, improving the management and operation of green production, and integrating the water industry chain. The executives of Gezhouba Group paid full attention to the acquisition of Kaidan Water, and promoted the use of Kaidan Water’s excellent resources from the aspects of corporate strategy and planning, and promoted the strong development of the water and environmental protection sec-tor. The green development concept of the executives and the encouragement and support at the strategic level have laid a good development foundation for the resource coordination and green transformation of the enterprise after Gezhouba Group’s acquisition of Kaidan Water. Gezhouba Group continued to introduce foreign green technology resources. It cooperated with Israel’s Ramim, Aqwise, AST, RWL and other leading technology companies on green technologies such as agricultural drip irrigation technology, sludge disposal technology, water plant upgrading and transformation, and highconcentration industrial sewage treatment. Gezhouba Group established an academician workstation, vigorously carried out technology research and development and technology incubation, adding strong endogenous power to the development of the water and environmental protection sector. In terms of management mode, Gezhouba Group took shortening the management chain and improving management efficiency as the basic idea, and comprehensively sorted out the Kaidan water management system. Relying on this system, the management of water affairs and water supply and drainage facilities is organically integrated to form the urban water affairs internet of things. The system analyzed and processed water information in a timely manner, and managed the entire process of the water system in a more refined and dynamic manner.
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After Gezhouba Group’s acquisition of Kaidan Water, it will help improve the layout of Gezhouba Group’s water conservancy and hydropower industry chain and promote its transformation and upgrading. The subsequent investment and mergers and acquisitions by Gezhouba Group based on strengthening the water business sector and improving the water business chain is more conducive to fully combining the advantages of the two companies. Through the integrated model of investment, construction and operation, Gezhouba Group will integrate its water industry chain and open up the market for comprehensive water environment management. Thirdly, in terms of resource application, Gezhouba Group applied resources both inside and outside the company. Internally, traditional business production has been optimized, thereby improving corporate performance. The environmental protection services provided to the outside world have found a new source of profit for Gezhouba Group.
4.3 Merger and Acquisition Results After Gezhouba Group acquired Kaidan Water, its performance has been significantly improved, which is embodied in three aspects: market performance, financial performance and environmental performance. Market performance. This paper adopts the event study method to analyze market performance. In the ten trading days before and after the acquisition of Kaidan Water by Gezhouba Group, the company’s excess rate of return and the overall change trend of the accumulated excess rate of return are roughly the same. Although the cumulative excess return has been negative, the company’s cumulative excess return has increased several times during these ten days. Specifically, during the period of [−5, −2], the company’s excess rate of return and the accumulated excess rate of return appear for the first time to increase. During the period of [1, 3], the excess rate of return of Gezhouba Group also showed a positive growth, which to a certain extent represented the approval of the shareholders for this green mergers and acquisitions. Although the excess rate of return declined during the periods of [−2, 1] and [3, 4], it did not affect the overall growth trend. Judging from the overall range of [−5, 5], after the completion of Gezhouba Group and the announcement of merger and acquisition, the change in yield is mainly growth. And the overall market performance after the merger is higher than before the merger. This to a certain extent shows that after Gezhouba Group’s green mergers and acquisitions, the stock market’s reaction to it is mostly positive. This also shows that the green mergers and acquisitions behavior of the company will improve the market performance of the company to a certain ex-tent. Financial performance. This paper mainly focuses on the financial performance of Gezhouba Group’s water company after the merger, because water segment is only one of the main business of Gezhouba Group [15]. This paper selects the operating income of the Gezhouba water sector from 2014 to 2016 as the financial performance indicator. Observing the operating income in 2015 and 2016, it can be found that
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the acquisition of Kaidan Water has significantly increased the operating income of the water sector. In 2016, the operating income of the water sector was 276 million yuan, with a growth rate of 124%. Environmental performance. This paper combines the Social Responsibility Guidelines Standard ISO26000 and General Framework Standards to measure environmental performance from two aspects of green operation and green production [16]. Among them, green operation is measured by environmental protection investment, and green production is measured by pollutant discharge [17], and the data are all from corporate social responsibility reports. Gezhouba Group’s investment in environmental protection in 2015 was 246.3 million yuan, and in 2016, it was 2.048 billion yuan, an increase of 10 times. The pollutant emissions of Gezhouba Group from 2014 to 2016 reached the discharge standard, and no environmental penalties or environmental violations have occurred. After allocating the pollutant consumption to the operating income of the current year, the pollutant discharge of Gezhouba Group from 2014 to 2016 still showed a downward trend year by year. This shows that Gezhouba Group attaches great importance to cleaner production after entering the environmental protection industry through green mergers and acquisitions, and the consumption of pollutants does not increase even when the volume of environmental protection projects increases.
5 Research Findings 5.1 Conclusions By taking Gezhouba Group as the research object, this paper studies the specific case of Gezhouba Group’s merger with Kaidan Water, and constructs a merger integration path of motive-process-result, as shown in Fig. 2. This paper explores the changes in market performance, financial performance, environmental performance of the company and their resource integration paths after mergers and acquisitions.
Fig. 2 Gezhouba Group green M&A motivation-behavior-result
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This paper draws the following conclusions. First, corporate green mergers and acquisitions is conducive to improving corporate performance. Gezhouba Group has improved its market performance, financial performance and environmental performance through green mergers and acquisitions. Through green mergers and acquisitions, companies send a strong environmental protection signal to the market and in-crease shareholders’ recognition of it [18]. Although the market performance may have negative changes in local cycles, from the overall trend, green mergers and acquisitions can promote the market performance of enterprises [19]. Gezhouba Group’s green mergers and acquisitions also significantly improved its financial and environmental performance. Second, the motivation for corporate green mergers and acquisitions mainly comes from two parts, external pressure and internal motivation. External factors mainly include regulatory constraints from the state and industry and non-regulatory constraints from the media and social residents [20]. The internal factors that drive Gezhouba Group’s green mergers and acquisitions include developing new business of water and environmental protection, improving the sustainable development of the enterprise and other factors. Through the acquisition of Kaidan Water, Gezhouba Group has created a new business segment, namely the environmental protection segment, on the basis of optimizing its tradition-al business. This will not only improve the legitimacy of Gezhouba Group’s production and operation, but also help it develop new business markets. Third, resource acquisition, resource integration and resource application is an important process path for green mergers and acquisitions behavior. Gezhouba Group has realized the integration of resources by updating the concepts of executives, integrating management operations, improving the industrial chain, and further developing green resources.
5.2 Limitations Due to the limitations of data acquisition, this paper has some limitations. For example, when analyzing the performance changes of Gezhouba Group, this paper only selects a few indicators to measure the performance changes. In fact, a complete evaluation of a company’s mergers and acquisitions performance needs to comprehensively consider more index systems. In addition, this paper only selects a typical enterprise as the research object when researching the impact and change of green mergers and acquisitions on enterprise performance and its role path. However, due to different industry backgrounds and enterprise scales, there may be differences in re-source integration paths and changes in corporate performance after green mergers and acquisitions by different companies.
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Carbon Emissions Reduction in Vehicle Routing Problems with Split Deliveries and Pickups Cheng Jin, Lijun Lu, and Jianing Min
Abstract In order to reduce the carbon emissions generated by vehicles during the logistics distribution, a green vehicle routing problem with split simultaneously deliveries and pickups model is established, by introducing-in an approximate calculation method for the fuel consumption and carbon emissions. The objective is to find environment-friendly green paths, and to minimize the total costs. A three-stage approach is designed to solve the problem. The effectiveness and feasibility of the proposed model and algorithms are verified by numerical experiments. The experimental results show that vehicle speeds, vehicle load rates, travel distances and the number of routes greatly affect the fuel consumption and carbon emissions. Heterogeneous vehicles will decrease the vehicles used and route number, and increase the clustering flexibility. The experimental results also show that it would be necessary for new energy vehicles to enter the transportation market. Keywords Pollution routing problem · Vehicle routing problem · Energy consumption · Carbon emissions · Green and low carbon
This work was financed by the Philosophy and Social Sciences Research Project of Jiangsu Province Education Commission (Grant No. 2021SJA0903), the National Natural Science Foundation of China (Grant No. 61872077). C. Jin University Office, Taihu University of Wuxi, Wuxi, China e-mail: [email protected] L. Lu (B) School of Management, Nanjing University, Nanjing, China e-mail: [email protected] L. Lu · J. Min School of Business, Taihu University of Wuxi, Wuxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_10
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1 Introduction Since the Copenhagen World Climate Conference, low-carbon economy and lowcarbon technologies have attracted widespread attention around the world. Energy conservation and emission reduction have become people’s consensus. In order to solve the negative impact of logistics activities on the environment, the concept of green vehicle routing problem (GVRP) in the logistics industry is rapidly emerging [1]. Sbihi and Eglese [2] in 2007, proposed that VRP research would be combined with energy saving and emission reduction, considering the relationship between green transportation and vehicle transportation path issues and the impact of logistics on the environment. Bektas and Laporte [3] in 2011, considered both economic and environmental factors, incorporating carbon dioxide emissions, fuel consumption, travel time, etc. into the vehicle routing planning to obtain an economical and environment-friendly vehicle routing planning, and proposed the pollution routing problem (PRP). Demira et al. [4] in 2012, proposed an adaptive large-scale neighborhood search algorithm (ALNS) to solve the path pollution problem in order to minimize fuel consumption. Jabali et al. [5] in 2012, established a time-dependent vehicle routing model considering travel time and carbon dioxide emissions, and solved it by a tabu search algorithm. Huang et al. [6] in 2012, studied a green vehicle routing problem with simultaneous pickups and deliveries (G-VRPSPD), by incorporating fuel consumption and CO2 emissions into the model. The experiments show that compared with the traditional distance minimization model, the G-VRPSPD model can generate greener routes without sacrificing too much total travel distance. Demira [7] in 2013, proposed an extended problem of the route pollution, the dual-objective pollution route problem, considering the two mutually exclusive objective functions of minimum fuel consumption and minimum driving time. Li and Zhang [8] in 2014, introduced a carbon emission measurement method that considered both vehicle load and travel speed for the vehicle routing problem under the carbon emission trading mechanism, and established a carbon emission trading model. Chen and Chen [9] in 2015, considered the impact of the vehicle fuel consumption on carbon dioxide emissions while delivering products to multiple customers under the premise of ensuring on-time delivery and the fuel consumption minimization, and established a multiobjective vehicle routing optimization model. Zhang et al. [10] in 2015, analyzed the characteristics of the heterogeneous vehicle VRP on the fuel consumption and carbon emission factors, and built a corresponding optimization model. Zhang [11] in 2016, studied the vehicle routing problem with uncertain travel time, which assumed that the service time of the vehicle at the customer was a linear function of the customer’s demand, and the vehicle selected one of multiple time points as the condition of the departure time. Duan et al. [12] in 2019, studied a multi-objective robust optimization model for the stochastic time-varying vehicle routing problem with hard time window constraints. Zhao et al. [13] in 2020, proposed a green vehicle routing problem with heterogeneous vehicles in traffic congestion areas, and constructed a
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dual-objective green vehicle routing model by introducing in a carbon emission rate metric function. Up to now, the research cases in this field have focused on pure delivery, and there are only few cases where both deliveries and pickups are completed at the same time. Huang et al. [6] studied fuel consumption and CO2 emissions of G-VRPSPD. The actual case of a logistics company in Europe was taken as experimental data. In this example, pickup and delivery demands could appear at the same customer, and all pickups had to collected at customers and all deliveries had to be from the depot. Based on the traditional distance minimization model, Huang et al. [6] compared it with the emission minimization model and the cost minimization model in numerical experiments, and analyzed the significant factors affecting carbon emission reduction in the models, namely, the fuel consumption rate, the average distance in the network and the distance range. They pointed out that the G-VRPSPD model can generate greener routes without affecting too much total travel distance. In this paper, we would explore the influences of the vehicle distance, speed and the load on the fuel consumption and the carbon emissions in the vehicle routing problem with simultaneously split deliveries and pickups (VRPSPDP) [14]. The VRPSPDP is a variant of VRPSDP, in which deliveries to and pickups from each customer are allowed to be split into multiple visits. By splitting customer demands, vehicle load rates can be further improved and the number of vehicles used can be further reduced. From the perspective of energy-saving and emission-reduction in the process of deliveries and pickups, a Green VRPSPDP (G-VRPSPDP) model is established, via leading-in the CMEM model [3]. The model takes the minimum total cost (including fuel consumption cost, carbon emission cost and driver cost) as the optimization objective to find environment-friendly green routes. A threestage approach is designed to solve this problem. Experiments were conducted on the constructed Solomon benchmark dataset to evaluate the feasibility and effectiveness of the algorithms, and to study the impact of speed, distance and load on fuel consumption, carbon emissions and driver costs. The rest of this paper is organized as follows: Sect. 2 introduces VRPSPDP and GVRP. Section 3 describes the three-stage heuristic approach in detail. Section 4 presents and analyses the computational results. Finally, Sect. 5 gives out the conclusion.
2 Problem Description In G-VRPSPDP, the transportation path is as a topology graph G = (V, A), where V = {0, 1, …, n} as the set of nodes, node 0 as the depot; V 0 as the set of customers; A = {(i, j) | i, j ∈ V and i /= j} as the set of arcs between each pair of nodes. A depot has a fleet of homogeneous vehicles with capacity Q to meet the delivery and pickup requirements of customers. In one tour, each vehicle leaves from and returns to the depot with all deliveries and pickups within its capacity limitation, and each stop is satisfied by both delivery and pickup demands. Let n and m represent the number of
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∑n ∑n customers and vehicles in the VRPSPDP, respectively. i=1 qd i and i=1 qpi are the cumulative values of deliveries and pickups from point 1 to point n, respectively. ⎡ (n ) ⎤ n ∑ ∑ qdi , qpi /Q Therefore, the minimum number of vehicles used is max i=1
i=1
[15, 16], where ⎡x⎤ is the greatest integer function denoting the smallest integer equal to or greater than x. As the primary research, we have not referred to time windows and time-dependent restrictions in this paper. The objective is to schedule environment-friendly green routes to fulfill the delivery and pickup demands for all customers within a minimum total travel cost, consisting of energy consumption, carbon emissions and driver wages, in order to achieve a win–win on the economic costs and the environmental protection. The notations and parameters used on the general VRP are as follows. Symbol
Definition
i, j
Node index; i, j = (1, 2, …, m)
k
Vehicle index; k = (1, 2, …, m)
d ij
Distance between point i and point j; (d ii = 0, d ij = d ji )
Q
Vehicle capacity
qd i
Delivery demand of customer i
qpi
Pickup demand of customer i
qd ij
Quantity of delivery load moved from customer i to customer j; qd ij ≥ 0
qpij
Quantity of pickup load moved from customer i to customer j; qpij ≥ 0
x ijk
If vehicle k goes from customer i to customer j, x ijk = 1; otherwise x ijk = 0
yik
If point i is served by vehicle k, yik = 1; otherwise yik = 0
The notations and parameters used on fuel consumption and CO2 emissions are as follows. Symbol Definition Pt
Total tractive power demand requirement in watts (W = kg (J = kg m2 /s2 )
E ij
CO2 emission over the arc (i, j); E ij = ε · Fi j
ε
the carbon emission factor of the fuel
q
caloric value of fuel
F ij
Fuel consumption over the arc (i, j)
Constant m2 /s3 ),
or joules
Fi j ≈ Pi j ≈ Pt (di j /υij )/q ≈ (αi j (w + f i j )di j )/q; load-induced energy requirements + (βυi2j di j )/q; speed-induced energy requirements (continued)
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(continued) Symbol Definition
Constant
vij
Speed (m/s) on arc (i, j)
d ij
Distance between node i and j
θ ij
The road angle of arc (i, j)
0o
(m/s2 )
a
The acceleration
Cr
The coefficients of rolling resistance (m/s2 )
0.01
g
The gravitational constant
α ij
α ij = α + g sin θ ij + g C r cos θ ij
9.81
ω
The curb (empty) weight (kg)
f ij
Vehicle load on arc (i, j) (kg)
A
The frontal surface area of the vehicle (m2 )—a light/medium heavy vehicle 5.0
Cd
The coefficients of drag
0.70
ρ
The air density
β
β = 0.5C d Aρ
Ff
The unit cost of fuel
Fe
The unit cost of CO2
Fd Td
Driver salary per unit time ∑n ∑n ∑m Driving time = i=0 j=0 k=1 x i jk d i j /vi j
t sij
Service time over the arc (i, j), e.g. loading or unloading
(kg/m3 )
1.2041
The G-VRPSPDP model is as follows. ⎧ ⎫ n ∑ m n ∑ ⎨ ⎬ ∑ { ⎡ ( ) ⎤ } αi j ω + f i j + βvi2j xi jk di j /q min (F f + ε · Fe ) ⎩ ⎭
(1)
i=0 j=0 k=1
⎧ ⎫ n ∑ n ∑ m ⎨ ∑ ( ) ⎬ di j /vi j + tsi j xi jk + Fd ⎩ ⎭
(2)
i=0 j=0 k=1
s.t. m ∑
qd j0 yik = qd j , j = 1, 2, . . . , n;
(3)
qp j0 yik = qp j , j = 1, 2, . . . , n;
(4)
k=1 m ∑ k=1 n ∑ i=1
qd 0i yik ≤ Q, k = 1, 2, . . . , m;
(5)
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qpi0 yik ≤ Q, k = 1, 2, . . . , m;
(6)
i=1 θ ∑
n ∑
qpi yik +
qdi yik ≤ Q,
i=θ +1
i=0
i = 0, 1, . . . θ, θ + 1 . . . , n; k = 1, 2, . . . , m; n ∑
(7)
qd i0 = 0
(8)
qp 0i = 0
(9)
i=0 n ∑ i=0 n−1 ∑ n ∑
(x i jk − x j ( j+1)k ) = 0, k = 1, 2, . . . , m;
(10)
i=1 j=i+1 n ∑
x0 jk = 1,
n ∑
j=0
x j0k = 1, k = 1, 2, . . . , m;
(11)
j=0
Equations (1) and (2) represents the total travel distance, where (1) denotes the cost of fuel consumption and CO2 emissions, and (2) denotes the service cost of derivers (including the transportation cost and other service cost). Equations (3) and (4) ensure that customer j’s delivery/pickup needs are satisfied through multiple visits. Equations (5) and (6) ensure that the total delivery/pickup loading are under the vehicle capacity. Equation (7) ensures that both delivery and pickup can occur at one node, and the gross loading of both the delivery and pickup at any node are not larger than the vehicle’s capacity per tour. In other words, the sum of the pick-up quantity before customer node θ (including the node θ ) and the delivery quantity after customer node θ cannot exceed the vehicle capacity. Equations (8) and (9) ensure that no delivery are directed to the depot, and also no pickup are directed from the depot. Equation (10) indicates that the vehicle arriving at node j must also leave that node. Equation (11) constrains that each vehicle enters/exits the depot only once per tour.
3 Three-Stage Approach A three-stage approach is proposed. In the first stage, an improved sweep algorithm is adopted to cluster customers according to the vehicle capacity and to determine the split points and values. In the second stage, a modified Clarke–Wright (C–W) saving algorithm is employed in each cluster to determine the visiting sequence of customer
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points based on the fluctuating load on each node, and to calculate the travel cost. In the third stage, the cost of the fuel consumption, CO2 emissions and driver services are calculated along each route.
3.1 First Stage-Clustering First A multi-restart-iterative-sweep-algorithm for both split deliveries and pickups (B-sMRISA) detailed in [14] is employed to cluster the problem domain to sub-domains under the vehicle capacity Q, and to determine the split points and values in each cluster of the VRPSPDP.
3.2 Second Stage-Routing Criterions After clustering, the problem becomes a VRPSDP in each cluster. A modified C–W algorithm [14] is adopted to meet the VRPSDP requirements. ∑n , • The load quantity at the starting point equals Rso = i=1 di yik ≤ Q. ∑n , • The load quantity at the endpoint equals Reo = i=1 pi yik ≤ Q. • Equation (7) must be satisfied that the total loading of both the deliveries and pickups (delivery first and pickup later) at any node in one tour should not be larger than the∑ vehicle’s capacity.∑The load quantity at the current point i equals , , i−1 R0,i = Rs0 − i−1 i=1 di yik − di + i=1 pi yik + pi ≤ Q.
3.3 Third Stage-Total Costs Calculation The costs of the fuel consumption, CO2 emissions and driver services are calculated along the route determined. ( ) • Fuel consumption over the arc (i, j) Fi j = (ai j w + f i j di j )/q+(βυ ij 2 d ij )/q). • CO2 emissions over the arc (i, j) E ij = ε· F ij . • The cost of the fuel and CO2 emissions = (F f + ε · ⎤} ∑n ∑n ∑m { ⎡ consumption F x . Fe ) i=0 i j i jk j=0 k=1 • The driving time over the arc (i, j) Tdi j = di j /vi j . • The other service times over the arc (i, j) = tsi j . ⎡( ) ⎤ ∑n ∑n ∑m • The cost of driver services = Fd i=0 j=0 k=1 Tdi j + tsi j x i jk .
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4 Experiments The Solomon benchmark datasets were used to verify the feasibility and effectiveness of the proposed algorithm. The Solomon datasets cannot be directly used in the VRPSPDP because they do not contain pickup demand data. Therefore, we constructed a new dataset from an original dataset by keeping its delivery demand unchanged, and extracting the delivery demand from another dataset and placing it as the pickup demand data. For an example, the new dataset CR101 was constructed by keeping the delivery demand in C101 unchanged, and extracting the delivery demand in R101 and placed it as the pickup demand. To make the weight more realistic, we assume that one unit of weight is equal to 10/1000 (T) shown in Table 1. The experiments were carried out in C on a 64-bit Windows 7 machine with a 2.50 GHz Intel (R) Core processor and 8 GB of memory.
4.1 Execution 1 on the Modified Constructed Dataset In order to be able easily to observe the execution results of split and unsplit, we modified the experiment dataset CR101-25 further. We kept the coordinates of nodes unchanged, but altered the delivery and pickup values as (values *4 + 0.1* Q) to all nodes, where Q denotes the vehicle capacity, similar to the method in [14]. The results are shown as columns D*4 and P*4 in Table 2. Thus, the total deliveries and pickups are 23.40 and 18.28 (T) respectively, and the vehicle capacity is 2 (T). Each node in the new dataset had delivery and pickup values between 12 and 110% of the vehicle capacity, and the averages of the delivery and pickup values are 46.8 and 36.56% of the vehicle capacity, respectively. The routes are 12 and loading rate is 0.98, and routes are 15 and loading rate is 0.78 respectively during split and unsplit. The experiment results performed “demand-unsplit” and “demand-split” on this dataset are shown in Tables 2 and 3, respectively. Comparing with two tables, we would conclude that each item in Table 3 is decreased because of the reduction of distance and route numbers, and the increment of load rate. • Load- and Speed-induced are decreased 11.60 and 16.62% with Dist. and load down respectively. • CO2 and Fuel are decreased 13.78, 14.78, 15.38, 15.76 and 15.98% at each speed respectively. • Cost of Driver is increased sharply 4.61, 11.34, 16.62, 20.87 and 24.35% with Dist. up respectively at each speed. • Total Cost is decreased 0.28, −0 25, 1.52, 3.94 and 6.23% respectively at each speed. The distinctive is that the Total Cost at speed 60 (Km/h) is as negative one. • Total Cost is at the lowest at speed 60 (Km/h).
66
69
85
35
35
25
10
11
12
68
70
38
38
66
69
65
68
8
40
7
70
66
9
42
40
5
42
4
6
45
42
2
3
68
50
40
45
0
1
Y (Km)
X (Km)
Point
0.2
0.1
0.1
0.1
0.2
0.2
0.2
0.1
0.1
0.1
0.3
0.1
0
D (T)
0.19
0.12
0.16
0.16
0.09
0.05
0.03
0.26
0.19
0.13
0.07
0.1
0
P (T)
1
0.6
0.6
0.6
1
1
1
0.6
0.6
0.6
1.4
0.6
0
D*4 (T)
Table 1 Constructed CR101-25 (vehicle capacity q = 2 (T))
0.96
0.68
0.84
0.84
0.56
0.4
0.32
1.24
0.96
0.72
0.48
0.6
0
P*4 (T)
25
24
23
22
21
20
19
18
17
16
15
14
13
Point
25
25
28
28
30
30
15
15
18
20
20
22
22
X (Km)
52
50
55
52
52
50
80
75
75
85
80
85
75
Y (Km)
0.4
0.1
0.1
0.2
0.2
0.1
0.1
0.2
0.2
0.4
0.4
0.1
0.3
D (T)
0.06
0.03
0.29
0.18
0.11
0.09
0.17
0.12
0.02
0.19
0.08
0.2
0.23
P (T)
1.8
0.6
0.6
1
1
0.6
0.6
1
1
1.8
1.8
0.6
1.4
D*4 (T)
0.44
0.32
1.36
0.92
0.64
0.56
0.88
0.68
0.28
0.96
0.52
1
1.12
P*4 (T)
Carbon Emissions Reduction in Vehicle Routing Problems with Split … 141
782.881 73.3598
120
509.1700
353.5856
226.2898
782.881 73.3598
782.881 73.3598
80
100
56.5714
127.2849
782.881 73.3598
782.881 73.3598
40
129.9295
582.4870
426.9390
299.6463
200.6510
10.6647 170.2450 138.2810 319.2100 20.7299 330.9470 112.1900 463.9100
15.1952 242.5650 122.6220 380.3800
1
1
Driver
1
Total cost ratio
4.4828 4.4830 0.5180 1.5724
3.2859 3.2858 0.5662 1.2893
2.3062 2.3061 0.6385 1.0819
1.5442 1.5442 0.7590 0.9677
1
Fuel
(c) Cost ratio changes with speed Total cost CO2
73.8230 216.5690 295.0350
Driver
7.1410 113.9970 164.3790 285.5180
4.6243
Fuel
(b) Cost changes with speed
Load-induced Speed-induced Total energy CO2
60
Speed Dist.
(a) Energy changes with speed
Table 2 Results of unsplit performed on the modified constructed dataset
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652.765 64.8520
120
424.5224
294.7991
188.6839
652.765 64.8520
652.765 64.8512
80
100
47.16876
106.1323
652.765 64.8513
652.765 64.8519
40
112.0227
489.3957
359.664
253.5323
170.9878
3.9869 97.1560 183.0200 286.2500
17.4171 278.0600 139.5100 434.9900
12.8004 204.3358 148.2113 365.3788
1
1
Driver
1
Total cost ratio
4.3686 4.3688 0.6158 1.4786
3.2106 3.2105 0.6542 1.2419
2.2632 2.2633 0.7118 1.0685
1.5264 1.5265 0.8079 0.9730
1
Fuel
(c) Cost ratio changes with speed Total cost CO2
63.6470 226.5500 294.2000
Driver
9.0232 144.0522 161.2638 314.3577
6.0856
Fuel
(b) Cost changes with speed
Load-induced Speed-induced Total energy CO2
60
Speed Dist.
(a) Energy changes with speed
Table 3 Results of split performed on the modified constructed dataset
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4.2 Execution 2 on the Modified Constructed Dataset When the capacities of vehicles could be selected according to the total customer demands (Assume: heterogeneous vehicles exist in the depot), the number of vehicles used could be decreased in this experiment, and also the drivers’ cost, the travel distances could be reduced. Table 4 shows the results of the experiments performed, where three larger capacity 9/8/7 (T) vehicles carry out the transportation task instead of the twelve 2 (T) vehicles. Figure 1a–d show that Dist., Fuel/CO2 , Driver, and T. Cost change with Speed in Tables 2, 3 and 4, respectively. These comparisons reveal that: • Selection of proper larger capacity vehicles can significantly reduce the route number, travel distances, fuel consumption and CO2 emissions, and the total cost in the logistics. • The clustering could be more flexible after abandoning the limit of vehicle capacity.
5 Conclusion This study developed a mathematical model for the G-VRPSPDP. The model takes the minimum total cost as the optimization objective to find environment-friendly green routes. A three-stage approach was designed to solve this problem. The first stage involves to cluster nearest customers into sub-groups, the second stage conducts to find the proper route to meet the fluctuating load on every node in each cluster, and the third stage devotes to calculating the total costs. Experiments on the modified constructed Solomon benchmark dataset are used to evaluate the feasibility and effectiveness of the proposed algorithms, and to analyze how far the vehicle speed, distance and load effect on the fuel consumption and the CO2 emissions. • The experiment results reveal that the Total Energy, the Cost of CO2 and Fuel increase with the speed up, and the Driver decreases with speed up, the lowest Total Cost is approximately at speed 60 (Km/h). • The proportion of Drive, Fuel and CO2 in Total Cost at speed 60 (Km/h) is approximately 63.9, 34 and 2.1% respectively. The proportion of Drive would reduce and the proportion of Fuel and CO2 in Total Cost would increment with speed up. • The selection of proper larger capacity vehicles can reduce routes number, distances travelled, energy consumption and carbon emissions, driver salary, and save the total cost in logistics distribution. • From the perspective of energy saving and emission reduction, the effect of choosing larger-capacity vehicles is much better than splitting goods.
251.388 99.9785
120
163.4854
113.5404
72.6652
251.388 99.9785
251.388 99.9785
80
100
18.1663
40.8724
251.388 99.9785
251.388 99.9785
40
118.1503
263.4680
213.5316
172.6541
140.8542
4.2050
Driver
98.0900
9.3770 149.7000
66.7600 225.8300
70.1100 199.0200
75.1400 179.3800
83.5200 168.5600
1
1
Driver
1
Total cost ratio
1.2340 1.2339 0.9522 1.1347
1.2368 1.2368 0.9331 1.1095
1.2256 1.2257 0.8997 1.0642
1.1922 1.1922 0.8329 0.9822
1
Fuel
(c) Cost ratio changes with speed Total cost CO2
67.1300 100.2800 171.6200 80.0300
7.5990 121.3200
6.1440
5.0130
Fuel
(b) Cost changes with speed
Load-induced Speed-induced Total energy CO2
60
Speed Dist.
(a) Energy changes with speed
Table 4 Results performed on the dataset by three larger capacity vehicles
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1000 800 600 400 200 0
II-Dist. III-Dist. IV-Dist.
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Dist. (Km)
Dist. changes with speed
40 60 80 100 120
350 300 250 200 150 100 50 0
II-Fuel III-Fuel IV-Fuel 40 60 80 100 120
Speed (Km/h)
Speed (Km/h)
(a)
(b) Total Cost changes with speed
250 200 150 100 50 0
500 II-Driver Iii-Driver
Cost (Money)
Driver (Money)
Driver changes with speed
iV-Driver 40 60 80 100 120
400 300
II-T.Cost
200
III-T. Cost
100
IV-T. Cost
0 40
60 80 100 120
Speed (Km/h)
Speed (Km/h)
(c)
(d)
Fig. 1 Results comparison among Tables 2, 3 and 4
• CO2 is a small proportion in Total Cost. In order to reduce CO2 emission, two ways can be adopted: one is to enlarge the carbon emission factor of the fuel; another is to encourage the usage of vehicles other energy sources. This paper is the primary exploration on carbon emissions in G-VRPSPDP. Future research will include more G-VRPSPDP case experiments, as well as develop more algorithms in time-dependent travel environments. Acknowledgements This research was financed by the Philosophy and Social Sciences Research Project of Jiangsu Province Education Commission (Grant No. 2021SJA0903), the National Natural Science Foundation of China (Grant No. 61872077), the Humanities and Social Sciences Research Base Fund of Jiangsu Province Education Commission (Grant No. 2017ZSJD020), and the Jiangsu Key Construction Laboratory of IoT Application Technology, Taihu University of Wuxi. Special thanks to the reviewers and editors, who have carefully reviewed the manuscript and provided pertinent and useful comments and suggestions.
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References 1. Zhou, X.C., Zhou, K.J., Wang, L., Liu, C.S., Huang, X.B.: Review of green vehicle routing model and its algorithm in logistics distribution. Syst. Eng.-Theory Pract. 41(1), 213–230 (2021) 2. Sbihi, A., Eglese, R.W.: Combinational optimization and green logistics. 4OR: Quart. J. Oper. Res. 5, 99–116 (2007) 3. Bektas, T., Laporte, G.: The pollution-routing problem. Transp. Res. Part B 45, 1232–1250 (2011) 4. Demir, E., Bektas, T., Laporte, G.: An adaptive large neighborhood search heuristic for the pollution-routing problem. Eur. J. Oper. Res. 223, 346–359 (2012) 5. Jabali, O., Van. Woensel, T., de Kok, A.G.: Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Prod. Oper. Manag. 21(6), 1060–1074 (2012) 6. Huang, Y.X., Shi, C.Y., Zhao, L., Woensel, T.V.: A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries. In: Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI), Suzhou, China, pp. 302–307 (2012) 7. Demir, E.: The bi-objective pollution-routing problem. Eur. J. Oper. Res. 232(03), 464–478 (2013) 8. Li, J., Zhang, J.H.: Study on the effect of carbon emission trading mechanism on logistics distribution routing decisions. Syst. Eng.—Theory Pract. 34(7):1779–1787 (2014) 9. Chen, Y.G., Chen, Z.Q.: Study on the vehicle routing problem with objectives of on-time delivery and oil consumption minimization. Chinese J. Manage. Sci. 23(SI), 156–164 (2015) 10. Zhang, D.Z., Qian, Q., Li, S.Y., Jin, F.P.: Research on an optimization model and its algorithm for vehicle routing problem based on CO2 emission. J. Railway Sci. Eng. 12(2), 424–429 (2015) 11. Zhang, M.Y.: Vehicle routing problem with uncertain factors. PhD dissertation, University of Science and Technology of China, Anhui, China (2016) 12. Duan, Z.Y., Lei, Z.X., Sun, S., Yang, D.Y.: Multi-objective robust optimisation method for stochastic time-dependent vehicle routing problem. J. Southwest Jiaotong Univ. 3, 1–9 (2019) 13. Zhao, Z.X., Li, X.M., Zhou, X.C.: Green vehicle routing problem optimization for multi-type vehicles considering traffic congestion areas. J. Comput. Appl. 40(3), 883–890 (2020) 14. Min, J.N., Lu, L.J., Jin, C.: A two-stage heuristic approach for split vehicle routing problem with deliveries and pickups. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds.) LISS 2021. Lecture Notes in Operations Research. Springer, Singapore, pp 479–490 (2022) 15. Mitra, S.: An algorithm for the generalized vehicle routing problem with backhauling. AsiaPacific J. Oper. Res. 22(2), 153–169 (2005) 16. Mitra, S.: A parallel clustering technique for the vehicle routing problem with split deliveries and pickups. J. Oper. Res. Soc. 59(11), 1532–1546 (2008)
Study on the Whole Process Coping Strategy of Supply Chain Disruption Risk Based on NVIVO Mengmeng Miao, Hongmei Shan, Zihao Li, and Qian Zhang
Abstract Nowadays, the external environment is turbulence and uncertainty for supply chain systems, the complexity and vulnerability of internal environment further enhance the possibility of supply chain disruption risk. How to deal with the disruption risk of supply chain has become a hot topic in theoretical and practical circles. This paper conducts coding analysis on 95 literatures based on the qualitative analysis of grounded theory, and proposes a whole process countermeasure model of supply chain disruption risk. Finally, this paper establishes a closed-loop control strategy of “beforehand prophylaxis-processing control-afterward treatment-beforehand prophylaxis”. Processing control is the key link to deal with the disruption risk of supply chain, and its core resolutions include dual-source procurement and information sharing. In addition, the research shows that the main strategies of beforehand prophylaxis are alternative supplier and inventory strategy; The afterward countermeasures include production decisions and reactive pricing strategies. This study provides scientific basis and theoretical guidance for enterprises to deal with the risk of supply chain disruption. Keywords Supply chain disruption risk · Grounded theory · Coping strategies · Coding analyses
The National Social Science Fund of China (21BJY216); Scientific Research Project of Shaanxi Provincial Department of Education (15JK1684); Graduate Innovation Fund of Xi’an University of Posts and Telecommunications (CXJJZW2021003). M. Miao (B) · H. Shan · Z. Li · Q. Zhang School of Modern Post, Xi’an University of Posts and Telecommunications, Xi’ an, China e-mail: [email protected] H. Shan e-mail: [email protected] Z. Li e-mail: [email protected] Q. Zhang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_11
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1 Introduction Impacted by various factors from political and economic fluctuations and natural disasters, supply chain will face unpredictable disruption risks. For example, the 2011 Earthquake in Japan caused a number of electronic manufacturers’ supply chain damaged; In 2018, the sanctions imposed by the US government on ZTE interrupted the supply of important components and software products, causing ZTE a direct loss of more than RMB 20 billion; The outbreak of COVID-19 has a huge impact on supply chain operations under the strict international traffic control. Gu and Huo (2019) found that supply chain disruption would lead to the 40% loss of the market value for an enterprise, and reduce 107% of business income, 114% of sales revenue and 92% of return on assets. It takes two years for enterprises to completely recover from supply chain disruption [1]. Demirel et al. (2017) assessed the costs and benefits associated with flexible procurement when suppliers are strategic price setters [2]. Di Weimin and Wang Ran (2021) proposed a method for reasonably designing the structure of supply chain network, and then established a supply chain site-inventory decision model aiming at minimizing the total expected cost of the system [3]. Liu Fan and Liu Jiaguo (2019) systematically reviewed on the studies on supply chain interruption risk from the perspectives of response time, response subject and response path of supply chain interruption [4]. Research on supply chain disruption risk such as concept, causation, influence and consequence has been widely concerned by scholars. However, most of them focus on one or two local prevention strategies or countermeasures. There is a lack of systematic and comprehensive theoretical research on coping strategies of supply chain disruption risk. And few scholars systematically engage in the practical operation methods in the whole process of beforehand prevention, processing control and afterward treatment of supply chain disruption risk. It is difficult to provide theoretical support for enterprises to solve practical problems and make the relevant risk decisions. Therefore, on the basis of a careful literature summary on coping strategies of supply chain disruption risk, this paper uses the qualitative research method of grounded theory to code and analyze the Chinese and English literatures on supply chain disruption risk. The closed-loop risk control process of supply chain disruption is put forward in three key links. Such as prophylaxis before disruption, controlling in the disruption and treatment after the disruption, which provide a clear theoretical reference for enterprises’ risk management decisions.
2 Research Design 2.1 Research Methods Grounded theory is a qualitative research method proposed by Anselm Strauss and Barney Glaser from Columbia University. Its core idea is to develop and summarize grounded theory by systematic procedure according to a certain phenomenon.
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Subsequently, Glase and Strauss et al. developed this method, which classified specific phenomena in the data into codes, extracted and condensed the research data step by step from bottom to top, and constructed a “theory that can solve problems” [5]. In order to make the literature coding more orderly, this paper uses Nvivo12.0 to manually code and analyze the selected literatures. It can extract the words, general ideas or observed phenomena in the interview draft and documents from the existing context, break them down into independent ideas and thoughts. Then reclassify and rename them to form a new theory.
2.2 Data Collection In order to search for coping strategies of supply chain disruption risk close to current conditions, this paper used “supply chain disruption risk” and “disruption coping strategies” as keywords, retrieved the latest domestic and foreign relevant literature in CNKI and Web of Science in recent 5 years. A total of 361 related literatures were retrieved from 2016 to 2020. Through careful interpretation and correlation analysis of these literature, 95 Chinese and English literatures (including 33 English literatures and 62 Chinese literatures) were selected as sample data set to analyze the coping strategies of supply chain disruption risk. As can be seen from Fig. 1, the main research objects are Manufacturer, Retailer and Customer, and the topics concerned are Supply Chain, Risk and Management, etc.; The characteristics of supply chain disruption risk can be understood from the words of Complexity, Dynamic, Random and Uncertainty in the Figure; Its research methods mainly include mathematical Model, Case study, Literature review, Example analysis and Theory; In addition, Enterprises can invest in Research, Inventory reservation, Recovery, Sharing, Network design, Flexibility and Agility improvement, Insurance, Collaboration and other coping strategies to achieve low Cost and high Performance. Although the word cloud has intuitive effect, it cannot make accurate judgement about coping strategies of supply chain disruption. Based on the grounded theory, this paper uses the manual coding function of “nodes” to study the supply chain disruption risk coping strategy through the number of coding times of “reference nodes”.
Fig. 1 Word cloud of the sample dataset
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3 Coding Analysis 3.1 Open Coding-Categorization of Concepts Open coding was used to interpret and analyze 95 literatures, encode and integrate concepts involved in the literature, sort out a total of 150 reference nodes, and classify them into different sub-nodes. At the same time, the nodes are definited based on research experience to form different categories. Finally, 49 conceptual categories (tertiary nodes) are obtained. These coding nodes are located at the bottom of the subordination relationship and are specific strategies and methods of supply chain disruption risk coping strategies.
3.2 Axial Coding-Determination of the Main Category On the basis of open coding, the axial coding induces and integrates the coded nodes, discovers the relations among various secondary categories according to classes, attributes and dimensions, and forms the main category. Nvivo12.0 was used to encode the literature. Through further analysis and sorting of the tertiary nodes, 49 tertiary nodes were summarized into 13 secondary nodes, and 13 secondary nodes were summarized. According to the stage of before and after the event of supply chain disruption risk, it can be divided into three first-level nodes: beforehand prophylaxis, processing control and afterward treatment. Through the information extraction and node coding of 95 literature, the node structures of all levels to deal with supply chain disruption risk were obtained, and the hierarchical structure of coping strategies was formed. The larger the number of coding reference nodes and literature, the larger the circle sector area, which means these treatments were paid more attention by scholars. As can be seen from Fig. 2, there are a large number of literatures concerning the level of material reserve and the development of alternative capacity in the strategies of beforehand prophylaxis. Supply chain flexibility, supply chain network design and information sharing have attracted much attention in the strategies of processing control. Emergency response is the most important strategy in the strategies of afterward treatment.
3.3 Selective Coding-Definition Core Category On the basis of axial coding, selective coding should identify dominant categories, clarify logical relations between categories, establish “story line”, and form theoretical views by combining research structure and actual situation. In order to determine the “core category”, three nodes obtained by axial coding are firstly abstracted at a higher level. In terms of the number of reference nodes, there are 150 reference nodes in the whole structure. There are 63 reference points in
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Fig. 2 Hierarchy diagram of coping strategies for supply chain disruption risk
processing control, accounting for 42% of all nodes, with global dominance. Further consideration of the role of nodes in the whole shows that processing control should be regarded as the “core category” to deal with supply chain disruption risk. The reference points for beforehand prophylaxis and afterward treatment account for 32 and 26% of the whole, respectively, and also play an indispensable role. They are inevitable nodes in the disruption risk of supply chain, so they will be analyzed separately as “supporting categories”. The closed-loop story line of “beforehand prophylaxis-processing controlafterward treatment-beforehand prophylaxis” is established as shown in Fig. 3, among them, the processing control is the core categorization with the beforehand
Fig. 3 Supply chain disruption risk response strategy closed-loop management process
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prophylaxis and afterward treatment as the supporting category, That is, preventive and improvement strategies are applied before the occurrence of supply chain disruptions; Timely control made available in the processing supply chain disruption risk; After the disruption occurs, the emergency treatments are benefit to recovery after the disruption, In a word, beforehand prophylaxis is to reduce the probability of disruption and reduce the loss; processing control is to reduce and control the impact of disruption; Afterward treatment is to restore normalcy as quickly as possible.
4 Research Discussion In order to further understand the specific coping strategies for supply chain disruption risk in different stages, this paper analyzes and explains the subordinate nodes in beforehand prophylaxis, processing control and afterward treatment, finds out the high-frequency measures and strategies discussed in theoretical or practical circles, and improves the feasibility of the supply chain risk coping strategies.
4.1 The Strategies of Beforehand Prophylaxis Beforehand prophylaxis refers to taking preventive measures for supply chain in advance to reduce the probability and loss of disruption risk. The strategies of beforehand prophylaxis include 4 secondary nodes and 13 tertiary nodes, namely material reserve level, alternative capacities, prospective strategies and R&D investment. 1. Material reserve level. Improving material reserve level is an important part of pre-prevention mechanism. Security, reliable and sustainable levels of material reserve can effectively reduce supply chain disruptions caused by shortages. It can be seen from Table 1 that appropriate redundancy and reserve capacity can improve the stability of supply chain system to a certain extent. It aims to ensure logistics capacity by maintaining a certain amount of inventory, so that enterprises can have sufficient buffer time when they encounter supply chain disruption. 2. Alternative capabilities. When confronted with supply chain disruption risk, the development of alternative capability can enable enterprises to obtain enough buffer time and backup plan, and enhance the flexibility of supply chain. The strategies for developing alternative capacity mentioned in the sample literature mainly include standby logistics and transportation and standby suppliers. It avoids the impact caused by supply disruption and can effectively reduce the cost and risk of the enterprise [6]. 3. Prospective strategies. Prospective strategy refers to the effective strategy that can reduce the probability of disruption by exploring possible events. Such as pricing, budget allocation and early warning model, etc. In most cases, companies can reduce supply chain disruption risks by adjusting pricing and budget allocations. In addition, the establishment of risk early warning mechanism can prevent the occurrence and spread of emergencies.
Study on the Whole Process Coping Strategy … Table 1 All nodes and coding reference points in beforehand prophylaxis Secondary nodes Tertiary nodes Material reserve level (15)
R&D investment (8)
Prospective strategies (12)
Alternative capabilities (13)
Redundancy Inventory Reserve capacit R&D intensity Supply chain capacity Innovation Distribution of procurement Pricing Budget allocation Early warning model Risk management culture Standby logistics and transportation Standby suppliers
155
Reference point 4 10 1 4 2 2 4 5 1 1 1 1 12
4. R&D investment. R&D investment refers to the investment of human and financial resources in order to acquire new technologies and products and improve the ability of enterprises to deal with the risk of supply chain disruption. Increasing R&D intensity and innovation can effectively enhance the ability to deal with supply chain disruption risk. In addition, investing in supply chain capabilities can effectively reduce the total expected cost and better deal with the supply chain disruption risk.
4.2 The Strategies of Processing Control Processing control is the key link with the largest number of reference nodes. It refers to the control that takes corresponding countermeasures immediately when the disruption occurs to alleviate the impact of disruption. The analysis results show that the strategies of processing control mainly include 6 secondary nodes and 22 tertiary nodes, such as supply chain flexibility, network design, sharing, contract, coordination and cooperation, and postponement strategies. 1. Supply chain flexibility. Supply chain flexibility is the secondary nodes with the largest number of reference nodes in processing control. Through the dynamic integration of various resources in the supply network, it forms the comprehensive competitive advantage of the whole chain and actively responds to the uncertainty from the market. Thome and Antonio et al. (2014) divide supply chain flexibility into internal supply chain flexibility and external supply chain flexibility [7]. Agility and flexibility strategies play a significant role in improving internal supply chain flexibility and mitigating the impact of disruption [8]. External supply chain flexibility can be realized by supplier selection strategy and multi-source procurement strategy.
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Table 2 All nodes and coding reference points in processing control Secondary nodes Tertiary nodes Reference point Postponement strategies (2) Coordination and cooperation (6) Sharing (11)
Supply chain flexibility (22)
Contract (11)
Network design (11)
Demand delay Supply delay Coordinated response Coordinate partners Resource sharing Knowledge sharing Information sharing Cost sharing Dual-source procurement Flexibility Supplier selection Agility Benefit-sharing contract Optimal contract Risk-sharing contract Cost-sharing contract Procurement contract Transportation disruption Site selection Facility disruption Stockout Node disruption
1 1 2 4 1 1 8 1 12 4 4 2 2 2 2 1 4 1 3 3 1 3
2. Network design. A supply chain network is a network system responsible for transferring products or services from suppliers to customers. Network design has a long-term impact on supply chain management, mainly involving supplier selection, procurement strategies, production plant, delivery mode, warehouse location and inventory strategies and so on. As can be seen from Table 2, the main types of supply chain network disruption are site selection, facility disruption and node disruption. In other words, the organizational structure and operational characteristics of supply chain network is the main compositions of the measurements to deal with supply chain disruption risk [9]. 3. Sharing. Sharing strategies could quick response to supply chain disruption risk, effectively reduce the possible impact of disruption, and improve the utilization efficiency of supply chain resources, knowledge and information. Table 2 shows that sharing resolutions include sharing of resources, knowledge, information and cost, among which information share attracts the most attention from academic and practical circles. Sharing disruption information with supply chain partners can not only enhance the transparency of supply chain, reduce the variability of order size, but also make rapid response to jointly deal with the risk of disruption.
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4. Contract. A contract refers to an agreement signed between supply chain parties so as to regulate the rights and obligations of each member in the market transaction [10]. Table 2 shows that common contract types include procurement contract, revenue sharing contract and risk sharing contract, etc. It can minimize the possibility of a shortage in the risk event. 5. Coordination and cooperation. Coordination and cooperation refers to the strategic behavior of cooperation and integration to gain the competitive advantage between upstream and downstream of supply chain [11]. When a supply chain disruption occurs, coordination with the partners in the supply chain can not only improve the operation efficiency of supply chain, but also help reduce the “high cost” or “resource availability” caused by the disruption. 6. Postponement strategies. A postponement strategy is a benefit solution for final manufacturing and product distribution after receiving customer orders, which can reduce the supply disruption caused by preferred suppliers. Waller and Dabholkar et al. (2000) divide postponement strategies in supply chain into “upstream postponement” and “downstream postponement” [12]. The postponement strategies are to innovate the business process and reduce the operation uncertainty of supply chain.
4.3 The Strategies of Afterward Treatment Afterward treatment is the response measure to quickly prevent permanent disruption or collapse of the supply chain after the disruption event of supply chain. According the analysis results, the strategies of afterward treatment of supply chain disruption risk include 3 secondary nodes (emergency response, demand management and recovery strategy) and 14 tertiary strategy nodes. 1. Emergency responses. Emergency responses are the secondary node with the largest number of coding points in the strategies of afterward treatment. As soon as the occurrence of supply chain disruption. It is indispensable for enterprises to make emergency response strategies quickly to reduce losses and recover from the disruption. Enterprises could obtain the solutions by thinking about what, how much, and how to produce in the short term after a disruption occurs. In addition, insurance can mitigate the loss of profits caused by business disruption, thereby transferring risks and allowing the enterprise to recover the production as soon as possible. 2. Demand management. Demand management refers to these ways that enterprises turn customer demand to substitute goods with sufficient inventory in order to reduce the demand pressure in the market after the occurrence of risks. Table 3 shows that reactive pricing strategy and demand transfer are hot topics for scholars. In the case of market demand fluctuations, reactive pricing strategies to satisfy the needs of enterprises or customers in the supply chain and promote sales through response mechanism. In addition, when an enterprise has multiple products with the similar functions at the same time, it can also reduce the pressure caused by disruption by guiding consumer demand to shift to unrestricted alternative products [13].
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Table 3 All nodes and coding reference points in afterward treatment Secondary nodes Tertiary nodes Reference point Emergency responses (25)
Demand management (10)
Recovery strategies (4)
Transshipment Production decision Emergency procurement Business continuity plan Emergency inventory Product outsourcing Insurance Financing Demand transfer Reactive pricing strategy Sales promotion Reorganization of the network Production disruption recovery Supply recovery
2 7 3 3 3 1 5 1 3 5 2 2 1 1
3. Recovery strategies. Recovery strategies refers to the renovation resolutions that repair and renovate the supply chain network system or revises the plan in the future to seek the best recovery after disruption in a certain stage. The recovery strategies mentioned by scholars include network, production and supply recovery. Sawik (2019) obtained corresponding mitigation and recovery decisions for supply chain disruption mitigation and recovery and compared with a multi-period approach [14]. In a word, the supply chain recovery strategy can effectively help the supply chain reduce collapse danger and control the loss and obtain sustainable development.
5 Conclusions and Future Study 5.1 Conclusions Based on the qualitative research method of grounded theory, this paper systematically collates the Chinese and English literature on coping strategies of supply chain disruption risk from 2016 to 2020, establishes the whole process countermeasure model of cracking supply chain disruption risk, and the coping strategy of supply chain disruption risk is divided into 3 first-level nodes, 13 secondary nodes and 49 tertiary nodes. The results show that coping of supply chain disruption risk is a closed-loop control process of “beforehand prophylaxis-processing controlafterward treatment-beforehand prophylaxis”. Among them, processing control is the key link to deal with the disruption risk of supply chain. Beforehand prophylaxis and afterward treatment belong to the supporting category. The core coping strategies of beforehand prophylaxis include alternative suppliers and inventory strategy.
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The core coping strategies of processing control are dual-source procurement and information sharing. The main coping strategies of afterward treatment include production decisions and reactive pricing strategies. The three links complement each other and build a “security firewall” of supply chain disruption risk in an all-round and whole process. This research improves the theoretical knowledge system of supply chain disruption risk, and provides decision-making suggestions for enterprise supply chain risk management practice.
5.2 Future Study There have been many richful researches on coping strategies of supply chain disruption in academic circles. However, due to the variety of supply chain risks and the complexity of trigger factors, the research on coping strategies of supply chain disruption risks is far from solving the urgent problems faced by supply chain enterprises. In the future, the following aspects need to be further researched: 1. Research on industry differences. Due to the complexity of coping strategies and the different risk factors involved in different industries, how to explore and study the response strategies of supply chain interruption risk in different industries needs to be refined. It should be a challenging but meaningful research field to summarize and improve the coping strategy mechanism of supply chain disruption on this basis. 2. Multi-dimensional and multi-angle research perspective. In this paper, the coping strategies of supply chain disruption are studied according to the stage of risk occurrence, but the causes and types of disruption risk and the dynamic evolution mechanism are not considered. Future research should be based on the existing theoretical analysis, from a variety of perspectives and deeply research different types of supply chain disruption risk coping strategies. 3. Actual case study. Future researches could turn the direction to case studies, and obtain the enlightenment by analyze and summary the successful practical experiences of well-known enterprises such as Huawei and ZTE in dealing with supply chain disruption risks. Namely, the theoretical height come from the practical point of view, which could provide powerful references and experiences for Chinese enterprises to deal with supply chain disruption risk.
References 1. Gu, M., Huo, B.: The impact of supply chain resilience on company performance: a dynamic capability perspective. Acad. Manage. Annu. Meet. Proceed. 2017(1), 16272 (2017) 2. Demirel, S., Kapuscinaki, R., Yu, M.: Strategic behavior of suppliers in the face of production disruptions. Manage. Sci. 64(2), 533–551 (2018) 3. Weimin, D., Ran, W.: Location and inventory decision model and optimization algorithm of supply chain considering multi-stage facility outages. Comput. integr. Manuf. Syst. 27(1), 270–283 (2021)
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4. Fan, L., Jiaguo, L.: Review on coping strategies of supply chain disruption. J. Zhongnan Univ. Econ. Law 2019(3), 148–156 (2019) 5. Glaster, B.G., Straussa, A.: The discovery of grounded theory: strategies for qualitative research. Nurs. Res. 3(2), 364 (1967) 6. Kamalahmadi, M., Parast, M.M.: An assessment of supply chain disruption mitigation strategies. Int. J. Prod. Econ. 184(feb), 210–230 (2017) 7. Parast, M.M.: A learning perspective of supply chain quality management: empirical evidence from US supply chains. Supply Chain Manage. Int. J. 25(1), 17–34 (2019) 8. Thome, M.T., Antonio, S., et al.: A multi-tier study on supply chain flexibility in the automotive industry. Int. J. Prod. Econ. 158(C), 91–105 (2014) 9. Chen, Y., Shu, T., Chen, S., et al.: Strong-weak collaborative management in coping supply chain disruption risk transmission based on scale-free networks. Appl. Econ. 49(39), 1–16 (2017) 10. Zhang, L., Wang, J., You, J.: Consumer environmental awareness and channel coordination with two substitutable products. Eur. J. Oper. Res. 241(1), 63–73 (2015) 11. Flynn, B.B., Huo, B., Zhao, X.: The impact of supply chain integration on performance: a contingency and configuration approach. Oper. Res. 28(1), 58–71 (2009) 12. Waller, M., Dabholkar, P., Gentry, J.: Postponement, product customization, and market oriented supply chain management. J. Bus. Logist. 21(2) (2000) 13. Bugert, N., Lasch, R.: Effectiveness of responsive pricing in the face of supply chain disruptions. Comput. Ind. Eng. 124(OCT), 304–315 (2018) 14. Sawik, T.: Two-period versus multi-period model for supply chain disruption management. Int. J. Prod. Res. 57(14), 1–17 (2019)
Service Quality Evaluation of New Retail Fresh E-commerce Based on AHP-Entropy TOPSIS Model Zhen Li and Yuping Xing
Abstract The outbreak of the COVID-19 and the rise of new retail models have promoted the vigorous development of the fresh e-commerce industry, but at the same time, some service quality problems have also been exposed. An accurate evaluation of the service quality of fresh e-commerce under the new retail background can provide theoretical support for the improvement of the service quality of fresh e-commerce, and has a certain practical significance. In this study, we propose a hybrid evaluation model based on fuzzy AHP-entropy TOPSIS (technique for order preference by similarity to ideal solution) model. Specifically, referring to SERVQUAL model and the service characteristics of fresh food industry, this paper firstly constructs the initial evaluation index; Then, the principal component analysis is further applied to adjust it, and an evaluation index system consisting of 6 dimensions and 25 indicators is finally constructed. By introducing AHP and entropy weight method, the problem of index weight distribution of TOPSIS evaluation model is solved. Finally, the above AHP-entropy TOPSIS model is applied to analyze the service quality of six fresh food e-commerce companies, the strategies that can promote service quality of fresh e-commerce are also put forward based on the results. Keywords Fresh e-commerce · Service quality · SERVQUAL model · AHP · Entropy TOPSIS
Z. Li · Y. Xing (B) Glorious Sun School of Business & Management, Donghua University, Shanghai 200051, China e-mail: [email protected] Z. Li e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_12
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1 Introduction In the context of the COVID-19, “home life” has spawned a “home economy”. Fresh e-commerce has ushered in explosive growth because of its advantages of fresh home, community and contactless distribution. At the same time, it has also imperceptibly affected consumers’ consumption habits of buying fresh food. According to iResearch consulting data, the scale of China’s fresh food e-commerce industry continued to expand in 2020, reaching 458.5 billion yuan, an increase of 64.0% yearon-year [1]. It is estimated that the scale of fresh e-commerce industry will exceed trillion yuan by 2023. With the vigorous development of the new retail and fresh food e-commerce industry, many capital parties have laid out the fresh food e-commerce field one after another, and platforms such as HEMA Fresh, MISSFRESH, Dingdong and so on are gradually rising. Although the fresh e-commerce under the new retail mode can meet the shopping demand of consumers to a great extent, there are still challenges in cold chain logistics, product standards and consumption experience, which hinder the further expansion of the fresh e-commerce industry to some extent [2]. Ensuring the service quality of fresh e-commerce is an effective way to solve the dilemma of fresh e-commerce at present, and it is also an important topic that fresh e-commerce enterprises pay attention to. This study aims to build a reasonable model and scientific and effective evaluation methods to comprehensively evaluate the service quality of fresh e-commerce, and further studies its service quality improvement strategies, so as to continuously promote the development of fresh e-commerce industry. The organizational structure of this paper is as follows. Section 2 introduces the theoretical basis and literature review. The evaluation index system and evaluation model are proposed and elaborated in Sects. 3 and 4 respectively. Section 5 applies the above model to analyze fresh e-commerce enterprises. Finally, Sect. 6 discusses the findings and limitations of the study.
2 Literature Review In this section, we review the theoretical background from two aspects: the evaluation of fresh e-commerce service quality and the improvement of TOPSIS method.
2.1 The Evaluation of Fresh E-commerce Service Quality The academic research on the service quality evaluation of fresh e-commerce mainly focuses on cold chain logistics and logistics distribution, while the research on the overall service quality of fresh e-commerce is less. Aiming at the cold chain logistics
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problem of fresh e-commerce industry, Qiu proposed an evaluation model based on catastrophe progression method, and applied the model to comprehensively evaluate SF and JD fresh [3]. Zhang and Huang constructed a fresh cold chain logistics service quality evaluation system based on SERVQUAL model and LSQ model, and used PCA-BP neural network method to carry out empirical analysis on SF cold chain logistics [4]. Aiming at the evaluation of fresh e-commerce distribution service quality, Zhou constructed an evaluation system, used AHP to determine the weight coefficients at all levels, and applied fuzzy comprehensive evaluation method to obtain the overall distribution quality [5]. In order to comprehensively evaluate the service quality of the fresh e-commerce industry, Pang established a service quality index system including five dimensions: product quality, cold chain distribution, technology platform, service flexibility and convenience, and further used the fuzzy comprehensive evaluation method to evaluate the service quality of HM fresh [6].
2.2 The Improvement of TOPSIS Method Domestic and foreign scholars combine TOPSIS with entropy theory, Delphi method and analytic hierarchy process to make up for the weight allocation problem of traditional TOPSIS method, so as to be better applied to complex decision-making problems in various industries. Wang et al. combined entropy weight theory with TOPSIS and applied it to information security evaluation, achieving the goal of reducing information security risks [7]. Kumar R. R. and Kumar C. also use entropy weight method to determine the weight of evaluation indicators, reducing the subjective impact of customers on cloud service selection [8]. Jiang et al. used fuzzy Delphi method and weighted TOPSIS method to establish an evaluation model to test the quality of community public health service organizations [9]. Ocampo L. et al. proposed a comprehensive SERVQUAL model, which combines analytic hierarchy process and TOPSIS to evaluate the service quality of government agencies related to employment [10]. According to the above literature review, it can be found that the research on the service quality of new retail fresh e-commerce is still slightly insufficient, and the selection of evaluation indicators and the determination of evaluation methods are also lack of scientific and applicability. Moreover, the weights among criteria in most existing TOPSIS models are directly determined by DMs. Thus, the goal of this paper is to propose a hybrid evaluation model based on fuzzy AHP-entropy TOPSIS model and apply it to provide a new train of thought for the evaluation of the service quality of fresh e-commerce. Specifically, in the process of determining weights among criteria, this paper proposes a AHP-entropy method, which is used to get objective and subjective weights. In the evaluation stage, TOPSIS is utilized to aggregate the evaluation matrices. The proposed method can comprehensively can consider the subjective and objective factors that affect the weight of the indicators, and more obtain reasonable evaluation results.
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3 Construction of Service Quality Evaluation Index System for New Retail Fresh E-commerce This section focuses on the construction process of the new retail fresh e-commerce service quality evaluation index system. First, the initial index system is constructed on the basis of the existing indicators from literature collation, and SERVQUAL model. Then, the principal component analysis method is used to optimize and adjust the system to meet the research needs.
3.1 Construction of Initial Evaluation Index System
Fig. 1 Statistics of occurrence times of evaluation dimension
Evaluation Dimension
By selecting more than 10 documents related to the evaluation of fresh e-commerce service quality, this paper sorts out and analyzes each evaluation dimension and occurrence times [3–6, 11–18] as shown in Fig. 1. It can be found that the four dimensions with the highest frequency are reliability, convenience, responsiveness and professionalism. SERVQUAL model measures service quality from five dimensions, including tangibility, reliability, responsiveness, assurance and empathy [19]. Based on the existing research results, combined with the service characteristics of the fresh e-commerce industry, the evaluation dimensions of this paper are obtained after adjusting the evaluation dimensions, including professionalism, reliability, responsiveness, empathy, economy and convenience, which is shown in Fig. 2. Specifically, the professionalism dimension includes the clothing of distribution personnel, complete cold chain facilities, product packaging professionalism and epidemic prevention measures [11–13]. The reliability dimension includes seven evaluation indicators: delivery timeliness, order accuracy, platform reputation, product freshness, product integrity, category richness and food source traceability [11, 12, 14]. The responsiveness dimension includes four evaluation indicators: order Professionalism Freshness Reliability Visibility Economical Convenience Responsiveness Empathy Timeliness Remedial Assurance Security Carefulness 0
2
4
6
Number of occurrences
8
10
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Fig. 2 SERVQUAL model and evaluation dimensions
response speed, logistics information update, return and replacement processing speed, and customer service response timeliness [13, 15, 16]. The empathy dimension includes four evaluation indicators: exclusive rights and interests of members, quality of delivery personnel, quality of customer service communication and quality of service recovery [13, 16]. The convenience dimension includes five evaluation indicators: diversity of payment methods, convenience of return and replacement, flexibility of picking up time, flexibility of picking up method, and coverage of distribution scope [12, 13, 17]. The economical dimension includes three evaluation indicators: commodity cost performance, price stability, and basic distribution fee [14, 18].
3.2 Optimization of Evaluation Index System Next, in order to optimize index system, we employ principal component analysis (PCA) by SPSS software and Table 1 shows the analysis result. According to Table 1, a total of 6 factors with eigenvalues greater than 1 are extracted, and the cumulative variance contribution rate is 63.213%, indicating that the interpretation ability of the extracted common factors to the original variables is acceptable, so as to avoid the overlapping of the content of each evaluation index. In order to make the connotation of the principal component clearer, the maximum variance method is further used to rotate the load factor orthogonally, and finally the variables with strong correlation are aggregated into six new dimensions, as shown in Table 2. Among them, the indicators with small load coefficients in each factor (the maximum factor load coefficient is less than 0.45) are deleted as an interference factor, which are exclusive rights of members and complete cold chain facilities. Rename the extracted principal components, and finally get a new retail fresh ecommerce service quality evaluation index system composed of 6 evaluation dimensions and 25 evaluation indicators. The adjusted evaluation index system is shown in Table 3.
Cumulative %
1.950
1.351
1.270
1.182
1.045
2
3
4
5
6
7.223
3.870
4.378
4.703
5.003
63.213
59.343
54.966
50.263
45.261
38.037
1.045
1.182
1.270
1.351
1.950
10.270
3.870
4.378
4.703
5.003
7.223
38.037
Variance %
63.213
59.343
54.966
50.263
45.261
38.037
Cumulative %
Total
38.037
Variance %
Total
10.270
Sum of squares of extracted loads
Initial Eigenvalue
1
Component
Table 1 Total variance interpretation
2.245
2.523
2.615
2.831
3.416
3.438
Total
8.316
9.343
9.686
10.485
12.651
12.733
Variance %
63.213
54.897
45.554
35.869
25.384
12.733
Cumulative %
Sum of squared rotating loads
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Table 2 Rotated component matrix Evaluation criteria
Component 1
Food source traceability
0.780
Product integrity
0.642
Variety richness
0.628
Product freshness
0.560
Packaging professionalism
0.467
Quality of distribution staff
2
3
4
5
6
0.788
Outbreak protection measures
0.769
Distribution staff dressing
0.540
Delivery punctuality
0.516
Logistics information update
0.512
Distribution coverage
0.473
Platform credibility
0.497
0.763
Timeliness of customer service response
0.603
Order response speed
0.585 0.491
Order accuracy rate
0.454
0.555
Exclusive member benefits Pick-up time flexibility
0.760
Flexibility in pick-up methods
0.738
Diversity of payment methods
0.543 0.718
Service remedy quality Speed of returns processing Quality of customer service communication
0.468 0.520
Convenience of return and exchange
0.598 0.541 0.464
Complete cold chain facilities Product cost performance
0.659
Basic delivery fee
0.590
Price stability
0.522
4 Topsis Evaluation Model Based on AHP-Entropy Weight Method 4.1 Determination of Weight Method Different weight distribution may lead to different evaluation results. The methods to determine the weight are roughly divided into subjective weighting method and objective weighting method. In the subjective weighting method, analytic hierarchy
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Table 3 New retail fresh E-commerce service quality evaluation index system Dimension
Evaluation criteria
Product quality (B1)
Food source traceability (C1) Product integrity (C2) Variety richness (C3)
Product freshness (C4)
Packaging professionalism (C5) Distribution services (B2)
Platform construction (B3)
Service remediation (B4)
Quality of distribution staff (C6)
Outbreak protection measures (C7)
Distribution staff dressing (C8)
Delivery punctuality (C9)
Logistics information update (C10)
Distribution coverage (C11)
Platform credibility (C12)
Timeliness of customer service response (C13)
Order response speed (C14)
Order accuracy rate (C15)
Service remedy quality (C16) Speed of returns processing (C17) Quality of customer service communication (C18)
Convenience (B5)
Convenience of return and exchange (C19)
Pick-up time flexibility (C20) Flexibility in pick-up methods (C21) Diversity of payment methods (C22)
Economical (B6)
Product cost performance (C23)
Basic delivery fee (C24)
Price stability (C25)
process is widely used in academia. It turns complex problems into multi-level single objective problems from the perspective of evaluators, and pays more attention to the qualitative analysis and judgment of problems. In the objective weighting method, the entropy weight method is based on the mathematical theory. The weight of indicators is determined by information entropy, which can better reflect the distinguishing ability between indicators. Thus, this paper uses analytic hierarchy process and entropy weight method to calculate the subjective weight and objective weight of the index, and then combines the two to obtain the comprehensive weight of the index, so as to achieve the internal consistency of subjective and objective, and ensure the authenticity and reliability of the evaluation results.
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4.2 Determination of Comprehensive Evaluation Method Among the many comprehensive evaluation methods, TOPSIS method was proposed by Hwang and Yoon [20]. It is a classical method in multi-attribute decision analysis. This method sorts the alternatives according to the distance between the alternatives and the positive and negative ideal solutions. This method has a wide range of applications, which is also applicable to the data with small sample size, and is widely used to evaluate the problems of multiple indicators and multiple evaluation objects. Therefore, it is easy to obtain reliable evaluation results by using this method to evaluate the objectives of this paper.
4.3 Decision Process of AHP-Entropy TOPSIS Model Assuming that there are m new retail fresh e-commerce evaluation objects and n evaluation indicators (which have been positively processed), the original data of the i-th new retail Fresh e-commerce under the j-th indicator is recorded as xi j , forming ( ) an original matrix X = xi j m×n . ( ) Step 1. Standardize the original matrix X to obtain matrix Z = z i j m×n . xi j z i j = /E m i=1
xi2j
, i = 1, 2, ..., m; j = 1, 2, ..., n
(1)
Step 2. Determine the comprehensive weight of indicators. αj + βj ( ) , j = 1, 2, ..., n j=1 α j + β j
ω j = En
(2)
α j is the weight calculated by the analytic hierarchy process, and β j is the weight calculated by entropy weight method. Step 3. Construct weighted standardization matrix. The standardized (matrix ) Z is weighted by the weight ω = (ω1 , ω2 , . . . , ωn ) to obtain the matrix V = vi j m×n = ( ) ω j z i j m×n . Step 4. Determine the positive ideal solution and negative ideal solution. ) ( PIS: V + = v1+ , v2+ , . . . , vn+ ) ( NIS: V − = v1− , v2− , . . . , vn− } { v +j = max v1 j , v2 j , . . . , vm j , j = 1, 2, . . . , n
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} { v −j = min v1 j , v2 j , . . . , vm j , j = 1, 2, . . . , n Step 5. Calculate the distance from each evaluation object to the ideal solution. Euclidean distance is used to calculate the distance between each evaluation object and PIS and NIS (recorded as Di+ , Di− ). | |E )2 | n ( + vi j − v +j , i = 1, 2, . . . , m Di = | j=1
| |E )2 | n ( − vi j − v −j , i = 1, 2, . . . , m Di = | j=1
Step 6. Calculate the relative closeness (Ci ) and rank the evaluation objects. Ci =
Di− , i = 1, 2, . . . , m Di+ + Di−
(3)
The higher the Ci value, the better the ith evaluation object is, and vice versa.
5 Empirical Analysis 5.1 Instance Selection and Data Collection According to the 2021 map of China’s fresh e-commerce industry chain released by iResearch consulting [1], this study selected six representative fresh e-commerce in new retail areas as the research objects, including HEMA fresh, MISSFRESH, Dingdong vegetables, Meituan vegetables, Meituan choose and Duoduo vegetables. The index system and evaluation model constructed above are used for example analysis. The questionnaire uses 5-level Likert scale to quantify consumers’ evaluation of the service quality of fresh e-commerce, and is distributed through Wenjuanxing platform. After deleting 19 invalid questionnaires, we obtained 211 valid questionnaires, and further applied SPSS software to analyze the reliability and validity. The results are shown in Tables 4 and 5. Cronbach’s α from reliability analysis is 0.936, and the KMO value obtained from validity analysis is 0.887, which is greater than 0.8, indicating that the questionnaire is reliable and credible.
Service Quality Evaluation of New Retail Fresh E-commerce Based … Table 4 Reliability analysis
Table 5 Validity analysis
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Cronbach’s α
Number of items
0.936
27
Kaiser–Meyer–Olkin Bartlett sphericity test
0.887 Approximate chi-square
2924.185
df
351
Sig
0.000
5.2 Decision-Making Process Use X 1 , X 2 , X 3 , X 4 , X 5 , X 6 to represent HEMA fresh, MISSFRESH, Dingdong vegetables, Meituan vegetables, Meituan choose and Duoduo vegetables respectively. The specific decision-making process is as follows. Step 1. Standardize the original matrix. The results are shown in Table 6. Step 2. Determine the comprehensive weight of indicators. This study designed an expert scoring table and invited three experts with rich experience in the field of fresh e-commerce to score. The subjective weight of the index (α j ) was calculated by using AHP method. The entropy weight method is used to process the original data and calculate the objective weight of the index (β j ). Combine the two weights to get the comprehensive weight of the index (ω j ), as shown in Table 7. Table 6 Standardized matrix C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
X1
0.44
0.45
0.43
0.43
0.44
0.43
0.42
0.43
0.42
0.43
0.43
0.46
0.41
X2
0.41
0.38
0.42
0.41
0.41
0.41
0.42
0.42
0.39
0.42
0.42
0.42
0.43
X3
0.37
0.38
0.39
0.35
0.39
0.43
0.41
0.38
0.38
0.38
0.38
0.30
0.36
X4
0.43
0.44
0.41
0.41
0.43
0.40
0.40
0.42
0.42
0.40
0.40
0.47
0.43
X5
0.40
0.39
0.41
0.42
0.40
0.39
0.40
0.40
0.42
0.41
0.42
0.38
0.42
X6
0.39
0.40
0.38
0.42
0.38
0.39
0.40
0.39
0.41
0.41
0.40
0.41
0.39
C14
C15
C16
C17
C18
C19
C20
C21
C22
C23
C24
C25
X1
0.41
0.44
0.43
0.42
0.42
0.42
0.44
0.45
0.43
0.45
0.42
0.44
X2
0.43
0.45
0.40
0.44
0.41
0.39
0.43
0.42
0.42
0.42
0.41
0.43
X3
0.38
0.38
0.38
0.37
0.39
0.37
0.39
0.39
0.37
0.36
0.36
0.35
X4
0.44
0.42
0.42
0.41
0.42
0.43
0.42
0.42
0.44
0.42
0.41
0.42
X5
0.40
0.38
0.43
0.40
0.41
0.42
0.39
0.40
0.39
0.40
0.41
0.41
X6
0.39
0.38
0.39
0.41
0.39
0.42
0.37
0.36
0.39
0.39
0.42
0.38
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Table 7 Comprehensive weight results Index
αj
βj
ωj
Index
αj
βj
ωj
C1
0.1220
0.0382
0.0801
C14
0.0178
0.0247
0.0212
C2
0.0327
0.0446
0.0387
C15
0.0152
0.0647
0.0400
C3
0.0309
0.0160
0.0234
C16
0.0533
0.0260
0.0396
C4
0.0309
0.0426
0.0367
C17
0.0533
0.0297
0.0415
C5
0.0295
0.0301
0.0298
C18
0.0496
0.0088
0.0292
C6
0.0815
0.0173
0.0494
C19
0.0143
0.0308
0.0226
C7
0.0707
0.0031
0.0369
C20
0.0353
0.0477
0.0415
C8
0.0454
0.0257
0.0356
C21
0.0309
0.0445
0.0377
C9
0.0275
0.0186
0.0231
C22
0.0135
0.0435
0.0285
C10
0.0170
0.0214
0.0192
C23
0.0546
0.0532
0.0539
C11
0.0159
0.0190
0.0175
C24
0.0103
0.0310
0.0206
C12
0.0833
0.2120
0.1477
C25
0.0290
0.0701
0.0496
C13
0.0356
0.0367
0.0361
Step 3. Construct weighted standardization matrix. The weighted standardization matrix can be obtained by weighting the standardization matrix with weights ω = (ω1 , ω2 , . . . , ω25 ). Step 4. Determine the positive ideal solution and negative ideal solution. Using the weighted standardized matrix to calculate the maximum and minimum values under each index, we can get the positive ideal solution V + and the negative ideal solution V − . V + = (0.0354, 0.0175, 0.0100, 0.0157, 0.0130, 0.0213, 0.0155, 0.0154, 0.0098, 0.0084, 0.0075, 0.0692, 0.0156, 0.0093, 0.0180, 0.0172, 0.0181, 0.0124, 0.0096, 0.0184, 0.0169, 0.0126, 0.0245, 0.0087, 0.0221) V − = (0.0298, 0.0148, 0.0090, 0.0129, 0.0113, 0.0193, 0.0148, 0.0134, 0.0087, 0.0072, 0.0066, 0.0443, 0.0131, 0.0081, 0.0150, 0.0150, 0.0153, 0.0115, 0.0083, 0.0152, 0.0137, 0.0106, 0.0196, 0.0074, 0.0172) Step 5. Calculate the distance from each evaluation object to the PIS and NIS, which is recorded as D+ and D− respectively. Step 6. Calculate the relative closeness and rank the evaluation objects. The results are shown in Table 8. According to the relative closeness, the final ranking is: X1 > X4 > X2 > X6 > X5 > X3 .
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Table 8 Evaluation results Evaluation object
D+
D−
Relative closeness
Ranking
X1
0.0021
0.0264
0.9258
1
X2
0.0094
0.0194
0.6732
3
X3
0.0278
0.0024
0.0802
6
X4
0.0038
0.0268
0.8764
2
X5
0.0153
0.0134
0.4665
5
X6
0.0135
0.0164
0.5495
4
Therefore, among the six selected fresh e-commerce platforms, X1 provides customers with the best quality of fresh service. Then there is X4 , X2 , X6 , X5 , which has a slight lack of service quality in some aspects, reducing the user’s consumption experience. The service quality of X3 is the worst, which shows that the platform cannot effectively meet the needs of users, and its service quality still has a lot of room to improve.
5.3 Service Quality Improvement Strategy The weights of the evaluation dimension layer in descending order are: platform construction (0.2450), product quality (0.2087), distribution services (0.1817), service remediation (0.1329), economical (0.1241), convenience (0.1077), as shown in Fig. 3. It can be seen that the main factors affecting the service quality of new retail fresh e-commerce are platform construction and product quality, with a total weight of 45.37%. Among them, the weight of platform construction accounts for 24.50%, which is the dimension with the largest weight among. In the evaluation index layer, the weights of platform reputation and food source traceability are 0.1477 and 0.0801 respectively, accounting for 22.78% of the 25 evaluation indexes, as shown in Fig. 4. Therefore, they are the key indicators influencing the comprehensive evaluation results. 0.2500
Fig. 3 Weight of evaluation dimension layer Weight
0.2000 0.1500 0.1000 0.0500 0.0000 B1
B2
B3 B4 Dimension
B5
B6
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Fig. 4 Weight of evaluation index layer
Weight
0.12 0.09 0.06
0.00
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25
0.03
Index
Based on the above analysis, we propose some strategies to improve the service quality from the macro and micro levels. At the macro level, fresh e-commerce should be committed to platform construction and ensure product quality, and constantly improve consumers’ purchase experience. At the micro level, fresh e-commerce should pay special attention to improving the reputation of the platform, ensuring that the evaluation in the platform is authentic and credible, and eliminating the phenomenon of false brush praise. On the other hand, fresh e-commerce should start to establish the food traceability system. Consumers can obtain the whole process information of food by scanning the QR code of food label, such as origin, processor, production date and time, safety report, planting environment, etc.
6 Conclusion Based on SERVQUAL model and fully referring to the existing research results, this study constructed an initial evaluation index system, further optimized and adjusted it by principal component analysis, and finally determined an evaluation index system consisting of 6 evaluation dimensions and 25 evaluation indexes, including product quality, distribution service, platform construction, service recovery, convenience and economy. In view of the weight problem of evaluation indicators, this paper, from the perspective of combination weighting, determines the subjective weight and objective weight of each indicator by introducing AHP method and entropy weight method respectively, and realizes the reasonable improvement of the traditional TOPSIS method. Finally, an example is given to verify the applicability of this method. Based on the evaluation results, we can find the deficiencies in the service quality of fresh e-commerce enterprises, which has important practical significance for the future development of the fresh industry. However, this study still has some limitations. First of all, the evaluation index system may still omit some key indicators, resulting in the imperfect evaluation index system in this paper. Secondly, the consumer groups covered by the questionnaire are not comprehensive enough, resulting in the limitations of data sources, which
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will affect the evaluation results to a certain extent. In addition, using Euclidean distance to measure the distance between the evaluation object and the ideal solution sometimes appears invalid. Thus, in future research on the field of fresh e-commerce, it is worth focusing on the above fields.
References 1. Research Report on China’s fresh e-commerce industry, 2021 IResearch consulting Series Research Report, 2021, vol. 5, pp. 337–365 2. Yu, M.Y.: The development and innovation of China’s industrial economy under the global epidemic situation—The rise and development of fresh food e-commerce in China. In: 2020 4th International Conference on Informatization in Education, Management and Business (2020) 3. Qiu, B.: Research on service quality evaluation of fresh e-commerce cold chain logistics based on catastrophe progression method. Beijing Jiaotong University (2017) 4. Zhang, Q.C., Huang, C.R.: Research on service quality evaluation of food cold chain logistics under fresh e-commerce environment—an empirical study based on PCA-BP neural network. J. Dalian Marit. Univ. 18 3, 70–77 (2019) 5. Zhou, X.: Research on service quality of fresh food e-commerce distribution based on AHP Fuzzy—Taking tmall and jd.com as examples. Foreign Trade Econ. Cooperation 8, 78–81 (2020) 6. Pang, J.R.: Research on service quality evaluation of fresh e-commerce under the new retail environment. Beijing Architecture University (2020) 7. Wang, D., Lu, Y., Gan, J.: An information security evaluation method based on entropy theory and improved TOPSIS. In: IEEE Second International Conference on Data Science in Cyberspace. IEEE (2017) 8. Kumar R.R., Kumar, C.: Designing an efficient methodology based on Entropy-TOPSIS for evaluating efficiency of cloud services. In: International Conference on Computer & Communication Technology. ACM, pp. 117–122 (2017) 9. Jiang, S., Yang, L., Jiang, P., et al.: Evaluating the quality performance of reconstructive community public health service based on weighted TOPSIS method. Springer Singapore (2017) 10. Ocampo, L., Bongo, M., Alinsub, J., et al.: Public service quality evaluation with SERVQUAL and AHP-TOPSIS: a case of Philippine government agencies. Socio-Econ Plann Sci 68 (2019) 11. Wu, J.: Research on fresh e-commerce logistics service evaluation index system based on customer perspective. Zhejiang University of Technology (2016) 12. Yi, Y.C., Shi, Y.X.: Research on improving the logistics service quality of fresh e-commerce under the normalization of the epidemic. J. Jiangxi Univ. Finance Econ. 1, 65–75 (2022) 13. Zhang, S.S.: Research on logistics service quality of fresh e-commerce of company B based on customer satisfaction. Henan University of Technology 14. Qiao, X.B.: Research on cold chain logistics service quality evaluation system of fresh products on e-commerce platform. Kunming University of Technology (2020) 15. Jiang, K.: Research on evaluation of logistics service quality of fresh e-commerce facing customer satisfaction. Hangzhou University of Electronic Science and Technology (2018) 16. Wu, C.Y.: Research on the evaluation of logistics distribution service quality of “the last mile” of fresh e-commerce. Hunan University (2018) 17. Han, S.G., Wu, J., Chen, Q.: Research on evaluation index system of logistics service quality of fresh e-commerce. J. Zhejiang Univ. Sci. Technol. 36(02), 138–143 (2016)
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18. Kuang, Y.L.: Research on evaluation of logistics service quality of fresh e-commerce. Hangzhou University of Electronic Science and Technology (2021) 19. Parasuraman, A., Zeithaml, V.A., Berry, L.L.: SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. J. Retailing 64(1), 12–40 (1988) 20. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making Methods and Applications. Springer-Heidelberg, Berlin, Germany (1981)
Research on Book Location Algorithm of Library Based on Improved LANDMARC Algorithm Yue Li, Shuihai Dou, Yanping Du, Zhaohua Wang, Xianyang Su, and Lizhi Peng
Abstract For the current problems of cumbersome book data, low accuracy of book positioning and difficulty of readers to find books in traditional libraries, this paper proposes an adaptive k-nearest neighbor LANDMARC positioning algorithm. In the traditional LANDMARC indoor localization algorithm, the number of k reference tags is fixed, which will bring in the information of lousy reference tags and has the problem of low localization accuracy. In this paper, the k-value selection rule for the reference tag number is improved, and the improved LANDMARC algorithm with adaptive optimal k-nearest neighbors is proposed. In order to verify the effectiveness of the adaptive optimal k-nearest neighbor LANDMARC algorithm, the simulation of the improved LANDMARC algorithm before and after the improvement is carried out using MATLAB, and the simulation results show that the optimized LANDMARC algorithm can improve the localization accuracy by at most 25.9%. In order to verify the effectiveness of the adaptive optimal k-nearest neighbor LANDMARC algorithm, MATLAB is used to simulate the traditional LANDMARC algorithm and the improved LANDMARC algorithm. The simulation results show that the optimized LANDMARC algorithm has a maximum improvement of 25.9% in localization accuracy. The reliability of the improved algorithm has been significantly improved, which meets the library positioning requirements, promotes readers’ reading stickiness and improves their reading experience, and helps to promote reading for all better.
Y. Li · S. Dou (B) · Y. Du · Z. Wang · X. Su · L. Peng Beijing Key Laboratory of Digitalized Printing Equipment, Beijing Institute of Graphic Communication, Beijing, China e-mail: [email protected] Y. Li e-mail: [email protected] Y. Du e-mail: [email protected] X. Su e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_13
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Keywords The library · LANDMARC · Adaptive k-nearest neighbor algorithm · Books positioning
1 Introduction At present, readers who borrow books usually go to specific areas to find them according to the book index number. However, most readers lack experience in book indexing, so it is time-consuming and challenging to find the target books among many books. In order to make paper books meet readers’ needs of checking out books quickly and accurately, domestic scholars and foreign scholars have proposed indoor positioning technologies such as ultrasonic technology, infrared technology, ultra-wideband technology, and radio frequency identification technology. Radio frequency identification technology began to be applied in the positioning field, such as storage goods positioning, because of its easy implementation, lower cost, and less interference. Due to the increasing demand for indoor positioning technology, the research of indoor positioning technology has been studied more and more deeply by domestic and foreign researchers. Its LANDMARC algorithm is a relatively mature indoor positioning scheme which introduces reference tag-assisted positioning and has the advantages of less stability affected by environmental changes and higher positioning accuracy at a lower cost. Jin et al. [1] applied the LANDMARC algorithm to scenic spots to solve the complex positioning problem where some reference tags cannot be evenly distributed. Liu [2] designed a parking management system based on LANDMARC algorithm to solve the problem of difficult parking and car finding. Huang [3] proposed a personnel positioning system using LANDMARC algorithm to accurately position the trajectory distribution of personnel in the cabins of ship repair enterprises. Wang et al. [4] improved the LANDMARC algorithm to solve the problems of tag failure and signal strength reading error. Han et al. [5] proposed LANDMARC algorithm to reduce the localization error in underground coal mines. In summary, few scholars have applied LANDMARC indoor localization algorithm to library book localization. The LANDMARC algorithm is a distanceindependent localization algorithm, which is a situational localization method, in other words, it determines the location coordinates of the tags to be located by introducing the regularly arranged reference tags for auxiliary localization, which makes the changes of the external environment have less influence on the localization results of the algorithm and thus has higher stability. However, the traditional LANDMARC algorithm can only locate in the regular environment, and there are errors in locating in complex indoor environments. If the number of reference tags k is too large, many irrelevant reference tags will be introduced, which will cause a signal collision and affect the localization performance. While if the number of reference tags k is too small, the reference tags that play a crucial role in localization will be lost. As a result, many scholars have proposed their optimization methods based on the traditional LANDMARC algorithm. Ying et al. [6] proposed a two-label LANDMARC
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improvement algorithm, which uses a localization model with one active label and one passive label to improve the stability of the algorithm. However, the global reference tag will lead to a large number of unnecessary calculations and affect the real-time and accuracy of localization. Based on the above, this paper improves the rules for selecting the k-value of the reference tag for the LANDMARC localization algorithm and proposes an adaptive k-neighborhood algorithm. The algorithm adaptively selects k neighboring reference tags according to the location of the target tag to locate the target tag location, avoiding different tags to be tested from bringing in bad reference tag information due to artificially set k values, which not only ensures the accuracy requirements of readers for book positioning but also reduces the cost of the positioning system.
2 Library Book Location Algorithm Based on LANDMARC Improved Algorithm LANDMARC positioning algorithm uses k reference tags to calculate the position of the tracking tag, where k value is always fixed, but for different environments to be tested, the optimal value of k is different. Finding the optimal value of k is one of the critical issues in improving the accuracy of the algorithm. First of all, this paper calculates the tag t under test and the reference tag the Euclidean distance matrix m, find the nearest reference tag as a “virtual tracking tag” t*. The next step is to choose a different k value, the k value from 1 to 9, the k value is applied to the virtual tracking tag, and get a new tag position. Here it is called the ' “estimated coordinate” t* and the positioning error between the estimated coordinate and the actual position of the object is calculated. By comparing the positioning error between the estimated coordinates and the actual position of the object, a k with the smallest error value is obtained. Finally, find the optimal k with the minimum error, and use the optimal k to compute the tracking tag coordinate [7]. Adaptive K-nearest neighbor algorithm is shown in Fig. 1. (1) Calculate the vector matrix of the Euclidean distance between the tracking tag and the nine adjacent reference tags, in the form of: | | D ∗ = D1t∗ , D2t∗ , . . . , D9t∗
(1)
wherein, the Euclidean distance D1t∗ , D2t∗ , …, D9t∗ is arranged in order from smallest to largest. (2) According to the Euclidean distance value from small to large, select the reference tag closest to the tracking tag, namely, the reference tag with the smallest Euclidean distance, which is set as “virtual tracking tag” t*. k*(1 < k* < 10) reference tags adjacent to the virtual tracking tag t* are selected to locate the virtual tracking tag t*.
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Fig. 1 Adaptive K-nearest neighbor algorithm
Start
The signal strength vector E for the reference tag
The signal strength vector S for the tracking tag
Euclidean distance D between the tracking tag t and the reference tag Select the first k minimum distance
Calculate the weight of reference label and coordinate information of the tracking tag
find the nearest reference tag as a "virtual tracking tag" t*
Euclidean distance D* between the virtual tracking tag t* and the reference tag Select different k (0 0.05, and there was no heteroskedasticity in the corrected model.
4.2.3
Serial Correlation Test and Correction
The LM test of the model shows that the p-values of et−1 andet−2 are greater than the significance level of 5%, that is, they do not pass the significance test of the variables, so the model does not have serial correlation.
4.2.4
Robustness Test
In order to avoid the influence of extreme values in the sample on the results, in this paper, individual outliers are excluded from the robustness test, and whether they remain robust by reducing the sample size. After reducing the sample size, the direction and significance of the regression results of the core variables do not change significantly, indicating that the regression results are robust.
4.3 Regression Results Y = −0.7013 + 0.0395 ln X + 0.0507 ln A1 + 3.93E − 08A2 + −7.08E − 10A3 + 4.51E − 10A4 (−8.25) (2.09) (18.92) (7.79) (−6.27) (9.13) 2
R 2 = 0.9729 R = 0.9716 F = 803.5857 D.W. = 1.9648
(9)
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From the above equation, it can be seen that the Digital Inclusive Finance Index is significant at the 5% significance level, indicating that there is a positive correlation between the two. Accelerating the development of digital inclusive finance is conducive to improving people’s living standards and meeting their needs for a happy life. For the control variables, people’s happy life is positively correlated with digital inclusive finance, GDP per capita, the number of unemployment insurance participants and year-end deposit balance, while it is inversely correlated with total import and export. The increase in the level of digital inclusive finance, the increase in the number of unemployment insurance participants and the increase in year-end deposits are all conducive to satisfying people’s daily lives, improving their quality of life and satisfying their well-being.
4.4 Cluster Analysis Further empirical analysis was carried out on the different tiers of cities in China according to the city rankings published in the City Business Attractiveness Rankings. The number of first-tier cities selected was 19, 29 s-tier cities, 69 third-tier cities, 83 fourth-tier cities and 87 fifth-tier cities (Table 4). In terms of the impact of digital inclusion on people’s well-being, differences in economic strength and development of cities cause the fit of the model to vary across city classes. In general, the degree of fit decreases as the city rank increases with Tier 1 cities being the best and Tier 4 cities being the worst. The reasons for this variation may be as follows. Tier 1 cities are the strongest, most economically developed, most densely populated and most radially driven among Chinese cities. Their political status is high, their economic growth is fast, their cities are developed, their innovation capacity is strong and their residents are highly educated, so their digital inclusive finance Table 4 Empirical results for different classes of cities Tier 1 cities
Tier 2 cities
Tier 3 cities
Tier 4 cities
Tier 5 cities
lnX
3.53
2.977
2.715
1.569
1.256
lnA1
−0.163
2.744
4.208
6.106
5.432
A2 A3 A4 R2 X Y
1.381 −0.81 4.347 0.937 288.53 0.401
−0.02
0.33
0.824
3.049
−0.593
−0.117
0.061
−0.513
3.653
2.782
2.358
1.327
0.641
0.632
0.452
0.532
275.082
251.176
240.259
225.449
0.186
0.098
0.077
0.06
X, Y Mean values of the digital inclusion index and the people’s well-being index, respectively
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operates smoothly, their coverage is large, their service quality is good and their people enjoy financial services more conveniently and efficiently, so the model fit is the highest. Tier 2 and 3 cities have a higher degree of model fit due to their comparative advantages. Their political status, economic development, scale of urban development, people’s living standards and digital inclusion financial development are higher than those of Tier 4 and 5 cities, but lower than those of Tier 1 cities, so the impact of digital inclusion financial development on people’s well-being is less enhanced than that of Tier 1 cities. The fourth and fifth tier cities, on the other hand, are at a disadvantage in terms of overall strength. The region’s slow economic growth, lagging urban development, low level of digital inclusion financial development, small scope of penetration and low per capita income make the impact of other factors on people’s well-being in the region more significant. The worst model fit for Tier 4 cities may be due to the higher marginal effect of progress in digital inclusion financial development on the level of people’s well-being in Tier 5 cities. As far as the control variables in the model are concerned, the impact of each control variable is not the same for different cities. The significant effect of GDP per capita on non-Tier 1 cities may be explained by the fact that Tier 1 cities have higher GDP per capita, which has less scope for improvement and less room to influence people’s well-being; the significant effect of the number of unemployed participants on Tier 1 and Tier 5 cities may be due to the fact that employment factors are more important in influencing people’s income than other cities; and the least significant effect of year-end deposit balance on Tier 5 cities may be due to the fact that Because of the low per capita income of residents in Tier 5 cities, the number of year-end deposits of residents is smaller, so its impact on people’s well-being is also smaller.
5 Conclusions and Recommendations This paper theoretically analyzes the influence mechanism of digital inclusive finance development on people’s happy life, uses the coefficient of variation method to measure the level of people’s happy life, and constructs a model to empirically study the influence of digital inclusive finance development on people’s happy life. The findings of the study found that: firstly, there is regional variability in people’s happy life level, and in general, people’s happy life decreases with the decrease of urban rank. Secondly, digital inclusive finance has a positive stimulating effect on people’s happy life, while there is obvious regional heterogeneity in its impact effect. The stimulating effect is strongest in Tier 1 cities, followed by Tier 2 and 3, and weakest in Tier 4 and 5. Based on the above findings, this paper makes the following recommendations.
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(1) Strengthen the infrastructure of digital inclusive finance and improve the organizational system of digital inclusive financial services. We will improve the coverage of cell phone payment and online banking services in backward regions, promote the establishment of digital communication service platforms in each region, and gradually improve network services for households in backward regions to ensure that the people enjoy the advantages of digital inclusive finance. At the same time, we will improve the organizational structure for the development of digital inclusive finance and promote cooperation with other organizations and government finance departments. (2) Increase the level of digital inclusive financial services and reduce the threshold and cost of financial participation for residents. The government guides and encourages financial institutions and emerging technology enterprises to develop diversified, multi-dimensional and multi-level digital inclusive financial products and services, broaden the scope of financial services, break the limitations of space and time, and provide the most appropriate products and services according to the actual needs of different customers; encourage financial innovation and Internet innovation to stimulate the vitality of digital inclusive finance, lower the threshold for residents to participate in finance, and at the same time protect the safety of participants and reduce risks so as to better meet people’s needs for financial services and enhance their well-being. (3) Implement differentiated digital financial inclusion policies to reduce the disparity in people’s well-being and living standards between regions. The government should formulate differentiated policies to promote the development of digital inclusive finance and enhance people’s happiness according to different local development conditions. Developed cities should play the role of radiation, optimize the allocation of financial resources, improve the utilization rate of resources, and drive the development of neighboring provinces and cities. In remote and rural areas, financial infrastructure should be improved, the layout of institutions should be reasonably optimized, innovation in financial products and services should be carried out, and the threshold of financial services should be lowered to effectively benefit the people and improve their well-being. The contribution of this paper is as follows. First, it broadens the economic effects of current research on digital inclusive finance to include the impact on people’s well-being in the research field. Second, it establishes an indicator system of people’s well-being using multiple indicators, which provides a reference for future evaluation of people’s well-being and related research. Finally, the study used data from 290 prefecture-level cities and conducted a cluster analysis of the results to make the data analysis more comprehensive and accurate, providing theoretical references for the government to formulate policies and evaluate the implementation effects of policies as well as financial institutions to improve the relevant systems and enhance their services. However, this study also has several limitations that pave the way for future research. First of all, the research sample in this paper lacks the support of countylevel data, and the study of people’s happy living standards in the county-level region is also very important to achieve common prosperity. Therefore, it is necessary to
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supplement county-level data and samples for further research in the future. In addition, considering that the impact of digital inclusive finance on people’s well-being is a long-term dynamic process, future research will expand the time span to consider the impact of time-varying factors on the results. Acknowledgements ➀ National Development and Reform Commission project “Research on the Development Strategy and Competitiveness of my country’s Coal-to-Liquid and Coal-to-Gas Industrialization in the New Era” (2020-1141); ➁ Hebei Province Social Science Fund Project “Research on the Impact of China’s Economic Policy Uncertainty on Commercial Banks’ Interest Rate Derivative Hedging” (HB21YJ030); ➂ Hebei Province Higher Education Scientific Research Program Project “Research on the Economic Mechanism and Countermeasures of Inclusive Finance Supporting Innovation and Entrepreneurship in Xiongan New Area” (SQ2021122); ➃ Hebei University of Economics and Business Scientific Research and Development Program “Research on the Mechanism and Policy of Green Finance Supporting Industrial Low-Carbon Development in Xiongan New Area” (2021YB10).
References 1. Li, S., Chen, G.: The employment effect of happiness—an empirical study of happiness, employment and implicit re-employment. Econ. Res. 50(03), 62–74 (2015) 2. Cai, Y., Huang, Y., Zheng, X.: Spillover effects of poverty eradication policies: a study based on the impact on the life satisfaction of non-poor households. China Ind. Econ. 11, 24–43 (2021) 3. Zhang, L., Yi, X., Yang, B.: Commercial insurance, digital economy and people’s happiness in the context of the common wealth goal: empirical evidence from household survey data. Res. Financ. Econ. 37(01), 42–60 (2022) 4. Zhang, J., Dong, X., Li, J.: Can digital inclusive finance advance common wealth?—an empirical study based on micro household data. Financ. Res. 1–15 (2022) 5. Zeng, Y., Jiang, C., Cui, Z.: The impact of digital finance on high-quality economic development—a study based on the spatial Durbin model. Technol. Econ. 41(412(04)), 94–106 (2022) 6. Song, J., Xinyue, Z.: Research on the impact of digital inclusive finance on high-quality economic development. Financ. Forum 26(08), 39–49 (2021) 7. Xu, M., Shen, Y., Zhou, P.: Research on the impact of digital inclusive finance on high-quality economic development. Resour. Dev. Markets 37(09), 1080–1085 (2021) 8. Xiaoyan, Z.: An empirical study on the impact of rural inclusive finance on the development of rural revitalization—Jiangsu Yancheng as an example. Southern Rural 37(01), 15–21 (2021) 9. Teng, L., Zhang, H., Tang, S.: Digital financial innovation and rural economic development in an inclusive perspective. Res. World (12), 34–42 (2021) 10. Xiaoling, S.: An empirical test of digital inclusive finance to narrow the urban-rural income gap. Financ. Econ. Sci. 06, 14–25 (2017) 11. Ma, W., Xu, X., Wen, J., Zhang, T.: An empirical analysis of the impact of digitalization of inclusive finance on urban-rural income gap. Cooper. Econ. Technol. (03), 78–79 (2022) 12. Yi, X., Zhou, L.: Does the development of digital inclusive finance significantly affect residential consumption—micro evidence from Chinese households. Financ. Res. (11), 47–67 (2018) 13. Guo, H., Zhang, Y., Peng, Y., He, Z.: A study of regional differences in digital financial development affecting rural residents’ consumption. Agric. Technol. Econ. 12, 66–80 (2020) 14. Li, S., Yu, W.: The social network effect of happiness—an empirical study based on Chinese residents’ consumption. Econ. Res. 55(633(06)), 172–188 (2020)
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15. Zhang, X., Cai, G.: Income, values and residents’ happiness—empirical evidence from adult survey data in Guangdong. Manag. World 216(09), 63–73 (2011) 16. He, L., Pan, C.: Solving the Easterlin paradox in China: income disparity, inequality of opportunity and residents’ happiness. Manag. World 215(08), 11–22+187 (2011) 17. Wang, Y., Tan, Z., Zheng, L.: A study on the impact of digital inclusive finance on social security. Quant. Econ. Tech. Econ. Res. 37(07), 92–112 (2020) 18. Chao, K., He, H., Li, J.: Construction and empirical evidence of national happiness index evaluation system. Stat. Decis. Mak. 04, 91–94 (2016) 19. Liu, Z.: Overview of overseas national happiness index development. Foreign Theor. Dev. 12, 30–35 (2013) 20. Wan, S., Wan, Q.: An empirical analysis of China’s national well-being coordination indexbased on 2008 panel data. Explor. Econ. Issues 11, 1–6 (2010) 21. Cheng, G., Xu, C., Xu, J., Xiang.: The concept of establishing an accounting system for national well-being in China. J. Geogr. (06), 5–15 (2005) 22. Hao, L., Zhang, Q.: Happiness index and its statistical measurement. Stat. Decis. Mak. 36(17), 38–42 (2020) 23. Guo, F., Wang, J., Wang, F., Kong, T., Zhang, X., Cheng, Z.: Measuring the development of digital inclusive finance in China: indexing and spatial characteristics. Economics (Quarterly) 19(04), 1401–1418 (2020)
Study on the Evaluation of Employment Quality in China’s Provinces Based on Principal Tensor Analysis Yingxue Pan and Xuedong Gao
Abstract Employment is the biggest livelihood of the people, we must adhere to the employment-first strategy and active employment policy to achieve higher quality and fuller employment. This paper takes 30 provinces, autonomous regions and municipalities directly under the Central Government in China from 2011 to 2020 as the research sample. From the six dimensions of employment environment, employment status, employability, labor remuneration, social security, and labor relations, an evaluation system for measuring provincial employment quality is constructed. The employment quality index data is expressed in the form of space–time tensor, and four principal components are extracted by using the tensor-based principal component analysis method (modulo-k advocated quantitative analysis model). According to the coefficients of the four principal components of the employment quality data in each dimension, the comprehensive score of the employment quality of each province, autonomous region and municipality directly under the Central Government is calculated, and a visual analysis of the development and evolution process of the employment quality is carried out. Keywords Quality of employment · Spatiotemporal data · Principal tensor analysis
1 Introduction Since 2010, China’s tertiary industry has developed rapidly, the contribution rate of the tertiary industry to GDP has increased year by year, and the employment structure has gradually changed. In 2020, the employment ratio of China’s tertiary industry was about 50%, but due to the impact of the current sluggish world economic growth, the Y. Pan · X. Gao (B) School of Economics and Management, University of Science and Technology Beijing, Beijing, China e-mail: [email protected] Y. Pan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_17
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employment capacity of the tertiary industry is constrained. Employment quality is closely related to people’s life and social economic growth. Improving the quality of employment is the basic prerequisite for achieving high-quality economic growth. Accurately assessing the development level of employment quality and analyzing the main factors affecting employment quality are of great significance for achieving high-quality employment and high-quality economic growth. In order to further implement the high-quality development policy proposed by the Chinese government and realize the high-quality development of China’s employment, it is of great significance to accurately evaluate the development of China’s inter-provincial employment quality. At present, different countries and regions have different standards for the construction of employment quality system. As early as the 1870s and 1880s, Western countries have started research on the construction of employment quality system, especially some countries in Europe. Chun Li Wang [1], Younga Kim [2], Errol Cocks and Syan H [3], Marcela Chrenekova and Katarna Melichova [4], Karen Van Aerden and Vanessa Puig-Barrachina [5] continuously improve and optimize the employment quality evaluation system based on the current research status. Qian Fang [6] built an employment quality evaluation system on five dimensions to evaluate the employment quality of migrant workers. Yang Yumei [7] constructed a three-level evaluation system for the employment quality of college students, and calculated the employment quality score by weighting the AHP. Ming Juan [8] used the equivalent income method and the objective index method to construct and measure the employment quality of migrant workers. Research on the quality of employment in various regions may be related to major issues such as the national economy and people’s livelihood, sustainable social development, and talent strategy. At present, although there are many studies on the construction of employment quality system, there is a lack of comprehensive quantitative expression of time and space for the comprehensive indicators of employment quality in various provinces in my country, and there is also a lack of comprehensive application research in terms of feature extraction, dimensional reconstruction and visualization of spatiotemporal employment quality data. This paper measures the quality of employment from the matching degree among employment environment, employment status and employability to the degree of labor remuneration, social security and labor relations. Based on Principal Tensor Analysis (PTAk) and principal component analysis, select 18 employment quality indicators to establish a comprehensive evaluation system. The research objects of this paper are 30 provinces, autonomous regions and municipalities directly under the Central Government (hereinafter referred to as provinces) except China’s Tibet Autonomous Region, Hong Kong, Macau and Taiwan. Collect the employment quality-related index data of 30 provinces in my country from 2011 to 2020, organize and construct this highdimensional data based on the tensor structure, use the PTA3 model to decompose it, and extract the principal components from it. Analyze the quantitative comprehensive evaluation index of key elements, and perform dimension reconstruction feature analysis and visual expression through tensor fiber operation and slicing operation.
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2 Research Method 2.1 PTAk Model of k-order Tensor (K > 2) PTAk is a method of decomposing high-dimensional array data, that is, N (N > 2)order tensors. It is a high-dimensional extension of principal component analysis. It uses low-order tensors to approximate high-order tensors, thereby realizing feature extraction for high-dimensional data. PTAk is essentially a generalized singular value decomposition model, and uses the alternating least squares method to achieve the calculation of the principal tensor. The orthogonal sub-tensor of the original tensor can be effectively obtained to approximate the high-dimensional space, and the reliability test and selection of the principal tensor can be performed based on the size of the singular value. For a long time, for the processing of spatiotemporal multidimensional data, it is often converted into vectors in order and analyzed in its linear subspace. Treating spatiotemporal multidimensional data in an ordinal manner often ignores its multilinear structure. Just like a bidirectional analysis table, a multidirectional analysis table must be collapsed or expanded in a table with two modes. Thus looking at 2nd-order interactions in multiple ways, rather than looking at multiple interactions. The framework used by the PTAk model extends some duality principles and thus extends the multidimensional analysis approach focusing on spatiotemporal data. The form of the PTAk model for a k-order tensor is as follows: The first principal quantity, the optimized form of singular value is as follows: σ1 = max X..(ψ ⊗ ϕ ⊗ φ) ||ψ||s = 1 ||ϕ||v = 1 ||φ||t = 1 = X (ψ1 ⊗ ϕ1 ⊗ φ1 )
(1)
Among them, “⊗” represents the tensor product, “..“ indicates the contraction operation, which is equivalent to the inner product operation of the tensor space, X represents matrix data or tensor data of the same data table, ψ1 , ϕ1 , φ1 are the first principal components, ψ1 ⊗ ϕ1 ⊗ φ1 is called the first principal component, From this, the best rank-one approximation and singular value of a given tensor X can be calculated. The calculation method of this step is called the RPVSCC algorithm. It is equivalent to TUCKALS3 which takes only one component per modulo. The proof of the uniqueness of the tensor solution given by Eq. (1) under orthogonal transformation was given by Leibovici in 1998 [9]. The calculation of the PTAk model is more convenient, because it does not directly calculate the tensor product of vectors by algebraic methods, but executes the contraction operator, as shown in Eq. (2): X..(ψ ⊗ ϕ ⊗ φ) = (X..ψ)..(ϕ ⊗ φ)
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= (X..ϕ)..(ψ ⊗ φ) = (X..ϕ)..(ψ ⊗ ϕ) = ((X..ψ)..ϕ)..φ
(2)
If an orthogonality constraint is added to Eq. (1), the second a principal tensor can be solved, The optimization process equivalent to the previous step in the solution process acts on the projection P Xψ ⊥ ⊗ϕ ⊥ ⊗φ ⊥ of the tensor X on the orthogonal tensor ( 1 1 1) of the first argument. Where ψ1⊥ ⊗ϕ1⊥ ⊗φ1⊥ represents the orthogonal tensor of the first principal tensor, which can also be written as (I d − Pψ1 ) ⊗ (I d − Pϕ1 ) ⊗ (I d − Pφ1 ). According to the algorithm pattern given in Eq. (3), PTAk decomposition yields a method of synthesizing data from the set of uncorrelated components. In the schemas implemented for the PTA3(X) and PTAk(X) functions, it is possible to distinguish between principal tensor and associated principal tensor. The latter are related to principal tensor because they display one or more components of this principal tensor in the first principal tensor’s component set. After a tensor of rank k performs a contraction operator on a given component, the associated principal tensor quantity can be decomposed by PTA(k-1) to obtain. This makes the PTAk algorithm a recursive algorithm. When k = 3, there are: P T A3(X ) = σ1 (ψ1 ⊗ ϕ1 ⊗ φ1 ) + ψ1 ⊗1 P T A2(P(ϕ1⊥ ⊗ φ1⊥ )(X..ψ1⊥ )) + ϕ1 ⊗2 P T A2(P(ψ1⊥ ⊗ φ1⊥ )(X..ϕ1⊥ )) + φ1 ⊗3 P T A2(P(ψ1⊥ ⊗ ϕ1⊥ )(X..φ1⊥ )) ⊥ + P T A3(P(ψ1⊥ ⊗⊥ 1 ⊗φ1 )X )
(3)
The notation ⊗1 means that in this full tensor product operation, the vector on the left will occupy the ith position in k positions, suah as ϕ1 ⊗2 (α ⊗ β) = α ⊗ ϕ1 ⊗ β.
2.2 PTA3 Model Algorithm Before introducing the algorithm of the PTAk model, the RPVSCC algorithm is explained first. The goal of the RPVSCC algorithm is to find the principal tensor quantity of the initial tensor X, and its pseudocode is as follows:
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) RPVSCC algorithm( Input: Tensor X, maximum iteration step size and stop iteration threshold Output: singular value and its principal tensor components , , step: 1: Initialize a set of principal tensor components 2: for from 1 to
If the extreme value of out of the iteration loop and output
is less than , jump
The pseudocode of the complete PTAk algorithm is as follows:
) PTAk algorithm( Input: tensor X, order =3, maximum iteration step size and stop iteration threshold Output: principal tensor , =1,2, , , associated principal tensor , =1,2, , step: 1: Run the RPVSCC algorithm to get the principal tensor 2: On the orthogonal tensor space of the solution obtained in step 1, continue with step 1 to get all principal tensors 3: for from 1 to For from 1 to
return j
In the pseudocode of the PTAk algorithm, for a fixed i, j, Ai does not necessarily j represent only an associated principal tensor quantity. Because the P T A(k − 1) ∗ X i optimization may select multiple association principal tensor. Moreover, the singular value of each principal tensor quantity obtained is not necessarily larger than the singular value of all associated principal tensor quantities, Because the singular value of the associated principal tensor of the ith principal tensor may be larger than the singular value of the i + 1th principal tensor.
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3 China’s Interprovincial Employment Quality Measurement and Its Visualization Firstly, an employment quality evaluation system is constructed in six dimensions: employment environment, employment status, employability, labor remuneration, social security, and labor relations; Then use the PTA3 model to perform principal tensor analysis on it, extract the principal components from it, analyze the quantitative comprehensive evaluation index of key elements, and calculate the comprehensive score of employment quality in each province; Finally, according to the visual expression of the employment quality scores of each province.
3.1 Employment Quality Measure (1) Construction of China’s Inter-provincial Employment Quality Evaluation Index System According to China’s employment status and the availability of data, this paper constructs an employment quality evaluation index system with 6 dimensions and 18 sub-indicators, and analyzes the panel data of 30 provinces in China from 2011 to 2020. The construction of the employment quality index system in this paper refers to previous research [10]. The index system and index interpretation are shown in Table 1. (2) Data preprocessing This paper uses a dataset of 30 provinces in my country from 2011 to 2020 on 18 indicators. The original data of the employment quality evaluation system in this paper are all from China Statistical Yearbook, China Population and Employment Statistical Yearbook, China Labor Statistical Yearbook and provincial statistical yearbook. For partial missing data, mean interpolation is used in this paper. The abnormal value in the statistical yearbook is adjusted on the basis of analyzing the development trend of the index. A comprehensive analysis of employment quality indicators related to major spatial patterns and temporal changes is the purpose of this study. Denote space modulo 1, time modulo 2, and indices modulo 3. After obtaining the preprocessed data, based on the tensor structure, R statistical software is used to organize it into multi-dimensional space–time data, forming tensor data that can be arbitrarily decomposed and combined in time and space dimensions, and the dimension of the tensor is (30, 10, 18). (3) PTA3 calculation result The above-mentioned organized tensor employment quality data is decomposed by PTA3 principal tensor quantity, and the tensor component coefficients of each principal tensor and each dimension are obtained. Figure 1 shows the correlation of
Study on the Evaluation of Employment Quality in China’s Provinces … Table 1 China’s provincial employment quality evaluation system
Level indicators
The secondary indicators
Employment environment
Per capita GDP level
The employment situation
Urban registered unemployment rate
233
Proportion of working-age population Proportion of employment in the tertiary industry Urban–rural income gap index Rate of industrial accidents
Employability
The number of years of education in the labor force Quality of skills training
Labor remuneration Wage level of employees in urban units Total wage level of urban employees The social security
Urban minimum living security coverage Per capita spending on social security and employment Endowment insurance participation rate Unemployment insurance participation rate Participation rate of industrial injury insurance Health insurance participation rate
Labor relations
Union participation rate Labor dispute settlement rate
principal tensor with variance contribution rate greater than 0.01%. The four largest singular values are 53.7118, 23.4939, 18.9946, and 8.8380, respectively. They correspond to the first principal tensor (principal tensor 1), principal tensor 6, principal tensor 7, and the second principal tensor (principal tensor 11), where 1, 6, 7, and 11 are the numbers corresponding to the principal tensors, Their variance contribution rates to the overall data are 53.60%, 10.25%, 6.70%, and 1.45%, respectively, with a cumulative contribution rate of 72%. (4) Principal Component Coefficient of Employment Quality Tensor After obtaining the first four largest singular values of the initial principal tensor and their corresponding principal tensor quantities, the score coefficients of these four tensors in any dimension can be calculated, Table 2 shows the score coefficients of these four principal tensors in the indicator dimension. Then the four principal components can be expressed as Ui = X Fi , i = 1, 2, 3, 4, where X = (X 1 , X 2 , · · · , X 18 ), and the weights of each principal component are ω1 = 53.6 = 0.7444, ω1 = 10.25 = 0.1424, ω1 = 6.7 = 0.0931, ω1 = 1.45 = 72 72 72 72
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Fig. 1 Third-order principal tensor analysis results
0.0201. If the composite index of employment quality score is Y, then Y can be expressed as: Y =
4 E
ωi Ui
i=1
3.2 Expansion and Visualization of Principal Tensors The following fiber operation and slicing operation based on tensors use the score coefficients of principal tensors in different dimensions to calculate the comprehensive score of employment quality in each combined dimension and display it visually. (1) Expansion and visualization in the spatial dimension According to the fiber operation of the tensor, by fixing the time dimension and the index dimension, 10 × 18 tensor fibers along the space dimension can be obtained. Here, the four fibers are used as examples for visualization, and in order to show the employment quality, the index dimension is fixed on the comprehensive index of employment quality score calculated by the score coefficient Y. In the time dimension, the fibers were fixed in 2011, 2014, 2017, and 2020, respectively. After ranking according to the comprehensive index score of employment quality, the scores of the
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Table 2 Score coefficients of principal tensors in the indicator dimension Variable
F1
F6
F7 −0.0003
F11
Percapita GDP level
0.2856
0.1602
Proportion of working-age population
0.2223
−0.0927
−0.357
−0.0528
Urban registered unemployment rate
0.0819
−0.2047
0.5966
−0.7025
Proportion of employment in the tertiary industry
0.2795
−0.1631
−0.0573
Urban–rural income gap index Rate of industrial accidents The number of years of education in the labor force
0.1783
0.0314
0.4092
−0.185
−0.0462
−0.2958
0.0295
0.0744
0.2455
0.0524
0.0103
−0.185 −0.5141
0.1493 −0.228
−0.1286
−0.0817
0.1102
Wage level of employees in urban units
0.2899
−0.1268
0.1029
0.0294
Total wage level of urban employees
0.2246
−0.4094
0.2163
−0.0198
Urban minimum living security coverage
−0.1515
−0.4546
−0.3649
0.0827
Per capita spending on social security and employment
0.157
−0.4317
−0.3979
−0.1513
Endowment in surance participation rate
0.3068
0.0103
0.0276
−0.0816
Unemployment in surance participation rate
0.3094
−0.0636
0.1321
−0.0499
Participation rate of industrial injury insurance
0.3123
−0.0217
0.0752
0.0940
Health insurance participation rate
0.3114
−0.0603
0.0566
−0.0633
Quality of skills training
Union participation rate Labor dispute settlement rate
0.2236
0.1597
−0.1292
−0.1496
−0.2996 0.0162
0.3343 −0.0084
top-ranked regions are quite different, and the scores of the middle and lower regions are more concentrated. It can also be seen from the results that with the implementation of the western development policy, the quality of employment in the western region is steadily improving. However, the development potential of employment quality in central China is insufficient, and it is gradually being overtaken by the western region. And from 2011 to 2020, the gap has shown a growing trend, and the development of employment quality is very unbalanced, which needs to be improved urgently. (2) Expansion and visualization in time and space dimensions According to the slicing operation of the tensor and fixing the index dimension, 18 tensor slices along the space and time dimensions can be obtained. Here is one of the slices as an example to show. Similarly, in order to express the comprehensive situation of the development of employment quality, the comprehensive index of employment quality Y calculated by the score coefficient is used as a fixed index dimension for visualization.
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From a national perspective, the average employment quality in China fluctuated around 1.3795 from 2011 to 2020, and the overall employment quality was relatively low. However, the overall trend is straight upward, indicating that the quality of our employment is developing in a healthy way and steadily improving. From the perspective of the provinces as a whole, Beijing, Shanghai, and Tianjin have the highest employment quality scores, followed by Zhejiang, Guangdong, and Jiangsu, and Jiangxi, Heilongjiang, and Henan have the lowest employment quality scores. Judging from the situation of each province, in the past 10 years, the employment quality scores of Beijing and Shanghai have grown rapidly, ranking the top two, and their advantages have become more and more obvious. Tianjin’s employment quality score ranks third, but its gap with the top two has gradually widened, and the gap with the following provinces in employment quality has gradually narrowed. In the past 10 years, the employment quality of all provinces has shown an upward trend. Although the rate of increase is different, the ranking of employment quality scores in 2020 has certain reference significance compared with 2011. Compared with 2011, the provinces whose employment quality scores have risen by more than 5 places are: Guizhou, Sichuan, Yunnan, Guangxi, Gansu. It is worth noting that these five provinces are all from the western region. This shows that the strategy of developing the western region of my country is very effective, and the quality of employment in the western region is steadily improving. In particular, Yunnan and Guizhou rose 14 and 10 places respectively. Compared with 2011, the provinces that have dropped more than 5 places are: Inner Mongolia, Anhui, Liaoning, Shanxi, Hebei, and Henan. The six provinces are all from the central and eastern regions. Although the quality of employment in the six provinces is also rising steadily, the increase is small and the growth is relatively flat. In particular, Shanxi Province dropped 17 places, and Inner Mongolia dropped 9 places.
4 Conclusion This paper studies the application of the principal component analysis method based on tensor-PTAK model in the measurement of inter-provincial employment quality in China. The conclusions drawn from the model are as follows: (1) The overall level of employment quality in China is not high and there are large differences in employment quality among provinces. The average employment quality in the past 10 years is only 1.3795. Beijing, Shanghai and Tianjin are firmly in the top three in terms of comprehensive employment quality scores, but the gap between Tianjin and Beijing and Shanghai is increasing, and the gap between Tianjin and the fourth place is gradually narrowing. In the past 10 years, the provinces with the rapid increase in the overall score of employment quality were all from the western region of my country, and the provinces that were lagging behind were all from the central and eastern regions.
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(2) Beijing and Shanghai, as the first-tier cities in China, have a strong development momentum. They have the fastest growth rate of employment quality among the 30 provinces, and their advantages are becoming more and more obvious. With the implementation of China’s western development policy, the quality of employment in the western region is steadily improving. However, the quality of employment in central China shows insufficient development potential and is gradually being overtaken by the western region. And from 2011 to 2020, the ten-year development cycle showed a trend of widening the gap in employment quality. The development of employment quality in the eastern, central and western regions of China is very uneven and urgently needs to be improved.
References 1. Wang, C. L.: Application of computer design of nearly linear numerical analysis model in research quality. Appl. Mech. Mater. 3207(556), 68–74 (2014) 2. Kim, Y.: Changes in precarious employment among south Korean women. Math. Popul. Stud. 22(2), 80–91 (2015) 3. Cocks, E., Thoresen, S.H., Lee, E.A.L.: Pathways to Employment and quality of life for apprenticeship and traineeship graduates with disabilities. Int. J. Disabil. Dev. Educ. 62(4), 131–136 (2015) 4. Chrenekova, M., Melichova, K., Marisova, E., Moroz, S.: Informal employment and quality of life in rural areas of Ukraine. Eur. Countryside 8(2), 90–102 (2016) 5. Van Aerden, K., Puig-Barrachina, V., Bosmans, K., Vanroelen, C.: How does employment quality relate to health and job satisfaction in Europe? A typological approach. Soc. Sci. Med. 02, 76–87 (2016) 6. Fang, Q., Dongyou, C., Xiaogang, Z.: Construction of the index system for measuring the employment quality of migrant workers. Jiangxi Soc. Sci. 09, 189–192 (2013) 7. Yumei, Y., Jina, L.: Research on employment quality evaluation of college graduates based on AHP and BP neural network. Chin. J. Educ. S1, 148–149 (2015) 8. Juan, M.: Situation and trend of migrant workers’ employment quality. Urban Problems 03, 83–91 (2016) 9. Leibovici, D., Sabatier, R.: A singular value decomposition of a k-way array for a principal component analysis of multiway data, PTA-K. Linear Algebra Appl. 269(1–3), 307–329 (1998) 10. Yingxue, P., Xuedong, G.: Measurement and Clustering Analysis of Interprovincial Employment Quality in China. LISS 2021, pp. 297–310. Springer, Singapore (2022)
Study on Low-Carbon Emissions in Vehicle Routing Problems with Split Deliveries and Pickups Cheng Jin, Lijun Lu, and Jianing Min
Abstract To reduce the pollution routing problem (PRP) generated by vehicles during logistics distribution, an approximate calculation method for fuel consumption and carbon emissions is introduced from the perspective of energy saving and emission reduction. Based on the vehicle routing problem with split simultaneous deliveries and pickups (VRPSPDP) model, a green VRPSPDP (G-VRPSPDP) model is established. The objective is to find environment-friendly green paths and minimize the total costs. A two-stage heuristic approach is designed to solve the problem. The effectiveness and feasibility of the proposed model and algorithms are verified using numerical experiments. The experimental results show that vehicle speeds, vehicle load rates, travel distances, and the number of routes greatly affect fuel consumption and carbon emissions. Multitype vehicles will decrease the number of vehicles used and route numbers and increase the clustering flexibility. The experimental results also show that it would be necessary for new energy vehicles to enter the transportation market. Keywords Pollution routing problem · Vehicle routing problem · Energy consumption · Carbon emissions · Green and low-carbon
C. Jin University Office, Taihu University of Wuxi, Wuxi, China e-mail: [email protected] L. Lu School of Business, Nanjing University, Nanjing, China e-mail: [email protected] L. Lu · J. Min (B) School of Business, Taihu University of Wuxi, Wuxi, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_18
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1 Introduction Since the Copenhagen world climate conference, low-carbon economy and lowcarbon technologies have attracted widespread attention worldwide. People have gradually advocated low-carbon and green concepts, such as low pollution, energy consumption, and emissions. Energy conservation and emission reduction have become people’s consensus. President Xi’s “dual carbon” goal has solemnly been promised to the world twice and has pointed out the direction for the future economic development model—a low-carbon economic development model marked by energy conservation and emission reduction. To solve the negative impact of logistics activities on the environment, the green vehicle routing problem (GVRP) concept in the logistics industry is rapidly emerging [1]. In 2017, Sbihi and Eglese [2] proposed that the vehicle routing problem (VRP) research would be combined with energy saving and emission reduction and the impact of logistics on the environment. Subsequently, research in this field began attracting the attention of scholars worldwide. In 2011, Bektas and Laporte [3] considered economic and environmental factors, incorporating carbon dioxide emissions, fuel consumption, travel time, etc. into the vehicle routing planning to obtain an economical and environmentally friendly vehicle routing planning, and proposed the pollution routing problem (PRP). In 2012, Demira et al. [4] proposed an adaptive large-scale neighborhood search algorithm (ALNS) to minimize fuel consumption by addressing the path pollution problem. In 2012, Jabali [5] established a timedependent vehicle routing model considering the travel time and carbon dioxide emissions and solved them with a tabu search algorithm. In 2013, Demira [6] proposed an extended problem of route pollution—the dual-objective pollution route problem, considering the two mutually exclusive objective functions of minimum fuel consumption and driving time. In 2013, Kwon et al. [7] considered the VRP of a fixed number and multimodel of vehicles with carbon emissions, and established a mixed-integer programming VRP model with buying/selling carbon emission rights. In 2013, Li and Fu [8] studied the low-carbon routing problem of multitype vehicles with a fixed number of vehicles based on energy consumption and carbon emissions. A multistarting tabu search algorithm based on path division was designed to solve this problem. In 2014, Li and Zhang [9] introduced a carbon emission measurement method that considers both vehicle load and travel speed for the VRP under the carbon emission trading mechanism. In 2015, Chen et al. [10] considered the impact of vehicle fuel consumption on carbon dioxide emissions while delivering products to multiple customers to ensure on-time delivery and the fuel consumption minimization. In 2015, Zhang et al. [11] analyzed the characteristics of the multitype VRP on the fuel consumption and carbon emission factors and built a corresponding optimization model. A heuristics algorithm was proposed based on the genetic algorithm. In 2015, Duan and Fu [12] expounded the hetero-type VRP considering carbon emissions, established a mathematical model, and designed a hybrid neighborhood tabu search algorithm based on optimal insertion and exchange sub-processes to solve the problem. In 2016, Zhang [13] studied a VRP with uncertain travel time, which
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assumed that the service time of the vehicle at the customer was a linear function of the customer’s demand, and an adaptive large-scale neighborhood algorithm was designed to find the Pareto non-dominated solution set of the problem. In 2019, Li et al. [14] studied the route optimization problem of low-carbon vehicle distribution in multichannel urban networks, designed the path rules to judge the minimum congestion level after considering the influence of the congestion, and constructed a low-carbon vehicle routing optimization model. In 2019, Zhou [15] studied the low-carbon timing-dependent VRP and the departure scheduling problem, comprehensively considered the distribution cost and carbon emission cost, and constructed a mathematic model. In 2019, Duan et al. [16] studied a robust multiobjective optimization model for the stochastic time-varying VRP with hard time window constraints. The ant colony algorithm was designed and the Pareto non-dominated solution set was obtained. In 2020, Zhao [17] proposed a GVRP for multivehicle type logistics distribution in traffic congestion areas. A dual-objective green vehicle routing model was constructed and a hybrid differential fused with a simulated annealing algorithm was designed for the problem-solving. In 2021, Di et al. [18] proposed a multi-vehicle green vehicle routing optomization model considering dynamic congestion to reduce logistics distribution cost and promote carbon reduction. So far, the research cases in this field have focused on pure delivery. There is few case where deliveries and pickups are completed simultaneously, i.e., VRP with simultaneous split deliveries and pickups (VRPSPDP). This paper will take the VRPSPDP as the research object based on [19] to explore the influences of vehicle speed and load on fuel consumption and carbon emissions in GVRP. From the perspective of energy-saving and emission reduction in logistics and distribution, a green VRPSPDP (G-VRPSPDP) is established, introducing the comprehensive modal emission model (CMEM) model [3]. The model takes the minimum total cost as the optimization objective to find environmentally friendly green routes. A twostage approach is designed to solve this problem. Experiments on the reconstructed Solomon benchmark dataset are used to evaluate the feasibility and effectiveness of the proposed algorithms and to study the vehicle speed and the load effect on fuel consumption and carbon emissions. The rest of this paper is organized as follows: Sect. 2 describes the VRPSPDP and GVRP. Section 3 describes the two-stage heuristic approach in detail. Section 4 presents and discusses the computational results. Finally, Sect. 5 gives out the conclusions.
2 Problem Description The transportation path in G-VRPSPDP is represented as a topology graph G = (V, A), where V = { 0, 1,…, n} as the set of nodes, node 0 as the depot; V 0 as the set of customers; A = { (i, j) | i, j ∈ V and i /= j} as the set of arcs between each pair of nodes. A depot has a fleet of single-model vehicles with capacity Q to meet the delivery and pickup demands of several customers. In one trip, each vehicle leaves
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from and returns to the depot with all deliveries and pickups within the capacity Q, and each stop is satisfied by both delivery and pickup demands. Let n and m represent theEnumber of customer nodes and the number of vehicles used in the En n qd i and i=1 qpi denote the cumulative values of deliveries and VRPSPDP. i=1 pickups|from (points ) |Therefore, the minimum number of vehicles En 1 to n,Erespectively. n qdi , i=1 qpi /Q [20, 21], where ⎡x⎤ is the greatest integer used is max i=1 function. For simplicity, there are no time window limitations and no restrictions on maximum distance and time in this paper. The objective is to schedule environmentally friendly green routes to minimize the total travel cost, which consists of energy consumption, carbon emissions, and driver wages, to fulfill the delivery and pickup demands of all customers, which is referred to as PRP in [3], to achieve a balance between the economic costs and the environmental protection. The symbols used in the general VRP are as follows. Symbol
Definition
i, j
Node index; i, j = (1, 2, …, m)
k
Vehicle index; k = (1, 2, …, m);
d ij
Distance between points i and j; (d ii = 0, d ij = d ji );
Q
Vehicle capacity;
qd i
Delivery demand of customer i;
qpi
Pickup demand of customer i;
qd ij
Delivery load moved from customer i to customer j; qd ij ≥ 0;
qpij
Pickup load moved from customer i to customer j; qpij ≥ 0;
x ijk
If vehicle k goes from customer i to customer j, x ijk = 1; otherwise, x ijk = 0
yik
If point i is served by vehicle k, yik = 1; otherwise, yik = 0
The symbols used for fuel consumption and CO2 emissions are as follows. Symbol Definition
Constant
Pt
Total tractive power demand requirement in watts (W = kg m2 /s3 ), or joules (J = kg m2 /s2 )
E ij
CO2 emission over the arc (i, j); E ij = ε × F ij
ε
The carbon emission factor of the fuel
q
Caloric value of fuel
F ij
Fuel consumption over the arc (i, j) F ij ≈ Pij ≈ Pt (d ij /υ ij )/q ≈ (α ij (w + f ij ) d ij )/q; Load-induced energy requirements + (βυ ij 2 d ij )/q; Speed-induced energy requirements (continued)
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(continued) Symbol Definition
Constant
vij
Speed (m/s) on arc (i, j)
d ij
Distance between node i and j
θ ij
The road angle of arc (i, j)
a
The acceleration (m/s2 )
Cr
The coefficients of rolling resistance
0°
(m/s2 )
0.01
g
The gravitational constant
α ij
α ij = α + g sin θ ij + g C r cos θ ij
9.81
ω
The curb (empty) weight (kg)
f ij
Vehicle load on arc (i, j) (kg)
A
The frontal surface area of the vehicle (m2 ) —a light/medium heavy vehicle 5.0
Cd
The coefficients of drag
0.70
ρ
The air density
β
B = 0.5C d Aρ
Ff
The unit cost of fuel
Fe
The unit cost of CO2
Fd Td
Driver salary per unit time En En Em Driving time = i=0 j=0 k=1 x i jk d i j /vi j
t sij
Service time over the are (i, j), e.g., loading or unloading
(kg/m3 )
1.2041
The G-VRPSPDP model is as follows: ⎧ ⎫ n E m n E ⎨ ⎬ E {| ( ) | } αi j ω + f i j + βvi2j xi jk di j /q min (F f + ε • Fe ) ⎩ ⎭
(1)
i=0 j=0 k=1
⎧ ⎫ n E n E m ⎨ E ( ) ⎬ di j /vi j + tsi j xi jk + Fd ⎩ ⎭
(2)
i=0 j=0 k=1
s.t. m E
qd j0 yik = qd j , j = 1, 2, . . . , n;
(3)
qp j0 yik = qp j , j = 1, 2, . . . , n;
(4)
k=1 m E k=1 n E i=1
qd 0i yik ≤ Q, k = 1, 2, . . . , m;
(5)
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qpi0 yik ≤ Q, k = 1, 2, . . . , m;
(6)
i=1 θ E
qpi yik +
n E
qd i yik ≤ Q,
i=θ +1
i=0
i = 0, 1, . . . θ, θ + 1, . . . , n; k = 1, 2, . . . , m; n E
(7)
qd i0 = 0
(8)
qp 0i = 0
(9)
i=0 n E i=0 n n−1 E E
(x i jk − x j ( j+1)k ) = 0, k = 1, 2, . . . , m;
(10)
i=1 j=i+1 n E j=0
x0 jk = 1,
n E
x j0k = 1, k = 1, 2, . . . , m;
(11)
j=0
Equations (1) and (2) represent the total travel distance, where (1) denotes the cost of fuel consumption and CO2 emissions, and (2) denotes the service cost of drivers (including the transportation and other service costs). Equations (3) and (4) state that the delivery/pickup demands of customer j can be satisfied by more than one visit. Equations (5) and (6) indicate that the delivery/pickup demands of a vehicle are within the vehicle’s capacity. Equation (7) constrains that the total demands of the deliveries and pickups at any node should not be larger than the vehicle’s capacity in one tour. That is to say, both delivery and pickup can occur at one node, and the sum of the pick-up quantity before node θ (including the node θ ) and the delivery quantity after node θ along the route cannot exceed the vehicle capacity. Equation (8) ensures that no delivery demands is directed to the depot, and Eq. (9) ensures that no pickup demands is directed from the depot. Equation (10) indicates that the vehicle arriving at node j must also leave that node. Equation (11) expresses that each vehicle enters/ exits the depot only once per tour.
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3 Proposed Two-Stage Approach A two-stage approach is proposed. In the first stage, an improved sweep algorithm is employed to cluster the customer domain according to the vehicle capacity Q and to determine the split points and values. Then, a modified Clarke–Wright (C– W) saving algorithm is adopted in each cluster to determine the order of access to customers based on Eq. (7), and to calculate the travel cost in each route on the basis of the traditional distance-minimizing model. In the second stage, the costminimizing model is used that the objective function includes distance cost as well as fuel consumption and carbon emission costs.
3.1 First Stage-Distance Minimizing (1) Ascertain Subdomains: The multi-restart-iterative-sweep-algorithm for split deliveries and pickups (B-s-MRISA) detailed in [19] is employed to cluster the problem domain to sub-domains based on the vehicle capacity Q and to determine the split points and values in each cluster of the VRPSPDP. (2) Routing Criterion: After clustering, the problem becomes a VRPSDP in each route. A modified C–W algorithm [19] is adopted to meet the VRPSDP requirements. En , • The load quantity at the starting point equals Rso = i=1 di yik ≤ Q. En , • The load quantity at the endpoint equals Reo = i=1 pi yik ≤ Q. • Eq. (7) must be satisfied that the total loading of both the deliveries and pickups at any node in one tour is not larger than the vehicle’s E capacity. The , , load quantity at the current point i equals R0,i = Rs0 − i−1 i=1 di yik − di + Ei−1 i=1 pi yik + pi ≤ Q.
3.2 Second Stage-Cost Minimizing The cost of the fuel consumption, CO2 emissions, and driver services are calculated along the route determined. ( ) • Fuel consumption over the arc (i, j)Fi j = (ai j w + f i j di j )/q + (βυi j2di j )/q). • CO2 emissions over the arc (i, j) E ij = ε•F ij . • The cost of the fuel and CO2 emissions = (F f + ε • |} En En Em {| consumption F x . Fe ) i=0 i j i jk j=0 k=1 • The driving time over the arc (i, j) Tdi j = di j /vi j . • The other service time over the arc (i, j) = tsi j . ) | En En Em |( • The cost of driver services = F d i=0 j=0 k=1 Tdi j + tsi j x i jk
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4 Experiments The Solomon benchmark datasets were used to verify the feasibility and effectiveness of the proposed algorithm. The Solomon datasets cannot be directly used in the VRPSPDP because they do not contain pickup demand data. Therefore, we constructed a new dataset from an original dataset by keeping its delivery demand unchanged, and extracting the delivery demand from another dataset and placing it as the pickup demand data. For an example, the new dataset CR101 was constructed by keeping the delivery demand in C101 unchanged, and extracting the delivery demand in R101 and placed it as the pickup demand. To make the weight more realistic, one unit of weight is equal to 10/1000 (T). The experiments were carried out in C on a 64-bit Windows 7 machine with a 2.50 GHz Intel (R) Core processor and 8 GB of memory.
4.1 Execution on the Constructed Dataset 1 Our experiment was on the constructed dataset 1, CR101-25. The vehicle capacity was 2 (T), the total deliveries were 4.6 (T), and the total pickups were (3.32) T. The total routes were 3, and the loading rate was 0.77. Table 1 shows the changes in CO2 emissions, fuel consumption, and driver salaries with speed. The results show that: • Dist. and load-induced are unchanged because the routes and loads are unchanged with speed up in one tour. Speed-induced increases rapidly with speed up. • Costs of CO2 and fuel increase with speed up. • The lowest total cost appears at speed 60 (Km/h); so does the total cost ratio in cost ratios. • Driver salaries decrease with speed up. The proportions of drivers in the total cost decreased from 78.47%, 65%, 53.69%, 42.00%, to 34.37%. Table 1 Results performed on dataset 1 Speed (a) Energy changes with speed Dist
(b) Cost changes with speed
Load-induced Speed-induced Total energy
CO2
Fuel
Driver
Total cost
40
236.365 22.5907
17.0802
39.6708 1.4119
22.5403 87.2726 111.2248
60
236.365 22.5907
38.4304
61.0211 2.1718
34.6711 71.5151 108.3579
80
236.365 22.5907
68.3207
90.9114 3.2356
51.6542 63.6363 118.5261 73.4897 58.9090 137.0021
100
236.365 22.5907
106.7512
129.3418 4.6034
120
236.365 22.5907
153.7217
176.3123 6.2751 100.1774 55.7575 162.2101
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Table 2 Results of “Pickup after Delivery” Performed on dataset 1 Speed (a) Energy changes with speed Dist 40
(b) Cost changes with speed
Load-induced Speed-induced Total energy
272.756 23.0502
19.7100
CO2
42.7602 1.5219
Fuel
Driver
Total cost
24.2956 94.4726 120.2901
60
272.756 23.0502
44.3476
67.3978 2.3987
38.2942 76.3151 117.0080
80
272.756 23.0502
78.8401
101.8903 3.6264
57.8922 67.2363 128.7549 83.0897 61.7890 150.0835
100
272.756 23.0502
123.1877
146.2379 5.2047
120
272.756 23.0502
177.3903
200.4405 7.1339 113.8866 58.1575 179.1780
4.2 Execution “Pickuping After Delivering” on the Constructed Dataset 1 In G-VRPSPDP, the delivery and pickup operations at any node are completed simultaneously, and the goal is to improve the loading rate. Considering that the increase in the load rate will affect the increase in fuel consumption, we tried to change the operation mode of delivery and pickup. In linehaul customers, only deliveries were carried on. The pickups would be done in backhaul. In this way, the load rate was reduced in one tour. Table 2 shows the expression results on the constructed dataset CR101-25. Comparing this with Table 1, we found that each item in Table 2 is enlarged. • Dist. and load-induced are enlarged at 15.40% and 2.03%, respectively. Speedinduced is enlarged by 15.40% because dist. is enlarged by 15.40%. • Cost of CO2 and fuel and total energy is increased by 7.79%, 10.45%, 12.08%, 13.06%, and 13.68% at each speed. • The change in the operation mode for delivery and pickup does not help fuel consumption.
4.3 Execution on the Constructed Dataset 2 In order to study the advantages of split-demands over the unsplit-demands, we enlarged values of the delivery and pickup of the constructed Dataset 1, and formed the constructed Dataset 2. We kept the coordinates of customer nodes but changed the delivery and pickup values as (value * 4 + 0.1* Q) to all nodes, where Q denotes the vehicle’s capacity, similar to the method in [19]. Thus, each node in the constructed Dataset 2 had delivery and pickup values between 12 and 110% of the vehicle capacity. The averages of the delivery and pickup were 46.8% and 36.56% of the vehicle capacity, respectively. The results performed on the constructed Dataset 2 show that split-demands has significant advantages compared to unsplit-demainds. We would further study the performances of G-VRPSPDP based on the constructed Dataset 2.
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Tables 3 and 4 show the performed results of the G-VRPSPDP experiments on unsplit-demands and split-demands, respectively. Comparing the two tables, we concluded that each item in Table 4 decreased because of the reduction in distance, route numbers and the increment in load rate. • Load- and speed-induced decreases by 11.60% and 16.62%, respectively, with dist. and load down. • CO2 and fuel decreased by 13.78%, 14.78%, 15.38%, 15.76%, and 15.98% at each speed, respectively. • Cost of drivers increased rapidly by 4.61%, 11.34%, 16.62%, 20.87%, and 24.35%, respectively, with dist. up at each speed. • Total cost decreased by 0.28%, −0 25%, 1.52%, 3.94%, and 6.23%, respectively, at each speed. The distinctive is that the total cost at speed 60 (Km/h) is negative. • Total cost is at the lowest at speed 60 (Km/h). • When the heterogeneous vehicles can be selected, the number of vehicles used decrease in this experiment, and the drivers’ costs and the travel distances are reduced. Table 5 shows the results of the experiments performed that three larger capacity 9/8/7 (T) vehicles carry out the transportation task instead of the twelve 2 (T) vehicles. Thus, the clustering could be more flexible after abandoning the limit of vehicle capacity.
5 Conclusion This study developed a mathematical model for the G-VRPSPDP. The model takes the minimum total cost as the optimization objective to find environmentally friendly green routes. A two-stage approach was designed to solve this problem. The first stage involves distance-minimizing approach, and the second stage involves costminimizing approach. Experiments on the constructed Solomon benchmark dataset are used to evaluate the feasibility and effectiveness of the proposed algorithms, and to analyze how far the vehicle speed and the load affect fuel consumption and carbon emissions. • The experimental results reveal that the total energy, the cost of CO2, and fuel increase with speed up, and the driver decreases with speed up. Also, the lowest total cost is approximately at speed 60 (Km/h). • The proportion of driver, fuel, and CO2 in total cost at speed 60 (Km/h) are approximately 63.9%, 34%, and 2.1%, respectively. The proportion of drivers reduces, and the proportion of fuel and CO2 in the total cost increases with speed up.
Total energy
73.3598
782.881
782.881
782.881
80
100
120
73.3598
73.3598
73.3598
782.881
73.3598
782.881
40
56.5714
509.1700
353.5856
226.2898
127.2849
582.4870
426.9390
299.6463
200.6510
129.9295
20.7299
15.1952
10.6647
7.1410
4.6243
330.9470
242.5650
170.2450
113.9970
73.8230
Fuel
(b) Cost changes with speed Speed-induced
CO2
Load-induced
(a) Energy changes with speed
Dist
60
Speed
Table 3 Results performed on unsplit dataset 2
112.1900
122.6220
138.2810
164.3790
216.5690
Driver
463.9100
380.3800
319.2100
285.5180
295.0350
Total cost
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Total energy
64.8520
652.765
652.765
652.765
80
100
120
64.8520
64.8512
64.8519
652.765
64.8513
652.765
40
47.16876
424.5224
294.7991
188.6839
106.1323
489.3957
359.664
253.5323
170.9878
112.0227
17.4171
12.8004
9.0232
6.0856
3.9869
278.0600
204.3358
144.0522
97.1560
63.6470
Fuel
(b) Cost changes with speed Speed-induced
CO2
Load-induced
(a) Energy changes with speed
Dist
60
Speed
Table 4 Results performed on split dataset 2
139.5100
148.2113
161.2638
183.0200
226.5500
Driver
434.9900
365.3788
314.3577
286.2500
294.2000
Total cost
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99.9785
251.388
251.388
251.388
80
100
120
99.9785
99.9785
99.9785
251.388
99.9785
251.388
40
163.4854
113.5404
72.6652
40.8724
18.1663
263.4680
213.5316
172.6541
140.8542
118.1503
9.3770
7.5990
6.1440
5.0130
4.2050
149.7000
121.3200
98.0900
80.0300
67.1300
Fuel
CO2
Total energy
(b) Cost changes with speed Speed-induced
Dist
Load-induced
(a) Energy changes with speed
60
Speed
Table 5 Results performed on dataset 2 by three larger capacity vehicles
66.7600
70.1100
75.1400
83.5200
100.2800
Driver
225.8300
199.0200
179.3800
168.5600
171.6200
Total cost
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• CO2 is a small proportion of the total cost. To reduce CO2 emission, two ways can be adopted: enlarge the carbon emission factor of the fuel and encourage the usage of vehicles with other energy sources. The limitation of this work is only one dataset experiments and the static road network. The future research on G-VRPSPDP solutions will include more case experiments, and consider the development of more algorithms in time-dependent travel environments. Acknowledgements This research was supported by the Philosophy and Social Science Research Project of Jiangsu Province Education Commission (Grant No. 2021SJA0903), the National Natural Science Foundation of China (Grant No. 61872077), Jiangsu Provincial Education Commission Humanities and Social Science Research Base Fund (Grant No. 2017ZSJD020), and Jiangsu Provincial Key Construction Laboratory of Internet of Things Application Technology, Taihu University of Wuxithe. Special thanks to the reviewers and editors for their careful review the manuscript and for their pertinent and useful comments and suggestions.
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14. Li, S.Y., Dan, B., Ge, X.L.: Optimization model and algorithm of low carbon vehicle routing problem under multi-graph time-varying network. Comput. Integr. Manuf. Syst. 2, 454–468 (2019) 15. Zhou, L.: Integrated optimization research on vehicle routing and scheduling in city logistics with time-dependent and CO2 emissions considerations. Comput. Eng. Appl. 4, 1–10 (2019) 16. Duan, Z.Y., Lei, Z.X., Sun, S., Yang, D.Y.: Multi-objective robust optimisation method for stochastic time-dependent vehicle routing problem. J. Southwest Jiaotong Univ. 3, 1–9 (2019) 17. Zhao, Z.X., Li, X.M., Zhou, X.C.: Green vehicle routing problem optimization for multi-type vehicles considering traffic congestion areas. J. Comput. Appl. 40(3), 883–890 (2020) 18. Di, W.M., Du, H.I., Zhang, P.G.: Optimization of multi-vehicle green vehicle routing problem considering dynamic congestion. Comput. Eng. Des. 09, 2614–2620 (2021) 19. Min, J.N., Lu, L.J., Jin, C.: A two-stage heuristic approach for split vehicle routing problem with deliveries and pickups. In: Shi, X., Bohács, G., Ma, Y., Gong, D., Shang, X. (eds.) LISS 2021. Lecture Notes in Operations Research, pp. 479–490. Springer, Singapore (2022) 20. Mitra, S.: An algorithm for the generalized vehicle routing problem with backhauling. AsiaPacific J. Oper. Res. 22(2), 153–169 (2005) 21. Mitra, S.: A parallel clustering technique for the vehicle routing problem with split deliveries and pickups. J. Oper. Res. Soc. 59(11), 1532–1546 (2008)
An Improved Contention-Based MAC Protocol Based on IEEE 802.11 Distributed Coordination Function Wenpeng Li and Xu Li
Abstract With the development of information technology, more and more devices need to access the network. Contention-based Media Access Control (MAC) protocol can better adapt to the changes in network topology and is widely used without complex synchronization and control scheduling algorithms. In the system using contention-based MAC protocol, as the number of access channel nodes increases, severe conflicts and collisions occur when the nodes compete for the channel, leading to a severe decline in system resource efficiency. This paper proposes an improved contention-based MAC protocol DCF* based on IEEE 802.11 distributed coordination function(DCF), which reduces the number of competing nodes and effectively enhances the system resource efficiency by adding the information maintenance of neighbor nodes. The relationship between system delay, system resource efficiency, and back-off parameters, neighbor maintenance times of the two protocols are simulated under the same node scale, and the resource efficiency of the two protocols is analyzed and compared with the increase of the number of nodes in the network. The results show that the resource efficiency performance of the DCF* protocol is better than that of the DCF protocol. Keywords Contention-based MAC protocol · IEEE 802.11 DCF · Resource efficiency optimization · Neighbor maintenance
1 Introduction IEEE 802.11 standard is one of the leading technology for wireless local area network (WLAN). In the past twenty years, based on the mechanism of contention in IEEE802.11 standard distributed coordination function (DCF) has become one of the important MAC protocols [1–3] for Mobile Ad hoc Networks (MANET). W. Li (B) · X. Li School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China e-mail: [email protected] X. Li e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_19
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In monitoring and protecting the environment, monitoring troops, equipment in war, and the detection of wards and patient care in medical treatment, numerous nodes need to be introduced to collect data information. Data collision and contention for access channels will waste many resources, resulting in increased delay and decreased resource efficiency of the communication system. Many scholars have put forward improvement schemes in the communication system using contention-based MAC protocol. Scholars generally assume that this node knows the information of other nodes in the network without considering whether this node can maintain other nodes. Therefore, it mainly optimizes resource efficiency based on priority or hybrid protocol. References [4–6] proposed a hybrid mechanism based on CSMA/CA and TDMA for the access channel of numerous Internet of things devices, which improves the resource efficiency and performance of using the CSMA/CA mechanism alone. Kobatake and Yamao [7] proposed an SS-CSMA/CA mechanism for wireless ad hoc networks, reducing the collision probability between messages and improving network performance. Shi et al. [8] proposed an improved CSMA/CA protocol, which uses a shorter preamble to build the network, shortens the contention time, and improves the resource efficiency and performance of the system. Dai and Yamao [9] proposed a CSMA/CA mechanism based on the time slot, which improves the packet transmission performance and resource efficiency in an interference environment. Fu and Ding [10] proposed a hybrid protocol based on CSMA/CA and SOTDMA for mobile ad hoc networks to improve network throughput. Lin et al. [11] proposed a TDMA and IEEE 802.11 DCF protocol hybrid method that provides contention-free TDMA cycles for high priority services and contention DCF cycles for low priority services, which improves resource efficiency. Iqbal and Lee [12] proposed a group based MAC layer contention protocol, which groups the nodes in the network, selects a leader node in each group, relays the data packets of this group to the base station, and uses the contention mechanism to compete for the channel in the group, to reduce the conflict and collision between nodes and improve the resource efficiency. The above studies do not consider whether the current node can accurately maintain its neighbor nodes. Therefore, this paper proposes an improved contention-based MAC protocol DCF* based on IEEE 802.11 DCF protocol. Based on the DCF protocol, the maintenance of neighbor node information is added. The number of nodes that can be maintained is regarded as the nodes within the contention range. The number of nodes participating in the contention is reduced to improve resource efficiency further. This paper studies the influence of backoff parameters and maintenance times on resource efficiency under the two protocols and sets reasonable backoff parameters and maintenance times. The results show that the DCF* protocol can improve the resource efficiency of the system. The rest of this paper is as follows: The second section puts forward the system model and explains the network structure and the DCF* protocol. The third section describes the calculation method of resource efficiency and delay in the DCF* protocol. The fourth section evaluates the DCF* protocol and makes a comparative analysis with the DCF protocol. The fifth section summarizes the full text.
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2 System Model 2.1 Network Structure In the one-hop range, the contention between nodes is considered, the hidden terminal and exposed terminal problems are not considered, each node is assumed to be saturated, the number of retransmission of packets is not limited, and packet transmission errors are only caused by conflicts. .r is the maximum communication distance between two nodes. With this node as the center of the circle and .r is the radius, all nodes in the circle are likely to collide with this node. All nodes are randomly distributed in the circle. The node density in the network is .λ, then the number of nodes in the network is . N . The network distribution model is shown in Fig. 1. .
N = λπr 2
(1)
2.2 DCF* Protocol Based on DCF protocol, the neighbor node of this node is firstly maintained through the election mechanism, and the neighbor node is maintained through the round of Network Configuration (NCFG) exchange. The number of nodes that can be maintained is . Nk , and the channel contention is carried out through the contention mechanism within . Nk . After the node enters the contention process, the node monitors the channel state. If the channel is idle, the node enters the back-off state. When the channel is always idle during the back-off process and the back-off time exceeds the distributed interframe space (DIFS) time slot, the node can send data packets. The packet is successfully transmitted if an ACK reply is received after the short interframe space (SIFS) time slot. If the node is in the back-off state and channel busy is detected in the DIFS
Fig. 1 Network model
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timeslot, the node freezes the back-off timer and enters the NAV state. After the completion of data packet transmission by other nodes, the node unfreezes the backoff timer and continues to back off until the data packet is sent.
3 Performance Analysis Model This paper proposes a performance analysis model and designs a calculation method for the delay .T and resource efficiency .η under the two protocols. .T should be the sum of maintenance delay .Tnc f g and contention delay .Tcsma , and .η should be the ratio of packet delay to system delay. The delay is: . T = Tnc f g + Tcsma (2) The resource efficiency is: η=
.
Tdata T
(3)
3.1 Maintenance Delay ex p is the backoff index, .basic is the backoff multiplier, the backoff interval length H and the election interval length .v are defined as follows:
. .
.
H = 2exp +basic
(4)
v = 2exp
(5)
.
According to the election mechanism, . K is the maintenance times of ncfg messages. After . K times of maintenance, the number of nodes in the network . Nk is obtained, and the election success probability . Pele should satisfy the following equation: v + P1ele 1 = (Nk − 1) ∗ . +1 (6) Pele H + P1ele If the node wants to send a data packet, the channel is still idle after the complete DIFS time slot, and the node can send the data packet. Solving the equation can get the election success probability . Pele , and the maintenance delay can be calculated from the election success probability. The maintenance delay .Tnc f g should be: T
. nc f g
= K (H +
1 ) Pele
(7)
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Fig. 2 Markov model
3.2 Contention Delay A Markov model is established to simulate the contention process between nodes, as shown in Fig. 2. The node backs off .m times in total, and the contention window is set according to .W . The . Suc state indicates that the back off is complete, and the packet can be sent. The time .Tw for a node to complete a packet transmission without collision is: T = TD I F S + Tdata + TS I F S + Tack
. w
(8)
. P f is the probability that the transmission fails to enter the next stage in the contention process,.W is the size of the contention window, and the node transmission probability . Ps [13] in the contention process can be expressed as:
.
.
Ps =
2(1 − 2P f ) (1 − 2P f )(W + 1) + P f W (1 − (2P f )m )
(9)
Nk is the number of nodes participating in the contention. . P f can be expressed as: .
P f = 1 − (1 − Ps ) Nk −1
(10)
The node sending probability . Ps can be solved from the above two equations, then the node sending success probability . Psuc is: .
Psuc = Ps (1 − Ps ) Nk −1
(11)
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The sum of the node sending success probability and the node collision probability should be 1, then the node collision probability . Pcollision is: .
Pcollision = 1 − Psuc = 1 − Ps (1 − Ps ) Nk −1
(12)
The value of the node’s back-off counter in the . j-th back-off stage randomly selects a value in the contention window .W j , and the average contention window .C W j of the node is: Wj − 1 2 j W0 − 1 = (13) .C W j = 2 2 In the back off process, when the one-hop neighbor node does not transmit data packets, the channel is idle, the back off counter of the node is decremented by one, and the probability . Pb f of being in the back off state is: .
Pb f = (1 − Ps ) Nk −1
(14)
If the node detects that the channel is busy during the backoff process, the node enters the NAV state, and the node probability . PN that the node is in the NAV state is: .
PN = 1 − Pb f = 1 − (1 − Ps ) Nk −1
(15)
The delay .Tcsma experienced by the data packet from entering the queue to being successfully sent to the receiving node can be expressed as: m ∑
T
. csma
=
Psuc,q [
q=0
+
q ∑
C W j (Pb f Tb f + PN Tw )]
j=0 m ∑
(16)
Psuc,q (qTw + Tw )
q=0
After simplification calculation, the expression .Tcsma is as follows: (Pb f + PN Tw ) T
. csma
m ∑ q=0
=
+Tw
Psuc,q [(2q − 21 )W0 − 21 (q + 1)] m ∑
Psuc,q (q + 1)
(17)
q=0
The system delay and system resource efficiency can be obtained from the sum of the two parts. The delay .T and resource efficiency .η are calculated as follows: .
T = K (H + η=
.
1 ) + Tcsma Pele
Tdata K (H + P1ele ) + Tcsma
(18)
(19)
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4 Numerical Simulation and Result The performance analysis of resource efficiency and delay shows that the backoff parameter and the number of ncfg maintenance times are related to resource efficiency and delay. Therefore, the relationship between resource efficiency and the backoff parameter in the two protocols is compared and analyzed. The relationship between resource efficiency and maintenance times in the DCF* protocol is analyzed when the backoff parameter takes the optimal value. After adding the optimal maintenance times to the DCF* protocol, The resource efficiency of the two protocols is compared and analyzed when the number of access network nodes increases. Simulation results show that DCF* resource efficiency performance is better than DCF. The established model is simulated using Matlab, and the simulation parameters are set as shown in Table 1.
4.1 Analysis of the Backoff Parameter The relationship between resource efficiency and back-off round .m, contention window .W in communication systems using DCF* and DCF protocols is analyzed and compared. When the values of .m and .W are better, the resource efficiency performance of the DCF* protocol is better than that of the DCF protocol. With the increase of .m and .W , the network’s overall performance will be improved, so the resource efficiency in the communication system will be improved, but if .m and .W continue to increase, a large amount of delay will be generated. The resource efficiency will be drastically reduced. As .m increases, resource efficiency first increases and then decreases; when .m takes a better value, as .W increases, resource efficiency first increases and then decreases. As the values of .m and .W increase, the delay in the communication system will continue to increase. Table 1 Parameter setting of numerical simulation Parameter Letter . Td . Tc .r .ex p .H .v . TD I F S . TS I F S . Tack . Tbacko f f . Tdata
Unit time slot Unit control time slot The radius of one hop range Backoff index Backoff period Electoral length DIFS SIFS Length of ack Length of backoff Packet length
Value .50 µs .30 µs .1 m
1 16 2 .128 µs .28 µs .50 µs .50 µs .150 µs
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Fig. 3 The relationship between .m and resource efficiency and delay
Fig. 4 The relationship between .W and resource efficiency and delay
When the number of nodes in the network is 20, the minimum value of .m is 2, and the maximum value is 10. As .m changes, the resource efficiency and system delay change, as shown in Fig. 3. When .W takes a fixed value, as .m increases, the resource efficiency first increases and then decreases, and the system delay increases. When the value of .m is 4, the resource efficiency of DCF* protocol reaches about 65%, and the resource efficiency of DCF protocol reaches about 60%. The resource efficiency of the two protocols is consistent with the changing trend of the back-off round .m. When the number of nodes in the network is 20, the minimum value of .W is 2, and the maximum value is 10. With the change of .W , the changes in resource efficiency and system delay are shown in Fig. 4. When .m takes a fixed value, with the increase of .W , the resource efficiency first increases and then decreases. When .W is 3 and 4, the resource efficiency of DCF* protocol is about 55%, and that of DCF protocol is about 40%. The resource efficiency of the two protocols is consistent with that of the contention window .W .
4.2 Analysis of the Maintenance Times Neighbor nodes are maintained after setting the optimal backoff parameters through ncfg interaction in the DCF* protocol. The contention-based MAC protocol is used for communication within the successfully maintained nodes. The resource efficiency and delay are related to the maintenance times K. With the increase of . K , the number of maintained nodes should gradually increase and tend to be stable. According to
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the change in the number of nodes, the contention parameters .m and .W should be changed continuously. When the number of nodes tends to be stable, the more reasonable the setting of .m and .W , the resource efficiency should rise. However, after the number of maintained nodes is stable, increasing . K can not make the value of .m and .W more optimized. Therefore, it will cause a waste of system resources, increase the system delay and reduce the system resource efficiency. The relationship between the ncfg maintenance times and maintenance time is analyzed when there are 25 nodes in the network. The optimal contention parameters .m and . W were selected to calculate the contention delay according to the number of nodes maintained. The relationship between system resource efficiency, system delay and maintenance times . K is analyzed. When the number of maintenance times is 3, the number of maintained nodes tends to be stable, and the resource efficiency reaches the maximum value, reaching about 55%. Take the minimum value of . K as 2 and the maximum value as 30 for simulation. The number of nodes that can be maintained is . Nk . The relationship between . Nk and . K is shown in Fig. 5. With the increase of maintenance times . K , the number of nodes that can be maintained gradually increases and finally tends to a fixed value. The maintenance delay increases with . K linearity. When . K takes 3, the number of maintenance nodes tends to be stable, and increasing . K will produce additional overhead and a large amount of delay. The delay of DCF* protocol is divided into two parts: ncfg maintenance delay and node contention delay. The relationship between resource efficiency, delay and maintenance times is shown in Fig. 6. Fig. 5 Relationship between the number of maintenance nodes, maintenance time and maintenance times
Fig. 6 Relationship between resource efficiency, latency and maintenance times
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Fig. 7 Comparison of DCF* and DCF protocol resource efficiency
With the increase in maintenance times, the resource efficiency will show an upward trend locally, but with the increase in the number of maintenance nodes and maintenance times, the resource efficiency will certainly decline. Therefore, the relationship between resource efficiency and . K should show an upward trend locally, a downward trend overall, and a linear upward trend in delay. When . K is 3, resource efficiency is the largest, reaching about 55%.
4.3 Analysis of the Number of Access Network Nodes After setting the optimal backoff parameters and maintenance times in the DCF* protocol, many simulations are performed to calculate the relationship between the resource efficiency and the number of nodes when the number of nodes in the network increases. The comparison of the resource efficiency between the two protocols is shown in Fig. 7. The results show that the resource efficiency of the DCF* protocol can be improved by about 10% after adding 3 ncfg message maintenance, and the resource efficiency performance of the DCF* protocol is better than that of the DCF protocol.
5 Conclusion This paper proposes an improved contention-based MAC protocol DCF* based on IEEE 802.11 DCF protocol. Adding the maintenance of neighbor nodes reduces the number of nodes participating in the contention, and resource efficiency can be improved when the number of nodes accessing the network increases. The relationship between resource efficiency of the DCF* protocol and maintenance times . K , number of network node . N , back off rounds .m, contention window .W is studied, and the relationship between resource efficiency of DCF* protocol and DCF protocol is compared and analyzed. The simulation results show that when . K is 3, .m is 4 and . W is 3, the resource efficiency of the DCF* protocol can be improved by about 10% compared with the DCF protocol.
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The analysis in this paper provides a theoretical basis for the contention-based MAC protocol, which has important practical significance.
References 1. Razaque, A., Almiani, M., Khan, M.J., Kakenuly, M.N., Jararweh, Y.: Energy efficient medium access control protocol for wireless sensor networks. In: 6th International Renewable and Sustainable Energy Conference (IRSEC). Rabat, vol. 2018, pp. 1–6 (2018) 2. Rehman, M.U., Uddin, I., Adnan, M., Tariq, A., Malik, S.: VTA-SMAC: variable trafficadaptive duty cycled sensor MAC protocol to enhance overall QoS of S-MAC protocol. IEEE Access 9, 33030–33040 (2021) 3. Villordo-Jimenez, I., Torres-Cruz, N., Menchaca-Mendez, R., Rivero-Angeles, M.E.: A scalable and energy-efficient MAC protocol for linear sensor networks. IEEE Access 10, 36697– 36710 (2022) 4. Shahin, N., Ali, R., Kim, Y.: Hybrid slotted-CSMA/CA-TDMA for efficient massive registration of IoT devices. IEEE Access 6, 18366–18382 (2018) 5. Shrestha, B., Hossain, E., Choi, K.W.: Distributed and centralized hybrid CSMA/CA-TDMA schemes for single-hop wireless networks. IEEE Trans. Wireless Commun. 13(7), 4050–4065 (2014) 6. Han, M., Cheng, X., Xu, F., Zhang, J.: Study of SPDS-TDMA time slot allocation protocol based on multi-channel communication. In: 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Nanchang, pp. 513– 517 (2021) 7. Kobatake, N., Yamao, Y.: High-throughput time group access SS-CSMA/CA for wireless ad hoc networks with layered-tree topology. In: Joint IFIP Wireless and Mobile Networking Conference (WMNC), Budapest, pp. 1–5 (2010) 8. Shi, C., Feng, B., Wu, Y., Zhang, W.: A preamble based MAC mechanism in ad-hoc network. In: 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP), Changsha, pp. 1–6 (2021) 9. Dai, J., Yamao, Y.: Effect of intra-flow interference canceller for large-scale ad hoc network. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, pp. 623–627 (2016) 10. Fu, Y., Ding, Z.: Hybrid channel access with CSMA/CA and SOTDMA to improve the performance of MANET. In: 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, pp. 793–799 (2017) 11. Lin, J., Wu, C., Ohzahata, S., Kato, T.: A QoS supporting ad hoc network protocol combing admission based TDMA and 802.11 DCF. In: The 16th Asia-Pacific Network Operations and Management Symposium(APNOMS), Hsinchu, pp. 1–4 (2014) 12. Iqbal, A., Lee, T.-J.: GWINs: group-based medium access for large-scale wireless powered IoT networks. IEEE Access 7, 172913–172927 (2019) 13. Bianchi, G.: Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Selected Areas Commun. 18(3), 535–547 (2000)
Method of Building Enterprise Business Capability Based on the Variable-Scale Data Analysis Theory Ai Wang and Xuedong Gao
Abstract Composable enterprise, as a new enterprise business capability (EBC) architecture, gains much attention in both software and business industry. This paper aims to study the enterprise business capability construction problem for enterprise digital transformation. A scale space model of packaged business capability (PBC) is established, to achieve data coordinate and management for the EBC construction process. Since CIOs always face the PBCs selection challenges when designing new business scenarios, we define the demand response list to obtain the PBCs structure improvement intention. Finally, an algorithm of enterprise business capability construction (EBC-VSDA) is put forward based on the variable-scale data analysis theory. Experiments in numerical dataset verify the accuracy and efficiency of the proposed EBC-VSDA method. Keywords Enterprise business capability · Variable-scale data analysis · Chief information officer · Digital transformation · Composable enterprise
1 Introduction Growing uncertainties in the economic and political environment push great pressure on enterprises worldwide, especially under the COVID-19 epidemic. Most of enterprise leaders have started to find efficient and environmentally friendly approaches to keep business performance and even create new growth. Therefore, digital transformation gradually becomes a wildly accepted solution for enterprises in various industries due to its economy and remote working-supported feature. Considering the solid knowledge foundation of new generation information technology and abundant business experience, chief information officers (CIO) are chosen A. Wang (B) · X. Gao University of Science and Technology Beijing, Beijing, China e-mail: [email protected] X. Gao e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_20
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to accomplish the digital transformation tasks without doubt. However, how to promptly plan and design digital business scenarios, as well as rapidly build matched workable platform, become the current two main challenges for CIOs. The advanced construction concept and powerful information tools are needed in urgent. Composable enterprise, as a new enterprise business capability (EBC) architecture, gains much attention in both software and business industry. In contrast to the traditional resource-oriented enterprise information systems (like ERP, CRM, PLM, etc.), EBC aims at building enterprise capabilities and value co-creation with customers, ecology, things and employees. The professional information technology research and analysis company Gartner forecasts that, by 2023, the EBC mode composable enterprises will achieve business innovation 80% faster than their competitors, and show more tenacity and flexibility in responding to rapid business change, unfamiliar operating risk, diverse customer experience, and uncertain geopolitical and economic environment [1]. Packaged business capabilities (PBC) are the encapsulated software components, that represent a well-defined business capability, for building the EBC platform for enterprises (see Fig. 1). There are different kinds of PBCs, such as application PBCs, process PBCs, data PBCs, and analytic PBCs, which jointly form every enterprise capability architecture under the control of application, data management and analysis platform. Different granularity PBCs interact with each other through API or event channels frequently.
Fig. 1 Building enterprise business capabilities: packaged business capability (PBC) components [1]
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With the help of PBC composable business platform, CIO could map enterprise business capabilities to information system. Moreover, the speed of business change or update also decides the type of PBC formed system. For instance, enterprise innovation capability related customized functions and applications usually change very fast (in 12–18 months), while enterprise operation capability related basic functions and applications are much stable (changed in 5–8 years). However, how to enable CIOs transfer their enterprises to EBC mode architecture with PBC components efficiently and accurately still needs more intelligent decision supported techniques. This paper studies the enterprise business capability construction problem for enterprise digital transformation. The main contributions of our research are as follows. Firstly, a scale space model of composable business platform is established, in order to achieve data coordinate and management for PBCs structure adjustment. Secondly, since CIOs always face the PBCs selection challenges when designing new business scenarios, we define the demand response list to obtain the structure improvement intention. Finally, an algorithm of enterprise business capability construction (EBC-VSDA) is put forward based on the variable-scale data analysis theory. Experiments in numerical dataset verify the accuracy and efficiency of the proposed EBC-VSDA method.
2 Related Works 2.1 Digital Transformation Digital transformation as one of the most popular topics at present, which not only exists within enterprises, but is also related to many industrial and government activities. The purpose or major mission of digital transformation is to win enhanced capabilities, especially from the organization external ecology, and keep economical and environmentally friendly at the same time. For example, Wang et al. [2] improves the cost control capability for intelligent manufacturing enterprises through establishing a three-dimension cost system, including enterprise internal actual cost and benchmarking industry standard cost, as well as external market data integrated testing cost. In order to build the inventory supply response capability for aerospace enterprises, a dynamic general, versatile and strategic materials recognition method is proposed via the digital supplier platform [3].
2.2 Enterprise Business Capability Platform Enterprise business capability (EBC) represents as a new type of enterprise application platform firstly announced by Gartner in 2019 [1]. With advanced cloud
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computing and artificial intelligent techniques, EBC mainly focuses on five business capability platforms, that is customerization, IT system, things, ecosystem, data and analysis. It was plenty of detailed packaged business capability (PBC) components that jointly form an ideal EBC. Figure 2 depicts an example of the extended planning and analysis (xP&A) capability for enterprises constructed by PBCs. Compared to the traditional integrated financial planning (IFP), xP&A break through the finance-only limitation and expand planning business to more domains like sales, marketing, supply chain human resource. However, there are still several problems in the construction process of enterprise business capability by CIOs. Although software developers have already provided a lot of pre encapsulated PBCs with different granularity, the connection or combination approach (path) of varied PBCs to form one specific business scenario is not unique. Also, the granularity of predefined PBCs might be inconsistent with what CIO needs. Thus, CIOs are easily confused when selecting PBCs with relevant subfunction or underlying data (like the sales management PBC, sales planning
Fig. 2 Example: enterprise extended planning and analysis capability (xP&A) [1]
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PBC, etc. in Fig. 2). To prevent missing key and protentional functions, CIOs tend to grab PBCs as much as possible. That will lead to fierce redundancy in the final EBC platform and add avoidable data interaction.
2.3 Variable-Scale Data Analysis Theory Scale effect clearly illustrates the widely existed phenomenon, that the properties or characteristics of the same object(s) may change under different observation scales, happened in both natural and social world [4]. That is critical for managers to make decisions. To improve the quality of analysis results in business scenarios, the scale effect strengthens the importance of identifying appropriate data observation scale first before making any concrete decisions on management object(s) [5]. Hence, the granularity of business service (PBCs) has closely relationship with business data scale. The variable-scale data analysis theory [6, 8] is just the intelligent decision-making theory that applied to solve the relation problem between data analysis scale (hierarchy) and analysis result quality during the decision-making process. Different scale transformation strategies (i.g. radical and conservative scale transformation strategy) reflect managers’ different decision-making preferences. According to the scale transformation mechanism and meta data analysis algorithms, the variablescale data analysis algorithms are proposed for various scenarios, that automatically obtain satisfied analysis results with clear scale feature to support managers make differentiated strategy and tactics [2, 3, 7]. Therefore, this paper studies the PBCs coordination and adjustment problem when building enterprise business capabilities, on the basis of the variable-scale data analysis theory.
3 Enterprise Business Capability Construction Method of Composable Enterprise 3.1 Scale Space Model of Composable Business Platform In order to facilitate the data management during the PBCs structure adjustment, the data scale representation model of composable business platform, that is scale space model, is established. Definition 1 (Scale space model of composable business platform): The scale space model Sn = {CC, V S} is to describe all candidate scales C H i of an observation ruler (dimension) and the structural relationships between scale values, where CC =< C H 0 , C H 1 , . . . , C H i , . . . , C H (n−1) , C H n > represent the
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Fig. 3 Example: scale space model of composable business platform
concept (hierarchy) chain under one observation dimension of the composable business platform, the value space V S = {Vi j | j ∈ N + } and Vi j is the jth scale value of scale C H i . Figure 3 shows the example of scale space model ( S5 ) of the observation ruler (dimension) A T on the composable business platform with five observation scales in total. Since the inappropriate data observation scale could largely explain the unsatisfying results obtained by the EBC composable platform due to the scale effect (see Sect. 2.3), we firstly define the demand response list to figure out the PBCs structure improvement intention. Definition 2 (Demand response list): The demand response list (DrL) is to map the relation between the granularity of packaged business capability and data analysis scale: Dr L = {δi |i = 1, 2, . . . , l}
(1)
⎧ ⎨ 1, when E BC r esults ar e too r ough δi = 0, when some dimensions ar e missing ⎩ −1, when E BC r esults ar e too detail
(2)
where l represents the number of times CIO gets unsatisfied PBCs structure in once EBC construction process, and Dr I /= ∅ represents that the PBCs structure and the quality of analysis results could successfully meet the EBC demand.
3.2 Enterprise Business Capability Construction Method Based on the Variable-Scale Data Analysis In this section, we propose the method of enterprise business capability construction based on the variable-scale data analysis (EBC-VSDA).
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Fig. 4 The basic idea of enterprise business capability (EBC) construction
Figure 4 illustrates the basic idea of the PBCs granularity adjustment through the data scale transformation. Algorithm steps of the EBC-VSDA are shown in Algorithm 1. Algorithm 1: Enterprise business capability construction algorithm based on the variable-scale data analysis Input: All scale space model of the EBC composable business platform Output: Satisfied PBCs structure schema P BC S , Demand response list Dr L Step 1: Clarity the target enterprise business capability and initially set up the PBCs application structure Step 2: Calculate the analysis results of the composable platform via the variable-scale data analysis method (see Sect. 2c) Step 3: If all analysis results of the current composable platform are qualified, input the selected PBCs to P BC S and go to Step 5; Otherwise, go to Step 4 Step 4: Obtain the demand response value δi from CIO and put it into list Dr L (see Eq. 1) Step 4.1: If δi = 1, it means the granularity of the current PBCs are much larger for the target business scenario, the scale down transformation process should be implemented. Replace those to the dimension-relevant PBCs with lower observation data scale and go to Step 2 Step 4.2: If δi = −1, it means the granularity of the current PBCs are much smaller for the target business scenario, the scale up transformation process should be implemented. Replace those to the dimension-relevant PBCs with higher observation data scale and go to Step 2 Step 4.3: If δi = 0, it means there are still key observation dimensions that are not included among the current PBCs. Add other business relevant PBCs and go to Step 2 Step 5: Output the P BC S and Dr L
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The time complexity of the EBC-VSDA is O(ltϕ), where l is number of times CIO gets unsatisfied PBCs structure, t is the maximum time complexity of meta data analysis algorithms, ϕ = min(m, n r ), r is the number of observation dimensions, n is the maximum number of observation data scales of one dimension, n is the number of management objects (instances).
4 Experiment Results and Discussion The comparative experiments are conducted in this section, so as to verify the efficiency and accuracy of the proposed method EBC-VSDA. The numerical experimental dataset is displayed in Table 1. The evaluation results on the basic data scale (C H 10 C H 20 C H 30 C H 40 ) proves that the average analysis quality of the EBC-VSDA is better than the meta data analysis method (k-modes) under all the evaluation indexes [9] (see Table 2). What’s more, the time consumption of meta data analysis method (k-modes) in locating target (optimal) data scale is 4.75/0.35 = 13.57 times longer than the EBC-VSDA, which demonstrates the efficiency of our proposed method (see Table 3).
5 Conclusions Digital transformation has become a widely accepted solution for enterprises to achieve business performance and create new growth, under the current uncertain circumstances. The EBC platform provides a powerful tool to assist enterprises design (new) digital business scenarios. This paper studies the enterprise business capability construction problem based on the variable-scale data analysis theory. Firstly, a scale space model of composable business platform is established to accomplish data coordinate and management for PBCs structure adjustment. Secondly, since CIOs always face the PBCs selection challenges when designing new business scenarios, the demand response list is also proposed to obtain the structure improvement intention. Finally, an algorithm of enterprise business capability construction based on the variable-scale data analysis (EBC-VSDA) is put forward. Experiments in numerical dataset verify the accuracy and efficiency of the proposed EBC-VSDA method. In the future research work, we will keep optimizing the scale transformation mechanism of the EBC platform, in order to better undertake the regulatory role of the “data blood” in the whole “PBCs business skeleton”, like the heart.
O19
O18
O17
O16
O15
O14
O13
O12
O11
O10
O9
O8
O7
O6
O5
O4
O3
O2
O1
Dimension object
A2 C H 20 2 V05 2 V05 2 V05 2 V05 2 V06 2 V01 2 V01 2 V02 2 V02 2 V02 2 V07 2 V06 2 V07 2 V08 2 V08 2 V08 2 V03 2 V03 2 V04
C H 12 1 V22 1 V22 1 V22 1 V21 1 V21 1 V22 1 V22 1 V22 1 V22 1 V22 1 V21 1 V21 1 V21 1 V21 1 V21 1 V21 1 V22 1 V22 1 V22
C H 10 1 V04 1 V04 1 V04 1 V05 1 V05 1 V01 1 V01 1 V01 1 V02 1 V02 1 V06 1 V07 1 V07 1 V07 1 V07 1 V07 1 V02 1 V03 1 V03
C H 11 1 V14 1 V14 1 V14 1 V13 1 V13 1 V12 1 V12 1 V12 1 V12 1 V12 1 V11 1 V11 1 V11 1 V11 1 V11 1 V11 1 V12 1 V14 1 V14
A1
Table 1 Numerical experimental dataset C H 21 2 V13 2 V13 2 V13 2 V13 2 V13 2 V11 2 V11 2 V11 2 V11 2 V11 2 V12 2 V13 2 V12 2 V12 2 V12 2 V12 2 V11 2 V11 2 V11
C H 22 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21 2 V22 2 V21 2 V22 2 V22 2 V22 2 V22 2 V21 2 V21 2 V21
C H 30 3 V03 3 V04 3 V01 3 V04 3 V02 3 V01 3 V01 3 V02 3 V03 3 V02 3 V05 3 V04 3 V05 3 V05 3 V06 3 V06 3 V03 3 V03 3 V02
A3 C H 31 3 V13 3 V11 3 V14 3 V11 3 V15 3 V14 3 V14 3 V15 3 V13 3 V15 3 V12 3 V11 3 V12 3 V12 3 V12 3 V12 3 V13 3 V13 3 V15
C H 32 3 V22 3 V22 3 V23 3 V22 3 V23 3 V23 3 V23 3 V23 3 V22 3 V23 3 V21 3 V22 3 V21 3 V21 3 V21 3 V21 3 V22 3 V22 3 V23
C H 41 4 V11 4 V12 4 V12 4 V11 4 V11 4 V12 4 V12 4 V12 4 V12 4 V11 4 V11 4 V11 4 V11 4 V11 4 V11 4 V11 4 V12 4 V11 4 V11
C H 40 4 V02 4 V01 4 V01 4 V02 4 V02 4 V01 4 V01 4 V01 4 V01 4 V02 4 V02 4 V03 4 V03 4 V04 4 V04 4 V04 4 V01 4 V02 4 V02
A4
Class A
Class C
Class C
Class E
Class E
Class E
Class E
Class E
Class E
Class A
Class F
Class F
Class F
Class F
Class B
Class B
Class D
Class D
Class C
(continued)
Predefined label
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O25
O24
O23
O22
O21
O20
Dimension object
Table 1 (continued)
C H 10 1 V03 1 V03 1 V05 1 V05 1 V06 1 V06
A1
C H 11 1 V14 1 V14 1 V13 1 V13 1 V11 1 V11
C H 12 1 V22 1 V22 1 V21 1 V21 1 V21 1 V21
C H 20 2 V04 2 V05 2 V06 2 V06 2 V06 2 V06
A2 C H 21 2 V11 2 V13 2 V13 2 V13 2 V13 2 V13
C H 22 2 V21 2 V21 2 V21 2 V21 2 V21 2 V21
C H 30 3 V03 3 V02 3 V03 3 V04 3 V01 3 V05
A3 C H 31 3 V13 3 V15 3 V13 3 V11 3 V14 3 V12
C H 32 3 V22 3 V23 3 V22 3 V22 3 V23 3 V21
C H 41 4 V12 4 V12 4 V12 4 V12 4 V11 4 V12
C H 40 4 V01 4 V01 4 V01 4 V01 4 V02 4 V01
A4
Class D
Class B
Class B
Class B
Class D
Class C
Predefined label
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Table 2 The comparative evaluation results under the basic data scale (C H 10 C H 20 C H 30 C H 40 ) Evaluation index
EBC-VSDA
Meta data analysis algorithm (k-modes)
Quality improvement rate (%)
Fmeasure
Average
0.6685
0.6051
10.46
Maximum
0.8436
0.7847
Minimum
0.4897
0.4643
Standard deviation
0.0592
0.0623
Average
0.6760
0.5948
Maximum
0.8400
0.7600
Minimum
0.5200
0.4800
Standard deviation
0.0621
0.0595
Average
0.6280
0.5431
Maximum
0.7712
0.7108
Minimum
0.4629
0.3744
Standard deviation
0.0565
0.0665
RI
NMI
13.65
15.63
Table 3 The comparative evaluation results under the target data scale (C H 11 C H 21 C H 31 C H 40 ) Evaluation index
EBC-VSDA
Meta data analysis algorithm (k-modes)
Quality improvement rate (%)
Fmeasure
Average
0.6685
0.6940
3.68
Maximum
0.8436
0.8436
Minimum
0.4897
0.5543
Standard deviation
0.0592
0.0615
Average
0.6760
0.6932
Maximum
0.8400
0.8400
Minimum
0.5200
0.5600
Standard deviation
0.0621
0.0661
Average
0.6280
0.6548
Maximum
0.7712
0.8537
Minimum
0.4629
0.4786
Standard deviation
0.0565
0.0669
Time in total
0.35
4.75
RI
NMI
t(s)
2.48
4.09
–
References 1. Kingdee International Software Group, Gartner: The newsletter of EBC 2021. China (2022) 2. Wang, A., Gao, X.: A variable-scale data analysis-based identification method for key cost center in intelligent manufacturing. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/ 2022/1897298
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3. Wang, A., Gao, X.: A variable-scale dynamic clustering method. Comput. Commun. 171(1), 163–172 (2021) 4. Tavakoli Mehrjardi, G., Behrad, R., Moghaddas Tafreshi, S.N.: Scale effect on the behavior of geocell-reinforced soil. Geotext. Geomembr 47, 154–163 (2019) 5. Wang, A., Gao, X.: Intelligent computing: knowledge acquisition method based on the management scale transformation. Comput. J. 64(3), 314–324 (2021) 6. Wang, A., Gao, X.: A variable scale case-based reasoning method for evidence location in digital forensics. Futur. Gener. Comput. Syst. 122(1), 209–219 (2021) 7. Wang, A., Gao, X., Tang, M.: Computer supported data-driven decisions for service personalization: a variable-scale clustering method. Stud. Inform. Control 29(1), 55–65 (2020) 8. Wang, A., Gao, X.: Variable-Scale Data Analysis Theory (in Chinese), pp. 103–150. Economic Science Press, Beijing (2022) 9. Wu, S., Gao, X., Bastien, M.: Data Warehousing and Data Mining. Metallurgical Industry Press, Beijing (2003)
Design of Coding Domain Non-orthogonal Demodulation Based on OFDM System He Liu and Xu Li
Abstract Orthogonal Frequency Division Multiplexing (OFDM) has been widely used in wireless communication. However, with the development of 5G, an unprecedented number of users will try to access the network, leading to frequent retransmissions, collisions and waste of resources. Reasonable use of limited wireless resources is a big challenge. Non-orthogonal multiple access (NOMA) technology which allows multiple users to share the same resources for data transmission at the same time can significantly improve the system capacity. However, the data will be aliased at receiver. So the premise of realizing non-orthogonal is how to recover the effective data of each user from the aliased signal at the receiver. Successive interference cancellation (SIC), as a non-orthogonal demodulation technology, has received great attention in the industry and academia in recent years due to its simple structure and ease of engineering implementation. In this paper, the combination of OFDM system and SIC is our key research direction. This paper focuses on the BER of each user in the non-orthogonal SIC demodulation in the coding domain. We take both synchronous and quasi-synchronous conditions into consideration, using across-slot SIC demodulation to process aliased signals in the coding domain. In addition, when quasi-synchronization occurs under the influence of interference or noise, a new frame structure is designed to improve across-slot SIC demodulation performance which copies long preamble to the end of the whole data. The simulation results show that the BER performance of across-slot SIC based on OFDM system is improved after using the new frame structure. Keywords OFDM · Across-slot SIC · Non-orthogonal aliasing in coding domain · Physical layer frame structure design
H. Liu (B) · X. Li School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China e-mail: [email protected] X. Li e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_21
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1 Introduction Non-orthogonal multiple access technology, as one of the important research technologies of 5G, has been studied at home and abroad. The serial interference cancellation multi-user detection algorithm (SIC) technology is an important means of baseband non-orthogonal multiple access reception. SIC was originally proposed by P. Patel et al. and subsequent researches on this technology have also been carried out all the time, which have important guiding value for improving the reliability of non-orthogonal demodulation [1–3]. Then ZTE Corporation proposed the coding domain non-orthogonal multiple access (Multiple User Shared Access, MUSA) technology [4]. Users occupy the same time–frequency resources, and SIC is used at the receiver to separate users. Another scholar conducts research on the Coded Slotted ALOHA (CSA) mechanism, and randomly selects multiple time slots in a frame to transmit the encoded access request message, forming a corresponding access pattern, then the base station monitors the access patterns, using across-slot SIC to separate messages from different users [5, 6]; While the above literatures are all researches on synchronous condition, instead of considering the quasi-synchronization. For multi-user channel estimation, the scheme [7] utilizes the original feature of OFDM, that is, using the Cyclic Prefix (CP) to overcome the asynchronous situation of the received signal [8, 9]. However, due to the uncertainty of the channel, when the delay between the two receiving preambles is greater than the length of CP, aliasing will inevitably occur in the orthogonal long preambles, which will lead to errors in the channel estimation and demodulation [10]. Paper [11] propose MMSE-SIC detection in single carrier to further eliminate the interference in the signal. Besides, many power control models have been proposed in power domain instead of coding domain[12–14]. At present, the existing research mainly focuses on theories and algorithms, while the research and implementation of aliased signal processing based on OFDM is very limited. Therefore, this paper considers a new frame structure based on the OFDM system, adding a long preamble both before and after the data, combined with the across-slot SIC demodulation technology, even if the system is under the quasi-synchronized situation, as long as the long preamble at the end of data isn’t interfered, the aliased data at receiver can be solved correctly and the system capacity can be improved as well. This method is also easy to implement in engineering. We focus more on the engineering implementation of aliased signal separation rather than theoretical analysis.
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Fig. 1 Baseband processing structure of traditional OFDM system Fig. 2 Traditional frame format in OFDM system
2 System Modle and Algorithm Design 2.1 Traditional OFDM System Figure 1 shows the baseband processing structure of traditional OFDM system. The transmitter undergoes operations such as coding, modulation, framing and FFT, then transmits through radio frequency. The receiver performs corresponding inverse operations on the signal to achieve successful demodulation of the received data. Figure 2 shows the traditional physical layer frame format. CP should be added before both the preamble and data to reduce inter-symbol interference. The short preamble is used for signal capture, carrier frequency offset estimation and synchronization, while the long preamble is used for channel estimation and equalization. When the received signal has non-orthogonal aliasing in the coding domain, if we still use the traditional frame format, long preamble used for channel estimation will also be aliased, which greatly reduces the success probability of SIC demodulation. Therefore, a new frame format is designed, which is especially important for improving non-orthogonal demodulation performance.
2.2 Across-Slot SIC Based on OFDM Description As shown in Fig. 3, the random access slot with only one user message in the access pattern is called a singleton slot, while the one with two or more user messages is
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Fig. 3 Coding domain non-orthogonal access pattern
called an aliasing slot (Reference [5]), the one without user messages is called a blank slot. In Fig. 3, firstly, we demodulate the information of user 1 in slot 1, and then remove the interference of user 1 from slot 2; Secondly, we demodulate the information of user 2 from slot 2, and eliminate the interference of user 2 from slot 3, further demodulate the information of user 3 from slot 3. The message of user 2 in slot 4 can be demodulated normally, preventing the message of user 2 in slot 2 from being corrupted. In the above process, the base station successfully demodulates the access request messages of all users by using across-slot SIC, which avoids access failure caused by collision between users, and can also effectively improve the system capacity. Take the first two slots as example, signals sent by user 1 and user 2 after passing through the OFDM system are s(t) and c(t) respectively, which are both simultaneous co-frequency signals with similar power, they can be expressed as follows: s(t) =
∞ N −1 1 ∑ ∑√ Ps · sn,k · exp[ j2π( f c + n Δ f )(t − kTs ) + φ0 ] · g(t − kTs ) N k=−∞ n=0
(1) c(t) =
∞ N −1 1 ∑ ∑√ Pc · cn,k · exp[ j2π( f c + n Δ f )(t − kTs ) + φ0 ] · g(t − kTs ) N k=−∞ n=0
(2) As formula shows, N is the number of subcarriers, while k is the number of OFDM symbols, Pc and Ps are the powers of two signals, cn ,k and sn ,k are the symbol sequences after constellation point mapping, f c is the carrier frequency and Δ f and T s are subcarrier spacing and OFDM symbol period respectively. T s = T u + T cp , T u = 1/ Δf , and g(t) is the root-generated cosine filter waveform. As shown in Fig. 3, the signal received in the first slot is s(t), and the data received in the second slot is: x(t) = s(t) + c(t)
(3)
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Assuming that the channel between the two communicating users is Additive White Gaussian Noise (AWGN) channel which is expressed as n(t), thus the signal received by the receiver in the first slot is r1 (t) = s(t) + c(t)
(4)
The signal received by the receiver in the second slot is r2 (t) = s(t) + c(t) + n(t)
(5)
The receiver first demodulates and decodes the user 1 signal s(t) in the first slot, then buffers it, and subtracts the reconstructed signal s(t) from the aliased signal in the second slot, thereby obtaining another user’s data which is r2, (t) = c(t) + n(t)
(6)
2.3 Model and Scheme Design of Across-Slot SIC Based on OFDM The process of base station performing multi-user data detection based on the access pattern is as follows. Firstly, we search for a singleton slot, that is, autocorrelating the signal of each random access slot with the local preamble sequence. Setting detection threshold and collision threshold, if no peak value exceeds the detection threshold in the detection interval, the time slot is considered to be a blank slot; if there is a peak value in the detection interval that is higher than detection threshold and lower than the collision threshold, the random access time slot is considered to be a single instance slot; other slots are regarded as collision slots or aliasing slot. If a singleton slot is found in the first round of search, the signal can be demodulated normally. While if an aliasing slot is found, across-slot SIC should be used to demodulate successfully. After each round of across-slot SIC process ends, the base station searches for a singleton slot in the updated slot, and repeats the above process. And so on, until all slots are detected as blank slots. Figure 4 shows baseband signal processing structure of OFDM system using across-slot SIC when two-way signals are sent in the second slot. The single-way signal processing structure in the first slot is the same as that of traditional OFDM in Fig. 1. Figure 5 shows the processing flow chart of sender and receiver, which is for the first two slots, after the first slot is normally received and demodulated, the data is reconstructed and buffered, and then processed using formula (7) in the second slot. X 2, =
(Y − H1 X ) Y H1 Y − H1 X = = − X H2 H1 H2 H2 H2
(7)
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Y is the aliased signal received in the second slot, H 1 and H 2 are the channel estimation values of the two signals respectively, and X is the value of the standard point mapped by the constellation map of the first signal. The key to successfully demodulate the data is the accuracy of channel estimation. During the transmission, due to interference or noise, the signal may be quasisynchronized, and the long preamble used for channel estimation will be aliased. Therefore, in order to improve the accuracy, Fig. 6 shows the newly designed frame format, which not only staggers the long preamble in the front, but also lengthens the preamble at the end of the frame (i.e. postamble) to ensure that the interference of long preamble used for channel estimation is minimized.
Fig. 4 Baseband processing structure of OFDM using across-slot SIC Fig. 5 Transceiver processing flowchart of OFDM using across-slot SIC
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Fig. 6 Improved frame format in OFDM system
Table 1 Simulation parameter settings
Coding type
RS + CC
Modulate type
QPSK
Number of subcarriers
256
Number of short preambles
64*4
Number of long preambles
128*2
Number of symbols
4
Channel type
AWGN
Channel estimation method
LS
3 Simulation and Results Analysis 3.1 Results and Analysis When Using Traditional Frame Format The system simulation parameters are shown in Table 1. Figure 7 shows the demodulation performance of the first two slots in the synchronization scenario in Fig. 3. Using traditional OFDM frame format, signal B (User 2) has slightly worse BER performance than Signal A (User 1), this is because there may be some residual interference after subtracting the reconstructed signal A from the mixed signal. In particular, the residual interference at preamble causes a large deviation in channel estimation of signal B, resulting in an increase in bit errors of signal 2.
3.2 Improved Method and Results for Quasi-Synchronous Scenario In the simulation, for the quasi-synchronous scenario, it can be determined whether the received signal is an aliased signal of two users through the correlation peak obtained by the synchronization correlation detection, but it is impossible to judge which user signal corresponds to a single slot. In order to solve this problem, the DATA BLK part obtained by demodulation in the first slot signal and the aliased signal in the second slot are used for correlation operation, and the first slot is judged as the A or B signal according to the peak appearance position, determine whether the first time slot is an A or B signal according to the peak occurrence position, so as to eliminate its interference from the second slot.
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Fig. 7 BER contrast between two received signals
The specific implementation scheme is as follows: (1) First slot is a single signal. After correct demodulation and the normal reconstruction of the signal, besides, the first DATA BLK is taken out for modulation and coding as a local sequence, as shown in Fig. 8a; (2) The second slot constructs an asynchronous arrival sequence of two signals (passing through two different AWGN channels), as shown in Fig. 8b; (3) Determine the time difference between the arrival of the two asynchronous sequences by the peak value obtained by cross-correlation of the local short preamble and aliased signal; (4) Cross-correlate the data block with the aliased signal, observe the position of the peak, if it is in the front, then separate the two signals (B, = A + B-A, ) and then take the second half, otherwise, separate the two signals (A, = A + B-B, ) and then take the first half; (5) Demodulate and decode the separated signal normally, and figure the BER-SNR; CP
SP
CP
LP
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DATA
CP
DATA
CP
DATA
CP
LP
DATA
LOCAL
(a) First slot arrival signal CP
SP
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CP
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(b) Second slot arrival signal
Fig. 8 a First slot arrival signal. b Second slot arrival signal
CP
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Fig. 9 Slot 1 sync correlation values
6) Repeat the above steps under the rayleigh channel to observe across-slot SIC reception performance.
3.3 Performance Contrast and Analysis The result of correlation between DATA BLK and the signal in the second slot is shown in Fig. 9, which indicates that the signal 1 is received in the second slot. While the synchronization peak obtained by the correlation between received signal in the first slot and short preamble is shown in Fig. 10. It can be seen that there are 8 correlation peaks totally, which are 4 short preambles of 2 users respectively, so it can be judged that the second slot is an aliased signal of 2 users. The subsequent processing is the same as across-slot SIC processing in the timealignment scenario of two signals, that is, reconstruct signal 1, subtract signal 1 from aliased signal, and obtain signal 2, then demodulate and decode it. The BER simulation results are shown in Fig. 11. It is found that BER of signal 1 is better than that of signal 2. It is guessed that there may be some residual interference, especially the residual at long preamble of signal 2. The interference leads to a large deviation of channel estimation result, which leads to an increase in bit error of the signal 2. In order to verify our guess and optimize it, we use the modified frame format, lengthen the preamble at the end of the frame (i.e. postamble), that is, adding a long preamble after B’s data for channel estimation of B, and eliminate the residual interference of user A’s data. At this time, the BER curve is shown by the blue line in Fig. 11. It can be seen that the error performance of signal 2 with postamble is improved compared with that of signal 2 without postamble, and the error performance is similar to that of signal 1. It is basically the same, which proves that our guess is correct. The interference of long preamble will lead to an increase in the bit error rate of the signal, and this problem
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Fig. 10 Slot 2 sync correlation values
Fig. 11 BER curve after improved frame structure
can be solved by the improved frame format. Figure 12 shows the simulation results after modifying the channel to rayleigh, the results are broadly the same with AWGN channel.
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Fig. 12 BER contrast using new or traditional frame format
4 Conclusion In OFDM system, in order to improve the system capacity, non-orthogonal technology can be used, and in order to solve the aliasing of received signals at the receiver caused by non-orthogonality, this paper focuses on the aliasing signal processing based on the OFDM system. Combined with the non-orthogonal access pattern of the upper layer in coding domain, across-SIC demodulation in coding domain is realized based on OFDM system. We can not only realize the aliasing signal processing at receiver in the case of synchronization but also consider the quasi-synchronous demodulation. In this case, a new frame format is proposed to improve the demodulation performance. The simulation results show that after using the new frame format, the aliasing part of long preamble used for channel estimation will be reduced, and the demodulation error performance of received signal is improved compared with the original frame format. Future work can analyze the optimization of BER of each user in the case of asynchronous across-slot SIC reception. And through the existing simulation process, the engineering implementation will be carried out on the OFDM system.
References 1. Benjebbour, A., Saito, Y., Kishiyama, Y., et al.: Concept and practical considerations of nonorthogonal multiple access (NOMA) for future radio access. In: 2013 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp. 770–774. IEEE Press, Okinawa (2013) 2. Saito, Y., Kishiyama, Y., Benjebbour, A., et al.: Non-orthogonal multiple access (NOMA) for cellular future radio access. In: 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE Press, Dresden (2013)
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Coordination Mechanisms for a Three-Echelon Fast-Moving Consumer Goods Supply Chain Considering Supply and Demand Effects Yi Wang, Bingkun Dong, Yefei Yang, and Shaobin Wei
Abstract This paper develops the coordination of supply and demand in the FastMoving Consumer Goods (FMCG) supply chain. By establishing a three-echelon supply chain model including a supplier, a distributor, and a retailer, the cost function of each member is analyzed on the basis of supply and demand, and the optimal strategy and Nash equilibrium strategy are calculated. An effective coordination mechanism is designed by comparing the optimal strategy with the Nash equilibrium strategy. Through the compensation mechanism, the supply chain can achieve the optimal scenario. Keywords Coordination mechanisms · Supply chain management · Supply and demand effects
1 Introduction Fast-moving consumer goods (FMCG) refer to consumer goods with a short service life and high consumption speed [1], mainly including food and beverages, personal care products, home care products, tobacco and alcohol, etc. Compared with other types of consumer goods, FMCG products belong to impulse purchase products. From the perspective of the supply chain, the FMCG industry includes raw material Y. Wang (B) · S. Wei Inner Mongolia Kunming Cigarette Limited Liability Company, Hohhot, China e-mail: [email protected] S. Wei e-mail: [email protected] Y. Yang School of Economics and Management, Beijing Jiaotong University, Beijing, China e-mail: [email protected] B. Dong China Tobacco Industry Development Center, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_22
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supply, product transformation, circulation, and consumption, involving manufacturers, distributors, and retailers, which together constitute the three-echelon supply chain of FMCG. In today’s economic globalization, no enterprise can independently complete the whole business process from product design, research and development, production, sales, logistics and distribution, etc. Because of the continuous shortening of product life cycles and industry segmentation, competition among enterprises is gradually shifting to competition among enterprises in each link of the supply chain. The FMCG supply chain has problems of low integration, low operating efficiency, and low return rate among enterprises, and there is a delicate competitive and cooperative relationship among enterprises. Take the tobacco industry as an example. In 2021, the tobacco industry achieved taxes and profits of 1.36 trillion yuan, an increase of 6.08% year-on-year. In terms of taxes and profits paid, the industry paid a total of 1.24 trillion yuan to the treasury for the year, an increase of 3.63% yearon-year, making a huge contribution to the national fiscal revenue. However, due to the historical legacy of institutional mechanisms, the complex evolution of the world situation, and the continued impact of COVID-19, they have brought greater uncertainties and challenges to the smooth operation of the three-echelon supply chain in the tobacco industry [2]. Increasing market competition, increasingly stringent tobacco control policies, and rising costs of raw materials and logistics have led the tobacco industry, especially tobacco commercial enterprises, to face low performance in general. Therefore, how to establish a three-echelon FMCG coordination mechanism composed of manufacturers, distributors, and retailers to maximize the benefits of the supply chain is important. At the same time, it is key to solve the existing problems of the FMCG industry and effectively improve the operational efficiency of the supply chain to realize a reasonable distribution of interests among the three main bodies. At present, the study of FMCG supply and demand effects is mainly based on case analysis [3, 4], but there is a lack of research on its quantitative judgment method. It is not clear how to describe the supply and demand law under different supply chain subjects, clarify its demand function, and achieve supply chain equilibrium under different conditions. In the traditional model scenario, the manufacturer sells his goods to FMCG end-users via the retailer. But in practice, a lot of manufacturers don’t interact with their shops directly. The distributor often serves as the intermediary between these two. In this paper, a supply–demand model of the three-echelon supply chain, including suppliers, distributors, and retailers, was established to analyze the Nash equilibrium solution when the supply chain reached equilibrium and, on this basis, to determine whether the strategies of each agent would maintain the Nash equilibrium solution. This paper proves that the optimal solution of a supplier’s basic inventory is always greater than the Nash equilibrium solution for the three-echelon supply chain. In order to achieve the most economical system, it is necessary to establish a corresponding compensation mechanism to adjust the supply and demand structure in the supply chain. In this paper, a corresponding coordination mechanism is designed, through which the optimal strategy of each agent can be consistent with the Nash equilibrium strategy, so that the whole system can be optimized. On the
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theoretical level, this paper contributes to the existing literature on FMCG supply chain coordination models by providing a detailed and complete analysis of the three-echelon structure considering supply and demand effects.
2 The Model Consider a three-echelon supply chain consisting of a supplier, a distributor, and a retailer. The supplier supplies a product to the distributor, which then supplies the product to the retailer, and the supplier is the only supplier of the product. Let i represent the node of the supply chain, and s, d, and r represent the supplier, the distributor, and the retailer, respectively. IN it is the current inventory level of node i at the period t, which equals the total quantity of products in stock minus the quantity out of stock. The three supply chain nodes use the basic inventory strategy to manage the inventory. That is, the three supply chain nodes continuously monitor the inventory so that its inventory position is always kept at a constant level, si , (i = {r, d, s}). In this case, the base inventory strategy is the optimal strategy for suppliers, distributors, and retailers. The three supply chain nodes are independent, and the basic inventory location is its private information. Any supply chain node only knows its own basic inventory location, and its goal is to minimize its average cost. The supplier produces with time L s . For the sake of simplicity, we assume that the production capacity of the supplier is infinite. The distributor purchases inventory from the supplier, and the time from order to delivery is related to the inventory of the supplier. The lead time is L d if the supplier has enough inventory to fill the distributor’s order; otherwise, the distributor must wait longer to meet the demand. Similarly, retailers purchase inventory from distributors, and the time from order to delivery is related to the inventory of the distributor. The lead time is L r if the distributor has enough inventory to fill the retailer’s order; otherwise, the retailer must wait longer to meet the demand. The market demand for a product is based on an independent and stationary random demand, Dτ indicates the total demand for τ interval, μτ indicates the average demand for τ interval, and the corresponding distribution function and density function are F τ (x) and f τ (x), respectively. Assuming that F 1 (x) is increasing, continuous, and differentiable, and F 1 (0) = 0, positive demand occurs in every period. Retailers store inventory to meet the needs of the customers, the unit time inventory cost is hr . The delayed delivery will be punished, and the unit penalty cost is β, shared by retailers and distributors. That is, αβ cost for the retailer, (1 – α)β punishment cost for the distributor, α ∈ [0,1]. Suppliers store inventory to meet the needs of distributors and retailers. The inventory cost per unit time is hs , and the penalty cost is omitted when the inventory is insufficient to meet the needs of distributors and retailers. The distributor’s inventory cost per unit time is hd . Based on the above assumptions, we can obtain the cost of suppliers, distributors and retailers, which are analyzed as follows.
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2.1 The Retailer’s Cost Function hr is the storage cost per unit time for the retailer. αβ is the retailer’s unit out-ofstock cost. cr (x) is used to represent the cost per unit time of the retailer’s existing inventory position IN rt at any time t, which is: [ ]+ ]− [ cr (x) = h r [x]+ + αβ[x]− = h r I Nr t − D 1 + αβ I Nr t − D 1
(1)
where [x]+ = max{0, x} and [x]– = max{0, – x}. So, the average cost function of the retailer in the interval [t, t + L r ) is: )] [ ( Cr (y) = E cr y − D Lr [ ( )+ ]− ] [ = E h r y − D Lr + αβ y − D L r { = hr
y
{ (y − x) f
Lr
(x)d x + αβ
0
y
(y − x) f L r (x)d x
0
( ) = h r y − μ L r + (h r + αβ)
{
+∞ y
(x − y) f L r (x)d x
{
= αβ(μ L r − y) + (h r + αβ)
y
F L r (x)d x
(2)
0
Since the effective inventory of the retailer at any time t is related to both their own base inventory S r and that of distributors S d . The retailer’s cost expectation function Πr (S r , S d ) per unit time is: { })] [ ( Πr (S r , Sd ) = E Cr min Sr + Sd − D L d , Sr { =
+∞ −∞
Cr (min{Sr + Sd − x, Sr }) f L d (x)d x { =
Sd
Cr (Sr ) f L d (x)d x
0
{ +
+∞
Cr (Sr + Sd − x) f L d (x)d x
Sd
= Cr (Sr )F L d (Sd ) { +
+∞ Sd
Cr (Sr + Sd − x) f L d (x)d x
(3)
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2.2 The Distributor’s Cost Function hd is the storage cost per unit time of the distributor. (1 –α)β is the distributor’s unit out-of-stock cost. cd (x) is used to represent the cost per unit time of the distributor’s existing inventory position IN dt at any time t. cd (x) = h d [x]+ + (1 − α)β[x]− [ ]+ ]− [ = h d I Ndt − D 1 + (1 − α)β I Ndt − D 1
(4)
where [x]+ = max{0, x} and [x]– = max{0, – x}. So, the average cost function of the distributor in the interval [t, t + L d ) is: )] [ ( Cd (y) = E cd y − D Ld [ ( )+ ]− ] [ = E h d y − D Ld + (1 − α)β y − D L d { = hd
y
(y − x) f L d (x)d x
0
{
+(1 − α)β
y
(y − x) f L d (x)d x
0
( ) = h d y − μL d { +(h d + (1 − α)β)
+∞
(x − y) f L d (x)d x
y
( ) = (1 − α)β μ L d − y { y +(h d + (1 − α)β) F L d (x)d x 0
(5)
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Therefore, the distributor’s expectation cost function Πd (S s , S d , S r ) per unit time is: { })] [ ( Πd (S s , Sd , Sr ) = E Cd min Sd + Ss − D L s , Sd { =
+∞ −∞
Cd (min{Sd + Ss − x, Sd }) f L s (x)d x { =
Ss
Cd (Sd ) f L s (x)d x
0
{ +
+∞
Cr (Sd + Ss − x) f L s (x)d x
Ss
= Cd (Sd )F L s (Sd ) { +
+∞
Cd (Sd + Ss − x) f L s (x)d x
(6)
Ss
2.3 The Supplier’s Cost Function At any unit time, the supplier’s storage cost per unit product is hs and at any time t, the retailer’s existing inventory is IN rt . The expected function Πs (S d , S s ) of supplier’s unit time cost during [t, t + L s ) is: { Πs (Sd , Ss ) = h s
Ss
(Ss − x) f L s (x)d x
(7)
0
Therefore, the expected cost function Π (S r , S d , S s ) under the supply and demand effect is: Π(Sr , Sd , Ss ) = Πr (Sr , Sd ) + Πd (Sr , Sd , Sd ) +Πs (Sd , Ss )
(8)
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3 Analysis Since in this model, retailers, distributors, and suppliers are all independent decision makers who want their own costs to be the lowest, the optimal solution needs to be compared with the Nash equilibrium solution. As a reference point of the system coordination mechanism, we use a centralized decision method to determine the base inventory of retailers and suppliers (S r * , S d * , S s * ). The expected cost function Π(S r , S d , S s ) under the supply and demand effect is: Π(Sr , Sd , Ss ) = Πr (Sr , Ss ) + Πd (Sr , Sd , Sd ) + Πs (Sr , Ss ) { = (Cr (Sr ) + Bs (Sr ))F (Ss ) + h s Ls
Ss
F L s (x)d x
0
{ +
+∞
[Cr (Sr + Ss − x) + Bs (Sr + Ss − x)] f L s (x)d x
(9)
Ss
Let C(y) = C r (y) + Bs (y), the Π(S r , S d , S s ) can be written as: { Π(Sr , Sd , Ss ) = C(Sr )F L s (Ss ) +
+∞
{ C(Sr + Ss − x) f L s (x)d x + h s
Ss
Ss
F L s (x)d x
0
(10) Because the expected cost function Π(S r , S d , S s ) is continuous, with S i ∈ [0, S], i = r, d, s, the optimal stocking strategy must exist and be satisfied the first order differential condition. ⎧ , ∂Π(sr ,sd ,ss ) = C (sr )F L s (ss ) ⎪ ∂s r ⎪ { +∞ , ⎪ ⎪ + ss C (sr + ss − x) f L s (x)d x = 0(11) ⎪ ⎪ ⎪ ⎨ ∂Π(sr ,sd ,ss ) = h F L s (s ) + C(s ) f L s (s ) s s r s ∂ss { +∞ Ls ⎪ + C(s + s − x)d f (x) r s ⎪ ss ⎪ ⎪ Ls ⎪ = h F (ss ) ⎪ s ⎪ { +∞ , ⎩ + ss C (sr + ss − x) f L s (x)d x = 0(12) When S s > 0, F Ls (S s ) > 0, so there is a S r 1 : C , (S r ) = h s
(13)
) , ,( 1 Cr (S r ) + Bs Sr1 = h s
(14)
1
and
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Therefore, it can be calculated that: 1
F L r (S r ) =
β + hs β + hr
(15)
Obviously, S r 1 exists and is unique. Since Π(S r 1 , S d , S s ) is a strictly convex function of ss , ss 1 also exists and is unique, and: {
sr1 +sd1 +ss1 ss1
( ) 1 F L r sr1 + sd1 + s s − x f L s (x)d x
=
β − (β + h s )F L s (ss1 ) β + hr
(16)
or {
sr1 +sd1 +ss1 ss1
F L r (x) f L r (sr1 + sd1 + ss1 − x)d x =
β β + hr
(17)
If ss 2 = 0, due to the F Lr (sr 2 ) = F Ls (0) = 0, in order to satisfy the first order conditions: { +∞ ) ,( C sr2 − x f L s (x)d x = 0 (18) 0
{
+∞ 0
( ) F L r sr2 − x f L s (x)d x = =
{
β β + hr
sr2 0
( ) F L s (x) f L r sr2 − x d x (19)
In short, the optimal solution of the model can be obtained through the above analysis. When Π(sr 1 , sd 1 , ss 1 ) < Π(sr 2 , 0, 0), the optimal solution (S r * , S d * , S s * ) of the model is (sr 1 , sd 1 , ss 1 ); When Π(sr 1 , sd 1 , ss 1 ) > Π(sr 2 , 0), the optimal solution of the model is (sr 2 , 0, 0). In the decision-making process, suppliers, distributors, and retailers select base stocks, ss , sd and sr at the same time. Retailers always choose the minimum cost base stock ss under the assumption that the base stock of suppliers and distributors is sd and sr . Similarly, distributors always choose base stock sd with the minimum cost under the assumption that the supplier’s and the retailer’s base stock are sr and ss . The supplier’s decision process is the same. In other words, retailers make the best response strategy according to suppliers’ basic inventory decisions, and suppliers make the best response strategy according to retailers’ basic inventory decisions. That is, the basic inventory is determined according to the following rules:
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rs (sr ) = {ss ∈ σs |Πs (sr , sd , ss ) = minΠs (sr , x, y)}
(20)
rd (sd ) = {sd ∈ σd |Πr (sr , sd , ss ) = minΠd (sd , x, y)}
(21)
rr (sr ) = {sr ∈ σr |Πr (sr , sd , ss ) = minΠr (sr , x, y)}
(22)
The Nash equilibrium in the supply chain satisfies the following conditions: ( ) ( ) sre ∈ rr sse , sde ∈ rd sde , sse ∈ rs (sre )
(23)
Proposition 1: if 0 < α < 1, the supplier’s inventory optimal solution is always greater than the Nash equilibrium solution, i.e., ss * > ss e . Proof: we know that ⎧ ∂Πs (sr ,sd ,ss ) = h s F L s (ss ) ⎪ ⎨ { ∂ss ] , +∞ [ , + ss Cr (sr + ss − x) + Bs (sr + ss − x) f L s (x)d x(24) ⎪ { +∞ , (s +s −x) f L s ⎩ ∂Πs (sr ,ss ) = h s F L s (ss ) + ss Bs r s (x)d x(25) ∂ss and ( ) F L r (sr ) > F L s srs =
αβ αβ + h r
so {
+∞ ss
{ =
,
Cr (sr + ss − x) f L s (x)d x
+∞ [
] −αβ + (h r + αβ)F L r (sr + sr − x) f L s (x)d x
ss
[ ] = −αβ 1 − F L s (ss ) + (h r + αβ)
{
+∞
F L r (sr + ss − x) f L s (x)d x
ss
[ ] = −αβ 1 − F L s (ss ) − (h r + αβ)F L r (sr )F L s (ss )
(26)
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{ +(h r + αβ)
+∞
F L s (x) f L r (ss + sr − x)d x
ss
[ ] < αβ 1 − F L s (ss ) − αβ F L s (ss ) + (h r + αβ)
{
+∞
f L r (sr + ss − x)d x < 0
ss
(27) That is: ∂Π(sr , sd , ss ) ∂Πs (sr , sd , ss ) < ∂ss ∂ss
(28)
Because the F Ls (•) is an increasing function, we finally obtain ss * > ss e .
4 The Coordination Mechanism As can be seen from the above analysis, when 0 < α < 1, the optimal strategy is not consistent with the Nash equilibrium strategy, so a corresponding coordination mechanism needs to be designed. Through a compensation mechanism, the optimal strategy of each subject can be consistent with the Nash equilibrium strategy so as to achieve the optimization of the whole system. In the supply and demand relationship, the distributor plays an intermediary role. In order to ensure the optimal inventory of suppliers and retailers and Nash equilibrium inventory, the missing part is compensated by distributors at another terminal of the supply chain. The compensation cost is as follows: T L (sr , sd , ss ) = t I Ir (sr , sd , ss )
(29)
where t I is the compensation parameter and I r (sr , sd , ss ) is the average inventory level of the retailer. Therefore, it can be obtained that if T L (sr , sd , ss ) > 0, it means that the retailer should be compensated, so the supplier compensates the retailer through the distributor. On the contrary, if T L (sr , sd , ss ) < 0, it means that the supplier should be compensated, so the retailer compensates the supplier through the distributor. After the adjustment of the compensation mechanism, the cost function of each node in the supply chain becomes: ΠrL (sr , sd , ss ) = Πr (sr , sd , ss ) − T L (sr , sd , ss )
(30)
ΠdL (sr , sd , ss ) = Πd (sr , sd , ss )
(31)
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ΠsL (sr , sd , ss ) = Πs (sr , sd , ss ) + T L (sr , sd , ss )
301
(32)
The compensation function can be substituted and calculated to obtain: { ΠrL (sr , sd , ss ) = (h r − t I )Ir (sr )F L s (ss ) +
+∞
(h r − t I )Ir (sr + ss − x) f L s (x)d x
ss
(33) ΠdL (sr , sd , ss ) = Π L (sr , sd , ss ) − ΠsL (sr , sd , ss ) − ΠrL (sr , sd , ss ) { ΠsL (sr , sd , ss ) = (h r − t I )
ss
F L s (x) + (h r − t I )Π L (sr , sd , ss )
(34) (35)
0
Let hr – t I = λhr , the Nash equilibrium can be calculated by: ( ) ∂ΠrL s∗r , s∗d , s∗s ∂Π(s r , s d , s s ) =λ =0 ∂ sr ∂ sr ( ) ∂ΠdL sr∗ , sd∗ , ss∗ =0 ∂sd ( ) ( ) ∂ΠsL sr∗ , sd∗ , ss∗ = (t I + λh s )F L s ss∗ ∂ss ( ) ∂Π sr∗ , sd∗ , ss∗ − tI +(1 − λ) ∂ss
(36)
(37)
(38)
then, the compensation mechanism t I is: t I = λh s
F L s (ss∗ ) 1 − F L s (ss∗ )
(39)
Equation (39) shows that, by reasonably designing t I , the optimal decisions of individuals will be consistent with the optimal decisions of the whole supply chain, which means that the supply chain achieves the goal of coordination. According to a three-echelon FMCG supply chain in Italy [5], the cost of safety stocks for manufacturers and distributors is e143.47/year/pallet and e136.51/year/pallet, respectively. Thus, the use of coordination mechanisms can bring economic benefits to enterprises, especially when the characteristics of products in the FMCG supply chain involve difficulties in demand prediction and product visibility. We designed a numerical case to demonstrate the compensation mechanism. We set h r = h d = h s = 1, α = 0.5, β = 3, and the market demand in the lead time follows a uniform distribution between 10 and 20. Figure 1 shows that when the compensation parameter t I = 0.2, the optimal strategy of each subject in the FMCG supply chain is consistent
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Fig. 1 The optimal strategy and Nash equilibrium strategy with t I
with the Nash equilibrium strategy. Note that when t I = 0, according to Proposition 1, the supplier’s inventory optimal solution is greater than the Nash equilibrium solution. To summarize, we find that the optimal solution of the system can be equal to the Nash equilibrium solution through the proposed compensation mechanism, and the system achieves the optimal.
5 Conclusion The FMCG supply chain consists of suppliers, distributors, and retailers, which is different from the two-echelon model used in traditional supply chain analysis. In this paper, a supply–demand model of the three-echelon supply chain was established to analyze the Nash equilibrium solution when the supply chain reaches equilibrium. This paper proves that the optimal solution of the supplier’s basic inventory is always greater than the Nash equilibrium solution for the three-echelon supply system. Therefore, in order to achieve the economic system, it is necessary to establish a corresponding compensation mechanism to adjust the supply and demand structure in the supply chain. Finally, a corresponding coordination mechanism is designed, through which the optimal strategy of each agent can be consistent with the Nash
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equilibrium strategy, so that the whole system can be optimized. Moreover, some limitations of the current paper can be relaxed. Firstly, collaboration for horizontal interactions can be proposed. Secondly, another extension can be made to a situation with asymmetric information types.
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Research on Knowledge Graph Platform of Logistics Industry Based on Big Data Fan Yang, Juntao Li, Ruiping Yuan, Fan Wang, and Huanli Zhao
Abstract China’s logistics industry started late, with the rapid development of the national economy, China’s logistics industry maintains a fast growth rate, and the data generated is growing explosively. Data and knowledge are the basis for the deep integration of new-generation information technology and intelligent manufacturing. How to efficiently and accurately analyze multi-source and heterogeneous data in various systems of the logistics industry is a major problem faced by people in the logistics industry. The knowledge graph is a new type of accurate knowledge representation method, which has gradually begun to land in medical, financial, and other industries. For the characteristics of the logistics industry, this paper conducts special research on five aspects, including knowledge acquisition, knowledge processing, knowledge graphing, knowledge application, and knowledge reasoning. It aims to promote the application of the knowledge graph platform in the logistics industry and provide decision-making support for auxiliary event-driven and industry-important events. Keywords Logistics industry · Knowledge graph · Big data
1 Introduction The rapid development of the mobile Internet makes it possible to interconnect everything, with data generated by this very interconnection continuously exploding. These massive and discrete data can be adopted as effective raw materials for discovering their potential value, endowing the knowledge graph with “an opportunity” to come in hand in many potentially interconnected tasks. The knowledge graph is a new type of accurate knowledge representation method, which, given its advantages of accuracy, structurization, and clarity, can help reflect the relationships between and within industry chains, assisting in event driving, important industrial events, accurate user decisions, etc., and express and analyze the whole picture of logistics industry scenes F. Yang · J. Li · R. Yuan (B) · F. Wang · H. Zhao Beijing Wuzi University, Beijing Key Laboratory of Intelligent Logistics Systems, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_23
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more vividly. Throughout future expansion from traditional logistics to smart logistics, the advancement of technology has promoted the development of the logistics industry towards the upgrading of logistics connections, accurate data analysis, and continuous innovation of the business models. The large-scale application of smart logistics, the continuous optimization of the structure, and the integration of new ideas, new models, new technologies, and new business forms giving full play to the advantages of smart logistics will jointly promote the revolutionary development of China’s logistics industry and realize the transformation and upgrading of the overall logistics industry.
2 Definition of Knowledge Graph The term Knowledge Graph originated from the semantic web in the 1960s and then experienced the evolution from ontology, to Web, semantic web, linked data, and then the knowledge graph. The semantic network, a graphical structure for representing knowledge in patterns of interconnected nodes and arc segments, was firstly proposed by Richard H. Richens of the Cambridge Language Research Group [1], which could perfectly represent the structure of the natural language and thus better extract its semantics. In the 1980s, the philosophical vocabulary ontology (Ontology), defined as “a formal description of concepts and relationships” by Tom Gruber [2], was introduced into the field of computer science for defining semantics, which belonged to the abstraction layer in the knowledge graph, i.e., the Schema layer. The emergence of the Web has exercised a profound impact on the knowledge graph, and Tim Berners-Lee’s “Information Management: A Proposal” published in 1989 proposed a vision for the Web [3], where each web page was a node, and the hyperlinks were edges. However, merely building links between Web pages was far from enough, and links between objects, concepts, things, or data were also necessarily important. Then, in 1998, Tim formally proposed the concept of the Semantic Web, a general framework for making data on the network machine-readable. In 2006, Tim proposed the concept of Linked Data [4], emphasizing that the purpose of the semantic Internet is to establish links between data, not just to publish structured data to the Internet. Afterwards, in 2012, Google formally proposed the concept of Knowledge Graph to improve the search quality, and it has continued to this day [5]. Knowledge graphs consist of generic knowledge graphs and domain-based knowledge graphs, among which, generic knowledge graphs are structured data encyclopedic knowledge bases for the general domain, while domain-based knowledge graphs are constructed with a top-down approach facing a specific domain [6]. Although there are some successfully constructed generic knowledge graphs, such as Freebase, YAGO, Wikipedia, etc. however, when faced with the relevant tasks in a specific field, it is usually difficult to construct specific domain knowledge graphs because there is no more regular and effective data source, the automated construction technology is not mature enough, the professionalism of the data source is not high. By establishing the association links between the data, the fragmented data is
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organized organically so that the data can be processed and used by people more efficiently. It is convenient for searching, mining, analysis, and so on.
3 Key Technologies and Processes for Knowledge Graph Realization in Logistics Industry The knowledge graph of the logistics industry is constructed based on the data concerning the logistics industry, and the construction process is divided into five links, i.e., knowledge acquisition, knowledge processing, knowledge graphing, knowledge application, and knowledge reasoning, as shown in Fig. 1. This paper mainly investigates and solves how to perform entity [8] and relationship extraction [9] from unstructured text in the logistics industry and obtain the relationship between entities and entities. And this paper’s main difficulties and innovations of this paper focus on the breakthrough in the accuracy of the entity recognition task of the traditional model [8, 10] and the relation extraction task based on unsupervised learning, and the knowledge inference of the triples obtained by the extraction.
3.1 Knowledge Acquisition Link Data are the basis for the construction of knowledge graphs. The data sources include structured data, semi-structured data, and unstructured data. The structured data of the logistics industry in this paper are derived from high-quality and enriched data that can be publicly downloaded and stored in a structured way. Semi-structured data are equipped with some structures, but the structures are incomplete and irregular, and require further extraction and collation of the data. For example, HTML documents
Fig. 1 Implementation process of knowledge graph of logistics industry
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are semi-structured data. To ensure the accuracy of the constructed logistics industry graph, this paper fixes the unstructured data source as the data downloaded from China Logistics and Purchasing Network and China Logistics Information Network, for the reason that the site has an inherent authority in the first place, and that there is no description deviation from the professional terms of the industry.
3.2 Knowledge Processing Link The core parts of knowledge graph construction are entity extraction, relation extraction, knowledge fusion, and knowledge storage [7]. In this paper, entity extraction and relationship extraction based on the deep learning model is adopted, which can capture the contextual features of sentences to obtain more comprehensive semantic information, and can significantly understand the relationship between entities, making the accuracy rate higher. For knowledge fusion work, a combination of machine learning techniques and manual methods are hereby selected in this paper to align entities and disambiguate entities from different data sources, thereby achieving semantic fusion across data sources. For the final triples, the Neo4j graph database is finally chosen for the knowledge storage.
3.3 Knowledge Application A variety of applications, including basic queries, important events in the industry, industry hot news, industry technology display, and intelligent Q&A, are involved in the logistics industry knowledge graph platform. First, the knowledge application reflects the relationship between and within the industrial chain; second, it is convenient for users to query logistics industry knowledge more quickly and assist users in decision-making; and third, it is convenient for venture capital shareholders to control all aspects of the enterprise.
3.4 Knowledge Reasoning The typical applications of knowledge reasoning mainly include intelligent search, intelligent recommendation, intelligent question answering, etc. For knowledge reasoning in the logistics industry, this paper adopts a rule-based knowledge reasoning model to complete the reasoning of the relationship between entities, realize the completion of the knowledge graph of the logistics industry, and explore the potential relationship between entities.
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4 Architecture Design of a Knowledge Graph Platform for the Logistics Industry Based on Big Data The purpose of constructing the logistics industry knowledge graph platform based on big data is to enable users to query logistics industry knowledge more quickly and efficiently, and solve problems such as information asymmetry, complex information, and inaccurate information. In the platform-building process, the multi-layer architecture model is used for research and development, when four architecture models, i.e., data layer, map layer, application layer, and service support layer, are involved. As shown in Fig. 2, the data layer mainly includes various structured data, semi-structured data, and unstructured data such as industries, regions, companies, products, patents, and people, also data cleaning and collation of the data; the graph layer supports the entire knowledge graph platform and is responsible for knowledge extraction and knowledge visualization; the application layer mainly consists of various applications such as industry knowledge query, industry hotspots, industry key events, industry technology display and intelligent reasoning; the service support layer is mainly to realize the development of platform basic service support, such as big data processing, system maintenance, message service, knowledge graph analysis, and search engine. The platform achieves basic intelligent processing using computer technology in the operation process, and conducts modular processing of various types of data in the database-model-building process, so that it can acquire and update knowledge quickly. The processed data can also be applied to other suitable scenarios, such as intelligent voice robots, and provide service support to users through intelligent dialogue. Fig. 2 Knowledge graph platform architecture for the logistics industry based on big data
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5 Application of Logistics Industry Knowledge Graph Platform With the support of massive amounts of data, mighty computing power, swarm intelligence computing and an endless array of models, traditional knowledge engineering solves the knowledge acquisition challenge, algorithms enable data-driven, largescale automated knowledge acquisition, and knowledge graph technology becomes the key to unlocking these data applications. The knowledge graph platform for the logistics industry fuses data from different data sources using knowledge graph technology for building an industry knowledge graph system and providing intelligent application services for users. The platform is divided into two parts: one is for users who are allowed to look up logistics industry knowledge and search for important events in the logistics industry, etc., while the other is for intelligent recommendations, where historical data, algorithmic models, and knowledge inference are adopted to mine information of interest to users.
5.1 Knowledge Graph Relationship Model for the Logistics Industry The relationships between entities and entities, such as industries, companies, products, people, places, and patents are hereby infiltrated into the logistics industry knowledge graph in this paper, and relationships are shown in Fig. 3. Currently, 5267 company entity data, 28 industry entity data, 294 region entity data, 15,264 patent entity data, and 86,000 relationship data created between entities are covered. Fig. 3 Logistics industry entity and relationship data
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Fig. 4 Part of the entity relationship graph
5.2 Knowledge Graph Visualization The Neo4j graph database, a high-performance NoSQL graph database also considered a high-performance graph engine, is hereby chosen as the knowledge store in this paper. This very graph database adopts B/S three-tier architecture and combines with PHP, H5, and other network programming technologies to realize the storage and complex query of graph data. The entities and relationships of the logistics industry are connected through the graph database, which can be queried not only through the path, but also through the single and multi-dimensional relationships. For example, to query all node relationships associated with a region and analyze them, the Cypher statement: match p = (n: Region{name: "北京市"})-[r*0..]- > ( m) return p limit 30 can be used to query the partial entity-relationship graph shown in Fig. 4.
5.3 Function Realization of Knowledge Graph Platform in the Logistics Industry The logistics industry knowledge graph platform is to meet the query needs of users, and mainly contains the following functions, as shown in Fig. 5: industry knowledge query: the created logistics industry node relationship graph is presented to users in a graphical form for providing concise and clear result information; industry hotspots: providing the hot spot information of the logistics industry to users in a personalized way and a personalized industry framework; important industry events:
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Fig. 5 The functions of the knowledge graph platform in the logistics industry
similar to hot spots in the same industry; industry technology display: displaying the latest technology of the current logistics industry disclosed for users’ reference, and pointing out the direction for the future development of industrial technology; and intelligent Q&A: the intelligent assistant can quickly find answers matching the users’ questions, answer them automatically, and provide an efficient and personalized experience.
6 Conclusion The knowledge graph is provided with the natural advantages of high performance, flexibility, and agility in the case of dealing with massive data and many-to-many complex entity connection scenarios. Therefore, the knowledge graph platform of the logistics industry based on big data can effectively solve the problem of multisource and heterogeneous knowledge in the logistics industry. Building relationships between entities and entities in the logistics industry can effectively improve the efficiency of services, which can in turn expand the service area. The architecture and construction of the knowledge graph platform based on the logistics industry are analyzed in this paper after clarifying the definition of the knowledge graph. In the future, the capabilities of graph computing and graph learning can be further explored based on the knowledge graph, so as to provide users with more functions of exploring the value of graph data in the logistics industry, and promote the developing direction of smartness and intelligence. Acknowledgements This paper was funded by the National Natural Science Foundation of China (72101033 and 71831001), the Beijing Key Laboratory of Intelligent Logistics Systems (BZ0211), the Canal Plan-Youth Top-Notch Talent Project of Beijing Tongzhou District (YHQN2017014), the Scheduling Model and Method for Large-scale Logistics Robot E-commerce Picking System based
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on Deep Reinforcement Learning (KZ202210037046), the Fundamental Research Funds for the Central Universities No.2015JBM125 and the Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2018KF-01).
References 1. Richens: Preprogramming for mechanical translation. Mechan. Transl. 3(1), 20–25 (1956) 2. Gruber, T.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995) 3. Berners-Lee, Tim: Information management: a propsal[J]. No. CERN-DD-89–001-OC (1989) 4. Tim Berners-Lee: Linked Data. Design Issues. W3C (2006) 5. Amit, S.: Introducing the knowIedge graph[R]. America: Offcial BIog of Google (2012) 6. Liu Qiao, Li Yang, Duan Hong, Liu Yao, Qin Zhiguang: Review of knowledge graph construction technology [J]. Comp. Res. Devel. 53(03), 582–600 (2016) 7. Yang Wei: Domain knowledge fusion and knowledge co-construction research [J]. Applic. Electr. Techn., 47–50 (2019) 8. Liu, Z., Wang, X., Chen, Q., et al.: Chinese clinical entity recognition via attentionbased CNN-LSTM-CRF[C]. International Conference on Healthcare Informatics Workshop. ICHIWorkshops 2018, New York, NY, USA, pp. 68–69 (2018) 9. Luo, Y., Cheng, Y., Uzuner, et al.: Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes[J]. J. Am. Med. Inf. Assoc. 25(1), 93–98 (2018) 10. Quan, C., Hua, L., Sun, X. et al.: Multichannel convolutional neural network for biological relation extraction [J]. BioMed Res Intern., 1–10 (2016) 11. Liu, Z., Wang, X., Chen, Q., et al.: Chinese clinical entity recognition via attentionbased CNN-LSTM-CRF[C]. International Conference on Healthcare Informatics Workshop. ICHIWorkshops 2018, New York, NY, USA, 68–69 (2018)
A Study on Market Development System and Competitiveness Evaluation of Energy Internet Weizheng Kong, Suxiu Li, and Xingtong Chen
Abstract With the rapid development of economy and revolution of information technology, energy internet enterprises emerge facilitating the strong integration of information technology and traditional energy infrastructure. Market competitiveness plays an important role for energy internet enterprises competing in the new era. In this paper, we initially analyze the status of market competitiveness of energy internet enterprises, and then taking the power grid enterprise as an example, we develop a market competitiveness evaluation index system for energy internet enterprises based on fuzzy comprehensive evaluation method. It can help energy internet enterprises evaluate their market competitiveness and improve their market value in the future. Keywords Energy internet enterprises · Market competitiveness · Evaluate
1 Introduction Energy Internet is the comprehensive use of advanced power electronics technology, information technology and intelligent management technology to interconnect a large number of distributed energy harvesting devices, distributed energy storage devices and new power networks, oil networks, natural gas networks and other energy nodes composed of various loads, so as to realize the sharing network of two-way flow of energy and sharing exchange [1]. As an energy technology revolution, the characteristics of energy Internet—interconnection and sharing—can help fundamentally change the energy industrial layout as well as the mode of production and W. Kong (B) · S. Li · X. Chen State Grid Energy Research Institute, Beijing, China e-mail: [email protected] S. Li e-mail: [email protected] X. Chen e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_24
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consumption. Energy Internet also can effectively ease the tension between energy consumption and economic development, and it has become an important guarantee of energy security. In the decades, energy Internet has gained a rapid development. Since The Economist published “Building the Energy Internet” in 2004 and first proposed the construction of the energy internet, the next generation of energy system has attracted great attention in the world wide, which sets off extensive researches on energy Internet [2]. Many technological breakthroughs in the use of clean energy have particularly promoted the further development of energy Internet. In 2018, the Global Energy Internet Development Cooperation Organization was established, and the “Global Energy Internet Conference” was held with achieving an important breakthrough in the development of energy Internet from “planning map” to “construction map” and from “new concept” to “operational” [3]. In 2019, the Global Energy Interconnection Development Cooperation Organization released the European Energy Interconnection Planning Research Report, which proposed the European Energy Interconnection development plan, key interconnection projects, and conducted a benefit assessment. In China, the layout and construction of the energy Internet is also actively carried out and has obtained strong support from the government. From 2014 to 2020, the state has released a total of 997 policies and regulations related to the energy internet [4]. Among them, 2020 is the year with the most frequent issuance of energy internetrelated policies, in which 375 national policies (including standard guidelines) were issued. Supported by such a policy system, the core technologies of the energy internet have developed rapidly, gradually shifting from a conceptual stage based on basic research to a practical stage based on applied research [5]. From 2014 to 2019, the number of patent applications related to the energy Internet industry has shown a steady growth as a whole reaching in 2018 with 450 patent applications. Although it has declined in 2019, it has remained above 400. Specifically, the energy network and the information network are increasingly integrated, and the electricity market has evolved from a limited information exchange model to a multi-party participation capability and information peer-to-peer exchange model. With various policy support and technological progress driving the rapid development of my country’s energy internet industry, my country’s energy internet business presents a multi-subject development trend, with both traditional and new industries, and the development of enterprises is thriving. The first batch of new energy and Internet companies have participated in the construction of the energy internet, promoting the longterm development of my country’s energy internet business. According to the “2021 National Energy Internet Development Annual Report” data, in December 2020, the number of registered companies in my country’s energy internet industry increased to 66,843, an increase of 70.6% compared with the end of 2019; at the same time, the number of listed companies in 2020 also increased rapidly. to 332 [6]. The development of energy Internet has received significant attention from scholars as well. There are three main research streams in the literature on energy Internet. The first stream is based on the physical system level of the energy internet, including multiple energy complementation and energy integration optimization [7].
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Energy integration optimization carries out horizontal coordination and optimization of multiple energy supply systems through energy coupling relationships to achieve various energy cascade utilization and optimization. Coordinated dispatching can promote the consumption of renewable energy and improve the efficiency of energy utilization [8]. The second stream focuses on the information technology analysis of the energy Internet, including the interaction, sharing and mining of data. The internal data of the energy Internet is generated by energy production, distribution and consumption systems, and its external data includes data that can reflect economic, policy and user characteristics [9]. From the perspective of system operation, Li et al. [10] proposed that sufficient information interaction and analysis are required to deal with the randomness of supply and demand to improve the stability of the power system, but they did not clarify the dependence of this interaction on market mechanisms; Sun et al. [11] claimed that big data technology can improve smart grid management level, operational efficiency and profitability. The third stream primarily pays attention to innovation on energy Internet integration. Each subject uses Internet thinking to innovate the energy operation model [12], through the integration of Internet operation and energy diversified business, to better meet people’s energy demand and improve the efficiency of the demand satisfaction process [13]. China is in a critical period of structural adjustment and institutional reform of the energy industry. It urgently needs all kinds of market actors to play an active role in facilitating the effective allocation of resources, exploring appropriate business models for the market promotion of renewable energy, and cultivating energy Internet-related businesses [14]. In this process, the market competitiveness is an important indicator for enterprises to recognize their market position and judge whether to develop specific energy Internet-related related businesses. However, due to that the development of China’s energy Internet industry started a little later and is still in the exploratory stage, there are few researches on the development mode of enterprises under the energy Internet era and how to realize the transformation to energy Internet-related business. Thus, in this paper, we first puts forward the strategic suggestions for enterprises to develop the energy Internet market, and then uses the fuzzy comprehensive evaluation method to construct the evaluation index system of the market competitiveness of energy Internet enterprises. This paper purpose to supplement the existing researches on the energy Internet, especially market development, but also provide effective and specific practical guidance for the reform and transformation of energy enterprises, promote the development of the energy internet industry.
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2 Energy Internet Market Development System and Market Competitiveness Evaluation 2.1 Constructing the Energy Internet Market Development System Comprehensive layout to accelerate the transformation of traditional enterprises. If energy internet enterprises want to be competitive in the market under the new background, traditional energy-based companies should increase investment and actively participate in the research and tackling of key technologies of energy internet. The key technological innovation of the energy internet is the most fundamental prerequisite for the innovation of the business model of energy companies. Therefore, it is very important for traditional energy companies to actively participate in the research and tackling of key technologies of the energy internet. Only with leading key technologies can they lead the industry standards. The times seize the opportunity and provide good support for creating better business model innovation. In the face of more complex business models, internal organizational structures, and data requirements of large and medium-sized enterprise customers. Energy internet enterprises should adjust the introduction strategy of emerging technologies in a timely manner to achieve a substantial improvement in technical capabilities. At the same time, it is necessary to pre-study the innovation of the business model of energy enterprises under the energy internet. In the context of Internet + , the business model of energy enterprises requires pre-research and experimentation by various business entities in the market. The business model needs continuous practice and experience in the market environment to mature. Finally, it is necessary to seize the opportunity of reform to dig deep into benefits, promote the revolution of energy production and consumption has become a major strategy of our country. At a certain stage of development, energy internet enterprises will fall into a bottleneck period due to problems such as resources and labor. “Internet plus” can use the advantages of new technologies and new applications to help the traditional energy industry integrate resources, reduce costs, and simplify intermediate links, achieve transformation and upgrading, break through bottlenecks, and get out of trouble. The application of new Internet technologies in related fields has a bright market prospect, constantly stimulating capital investment, and frequent innovation investment will further release the potential of social investment and promote the maturity of related industries. Accelerate the opening and sharing of data resources, support the development of new business formats in the energy field, and cultivate data-driven big data application demonstrations in the energy industry. Promote the digitalization of the energy industry, vigorously develop business outsourcing based on digital delivery, and enhance the competitiveness of digital energy products and technologies. Make full use of the Internet platform to integrate online and offline resources, promote optimize social resource allocation and utilization efficiency, and improve the integrated development of business formats.
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Improve market mechanism based on demand. The revolution of Internet + energy has kicked off, and it has become a policy dividend for the energy industry to usher in a new round of qualitative changes. The market environment will become more relaxed, and all forms of energy will be produced and stored to energy consumption. It will be opened up, and the circulation of energy in the market will be more convenient. Taking the integrated energy service platform as an example, it should promote its key projects, at the same time keep an eye on potential customers, and actively apply technologies such as energy storage in the direction of multi-energy complementary integration optimization and regional energy internet, and build large-scale, demonstration project of comprehensive energy service with good benefit and advanced technology. Actively refine project construction experience, form a model that can be promoted and replicated, and help expand existing business scale and improve operating efficiency. As an energy enterprise, it should seize the opportunity of reform, actively innovate its business model, dig out new profit growth points, create greater value and achieve better development. The normal operation of any business model requires a relatively complete mechanism to maintain it. The realization of the energy internet business model is based on a sound market mechanism. First of all, it is necessary to establish a communication mechanism between energy enterprises and users to ensure the effective connection of information between the two, conduct good communication and convey timely information; secondly, strictly control the quality, standardize the development of the new energy industry, and avoid product quality and mixed performance has a negative impact on the energy industry, ensuring user confidence in energy products. Regardless of the model, the emergence of the energy internet will involve a variety of market transaction entities and trading platforms. A credible transaction credit system and the credit status of both parties determine the length of the cooperation between the two parties and the stable development of the energy interconnection business model. At the same time, in order to ensure the orderly energy trading platform, the government and relevant management departments should also formulate relevant laws, regulations and technical guidelines to do a good job in supervision.
2.2 Market Competitiveness Evaluation Based on Multi-level Fuzzy Comprehensive Evaluation Method Multi-level fuzzy comprehensive evaluation, first of all, the various factors of a certain thing to be evaluated are divided into several categories of major factors according to their attributes; then a preliminary comprehensive evaluation is carried out for each category of major factors; Table 1 comprehensive evaluation. Fuzzy comprehensive evaluation method is a very effective multi-factor decision-making method. Based on Analytic Hierarchy Process (AHP), combined with the theory of fuzzy sets and membership functions, the overall evaluation of the membership level of things from
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multiple aspects can better deal with problems such as multiple factors, ambiguity and subjective judgment. In this paper, we take a power grid enterprise as an example to show how to analyze an enterprise’s market competitiveness with this method. To be specific, with analyzing competitiveness connotation and its influencing factors, we develop a market competitiveness evaluation index system for the power grid enterprise based on fuzzy comprehensive evaluation method, which both takes account of the enterprise’s operational characteristics and can fully reflect the competitiveness level and influencing factors of a power grid enterprise. The first model calculation process is establishing a single-factor evaluation matrix and determine the weight distribution of the fuzzy comprehensive evaluation model, usually using statistical experiments or expert scoring methods. Combined with the market operation characteristics of the State Grid, the evaluation index is constructed according to the characteristics and classification of the influencing factors by referring to domestic and foreign scholars’ research on competitiveness evaluation [7]. This paper divides the national grid competitiveness evaluation index system into 3 levels and 15 indicators. The first layer (target layer): U = (State Grid Market Competitiveness). The second level (criteria level): U = (U1 , U2 , U3 , U4 , U5 ) = (Market, Management Organization, Service Quality, Financial Status, Sustainability). The third level (indicator level): U1 = (U11 , U12 , U13 )
(1)
U2 = (U21 , U22 , U23 )
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U3 = (U31 , U32 , U33 , U34 )
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U4 = (U41 , U42 , U43 )
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U5 = (U51 , U52 )
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The second model calculation process is building a review set. The evaluation set V is a set of evaluation grades, and an evaluation set is established according to the competitiveness evaluation index system. That is, V-{V1, V2, V3, V4, V5}-{powerful, strong, general, weak, weaker} According to the evaluation set y, the establishment of the critical value is carried out. Powerful: 90–100, Strong: 80–90, Normal: 70–80, Weak: 60–70, Weaker: 60 or less. The third model calculation process is to determine the indicator membership matrix. In fuzzy sets, elements and fuzzy sets have a certain degree of membership, that is, membership degree. 100% membership is recorded as membership degree R = 1. There must be no membership relationship, then it is recorded as membership
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degree R = 0.Therefore, the value interval of the membership degree is [0, 1]. The method of determining the membership degree of a single index adopts the fuzzy statistical method. The fuzzy statistical method is to let the experts participating in the evaluation classify each evaluation index according to the evaluation set V given in advance, and then count each evaluation index in turn. The frequency of belonging to each evaluation level Vq (q = 1, 2, 3, 4, 5) is used to calculate the degree of membership of the evaluation index to the level. ⎫ ⎧ r11 , r 12 , . . . , r1m ⎪ ⎪ ⎪ ⎪ ⎬ ⎨ r21 , r22 , . . . , r2. N ⎪...,...,...,...⎪ ⎪ ⎪ ⎭ ⎩ rn1 , rn2 , . . . , rnm
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The fourth model calculation process is dto determine the weight set of each indicator. According to the importance of each index factor, the corresponding weight is assigned to each index, and its size should be consistent with the degree of influence of the evaluation index on the upper-level index, so as to form the weight set of the evaluation index factors. Wk = (wk1 , wk2 , . . . , wkm ),
Em j=1
Wk j = 1#
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The fifth model calculation process is calculating fuzzy comprehensive membership value set B and comprehensive evaluation. There are many ways to determine the weight, this paper chooses the analytic hierarchy process to determine the weight of the index. AHP is a decision-making method combining qualitative and quantitative analysis, and it is also an effective method to objectively describe people’s subjective judgments. This method stratifies and quantifies people’s thinking process, and quantifies and ranks the importance of evaluation indicators through the judgment and selection of experts. The evaluation expert group needs to carry out fuzzy evaluation on the indicators at the same level and compare them in pairs. The 1–9 scale method can be used, and each expert constructs a judgment matrix respectively, and then the final judgment matrix is obtained from the average value. According to the final judgment matrix, firstly perform the single-level sorting and its consistency test, and solve the maximum eigenvalue A of the judgment matrix. and the ranking indicator (weight) of the relative importance of a factor in its corresponding layer. The ccomputational processes are shown in the following steps: Step 1: Determine the fuzzy evaluation index set U. The enterprise’s market competitiveness evaluation index system is a set of evaluation indexes and it is divided into three levels. The first layer (target layer): U = (State Grid Market Competitiveness). The second level (criteria level): U = (U1 , U2 , U3 , U4 ) = (Competitiveness Index).
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The third level (indicator level): U1 = (U11 , U12 , U13 , . . . )#
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U2 = (U21 , U22 , U23 , . . . )
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U3 = (U31 , U32 , U33 , . . . )
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Step 2: Obtain the weight of each level index and the membership degree of each secondary evaluation index to the evaluation set V. Step 3: Determine the index membership matrix (evaluation matrix) R. Step 4: Determine the weight set of each indicator W Wk = (Wk1 , Wk2 , . . . , Wkm )
(11)
Step 5: Calculate fuzzy comprehensive membership value set B and comprehensive evaluation U. According to the membership degree Rk of each evaluation index of State Grid’s market competitiveness and the comment set µ, the scores of each specific index can be obtained: Bk = Rk · µT
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From Bk and index weight Rk , the evaluation result U k of each first-level index can be obtained: Uk = Wk · Bk #
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Repeat the above calculation steps, according to the evaluation results of each first-level index U k (k = 1, 2, …, 6) and the first-level indicator weight set W, Let B = (U 1 , U 2 , …, U n )T , get a comprehensive evaluation U: U =W·B
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The comprehensive membership degree U is the total evaluation score obtained by the evaluation object.
3 Conclusion The process of promoting the upgrade of the energy Internet is the process of promoting the construction of a new type of power system, which is ultimately implemented in the practice of the company’s strategy implementation and market
A Study on Market Development System and Competitiveness … Table 1 Market competitiveness evaluation index system
The first layer
The second level
Market U 1
Transaction size U 11
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Domestic market share U 12 International market share U 13 Management Organization U 2 Management skills U 21 Management system perfection U 22 Employee turnover U 23 Service Quality U 3
Transaction efficiency U 31 Technique level U 32 Service Level U 33 Safety level U 34
Financial Status U 4
Profitability U 41 Solvency U 42 Operating capacity U 43
Sustainability U 5
Transaction size growth rate U 51 Profit growth rate U 52
competitiveness. In this paper, we mainly make two contributions. First, we discuss what enterprises should do to promote their self-development and business transformation in energy Internet era, from the aspects of key technology innovation, resource opening and integration, demand orientation and business model innovation. Second, we develop a market competitiveness evaluation model, and it provides an effective analytical tool for enterprises to evaluate and decide whether to cultivate new energy Internet-relevant businesses. More further researches are needed in the future construction of energy Internet. Innovate the development mode of the power grid, enhance the system adjustment ability, do a good job in major technical research, deepen the research on major issues, and steadily promote the construction of a new power system.
References 1. Wu, Y., Zhang, T., Wu, C.: Research on construction decision of varied energy internet storage projects based on fuzzy environment. Sci. Technol. Manag. Res. 41(03), 164–169 (2021) 2. Lund, H., Mathiesen, B.V.: Energy system analysis of 100% renewable energy systems-the case of Denmark in years 2030 and 2050. Energy 34, 524–531 (2009) 3. Liu, W., Gu, W., Wang, J., Yu, W., Xi, X.: Game theoretic non-cooperative distributed coordination control for multi-microgrids. IEEE Trans. Smart Grid 9, 6986–6997 (2018)
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4. Moazami, A., Nik, V.M., Carlucci, S., Geving, S.: Impacts of future weather data typology on building energy performance–investigating long-term patterns of climate change and extreme weather conditions. Appl. Energy 238, 696–720 (2019) 5. Wang, Z., Perera, A.T.D.: Integrated platform to design robust energy internet. Appl. Energy 269, 114942 (2020) 6. Perera, A.T.D., Nik, V.M., Wickramasinghe, P.U., Scartezzini, J.-L.: Redefining energy system flexibility for distributed energy system design. Appl. Energy 253, 113572 (2019) 7. Zhong, D., Li, Q.M., Zhou, X.: Research status and development trends for key technologies of multi-energy complementary comprehensive utilization system. Therm. Power Gen. 47(2), 1–5 (2018) 8. Ai, Q., Hao, R.: Key technologies and challenges for multi-energy complementarity and optimization of integrated energy system. Autom. Elect. Power Syst. 42(4), 2–10 (2018) 9. Liu, S.C., Zhang, D.X., Zhu, C.Y.: A view on big data in energy Internet[J]. Autom. Elect. Power Syst. 40(8), 14–21 (2016) 10. Li, C.B., Li, X.P., Tian, S.M.: Challenges and prospects of risk transmission in deep fusion of electric power and information for energy Internet. Autom. Elect. Power Syst. 41(11), 17–25 (2017) 11. Sun, H.F., Peng, L., Ni, J.R.: Smart grid big data ability promotion and value creation in the energy Internet environment. Manag. Technol. SME 8, 167–169 (2017) 12. Liu, S.C., Han, X., Wang, J.Y.: Research on the influence of“Internet+”initiative on power industry. Elect. Power Inf. Commun. Technol. 14(4), 27–34 (2016) 13. Bai, C.F., Han, X.Y., Yang, F.: Research on the functional architecture of the energy internet. Elect. Power 51(8), 31–37 (2018) 14. Wang, J., Gao, H., Yi, Y.: Business model innovation of energy internet and chinese utilities. Sci. Technol. Manag. Res. 37(08), 26–32 (2017)
Judgment Model of Pollution Source Excessive Emission Based on LightGBM Wenhao Ou, Xintong Zhou, Zhenduo Qiao, Liang Shan, Zhenyu Wang, and Jiayi Chen
Abstract Big data has become a social consensus to improve the modernization level of national governance, support the innovation of government management and social governance modes. This paper aims to strengthen the application of big data in the field of ecological environment. It monitors and analyzes abnormal production behaviors based on energy consumption data. Thus, it assists regulatory authorities to improve supervision efficiency and further protects the legitimate rights of legal enterprises, maintains market fairness, and help the country win the defense of blue sky. By participating in the 5th Digital China Innovation Contest(DCIC), whose topic is ‘Pollution Source Excessive Emission Research and Judgment’, we proposed a multi-feature model for pollution source excessive emission using LightGBM. The final F1 score of model is 0.61203524. Keywords Big data in electric power industry · Pollution prevention · Excessive emission · LightGBM · DCIC 2022
W. Ou (B) · X. Zhou · Z. Qiao · L. Shan · Z. Wang · J. Chen State Grid Commercial Big Data Co., Ltd, Beijing, China e-mail: [email protected] X. Zhou e-mail: [email protected] Z. Qiao e-mail: [email protected] L. Shan e-mail: [email protected] Z. Wang e-mail: [email protected] J. Chen e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_25
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1 Introduction In recent years, the Central Committee of the Communist Party of China and the State Council have attached great importance to the construction of ecological civilization. It has continuously promoted the improvement of ecological environment quality through a series of measures such as the ‘battle against pollution’. Among those measures, the monitoring of pollution emission on specific enterprises has become an important auxiliary approach for the ecological environment department to obtain real-time emission data of enterprises and make reasonable decisions. Although more and more data are available for analysis, the big amount of these data has exceeded the processing capacity of the enterprise [1]. Traditional data processing and analysis methods are powerless confronting these data. At the same time, corporate decisionmaking and strategy formulation are increasingly relying on data. Hence, enterprises urgently need the ability to discover important information from massive business data [2]. At present, the industrial chain division of environmental protection monitoring industry is gradually formed. Most enterprises focus on the manufacture and system integration of automatic monitoring equipment for pollution sources. Most of the monitoring methods are traditional non-real-time monitoring. This monitoring method has shortcomings such as low monitoring frequency, slow response, large sampling error, scattered monitoring data, and inability to reflect environmental changes in a timely manner, which make it difficult to meet the environmental management needs of the government and enterprises. According to the development trend of the monitoring equipment industry and the international advanced environmental monitoring experience, online monitoring has become an effective and timely method for relevant departments to obtain continuous monitoring data. At the same time, there are difficulties and pain points in the online monitoring of enterprise pollution prevention and control, such as massive investment in equipment and projects, and long construction period. Therefore, how to meet the requirements of high monitoring frequency, fast response, high accuracy while controlling the cost to the greatest extent is an important issue that needs to be solved urgently. With the rapid development of artificial intelligence and big data technology, it has become a social consensus to use big data technology to improve the modernization level of national governance and promote innovation in government management and social governance modes. It also provides new technical ideas for online monitoring on corporate pollution prevention and control. This paper uses the big data technology in electric power industry, combining high-coverage electric power consumption data and environmental protection department’s regulatory requirements, to solve the pollution source excessive emission judgment problem. It uses the gradient boosting decision tree algorithm to identify users with excessive emission behaviors. This provides an important reference for the formulation of environmental protection policy and assists the ecological environment department to manage and control these enterprises, which is conducive to the modernization of the ecological environment governance system and governance capacity.
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2 Analysis of Multi-Feature Model of Pollution Source Excessive Emission 2.1 LightGBM Model LightGBM is a commonly used GBDT toolkit developed by Microsoft Research Asia. It improves the classical GBDT algorithm and let it have better prediction accuracy and lower memory usage. Therefore, it is very suitable for dealing with the problems with large amount of data [3]. The reason why LightGBM has a better performance is that it get improved mainly in the following aspects: Histogram-based decision tree algorithm, unilateral gradient sampling, mutually exclusive feature bundling [4], etc. With these improvement, LightGBM reduces the complexity of the model, leading to a lower memory usage, a faster running speed, and a better prediction accuracy. First of all, the improvement of the tree growth strategy is different from the tree growth method in the GBDT algorithm and the XGBoost algorithm, which grows the tree level-by-level [6]. The LightGBM algorithm uses the leaf-wise tree growth strategy. Calculate the splitting gain, find the leaf node with the largest information gain among all the current leaf nodes, and proceed in turn to find the optimal tree structure q(x). Compared with the level-wise tree growth method of the GBDT algorithm and the XGBoost algorithm, the leaf-wise method can reduce the loss, thereby improving the accuracy of prediction results. It can also limit the minimal number of data in one leaf and the depth of the tree to avoid overfitting. Secondly, LightGBM uses histogram-based gradient boosting. By discretizing the continuous input variables into k bins, the information containing k groups forms a histogram with a width of k. When splitting with the XGBoost algorithm, the original data of the indicator is first divided. Compared with pre-sorting-based algorithm, the histogram-based algorithm divides the original data of the indicator into a series of discrete bins, traverses the discrete data, and finds the optimal split point. In the process of traversing attributes, since only k times of information gain need to be calculated, the number of operations is reduced. This speeds up the training process and reduces the memory usage. The split point we find is not necessarily the most accurate one, but a large number of experiments show that the impact of discretization on the accuracy of the model is limited. The third is the unilateral gradient sampling algorithm, which uses the information of the gradient size of the sample as a consideration of the importance of the sample. It believes that the smaller the gradient of the sample, the smaller the error and the better the model fitting effect. Random sampling is used for such samples as the extracted strategy. All samples with large gradients are reserved to increase the attention to samples with poor training conditions, thus improving the recognition accuracy of the model, greatly reducing the computational load of the model, and improving the running speed. The fourth is the mutually exclusive feature bundling algorithm. This algorithm is used to solve the feature sparsity problem of high-dimensional samples. In the sparse
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space, many features are often mutually exclusive, that is, several features will not be non-zero at the same time (for example, after one-hot coded data). LightGBM algorithm converts these features into graph coloring problem processing. It forms a weighted undirected graph based on the relationship between mutually exclusive features according to the relationship between feature vectors. After that, based on the principle of minimum overall feature conflict, it sorts the obtained nodes in the A feature and assigns the node with a larger degree to an existing feature package, or to directly form a new feature package. In this way, the mutually exclusive feature bundling algorithm improves the running efficiency of the model by reducing the number of data features.
2.2 Improved Multi-feature Model The datasets have a total of 22 features. The first step is to do data proprocessing. Based on data quality, there are some values missing for some users. There are also some unreasonable values like negative electricity consumption data. These types of data, so-called outliers, are handled first. According to the quantity of outliers, choose different dealing method like deletion or using the average value to fill them or some others. Some exploratory data analysis are also conducted in this step. Secondly, form more features through feature crosses, aiming to dig more cross information from the data. For instance, ‘user electricity consumption ratio of peak period and valley period in the 4th Quarter’ is a new feature relying on ‘user electricity consumption in the 4th Quarter peak period’ and ‘user electricity consumption in the 4th Quarter valley period’. Some statistics features are also added to the model, like ‘the mean/median/standard deviation/skew of the electricity consumption in peak period’. Then we do some exploratory data analysis work. We plot the scatter matrix of feature correlation, as shown in Fig. 1. It can be noticed that ‘contract_cap’ and ‘run_ cap’, ‘avg_settle_pq_mean’ and ‘avg_settle_pq_median’ both have a complete linear positive correlation. Therefore, their collinearity should be considered in further model work. There are also other feature pairs which have strong positive correlation that we need to pay attention to. ‘weekend_f_ratio’ and ‘weekend_fgsum_ratio’ in Fig. 2 is a pair of example. After that, 30 features enter the LightGBM model. We run the model and then tune the parameters to get a better fitting effect. As one of the important parameters affecting the results, once the learning_rate is too small, the convergence speed of the results will become slower. If it is too large, the results may lose oscillation or even become larger. Through calculating statistics of the experimental data, testing different parameter values and comparing the experimental results, it is determined that when the learning rate is 0.015, we obtain the lowest logloss.
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Fig. 1 Scatter matrix for some features
Since LightGBM uses the algorithm of leaf-wise, when adjusting the complexity of the tree, num_leaves is used instead of max_depth. However, we still need to make sure that the value of num_leaves is less than 2max_depth . The experiment uses grid search method to determine the optimal value of the parameter, as shown in Table 1. We plot the feature importance to determine features with top 10 feature importance as Fig. 3. Notice that the feature importance in this figure is the number of times the feature is used in the model. It can be seen that ‘d_kwh_g_skew’ has the highest feature importance - 362 - among all features. We also use LSTM as a compared algorithm to deal with this problem. However, it turns out that the F1 score is always lower than 0.6, which means there is a gap in their performance.
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Fig. 2 Scatter matrix for some features Table 1 Parameter values of LightGBM model
Parameters
values
learning_rate
0.015
boosting
gbdt
n_estimators
2000
max_depth
6
num_leaves
40
min_data_in_leaf
32
feature_fraction
0.9
bagging_fraction
0.8
lambda_l2
1e-4
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Fig. 3 Importance of top 10 features
3 Experiment Analysis 3.1 Dataset The electricity consumption data for pollution sources excessive emission judgment are obtained from the DCIC 2022 competition. The datasets contain training datasets and test datasets, with each including three parts: basic user information data, user daily electricity consumption data, and industry average monthly electricity consumption data. The date range of industry average monthly electricity consumption data is from October 2020 to December 2021. The date range of user daily electricity consumption data is from October 01, 2020 to December 31, 2020.
3.2 Experiment Environment The operating system used in this experiment is a 64-bit Windows10 system, the processor is Intel(R) Core(TM) i7-8550U, and the CPU is 2.0 GHz. 8 cores CPU, 16 GB memory, hard disk size 1 T. Based on the JetBrains PyCharm Community Edition platform, the Python3.7 scripting language is used for development. The machine learning framework version includes LightGBM 2.3.1. In addition, Anaconda3 is used to load some commonly used python packages, such as: Sklearn, Numpy and so on.
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3.3 Results Take one tree of the multi-feature LightGBM model as an example to illustrate the experiment results. The Fig. 4 represents the specific classification in this tree. The figure shows one of the trees of multi-feature model of pollution source excessive emission based on LightGBM. Each node in the graph represents a node in the tree. Non-leaf nodes have labels like ‘d_kwh_f_skew ≤ 0.674’, which means the node splits on the feature named ‘d_kwh_f_skew’, with the threshold 0.674. Leaf nodes have labels like ‘leaf 0: 0.002’, which means the node is a leaf node and the predicted value for records that fall into this node is 0.002.. The plot demonstrates the trend of binary logloss during the training process as Fig. 5 shows. With the settlement of early stopping round (100), we can get the metric stable in 250 rounds. Finally we got the 13th place in the competition with an F1 score of 0.61203524 as Fig. 6 represents [7].
4 Conclusion In this paper, based on the users’ electricity consumption data in a given area, we conduct a in-depth data mining work into electricity consumption data. We use LightGBM to construct a judgment model for excessive emission of pollution sources. Using feature crosses and parameter tuning, we improve the model effect. Users with excessive emission behaviors are identified in the results. Thus, we can intelligently diagnose abnormal electricity consumption behavior, analyze and draw scientific conclusions. It can provide support for decision-making and supervision. In the future, further research will be conducted on the portraits of polluting enterprises, the identification of pollution sources, and the electric power environmental protection credit system. These applications can further expand the application scenarios of electric power big data in the field of environmental protection, and help modernize the ecological environment governance system and governance capabilities.
Fig. 4 One of the trees of multi-feature model of pollution source excessive emission based on LightGBM
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Fig. 5 The trend of binary logloss during the training process
Fig. 6 The final rank of the 5th DCIC pollution source excessive emission research and judgment
References 1. Merendino, A., Dibb, S., Meadows, M., et al.: Big data, big decisions: the impact of big data on board level decision-making [J]. J. Bus. Res. 93, 67–78 (2018) 2. Song Xinping, Lu Guodong, Du Nian, Liu Haolai: Research on the use behavior of competitive intelligence network information sources of small and medium-sized enterprises under big data [J]. Mod. Intell. 41(01), 88–93+110 (2021) 3. Binbin, Z., Yan, J., Zhenyu, Z., et al.: Research on LightGBM ultra-short-term load forecasting based on particle swarm optimization algorithm [J]. Ener. Energy Cons. 02, 2–6 (2021) 4. Zhu Yangbao: Design of multi-factor stock selection scheme based on XGBoost and LightGBM algorithms [D]. Nanjing University (2022)
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5. Wu Haoxin: Research on obstacle avoidance path planning method of vision-guided space manipulator [D]. Beijing University of Posts and Telecommunications (2018) 6. Meng, Q.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree (2018) 7. Digital China Innovation Contest: Energy big data subtrack - research and judgment on excess emissions of pollution sources (2022). Available at: https://www.dcic-china.com/competitions/ 10024/ranking?sch=10039&stage=B (Accessed: 21 June 2022)
Spatial–temporal Evolution of Green Patent Cooperation Network in BTH Region Mingxuan Yang and Shuo Zhang
Abstract Beijing-Tianjin-Hebei (BTH) region coordinated and green development strategy has been upgraded to a national development strategy since 2014. The study of green patent cooperation among various innovation units in the BTH region can provide an empirical perspective for examining the effectiveness of the BTH coordinated development strategy. Based on the data of green invention patents in China Beijing-Tianjin-Hebei (BTH) region from 2010 to 2018, the social network analysis method is used to study the evolution of the overall network and core nodes from three aspects of time, cooperation agents and space. In terms of time dimension, the network center potential kept at a high level from 2013 to 2016, showing a certain aggregation effect, but gradually tended to be diversified after 2016. From 2010 to 2018, although the network density decreased and the network tended to be loose generally, a few enterprises and scientific research institutions in Beijing kept the core position of the network. In terms of spatial dimension, the green patent cooperation between BTH presented an unbalanced phenomenon as a whole, and Beijing occupied an absolute dominant position. Despite this, the cooperation among the three regions was increasing year by year, and the driving effect of Beijing as the leader was gradually emerging, indicating that the BTH coordinated development strategy is working. Based on the research of this paper, the scientific decision support for the allocation of resources and the formulation of relevant policies in BTH region can be provided. Keywords BTH region · Green patent · Patent cooperation network · Temporal and spatial evolution
M. Yang School of Economics and Management, University of Science and Technology, Beijing 100083, China e-mail: [email protected] S. Zhang (B) School of Economics and Management, North China Electric Power University, Beijing 102206, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_26
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1 Introduction For a long time, the resource allocation and economic development of China Beijing, Tianjin and Hebei have been unbalanced. To solve this problem, the Beijing-TianjinHebei (BTH) coordinated development strategy has been upgraded to a national development strategy since 2014. From then, new policies of coordinated development of BTH region were proposed by adjusting and optimizing the industrial structure, promoting industrial upgrades and transfer, and accelerating market integration. Under this background, the study of patent cooperation among various innovation nodes in the BTH region can provide an empirical perspective for examining the effectiveness of the BTH coordinated development strategy. At the same time, environmental issues receive high attention in this strategy. Green technology innovation is not only the key point of development but also the internal demand and key breakthrough direction of sustainable development. Therefore, based on the green patent cooperation network of BTH region, the evolution trend of the network and core node were revealed by analyzing the spatial–temporal evolution characteristics of the network in this paper, so as to provide scientific decision support to the green development policy of BTH region. Constructing a patent cooperation network with patent data has been a hot topic in the field of collaborative innovation. Tsay et al. constructed a global patent cooperation network in the field of artificial intelligence and analyzed the evolution characteristics of the network [1]. Chen et al. built an inter-organizational patent cooperation network by collecting patent cooperation application data of different types of organizations in China from 2007 to 2015 [2]. Liu et al. studied the relevant patent network of Tsinghua University in China and analyzed the important role of universities in collaborative innovation [3]. Liu et al. studied the collaborative network of China’s wind energy industry by using complex network theory and social network analysis methods based on the patent collaborative application data of the State Intellectual Property Office [4]. Mei et al. constructed a collaborative innovation network based on the collaborative patent and paper data of BTH region from 2010 to 2016, so as to investigate the characteristics and efficiency of innovation cooperation among BTH cities [5]. Although patent data has been widely used to study the characteristics of cooperated innovation networks, there are only several studies on green patent data and green innovation. Liu et al. used Chinese patent data to construct a green patent cooperation network from 2007 to 2017, and analyzed the characteristics of the network from multiple perspectives such as time, region and spillover effect [6]. Zhou et al. collected the green patent data of urban agglomeration in The Yangtze River Delta from 2000 to 2016, constructed a patent cooperation network and analyzed the evolution patterns of green collaborative innovation in the Yangtze River Delta region of China [7]. Fan et al. used the gravity model and the social network analysis model to analyze the spatial correlation network of China’s green innovation. The results showed the existence of spatial effects in China’s green innovation correlation network. They also found that regions with strong green technology innovation capabilities did not
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affect the green innovation of other provinces or cities, and regions with medium innovation strength may play an important role within the green innovation linkage network [8]. Wang et al. explored the spatial correlation network structure of green innovation efficiency in the Yangtze River Delta, China. The results showed that in spatial terms, the Yangtze River Delta’s green innovation efficiency is extremely unbalanced, and the spatial network association density is low [9]. Bai et al. proposed a framework for determining the impacts of a multiple relationship network on green innovation. They use text mining to construct the multiple relationship network and use content analysis to identify green patents from the massive patent data. Then they use fuzzy-set qualitative comparative analysis (fsQCA) to study the equivalent influencing paths of green innovation [10]. Through the analysis of the existing literature, it can be found that although there were several studies on green patent cooperation networks, there was no one targeted the BTH region. Then, what are the characteristics of the current green patent cooperation network in the BTH region in terms of time and space? Starting from this question, this paper constructed the green patent cooperation networks in BTH region from 2010 to 2018, and analyzed the synergistic effect of green innovation across regions, so as to reveal the temporal and spatial evolution characteristics of the green patent cooperation network in BTH region.
2 Data Sources and Research Methods 2.1 Data Sources The patent database of the State Intellectual Property Office (SIPO) is the data source of this paper. The data of this study were obtained from invention patents in Beijing (BJ), Tianjin (TJ) and Hebei (HB). Patent information includes patent name, patent number, abstract, application date, applicant, applicant province code, applicant type, IPC code and et al. Since there is a lag of one to three years between patent application time and publication time, data from 2010 to 2018 were selected. Finally, 17,648 patent data including IPC code were obtained. Next, this study chose the green patent data of the cooperative application in the above patent data as the research object. Based on the patent classification number provided by IPC Green Inventory [11], this paper screened out the green patents applied by cooperation in BTH region and finally obtained 3793 valid data.
2.2 Network Construction and Measurement Index For the BTH Green Patent Cooperation network, the nodes of the network are the applicants in the cooperative patents, which belong to three types – universities (U),
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research institutes (I) and enterprises (E). The edge of the network represents the connection of nodes in the BTH Green patent cooperation network. In the BTH green patent cooperation network, the cooperation between nodes is bilateral cooperation, so it is an undirected network. In the global network, network size, network density, and center potential are selected as the measurement indexes. Among them, the network size N refers to the number of participants in the BTH green patent cooperation network. The more participants, the larger the network scale. Network density refers to the closeness of connections between nodes in the network, which is equal to the ratio of the number of network edges to the maximum possible number of edges and can be calculated by Eq. (1): d(G) =
2L N (N − 1)
(1)
where L is the number of edges, N is the number of nodes of the network. The network center potential C AD reflects the centrality of the network as a whole, which can show the extent to which the network is constructed around some special nodes and can be calculated by Eq. (2): ∑ C AD =
i (C D Dmax− C D Di )
(n − 1)(n − 2)
(2)
where C DDmax is the maximal value of the node degree centricity, C DDi is the degree centricity of node i. Additionally, degree centricity and intermediary centricity are selected to measure the nodes. Where the degree centrality of a node C DDi refers to the number of other points directly connected with the node. The larger its value is, the more centrality it is in the network, indicating that the node has an important position in the network. The degree centricity can be calculated by Eq. (3): C D Di =
∑
Xi j
(3)
j
where X ij is the number of connected edges from node i to node j. The betweenness centrality of nodes C ABi measures the proximity of nodes to other nodes. The higher the betweenness centrality, the stronger the bridge function of nodes. The betweenness centrality can be calculated by Eq. (4): C ABi =
∑
g jk (i)/g jk
(4)
j M but less than a certain threshold, time slot multiplexing and codeword multiplexing can be implemented in
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Fig. 6 The performance of TDMA-CDMA
Fig. 7 The scheduling delay under different .rt
the network, which reduces the scheduling delay. (3) When . RrtA is greater than the certain threshold, the number of retransmissions of CSCR and CSCG is large, which leads to the continuous increase of the scheduling delay. From Fig. 7, we can observe that for a specific . Nsum , there is an optimal interval of . RrtA that minimizes the scheduling delay. For the centralized ad hoc network with 100 nodes, when . RrtA = 10, that is, .rt = 100 m, the scheduling delay of the TDMACDMA scheduling scheme reaches a minimum value of .20 ms.
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Fig. 8 The scheduling delay under different .ψ
5.3 Influence of .ψ The scheduling delay of 100 nodes under different .ψ is shown in Fig. 8. From the figure, we can see that with the increase of the one-hop transmission failure probability multiple (.ψ), the scheduling delay is also increasing. When . RrtA = 10 , that is, .r t is set to 100 m, the scheduling delay curves under different .ψ all reach the lowest value.
6 Conclusion In this paper, the author proposes a hybrid MAC protocol suitable for centralized networks, named TDMA-CDMA, to improve the latency performance of centralized ad hoc networks. The analysis in this paper shows that: (1) When the node density is greater than .11 nodes/km2 , the TDMA-CDMA scheduling scheme proposed in this paper can reduce the scheduling delay. (2) For a specific . Nsum , there is an optimal interval of . RrtA , which can make the scheduling delay reach the lowest value. (3) The scheduling delay under different value of one-hop transmission failure all reach the lowest value when . RrtA = 10. This paper analyzes the scheduling delay of TDMA-CDMA scheduling scheme under different node density, node transmission radius and one-hop failure probability through simulation, which can provide a theoretical basis for engineering practice.
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References 1. IEEE standard for air interface for broadband wireless access systems. In: IEEE Std 802.162017 (Revision of IEEE Std 802.16-2012), pp. 1–2726 (2018) 2. Miya, T., Ohshima, K., Kitaguchi, Y., Yamaoka, K.: Experimental analysis of communication relaying delay in low-energy ad-hoc networks. In: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pp. 1–2 (2021) 3. Kumar, S., Kim, H.: BH-MAC: an efficient hybrid MAC protocol for vehicular communication. In: International Conference on COMmunication Systems & NETworkS (COMSNETS), vol. 2020, pp. 362–367 (2020) 4. Xin, S., Ben-yuan, W., Li, G., Liton, A.M.: Low delay and low overhead terahertz wireless personal area networks directional MAC protocols. In: 2021 6th international conference on intelligent computing and signal processing (ICSP), pp. 687–691 (2021) 5. IEEE standard for air interface for broadband wireless access systems. In: IEEE Std 802.162012 (Revision of IEEE Std 802.16-2009), pp. 1–2542 (2012) 6. Arafat, M.Y., Poudel, S., Moh, S.: Medium access control protocols for flying ad hoc networks: a review. IEEE Sens. J. 21(4), 4097–4121 (2021). https://doi.org/10.1109/JSEN.2020.3034600 7. Tong, X., Li, X., Liu, Y.: Research on resource efficiency optimization model of TDMA-based distributed wireless ad hoc networks. IEEE Access 8, 96249–96260 (2020) 8. Zhou, M., Wu, M., Ding, Z., Liu, Z., Zhao, F.: Performance evaluation of hybrid distributedcentralized TDMA in high-density vehicular networks. IEEE Commun. Lett. 9. Hadded, M., Muhlethaler, P., Laouiti, A.: TDMA scheduling strategies for vehicular ad hoc networks: from a distributed to a centralized approach. In: 2018 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–6 (2018) 10. Mao, Q., Yue, P., Xu, M., Ji, Y., Cui, Z.: OCTMAC: a VLC based MAC protocol combining optical CDMA with TDMA for VANETs. In: 2017 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 234–238 (2017) 11. Mary, P., Fijalkow, I., Poulliat, C.: Time slot allocation for TDMA/CDMA TH-UWB ad-hoc networks. In: 2010 IEEE 11th international workshop on signal processing advances in wireless communications (SPAWC), pp 1–5 (2010) 12. Luan, T.H., Shen, X.S.: A queuing based model for analyzing multihop performance in VANET. In: IEEE International Conference on Communication Systems (ICCS), vol. 2018, pp. 186–191 (2018) 13. Hasegawa, M., Kobayashi, K., Okada, H., Katayama, M.: Synchronous CDMA for a PLCbased multi-machine control system. In: IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, pp. 559–564 (2019) 14. Karabulut, M.A., Shah, A.F.M.S., Ilhan, H.: OEC-MAC: a novel OFDMA based efficient cooperative MAC protocol for VANETS. IEEE Access 8, 94665–94677 (2020). https://doi. org/10.1109/ACCESS.2020.2995807
A Data-Driven Pharmacists Scheduling Problem in a Pharmacy with Fairness Concerns Yuyao Feng and Xiang Jie
Abstract In a field investigation of the outpatient pharmacy of one hospital in Chengdu, we found it bears problems including the poor working experience of pharmacists, long patient waiting time, and low service efficiency. One major reason is traced to the lack of a scientific scheduling method, therefore, in this study, we proposed an optimization scheduling method based on the data on prescriptions and pharmacist preferences. We built a data-driven pharmacist scheduling model to minimize the difference in work experience and intensity among pharmacists. Specifically, the objective is to minimize the variance of the weighted working time among pharmacists and to lower the work intensity and negative emotions of pharmacists. The case study demonstrates that this model has considerable advantages in strengthening the fairness concerns of pharmacists as well as their work experience. The results also show that the model has good properties regarding stability and robustness in varied circumstances. Through this optimization, we could improve the efficiency of pharmacists and the service quality of the pharmacy. Keywords Scheduling optimization · Fairness concerns · Outpatient pharmacy · Data driven · Efficiency · Integer programming
Y. Feng College of Design and Engineering, National University of Singapore, Singapore, Singapore e-mail: [email protected] X. Jie (B) Business School, Sichuan University, Chengdu, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_29
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1 Introduction 1.1 Research Background The outpatient pharmacy serves as the cornerstone of the hospital service system and it is the place where patients finish their medical care [1]. It is also a medical facility service terminal and the distribution center for hospital medications. The number of prescriptions that need to be processed by pharmacies and the number of patients that need to be served are both rising daily due to the ongoing growth in medical demand. As a result, the patient waiting time is getting longer and more space is needed for queuing, which leads to patients unsatisfactory with the pharmacy service [2]. In our investigation on one large hospital in Chengdu, China, we discovered that the pressure placed on outpatient pharmacy was primarily focused on the accumulation of prescriptions and inefficient work. Usually, the elderly make up the majority of patients and most of them are psychologically and physically less robust than young people. They frequently ask the outpatient pharmacy doctors at the counter rather than the specialized instructor at information center. The reason is because they are difficult to learn and understand the consultation process during consultation or using the digital consultation system. The additional and repeated inquires occupied a large proportion of the regular pharmacy work time, concurrently, the drug collection process also requires a long time to complete. Therefore, a backlog of drug order occurs in the outpatient pharmacy. The repeat inefficient consultation and longtime intensive workload also engender negative feelings in the pharmacist, such as depression and irritability. How to increase the efficiency of the pharmacy work and improve the patient satisfactory becomes more critical when there is little space to increase the number of pharmacists. The field investigation revealed a number of issues, include the fact that the scheduling of pharmacists in the pharmacy is based on manual experience instead of any scientific method. We also found that different pharmacists have their own preferences regarding working periods. The dis-match of pharmacists’ preferences and the working time also result in negative feelings for pharmacists. In order to address this issue, we proposed a scheduling model based on the prescription data and the pharmacist preferences collected from the survey. We tried to reduce the backlog of prescriptions, inconsistent busy times of pharmacists, and unfairness concerns of pharmacists during their work.
1.2 Literature Review The scheduling problem and fairness concerns of pharmacists in health care system had attracted great attention from the academic and emerged abundant literature on this topic. The workload of pharmacists has been measured in research using a variety
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of methods, including data-driven analysis, which has been studied and used for calculating work intensity in many academic works [1]. Investigated outpatient pharmacies from the perspectives of pharmacists and patients, the results show that the pharmacist scheduling seriously affects the job satisfaction of pharmacists. Based on the calculation of the work intensity of outpatient pharmacists at West China Hospital, Chen et al. [3] proposed to optimize the work intensity by adjusting the work periods. The management of employees’ emotions and the assurance of high-quality work are both benefited from better scheduling, because it can help dispatchers maintain a calm attitude at work and boost team spirit. In order to precisely calculate the ideal work intensity curve and apply it to scheduling optimization, Wu et al. [4] used the work intensity calculation method at the Third Affiliated Hospital of the Third Military Medical University and the results show a significant growth in satisfaction. In West China Second Hospital, Huang et al. [5] established a number of scheduling rules with humanistic care as their central tenet, the result show fewer people in the line and much less pointless controversies and errors in their work. Flexible scheduling was introduced into the inpatient pharmacy scheduling system, so that physicians’ work preferences and work intensity could be matched and the working time could be distributed fairly and sensibly [6]. Jiang et al. [7] investigated the scheduling practices of pharmacy physicians using a two-person three-shift scheduling model. For evaluation and simulation analysis, Liu et al. [8] built the patient waiting and doctor scheduling models to evaluate and simulate the pharmacist workload intensity. Bowie et al. [9] focused on the nurse scheduling, they developed a prediction model which significantly increased the accuracy. Chern et al. [10] adopted heuristic algorithms to do the scheduling of health examination in a hospital. Chen et al. [11] also used heuristic algorithms to make medical staff scheduling. The staff are classified into different groups, based on which the schedule is obtained. Benchoff et al. [12] adopted integer programming to optimize the surgery arrangement. The data-based industrial engineering methods have been applied to solve problems in health care system. Hu et al. [13] identified the bottleneck department and investigated the hospital department scheduling. They adopted the production planning and control method to develop scheduling rules to reduce the overall waiting time and equipment idle time of the bottleneck department. Cincar and Ivascu [14] proposed an intelligent hospital management system to make medical staff and patients schedule. The results show a significant improvement in resource utilization regarding medical personnel, patients, and other hospital resources. Feng et al. [15] built a prediction model based on the patient data to identify the busy and busy conditions of the hospital in the future and then optimized the schedule of doctors. Göbel et al. [16] optimized and enhanced the working environment for pharmacists by conducting research from a human factors perspective. Chui et al. [17] showed that pharmacist job satisfaction was negatively correlated with workload through the data analysis. In summary, the work intensity calculation and modeling optimization, have each been applied in previous literature to solve the scheduling problem in healthcare. However, very few researchers have combined the two approaches. If shifts are only scheduled using the work intensity calculation, the accuracy and robustness
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Fig. 1 Map of research idea
are absent, in addition, there will be more instability due to the influence of human factors. Therefore, in this work, we are trying to fill this gap by taking both work intensity based on prescription data and humanistic factors into account. The goal of this paper is to calculate work intensity and include it as a key parameter in model optimization, based on which, the fairness concerns of pharmacists will be further considered.
2 Research Outlines 2.1 Research Design In this study, we completed the optimization of pharmacist scheduling in two steps. The first is to evaluate the workload demand and the work preference of each pharmacist in all periods. The second is to optimize the pharmacist scheduling problem considering the workload intensity and pharmacist preference. Since pharmacists have their own preferences on working periods, we adopted the preference weight by combining the workload demand and work preference. This is different to many previous literature which either considers the workload demand only or the preference of pharmacists. As one of the most important service department of a hospital, the outpatient pharmacy is constrained by many principles, e.g., the longest patient waiting time and pharmacist working time. Therefore, we examined some hard and soft constraints in the proposed scheduling model. The outline of the research is shown in Fig. 1.
2.2 Pharmacy Overview This research is based on the current situation of an outpatient pharmacy in Chengdu, China, of which the majority patients are people having mental illnesses. The pharmacy occupied less than 200 square meters with 20 physicians serving for 7 windows. The outpatient pharmacy serves about 2,000 prescriptions per day on average, and up to 3,000 per day during peak times. For each window, a medication collector and a
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teller at the counter are needed and the double-check rule is applied according to the regulation. The teller is in charge of communicating with the patients and distributing the medication, while the other is responsible for distributing the prescription and collecting medications. The primary problem of this pharmacy is that it is very crowed especially in the morning during the work days. The reason behind is the number of patients is increasing over the years, however, the pharmacy space and medical crew are limited. The result is the average patient queue during peak hours greatly exceeds the pharmacy capacity. On the other hand, pharmacists working in the pharmacy are bearing a very high level of mental and physical pressure. Because of the traditional scheduling method based on experience, the work hours and work intensity of pharmacist are unbalanced. The workload and work pressure for the pharmacist are far above normal working conditions under the current shift arrangement. They are not allowed to choose the days and periods they want to work. The work arrangements require that all living arrangements be temporarily changed, which may disrupt the doctor’s routine, and in some cases, the pharmacy staff may also become disorganized due to physical health issues and other potential negative factors. Besides, the pharmacists did not have a proper plan for their downtime, which refers to the non-pure operation time in the work as well as the time needed to make up for some operations, like drinking and using the restroom. In addition, the uneven work intensity distribution is another important issue for pharmacists. The field observation shows that some pharmacists may have more busy hours than others, which may significantly impact their work efficiency as well as their feelings. All these issues and reasons behind call for a better scheduling solution for this pharmacy. In this work, we divided the working day into five periods from 8:00 am to 6:00 pm in accordance with the current situation. Based on the field investigation of the pharmacy, we assigned the workload (number of prescriptions per person) in each period of a week (shown in Table 1), considering the volume of prescriptions, the workload intensity, and a number of other external factors (such as relaxation time). The peak times occur between 10:00 to 14:00 while the last period, i.e., 4:00 p.m. to 6:00 p.m., has the fewest patients. Following the current practice, 14 pharmacists are going to be assigned to work in a pharmacy for a given week. Each pharmacist works no more than eight hours per day (i.e., four time periods) according to related regulations and each one has their own preference on different working periods. After conducting an on-site interviews and investigations, we assigned the five time periods with weights of 0.8, 0.85, 0.9, Table 1 Window working hours and arrangements Period
8:00–10:00 10:00–12:00 12:00–14:00 14:00–16:00 16:00–18: 00
Minimal windows
3
Prescriptions per person 50
5
4
4
4
70
60
50
30
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Table 2 Preference table of pharmacists Period/Number
8:00–10:00
10:00–12:00
12:00–14:00
14:00–16:00
16:00–18:00
1
0.85
0.9
1
0.95
0.8
2
1
0.85
0.9
0.95
0.8
3
0.8
0.85
1
0.95
0.9
4
0.9
0.95
0.85
0.8
1
5
0.85
0.8
0.9
0.95
1
6
0.9
0.8
0.85
0.95
1
7
0.95
1
0.9
0.85
0.8
8
0.95
1
0.9
0.8
0.85
9
0.8
0.9
0.85
1
0.95
10
1
0.8
0.95
0.9
0.85
11
1
0.8
0.95
0.85
0.9
12
0.95
0.85
0.8
1
0.9
13
1
0.95
0.9
0.8
0.85
14
0.95
1
0.85
0.9
0.8
0.95, and 1.0, respectively. A smaller weights mean a more preferred period. The weights of 14 pharmacists are given by Table 2.
3 Scheduling Optimization Model 3.1 Pharmacist Work Experience Weight In many previous literature, the work intensity is identified as the objective workload, i.e., the number of orders per unit time, ignoring many human factors, like nervous, exhaustion, and paining of workers. In a pharmacy, three factors are affecting the work experience of a pharmacist, the number of prescriptions per unit time (i.e., the workload), total work time, and work time preference. It is impossible to avoid such situations when a pharmacist works longer hours and with greater intensity than others. To abstract the three factors in this case, we adopted the work experience weight to optimize the scheduling of pharmacists. We define the following parameters as. yij is the work experience weight of doctor i in period j. Aij is the work preference of doctor i in period j (0 < Aij < 1). Bj is the workload in period j (0 < Bj ≤ 1) and B j = C j /max{C j }
(1)
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C j is the average number of prescriptions per pharmacist in period j. The work preference Aij is obtained from Table 2, note that it has 5 values which denote the ranking of each period given by each physician via a questionnaire. Then we defined the work experience weight as yi j = Ai j ∗ B j
(2)
3.2 Scheduling Model (1) Variables and parameters Variables and parameters used in this work are defined as follows,k is the total number of pharmacists can be assigned. T is the duration of a duty cycle, which is divided into s segments, and t j ( j=1, 2, ..., s) represents period j in a cycle time segment. d j ( j=1,2,..., s) represents the minimum number of pharmacists required in period j. n represents the maximum number of periods a doctor can work. E represents the weight of the fairness concerns. F i (i=1, 2, ..., k) represents the weight of doctor i’s total weighted working time. x ij (i=1, 2, ..., k; j=1, 2, ..., s) indicates whether pharmacist i works at period j. (2) Objective functions The purpose of this model is to improve the fairness and accuracy of pharmacist scheduling. Therefore, one objective is to reduce the variance of total working time of each pharmacist in the planning horizon. The total working hours of pharmacist 1 to k are given as, Es j=1
x1 j ,
Es j=1
x2 j , · · · ,
Es j=1
xk j
(3)
The variance of working time of pharmacists is, minV ar {
Es j=1
x1 j ,
Es j=1
x2 j , · · · ,
Es j=1
xk j }
(4)
As mentioned before, the feeling of a pharmacist is highly associated to the workload intensity and work preference. That is why the work experience weight yij is introduced. Then, the total weighted working hours of the first to k th pharmacist are given as, Es j=1
x1 j y1 j ,
Es j=1
x2 j y2 j , · · · ,
Es j=1
xk j yk j
(5)
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Then the objective function is given as, minV ar {
Es j=1
x1 j y1 j ,
Es j=1
x2 j y2 j , · · · ,
Es j=1
xk j yk j }
(6)
Except the fairness concern of pharmacists, we also should let them work at their preferred periods, in another word, to minimize their total work experience as, min
Es j=1
x1 j y1 j , min
Es j=1
x2 j y2 j , · · · , min
Es j=1
xk j yk j
(7)
This makes the pharmacist scheduling problem a multi-objective planning and the weighted method is used to solve this problem. (3) Constraints There are also some constraints regarding pharmacists and other concerns. All constraints are given as follows, The total working time of a pharmacist should not exceed the specified limit. Es j=1
xi j ≤ n
(8)
The total number of on-duty pharmacists in each period is greater than the demand, and no more than the total number of pharmacists. dj ≤
Ek i=1
xi j ≤ k
(9)
We also should ensure that pharmacists are assigned in their preferred working hours as much as possible, and all pharmacists have the same weight. E > Fi , F1 = F2 = . . . . . . = Fi
(10)
E + F1 + F2 + · · · + Fi = 1
(11)
3.3 Case Analysis The pharmacist scheduling problem is a typical 0–1 integer programming, we could adopt many software, i.e., Lingo, MATLAB, to solve it in a short time since our focus is to highlight the accuracy and fairness concern of the scheduling other than solution algorithm. In this research, we used the real data of a pharmacy in Chengdu to demonstrate the feasibility and efficiency of the model. Parameter values are obtained
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from the field investigation of the pharmacy in Chengdu. Specifically, k = 14, T = 10, n = 8, E = 0.3, and F 1 , F 2 , …, F 14 are all set to 0.05. The work demand d j , Bj and work preference Aij are obtained from Tables 1 and 2, respectively. The results are given by Table 3. The outcomes show that all pharmacists work 6 h per day and that the number of employed pharmacists during each period is greater than or equal to the minimum requirement. We could compute the weighted work experience of each pharmacists, shown in Table 4. The average weighted work experience of pharmacists is 1.98 with a variance of 0.0309. This variance result demonstrates that the workload and work experience of each physician is nearly identical and that there is virtually no difference in the work intensity of pharmacists during this shift work. Table 3 Optimization results of pharmacist work schedule Number
8:00–10:00
10:00–12:00
12:00–14:00
14:00–16:00
16:00–18:00
1
0
0
1
1
1
2
0
1
1
0
1
3
0
1
0
1
1
4
0
1
0
1
1
5
0
0
1
1
1
6
0
0
1
1
1
7
0
1
1
0
1
8
0
1
1
0
1
9
1
0
1
1
0
10
1
1
0
0
1
11
1
1
1
0
0
12
1
1
0
1
0
13
1
1
0
1
0
14
1
1
0
0
1
Table 4 Pharmacist weighted work experience Number
1
2
3
4
5
6
7
Value
1.82
1.91
1.85
1.88
1.81
1.76
2.06
8
9
10
11
12
13
14
AVG
2.08
2.01
1.82
2.33
2.24
2.24
1.96
1.98
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3.4 Robustness Analysis Considering the fact that the outpatient staff changes frequently, this study used multiple groups of randomly generated data on pharmacists’ preferred working hours to simulate check the robustness of the model. We compared the variance of each group with the average weighted work experience of pharmacists. Specifically, 30 additional groups of preferences were randomly generated and the results are presented in Table 5. This table shows that the minimum and maximum variances are 0.0091 and 0.0283, respectively. These 30 sets of data demonstrate that the model can produce reasonably fair scheduling results considering various pharmacists are available.
4 Extentions The previous model considered the scheduling accuracy and fairness concern of pharmacist, the result shows a good performance regarding the feasibility and robustness. In this subsection, we further took the work content of pharmacists into consideration and studied a more detailed scheduling problem based on the field investigation of the case pharmacy. Table 5 Multi-group data model test results Group (G)
Variance (Var)
Work experience (WE)
G
Var
WE
1
0.0243
2.20
16
0.0145
2.24
2
0.0101
2.09
17
0.0229
2.11
3
0.0091
2.14
18
0.0162
2.07
4
0.0178
2.18
19
0.0093
2.07
5
0.0112
2.23
20
0.0212
2.18
6
0.0119
2.17
21
0.0123
2.22
7
0.0194
2.06
22
0.0208
2.13
8
0.0226
2.21
23
0.0132
2.12
9
0.0163
2.19
24
0.0256
2.10
10
0.0195
2.08
25
0.0178
2.16
11
0.0262
2.05
26
0.0254
2.22
12
0.0161
2.07
27
0.0283
2.11
13
0.0281
2.19
28
0.0243
2.25
14
0.0124
2.08
29
0.0116
2.10
15
0.0112
2.11
30
0.0093
2.08
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4.1 Optimization of Work Content There are many pharmacies adopt the double-check rule in their work just like the case pharmacy does. In general, the pharmacist on-duty has two tasks, the medication collection and distribution. Obviously, the two tasks have different work intensity and it is reasonable to consider such difference in making the pharmacist scheduling. Following the human factors and ergonomics analysis, we assumed that the distribution work intensity at the counter is equal to 1, as a benchmark. As to the medication distribution task, there are 6 medicine cabinets and we assume all prescriptions are processed at a constant rate. We blurred all data for confidential reason and the number of prescriptions and visits of each medical cabinets are presented in Table 6. The prescription type reveals how challenging to collect medications and a score is applied based on the frequency of appearance, difficulty of searching, and collecting of medications. A maximum score of 60 is used and the higher the score is, the easier it is to collect the medication. The layout of the medicine cabinets in the pharmacy is as follows (Fig. 2). Then the medication collection work intensity is determined by adding the weight of all medicine cabinets, which is done by dividing each medicine cabinet work weight by the moving distance in relation to the table above. Table 6 Pharmacist’s visit to the medicine cabinet Cabinet
1
2
3
4
5
6
Total
49
55
30
35
47
14
230
Visits
4961
5670
2733
3379
4447
1370
22,560
Ratio
101
103
91
97
95
98
585
Type of prescription
Fig. 2 Map of cabinets location
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Table 7 The work intensity of the drug collection pharmacist Cabinets
1
2
3
4
5
6
Total
Weight
0.20
0.12
0.18
0.34
0.30
0.36
1.50
W eight = Ratio ÷ 5 × 0.01 × Ok
(12)
where Ok is the distance weight of cabinet k. Set O1 as 1, then O2 , O3 , O4 , O5 , O6 will 0.6, 1, 1.8, 1.4, 1.8, respectively (Table 7). In this section, the time period is divided into two tasks for scheduling, and the previous j is replaced by j1 , j2 , representing the first and second task (i.e., distribution and collection) in period j. Accordingly, parameters P1 and P2 present the work intensity value of the first and second task. As we have discussed, in this work, we have P1 = 1 and P2 = 1.5. Then the objective function of the scheduling model considering the work content is, minV ar {
Es j=1
(x1 j1 y1 j1 + 1.5x1 j2 y1 j2 ),
Es j=1
Es
j=1
(x2 j1 y2 j1 + 1.5x2 j2 y2 j2 ), · · · , (xk j1 yk j1 + 1.5xk j2 yk j2 )}
(13)
The new constraints require that the number of people who take on the first task is equal to that of the second task in each period. Ek 1
xi11 =
Ek 1
xi12 ,
Ek 1
xi21 =
Ek 1
xi22 , · · · · · · ,
Ek 1
xi j1 =
Ek 1
xi12
(14)
Adopting the same parameter values, we solved the scheduling problem considering the work content and the results are given in Table 8. The results show that every pharmacist also works for three periods per day with an average weighted work experience 2.27. Since we assume the second task is increased by 1.5 times in work intensity weight, the 8% increase of the weighted work experience is negligible. By the same way, we could compute the variance and the result is 0.0178. The results show that the model performs well when considering the work content.
4.2 Extention with Overtime Working In previous models, there is an assumption, that is the number of pharmacists is larger than the total demand, which means no pharmacist has to work overtime. However, this is not hold for many cases, especially for some big pharmacies. In this subsection, we tried to address this issue by relaxing this assumption and introduced a penalty to
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Table 8 Optimizing model results Time 8:00–10:00
10:00–12:00
12:00–14:00
14:00–16:00
16:00–18:00
Task
1
1
1
1
1
2
2
2
2
2
1
0
0
0
0
0
1
0
1
1
0
2
0
0
0
1
0
1
0
0
0
1
3
0
0
0
1
0
0
0
1
0
1
4
0
0
0
1
0
0
0
1
1
0
5
0
0
0
0
1
0
0
1
1
0
6
0
0
0
0
0
1
1
0
0
1
7
0
0
1
0
0
1
0
0
1
0
8
0
0
1
0
1
0
0
0
0
1
9
0
1
0
0
1
0
1
0
0
0
10
0
1
0
1
0
0
0
0
0
1
11
1
0
1
0
1
0
0
0
0
0
12
1
0
1
0
0
0
1
0
0
0
13
1
0
1
0
0
0
1
0
0
0
14
0
1
0
1
0
0
0
0
1
0
Total 3
3
5
5
4
4
4
4
5
5
avoid unnecessary working overtime. It is obviously impossible to accommodate all pharmacists’ preferences when considering overtime working, the multi-objective optimization is reduced to a single-objective optimization that satisfies fairness as much as possible. Let li denotes the unit penalty for pharmacist i to work overtime. Then we have our objective function as minV ar
{E s j=1
x1 j y1 j ,
Es j=1
x2 j y2 j , · · · ,
Es j=1
} E k xk j yk j +
i=1
li ×
(E
s j=1
) xi j − n
(15) We adopted the same parameter values as previous and the first 12 pharmacists are available in this case. Normally, we assumed that a pharmacists cannot work more than three periods in a single day, otherwise the overtime penalty will occur. Note that the minimum demand is 40 (from Table 1) while the total number of working periods in a day is 36, which means some pharmacists have to work overtime. In general, the attitude of a pharmacist work overtime differs from one to another, therefore, a questionnaire was applied to quantify each pharmacists’ willingness to work overtime. A higher value means a greater impact of overtime work and a greater penalty (Table 9). We multiplied all impact values by 0.01 to ensure the penalty li and the variance has the same order of magnitude (otherwise the impact of penalty will dominate the fairness concern too much). Then we solved the problem and the results are presented in Table 10.
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Table 9 Pharmacist overtime work impact Number
1
2
3
4
5
6
Value
1.05
0.88
1.16
0.85
0.92
1.09
7
8
9
10
11
12
0.88
0.93
1.20
1.14
0.83
0.99
Table 10 Solution results for soft and hard constraints Number
8:00–10:00
10:00–12:00
12:00–14:00
14:00–16:00
16:00–18:00
1
0
0
1
1
1
2
0
1
1
0
1
3
0
1
0
1
1
4
1
1
0
1
1
5
0
1
1
1
1
6
0
1
1
1
0
7
1
1
1
0
1
8
0
1
1
0
1
9
1
0
1
1
0
10
1
1
0
0
1
11
1
1
1
1
0
12
1
1
0
1
0
The result show that 4 pharmacists have to work overtime and the pharmacist’s weighted work experience is 2.34 on average, which is still below average number of periods they worked. By the same way, we could compute the variance of the weighted work experience of pharmacists, the result is 0.1325, which is very small compared to the average weighted work experience. This means the model also works well for this extensions.
5 Summary The pharmacy of a hospital plays an important role in serving the patients and it highly affects the efficiency of the hospital and the satisfaction of patients. In a field investigation of an outpatient pharmacy in Chengdu, China, we found the pharmacy is suffering some important issues like crowds. Based on the prescription statistical data and the field survey, we proposed the pharmacist scheduling problem considering the fairness concern. The result shows that the model works well regarding the accuracy and fairness of pharmacists by a case study using data from the pharmacy. In addition, we further considered two extensions considering the work content and overtime
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working, respectively. The model also shows a good performance by numerical studies. There are some future directions for further studies, the first is to consider heterogeneous work in the scheduling problem. The second is to combine the theoretical analysis with an empirical study to generate better managerial insights. Acknowledgements This work was supported by the Sichuan Science and Technology Program with grant number 2022JDR0322. The authors also appreciate the support of the Second Outpatient Pharmacy of West China Hospital of Sichuan University in this research.
References 1. Surur, A.S., Teni, F.S., Girmay, G., Moges, E., Tesfa, M., Abraha, M.: Satisfaction of clients with the services of an outpatient pharmacy at a university hospital in northwestern Ethiopia: a cross-sectional study. BMC Health Serv. Res. 15(1), 1–8 (2015) 2. Sadi, B.M.A., Harb, Z., El-Dahiyat, F., Anwar, M.: Improving patient waiting time: A quality initiative at a pharmacy of a public hospital in United Arab Emirates. Int. J. Healthc. Manag. 14(3), 756–761 (2021) 3. Chen, L., Wang, F., Liu, Y., Zhang, L.: Application of work intensity calculation method in optimizing outpatient dispatching work schedule (in Chinese). Chin. Pharm. 19(28), 2199–2200 (2008) 4. Wu, H., Zhang, P., Meng, D.: Using work intensity calculation method to optimize outpatient pharmacy scheduling (in Chinese). Chin. Pharm. 15(11), 1663–1666 (2012) 5. Huang, H., Yuan, H., Li, Y., Zhang, L.: Discussion on the rational scheduling experience of optimizing the process of outpatient pharmacy in our hospital (in Chinese). Chin. J. Hosp. Pharm. 29(07), 578–580 (2009) 6. Gan, H., Yang, C., Yang, S.: Application of flexible scheduling in inpatient pharmacies (in Chinese). Appl. Mod. Med. China 3(18), 213 (2009) 7. Jiang, S., Zhan, H., Yu, B., He, Q.: Application of two-person three-shift through-shift scheduling mode in pharmacy management (in Chinese). Jiangxi Med. 48(10), 913–914 (2013) 8. Liu, Q., Xie, X., Liu, R., Chen, E., Yang, Z.: Research on the scheduling of emergency physicians for dynamic time-varying needs (in Chinese). Ind. Eng. Manag. 20(06), 122–129 (2015) 9. Bowie, D., Fischer, R., Holland, M.L.: Development and implementation of a forecasting model for inpatient nurse scheduling. Nurs. Econ. 37(3), 144–151 (2019) 10. Chern, C.C., Chien, P.S., Chen, S.Y.: A heuristic algorithm for the hospital health examination scheduling problem. Eur. J. Oper. Res. 186(3), 1137–1157 (2008) 11. Chen, P.S., Huang, W.T., Chiang, T.H., Chen, G.Y.H.: Applying heuristic algorithms to solve inter-hospital hierarchical allocation and scheduling problems of medical staff. Int. J. Comput. Intell. Syst. 13(1), 318–331 (2020) 12. Benchoff, B., Yano, C.A., Newman, A.: Kaiser Permanente Oakland Medical Center optimizes operating room block schedule for new hospital. Interfaces 47(3), 214–229 (2017) 13. Hu, X., Wu, H., Zhang, S., Dai, X., Jin, Y.: Scheduling outpatients in hospital examination departments. In: IEEE International Conference on Industrial Engineering and Engineering Management 2009, 335–338 (2009) 14. Cincar, K., Ivascu, T.: Agent-Based hospital scheduling system. In: 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 337–338 (2019)
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15. Feng, D., Mo, Y., Tang, Z., Chen, Q., Zhang, H., Akerkar, R., Song, X.: Data-driven hospital personnel scheduling optimization through patients prediction. CCF Trans. Pervasive Comput. Interact. 3(1), 40–56 (2021) 16. Göbel, M., Franck, M., Friesdorf, W.: Work duration and work scheduling for hospital doctors, Qual. Work. Prod. Enterp. Futur., (2003) 17. Chui, M.A., Look, K.A., Mott, D.A.: The association of subjective workload dimensions on quality of care and pharmacist quality of work life. Res. Social Adm. Pharm. 10(2), 328–340 (2014)
Research on Book Publishing Enabled by Digital Twin and NFT Kehan Li and Liang Wang
Abstract With the explosion of “metacmos”, the idea of various technologies enabling reality has gradually entered the public view. Whether it’s digital twins, big data, or blockchain, they’re essentially technology enabling the physical world. The “digital twins” of physical publications are constructed through digital twinning technology, followed by data storage. Not only can ensure that the book board type, the content is not damaged by the passage of time. It can also use the NFT blockchain technology to ensure that the “digital book” data itself will not be stolen or tampered with, with a verified identification certificate and an independent collection number. As the vision of virtual space life becomes more and more real, people collecting “precious physical books” from ancient times may gradually give way to the collection of “digital books” as a collection value due to the emergence of technology. It is foreseeable that the application of digital technology will bring a series of irreversible changes to the production, sales and service of the publishing industry. Keywords Digital twin · NFT · Blockchain · Book collection · Virtual books
1 Introduction With the explosion of the concept of “metacoverse”, various cutting-edge technologies are connected to the real world under the power of the Internet, and even give birth to the idea of a virtual world.In the integrated development of publishing industry, the empowerment and innovation of publishing has always been a hot topic in the This work is partially supported by National Social Science Fund of China under contract with No. 21BXW037 K. Li · L. Wang (B) School of Economics and Management, Beijing Institute of Graphic Communication, Beijing, China e-mail: [email protected] K. Li e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_30
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academic and industry. Technology’s real-world enabling behavior can spatially help publications extend their value chain with more developmental possibilities. Blockchain attracts public attention because of its characteristics of decentralization, invariance, data integrity and transparency [1]. In early 2021, Non-Fungible Token, abbreviated as NFT, a new type of blockchain-based digital asset, appeared in the public view.In particular, American digital artist Mike Winkelmann pieced together his 5,000 photos a day since 2007 into NFT painting Everydays The First 5000 Days (5,000 Days), after Christie’s sold for $69.3 million. NFT began to show explosive growth, and gained fame in the international market. Regardless of the bubble risks of NFT, NFT, as a cutting-edge application of publishing fusion technology, can bring copyright protection, business expansion, space continuation and other changes to the publishing industry [2]. In the past few years, the publishing industry has accumulated a lot of successful integration cases and its relative practice and understanding experience. As the trend of the digital age becomes more and more intense, the publishing industry needs more advanced the application of digital technology in the publishing field more and more frequently. For a long time, the development of the publishing industry mainly focuses on the talent mechanism, the content creation mechanism, the science and technology enabling innovation and the publishing main body mechanism construction. A deep understanding of scientific and technological innovation shows that books, as the media carrying knowledge and data content, their changes and the application of new technologies play a key role in the process of technology participation in the development of the publishing industry [3]. Such as cutting-edge technology by making up for the traditional publishing in the fusion development of media form limit, low transmission speed, basic energy consumption and terminal equipment compatibility problems, in space extension publications value chain, thus in the era of new technology will be publishing and education, entertainment, social more from the function together [4]. Digital technology also enables people to access and explore cultural products in a way that goes beyond space and time [5]. Smart bookstores and smart book retail with the participation of technology have emerged across the country, and electronic publications are also regarded as an important strategy for publishing development in many countries because of the demand of market development [6].
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2 The Dilemma Existing in the Development Process of Publishing Integration 2.1 Paper Books Face the Dilemma of “Losing Their Charm” Traditional physical publications, whether paper books or audio-visual products, are facing the dilemma of gradually losing the market and losing its charm in the face of the Internet digital wave. Since the purchase price of mobile devices and the cost of Internet communication services have continuously fallen, people have used mobile devices much longer than they can read paper books. As one of the many ways of entertainment, paper reading is not the first choice for people’s entertainment. Network world information flood make users dizzying, not to mention mobile devices and its APP extract user fragments of life time, in order to meet the challenge of the market change, press also transformation, launched electronic books, podcasts, listening and other new book services, the new market environment is squeezing the survival and development space of paper books. At present, the era of digital reading has arrived, and the changes of the form of publishing are also virtually changing people’s reading habits. Fragmented reading and fragmented publishing have become what more and more people like about it. According to the results of the 19th survey on Chinese readers’ reading released by China Academy of Press and Publication, the book reading rate of Chinese adult Chinese readers in 2021 was 59.7% and 24.6%, down 0.9% from 2020; the journal reading rate was 18.4%, down 0.3% from 2020, and the contact rate of digital reading methods was 79.6%, up 0.2 percentage points from 79.4% in 2020.In general, although the individual reading rate in China is still good, and many people still choose paper publications, the reading contact rate of traditional paper media is declining under the overall trend. Although e-books and network reading both in product form or build environment performance than paper books, but later as the digital technology fully permeate people’s life, and all kinds of knowledge services, digital publishing product services in various forms and service quality gradually improved, users in the choice will be more inclined to digital publishing products, paper publications “charm” will gradually weaken, so the traditional publishing industry is actively seeking digital integration development. The integration of publishing does not mean abandoning paper books, but integrating digital technology and paper books, bursting out new vitality (Fig. 1).
2.2 Electronic Book Piracy is Rampant The development of the Internet has been very mature, and the accessibility of the network media has been continuously improved because of the development of the network communication technology. The Internet has become a necessary tool for
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Fig. 1 Survey results of the 19th Chinese national reading media contact rate. Data source China academy of press and publication, the 19th Chinese national reading survey report
the daily communication among the public. This brings about a strange phenomenon: authors, readers, creators, consumers and other identities and characters mix together. This undermines the relatively balanced “reader-editor-author” model of the traditional publishing industry. While the convergence of publishing has brought them closer together, they are becoming more distant from their content. This is reflected in the current environment, digital reading is very convenient, available and accessible at any time. But in the end is composed of binary data, even if all kinds of anti-theft measures emerge in endlessly, piracy means has become more clever. In the face of various potential piracy threats in the network environment, even if the use of legal means. In the tug of war of rights protection, the author and the editor have to give up their rights protection in the face of a long process and a large number of objects. The well-known difficulty in safeguarding rights has made piracy even more rampant.
2.3 Profit Distribution of Recoverable Copyright Although the diversified communication environment has brought the “blue ocean” that the publishing industry has never touched, it is also eroding the economic territory of the traditional publishing industry. The emergence of various non-written media has given birth to cross-border integrated publishing forms such as audio books, short videos and augmented reality, as well as new industrial practices and new economic entities emerging together with them, and audiences’ thinking and preferences have been constantly differentiated. The boundaries between creators and consumers are no longer clear, and collaborative publishing like Zhihu appears. However, there are also huge problems in the new business format. In specific practice, collaborative
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publishing may face problems such as uncertain contribution degree and difficulty in copyright and profit distribution. Although electronic publications such as e-books, electronic journals, and emagazines are not new. In addition, the works are published electronically and uploaded by the author himself or others to the network platform for sale. It has an international history of more than ten years, and it is also a very common and direct way. This approach coincides with the fragmented reading of current users. But due to the special sales model, most platform will choose membership, many e-books can not directly produce sales, creators can share is only a small part of the benefits of platform restrictions make e-books cannot be converted into transfer or cross-platform transfer, users in a platform paid books, but cannot read the books in other platforms. Consumers’ rights and interests need to be attached to the centralized platform, and if the platform is closed, then readers cannot enjoy the corresponding services. The high degree of centralization not only affects the rights and interests of consumers, but also makes the interest distribution of copyright cannot be reduced.
3 The NFT and Digital Twins Make the “Digital Scarcity” of Books Possible As a material media carrier of information and culture, books have the attributes of recording, transmission and collection.China is one of the earliest countries in the world to have book collection institutions and administrators, and its history can be traced back to the Zhou Dynasty. In Chinese history, the collection system can be divided into three categories: national collection, commonly known as official collection; private collection, also called private collection; academy collection and temple collection, namely private office support collection organization [7]. As both practical and collectible precious items, books are circulated and collected by people, and even regarded as a symbol of wealth. Traditional publishers also highlight the physical scarcity of physical books through a more elaborate design and arrangement, production and binding, as well as some limited distribution and hardcover books. But with the advent of the information revolution and the birth of computers, books bid farewell to the “lead and fire” printing, all information can be stored in binary. Not only does this mean that the market for physical publications is gradually compressed, and that mass copying and production at low cost makes books dependent on content quality, but the slight scarcity of digital charm in the digital market is diluted by the low cost of also copying and making. On the other hand, information and technology have also flooded the market with uncertainties, and copyright issues and improper use of information technology have plagued authors and publishers, so the digital scarcity of books has been at stake. The emergence and adoption of new technologies means that old spatiotemporal environments, relationships are deconstructed, and strategies to provide their accompanying problem solving. So what changes can the digital twin and NFT, which have
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only emerged in recent years, bring to the publishing industry, especially to the struggling physical book market?In terms of the technical characteristics, the digital twin technology, as a 1:1 digital twin for manufacturing the physical entities, can realize the complete replication of the physical entities, and facilitate the subsequent processing and data management through the data. The use of blockchain technology has surged around 2016–2017. The use of blockchain technology includes cryptocurrencies, smart contracts, and irreplaceable tokens (NFTs). As one of the applications of blockchain technology, NFTs themselves are decentralized, unchangeable, and irrevocable. However, unlike homogenized tokens, NFT s has an inseparable, irreplaceable and unique characteristic [8]. Therefore, this paper will focus on the changes brought about by the two technologies to physical books.
3.1 Deconstructing the Digital Twin of the Recombinant—Entity Books from a Unique Perspective Digital twin, also known as digital mapping or digital mirror. As the name suggests, it is one or more interrelated digital twins formed by digitally transforming the physical things in the real world. It was first called the digital twin, and its concept originated in the National National Space Agency (NASA) Apollo moon landing project. In the later development and progress of technology, it is gradually applied in all walks of life, although it is currently mainly concentrated in the manufacturing industry and experiments. However, because it can be accurately mapping by sensors, so that physical entities recorded into a large amount of data, to achieve the purpose of simulation presentation in the Internet.Many scholars have also proposed to use digital twin technology to realize digital museum curator, establish digital twin system, and finally realize the urgent demand for the data of cultural relics ontology in terms of content transmission methods [9]. With today’s Chinese Z era people have become the main consumers in the market, their distinct digital preferences and digital characteristics promote the application of more digital technology in all walks of life. Data can provide people with a brand new thinking and means to understand and understand complex systems and things. Through the 1:1 accurate digital reconstruction of real things on a limited time length and spatial scale, the almost consistent digital virtual images are created. hrough the powerful algorithm and process implantation in the later stage, the image has an immeasurable prospect of use. Digital twin can give the existing and upcoming physical books a new deconstruction and restructuring perspective, through the construction of treasured books, cultural relics books, classic content can maximize the fine data preservation, through the existing book planning into digital twin technology, can provide readers with a new reading experience and interactive content; through the digital twin can realize the effective linkage between online data and offline books, so that the two under
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the Internet. All this is based on the cutting-edge digital technology for entity book “charm” can assign, these digital technology can provide traditional book promotion, marketing, protection, communication does not have the advantage, a greater degree of digestion, even better solve the books in the future digital age and personalized interaction between individual users.
3.2 Create Digital Scarcity—NFT Continues the Value Chain and Application Scenarios of Books Blockchain technology has always been recognized as one of the most secure data technologies. By building on data blocks to achieve its fast, open, transparent, cheap, accessible and programmable features, blockchain, as a protocol to manage value exchange rules, allows the rapid transfer of information or one place in the world of the total financial assets to another place. It can contain any form, any type of information, and the connection between blocks and blocks is called a chain, and is an immutable way [10]. So it is very safe. And the Non-Fungible Token is a nonhomogeneous currency. It is related to traditional cryptocurrencies because they are all represented by a tokized value item. But the NFT has its own unique features or identifiers, which means that it can give any digital collection a unique number that cannot be tampered with and stored forever, and that is very easy to verify. As a result, the NFT is presented most often in the unique form of the collections being set up, such as digital art, digital collectibles, games, and meta-versions, and is not used as a medium of exchange [11]. So any two NFT’s in the ecosystem can be sold at significantly different prices, which are different from traditional cryptocurrencies: “One Bitcoin is worth a single bitcoin.” In addition, the smart contract through a unique identifier attached to each NFT, it can not be falsified, less likely to be copied, nor exchanged with any other tokens. If the owner decides to sell the NFT, then the owner can only go through the NFT-based NFT market, with no need for a middleman, and will not be locked on any platform, which greatly ensures the standardization and safety of the NFT circulation. In some cases, the original creator also receives resale royalties. And the owner can also decide to hold the NFT permanently, without worrying about losing its assets because the platform disappears, because it is held in the wallet of the Ethereum blockchain [12]. These features make it a hot trading option in virtual markets. Because of the limitations of the standards, the irreplaceable nature of the tokens is made powerful. These standards provide developers to safe assets, with traditional alternative tokens regulated by ERC-20 and the NFT through ERC-721.ERC-721 represents the first standard of irreplaceable digital assets. And it is an inheritable and highly reliable intelligent contract standard. Developers and Foundry can easily create new contracts for ERC-721 compliance. It links a unique identifier to the address. In addition, it provides control methods for transferring these assets.
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Before the advent of NFT, digital information was copied and stolen at almost zero cost. But the emergence of NFT makes digital scarcity into the digital world. People prove their ownership of their digital assets through the NFT. Ordinary ebooks can only meet reading needs, and fans of books and works, huge fans and book collectors tend to want more access to the author himself and the world they create. In the past, such needs could only be met by collecting books or books, hardly in the digital age. But with the NFT in the public eye, publishers and authors can meet the needs of their readers by providing a book-related NFT. And make more profits. All in all, the NFT makes digital scarcity possible, books have a long history of being collected and appreciated, and its related NFT can also create a whole new collection and investment market. Publishing integration pays more attention to spatial bias, so how to extend the spatial value chain of publications to the greatest extent through thinking innovation and technology empowerment has become a hot topic for many scholars. As a brand new cutting-edge technology, NFT can enable physical books to extend their value chain and enrich the form of digital publications.
4 Reality and Potential Applications of NFT and Digital Twin in the Field of Book Publishing 4.1 Books: From “Collected Physical Books” to “Collected NFT Books” Through digital twins to collect, store, organize books, map physical books, save metadata, and then certified the electronic copy as the first digital version through NFT, sold at a higher premium; several or hundreds of limited copies with the author’s digital signature can also be sold at a lower premium. By limiting the number or time of NFT books sold, it will not only attract consumers who pursue individuality and uniqueness, but also attract investors who regard NFT as an investment. Even after the sales window closes, collectors are eager for limited edition digital copies and a willingness to pay a high premium for these NFT books (Fig. 2). Due to the initial use of digital twin technology of metadata version, can modify through part of the chapters or content, form “egg” unique book content, and its limited sale, which means that readers / collectors will have truly unique works and books, enhance the ability of collectible and personalized book itself. In addition, for the artistic creation of works in books, it can also be NFT. There will be multiple design versions for the book cover and binding design, all of which can be NFT. They represent the process of book forming, which can not only meet the readers and collectors to explore the process of book publishing and the “desire” of the background, but also create economic benefits for publishing houses and designers. No matter what kind of existing or potential practical applications, digital technology led by digital twin and NFT creates untapped markets and service areas
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Fig. 2 The NFT book adoption framework
enabled by the Internet by giving physical books their digital scarcity and uniqueness. In the digital age, the traditional books “shelved” will also become one of the treasures of the virtual world collection room, extending its value chain from the real world to the cyber space.
4.2 Users: Digital Books Become an Asset, No Longer Limited by the Platform According to the 19th Chinese National Reading survey data released by the Chinese Academy of Press and Publication in April 2022,45.6 percent of the readers still prefer physical books, but more than half of the readers prefer a variety of digital reading methods. As people’s lives become more light and mature, While the transfer and extension of physical books to digital books is close hand, Digital publishing is also actively expanding its territory, But there is still a certain gap between the existing e-books and physical books, First, the platform restrictions, Books purchased away from the platform can not be read again; next, Ebooks are just mere binary characters, Without the exquisite arrangement and advanced texture of the paper book, Even with the e-readers that are currently available today, Even reading platforms are trying to emulate the page color, print font, and page turning experience of physical books, But it still cannot replace the reading experience of physical books (Fig. 3). Therefore, the biggest role of digital twin in the integration of publishing is to make digital mapping through accurate measurement, and then cast the data into NFT books, so that readers can obtain the appearance and exquisite details comparable to that of physical books. Different from physical books, it does not have to carry, both real and light. Even readers can get NFT books that meet their personal aesthetic and needs through personalization. And on the assets he can surpass the traditional entity books most hands discount and easy to damage, or books on the platform and piracy situation, as one of the digital assets, NFT books and paper books have many similarities, NFT books is different from ordinary ebooks because it can realize
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Fig. 3 Statistical chart of personal reading mode selection. Data source China academy of press and publication, the 19th Chinese national reading survey report
digital real rights, and the ownership will not disappear because the buyer and seller of either side exit. As a buyer’s asset, it will not target the platform just like readers buy Amazon, wechat reading members or books, leaving the platform. NFT simulates some of the ownership properties of physical entities in the digital world. And this is not the end. It can be predicted that in the future of continuous technological progress, more attribute rights of the physical world will be copied by developers to the virtual world through technology. The emergence of NFT books also makes the digital life in the “metaverse” imagination one step closer to people.
4.3 Author: Digital Rights Protection and Multi-author Rights Confirmation However, e-reading platforms led by K indle, have been emerging since 2007, but the safety of the authors has been deteriorating, according to the survey. Even with platform rules, American writers’ revenues plummeted by 42 percent from 2009 to 2014 due to piracy and other imaging factors [13]. NFT books cast after digitized books, after purchase, do not mean that the purchaser has full ownership of the NFT. In terms of the rules, the scope of powers granted by NFT is determined by
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smart contracts, which are formulated by NFT issuers. Therefore, smart contracts can stipulate the nature, scope and exercise of rights that can be obtained by purchasing NFT [14]. The issuance of NFT does not affect the author’s reproduction, exhibition and dissemination of the created digital works, even if the copyright of the digital assets is implemented in other forms. The copyright owner will always be the original author that the NFT book points to, and cannot be tampered with. This greatly guarantees the digital interests of the author. In addition, the nature of decentralization helps authors stay away from the inequality of “stripping” of large income after working with centralized platforms. Moreover, the traceable nature of the blockchain can ensure the recording and continuation of each retransaction. This breaks the traditional paper book multi-hand monopoly and other transactions, publishers and authors will not get income. Each time an NFT book is sold, the publisher and authors receive a share of the benefits of the smart contract. Whether in digital books or physical books, the phenomenon of co-creation often appears, but in previous practice, determining the respective workload and copyright need to be determined in the presence of a third party. Now, thanks to the recording nature of blockchain for any modification or added behavior, it can quickly and accurately record anyone’s record of digital content during book creation, ensuring the determination of collaborative copyright. NFT will greatly protect the author’s digital rights and interests, and will also give the transaction a reasonable, legal and compliant attributes. At present, international publishing companies have implemented NFT books internationally. NiftyLit Book Publishing Company promises to distribute most of the income from NFT sales to writers, establishing a more “fair publishing, community participation, writer and artist compensation system” to attract more writers and artists to work with the company (Fig. 4). Fig. 4 The Flow chart of NFT instant recording created by multiple authors
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4.4 Publishing House: Visualize Readers on the Basis of Copyright Protection, and Distribute Publishing on Demand For most publishing houses, copyright protection and overpublishing have been an important problem in their operation and management. Therefore, through the application of blockchain-based digital copyright management system, which enabling the process of data storage, recording, verification, circulation and maintenance, the decentralized distributed system constructed by mathematics and algorithm[15]. Through the application of NFT, it can protect the copyright of books, not being copied and fake, and on the other hand, for most publishers, their main customer base is not readers, but middlemen such as bookstores. In the traditional publishing period, in order to increase the shipping rate, most publishers will promise the middlemen to return the remaining packages, namely, to entrust the commission basis. It is mainly to stimulate and encourage middlemen to cooperate with publishers by reducing the risk that middlemen need to bear. However, the risk is passed to the publishing house here, even if the middleman orders a large number of books, and represents the good sales of books, once there is the tide of returning books, it will seriously affect the publishing house’s judgment of the actual inventory and cause economic losses. Therefore, the book sales in the NFT ecological environment is no longer from the “publishing house intermediary readers”, but from the publishing house docking with the readers.On-demand publishing can well solve the problem of traditional publishing inventory backlog and unmarketable. The NFT simulates certain real-world ownership properties in the digital world. By making full use of the advantages of digital printing technology, according to the user’s needs of time, place, content, quantity, to provide on-demand and personalized service of the new publishing methods. On the one hand, NFT and digital twin technology can meet the precise needs of publishers in the digital age; on the other hand, they can protect the rights of publishers and authors.The decentralization of NFT can realize the straight-line connection of “publisher readers” and help books to make printing decisions according to actual needs. Casting the on-demand publishing process of the digital age, users’ every browsing and purchase records are clearly visible. By reflecting the users’ attention degree and consumption intention of a certain NFT book, we can help the publishing houses to make important decisions of operation, management and marketing and operation. In addition, the uniqueness of the NFT logo coincides with the book number of “One Book One” in the real world. It realizes the physical publishing of “One Book One” in the virtual world, and can also avoid inventory backlog problems and copyright theft (Fig. 5).
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Fig. 5 Schematic diagram of the NFT Books-assisted on-demand publishing process
5 Conclusion Both digital twin and NFT are cutting-edge digital technologies. Through digital images and simulated ownership of physical books, they not only bring a new development direction to the book market, to the development of the publishing industry; make the realization of digital scarcity of books in the cyber space possible, give its collection value, making the traditional physical book collection in the digital age derived the e-book collection idea. It’s not that technology will make books completely, but hidden dangers behind NFT such as fraud, financial bubbles and environmental issues are waiting to be solved. There are still many risks and challenges in the book integration of technology-enabled publishing market, but for present, digital twin and NFT have good development prospects in terms of expressiveness, personalization and scarcity of books, which is worth further exploration and deep cultivation by industry insiders and industry scholars. They deserve the close attention and practical support from the publishing industry.
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References 1. Denter Nils M., Seeger Fabian, Moehrle Martin G.: How can Blockchain technology support patent management? A systematic literature review[J]. Int. J. Inf. Manag. (2022) (prepublish) 2. Chalmers Dominic, Fisch Christian, Matthews Russell, Quinn William, Recker Jan.: Beyond the bubble: Will NFTs and digital proof of ownership empower creative industry entrepreneurs?[J]. J. Bus. Ventur. Insights, 17 (2022) 3. Wen Zhihong, teacher Zeng Zhi. From content supply, technology momentum to manement innovation: the challenge of the deep integrated development of publishing [J]. Publ. Wide Angle, (09):35–39. https://doi.org/10.16491/j.cnki.cn45-1216/g2.2022.09.006.ag 4. Dawei, Z.: The development trend and institutional innovation of the publishing industry in the 5G era: the perspective of technology and system interaction [J]. Ed. J. 01, 6–11 (2020) 5. Berndt, R., Buchgraber, G., Havemann, S., Settgast, V., Fellner, D.W.Publishing Workflow for Cultural Heritage Artifacts from 3D-Reconstruction to Internet Presentation.[A]. IEuroMed 2010: Digital Heritage [C]. Lect. Notes Comput. Sci. pp. 166–178 (2010) 6. Wang, B., Mao, W., Li, G.: China’s digital publishing moving towards In-Depth integrated development[J]. Pub. Res. Q 35, 648–669 (2019). https://doi.org/10.1007/s12109-019-09697-x 7. Jiao Shuan.: Chinese book collection history dialect [M]. China Radio Int. Press. 1.1.201 8. Cornelius Kristin.: Betraying Blockchain: Accountability, transparency and document standards for Non-Fungible tokens (NFTs)[J].Information, 12(9) (2021) 9. Qunhua, Z., Shanshan, T.: Digital twin system construction of shanghai history museum and digital transformation of shanghai history museum [J]. Museum of China 02, 30–35 (2022) 10. Antonios Maniatis.: Blockchain law[J]. Int. J. Big Data Manag. 2(1) (2022) 11. Andrew Park, Jan Kietzmann, Leyland Pitt, Amir Dabirian, Amir Dabirian.: The evolution of nonfungible tokens: Complexity and Novelty of NFT Use-Cases[J]. IT Prof. Mag. 24(1) (2022) 12. Muddasar Ali, Sikha Bagui.: Introduction to NFTs: The future of digital collectibles[J]. In Ternational J. Adv. Comput. Sci. Appl. (IJACSA), 12(10) (2021) 13. Larson Christine.: Open networks, open books: gender, precarity and solidarity in digital publishing[J]. Inf., Commun. & Soc. 23(13) (2020) 14. Bo, C., Qian, X.: Study of NFT in publication fusion [J]. Publishing on the Wide Angle 11, 35–41 (2022). https://doi.org/10.16491/j.cnki.cn45-1216/g2.2022.11.007 15. Li Yongming, Rilina.: The dilemma and outlet of the whole chain of digital copyright protection under the background of blockchain [J]. Sci. Technol. Manag. Res. 42 (10): 140–150 (2022)
Key Technology of Cloud Service for the Aggregation of Satellite, Aerial and Terrestrial Multi-source Spatiotemporal Information WenHao Ou, Peng Luo, LinLin Liu, Liang Shan, JiaYi Chen, and Zhenyu Wang
Abstract In this paper, a spatiotemporal data model and efficient indexing method suitable for cloud service management is proposed, a unified and high-precision registering technology system for spatiotemporal reference of satellite, aerial and terrestrial multi-source sensing information is constructed and a multi-source spatiotemporal sensing information interaction interface standards and distribution mechanism scheme are developed to improve the accuracy and reliability of multi-source sensing data of power IOT, promote the sharing of power IOT data effectively, and provide the necessary conditions for the integrated application of multi-source spatiotemporal data in the core business scenarios of power grid. Keywords Cloud service · Aggregation · Multi-source spatiotemporal information
W. Ou (B) · P. Luo · L. Liu · L. Shan · J. Chen · Z. Wang State Grid Commercial Big Data Co., Ltd, Beijing, China e-mail: [email protected] P. Luo e-mail: [email protected] L. Liu e-mail: [email protected] L. Shan e-mail: [email protected] J. Chen e-mail: [email protected] Z. Wang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_31
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1 Introduction With the progress of social economy and the development of science and technology, people’s demand for energy is ever growing and the pressure of energy supply is increasing accordingly, while the use of large amount of energy has caused serious impact on global climate. Therefore, sustainable economic energy has become an important driving energy for development. At present, oil, coal and other energy sources are still the main power energy sources in the electric power system, and the development of sustainable economic energy sources will definitely bring huge security challenges to the electric power system. The introduction of IOT technology in the power system can effectively sense the former state of the power system, integrate the massive data information in the power system, enhance the informatization level of power grid, realize intelligent control, and ensure the safe and stable operation of the power system.
2 Study Status of Massive Spatiotemporal Data Model and Cloud Service Architecture 2.1 Massive Spatiotemporal Data Models Space and time are the basic attributes in the real world, and many spatiotemporal application systems need to describe the temporal or spatial attributes of geographic entities. Traditional static GIS-based spatial models cannot handle and analyze the spatial migration with time change. The spatiotemporal data model abstracts the organization and usage of spatiotemporal data, thus providing a unified reference model for the organization of spatiotemporal data and the development of spatiotemporal applications. It is crucial to design and construct a reasonable and efficient spatiotemporal data model for the development of spatiotemporal GIS. The spatiotemporal data model is the basis of spatiotemporal data business application and is significant for spatiotemporal application system, which not only defines the structure, interrelationship and operation combination inside spatiotemporal data objects, but also can maintain the association rules of spatiotemporal data. In terms of spatiotemporal state inference and spatiotemporal model application, Zhang used a process-based spatiotemporal data model in the representation of spatiotemporal phenomena in watersheds, which described the spatiotemporal object change process as three types of observed change processes in space, time and attributes at four granularity levels: process, sub-process, stage and sub-stage unit, and was able to describe the process of water level rise, area flooding and water level fall during precipitation process along the river [1]. A spatiotemporal data model based on feature elements was applied in monitoring land use by Mao et al. [2]. Jiang et al. proposed a graph-based approach to describe spatiotemporal relationships and entities, which was extended from the snapshot sequence model for studying spatially
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discontinuous and temporally continuously varying spatiotemporal data, with the edges of the graph describing the spatiotemporal changes of entity objects [3]. A geographical event-based spatiotemporal data model (GESTEDM), which combines implicit and explicit geographic events, was adopted by Liu et al. and applied to land use management in Dongguan City, Guangdong Province [4]. By introducing the object-oriented approach to geographic entity description, Liu et al. [5] introduced the base state distance influence factor in the base state correction model to improve the efficiency of temporal query. Luo constructed a full time domain mobile objectoriented spatiotemporal model by combining the mobile object spatiotemporal model [6]. During the development of spatiotemporal data models in the past 50 years, many spatiotemporal data models have been proposed, but there exist spatiotemporal data models that only focus on the semantic design but have not been verified in practice, and thus it is necessary to deepen the basic semantic and theoretical study while strengthening the empirical study of practical applications. Most of the existing spatiotemporal data models are designed for a single data type, such as vector data or raster data structure, but in the context of informationization, digitalization, big data and Internet Plus, it is especially necessary to explore spatiotemporal big data models suitable for supporting multiple sources of heterogeneity. The current spatiotemporal data model is relatively separated for the expression of temporal and spatial information of geographic entities, for which the linkage between time and space should be strengthened and a spatiotemporal data model combining multiple spatial scales and multiple temporal granularities should be constructed.
2.2 Cloud Service Architecture for Massive Spatiotemporal Data Model From the architecture of massive remote sensing image data storage system, there are mainly three forms: Centralized storage architecture, network storage architecture and distributed file system storage architecture. The centralized storage architecture is characterized by storing the massive image data on the central server and organizing and managing the image data by means of files. The central server stores image data and is also responsible for the control and maintenance of the whole system, which is generally composed of high-performance servers. The structure of image data is complex and not easily managed by a purely relational database, while the file system is well adapted to various complex image data structures and can efficiently support the operation and maintenance of data. However, relationships cannot be established between data files, and the redundancy is large, which is not suitable for expansion and maintenance; the system is not very expandable. The centralized management mode can be adopted to reduce the maintenance workload of the whole system and facilitate the management operation of the managers, but due to the heavy workload
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of the central server, it can easily become the bottleneck of the whole system and cause the whole system to be paralyzed when the server crashes. The network-oriented storage architecture is adopted, featuring the separation of data processing and data storage. The network storage architecture combines the advantages of network and I/O, and extends the I/O capability to the network; especially the flexible network addressing capability, long-distance data transfer capability, and efficient I/O throughput capability. Connecting servers and storage resources through the network eliminates the connection barriers between different storage devices and servers, and improves data sharing and availability. And it can realize centralized data management with fault tolerance and no single point of fault for the whole network. However, network storage devices such as network attached storage (NAS) and storage area network (SAN) are generally expensive and have higher initial installation and equipment costs. The design goal of distributed file system is to build a high-capacity, highthroughput and scalable distributed storage architecture on a common, inexpensive hardware platform. The distributed file system storage architecture is characterized by deploying the whole system on top of a large-scale cluster built by general-purpose desktop computing devices, storing image data on each data node of the distributed file system, with the central node organizing and managing image data by means of files, and numerous nodes communicating and transferring data through the network to form a huge file system. The representative distributed file systems include Google file system (GFS), Hadoop distribute file system (HDFS), Kosmos distributed file system (KFS), Sector, MooseFS, etc., which basically adopt Master/Slave structure. Among the distributed file systems, HDFS is the most widely used, which provides the mechanism of writing once and reading many times, and distributes the data in the form of data blocks on different physical machines in the cluster. To ensure data validity, the same file is backed up redundantly on different physical machines, and data is transferred between different data nodes through computer networks to ensure even data distribution. In recent years, scholars have been carrying out study on massive spatiotemporal data on distributed cloud service architecture. Wan et al. proposed two tile-pyramid storage models based on Hadoop’s Mercator projection and geographic latitude– longitude coordinate projection for massive raster tile image data, and also provided data storage and access interface services via the data access middle layer [7]. Chen et al. proposed a storage scheme of distributed raster tiles based on data storage in Hadoop cloud environment, and introduced Hbase distributed indexing mechanism to optimize data storage structure [8]. Lu et al. proposed a geographical spatiotemporal big data model based on streaming data cubes, and completed the storage and management of spatiotemporal data under the coexistence of relational and nonrelational data [9]. Based on the Hadoop platform, Xu proposed the ST-Open GIS model—a storage model for massive GIS spatiotemporal data in the cloud environment, and combined with the Geotools spatiotemporal data engine and Accumulo database to realize the storage and query of spatiotemporal data [10].
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2.3 Indexing Technology for Multi-source Spatiotemporal Data Query Spatiotemporal data is continuously accumulated to form massive spatiotemporal data, but it will be slower and slower to directly query data content only by relying on traditional spatiotemporal data storage, so it is necessary to establish efficient spatiotemporal data index in the process of studying spatiotemporal data. If no index is established or the index is not efficient enough, the retrieval of massive multi-source spatiotemporal data and data analysis will be very time-consuming and directly affect the user experience. Spatiotemporal data is typical multi-dimensional data, and the analysis on spatiotemporal data must take into account the temporal and spatial attributes of the data, thus increasing the difficulty of spatiotemporal big data analysis, i.e. “curse of dimensionality”. The storage, query and analysis on spatiotemporal data are often performed in the databases, and traditional relational databases and cloud-based nonrelational databases NoSQL have their respective strengths in data modeling, storage and query analysis. Regardless of the database environment, applying spatiotemporal indexing techniques to manage and maintain spatiotemporal data can improve the efficiency of querying massive spatiotemporal data and directly affect the overall performance of spatiotemporal data storage and query services. Spatiotemporal indexing method usually draws on the traditional B-tree index, R-tree index, Kdtree, grid index, quadtree index and space-filling curve that have been widely used in relational databases. In recent years, space-filling curves with dimensionality reduction characteristics are gradually used for processing spatiotemporal data and related multidimensional data, among which Z order and Hilbert curves are widely used. With the maturity of cloud computing and big data technology, the big data system in cloud environment is urgently needed to support multidimensional data query. The multidimensional data index built by EEMIC is a two-layer structure, indexing in twolayer structure of R-tree and k-d tree, which can effectively process multidimensional point query and range query of data. MD-HBase is an architectural platform based on HBase to support multidimensional data storage and query. It uses Hbase—a column family cloud database, to downscale multidimensional data according to the space-filling Z-curves, and then constructs a layer of k-d tree and quadtree indexes, which is more efficient for queries, but with the disadvantage of complex structure. HE Lizhi proposed to use a combined indexing method of quadtree network as the primary index and 3DR tree as the secondary index, while integrating three query methods: Time-period window query, time-period K-nearest neighbor query and specific object topology query.
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3 Improvement of Spatiotemporal Data Read–Write Framework For the characteristics of massive spatiotemporal data, the study content of this paper is encapsulated into a multi-source spatiotemporal data management microservice module in order to meet the needs of massive spatiotemporal data storage, processing and query as well as the access needs of other information projects or modules in the provincial-level and municipal-level power grid platforms, so that it can provide other microservice modules or foreground modules with multi-source spatiotemporal data management needs, including data storage, data governance, data calibration and data query etc. In this paper, an improved common read–write architecture for massive spatiotemporal data is propose during the multi-source spatiotemporal data microservices, which can be adapted to the improved spatiotemporal object model ISTM-OpenGIS, improved spatiotemporal data indexing algorithm IHB, common indexing method, improved high-precision registering algorithm and other study contents proposed herein, and the improved spatiotemporal data read–write architecture is shown in Fig. 1, which includes three layers: Client layer, interface layer and data layer. The client layer mainly realizes encapsulation, analysis and scheduling of various spatiotemporal processing requests, including batch processing operations, timed data operations, online operations and other job types, and also supports the operation scheduling allocation via the operation scheduling engine. The operation encapsulation and analysis involve the format analysis on interaction model with SG-ICM. Fig. 1 Improved spatiotemporal data read–write framework
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The interface layer mainly encapsulates data parsing, data preprocessing, spatiotemporal processing engine, indexing algorithm and other parts. The data parsing is mainly for parsing spatiotemporal data types, constructing specific adapters for various spatiotemporal data types, realizing the normalization and unification of temporal, spatial and attribute dimension data of different spatiotemporal data types, and ensuring the consistency of subsequent operation logic. The data preprocessing function focuses on data preprocessing content, including the spatial coordinate system conversion, temporal coordinate conversion, high-precision data registering algorithm and other data processing in subsequent study, and the data preprocessing work performs different preprocessing content for different data types. The spatiotemporal data processing engine encapsulates the data into ISTM-OpenGIS spatiotemporal object model when storing data, and is responsible for the creation, updating and scheduling functions of the spatiotemporal data index; in addition, the spatiotemporal data processing engine is also responsible for encapsulating the spatiotemporal object model to return to the data requesting party as query results. The indexing algorithms include the improved spatiotemporal indexing algorithm IHB, ID indexing algorithm, B + attribute indexing algorithm and inverted indexing algorithm proposed in this paper, and can be flexibly adjusted according to the actual business requirements and data situation. The storage layer is based on Hbase big data management platform for model data storage of ISTM-OpenGIS spatiotemporal data model and index data storage generated by indexing algorithm, and is also responsible for fast chunking and distributed storage management technology of multi-source spatiotemporal data in distributed cloud environment. In this paper, based on the improved spatiotemporal data read–write framework, the storage process of spatiotemporal data is as follows: The original data is transferred to the interface layer through various operations; the interface layer performs data parsing, data preprocessing and data processing engine to encapsulate the data into spatiotemporal objects based on the requirements of ISTM-OpenGIS specification, and uses indexing algorithms to generate the corresponding index data; finally, the spatiotemporal object data and index data are stored in the HBase data management environment. The query workflow of spatiotemporal data is as follows: The data query request is converted into a uniform spatiotemporal data request, as shown in Table 1, the interface layer performs data retrieval in the index storage structure according to the spatiotemporal data request, and returns the data OF source data table corresponding to the retrieval result to the data requesting party in the form of ISTM-OpenGIS spatiotemporal data. Data format conversion (SG-ICM interaction model) can also be performed according to the data requesting party’s requirements.
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Table 1 Standard spatiotemporal data request format Parameters
Meaning
Optional / Mandatory
Box
Four boundaries (latitude and longitude information on four boundaries—east, west, north and south)
Optional
Geomeotry
2D graphics (point, line, surface, multi-point, multi-line, multi-surface)
Optional
Uid
Temporal object UUID
Optional
Lat
Longitude
Optional
Lon
Latitude
Optional
Radius
Distance radius
Optional
GeoRelation
Spatial topological relations (e.g. disjoint, within, overlap, etc.)
Optional
Start
Starting time
Optional
End
Ending time
Optional
TemporalRelation
Time topology relationship (e.g. after, before, during, etc.)
Optional
4 Study on Improved Spatiotemporal Indexing Method Based on the summary of other spatiotemporal indexing methods and the study of actual spatiotemporal objects on the power grid, this study proposes an improved spatiotemporal indexing algorithm IHB (Index Algorithm Based on Hlibert Curve and B+ Tree), which combines Google S2 encoding of spatial information, temporal information encoding and unique identification information to form a spatiotemporal object information encoding, and then combines them with B+ tree to build an index tree structure, so as to express the spatiotemporal information at any resolution. With the help of IHB spatiotemporal indexing algorithm, the spatiotemporal multidimensional data is reduced to one-dimensional data with the Hilbert curve filling method, and the B+ tree is used to realize efficient multidimensional range query, which can realize the joint spatiotemporal query, reasonable expression and efficient retrieval of spatiotemporal data. In the IHB spatiotemporal indexing algorithm, the spatiotemporal object encoding mainly consists of three parts: Unique identification of geographic element, time dimension information encoding and spatial dimension information encoding, among which unique identifier of geographic element is used mainly to distinguish different spatiotemporal objects in the same time dimension and space dimension. It is mainly generated by Twitter’s SnowFlake algorithm, and is sorted according to the time of ID generation, including identifiers, timestamps, cluster IDs, node IDs, sequence number and other parts. In this paper, the unified time reference system is based on Beijing time (UTC+8h) in order to unify the time in different time zones around the world during time information encoding, and the calendar day is used for pre-division with time precision
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Fig. 2 IHB indexing encoding of spatiotemporal objects
of seconds. The time information encoding is mainly composed of the year of that moment and the number of seconds in that year. In this paper, CGCS2000 datum is used as the spatial reference coordinate system in spatial information encoding, and the spatial objects in each layer can be identified with one and more grids that form an envelope to them with the Google S2 index calculation method. The calculation process is generally to transform the latitude– longitude coordinates of ellipsoidal surface into spatial rectangular coordinates XYZ, then into coordinates on the projection plane of the external tangent cube, and finally into corrected coordinates, then mapped to the interval range of [0,2^30–1] by the coordinate system transformation, and finally to the Hilbert curve. The spatial information is encoded by the Cell ID on the Hilbert curve. The overall index encoding of spatiotemporal objects can be obtained from the above description as shown in Fig. 2. The main idea of spatiotemporal data query based on IHB indexing algorithm is to transform the spatiotemporal range query into a one-dimensional interval query to query the spatiotemporal objects corresponding to the boundary points of the spatiotemporal range, and its workflow is as follows: (1) To calculate the minimum boundary and maximum boundary of the spatiotemporal range corresponding to the spatiotemporal query; (2) to calculate the spatiotemporal object codes—Hmin and Hmax of the minimum boundary and maximum boundary; (3) to combine the spatiotemporal object codes with the minimum boundary and maximum boundary to form a query interval [Hmin, Hmax], thereby converting the spatiotemporal range query into one-dimensional interval query; (4) to perform spatiotemporal data retrieval in the index tree with the help of tree search algorithm, first searching to the leaf node where Hmin is located, and then traversing the leaf nodes as per the horizontal leaf pointer to the leaf node where Hmax is located.
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5 Conclusion The study of this project can make up for the shortage of spatiotemporal data used by each unit, lay the foundation for the integrated management, fusion analysis and c integrated application of multi-source spatiotemporal data, and powerfully promote the construction of the enterprise’s data center station strategy and threedimensional sensing technology system. At the level of data management, unified spatiotemporal benchmark and spatiotemporal correlation technology are provided by improving spatiotemporal data model, and multi-source spatiotemporal information interaction interface standard and distribution mechanism are established to promote the understanding and application of satellite remote sensing data by all units of the company and form the company-level “multi-source spatiotemporal data based on one source”, which becomes an important part of the company’s data center construction, providing accurate and sufficient spatiotemporal correlation data guarantee for various core business scenarios of power grid. At the data service level, a common development platform is provided in the field of spatiotemporal application and visualization for various core business scenarios of power grid via microservice architecture design, comprehensive data fusion analysis related core algorithm construction and development of aggregation and component service platform for power IoT spatiotemporal data for the purpose of promoting flexible and rapid iteration of micro applications for various core business scenarios of power grid, and enriching the component content, service capability and application content of the company’s business data center station.
References 1. Zhang, G.: Study on time-space data model of watershed water conservancy based on procession. China University of Geoscience (2014) 2. Mao, X., Wen, Y., Ma, X., Huang, H., Zhang, H., Yu, R.: Research and application of multisource heterogeneous data fusion technology based on power big data. Power Big Data 23(8), 33–39 (2020) 3. Jiang, Y., Peng, M., Ma, K.: Power transformer condition evaluation method based on multisource heterogeneous data fusion. Guangdong Electr. Power 32(9), 137–145 (2019) 4. Liu, D., Ma, L., Liu, X.: Power big data fusion and anomaly detection method based on deep learning. Comput. Appl. Softw. 35(4), 61–64 (2018) 5. Liu, J., Zhang, C., Sun, H., Wu, S., Wan, X.: Research on power market active service aware sharing platform based on power grid. Power Inform. Commun. Technol. 17(7), 16–20 (2019) 6. Luo, J.: Improving the capacity of marine meteorological equipment support. China Meteorol. News, 27 May 2016 (007) 7. Wan, Q., Wang, S., He, X.: Application method of SG-CIM model in data center. Telecommun. Sect. 36(3), 136–143 (2020)
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8. Chen, Y., Lu, Y., Luo, S., Shu, J.: Research overview of distributed storage system based on RDMA. Comput. Res. Dev. 56(2), 227–239 (2019) 9. Lu, Y., Li, J., Ye, S., Jiang, J., Yin, M., Zhou, Y.: GIS time-space big data organization method of extended flow data cube. Gen. Bull. Surv. Map. (8), 115–118 (2018) 10. Xu, Q.: Research and implementation of storage method of massive GIS time-space data in cloud environment. Xi’an Dianzi University (2018)
The Dilemma and Reflection of Short Video Infringement from the Perspective of Game Theory: Publicity and Protection of Film and Television Works Anyi He and Liang Wang
Abstract In the context of the popularization of digital technology, more and more film and television works are troubled by the infringement of short video, and publishers have to rely on short video platforms for publicity while gaining losses. The infringement of short video needs to be regulated, but as a propaganda tool in the Internet era, short video does bring traffic to film and television works and platforms. It is difficult for copyright holders, short video platforms and copyright users to balance in the game of traffic and interests. Therefore, a balance should be sought between the three, and a win–win situation should be sought by breaking through the traditional business model and formulating reasonable game rules. This paper takes the infringement phenomenon of short video as the breakthrough point and analyzes it, and proposes to solve the problem of propaganda and protection of film and television works through short video platform by making reasonable game rules through technology and strengthening control. Keywords Digital copyright · Short video · Copyright asset management · Game theory
This work is partially supported by the Key Program of Beijing Social Science Fund under contract with No.19JDXCA001. A. He School of Journalism and Publication, Beijing Institute of Graphic Communication, Beijing, China e-mail: [email protected] L. Wang (B) School of Economics and Management, Beijing Institute of Graphic Communication, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_32
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1 Introduction At present, short video has become a new trend in the development of the media industry, and its short, flat and fast characteristics have attracted a large number of network users and fans. As the product of the Internet era, the production mode and operation logic of short video are different from traditional audio and video products, which impact the original copyright system. In recent years, the infringement of short videos has been repeatedly banned, most of which focus on the cutting and handling of existing film and television works, including but not limited to editing, splicing, interpretation and other ways. On February 15, 2021, the Standard Rules for The Examination and Approval of Network Short Video Content was released. Article 93 clearly stipulates that short video shouldn’t be cut or adapted to movies, TV dramas, network movies and TV dramas without authorization. The voice of the society for the protection of film and television copyright is getting higher and higher. Were successfully “notified and deleted”. Among them, 150 film and television works were included in the early warning list of key works of the National Copyright. Administration, and a total of 852,800 short videos of second and creation infringement were detected. The number of short videos of single TV series was the second with 7,556. On the one hand, the definition of “secondary creation” of short video is complicated, and the infringement of film and television works is rampant, which damages the interests of copyright owners. On the other hand, in the era of the Internet of everything, relying on short video platforms to simplify films and let the audience know the content of films is also a means of publicity to improve the popularity of film and television works, and can also bring traffic to the platform. Therefore, it is particularly important to promote the cooperative game between film and television copyright holders and short video platforms under reasonable rules to achieve a win–win situation.
2 Infringement Mode of Short Video—Subject and Performance of Copyright Game of Film and Television Works There are many cases of short video infringement mode. This paper only discusses the short video infringement phenomenon of TV series, movies and other film and television works. This kind of infringement phenomenon is the result of the combination of direct infringement and indirect infringement.
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2.1 Game Between Copyright Holders and Copyright Users The most direct manifestation of the conflict between copyright holders and copyright users is direct infringement, which is mainly the second creation of film and television works by the infringer. This secondary creation is to edit the original work, edit one or more film and television works into several segments, add commentary and background music, and transform the film and television works into “new” works by new means, for example, “Take you to watch the funny action movie Regional Garden in X minutes”. In order to obtain traffic, and direct appropriation is convenient, copyright users directly infringe on film and television works. This kind of noncooperative game usually benefits one party at the expense of the other. The right to copy is the basis of copyright protection. With the development of the Internet, people have more extensive access to information, and the role of copying the released film and television works has changed from the traditional minority publishers to the general public, which is also one of the reasons for the proliferation of infringement. At the same time, in the identification of this kind of behavior, whether it is infringement or fair use, the boundary between the two needs to be further discussed. Short video of the openness of the second creation is stronger, in general, the same film and television works will not be cut into a single short video clips, the users tend to be from different aspects and angles to development, creation, film and television works, combined with the diversity of film and television works, can cut material is rich, the amount of short video will lead to infringement. If the copyright owner wants to initiate a lawsuit against the infringement, he will face not a single subject, but a large number of infringers, and at the same time, he needs to collect a large amount of evidence. The time, energy and litigation cost are not equal to the compensation for the infringement, so the copyright owner will eventually give up protecting his rights. Secondly, it is also difficult for content publishers to contact copyright owners to obtain authorization. The Copyright Law stipulates that a licensing contract shall be concluded with the copyright owner to use a work created by others. That is, only with the permission of the copyright owner can a work be used. Video publishers need to find copyright owners and sign contracts. Most video publishers are ordinary users, and it is difficult to contact the authors on the Internet, which will undoubtedly increase costs for them. Objectively speaking, general video publishers will choose infringement.
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2.2 Game Between Short Video Platforms and Copyright Holders There is also a non-cooperative game between platform and copyright holders. Specifically, it is indirect infringement, which is more obscure and mainly manifested in the inclusion and help of the publishers of infringing short video content. Releasing such videos on short video platforms will attract a lot of traffic to the platforms, so some short video platforms will turn a blind eye to infringing videos. At the same time, short video platforms will also carry out activities similar to video solicitation to urge video publishers to produce infringing videos and provide certain rewards for publishers. For example, Douyin also launched free “Dou+ ” activities to provide free push stream for publishers, which also indirectly promoted the occurrence of infringement. This kind of behavior can constitute the Internet service provider’s abetting infringement and aiding infringement in the judicial interpretation issued in 2021. At the same time, the principle of safe harbor also provides the possibility of asymmetry for the game between the two sides. The so-called safe harbor principle means that in case of copyright infringement, short video platforms only provide space services and are not responsible for the content. If short video platforms are informed of infringement, they have the obligation to delete, otherwise they will be regarded as infringement. If the infringing content is neither stored on the server of the short video platform, nor is it told which content should be deleted, it is not liable for infringement. This principle provides convenience for the platform, but for infringement can not simply use the principle of safe harbor to identify. The aforementioned platform’s implicit behavior of tolerating and helping short video content providers can also constitute infringement. Platforms can evade responsibility due to the principle of safe harbor, making short videos into infringement dilemma.
3 Reflections on Short Video Tort—The Cause of Non-Cooperative Game 3.1 Network Audio-Visual Publishing Products Have Not Been Included in the Scope of Management According to China’s Regulations on the Administration of Audio and Video Products, The National Library of China has officially become an institution for the preservation of audio and video samples in China. According to relevant regulations, all audio-visual products published in China must be deposited with the National Library 30 days after publication by audio-visual publishing units [1]. The country
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to the traditional audio and video products have deposit system and the requirement of the management, but for the network audio and video works have not been requested. Short video, as a product of the Internet era, has low production cost, and the professional threshold of release is gradually lowered, because its distribution mode is different from the traditional publishing mode of audio and video materials, and it is only published as a form of content. In addition, the current network drama, network variety shows and other programs completely conform to the scope of network publishing in China’s interim Provisions on the Administration of Internet Publishing [1]. State if the network audio and video publications deposit scope and model for a certain regulation, network audio and video publications should be brought into the deposit, will help standardize the management of film and television works better, each works in the national library has a backup can guarantee the integrity of the work, to prevent the secondary creation to release a core content is tampered with, At the same time, it is of progressive significance to protect the copyright of film and television works and cultural resources.
3.2 “Rational Utilization” of Second Creation of Short Video Short video infringement has brought losses to copyright owners, but it is undeniable that propaganda of film and television works through the Internet is the general trend. Major TV production companies like Daylight Entertainment will create their own Douyin accounts to re-create and release their dramas, which can attract fans and increase their popularity. Therefore, short video is not a bad thing for the second creation of film and television works, as long as it is carried out under the proper premise, it can achieve a win-win situation. Secondly, there are many well-made but time-honored old films and television works facing the dilemma of being forgotten by the market. Among them, there is no lack of documentaries propagating Traditional Chinese culture. Due to the relatively small number of subjects and the relatively long distribution time, it is difficult to adapt to the current mode of Internet communication and audience’s taste. The second creation of short videos is a good channel to break through this dilemma. In this way, the ancient film and television works can be publicized and protected, and the short video platform can bring traffic. As long as the secondary creation is in the range of reasonable utilization, both sides can profit. The governance of short video infringement can not only be limited to the maintenance of intellectual property rights, and it is important to balance the interests of the platform and copyright parties to reach a cooperative game. Instead of suppressing the development of short videos blindly, a breakthrough in business model is needed to ensure that film and television works can be publicized in the network environment. Copyright protection needs to be strengthened, but today’s Internet emphasizes sharing. Blindly carrying out copyright protection will intensify the competition between copyright owners and platforms, which will violate the universal needs of
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users, and it is still consumers who pay for the results. It is particularly important to make reasonable use of the second creation of short video.
4 Publicity and Protection of Film and Television Works—Formulating Reasonable Game Rules 4.1 Improve the Copyright Protection Mechanism Both sides of the game players should clarify the boundaries of their respective interests firstly,the principle of “safe habor” cannot be escape the umbrella of tort liability and to abide by the principle of “red flag principle” but not to abuse the principle of “red flag principle”, to avoid short video platform high filtering pressure in advance, in case of the rational use of video “friendly fire”. In general, the objective criteria for determining whether short video platforms constitute infringement should be judged from three aspects:(1) whether the use of short video causes the users to gain objectively and the copyright owners to suffer losses, which is consistent with the concept of “unjust enrichment” in China’s civil code. (2) Whether there is an inevitable connection between benefit and loss. (3) The user cannot raise a reasonable defense. The above three criteria should be applied comprehensively. If they do not meet any point, it should be deemed that the user does not constitute a fair use of copyright [2]. At the same time, the rationality of objective standard lies in the constant change of short video copyright infringement mode brought by the development of science and technology. Secondly, although Internet platforms are not liable for infringement without their knowledge, they need to take the responsibility of screening infringing short videos. As the intermediate protocol level of copyright governance, the platform is the bridge linking copyright owners and users, and the key subject of governance effect. Short video copyright platforms can introduce filtering mechanism in governance by improving acceptance and quick processing mechanism, so as to strengthen supervision and protect the rights and interests of the original author, participant, platform and other subjects in the link of rights confirmation, use and rights protection. If the copyright norms and standards of the short video industry are established, the government can actively supervise and use third-party copyright services through platform review and filtering to establish a healthier copyright order in the short video industry. Strengthening copyright autonomy makes it possible to protect the legitimate rights and interests of originators.
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4.2 Establish a Mutually Beneficial Business Model Game theory proves that in the absence of cooperation, the action of maximizing individual utility may be the worst outcome for both the individual and the cooperator. Therefore, cooperation becomes the inevitable result of rational choice under the condition of competition. Excessive competition requires high competition costs and can lead to lose-lose. In order to avoid the miserable game, we can find a better competition model [3]. In digital publishing and traditional paper publishing, the way of profit distribution mainly includes sharing system, edition tax system and one off buyout system. At present, the distribution of the system of the publishing industry to take more into form, the author will create content for the Internet, then, the Internet service providers to provide the supply network platform, the two sides according to the agreement before into proportion, until he be taken into the digital publication market, a corresponding yield, the copyright in proportion to the island’s peace respectively in accordance with the contract, get their own income. For the copyright owner, a reasonable sharing mechanism can be set up with the platform to grant the platform the authorization of the original version, so that the copyright owner can get profits and the film and television works can also get publicity. Chinese publications generally adopt two authorization modes: one is the one-toone mode in which the copyright owner sells the copyright to the publisher and the publisher uses the work; the other is the collective management mode, in which the copyright owner entrusts a third party organization to authorize the publisher to use the work. It may include further licensing of publishers as copyright licensing agencies, licensing of digital works by professional copyright companies, obtaining copyright licensing from copyright collective management organizations, etc. Short video platforms can be viewed as collective management organizations, which provide services and charge fees to users after obtaining authorization. At the same time, member and non-member systems can be adopted for users, which can not only manage film and television resources, but also create a new business model for profit.
4.3 Use Technology to Strengthen Copyright Protection Platforming is the trend of digital copyright industrialization. After the collective management of film and television works, the platform can be protected. With the widespread use of 5G technology, the short video platform can help the platform better fulfill the screening obligation through technical means. Block chain technology is immutable and can completely record all changes of works, which is conducive to the realization of transparency in copyright transactions. Smart contracts in blockchain can automatically regulate the exercise and
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traceability of all rights, reduce the cost of rights confirmation and improve transaction efficiency. The features of consensus trust in blockchain make it convenient for authors to manage all subdivided copyright authorization on a unified platform [4], provide better exposure and trading opportunities for film and television works. It is also possible to build the information database of copyright authors, which makes it possible to reinforce the evidence of short video products and trace back the propagation path. Using visual artificial intelligence technology to strengthen process supervision is also an effective measure to promote the copyright protection of film and television works. Short video traffic is high, the efficiency of manual review is too low, can not meet the demand [4]. Use the filter mechanism of the technology of short video platform management can greatly improve the efficiency, at the same time of establishing and perfecting the law, manage with block chain technology, plus the integration application of intelligent algorithm, so that you can build for short video industry integration system of global governance, for the short video industry healthy and orderly ecology provide legal support. Therefore, through the filtering responsibility mechanism, the platform of short video copyright can refine the platform’s duty of care and adopt the responsibility mechanism of “above the safe harbor”, so as to avoid the defects of the power structure of the governance model to the maximum extent.
5 Conclusion As 5G technology promotes the further development of short video, the operation logic of short video platform will have new characteristics and trends, which challenges the existing copyright game rules. The strength of one party may increase the cost of protecting the interests of the other party. This will bring new difficulties to judge the originality of short video, deal with short video infringement, construct short video governance system and mode, and prevent short video infringement. Short video platform is a powerful tool to promote films and TV series. Only by using it reasonably can it play its maximum effect and role. For film and television works, they should not be buried in the wave of Internet development, master the “traffic password” of short videos, do a good job in copyright protection and realize the cooperative game among the three parties, so as to ensure the integrity of their cultural resources and continue to bring economic value.
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References 1. Meng, X., Cui, Y.: Problems and optimization of the deposit work of Chinese audio-visual products in the national library of China [J]. Henan Library J. 40(09), 88–90 (2020) 2. Huang, J.: Imputation and exemption in short video copyright infringement [J]. Legal Expo 07, 39–41 (2022) 3. Zeng, W.: Research on the optimization of digital publishing of scientific and technological journals in my country from the perspective of game theory [J]. J. Jiangxi Univ. Sci. Techn. 42(06), 101–104 (2021). https://doi.org/10.13265/j.cnki.jxlgdxxb.2021.06.016 4. Nie, J., Cheng, H.: Research on copyright protection of short video content dissemination [J]. China Publ. 03, 9–12 (2020) 5. Jinbao Li, Liping Gu: Short video infringement dilemma and governance strategy under the background of digital copyright—From the perspective of short video originality [J]. Friends Edit. (11), 77–85 (2021). https://doi.org/10.13786/j.cnki.cn14-1066 /g2.2021.11.012
The Evolution of Public Opinion and Its Emotion Analysis in Public Health Emergency Based on Weibo Data Jiazheng Sun, Xiaodong Zhang, and Shaojuan Lei
Abstract Since the occurrence of the Corona Virus Disease 2019, relevant online public opinion has spread rapidly, which has had an important impact on social order. How to identify, prevent and control public opinion crisis of public health emergencies has become a practical problem that urgently needs to be studied. First data source comes from Weibo comments, comparing with the three models of Naive Bayesian Model, Support Vector Machine and Logistic Regression, Long Short Term Memory (LSTM) model based on word to vector (Word2vec) model is selected for emotion classification. Secondly, the evolution of public opinion is divided into three stages base to the Baidu search index, use visualization methods to study emotional tendencies at various stages and analyze the temporal and spatial laws of public opinion. At last, according to the evolution law and characteristics of public opinion in each stage, relevant optimization strategies are proposed. Research shows, the Word2Vec-LSTM model can effectively predict the emotional state of netizens; analyzes the law of public opinion evolution of public health emergencies, provide a basis for optimizing the network environment and preventing public opinion crisis. Keywords Corona virus disease 2019 · Emotion analysis · Word2Vec-LSTM model · Public opinion evolution
J. Sun (B) · X. Zhang · S. Lei College of Economics and Management, University of Science and Technology Beijing, Beijing, China e-mail: [email protected] X. Zhang e-mail: [email protected] S. Lei e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_33
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1 Introduction Corona Virus Disease 2019 (COVID-19) refers to pneumonia caused by the novel coronavirus infection that started in 2019. The sudden outbreak spread with unimaginable speed and intensity, and quickly evolved into a major public health emergency, posing a huge challenge to my country’s and even the global health system. The development of the epidemic has prompted a surge in related online hotspots, and online public opinion has formed rapidly and continues to brew. According to the 45th “Statistical Report on Internet Development in China”, as of March 2020, the number of Internet users in my country reached 904 million, an increase of 75.08 million from the end of 2018, and the Internet penetration rate was 64.5%, an increase of 4.9 percentage points from the end of 2018. With the popularization of the network, the network environment becomes more and more complex. Due to the characteristics of timely, extensive and real-time interaction, Weibo has become the main platform for the development and evolution of my country’s network public opinion [1]. A large number of netizens’ comments on hot topics quickly spread to form network public opinion, and negative network public opinion will increase social dissatisfaction and seriously affect social order [2]. After the outbreak of COVID-19, netizens had heated discussions, and the number of Weibo comments remained high [3]. In order to prevent the management of public opinion from getting out of control, it is necessary to strengthen the emotion analysis of online public opinion and grasp the evolution of public opinion. This paper uses some Weibo comments of the new coronary pneumonia incident as the data source, constructs an Long Short Term Memory (LSTM) emotion classification fusion model based on word to vector (Word2vec), and uses statistical analysis and data visualization methods to study the evolution of public opinion and emotion tendencies in public health emergencies.
2 Related Research 2.1 Weibo Emotion Analysis Emotion analysis, also known as emotion tendency analysis, is the process of analyzing, processing, summarizing and reasoning on subjective texts with emotional colors. Netizens participate in the comments on characters, events, products, etc. through the Internet (such as Sina Weibo, forums, and post bars), express their subjective emotion tendencies, and then form public opinion. Commonly used emotion analysis methods include emotion dictionary based classification method and machine learning based method. The pioneers of emotion analysis, Pang et al. [4] used the N-grams and part-of-speech of the text as emotion features, and used naive Bayes, support vector machine and maximum entropy classification to classify movie reviews binary emotion, and the results showed that support Vector machines
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work best for classification. Wilson et al. [5] constructed a emotion dictionary by analyzing different parts of speech, contexts and other related factors, and used this as a standard to determine emotion tendency. Emotion analysis on Weibo refers to crawling Weibo comment data, structuring the data, calculating emotion trends, and mining the evolutionary characteristics of public emotion. Since Weibo is the main platform for Chinese netizens to express their opinions and emotions, a large number of scholars have begun to use the Weibo platform to conduct emotion analysis, as shown in Table 1. It can be seen from the above literature that relevant scholars have used different methods to analyze Weibo emotion and have achieved rich results. The emotion dictionary-based method can make good use of emotion words to express the emotional tendency of sentences, but this method relies too much on the construction of emotion thesaurus; the traditional machine learning method requires a large amount of manual labeling data and relies too much on background knowledge and data labeling quality. With the rapid development of deep learning in the field of natural language, relevant scholars have begun to introduce this method into the field of text emotion analysis. The research found that the accuracy and efficiency of emotion classification based on deep learning are better than those based on emotion dictionary and machine learning. Therefore, this paper adopts the emotion analysis method combining Word2Vec and LSTM. The process includes: using the Word2Vec model to extract text feature words, converting the words in the text into vectors as the input of the LSTM model, and then using the LSTM model to analyze the text. Emotional tendencies judgments. Table 1 Research on emotion analysis of weibo Author
Object
Methods
Conclusion
Liang Yawei [6]
Weibo
Construct an emotion dictionary to identify degree words and negative words in text
Realize Weibo emotion analysis based on emotion dictionary
Wu Jiesheng [7]
Weibo
Construct emotion dictionary in Weibo field
Realize emotion analysis of Weibo
Liu Bing [8]
Twitter
Opinion Parser system
Predict movie box office by analyzing movie reviews
Liu Liqun [9] Weibo
Naive Bayes model
Implemented emotion analysis of Weibo topics
Li Tingting [10]
Weibo
SVM and CRF
Comparative analysis of Weibo emotion analysis based on multi-feature combination
Wu Jie [11]
Weibo
LSTM model
Constructs a user emotion analysis method based on LSTM
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2.2 Analysis of Weibo Public Opinion Evolution Public opinion refers to the synthesis of emotions and opinions held by the public on social public affairs within a certain time and space [12]. The Weibo public opinion pointed to in this article refers to the sum of the emotions, attitudes and opinions expressed by netizens on a specific hot event based on Weibo. Based on different perspectives and methods, scholars have conducted comprehensive research on the evolution of Weibo public opinion. These studies divide Weibo public opinion into different stages according to the occurrence time and development life cycle of events. Jin Jianbin et al. [13] took the research topics of network public opinion as the research object, and divided the evolution process of public opinion into three stages: the emergence of the topic, the survival of the topic, and the trend of the topic; Ji Shunquan [14] from the perspective of the information life cycle Starting from this, the evolution process of Weibo public opinion is divided into four stages: fermentation period, climax period, subsidence period and stable period; An Lu [15] divides the evolution period of public opinion into initial period, recession period and outbreak period based on the theme analysis of Weibo. Fu Yating [16] divides the evolution process into five stages of formation, development, alienation, continuation and dilution by studying the network public opinion about college student group events. No matter what standard is used to divide the evolution stage of public opinion, the evolution process always follows the law of initiation-development-settlement. It can be seen that scholars have carried out comprehensive and diverse research on the evolution of online public opinion based on communication studies. However, as far as the current research is concerned, on the one hand, although scholars have divided the evolution of public opinion into stages to illustrate the process characteristics of the evolution process, but, it is easy to ignore the continuity of time, resulting in insufficient analysis of the internal laws and mechanisms in the evolution of public opinion; On the other hand, there are few studies on the evolution of network public opinion in the field of public health emergencies, and few practical cases have been used to combine the evolution of network public opinion and emotion.
2.3 Research on the Evolution of Public Opinion and Emotion in Public Health Emergencies Public health emergency refers to a disease, epidemic situation, poisoning and other events seriously endangering public health that occur suddenly and cause serious losses to public health [17]. It is characterized by wide coverage, great harm and high international participation. By tracking and analyzing the emotional changes of netizens in public health emergencies, we can not only observe the impact of the incident on society, but also help the public opinion control department to make accurate and effective guidance. The scholar Gomide [18] built a dengue disease
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surveillance system based on Twitter information to monitor the emotional changes of the people during the epidemic. Zhang [19] collected information about the Zika virus in the Yahoo community, and used the methods of time analysis and inference statistics to study the changes in the focus points of netizens in different periods. To sum up, the methods of public opinion emotion analysis are complex, and the performance of the same method in different fields is very different. At the same time, the evolution of public opinion emotion often presents the characteristics of different time stages, and there are many evolutionary patterns. However, the sudden, public and harmful characteristics of public health emergencies determine that the evolution of public opinion and emotional texts of this type of incident have a certain similarity. Therefore, it is extremely important to construct a emotion analysis model suitable for public health emergencies and reasonably divide the evolution stages. Compared with existing studies, the main contributions of this study are: (1) Construct an LSTM emotion classification model based on Word2Vec. The experimental results show that compared with traditional machine learning, the method proposed in this paper can significantly improve the effect of microblog emotion classification. (2) Introduce visualization technology into public opinion research on epidemics, and fully tap the evolution law and emotional situation of public opinion in public health emergencies.
3 Public Opinion Emotion Evolution Model for Public Health Emergencies 3.1 Word2vec Model Word2Vec is a word semantic computing technology proposed by Google in 2013 [20]. This technology abandons the traditional one-hot encoding method, converts words into vectors and maps them to high-dimensional space, and predicts the similarity between words by seeking the characteristics of words, thereby solving the problems of vector sparseness and semantic association. Word2Vec includes CBOW and skip-gram models. The CBOW model predicts the probability of a feature word according to the context of a feature word, and the latter predicts the probability of the context by inputting the feature word. This paper adopts the CBOW model of Word2Vec, which consists of a three-layer network model, namely the input layer, the projection layer, and the output layer, where W represents the feature word, and the structure is shown in Fig. 1. In the CBOW model, the feature word x is predicted according to the vector Q(x) of the feature word context, where x represents the wrongly predicted sample of the feature word, and the negative sample set N eg(x) is obtained by negative sampling. The features of words are expressed as:
420 Fig. 1 CBOW model structure
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W(t+1)
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W(t+n) input layer
SUM
W(t)
{ F x (x) =
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output layer
x=x x /= x
The objective function is expressed as: |
W (x) =
P(c|Q(x))
c∈(x)∪N eg(x)
Among them: { P(c|Q(x)) =
σ (X(xT θ c ), )F x (c) = 1 1 − σ X xT θ c , F x (c) = 0
3.2 LSTM Model and Emotion Classification The LSTM model is derived from the recurrent neural network (RNN), which was first proposed by Hochreiter [21] in 1997, by introducing a constant error flow (CEC) to solve the problem of gradient explosion and gradient disappearance. The core of the LSTM model is the memory cell and gate structure, the memory cell is used to record historical information, the gate structure determines which information can pass through and which information needs to be blocked, it is essentially a feature selection method. LSTM mainly includes four stages: forgetting, selecting memory, updating and outputting. The hidden layer logic of the LSTM model is shown in Fig. 2.
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Fig. 2 The LSTM model hides the inner structure of the layer
The forgetting stage is mainly to selectively discard the input from the previous node, which is realized by multiplying the forgetting gate and the previous sequential state C (t−1) , the expression formula of the forgetting gate is: f orget (t) = δ(w f h (t−1) + μ f x (t) + b f ) The selection memory stage refers to the information obtained by multiplying the input gate and the input of the current sequence, this part of the information is the information that needs to be remembered, the expression of the input gate is: in (t) = δ(wi h (t−1) + u i x (t) + bi ) The input for the current timing is: C'
(t)
= tan h(wc h (t−1) + u c X (t) + bc )
The update phase refers to the addition of new information plus previous information that needs to be remembered, the formula is expressed as: C (t) = C (t−1) × f orget (t) + in (t) × C '
(t)
Output stage: the output gate multiplied by the activated long-term state variable is the real hidden layer output, and the output formula is expressed as: ) ( h (t) = tanh C (t) × δ(w0 h (t−1) + u 0 x (t) + b0 ) Among them, in (t) 、 f orget (t) and h (t) represent input, forgetting and output, respectively; C (t) represents the vector after information update; w f 、wi 、wc and w0 represent the weight matrix of forget gate, input gate, current input unit state
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Fig. 3 Flow chart of emotion classification model
and output gate, respectively; b f 、bi 、bc 、b0 represent their corresponding offset items. In terms of emotion classification, the Word2Vec model is used to obtain the word vector of the text, and the set of word vectors is corresponding to a sentence to obtain the sentence vector, which is used as the input of the LSTM model to classify the text emotion. The classification flowchart is shown in Fig. 3.
3.3 Model Flow Chart Construction Based on Weibo data, this study established a public opinion evolution and emotion analysis model for public health emergencies. The model process includes data acquisition, data preprocessing, Word2Vec training word vectors, LSTM for emotion classification, and exploration of the evolution of public opinion. (1) Data acquisition and data cleaning Search topics related to the epidemic through Weibo, and crawl the comment set under the event, the data comes from the official Weibo of People’s Daily, the official Weibo of CCTV News, the official Weibo of China News Network and other related Weibo comment information of the official media. In order to improve data quality and utilization efficiency, the crawled raw data is preprocessed, and the processed data is saved in csv format. (2) Emotion analysis Emotion classification of textual information using Python. Firstly, the microblog comment information is processed by word segmentation, and the stop words in the comment information are removed. Some of the extracted relevant comments are labeled as positive emotion, negative emotion and neutral emotion. Then, the labeled text is input into the Word2Vec model as a data set to obtain the sentence vector, which is used as the input of the LSTM model, after
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deep feature selection in the hidden layer, the emotion tendency of the sentence is output. Finally, the emotional tendencies of the rest of the text information are predicted by Word2Vec and LSTM models. (3) The law of public opinion evolution On the basis of the research on the life cycle of public opinion dissemination, according to the Baidu search index, the evolution stage of public opinion is divided into three stages: incubation period, outbreak period and calm period. Through statistics and analysis of the proportion of different emotions, age and geographical distribution at each stage, the evolution law of public opinion is explored.
4 Emotional Evolution Analysis of COVID-19 Event 4.1 The Experimental Data At the beginning of the outbreak of the COVID-19, the virus did not have an official name, and because the virus was detected in Wuhan in the early stage, the Weibo searched for the keywords “new crown pneumonia”, “Wuhan pneumonia” and so on. We selected Weibo posts reported by authoritative media officials on Weibo with more than 1,000 Weibo comments, in order to fully demonstrate the impact of time continuity on public opinion and emotion, from December 31 to March 31, the scattered selection and A total of 47,133 Weibo comment information was crawled from 32 Weibo with highly relevant research contents, including comment content, time, and commenter’s id and nickname. In terms of data preprocessing, the first is to eliminate duplicate data, missing data and invalid data. For example, only the words “Forward Weibo”, “Follow” and other irrelevant research content or comments with less than four words in the comments will be deleted. The second is that there are some abnormal data in the crawled information, such as the selection of age beyond the cognitive range, delete this part of the data attributes. Finally, 40,818 pieces of available data were obtained. Since the LSTM classification model belongs to supervised learning, the crawled 40,818 comment data cannot be directly input into the model as a data source, and part of the data needs to be extracted and manually labeled as experimental data and input into the model. In order to improve the prediction quality of the model, this paper will equally label three emotion categories, namely positive, neutral and negative, which are represented by 2, 1, and 0. Each emotion category is labeled with 1350 corpora, and a total of 4050 samples are labeled.
424 Table 2 Classification mix-up matrix
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Predicted results
Real results
Belongs to category Out of the category X X Belongs to category a X
b
c
d
Out of the category X
4.2 Selection of Evaluation Indicators In this paper, the accuracy rate P, recall rate R, and F values are selected as evaluation indicators [22]. As shown in Table 2. P is used to detect the accuracy of the model classification, which refers to the proportion of the samples of the true category in the samples predicted to be a certain category, namely: P=
a a+c
R is used to detect the coverage of the model, predict the proportion of the true class in a certain class to all true classes, namely: R=
a a+b
F refers to the weighted harmonic mean of P and R, which is used to measure the final classification effect, namely: F=
2× P × R P+R
Because this paper divides the corpus into three emotion categories, the average accuracy AV P, average recall rate AV R and average F value AV F corresponding to the three categories are used as the final evaluation indicators to measure the performance of the emotion classifier.
4.3 Experimental Process and Results In this experiment, the Keras deep learning tool is used to train the classification model, and various parameters of the LSTM model are set, including the selection of the activation function, the value experiment of dropout, and the value experiment of the number of training epochs, as shown in Table 3. After the training times reached 26 rounds, the accuracy of the training set has gradually approached 0.97; after the
The Evolution of Public Opinion and Its Emotion Analysis in Public … Table 3 Model parameter setting
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Hyperparameter
Parameter value
Activation function
tanh
Optimize the way
Adam
Dropout
0.2
Word vector dimension
120
Batch size
64
Epoch
50
Training window size
7
Minimum word frequency
3
training times reached 39 rounds, the information loss value has gradually approached 0.11. The results show that the experimental model has better performance (Table 2). Xu Dongdong [23] used the TF-IDF method to vectorize the text, and achieved the expected goal of the model; Gao Huan [22], Luo [24] and Melville [25] used logistic regression, support vector machine and naive Bayes methods for emotion classification respectively, and achieved good classification results. In order to verify the effectiveness of the emotion classification method based on word2vec and LSTM model proposed in this paper, the method in this paper is compared with Support Vector Machine (SVM), Naive Bayesian Model (NBM) and logistic models based on TF-IDF. The experimental results are shown in Table 4. Through the above experiments, it can be found that the performance of the three traditional machine learning models based on the data in this paper is not much different, and the values of AV P、 AV R and AV F are all lower than the results obtained by the method in this paper. Therefore, the emotion classification model based on Word2Vec and LSTM proposed in this paper can better complete the emotion classification task. Table 4 Comparison of model results Performance
TF-IDF + SVM
TF-IDF + NB
TF-IDF + logistic
Word2Vec + LSTM
AVP
0.74
0.75
0.75
0.89
AVR
0.73
0.76
0.76
0.83
AVF
0.73
0.75
0.76
0.83
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5 Public Opinion Evolution Analysis 5.1 Evolutionary Stage Division In the early days of the epidemic, due to the lack of awareness of the virus, many netizens used Wuhan pneumonia as their name, and with the spread of the epidemic, COVID-19 began to break out around the world, and the number of infections in the United States rose sharply. Therefore, in order to better analyze the evolution of public opinion about epidemic events, this article uses Baidu search index to search by the combination words of “Wuhan Pneumonia + COVID-19”, “Epidemic abroad + epidemic in the United States”, and the time span is from December 30, 2019 to March 31, 2020. From the Baidu search index data, the search for “Wuhan Pneumonia + COVID19” began on December 30, 2019. Before January 18, 2020, due to the small number of infected cases, COVID-19 did not attract widespread attention, and the search index was always low. With the rapid spread of the epidemic, the number of infected people has risen rapidly, and the search volume has entered a stage of explosive growth, and the index peaked on January 23, with 613,238 searches. Since then, with the timely and effective release of relevant epidemic information, the panic of the public has been reduced to a certain extent, and the search volume has gradually declined, and experienced the last small peak on February 13. After February 15, the domestic epidemic was basically controlled, and the search index gradually subsided. Therefore, based on the Baidu search index and combined with the life cycle theory of public health emergencies [26], this paper divides the public opinion development process of COVID-19 incident into the incubation period (2019/12/30–2020/01/17), the outbreak period (2020/01/18–2020/02/15) and calm period (2020/02/16–2020/ 03/31) three stages.
5.2 Proportion Analysis of Emotional Tendency in Different Stages Figure 4 shows the proportion of different types of emotions in each stage. It can be seen from the figure that no matter what stage of public opinion is in, the proportion of negative emotions is the highest, followed by neutral emotions and the lowest positive emotions. During the incubation period, the government’s weak monitoring and the relative lack of information about COVID-19 available to netizens have led to rumors, which seriously misled netizens’ rational judgments and caused negative emotions to rise. As public opinion entered the outbreak period, netizens fully realized the danger of COVID-19 virus and panicked, so that the proportion of positive emotions at this stage was less than 0.1. In the stage of calming public opinion, the domestic epidemic was basically under control, the people gradually returned to normal life, and positive emotions increased, and compared with the previous two stages, the
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Fig. 4 Proportion of emotional tendencies at each stage in the evolution of public opinion
proportion reached the highest level. However, due to the rapid development of foreign epidemics, domestic people were more concerned about the global epidemic, As a result, the proportion of negative emotions in the calm period to remain high.
5.3 Analysis of Age Characteristics at Different Stages and the Proportion of Emotional Tendencies in Each Age Stage In order to better analyze the age characteristics of the emotional evolution of public opinion, First, the crawled raw data is processed, invalid and false age information is deleted, and the data information of the age of 12 to 80 is selected. Secondly, it is divided into 5 age ranges according to age group, which are 12–17 years old, 18–27 years old, 28–38 years old, 39–54 years old, and 55–80 years old [27]. Finally, through statistical analysis of the number of people and emotional tendencies of each age group. The details are shown in Figs. 5 and 6. It can be seen from Fig. 5 that no matter what stage of public opinion evolution is in, the proportion of the population of each age group has the same trend. The age of commenters is mainly concentrated in the two age groups of 18–27 years old and 28–38 years old. Among them, the group of 18–27 years old reaches the peak, and the proportion in all three stages exceeds 0.5, it is the main force of commenting on COVID-19 incident. However, the proportions of the three groups of 12–17 years old, 39–54 years old and 55–80 years old are less than 0.1 in each stage of public opinion evolution, it shows that netizens in these three age groups have a low degree of public opinion participation in the event. Therefore, we should focus on strengthening the guidance of young people aged 18–27 and 28–38. In the 18–27-year-old group, these netizens are relatively young and curious, but they are also easily misled by false information and have irrational emotions, therefore, the young people in this group should focus on monitoring, and guide them in a way suitable for young people to
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Fig. 5 The proportion of people in different age groups at each stage
Fig. 6 The proportion of emotional tendencies at different ages
accept, especially to strengthen the guidance in the first stage and the third stage; The 28–38-year-old group of netizens has a certain ability of analysis, and has a strong interest in participating in social hotspots, and the netizens in this group have the highest degree of participation in the second stage. Therefore, in the second stage of public opinion development, focus should be on strengthening the Guidance for netizens aged 28–38. As can be seen from Fig. 6, no matter which age group, the proportion of negative emotions has an absolute advantage. In the trend of the proportion of negative emotions, the proportion of 12–17 years old is the lowest, and people in this stage are more optimistic than other groups, reaching the peak at 18–27 years old, and then gradually falling. The negative emotions and positive emotions of the four groups of age showed opposite trends. However, in the group aged 55–80, although the proportion of negative emotions fell, the proportion of positive emotions also reached the
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lowest point of the five groups. The reason is as follows: the decrease in the proportion of negative emotions in this group is mainly driven by neutral emotions, and has nothing to do with positive emotions. People aged 55–80 years have poor immune resistance and the highest fatality rate among COVID-19 infected people, which makes people in this group lack confidence in the face of the epidemic and generally show negative emotions.
5.4 Distribution Characteristics of Domestic Provinces at Different Stages In order to analyze the evolution law of public opinion in different regions and stages, this paper drew relevant thermal maps for the proportion of the number of comments in each province at each stage, as shown in Fig. 7. First, we screened the data from 34 Chinese netizens in provincial administrative regions, and excluded “other” and “overseas” data. Secondly, according to the classification results of public opinion evolution in this paper, the proportion of netizens participating in the discussion of this event in all provincial administrative regions in China at each stage is calculated, the red dot in the figure represents the location of the provincial capital. Finally, in order to better highlight the color hierarchy, low–High is set to [0–100], and the proportion of relevant data is increased so that the data with the highest proportion corresponds to 100 in high, so as to carry out regional visualization analysis. As can be seen from Fig. 7, heat in the whole evolution process was mainly concentrated in the central region with Hubei as the center and the coastal region. The reason may be: on the one hand, the epidemic was first reported in Wuhan, which made Hubei and surrounding provinces pay more attention to the incident; On the other hand, the coastal areas are economically developed, the usage rate of Weibo is relatively high, and the speed of information dissemination is relatively fast. In the incubation period, the public opinion heat is mainly concentrated in Hubei Province, although there are netizens in Beijing, Guangdong, Henan and other places to participate in comments, the participation is significantly lower than that in Hubei Province. During the outbreak period, with the spread of the epidemic across the country, the heat of public opinion in central and coastal areas increased rapidly and gradually showed a balanced trend, and Hubei province was no longer “the dominant one”. In the calm phase, due to the high population density and high mobility of Beijing and Guangdong, the two regions attracted high attention to this event and became the regions with the highest public opinion heat on COVID-19. It can be seen that the evolution of public opinion has obvious regional characteristics, therefore, the evolution of public opinion of the epidemic should be controlled and guided by different provinces in stages.
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PhaseⅠ PhaseII PhaseIII Fig. 7 Heat map of domestic provinces in three stages of public opinion evolution
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6 Conclusion The conclusions obtained in this paper are based on the following assumptions: • It is assumed that the manually annotated samples can accurately represent the emotion tendency of the review data; • It is assumed that the personal information of Weibo users is authentic; • It is assumed that the comment data sample can reflect the characteristics of all the comment information of the event on Weibo. • It is assumed that the Weibo backend does not restrict comment information and commenter information. At present, the academic community has carried out extensive research on the evolution of online public opinion, however, compared with other fields, because public health emergencies have the characteristics of rapid outbreak time, wide coverage and great harm, it is more urgent to study the network public opinion of public health emergencies. In addition, effectively identifying the emotional changes of netizens is the basis for the study of online public opinion, but the current research shows that there is still much room to improve the accuracy of emotional prediction. Therefore, this paper takes the covid-19 incident as an example, and analyzes the public opinion evolution and emotion of public health emergencies according to the microblog data. There are two main contributions: first, at the theoretical level, the public health emergency public opinion evolution and emotion analysis related theory on the basis of combing, use the length of the depth of learning memory neural network algorithm, to build a public health emergency public opinion emotional evolution model, to a public health emergency public opinion emotional evolution analysis provides a new theoretical framework. Second, on the practical level, the validity of the model established in this paper is verified by comparing the performance of four models, namely logistic regression, NB, SVM and LSTM, with empirical analysis of COVID-19 event. Visual analysis of emotional evolution was conducted based on time, age and region, so as to verify the process of emotional evolution analysis model of public opinion about public health emergencies based on Weibo data and the application of visual analysis results. The research of this paper is of great practical significance for the relevant regulatory departments of the government to understand the emotional evolution law of public opinion and guide public opinion correctly in public health emergencies. In order to enable relevant government departments to effectively identify the risk of public opinion on the Internet of public health emergencies and formulate targeted prevention and control measures, based on the analysis of the overall evolution of public opinion and emotion of the event, this paper divides the evolution process into three stages, and then analyzes its
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evolution law from the three dimensions of time, age and region, and puts forward the following three suggestions. (1) The incubation period: strengthen public opinion risk monitoring and early warning The incubation stage of public opinion covers the causes and details of public health emergencies. If the public opinion response is inappropriate or slow, it will lead to the rapid formation of public opinion storm and seriously affect the normal order of society. First, monitor the possible public opinion risks. Use big data technology to automatically capture, filter, classify and integrate relevant network data, so as to obtain first-hand data of public opinion information and monitor risk sources around the clock. Secondly, early warning of possible public opinion risks. Through the in-depth mining of public opinion information, such as theme word mining, emotion recognition, semantic analysis and other methods, we can timely and comprehensively capture and perceive the changes of netizens’ emotional attributes in public health emergencies. Finally, establish and improve the risk assessment mechanism of public health emergencies. From the aspects of public opinion risk prevention and control mode, measures and effectiveness, we should build a social public opinion risk prevention and control evaluation system for public health emergencies, conduct risk assessment, and promote and improve the scientific and effective operation of public opinion risk prevention and control mechanism for public health emergencies. (2) The outbreak period: explore the evolution characteristics of public opinion and give targeted guidance After the evolution of public opinion to the outbreak period, public opinion becomes more and more complex. In order to quickly “extinguish” the public opinion storm, we need to implement targeted measures according to the evolution characteristics of public opinion. Statistical analysis, visualization technology and other methods are used to mine the characteristics and needs of netizens in public health emergencies. For example, through the analysis of age levels, it is found that the age of netizens participating in the discussion of the event is mainly 18–27 years old and 28–38 years old; Through the analysis of the regional thermal map, it is found that each region pays attention to the event. Therefore, according to the evolution characteristics of public opinion in public health emergencies, the following measures can be formulated: first, the guidance of public opinion should pay attention to the analysis of young people’s cognitive style and evaluation angle, and choose the angle that is in line with the understanding of young people to release guidance information. Second, for areas with high public opinion, we can organize some special publicity and activities to try to control negative emotions within a certain area and prevent the nationwide spread of negative emotions. (3) The calm period: maintain the image of the government and prevent the recurrence of public opinion
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At this stage, we should focus on avoiding people’s “negative return” to domestic related events, reshaping people’s confidence, repairing the government’s image, and improving the public opinion management mechanism. There are three specific measures: first, set an example and carry forward the positive energy of society, which can not only better guide the trend of public opinion and emotion and concentrate, but also improve people’s literacy and cognitive ability and prevent the “rebound” of public opinion; Second, relevant government departments should constantly reflect, dare to take responsibility, publish information in a timely manner, and make the disposal results transparent, so as to eliminate people’s doubts and worries about the government and enhance people’s trust in the government; Third, the relevant government departments should find and repair the loopholes in the public opinion management mechanism in time, draw lessons from failures, accumulate useful experience, and do a good job in public opinion management. Acknowledgements The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China under grant No. 71871018.
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Research on the Impact of Digital Economy Development on Enterprise Innovation from the Perspective of Financing Constraints Junyue Zhou and Qi Qiu
Abstract The digital economy has flourished in China in recent years, and it has played an important role in stimulating consumption, stimulating investment, and creating jobs. At present, digital technology has become a key driving force to achieve high-quality economic development. With the rapid rise of emerging technologies represented by digitization and intelligence, the digital economy has achieved effective penetration into the traditional economy with its unique means of information transmission, promoted the reform of enterprise management concepts, and injected strong vitality into the innovation development of enterprises. This article focuses on the relationship between the digital economy and enterprise innovation, explores the impact of the digital economy on enterprise innovation, and analyzes its impact mechanism from the perspective of financing constraints. Based on the data of China’s A-share listed companies from 2014 to 2020, regression analysis and mediation effect model are applied to empirical research, and heterogeneity analysis is carried out for different industries, regions and enterprise types. The research results show that the digital economy can significantly promote enterprise innovation, and financing constraints play an intermediary role. The digital economy can alleviate financing constraints thereby provide vitality for enterprise innovation. The digital economy has a more significant role in promoting the innovation of non-state-owned enterprises and large enterprises, and has a greater role in driving innovation in enterprises in the eastern and central regions and enterprises in the primary industry. The research enriches the literature on the digital economy and enterprise innovation, providing theoretical support for the necessity of developing the digital economy, which has implications for the high-quality development of the digital economy and the enhancement of enterprise innovation capabilities. Keywords Digital economy · Enterprise innovation · Mediation effect · Financing constraints J. Zhou · Q. Qiu (B) School of Economics and Management, Beijing Jiaotong University, Beijing, China e-mail: [email protected] J. Zhou e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_34
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1 Introduction In recent years, in China, the digital economy has flourished, and it has played an important role in stimulating consumption, stimulating investment, and creating jobs. In 2020, the scale of China’s digital economy will reach 39.2 trillion yuan, accounting for 38.6% of GDP, ranking second in the world, forming a characteristic digital economy development path based on industrial foundation and market advantages to strengthen digital economy innovation. Against the background of the complex and severe global economic situation, China’s digital economy still maintains a high growth rate of 9.7%, which is more than 3.2 times the nominal GDP growth rate during the same period. 2021 Digital Economy Report released by the United Nations Conference on Trade and Development also pointed out that China and the United States account for about 90% of the market value of the world’s largest digital economy platforms, and their outstanding advantages in data use lead the world. At present, digital technology has become a key driving force for the high-quality development of the national economy, and has gradually developed into an important economic form that undertakes the agricultural economy and the industrial economy. Following the general trend of the development of the digital economy and continuously and effectively releasing the development potential of the digital economy is what must be done to realize the development strategy of “digital power”. With the rapid rise of emerging technologies represented by digitization and intelligence, the digital economy has achieved effective penetration into the traditional economy with its unique means of information transmission, promoted the reform of enterprise management concepts, and injected strong vitality into the innovation and development of enterprises. The innovation-driven effect of the digital economy can stimulate new kinetic energy for economic development and help build a new pattern of national innovative development, which is meaningful for both the development of the digital economy and innovation. Therefore, in recent years, it has received more and more attention from scholars and policy makers that how to effectively release the boosting power of the digital economy to the innovative development of enterprises, to provide an important driving force for the higher-quality economic development. Whether micro-enterprises can seize innovation opportunities and realize the full application of digital technology, to effectively exert their innovation empowerment effect and promote the innovation and development of enterprises, has also become one of the hot topics that academic researchers and enterprise managers pay attention to. Today, digital methods are effectively breaking the barriers of time and space, improving the level of inclusiveness of limited resources, greatly facilitating people’s lives, and meeting diverse and personalized needs. The development of the digital economy is allowing the masses to enjoy tangible and visible benefits. At present, China is vigorously promoting the development of the digital economy, and
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promoting the deep integration of the digital economy and the real economy. Innovation also occupies an important position in the overall situation of China’s modernization. It is worthy to study that how to stimulate the innovation vitality of enterprises and improve the level of innovation. This article will focus on the relationship between the digital economy and enterprise innovation, exploring the impact of the digital economy on enterprise innovation, and analyzing its impact mechanism. In 1995, after Don Tapscott, the Father of the digital economy, first proposed the concept of the digital economy, scholars have conducted in-depth discussions on the digital economy. Changhong Pei et al. [1] pointed out that compared with the traditional economy, the digital economy can use its unique data information and information technology transmission methods to achieve effective penetration into the traditional economy, and ultimately promote high-quality and sustainable economic development. However, there is a lack of micro-empirical research on the relationship between the digital economy and enterprise innovation. Tan and Zhan [2] found that the application of digital technology to the enterprise innovation process enables enterprises to significantly shorten the time-to-market of products, increase consumer acceptance of new products and reduce R&D costs. There are also some literatures that discuss the impact of the “Internet +” strategy on the performance of enterprises’ independent innovation [3], as well as the impact of technological financial policies on the level and intensity of digital application of enterprises [4]. In terms of the influence mechanism, Lin Dang et al. (2021) found that the development of the digital economy mainly promotes foreign investment by improving the regional innovation environment, increasing the activity of VC/PE, and then improving the level of cooperative innovation of manufacturing enterprises. From the perspective of technology application, enterprise innovation needs to rely on irreproducible and unique resources [5]. The development of digital economy provides convenience for enterprises to use digital technology to integrate resources and match market demand information [6], which is helpful to enterprise identify resource needs, greatly reducing innovation risks and improving the efficiency of resource allocation and innovation [7, 8]. From the perspective of market development, the digital economy integrates and converts production factors digitally, breaking the geographical restrictions of traditional markets, that improves the market liquidity and allocation efficiency of production factors, and expands the space for enterprises to explore new paths for technological innovation [9]. The digital economy relies on digital platforms to integrate existing innovation resources, breaking through the time and space limitations of factor supply and innovation demand, which improves the matching degree of factor supply and demand [10], and improves the quality of enterprise innovation and market efficiency [11]. This article will analyze from the perspective of financing constraints. According to the different sources of financing, corporate financing methods can be mainly divided into two types: one is internal financing, that is, financing is obtained from the company’s own funds; the other is external financing, that is, financing is obtained from external financial markets and financial institutions. Internal financing has the characteristics of easy access and low cost, but with the expansion of the scale of enterprise development, relying only on internal financing is no longer enough
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to meet the daily capital needs of enterprises. At this time, external financing has become an important channel for enterprises to obtain cash flow [12]. When there is a big difference between the external financing cost and the internal financing cost, the enterprise may abandon some R&D innovation projects that bring positive returns due to the excessive external financing cost, which means, there is a financing constraint problem. Financing constraints limit the innovation ability of enterprises [13], and enterprises facing financing constraints will improve the level of R&D innovation with the increase in the amount of external funds, that is, alleviating the financing constraints faced by enterprises is conducive to improving the innovation level of enterprises [14]. Innovation requires substantial capital investment that is sustained over a long period of time. Insufficient R&D investment and financing constraints are often the key obstacles to the orderly development of corporate scientific research projects [15]. It has great significance for enterprises to achieve technological breakthroughs at an early date and improve the quality of innovative output as soon as possible, by filling the financing gap in time and effectively alleviating the financing constraints. The digital economy can provide more financial support for enterprise innovation by reducing external financing costs and operating costs of enterprises. The development of the digital economy can improve the utilization efficiency of credit resources of financial institutions, thereby promoting regional financial development, dredging external financing channels for enterprises, and improving the operational efficiency of financial markets. The ability of financial institutions to obtain information is enhanced, and they play the role of financial intermediaries, thereby reducing the external financing costs of enterprises and alleviating the problem of financing constraints [16]. The digital economy can also improve the utilization efficiency of the production materials of enterprises, reducing the internal operating costs and alleviating the problem of capital shortages. In recent years, with the development of digital technologies such as artificial intelligence, big data and the Internet of Things, the sharing economy has gradually penetrated from the field of living and consumption to the field of manufacturing. The excess production and manufacturing capacity, product testing capacity, and logistics and distribution capacity of enterprises can be traded through the sharing economy platform to promote idle equipment and idle factories to be put back into use [17]. This not only contributes to the intensive allocation of manufacturing resources in the whole society and reduces the repeated investment on the whole, but also reduces the equipment management and maintenance cost of large enterprises and the resource utilization threshold and production cost of small and medium-sized enterprises, effectively alleviating the problem of enterprise capital shortage. Based on the data of China’s A-share listed companies and the digital economy development index [18] from 2014 to 2020, this article uses the ordinary least squares method and the mediation effect test method to conduct empirical research. The results of the study show that the development of the digital economy can significantly promote enterprise innovation. In terms of influence mechanism, the digital economy will provide activities for enterprise innovation by easing the difficulties of corporate financing constraints. The contributions of this article are mainly reflected
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in the following aspects: Most of the previous studies have focused on high-quality economic development and other aspects, taking the impact of the digital economy on enterprise innovation as one of the transmission mechanisms. In terms of innovation, previous studies have also considered it as a macro variable. This article directly focuses on the impact of digital economy development on enterprise innovation, and tests the impact mechanism from the perspective of micro-enterprise financing constraints, deepening the research on the impact of existing literature on the micro-level impact of the digital economy. When examining the issue of financing constraints and enterprise innovation, in addition to the characteristics of enterprises, other perspectives have been added.
2 Data and Method 2.1 Data This article takes A-share listed companies from 2014 to 2021 as the initial sample. Referring to the common practice in the existing literature, this article does the following processing for the initial sample: . Eliminate listed companies with missing data on major variables during the sample period; . Exclude listed companies with a listing date after 2013; . Exclude ST-share listed companies; . Two-sided tailing was performed on all continuous variables at the 1 and 99% levels. The data of listed companies comes from CSMAR, iFind Database, etc. The calculation method of the provincial digital economy development index adopts the method provided by Jun Liu, Yuanjun Yang, and Sanfeng Zhang [19] in the previous study. The raw data comes from National Bureau of Statistics and the China Statistical Yearbook, as well as local statistical bureaus and statistical bulletins. Due to the serious lack of data in Tibet, it is excluded in this article.
2.2 Variables a. Explained variable: Enterprise Innovation. The development of innovation is measured by corporate patent applications. The calculation method is to select the number of enterprise invention patent applications, add 1 to it, and take the natural logarithm.
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b. Explanatory variable: Digital Economy Index. At present, there is still a lack of unified standards for the measurement of the digital economy. The existing literature mainly measures the degree of development of the digital economy from two perspectives: one is the measurement from the dimensions of the development foundation and application of the digital economy; the other is based on the dimensions of digital industrialization and industrial digitization. This article measures the degree of development of the digital economy from the first perspective, using the digital economy index calculation method provided by Jun Liu, Yuanjun Yang, and Sanfeng Zhang [19]. The index is calculated from three perspectives: informatization, Internet, and digital transactions. c. Mediating variable: Financing Constraints. The intermediary variable tested in this article is the financing constraint. The measurement methods of financing constraints can be divided into single indicator and multivariate indicators. Single indicator is mainly constructed with the help of corporate financial index, such as asset size, interest expense, etc., while there is a large endogeneity, which will lead to biased research results. Multi-indicator is an index constructed by a combination of multiple factors, such as SA index, KZ index and WW index. Since the KZ index and the WW index contain a variety of endogenous financial indicators, in order to avoid the endogenous interference caused by the company’s own financial indicators, this article refers to the practice of Minggui Yu et al. [20]. The SA index constructed by Hadlock and Pierce [21] is selected to measure financing constraints. The formula for calculating the SA index is as follows: SA = 0.043 × Size2 − 0.737 × Size − 0.040 × Age
(1)
Size is the natural logarithm of the size of the company’s assets (unit: million yuan); Age is the length of the company’s establishment (unit: years). The SA index obtained by the above calculation method is all negative. In order to better compare the financing constraints of each enterprise, the absolute value of the SA index is taken, which is the financing constraint value required for the empirical research in this article. The larger the absolute value of the SA index, the more serious the financing constraints faced by enterprises. d. Control variables: With reference to relevant researches on enterprise innovation, the following related variables are controlled: . Enterprise Size (Size): measured by the natural logarithm of total assets; . Age of the Enterprise (Age): expressed by the natural logarithm of the duration of the enterprise; . Leverage (Lev): measured by the ratio of total liabilities to total assets; . Return on Assets (ROA): measured by the ratio of net profit to total asset balance; . Cash Holdings (Cash): measured by the natural logarithm of the balance of cash and equivalents at the end of the period; . Growth Capability (Growth): measured by the growth rate of the company’s operating income;
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Table 1 Research variables table Variable
Name
Short Name Calculation
Explained variable
Enterprise innovation
EI
Explanatory variable
Digital economy DEI index
The method proposed by Jun Liu (2021) et al.
Mediating variable
Financing constraints
The absolute value of the SA index
Control variables
Enterprise sizea
Size
Natural logarithm of total assets
Age of the enterprise
Age
The natural logarithm of the duration of the firm
Leverage
Lev
FC
The number of invention patent applications, add 1 to take the natural logarithm
Total Liabilities/Total Assets
Return on assets ROA
Net profit/total asset balance
Cash holdings
Cash
Natural logarithm of closing cash and equivalents balances
Growth capability
Growth
(Total operating income of the current year—Total operating income of the previous year)/Total operating income of the previous year
Ratio of independent directors
RID
Number of Independent Directors/Number of Board of Directors
State-owned enterprise
SOE
The value of state-owned enterprises is 1, and the value of non-state-owned enterprises is 0
The Size and Age here are different from the previous.
. Ratio of Independent Directors (RID): measured by the ratio of independent directors to the number of directors; . Whether it is a State-owned Enterprise (SOE): the value is 1 for state-owned enterprises and 0 for non-state-owned enterprises. The raw data comes from the CSMAR and iFind databases. Table 1 is a list of variables. After preliminary data processing, there are a total of 13,720 sets of observations. Table 2 shows the descriptive statistics of the main variables.
2.3 Method and Design The benchmark regression model for this article is set as follows: EIi,j,t = α0 + α1 DEIi,t + α2 Controls + Year + Industry + εi,j,t
(2)
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Table 2 Descriptive statistics of primary variables Short name
Obs
EI
13,720
Mean 1.6334
Std
Min
Medium
Max
1.3940
0
1.6094
8.8279
DEI
13,720
5.6491
2.3480
1.4704
5.4062
11.2750
FC
13,720
3.7361
0.3542
0.6305
3.7603
5.6379
Size
13,720
21.6754
1.5704
16.0223
21.5260
30.9679
Age
13,720
2.8056
0.3763
0.6931
2.8332
4.1271
Lev
13,720
0.3964
0.1991
0.0091
0.3825
3.2619
ROA
13,720
0.0615
0.0856
−1.6830
0.0542
1.1218
Cash
13,720
19.5098
1.6621
10.1586
19.4525
28.1549
Growth
13,720
0.1866
1.0517
−0.9486
0.1123
89.4786
RID
13,720
0.33704
0.1244
0
0.3333
1
SOE
13,720
0.2587
0.4379
0
0
1
The subscripts i, j and t represent the province, enterprise and year, respectively. The explanatory variable EI represents enterprise innovation, and the explanatory variable DEI represents the digital economy index; Controls represents a series of micro-enterprise-level control variables that affect innovation; Year and Industry represent the year fixed effect and industry fixed effect respectively; ε represents random errorterm. The sign and saliency of α1 is the focus of this paper. If α1 > 0, it means that the more developed the digital economy is, the stronger the innovation capability of the enterprise, and vice versa. In order to further test whether the digital economy promotes innovation activities by easing financing constraints, this article establishes the following model with reference to the mediation effect test procedure of Zhonglin Wen et al. [22]: FCi,j,t = β0 + β1 DEIi,t + β2 Controls + Year + Industry + εi,j,t EIi,j,t = γ0 + γ1 DEIi,t + γ2 FCi,j,t + γ3 Controls + Year + Industry + εi,j,t
(3) (4)
FC is the intermediary variable Financing Constraint, and the definitions and measurement methods of other variables are consistent with the above. The coefficient β1 reflects the impact of the digital economy on corporate financing constraints; the coefficient γ1 reflects the direct effect of the digital economy on corporate innovation, and the product of γ2 and β1 reflects the intermediary effect of financing constraints.
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3 Results and Discussion 3.1 Main Regression Result After confirming that there is no significant correlation between the explanatory variables and the control variables, this paper performs regression according to Model Eq. 2. Table 3 shows the regression results of the digital economy index and enterprise innovation. Column (1) is the result of regression of the explanatory variable digital economy after introducing the year fixed effect and the industry fixed effect. From the test results, it can be seen that the impact of the digital economy on enterprise innovation is significantly positive at the 1% level. Column (2) introduces a series of control variables, and the results show that the positive impact of the digital economy on enterprise innovation still exists and is significant at the 1% level. These results show that the development of the digital economy will significantly promote the innovation of enterprises. Table 3 Main regression result
Variable
EI (1)
(2)
DEI
0.0373***(5.75) 0.0261***(4.11)
Size
0.0624*** (3.19)
Age
−0.3315***(−9.29)
Lev
0.5215***(6.92)
ROA
2.0062***(13.27)
Cash
0.1348***(9.14)
Growth
0.0012 (0.07)
RID
− 0.4593***(−4.46) − 0.1936***(−6.64)
SOE Constant term
1.0914***(8.38)
− 3.0995*** (−9.02)
Year fixed effect
Yes
Yes
Industry fixed effect Yes
Yes
R2
0.0646
0.1163
N
13,720
13,720
*,
**, and *** indicate significance at the 10, 5, and 1% levels, respectively, with t values in parentheses
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3.2 Mediation Test In order to test the mediating effect of financing constraints in the drive of digital economy development to corporate innovation activities, this article tests the mediating effect of financing constraints through stepwise regression. Table 4 shows the results of the mediation effect test. Column (1) reports the impact of the digital economy on corporate innovation, while column (2) reports the regression results of the digital economy index on financing constraints, and column (3) reports the regression results of the digital economy on corporate innovation and financing constraints. Table 4 Mediation test result
Variable DEI
(1)
(2)
(3)
EI
FC
EI
0.0261*** (4.11)
−0.0049*** (−6.19)
0.0233*** (3.68) −0.5733*** (−6.58)
FC Size
0.0624*** (3.19)
Age
− 0.3315*** − 0.0313*** 0.0673 (−9.29) (90.78) (1.00)
Lev
0.5215*** (6.92)
− 0.0313*** 0.0504*** (−2.82) (6.74)
ROA
2.0062*** (13.27)
− 0.2569*** 1.8589*** (−10.88) (12.36)
Cash
0.1348*** (9.14)
0.0088*** (3.82)
0.1399*** (9.50)
Growth
0.0012 (0.07)
0.0026 (1.01)
0.0027 (0.17)
RID
− 0.4593** (−4.46)
0.2568*** (14.01)
− 0.3120*** (−3.04)
SOE
− 0.1936*** − 0.0176*** − 0.2037*** (−6.64) (−4.86) (−6.97)
Constant term
− 3.0995*** 0.5560*** (−9.02) (4.56)
− 2.7808*** (−8.38)
Year fixed effect
Yes
Yes
Yes
Industry fixed effect
Yes
Yes
Yes
0.6958*** (−2.80)
0.0551*** (2.92)
R2
0.1163
0.7495
0.1216
N
13,720
13,720
13,720
* , **, and *** indicate significance at the 10%, 5%, and 1% levels,
respectively, with t values in parentheses
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The results show that the coefficients β1 , γ1 and γ2 are all significantly positive at the 1% significance level, indicating that financing constraints have a significant mediating effect on the impact path of the digital economy on enterprise innovation, and the mediation effect accounts for 10.76% of the total effect. The digital economy provides vitality for enterprise innovation by easing the financing constraints of enterprises.
3.3 Heterogeneity Analysis In the following sections, this article conducts group tests for different ownership properties, firm size types, industries and regions. Table 5 is the test results. Column (1) reports the group test results of the benchmark regression model; column (2) reports the impact of the digital economy index on financing constraints; column (3) reports the regression results of the digital economy index on corporate innovation and financing constraints. To make the data more reliable, no industry fixation was performed for the analysis of industry heterogeneity. (1) Heterogeneity in the nature of corporate equity: From the results in Table 5, it can be seen that the digital economy has a significant positive impact on corporate innovation of non-state-owned enterprises, and the test results of financing constraints as an impact channel are also significant, while the impact on corporate innovation of state-owned enterprises is not significant. This may be because, compared with state-owned enterprises, non-state-owned enterprises often rely more on the way to achieve technological breakthroughs by organizing high-quality innovation projects to obtain market competitive advantages, so they will more actively apply digital technology to effectively integrate internal and external R&D. elements. (2) Heterogeneity of size and type of enterprises: From the results in Table 5, it can be seen that the digital economy has a significant positive impact on enterprise innovation of large enterprises, and the test results of financing constraints as an impact channel are also significant, while the impact on innovation of small, medium and enterprises is not significant. This may be due to the fact that large enterprises have a longer existence than small, medium and micro-sized enterprises, which have accumulated more R&D and innovation resources, and richer social network relationships. The digital economy can help large enterprises integrate internal resources at a lower cost in a short period of time. At the same time, large enterprises have a more solid foundation to apply the development of digital economy technology to themselves. (3) Industrial heterogeneity: The results show that the digital economy significantly promotes the innovation behavior of enterprises in the primary industry, the secondary industry and the tertiary industry, and has the greatest impact on enterprises in the primary industry. However, the results show that the digital economy
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Table 5 Heterogeneity test result Classification State-owned enterprise
Variable DEI
(1)
(2)
(3)
EI
FC
EI
0.0065 (0.50)
− 0.0020 (−1.11)
0.0044 (0.35) − 0.9938*** (−8.50)
FC
Non-state-owned enterprise
Controls
− 5.2104*** (−9.90)
2.1704*** (28.89)
− 3.0535*** (−5.27)
DEI
0.0366*** (4.97)
− 0.0010** (−2.17)
0.0363*** (4.93) − 0.3127*** (−3.16)
FC
Large enterprise
Controls
− 2.5504*** (−7.29)
1.0465*** (38.79)
− 2.6026*** (−7.43)
DEI
0.0367*** (4.79)
− 0.0058*** (−6.39)
0.0318*** (4.17) − 0.8216*** (−9.92)
FC
SMEs
Controls
− 3.5607*** (−10.22)
1.2046*** (28.92)
− 2.5710*** (−7.12)
DEI
− 0.0005 (−0.05)
0.0031** (2.27)
− 0.0004 (−0.03) − 0.0542 (−0.41)
FC
Primary industry
Controls
0.7011 (1.10)
1.1844*** (−14.66)
0.6369 (0.97)
DEI
0.1762** (3.85)
0.0073** (2.23)
0.2208*** (5.27) −6.0950*** (−6.71)
FC
Secondary industry
Controls
− 2.8979 (−1.63)
2.0474*** (20.93)
10.1892*** (4.52)
DEI
0.0518*** (7.28)
− 0.0020*** (−2.72)
0.0499*** (7.05) − 0.8935*** (−9.88)
FC
Tertiary industry
Controls
− 1.6346*** (−6.67)
1.3409*** (51.94)
− 0.4364* (−1.69)
DEI
0.0544*** (3.84)
− 0.0128*** (−3.74)
0.0498*** (3.52)
FC
− 0.3616*** (−4.42) (continued)
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Table 5 (continued) Classification
Eastern region
Variable
(1)
(2)
(3)
EI
FC
EI
Controls
0.5987* (1.77)
4.6787*** (57.22)
2.2908*** (4.49)
DEI
0.0511*** (5.46)
− 0.0047*** (−3.53)
0.0486*** (5.21) − 0.5279*** (−7.67)
FC
Central region
Controls
− 2.9927*** (−9.51)
0.6358*** (14.17)
− 2.6571*** (−8.38)
DEI
0.7087*** (5.70)
− 0.0190* (−1.84)
0.6985*** (5.79) − 0.5335* (−1.93)
FC
Western region
Controls
− 5.1168*** (−6.21)
0.8126*** (11.68)
− 4.6833*** (−5.49)
DEI
0.0498 (0.95)
0.0035 (0.64)
0.0554 (1.08) − 1.6005*** (−6.49)
FC Controls
− 4.5473*** (−5.37)
0.5002*** (5.62)
− 3.7467*** (−4.44)
*,
**, and *** indicate significance at the 10%, 5%, and 1% levels, respectively, with t values in parentheses
makes the financing constraints of primary industry enterprises more serious, which may be due to the small sample data of primary industry enterprises. (4) Regional heterogeneity: The results show that the development of the digital economy has a significant role in promoting the innovation behavior of enterprises in the eastern and central regions, while the impact on the western region is not significant, which may be related to the imbalance of regional development. When formulating policies related to the digital economy, policy makers should pay attention to development gaps such as unbalanced and insufficient regional development, and should also consider the development potential and development momentum of different regions.
3.4 Robustness Check In order to ensure the robustness of the regression results and reduce the adverse effects of measurement error bias, this article also conducts a robustness test. First of all, this article replaces the enterprise innovation indicator, using the ratio of the
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number of patents granted to the company to the number of employees to measure enterprise innovation, or replaces the digital economy index indicator and uses the index system proposed by Jun Wang, Jie Zhu, and Qian Luo [18] to measure the development of the digital economy. The regression results after replacing the indicators are basically consistent with the benchmark regression. Then, this article excludes financial enterprise data for testing, and the results are basically consistent with the benchmark regression. In addition, the paper adopts a fixed effect model to incorporate more enterprise characteristic factors that may have an impact on enterprise innovation without changing with time into the regression model, and the results are basically consistent with the benchmark regression.
4 Conclusion Based on the data of Chinese A-share listed companies from 2014 to 2020, this article empirically studies the impact of digital economy development on corporate innovation. The research results show that the digital economy can significantly promote enterprise innovation, and financing constraints play an intermediary role. The digital economy can alleviate financing constraints to provide vitality for enterprise innovation. The digital economy has a more significant role in promoting the innovation of non-state-owned enterprises and large enterprises, and has a greater role in driving innovation for enterprises in the eastern and central regions. Among the primary, secondary and tertiary industries, the digital economy plays a greater role in promoting innovation of enterprises in the primary industry. This article enriches the literature on the digital economy and enterprise innovation, and also has important implications for enhancing enterprise innovation and promoting high-quality economic development. The theoretical support for the necessity of developing the digital economy is provided, since the digital economy will help improve the independent innovation capabilities of Chinese enterprises, thereby improving the quality, efficiency and core competitiveness of economic development. This study considers the relationship between the digital economy level at the macro level and enterprise innovation at the micro level, expands research related to micro-enterprises, and provides ideas for how the government can promote the implementation of innovation-driven development strategies at the enterprise level. In addition, this article proves the incentive effect of digital economy development on enterprise innovation from the perspective of financing constraints, which provides theoretical reference and practical guidance for solving problems such as low efficiency of enterprise resource allocation and insufficient innovation awareness. It also provides a scientific basis for enterprises and government departments to formulate and implement relevant policies. These following inspirations are given: Under the background that the digital economy can promote enterprise innovation, vigorously developing the digital economy and improving information infrastructure are of great significance to assisting enterprise technological innovation. Advanced digital technologies such as
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cloud computing, big data, and the Internet of Things can help promote massive information sharing, breaking down information barriers between consumers and R&D personnel, and meeting consumers’ most urgent consumption needs. It also provides financial institutions with more information and clears the external financing channels of enterprises, and also promotes the utilization of internal resources of enterprises, reducing internal operating costs, and alleviating financing gaps. On the one hand, policy makers should actively promote the development of advanced digital technologies, accelerating the penetration of the digital economy from the field of daily consumption into the field of production. Large enterprises should be supported in building large-scale integrated networked collaborative innovation platforms, and manufacturing enterprises should be assisted in building industrial Internet, intelligent platform etc. At the same time, it is worthy to support promoting more application of digital technology in commercial economic activities and financial investment activities, which promote the development of digital finance. Despite the rapid expansion of China’s digital economy, the problems of unbalanced, insufficient and non-standard development are also prominent, and the problem about the development of the western region requires special attention. Policy makers should steadily not only promote the development of the digital economy but also expand the coverage of digital economy services, thereby promoting the equalization of the development level of digital technology and digital finance. Only by speeding up the improvement of shortcomings and weaknesses and improving the level of digital economy governance can we embark on a high-quality development path. On the other hand, enterprises should grasp the development direction of the times, while strive to transform in the direction of digitalization, and adapt to the development trend of the digital economy, so as to improve their competitiveness and anti-risk capabilities. Small, medium and micro enterprises could closely follow the revelation of the digital economy policy and make better use of the digital economy to provide resources and financial guarantees for their own innovation activities.
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8. Cai, L., Yang, Y., Lu, S., Yu, H.: Review and prospect of research on the impact of digital technology on entrepreneurial activities. Sci. Res. 37, 1816–1824+1835 (2019) 9. Biswas, T., Kennedy, P.L.: Cross-border trade in the era of the Internet. J. Int. Agric. Trade Dev. 10, 1–18 (2014) 10. Huang, P., Chen, L.: World economic operation mechanism and rule construction under the globalization of digital economy: From the perspective of factor flow theory. World Econ. Res. 3, 3–13+134 (2021) 11. Jing, W., Sun, B.: Digital economy promotes high-quality economic development: a theoretical analysis framework. De Econ. 02, 66–73 (2019) 12. Myers Stewart, C., Majluf Nicholas, S.: Corporate financing and investment decisions when firms have information that investors do not have. North-Holland 13 (1984) 13. Gorodnichenko, Y., Schnitzer, M.: Financial constraints and innovation: why poor countries don’t catch up. J. Eur. Econ. Assoc. 11 (2013) 14. Czarnitzki, D., Hottenrott, H.: R&D investment and financing constraints of small and mediumsized firms. Small Bus. Econ. 36 (2011) 15. Zhang, J., Lu, Z., Zheng, W., Chen, Z.: Financing constraints, financing channels and R&D investment of enterprises. World Econ. 35, 66–90 (2012) 16. Shen, H., Kou, H., Zhang, C.: An empirical study on financial development, financing constraints and enterprise investment. China Ind. Econ. 06, 55–64 (2010) 17. Zhao, C., Wang, W., Li, X.: How digital transformation affects enterprise total factor productivity. Financ. Trade Econ. 42, 114–129 (2021) 18. Wang, J., Zhu, J., Luo, Q.: Measurement of the development level and evolution of China’s digital economy. Quant. Econ. Technol. Econ. Res. 38, 26–42 (2021) 19. Liu, J., Yang, Y., Zhang, S.: Research on the measurement and driving factors of China’s digital economy. Shanghai Econ. Res. 06, 81–96 (2020) 20. Yu, M., Zhong, H., Fan, R.: Privateization, financing constraints and corporate innovation: evidence from Chinese industrial enterprises. Financ. Res. 04, 75–91 (2019) 21. Hadlock, C.J., Pierce, J.R.: New evidence on measuring financial constraints: Moving beyond the KZ index. Rev. Financ. Stud. 23, 1909–1940 (2010) 22. Wen, Z., Zhang, L., Hou, J., Liu, H.: Intermediate effect test procedure and its application. Acta Psychol. Sin. 05, 614–620 (2004) 23. Chen, Z., Tian, H., Wu, F.: Does the development of science and technology finance promote the application of digital technology in enterprises?—A quasi-natural experiment based on the policy of “combining science and technology with finance.” Mod. Manag. Sci. 01, 97–105 (2022) 24. Wen, Z., Ye, B.: Mediation effect analysis: method and model development. Adv. Psychol. Sci. 22, 731–745 (2014)
How to Improve Innovation Ability of Graduate Students Through Science and Technology Competition? Dandan Li, Yan Huang, and Shiyin Yan
Abstract Taking the academic postgraduates majoring in business administration in Beijing Union University as an example, and an innovation education system is proposed based on the main line of “accumulation of knowledge of innovative methods—improvement of innovative quality—cultivation of innovative ability”. There is a stepwise path to enhance the innovation ability of graduate students based on science and technology competitions int this article. A curriculum system to enhance the innovation ability of graduate students with science and technology competitions as the “second classroom” is constructed, and it is to enhance the innovation ability of graduate students. Keywords Innovation ability · Science and technology competition · Postgraduate education
1 Introduction In 2013, Comrade Liu Yandong made an important speech at the 30th meeting of the National Postgraduate Education and Academic Degrees Committee of the State Council. He emphasized that “postgraduate education is the main source of innovative talents and an important area for building an innovative country” and “strengthening the cultivation of innovation and practical ability of postgraduates”. In September 2014, Premier Li Keqiang issued a call for “mass entrepreneurship and innovation” at the Summer Davos Forum.
D. Li · Y. Huang (B) · S. Yan School of Management, Beijing Union University, Beijing, China e-mail: [email protected] D. Li e-mail: [email protected] S. Yan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_35
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A national plan of education for Medium and long term Development is announced by Chinese government. One of its aims is to vigorously promote the reform of postgraduate training mechanism, implement the “Innovation Plan for Postgraduate Education”, and focus on improving “innovation ability”. At present, innovation and entrepreneurship education, which is the “third education passport”, is highly valued by the Party Central Committee and the government (the other two passports are academic education and vocational education). Therefore, the innovative development of postgraduate students is a major practical problem that needs to be faced by postgraduate education, and it is an inevitable trend for the adjustment of the structure and scale of postgraduate education in the new era. Postgraduate scientific and technological competitions (including postgraduate scientific research and innovation competitions, postgraduate mathematical modeling competitions, postgraduate electronic design competitions, etc.) have an important influence on promoting the cultivation of postgraduate innovation ability [1]. At present, postgraduate scientific and technological competitions have become an important carrier for cultivating innovative thinking and innovative ability of postgraduates, and an effective means and measure to enhance the innovative practice ability of postgraduates. The Innovation and Entrepreneurship Education Center of Beijing Union University (short for BUU) was established in 2014, and it has been exploring for years about how to systematically improve the innovation ability of students. There are some very good experience, including integrate the ability of science and technology competition, foreign innovation and entrepreneurship. Then put them into international innovation course content, and enterprise innovation methods, also including curriculum system and cultivation program, to develop and form a set of all-round and systematic strategies and programs. Finally, a series of courses of innovation and entrepreneurship education are developed, forming a multi-structure tutor team, forming a value co-creation oriented project incubation chain, forming the ability of a scientific and technological competition, and finally realizing the organic unity of knowledge, practice, thinking and innovation, cultivating and improving for students innovation and entrepreneurship. An innovative education system for graduate students in the School of Management of Beijing United University is constructed in the paper, which is based on the main line of “innovative method knowledge accumulation-innovative quality improvement-innovative ability training”. The implementation plan is constructed from the aspects of curriculum development, teaching reform, teacher team training and school-enterprise cooperation teaching resources construction, so as to put forward the promotion strategy of graduate students’ innovative ability based on scientific and technological competition.
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2 Related Work 2.1 Cultivation Mode There are many scholars have studied on graduate education, which are mainly in terms of cultivation mode, evaluation of cultivation quality, and cultivation of innovation ability. Through literature analysis, cultivation mode is the key area of attention for scholars of graduate education research. Through the historical study of cultivation mode and comparative study at home and abroad, scholars found that the historical evolution of postgraduate cultivation mode in Chinese universities has experienced from single to dual mode, that is academic and application-oriented, and then developed into multiple mode. But the cultivation goals in many universities did not be set according to the actual needs of society [5]. The cultivation modes of various types of postgraduates are more or less the same. Some scholars have located and distinguished again about the value orientation, nature characteristics, cultivation target and hierarchical structure of academic, application-oriented postgraduate cultivation modes. Also they have built two different postgraduate cultivation modes with their own characteristics by taking into account the national conditions of China. Other scholars have analyzed and justified the cultivation program, cultivation elements, and cultivation process studied in China after studying the cultivation of innovation ability of graduate students among the United States, Japan, and Germany [6]. Based on the discuss of the update concept, building platform, paying attention to foundation, people-oriented, exerting advantages, they proposed the training mode construction strategy from five aspects, including scientific and reasonable training plan, strengthen discipline construction, improve teaching quality, build high-quality tutor team, improve moral education work. In addition, the main issue is about how to innovate the postgraduate cultivation mode [7]. In view of the difference in cultivation goals between academic and professional (application-oriented) postgraduates, the innovation of cultivation mode of professional type needs to start from various aspects such as faculty, professionalism and practical ability. We should pay attention to practical links and formulate targeted cultivation programs. At the same time, we should start from the source. In view of the problems of single enrollment channel and insufficient publicity for postgraduates, wide enrollment channels and school-enterprise cooperation should be adopted to comprehensively improve the effectiveness of graduate training. About the academic graduate degree cultivation mode, the system construction should be improved firstly. Otherwise, the positioning of degree should be grasped clearly, and then we should strengthen the importance of practical research based on basic theoretical research to cultivate more compound talents. The main issue of cultivation quality evaluation is about four aspects [8]. First, it is the origin, development history and current development of the quality evaluation system of graduate education at home and abroad. Second, it is the connotation of graduate student quality evaluation. Third, the evaluation of graduate student
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cultivation quality by different quality concepts. At last, the quality evaluation of different types of graduate student cultivation in different majors, such as professional postgraduates of sports majors.
2.2 Innovative Ability The innovative ability of graduate students has mainly focused on the improvement of research ability and personalized training [9, 10]. Domestic scholars generally agree that the factors affecting innovative ability of graduate students include literature reading, teacher-student relationship (academic interest as a mediating variable), practice and learning ability, mentor guidance and sense of mentor responsibility, learning behavior and learning purpose, and undergraduate experience [11]. Some scholars discussed the relationship between questioning spirit and cultivation of critical thinking, the relationship between personality and innovative ability, the relationship between broad knowledge base and innovative ability, and the relationship between interest and innovative ability. They concluded that the academic graduate student group is an important innovative force in a country [12]. And it is very important for the long-term improvement of science and technology of a country. Especial the innovative ability of academic graduate students. While cultivation of questioning and critical thinking is a gradual and consistent process. In addition, some scholars believe that in the process of postgraduate training, course teaching, supervisory guidance, research practice, academic atmosphere and academic standards can influence the innovation ability of postgraduate students. Among them, academic standards have the strongest influence, while research practice has the weakest influence and no significant influence on the innovation ability of doctoral students. However, academic activities and incentive policies do not have significant effects on the innovation ability of graduate students. There are some other scholars believe that schools should pay attention to strengthening the implementation of graduate academic norms and systems [13]. For example, how to improve the quality of graduate scientific research practice and academic activities? How to stimulate the confidence of graduate students in scientific research and innovation? In conclusion, scholars generally believe that subjective factors, objective factors, human factors, and environmental factors can affect the innovation ability of graduate students at different degrees. With the development of “mass innovation and entrepreneurship”, science and technology competitions which are hosted by major ministries and commissions, have received more and more attention from education departments and educators in recent years. As an effective extension of classroom teaching, science and technology competitions are a practical platform to test the quality of theoretical teaching. At the same time, with the continuous improvement of competition system and technology, they play an irreplaceable and important role on the cultivation of innovation ability, which making them gradually become a new platform to enhance innovation ability. However, scholars have not paid enough attention to their role in postgraduate
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innovation cultivation, and there is a lack of relevant theoretical research. So, from the function of science and technology competitions for postgraduate, the important role of science and technology competitions in innovation capacity enhancement is argued in this paper. At the same time, for graduate students majoring in business management in BUU, we design strategies for enhancing innovation capacity. They are based on the actual situation of participation in science and technology competitions. the strategies include technology competition system construction and postgraduate curriculum setting.
3 Strategies About How to Improving Innovation Ability of Graduate Student 3.1 A Ladder-Type Path to Improve Innovation Ability of Graduate Students With the main line of “knowledge accumulation of innovative methods–improvement of innovative quality–cultivation of innovative ability”, the three-year learning stage of graduate students is planned as three stages, which are “knowledge accumulation, quality shaping, and ability improvement”. For the different stages, there are different innovative ability training courses, practical projects and discipline competitions. The ladder-type path to improve the innovation ability of graduate students is established int this part. Specifically, through the integration of courses, projects and competitions, students can learn innovation ability knowledge and professional knowledge in class. through practical projects, students can solve problems by applying learned knowledge. through the application of knowledge in competitions students can obtain recognition. The ladder-type path is shown in Fig. 1. In the first year of graduate school, students need to consolidate their existing foundation of creative skills classes and accumulate their own knowledge. Some graduate students already have a good knowledge foundation in the undergraduate stage, but the foundation of graduate students who is majoring in business management in the School of Management of BUU is relatively weak in this area. Most of them have not systematically studied innovative methods and innovative ability courses, such as “innovative thinking methods” and “TRIZ innovative methods” at the undergraduate stage. So these basic courses need to be supplemented. Limited to class time, they cannot be covered all, and students should do self-study by guiding with their tutors, and it needs to conjunct with both their research direction and major. We recommend the following innovation courses and knowledge which are “Internet Business Model Innovation”, “Innovative Thinking and Design of Financial Products”, “Case Study of Management Innovation of Science and Technology-based Enterprises”, etc. They should deeply integrate innovation knowledge and professional knowledge. Then, their innovative and creative thinking can be consolidated by applying or participating in some projects, such as “QiMingXing” Science and Technology Innovation Project
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Fig. 1 Ladder-type path of graduate students’ innovation ability
of undergraduates (or graduate students), social research projects on summer vacation, and innovative thinking training projects. The ability to apply what they have learned to solve problems can be exercised, and also to improve the innovation ability of graduate students. It is can be achieved through participating in “Zhiyong Cup” Innovation and Entrepreneurship Competition of BUU, the China Students Innovation Method Application Competition, and the Entrepreneurial Survival Challenge and other competitions. In the second year of graduate school, recommended courses include “Innovative Thinking Methods (middle and high level)”, “TRIZ Innovation Methods (middle and high level)” etc., students can enter the advanced stage of innovative science and technology competitions, such as China College Students “Internet Plus” Innovation and Entrepreneurship Competition, Graduate Students Research and Innovation Competition, Graduate Students Mathematical Modeling Competition, Graduate Students Electronic Design Competition, China Students E-Commerce Innovation/ Creativity/Entrepreneurship Competition, the “Challenge Cup” National Undergraduate Curricular Academic Science and Technology Competition, and China Students Entrepreneurial Competition. The students can further improve their comprehensive ability by using comprehensive knowledge to solve problems. Some of outstanding projects can also be funded. For example they could join in BUU Hive Business Incubation Project, or Beijing College Students Innovation and Entrepreneurship Training Program Project. These projects both could further incubate the project content into the ground. This not only provides more choices for their topics of graduation thesis, but also provides empirical data and materials for writing relevant research papers, which greatly enhances the innovation ability and academic level of the graduate students. In the third year of graduate school, the role of graduate students in science and technology competitions can change from leader to team member. On the one hand, their successful or unsuccessful experience can be passed on to their younger
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siblings. On the other hand, by combining the achievements of the first two stages with their research directions and graduation thesis, they could make graduation thesis good, moreover, they can declare some other projects, such as Beijing Highlevel Real-training Bibliography Project, Excellent Innovation Team, Future CEO Training Camp. All of theses could provide enhance-training for their future in-depth research and work. They can also further practicing and verifying their innovative ability. The implementation of these projects provides valuable soil for improvement of graduate students’ innovative ability. The transmission of excellent projects and valuable experiences makes this laddertype path to be a closed loop. It is an effective strategy to enhance the innovation ability of BUU graduate students who are majoring in business management.
3.2 Taking Science and Technology Competition as the “Second Classroom” Since the training goal of graduate students is very clear, that is, to cultivate scientific research talents, so the quality of academic graduate students training is closely related to the quality f innovative talents. The features of “second classroom” including flexibility and scalability should be more used to build the curriculum system. The system is collaborative between “second classroom” and “first classroom”, and it can cultivate talents with innovative ability. So, there are five dimensions and two hands. The first hand is “first classroom” that is general knowledge, professional, theoretical and practical. The second hand is “second classroom” that is community, project, subject competition. It is shown in Fig. 2. It can achieve the goal of cultivating scientific research talents and improving innovation ability. Fig. 2 Taking science and technology competition as “Second Classroom”
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The courses in “first classroom” are general knowledge innovation elective courses (Class one), self-study courses based on innovation and entrepreneurship suggestions (Class two), and professional innovation and entrepreneurship elective courses (Class three). Class one includes “Innovative Thinking Methods (middle and high level)”, “TRIZ Innovation Methods (middle and high level)” etc.. Class two includes “Innovation Management”, “Entrepreneurship Management” and “Entrepreneurship Training” etc.. Class three includes “Internet Business Model Innovation”, “Entrepreneurial Finance”, “Innovative Thinking and Design of Financial Products”, “Innovative Case Study of Technology-based Enterprise Management”. Furthermore, innovative thinking, innovative methods and other innovative knowledge is integrated into existing courses. Such as, we can hold professional course cluster seminars and add innovative application modules to existing relevant professional courses. So that each course is no longer separate and can be complement to each other. Then, connection of every courses are more closely. It is consequent improvement for students to learn innovative courses. The main form of “second class” is science and technology competition. Innovation is the soul of graduate students’ academic research. Nowadays, many graduate students are “innovating for the sake of innovation”. Because their articles cannot be published without “innovation”. They cannot write a qualified thesis without “innovation”, so that they cannot graduate successfully. As for that, they write many strange articles, which look like esoteric. Many strange methods and novel ideas appeared in their article. But in fact, there is no theoretical breakthrough at all. Sometimes there is even no practical application value. In fact, it is difficult to have theoretical breakthroughs at the postgraduate stage. Therefor, we believe that innovation at this stage should mainly reflect the practical application value. In the way of “project plus competition”, students can expand their knowledge of innovative thinking and methods. Then they can pay attention to the painful problems of society, discover and research these problems. So that graduate students can have the ability to discover and propose problems, which is also one of the academic innovation ability. Science and technology competitions can be used as a carrier of innovation and entrepreneurship practice training. We can set up graduate students innovation societies (short for GSI) throughout school. It can gather a group of graduate students who are interested in the improvement of innovation ability. Real projects from enterprises and society can be introduced to GSI. It will lead graduate students to pay attention to social problems and apply their learned knowledge to collect and analyze references. Then they can propose academic propositions, design research processes, and finally propose innovative solutions. Through participating in various innovative science and technology competitions, students’ knowledge in the classroom is deeply integrated with implemented projects. Their ability of applying knowledge to solve social problems innovative is exercised, because of writing competitions reports, project defense and technological innovation. Their achievements are affirmed to a certain extent by winning the competitions, which stimulates students’ learning motivation and enthusiasm. By carrying out “learning through competition” and “promoting learning through practice”, the “first classroom” and “second classroom” are connected to each other. The synergistic
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curriculum system for cultivating innovative talents is built. It provide a boost for students’ innovative ability. On the one hand, the ability of graduate students to solve practical problems is improved through participating science and technology competition. It can realize the practical application value. On the other hand, new methods to solve problems are found. It doesn’t matter if they solve new problems with new methods, or solve old problems with new methods, it reflects the improvement of graduate students’ academic innovation ability.
4 Conclusion During the three-year training period in Management School of BUU, graduate students will obtain the inspiration of innovative thinking, the accumulation of innovative method knowledge and the exercise of innovative solution to practical problems It is achieved two practices training. One of them is innovation and entrepreneurship education system training with the main line of “innovative method knowledge accumulation-innovative quality improvement-innovative ability cultivation”. The other training is innovative ability improvement with the integration of “curriculum + project + competition”. After three years, innovative ability of graduate students is effectively improved. It is a reserve of innovative and entrepreneurial application ability for their further scientific research. Acknowledgements Educational Teaching Research and Reform Project in Beijing Union University (JY2020Y007), Premium Funding Project for Academic Human Resources Development in Beijing Union University (BPHR2020CS05).
References 1. Shanshan, Z., Ying, S., Wei, L.: Influence of supportive guiding style on innovation ability of college graduate students: the chain mediating effect of team atmosphere and learning engagement. Chin. J. Health Psychol. 30(3), 448–452 (2022) 2. Haifeng, X.: Some thoughts on the cultivation of the innovation ability of academic postgraduates. Sci. Educ. Art. Coll. (8), 5–7 (2022) 3. Binru, P., Jianmin, G.: What are the factors that affect postgraduates’ innovation ability in the training process. Jiangsu High. Educ. (2), 74–81 (2022) 4. Xianwei, L., Wenjing, Y.: An integrative study of the factors influencing the innovation ability of Chinese graduate students: a MASEM analysis based on the scientific and technical human capital theory. Chongqing High. Educ. Res. 1–14 (2022) 5. Mengdi, Y.: Analysis on the factors affecting graduate students’ innovation ability: taking forestry engineering as an example. Sci. Educ. Art. Coll. (Upper Part of the Journal) (5), 4–7 6. Hongmei, S., Ying, L., Shuang, X.: An empirical study on the analysis of factors influencing the innovation ability of graduate students in higher education—based on a survey and research in five universities in Shaanxi Province. Policy Sci. Consult (Sci. Technol. Manag.) (1), 4–5 (2020)
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7. Guofang, X., Shulian, P.: Bottleneck problem and system construction of the cultivation of doctoral students’ innovation ability. Sci. Manag. Res. 37(5), 127–132 (2019) 8. Yuanji, Z.: The study of cultivation quality evaluation and promotion strategy of full-time professional master’s degree in sports. Beijing Sport University, no. 9 (2019) 9. Pei, Z.: Research on the training mode of finance and economics postgraduate students based on improving their innovation ability-take Shanxi University of finance and economics for example. Shanxi University of Finance and Economics, no. 1 (2018) 10. Jianzhu, B.: Exploring the system of cultivating innovation ability of graduate students. Continue Educ. Res. (6), 110–112 (2016) 11. Binru, P., Jianmin, G.: On the influence of supervisors’ guidance on graduate students’ innovative abilities. Acad. Deg. Grad. Educ. (4), 52–60 (2022) 12. Haifeng, X., Huaxiang, Y.: Research on the cultivation strategy of innovative ability of academic postgraduates. Educ. Forum (10), 13–16 (2022) 13. Xueyan, M., Yanan, W., Jiqin, R., Jing, Z.: Research on the cultivation of innovative ability of doctoral students under cross-disciplinary cultivation model. J. High. Educ. 8(6), 40–44 (2022)
Analyses and Suggestions on the Reform of Land Requisition Comparing with the Regimes of the US Qianxiang Yu
Abstract There are a lot of conflicts and arguments in land requisitions in China these years. Such as the right land requisition to be abused, the interests of farmers were harmed. The efficiency of factors allocation was lost too. It is undoubted that the current requisition system should be reformed eagerly. However, it is very complicated to improve the system. The Chinese governments have adopted the reforms of land requisition policies. There are quite different opinions on the reforms in China, such as the direction, approaches and steps of the reforms in this field. The US is the most developed country in the world. There are relatively advanced policies in this field too. The regime of land requisition of the US seems a bit ideal. Therefore, learning from the US land management experience, we can get beneficial inspiration and reference from the US. It will be helpful to the reform of China’s land requisition system. But we cannot copy the US system directly, because of the quite different background and tasks between the two countries. The Chinese regimes need focus on more issues than that in the US. For example, how to keep the balance between industrialization and urbanization and security of food, how to protect the environment and the land resource. My research will compare the land requisition policies between China and the US. Then it will analyze the advantages and disadvantages of the policies in China. Furthermore, it will compare the background and relative conditions between the two countries. Moreover, it will analyze what we should learn from the US, what is the right direction and approaches of the reforms in this field in China. At last, it will give some advice on the land requisition reform in China. For example, Chinese government should set a quota market, in which the national government sells the quota of converting agricultural land to commercial land. In this way, the market mechanism can be more efficient than that the local government monopolies the supply of land. The land requisition will not be abused too. Furthermore, the land requisition compensation standard will be increased. Therefore the owners can get obviously more compensation of land requisitions. It can help millions of farmers to become richer than before. Keywords Land requisitions · China · The US · Reform · Quota · Suggestion Q. Yu (B) Cardiff University, Cardiff, UK e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_36
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1 The Basic Land Regimes Between China and the US In China, there are two types of land ownership, the state owns all the urban land, while rural and suburban land is collectively owned by rural economic organizations, typically based on small villages, except for portions that are state-owned according to the law. Individual households have been regaining control over the practical use and profits of arable land. However, the use of rural land is mainly restricted to agricultural purposes, except for certain exceptions such as the construction of facilities for township-village enterprises (TVEs), building housing for collective members, and public facilities, as stated in articles 14, 44, and 59 of the Land Management Law (LML). According to the Land Management Law (LML), any changes in the use of agricultural land require government approval at the county or higher level, regardless of whether the land is owned by the state or a collective. The government aims to increase the amount and quality of cultivated land, which may involve reducing the amount of cultivated land at the provincial level. Land-use planning is implemented at all levels, from national to village. New construction is only allowed on stateowned land, except for facilities for TVEs, farmers’ housing, or public facilities, as stated in Article 43 of the LML. The state can expropriate or requisition land for public use but must compensate the owners in accordance with the law, as stated in Article 2 of the LML. This means that the local government monopolizes the supply of commercial land for construction in the land market. The occupied cultivated land needs to be supplemented with the same quantity and quality of cultivated land. The amount of cultivated land should not be reduced at any province. Unless the uses of the TVE facilities, farmers’ housing, or public facilities, the new construction should happen on state-owned land. (Article 43 of LML) The governments may requisition land for the public interest, and should make compensation for the land requisitioned in accordance with the law (Article 2 of LML). It means that the commercial land supply for construct is monopolized by the local government. At the same time, the scale of construction land of the lower administrative district is limited by the overall plan of land-use. The plans were developed and implemented by the central government or government at the higher level. The amount of new construction land will be issued each year by the central government or government at the higher level. Undoubtedly, no matter there is the public interest or not, almost all the new construct lands have to be requisitioned from the land owners in the rural by the local governments. Private ownership of land is the basic feature of the US. Although for private ownership of land, today the US still has about 40% of land for public use, such as the national parks, unused land, ecological reserves, public facilities and public welfare undertakings and private land cannot be used. Another 60% land is private land [1]. Various methods have been employed throughout the United States to prevent agricultural land from being used for other purposes. These techniques include policies in
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zoning ordinances, comprehensive plans, programs for the purchase and transfer of development rights, and ordinances protecting the right to farm, differential property tax assessment. According to Michael D. Smith and Deborah Giraud’s case study published in “Planning, Practice and Research” in November 2006, these are the most commonly used methods [2]. The laws in the United States that pertain to land acquisition are scattered throughout various legal provisions, such as the US Constitution, US property law, the US Public Rangeland Improvement Act, and the US Federal Land Policy Management Act [3]. The 14th amendment provides that “prohibited by it to the States, deprive any person of life, liberty, property, without due process of law.” The US federal land policy management Act provides the Government the rights to sell, Exchange, donation or is imposed by the acquisition of land or interests in land. All of the state constitutions also made corresponding to the right provisions in the United States.
2 Range of Requisition The Constitution of China’s Article 10 states that the government has the power to expropriate or requisition land for public use and must provide compensation for the land in accordance with the law [4]. This provision, similar to the Fifth Amendment in the US Constitution, limits the government’s authority to requisition land in two ways. Firstly, requisition can only be done in the public interest, but this term is not clearly defined in the relevant laws or the Constitution. Despite this requirement being repeated in the Civil Code and other statutes, there is no guidance on how to determine whether a requisition is genuinely in the public interest [5]. Recent practice indicates that the phrase “in the public interest” encompasses a broader meaning than “for public use”. In some areas of China, local governments requisition collective-owned land and promptly transfer the use rights to private developers or for-profit ventures in which the government entity may or may not hold an equity stake. It is challenging to distinguish between public and private use in the Chinese context, particularly as some local governments intend to requisition land as business entities in their own right to obtain revenues. Additionally, all new commercial lands are typically requisitioned from the collectives by the local governments, extending the range of requisition widely. In fact, as the regime stipulates that new construction must use state-owned land, unless for TVE facilities, farmers’ housing, or public facilities, all the new commercial lands are requisitioned from the collectives by the local governments—the range of requisition is extended widely. In the US, before the Kelo Decision, due to the Federal Court and the State courts have expanded the explain on “public”. It led to the range of public purposes very
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broad, such as built road, and street, and Park, and government office, and utilities, belongs to public range, to eliminate slum and decline area belongs to public range, to development economic also belongs to public range; not only requisition of land for public, and government institutions and public institutions, belongs to public range, even requisition of Land private entities occupy, use and enjoy also belong to the public purposes. In order to solve the problem of the rights excessively used, many states have introduced amendments to narrow the scope of public purpose. Many states amendment the rules to ban the requisition for economic development purposes, to ban the transfer of assets to a private, such as Michigan, Florida, To change the State of decline area criterion to narrow the scope of property can be imposed on, To ban the requisition of non-declining property from the decline area [6].
3 Compensation of Land Requisition The Chinese Constitution imposes two restrictions on state requisition of land, which are similar to those found in the US Constitution’s Fifth Amendment. The first restriction is that land requisition can only be done “in the public interest”, but this phrase is not clearly defined in the major documents governing land requisition or elsewhere in the Constitution. Recent practices suggest that the phrase encompasses broader purposes than “for public use”. In some areas, local governments requisition land and transfer its use rights to private developers or joint private–public for-profit ventures. The second constitutional limitation is that “just compensation” must be paid “in accordance with the law”. According to the Land Management Law (LML), three kinds of payments must be made when rural land is requisitioned: compensation for the land itself, resettlement assistance payments for the individuals occupying the land, and compensation for personal property attached to the land and for standing crops. The amount of each form of compensation is determined by provincial and municipal governments. Basic compensation payments should range from six to ten times the average value of the agricultural output of the requisitioned land for the three years prior to requisition, and resettlement assistance payments are determined by dividing the quantity of land requisitioned by the per-capita land allocation in that collective. The sum of basic compensation and resettlement assistance payments is capped at thirty times annual average output. However, the total compensation standard is generally lower than the market price of the land. Furthermore, the distribution of compensation is very complicated and uncertain. The basic land compensation and resettlement assistance are paid to the collective. Then the collective allocates part of the total compensation to the individual farmers whose losses the use right of the land. In the US, to the requisitioned person, the compensation standard is fair market value which refers to “the price of land that the owner voluntary and not was forced to sold should get of amount”. After the Kelo Decision, some States increased the amount of requisition compensation, impose a specific property,
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specific subject collection property or for the purposes of a particular collection property, compensation should be paid more than fair market value. For example, Indiana has a provision that states that if someone’s primary residence is taken for public use, they must be compensated fairly, and the compensation should be at least 150% of the fair market value of the property [6].
4 The Advantages and Disadvantages of China’s Land Requisition Regime We can find that from the materials above, the governments in both countries tend to enlarge the scope of the public use or purposes in land requisition. The court plays an important role in this field in the US and the power of the governments was limited by the courts. Comparing with the US, the governments of China participate in the land requisition system more deeply. The compensation standards are quite different. In China, the compensation for land owners is primarily based on the value of original use. It is much lower than the market price. Generally, it cannot be denied that China’s land requisition policy provided the basis for the development of economy, accumulated the industrialization and urbanization. It helped to reserve the arable land too. But it obviously is not good enough in efficiency, justice and equity. It has the following disadvantages: Firstly, the public interest is not clearly define of land requisition to abuse. Both the Constitution and the LML stipulate that, based on public interest, the governments can requisition land from the land owners. But what the public is was not made further specific provisions. This provision leads to the abuse of power easily and causes the scope of land requisition expanded. Secondly, the land requisition compensation standard is relatively low. This compensation standard is based on the agricultural land in terms of revenue. It has not accurately reflected the agricultural land into non-agricultural land of the expected benefits. Since there is enormous growth potential of land, the compensation standard is far less than the selling price of land. So the existing requisitioned land compensation standard is inadequate. In the first place, modern agriculture is no longer the traditional sense of agriculture. Modern urban agriculture output value of the land is no longer an ordinary food or vegetable comparable value. In the second place, the land requisition compensation standard does not include the value-added part of the land. On requisition of agricultural land, its land-use change will usually result in price increased, but the regime of land requisition compensation standard has not been considered in this part of the value-added factor. In the third place, the land requisition compensation standard has not reflected the land location and the local levels of economic development (Table 1).
466 Table 1 Rural land requisition compensation standards of Beijing [7]
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1. Classes of common cultivated land
Average compensation (yuan/ mu)
Paddy field
90,000
Dry field
53,000
Vegetable field
150,000
2. Classes of basic farmland
Average compensation (yuan/ mu)
Paddy field
99,000
Dry field
58,000
Vegetable field
156,000
3. Forest land and other agricultural land
133,800
4. Land for villages’ residences, roads, etc
136,000
5. Wasteland, idle land and other unused land
21,000
Thirdly, the compensation was unreasonable distributed. As the main body of rural collective land ownership is very confusing, leading to the allocation of compensation of land to a “village detention”. “Village detention” caused many social problems. The distribution of benefits among the farmers and the collective was unreasonable. The money will really reach farmers mainly include the ground attachments and young crops compensation fee, farmers eventually the actual income received is relevant little and uncertain. The collective retained quite a part of it. According to statistics, after the transfer of land requisition, famers only get about 8% of the land value added as compensation, village collective gets 27%, 65% as government income [8]. Fourthly, it to some extent lost the efficiency of factor allocation. As the local governments can expropriate land in a low price, they will intend to expropriate as more as possible. Then the government will sell the land at a relatively low price to attract investment or just leave it unused. It caused a large quantity of arable land was wasted. Fifthly, influence the stability of the society. Some farmers whose lands were expropriated would strive with the local government for a long time. The costs of resolving this kind of conflicts were rising heavily in the past years (Fig. 1).
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individul
8%
27% 65%
colletive government
Fig. 1 The distribution of land value-added income
5 Establish the Quota of Land Converted Trading Mechanism by Drawing Lessons from the Us System of Land Development Rights Since there are so many shortcomings in the current land requisition system, just as what had happened in the US, Chinese government and society have made efforts to improve it. The compensation standard of land requisitions has been rised constantly in the past years. Providing social insurance for the peasants whose land was expropriated is an obligation now. The steps of reform have been accumulated rapidly recently. It is undoubted that the current requisition system should be reformed eagerly, but how to improve the system is the key point. The situation is very complicated. The regime of land requisition in the US seems more ideal. Therefore, learning from the US land management experience, we can get beneficial inspiration and reference from the US. It will be helpful to the reform of China’s land requisition system. But we cannot copy the US system directly, because of the quite different background and tasks between the two countries. China and the US are similar in the size of land, but quite different in national conditions and land requisition regimes. China is the largest developing country. The US is the most developed country. Land resource of China is scarcer than that of the US. For example, based on the 2021 National Economic and Social Development Statistics Bulletin (issued by the National Bureau of Statistics of China, 2022, 2, 28), surface area of China is 9596961 km2 . Population of China is about 1,412,600,000, population density is 146.8 km2 . Surface area of the US is 9629091 km2 . Population of the US is about 332,400,000 in 2020, population density is 34.4 km2 (issued by the United States Census Bureau, 2022, 1, 1). The area of cultivated farmland is 2,030,770,000 acres in China [9]. Crop land of the US is about 367.9 million acers (2012 the US Statistics Yearbook). So Chinese per capita land area is only about one fourth of that of USA, Chinese per capita area of cultivated farmland is only about one eighth of that of the US.
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The quality of cultivated land is low and the reserve resources of cultivated land is scarce too. 5.649 million ha (84.74 million acres) of arable land is located in the forest areas of the Northeast, Northwest, grasslands and river Lakes flood control range, 4.314 million ha (64.71 million acres) of cultivated land in steep slope of more than 25° [9]. Security of food does be the most important strategic issue in China. Crops output is less than the amount of consumption. China have to import huge quantity of oil and crops. The balance of supply and demand is tight and fragile. The certain amount of arable land is necessary basis to gain relatively adequate crops output. China is in the process of industrialization and urbanization, non-agricultural land demand is huge in the coming decades. Since the arable land decreases rapidly in the past decades. The government had to exploit new arable land to keep the amount of arable land. These years, the quantity of arable land has closely approached the deadline and the reserved resources of arable land is very limited. If the government don’t restrict firmly the converting from arable land to construct land, the decreasing of arable land will cause a huge gap between the demand and supply of crops. In this case, the import of crops from China will be too much to be supported by the international market. Moreover, the arable land is important to keep the balance of the ecology environment too. Though the US is relatively adequate in land resource, a wide variety of policies have been adopted to stem the loss of agricultural land to other uses. The Chinese system must focus on more issues than that in the US, such as how to keep the balance between large demand of land in the stage of industrialization and urbanization and security of food, how to protect the environment and the land resource. So Chinese government must control the loss of agricultural land more restrictedly than that in the US. Furthermore, adopting the compensation standard as the market price is not rational in China. It is not good enough for promoting the equity and equality in the allocation of the benefits in land exploitation as the US does. The market price of land is composed by two parts: one is agricultural value which is “land itself by value”; the plus is value-added part, which is caused by the use converted from agriculture to commerce and natural value-added. Natural value-added means that economic activities cause external returns on land, such as the infrastructure that was invested by the government and so on. The quotas of Land converted from agriculture to commerce should be paid by the land user. The value of land mainly depends on its use, location and peripheral matching. Only a little part of land can be converted from agriculture to commerce. It means that only a few of farmers have the opportunities to convert the use of their land, and the opportunities given by the decision of the government. So the compensation price for the land owner should be just part of the market price. Because the government investment in supporting facilities and the interests of other farmers who have not been given opportunities for land conversion also need to be taken into account. The value of land converting is similar to the land development rights in the US. As land is quite scarce in China, the market price of land is very high. That means if
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Chinese governments want to buy the land development rights from the land owners, they will pay a huge amount of money. It should be many times more than that of the US. Evidently, Chinese governments cannot afford it. Meanwhile, though the per capita GDP of China is much less than that of the US, but the average price of land is more expensive. Comparing with the low income of the farmers, the compensation will obviously increase the assets of the farmer’ family. So considering the historical continuity, if the compensation jumps up many times higher than that before the reform, what is the reflection of the farmers whose land was expropriated before, especially in recent years? If the governments pay the compensation as high as the market price, they will encounter the dilemma that, how to persuade the land owners whose land had be requisitioned before, how to persuade the land owners whose land is permitted to be exploited. On the other hand, refers to the “market price” and “compensation for what the owner’s loss” principles in the US, the loss of the land owner in China is the agricultural use right and the potential opportunities of converting to commercial land. So the compensation price should base on the same principles. It should be all the agricultural value and part of the appreciation. Refers to the land development rights in the US, Chinese government shall set a quotas market, in which the national government sells the quotas of converting agricultural land to commercial land. In accordance with plans and related provisions, the central government limits the number of annual quotas for each administrative region. If the commercial land user want to buy a piece of land from the owner, he must buy the relevant quotas from the market. So the market price can be formed through the transaction between the buyers and the sellers. The land owner should pay the progressive taxes for the revenue. The income by selling the quotas should be allocated in two ways: first, to reclaim new arable land in order to maintain the total amount of arable land stable; second, use to all of the farmers in China. If the national government sets the quotas market, it will take the following advantage: Firstly, the national government sells the quotas of converting agricultural land to commercial land. In this way, the market mechanism can be more efficient than that the local government monopolies the supply of land. The market will find the true price of the quotas. Since that it can find the true value of the land too. Secondly, basing on the true value of the land, requisition compensation standard will be increased. Therefore the owners can get obviously more compensation of land requisitions. It can help millions of farmers to become richer than before. Thirdly, because of the true price of land was found and requisition compensation standard was increased, the local governments will not intend to abuse the power of land requisition too. For example, a local government set up a market for quotations on December 4, 2008. By the end of 2010, the Chongqing Rural Land Exchange conducted 85 transactions, a total of 1,200 ha. The average price of the quotas is 1,554,000 yuan per ha. And the compensation standard for land requisition in the same period is 22,500 yuan [z]. (Wei Feng, Zheng Yi, Liu Fuwen, “Study on the market allocation mechanism of
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rural collective land of construction in China”, the eighth issue, Modern agricultural technology, 2010). It had obviously increased the price of land. Meanwhile, the public interest scope should be clearly defined and restricted. That should be expressed in the way combined by principle provision and specific cases. The keen point is that the requisition compensation standard must be higher than the market price.
Improve the land requisition process, safeguard the legitimate rights and interests of land owners. Land requisition compensation standard should base on the revenue of commercial land transaction. The compensation should consist by the following parts: first, full of the value of the original use; second, the personal property attached to the land and the value of standing crops. A fixed or diminishing rate of land appreciation should be adopted. The allocation of compensation should be clarified between the collective and individuals too. In this way, the efficiency of land resource allocation and social equality can be gained. And the reform can be implemented easily and smoothly.
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References 1. Xiaohong, Y.: United States law of land management system. The second issue, Shaanxi land and resources magazine (2011) 2. Smith, M.D., Giraud, D.: Traditional land-use planning regulation and agricultural land conservation: a case study from the US. Planning, Practice Res. 21(4), 407 (2006) 3. Yin, S.: Power vs checks and balances—the collective land requisition and compensation system needs to be reformed urgently. The South Real Estate 337, (2013) 4. The Constitution of China: Adopted at the Fifth National People’s Congress 12(4) (1982) 5. The Civil Code: Adopted at the Third session of the thirteenth National People’s Congress 5(28) (2020) 6. Aihua, Z.: The new development of land requisition law in the United States and its inspiration to China. The Modern Jurisprudence 4 (2013) 7. Rural land requisition compensation standards of Beijing: Issued by the Beijing Municipal Government 7(6) (2019) 8. Yaonan, Z.: Thoughts on land requisition system in China. Res. Discuss. 6 (2007) 9. Communique on the outcome of the second national land survey data: The Ministry of land and resources of China, National Bureau of statistics of China 12(30) (2013)
The Market Effectiveness of China’s Shipping Derivatives Under the Background of Financial Support for Shipping Logistics Siyuan Wang and Xiaojie Liang
Abstract The development of shipping logistics is inseparable from financial support. Shipping financial derivatives is one of the important means to avoid risks in the shipping market. China shipping derivatives trading starts relatively late. This paper studies the market effectiveness of China coastal coal shipping derivatives with Wildbootstrap variance ratio test and other empirical method and provides a theoretical reference for shipping companies. The result shows the China coastal coal shipping derivatives market is not efficient in the past six years, but it can achieve weak form efficiency in the long run. Keywords Inancial support · Shipping logistics · Shipping financial derivatives · Market effectiveness
1 Introduction The outbreak of COVID-19 has had a great impact on all walks of life in China. The Chinese government has increased its financial support for shipping logistics to ensuring the smooth circulation of the national economy. In this background, the development of shipping finance has attracted more and more attention. Shipping financial derivatives are one of the important contents of shipping finance. It plays an important role in helping shipping enterprises avoid market risks. In the past three years, the freight rate fluctuation in the domestic dry bulk shipping market has increased significantly. After experiencing a sharp rise in 2020, China’s coastal coal freight index began to decline rapidly in October 2020. Relevant enterprises of dry bulk transportation need to take appropriate measures to control the risk of freight rate fluctuation, and shipping derivatives have also received more attention. S. Wang · X. Liang (B) China Academy of Transportation Science, Beijing, China e-mail: [email protected] S. Wang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_37
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Shipping derivatives are financial derivatives with shipping price or capacity as the subject matter, which have the functions of hedging, price discovery, and investment arbitrage. Shipping companies and other market participants control the expected freight rate within a certain range through derivatives transactions to avoid huge losses caused by sharp price fluctuations. Compared with international shipping derivatives, China’s derivatives market started late, the trading mode is not mature, and the market participation is relatively low. China’s shipping derivatives are mainly launched and listed by Shanghai Shipping Tariff Co., Ltd., and provide a trading platform for traders. Shanghai Shipping Tariff Co., Ltd. provides electronic derivatives trading system to provide services for all types of investors. Shipping derivatives listed earlier include container freight futures and China coastal coal freight futures, both of which entered the market in 2011. The launch of coastal coal shipping derivatives provides a way for relevant enterprises to avoid risks. Under the background of China’s increasing financial industry to help the development of shipping logistics, studying the market features of China’s shipping derivatives can not only provide theoretical reference for shipping enterprises to avoid market risks, but also guide financial investors to participate in the shipping financial market more actively and normatively, promote the healthy and stable development of China’s shipping derivatives market, and provide assistance for the high-quality development of China’s shipping logistics industry. This paper takes China’s shipping financial derivatives as the research object, uses empirical models to analyze the market effectiveness of China’s coastal coal shipping derivatives, more carefully depicts the market features of shipping derivatives, and provides theoretical reference for investors to participate in financial derivatives transactions, and provides technical support for relevant management departments to promote the high-quality development of derivatives market. At present, there are still some limitations in this research work, which are mainly reflected in: the current research results on the features of China’s existing shipping derivatives market are relatively limited, and the acquisition of derivatives related transaction information is difficult, and the analysis of the market features of China’s existing shipping derivatives may not be comprehensive and in-depth enough. Other market features of China’s shipping derivatives, such as price fluctuation features and hedging effect, will be the next research direction.
2 Literature Review For the research scope of market effectiveness, the narrow sense refers to the effectiveness of futures prices reflecting market information; In a broad sense, it also includes the effectiveness of the futures market in realizing its functions, that is, functional effectiveness. Therefore, the study of market effectiveness can be divided into information effectiveness and functional effectiveness. Regarding the study of information effectiveness, early method of judging whether the market is effective by analyzing the law of change in market price, and using
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statistical methods to study whether there are significant laws [1]. Bariviera and FontFerrer [2] studied the information effectiveness of the gold market from 1968 to 2017, and found that its market effectiveness is susceptible to major economic activities, and price fluctuations are highly persistent. The informational effectiveness of the bitcoin spot market is examined by evaluating the predictive power of mechanical trading rules designed to exploit price continuation. It shows that Bitcoin futures have altered the state of informational efficiency in bitcoin prices [3]. Xingzhi [4] based on the actual operation of the Shanghai stock market, studied the efficiency of Shanghai stock market and put forward relevant suggestions to improve efficiency of the stock market. Dandan et al. [5] took the prices of seven mainstream digital currencies 24 h of each day as the research object to test the market effectiveness and found that Bitcoin and Litecoin achieved weak form efficiency in the entire sample. About functional effectiveness, It is analyzed India’s bulk commodity index and its corresponding futures market through the VECM model and Grager causality test, and believed that the futures market had the ability of price guidance [6]. The indicator is calculated for the period 1975–2015 and for several subperiods, to test functional effectiveness, before and after the 2007–2008 spikes in the prices of agricultural commodities [7]. Ju Ronghua [8] analyzed the changes in the functional effectiveness of the six Chinese agricultural futures markets and the results suggested that the longer it takes for an agricultural futures contract to reach maturity, the lower the functional efficiency of its market. Xinqi [9] found that China’s crude oil futures market failed to effectively transmit information to the crude oil spot market, the international crude oil futures market, and the crude oil transportation market, and believed that the effectiveness of China’s crude oil futures price discovery function was insufficient [10–12]. In summary, the existing literatures have studied the market functions of China’s shipping derivatives such as price discovery or hedging, but few have analyzed the market features of shipping financial derivatives from the perspective of market effectiveness. China’s coastal coal shipping derivatives are less analyzed as research subjects. Therefore, the innovation of this paper is reflected in the empirical analysis of the effectiveness of China’s coastal coal shipping derivatives, and then provides a theoretical reference for relevant departments to formulate policies to promote the healthy and stable development of shipping derivatives.
3 Research Method The theory of market effectiveness is an important part of the basis of modern financial theory, which recognizes that a market is effective when it can reflect all the information of the market in timely adequate manner. Efficient markets are the end result of rational investor behavior that seeks to maximize profits and it is also a sign that the market has reached maturity and stability. In the research of market effectiveness, different methods are used to conduct empirical research on information effectiveness and functional effectiveness.
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3.1 Information Effectiveness Random walk test is one of the methods to study the informational effectiveness. The random walk tests whether the historical price is predictable. When the price is a random walk, it is impossible to acknowledge the price trend in the future through the previous ones and the market is efficient. Otherwise, it is considered that the market has not reached information efficiency. According to the strictness degree of the random term assumptions, the models are mainly divided into three categories, the most stringent random walk model, the looser random walk model and the most basic random walk model [13–17]. The most rigorous random walk model, in which the asset return rate is independent and identically distributed with mean of zero, and its efficiency is the highest. But it is usually difficult to happen in the actual market. The looser model, with the assumption that the distribution of the return sequence is independent, and historical prices are not predictive. The most basic model requires only that the return sequence is an uncorrelated distribution, and the market has the weakest efficiency. (1) Runs Test In the random walk test models, the run test and variance test have obvious advantages. The runs test is a nonparametric test, which focuses on the positive and negative directions of the sample series, rather than the size of the sample number value. Runs test can overcome the disadvantage that individual large value samples affect the test results, making the results closer to the actual situation. The price increment ΔP = Pt − Pt - 1 , ΔP > 0 is a positive, ΔP < 0 is a negative. ΔP = 0 is a zero run n1 , n2 and n3 represent the number of positive, negative, and zero runs respectively. The total of runs n = n 1 + n 2 + n 3 , the expected number E(R) and its standard deviation σR are E(R) = n + 1 −
3 E n2 i
i=1
σR =
n
| | 3 | |E 3 E | 3 2 E | ni n i2 + n(n + 1) − 2n n i2 − n 3 / i=1 i=1 i=1 n 2 (n − 1)
(1)
(2)
and the Z-statistic to test the hypothesis is Z=
R − E(R) σR
(3)
When the test result is that at a certain confidence level, the Z-statistic follows the standard normal distribution N(0, 1). Then the assumption that the price change is a random walk is accepted and believed that the market has reached weak-form
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efficiency. Conversely, it indicates that the sample does not meet the random walk, and the price changes can be predicted. Therefore the market has not reached information efficiency. (2) Variance ratio test The variance ratio test was proposed by A.W. Lo and A.C. MacKinlay in 1988, Its basic principle is to judge whether the sample series meet its increment with uncorrelated features by testing the additive features based on the additive features of the variance of the random walk. Computational statistics of difference ratio test under different assumptions, the Z(q) and Z’ (q) expressions are as follows: V ar (Pt+q − Pt ) q V ar (Pt+q − Pt )
V R(q) =
(4)
Z(q) =
V R(q) − 1 ∼ N (0, 1) √ 0(q)
(5)
Z'(q) =
V R(q) − 1 ∼ N (0, 1) √ 0'(q)
(6)
where VR(q) represents the variance ratio, 0(q), 0' (q) respectively represent the asymptotic statistics with the same and different variance. The model is constructed as follows: 2(2q − 1)(q − 1) 3q(mq)
(7)
| q−1 | E 2(q − j ) 2 σ ( j) q j=1
(8)
0(q) = 0' (q) = Enq σ (j) =
t= j+1
( )2 (Pt − Pt−1 − μ)2 Pt− j − Pt−− j−1 − μ | | Enq 2 2 t= j+1 (Pt − Pt−1 − μ)
(9)
3.2 Functional Effectiveness The market functions of futures mainly include price discovery, hedging and speculative arbitrage. Different futures markets may not able to utilize these functions in actual transactions. The research on functional effectiveness mainly measures the role of futures market in price discovery, hedging and speculative arbitrage, as well as the efficiency of its function. Cointegration test is one of the important methods to study the functional effectiveness. Because the research of the asset price needs to analyze a large number of
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financial time series, most of which usually have the features of high kutrosis and fat tailed, non-stationary and so on. In empirical research, these series can’t be directly analyzed by regression, otherwise, there will be many problems such as pseudo regression. It is need to be studied by a method different from ordinary regression analysis. Therefore, Engle and Granger put forward the concept of cointegration. Cointegration refers to an equilibrium relationship between several variables, which is long-term in time and tends to be stable in the long run. When studying financial time series, we can analyze the possible long-term relationship between research objects by studying their cointegration relationship, so as to avoid the impact of non-stationary series. There is a k-dimensional series vector yt = (y1t , y2t , . . . , ynt ), , if it satisfies: (1) yt ∼ I (d), i = 1, 2, . . . , k. (2) There is a non-zero vector β = (β1 , β2 , . . . βn ) that the following formula holds: β ' yt =
k E
βt yit ∼ I (d − b), 0 < b ≤ d
(10)
i=1
Then there is a co-integration relationship between the components of yt , denoted as yt ∼ C I (d, b). β is the cointegration vector. In the cointegration test, the stationarity of the sequence needs to be tested first. By measure the value of the statistics and the critical value, it is analyzed the possible cointegration between the sequences. Since there may be multiple cointegration, repeated experiments may be required [18–20].
4 Empirical Analysis This paper takes China coastal coal shipping derivatives as the research object and selects the daily closing prices from June 2014 to June 2020. The sample is divided into three sub intervals in two-year units to study the changes of its market effectiveness. All data are from Shanghai Shipping Exchange. The three groups of sample are taken the natural logarithm to obtain the variables R1 , R2 , R3 . The overall sample of three-year is Rt . Table 1 presents basic statistical features of the variables. The standard error of the R2 is the largest, indicating the greater fluctuations in the second two years. The skewness are all greater than zero. The right skew shows the price with a strong rising. The four kurtoses are higher than the normal distribution, representing the characteristic of spikes. At the 1% confidence level, it is considered that all sample series do not follow the normal distribution.
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Table 1 Basic statistical features of variables Variable
Mean
Std.Dev
Skewness
Kurtosis
J-B statistics
R1
2.932
0.140
0.939
4.145
48.376a
R2
3.100
0.260
0.041
1.735
15.126a
R3
3.227
0.244
0.184
2.579
40.477a
Rt
3.116
0.249
0.142
1.974
33.237a
Note a indicate the significance at confidence levels of 1%
4.1 Information Effectiveness Text First-order difference is carried out on the R1 , R2 , R3 and Rt to get the rate of return variable ΔR1 , ΔR2 , ΔR3 and ΔRt . Firstly, the autocorrelation test is carried out to determine whether the variables meet the random walk state. The null hypothesis of autocorrelation test is that there is no correlation between the series. The accompanying probabilities of the autocorrelation test results of ΔR2 and ΔR3 are basically higher than 10%, indicating that the null hypothesis can’t be rejected, that is, the incremental series is irrelevant. Therefore, coastal coal derivatives meet the random walk state after June 2016 while prices from 2014 to 2016 and the overall six years do not. (1) Runs Test The results of the autocorrelation test are easily affected by individual extreme data, so the run test is further used to analyze the effectiveness of coastal coal derivatives. The null hypothesis of the run test is that there is no correlation between the samples and it meets the random walk model. The results of the run tests are shown in Table 2. With the median as the test value, at a 95% confidence interval, the Z statistic of the ΔR2 and ΔR3 series falls between (−1.96, 1.96), and the accompanying probability is significantly higher than 5%, indicating that the changes of the two series are not correlated and meet to the random walk state. While the absolute value of the Z values corresponding to the ΔR1 and the ΔRt are larger and the P-values are closed to zero, showing that the variables are correlated in the run test. Table 2 Results of the runs test Variable
Median test
Runs
Z Statistic
Pvalue
Mean Test
Runs
Z Statistic
Pvalue
ΔR1
0.0000
96
-3.176
0.001
0.0006
93
-3.560
0.000
ΔR2
0.0000
107
-0.868
0.385
0.0034
93
-2.651
0.008
ΔR3
0.0000
107
-1.037
0.149
0.0031
96
-2.788
0.001
ΔRt
0.0000
278
-4.272
0.009
0.0003
274
-5.195
0.000
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In comparison, when taking the mean value as the test value, the absolute values of the Z value of the four sample series are higher than 1.96, and the P-values are all less than 5%. The original hypothesis is rejected, and it is considered that there is a certain trend in the price change, that is, the return series within six years have not reach the random walk state. (2) Wild bootstrap variance ratio test Becauce of different results in the run tests, the wild bootstrap variance ratio test is used for further analysis. The paper uses the Rademacher distribution automatically selected by the model. The number of repetitions is 1000, and the lag order is selected (2, 4, 8, 16). The test results are shown in Table 3. In Table 3, the P-values of three samples in different lag periods are all less than 5%, so the null hypothesis is rejected. That is, the derivatives in the three periods do not satisfy the random walk process. The Table 4 shows similar results. The P-value of ΔRt in different lag periods of is less than 5%, again indicating that the coastal coal derivatives market is not efficient from 2014 to 2020. Based on the above tests, it can be seen that the autocorrelation test and runs test with the median value as the test value show that after the second half of 2016, the price of China’s coastal coal derivatives meets the random walk, but the overall market price can’t meet the random walk within three years, and the market has not reached weak efficiency. Furtherly, the run test with the mean value has the same results with the Wild bootstrap variance ratio test, indicating that the derivatives in each the three partitions and the entire interval do not follow a random walk. Therefore, through the analysis results of various models, it is concluded that China’s coastal coal derivatives have not reached the market efficiency within six years. Table 3 Results of the wild bootstrap variance ratio test of ΔR1 , ΔR2 , ΔR3 Lag ΔR1
ΔR2
ΔR3
Variance Z P-value Variance Z P-value Variance Z P-value ratio Statistic Statistic ratio Statistic Ratio 2
0.46
−4.48
0.0000
0.43
−3.75
0.0000
0.42
−2.39
0.0000
4
0.27
−3.66
0.0010
0.25
−3.08
0.0020
0.26
−2.97
0.0016
8
0.18
−3.01
0.0080
0.13
−2.64
0.0110
0.14
−2.31
0.0220
16
0.09
−2.58
0.0280
0.07
−2.16
0.0340
0.08
−2.29
0.0110
Table 4 Results of the wild bootstrap variance ratio test of ΔRt
Lag
Variance ratio
Z statistic
P-value
2
0.4592
−4.0425
0.0000
4
0.2623
−3.5796
0.0000
8
0.1393
−3.3379
0.0000
16
0.0686
−2.9997
0.0020
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Table 5 Descriptive statistics of the variables Variable
Mean
Std.Dev
Skewness
Kurtosis
J-B statistics
St
3.1461
0.2597
0.7315
2.9743
11.6248a
Ft
3.1120
0.2621
0.1761
1.9051
6.8898a
Note a indicate the significance at confidence levels of 1%
4.2 Functional Effectiveness Test (1) Co-integration test The co-integration test is used to study the functional effectiveness by analyzing the long-term relationship between derivatives and spots. Select the China Coastal Coal Freight Index (CBCFI) from June 2014 to June 2020 as the spot price (St ), and the main contract of the China Coastal Coal Derivatives as the derivative price (Ft ), with the same sample period. Since the freight rate of the CBCFI is weekly data, the trading price of derivatives is also from weekly closing price. The data are selected from Shanghai Shipping Freight Trading Co., Ltd. The descriptive statistics of the variables are shown in Table 5. In Table 5, the average value of Ft is less than St , indicating that the derivatives has a premium. The standard errors of the two variables are relatively close, showing that their vibration amplitudes are similar. They are relatively unstable since their kurtosis values are less than 3 and the spot prices are more likely to have the characteristics of “peak and thick tail”. At the 1% confidence level, it is considered that there are no variables obeying the normal distribution. The ADF test is first used for the stationarity of samples. The results show that they are not stable at the 5% confidence level, and the first-order difference are both stable at the 1% confidence level. Therefore, the spots and derivatives are both first-order single-integration sequences, which can be tested for co-integration. The VAR model is established to select the optimal lag period which is second order from the indicators. First order is selected in the co-integration test since it is recorded as the lag order calculated by the VAR model to minus 1. Results of Johansen characteristic root trace test are shown in Table 6.
Table 6 Results of Johansen co-integration test
CE
Trace statistic
5% Critical value
Prob
None
21.3151
15.4947
0.0059
At most 1
3.1557
3.8415
0.0757
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Table 7 Cointegration coefficient of St and Ft
St
Ft
Cointegration coefficient
−4.246
3.562
Standardized cointegration coefficient
1.000
−0.839
Table 8 Results of the VECM model
ΔSt ecmt-1 C
ΔFt
Coefficient
−0.0957
0.0828
T statistic
[−2.5539]
[3.4412]
Coefficient
6.66E-05
−0.0002
T statistic
[0.0075]
[−0.0395]
Table 6 shows that, under the signification level of 5%, there is a co-integration relationship between spots and derivatives. Table 7 presents the standardized cointegration coefficient. The equation of St and Ft are presented in Formula (11) St = 1.631 + 0.487Ft + εt
(11)
where the P-value shows that the constant term and the coefficient are both significant. The F statistic and the P-value show that the estimation is good, indicating the two variables have a long-term relationship. The results of WALD test show that the Fstatistic and Chi-square statistic are both closed to zero. Therefore, in the equation St = α + β · Ft + εt , α = 0 and β = 1 cannot be rejected, implying that the coastal coal shipping derivatives market is efficiency in the long run. (2) VECM model VECM model is established for St and Ft , the optimal lag order is the same as the cointegration test, and the estimated results of VECM equation parameters are shown in Table 8. ecmt-1 is an error correction term, ΔSt error correction term coefficient is -0.0957 but not significant, and it is believed that it may have a negative guiding effect on the futures price, and its force is weak. The error correction coefficient of ΔFt is 0.0828 and significant, indicating that it has a positive guiding effect on the spot price and has a certain degree of strength. It is believed that China’s coastal coal derivatives market can achieve weak from efficiency in the long term.
5 Conclusions and Recommendations The efficient market can timely and fully reflect and help investors to obtain market information in a timely manner. This paper establishes models to study the information effectiveness of China’s coastal coal shipping derivatives. The results show that
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through the autocorrelation test and the runs test with the median value as the test value, the derivative price meets the random walk after the second half of 2016, but the overall market price can’t meet the random walk within six years, and the market can’t reach weak form efficiency; The runs test results with the mean value as the test value are the same as the Wild bootstrap variance ratio test, which shows that the derivatives revenue series between the three partitions and the entire sample interval can’t meet the random walk process, so it is considered that the derivatives have not reached the market efficiency within six years. In terms of functional effectiveness, results of co-integration tests and VECM models show that there is a long-term equilibrium relationship between the spots and the derivatives in China coastal coal shipping market. The spot prices have a relatively weak negative guiding effect while derivative prices have a positive effect on spots. Therefore, it is believed that China’s coastal coal shipping derivatives market can be efficient in the long term. Based on these research results, we put forward the following countermeasures and suggestions to improve the effectiveness of China’s coastal coal shipping derivatives: First, attract international investors and expand trading volume. The daily trading volume of China’s coastal coal shipping derivatives is low, which is an important reason to hinder the market effectiveness. Therefore, Shanghai Shipping Exchange and relevant departments need to improve the information exchange platform to attract more investors at home and abroad to participate. First of all, we should not only expand publicity within the shipping and related industries, but also release information to the financial industry and various capital markets, and promote it to large enterprises and institutions to increase investors’ understanding of the product. Introduce the latest situation of coastal coal transportation industry in a timely manner through the public websites and various new media. Secondly, we should promote the opening of RMB to the outside world, improve the trading rules for foreign investors to participate in China’s financial market, promote the docking of domestic trading panels with foreign countries, and attract large foreign enterprises to enter the coastal coal shipping derivatives market. Second, optimize the structure of investors and increase market liquidity. Investors in the market mainly include legal person investors and natural person investors. Legal person investors represent an enterprise, whose investment generally has a relatively mature decision-making process and strong prediction and evaluation of market risks. The investment decision-making ability of natural person investors may be inferior to enterprise investors. Therefore, China’s coastal coal shipping derivatives market should attract more enterprise investors, including potential domestic and foreign participants. Shanghai Shipping Exchange and relevant departments can encourage and guide professional investment institutions to participate in transactions, improve market liquidity, so that derivatives prices can timely and effectively reflect the transportation market situation, and then achieve market effectiveness. Third, strengthen supervision and prevent market risks. China’s shipping derivatives have entered the market for a relatively short time, and the market operation and transaction management are not perfect, so we need to pay more attention to the
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control of market risk. First of all, we should fully implement the national management measures on financial transactions, strengthen the management and testing of hardware facilities, strengthen the training of employees’ professional operation and ethics, improve the rules and regulations of the exchange, and improve the ability of self-regulation. Secondly, we should actively learn the supervision concepts and methods of foreign financial markets, and combine the actual situation of our country, selectively apply them to the daily supervision of the shipping derivatives market, and improve the emergency measures in case of accidents. In addition, the current coastal coal shipping derivatives market is relatively small, which is more prone to insider trading, market manipulation and other illegal acts. Therefore, the management of trading accounts and irregular inspections need to be strengthened.
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18. Sizhe, L.: The study of Shanghai fueloil futures market efficiency And its international pricing power. Hunan Normal University (2010) 19. Xutong, W., Dongwu, N.: Weak form efficiency test of China’s gold futures market based on multivariate linear regression model. Modern Econ. Inf. (22), 251–253 (2016) 20. Jiajia, Y., Xinran, Z.: A comparative study on the effectiveness of offshore and onshore RMB exchange rate market—a test of RMB’s entry into SDR. Financ. Theory Prac. (09), 30–34 (2017)
Service Supply Chain Optimization of Community Elderly Care-A Case Study in Beijing Yongkang Liu, Xufan Zhang, and Yi Zhang
Abstract China’s community-based aged care services are under immense pressure due to an aging population. Increasing the capacity of community aged care services is helpful for preserving older citizens’ lives and fostering steady societal advancement. Many academics have studied how to enhance the supply chain for elderly care services because China’s community care sector is still in its infancy. However, there is a gap in the research on improving the supply chain and ultimately the improve the elderly services by enhancing the satisfaction from the elder. Therefore, in order to fill this gap, this paper looks at elderly satisfaction. This paper uses community D in Beijing as the research object and uses the kano model and AHP analysis to identify optimum service indicators to improve the service supply chain and enhance service capacity in community D through improving the service supply chain. This article provides a new way for improving the capacity of community elderly care services by optimizing the service supply chain in terms of enhancing the satisfaction of the elderly. Keywords Service supply chain · Community elderly care · The kano model · The AHP model
Y. Liu · X. Zhang (B) · Y. Zhang Beijing Wuzi University, Beijing, China e-mail: [email protected] Y. Liu e-mail: [email protected] Y. Zhang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_38
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1 Introduction 1.1 Background of Study and questions raised As China enters a new era of economic development, the country’s ageing population continues to rise due to declining fertility rates and increasing life expectancy. According to chinanews.com (2018), as shown in the Fig. 1. We can see the number of older people increased of different age groups in China every five years since 2010, with these figures showing an increasing trend that will continue until 2050. China has increasingly established various service models to help relieve the mounting burden on aged services and the current elder care approaches are divided into four categories by Li and Chang (2022): home care, community care, institutional care, and new ecological care [1]. Well, the majority of the elderly are tough to cover with home care, institutional care, and new ecology. For example, home care, the elder cannot receive professional assistance at home. For institutional care, most of the family couldn’t afford the cost, and new ecological care are not widely popular, therefore, the community care should take more responsibility. According to the green paper China Social Security Development Report (2014), community-based care is the preferred way of ageing in the future. The “community” will play the role of a link between families and institutions, compensate for the weakened function of family care and the lack of resources of social institutions for the elderly, and enable the elderly to enjoy their old age in their own familiar community environment. This is in line with China’s national conditions and traditional culture, and is also the future development trend of the elderly care method.
Fig. 1 The development trend of the elderly population over 60 years old in China (100 million)
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According to Wang and Jia (2017), the original and currently most prevalent model of community care is the existence of aged care facilities in regular communities. The second is the retirement community model, where elderly people move in; this is comparable to the old-age home concept. The act of elderly people buying property as part of a “retirement + real estate” development [2]. We conduct our research on community D since all of the community care stations in Beijing are members of the first model and can be represented by the community D service model. However, the development of community-based elderly care in China has started late and is facing greater pressure on elderly care services. The current problems of community-based elderly care services are summarized as follows: (1) It is difficult to meet the diversified elderly care needs of the elderly, and the elder are not satisfy with the community care service. The capability of community elderly care services is still relatively low and the development is unbalanced, making it difficult to meet the needs of the elderly [3, 4] (Yin, 2016; Li, 2021). (2) The relevant rule of law construction is not perfect, the relevant standards for elderly care services are not unified, and the supervision of the provision of services has not been described in detail [5] (Li and Zhang, 2021; Wang and Jia, 2017). (3) The level of service provided from the staff is low and the quality of service cannot be guaranteed, and many elderly people are skeptical about the level of elderly care services in the community. The reward and punishment system, regulatory mechanism, development goals and many other aspects are still unorganized and the relevant legal system should be further improved [6, 7, 8] (Shi, 2022; Yang, 2021; Li, 2021).
1.2 Purpose and Significance of Study Purpose: Through the community D service station, we discovered in this research that its development commenced late. And using data from research on the elderly, we built a service supply chain and discovered that it wasn’t able to satisfactorily serve their needs. Therefore, this article enhanced the supply chain framework by raising older satisfaction in order to boost the elderly service capability of the community D station. Theoretical significance (a) Expanding the use of service supply chain in elderly care. (b) We improved the community D elderly care service supply chain framework and give some advice. Practical significance (a) We optimal the supply chain by integrating effective strategies to increase older people’s satisfaction, enhancing feedback and evaluation procedures, adding management systems, training the service workers.
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(b) This article enhances the capacity of community-based elderly care services by optimizing the service supply chain. This not only protects the interests of the elderly, but also promotes the development of community elderly care services and social stability.
1.3 Methodology The research methods in this article include field method, Kano model analysis method, questionnaire survey method and AHP hierarchical analysis method. (1) Field method: The first step was to conduct research on the community D senior service station. The integrated services discovered by the research were taken as the major service indicators, and the service items included in it were taken as the secondary service indicators. (2) Questionnaire method: Questionnaires were set up according to the Kano model and AHP model features and primary and secondary service indicators respectively. The questionnaires were used to understand the needs of end customers. (3) Kano model analysis method: We determine the first level of service indicators based on the research and set up a kano questionnaire to survey the elderly in the service station. The Kano model analysis method is used to determine the priority relationship between the classification of service items and the importance of enhancing satisfaction for the elderly. (4) AHP model analysis method: Our paper introduces the AHP model for customers to compare the importance of secondary service indicators. According to the AHP model, we set up the questionnaire with the secondary service indicators and conducted with the elderly representatives to determine the weighting of the secondary service indicators through the AHP model analysis. The items with the highest weighting in each level of indicators are optimized under the priority relationship to achieve the effect of increasing satisfaction. In this paper, a combination of Kano and AHP is used for this purpose. Compared to the two-tier AHP analysis method, this paper optimizes the services with the highest weighting according to the ranking of importance can make more effective use of funds and resources to improve customer satisfaction. In addition, according to the classification, the part of the non-differentiated attributes with the lowest weighting can be reduced and the available resources can be allocated more rationally. The research roadmap is shown in Fig. 2.
Service Supply Chain Optimization of Community Elderly Care-A Case … Introducing the kano model for projects looking to improve customer satisfaction Analysis of the existing supply chain framework
Existing supply chains do not provide services that meet the needs of the elderly
Questionnaire set according to Kano model characteristics, combined with first level service indicators
Research on elderly people in Community D to obtain data
Optimization of the items with the highest weighting of secondary indicators within each level of indicators in a priority relationship to achieve increased satisfaction
Determine the weighting of secondary service indicators through AHP model analysis
Conduct research with elderly representatives to obtain data
Questionnaire set according to AHP model characteristics, combined with secondary service indicators
Optimization of the existing service supply chain framework by improving the satisfaction of elderly customers
Give advice
Issues identified through interviews with elderly people in Community D
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The kano model analysis identifies the priority relationship between older people's classification of services and the importance of enhancing satisfaction
Introducing an AHP model for customers to compare the importance of secondary service indicators
Fig. 2 Research roadmap
2 Literature Review 2.1 Domestic and International Research on Service Supply Chain Theory and Elderly Service For the study of service supply chain foreign scholars have studied earlier. There were four scholars studied the service supply chain from different perspectives, and gave the concept of the service supply chain, which laid the foundation for future research [9–12] (Andrea, 2004; Margeetal, 2006; Waart, 2004; Ellram, 2006). In the past serval years, the question of how to improve community elderly care has attracted the attention of academics. Chen (2021) analyses the necessity and feasibility of government action in community care for the elderly and discusses the role played by the government in the community care system, the important role it plays and the specific initiatives it has taken to promote the development of elderly services from the government’s perspective [13]. Drawing on the experience of NORC’s community elderly service project in the United States, Zhang (2014) proposed that the actual needs of the elderly in community elderly service should be “empirical”, combined with the data model of field research, using Internet of Things technology and big data analysis to provide better services for the elder [14]. The use of IoT technology and big data analysis is needed to better serve
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the elderly. Zhang (2012) suggested that in order to manage the fragmented and imperfect elderly care system at a high level and to better meet the actual needs of the elderly, a community-based elderly care service supply chain needs to be built [15]. The community aged care service supply chain connects service integrators upstream providers and end customers, it is important to build and operate, and plays an important role in community elderly care services. These are some of the scholars studied how to enhance elderly care from other perspective. We also found that some scholars have studied elderly services from the perspective of service supply chain. Wang (2012) first explored the innovation model of senior care service based on service supply chain, and integrated the resources of senior care service by combining service architecture theory [16]. Zhang, Lu and Shi (2016) conducted a relevant study on the coordination of quality control of the supply chain of senior care services based on the reward and punishment contract, and studied and analyzed the service integrators and service providers’ quality games in individual decision making, joint decision making, and the use of reward and punishment contracts [17]. Ma, Liu and Yi (2020) used the rooting theory to construct a risk identification framework for the senior care service supply chain [18]. Ren, Liu and Zhou (2021) studied the pricing and coordination strategies of community-based elderly service supply chain based on elderly service preferences, and incorporated the factors of elderly service preferences on the demand side into the research framework of elderly service supply chain pricing and coordination strategies by establishing elderly satisfaction [19]. Zhou and Lu (2018) used TOC theory to identify the risks in the community aged care service supply chain and propose countermeasures to solve the problems in community aged care, so as to improve the aged care service [20]. Studies have shown that the level of customer satisfaction affects the purchasing behavior of customers [21] (Zarei, Nuri and Noroozi, 2019). Therefore, improving service capabilities in terms of increasing satisfaction can better attract older customers to make purchases. However, the above-mentioned studies have not taken into account the satisfaction of the elderly and have not been able to effectively measure whether services have been effectively improved for them. Thus, there is a gap in the research on improving the service supply chain by increasing satisfaction and thus enhancing elderly care services, therefore, this paper was conducted to fill this gap.
2.2 Kano Model and AHP Model for Elderly Care Services Our work is also related to previous studies on kano model and AHP model analysis in elderly care services. Zheng (2021) studied the design of community health care integration, and used the Kano model to screen and rank the acquired user needs, so as to build a strategy and model for the design of community health care integration services for the elderly [22]. Wu (2015) uses Kano model and AHP analysis to classify and optimize the medical service demand management [23]. Chen (2020) uses AHP analysis to evaluate and optimize the ageing suitability of open spaces in urban parks [24].
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2.3 Innovation This paper optimizes the community aged care service supply chain from the perspective of enhancing the satisfaction of the elderly, constructs a new supply chain framework for aged care services through research and model analysis, and puts forward relevant suggestions that can improve the satisfaction of the elderly, optimize aged care services and enhance service capacity. Optimization from the perspective of enhancing the satisfaction of the elderly can effectively improve the service capacity and help the service stations attract more elderly people.
3 Field Research and Model Data Analysis 3.1 Community D Elderly Care Survey The community D nursing home is still in the early stages of growth, and the service equipment is not ideal, the service content is not complete, and the related industry chain needs to be further improved. From Figs. 3, 4 and 5, the research photos below were taken in community D elderly care station in Beijing. According to the field investigation, there are 57 elderly people in community D nursing home, all of them are elderly in community D. Services involved in community elderly care institutions includes convalescence services (physical examination, acupuncture massage, health care products); entertainment services (chess and card rooms, gym, library); medical services (equipment with medical devices, pharmaceutical, medical insurance); daily services (care and escort, psychological counseling, exercise recovery) and living services (level of food, level of living, level of hygiene), In order to further develop old-age service institutions and provide more satisfactory services for elderly customers, in order to further develop elderly care service station and provide more satisfactory services for elderly customers, we are now conducting surveys on elderly customers in community D by issuing questionnaires. We interviewed several elderly people who were present during the research and found the following problems with the service stations: (1) In the event of an emergency such as an epidemic, there is often a shortage of medicines needed by the elderly in the service stations, and this also occurs with regard to food. (2) When receiving services from professional caregivers, there is no good monitoring mechanism and there is no guarantee that the services are effective. (3) Some of the services provided are not purchased by clients and take up resources. On the contrary, some popular services are in short supply in terms of manpower and demand exceeds supply. (4) The level of satisfaction of the elderly with the service stations is not high. In summary, we found that the services provided by the existing supply chain did
494 Fig. 3 Elderly care station
Fig. 4 Living room
Fig. 5 Psychological counseling room
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not meet the satisfaction of the elderly. Therefore, we optimized the service supply chain framework in terms of improving end-point satisfaction.
3.2 Community D Elderly Care Service Supply Chain Framework Through the investigation of community D elderly care service station, we construct the current service supply chain mode as shown in Fig. 6. It shows the current community D elderly care service supply chain framework. In the service flow, the providers of various segmented services are on the right. Through the integration of services by elderly care service integrators, the services provided by various providers are classified and integrated to form five integrated services for the elderly. Integrated services for convalescence; Living integration services; Entertainment integration services; Daily service integration service and medical integration service. Cooperate with the help provided by the neighborhood committee (the neighborhood committee provides room, etc.) to sell integrated services to the elderly group in community D. In terms of information flow: service demand management is carried out to collect and sort out the information about the needs of the elderly, and feed back to the community D elderly care service integrator, so that the integrator can adjust the supplier selection strategy according to the information. Finally, service integrators act on suppliers through supplier selection to select service flows.
Fig. 6 Community D elderly care service supply chain framework
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3.3 Results of the Questionnaire Survey Based on the above analysis and research, a one-week survey was conducted for the elderly customers in the community D elderly care station in Beijing, and a total of 57 questionnaires were distributed, of which 53 were valid questionnaires. As the Table 1 shows: (1) Reliability Analysis of Survey Questionnaire: In order to test whether the recovered questionnaire data has high reliability and the value of the recovered questionnaire, the reliability and validity of the collected questionnaire data should be analyzed to test the reliability and internal consistency of the collected data. In the questionnaire survey, we usually use the means of reliability analysis to measure whether the test results are stable and consistent. For the reliability index, we usually use the Cronbach’s α coefficient proposed by Cronbach’s. The Cronbach α coefficient is the most commonly used credit coefficient at present. The minimum value of the coefficient is 0 and the maximum value is 1. The larger the value, the higher the reliability. According to the research of line pipe scholars, when the coefficient is greater than 0.7, the measurement results of the questionnaire are Table 1 Basic questions Basic question
Options
Number
Ratio (5)
Gender
Male
31
58.49
Female
22
41.51
Age
Average monthly income
Percent of total revenue spent on aged care facilities
Children
60–70
15
28.30
70–80
21
39.62
80–90
11
20.75
Over 90
6
11.32
Under 1000
18
33.96
1000–2000
17
32.08
2000–3000
10
18.87
3000–4000
1
1.89
Over 4000
7
13.21
Under 30%
14
26.42
30%-40%
14
26.42 28.30
40%-50%
15
50%-60%
5
9.43
Over 60%
5
9.43
None
15
28.30
One
17
32.80
Two
14
26.42
Three and more
7
13.21
Service Supply Chain Optimization of Community Elderly Care-A Case … Table 2 α Analysis coefficient
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Sample size
Number of items
Cronbach.α coefficient
53
15
0.900
basically considered to be reliable. The coefficient value is 0.7–0.8, which can be considered as good reliability, 0.8–0.9 can be considered as excellent reliability coefficient, and above 0.9 The reliability coefficient is excellent. Conversely, if the reliability coefficient is lower than 0.6, the results of the questionnaire are considered to be unreliable, and the items of the questionnaire need to be optimized or redesigned. The results obtained through the correlation calculation are as follows in Table 2: The reliability analysis showed that the α coefficient was higher than 0.7 and 0.900, the reliability coefficient was excellent, and the results of the questionnaire were reliable. The next step was to carry out the validity analysis. (2) Validity Analysis of the Questionnaire: Validity refers to the degree of validity of the measurement. The degree to which the measurement tool can measure the trait to be measured. The validity is about high, which means that the higher the degree to which the trait of the measurement object is measured, and the more correct and effective the measurement tool is. KMO The value is between 0 and 1. The larger the value, the higher the correlation of the questionnaire index items, and the lower the correlation. If the KMO is higher than 0.7, it can be considered suitable for factor analysis. The higher the value, the higher the degree of suitability. On the contrary, if the value is lower than 0.7, the degree of suitability is relatively limited; if it is lower than 0.5, it can be considered significantly unsuitable for factor analysis, and the total variance explained is greater than 50%, indicating good validity. Through the analysis, the result as follows in Table 3: After the validity analysis test, the KMO value is greater than 0.7, which proves that the questionnaire is effective and can be further analyzed. Table 3 Validity analyysis data Items Eigenvalues (before rotation)
Factor 1
Factor 2
Factor 3
7.99
1.44
1.23
Variance explained rate % (before rotation)
53.27%
9.63%
8.21%
Cumulative variance explained rate % (before rotation)
53.27%
62.90%
71.11%
Variance explained rate % (after rotation)
52.81%
9.68%
8.62%
Cumulative variance explained rate % (after rotation)
52.81%
62.49%
71.11%
KMO value Barth’s spherical value
0.904 626.457
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3.4 Satisfaction Analysis Based on Kano Model The “two-dimensional model” of the Kano model reflects the movement of customer needs. It examines the different types of product quality characteristics from the customer’s point of view. Kano identifies the different types of customer needs, and the quality characteristics that are most beneficial for customer satisfaction, and enables a very systematic understanding of customer needs from the point of view of elderly satisfaction. It helping us to better identify which quality characteristic are most important and have a systematic understanding about the satisfaction. From the perspective of research method, the Kano model belongs to qualitative research method. This paper uses Kano model and Better-Worse coefficient as the basic class to analyze customer needs, determine key service elements and their priorities. (1) Based on the five service quality classifications of the Kano model, a structured questionnaire with elements of different dimensions is designed, and each element contains questions in both positive and negative directions, so as to determine the frequency of the attribute. Finally, the collected data of the Kano questionnaire is sorted, and the one with the largest total frequency of each element is the Kano category to which the element belongs. The Better-Worse coefficient is calculated to reflect the influence of this factor on increasing user satisfaction or eliminating user dissatisfaction. The Better coefficient is usually a positive value, indicating that a certain service or function will improve user satisfaction. The Worse coefficient is negative. (2) On the basis of the Kano attribute discussion, the Better-Worse coefficient is calculated by calculating the percentage of attribute classification, indicating that the element can increase the degree of satisfaction or eliminate the influence of dissatisfaction. (3) And then, based on the both coefficients to determine the importance of the first level of service indicators to customers and to determine the order of service enhancement. In the Kano model, the basic attributes of product and service quality are divided into five main categories: Must-be/basic quality (M), One-dimensional/performance quality (O), attractive quality (A), indifference quality (I) and reverse quality (R). (1) The 53 questionnaires are classified and summarized by the kano model and the customer’s choice and actual expectations are highlighted by means of charts in Table 4 Figure 7 shows the status analysis of primary service indicators, so we can see the current situation and elder’s preference. (2) The formula of SI and DI is: Increased satisfaction coefficient (better coefficient): SI =
( A+O) ( A+O+M+I )
(1)
Dissatisfaction coefficient after elimination (worse coefficient): DI =
−(O+M) ( A+O+M+I )
(2)
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Table 4 Kano analysis data Attribute
O
I
M
A
R
Q
number WORSE BETTER O category + M +I + A
Convalescence
13 21
6
6
7
0
53
41.30%
41.30%
46
I
Entertainment
3
15 11
11
13 0
53
35.00%
35.00%
40
I
Daily services
15 10 12
8
8
0
53
60.00%
51.11%
45
O
Medical treatment
6
9
11 1
53
53.66%
36.59%
41
M
Living
18 17
2
4
53
57.78%
44.44%
45
O
49.55%
41.69%
10 16 8
Average
4
Fig. 7 Kano model analysi
BETTER and WORSE coefficient are shown in Figs. 8 and 9. (3) Then we can calculate the satisfaction improvement factor by using SI and DI: Satis f actionimpr ovement f actor = S I − D I #
(3)
When the satisfaction improvement factor is close to 1, it proves that enhancing the service will improve customer satisfaction faster. We can see from the Fig. 7 that daily services and living are in the first quadrant, medical treatment in the second quadrant and recreation in the fourth quadrant. Medical treatment is an essential attribute, while convalescence and entertainment are indifference attributes. As a service with aspirational attributes, the more services there are and the wider the range of services, the higher the customer satisfaction and the higher the sense of presence should be. The provision of this function increases
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Fig. 8 Better coefficient
Fig. 9 Worse coefficient
customer satisfaction and the elimination of this function decreases the customer satisfaction factor. Essential as a service with basic attributes means that medical treatment is a mandatory feature for nursing homes. Elderly users will not be more satisfied with this feature, but elderly people are very dissatisfied with the removal of this feature. At the same time, we can see that users are not very satisfied with their services as a must-have service and that the quality of medical treatment should
Service Supply Chain Optimization of Community Elderly Care-A Case … Table 5 Satisfaction improvement factor
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Items
Worse
Better
Satisfaction improvement factor
Convalescence
−0.413
0.413
0.826
Entertainment
−0.35
0.35
0.7
Daily services
−0.6
0.511
1.111
Medical treatment
−0.537
0.366
0.903
Living
−0.578
0.444
1.022
be improved. Finally, the convalescence and entertainment are indifference, which means that they are not important for older people and which they perceive as having a low presence. Whether or not this feature is provided, the user’s satisfaction is not greatly affected. After obtaining the BETTER-WORSE coefficients, as shown in Table 5, Figs. 8 and 9, the order of the degree of importance that older people attach to the first level of service indicators is obtained by calculating the satisfaction improvement coefficient. According to the definition of the Kano model, we can see it clearly that when the satisfaction improvement coefficient of a service indicator is closer to 1, it means that the satisfaction of elderly services can be improved faster if the service indicator is improved so the customer satisfaction coefficient increases. The ranking is living > medical treatment > daily services > convalescence > entertainment from Fig. 10. This is the order in which customers rank the importance of the first level indicators as the order in which to optimize the always service indicators in this article.
Fig. 10 Satisfaction improvement factor
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3.5 Weighting Analysis Based on AHP Model We used AHP analysis to analyze the weighting of the secondary indicators for this research. We constructed a matrix by aggregating the scores obtained from the research conducted by the elderly representatives. In order to improve the accuracy, when determining the weight between the factors, the two factors are compared with each other. For example, for a certain standard, each scheme under it will be compared in pairs, and the matrix formed by the result is called the judgment matrix (U). Assume that the problem A has B1, B2, …, Bn indicators, and they are compared in pairs, and the matrix formed by them becomes the judgment matrix. Suppose problem A has B1, B2, …Bn indicators. Then the constructed judgment matrix B is: ⎡
b11 · · · ⎢ .. . . ⎣ . . bn1 · · ·
⎤ b1n .. ⎥ . ⎦
(4)
bnn
In the formula: Bi j represents the result of comparing the column Bi and B j, bi j =
Bi Bj
(5)
bi j =
1 bji
(6)
And
We use the Arabic numerals 1–9 and their reciprocals as a ruler to determine the value of Bi j. Assuming two factors x and y, when x and y are of the same importance, the eigenvalue is 1; when x and y are compared, and x is slightly more important than factor y, the eigenvalue is 3; when x and y are both in a pairwise comparison, when x is significantly more important than y, the eigenvalue is 5; when x is compared pairwise and x is more important than y, the eigenvalue is 7; when x and y are compared pairwise, x is extremely important than y, The eigenvalue is 9. The second step is to perform hierarchical single sorting, obtain multiple judgment matrices according to the previous step, calculate the sorting weights (W) of the factors related to the current layer and the upper layer, and apply the sum-product method to calculate the maximum eigen root and eigenvector of the judgment matrix. For Judgment matrix B is: U W = λmaxW #
(7)
The normalized eigenvector is the ranking weight of this layer factor for its subordinate factor B. The third step is to carry out the consistency test, and it is concluded that the weights are affected by various subjective and objective factors, so after
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obtaining λmax, the consistency test of the judgment matrix needs to be carried out. For the test of the consistency of the judgment matrix, it is necessary to calculate its consistency index C I , that is: CI =
λmax−n n−1
CR =
(8)
CI RI
(9)
When 0, ∂t∂ pe = 0. F∗
T∗
< 0, ∂t∂c1 < 0, ∂t∂c2 < 0; ∂t < 0. ∂cm F∗
j∗
Proposition 5(1) indicates that the optimal tariff decreases with the credit price in case E, vice versa in case F. This reflects that, the DCP stimulates the NEV adoption. However, due to the negative CAFC credit, the optimal tariff of the government may vary in these two cases. Besides, the credit price has no impact on the optimal tariff in case T, since the credit price does not exist in the model. Proposition 5(2) reflects the fact that, in these three cases, when the production line of vehicles is located abroad, with the increase of the production (transportation) cost, the government will implement lower tariffs to gain more social welfare from this international supply chain. The reasons can be explained as follows. The higher the production (transportation) cost of vehicles made abroad, the higher the retail price sold in domestic market. In order to promote the diverse development of domestic vehicle industry, the government will reasonably lower the tariffs so that the retail prices will go down and consumers’ purchasing power on imported cars can be improved.
5 Decision Analysis To obtain some managerial insights, this section will compare some results obtained in previous Sections, and discuss the impact of credit ratio on the optimal profit and the social welfare. By comparing the optimal tariff among different cases, we can obtain the following theorems.
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Theorem tariffs of the government in the cases E, F, and T sat⎧ 1E∗ The Toptimal ⎨ t < t ∗ < t F∗ , i f ν1 − ν2 < h 1 isfy . t E∗ < t F∗ < t T ∗ , i f h 1 < ν1 − ν2 < h 2 where .ν1 = c1 − /μ1 θ1 , ν2 = c2 − ⎩ t F∗ < t E∗ < t T ∗ , i f ν1 − ν2 > h 2 3(1−λ)+4(η−λg ) pe 3(1−λ)+4(η−λg +λλe ) pe /μ2 θ2 , h 1 = , h2 = . 3 3 Theorem 1 indicates that .t E∗ < t E∗ always holds, that is, comparing the automakers who choose NEV for domestic production and FV for overseas production with the ones who choose both NEV and FV for overseas production, the government should set the unit tariff on the former smaller than on the latter. Besides, when OEMs choose NEV for overseas production and FV for domestic production, the size relationship among three optimal tariffs is related to the production cost, credit coefficient of NEV, credit price and consumers acceptance for FV. Under the condition that ./μ1 θ1 = /μ2 θ2 , if the production cost difference between NEV and FV is below the threshold, the optimal tariff is the largest in case F; if the production cost difference between NEV and FV exceeds the threshold, i.e.,.h 1 < ν1 − ν2 < h 2 , or.ν1 − ν2 > h 2 , then the optimal tariff is always the largest in case T. Furthermore, the threshold difference between and increases with the credit price, credit coefficient and the consumer preference for FV. Thus, we can conclude that the implementation of DCP affects the optimal tariffs in different production cases and the tariff policy makers should comprehensively consider the features in production and consumption. Since .ω1C∗ = ω1E∗ , p1C∗ = p1E∗ , ω2C∗ = ω2F∗ , p2C∗ = p2F∗ , we compare the wholesale price and retail price of NEV and FV under cases: E, F, T. and we can obtain the following theorems. Theorem 2 (1) For the wholesale price of NEV, if .k1 < /72 , then .ω1F∗ < ω1E∗ < { E∗ ω < ω1F∗ < ω1T ∗ , i f /2 > /3 /2 T∗ ω1 ; if .k1 > 7 , then . 1E∗ ω1 < ω1T ∗ < ω1F∗ , i f /2 < /3 { E∗ p < p1F∗ < p1T ∗ , i f /2 > /3 (2) For the retail price of NEV, . 1E∗ p1 < p1T ∗ < p1F∗ , i f /2 < /3 Theorem 2 (1) shows that.ω1E∗ < ω1T ∗ always holds, that is, the wholesale price of only NEV with domestic production channel is always smaller than the wholesale price of both NEV and FV with overseas production channel. In addition, when OEMs choose NEV with overseas production and FV with domestic production, the size relationship among three optimal wholesale prices is related to the production cost, the transportation cost, credit coefficient, and credit price. We can analyze that the difference between .k1 and . /72 increases with the production cost and transportation cost. Thus, with the foreign production located far away from domestic market, the automakers with only NEV production at home can always maintain the lowest wholesale price of NEV. Theorem 2(2) shows that the variation trend of NEV’s retail price is consistent with the trend of NEV’s wholesale price, except that it is independent of .k1 . ⎧ / ⎨ ω2T ∗ < ω2E∗ < ω2F∗ , i f k2 < 71 /1 T ∗ F∗ E∗ Theorem 3 (1) For the wholesale price of FV, . ω2 < ω2 < ω2 , i f 7 < k2 < ⎩ ω2F∗ < ω2T ∗ < ω2E∗ , i f k2 > /73
/3 7
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⎧ ⎨
p2F∗ < p2E∗ < p2T ∗ , i f k3 < /71 (2) For the retail price of FV, . < p2F∗ < p2T ∗ , i f /71 < k3 < /73 ⎩ p2E∗ < p2T ∗ < p2F∗ , i f k3 > /73 where .k1 = cm + /μ1 θ1 + λe pe , k2 = cm + /μ2 θ2 − pe (η − λg ), k3 = pe (η − λg ) − cm − 3/μ2 θ2 . p2E∗
Theorem 3(1) shows that .ω2T ∗ < ω2E∗ always holds, that is, opposite to the optimal tariff in Theorem 1 and the wholesale price of FV in Theorem 2, the wholesale price of FV in case E is always larger than the wholesale price in case T. Furthermore, the greater consumer preference for FV and the higher credit price result in a higher wholesale price gap between case E and case T. There is reasonable concordance among the three sets of results. It is common that the automakers product NEV with domestic channel and FV with overseas channel. Under such circumstance, the supply chain members’ pricing strategies can be more sensitive to the complex market environment such as the consumer preference for NEV, the credit price etc. With Theorem 3 (2) we can know that the variation trend of the retail price of FV is contrary to that of the wholesale price of FV, except for the threshold .k3 instead of .k2 . Additionally, considering the size relationship among three wholesale / retail prices, many factors are involved such as the production cost, the transportation cost, credit coefficient, and credit price. We can also see that the difference between .k2 (k3 ) and /3 . increases with the production cost and transportation cost. Thus, the automakers 7 with only FV production domestically can always keep the lowest wholesale price of FV while the retail price holds the opposite. By discussing the impact of credit ratio on the optimal profits and the social welfare in case C and case F, we can obtain the following theorems. Theorem 4 Under case C, if.0 < η < η(1) ,then. case η(1) =
.
∂πmC∗ ∂η
< 0;otherwise.
∂πmC∗ ∂η
> 0. Under
∂π F∗ ∂π F∗ F, if .0 < η < η ,then . ∂ηm < 0; otherwise . ∂ηm > 0. where λc1 −c2 −λλe pe + pe λg 37λ−28λ2 +12λ(c1 +cm −/μ1 θ1 )+(49−40λ) pe λg −(49−28λ)c2 , η(2) = . pe (49−40λ) pe (2)
Theorem 4 shows that there exists a certain threshold .η(1) , η(2) under case C and case F respectively, and the profits vary with the credit ratio in the same trend. When the credit ratio is below the threshold, the profits decrease with the credit ratio; otherwise the opposite result will occur. This finding can be interpreted as follows. At the beginning of the development of NEV, R&D investment accounts for the majority of production cost and the NEV market share is small. As a result, the earnings of NEV cannot completely offset the cost of inputs. Only when the credit ratio exceeds the threshold can the production quantities of NEV increase with the credit ratio and the automakers improve the overall revenue. Theorem 5 Under case C, if .0 < η < η(3) ,then . ∂ SW < 0;otherwise . ∂ SW > 0. ∂η ∂η { F∗ ∂ SW (4) > 0, i f 0 < η < η 7 7 ∂η Under case F, if .0 < λ < 16 < λ < 1, the ,then . ∂ SW if . 16 F∗ (4) < 0, i f η > η ∂η C∗
opposite
holds.
where
9λ+12λ(c1 +cm −/μ1 θ1 )−21c2 +(−7+16λ) pe λg . (−7+16λ) pe
η(3) =
.
C∗
−3λc1 +3c2 −λλe pe + pe λg , η(4) pe
=
Note that all proofs are provided in Appendix.
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Theorem 5 indicates that there exists a certain threshold .η(3) , η(4) in case C and case F respectively. In case C, when the credit ratio is below the threshold .η(3) , the social welfare decreases with the credit ratio. Otherwise, the opposite result will happen. In case F, the variation trend of social welfare is related to the consumer preference for FV. If the consumer preference is low, the social welfare retains the trend consistent with case C; otherwise SW shows the opposite trend. In fact, due to consumers’ concerns about the range and coverage of NEV charging, the level of research and development also needs to be improved. For example, the Nissan Leaf can be driven for 150 km or so before it needs to be recharged for six to eight hours. The company is refining its battery and other electric power source technology to expand the cruising range of electric vehicles and promote their widespread adoption. This indicates that the consumer surplus and social welfare are low at the beginning of the NEV adoption. Therefore, based on reality and the above findings, we recommend that the government can provide subsidies for the automakers that produce both NEV and FV domestically. With the maturity of the NEV market, the credit ratio gradually increases beyond a certain threshold and the social welfare will increase with the credit ratio. From Theorem 4 and Theorem 5, further analysis shows .η(1) < η(2) , η(3) < η(4) , which indicates that case C is more conducive to improving corporate profits, and the market environment can reduce the negative effects of the DCP.
6 Numerical Analysis This section represents a numerical simulation to investigate three main areas: (a) the optimal solutions in four cases; (b) sensitivity analysis of the optimal tariffs and production decisions; (c) the impact of DCP and tariff policies to identify the optimal production strategies. Based on the assumptions above and the previous researches [15, 26, 28], the following parameter’s values are normalized to [0 1]. .c1 = 0.32, c2 = 0.24, cm = 0.08, λe = 1.6, λg = −0.5, η = 0.1, λ = 0.9, /μ1 = /μ2 = 0.6, θ1 = θ2 = 0.5, pe = 0.01. Table 6 shows that the optimal tariffs satisfy .t E∗ < t T ∗ < t F∗ , which verifies Theorem 1.
Table 6 Results obtained in four cases ∗ ∗ .t .ω1 Variables Case C Case E Case F Case T
N/A 0.369 0.389 0.377
0.652 0.652 0.655 0.661
∗
∗
∗
.ω2
. p1
. p2
0.573 0.576 0.573 0.571
0.826 0.826 1.172 1.169
0.737 1.072 0.737 1.074
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6.1 Sensitivity Analysis The vehicle sales can be impacted by various factors such as the tariff, credit price and consumer preference. Figure 2 depicts the surfaces of NEV and FV sales in four cases with respect to different parameters. NEV sales in case C can be simultaneously affected by credit price and consumer preference, as shown in Fig. 2a. When the credit price exceeds certain threshold, the credit price has greater impact than consumer preference on the quantities, and thus, the quantities of NEV can be positively correlated with consumer preference. Practically, in the initial implementation of DCP policy, the credit price is usually relatively low, which leads to the reduction of NEV quantities with consumer preference for a period of time. Notably, FV sales is contrary to the above analysis. Figure 2b and c shows the impact of credit price and tariffs on the vehicle sales, which verify the previous analysis in Proposition 2 and 4. In addition, Fig. 2e indicates that the consumers environmental awareness exhibits positive impact on the demand for NEV, and vice versa for FV. The tariff level has no effect on NEV sales, but generates negative impact on FV sales. The optimal tariffs can be impacted by various factors such as the production cost, transportation cost and consumer preference. For concisely, we take cases E and T for example, due to the similar analysis in case F to case E. Figure 2f shows that the optimal tariff decreases with transportation costs and production costs, which verifies Proposition 5. Furthermore, the optimal tariffs in cases E and T decrease with consumers’ green awareness. This reflects that, with the domestic NEV market continuously maturing, more automakers will engage in the domestic production of NEV. Besides, for the FV with overseas production, government is supposed to reduce tariff within a certain range in order to meet the diversified consumer demand, increase consumer surplus and improve social welfare. We can also conclude that the factors of market and cost can offset the uncertainty of tariff. As can be seen from Fig. 2f, considering the non-negative nature of tariff, automakers should be constrained by the production costs when they produce in different channels.
6.2 Impact of Credit Ratio and Tariff on the Profits and Social Welfare Figure 3 shows the profits in cases C and F decrease with the credit ratio, which verify Theorem 4. And so to the social welfare, as shown in Fig. 4, verify Theorem 5. The credit ratio causes little impact on the profit and social welfare in case E and T basically. According to Theorem 1, there are three cases of optimal tariff size relationship under different cases. Without loss of generality, we take (1) .0 < η < η , 0 < η < η(2) , 0 < η < η(3) , 0 < η < η(4) and .t E∗ < t T ∗ < t F∗ as an example for analysis. Hence, we suppose the value as .t = 0.2, 0.4, 0.6, represented by subscripts .1, 2, 3, and the impact of tariff and credit ratio on the automakers’ profits and social welfare are studied. When the tariff is less than the optimal tariff (Fig. 3,
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Fig. 2 The sensitivity analysis of vehicle sales and optimal tariffs
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Fig. 3 The profits of automaker as function of credit ratio
t = 0.2), the profits in four cases are basically equal. When the tariff is higher than or equal to the optimal tariff (Fig. 3, .t = 0.4, 0.6), the profits in cases E and F is significantly higher than that in cases C and T. Under the same credit ratio, when the tariff is beyond the range of the optimal tariff (Fig. 4,.t = 0.2, 0.6), the social welfare in case C is always the largest; otherwise (Fig. 4, .t = 0.4), . SW2T ∗ < SW C∗ < SW2E∗ < SW2F∗ hold. This reflects that the social welfare in case E is always smaller than that in case F. The results can be relative with the range of credit price. Therefore, in the next subsection, the sensitivity of credit price and the equilibrium state of the game are explored.
.
6.3 Impact of Credit Price and Tariff on the Profits and Social Welfare Similar to the previous value setting, we analyze the impact of credit price and tariff on the profits and social welfare in this subsection. When government tariff is the same, the profits in case C and E increase with credit price while the profit in case F holds the opposite trend, as shown in Fig. 5a and b. The profits of case E and F indicate positive relationship with tariff while the profits of case T holds the opposite trend as shown in Fig. 5c. Some applications can be found in practice. Generally speaking, the credit price is determined by the market supply and demand of credits. Due to lack of credit in the initial development of NEV, the average credit price is increasing year by year. The Ministry of Industry and Information Technology reported that the credit price has risen from one point 300–500 yuan to 2500–3000 yuan. In addition to the
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Fig. 4 The social welfare as function of credit ratio
constant trade conflicts between countries, tariffs have been raised. For traditional auto companies that produce FV and NEV, production channel of cases C and E can reduce the negative impact of credit price or tariffs on the profits. To further validate our findings, we compare the profits in cases C and E under different credit price. From Fig. 5a, we can see that when . pe is low, then .π C∗ < π1E∗ < π2E∗ < π3E∗ ; otherwise,.π1E∗ < π C∗ < π2E∗ < π3E∗ hold. Consequently, with higher tariff barriers, only NEV produced at home (case E) is always more profitable than both NEV and FV produced domestically (case C). By contrast, under a loose tariff policy, the profits relationship in the two cases can be influenced by credit price. To be specific, with higher credit price, case C gains more profit than that of case E and vice versa. Figure 6 depicts the impact of credit price on social welfare under four cases. Figure 6a shows that the social welfare in case C increases with credit price while the trend of social welfare in cases E and F is associated with tariff. When the tariff is lower than or equal to the optimal tariff, i.e., .t = 0.2 or 0.4, . SW E∗ increases with . pe , otherwise, the opposite holds. . SW F∗ varies with . pe on the contrary and T∗ . SW has no relationship with the credit price, as shown in Fig. 6b and c. We further compare social welfare in Fig. 6a and obtain the following findings. If . pe < pe∗ ,then E∗ . SW3 < SW1E∗ < SW C∗ < SW2E∗ ; otherwise, . SW3E∗ < SW1E∗ < SW C∗ < SW2E∗ E∗ or . SW3 < SW2E∗ < SW1E∗ < SW C∗ . Obviously, the regulation of high tariff can harm social welfare (.t = 0.6), since . SW3E∗ is always the smallest. With tariffs lower than or equal to the optimal tariff (.t = 0.2, 0.4), if the credit price is relatively high, C∗ . SW is the largest and . SW1E∗ > SW2E∗ ; otherwise, . SW2E∗ is always the largest. This can be interpreted as follows. Social welfare with lower tariff is superior to that with the optimal tariff, due to the dominant impact of credit price on social welfare under such production channel. Conclusively, to maximize social welfare, the government set tariffs lower than or equal to the optimal tariff. When the credit price is low, the government carry out
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Fig. 5 The profits of automaker as function of credit price
Fig. 6 The social welfare as function of credit price
tariff policy with the optimal tariff and the production channel of case E reaches the state of balance. When the credit price is larger, social welfare of case C is the largest, but automakers’ profit is not optimal. Under such circumstance, the automakers will maintain the production channel of case E, so that the government gains sub-optimal social welfare (. SW1E∗ or . SW2E∗ ). These findings reveal that the implementation of dual-credit policy in auto supply chain can be influenced by the tariff policy from social welfare perspective. The increase in credit price is a further reflection of credit value, indicating that the automakers are more and more enthusiastic in developing NEV technologies such as driving range, electric energy consumption and power battery cost reduction. Mean-
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while, in order to guarantee consumer surplus and social welfare, the government should formulate corresponding policies to control the fluctuation of credit price, form a fair trading environment, and actively carry out related mechanisms with carbon trading market.
7 Conclusions The DCP policy has brought great challenges to traditional automobile supply chain. With the tension of energy-saving technologies and the uncertainty of tariff policy, the manufacturer will be in a dilemma to select his/her production structures and pricing strategies for different types of vehicles. Therefore, we formulate the Stackelberg game consisting of the government, an automaker and a dealer. The decision-making order is as follows. First, the government aiming to maximize social welfare set the imported tariff; then, the auto supply chain automakers and dealers make the wholesale and retail price sequentially to realize their profits maximization. According to backward induction, we calculate the optimal equilibrium solutions of tariffs and prices under four cases and derive the demand sales, profits, consumer surplus and social welfare respectively. By means of comparative analysis and numerical examples, the equilibrium solutions under the four models are compared, and the impacts of credit ratio and credit price with tariff changes on the profits and social welfare are analyzed. The main findings of this paper are summarized as follows. (1) From the perspective of social welfare maximization, no matter which channel production strategy the automaker adopts, the optimal tariff for government always exists. Moreover, the optimal tariff in only NEV produced domestically (case E) is always lower than that in both NEV and FV produced abroad (case T). Only when the cost gap between NEV and FV exceeds a certain threshold, will the optimal tariff in only FV produced domestically (case F) be the smallest. (2) To make a better comparison, we further address the issues such as the wholesale and retail price of two vehicles. On the one hand, the wholesale price of NEV in case E is always lower than that in case T, and with the higher transportation cost, the wholesale price of NEV in case E can always keep the lowest among three cases. In a similar vein, the automakers can always keep the lowest wholesale price of FV in case F. On the other hand, the wholesale price of FV in case E is always larger than that in case T. Regardless of the threshold, NEV’s retail price changes in line with the wholesale price, while the trend of FV’s retail price is opposite to that of the wholesale price. (3) When the credit ratio exceeds a certain threshold, corporate profits increase with the credit ratio. By raising consumers’ preference for environmental awareness, the credit ratio can positively impact social welfare within a specified threshold under case F. Considering the different production channel strategies, the market environment will offset the negative impact of dual-credit policy on social wel-
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fare. The cooperation between the government and SC members can promote the development of the economy and the environment. (4) Numerical analysis indicates that the social-welfare-maximizing governments will not impose tariffs that exceed the optimal tariff. When the market credit price is low, the production channel of case C is a balanced state under the optimal tariff. Otherwise, the social welfare is the largest in case C, but the automakers’ profit is not optimal. OEMs will maintain the production channel of case E, giving the government sub-optimal social welfare. As the core of the thesis, the optimal pricing and production solutions are obtained, and the equilibrium state of production channel is proposed. For the domestically produced automakers, to cope with the negative effect of DCP policy, the multinational SC members should cooperate with each other to advocate environmental protection and raise the consumption of NEV. With one or two types of vehicles production abroad, it is necessary to consider tariffs, transportation cost, consumers’ valuations and preferences for pure imported NEV and FV. Additionally, under the DCP regulation, the development of energy-saving technology and battery technology are becoming increasingly important. Automotive companies should strengthen the level of technological innovation of NEV and FV to narrow the production cost gap, keep the government tariffs low, and encourage domestic imported auto production. Finally, game-theorical method can provide for the evaluation of the channel strategies, giving important insights into the most suitable policy scenarios for achieving social welfare maximization. The findings will add weight to calls for more interdisciplinary researchers to consider studying environmental policies, which has traditionally been seen as purely engineering subject. The deficiencies of this paper are that it only studies one type of NEV and FV respectively and does not add the case of multiple products into the model. Further studies can also consider how to effectively relate the dual-credit policy with the mechanism of carbon trading and consider the impact of economic and environmental benefits. Acknowledgements The authors are grateful to the editor and the anonymous referees for their constructive comments, which substantially helped the authors improve the quality of the manuscript. This work is supported by the National Natural Science Foundation of China (No. 72171024, 71801016); the Key project of National Social Science Foundation of China (No. 20AJY016) [33– 36].
8 Appendix 8.1 Proof of Demand Function According to the utility function of Yu et al. [28] and Zhang et al. [35], the utility function of two consumers choices in case C can be defined as follows:
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Fig. 7 Purchasing behaviors of the heterogeneous consumers in case C
Table 7 The optimal prices and profits obtained in four cases j∗
j∗
Case j . p1 C E F T
3A+c1 −λe pe . 4 3+c1 −λe pe . 4 6(1+/μ1 θ1 )+c1 +cm + pe (η−λg ) . 7 7(3+c1 +3/μ1 θ1 )−3(c2 −/μ2 θ2 −λ)+4cm . 28
j∗
. p2
.πm
3λA+c2 +(η−λg ) pe . 4 6(λ+/μ2 θ2 )+c2 +cm −λpe λe . 7 3λ+c2 + pe (η−λg ) . 4 c +c +6(λ+/μ2 θ2 ) . 2 m 7
Q +Q 2 +Q 3 +λpe2 λe . 1 8λ(1−λ) Q 4 +Q 5 . 392λ(1−λ) 49y 2 Q 26 +4x Q 7 . 392λ(1−λ) +ν2 )+4Q 9 (ν2 +cm −λ) . λQ 8 (1−λ−ν156λ(1−λ)
μ1 = μ0 − p1 , μ2 = λμ0 − p2
.
where . p1 , p2 are respectively the retail prices of NEV and FV; .μ0 , λ have been assumed in Assumption 3. The choice of consumers completely depends on the utility harvested from NEV and FV. If the utility gained from the former, namely .μ1 > μ2 , the consumers will prefer to NEV. Otherwise, the consumers will prefer 1 − p2 < μ0 < A, and .μ1 < μ2 yields .0 < to FV. According to A1, .μ1 > μ2 yields . p1−λ p1 − p2 μ0 < 1−λ . Here we assume that .0 < p2 < λp1 , which means both NEV and FV are in need, as shown in Fig. 7. Then according to the analysis of utility functions, the demand functions can be obtained below: {A q =
. 1
p1 − p2 1−λ p1 − p2 1−λ
{
q2 = p2 λ
1 p1 − p2 dμ0 = 1 − A A(1 − λ) 1 λp1 − p2 dμ0 = A λA(1 − λ)
Similar to the derivation above, the utility functions and demand functions under four cases are summarized in Table 2. The optimal prices and profits are obtained in Table 7.
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8.2 Proof of Results in Case C According to backward induction, we first consider the second stage with respect to the dealer’s decision. Obviously, the profit functions of two types of vehicles are respectively the concave functions of price. Therefore, by solving Eq. (1), the optimal prices of NEV and FV are obtained as follows, λA + c2 + (η − λg ) pe A + c1 − λe pe c∗ , w2 = 2 2 3λA + c2 + (η − λg ) pe 3A + c1 − λe pe c∗ , p2 = = 4 4
w1c∗ = .
p1c∗
(13)
Then the production quantities of NEV and FV are obtained, i.e., q1c∗ = .
A(1 − λ) − c1 + c2 + (λe + η − λg ) pe 4 A(1 − λ) − c2 − (λλe + η − λg ) pe λc 1 q2c∗ = 4 Aλ(1 − λ)
(14)
Substituting Eqs. (13), (14) into Eqs. (1), (2) and (3), we can derive the optimal profits of the manufacturer, the consumer surplus and the social welfare under case C, i.e., Q 1 + Q 2 + Q 3 + λpe2 λe 8λ(1 − λ) Q 1 + Q 2 + Q 3 + λpe2 λ2e = 32λ(1 − λ) 7(Q 1 + Q 2 + Q 3 ) + λpe2 (6 + λe ) = 32λ(1 − λ)
π C∗ =
. m
C S C∗ SW C∗
where . Q 1 = λc12 + c22 + (η − λg )2 pe2 , Q 2 = −2λc1 (c2 + pe (λe + η − λg )) + 2c2 pe (λλe + η − λg ), Q 3 = λ(1 − λ)(1 + 2λe pe − 2c1 ) + 2λpe2 λe (η − λg ).
8.3 Proof of Results in Case E We apply the backward induction method to obtain the optimal tariff of the government and the optimal pricing strategies of the vehicle manufacturer and the dealer. By solving Eq. (5),we can obtain the retail prices of NEV and FV,respectively, i.e., .
p1E =
3Aλ + c2 + cm + 3/μ2 θ2 + t E 3A + c1 − λe pe E , p2 = 4 4
(15)
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Then, we can obtain the production quantities of NEV and FV,respectively, i.e., q1E = .
A(1 − λ) − c1 + c2 + cm + λe pe − /μ2 θ2 + t E 4 A(1 − λ) − c − cm + /μ2 θ2 − λλe pe − t E λc 1 2 q2E = 4 Aλ(1 − λ)
(16)
Obviously, . A(1 − λ) − c1 + c2 + cm + λe pe − /μ2 θ2 + t > 0 and .λc1 − c2 − cm + /μ2 θ2 − λλe pe − t > 0 can ensure the production quantities of NEV and FV t2 must be satisfied where .~ are both non-negative. Subsequently, .~ t1 < t < ~ t1 = c1 − c2 − cm − λe pe + /μ2 θ2 − A(1 − λ),.~ t2 = λc1 − c2 − cm + /μ2 θ2 − λλe pe . Therefore, by substituting Eqs. (15), (16) into (4), the optimal tariff can be obtained 2 +cm −/μ2 θ2 )+(A−5)λλe pe . For simplicity, it is customary to as .t E = 3λ+(1−A)λc1 +(A−4)(c8−A set A=1 to denote the demand density of the market is one unit. Then the optimal tariff can be simplified as.t E∗ = /71 , where./1 = 3(λ − c2 − cm + /μ2 θ2 ) − 4λλe pe > 0 can ensure the optimal tariff is non-negative. By replacing .t E∗ in Eqs., the optimal prices and production quantities are as below, 3 + c1 − λe pe 4 6(λ + /μ2 θ2 ) + c2 + cm − λλe pe = 7 7(1 − c1 + λe pe ) + 4(ν2 + cm − λ − λλe pe ) = 28(1 − λ) 7λc1 − 3λ(1 + λe pe ) − 4(ν2 + cm ) = 28λ(1 − λ)
p1E∗ = p2E∗ .
q1E∗ q2E∗
(17)
By substituting Eq. (17) into Eqs. (4), (5), (6) we can obtain the profit, the consumer surplus and the social welfare in case E, i.e., Q4 + Q5 392λ(1 − λ) Q 4 + Q 5 − 3136λ/μ2 θ2 = 1568λ(1 − λ) Q 4 + Q 5 + 56λ(1 − λ) pe λe (c1 − pe λe − 1) − 448λ/μ22 θ22 = 224λ(1 − λ)
π E∗ =
. m
C S E∗ SW E∗
where . Q 4 = λ(49 − 40λ)(1 + pe λe )2 + 49λc12 + 16(ν2 + cm )2 + 24λ(1 + pe λe ) (ν2 + cm ), Q 5 = −14λc1 (4(ν2 + cm ) + (7 − 4λ)(1 + pe λe )).
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8.4 Proof of Results in Case F According to backward induction, by solving Eq. (8), we can easily obtain the retail prices of NEV and FV, i.e., 3A + c1 + cm + 3/μ1 θ1 + t 4 . + p 3λA + c 2 e (η − λg ) p2F = 4 p1F =
(18)
Then we can obtain the production quantities of NEV and FV, respectively, i.e., A(1 − λ) − c1 + c2 − cm + pe (η − λg ) + /μ1 θ1 − t 4 A(1 − λ) . + c − /μ λ(t + c 1 m 1 θ1 ) − c2 − pe (η − λg ) q2F = 4 Aλ(1 − λ) q1F =
(19)
Obviously, . A(1 − λ) − c1 + c2 − cm + pe (η − λg ) + /μ1 θ1 − t > 0 and .λ(t + c1 + cm − /μ1 θ1 ) − c2 − pe (η − λg ) > 0 can ensure that the production quantities t3 < t < ~ t4 must be satisof NEVs and FVs are both non-negative. Subsequently, .~ c2 + pe (η−λg ) ~ ~ fied, where .t3 = − c1 − cm + /μ1 θ1 , t4 = A(1 − λ) − c1 + c2 − cm + λ pe (η − λg ) + /μ1 θ1 . Therefore, by substituting Eqs. (18), (19) into (7), the optimal 15A−12+(1−A)(15λ+c2 )−(4−A)(c1 +cm −/μ1 θ1 )+(5−A) pe (η−λg ) tariff can be obtained as .t F = . 8−A /2 F∗ Similar to case E, by setting A=1 we can simplify the optimal tariff as .t = 7 , where ./2 = 3(1 − c1 − cm + /μ1 θ1 ) + 4 pe (η − λg ) can ensure the optimal tariff is non-negative. By replacing.t F∗ in Eqs. (18), (19), the optimal prices and production quantities are as below, 6(1 + /μ1 θ1 ) + c1 + cm + pe (η − λg ) 7 3λ + c2 + pe (η − λg ) = 4 4(1 − ν1 − cm ) + 7(c2 − λ) + 3 pe (η − λg ) = 28(1 − λ) λ(3 + 4(ν1 + cm + pe (η − λg ))) − 7(c2 + pe (η − λg )) = 28λ(1 − λ)
p1F∗ = p2F∗ .
q1F∗ q2F∗
(20)
substituting Eqs. (20) into Eqs. (7), (8), (9) the profit, the consumer surplus and the social welfare in case F, i.e.,
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49y 2 Q 26 + 4x Q 7 392λ(1 − λ) 49y 2 Q 26 + Q 7 ((4 − 8λ)x + 7y) = 1568λ(1 − λ)2 7Q 6 (4 pe (η − λg ) − 3y) + 4Q 7 (3x − 7 pe (η − λg )) = 784λ(1 − λ)
π F∗ =
. m
C S F∗ SW F∗
where . Q 6 = 4λx − 7y, Q 7 = 4λx − 7λy, x = c1 + cm + pe (η − λg ) − /μ1 θ1 , y = c2 + pe (η − λg ) − λ, ν1 = c1 − /μ1 θ1 , ν2 = c2 − /μ2 θ2 .
8.5 Proof of Results in Case T Similar to previous proofs, the optimal results are derived as follows, tT∗ =
.
p1T ∗ = p2T ∗ = q1T ∗ = q2T ∗ =
/3 7 7(3 + c1 + 3/μ1 θ1 ) − 3(c2 − λ − /μ2 θ2 ) + 4cm 28 c2 + cm + 6(λ + /μ2 θ2 ) 7 1 − λ − c1 + c2 + /μ1 θ1 − /μ2 θ2 4(1 − λ) 7λ(c1 − /μ1 θ1 ) − (4 + 3λ)(c2 − /μ2 θ2 ) − (1 − λ)(3λ + 4cm ) 28λ(1 − λ) λQ 8 (1 − λ − ν1 + ν2 ) + 4Q 9 (ν2 + cm − λ) 56λ(1 − λ) 2 7Q 9 + Q 10 (1 − λ − ν1 + ν2 ) = 224λ(1 − λ)2 2 7Q 9 + Q 10 (1 − λ − ν1 + ν2 ) 3(ν2 + cm − λ)2 = + 224λ(1 − λ)2 49λ
πT∗ =
. m
C ST ∗ SW T ∗
./3 = 3(λ − c2 − cm (4+3λ)ν2 +(1−λ)(3λ+4cm )−7λν1 , Q 10 7 (1 + 6λ)ν2 .
where
+ /μ2 θ2 ), Q 8 = 7 − 3λ − 7ν1 + 3ν2 − 4cm , Q 9 = = (1 − λ)(7 − 6λ − 8cm ) − 7ν1 (1 − 2λ) −
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Analysis of Reading Promotion in the Digital Twin Library Xiaoyu Liu and Yuqing Ma
Abstract During the 14th Five-Year Plan period, China will promote national reading as an important task for public libraries at all levels. However, in the process of reading promotion, public libraries have some problems, such as limited content supply, uneven allocation of resources, insufficient development ability, and unable to meet the reading demand. Digital Twin library can be assigned to transform and upgrade the public library, using digital twin technology to strengthen the library published high quality content supply, optimizing the resources distribution of basic reading, improving the reading conditions of public places, ensuring basic reading needs of grassroots groups, thus further solve the public library reading promotion problem, promote nationwide reading activity. Keywords Digital twin library · Public library · Reading promotion path
Under the background of promoting nationwide reading and building a literate China, libraries have become one of the important places to promote nationwide reading activity. However, although public libraries have abundant collection resources and extensive reading and publicity channels, however, there are still difficulties in content supply, site construction, resource allocation, reading guidance and other aspects. This study will explore the deep integration path of digital twin technology and public libraries to build digital twin libraries, use emerging technologies to promote nationwide reading.
X. Liu (B) · Y. Ma School of Economics and Management, Beijing Institute of Graphic Communication, Beijing, China e-mail: [email protected] Y. Ma e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 X. Shang et al. (eds.), LISS 2022, Lecture Notes in Operations Research, https://doi.org/10.1007/978-981-99-2625-1_41
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1 The Predicament of Reading Promotion in Public Libraries 1.1 Insufficient Content Supply Public libraries are divided into “city, district, street township, community village” and other levels, different levels of library collections, books and newspapers are different, which leads to differences in content supply. Some public libraries are unable to meet readers’ reading needs due to limited collections, single types, insufficient content supply and other reasons [1]. In addition, public libraries are in short supply of some scarce content, such as rare ancient books and sample books. Some rare ancient books cannot be read by readers, and sample books can only be borrowed by readers in the reading room, these restrictions not only affect readers’ reading enthusiasm, but also restrict the reading promotion and dissemination of high-quality content.
1.2 Limited Reading Space Conditions The allocation of area resources in urban and rural public libraries are uneven, due to the lack of sufficient financial resources, material resources and manpower, some public libraries, especially rural libraries, are faced with the dilemma of great difficulty in construction and high operating costs. Their narrow reading space and outdated collection of books not only fail to provide high-quality venues, but also fail to meet the increasing reading needs of readers. In addition, affected by the epidemic, some public libraries will take anti-epidemic measures such as restricting access and closing their libraries, and reduce offline reading activities. As a result, some readers cannot enter the public library to borrow books offline or participate in the library’s offline reading promotion activities.
1.3 Lack of Professional Reading Instruction Most books and newspapers in public libraries are sorted according to their types, lacking of special reading guarantees and specialized, diversified and personalized reading guidance. Public libraries have rich collection resources, but offline reading is difficult to create a self-growth, self-development of intelligent reading mode [2]. Readers can enter the library to read independently, but the existing public library cannot provide personalized, customized and professional reading guidance services for multiple users, and it is necessary to strengthen the reading service guidance for minors, the elderly, the disabled and other special groups and reading promotion methods.
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With the development and popularization of emerging technologies such as 5G/ 6G, big data, cloud computing, block chain, virtual and augmented reality and so on, digital twin technology has emerged. Digital twin technology is an advanced stage of information development. Public libraries introduce digital twin technology to create digital twin libraries which can effectively solve the dilemma of library reading, promote the deep integration of reading services and information technology, and assist nationwide reading.
2 An Overview of the Digital Twin Library 2.1 The Concept of Digital Twin Technology Digital twin technology is the integration of simulation process by making full use of physical model, sensor update, operation history and other data to complete realtime mapping in virtual space, so as to reflect the full life cycle process of the corresponding entity [3]. Digital twin technology has four characteristics. First one is mapping accurately. Digital twin technology makes use of physical entities, through data feedback and technology application, real-time mapping in virtual space, accurately reflecting the whole life cycle process of entities. The second one is the virtual-real interaction. Physical entities interact with virtual entities to realize data flow automatically. The third one is software definition. Digital twin technology in the form of software builds virtual models for physical entities and monitors the real running state of simulated physical entities. The fourth one is intelligent decision-making. Digital twin technology is combined with emerging technologies, virtual and real integration and interaction, through data collection and analysis, model mapping simulation to provide data support, realize the optimization of physical entity management, intelligent decision-making. From the point of view of data fusion, digital twin technology has the characteristics of all factors, all processes and all services; from the perspective of technical services, digital twin technology provides intelligent, personalized and precise services on demand; from the perspective of mapping interaction, digital twin technology has the characteristics of comprehensiveness, real-time, dynamic and operability. Because of its various advantages and characteristics, many fields are scrambling to explore the application of digital twin technology in the industry, and the concept of digital twin library comes into being.
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2.2 The Concept of Digital Twin Library The digital twin library depends on the physical library, sensors update, real-time data collection, etc. Virtual library is generated in virtual space to map physical library in real time, so as to reflect the current situation of physical library comprehensively and quickly. It can effectively generate twin data of library, connect all resources of library through data drive, and realize real-time interaction and iterative upgrade of library, as shown in Fig. 1. Physical library is an important part of the application of digital twin technology. It includes library entities, offline users, various resources, hardware and software facilities, etc. Digital twin technology relies on physical entities to build virtual space, realize dynamic mapping, execute tasks precisely, augmented reality sensing and interaction, so as to enhance the deep integration and construction of “human– machine-object-environment”. Virtual library is a virtual model that restores physical library in real time, which is also the virtual twin of physical library. Sensors, big data, integration, analysis techniques and other driving factors in the physical library integrate various models. Virtual twin reflects the status quo of physical library entity through digital mapping, and uses simulation technology and modeling technology to build virtual model in real time. Big date of library twin is an important driving force of digital twin libraries, which contains all the data generated by the construction and operation of digital twin
Fig. 1 Digital twin library model
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libraries. Digital twin library relies on twin big data to generate virtual library, through data acquisition, big data processing, data simulation mapping, data transmission and storage and other forms through two-way feedback with the physical library to achieve real-time interaction. The front display of the library visualizes the data. Through the integration of media devices, sensors and other external entities, the front display and reflection of all the characteristics of the physical library from micro to macro as well as the overall operation situation, showing the evolution process of the life cycle of the physical library. The service function of library is mainly divided into functional service and business service. The former relies on tool components, interactive sensing devices, module engines and human–computer interaction data to realize the internal operation of the digital twin library. The latter is the presentation of external functions that satisfy different object users, different business types and different subdivisions through software, platform and mobile client. Connection is an important link in the construction of digital twin libraries, which makes physical libraries, virtual libraries, library twin big data, and library digital twin system services and displays interconnect with each other. Digital twin library based on physical library to collect data, data driven by digital twin, using technology to build open, interactive, symbiotic reading knowledge service system, and optimizing the library service and the ability of reading guide, greatly expand the library reading scene application, solve the public library’s existence predicament, further meet the demand of high-quality library reading promotion.
3 The Digital Twin Library Reading Scene Application 3.1 Dynamic Monitoring to Meet the Individual Needs of Users With the rapid development of science and technology network, the traditional physical library has been unable to meet the diversified and personalized needs of readers. If the library wants to survive, develop or innovate, it must adapt to the change of times and the needs of readers and carry out corresponding transformation. The library is the representative of public cultural service institutions and has a wealth of library collection resources. The ultimate goal of modern libraries to meet individual needs of users is to analyze and construct reading labels of different groups of readers, accurately match library reading resources with diverse needs of readers, and efficiently present them to readers in innovative service ways. The digital twin library is under the action of emerging technologies such as Internet of Things, virtual/ augmented reality technology, artificial intelligence, big data and cloud computing
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to form users-twin body [4]. The digital twin library rely on physical sensor and algorithm analysis to make user’s behavior digital, via the user-twins body to analyze and forecast data, objectively reflect the user behavior, interests, cognitive level and so on, transform users into user-twins to form twin data. Through the analysis of the digital twin system, the digital twin library can understand and evaluate users from multiple dimensions, cultivate users’ self-perception ability, realize users’ self-exploration and innovation, meet users’ personalized needs, and enhance users’ interest in reading.
3.2 Optimize Allocation and Integrate Supporting Resources Reasonably The digital twin technology can highly simulate the physical library and form a virtual library, and they can interact with each other in real time. The digital twin technology can model the hardware and software of the physical library, and make real-time analysis and diagnosis of the equipment, server, borrowing and returning platform and Internet in the physical library through data feedback, so as to eliminate the hardware and software equipment faults for the first time and maintain the overall operation of the physical library. The digital twin technology can optimize the service and space configuration of the library in real time. Through the intuitive data collection and objective data analysis of the Internet of things technology, the energy consumption data of the physical library can be obtained, and reasonable and feasible suggestions for reducing energy consumption of the library can be put forward. In addition, the physical library and virtual library of the digital twin library are mapped in real time [5]. If the physical library is closed, the virtual library can meet the readers’ digital reading needs, and update the library twin data through data collection in real time. On the one hand, it can realize data driving, and on the other hand, it can provide readers with digital reading places.
3.3 Intelligent Interaction to Create a System and Service System The digital twin library captures the user behavior, dynamically tracks the user’s movement trajectory, stay time, reading habits and other behaviors in the library, and explores the potential needs of users according to their preferences. It can use VR/ AR technology to provide immersive digital reading experience, construct intelligent reading service scene, provide scene reproduction, and deepen users’ reading understanding and perception ability of knowledge [6]. In addition, the digital twin technology can also realize the online teaching of the library. According to the user’s own situation, the learning model and knowledge system with different degrees of difficulty can be formed to enhance the user’s learning and reading attraction [7]. It can
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also combine emerging technologies to restore the real scene of the physical library, break through the restrictions of time, space, virtual and reality, and provide users with remote display of high-quality library content resources and reading services, such as the reproduction of ancient books, cultural dissemination, immersion experience, etc., so as to realize intelligent interaction and create a systematic user service system.
4 The Reading Promotion Path of the Digital Twin Library 4.1 The Digital Twin Library Provides High Quality Content There are obvious differences in the supply of reading content in different regions and different types of public libraries. At present, the physical library is still the core of the library, and the network service is only the effective extension of the physical library. Physical libraries are now faced with some problems that are difficult to solve. For example, with the increasing number of readers, physical space resources are increasingly strained and the conditions for expansion are relatively high. And some physical libraries in remote areas have fewer books and documents, which cannot meet the reading needs of local readers. The reading resources are not digitized and the utilization rate is low. However, most physics libraries have a large and diverse collection, and contain some unique rare ancient books, which add to the value of the physics library. Digital twin library can realize the sharing mode of book resources with physical library through the characteristics of intelligence, digitization and synchronization of digital twin technology, which can broaden the mobility and utilization rate of reading resources. With the help of emerging technologies, digital twin library can truly restore and simulate, and build a digital platform for ancient books by mapping books in virtual space. Through data collection, data driven, the digital twin library build a virtual space and use it to integrate offline resources into digital content, makes the collection of ancient rare book “out” of the real library, let readers stay at home to read, further enhance the library supply of high-quality content, promote nationwide reading (Fig. 2).
4.2 The Digital Twin Library Optimizes the Allocation of Reading Resources Digital Twin libraries empower learning Spaces. Users can realize dynamic simulation of real and virtual space through digital twin technology, which can be learned repeatedly, tested, applied and verified in virtual space [8]. This enables users to feel the pleasure of creation in an immersive way and greatly improves their own
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Fig. 2 Digital twin Library flowchart
creativity. Digital twin library can reasonably optimize the library space and accurately optimize the allocation of library reading resources. Readers can switch between the physical library and the digital twin library at will, thus expanding the space resources of the library. Digital Twin Library provides readers with realtime reading services through immersive visual reading, which can provide more fault-tolerant space for readers’ learning. Through the analysis of readers’ problems in reading in the digital twin library and readers’ feedback, combined with realtime monitoring and demand mining, digital twin library and physical library can be optimized and upgraded. Through digital twin technology, digital twin library can collect readers’ information in multiple dimensions and all-round ways, so as to achieve accurate matching of readers’ services. Firstly, by analyzing the basic data of readers, such as relevant professional cultural background and career direction, and then analyzing the behavior data of readers, such as reading behavior, preference and interest, borrowing frequency, etc., different groups of readers are divided and labeled with different portrait labels. The digital twin library can accurately locate the reader’s portrait, and can be mapped to the physical library, so as to realize the synchronous update of the digital twin library and the physical library. With the help of real-time monitoring, association analysis and intelligent mining of twin big data, the needs of readers with the same portrait label are analyzed and predicted. Discover their existing and hidden reading interests and hobbies, develop personalized reading programs for readers, and further optimize the allocation of reading resources through user feedback, improve user engagement, and promote users to increase reading frequency and nationwide reading (Fig. 3).
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Fig. 3 Digital twin library optimization method
4.3 The Digital Twin Library Improves Reading Space Conditions Digital twin library can realize intelligent management, emergency response and library culture construction through digital twin technology. In the process of actual operation, library unable to monitor operation status in real time, but through the technology of sensors and artificial intelligence, the digital twin library can realize the virtual library, through data collection, summary, analysis and arrangement, the staff can accurately judge the reality of library operation state to effectively remote monitoring, troubleshooting, emergency disposal, and reduce the actual operating pressure of the library and optimize the conditions of reading places. The digital twin library can improve the reading service and optimize the reading place, and realize the virtual restoration of remote physical library through the establishment and integration of the elements of “man–machine-object-environment” [9]. Readers can enter the physical library for an interactive, immersive reading experience through virtual/ augmented reality, artificial intelligence and other technologies. During the epidemic prevention and control period, if the library is closed, readers can dynamically read online through the virtual library (Fig. 4).
4.4 The Digital Twin Library Guarantees the Basic Reading Needs of Grassroots Groups The 19th National Congress of the Communist Party of China made a major plan to implement the rural revitalization strategy, and cultural revitalization has become an important part of rural revitalization. Although the gap between urban and rural areas is gradually narrowing, there is still a phenomenon of unbalanced development between urban and rural libraries. Compared with urban libraries, rural libraries have problems such as lack of book varieties, insufficient hardware facilities and weak service awareness. Libraries in some remote areas can hardly meet the basic reading needs of grass-roots groups, such as rural left-behind children and the children of urban migrant workers. The digital twin library can solve this problem effectively.
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Fig. 4 Digital twin library advantage
Digital twin technology restores the 3D modeling of the real library through 5G network, virtual augmented reality technology, big data, cloud computing and other emerging technologies. Through the rapid spread of the network, the virtual reality sensing of wearable devices, the whole picture of the library is restored for users. The digital twin technology provides readers with a virtual library to meet their reading needs and enhance their immersive virtual reading experience. Readers can feel the library’s reading atmosphere without leaving home, use the library’s supporting resources, or browse online through virtual/augmented reality technology [10]. No matter left-behind children in rural areas, children of migrant workers, or elderly readers and people with disabilities can realize personalized self-learning and online reading through the digital twin library, so as to guarantee the basic reading needs of grassroots groups, effectively gather the resources of public libraries, and create a strong atmosphere of reading (Fig. 5).
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Fig. 5 Digital twin library process
5 Conclusion The digital twin library uses emerging technologies to innovate reading methods. Relying on the unique characteristics of the library, it excavates library cultural resources deeply. It not only provides users with new reading choices and personalized reading customized services, but also contributes to the development of libraries and the construction of smart cities. The digital twin library can meet the readers’ reading needs through the popularization of technology, so as to realize the optimization and improvement of physical libraries, enhance the readers’ reading participation and coverage, and further help promote nationwide reading and the implementation of the national reading strategy.
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