Sharing Behavior of Brand Crisis Information on Social Media: A Case Study of Chinese Weibo 9811666660, 9789811666667

Table of contents :
Foreword
Contents
About the Author
1 Introduction
1.1 Research Background
1.1.1 Severity of Brand Crisis
1.1.2 Rapid Development of Microblog/Weibo
1.2 Research Purposes and Significance
1.2.1 Research Purposes
1.2.2 Research Significance
1.3 Research Questions and Structure
1.3.1 Research Questions
1.3.2 Research Contents
1.3.3 Structure
1.4 Research Methods and Technical Route
1.4.1 Research Methods
1.4.2 Technical Route
1.5 Research Innovation
References
2 Literature Review and Theoretical Foundation
2.1 Research Status Quo
2.1.1 Brand Crisis Dissemination
2.1.2 Information Dissemination on Microblogging Platforms
2.1.3 Research on Information Behavior
2.1.4 Contextual Factors of Behavior
2.1.5 Research Review
2.2 Theoretical Foundation
2.2.1 Information Context Theory
2.2.2 Information Grounds Theory
2.2.3 Information Processing Theory
2.2.4 Field Theory of Psychology
2.2.5 Information Behavior Theory
2.3 Summary
References
3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users
3.1 Data Collection and Descriptive Statistics
3.1.1 Data Acquisition and Data Preprocessing
3.1.2 Data Features and Descriptive Statistical Analysis
3.2 Fluctuation Features of Reposting Behavior
3.2.1 Fluctuation Features of the Whole Information Spreading Process
3.2.2 Weekly Fluctuation Features
3.2.3 Weekly Fluctuation Features
3.3 Fluctuation Features of Comment Behavior
3.3.1 Fluctuation Features of the Whole Communication Process
3.3.2 Weekly Fluctuation Features
3.3.3 One-Day Fluctuation Features
3.4 Summary
References
4 Contextual Factors Affecting Brand Crisis Information Sharing by Weibo Users
4.1 Selection and Construction of Contextual Factors
4.2 Testing and Analysis of Classified Contextual Factors
4.2.1 Static Contextual Factors
4.2.2 Dynamic Contextual Factors
4.3 Testing and Analysis of Static Contextual Factors
4.3.1 Data Sources
4.3.2 Reliability and Validity Analysis
4.3.3 Correlation Analysis
4.3.4 Causality Test
4.4 Testing and Analysis of Dynamic Contextual Factors
4.4.1 Time Series Level
4.4.2 Data Integrity Level
4.5 Summary
References
5 Static Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases
5.1 Introduction
5.2 Hypothesis
5.2.1 Relationship Between Information Context and Physiological Stimulation
5.2.2 Relationship Between Physiological Stimulation and Perceptual Attributes
5.2.3 Relationship Between Perceptual Attributes and Behavior Intention
5.2.4 Moderating Effect of Harm Relevance
5.2.5 Theoretical Framework for Research
5.3 Research Design and Data Collection
5.3.1 Research Methods
5.3.2 Design of Scale and Questionnaire
5.3.3 Data Collection
5.4 Data Processing, Testing and Analyzing
5.4.1 Reliability and Validity Analysis
5.4.2 Hypothesis Testing
5.4.3 Analysis of Results
5.5 Cluster Analysis
5.5.1 Population Sample
5.5.2 Gender Group
5.5.3 Age Group
5.5.4 Education Group
5.5.5 Occupational Group
5.6 Conclusion and Discussion
5.6.1 Conclusion
5.6.2 Discussion
5.7 Summary
References
6 Dynamic Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases
6.1 Impact of Total Number of Reposts and Comments
6.1.1 Causality Test
6.1.2 VAR Model Construction
6.1.3 Impulse Responses Analysis
6.1.4 Marginal Influence
6.2 Impact of the Blogger’s Number of Follows and Followers
6.2.1 Causality Test
6.2.2 VAR Model Construction
6.2.3 Impulse Responses Analysis
6.2.4 Marginal Influence
6.3 Impact of the Number of Follows and Followers of Source Information
6.3.1 Causality Test
6.3.2 VAR Model Construction
6.3.3 Impulse Responses Analysis
6.3.4 Marginal Influence
6.4 The Impact of Information Temporal Distance
6.4.1 Causality Test
6.4.2 VAR Model Construction
6.4.3 Impulse Responses Analysis
6.4.4 Marginal Influence
6.5 Decomposition Analysis of Impact Contribution Ratio
6.5.1 Reposting Behavior Fluctuation
6.5.2 Commenting Behavior Fluctuation
6.6 Summary
References
7 Strategies for Monitoring Brand Crisis Information Sharing by Weibo Users
7.1 Positioning Monitoring Time
7.1.1 Monitoring Times for Reposting Behavior
7.1.2 Monitoring Times for Commenting Behavior
7.2 Monitoring of Static Contextual Factors
7.2.1 Indicator of Information Visualization (IV)
7.2.2 Indicator of Information Sentiment (IS)
7.2.3 Indicator of Information Authority (IA)
7.2.4 Indicator of Harm Relevance (HR)
7.2.5 Indicator of Group Difference
7.3 Monitoring of Dynamic Contextual Factors
7.3.1 Monitoring of Reposting Behavior
7.3.2 Monitoring of Commenting Behavior
7.4 Summary
8 Conclusion and Suggestions
8.1 Research Findings
8.1.1 Accurate Analysis of the Fluctuation Characteristics of Brand Crisis Information Sharing by Weibo Users
8.1.2 Identifying and Quantitative Analysis of the Contextual Influencing Factors of Information Sharing Behavior on Weibo in Brand Crisis
8.1.3 Study on the Influencing Mechanism of Static Contextual Factors of Weibo Users’ Information Sharing of Brand Crisis Cases
8.1.4 Study on the Influencing Mechanism of Dynamic Contextual Factors of Brand Crisis Information Sharing by Weibo Users
8.1.5 Study of the Strategies for Targeted Monitoring of Brand Crisis Information Sharing by Weibo Users
8.2 Research Contributions
8.3 Limitations and Suggestions
References
Appendix A Original Data Retrieval Formats
Appendix B Questionnaire

Citation preview

Changzheng Yang

Sharing Behavior of Brand Crisis Information on Social Media A Case Study of Chinese Weibo Translated by Feng Yue, Hanxiong Zhu, and Li’e Liang

Sharing Behavior of Brand Crisis Information on Social Media

Changzheng Yang

Sharing Behavior of Brand Crisis Information on Social Media A Case Study of Chinese Weibo

Changzheng Yang School of Liberal Arts, Journalism and Communication Ocean University of China Qingdao, China Translated by Feng Yue Xi’an, China

Hanxiong Zhu Xi’an, China

Li’e Liang Xi’an, China

The National Social Science Fund of China “Research on the Public Cognition Bias and Guidance Mechanism of Network Emergency Events Based on Information Cascade”. Project Number: 19CXW041. ISBN 978-981-16-6666-7 ISBN 978-981-16-6667-4 (eBook) https://doi.org/10.1007/978-981-16-6667-4 Jointly published with Xiamen University Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Xiamen University Press. ISBN of the Co-Publisher’s edition: 978-7-5615-7367-9 © Xiamen University Press 2022 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 publishers, 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 publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain 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

Foreword

With the rapid development of information technology, new media represented by the Internet has greatly changed the way information spreads. Social media is among the most popular new media at present. According to the 43rd Report on the Development of China’s Internet, social media users have been on the rise in China. The usage rate of WeChat and QQ reached 83.4% and 58.8% by the end of 2018. As for Weibo (China’s microblog), the usage rate is 42.3%, a 1.4% rise over the year 2017. Since social media has become the top information source on the Internet, surpassing all search engines, it is changing people’s information behavior in a significant way. Business competition grows ever more fiercely with the development of social economy. As enterprises attach greater importance to branding and people attach more importance to consumer rights, brand crisis has become a common phenomenon that enterprises have to deal with in the process of development. There are more and more cases of brand crisis, and when they occur, social media aggregates the situation if information go viral on the social network. Apart from the role company leaders play when coping with the crisis, social media can exert significant impact on rapid spreading of the crisis through uncontrolled posts and reposts which may cause uncontainable public opinion in the virtual and real world. The 2017 Ctrip bundle sale and the 2018 fake news of Starbuck’s coffee causing cancer are two such cases. As social media is booming, it is vital for business leaders to be able to understand the characteristics of brand crisis information spread in social media in order to quickly adopt suitable crisis response strategies, minimize the negative impact, and avoid economic losses. The present study is a timely answer to the call for brand crisis management in the era of users’ information sharing behavior in the social media. In recent years, the study on social media has attracted much attention in the academic circle and the business world. Dr. Yang Changzhen’s work unveils the characteristics, influencing factors, static influencing mechanism, dynamic influencing mechanism, and strategies for behavior monitoring and orienting of social media users’ information sharing behavior over brand crisis. The study will provide valuable insights and references for future work in related areas.

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Foreword

It is an honor to be asked by Dr. Yang to present this Foreword. Li Mingde Director of School of Journalism and New Media of Xi’an Jiaotong University Xi’an, China

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Severity of Brand Crisis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Rapid Development of Microblog/Weibo . . . . . . . . . . . . . . . . 1.2 Research Purposes and Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Research Purposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Research Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Research Questions and Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Research Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Research Methods and Technical Route . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Technical Route . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Research Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 1 2 3 3 4 5 5 8 9 10 10 13 13 17

2 Literature Review and Theoretical Foundation . . . . . . . . . . . . . . . . . . . . 2.1 Research Status Quo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Brand Crisis Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Information Dissemination on Microblogging Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Research on Information Behavior . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Contextual Factors of Behavior . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5 Research Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Theoretical Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Information Context Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Information Grounds Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Information Processing Theory . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Field Theory of Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Information Behavior Theory . . . . . . . . . . . . . . . . . . . . . . . . . .

19 19 19 23 26 28 31 32 32 34 36 38 38

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2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40 41

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Data Collection and Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Data Acquisition and Data Preprocessing . . . . . . . . . . . . . . . . 3.1.2 Data Features and Descriptive Statistical Analysis . . . . . . . . 3.2 Fluctuation Features of Reposting Behavior . . . . . . . . . . . . . . . . . . . . 3.2.1 Fluctuation Features of the Whole Information Spreading Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Weekly Fluctuation Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Weekly Fluctuation Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Fluctuation Features of Comment Behavior . . . . . . . . . . . . . . . . . . . . 3.3.1 Fluctuation Features of the Whole Communication Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Weekly Fluctuation Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 One-Day Fluctuation Features . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89 102 108 116 117

4 Contextual Factors Affecting Brand Crisis Information Sharing by Weibo Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Selection and Construction of Contextual Factors . . . . . . . . . . . . . . . 4.2 Testing and Analysis of Classified Contextual Factors . . . . . . . . . . . . 4.2.1 Static Contextual Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Dynamic Contextual Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Testing and Analysis of Static Contextual Factors . . . . . . . . . . . . . . . 4.3.1 Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Reliability and Validity Analysis . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Testing and Analysis of Dynamic Contextual Factors . . . . . . . . . . . . 4.4.1 Time Series Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Data Integrity Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

119 119 123 123 126 127 127 128 130 132 133 133 138 148 151

5 Static Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Relationship Between Information Context and Physiological Stimulation . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Relationship Between Physiological Stimulation and Perceptual Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47 50 50 56 62 62 75 81 89

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Contents

5.2.3 Relationship Between Perceptual Attributes and Behavior Intention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Moderating Effect of Harm Relevance . . . . . . . . . . . . . . . . . . 5.2.5 Theoretical Framework for Research . . . . . . . . . . . . . . . . . . . . 5.3 Research Design and Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Research Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Design of Scale and Questionnaire . . . . . . . . . . . . . . . . . . . . . 5.3.3 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Data Processing, Testing and Analyzing . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Reliability and Validity Analysis . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 Analysis of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Population Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Gender Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3 Age Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.4 Education Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.5 Occupational Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Dynamic Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Impact of Total Number of Reposts and Comments . . . . . . . . . . . . . . 6.1.1 Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 VAR Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Impulse Responses Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Marginal Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Impact of the Blogger’s Number of Follows and Followers . . . . . . . 6.2.1 Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 VAR Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Impulse Responses Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Marginal Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Impact of the Number of Follows and Followers of Source Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 VAR Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Impulse Responses Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Marginal Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 The Impact of Information Temporal Distance . . . . . . . . . . . . . . . . . . 6.4.1 Causality Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 VAR Model Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.4.3 Impulse Responses Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Marginal Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Decomposition Analysis of Impact Contribution Ratio . . . . . . . . . . . 6.5.1 Reposting Behavior Fluctuation . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Commenting Behavior Fluctuation . . . . . . . . . . . . . . . . . . . . . 6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

239 242 244 244 244 246 248

7 Strategies for Monitoring Brand Crisis Information Sharing by Weibo Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Positioning Monitoring Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Monitoring Times for Reposting Behavior . . . . . . . . . . . . . . . 7.1.2 Monitoring Times for Commenting Behavior . . . . . . . . . . . . 7.2 Monitoring of Static Contextual Factors . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Indicator of Information Visualization (IV) . . . . . . . . . . . . . . 7.2.2 Indicator of Information Sentiment (IS) . . . . . . . . . . . . . . . . . 7.2.3 Indicator of Information Authority (IA) . . . . . . . . . . . . . . . . . 7.2.4 Indicator of Harm Relevance (HR) . . . . . . . . . . . . . . . . . . . . . 7.2.5 Indicator of Group Difference . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Monitoring of Dynamic Contextual Factors . . . . . . . . . . . . . . . . . . . . 7.3.1 Monitoring of Reposting Behavior . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Monitoring of Commenting Behavior . . . . . . . . . . . . . . . . . . . 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

249 250 251 253 255 255 256 256 256 257 257 258 261 263

8 Conclusion and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Research Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Accurate Analysis of the Fluctuation Characteristics of Brand Crisis Information Sharing by Weibo Users . . . . . . 8.1.2 Identifying and Quantitative Analysis of the Contextual Influencing Factors of Information Sharing Behavior on Weibo in Brand Crisis . . . . . . . . . . . . . . 8.1.3 Study on the Influencing Mechanism of Static Contextual Factors of Weibo Users’ Information Sharing of Brand Crisis Cases . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Study on the Influencing Mechanism of Dynamic Contextual Factors of Brand Crisis Information Sharing by Weibo Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.5 Study of the Strategies for Targeted Monitoring of Brand Crisis Information Sharing by Weibo Users . . . . . . 8.2 Research Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Limitations and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix A: Original Data Retrieval Formats . . . . . . . . . . . . . . . . . . . . . . . . 277 Appendix B: Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279

About the Author

Changzheng Yang is professor and doctoral supervisor at the School of Liberal Arts, Journalism & Communication, Ocean University of China. His main research interests are new media user, crisis communication, and brand communication. In July 2017, he received a doctorate degree in media management from Shanghai Jiaotong University, and in 2015, was awarded the National Scholarship for Doctoral Candidates from the Ministry of Education of the People’s Republic of China.

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Chapter 1

Introduction

1.1 Research Background 1.1.1 Severity of Brand Crisis Competition among enterprises has become ever more intense as economy continues to develop in recent years, forcing enterprises to attach greater importance to branding. In today’s turbulent market environment, enterprises face challenges brought by external and internal uncertainties. They also have to cope with brand crisis brought by consumers’ increasing awareness of their rights. Some of these crises are bound to threaten and destroy the construction and development of corporate brands. Therefore, in the daily operation and management, brand crisis has become emergencies that enterprises must prevent and deal with at any time. In this context, how to respond quickly and effectively in the face of a sudden crisis has become an urgent concern for every business leader. In recent years, brand crises have struck some well-known enterprises related to their products or services, such as “Drinking Water Standard Gate” of Nongfu Spring; “Instant Chicken Scandal” of KFC; and “Poisonous Milk Powder” of Fonterra. Such crises have seriously damaged consumers’ trust in corporate brands and created a sense of insecurity among consumers. At the same time, the continuous emergence and rapid development of new media, especially the internet-based new media, has made it possible to spread information fast and far and wide in the virtual and real world to form an unstoppable public opinion. Therefore, in the new media era represented by the Internet, when the brand crisis breaks out, enterprises should know how to effectively deal with it as it is related to the survival and development of corporate brands. If enterprises already have some advanced and in-depth understanding of the laws and characteristics of crisis information dissemination through new media, it is possible for them to take effective measures fairly quickly and reduce negative impacts accordingly. If not, the crisis may be extremely costly or even fatal to enterprises. In early 2011, China’s Central TV(CCTV) exposed the use of Clenbuterol, a forbidden Lean Meat Powder, in Shuanghui meat products. Since © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_1

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1 Introduction

the first report by CCTV’s Weekly Quality Report program, the news went viral on new media platforms including microblogs, forums and blogs, attracting continuous follows and interactions among users. As a result, Shuanghui Group suffered the most serious economic losses and brand damage. A similar case occurred to Nongfu Spring concerning “Drinking Water Standard”. Media first exposed the scandal on a small scale but the news went viral through users’ continuous reposts and comments on various new media platforms. Negative information spread online and offline, causing huge public outcry and sharp sales decline. After Mengniu’s aflatoxin-contaminated milk was exposed, it took just days to cause a 35% drop in sales of Mengniu milk. A similar scandal over tainted baby milk powder by Sanlu brought the enterprise and brand to near extinction. These events prove that the worsening of crisis is not only related to the crisis response ability of business operators, but also to the promotional work conducted by media users through reposts and comments. Therefore, in today’s new media booming environment, a new challenge for every enterprise manager is how to accurately understand the laws and characteristics of brand crisis information dissemination in order to quickly adopt crisis response strategies to minimize the negative impact of the crisis and consequent economic losses.

1.1.2 Rapid Development of Microblog/Weibo In recent years, with the rapid development of electronic information technology, Internet-based new media platforms continue to emerge, such as blogs, microblogs, WeChat, forums and so on. The development momentum of Weibo, China’s microblog, is particularly rapid as it is widely used in people’s daily information acquisition and dissemination. On December 25, 2015, President Xi Jinping sent New Year’s greetings to all Chinese military officers and soldiers via Weibo during a visit to the PLA Newspaper Press, which fully shows that Weibo, as a new media, has become a focal point for governments of all levels in China and a favored platform for a vast number of netizens. Statistics show that as of December 2014, Twitter’s global users have reached 1 billion, with an average of 241 million active users per month.1 According to a 2015 Nielsen survey, 97% of mainstream media users also use microblogging, while about 70% of the microblog users use it to obtain information and share it. According to the 38th Statistical Report on Internet Development in China released by the China Internet Network Information Center (CNNIC), as of June 2016, China’s Internet users reached 710 million, with an Internet penetration rate of 51.7%, up 1.3% points from the end of 2015. As of June 2016, China’s Weibo users were 242 million, accounting for 34% of Internet users, which clearly reflects the important role Weibo plays in information dissemination due to its rapid growth and development.2 When a brand crisis occurs, Weibo has become an important platform for rapid information dissemination. As a double-edged sword, it can 1 2

China Internet Network Information Center (CNNIC) (2015). China Internet Network Information Center (CNNIC) (2016).

1.1 Research Background

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be a crisis resolution booster by helping enterprises carry out crisis communication management and turn the crisis into an opportunity; or it can flame up the crisis information spread and generate an unstoppable public opinion. Therefore, the emergence and wide application of Weibo not only brings development opportunities to enterprises, but also brings new challenges and shocks to enterprise crisis management. Survey on the information behavior of Weibo users in China shows that when users use Weibo, the four most popular information functions are reposting, commenting, following and hot topics, among which reposting and commenting can enable users to share specific information and express and transmit emotions. Thus, they are the main Weibo users’ information sharing behavior.3 In this context, it is of great significance to study the influence mechanism of information sharing behavior of Weibo users in the brand crisis to manage the brand crisis in the new media environment.

1.2 Research Purposes and Significance 1.2.1 Research Purposes This study combines communication, psychology, sociology and management theories, collects data through official APIs, web crawler and questionnaire, processes and analyzes data using quantitative means such as time series analysis and structural equation models, and studies the influence mechanism of information sharing behavior of Weibo users over brand crisis from the information contextual perspective. The purposes are as follows: Firstly, through the decomposition of the fluctuation characteristics of user information sharing behavior, the autocorrelation, trend characteristics, periodic characteristics and cluster characteristics of fluctuations are analyzed accurately in order to discover the basic laws of fluctuation of Weibo users’ information sharing behavior over brand crisis. Secondly, static and dynamic contextual influencing factors of user information sharing behavior are to be studied and analyzed in order to reveal which factors exert significant impact on users’ information sharing behavior. Thirdly, the influencing mechanism of static contextual factors of Weibo users’ information sharing behavior is to be studied in order to reveal how static contextual factors influence users’ information sharing behavior and differences that exist among users of different groups. Fourthly, the influencing mechanism of dynamic contextual factors of Weibo users’ information sharing behavior is to be studied in order to reveal the features of dynamic contextual factors on the disturbance process, marginal impact and fluctuation contribution rate of user information sharing behavior.

3

Zhang and Zhao (2015).

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1 Introduction

Finally, based on the above-mentioned research conclusions, the specific monitoring strategy of information sharing behavior of Weibo users over brand crisis is proposed, so as to improve the efficiency and effect of brand crisis communication management.

1.2.2 Research Significance 1.2.2.1

Theoretical Significance

At present, research on the dissemination of Weibo information has become a hot topic in academic circles, but most of the research focuses on the analysis of the dissemination mode, dissemination effect, user attitude, communication value and marketing value of Weibo information. There are many studies on the information behavior of Weibo users, but most of them mainly focus on description of behavior characteristic, user analysis and static path and very few of them touch upon issues concerning precise analysis of the fluctuation characteristics of information sharing behavior or the influencing mechanism of Weibo users’ information sharing behavior in brand crisis from the perspective of contextual factors. In this context, with the help of related theories from communication, psychology, sociology and other disciplines, this study uses trend decomposition of time series, ARIMA model, vector auto regression (VAR), and structural equation model (SEM) to study the fluctuation characteristics, static influencing mechanism and dynamic influencing mechanism of Weibo users’ information sharing behavior. Because the study integrates multidisciplinary theory and uses a variety of research methods, it further promotes the idea of interdisciplinary research on information behavior, and also strengthens the consciousness of studying information behavior from the perspective of contextual factors. Through the comprehensive explanation of the fluctuation characteristics of information sharing behavior and the specific analysis of the influencing mechanism of contextual factors, the relevant research conclusions can not only enrich the research results on the information behavior of Weibo and other social media users, but also help to fully understand the objective law and internal influencing mechanism of the information sharing behavior of Weibo users over brand crisis.

1.2.2.2

Practical Significance

First, through the decomposition of the fluctuation characteristics of Weibo users’ reposting and commenting of brand crisis information, the autocorrelation, trend characteristics, periodic characteristics and cluster characteristics of fluctuations are analyzed accurately. The conclusion of this study can help enterprise managers to predict the development of Weibo users’ information reposting and commenting behavior, identify key periods in the management process of brand crisis information dissemination, and then focus the crisis response strategy and public relations

1.2 Research Purposes and Significance

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activities on the time node of the largest marginal growth rate of fluctuation, behavior peak and group gathering, so as to achieve effective and efficient crisis management. Secondly, through the research on the influencing mechanism of static contextual factors influencing the information sharing behavior of Weibo users, the author reveals the influence path of information visualization, information sentiment and information authority on the behavior of reposting and commenting respectively. Differences of influence effect between different genders, ages, academic qualifications and occupational groups are compared. In the management of brand crisis information on Weibo, the relevant contextual factors can be regarded as the monitoring index of crisis information sharing behavior which can be used to identify what kind of contextual factors is more likely to lead to user reposting and commenting and to adopt targeted monitoring strategies based on differences in gender, age, education and occupation. Thirdly, through the study of the influencing mechanism of dynamic contextual factors of information sharing behavior of Weibo users, the author reveals the dynamic fluctuation characteristics of time lag, pulse disturbance, marginal impact and contribution rate under the influence of the total number of information reposts and comments, the number of blogger’s follows and followers, the number of follows and followers of IS and information temporal distance. In the management of brand crisis information on Weibo, the relevant contextual factors can be regarded as dynamic monitoring index of information sharing behavior and be divided into different levels so as to determine the effective time length of tracking the relevant monitoring indicators. The effect of each factor on the sharing behavior can be predicted at different time nodes, so as to accurately locate the key time period for tracking and monitoring the various influencing factors.

1.3 Research Questions and Structure 1.3.1 Research Questions Weibo has provided users with information functions such as reposting, commenting, following and being followed, which enable users to obtain information and share and interact with other users. A review of the information behavior of Weibo users in China shows that information reposting, commenting, following and hot topics are the four most popular information functions chosen by Weibo users. Among them, the first two functions enable users to share specific information, as well as the expression and transmission of various emotions. They are the major user behavior on Weibo. Information sharing refers to the process by which users communicate and interact with others mainly through reposting and commenting of relevant information and personal opinions and attitudes.4 Therefore, the information sharing behavior of 4

Zhao and Zhang (2013).

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1 Introduction

Weibo users involved in this study mainly refers to the behavior of Weibo users to repost and comment on information. In today’s volatile market environment, brand crisis occurs more and more frequently. Weibo, as an important way for people to obtain information, share information and interact with each other, has an important impact on the dissemination and spread of crisis information. At present, the research on the dissemination of Weibo information has become a hot topic in academic circles, and the research on the behavior characteristics and influence mechanism of brand crisis information on Weibo has received wide attention from academic circles and practitioners at home and abroad. There are also studies on the information behavior of Weibo users, but most of the relevant research focuses on the mode of dissemination of Weibo information, the effect of dissemination, user attitude, description of behavior characteristics and static path analysis. The research on the characteristics of information behavior, shock response, marginal impact, contribution rate of disturbance and behavior prediction needs to be improved. There is still a lack of literature on the fluctuation characteristics of Weibo users’ information behavior from the perspective of dynamic and contextual factors as well as study of the influencing mechanism. At the same time, due to the rapid development of computer information technology, user information behavior is greatly influenced by contextual factors, whose impact path can be described as: originated from certain condition, impacted on psychology and manifested in behavior. Therefore, it is necessary to explore information behavior from the perspective of contextual factors, in order to better reveal the mechanism of user behavior from the source and draw more comprehensive research conclusions and constantly improve the existing research. In this context, the author puts forward the following general research questions:

General Research Question: Based on the perspective of contextual factors, what are the fluctuation characteristics of the brand crisis information sharing behavior by Weibo users and what is its influencing mechanism process? Previous studies have gained results in the relevant fields of micro-blogging information dissemination, but they are inadequate for the contents and target of the present study. They have not touched upon issues such as autocorrelation analysis of information behavior fluctuation and the analysis of fluctuation characteristics is mainly through descriptive statistical analysis or through the analysis of the trend of the total line chart to draw relevant research conclusions. The findings lack precise conclusions on the autocorrelation of fluctuations, trend characteristics, periodic characteristics, irregular features and cluster characteristics. In terms of influence mechanism research, most of the previous research has been carried out from the perspective of static mechanism, with relatively few research literatures from the perspective of dynamic mechanism, especially from the perspective of contextual factors to study

1.3 Research Questions and Structure

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the dynamic influence mechanism of crisis information sharing behavior. Among them, in the study of static influence mechanism, the literature in the past concerns with the perspective of “personal characteristics – motive – behavior”, or through regression model (such as logistic regression), analysis of variance(ANOVA) and other methods to study the influence mechanism of the impact of related factors. Regression model and ANOVA usually draw conclusions about the significance, size and direction of the influence of independent variables on dependent variables, but research conclusions can’t be drawn about the specific paths of the influence and the size of the path coefficients, resulting in “knowing the phenomenon but not knowing the reason”. In addition, in the past, previous scholars have put forward corresponding information sharing behavior supervision schemes from the macro and meso levels, while relatively few studies have been done on specificity and precision monitoring strategies. It is likely that enterprises will have difficulty in regulating crisis information sharing behavior according to such macro- and medium-term strategies, such as not being targeted and difficult to implement. Therefore, the accuracy of its research conclusions still needs to be further improved. Based on the current situation of theoretical research, in order to convert the above-mentioned general research problems into corresponding operability research questions, the author proposes the following concrete sub-questions:

SQ1: What are the autocorrelation characteristics of the fluctuation of information sharing behavior of Weibo users in brand crisis? What are its trend characteristics, periodic characteristics, and cluster characteristics? SQ2: What static and dynamic contextual factors have a significant impact on the information sharing behavior of Weibo users in brand crisis? SQ3: What is the influence mechanism of static contextual influence factors on user information sharing behavior in brand crisis? How does the effect size differ between user groups? SQ4: What is the influence mechanism of dynamic contextual influence factors on user information sharing behavior in brand crisis? What are the dynamic changing characteristics of pulse disturbance, marginal impact and contribution rate? SQ5: On the basis of the above-mentioned research conclusions, how to implement specific monitoring of the information sharing behavior of Weibo users in brand crisis?

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1 Introduction

1.3.2 Research Contents In order to explore the influence mechanism of brand crisis information sharing behavior of Weibo users from the perspective of contextual factors, combined with the research questions raised in this book, it is necessary to analyze the fluctuation characteristics of information sharing behavior of Weibo users accurately. On the basis of a detailed analysis of the phenomenon characteristics of the problems, it is necessary to find the factors behind the phenomenon. This book mainly looks for and excavates the more significant factors from the perspective of the contextual factors. Based on a comprehensive analysis of the characteristics of the phenomenon and the acquisition of relevant influencing factors, it further examines how the corresponding influencing factors affect the user’s information sharing behavior, i.e., the influence mechanism process. Finally, on the basis of the above-mentioned research on the characteristics, influencing factors and mechanism of information sharing behavior, the practical value of the study is found and its conclusions are applied to practice so as to propose strategies and measures for accurate monitoring of Weibo users’ information sharing behavior in brand crises. The specifics of the study are as follows: First, analysis of the fluctuation characteristics of information sharing behavior of Weibo users in brand crisis. In order to explore the influence mechanism of brand crisis information sharing behavior, it is necessary to analyze the behavior characteristics accurately in order to have a deeper understanding and grasp of the research problem. It is necessary to lay a theoretical foundation for extracting the influencing factors that produce the phenomenon and exploring its mechanism of action. Because people’s network information behavior is usually influenced by behavior habits and time periods, the fluctuation process of information sharing behavior of Weibo users usually includes autocorrelation, trend characteristics, periodic characteristics, cluster characteristics and irregular characteristics. In order to understand and analyze the behavior phenomenon in depth, it is necessary to decompose and separate the component elements of the behavior, so as to accurately analyze its autocorrelation, the propagation process, weekly and daily fluctuation trend characteristics, periodic characteristics, irregular features and cluster characteristics. Second, analysis of the influencing factors of information sharing behavior of Weibo users in brand crisis. On the basis of the phenomenon analysis of the abovementioned user information sharing behavior characteristics, in order to provide concrete and feasible influence factors for the follow-up influence mechanism, it is necessary to extract and analyze the specific influencing factors behind the phenomenon. This study mainly explores and excavates the static and dynamic contextual influence factors that have a significant impact on the user’s information sharing behavior from the perspective of the contextual factors. Third, study on the influence mechanism of static contextual factors of information sharing behavior of Weibo users in brand crisis. On the basis of the precise analysis of the above information sharing behavior characteristics and the extraction of significant influence factors, it is necessary to further explore how the factors affect the user’s information sharing behavior, i.e., the mechanism of influence, which mainly

1.3 Research Questions and Structure

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includes static influence mechanism and dynamic influence mechanism. Static influence mechanism is the preliminary mechanism analysis, and the dynamic mechanism is the higher level of mechanism analysis. Based on the extracted static contextual factors, this paper first studies the static influence mechanism of information sharing behavior of Weibo users and explores the influence mechanism and action path size of information visualization, information sentiment and information authority on reposting and commenting behavior. It then analyzes gender, age, education and occupational groups on the basis of the correct theoretical model after verification, so as to reveal the differences in different groups under different path coefficients. Fourth, study on the influence mechanism of dynamic contextual factors of information sharing behavior of Weibo users in brand crisis. On the basis of the above research, in order to find out the dynamic influence process of contextual factors on user information sharing behavior, it is necessary to further study the static influence mechanism of information sharing behavior of Weibo users according to the extracted dynamic situation factors, and to dynamically decompose the total number of blogger’s reposts and comments, the number of their followers and follows, the number of followers and follows of IS, and information temporal distance on the dynamic change process of time lag, pulse disturbance, marginal influence and contribution rate of information sharing behavior influence. Finally, study on the monitoring strategy of information sharing behavior of Weibo users in brand crisis. Based on the user information sharing behavior characteristic analysis, influence factor extraction and mechanism of action research, the study will refine the practical value of the research, applying its conclusions to practice and proposing specific strategies to monitor the information sharing behavior of Weibo users in brand crisis from the three aspects of monitoring period positioning, static contextual index and dynamic contextual index.

1.3.3 Structure Depending on the process and steps of academic research, a complete research is usually conducted in the logical order of “raising questions – analyzing problems – solving problems”. That is, according to the actual background and theoretical research status quo, research questions are raised and the problems are clearly defined. Then, the characteristics of the research problems are described and analyzed in depth. Then, causes of the phenomenon and its formation mechanism, i.e., the causes of the problems and the mechanism of action will be explored and studied accordingly. Finally, on the basis of in-depth analysis and understanding of the characteristics of the problem and its mechanism of action, the solution and strategy of the problems are put forward, transforming the theoretical research into practical application to guide future practice. According to the above research process and paradigm, the corresponding structural order of this study is arranged as follows: Chapter 1 Introduction;

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1 Introduction

Chapter 2 Literature review and theoretical foundation; Chapter 3 Fluctuation features of brand crisis information sharing by Weibo users; Chapter 4 Contextual factors affecting brand crisis information sharing by Weibo users; Chapter 5 Static influencing mechanism of Weibo users’ information sharing of brand crisis cases; Chapter 6 Dynamic influencing mechanism of Weibo users’ information sharing of brand crisis cases; Chapter 7 Strategies for Monitoring brand crisis information sharing by Weibo users; Chapter 8 Conclusion and suggestions. Figure 1.1 provides the research framework.

1.4 Research Methods and Technical Route 1.4.1 Research Methods Firstly, questionnaire. It is used to collect data on the reposting and commenting behavior of Weibo users in brand crisis, including the survey scale data on the demographic characteristics of respondents and static contextual influence mechanism. Secondly, time series ARIMA analysis. It is used to construct and analyze the evolution model and autocorrelation characteristics of information sharing behavior of Weibo users in brand crisis. ARIMA analysis is used to determine whether the time variable has autocorrelation and the specific characteristics of the pulse response caused by the self-correlation of the variable itself through the model construction and estimation of a time variable with autocorrelation. By analyzing the past research results, this study finds that the corresponding information behavior will be influenced by the past self-behavior, and combined with the needs for self-correlation characteristic analysis, while the information sharing behavior data also meets the requirements of the corresponding analysis of time series. Therefore, based on the content of this study, the purpose of the study and the characteristics of the data, the use of ARIMA to test and analyze the self-relevance of information-sharing behavior is suitable. Thirdly, time series trend decomposition method. It is used to accurately analyze the trend characteristics, periodic characteristics and irregular characteristics of the fluctuation characteristics of information sharing behavior of Weibo users in brand crisis. The trend decomposition of time series is mainly the precise decomposition and separation of trend variables, periodic variables and irregular variables from a certain time variable. By analyzing the past research results, this study finds that the corresponding information behavior fluctuations are composed of trend characteristics, periodic characteristics and irregular characteristic components, combined with

1.4 Research Methods and Technical Route

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Fig. 1.1 Research structure

the needs of this study for accurate analysis of the size and characteristics of each characteristic component, while the information sharing behavior data also meets the requirements of the corresponding analysis of time series. According to the content, purpose and data characteristics of this study, time series trend decomposition method

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1 Introduction

is suitable for the accurate decomposition and analysis of trend characteristics, periodic characteristics and irregular characteristic component variables of information sharing behavior fluctuation. Fourthly, auto regression conditional heteroscedasticity (ARCH). It is used to analyze the cluster characteristics of the fluctuation of information sharing behavior of Weibo users in brand crisis. ARCH is mainly based on the autoregressive equation residual square correlation and ARCH effect test, so as to determine whether the time series has the cluster characteristics of fluctuation. Due to the existence of cluster, time series often show the characteristics of less fluctuation in one period and relatively large fluctuation in another. On this basis, the characteristics of time series clusters can be analyzed accurately by ARCH’s residual sequence diagram and conditional variance chart. After analyzing previous research results, this study finds that the corresponding information behavior fluctuations usually have cluster characteristics. For ARCH provides accurate analysis of cluster effect size and distribution and information sharing behavior data meets the requirements of the corresponding analysis of time series, ARCH can be used to extract and accurately analyze the cluster characteristics of fluctuations of user information sharing behavior. Fifthly, Structural Equation Model (SEM). It is used to estimate and test the influencing mechanism model of static contextual factors of crisis information sharing behavior, and to compare and analyze the differences between different gender, education, age and occupational groups. In the study of multiple variable relationships, the traditional method mainly uses correlation analysis and regression model, which requires that each structure should only be composed of a single measurement index or problem item, and it is difficult to deal with the research framework of each structure involving multiple measurement dimensions or question items. SEM model is a multi-regression research method that can deal with each structure at the same time, involving multiple measurement indicators or problem items. SEM can estimate the error in the measurement and be used to calculate the reliability and validity of the measurement results. It is not limited by basic assumptions in classical measurement theory, and it can easily detect the correlation between certain errors. This part mainly explores the impact path and size of static contextual factors on reposting and commenting behavior, in which the theoretical model contains several independent variables, mediating variables and dependent variables, where each variable involves multiple measurement indicators and question items, and the data processing process will involve the fitting and estimation of multiple sets of regression equations, each of which contains some errors in the measurement. Therefore, according to the content, purpose and data characteristics of this study, SEM should be used to process and analyze the theoretical framework of influencing mechanism of static contextual factors, in order to reduce the errors and defects of traditional regression models in data processing. Sixthly, vector auto regression (VAR). It is used to decompose and analyze the dynamic change process of time lag characteristics, pulse disturbance and fluctuation contribution rate of the dynamic contextual influencing factors the information sharing behavior of Weibo users in brand crisis. It is mainly used to model a group of variables with autocorrelation and hysteresis endogenous phenomenon.

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Pulse response estimation can be used to analyze the dynamic effect on the system when the random perturbation term of an endogenous variable changes, so as to better reveal the dynamic disturbance characteristics between variables. At the same time, the variance decomposition of the fluctuation contribution rate of each influencing factor can be made to identify the importance of the influence of different influencing factors on an endogenous variable, so as to analyze the rate of change contribution of the structural impact of some related endogenous variables to a particular endogenous variable in the model. In this study, due to the existence of autocorrelation of information sharing behavior time series, as well as the influence of sharing behavior and corresponding contexts, the factors are affected by hysteresis endogenous impact. Therefore, according to the content and of purpose of the study and the characteristics of the data, to conduct precise analysis of the dynamic impact process and contribution rate, it is suitable for the model construction and dynamic analysis of the dynamic contextual influence mechanism of information sharing behavior with VAR, so as to draw more accurate and detailed research conclusions. Seventhly, the state space model. It is used to analyze the dynamic change process of the marginal influence of dynamic contextual influencers on reposting and commenting behavior of Weibo users. It is mainly used to construct models of time series or sequence groups affected by unobservable factors, so as to accurately and dynamically analyze the marginal influence effect at a particular point in time, which mainly include measurement error, rational expectation and so on. In view of the analysis of the marginal influence of dynamic contextual factors on information sharing behavior in this study, in the dynamic change process of the marginal influence of dynamic contextual factors on reposting and commenting behavior, in addition to being influenced by independent variables, it is also influenced by many difficult observation factors such as measurement error and rational expectation. Therefore, according to the content, research purpose and data characteristics of this study, dynamic analysis of the change process of the marginal influence of relevant factors from the measurement equation and the state equation by the state space model can better reduce the measurement error caused by the interference of non-observable factors, and thus draw more accurate research conclusions.

1.4.2 Technical Route Figure 1.2 provides a description of technical route of the present study.

1.5 Research Innovation The first innovation is the study of the influence mechanism of dynamic contextual factors of information sharing behavior of Weibo users in brand crisis. Previous researches mainly use the relevant analysis, variance analysis and regression model to

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Fig. 1.2 Technical route

1 Introduction

1.5 Research Innovation

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explore and analyze the influencing factors and effects of reposting and commenting behavior of users, which can draw conclusions such as whether the influence of independent variables on dependent variables is significant, how big it is, and whether it is positive or negative. But they cannot reveal the characteristics of dynamic processes in which independent variables affect dependent variables nor the fluctuation process of influence size. At the same time, since the corresponding independent variables and dependent variables exist in time series, the size relationship between them and the positive and negative effects are constantly changing at different points in time, i.e., the variable relations is dynamic. It is difficult to achieve an accurate description of the dynamic relationship between independent variables and dependent variables only through the correlation analysis or regression analysis of a certain point in time or the total sum. Most of the research on the influence mechanism of information behavior in the past has been carried out from the perspective of static mechanism, while the research literature from the perspective of dynamic mechanism is relatively few. There are even fewer literatures on the dynamic influence mechanism of crisis information sharing behavior from the perspective of contextual factors. This has provided the research space for the present study. The second innovation is the precise analysis of the fluctuation characteristics of Weibo users’ information sharing behavior in brand crisis. Through the time series ARIMA model, trend decomposition and autoregression conditional heteroscedasticity model, the autocorrelation, trend characteristic, periodic characteristic and cluster characteristic of information sharing behavior fluctuation are decomposed and analyzed accurately. Previously, autocorrelation analysis of information behavior fluctuation has rarely been studied, and the analysis of fluctuation characteristics has been mainly based on descriptive statistical analysis and through the analysis of the trend of the total line chart. In reality, these fluctuations are a combination of trend components, periodic components and irregular components, possessing autocorrelation and cluster characteristics. Therefore, descriptive analysis without the decomposition and separation of the characteristic components can only draw fairly preliminary conclusions, and cannot offer a more accurate study of the autocorrelation, trend characteristics, periodic characteristics, irregular characteristics and cluster characteristics of fluctuations. Therefore, the imperfection of the past researches is turned into a research opportunity for the present study. The third innovation is the study made on the influencing mechanism of static contextual factors of information sharing behavior of Weibo users in brand crisis. It has constructed a influence mechanism model of information visualization, information sentiment and information authority on reposting and commenting behavior, and analyzed the differences in the size of each path among different genders, ages, academic qualifications and occupational groups. In the study of static influence mechanism, the literature in the past concerns with the perspective of “personal characteristics – motive – behavior”, or through regression model (such as logistic regression), variance analysis and other methods to study the influence mechanism of related factors. It follows the traditional psychological research paradigm, and its independent variable selection often comes from the user psychology variable.

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1 Introduction

With the rapid development of computer information technology, user information behavior is greatly influenced by contextual factors, whose impact path can be described as: originated from certain condition, impacted on psychology and manifested in behavior. Therefore, in the study of information behavior influence mechanism, it is more suitable and quite necessary to start from the perspective of contextual factors in order to better reveal the mechanism of user behavior from the source of power and draw more comprehensive research conclusions to improve the existing research. Former studies can draw conclusions about the significance, size and direction of the influence of independent variables on dependent variables, but cannot explain the concrete influence path or the size of the path coefficient, resulting in knowing the hows but not the whys. In this context, based on information contextual theory, information field theory and information processing theory, this study takes information visualization, information sentiment and information authority as independent variables, perceived fluency, cognitive absorption and cue dependence as intermediary variables, and constructs theoretical models with harm relevance as the regulating variable, so as to study the mechanism of static contextual factors on crisis information sharing behavior. This study is innovative both in the selection of independent variables, mediation variables and moderating variables, as well as in the construction of impact paths and the comparative analysis of the differences in the effects of different user groups. The fourth innovation is that the study has put forward precise monitoring strategies of information sharing behavior of Weibo users in the brand crisis. Specific and accurate monitoring strategies are proposed from the three aspects of monitoring period positioning of information sharing behavior, static contextual indicators and dynamic contextual indicators based on analysis of user information sharing behavior characteristic, factor extraction and impact mechanism. Previous scholars have put forward information sharing behavior supervision schemes from the macro and meso levels, while there has been relatively little research on specific and precise monitoring strategies. However, such measures may be difficult for enterprises to implement in brand crisis as they lack clear target. At the same time, the past research on the classification of monitoring indicators and monitoring period positioning is mainly based on qualitative analysis and descriptive statistical analysis. Because of the limitations of these methods, the accuracy of their research conclusions still needs to be further improved. There is still very little research on the monitoring of information behavior from the perspective of contextual factors. Therefore, the present study, which is based on the perspective of contextual factors, has put forward specific and accurate monitoring strategies for the information sharing behavior of Weibo users in brand crisis, which is innovative in both theory and practice.

References

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References China Internet Network Information Center (CNNIC). (2015). The 35th Statistical Report on Internet Development in China [EB/OL]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/201502/P02015020 35518020054676.pdf China Internet Network Information Center (CNNIC). (2016). The 38th Statistical Report on Internet Development in China [EB/OL]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201608/P02 0160803367337470363.pdf Zhang, J., & Zhao, L. (2015). Review of studies on Weibo user behavior. Information Science, 8, 27. Zhao, L., & Zhang, J. (2013). Multi-dimensional analysis of microblog user behavior research. Information and Documentation Services, 34(5), 65–70.

Chapter 2

Literature Review and Theoretical Foundation

Through literature combing and theoretical exploration, this chapter lays the theoretical foundation and provides theoretical support for the following research. Literature review covers studies on brand crisis dissemination, microblogging information dissemination, information behavior research and behavior contextual factors. At the same time, information contextual theory, information ground theory, information processing theory, the theory of psychological fields and the theory of information behavior are expounded. The research framework for this chapter is shown in Fig. 2.1.

2.1 Research Status Quo 2.1.1 Brand Crisis Dissemination Regarding the spread of brand crisis, this book mainly combs the relevant literature at home and abroad from the three aspects of definition of brand crisis, crisis information dissemination and crisis information supervision.

2.1.1.1

Definition of Brand Crisis

Different scholars have different definitions on crisis. Rosenthal and Pijnenburg (1991) defines a crisis as an event that poses a serious threat to the organization’s basic value system and operational structure, requires critical decisions in a short period of time and in uncertain situations, and believes that the crisis will have a significant impact on the core values of the organization. This definition is now widely recognized in the relevant research areas. Barton (1994) defines a crisis as an event with considerable uncertainty and unpredictable negative effects on the organization or business, which, if mishandled, can result in significant or serious consequences for the organization, employees, reputation, products, assets, and services. Perse (2001) © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_2

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Fig. 2.1 Structure of Chap. 2

also believes that crisis is uncertain and sudden, prone to loss of control and emotional disturbance which will affect a large number of people, and may even endanger the survival or property security of an organization. As for brand crisis, Dawar and Lei (2009) believe that the brand crisis refers to the sudden changes in the enterprise itself, customer structure, competitors and other external environment, or the implementation of improper brand strategy, thus damaging the brand image or corporate image, resulting in a reduction in people’s brand trust, and thus directly posing a serious threat to the brand and even the survival of the enterprise. Since the concept of branding was introduced into China, scholars in various fields have come up with different definitions. Previous studies define brand crisis as due to abnormal external environment, or the implementation of the enterprise’s non-effective or inappropriate brand operation strategy, the corporate image or brand image is damaged, and people’s recognition and trust in the brand is reduced in a short period of time, thus affecting the willingness of consumers to buy or continue to buy and posing a serious threat to the survival and development of the brand. Guo (2006) stresses the important role media and information plays in the crisis communication. She thinks that the brand crisis is due to invalid or improper brand operation or marketing management of the enterprise, or because of the competitive landscape, customer characteristics and other unexpected changes of

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external environment, so that the enterprise management, brand image and product services and other aspects of negative information quickly and widely spread to the public, causing people to doubt the brand and reducing the public’s goodwill and willingness to buy. Refusal and hostility towards the brand may arise, thus posing a serious threat to brand maintenance and development. The above literature defines and describes the brand crisis from different perspectives, but with the rapid development of information technology, the emergence of new media represented by the Internet has greatly affected the formation of brand crisis, the dissemination of information and the evolution of crisis. Based on previous studies, this book defines brand crisis from the perspective of media communication as follows: When the operation or management of an enterprise or organization is ineffective or abnormal, or because of unexpected changes in the external environment such as competitors or customers, or because of malicious rumors from the outside world, negative information about corporate image, organizational image and product services is widely disseminated and spread rapidly, leading to a situation out of control, thus causing people’s doubts over the brand, reducing the public’s good feelings about the brand, or causing a refusal and hostility to the brand, which poses a serious threat to the survival and development of the enterprise or organization.

2.1.1.2

Dissemination of Crisis Information

From an Informational Perspective Fearn-banks (2010) defines crisis communication as the dialog between the organization and its public (s) prior to, during, and after the negative occurrence. The dialog details strategies and tactics designed to minimize damage to the image of the organization. Austin and Fisher (2012) summarize the characteristics of user’s choice of information channels by using the social-mediated crisis communication (SMCC) model and come to the conclusion that: audiences use social media during crises for insider information and checking in with family/friends and use traditional media for educational purposes. Convenience, involvement, and personal recommendations encourage social and traditional media use. Duggan and Banwell (2004) analyze the influencing factors in the process of sending and receiving information from both internal and external aspects, and construct the model based on the process perspective of information transmission and information reception. However, with the rapid development of Internet and information technology, the research on information dissemination has become the research focus. Because of the complex characteristics of Internet information dissemination mechanism and influencing factors, scholars at home and abroad mainly use complex network theory and system simulation to study the dissemination of crisis information on the Internet. Starting from the network system characteristics, Monge et al. (2003) explored and studied the interaction between the various influencing factors in the process of crisis transmission and its dynamic mechanism based on the theory of complex adaptation system by using computer simulation technology. Moreno et al. (2004) constructed a

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scaleless network crisis information dissemination model with a focus on the evolutionary laws and characteristics of crisis information in the process of dissemination based on complex network theory by using computer simulation technology. Coombs (2002), Coombs (2004), Coombs (2007) proposed situational crisis communication theory (SCCT) from the angle of situational factors of information dissemination. The theory mainly expounds that information will be influenced by different situational factors in the process of dissemination, while the unsympathetic factors have different influences on the communication process and dissemination effect of crisis information through the differentiation mechanism, which can help enterprises or groups to identify different situational factors in the spread of crisis information and to put forward crisis response strategies for different situations.

From a Process Perspective In addition to information-based perspective, scholars have benefited from studying brand crisis from the information dissemination process perspective. They believe that the communication process of crisis information is composed of several stages, which are interconnected and have different characteristic trends. Fink (1986) divided crisis communication process into four stages, i.e., prodromal crisis stage, acute crisis stage, chronic crisis stage, and crisis resolution stage. Fink’s Crisis Life Cycle model is a representative work and has been recognized by a large number of scholars. Cao (2010) takes the timeline of the crisis information dissemination process as the criterion for segmentation, and thinks that the network crisis information communication experiences four stages of diffusion, heated discussion, sublimation and continuation.

2.1.1.3

Crisis Information Supervision

From the perspective of the function and role of crisis communication, Heath (1998) believes that different management priorities and functional characteristics should be different at different stages of crisis information dissemination. Heath’s famous 4R model holds that the complete crisis management process should have four stages: reduction, readiness, response and recovery. During the readiness stage, the crisis development situation’s monitoring and early warning should be implemented and equipped with the corresponding personnel and resources to track and identify the crisis. During the response stage, the crisis development situation should be analyzed to design effective and efficient crisis response strategy to monitor and manage crisis. It can be seen that in crisis management, understanding and mastering the regular characteristics of crisis information dissemination and effective monitoring and prediction of crisis information dissemination process are important basis for the correct formulation of crisis response strategies and effective intervention in crisis communication. He (2003) thinks that monitoring and forecasting the process of crisis information dissemination is the starting point of all crisis management work. In the process of crisis information dissemination, enterprises or organizations should pay

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enough attention to the regular characteristics of the evolution of crisis information dissemination and its monitoring links, and pay attention to systematic, comprehensive and continuous data collection, analysis and utilization. Wei and Zhao (2006) developed an intelligent and real-time crisis information dissemination monitoring system based on the meta search engine theory. The system helps to monitor and track the process of crisis information dissemination and use this information to help enterprises or organizations to develop effective crisis response strategies, thereby improving the efficiency of crisis management and helping enterprises to minimize crisis damages. Through the induction and analysis of specific cases, Ye and Pang (2003) found that some organizations or enterprises failed to effectively intervene in the crisis information dissemination process in the face of crisis information dissemination. The cause is that managers fail to effectively monitor the dissemination of crisis information, so that they cannot implement information feedback and information intervention. Wang and Song (2006) analyzed and explored the process of brand crisis information dissemination and coping strategy from the perspective of competitive intelligence theory. They think that the crisis communication management process should strengthen the monitoring, identification and sharing of crisis information, and use the monitoring and identification results to integrate information networks, organizational networks and networks effectively, so as to achieve effective and efficient strategy development and crisis management.

2.1.2 Information Dissemination on Microblogging Platforms In recent years, with the rapid development of microblogging information platform, research on the information dissemination on microblogs has become a hot topic for scholars. At the same time, with the continuous expansion of research work, related research areas and research topics are also expanding, including hot topics such as microblogging information flow, information dissemination model, and communication impact mechanism.

2.1.2.1

Definition and Characteristics of Microblogging

With regard to the definition and characteristics of microblogging, different scholars have offered different definitions and explanations. Kaplan (2015) consider microblogging to be an information platform for information posting, dissemination, sharing and interaction. It makes full use of the advantages of Internet information dissemination to enable users to post and interact with information of required size, including text, video, audio, pictures and other formats of data information. Li (2011) defines microblogging as a convenient and timely platform for the dissemination and exchange of information, also known as micro-blogs. With it, users can establish

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various forms of user relationship networks and participate in or build a specific virtual community according to their own needs. Users can post instant message up to 140 words, or other audio, visual and video formats of information publishing, dissemination, sharing and communication. Regarding the attribute characteristics of microblogging, most scholars use the theory of communication to elaborate on the characteristics of information dissemination and mode of communication. Yang (2010) summarizes the characteristics of microblogging communication from the perspective of microblogging communication process, user characteristics and information characteristics, and thinks that microblogging communication has the characteristics of formal interactivity, widespread content, information fragmentation, timeliness of dissemination, information miniaturity and user diversity. Zhao and Zhang (2013) believe that the microblogging information platform mainly provides information behavior functions such as following, being followed, reposting and commenting. Users acquire information by following other people’s microblogs, spread their own information by being followed by others, and share information by reposting and commenting on other people’s microblogs.

2.1.2.2

Factors Influencing the Dissemination of Microblogging Information

Suh and Hong (2010) have used the main component analysis method to process a large amount of microblogging data, and explored the main factors influencing the behavior of information reposting in microblogging. Their study shows that the hashtags and uniform resource locators (URL) in the blog post have the greatest influence on the reposting/retweeting behavior. Amongst contextual features, the number of followers and followees as well as the age of the account seem to affect reposting, while, interestingly, the number of past posts does not predict reposting of a user’s post. Liu et al. (2012) applies Heuristic Systematic Model (HSM) to study the impact of microblogging information dissemination and diffusion factors. Using the reposting data of emergency information in Sina Weibo as the empirical data source, the causal relationship between the variables is tested and analyzed by regression model. The results show that source trustworthiness, source expertise, source attractiveness, and the number of multimedia have significant effects on the information retweeting. Stieglitz and Xuan (2011) have studied the impact of information about political events being reposted on Weibo from the perspective of emotional differences in information and found that information with positive or negative emotions was more likely to be reposted than information with neutral emotions. Cha et al. (2010) have compared and analyzed the impact of indegree, retweets, and mentions “@user” (the user nickname) of the source user from the perspective of the author’s attributes, and found that the user characteristic factors were significantly related to and had a significant impact on the reposting and being reposted of their information. Hansen and Arvidsson (2011) have divided Twitter information into news and non-news categories through machine learning methods, and used content analysis to evaluate the emotionality of the information posted daily on the microblog. They have

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built a regression model to study the relationship between information emotionality and information dissemination characteristics. The results show that the emotionality of information on the Twitter platform is significantly related to the characteristics of information dissemination, and negative emotional content can easily lead to the dissemination of news information, whereas positive emotional content can easily lead to the spread of non-news information. In China, Zhang (2013) have studied users’ microblogging marketing information behavior and found that the professionalism of Weibo bloggers have a significant impact on information reposting and commenting. Sun and Li (2012) have analyzed the top reposts on Sina Weibo and found that the content characteristics of the microblogging information were the most important for people’s reposting. Pleasant and practical information with easy content has the highest rate of reposting and graphic information is more likely to be retweeted and shared than complex and lengthy one.

2.1.2.3

Information Dissemination Model in Microblogging

Kwon et al. (2013) have made an empirical study on the characteristics and influence mechanism of information reposting behavior through the collection and processing of microblogging data, and constructed a theoretical model of Weibo user’s reposting behavior, which can be used to predict and analyze users’ reposting behavior. Wang and Jin (2012) believe that users who have not followed the source blogger may still repost the message, while other users may repost messages with the same contents multiple times. They incorporate these two reposting behaviors into a modified SIS (susceptible–infected–susceptible) model as variables, thus forming a new information propagation model, which can be used to dynamically describe the information reposting behavior. By studying Twitter, Boyd and Golder (2010) have analyzed the different forms of information reposting behavior in microblogging, and explored the various possible motivations. Yang and Counts (2010) sum up 22 characteristics of information reposting behavior from the aspects of time distance, user attributes and blog features through empirical research on Twitter, and the evolution of user’s reposting behavior can be predicted according to these characteristic variables. Yi et al. (2013) have summed up seven information dissemination modes according to the different network characteristics, each of which has a different information dissemination mechanism and dissemination form. Ping and Zong (2010) built a microblogging information network dissemination model based on complex network and social network theory. They have collected relevant data in Sina Weibo, taking the users’ follows and followers as the research focus and adopting the perspective of centrality, degree, node and other network attributes.

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2.1.3 Research on Information Behavior 2.1.3.1

Characteristics and Classification of Information Behavior

According to Davenport (1997), “simply put, information behavior refers to how individuals approach and handle information. This includes searching for it, using it, modifying it, sharing it, hoarding it, even ignoring it.” Sonnenwald (1999) sees information behavior as a process by which users interact with information resources to meet specific information needs, including information exploration, seeking, filtering, use, and communication. In the Internet environment, because of the convenience and powerful function of the network, the information behavior of network users has more unique and extensive characteristics. Li (2004) describes the information behavior of network users as a collection of users who are stimulated by their own information needs and influenced by specific motivations in the network environment, thus using a tool or information platform to retrieve, select, share, communicate and post information. Because information behavior is human specific, it has a variety of social attributes. Deng and Li (2008) point out that people’s information behavior has six main characteristics, namely, sociality, convenience, economy, purpose, habit and accumulation. Because information behavior is the sum of all related activities carried out by people with information as the object, it can be divided into different types according to different purposes and functions. Through the summary of network user information behavior, Wang and Deng (2009) think that there are six major types of user information behavior, namely, information search, information preservation, information processing, information utilization, information sharing, information interaction.

2.1.3.2

Factors Influencing Information Behavior

Harris and Dewdney (1994) put forward six basic principles for information seeking and acquiring behavior. They are: (1) the nature of an information need and the type of help needed depend on the helpseeker’s situation; (2) the decision to seek help or not seek help is affected by many factors; (3) people tend to seek out information that is most accessible; (4) people tend first to seek help or information from interpersonal sources, and especially from people like themselves; (5) information-seekers expect emotional support; and (6) people follow habitual patterns in information seeking.

It can be seen that in the process of information seeking, acquiring and using, the internal and external environment will have an important impact on the user’s information behavior. On the one hand, user’s information behavior will be influenced by the difference of the actor’s demographic characteristics, such as age, education, occupation, sex, cognitive style and information skills; on the other hand, it is also affected by external factors such as network environment, information context, software and hardware.

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Wang and Deng (2009) believe that the information behavior of users in the network environment will be affected by a variety of factors, including information characteristics and information environment factors. Among them, the information characteristic mainly refers to the characterization and organization form of information, the symbolic form of information. The information environment factors are mainly reflected in the network hardware and software, interactive form, information system, social norms and time and space of action. Contextual factors of information behavior mainly include activating and obstructing factors. The individual characteristics of information users mainly include work role, media literacy, cognitive style, time frame, knowledge structure, social experience and so on. Cao and Deng (2006) point out that the information behavior of network users is generally influenced by personal characteristics, social relations, interpersonal relations and internal and external environment. Gan et al. (2008) divide the network information users into field-dependent and field-independent from the perspective of information cognition style, and point out that field-dependent users are more susceptible to the influence of external environment than field-independent users. Through the analysis of a large number of user information behavior data, Lin (1996) believes that people’s information behavior will be influenced by factors from the internal and external environment of the subject, such as the users’ information literacy, cognitive style, information environment and drivers of information need.

2.1.3.3

Information Behavior Patterns

The theory of information behavior and related models can reveal the functional structure, constituent elements and mechanism of action of information behavior, and can provide theoretical basis and support for the research of information behavior of Internet users. Foreign scholars began to study information behavior relatively early and many were inclined to use psychology as the basis for theoretical research and model construction. Wilson (1981) was an early scholar who studied information behavior models, and the Wilson Information Model still carries importance today. The model holds that the individual information behavior starts from the information need, which leads to information seeking, disseminating, using and sharing, which is influenced by many external factors during the whole process. Wilson (1999) later perfected the model, incorporating the contextual influencing factors and related intermediary variables on the basis of the original model, and believed that there is a dynamic mechanism in all aspects of the behavior process. Because Wilson first considered the influence of contextual factors, the theory created during this period focused more on the study of the causes and ways of user information behavior, and emphasized that the environmental factors related to information behavior should be put into the theoretical model. His study has laid a solid foundation for the development of contextual theory on information behavior. Niedzwiedzka (2003) has presented an integrated model of information behavior, which contain views such as:

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Spink and Cole (2006) proposed the Primary Model of Information Behavior, recognizing that some of the “pre-situations” in which people are located stimulate them to conduct information inquiry, collection, processing and utilization within a certain range according to their existing knowledge structure. The information behavior process is that users constantly adjust their behavior to adapt to the changing external environment or situation, so as to maintain the cognitive balance. Taylor (1986a) studied the environmental factors of information behavior and put forward the theory of information use environment (referred to as IUE), which was enriched and perfected in 1991. He points out that the information behavior environment is beginning of user’s information needs, retrieval, selection and utilization and different information environment or situation creates different information behavior subjects, leading to manifestation of different characteristics and states of information behavior. Sonnenwald (1999) has constructed the theory of information horizons, in which situation, context, and social network form the main framework of the model, which can be used to analyze the characteristics of user information behavior and its influencing factors. On this basis, it is also found by empirical research that situational factors play an important role in the formation of people’s information behavior. Bystrom and Jarvelin (1995) point out that contextual factors, individual differences, environmental structures, and query types all have important effects on user information behavior, and that different individual experiences and cognitive styles contribute to different information behaviors. Chinese scholars have also been conducting research in this area. Wang and Deng (2009) applied related theories on behavior, communication and sociology to study network users’ information behavior. Through the study of scientific and technological user information behavior, Gan Liren (2007) found that in the Internet environment, network environment characteristics, system characteristics and personal characteristics have an important impact on user information behavior and thus built an information behavior factor analysis model.

2.1.4 Contextual Factors of Behavior 2.1.4.1

Concepts and Classifications of Context

Schilit et al. (1994) define context as individual-perceived physical presence, the collection of nearby people, hosts, and accessible devices, as well as changes to such things over time. They consider contextual factors to be composed of physical context, computer context and user context. Schmidt et al. (1999) define context as “the collection of nearby people and objects, as well as changes to those objects over time”. Context includes subject, behavior and environment, each relating to

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a set of relevant contextual information, such as infrastructure (network connection, connectivity, nearby information resources), physical conditions, time, user and social conditions. According to Dey (2001), context is any information that can be used to characterize the situation of an entity, which can be a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves. This definition has now been endorsed by a large number of scholars. Contextual factors can be divided into different types, depending on the criteria. Dey (2001) believes that contextual information mainly includes entities, locations, directions, emotional states, time information, and concerns in the user’s environment. Christiansen and Dahl (2005) consider context to be the sum of any perceptible or non-perceived entities and information related to the physical and social nature of user’s interactions with others. Snowdon and Grasso (2000) believe that context has multiple hierarchies, including personal, project, group and organization. Henricksen and Indulska (2002) expand context to mean the sum of information about all characteristics, states, and trends of the user, the computer, and the environment in which they are located. Chinese scholars have also carried out an active study of the definition and characteristics of context. Jiang and Huang (2009) define context as the evolution of the situation, the state of affairs and the future development trend faced by the actor after an emergency. Li (2010) believes that context is a collection of trends and conditions that things present in the course of development and evolution. Trends are the embodiment of information of evolution and the process. Conditions are the result of evolution. Gu (2009) divides contextual factors into: user context, which mainly refers to user demographic characteristics, user identity and the location of the user; computer context, which mainly refers to system characteristics, network conditions and network relations; social context, which mainly includes social norms, ethics, habits, systems, laws, etc.; physical context, which mainly refers to hardware facilities, orientation, decoration, brightness, temperature and so on. In addition, according to the degree of context detection, it can be divided into explicit context, which refers to factors the user can perceive and feel, such as facility status, data and temperature, and implicit context, which refers to factors that are difficult for the user to detect or feel, such as network, user preferences and individual identity. On the basis of previous research, this study defines context as the sum of all the information about the state of the subject and the change of the surrounding environment from the perspective of the network environment, which can be used to describe the state, environmental characteristics and development trends of the subject and object, including the subject, behavior and environment. Context can be static or dynamic, the former refers to the inherent attributes of subject, behavior or environment, or factors that do not change with time, while the latter refers to factors that change with time.

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Effects of Contextual Factors

Biederman (1972) believes that the information query process is always disturbed by a variety of factors, the combination of which constitutes a special type of information—context (also known as background information). However, people’s information behavior does not exist independently, and its evolution and state are always the result of the continuous action of the situation and environment. Gibson and Pick (2000) believes that there are always complex and rich scenes and situational structures in people’s views, once something has changed, it can have an important impact on other related things. He calls this covariation. Kintsch and Dijk (1978) believe that users experience three levels of comprehension in the process of reading, i.e., the surface structure, the propositional text base, and the situation model. Among them, situational model refers to the integration of information by users based on their own experience and background knowledge in order to form a global and coherent understanding of text information, which is the highest level of information processing and understanding. Zwaan and Radvansky (1998) believe that when people process and analyze information, they simultaneously monitor temporal, causal, goal-related, and protagonist-related dimensions during narrative comprehension. Zwaan (1995) offers theevent-indexing model to explain how information users are affected by time and space. When time or location changes, users will form a different contextual model in their minds to characterize and understand the same information. Through the study of information recommendation models, Adomavicius et al. (2005) present a multidimensional recommendation model that incorporates contextual information into the recommendation process and makes recommendations based on multiple dimensions, profiles, and aggregation hierarchies. Cho and Lee (2006) have carried out information multidimensional collaborative filtering research based on context. They believe that contextual factors will have an important impact on the recommendation effect of information, so they should be introduced to the information recommendation model and the study of the characteristics of contextual factors and their influencing mechanism should be strengthened. Oku and Nakajima (2006) have studied the scoring sequencing of information recommendations from the perspective of contextual factors. The results show that there are significant differences in the perception of the importance of information content by the same users in different contexts, indicating that contextual factors have an important impact on people’s information processing. Jiang (2012) analyzes the important influence of situational environment on people’s behavior patterns from the perspective of cognitive model and human-caused reliability. The study mainly uses the theory of control science to explore how the contextual environment produces errors in people’s behavior, and divides the contextual environment into static and dynamic ones.

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2.1.5 Research Review 2.1.5.1

Research Results

With the rapid development of microblogging information platform and its wide application in many fields, the research in the field of crisis information transmission on microblog has also become a hot topic, and some achievements have been made. Most of the studies revolve around the subject of public crisis events or emergencies. The research focuses on the communication structure, communication mode, user characteristics, influencing factors, information behavior, information mining, user relationship and so on. The main research methods are text analysis, descriptive statistics, regression model, variance analysis, main component analysis, etc. In sample selection and data collection, Twitter and Sina Weibo are mostly used.

2.1.5.2

Research Gaps

Although much progress has been made in the relevant researches on microblogging information dissemination in the past, but they are quite limited for the present study in terms of contents and objectives. Firstly, research on the dynamic mechanism of user information behavior is still lacking. Previous studies mainly used the relevant analysis, variance analysis and regression model to explore and analyze the influencing factors, influence path and process characteristics of user behavior, which can draw conclusions about whether independent variables have a significant effect on dependent variables, and the size and direction of the effects, but cannot draw relevant conclusions about the dynamic characteristics of the effects. Since the corresponding independent variables and dependent variables exist in a time series, the size relationship between them and the positive and negative effects are constantly changing at different points, i.e. the variable relationship is dynamic. It is difficult to achieve an accurate description of the dynamic relationship only through correlation analysis of the sum of a certain point in time or population and regression analysis. Most of the research on the influence mechanism of information behavior in the past has been carried out from the perspective of static mechanism; very few from the perspective of dynamic mechanism, even fewer from the perspective of contextual factors. Secondly, research on microblogging started late in China. The accuracy of the research on information behavior characteristics, impact response, influence, disturbance contribution rate and behavior prediction need to be improved, and the autocorrelation analysis of information behavior fluctuation is hard to find in the existing literature. Past studies on fluctuation characteristics have been based mainly on descriptive statistical analysis and the trend analysis of the total line chart. In fact, these fluctuations are a combination of trend components, periodic components and irregular components, possessing both autocorrelation and cluster characteristics. Therefore,

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the conclusion drawn by descriptive analysis only is not precise. Only by decomposing and separating the components can more accurate conclusion about the autocorrelation of fluctuations, trend characteristics, periodic characteristics, irregular characteristics and cluster characteristics be drawn. Thirdly, although past literature has elaborated contextual factors have an impact on user information behavior, the impact has not been specified. Previous research on the influence mechanism of information sharing behavior mainly studied the influence mechanism of related factors from the perspective of “personal characteristics—motive—behavior”, or through regression model (such as logistic regression), variance analysis and so on. The former follows the traditional paradigm of psychological research, and its selection of variables comes more from the user psychology variable. The latter may discover that the influence of the independent variables on the dependent variables is significant, but fail to discover its size or direction. Due to the rapid development of computer information technology, user information behavior is greatly influenced by contextual factors and the influencing path may be manifested as: originated from the situation, impacted on psychology, and manifested on behavior. To explore information behavior from the perspective of contextual factor scan better reveal the mechanism of user behavior from the source and draw more comprehensive research conclusions. Finally, research on the specific monitoring strategy of user information behavior is hard to find. Previous studies mainly cope with macro or meso-level strategies, while micro-operational, specific and precise monitoring strategies are still few. It is likely that enterprises will have some difficulty in carrying out practical supervision of crisis information sharing behavior according to such macro and meso-strategies. In terms of the classification of monitoring index levels and the positioning of monitoring periods, most of them are based on qualitative analysis and descriptive statistical analysis, and the accuracy of their conclusions needs to be further improved due to the limitations of qualitative analysis and descriptive statistical methods. Research on the monitoring of information behavior from the perspective of contextual factors is still lacking, and the related research needs to be further improved.

2.2 Theoretical Foundation 2.2.1 Information Context Theory 2.2.1.1

Information Use Environment Theory

Taylor (1986a) proposed the theory of information use environment (IUE), which was supplemented and refined in 1991. The theory holds that the environment of information use can encourage users to form information need and drive them to actively carry out information search, query and utilization behavior. It is the starting point of all information behavior, such as information need, information search, evaluation

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and utilization. Through the analysis of information use environment, combining with internal and external information, a series of activities such as the use of information resources, decision-making, proposal and improvement of measures can be implemented. IUE consists of four main aspects: the individual user, the problem to be solved, the coping strategy, and the information environment (Taylor, 1986b, 1991). In IUE, users seek and acquire information that is valuable to them at a specific time according to their own information needs. Various factors can have a significant impact on their information screening and selection. In short, the flow, transmission and utilization of information between users are affected by IUE, which can be used to determine the usefulness and the value of information.1 IUE theory emphasizes that information behavior analysis should be centered on information users, and considered information environment in the process of information behavior, which fully reflects the research concept that information behavior research is good at combining user’s psychological characteristics and social orientation. The theory also points out that the different occupations and social roles of users can have an important impact on people’s information behavior, and these factors lead to the different characteristics of user information behavior to some extent. It also divides users into different occupational groups according to differences in people’s work attributes and social roles, which helps to deepen the study of information behavior in different occupational areas (Taylor, 1996).

2.2.1.2

Information Horizons Theory

Based on empirical research, Sonnenwald (1999) puts forward the theory of information horizons, which mainly contains three basic concepts: social network, context, and situation. It studies the influence of different contextual factors on user information search behavior and provides a structural analysis framework and means for the study of people’s information behavior, which can be used to analyze the characteristics of information behavior, such as user’s information retrieval, utilization and sharing. Below is a list of the five basic propositions: Proposition 1: Human information behavior is woven around, i.e., is shaped by and shapes, individuals, social networks, situations and contexts. Proposition 2: Individuals or systems within a particular situation and context, may perceive, reflect and/or evaluate change in others, self, and/or their environment. Information behavior is constructed amidst a flow of such reflections and/or evaluations, in particular, amidst reflections and/or evaluations concerning a lack of knowledge. Proposition 3: Within a context and situation is an “information horizon” in which we can act. When an individual has decided to seek information, there is an information horizon in which they can seek information. Proposition 4: Human information behavior may, ideally, be viewed as collaboration among an individual and information resources. Collaboration with (and among) information resources ideally includes reflexive interaction, and/or reflexive provisioning of information.

1

Chiang and Yang (2015).

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2 Literature Review and Theoretical Foundation Proposition 5: Information horizons may be conceptualized as densely-populated solution spaces. (Trusina et al., 2004)

This theory can be used to analyze the ideas, intentions and ultimately formed decision-making behaviors of users in the conduct of information behavior, including personal preferences, interpersonal relationships, information environment, contextual factors, sharing will and so on. (Zhang, 2012).

2.2.2 Information Grounds Theory Previous studies finds that the environment around people does not exist independently, but is a vast community of factors that interact with each other. On this basis, she defines information grounds as: synergistic environment temporarily created when people come together for a singular purpose but from whose behavior emerges a social atmosphere that fosters the spontaneous and serendipitous sharing of information’ put forward the informatics concept of information field, and called this huge environment composed of many influencing factors information field. (Fisher et al., 2006)

Fisher perfected the theory in 2002 and formally put forward seven basic propositions of the information grounds theory: (1) (2) (3) (4) (5) (6) (7)

People gather at ‘information grounds’ for a primary, instrumental purpose other than information sharing. Information grounds are attended by different social types, most if not all of whom play expected and important, albeit different roles in information flow. Social interaction is a primary activity at ‘information grounds’ such that information flow is a by-product. People engage in formal and informal information sharing, and information flow occurs in many directions. Information grounds can occur anywhere, in any type of temporal setting and are predicated on the presence of individuals. People use information obtained at ‘information grounds’ in alternative ways, and benefit along physical, social, affective and cognitive dimensions. Many sub-contexts exist within an ‘information ground’ and are based on people’s perspectives and physical factors; together these sub-contexts form a grand context.

(Fisher & Naumer, 2006) The people–place-information trichotomy emphasizes that these three factors have an important influence on information sharing behavior. People. Because information behavior such as information inquiry and information sharing is the product of society, human factors have the most important influence

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Fig. 2.2 The People–Place-Information Trichotomy of the information grounds

on information search and information sharing behavior in the information grounds (Fisher & McKechnie, 2005). Place. Because the information ground is composed of all the implicit and explicit elements in the place, the physical and social attributes of the place will have an important influence on the direction and intensity of people’s information sharing behavior. The basic conditions of the place may promote or inhibit people’s information seeking and information sharing behavior, thus affecting the effect and willingness of people to exchange or share information.2,3 Information. Characteristics of information such as frequency, subject matter, hot topics, information sources, information credibility, ease of use, and familiarity of information reposting and comments can have an important impact on people’s information-sharing behavior (Fisher-Julien, 2009; Ma & Wang, 2014). The People–Place-Information Trichotomy of the information grounds is shown in Fig. 2.2.4,5 With the rapid development and wide application of Internet technology, network media has become an important way for people to obtain information and an important gathering place in the virtual environment. Counts and Fisher (2008) have introduced the concept of information grounds into the virtual network environment when they studied the information behavior of mobile social network. They point out that the social network in which users exchange and share information belongs to a new kind of information ground, which is different from that of the real environment, and its main functions are also different. Information exchange and sharing are the 2

Ma and Wang (2014). Lin et al. (2010). 4 Fisher et al. (2006). 5 Zhao and Zhou (2015). 3

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main functions and purposes of social networks, while information sharing in the real information ground is only a derivative of people’s social activities (Counts & Fisher, 2010). Savolainen (2009) has conducted similar research. After comparing the information ground theory and the small world theory in information search and sharing behavior, he finds that because of the rapid development and popularization of the Internet, the information ground theory is more suitable for the analysis of information behavior in virtual social networks.

2.2.3 Information Processing Theory 2.2.3.1

The Elaboration Likelihood Model

The elaboration likelihood model (ELM) is one of the main theories about information processing and the effectiveness of persuasive communications. There are two routes in information processing, one at the center and the other at the peripheral. The different routes have different persuasive effects on people, which reflects the whole process of change from information receiving to information processing to attitude change (Cacioppo and Petty 1984). One route is based on the thoughtful consideration of arguments central to the issue, whereas the other is based on the affective associations or simple inferences tied to peripheral cues in the persuasion context. The former emphasizes that people analyze and think carefully about the information after obtaining the information, identifies the relevant variables and carefully looks for the relevant clues. Users have a finer awareness of the information. The latter refers to people’s identification and judgement of information content and viewpoints through perceptual cognition after obtaining information. The fineness degree of information processing is relatively low to enable a change of attitude more quickly and directly. ELM holds that people’s attitude change is influenced by the high refinement degree of their information processing (Pierro et al. 2004). If individuals are capable and willing to analyze and think deeply about information, or if the information variables are sufficient, logical, and accurate, then they are more likely to process the information using the central route, resulting in more effective persuasion. If individuals do not have the ability and will to analyze and think deeply about information, they process and judge the information through some easily perceived or easily accessible clues, resulting in rapid changes in attitude (O’Keefe, 2008).

2.2.3.2

Heuristic-Systematic Model

The heuristic-systematic model (HSM), similar to ELM, is a theory of persuasion that suggests attitudes can change in two fundamentally different ways. One way is through systematic processing, whereby people think carefully about any available information when forming an opinion to determine whether the information is accurate or valid. This kind of thinking takes a lot of efforts. The other is called heuristic

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processing, and it involves using rules of thumb known as heuristics to decide what one’s attitudes should be. The systematic approach is similar to the central route of ELM, on which people think comprehensively and carefully about the explicit and implicit clues of information, and on this basis form an attitude response. The heuristic approach is similar to ELM’s peripheral route, but it is finer and processes information under the “minimum cognition effort” principle. HSM regards the degree of people’s efforts in information processing as a continuum, and the differences in processing will and ability affect people’s use of different processing methods, thus producing different persuasive effects on people. When individuals have a high level of willingness and cognitive ability to process information, they change their attitude through systematic processing. When processing intentions and cognitive abilities are low, individuals develop their own attitudes through initiative processing (Griffin et al., 2002).

2.2.3.3

Construal Level Theory

Construal level theory (CLT) describes the relationship between psychological distance and people’s information processing and understanding. Psychological distance consists of temporal distance, spatial distance, social distance, and hypothetical distance,6 which can lead to high construal level or low construal level: high-level construal—representing events by their abstract and essential features and low-level construal—representing events in terms of their more concrete and idiosyncratic features.7 When the psychological distance is large, the subject tends to process the information with high-level construals; when the psychological distance is small, the subject tends to use low-level construals for information processing. As one of the theories in the field of cognition, CLT holds that different situational characteristics usually lead to differences in people’s perception of things. Some scholars have studied the relationship between situational factors and the level of construals, and believe that the degree of social information sharing is related to people’s construal level.8 Previous studies mainly focus on the dimension of temporal distance. When the temporal distance is large, the subject tends to process the information with highlevel construals; when the temporal distance is small, the subject tends to use lowlevel construals for information processing.9 Differences in temporal distance cause differences in construal. For some, the valence is higher with high-level construals; for others, the valence is higher with low-level construals.10 Therefore, the differences in temporal distance lead to differences in construal level, which lead to differences in perceived valence, which eventually affect people’s behavior and decision.

6

Trope et al. (2007). Trope and Liberman (2010). 8 Jiga-Boy et al. (2013). 9 Trope and Liberman (2003). 10 Sun et al. (2007). 7

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2.2.4 Field Theory of Psychology The theory was put forward by Lewin (1951), in which fields of psychology is the core concept. For better illustration, Lewin used another concept, namely, the psychological living space (life space, also known as the living space), which is mainly composed of personal and environmental factors. In his view, a person’s behavior always occurs in a specific time and space, and is influenced by the interaction between internal factors and the external environment, i.e. the individual’s mental living space determines their behavioral characteristics (Cartwright, 1951). The theory can be expressed as: B = f (PE) = f (L × S)

(2.1)

where B represents behavior; f for functional relationships (also known as law), P for person, E for environments, and L × S for life space. The formula shows that human behavioral characteristics are the result of the interaction between individuals and environmental factors. With the help of topology theory, Levin explains the action mechanism of psychological fields and he believes that the dynamic process of psychological fields consists of desire, tension, valence, vector, obstacle and balance.11,12 Desire refers to a driving force produced by the absence of certain physiological conditions. Tension is an emotional state in which people suffer from psychological disorders due to the existence of desire. Valence indicates the subjective judgment and emotional experience of a certain desire in the current internal and external environment. Vector refers to the expression of the subject’s motivation to the object in terms of strength and direction. Barriers restrict the individual’s movements. Desire creates tensions in the individual and the person acts until tensions come to a balancing state (Lewin et al., 1936; Lewin, 1939, 1951; Tao, 2012). The theory of the psychological field shows that the behavior of an individual at any point in time is the result of the interaction between individual factors and the external environment, and keeps changing all the time, which is a dynamic process in which any change in the environment will have certain effects on the behavior of the individual within the environment.

2.2.5 Information Behavior Theory Scholars have made various explorations on people’s information behavior from different perspectives, thus forming a variety of theoretical systems on behavior. For the need of the present study, two theories are selected because of their wide application and influence. 11 12

Lewin et al. (1936). Tao (2012).

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2.2.5.1

39

A General Model of Information Behavior

Wilson’s general information behavior theory regards user information behavior as an orderly circular process, with information need as the starting point and information utilization as the end (Wilson, 1999). It contains the following basic points of view. Firstly, information need is the focus of the entire model. Individuals in the process of information seeking and information utilization will be affected by a variety of interference factors, which may enhance the user’s search effect and use behavior, or hinder them. Secondly, information seeking behavior is influenced by many factors within various dynamic mechanism links, of which active retrieval is the key to individual information behavior. Thirdly, the processing and utilization of the obtained information is an important part of the whole process. There are many kinds of intermediary variables that have an important influence on the information behavior and dynamic mechanism, including psychological characteristics, population statistics, social roles, interpersonal relationships, environmental characteristics and source information characteristics. Fourthly, the theory pays special attention to the factors and dynamic mechanisms of information acquisition process, including individual cognitive style, social environment, information exchange, information sharing and information utilization. Finally, the theory also emphasizes that an individual’s need for information may come from work or living environment, from a user’s work role or from an individual himself, and that users in a similar situation play an important role in interfering with their information behavior (Wilson 1997). In a word, the general information behavior theory integrates individual information need, dynamic mechanism, influencing factors and information response into the model, which makes the situational factors, interference variables and the dynamic mechanism in the process of information behavior more clearly visible, so that it can be better used to predict and analyze the user’s information behavior. The theoretical model is shown in Fig. 2.3.13

2.2.5.2

Integrated Model of Information Behavior

On the basis of in-depth analysis and empirical research on user information behavior, Niedzwiedzka critiques Wilson’s General Information Behavior Theory because people’s information behavior almost rarely occurs in a completely independent environment, which is usually accompanied by other people’s information behavior, and interacts with other people’s behavior, influencing each other, thus exerting important influence and interference on their own information behavior. Niedzwiedzka proposes an integrated model of information behavior, which inherits the core concept of Wilson’s general model, such as conducting information behavior research by reference to multidisciplinary theory and methods. User information behavior is always the product of specific contextual factors, social roles and individuals and

13

Song and Wang (2010).

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Fig. 2.3 Wilson’s general information behavior model

environment play an important role in information behavior. The model has the following core views: (1) the intermediary variables in information behavior are integrated with the situational factors; (2) the information behavior always occurs in certain situations, that is, the user information behavior is the product of the specific situation; (3) there is a dynamic mechanism in all aspects of the process of information behavior; (4) there are two ways for users to search for information, namely,—intermediary-based or personal. (Song and Wang, 2010).

2.3 Summary This chapter contains literature review of related documents and theories mainly from the four areas in brand crisis communication, microblogging information dissemination, information behavior research and behavior contextual factors. Results show that previous researches mostly center around public crisis events or emergencies with topics on information dissemination mechanism, structural model, behavior characteristics, information mining, user relations and influencing factors. Twitter and Sina Weibo have been most frequently studied or used as data sources. Research methods mainly cover text analysis, descriptive statistics, regression model, variance analysis and so on. In general, previous researches have yielded fruitful results, but there are also limitations. Further research is needed in the following areas. The first is to carry out dynamic research on the fluctuation characteristics of information

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behavior to fill in the research gap. Secondly, previous analysis of fluctuation characteristics of information behavior is rough and the accuracy needs to be improved. Thirdly, study on dynamic influence mechanism and specific study on the influence of contextual factors on information behavior should be carried out to fill in the research gaps. In addition, the specific monitoring strategy of information behavior should be studied further. Therefore, the theory of information context, the theory of information field, the theory of information processing, the theory of psychological grounds and the theory of information behavior are discussed in this chapter so as to provide theoretical basis and theoretical support for the research of the following chapters.

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Oku, K., Nakajima, S., Miyazaki, J., et al. (2006). Context-aware SVM for context-dependent information recommendation. In Proceedings of the 7th International Conference on Mobile Data Management, (Vol. 109). IEEE Computer Society Perse, E. M. (2001). Media effects and society. Routledge. Pierro, A., Mannetti, L., Kruglanski, A. W., et al. (2004). Relevance override: on the reduced impact of “cues” under high-motivation conditions of persuasion studies. Journal of personality and social psychology, 86(2), 251. Ping, L., & Zong, L. (2010). Research on microblog information dissemination based on SNA centrality analysis—a case study with Sina Microblog. Document, Information & Knowledge, 6, 92–97. [平亮, 宗利永.基于社会网络中心性分析的微博信息传播研究--以Sina微博为例. 图书情报知识, 2010(6):92–97]. Rosenthal, U., & Pijnenburg, B. (1991). Crisis management and decision making: simulation oriented scenarios. Springer Science & Business Media. Savolainen, R. (2009). Small world and information grounds as contexts of information seeking and sharing. Library & Information Science Research, 31(1), 38–45. Schilit, B., Adams, N., & Want, R. (1994). Context-aware computing applications. In Mobile computing systems and applications (pp. 85–90). IEEE. Schmidt, A., Beigl, M., & Gellersen, H. W. (1999). There is more to context than location. Computers & Graphics, 23(6), 893–901. Snowdon, D., & Grasso, A. (2000). Providing context awareness via a large screen display. In Proceedings of the CHI 2000 Workshop on “The What, Who, Where, When, Why and How of Context-Awareness”. IEEE. Song, X., & Wang, P. (2010). Yonghu xinxi xingwei yanjiu shuping (Review on customer information behavior research). Information Science, 4, 625–629. [宋雪雁, 王萍.用户信息行为研究述 评.情报科学, 2010(4):625–629]. Sonnenwald, D. H. (1999). Evolving perspectives of human information behavior: contexts, situations, social networks and information horizons. Exploring the contexts of information behavior. In Proceedings of the Second International Conference in Information Needs. Taylor Graham. Spink, A., & Cole, C. (2006). Human information behavior: Integrating diverse approaches and information use. Journal of the American Society for Information Science and Technology, 57(1), 25–35. Stieglitz, S., Dang-xuan, L. (2011). The role of sentiment in information propagation on Twitter-an empirical analysis of affective dimensions in political tweets. In International Conference on Systems Science. Hawaii. Suh, B., Hong, L., Pirolli, P., et al. (2010). Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In Social Computing (socialcom), 2010 IEEE Second International Conference. IEEE, 177–184. Sun, H., & Li, L. (2012). Analysis on the characteristics of high-frequency microblog forward and users’ forward motivation based on the content analysis of Sina Weibo forward rankings of the day. Modern Communication, 34(6), 137–138. [孙会,李丽娜.高频次转发微博的特征及用户转 发动机探析--基于新浪微博“当日转发排行榜”的内容分析. 现代传播: 中国传媒大学学报, 2012,34(6):137–138] Sun, Z., & Wu. (2007). Construal level theory: A review of the literature. Applied Psychology, 2007(02), 181–186. Tao, P. (2012). Modeling of driving behavior based on psychology field theory. Jilin University. [ 陶鹏飞.基于心理场理论的驾驶行为建模.吉林大学,2012]. Taylor, R. S. (1991). Information use environments. Progress in Communication Sciences, 10(217), 55. Taylor, R. S. (1996). Information use environments. Managing information for the competitive edge (pp. 93–135) Taylor, R. S. (1986a). On the study of information use environments. In Proceedings of the 49th Annual Meeting of the American Society for Information Science (ASIS’86), (Vol. 23, pp. 331– 334). IEEE.

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Taylor, R. S. (1986b). Value-added processes in information systems. Greenwood Publishing Group. Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110(3), 403. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440. Trope, Y., Liberman, N., & Wakslak, C. (2007). Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. Journal of Consumer Psychology: The Official Journal of the Society for Consumer Psychology, 17(2), 83. Trusina, A., Rosvall, M., & Sneppen, K. (2004). Information horizons in networks. Physical Review Letters, 94(23). Wang, Y., Jin, Z., Yang, Z., et al. (2012). Global analysis of an SIS model with an infective vector on complex networks. Nonlinear Analysis: Real World Applications, 13(2), 543–557. Wang, Y., & Deng, X. (2009). Discussion on basic issues of network user information behavior. Library and Information Service 53(16), 35–39. [王艳,邓小昭.网络用户信息行为基本问题探 讨.图书情报工作, 2009,53(16):35–39]. Wang, Z., & Song, Z. (2006). The characteristics of web2.0 and its influence on network information exchange. New Century Library, 3, 10–13. [王知津, 宋正凯.Web2.0的特色及其对网络信息交 流的影响.新世纪图书馆, 2006(3):10-13]. Wei, J., & Zhao, D. (2006). Research and realization of crisis alert system based on the meta-search engine. Journal of Management Science 18(5), 36–42. [魏玖长, 赵定涛.基于元搜索引擎的危 机信息监控系统的研究与实现.管理科学, 2006,18(5):36–42]. Wilson, T. D. (1981). On user studies and information needs. Journal of Documentation, 37(1), 3–15. Wilson, T. D. (1997). Information behaviour: an interdisciplinary perspective. Information Processing & Management, 33(4), 551–572. Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270. Yang, J., & Counts, S. (2010). Predicting the speed, scale, and range of information diffusion in Twitter. ICWSM, 10, 355–358. Yang, X. (2010). Microblog research in the horizon of communication studies. Contemporary Communication 2, 73–74. [杨晓茹.传播学视域中的微博研究.当代传播,2010(2):73–74]. Ye, B, & Pang, Y. (2003). Top ten corporate crisis public relations cases in 2003. HEBEI QIYE 11, 44–47. [叶秉喜, 庞亚辉.2003年度十大企业危机公关案例(上).河北企业, 2004(11):44–47]. Yi, C., Bao, Y., & Xue, Y. (2013). Research on mechanism of large-scale information dissemination based on sina weibo. Journal of Frontiers of Computer Science and Technology, 7(6), 551–561. [易成岐, 鲍媛媛, 薛一波, 等.新浪微博的大规模信息传播规律研究.计算机科学与 探索,2013,7(6): 551-561]. Zhang, H. (2012). Theoretical evolution of information behavior research. Information and Documentation Services, 5, 41–45. [张海游.信息行为研究的理论演进.情报资料工作,2012(5):41– 45]. Zhang, Y. (2013). Is the expert more influentia—microblogger and information characteristics’ impact on consumer behavior. Zhejiang University. [张媛伊.微博博主和信息特征对消费者行 为影响研究.浙江大学, 2013]. Zhao, L., & Zhang, J. (2013). Multi-dimentional analysis of microblog user behavior research. Information and Documentation Services, 34(5), 65–70. [赵玲, 张静.微博用户行为研究的多 维解析.情报资料工作, 2013, 34(5):65–70]. Zhao, J., & Zhou, X. (2015). Research review of information grounds theory in user information behavior. Information Science, 4, 9. Zwaan, R. A., Radvansky, G. A., Hilliard, A. E., et al. (1998). Constructing multidimensional situation models during reading. Scientific Studies of Reading, 2(3), 199–220. Zwaan, R. A., Langston, M. C., Graesser, A. C. (1995). The construction of situation models in narrative comprehension: an event-indexing model. Psychological Science, 292–297.

Chapter 3

Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

In recent years, with the rapid development and wide application of Weibo/microblog, the number of Weibo users is increasing. Weibo is exerting great impact on the social media market, whose soaring is known as a “micro-revolution” in the Internet era with a far-reaching significance. Celebrities, influencers, and the general grassroots are actively moving from traditional media, forums, communities, podcasts to microblogging information platforms. At the same time, as brand crisis has become quite normalized, Weibo will undoubtedly help to add fuel to the spread of crisis information as a popular information sharing and communication tool for the general public and consumer groups alike. In this context, enterprises need to effectively respond to and deal with the rapid dissemination of brand crisis information on Weibo, and to monitor the sharing of information to minimize the losses caused by the crisis to enterprises. It is, therefore, essential for enterprise managers to have a deeper understanding of the features of brand crisis information sharing behavior on Weibo. General information behavior theory holds that user information behavior is an orderly circular process, beginning with information need and ending with information utilization. Information need is the key point of the whole process of information behavior. Users in the process of information seeking and information utilization will be affected by a variety of interference factors, which may promote or hinder the user’s search effect and utilization behavior. There are multiple dynamic mechanisms in this process, in which active retrieval is the key to user information behavior (Wilson, 1997). At the same time, a variety of intermediary variables in the process can have an important impact on information behavior and dynamic mechanism, including psychological features, demographic features, social roles, interpersonal relationships, environmental features and features of the source information (SI). An individual’s need for information may come from work or living environment, from his work role or from himself. Users in a similar situation play an important role in interfering with their information behavior (Wilson, 1997). Niedzwiedzka holds that people’s information behavior almost rarely occurs in a completely independent environment, but is usually accompanied by other people’s information © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_3

47

48

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

behavior, and interacts with other people’s behavior, influencing each other, thus exerting important influence and interference on their own information behavior. Niedzwiedzka’s integrated model of information behavior points out that there is a dynamic mechanism in all aspects of the process of information behavior. As for the features of information sharing of Weibo users, because users’ habits have an important impact on their information behavior, and their habits are different due to gender, age, occupation, educational background and other individual differences, habitual effects that are often autocorrelated and periodic always appear in Weibo users’ information sharing. At the same time, as a social network, Weibo belongs to a kind of self-organizing system, wherein the information behavior is not only affected by the user’s habitual features, but can also be self-organized and change abruptly. After a period of hibernation, the user’s information behavior will follow the power law distribution to frequent post and share information at high frequencies.1 In addition, behind the massive reposts and comments of Weibo information lies the celebrity effect and the public effect. This psychological characteristic manifests itself in people’s love and follow of opinion leaders (e.g. stars, professors, wellknown scholars, entrepreneurs, etc.), or in finding a certain psychological “group identity” and deliberately fashioning their own behavior after the group under the influence of psychological pressure of group convergence (Han, 2012). At the same time, users can also choose to share and interact with specific user groups according to their own needs and interests, resulting in differences in the self-organization and cohesion of the network between different user groups, and the combined effect of these factors will make the information sharing behavior show obvious cluster features (Linhong & Rongrong, 2013). Consequently, how to make an in-depth analysis of the features of brand crisis information sharing behavior of Weibo users in order to effectively monitor and guide the behavior has become the focus of attention in the academic circles and the industry. Related researches are on the rise,2,3,4,5,6 but most of them adopt descriptive statistics and total line chart research methods to study trend features, periodic features and cluster features of behavior fluctuations, which is quite preliminary and rough. This is because users’ information reposting and commenting behavior is autocorrelated, and its overall waveform is composed of trend features, periodic features, cluster features and irregularities. It is difficult to draw specific and accurate research conclusions by descriptive statistics and total line chart analysis. To solve this problem, it is necessary to model the information sharing behavior and decompose the characteristic variables in order to accurately analyze the autocorrelation of the corresponding information behavior and its fluctuating trend features, periodic features, cluster features and irregular features.

1

Peng et al. (2015). Yi (2012). 3 Fu J. (2015). 4 Gao et al. (2012). 5 Guan et al. (2014). 6 Buccafurri et al. (2015). 2

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

49

Fig. 3.1 Structure of Chap. 2

In this chapter, data are selected from Sina Weibo and through autocorrelation analysis and construction of ARIMA model of information sharing of Weibo users, the whole process of reposting and commenting, its weekly and daily features, its periodical features, group features and irregular features are precisely decomposed and analyzed. The research framework for this chapter is shown in Fig. 3.1.

50

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

3.1 Data Collection and Descriptive Statistics 3.1.1 Data Acquisition and Data Preprocessing 3.1.1.1

Sample Selection

Selection of Brand Crisis Cases The development of microblogging/Weibo in China started in 2009. The period of 2010 to 2016 has been used as the time frame for choosing brand crisis information sharing cases. Sixty-six brand crisis events that occurred from 2010 to 2016 are sorted from Sina Weibo based on the selection criteria of the six dimensions of brand awareness,7 media coverage, crisis attention, crisis continuity, crisis impact and crisis damage. Top Ten Brand Crisis Events issued by the Brand China Industry Alliance Development Research Center has been consulted as well. The sixty-six cases are shown in Table 3.1. Brands cover industries including automobile, electronics, food, pharmaceuticals, home, media, enterprises and public institutions. Most cases are related to products of famous brands and are in contact with people’s daily life. They have a greater comprehensive impact and more public involvement, therefore, the final conclusion of the study has a better universality.

Selection of Weibo Platform At present, the more mature microblogging platforms in China mainly include Sina Weibo, NetEase Weibo, Sohu Weibo and Tencent Weibo. Sina Weibo is currently the most successful operator, having the largest number of visits, registrations and use rate. According to the 36th China Internet Development Statistics Report released by the China Internet Network Information Center (CNNIC), since 2013, Sohu, NetEase, Tencent and other companies have been reducing their investment in Weibo operation, and the overall market for Weibo has entered a shuffling period. By June 2015, the competition pattern in the Weibo market has become clear: more and more users are turning to Sina Weibo, taking up to 69.4% of Weibo users in China. Sina Weibo usage in Tier 1 to Tier 5 cities is above 65%, surpassing that of other Weibo operators. It is now the number one operator in the market.8 Meanwhile, Sina Weibo Data Center released the 2015 Weibo User Development Report. As of September 30, 2015, the number of monthly active users of Weibo (MAU) had reached 212 million, an increase of 48% over the same period last year, of which mobile MAU accounted for 85% of the total MAU in September. The average daily number of daily users

7 8

Li and Chen (2013). China Internet Network Information Center (CNNIC) (2016).

3.1 Data Collection and Descriptive Statistics

51

Table 3.1 A list of 66 brand crisis cases No

Brand name

Case description

Year

Business type

1

Toyota

Recall Gate

2010

Automobile

2

Hewlett Packard

Cockroach incident

2010

Electronics

3

KFC

SecKill scandal

2010

4

Midea

Purple clay cooker incident 2010

Home products

5

Bawang

Shampoo containing cancer-causing chemical

2010

Other

6

Zkungfu

Use of substandard imported ribs

2010

Food

7

Jinhao Camellia Oil

Secret recalls of problematic oil

2010

Food

8

Mengniu Dairy

Smear scandal against business rivals

2010

Food

9

Tencent

QQ VS Qihoo 360

2010

Media

10

Carrefour

Fraud price

2011

Enterprises and public institutions

11

Shuanghui

Lean-meat powder

2011

Food

12

Kumho Tire

Recall incident

2011

Others

13

Ku6

Layoff disturbance

2011

Media

14

Otis

Elevator incident

2011

Home products

15

Da Vinci Furniture

Labeling China-made furniture as foreign products

2011

Home products

16

Siemens

Luo Yonghao smashing Siemens refrigerators

2011

Home products

17

Mengniu Dairy

Milk with carcinogenic substance

2011

Food

18

Taobao

Fraudulent pricing

2011

Media

19

McDonald

Use of expired meat

2012

Food

20

JDB VS Wanglaoji

Trademark dispute

2012

Food

21

Baidu VS Qihoo 360

Fight over search engine

2012

Media

22

Xiuzheng

Toxic gelatin capsules

2012

Pharmaceutical

23

Guizhentang

Extraction of bear bile alive

2012

Pharmaceutical

24

Coca-Cola

Chlorine contamination scandal

2012

Food

25

KFC

Fast-growing chicken scandal

2012

Food

26

Jiugui Liquor

Plasticizer found in liquor

2012

Food

27

Bright Dairy

Dairy quality crisis

2012

Food

Food

(continued)

52

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Table 3.1 (continued) No

Brand name

Case description

Year

Business type

28

Besunyen

Advertising incident

2012

Food

29

Anxin Flooring

Excessive formaldehyde

2012

Home products

30

Everbright Securities

Program bug incident

2013

Enterprises and public institutions

31

YTO Express

Fatal chemical contaminated packages

2013

Enterprises and public institutions

32

Kangtai Bio Co

Death-causing vaccine

2013

Pharmaceutical

33

Fonterra

Tainted milk powder

2013

Food

34

Nongfu Spring

Standard scandal

2013

Food

35

Tongrentang

Quality scandal

2013

Pharmaceutical

36

Little Sheep

Fake meat

2013

Food

37

Walmart

Beef containing fox meat-

2013

Enterprises and public institutions

38

Dumex

Bribery scandal

2013

Food

39

Starbucks

Scandalous profiteering in China

2013

Food

40

Volkswagen DSG

Gearbox fault incident

2013

Automobile

41

Malaysia Airlines

Disappearance of Flight MH370

2014

Enterprises and public institutions

42

Ctrip

Leaking of customers’ private information

2014

Media

43

Nikon Camera

Dark spot incident

2014

Electronics

44

McDonald

Murder in a McDonald in Zhaoyuan, Shandong Province

2014

Food

45

McDonald

Husi rotten-meat scandal

2014

Food

46

Xiamen University

Seduction of female students by a faculty member

2014

Enterprises and public institutions

47

Youku

IP infringement

2014

Media

48

Tesla

Dispute with Pinduoduo over car delivery

2014

Automobile

49

Apple

Bendgate iPhone

2014

Electronics

50

FAW Audi

Water-damaged cars scandal

2015

Automobile

51

Didi

Logo copying scandal

2015

Media

52

Ctrip

System breakdown incident

2015

Media

53

Uniqlo

Fitting room sex video scandal

2015

Others (continued)

3.1 Data Collection and Descriptive Statistics

53

Table 3.1 (continued) No

Brand name

Case description

Year

Business type

54

Fudan University

Plagiarism scandal of FU promotional video

2015

Enterprises and public institutions

55

Qingdao City

City image damaged by shrimp scandal

2015

Enterprises and public institutions

56

JD.com

Consumer rights protest by 2015 writer Liuliu

Media

57

Master Kong

Reuse of cooking oil 2015 complained by tourist from Taiwan

Food

58

Movie: A Fool

Movie release threatened by the arrest of leading actor over drug abuse

2015

Others

59

Baidu

Wei Zexi’s death scandal

2016

Media

60

Taobao

Taobao shop fake sales scam

2016

Media

61

Ele OTO

Scandalous food-processing workshop

2016

Food

62

BTG Home Inns Heyi Hotel

Assault of a woman at the hotel lobby

2016

Enterprises and public institutions enterprise

63

Douyu

Live streaming of obscene behavior

2016

Media

64

Didi

Murder of a female passenger by the hitch driver

2016

Media

65

S.F. Express

S.F. delivery man being beaten

2016

Enterprises and public institutions

66

Walmart

Live fish contaminated with malachite green

2016

Enterprises and public institutions

(DAUs) reached 100 million in September, up 30% from a year earlier.9 Therefore, Sina Weibo can best represent the features and attributes of Weibo in China.

3.1.1.2

Data Collection Methods

Sina official API and web crawler technology have been used for data collection.

9

Sina Weibo Data Center (2015).

54

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Official API API refers to application programming interface. In the form of pre-defined functions, API can be easily used by program developers to access a set of routines in a website by writing applications without having to access the source code or having a detailed understanding of how it works internally (Zhaoyun et al., 2014). Weibo is an open information platform built on its own information system to provide users with tools for information dissemination, communication and sharing. It stores a large amount of user information, user relationships and dissemination of information and other related data resources. As long as the developer or user logs on to the site, through the platform’s open interface (open API) application creation, they can access the relevant data resources after obtaining official authorization. Access to data resources using official APIs usually has the advantages of accuracy, efficiency, and format standardization (Jie et al., 2011).

Web Crawler Technology Web crawler is a collection technology that automatically parses and crawls web page information according to certain search rules, mainly by storing the pages on the corresponding site on the local hard disk to create a mirror backup of the pages visited by the program, on the basis of which search engines can quickly access and retrieve saved copies. It allows certain links on the web to be executed automatically or to automatically confirm html code, as well as to obtain specific information in certain sites. During its data fetching process, users first need to set certain URLs (unified resource locators) as the starting point for crawling, which is called seed. Users can first access these links, identify all the other links on the page and crawl the corresponding pages of the URL one by one, and then store them in the URL list. Users repeat the same operation to the above URL and get new URLs continuously to obtain new information and content until the end of the program. There are usually three strategies for crawlers to parse Web pages, the best-first, depth-first, and breadth-first. Among them, the depth-first strategy is prone to fall into traps when crawling, so the best first and breadth-first strategies are more common web crawl means. In this study, the breadth-first strategy is used. It is also known as width-first strategies, i.e. starting with the start page, fetching all of the linked pages, selecting one of them, and continuing to crawl all the linked pages. It has the following advantages.10 First, the actual depth of the World Wide Web can reach up to 17 layers, and the web pages are connected in all directions, so there is a shortest path from one page to another. If depth-first is used, it is possible to crawl from a low PageRank to a very high PageRank, making it difficult to calculate PageRank. The links provided by portals tend to be the most valuable whose PageRank is high, and with each level of depth, their page value and PageRank decrease accordingly. It means that important 10

360doc Personal Library (2014).

3.1 Data Collection and Descriptive Statistics

55

Fig. 3.2 Data acquisition process

pages are often closer to the seed, while pages that are crawled too deeply are of low value. If the best-first strategy is used to predict the similarity of the potential URL to the target page, or the relevance of the topic, according to a web analytics algorithm, and to select one or more of the best rated URLs for further crawling, many related pages may be ignored. Secondly, the breadth-first strategy facilitates parallel crawling of multiple crawlers. This way of crawler cooperation usually fetches the station link first, and crawls out when encountering the off-site links, enabling a well-closed crawling. Thirdly, the advantage of the breadth-first strategy is that it is relatively simple to design and implement. The basic idea is that web pages closer with the seed are more important and in line with reality. In addition to the official API, web crawler technology is used to fetch data, which can effectively reduce the degree of dependence on the official API, while a high degree of autonomy is maintained (Yuanchao et al., 2007).

3.1.1.3

Data Acquisition Process

API-Based Data Acquisition In order to enable researchers to share and access the data resources, Sina Weibo platform specifically provides an official API for accessing data through programming. People first need to obtain the official authorization from Sina, and after obtaining authorization, they can access relevant data resources such as user information, blog information and so on through an open system (Xiangyang et al., 2015).

Web Crawler-Based Data Acquisition The web crawler is used to extract data on the Sina Weibo platform. Its workflow is to first set the seed URL as the starting address of page crawl, use the breadth-first crawling strategy to grab the page information pointed to by each URL, and parse the content of the page, and then continue to extract the next link URL for page crawling and parsing. The process is repeated until the end of the program (Dingzhong et al., 2009). Figure 3.2 shows the data acquisition process.

56

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Table 3.2 Fields in dataset 1 User ID

Nickname

Location

1749267314

Sprite Cat

Wuhan, Hubei

M

6/1/2011

YES

1749267315

Pistachio

Liuzhou, Guangxi

F

9/3/2010

NO

…

…

…

…

…

…

3.1.1.4

Gender

Date of registration

Authentication status

Results of Data Collection

In this study, web crawlers are programmed with Java language programming. Taking into account the effective duration of the sample information dissemination process, the number of days of reposting and commenting on each sample information in Sina Weibo is 21 days. In the process of data collection, for the convenience of data collation and expression, the obtained data is uniformly numbered, and each user and the information they repost are also given an ID number. Appendix 1 of this book contains original data acquisition formats. On the basis of raw data obtained through official API and web crawler, data cleaning is the next step, i.e. non-compliant data and documents that can be identified, such as data consistency, duplicate data, invalid data, missing values, error data, etc. are reviewed, verified, corrected and processed. After data cleaning, the final number of valid reposts is 358,014 and the number of valid comments is 376,492. All data are placed into two data sets, Dataset 1 and Dataset 2. Dataset 1 is the specific information of the user collected, mainly contains the following variables: user ID, nickname, location, gender, registration time, authentication status. The data format is shown in Table 3.2. Dataset 2 contains data on the number of relevant followers, related follows and information temporal distance of the users, mainly containing the following variables: YID (user ID), WID (Weibo ID), ZF (reposting), PL (commenting), ZS (time of reposting)), PS (time of commenting), ZZ (total number of reposts), PZ (total number of comments), ZiF (number of user’s followers), ZiG (number of user’s follower), XF (number of followers of SI), XG (number of follows of SI), SJ (information temporal distance). The data format is shown in Table 3.3.

3.1.2 Data Features and Descriptive Statistical Analysis 3.1.2.1

Features of Sample Distribution

The sample features analysis of brand crisis chiefly includes the distribution of brand types and sample time. The corresponding distribution features are shown in Figs. 3.3 and 3.4 respectively. The distribution features of brand crisis types in Fig. 3.3 show that automobile brands account for 6.06%, electronics 4.55%, food 33.33%, pharmaceutical 6.06%,

WID

289144

289145

…

YID

1749267314

1749267314

…

…

Yes

Yes

ZF

Table 3.3 Fields in dataset 2

PL

…

Yes

Yes

ZS

…

24-Oct-2013 17: 36: 19

23-Oct-2013 16: 28: 51

PS

…

24-Oct-2013 11: 25: 47

24-Oct-2013 21: 46: 29

ZZ

…

1503

925

PZ

…

1695

1362

ZiF

…

68

68

ZiG

…

278

274

XF

…

126

91

XG

…

249

186

SJ

…

34 6

192 47

3.1 Data Collection and Descriptive Statistics 57

58

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Fig. 3.3 Distribution of types of industries of the brands

Fig. 3.4 Distribution of years of brand crisis

home products 7.58%, media 19.70%, enterprises and public institutions 16.67%, and other brands 6.06%. The samples have covered brands in most industries that people come into contact with on a daily basis. The time distribution features of the samples in Fig. 3.4 display that the number of samples in 2010 accounted for 13.64%, that of samples in 2011 13.64%, that of samples in 2012 16.67%, that of samples in 2013 16.67%, that of samples in 2014 13.64%, that of samples in 2015 13.64% and that of samples in 2016 12.12%. It can be seen that the brand crisis samples selected cover all the years since the emergence of Weibo in China.

3.1 Data Collection and Descriptive Statistics

3.1.2.2

59

Data Descriptive Statistical Analysis

In statistics, the average value is a measure of the degree of concentration of a group of data, which can represent the feature of each data in the group. It can minimize the sum of the square of the error value in mathematics, that is, minimize the value of the quadratic loss function, indicating that it can comprehensively comprise all the information of each member of the group of data, and can better represent the features of each member. Whereas, the major disadvantage of the average lies in that it is easily influenced by the existence of extreme values in the group of data. In this study, since brand crisis samples are mainly selected from six dimensions, namely brand awareness, media coverage, crisis concern, crisis sustainability, crisis influence and crisis destructive power, the probability of extreme values in each sample data is tiny. This study uses the average value of samples for data processing and analysis. Thanks to including the information features of each sample member and avoiding the impact of extreme values through the design of samples, it can effectively apply and integrate the attribute features of all sample data, then it can reflect the general features of all sample data more clearly. In order to find out the universal law features of the evolution of the reposting and comment behaviors of brand crisis information in Weibo from the sample data of each crisis event, the research adopts the average value of the reposting and comment number of all brand crisis samples at each time point as the research variable. Among them, the bar and line charts of the average number of reposts and the average number of comments of the samples are shown in Figs. 3.5 and 3.6 respectively. From Figs. 3.5 and 3.6, it can be concluded that the evolutionary features of repost and comment of brand crisis information are roughly similar, exhibiting distinct life cycle features which can be divided into five stages: incubation period, outbreak

Comments number / piece

Bar chart of average number of reposts Line chart of average number of reposts

Time/day Fig. 3.5 Bar chart and line chart of average number of reposting

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users Bar chart of average number of comments Line chart of average number of comments

Comments number / piece

60

Time/day Fig. 3.6 Bar chart and line chart of average number of comments

period, climax period, recession period and long tail period. The first day after the crisis can be called the incubation period of crisis information sharing behavior; from the second day to the third day, the sharing behavior is rocketing, showing an explosive growth, which can be named the outbreak period; the third and the subsequent days after the crisis, this kind of behavior reaches a high peak, which can be called the climax period; after the climax period of information sharing behavior, there would be a decline process, and the information spreading situation in this stage would fluctuate slightly until about the fourteenth day after the crisis, which is known as the recession period. In the recession process, the information sharing behavior does not disappear immediately, but has a long fallout, whose influence is small. Its duration is varied due to the different attributes of brand crisis. Thus, this stage is the long tail period of crisis information sharing behavior. To further understand the distribution features of the repost and comment behavior of brand crisis information in Weibo and then apply appropriate research methods to build the model, this paper first makes histogram drawing and normal distribution test on the average number of reposts and comments. The corresponding results of the histogram and normal distribution test are shown in Figs. 3.7 and 3.8. In Figs. 3.7 and 3.8, the columns represent the frequency distribution of the number of reposts and comments respectively, and the parabolas show the standard normal distribution curve of the corresponding data. Among them, the frequency distribution features of the number of reposts and comments are quite different from the corresponding standard normal distribution curve, and the corresponding normal distribution test results show that the p value of the test is less than the significant level of 0.05, so the original hypothesis that the sample data obeys the normal distribution is rejected. It can be considered that the sample data does not have the normal distribution features, so the subsequent construction of the relevant model requires to carry out the corresponding transformation of the two series or only use the research method of non-normal distribution data for data processing and analysis.

3.1 Data Collection and Descriptive Statistics

Fig. 3.7 Histogram and test results of the mean of the reposting number

Fig. 3.8 Histogram and test results of the mean of comments number

61

62

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

3.2 Fluctuation Features of Reposting Behavior 3.2.1 Fluctuation Features of the Whole Information Spreading Process

Reposting number / piece

The changes of the average number of brand crisis information reposting in the whole communication process is shown in Fig. 3.9. From Fig. 3.9, we can see that the first three days after the crisis enjoy a prompt growth, reaching the maximum on the third day. And then the number decreases rapidly, but there is a small upward fluctuation on the eighth day, followed by a slow decline process. The whole evolutionary process gradually developed and changed in the light of five states: the incubation period, the outbreak period, the climax period, the recession period and the long tail period. The chart demonstrates that the line trend features of the whole reposting process are not a simple linear relationship, but a more complex fluctuation process. Therefore, it is hard to find the deeper and more specific reasons behind the fluctuation phenomenon merely through descriptive statistical analysis, and we learn further that it requires the assistance of more complicated feature component decomposition method to accurately separate the relevant feature elements, so as to better analyze the more profound causes behind the fluctuation phenomenon. However, in the whole spreading process as the reposting fluctuation features have autocorrelation and are composed of trend features, cluster features and irregular features. In order to learn the features of the overall spreading process of reposting behavior in detail, it is necessary to make autocorrelation analysis on the reposting mean sequence and separate the trend features, cluster features and irregular features accurately.

Time/day Fig. 3.9 Line chart of the whole process of average reposting number

3.2 Fluctuation Features of Reposting Behavior

3.2.1.1

63

Autocorrelation

In order to analyze the autocorrelation of reposting behavior, we have to analyze the autocorrelation and partial autocorrelation of reposting mean time series to detect the existence of autocorrelation. In this case, autocorrelation refers to the measurement of the correlation between a certain time series and the sequence formed after the sequence lags N order; partial autocorrelation refers to the measurement of the conditional correlation between a certain time series and the sequence lagging N order when other time series are given. It can be calculated by the following formula: Autocorrelation coefficient of reposting behavior: T r zhuan f a,k =

t=k+1

(yzhuan f a,t − y zhuan f a )(yzhuan f a,t−k − y zhuan f a ) T 2 t=1 (yzhuan f a,t − y zhuan f a )

(3.1)

y zhuan f a is the sample mean of the reposting mean sequence. Partial autocorrelation coefficient of reposting behavior: zhuan f a

φk,k

=

⎧ ⎨ r zhuan f a,1

k=1

⎩

k>1

 zhuan f a r zhuan f a,k − k−1 j=1 φk−1, j r zhuan f a,k− j k−1 zhuan f a 1− j=1 φk−1, j r zhuan f a,k− j

(3.2)

r zhuan f a,k is the value of the autocorrelation coefficient of the order lag. zhuan f a

φk, j

zhuan f a

= φk−1, j

zhuan f a

− φk,k

zhuan f a

φk−1,k− j

(3.3)

The corresponding spike graphs of autocorrelation and partial autocorrelation are shown in Figs. 3.10 and 3.11 respectively. The shadow areas in Figs. 3.10 and 3.11 marks 95% confidence intervals, indicating the significant correlation individuals of the points falling outside the area. From Fig. 3.10, we can see that the autocorrelation of the reposting time series is significant in the lag period 2. According to Fig. 3.11, the partial autocorrelation is obvious in the lag period 3. It can be concluded that the reposting number sequence has outstanding autocorrelation, that is, the reposting behavior has the obvious autocorrelation, and it is apparent in the range of lag period 2–3. The autocorrelation function of reposting behavior is exponential decay, but its attenuation speed is slow, so it can be preliminarily considered as unstable sequence. To reveal the autocorrelation features of reposting behavior, we need to construct ARIMA model for reposting mean time series, so as to conduct in-depth analysis. However, in order to prevent the pseudo regression, ARIMA model construction should be based on the stable sequence. Hence, before the ARIMA model is constructed for the reposting number, it is necessary to make unit root test of the stability for the sequences. (Hamilton, 1994). Based on the rough judgment of the instability of the repost sequence, the unit root test of the stability is carried out to determine the appropriate method to make the

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

“Daily reposting number”

64

“Daily reposting number”

Fig. 3.10 Reposting number autocorrelation graph

Fig. 3.11 Partial correlation graph of reposting number

3.2 Fluctuation Features of Reposting Behavior

65

Table 3.4 Unit root test of logarithmic sequence of reposting number Sequence

ADF value

Critical value 1%

In(zhuanfa) D(ln(zhuanfa))

5%

p value

Test results

10%

0.978

– 3.808

– 3.021

– 2.650

0.994

Unstable

–4.540

– 3.832

– 3.030

– 2.655

0.002

Stable

sequence stationary. In order to reduce the instability caused by the large fluctuation of data, the natural logarithm transformation of reposting number sequence should be made before the test. ADF (Augment Dicky-Fuller) test is the most commonly used stationarity test method (Box et al., 2011). In this study, Eviews 8.0 statistical software is adopted to analyze the stationarity of its natural logarithm series and its difference series, and the test results are shown in Table 3.4. Table 3.4 shows that the absolute value of the test statistic of the natural logarithmic sequence of the reposting is less than 5% of the absolute value of the critical level statistic, that is, the test value p is greater than 0.05 significance level, and the original hypothesis of “single root exists” cannot be rejected, indicating that there is at least one single root in the sequence of In (zhuanfa), which means that the sequence is not stationary. At the same time, as for In (zhuanfa), the absolute value of the firstorder differential sequence statistics is greater than the absolute value of 5% critical level statistics, that is, the test value p is less than 0.05 significance level, rejecting the original hypothesis, which indicates that there is no single root in the first-order differential sequence of the sequence, that is, the In (zhuanfa) first-order differential sequence is stable. Based on the stable first order differential sequence of the reposting logarithm In (zhuanfa), autocorrelation and partial correlation analysis are performed to identify and determine the p and q values in the ARIMA (p, d, q) model. The correlation analysis of the first-order differential sequence of In (zhuanfa) is shown in Fig. 3.12. In Fig. 3.12, we can see that the logarithmic first-order differential partial correlation function of reposting exceeds 95% confidence intervals in the first three phases, while the rest of the phases are within the confidence intervals. And the values of each order function decay slowly, indicating that the p value in the ARIMA (p, d, q) model can be tried to get the value 3; Autocorrelation function in the first 3 period exceeds 95% confidence intervals, and the rest phases are within the confidence intervals, and there is a trailing phenomenon in the values of each order function, showing that the q value in the ARIMA (p, d, q) model can be tested as numeric value 3. The d value presents that the No. d order difference sequence is stable, where the d value is 1, so ARIMA (3,1,3) Model can be constructed for the logarithmic first-order differential sequence of the reposting. On this basis, examine the validity and suitability of the model, and the results are shown in Figs. 3.13 and 3.14, respectively. Figure 3.13 shows that the autocorrelation and partial correlation function values of the reposting logarithmic first-order differential sequence ARIMA (3,1,3) model residuals are within 95% confidence intervals on all lag orders, and the p values of the correlation tests for each order of the residuals series are greater than the

66

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Fig. 3.12 First-order logarithmic difference correlation diagram of reposting numbers

Fig. 3.13 Residual correlation diagram of ARIMA (3,1,3) model for D (In (zhangfa)) sequence

3.2 Fluctuation Features of Reposting Behavior

67

Fig. 3.14 Fitting effect diagram of ARIMA (3,1,3) model for D (In (zhangfa)) sequence

significant level of 0.05, that is, accepting the original hypothesis, which indicates that there is no sequence correlation in the residuals series of the constructed ARIMA (3,1,3) model. For Fig. 3.14, the actual values of the reposting logarithmic first-order difference fit well with the ARIMA (3,1,3) model estimates, and that all residual values are within 95% confidence interval, indicating that the model settings and model estimates are valid. The correlation of model ARIMA (3,1,3) and the number of lag significant periods indicate that reposting behavior has an important impact on self-behavior within the lag 3 period, that is, the user’s own past behavior of participating in reposting has a significant impact on his own current behavior of participating in reposting, and there is a significant dependence between current and past reposting behavior in the lag 3 period. The estimated results of the ARIMA (3,1,3) model reposting logarithmic firstorder differential sequences are shown in Table 3.5. Table 3.5 presents that the intercept items and the corresponding coefficients of each variable in the model have significant test values p less than 0.05 significant levels, and the goodness of fit index R2 and adjusted R2 values of the model are both greater than 86%.The p value of model integrity fit test reaches 0.001 significant level, indicating that both model setup and model estimation results are good, indicating that the model can be used to effectively predict and estimate the number of reposts. The expression of ARIMA (3,1,3) for this model can be written as: Ln(zhuan f at ) = −0.297 + 0.311 × Ln(zhuan f at−1 ) + 0.388 × Ln(zhuan f at−2 ) − 0.387 × Ln(zhuan f at−3 ) + εˆ t + 1.076 × εˆ t−1 − 1.137 × εˆ t−2 + 3.239 × εˆ t−3

(3.4)

68

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Table 3.5 Estimated results of ARIMA (3,1,3) model for D (In (zhangfa)) sequence Variable

Coefficient

Std. Error

t-Statistic

C

−0.2966

0.1009

−2.9384

0.0148 0.0008

AR(1)

0.3110

0.2311

−4.5826

AR(2)

0.3879

0.0768

5.0469

AR(3)

−0.3866

0.0934

−4.1388

Prob

0.0005 0.002

MA(1)

1.0760

1.2656

4.1459

0.0016

MA(2)

−1.1370

1.1216

−2.3767

0.0367

MA(3)

3.2390

1.1641

2.7822

0.0194

R-squared

0.9154

Mean dependent var

Adjusted R-squared

0.8646

S.D. dependent var

S.E. o£ regression

0.0694

Akaike info criterion

0.0482

−0.275 0.1888 −2.2029

Schwarz criterion

−1.8598

Log likelihood

25.7252

Hannan-Quinn criter

−2.1688

F-statistic

18.0342

Durbin-Watson stat

Sum squared resid

0.00007

Response value

Prob ( F-statistic)

1.6002

Time / day Fig. 3.15 Impulse diagram of ARIMA (3,1,3) model for D (In (zhangfa)) sequence

The impulse response features of this model are shown in Fig. 3.15. Figure 3.15 shows that each shock of the Weibo reposting behavior of brand crisis information will have a huge impact on the information reposting behavior of the lag period 1, 2, 4 and 6, while the impact on the remaining lag periods are relatively small.

3.2.1.2

Trend Features

The trend decomposition method of time series is used to decompose the characteristic variables of reposting behavior fluctuation. Since the time series is nonquarterly or monthly data, in the whole spreading process of the reposting time series

3.2 Fluctuation Features of Reposting Behavior

69

Whole_zhuanfa, there is no influence of seasonal elements, thus the characteristic components of the series can be divided into the following parts: YWhole_zhuanfa,t = T CWhole_zhuanfa,t + IWhole_zhuanfa,t

(3.5)

TC Whole_zhuanfa,t represents the trend cycle elements and I Whole_zhuanfa,t represents the irregular elements. For the formula of T C + YWhole_zhuanfa,t T CWhole_zhuanfa, t = YWhole_zhuanfa,t

(3.6)

C T YWhole_zhuanfa,t is the trend component in the time series; YWhole_zhuanfa,t is the periC odic component. As the time series is not quarterly or monthly data, YWhole_zhuanfa,t C T does not exist, i.e., YWhole_zhuanfa,t = 0, so T CWhole_zhuanfa, t = YWhole_zhuanfa,t . Among them, Henderson weighted moving average method (MA) can be used to calculate the trend cycle elements of whole-Zhuanfa reposting time series, namely: T T Cwhole_zhuan f a,t = Ywhole_zhuan f a,t = M Awhole_zhuan f a,t

=

H 

+1 h 2H Ywhole_zhuan f a,t+i j

j=−H

H +1≤t ≤ T − H

(3.7)

Based on this, the irregular elements in the reposting time series Whole_Zhuanfa can be calculated as: Iwhole_zhuan f a,t = Ywhole_zhuan f a,t − T Cwhole_zhuan f a,t T = Ywhole_zhuan f a,t − Ywhole_zhuan f a,t

(3.8)

The decomposition results are shown in Fig. 3.16. It can be seen from Fig. 3.16 that the reposting trend of the whole communication process presents a unimodal curve, which witnesses a rapid rise after the crisis, reaches the maximum value on the fourth and fifth day after the crisis, then shows a prompt decline, and presents a slow decline trend on the fourteenth day. However, the irregular characteristic curve of reposting is bimodal and has no obvious regularity. It shows the fluctuation characteristic that each period increases followed by the next decreasing period. And on the fifth and twelfth day, the irregular characteristic extends a great impact. In order to analyze the change rate features of the crisis information reposting behavior over time, it is necessary to analyze the marginal change rate of reposting behavior. Among them, margin refers to the change of dependent variable with the increase of independent variable. The marginal change rate of reposting refers to the change of reposting number per unit time over time, which can be applied to learn

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Reposting number / piece

70

Time/day Irregular features

Trend features

Fig. 3.16 Decomposition of fluctuation features of reposting number in the whole process

the change of reposting increment per unit time. The index can be calculated by the following formula: M Q i (t) = Yi (t) =

Y (t)whole_zhuan f a,i t

(3.9)

i is the ith day, and the values are 1, 2, 3, …, 21. Y (t)whole_zhuan f a,i = Y (i + 1)whole_zhuan f a − Y (i)whole_zhuan f a

(3.10)

Among them, t is the unit time. The calculation results are shown in Fig. 3.17. Figure 3.21 illustrates that the marginal growth rate of crisis information reposting behavior is positive until it approaches the fourth day. The value of the adjacent places on the eighth day and the ninth day fluctuates slightly in a positive direction, then tends to 0 after the 17th day, and the other periods are negative.

3.2.1.3

Cluster Features

In the whole process of crisis communication, information sharing behavior has not only the trend features and irregular features, but also the features of relatively small fluctuation in one period and relatively large in another period, namely, the volatility clustering. For analyzing whether the cluster features of the reposting behavior exist in the process of crisis information communication, the autoregressive conditional

71

Reposting marginal growth rate / piece

3.2 Fluctuation Features of Reposting Behavior

Time / day Fig. 3.17 Marginal change of reposting number in the whole process

heteroscedasticity (ARCH) model of the reposting behavior is estimated. According to the square correlation diagram of the residual of the reposting behavior and the ARCH effect test results, we can judge whether the volatility clustering exists in the whole spreading process of the reposting behavior. When constructing ARCH (q) model, in order to reduce the error caused by data fluctuation, we first take the natural logarithm of Ywhole_zhuanfa series. Then the corresponding ARCH (q) model is: ⎧ ln Ywh_zh,t = βwh_zh,0 + βwh_zh,1 ln Ywh_zh,t−1 + βwh_zh,2 ln Ywh_zh,t−2 ⎪ ⎪ ⎨ +··· + β wh_zh,k ln Ywh_zh,t−k + u wh_zh,t 2 2 2 σ = α ⎪ wh_zh,0 + αwh_zh,1 u wh_zh,t−1 + αwh_zh,2 u wh_zh,t−2 ⎪ ⎩ wh_zh,t 2 + · · · + αwh_zh,q u wh_zh,t−q

(3.11)

In the formula, i is the number iday, and its values are 1, 2, 3, …, 21 respectively. The model is fitted by Eviews 8.0. The results are as follows: ln Ywh_zh,t = 2.56 + 0.16 ln Ywh_zh,t−1 + 0.52 ln Ywh_zh,t−2 + uˆ wh_zh,t

(3.12)

Among them, the p value of the overall F statistical test of the model is 0.000, indicating that the model is overall significant; the p value corresponding to constant term and each variable coefficient is 0.024, 0.000, 0.017, which suggests that each fitting coefficient is significant; and R 2 value is 0.995, which shows that the fitting effect is good, so the fitting results of the model are effective. The corresponding residual square correlation diagram is shown in Fig. 3.18. It can be concluded from Fig. 3.18 that the autocorrelation and partial correlation functions of the square of the residuals have a lag of at least one period, which exceeds

72

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Fig. 3.18 Square correlation diagram of residual error of the overall process of reposting number

the 95% confidence intervals, indicating that the correlation of the sequence is not 0, and the autocorrelation function presents a slow decline state, namely “tailing phenomenon”. Meanwhile, the corresponding Q-test shows that p values are less than 0.001, that is, the test results are extremely significant, which testifies that there is autocorrelation in the residual square sequence of ARCH model, that is, there is ARCH effect in this series. In the process of model estimation, in order to avoid improper selection of lag length q of ARCH model which may lead to violation of the constraint condition that the value 62 wh_zh,t should not be negative, so that the condition that the conditional variance is positive cannot be satisfied, which makes the whole model estimation invalid, the generalized ARCH model (GARCH) is used to fit the conditional variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as:

3.2 Fluctuation Features of Reposting Behavior

73

⎧ ⎪ ln Ywh_zh,t = γwh_zh,0 + γwh_zh,1 ln Ywh_zh,t−1 + γwh_zh,2 ln Ywh_zh,t−2 ⎪ ⎪ ⎪ ⎪ ⎨ + · · · + γwh_zh,k ln Ywh_zh,t−k + u wh_zh,t 2 σwh_zh,t = αwh_zh,0 + αwh_zh,1 u 2wh_zh,t−1 + αwh_zh,2 u 2wh_zh,t−2 ⎪ ⎪ ⎪ + · · · + αwh_zh,q u 2wh_zh,t−q ⎪ ⎪ ⎩ 2 2 2 +βwh_zh,1 σwh_zh,t−1 + βwh_zh,2 σwh_zh,t−2 + · · · + βwh_zh,q σwh_zh,t− p

(3.13)

The final fitting results of GARCH model are shown in Table 3.6. In the fitting results of Table 3.6, the z-test p values corresponding to the coefficients of mean equation and variance equation reach the significant level of 0.05, and the fitting index R-squared value of the whole model is close to 1, so the model construction and fitting results are effective. Thanks to the clustering effect of reposting behavior in the whole communication process, the cluster features of reposting behavior can be analyzed by residual sequence diagram and conditional variance diagram of GARCH model. The corresponding residual sequence line chart and conditional variance line chart are shown in Figs. 3.19 and 3.20 respectively. Figure 3.19 shows that there is volatility clustering phenomenon on the second day, the third day and the seventh day after the crisis, and the cluster effect is obvious, while the cluster effect is weak in other periods. The conditional variance line in Fig. 3.20 demonstrates that the conditional variance is the largest from the second day to the seventh day after the crisis, and the conditional variance is larger on the first day and the eighth to the tenth day, indicating that the reposting behavior has Table 3.6 GARCH model fitting results of overall reposting process, GARCH = C(4) + C(5) * RESID(−1)∧ 2 + C(6) *GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

Mean equation C

0.119807

0.228161

2.19068

0.0285

lnY wh_zh (−1)

1.539718

0.173131

8.89335

0.0000

lnY wh_zh (−2)

−0.573092

0.178239

−3.215304

0.0013

0.004668

0.003111

2.646242

0.0081

−0.199248

0.125982

3.881121

0.0001

1.02175

0.187706

5.443347

0.0000

Variance equation C RESID(−1T2) GARCH(−l) Fitting index R-squared

0.984854

Mean dependent var

8.451398

Adjusted R-squared

0.98296

S.D. dependent var

1.699413

S.E. o£ regression

0.221835

Akaike info criterion

−0.433337

0.787374

Schwarz criterion

−0.135093

Hannan-Quinn criter

−0.382863

Sum squared resid Log likelihood Durbin-Watson stat

10.1167 2.093343

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Residual

74

Time/day

Condition program

Fig. 3.19 Line chart of residual error in the whole process of reposting number

Time/day Fig. 3.20 Line chart of conditional variance in the whole process of reposting number

great fluctuations in the corresponding period, and the conditional variance is smaller after the tenth day.

75

Reposting number / piece

3.2 Fluctuation Features of Reposting Behavior

Time/day Fig. 3.21 Line chart of reposting number

3.2.2 Weekly Fluctuation Features 3.2.2.1

Trend and Periodic Features

Figure 3.21 shows the average weekly reposting value of brand crisis information. Figure 3.21 shows that the rise from Monday to Tuesday is relatively placid, and it rises rapidly from Wednesday to Friday, declines quickly on Saturday, and decreases slowly on Sunday, with the maximum value on Friday. The results show that Monday is the warm-up period, Tuesday and Wednesday are the warming period, Thursday and Friday are the climax period, and Saturday and Sunday are the cooling period. The results also manifest that the trend feature of the weekly reposting broken line is not a simple linear relationship, but a more complex fluctuation process. Hence, it is difficult to find the deeper and more specific reasons behind the fluctuation phenomenon only through descriptive statistical analysis, and the more sophisticated feature component decomposition method is required to accurately separate the related feature elements. Only in this way, can we better analyze the deeper roots behind the fluctuation phenomenon. The time series trend decomposition method is used to decompose the characteristic variables of reposting behavior fluctuation. In the weekly reposting time series Week_zhuanfa, as the time series is not quarterly or monthly data, there is no influence of seasonal elements, so the characteristic components of the series can be decomposed into: YWeek_zhuanfa,t = T Cweek_zhuanfa,t + IWeek_zhuanfa,t

(3.14)

In the formula, TC Week_zhuanfa,t represents the trend cycle elements, and I Week_zhuanfa,t represents the irregular elements.

76

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users T C For the formula of T Cweek_zhuanfa, t = YWeek_zhuanfa,t + Yweek_zhuanfa,t

(3.15)

C T YWeek_zhuanfa,t is the trend component in the time series, YWeek_zhuanfa,t is the periodic component. Among them, Henderson weighted moving average method (MA) can be used to calculate the trend cycle elements of Week_zhuanfa reposting time series, namely:

T Cweek_zhuan f a,t = M Aweek_zhuan f a,t =

H 

+1 h 2H Yweek_zhuan f a,t+i j

j=−H

(H + 1 ≤ t ≤ T − H )

(3.16)

Among them, HP (Hodrick-Prescott) filtering can be used to separate T T Yweek_zhuan f a,t the trend component of T C week_zhuan f a,t = Yweek_zhuan f a,t + C T Yweek_zhuan f a,t from TC week_zhuanfa,t . The trend components Yweek_zhuan f a,t can be separated by solving the following minimization problem: min

T  

T T Cweek_zhuan f a,t − Yweek_zhuan f a,t

2

2  T (3.17) + λ c(L)Yweek_zhuan f a,t

t=1

the parameter λ is a given prior value, its value λ ∈ [0, ∞), and c(L) is the delay operator polynomial, that is c(L) = (L −1 − 1) − (1 − L). The corresponding HP filtering problem is transformed into the following minimization loss function: min

 T 

T T Cweek_zhuan f a,t − Yweek_zhuan f a,t

2

t=1

+λ

T −1  





T T T T Yweek_zhuan f a,t+1 − Yweek_zhuan f a,t − Yweek_zhuan f a,t − Yweek_zhuan f a,t−1

t=2

(3.18) Finally, the irregular element components in the forwarding time series Week_zhuanfa can be calculated as: Iweek_zhuan f a,t = Yweek_zhuan f a,t − T Cweek_zhuan f a,t = Yweek_zhuan f a,t −

H 

+1 h 2H Yweek_zhuan f a,t+i j

(3.19)

j=−H

After calculation, the separation results of trend features, periodic features and irregular features of weekly reposting number fluctuation are shown in Fig. 3.22. Figure 3.22 shows that the weekly reposting trend presents a single-peak curve, with an upward trend from Monday to Friday, reaching a maximum on Friday, and

2



77

Reposting number / piece

3.2 Fluctuation Features of Reposting Behavior

Time/day Trend feature

Periodic feature

Irregular

Fig. 3.22 Decomposition of weekly reposting number fluctuation features

a rapid downward trend on Saturday and Sunday. The cycle is characterized by a single-peak curve process, rising rapidly on Tuesday, rising slowly on Wednesday and Thursday, and declining from Friday to Sunday, with a larger decline on Saturday. In terms of cycle features, Monday has the smallest cyclical effect, followed by Sunday, while the cyclical reposting behavior from Tuesday to Friday is more active, with the maximum on Thursday. The irregular characteristic curve has a bimodal characteristic and does not have apparent regularity. It is manifested as the volatility characteristic of increasing in each period and then decreasing in the next period, and its irregular influence on Tuesday and Friday is greater. In order to analyze the features of the rate of change of the number of reposting of crisis information in a week, it is necessary to analyze the marginal rate of change. The index can be calculated with the following formula: M Q i (t) = Yi (t) =

Y (t)week_zhuan f a,i t

(3.20)

i is the week i, and the values are 1, 2, 3,…, 7. Among Y (t)week_zhuan f a,i = Y (i + 1)week_zhuan f a − Y (i)week_zhuan f a , t is the unit time. The calculation results are shown in Fig. 3.23. Figure 3.23 shows that the marginal growth rate is positive from Monday to Friday and negative from Saturday to Sunday, and the marginal growth rate from Wednesday to Friday is larger.

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Reposting marginal growth / piece

78

Time/day

Fig. 3.23 Marginal change of reposting number in one week

3.2.2.2

Cluster Features

For the purpose of testing whether the weekly reposting behavior has clustering effect, the weekly reposting sequence is estimated by ARCH model, and then the correlation of its residual square sequence is tested, so as to determine whether the weekly reposting behavior fluctuation has clustering features. When constructing ARCH (q) model, in order to reduce the error caused by data fluctuation, we first take the natural logarithm of the Y whole_zhuanfa sequence. Then the corresponding ARCH (q) model is: ⎧ ln Ywk_zh,t = βwk_zh,0 + βwk_zh,1 ln Ywk_zh,t−1 + βwk_zh,2 ln Ywk_zh,t−2 ⎪ ⎪ ⎨ +··· + β wk_zh,k ln Ywk_zh,t−k + u wk_zh,t 2 ⎪ σwk_zh,t = αwk_zh,0 + αwk_zh,1 u 2wk_zh,t−1 + αwk_zh,2 u 2wk_zh,t−2 ⎪ ⎩ + · · · + αwk_zh,q u 2wk_zh,t−q

(3.21)

In the formula, i is the ith day, and its values are 1, 2, 3 … 7 respectively. The model is fitted by Eviews 8.0. And the results are as follows: ln Ywk_zh,t = 2.89 + 0.64 ln Ywk_zh,t−1 + 0.32 ln Ywk_zh,t−2 + uˆ wk_zh,t

(3.22)

Among them, the p value of the overall F statistical test of the model is 0.002, indicating that the model is overall significant; the p values corresponding to constant term and each variable coefficient are 0.001, 0.005, 0.000, which suggest that each fitting coefficient is significant; and R 2 value is 0.971, which shows that the fitting effect is good, so the fitting results of the model are effective. The corresponding residual square correlation diagram is shown in Fig. 3.24.

3.2 Fluctuation Features of Reposting Behavior

79

Fig. 3.24 Square correlation chart of reposting number residuals in one week

It can be seen from Fig. 3.24 that the autocorrelation and partial correlation functions of the residual square sequence have a lag of at least one period, which exceeds the 95% confidence interval, indicating that the correlation of the residual sequence is significantly not zero, and the autocorrelation function shows a slow decay state, which is called “tailing phenomenon”. At the same time, the corresponding p value of Q statistical test is less than 0.001, which means that the test result is extremely significant, thus indicating that there is autocorrelation in the residual square sequence of one week reposting behavior, that is, there is ARCH effect in the sequence. In the process of model estimation, in order to avoid the improper choice of the lag length q of ARCH model, which may lead to violation of the constraint that 62wh_zh,t value should not be negative, making the condition that conditional variance is positive unsatisfied, and thus render the whole model estimation invalid, the generalized ARCH model (GARCH) is used to fit the conditional variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as: ⎧ ln Ywk_zh,t = γwk_zh,0 + γwk_zh,1 ln Ywk_zh,t−1 + γwk_zh,2 ln Ywk_zh,t−2 ⎪ ⎪ ⎪ ⎪ ⎪ + · · · + γwk_zh,k ln Ywk_zh,t−k + u wk_zh,t ⎪ ⎪ 2 ⎨ σwk_zh,t = αwk_zh,0 + αwk_zh,1 u 2wk_zh,t−1 + αwk_zh,2 u 2wk_zh,t−2 (3.23) + · · · + αwk_zh,q u 2wk_zh,t−q ⎪ ⎪ ⎪ ⎪ 2 2 ⎪ +βwk_zh,1 σwk_zh,t−1 + βwk_zh,2 σwk_zh,t−2 ⎪ ⎪ ⎩ 2 + · · · + βwk_zh,q σwk_zh,t− p The final fitting results of GARCH model are shown in Table 3.7. In the fitting results of Table 3.7, the z-test p values corresponding to the coefficients of mean equation and variance equation both reach a significant level of 0.05, and the fitting index R-squared value of the whole model is close to 1 value. Therefore, the model construction and fitting results are effective. Due to the existence of clustering effect, the cluster features of one-week reposting behavior can be further analyzed by the residual sequence diagram and conditional variance diagram of GARCH model.

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3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Table 3.7 Fitting results of GARCH model of reposting number, GARCH = C(3) + C(4)*RESID(−1)ˆ2 + C(5)*GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

C

−0.466034

0.219452

−2.123633

0.0337

lnYwk _zh (−1)

1.025609

0.023299

44.01917

0.0000

Mean equation

Variance equation C

0.007513

0.0074

−2.46476

0.0137

RESID(−1)ˆ2

−0.325896

0.324164

3.64471

0.0003

GARCH(−l)

1.199276

0.624148

1.921462

0.0547

R-squared

0.973278

Mean dependent var

8.544772

Adjusted R-squared

0.971794

S.D. dependent var

1.705983

S.E. o£ regression

0.286514

Akaike info criterion

0.045052

Sum squared resid

1.477627

Schwarz criterion

0.293985 0.093647

Fitting index

Log likelihood

4.549477

Hannan-Quinn criter

Durbin-Watson stat

0.757296

Schwarz criterion

Residuals

The residual line diagram and conditional variance line diagram are shown in Figs. 3.25 and 3.26 respectively. From the residual line Fig. 3.25, we can see that there is fluctuation clustering phenomenon on Wednesday, Thursday and Friday, and the clustering effect is obvious, while the clustering effect is weak in other periods. It can be seen from

Time / day Fig. 3.25 Line chart of reposting number residuals in one week

81

Condition program

3.2 Fluctuation Features of Reposting Behavior

Time / hour Fig. 3.26 Line chart of conditional variance of weekly reposting number

the line Fig. 3.26 of conditional variance that the conditional variance is the largest on Thursday morning, followed by Wednesday morning, Friday afternoon, Tuesday morning and Saturday morning, which indicates that the reposting behavior of a week has a large fluctuation in the corresponding period, while the fluctuation range of other periods is small.

3.2.3 Weekly Fluctuation Features The line chart of daily reposting number of brand crisis information in one week is shown in Fig. 3.27. Figure 3.27 demonstrates that daily reposting behavior in one week shows common regular features, namely, the reposting volume of 7:00–11:00 a.m. rises promptly, then decreases slightly until noon, and increases slightly from 2:00 to 4:00 p.m., followed by a decline process. There is another rise from 8:00 to 11:00 p.m., and the trend of the decline is significant from then to 6:00 a.m. the next day. In order to further understand the more accurate features of daily reposting behavior of crisis information, it is necessary to conduct time analysis on the daily reposting average. The average line graph of daily reposting number is shown in Fig. 3.28. It can be seen from Fig. 3.32 that the average daily reposting number rises rapidly from 7:00 a.m. to 11:00 p.m., decreases slightly to 2:00 p.m., increases modestly from 2:00 p.m. to 4:00 p.m., decreases to 7:00 p.m., increases quickly from 8:00 p.m. to 10:00 p.m., and then decreases sharply to 6:00 a.m. the next day. The results show that the reposting behavior presents the features of “three peaks and three valleys” in 24 h of a day. The order of fluctuation process of “three peaks” is from 9:00 a.m. to

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Reposting number / piece

82

Time / hour Monday Friday

Tuesday Saturday

Wednesday Sunday

Thursday

Reposting number / piece

Fig. 3.27 Line chart of daily reposting in one week

Time / hour

Fig. 3.28 Line chart of the average number of reposting in one day

12:00 p.m., from 9:00 p.m. to 11:00 p.m., and from 3:00 p.m. to 5:00 p.m.. The order of fluctuation process of “three valleys” is from 11:00 p.m. to 9 a.m., from 5:00 p.m. to 9:00 p.m., and 12:00 a.m. to 3:00 p.m. The chart manifests that the trend features of daily reposting broken line is not a simple linear relationship, but a more complex fluctuation process. Therefore, it is hard to find the deeper and more specific reasons

3.2 Fluctuation Features of Reposting Behavior

83

behind the fluctuation phenomenon only through descriptive statistical analysis, and it needs the help of more complicated feature component decomposition method to accurately separate the relevant feature elements, so as to better analyze the deeper causes behind the fluctuation phenomenon.

3.2.3.1

Trend and Periodic Features

The time series trend decomposition method is applied to decompose the fluctuation feature variables of reposting behavior. In the one-day reposting time series Day_Zhuanfa, as the time series is not quarterly or monthly data, there is no influence of seasonal factors, so the feature components of the series can be decomposed into: Yday_zhuanfa,t = T Cday_zhuanfa,t + Iday_zhuanfa,t

(3.24)

In the formula, TC day_zhuanfa,t represents the trend cycle elements, and I day_zhuanfa,t represents the irregular elements. C T T +Yday_zhuanfa,t , Yday_zhuanfa,t is the trend component In T Cday_zhuanfa, t = Yday_zhuanfa,t C in the time series. Yday_zhuanfa, t is the periodic component. Among them, Henderson weighted moving average method (MA) can be used to calculate the trend cycle elements of Week_zhuanfa reposting time series, namely: Yday_zhuan f a,t = T Cday_zhuan f a,t + Iday_zhuan f a,t

(3.25)

In the formula, TC day_zhuanfa ,t represents a trend cycle element, and I day_zhuanfa,t represents an irregular element. T T Among them, Yday_zhuan f a,t of the formula T C day_zhuan f a,t = Yday_zhuan f a,t + C C Yday_zhuan f a,t is the trend component in the time series and Yday_zhuan f a,t is the periodic component. The Henderson weighted moving average method (MA) can be used to calculate the trend cycle element component of the forwarding time series Week_zhuanfa, which is: T Cday_zhuan f a,t = M Aday_zhuan f a,t =

H 

+1 h 2H Yday_zhuan f a,t+i , H + 1 ≤ t ≤ T − H j

(3.26)

j=−H

Here, HP (Hodrick-Prescott) filtering can be used to separate the trend component T T Yday_zhuan f a,t from TCday_zhuanfa,t. The trend components Yday_zhuan f a,t can be separated by solving the following minimization problem: min

T   t=1

T T Cday_zhuan f a,t − Yday_zhuan f a,t

2

2  T + λ c(L)Yday_zhuan f a,t

(3.27)

84

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

the parameter λ is a given prior value, its value λ ∈ [0, ∞), and c(L) is the delay operator polynomial, that is c(L) = (L −1 − 1) − (1 − L). The corresponding HP filtering problem is transformed into the following minimization loss function: min

T  

T T Cday_zhuan f a,t − Yday_zhuan f a,t

2

t=1

+λ

T −1  

T Yday_zhuan f a,t+1

−

T Yday_zhuan f a,t



−



T Yday_zhuan f a,t

−

T Yday_zhuan f a,t−1

2



t=2

(3.28) Finally, the irregular element components in the forwarding time series Week_zhuanfa can be calculated as: Iday_zhuan f at = Yday_zhuan f at − T Cday_zhuan f at = Yday_zhuan f at −

H 

+1 h 2H Yday_zhuan f at+i j

(3.29)

j=−H

Reposting number / piece

After calculation, the trend features, periodic features and irregular features of the average fluctuation of one day’s reposting number are divided into three parts. The results are shown in Fig. 3.29. Figure 3.29 shows that the change trend of one-day reposting behavior presents a bimodal curve, showing a rapid upward trend from 6:00 a.m. to 4:00 p.m., a rapid decline from 6:00 p.m. to 8:00 p.m., a quick rise from 8:00 p.m. to 10:00 p.m.,

Time / hour Trend feature

Periodic feature

Irregular feature

Fig. 3.29 Decomposition chart of fluctuation features of daily reposting number mean

3.2 Fluctuation Features of Reposting Behavior

85

and then a downward trend until 6:00 a.m. the next day. The periodicity shows an upward trend from 8:00 a.m. to 10:00 p.m. and a downward trend from 10:00 p.m. to 6:00 a.m. the next day. Among them, 6 a.m. is the time when the periodic effect of one-day reposting behavior is the smallest, 4 p.m. and 10 p.m. are larger, indicating that the periodic reposting behavior of 4 p.m. and 10 p.m. is more active. Nevertheless, the irregular characteristic curve is characterized by multi-peak and no obvious regularity, showing the fluctuation features of increasing in every two periods and then decreasing in the following two periods, the main performance of which is that the large irregular changes at 10:00 a.m., 2:00 p.m., 6:00 p.m. and 10:00 p.m. are greater. To analyze the features of one-day fluctuation rate of crisis information, we should analyze the marginal change rate of one-day reposting behavior. The index can be calculated by the following formula: M Q i (t) = Yi (t) =

Y (t)day_zhuan f a,i t

(3.30)

Reposting marginal growth/ piece

i is the day of the week, and the values are 1, 2, 3, ……, 7. Where Y (t)day_zhuan f a,i = Y (i + 1)day_zhuan f a − Y (i)day_zhuan f a , t is unit time. The calculation results are shown in Fig. 3.30. Figure 3.30 shows that the marginal growth rates of the corresponding periods from 6:00 a.m. to 12:00 p.m., from 2:30 p.m. to 4:30 p.m. and from 8:00 p.m. to 11:00 p.m. are all positive, while the marginal growth rates of other periods are negative. Among them, the marginal growth rate is the largest from 7:00 a.m. to 11:00 p.m., followed by 8:00 p.m. to 4:30 p.m., and the marginal growth rate is relatively small.

Time / hour

Fig. 3.30 Marginal change of average daily reposting number

86

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

3.2.3.2

Cluster Features

In order to test whether one-day reposting behavior has clustering effect, ARCH model is adopted to analyze the correlation of square sequence of one-day reposting behavior residuals, so as to determine whether one-day reposting behavior has fluctuating clustering features. When constructing ARCH (q) model, in order to reduce the error caused by data fluctuation, we first take the natural logarithm of Y day-zhuanfa sequence. The corresponding ARCH (q) model is as follows: ⎧ ⎪ ln Yday_zh,t = βday_zh,0 + βday_zh,1 ln Yday_zh,t−1 + βday_zh,2 ln Yday_zh,t−2 ⎪ ⎪ ⎨ +··· + β day_zh,k ln Yday_zh,t−k + u day_zh,t 2 σ = αday_zh,0 + αday_zh,1 u 2day_zh,t−1 + αday_zh,2 u 2day_zh,t−2 ⎪ day_zh,t ⎪ ⎪ ⎩ +··· + α 2 day_zh,q u day_zh,t−q (3.31) In the formula, i is the number i hour, and its values are 1,2,3, …, 24 respectively. The model is fitted by Eviews 8.0. And the results are as follows: ln Yday_zh,t = 2.61 + 0.73 ln Yday_zh,t−1 + 0.15 ln Yday_zh,t−2 + uˆ day_zh,t

(3.32)

Among them, the p value of the overall F statistical test of the model is 0.005, indicating that the model is overall significant; the p value corresponding to constant term and each variable coefficient is 0.002, 0.000, 0.001, which suggests that each fitting coefficient is significant; and R2 value is 0.984, which shows that the fitting effect is good, so the fitting results of the model are effective. The corresponding residual square correlation diagram is shown in Fig. 3.31 Figure 3.31 shows that the autocorrelation and partial correlation functions of the residual square sequence have a lag of at least one period, which exceeds the 95% confidence interval, indicating that the correlation of the sequence is significantly not zero, and the autocorrelation function shows a slow attenuation trend, namely “tailing phenomenon”. At the same time, the corresponding p value of Q statistical test is less than 0.001, which means that the test result is very significant, indicating that the fluctuation of one-day reposting behavior has obvious cluster effect. In the process of model estimation, the improper choice of the lag length q of ARCH model may lead to violation of the constraint condition that αday_zh,t value should not be negative, so that the condition that the conditional variance α2day_zh,t is positive can not be satisfied, which makes the whole model estimation invalid. For the sake of avoiding this problem, the generalized arch model GARCH (p, q) is used to fit the conditional variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as:

3.2 Fluctuation Features of Reposting Behavior

Residuals

Fig. 3.31 Square correlation chart of mean residuals of one-day reposting number

Time / hour Fig. 3.32 Line chart of residuals of one-day reposting number

87

88

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Table 3.8 Fitting results of GARCH model for one-day reposting number, GARCH = C(4) + C(5)*RESID(−1)ˆ2 + C(6)*GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

C

−0.071621

0.390063

2.990442

0.0028

lnY day_zh (−1)

1.142787

0.207418

5.509585

0.0000

lnY day_zh (−2)

−0.162827

0.223682

14.35649

0.0000

0.019092

0.02348

−3.19232

0.0014

Mean equation

Variance equation C RESID(−1)− 2

−0.198747

0.174676

27.3212

0.0000

GARCH (−1)

0.812644

0.57868

3.064

0.0022

Fitting index R-squared

0.969055

Mean dependent var

8.441642

Adjusted R-squared

0.965187

S.D. dependent var

1.730489

S.E. of regression

0.322877

Akaike info criterion

0.300406

Sum squared resid

1.667993

Schwarz criterion

0.59865

Log likelihood

3.146144

Hannan-Quinn criter

0.350881

Durbin-Watson stat

1.854139

⎧ ln Yday_zh,t = γday_zh,0 + γday_zh,1 ln Yday_zh,t−1 + γday_zh,2 ln Yday_zh,t−2 ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ + · · · + γday_zh,k ln Yday_zh,t−k + u day_zh,t 2 σday_zh,t = αday_zh,0 + αday_zh,1 u 2day_zh,t−1 + αday_zh,2 u 2day_zh,t−2 ⎪ ⎪ ⎪ + · · · + αday_zh,q u 2day_zh,t−q ⎪ ⎪ ⎩ 2 2 2 +βday_zh,1 σday_zh,t−1 + βday_zh,2 σday_zh,t−2 + · · · + βday_zh,q σday_zh,t− p (3.33) The final fitting results of GARCH model are shown in Table 3.8. In the fitting results of Table 3.8, the z-test p values corresponding to the coefficients of mean equation and variance equation reached the significant level of 0.05, and the fitting index R-squared value of the whole model was close to 1, so the model construction and fitting results were effective. Due to the existence of clustering effect, we can further analyze the clustering features of one-day reposting behavior through residual sequence diagram and conditional variance diagram of GARCH model. The corresponding residual sequence line chart and conditional variance line chart are shown in Figs. 3.32 and 3.33 respectively. From the residual line Fig. 3.36, it can be seen that there are fluctuating clustering phenomenon from 9:00 a.m. to 11 a.m., from 3:00 p.m. to 5:00 p.m., from 9:00 p.m. to 11 p.m., and the clustering effect are apparent in these periods, while the clustering effects in the remaining periods are relatively weak. From the conditional variance line in Fig. 3.37, we can see that the conditional variance is the largest from 8:00 a.m. to 11 a.m. and from 9:00 p.m. to 11 p.m. in one day, and the conditional variance

3.2 Fluctuation Features of Reposting Behavior

89

Time / hour Fig. 3.33 Line chart of conditional variance of one-day reposting number

from 3:00 p.m. to 4:00 p.m. is relatively large, indicating that the one-day reposting behavior has a large fluctuation in the corresponding period, while the fluctuation amplitudes of the rest of the times are small.

3.3 Fluctuation Features of Comment Behavior 3.3.1 Fluctuation Features of the Whole Communication Process The broken line chart of the average value of brand crisis information comments in the whole communication process is shown in Fig. 3.34. From Fig. 3.38, we can know that the number of comments in the first three days after the crisis increases quickly, and reaches the maximum on the third day, then decreases rapidly on the eighth day, and there is a small upward fluctuation from the eighth day to the ninth day, followed by a slow decline process. The whole evolutionary process gradually developed and changed in five life cycles: incubation period, outbreak period, climax period, recession period and long tail period. The chart shows that the trend of the broken line in the whole process of the comments is not a simple linear relationship, but a more complex fluctuation process. Therefore, it is difficult to find more profound and specific reasons behind the fluctuation phenomenon only through descriptive statistical analysis, and it is necessary to accurately separate the relevant feature elements with the help of more complex feature component decomposition method, so as to better analyze the deeper causes behind

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Comments number/piece

90

Time / hour Fig. 3.34 Line chart of the whole process of the average number of comments

the fluctuation phenomenon. However, in the whole communication process, as the fluctuation features of comments have autocorrelation, which is composed of trend features, cluster features and irregular features. In order to learn the behavior features of comments in the whole communication process, we need to analyze the autocorrelation of the mean series of comments, and separate the trend features, cluster features and irregular features accurately.

3.3.1.1

Autocorrelation

In order to analyze the autocorrelation of the comment behavior, we first need to analyze the autocorrelation and partial autocorrelation of the comment mean time series to detect whether there is autocorrelation in the series. It can be calculated by the following formula: The autocorrelation coefficient of comment behavior was as follows T t=k+1 (y pinglun,t − y pinglun )(y pinglun,t−k − y pinglun ) (3.34) r pinglun,k = T 2 t=1 (y pinglun,t − y pinglun ) y pinglun is the sample mean of the comment mean sequence; The partial autocorrelation coefficient of comment behavior is as follows: pinglun

φk,k

=

⎧ ⎨ r pinglun,1

k=1

⎩

k>1

 pinglun r pinglun,k − k−1 j=1 φk−1, j r pinglun,k− j k−1 pinglun 1− j=1 φk−1, j r pinglun,k− j

(3.35)

91

“Daily comments number”

3.3 Fluctuation Features of Comment Behavior

Fig. 3.35 Autocorrelation graph of comments number

r pinglun,k is the value of pinglun pinglun pinglun φk−1, j − φk,k φk−1,k− j .

pinglun

self phase relation with k-order delay, φk, j

=

The corresponding spike graphs of autocorrelation and partial autocorrelation are shown in Figs. 3.35 and 3.36 respectively. The 95% confidence interval is marked in the shadow area in Figs. 3.35 and 3.36, which indicates that the correlation individuals of the points falling outside the area are significant. It can be seen from Fig. 3.35 that the autocorrelation of the comment time series is significant within the lag four terms; it can be seen from Fig. 3.36 that the partial autocorrelation is significant within the Lag 4 period. Therefore, it can be judged that there is significant autocorrelation in the number of comments sequence, that is, the behavior of comments has significant autocorrelation features, and it is more obvious in the range of the Lag 3–4 periods. Among them, the autocorrelation function sequence of comments decays exponentially, but its decaying speed is slow, so it can be preliminarily considered that the comment sequence is unstable. For further learning the autocorrelation features of comment behavior, we are ought to build ARIMA model for comment mean time series to conduct in-depth analysis. Whereas, in order to prevent the occurrence of pseudo regression, the construction of ARIMA model needs to be based on the stationary series. Thus, before the construction of ARIMA model for comment behavior, it is necessary to carry out the stationary single root test on the comment time series. On the basis of the rough judgment of the instability of the comment sequence, it is necessary to test the unit root of stationarity in order to make the sequence stable by appropriate transformation method. In order to reduce the instability caused by the large fluctuation of data, carry out the natural logarithm transformation of the

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

“Daily comments number”

92

Fig. 3.36 Partial correlation graph of comments number

Fig. 3.37 Log first-order differential correlation chart of comments number

3.3 Fluctuation Features of Comment Behavior

93

Fig. 3.38 Residual correlation diagram of ARIMA (4,1,3) model for D (In (pinglun)) sequence

Table 3.9 Single root test for logarithmic series of comments Sequence

ADF value

Critical value

Value

Test result

1%

5%

10%

In(pinglun)

0.766

−3.809

−3.021

−2.650

0.991

Unstable

D(ln(pinglun))

−3.512

−3.832

−3.030

−2.655

0.019

Stable

comment number sequence before the test. The stationarity test results of natural logarithm series and difference series are shown in Table 3.9. The test results in Table 3.9 show that the absolute value of the statistics of natural logarithm series is less than the absolute value of the statistics of 5% critical level, that is, the test p value is greater than 0.05 significance level, and the original hypothesis of “single root” cannot be denied, which means that there is at least one single root in the In(pinglun) series, that is, the series is non-stationary. Meanwhile, the absolute value of In(pinglun) first-order difference sequence statistics is greater than the absolute value of 5% critical level statistics, that is, the test p value is less than 0.05 significance level. And the original hypothesis is rejected, which indicates that there is no single root in the first-order difference sequence, that is, In(pinglun) first-order difference sequence is stable. On the basis of commenting on the stationarity of logarithmic In(pinglun) firstorder difference sequence, the autocorrelation and partial correlation of the difference sequence are analyzed to identify and determine the p value and q value in ARIMA

94

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

(p, d, q) model. The correlation analysis of In(pinglun) first order difference sequence is shown in Fig. 3.37. Figure 3.37 shows that the log first-order differential partial correlation function of the number of comments exceeds the 95% confidence interval in the first four periods, and the rest of the periods are within the confidence interval, and it presents a slow decline trend, indicating that the value of p in ARIMA (p, d, q) model can be taken as 4; the autocorrelation function of ARIMA (p, d, q) model exceeds the 95% confidence interval in the first three periods, and the other periods are in the confidence interval, and there is a tailing phenomenon, which reflects that the value of q in ARIMA (p, d, q) model can be taken as 3. Where the value of d means that the dth order difference sequence is stationary, where d value is 1, so ARIMA (4,1,3) model can be initially constructed for the log first order difference sequence. On this basis, the validity and fitness of the model are tested, and the test results are shown in Figs. 3.38 and 3.39 respectively. Figure 3.38 shows that the residual autocorrelation and partial correlation function of ARIMA (4,1,3) model of log first order difference sequence are in the 95% confidence interval on all lag periods, and the p value of correlation test of each order of residual sequence is greater than the significance level of 0.05, that is, the original hypothesis is accepted, which indicates that the residual sequence of ARIMA (4,1,3) model constructed does not have sequence correlation. From Fig. 3.39, we can see that the actual value of the log first-order difference fits well with the estimated value of ARIMA (4,1,3) model, and all the residuals are within the 95% confidence interval, implying that the setting and estimation results of the model are effective. Among them, the correlation of ARIMA (4,1,3) and the number of significant lag periods show that the comment behavior has an important impact on their own

Fig. 3.39 Fitting effect of ARIMA (4,1,3) model for D (In (pinglun)) sequence

3.3 Fluctuation Features of Comment Behavior

95

Table 3.10 Estimation results of ARIMA (4,1,3) model for D (In(pinglun)) sequence Variable

Coefficient

Std. Error

t-Statistic

Prob

C

−0.3103

0.1264

−5.7753

0.0003

AR(1)

0.4175

0.2063

−3.0480

0.0138

AR(2)

0.0284

0.0948

10.1392

0.0000

AR(3)

0.3673

0.1754

2.7086

0.024

AR(4)

0.3816

0.1439

5.9640

0.0002

MA(1)

−1.3709

0.1949

3.0078

0.0148

MA(2)

−2.2954

0.2644

−3.2702

0.0097

4.2724

MA(3)

0.1990

0.1141

R-squared

0.9596

Mean dependent var

−0.2886

0.0009

Adjusted R-squared

0.8969

S.D. dependent var

0.2171

S.E. of regression

0.1113

Akaike info criterion

−1.2448

Sum squared resid

0.0992

Schwarz criterion

−0.8585

Log likelihood

17.9587

Hannan-Quinn criter

−1.2250

F-statistic

7.0025

Durbin-Watson stat

2.4334

Prob (F-statistic)

0.0067

behavior within three lag periods, that is, the user’s past participation in comments has a significant influence on their current participation in comments, and there is a significant dependence between the current comment behavior and the past comment behavior within three lag periods. The estimated results of ARIMA (4,1,3) model for log first-order difference sequence are shown in Table 3.10. Table 3.10 shows that the p values of intercept term and coefficient significance test corresponding to each variable in the model are less than the significant level of 0.05, and the R2 values of goodness of fit and adjusted R2 of the model are greater than 89%, And the p value of the model’s overall goodness of fit test reached 0.01 significant level, indicating that the model setting and model estimation results are both good, that is, the model can be used to effectively predict and estimate the number of comments. The expression of ARIMA (4,1,3) can be written as follows: Ln( pinglun t ) = −0.310 + 0.418 × Ln( pinglun t−1 ) + 0.028 × Ln( pinglun t−2 ) +0.367 × Ln( pinglun t−3 ) + 0.382 × Ln( pinglun t−4 ) + εˆ t −1.371 ׈εt−1 −2.295 × εˆ t−2 + 0.199 × εˆ t−3 (3.36) The impulse response features of the model are shown in Fig. 3.40. Figure 3.40 shows that each impact of Weibo comment behavior on brand crisis information will have a greater impact on the information comment behavior lagging

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Response value

96

Time / day

Fig. 3.40 Pulse diagram of ARIMA (4,1,3) model for D (In (pinglun)) sequence

behind the first, third, fourth and sixth periods, while the impacts on other lag periods are relatively small.

3.3.1.2

Trend Features

The time series trend decomposition method is used to decompose the characteristic variables of comment behavior fluctuation, and in the comment time series Whole_zhuanfa of the whole spreading process, since the time series is non quarterly or monthly data, there is no influence of seasonal factors, so the characteristic components of the series can be decomposed into: Ywhole_pinglun,t = T Cwhole_pinglun,t + Iwhole_pinglun,t

(3.37)

In the formula, TC whole_pinglun,t represents the trend cycle elements, and I whole_pinglun,t represents the irregular elements. C T T + Ywhole_pinglun,t , Ywhole_pinglun,t For the formula of T Cwhole_pinglun,t = Ywhole_pinglun,t is the trend component in the time series. As the time series is non quarterly or C monthly, the Ywhole_pinglun,t , which is the periodic component, doesn’t exist. Among them, Henderson weighted moving average method (MA) can be used to calculate the trend cycle elements of whole_pinglun comments time series, namely: T T Cwhole_ pinglun,t = Ywhole_ pinglun,t = M Awhole_ pinglun,t

=

H 

+1 h 2H Ywhole_ pinglun,t+i j

(3.38)

j=−H

H +1≤t ≤ T − H Based on this, the components of irregular elements in Whole_pinglun can be calculated as follows:

97

Reposting number / piece

3.3 Fluctuation Features of Comment Behavior

Time / day Trend feature

Irregular feature

Fig. 3.41 Decomposition of fluctuation features of the whole process of comments number

Iwhole_ pinglun,t = Ywhole_ pinglun,t − T Cwhole_ pinglun,t T = Ywhole_ pinglun,t − Ywhole_ pinglun,t

(3.39)

The calculation results are shown in Fig. 3.41. It can be concluded from Fig. 3.41 that the comment trend in the whole communication process is characterized by a single peak curve, which shows a soaring rise after the crisis, reaches the maximum value of comment behavior trend effect on the fourth and fifth day, then presents a quick decline, and has a slow decline trend about the fourteenth day. But the irregular characteristic curve has the features of multi peak and it possesses no obvious regularity. It shows the fluctuation features that the increase in each period is followed by the decrease in the next period, and the irregular effect is greater from the second day to the sixth day after the crisis. For the purpose of studying the change rate features of crisis information comment behavior over time, it is necessary to analyze the marginal change rate of comment behavior. The index can be calculated by the following formula: M Q i (t) = Yi (t) =

Y (t)whole_ pinglun,i t

(3.40)

i is the day, and the values are 1, 2, 3, …, 21. Where Y (t)whole_ pinglun,i = Y (i + 1)whole_ pinglun − Y (i)whole_ pinglun , t is the unit time. The corresponding analysis results are shown in Fig. 3.42.

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users Reposting marginal growth rate / piece

98

Time / day

Fig. 3.42 Marginal change in the overall process of the number of comments

From Fig. 3.42, we can know that the marginal growth rate of crisis information comment behavior is positive when it approaches the fourth day, and it fluctuates slightly in the vicinity of the eighth day and the ninth day, and tends to zero after the fifteenth day, while it is negative in other periods.

3.3.1.3

Cluster Features

To find out whether the cluster features of comment behavior exist in the whole process of crisis communication, we need to estimate the comment behavior with ARCH model first, and then judge its existence according to the square correlation graph of comment behavior residuals and its significance test results. When constructing the ARCH (q) model, we first take the natural logarithm of Y whole_pinglun sequence in order to reduce the error caused by data fluctuation. The corresponding ARCH (q) model is as follows: ⎧ ⎪ ln Ywh_ pl,t = βwh_ pl,0 + βwh_ pl,1 ln Ywh_ pl,t−1 + βwh_ pl,2 ln Ywh_ pl,t−2 ⎪ ⎪ ⎨ +··· + β wh_ pl,k ln Ywh_ pl,t−k + u wh_ pl,t 2 ⎪ σwh_ pl,t = αwh_ pl,0 + αwh_ pl,1 u 2wh_ pl,t−1 + αwh_ pl,2 u 2wh_ pl,t−2 ⎪ ⎪ ⎩ +··· + α 2 wh_ pl,q u wh_ pl,t−q

(3.41)

In the formula, i is the number iday, and its values are 1, 2, 3, ·, 21 respectively. The model is fitted by Eviews 8.0. And the results are as follows: ln Ywh_ pl,t = 2.14 + 0.28 ln Ywh_ pl,t−1 + 0.69 ln Ywh_ pl,t−2 + uˆ wh_ pl,t

(3.42)

3.3 Fluctuation Features of Comment Behavior

99

Among them, the p value of the overall F statistical test of the model is 0.000, indicating that the model is overall significant; the p value corresponding to constant term and each variable coefficient is 0.000, 0.000, 0.009, suggesting that each fitting coefficient is significant; and R2 value is 0.946, which shows that the fitting effect is good, so the fitting results of the model are effective. The corresponding residual square correlation diagram is shown in Fig. 3.43. It can be seen from Fig. 3.43 that both the residual autocorrelation and partial correlation function have a lag of at least one period, which exceeds the 95% confidence interval, indicating that the residual correlation is not significantly zero, and the autocorrelation function is in a slow decay trend, which is called “tailing phenomenon”. At the same time, the corresponding p values of Q statistical test are less than 0.001, which means that the test results are very significant, suggesting that there is autocorrelation in the residual square sequence of ARCH model in the whole communication process, that is, there is ARCH effect in this sequence. In the process of model estimation, The improper selection of lag length g of ARCH model may lead to violation of the constraint that α wh_pl,t value should not be negative, resulting in the condition that conditional variance 62wh_pl,t is positive cannot be satisfied, which makes the whole model estimation invalid. In order to avoid this problem, the generalized arch model (GARCH) is applied to fit the conditional

Fig. 3.43 Square correlation chart of the residuals of the whole process of the comments number

100

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as: ⎧ ln Ywh_ pl,t = γwh_ pl,0 + γwh_ pl,1 ln Ywh_ pl,t−1 + γwh_ pl,2 ln Ywh_ pl,t−2 ⎪ ⎪ ⎪ ⎪ ⎪ + · · · + γwh_ pl,k ln Ywh_ pl,t−k + u wh_ pl,t ⎪ ⎪ ⎨σ2 2 2 wh_ pl,t = αwh_ pl,0 + αwh_ pl,1 u wh_ pl,t−1 + αwh_ pl,2 u wh_ pl,t−2 2 + · · · + αwh_ pl,q u wh_ pl,t−q ⎪ ⎪ ⎪ ⎪ 2 2 ⎪ +βwh_ pl,1 σwh_ ⎪ pl,t−1 + βwh_ pl,2 σwh_ pl,t−2 ⎪ ⎩ 2 + · · · + βwh_ pl,q σwh_ pl,t− p

(3.43)

The final fitting results of GARCH model are shown in Table 3.11. In the fitting results of Table 3.11, the z-test p values corresponding to the coefficients of mean equation and variance equation reached the significant level of 0.05, and the fitting index R-squared value of the whole model was close to 1, so the model construction and fitting results were effective. Due to the existence of the cluster effect in the whole communication process, this research analyzes the cluster features of comment behavior via the residual sequence diagram and conditional variance diagram of GARCH model. The corresponding whole process residual line chart and conditional variance line chart is shown in Figs. 3.44 and 3.45 respectively. The residual line Fig. 3.44 shows that there is fluctuation cluster phenomenon from the first day to the fourth day and the seventh day after the crisis, and the cluster effect is apparent, while the cluster effect is relatively weak in other periods. The Table 3.11 Fitting results of GARCH model for overall comment process, GARCH = C(4) + C(5)*RESID(−1)ˆ2 + C(6)*GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

Mean equation C

1.180381

0.26341

4.481156

0.0000

InYwh_pl (−1)

1.656943

0.091788

18.05178

0.0000

InYwh_pl (−2)

−0.797711

0.086799

−9.190308

0.0000

Variance equation C

−0.000615

0.000856

2.732931

0.0063

RESID(—1)ˆ2

−0.082028

0.134132

2.342168

0.0192

GARCH(—l)

1.01173

0.152835

6.619763

0.0000

R-squared

0.973011

Mean dependent var

8.11529

Adjusted R-squared

0.97017

S.D. dependent var

0.970983

S.E. of regression

0.167702

Akaike info criterion

−0.948658

Sum squared resid

0.534355

Schwarz criterion

−0.651101

Log likelihood

16.43524

Hannan-Quinn criter

−0.878563

Durbin-Watson stat

1.704925

Fitting index

101

Residuals

3.3 Fluctuation Features of Comment Behavior

Time / day

Conditional Program

Fig. 3.44 Line chart of the overall process residuals of the comments number

Time / day Fig. 3.45 Line chart of conditional variance of the whole process of the comments number

conditional variance line chart in Fig. 3.45 shows that the conditional variance is the largest on the second day and the third day after the crisis, followed by the first day and the seventh day, indicating that the comment behavior has a large fluctuation in the corresponding period, and the conditional variance is small after the tenth day.

102

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

3.3.2 Weekly Fluctuation Features 3.3.2.1

Trend and Periodic Features

Figure 3.46 shows the line chart of the number of comments on brand crisis information in one week. Figure 3.46 shows that the rise from Monday to Tuesday is relatively smooth, and the number of comments goes up rapidly from Wednesday to Friday, falls quickly on Saturday, and decreases slowly on Sunday, with the maximum value on Friday. The results reveal that Monday is the warm-up period, Tuesday and Wednesday are the warming period, Thursday and Friday are the climax period, and Saturday and Sunday are the cooling period. The chart shows that the trend of weekly comments is not a simple linear relationship, but a more complex fluctuation process. Therefore, it is difficult to find the deeper and more specific reasons behind the fluctuation phenomenon only through descriptive statistical analysis, and it is necessary to accurately analyze the relevant characteristic elements with the help of more complicated feature component decomposition method. Only in this way can we better analyze the deeper causes behind the fluctuation. The time series trend decomposition method is used to decompose the characteristic variables of comment behavior fluctuation. In the weekly comment time series Week_zhuanfa, as the time series is non quarterly or monthly data, there is no influence of seasonal factors, so the characteristic components of the series can be decomposed into:

Comments number / piece

Yweek_ pinglun,t = T Cweek_ pinglun,t + Iweek_ pinglun,t

Time / day Fig. 3.46 Line chart of comments in one week

(3.44)

3.3 Fluctuation Features of Comment Behavior

103

where TC week_pinglun,t is the trend cycle element and I week_pinglun,t is the irregC T ular element. Where T Cweek_ pinglun,t = Yweek_ pinglun,t + Yweek_ pinglun,t , where C T Yweek_ pinglun,t is the trend component in the time series and Yweek_ pinglun,t is the periodic component. Among them, Henderson weighted moving average (MA) method can be used to calculate the trend cycle elements of the review time series Week_pinglun: T Cweek_ pinglun,t = M Aweek_ pinglun,t =

H 

+1 h 2H Yweek_ pinglun,t+i , H + 1 ≤ t ≤ T − H j

(3.45)

j=−H T The trend component Yweek_ pinglun,t can be separated from TC by HP (Hodrick T Prescott) filter. The trend component Yweek_ pinglun,t can be separated by solving the following minimization problems:

min

T  

T T Cweek_ pinglun,t − Yweek_ pinglun,t

2

2  T (3.46) + λ c(L)Yweek_ pinglun,t

t=1

where the parameter λ is a given prior value, λ ∈ [0, ∞), and C (L) is a delay operator polynomial, that is, c(L) = (L −1 − 1) − (1 − L). The corresponding HP filtering problem is transformed into the following minimization loss function:  min

T 

T T Cweek_zhuan f a,t − Yweek_zhuan f a,t

2

t=1

+λ

T −1 



T Yweek_ pinglun,t+1

−

T Yweek_ pinglun,t



−



T Yweek_ pinglun,t

−

T Yweek_ pinglun,t−1

2



t=2

(3.47) Finally, the irregular elements in the comment time series Week_zhuanfa can be calculated as follows: Iweek_ pinglun,t = Yweek_ pinglun,t − T Cweek_ pinglun,t = Yweek_ pinglun,t −

H 

+1 h 2H Yweek_ pinglun,t+i j

(3.48)

j=−H

After calculation, the separation results of trend features, periodic features and irregular features of weekly reviews are shown in Fig. 3.47. From Fig. 3.47, we can see that the weekly comment trend shows a unimodal curve, presenting a rapid upward trend from Monday to Friday, reaching the maximum value on Friday, showing a quick decline on Saturday and a small decline on Sunday.

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Comments number / piece

104

Time / hour Trend feature

Periodic feature

Irregular feature

Fig. 3.47 Decomposition of comment fluctuation in one week

The periodic features show a single peak curve process, rising rapidly on Tuesday and Wednesday, reaching the maximum value of periodic effect on Thursday, and declining from Friday to Sunday with a large degree. In terms of periodic features, the comments on Monday are the smallest, followed by that on Sunday, and the periodic comments from Tuesday to Friday are more active, reaching the maximum on Thursday. However, the irregular characteristic curve has bimodal features without obvious regularity. It generally shows the fluctuation characteristic that the increase of each period is followed by the decrease of the next period, among which the irregularity of Tuesday and Thursday has a greater influence. In order to further analyze the change rate features of crisis information weekly comment behavior, we need to analyze its marginal change rate. The index can be calculated by the following formula: M Q i (t) = Yi (t) =

Y (t)week_ pinglun,i t

(3.49)

i is the day of the week, and the values are 1, 2, 3,…, 7. Where Y (t)week_ pinglun,i = Y (i + 1)week_ pinglun − Y (i)week_ pinglun , t is unit time. The calculation results are shown in Fig. 3.48. Figure 3.48 shows that the marginal growth rates is positive from Monday to Friday and negative from Saturday to Sunday. The marginal growth rate from Wednesday to Friday is larger.

105

Comments’ marginal growth/piece

3.3 Fluctuation Features of Comment Behavior

Time / day Fig. 3.48 Marginal change of comments per week

3.3.2.2

Cluster Features

To test whether the comment behavior in one week has clustering effect, we need to estimate the weekly review sequence by ARCH model, and then analyze the correlation of the square sequence of the comment behavior residuals in one week, so as to judge whether the weekly comment behavior fluctuation has clustering features. When constructing ARCH (q) model, we first take the natural logarithm of Y Series in order to reduce the errors caused by data fluctuation. The corresponding ARCH (q) model is as follows: ⎧ ⎪ ln Ywk_ pl,t = βwk_ pl,0 + βwk_ pl,1 ln Ywk_ pl,t−1 + βwk_ pl,2 ln Ywk_ pl,t−2 ⎪ ⎪ ⎨+··· + β wk_ pl,k ln Ywk_ pl,t−k + u wk_ pl,t 2 σ = αwk_ pl,0 + αwk_ pl,1 u 2wk_ pl,t−1 + αwk_ pl,2 u 2wk_ pl,t−2 ⎪ wk_ pl,t ⎪ ⎪ ⎩+··· + α 2 wk_ pl,q u wk_ pl,t−q

(3.50)

i is the day of the week, and the values are 1, 2, 3,…, 7. Eviews 8.0 is used to fit the model and the result is: ln Ywk_ pl,t = 2.06 + 0.51 ln Ywk_ pl,t−1 + 0.49 ln Ywk_ pl,t−2 + uˆ wk_ pl,t

(3.51)

Here, the p value of the overall F-test of the model is 0.000, which indicates that the model is significant as a whole; the corresponding p values of constant term and variable coefficients are 0.004, 0.000, 0.007, which means that the fitting coefficients are significant; and the R2 value is 0.984, which indicates that the fitting effect is good, so the fitting result of the model is effective. The corresponding residual square correlation diagram is shown in Fig. 3.49.

106

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Fig. 3.49 Square correlation of weekly comments

It can be seen from Fig. 3.49 that the autocorrelation and partial correlation functions of the residual square sequence have a lag of at least one period, which exceeds the 95% confidence interval, indicating that the correlation of the residual sequence is significantly not zero, and the autocorrelation function shows a slow decay state, which is called “tailing phenomenon”. Meanwhile, the corresponding Q statistical test p values are less than 0.001, and the test results are extremely significant, which shows that there is autocorrelation in the square sequence of comment behavior residuals in one week, that is, there is ARCH effect in the sequence. In the process of model estimation, the improper choice of the lag length q of ARCH model which may lead to the violation of the constraint condition that α wk_pl,t value should not be negative, the condition that 62wk_ pl,t value is positive cannot be satisfied, so that the whole model estimation is invalid. In order to avoid this condition, the generalized ARCH model (GARCH) is applied to fit the conditional variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as: ⎧ ln Ywk_ pl,t = γwk_ pl,0 + γwk_ pl,1 ln Ywk_ pl,t−1 + γwk_ pl,2 ln Ywk_ pl,t−2 ⎪ ⎪ ⎪ ⎪ ⎪ + · · · + γwk_ pl,k ln Ywk_ pl,t−k + u wk_ pl,t ⎪ ⎪ ⎨σ2 2 2 wk_ pl,t = αwk_ pl,0 + αwk_ pl,1 u wk_ pl,t−1 + αwk_ pl,2 u wk_ pl,t−2 (3.52) 2 + · · · + αwk_ pl,q u wk_ pl,t−q ⎪ ⎪ ⎪ ⎪ 2 2 ⎪ ⎪ ⎪ +βwk_ pl,1 σwk_ pl,t−1 + βwk_ pl,2 σwk_ pl,t−2 ⎩ 2 + · · · + βwk_ pl,q σwk_ pl,t− p The final fitting results of GARCH model are shown in Table 3.12. In the fitting results of Table 3.12, the Z-test p values corresponding to the coefficients of mean equation and variance equation reached the significant level of 0.05, and the fitting index R-squared value of the whole model was close to 1, so the model construction and fitting results were effective. Due to the cluster effect of comment behavior, we can analyze the cluster features of one week’s comment behavior through residual sequence diagram and conditional

3.3 Fluctuation Features of Comment Behavior

107

Table 3.12 Fitting results of weekly comments of GARCH model, GARCH = C(3) + C(4)*RESID(−1)ˆ2 + C(5)*GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

Mean equation C

2.815129

0.952387

2.955866

0.0031

InYwk-pl (−1)

0.68922

0.104938

6.567893

0.0000

C

0.004818

0.008433

2.381022

0.0173

RESID(−1)ˆ 2

0.451094

0.868947

2.825329

0.0047

GARCH (−1)

0.255864

0.824757

17.11379

0.0000

R-squared

0.966919

Mean dependent var

8.546476

Adjusted R-squared

0.95582

S.D. dependent var

0.870592

S.E. of regression

0.430199

Akaike info criterion

0.151021

Sum squared resid

3.886501

Schwarz criterion

0.397868

Log likelihood

3.263258

Hannan-Quinn criter

0.213102

Durbin-Watson stat

0.230188

Variance equation

Fitting index

Residuals

variance diagram of GARCH model. The residual line chart and conditional variance line chart are shown in Figs. 3.50 and 3.51 respectively. From the residual line chart in Fig. 3.50, we can see that there is fluctuation cluster phenomenon on Wednesdays, Thursdays and Fridays, and the clustering effect is obvious, while the clustering effect in other periods is relatively weak. As can be seen from the line chart in Fig. 3.51 of conditional variance, the conditional variance

Time / day Fig. 3.50 Line chart of residuals of weekly comments number

108

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Time / day Fig. 3.51 Line chart of conditional variance of weekly comments number

on Friday afternoon is the largest, followed by that on Thursday morning, Wednesday morning, Saturday morning and Tuesday morning, which indicates that there is a large fluctuation of comment behavior in the corresponding period, while the fluctuation of other periods is small.

3.3.3 One-Day Fluctuation Features The broken line chart of daily comments of brand crisis information in one week is shown in Fig. 3.52. From Fig. 3.52, we can see that daily comment behavior in one week shows common regular features, that is, it rises promptly from 7:00 a.m. to 11:00 a.m., decreases slightly until 1:00 p.m., increases slightly from 2:00 p.m. to 5:00 p.m., declines from 7:00 p.m. to 11:00 p.m., and then decreases sharply until 6:00 a.m. the next day. On this basis, in order to further understand the more accurate regular features of daily comment behavior of crisis information, we need to analyze the mean value of daily comments at all times. The broken line chart of the average daily comments is shown in Fig. 3.53. As can be seen from Fig. 3.53, the average value of daily comments increases rapidly from 7:00 a.m. to 12:00 p.m., then decreases slightly from 2:00 p.m. to 5:00 p.m., then goes down from 7:00 p.m., then rises rapidly from 8:00 p.m. to 11:00 p.m., and then declines significantly from 6:00 a.m. the next day. The results show that in the 24 h of a day, the comment behavior presents the features of “three peaks and three valleys”: the fluctuation processes of “three peaks” are: from 9:00 p.m. to 11:00 p.m., from 9:00 a.m. to 12:00 a.m., from 3:00 p.m. to 9:00 p.m., and the fluctuation

109

Reposting number / piece

3.3 Fluctuation Features of Comment Behavior

Time / hour Monday

Tuesday

Wednesday

Friday

Saturday

Sunday

Reposting number / piece

Fig. 3.52 Line chart of daily comments in one week

Time / hour Fig. 3.53 Line chart of average daily comments

Thursday

110

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

processes of the “three valleys” are: from 11:00 p.m. to 9:00 a.m., from 5:00 pm. to 9:00 p.m., and from 12:00 a.m. to 3:00 p.m.. The Fig. 3.53 shows that the trend features of daily comments are not a simple linear relationship, but a more complex fluctuation process. Therefore, it is difficult to find the deeper and more concrete reasons behind the fluctuation phenomenon only through descriptive statistical analysis, and it is necessary to accurately separate the relevant characteristic elements with the help of more complicated feature component decomposition method, so as to better analyze the underlying causes of the fluctuation phenomenon.

3.3.3.1

Trend and Periodic Features

The time series trend decomposition method is used to decompose the characteristic variables of the comment behavior fluctuation. In the one-day review time series Day-pinglun, because the time series is non-quarterly or monthly data, there is no influence of seasonal elements, so the characteristic components of the series can be decomposed into: Yday_ pinglun,t = T Cday_ pingluna,t + Iday_ pinglun,t

(3.53)

in the formula, TC day_pinglun,t represents trend cycle elements and I day_pinglun,t represents irregular elements. C T T Here, T Cday_ pinglun,t = Yday_ pinglun,t + Yday_ pinglun,t , Yday_ pinglun,t is the trend C component in the time series, and Yday_ pinglun,t is the periodic component. Among them, Henderson weighted moving average (MA) method can be used to calculate the trend cycle elements of the review time series Week_pinglun, namely: T Cday_ pinglun,t = M Aday_ pinglun,t =

H 

+1 h 2H Yday_ pinglun,t+i , H + 1 ≤ t ≤ T − H j

(3.54)

j=−H T The trend component Yday_ pinglun,t can be separated from TC day_pinglun,t by HP T (Hodrick Prescott) filter. The trend component Yday_ pinglun,t can be separated by solving the following minimization problems:

min

T  

T T Cday_ pinglun,t − Yday_ pinglun,t

2

 2 T + λ c(L)Yday_ pinglun,t

(3.55)

t=1

where the parameter λ is a given prior value, λ ∈ [0, ∞), and C (L) is a delay operator polynomial, that is, c(L) = (L −1 − 1) − (1 − L). The corresponding HP filtering problem is transformed into the following minimization loss function:

3.3 Fluctuation Features of Comment Behavior

 min

T 

T T Cday_ pinglun,t − Yday_ pinglun,t

111

2

t=1

+λ

T −1 



T Yday_ pinglun,t+1

−

T Yday_ pinglun,t



−



T Yday_ pinglun,t

−

T Yday_ pingluna,t−1

2



t=2

(3.56) Finally, the irregular elements in the comment time series Week_pinglun can be calculated as follows: Iday_ pinglun,t = Yday_ pinglun,t − T Cday_ pinglun,t = Yday_ pinglun,t −

H 

+1 h 2H Yday_ pinglun,t+i j

j=−H

(3.57)

Reposting number / piece

After calculation, the separation results of trend features, periodic features and irregular features of daily comment behavior fluctuation are shown in Fig. 3.54. Figure 3.54 shows that the trend of one-day comments presents the features of three peak curve, which shows that the periodical peaks are reached at 10:00 a.m., 4:00 p.m. and 10:00 p.m., and the trend effect is large, while the trend influence is the smallest at 4:00 a.m. The periodic features also show the features of three peak curve, reaching the periodic peak at 12:00 at noon, 4:00 p.m. and 10:00 p.m., and the periodic effect is large, and the influence of 4:00 a.m. is the smallest. However, the irregular

Time / hour Trend feature

Periodic feature

Fig. 3.54 Decomposition of fluctuation features of daily comments

Irregular feature

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Reposting marginal growth / piece

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Time / hour Fig. 3.55 Marginal change of daily comments

characteristic curve showed multi-peak features with no obvious regularity, which showed the fluctuation features of increasing every two periods and then decreasing in the following two periods. The irregular effect was greater at 10:00 a.m., 6:00 p.m. and 10:00 p.m. To analyze the change rate features of one-day comment behavior of crisis information, it is necessary to analyze its marginal change rate. The index can be calculated by the following formula: M Q i (t) = Yi (t) =

Y (t)day_ pinglun,i t

(3.58)

I is the day of the week, the values are 1, 2, 3 …7. In the above formula, Y (t)day_ pinglun,i = Y (i + 1)day_ pinglun − Y (i)day_ pinglun , t is the unit time. The corresponding analysis results are shown in Fig. 3.55. Figure 3.55 shows that the marginal growth rates of the corresponding periods from 6:00 a.m. to 12:00 p.m., from 2:00 p.m. to 5:00 p.m. and from 7:30 p.m. to 11:00 p.m. are all positive, while the other periods are all negative. Among them, the marginal growth rate is the largest from 7:00 to 10:00 in the morning, followed by 8:00 p.m. to 11:00 p.m., and the marginal growth rate is relatively small from 2:00 p.m. to 5:00 p.m. in the afternoon.

3.3.3.2

Cluster Features

In order to test whether the one-day comment behavior has clustering effect, we need to use ARCH model to analyze the correlation of the square series of the one-day

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113

comment behavior residuals, so as to judge whether the one-day comment behavior has volatility clustering features. When constructing ARCH (q) model, in order to reduce the errors caused by data fluctuation, we first take the natural logarithm of Y day_pinglun sequence. The corresponding ARCH (q) model is as follows: ⎧ ⎪ ln Yday_ pl,t = βday_ pl,0 + βday_ pl,1 ln Yday_ pl,t−1 + βday_ pl,2 ln Yday_ pl,t−2 ⎪ ⎪ ⎨ +··· + β day_ pl,k ln Yday_ pl,t−k + u day_ pl,t 2 2 2 σ = ⎪ ⎪ day_ pl,t αday_ pl,0 + αday_ pl,1 u day_ pl,t−1 + αday_ pl,2 u day_ pl,t−2 ⎪ ⎩ +··· + α 2 day_ pl,q u day_ pl,t−q (3.59) where i is the time in the day, and the values are 1, 2, 3 … 24. Eviews 8.0 was used to fit the model: ln Yday_ pl,t = 2.37 + 0.61 ln Yday_ pl,t−1 + 0.38 ln Yday_ pl,t−2 + uˆ day_ pl,t

(3.60)

Among them, the p value of F statistical test of the whole model is 0.013, which indicates that the model is significant on the whole; the P values corresponding to constant terms and variable coefficients are 0.000, 0.027 and 0.004 respectively, indicating that the fitting coefficients are significant; and the value is 0.991, indicating that the fitting effect is good, so the fitting result of the model is valid. The corresponding residual square correlation diagram is shown in Fig. 3.56. Figure 3.56 shows that both the autocorrelation and partial correlation functions of the residual square sequence have a lag of at least one period, which exceeds the 95% confidence interval, indicating that the correlation is significantly not zero, and the autocorrelation function shows a slow attenuation characteristic, namely “tailing phenomenon”. At the same time, the corresponding Q statistical test p values are less than 0.001, and the test results are very significant, indicating that the fluctuation of one-day comment behavior has a significant clustering effect. In the process of model estimation, the improper selection of lag length g of ARCH model may lead to violation of the constraint that α day_pl,t value should not be negative, resulting in the condition that conditional variance 62day_ pl,t is positive cannot be satisfied, then makes the whole model estimation invalid. For avoiding this problem, the generalized ARCH model (GARCH) is used to fit the conditional variance of random error term. Then the corresponding GARCH (p, q) model can be expressed as:

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3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Fig. 3.56 square correlation chart of the residuals of the comments number in one day

⎧ ln Yday_ pl,t = γday_ pl,0 + γday_ pl,1 ln Yday_ pl,t−1 + γday_ pl,2 ln Yday_ pl,t−2 ⎪ ⎪ ⎪ ⎪ ⎪ + · · · + γday_ pl,k ln Yday_ pl,t−k + u day_ pl,t ⎪ ⎪ 2 ⎨ σday_ pl,t = αday_ pl,0 + αday_ pl,1 u 2day_ pl,t−1 + αday_ pl,2 u 2day_ pl,t−2 (3.61) + · · · + αday_ pl,q u 2day_ pl,t−q ⎪ ⎪ ⎪ ⎪ 2 2 ⎪ +βday_ pl,1 σday_ ⎪ pl,t−1 + βday_ pl,2 σday_ pl,t−2 ⎪ ⎩ 2 + · · · + βday_ pl,q σday_ pl,t− p The final fitting results of GARCH model are shown in Table 3.13. In the fitting results of Table 3.13, the z-test p values corresponding to the coefficients of mean equation and variance equation reached the significant level of 0.05, and the fitting index R-squared value of the whole model was close to 1, so the model construction and fitting results were effective. Due to the clustering effect, we can further analyze the cluster features of oneday comment behavior through residual sequence diagram and conditional variance

3.3 Fluctuation Features of Comment Behavior

115

Table 3.13 Fitting results of GARCH model for daily comments, GARCH = C(4) + C(5)*RESID(−1)ˆ2 + C(6)*GARCH(−1) Variable

Coefficient

Std. Error

z-Statistic

Prob

Mean equation C

0.754185

0.090656

8.319196

0.0000

InYday_pl (−1)

1.585483

0.00755

210.005

0.0000

InYday_pl (−2)

−0.673541

0.005409

−124.5254

0.0000

0.001607

0.001877

2.342168

0.0192

Variance equation C RESID(−1)ˆ 2

−0.319658

0.229456

−16.7374

0.0000

GARCH(−l)

1.299065

0.191462

6.784964

0.0000

Fitting index R-squared

0.960473

Mean dependent var

8.57842

Adjusted R-squared

0.956312

S.D. dependent var

0.877174

S.E. of regression

0.183345

Akaike info criterion

−0.844825

Sum squared resid

0.63869

Schwarz criterion

−0.547268

Log likelihood

15.29308

Hannan—Quinn criter

−0.77473

Durbin-Watson stat

1.934s042

Residuals

diagram of GARCH model. The corresponding residual line chart and conditional variance line chart are shown in Figs. 3.57 and 3.58 respectively. From the residual line chart 3.57, it can be seen that there are apparent volatility clusters from 8:00 a.m. to 11:00 a.m., 3:00 p.m. to 5:00 p.m. and 9:00 p.m. to 11:00 p.m., and the clustering effect in other periods is relatively weak. It can be found from the broken line Fig. 3.58 of conditional variance that the conditional variance

Time / hour Fig. 3.57 Line chart of residual for daily comments

3 Fluctuation Features of Brand Crisis Information Sharing by Weibo Users

Conditional Program

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Time / hour Fig. 3.58 Line chart of conditional variance of daily comments

is the largest from 8:00 a.m. to 10:00 a.m. and 9:00 p.m. to 11:00 p.m. in one day, the conditional variance is larger from 3:00 p.m. to 4:00 p.m., which indicates that there is a large fluctuation in the one-day comment behavior in the corresponding period, while the fluctuation in the remaining periods is smaller.

3.4 Summary Based on Sina Weibo platform, this chapter analyzes the fluctuation features of information sharing behavior of Weibo users in the brand crisis by using 66 brand crisis events with great influence from January 2010 to July 2016. Through Sina’s official API and web crawler technology, the relevant data of brand crisis information reposting and commenting behavior in Sina Weibo are collected, and quantitative research methods such as time series ARIMA model, trend decomposition and autoregression conditional heterodox model are used to analyze the autocorrelation of information reposting and commenting behavior fluctuations, and accurately analyze the whole communication process, weekly and daily trend features, periodic features, cluster features and irregular features. The study reveals that users are influenced by a variety of factors, including demographic characteristics and social roles, in the process of information seeking and sharing. Among them, the user’s living habits have an important influence on their information behavior, and people’s habit characteristics are often different because of their gender, age, occupation, education and other individual differences. Therefore, the user information sharing behavior will always show autocorrelation and periodic features. The individual’s information need may come from work, but environment also plays an important role in it. User’s information behavior rarely occurs in a

3.4 Summary

117

completely independent environment, and is often accompanied by other people’s information behavior, interweaving and interacting with one another to generate impact on one’s behavior. Users in similar situations have a more significant impact on their behavior, such as opinion leadership effect and group convergence. Under the joint action of these factors, the information sharing behavior shows obvious clustering in a power law distribution manner as it reappears with frequent posting and sharing after a period of hibernation. The relevant research conclusions of this chapter can help enterprise managers to clearly identify the key period of monitoring and management of brand crisis information sharing behavior on Weibo so as to formulate a targeted crisis management strategy. The key is to focus management on the time node with the largest marginal growth rate, the largest trend value, periodic peaks and group gathering, so that the management of brand crisis information sharing behavior on Weibo can achieve more with less effort.

References 360doc Personal Library. (2014). A Brief Analysis of the Web Crawler Technology [EB/OL]. http:// www.360doc.com/content/14/1001/12/19598626_413646742.shtml. Accessed 1 Oct 2014. Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2011). Time Series Analysis: Forecasting and control. John Wiley & Sons. Buccafurri, F., Lax G, Nicolazzos, et al. (2015). Comparing twitter and Facebook user behavior: Privacy and other aspects. Computers in human behavior, 52, 87–95. Chen, X., Chen, L., & Jiang, Z. (2015). Jiyu API jiekou de tengxun weibo shuju wajue (Data Mining of Tencent Microblog Based on API Interface). Modern Computer (6), 47–50. [陈向阳, 陈丽萍, 姜振国.基于API接口的腾讯微博数据挖掘.现代计算机:上下旬, 2015(6):47–50]. China Internet Network Information Center (CNNIC). (2016). The 36th Statistical Reports on Internet Development in China [EB/OL]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201 507/P020150723549500667087.pdf,2015-7-23. Ding, Z., Jia, Y., & Zhou, B (2014). Weibo shuju wajue yanjiu zongshu (Survey of Data Mining for Microblogs). Journal of Computer Research and Development, 51(4), 691–706. [丁兆云, 贾焰, 周斌,微博数据挖掘研究综述.计算机研究与发展, 2014,51(4):691–706]. Fu, J. (2015). Research and analysis on microblog user behavior model. Beijing University of Posts and Telecommunications. [付江丽.微博用户行为分析及建模研究[D].北京邮电大学, 2015.] Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press. Gao, Q., Abel, F., Houben, G. J., & Yu, Y. (2012). A comparative study of users’ microblogging behavior on Sina Weibo and Twitter (pp. 88–101). Springer. Guan, W., Gao, H., Yang, M., Li, Y., Ma, H., Qian, W., Cao, Z., & Yang, X. (2014). Analyzing user behavior of the micro-blogging website Sina Weibo during hot soceial events. Physica A: Statistical Mechanies and its Applications, 395, 340–351. Li, H, Chen, X. (2013). Case study report of China’s crisis public relations. Huazhong University of Science and Technology Press. [李华君,陈先红.中国危机公关案例研究报告[M].华中科技 大学出版社,2013.] Li, L., & Li, R. (2013). Xinlang weibo shehui wangluo de zizuzhi xingwei yanjiu (Studies on Self-organization Behavior of Sina Micro-blog Social Network). Statistics & Information Forum (1), 88–94. [李林红, 李荣荣.新浪微博社会网络的自组织行为研究.统计与信息论坛, 2013(1):88–94].

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Lian, J., Zhou, X. & Cao, W (2011). Xinlang weibo shuju wajue fangan (Sina Microblog Data Retrieval). Journal of Tsinghua University(Science and Technology) 51(10), 1300–1305. [廉 捷, 周欣, 曹伟, 等.新浪微博数据挖掘方案.清华大学学报: 自然科学版, 2011, 51(10):1300– 1305]. Peng, X., Zhu, Q., & Liu, X. (2015). Research on behavior characteristics and classification of micro-blog users—Taking “Sina micro-blog” as an example. Information Science, 1, 14. Sina Weibo Data Center. (2015). Microblog User Development Report[EB/OL]. http://www.useit. com.cn/thread-10921-1-1.html.2015-12-16. Wilson, T. D. (1997). Information behaviour: An interdisciplinary perspective. Information Processing & Management, 33(4), 551–572. Xu, Y., Liu, J., & Liu, L. (2007). Design and implementation of spider on web-based full-text search engine. Microcomputer Information (21), 119–121. [徐远超, 刘江华,刘丽珍, 等.基于Web的网 络爬虫的设计与实现. 微计算机信息, 2007(21):119–121]. Yang, D., Zhao, G., & Wang, T (2009). Application of WebCrawler in Information Search and Data Mining. Computer Engineering and Design (24), 5658–5662. [杨定中, 赵刚, 王泰.网络爬虫 在Web信息搜索与数据挖掘中应用计算机工程与设计,2009(24):5658–5662]. Yi, L. (2012). Research on statistical characteristic analysis and modeling for user behavior in microblog community based on human dynamics. Beijing University of Posts and Telecommunications. [易兰丽.基于人类动力学的微博用户行为统计特征分析与建模研究[D].北京邮电 大学,2012.]

Chapter 4

Contextual Factors Affecting Brand Crisis Information Sharing by Weibo Users

In Chap. 3, by analyzing the fluctuation features of brand crisis information sharing behavior of Weibo users, it’s founded that users’ information behavior fluctuation exhibits autocorrelation, trend, periodical and cluster features, which are caused by various internal and external factors. In order to understand the specific factors influencing the fluctuation of user behavior, scholars have conducted research from different perspectives. Literature review shows that past researches mainly focus on user habits, characteristics of hot topics, distribution of communication nodes, Weibo information characteristics, user characteristics and so on. At present, with the rapid development of information technology and network technology, the user information behavior is greatly influenced by contextual factors which reflect the environment, development trends and social network characteristics that foster the user information behavior (Xiangyang et al., 2012). Therefore, it is necessary to conduct the study based on contextual factors in order to draw more comprehensive and multi-perspective research conclusions. Although the importance of contextual factors on user information behavior has been mentioned or expounded in past literature, specific studies are quite rare. In order to further explore the influencing mechanism of brand crisis information sharing of Weibo users from the perspective of contextual factors, it is necessary to understand which factors exert significant impact on user behavior. Before studying the influence mechanism, Chapter 4 will explore the static and dynamic contextual factors of user information sharing behavior in brand crisis by means of Granger causality test, panel data model and Probit model. The research framework of this chapter is shown in Fig. 4.1.

4.1 Selection and Construction of Contextual Factors In previous study of information behavior theory, general information behavior theory regards user information behavior as an orderly circular process, with information need as the starting point and information utilization as the end point of the © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_4

119

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4 Contextual Factors Affecting Brand Crisis Information …

Fig. 4.1 Structure of Chap. 4

loop. Information need is the focus of the whole process and users’ information seeking and utilization are affected by a variety of interference factors, which may promote or hinder the effectiveness of users’ information search and utilization. In this process, there are several dynamic mechanism links, in which active retrieval is the key to individual information behavior (Wilson, 1997). At the same time, there are many intermediary variables in the loop that have an important influence on information behavior and dynamic mechanism, including psychological characteristics, demographic characteristics, social roles, interpersonal relationships, environmental characteristics and characteristics of source information. Individuals’ information need may come from work or living environments, or from themselves or their work roles. People in similar situations often play an important role in interfering with user information behavior (Wilson, 1999). The integrated model of information behavior, on the other hand, holds that people’s information behavior rarely occurs in a completely independent environment, and usually accompanies other people’s information behavior and interacts with it, which has an important impact and interference on their own information behavior. The theory also holds that information

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121

behavior is always happened in certain situations or contexts, i.e., the user information behavior is the product of a specific situation, and the information behavior process of each node has a dynamic mechanism (Niedzwiedzka, 2003). Regarding the study of behavioral influencing factors, the theory of the psychological field holds that a person’s behavior always occurs in a specific time and space, that is, the psychological life space, which is mainly composed of two factors: individual and environmental. Human behavior is influenced by the interaction between internal factors and external environment. The individual’s psychological life space determines their behavior characteristics, and their behavioral characteristics are the result of the interaction between individual and environmental factors (Lewin, 1951). In the field of information behavior, the theory of information use environment (IUE) holds that the information behavior environment is the beginning of user’s information needs, retrieval, selection and utilization and different information environment or situation creates different information behavior subjects, leading to manifestation of different characteristics and states of information behavior (Taylor, 1986a, 1991). Through the analysis of IUE, combined with internal and external information, users can implement a series of activities such as the utilization of information resources, decision-making, the formulation of programs and the improvement of measures. Among them, IUE mainly includes the user, the problem to be solved, the coping strategy, and the information environment (Taylor,1986b ). Users obtain information according to their own needs and within a specific period of time. The various IUE factors will have an important impact on their information screening and selection, that is, the flow, transmission and utilization of information between users are affected by IUE. Therefore, IUE can be used to judge the usefulness and value of information. The theory also points out that different occupations and social roles of users can have an important impact on people’s information behavior, and to some extent these factors lead to differences in user information behavior (Taylor, 1996). Sonnenwald (1999) has constructed the theory of information horizons, in which situation, context, and social network form the main framework of the model. It is believed that the user information behavior is mainly composed of four parts: the users, the contextual factors, the environment and the social relationship. The information user can perceive the change of the environment, evaluate and react to it, and the user information behavior is a series of evaluation, choice and reaction behavior process formed by their lack of certain knowledge (Sonnenwald, 1999). Users usually go into information search, acquisition, utilization and other information activities within their own information horizon. User information search behavior is the process of individuals constantly adjusting their own behavior and maintaining interaction and coordination with information resources. The user information horizon contains a variety of information resources that can be used to respond to what happens to them, and in this information domain, the user will use the optimal scheme of information search, query, acquisition, and utilization according to their own conditions (Trusina et al., 2004). According to the relevant theory and past research results, the information contextual factors are important influencing factors of brand crisis information sharing behavior on Weibo. This study will adopt the perspective of information contextual

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4 Contextual Factors Affecting Brand Crisis Information …

factors. Yingjie (2012) analyzes the important influence of contextual environment on people’s behavior patterns based on cognitive model and human-caused reliability perspective. By using the theory of control science to explore how the contextual environment causes errors in people’s behavior, he divides the contextual environment into static and dynamic contexts. Based on Jiang’s findings, this study defines static contexts as factors that mainly refer to the dimensions of attributes inherent in subjects, behaviors, or environments, or that do not change over time; whereas, dynamic factors refer primarily to factors that change over time with subjects, behaviors, or environments. Firstly, according to the theory of persuasion effect, Weibo user’s reposting or commenting of information can be regarded as the activity process of the user to evaluate the information and make decisions about the behavior under various factors after receiving the information. The results of user’s decision-making behavior are affected by differences in the processing and treatment of information they use. On Weibo, the information of different contextual characteristics has produced differential physiological stimulation to the user, in turn causing the user to take different information processing paths, which finally leads to different user behaviors. In this process, the information contextual factors act directly on the user’s psychological variables, and produce different persuasion effects eventually. Information in different forms, with different content characteristics and from different sources will affect the user’s willingness to process and share information, resulting in users forming different information sharing will, resulting in fluctuations in the process of information sharing by users. Secondly, in the user environment and information horizon, there may be a variety of contextual factors that cause the autocorrelation and cluster characteristics of information reposting and commenting by Weibo users. The overall fluctuations are divided by a combination of trend features, periodic features and irregular features. In the context of user information behavior, the total number of reposts and comments on Weibo will have a crowd effect on the users. Because of the influence of psychological pressure of group convergence, users tend to actively look for some kind of psychological “collective identity” to keep their behavior consistent with group behavior. This is the cause for trend features of fluctuations in user information behavior. Weibo users’ number of follows and followers reflects the extent to which others follow them and the breadth and depth of the scope of their own access to information. The numbers will continue or stimulate similar behavior at a later time, resulting in a kind of autocorrelation in the fluctuations in user information sharing behavior. Users can judge and choose to share and interact with a certain type of user group according to the number of follows and followers, resulting in the differences of self-organizing and cohesiveness between different user groups. This is the cause for cluster features in the fluctuations in user information sharing behavior. In addition, information sharing behavior is also closely related to the temporal distance of information release, which has an important impact on information sharing behavior as different temporal distance will result in different frequencies in information reposting and commenting. As a social media platform, Weibo is a kind of self-organizing system which is ad hoc and mutable. The user information

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123

behavior can go through a period of hibernation before reemerging with frequent posting and sharing with the power law distribution of the fluctuating characteristics, thus forming the cluster features and periodic features of information behavior. Finally, the constantly updated and changing number of information reposts and comments, the number of users’ follows and followers, the number of the follows and followers of SI, as well as the temporal distance of information release and other dynamic factors constitute a larger information environment, i.e., the Weibo information field. The information field created by the user gathering is mainly displayed by the total number of reposts, the total number of comments, the number of users’ follows and followers, the total number of follows and followers of SI. Users discover, feel, and experience information through these factors. It is the information field composed of these factors that creates an environment and atmosphere to encourage active participation and extensive communication of other users, thus influencing the information sharing behavior of other users. According to the definition of static factors and dynamic factors in this book, combined with all possible static and dynamic contextual factors, the static contextual factors will be studied from the inherent attributes of information and related dimensions in this chapter. The study of dynamic contextual factors will be carried out from the dimensions of dynamic differences such as total number of information shares, number of users’ follows and followers and information temporal distance.

4.2 Testing and Analysis of Classified Contextual Factors 4.2.1 Static Contextual Factors Persuasion effect refers to a changed state in an individual’s decision-making followed by the individual attitude change upon receiving persuasive information and following its viewpoint (Xiangyang et al., 2012). It exists in all aspects of life and is widely used in research areas covering consumer willingness to buy, brand advertising, and marketing. many scholars also actively use the theory of informatics related fields of research. It is also used by scholars in informatics-related fields. With the emergence of new media, the persuasion effect has been widely used in media. Weibo has grown to be a popular social media in recent years, and many scholars have begun to actively study the information behavior of Weibo users with the theory of persuasion effect (Liu et al., 2012). Weibo users’ reposting or commenting behavior can be regarded as the process of evaluating information and making behavioral decisions under the influence of various factors after receiving the information.1 Since user’s decision-making behavior results are influenced by differences in the way information is processed and treated, these studies are still under the category of the theory of persuasion effect (Cheung et al., 2008; Watts & Zhang, 2008). 1

Guo (2013).

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Persuasion effect theory mainly includes Elaboration Likelihood Model (ELM), Heuristic and System Model (HSM), self-efficacy theory and the newer AssociativePropositional Evaluation model (APE). The first two are most widely used (O’keefe, 2015; Xiaoxuan, 2013). Combined with the needs of this study, ELM and HSM models are used to explore the contextual factors of Weibo user’s information sharing behavior in the brand crisis. On the influencing factors related to the form of information. The Fluency Theory emphasizes that the differences in information forms affect how easily people feel and experience when processing information, thus affecting people’s willingness to exert efforts for information processing. According to ELM persuasion effect theory, if individuals have the ability and willingness to think and analyze the information in depth, they will be more inclined to process the information using the central path. Otherwise, they will be more inclined to process the information from the peripheral. Different information processing methods will lead to different persuasion effects, which will in turn lead to different user behaviors. By analyzing Weibo reposts, scholars have found that information form has an important influence on whether the information is reposted or commented on (Rosson, 2009). In general, video and photographic information is more attractive to users than wordy information and is more favored by Sina Weibo users in their reposting and commenting behavior (Yu et al., 2011). Similarly, messages that are direct, interesting, and easy to read can get high reposting rates (Hui & Lina, 2012). The research shows that users have different information sharing frequency for information is carried by different forms, namely, text type, photo type and video type. Such differences are manifested in the different degrees of information visualization. It can be seen that information visualization also has an important impact on the brand crisis information sharing by Weibo users. On the influencing factors related to the content of information. According to ELM, systematic processing reflects the information receiver’s in-depth analysis and careful consideration of the intrinsic attributes of information, thus forming the final decision-making behavior. In a network environment, information quality (IQ) is an important factor in the systematic processing of information by users when people communicate information online through computers (Chaiken & Eagly, 1989). Past research has shown that IQ has an important impact on user reposting behavior, and users are more willing to repost if the information is timely, accurate and matches the user needs (Zhongling, 2012). However, the user’s perception of IQ is mainly reflected in information arguments (Cacioppo & Petty, 1989), the amount of information (Slater & Rouner, 1996), and the emotional framework of information (DeSteno et al., 2004). According to the emotional framework of information, different messages have different emotional components: positive, negative or neutral, and people’s behavior is often influenced by the emotional differences that information conveys.2 On microblogging platforms, differences in emotional types and emotional levels are interwoven and interact with the content form of information or people’s inherent cognitive patterns (Hansen et al., 2011a, 2011b), resulting

2

Donohew et al. (2015).

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in different persuasive effects3 and eventually different reposting and commenting behaviors (Stieglitz & Xuan, 2011). It can be seen that the information sensibility framework will have an important impact on the brand crisis information sharing by Weibo users. On the influencing factors related to information source (IS). HSM persuasion theory emphasizes that heuristic information processing is mainly to process information by obtaining intuitive clues in a convenient and fast way, while online community information users in network environments often process information using the shallow features of the information as heuristic clues (Watts & Zhang, 2008). However, when people process information, IS is one of the most important surface factors affecting people’s information cognition. On Weibo, user characteristics have an important impact on the formation of centrality, which reflects the importance of the user node on the platform. User authority can bring together a large number of users, thus generating a group effect on the information reposting and commenting, which in turn exacerbates the dissemination and diffusion of information (Pal & Counts, 2011). Therefore, the characteristics of IS have an important impact on the persuasion effect of information. During information processing, the reliability, professionalism, credibility, attractiveness and amount of multimedia involved in IS all affect the user’s choice of information processing methods, which in turn can lead to different decision-making (Liu et al., 2012). The reliability of the source reflects the degree of authority of the source channel, while the professionalism reflects their authority in a particular area. Both have an important impact on the reposting or commenting behavior of Weibo users. The reliability of the source reflects the authoritative characteristics of the source in certain attributes (Chaiken, 1980). Reliable IS is usually defined as a trustworthy and competent sender of information considered by other information users (Petty & Cacioppo, 1986), and the credibility and attractiveness of IS can have an important impact on the choice of user-heuristic processing methods (Chaiken, 1980). It can be seen that the IS authority will have an important impact on the brand crisis information sharing by Weibo users. Based on the theory of ELM and HSM persuasion effect, the static contextual factors of brand crisis information sharing behavior of Weibo user are discussed in this part. It is concluded that the import static factors mainly include information form, information content and information source. The factors of information form are mainly reflected in the differences in information visualization. The content factors are mainly reflected in the difference of information sentiment. The factors of IS are mainly reflected in the difference of source information authority. Based on this, this book summarizes the static contextual factors of information sharing behavior of Weibo users in brand crisis into the following three dimensions: information visualization (IV), information sentiment (IS) and information authority (IA).

3

Petty et al. (2015).

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4.2.2 Dynamic Contextual Factors Information acquisition and dissemination on Weibo is not just a simple transmission of information. It also provides users with a platform for information exchange and interaction. The platform is composed of a number of sub-environments, which constitute a huge environment where users gather for information dissemination activities, thus creating a social atmosphere that enables collaboration and interaction where users are encouraged to spontaneously exchange and share information. Such a platform constitutes an information field (Fisher, 2005). Weibo information field contains various types of user groups who play different social roles in the process of information sharing and information exchange, which is conducive to the effective composition of the information field. Here, information can flow and transmit in any direction; users can obtain information in any form; and information exchange and sharing can be conducted in formal or informal ways, wherein the acquisition and exchange of relevant information will have an important positive impact on the individual’s physiological, cognitive, emotional and social aspects.4 The dissemination of information on Weibo not only conveys information to other user groups, but also promotes different users to actively share and exchange information. Although users’ information reposting and commenting do not contribute to the value of the information itself, such behavior creates an environment and atmosphere that promotes the active participation and wide communication of other users. Weibo information field is created when the platform constantly updates dynamic contextual factors such as the total number of information reposts and comments, the number of users’ own follows and followers, the number of follows and followers of IS, as well as the temporal distance of information release. These factors constitute a larger information environment which has an important impact on other users’ information sharing behavior. To some extent, the number of follows and followers reflects the amount of attention paid to the user by the others and the breadth and depth of the user’s access to information, which can significantly affect the willingness of other users to repost or comment. Using main component analysis, scholars have analyzed a large number of blog posts, and found that the number of blog authors’ followers had a significant impact on the reposting behavior, while the number of bloggers’ posts had no obvious effect on information reposting (Suh et al., 2010). If Weibo users have a larger number of follows or followers, they will be more influential in the network in which they live, and their messages will be more likely to be reposted or commented on (Sun & Lina, 2012). In addition to being influenced by user attention and the number of followers, information sharing behavior is closely related to the temporal distance of information release, which has an important impact on information reposting features (Savolainen, 2006). Construal level theory holds that differences in temporal distance cause differences in construal. For some, the valence is higher with high-level construals; for others, the valence is higher with low-level construals. Therefore, the differences in temporal distance lead to differences in construal level, which lead 4

Pierce (2012).

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to differences in perceived valence, which eventually affect people’s behavior and decision. In general, the shorter the temporal distance, the easier it is for information to be reposted and commented on, and the less likely it is to be shared with time passing. The study discovers that 90% of these reposting behaviors occur within one month of the release of source information (Lee et al., 2010). The dynamic contextual factors affecting Weibo user’s information reposting and commenting behavior is analyzed from the perspective of information field theory. The conclusion is that the dynamic situational factors mainly include, the number of users’ own follows and followers, the number of follows and followers of IS, as well as the temporal distance of information release. On this basis, this book summarizes the dynamic contextual factors of information sharing behavior of Weibo users in brand crisis into the following seven dimensions: total number of information reposts (TNR), total number of information comments (TNC), number of user’s follows (NUF), number of user’s followers (NUFF), number of follows of information source (NISF), number of followers of information source (NISFF), and information temporal distance (ITD).

4.3 Testing and Analysis of Static Contextual Factors 4.3.1 Data Sources Scale and questionnaires can be used to measure related constructs in order to verify the causality between variables. The Probit model can be used to estimate the corresponding regression equations in order to carry out the causal analysis of variables. The design of the scale and questionnaire in this study is based on the research results of the classical scale and related literature in the past, further modified to meet the specific needs of this study. The following constructs are used in Chap. 4: information visualization (IV), information sentiment (IS), information authority (IA), forwarding intention (FI), comment intention (CI). Table 5.1 contains the specific contents and structure of the scale. The design results can be found in Appendix II Questionnaire at the end of this book. It is to be noted that the Questionnaire also contains the five constructs for the study of static influencing mechanism of brand crisis information sharing by Weibo users in Chap. 5. They are: perceptual fluency (PF), cognitive absorption (CA), cue dependence (CD), perceived harm (PH), and harm relevance (HR). This study mainly uses official APIs and web crawlers to obtain relevant user information as data. A random sampling is taken of users who have participated in crisis information reposting or commenting behavior on Weibo. Then, the questionnaire is designed to obtain relevant data (see 5.3.3 for the details of investigation process). The demographic variable distribution characteristics of valid sample data (see Table 5.2 Demographic characteristics of samples) are similar to the distribution

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characteristics of user demographic variables in the 2015 Sina Weibo User Development Report. They have covered Sina Weibo user groups of different gender, age, educational background and occupation. It shows that the chosen sample data can well represent the overall characteristics of all Sina Weibo users.

4.3.2 Reliability and Validity Analysis The number of valid samples in this study is 2092. First, the extreme value samples are processed by drawing the box plot of the sample data. It is found that there are 21 singular values in the 2092 samples, so the corresponding sample data needs to be removed from them to ensure the accuracy and reliability of the research results.

4.3.2.1

Reliability Analysis

Using SPSS 22.0 to test the internal consistency of the questionnaire item data, the processing results show that the Cronbach’s a value of the subscales of IV, IS, IA, FI, and CI are 0.79, 0.86, 0.76, 0.83 and 0.87, respectively. The total Cronbach’s a value of the overall scale is 0.84, that is, the Cronbach’s a value of each subscale and the overall scale is greater than the standard of 0.70, indicating that the questionnaire design and sample data show relatively good reliability.

4.3.2.2

Validity Analysis

Construct Validity Construct validity indicates the degree to which the scale design can effectively reflect the theoretical structure and framework characteristics, and reflects the consistency between the scale and the theory. It is mainly analyzed and judged through the cumulative explanatory variance and factor loading indicators in the exploratory factor analysis (EFA), as well as the single-dimensional test. Before performing EFA analysis on each variable, perform KMO measurement and Bartlett sphere test to determine whether the sample data is suitable for EFA analysis. The processing result shows that the KMO value is 0.859, which is greater than the standard value of 0.70; the value of Bartlett’s test is 0.002, which is less than 0.01, and the original hypothesis that the correlation coefficient matrix is a unit matrix is rejected, indicating that there is a correlation between variables. The scale and the sample data have significant correlations and are suitable for EFA analysis. Firstly, EFA analysis is conducted on the overall scale. The results show that it can be extracted 5 factors and the cumulative explained variance of the 5 factors is 94.02%. Secondly, the EFA analysis of each subscale shows that the cumulative explained variance of each subscale is greater than 87.41%, except for item IV3,

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whose factor load is 0.42, which is smaller than 0.60. The factor loads of the remaining items on the corresponding variables are greater than 0.60, so IV3 items need to be removed from the sample data. Finally, a single-dimensional test is performed on each item to determine multiple items that measure the same construct. The items can only be loaded on the same construct. The single-dimensional test results show that each test value is greater than the standard value of 0.50, indicating that each construct meets the single-dimensionality, and the scale has good overall construct validity.5

Convergent Validity Convergent validity indicates the degree to which a measured variable can effectively reflect the characteristics of its latent variable, which is mainly analyzed and judged by indicators such as the standard factor load, average extracted variance (AVE) and composite reliability (CR) in confirmatory factor analysis (CFA). On the basis of the above construct validity test, item IV3 is deleted from the data, and the data is analyzed by CFA. The standard load coefficients between each measurement item and the measured latent variable are all greater than the standard value of 0.60, and each corresponds to the t value of the significance test is greater than the critical value of 3.31 (at this time p = 0.001) (see Table 5.3 Results of confirmatory factor analysis), indicating that each measured variable can be used effectively to measure each latent variable. The average extracted variance (AVE) refers to the degree to which the latent variable can explain the variability of its observed index, and indicates the extent to which the index can effectively reflect the characteristics of its latent variable. The composite reliability value (CR) of a latent variable is the reliability combination of all its observed variables. This indicator is used to analyze the degree of consistency between the latent variable and the observed indicators. In the results of the validity analysis, the AVE value of each variable is greater than the standard value of 0.50, and the CR value is greater than the standard value of 0.70 (see Table 5.3 Results of confirmatory factor analysis), It shows that the measured variables can effectively reflect the characteristics of each latent variable, and there is good consistency among the measured indicators in each group, which shows that the scale and the sample data have good convergent validity.6

Discriminant Validity Discriminant validity indicates the degree to which each construct can be distinguished during measurement. When the square root of the AVE value of all latent variables is greater than the absolute value of all correlation coefficients corresponding to the variable and other variables, it indicates that there is a good discriminant validity 5 6

Xue et al. (2015). Xue et al. (2015).

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between latent variables and other variables. After calculating the correlation coefficient between the variables and the square root of AVE, and the results show that the square root of AVE of all latent variables in the scale (that is, the first the values on the diagonal in Table 5.4 Discriminant Validity Analysis Results) are greater than the absolute values of all correlation coefficients corresponding to this variable and other variables, indicating that the scale does not have overlapping variable (or item) across multiple constructs. The constructed measurement indicators all fall on the expected constructs, indicating good discriminant validity of the scale and sample data.7

4.3.3 Correlation Analysis In order to explore whether information visualization, information sensibility, and source authority have a significant impact on Weibo information reposting and comment behavior in brand crisis, the author constructs a regression model and analyzes the significance of the model and its regression coefficients to determine whether there is a significant causal relationship between influencing factors and reposting and commenting behaviors. Since the construction and estimation of the regression model need to be based on the premise that there is a significant correlation between the independent variable and the dependent variable, before the regression model is constructed, the correlation between the dependent variable and the independent variable in the equation needs to be analyzed to ensure the establishment and estimation of the regression equation has practical significance. In the correlation analysis of pairwise variables, the Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall rank correlation coefficient are mainly used for judgment. However, among the above three indicators, one or two correlation coefficients may be significant, while the other or two correlation coefficients are not significant. In this case, if only one or two of the coefficients are selected as a criterion, unreliable research conclusions may be drawn. Therefore, in the analysis of the correlation between two variables, in order to improve the reliability of the correlation determination, the above three correlation indicators are usually calculated at the same time, and the relevant conclusions are finally reached through comprehensive analysis (Keller, 2015). This study uses SPSS 22.0 statistical software to calculate the three correlation coefficients of information visualization, information sensibility and authority of the source, and the willingness to repost crisis information and the willingness to comment behavior. Among them, the corresponding coefficient values are shown in Table 4.1. Table 4.1 shows that, except for the significance p value of the Kendall rank correlation coefficient between IV and CI is 0.08, and the significance p value of the Spearman rank correlation coefficient between IA and FI is 0.063, both of which are 7

Xue et al. (2015).

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Table 4.1 Correlation coefficient value Coefficient type

Sharing behavior

Coefficient and testing

IV

IS

IA

Pearson coefficient

Reposting

Correlation coefficient

0.431

0.494

0.472

Significance (bilateral)

0.012

0.005

0.000

Commenting

Correlation coefficient

0.563

0.640

0.571

Significance (bilateral)

0.024

0.000

0.019

Reposting

Correlation coefficient

0.414

0.489

0.572

Significance (bilateral)

0.000

0.004

0.001

Correlation coefficient

0.531

0.376

0.463

Significance (bilateral)

0.08 *

0.000

0.016

Reposting

Correlation coefficient

0.379

0.342

0.496

Significance (bilateral)

0.014

0.001

0.063 *

Commenting

Correlation coefficient

0.545

0.412

0.537

Significance (bilateral)

0.038

0.042

0.008

Kendall coefficient

Commenting Spearman coefficient

Note * indicates that the coefficient has not reached the significant level of 0.05

above the significance level of 0.05, all other correlation test values are lower than the significance level of 0.05, indicating that the correlation between the corresponding variables is significant on the whole, that is, there is significant correlation between IV, IS, IA and FI and CI. Correlation analysis is mainly conducted through the calculation of the corresponding coefficient between two variables to judge whether the correlation between the variables is significant and the strength of certain linear correlation. In the multivariate correlation analysis, due to the influence of other variables, the correlation coefficient between two variables can only reflect the relationship between the two variables as a whole, that is, it is difficult to ensure an accurate determination of the correlation between the two variables through this indicator alone. Therefore, when performing multivariate correlation analysis, it is still necessary to immobilize all the relevant variables except the two variables, that is, to set them as control variables, and then further analyze the correlation between any given two variables on this basis, namely to perform partial correlation analysis. Partial correlation analysis is mainly used to calculate the partial correlation coefficients between variables in order to more accurately determine the significance of the correlation between the variables and the degree of correlation (Field, 2013). Among them, the partial correlation coefficients among static contextual variables are shown in Table 4.2. Table 4.2 shows that the p-value of the correlation test corresponding to each partial correlation coefficient is lower than the significance level of 0.01, indicating that the partial correlation coefficients corresponding to each variable are all significant. On the basis of the above correlation analysis, combined with the partial correlation analysis results, it can be determined that there is a significant correlation between IV, IS, IA and FI and CI.

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Table 4.2 Value of the partial correlation coefficients Sharing behavior

Coefficient and testing

IV

IS

IA

Reposting

Partial correlation coefficient

0.427

0.391

0.543

Significance (bilateral)

0.000

0.028

0.000

Commenting

Partial correlation coefficient

0.416

0.408

0.614

Significance (bilateral)

0.000

0.006

0.002

4.3.4 Causality Test The above correlation analysis and partial correlation analysis results show that there is a significant correlation between IV, IS, IA and FI and CI. On this basis, a regression model is established for related variables, and the significance of the model and regression coefficients is analyzed to determine whether the causal relationship between the independent variable, and thus revealing whether IV, IS, IA have a significant impact on the Weibo users’ reposting and commenting intention of crisis information. All variables are measured by the Likert five-point scale, the assignment of each variable is an integer ranging from “1” to “5”, so the ordered Probit model should be selected to fit the sample data. Among them, the ordinary Probit model can be expressed as: P(Y = 1) = f(X), that is, the probability of Y = 1 is a function of X, where f (•) obeys the standard normal distribution, while the ordered Probit model can be regarded as an extended form of the ordinary Probit model (Daganzo, 2014). An ordered Probit regression model is constructed using crisis information FI and CI as dependent variables, and IV, IS, and IA as independent variables. STATA 13.0 is used to fit and estimate the regression model. The results are shown in Table 4.3. In Table 4.3, in order to ensure the identification of the parameters, STATA 13.0 statistical software has standardized the parameters. Therefore, the constant term is not included in Table 4.3. The results show that in the models with FI and CI as Table 4.3 Estimation results of ordered probit model Dependent variable

Independent variable

Coefficient Standard value error

Z test Value

Test p-Value

Model likelihood ratio p(chi2) value

Quasi R2 Value

FI

IV

0.61

0.093

4.257

0.000

0.000

0.794

IS

0.43

0.014

−8.513

0.000 0.000

0.829

CI

IA

0.75

0.048

−9.242

0.000

IV

0.54

0.072

−3.261

0.001

IS

0.39

0.036

6.839

0.000

IA

0.62

0.059

4.603

0.000

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the dependent variables, the probability p value of each model’s likelihood ratio chisquare test is 0.000, which both reach the significance level of 0.01, indicating that former hypothesis about invalid regression model is rejected and the significance of the model construct is effective. Among them, the quasi R2 value is used to measure the proportion of the actual value added of the log-likelihood function to the maximum possible value added, which reflects the degree of interpretation of the independent variable in the model to the change of the dependent variable, and can better measure the quasi-value of the model with close accuracy. In the above two models, the corresponding quasi-paste values are 0.752 and 0.829 (the closer the value of R2 is to 1, the better the model fits). Both values are larger, indicating that the two models have better fit. At the same time, the p value of the z test of each coefficient in the two models is less than 0.01, indicating that the estimated value of each coefficient in the two models has passed the significance test at a confidence level of 1%, and combined with the positive and negative properties of the coefficients in the two models, it can be judged that there are significant causal relationships between IV, IS, IA, and brand crisis information reposting and commenting behavior on Weibo. All have positive effects.

4.4 Testing and Analysis of Dynamic Contextual Factors In order to explore whether the total number of information reposting (TNR), the total number of comments (TNC), the number of user’s follows (NUF) and followers (NUFF), the number of follows (NSIF) and followers of the information source (NSIFF), and the temporal distance of information (ITD) have a significant impact on Weibo users’ information reposting and commenting behavior in brand crisis, this study tests and analyzes the causal relationship between these dynamic contextual factors and information sharing behaviors from the time series level and the data integrity level separately so as to comprehensively test the existence of causal relationships between variables and to avoid discrepancies in significant level of causality. The data are collected mainly through Sina official API and web crawler technology.

4.4.1 Time Series Level In order to explore from the time series level of data whether TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD have a significant impact on Weibo users’ information reposting and commenting behavior in brand crisis, Granger causality test is used to test and analyze the significance of the causal relationship between the corresponding time series (Sreenivasulu & Pagadala, 2014) to determine whether each influencing factor has a significant causal relationship with reposting and commenting behavior.

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Fig. 4.2 Cross correlation between the no. of reposts and TNR

Since the Granger causality test is based on the premise that there is a significant correlation between the time series, and the different settings of the number of lag periods in the test will have an important impact on the test results, it is necessary to cross the corresponding time series before performing the Granger causality test analysis to ensure the correctness of the setting of lag attribute test and the practical significance of the test results (Ling & McAleer, 2015).

4.4.1.1

Cross-Correlation Analysis

Correlation Analysis with the Number of Reposting This part analyzes the cross-correlation between TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD and the number of reposts. The analysis results are shown in Figs. 4.2, 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8. From Figs. 4.2, 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8, it can be seen that TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD are related to the number of reposts. The crosscorrelation function values of all have at least two periods of lagging beyond the

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Fig. 4.3 Cross correlation between the no. of reposts and TNC

95% credible interval range, indicating that the corresponding dynamic contextual factors have significant cross-correlation with reposting behavior.

Correlation Analysis with the Number of Comments This part analyzes the cross-correlation between TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD and the number of comments. The results are shown in Figs. 4.9, 4.10, 4.11, 4.12, 4.13, 4.14 and 4.15. From Figs. 4.9, 4.10, 4.11, 4.12, 4.13, 4.14 and 4.15, it can be seen that TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD are related to the number of comments. The cross-correlation function values of all have at least two periods of lagging beyond the 95% credible interval range, indicating that the corresponding dynamic contextual factors have significant cross-correlation to users’ commenting behavior.

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Fig. 4.4 Cross correlation between the no. of reposts and NUFF

4.4.1.2

Granger Causality Test

Granger Causality with the Number of Reposting The cross-correlation analysis results in Figs. 4.2, 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 show that TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD are significantly correlated to the number of reposts. The cross-correlation function values of the maximum lags of 7, 3, 4, 4, 3, 2, and 3 exceed the 95% confidence interval range, indicating that the correlations in each corresponding lag period are equally significant. To ensure the accuracy and validity of the Granger causality test results, the minimum value of the largest significant lag period should be selected as the lag period value of the Granger causality test, and the minimum value of all the maximum lag periods in the group is 2, which means that the number of lag periods in the test should be set at 2. The corresponding testing results are shown in Table 4.4. Table 4.4 shows that the p-value of the Granger causality test of TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD with the number of reposts are all lower than the significant level of 0.05. Thus, the original hypothesis that each factor “cannot Granger cause changes in the number of reposts” is rejected, indicating that the

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Fig. 4.5 Cross correlation between the no. of reposts and NUF

corresponding dynamic contextual factors are all Granger causes for the change of number of reposts.

Granger Causality with the Number of Comments The cross-correlation analysis results in Figs. 4.9, 4.10, 4.11, 4.12, 4.13, 4.14 and 4.15 show that TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD are significantly correlated to the number of comments. The cross-correlation function values of the maximum lags of 7, 7, 3, 2, 2, 5, and 4 exceed the 95% confidence interval range, indicating that the correlations in each corresponding lag period are equally significant. To ensure the accuracy and validity of the Granger causality test results, the minimum value of the largest significant lag period should be selected as the lag period value of the Granger causality test, and the minimum value of all the maximum lag periods in the group is 2, which means that the number of lag periods in the test should be set at 2. The corresponding testing results are shown in Table 4.5. Table 4.5 shows that the p-value of the Granger causality test of TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD with the number of comments are all lower than

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Fig. 4.6 Cross correlation between the no. of reposts and NSIFF

the significant level of 0.05. Thus, the original hypothesis that each factor “cannot Granger cause changes in the number of comments” is rejected, indicating that the corresponding dynamic contextual factors are all Granger causes for the change of number of comments.

4.4.2 Data Integrity Level To explore from the perspective of data integrity whether TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD have a significant impact on the information reposting and commenting behavior of Weibo users in brand crisis, a panel data regression model is constructed and the significance of the model and regression coefficients is analyzed to determine whether each influencing factor has a significant causal relationship with Weibo users’ reposting and commenting behavior. Since the construction and estimation of the regression model need to be based on the premise that there is a significant correlation between the independent variables and the dependent variables, before the regression model is constructed, the correlation between the dependent variable and

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Fig. 4.7 Cross correlation between the no. of reposts and NSIF

the independent variable of the regression equation needs to be tested and analyzed to ensure the establishment and estimation of the regression equation has practical significance.

4.4.2.1

Correlation Analysis

Pearson correlation coefficient, Spearman rank correlation coefficient and Kendall rank between TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD and brand crisis information reposting and commenting are calculated. The results are shown in Table 4.6. Table 4.6 shows that, except for the Spearman rank correlation coefficient significance test p value of NSIF and reposting behavior is 0.13, and the Kendall rank correlation coefficient significance test p value of NUFF and comment behavior is 0.096, which are above the significance level of 0.05, the p-values of all other correlation tests are lower than the significance level of 0.05, indicating that the correlation coefficients have an overall significance, that is, TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD are significantly correlated with user commenting behavior.

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Fig. 4.8 Cross correlation between the no. of reposts and ITD

On this basis, the partial correlations are analyzed and the partial correlation coefficients of the variables are shown in Table 4.7. Table 4.7 shows that in the significance test of all partial correlation coefficients, except for the p value of ITD and comment behavior coefficient is 0.084, which is above the significance level of 0.05, the significance test of the other partial correlation coefficients p values are all lower than the significance level of 0.01, indicating that the partial correlations between the corresponding variables are overall significant. Based on the correlation analysis of the above variables, combined with the partial correlation analysis results, it is possible to conclude TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD have significant correlations with user reposting and commenting behavior.

4.4.2.2

Causality Test

Previous analysis proves that there is significant correlation between TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD and Weibo users’ reposting and commenting behavior. On this basis, a panel data model is constructed for each variable, and the

4.4 Testing and Analysis of Dynamic Contextual Factors

141

Fig. 4.9 Cross correlation between the no. of comments and TNR

significance analysis of the model and regression coefficients is used to determine whether there is a significant causal relationship between the independent variables and the dependent variables, thereby revealing the impact of various dynamic contextual factors on user behavior in brand crisis. Since all data about TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD include information of individual, time and indicators, it belongs to the panel data structure.8 Therefore, when testing the relationship between variables, the panel data model should be used to fit and analyze the data. There are three common forms of panel data models9 : First, the mixed regression model. If the individual differences in the time series are not significant, and the cross-sectional differences are not significant, the coefficients and intercept terms of the explanatory variables in the model are estimated to remain unchanged for all individuals. The model can be expressed as: yit = α + βxit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

8 9

Baltagi (2008). Hsiao (2014).

(4.1)

142

4 Contextual Factors Affecting Brand Crisis Information …

Fig. 4.10 Cross correlation between the no. of comments and TNC

Second, the variable intercept model. The model shows that there are individual differences but no structural differences. The coefficients corresponding to all explanatory variables in the model remain unchanged, while the estimated values of the intercept term are different. Among them, the variable intercept model can be changed according to the source of the difference in the intercept term. It can be divided into two types: fixed effects and random effects. The model is expressed as: yit = αi + βxit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

(4.2)

Finally, the variable coefficient model. The model shows that the panel data have differences in both individual members and structures, among which individual differences are reflected by changes in the intercept term of the model, and structural differences are reflected by changes in coefficients of explanatory variables. The model is expressed as: yit = αi + βi xit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T Generally, the panel data model can be expressed as:

(4.3)

4.4 Testing and Analysis of Dynamic Contextual Factors

143

Fig. 4.11 Cross correlation between the no. of comments and NUFF

yit = αi + β1i x1it + β2i x2it + β3i x3it + . . . + βki xkit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

(4.4)

When selecting and setting the model, the following two original hypotheses need to be tested: H1: For individuals on all cross-sections, the coefficients of the explanatory variables in the model remain the same (that is, the slope coefficient has the characteristic of homogeneity), but there are differences in the intercept term in the model. This model is the variable intercept model: yit = α + β1 x1it + β2 x2it + β3 x3it + . . . + βk xkit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

(4.5)

H2: For individuals on all cross-sections, the estimated values of coefficients and intercept terms of the explanatory variables in the model remain unchanged. This model is a mixed regression model:

144

4 Contextual Factors Affecting Brand Crisis Information …

Fig. 4.12 Cross correlation between the no. of comments and NUF

yit = α + β1 x1it + β2 x2it + β3 x3it + . . . + βk xkit + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

(4.6)

The suitability of the model is tested by calculating the following two F-test statistics: (S3 − S1 )/[(N − 1)(k + 1)] ∼ F[(N − 1)(k + 1), N T − N (k + 1)] S1 /[N T − N (k + 1)] (S2 − S1 )/[(N − 1)k] ∼ F[(N − 1)k, N T − N (k + 1)] (4.7) F1 = S1 /[N T − N (k + 1)] F2 =

Here, N is the number of individual members, T is the number of observation periods for each member and k is the number of variables. S1 , S2 and S3 are the sum of squared regression residuals of variable coefficient model, variable intercept model and mixed model respectively. F2 and F1 are calculated separately and compared with the critical value of the F distribution corresponding to a specific degree of freedom in order to determine whether the original hypotheses H1 and H2 are to be accepted, and then to determine the form of the panel data model setting.

4.4 Testing and Analysis of Dynamic Contextual Factors

145

Fig. 4.13 Cross correlation between the no. of comments and NSIFF

In this study, the statistical values of and F1 are calculated based on the sample data, and the F distribution table is consulted. The result is and F2 > F2 critical value, indicating that the original hypothesis H2 can be rejected. At the same time, F1 < F1 critical value, so the original hypothesis H1 is accepted. The variable intercept model can be used to better fit the sample data. In order to further determine whether the model should be set in the form of random effects or fixed effects, the Hausman test needs to be performed on the model. The Hausman test results show that the corresponding statistic value is less than the critical value of xˆ2 at the 0.05 significance level, and the original hypothesis that the individual factors in the random effects model are not related to the independent variables cannot be rejected, so the random effects variable intercept model should be constructed,10 which is: yit = α + β1 x1it + β2 x2it + β3 x3it + β4 x4it +β5 x5it + β6 x6it + β7 x7it + γi + u it , i = 1, 2, 3, . . . , N ; t = 1, 2, 3, . . . , T

10

Baltagi (2014).

(4.8)

146

4 Contextual Factors Affecting Brand Crisis Information …

Fig. 4.14 Cross correlation between the no. of comments and NSIF

Here, y represents the number of crisis information reposts or comments, x1 , x2 , x3 , x4 , x5 , x6 and x7 respectively represent TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD. α is the average value of the number of information reposting or comments of all brand crisis events, and γi is a random variable, representing the random impact of different brand crises, to reflect the differences in the attributes and characteristics of different brand crisis. uit is random error. EViews 8.0 statistical software is used to fit and estimate the model. The results are shown in Table 4.8 (according to the needs of this research, this book only lists the estimated results of the coefficients and intercept terms, and the γi values are not shown). According to Table 4.8, the p values of the overall F test of the two models are below the significance level of 0.01, indicating that the two models have overall significance. At the same time, the goodness of fit Rˆ2 value and the modified Rˆ2 value are both greater than 0.90, indicating that the two models have good fitting effects. In addition, the t-tests corresponding to the estimated values of the explanatory variable coefficients and intercept items all meet the significance level of 0.01, indicating that the factors TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD have a significant impact on Weibo users’ information reposting and commenting behavior in brand crisis.

4.4 Testing and Analysis of Dynamic Contextual Factors

147

Fig. 4.15 Cross correlation between the no. of comments and ITD Table 4.4 Granger causality test of contextual factors and the number of reposts Ind.variable Dependent variable: no. of reposts Original hypothesis

F test value Probability p value

TNR

TNR cannot Granger cause change of no. of reposts

7.80373

0.0125

TNC

TNC cannot Granger cause change of no. of reposts

5.53885

0.0309

NUFF

NUF cannot Granger cause change of no. of reposts

NUF

NUFF cannot Granger cause change of no. of reposts

NSIFF

NSIF cannot Granger cause change of no. of reposts

NSIF

NSIFF cannot Granger cause change of no. of reposts

ITD

ITD cannot Granger cause change of no. of reposts

32.9325 6.86951 11.8182 5.42933 20.6030

0.0000 0.0179 0.0031 0.0324 0.0003

148

4 Contextual Factors Affecting Brand Crisis Information …

Table 4.5 Granger causality test of contextual factors and the number of comments Ind. variable Dependent variable: no. of comments Original hypothesis

F test value Probability p value

TNR

TNR cannot Granger cause change of no. of comments

11.2279

0.0038

TNC

TNC cannot Granger cause change of no. of comments

18.0332

0.0005

NUFF

NUF cannot Granger cause change of no. of comments

NUF

NUFF cannot Granger cause change of no. of comments

17.0513

0.0007

NSIFF

NSIF cannot Granger cause change of no. of comments

25.6695

0.0001

NSIF

NSIFF cannot Granger cause change of no. of 15.9578 comments

0.0009

ITD

ITD cannot Granger cause change of no. of comments

0.0113

7.58991

8.06075

0.0135

4.5 Summary Based on the perspective of contextual factors, this chapter explores and analyzes the static and dynamic contextual factors that affect Weibo user information sharing behavior in brand crisis. Firstly, based on previous researches, through reasoning, induction and analysis, the static and dynamic factors that have a significant impact on the information sharing behavior of Weibo users in brand crisis are studied. The static factors mainly include IV, IS and IA. The dynamic factors mainly include TNR, TNC, NUF, NUFF, NSIF, NSIFF, and ITD. Collected data are treated and analyzed for the study of the causal relationship between each factor and information reposting and commenting behavior to determine whether the factors have a significant impact on the information sharing behavior of Weibo users in brand crisis. In the causality test of information sharing behavior by static contextual factors, correlation analysis and ordered Probit model are mainly used. In the causality test of information sharing behavior by dynamic contextual factors, it is mainly carried out from the time series level and the data integrity level while using the cross-related analysis, Granger causality testing and panel data models. This study reveals that social media users’ information behavior is influenced by many factors, which can usually be summed up in two major categories: one is individual and the other is environmental. Because user information behavior always occurs in a specific time and space, the behavior characteristics of individuals in a virtual environment at any point in time are the result of the interaction of personal factors with the external environment, which always occur in certain contexts. Behavior can be regarded as the product of a particular context. However, environmental factors do not exist in isolation. They are always made up of multiple

Commenting

Reposting

Commenting

Reposting

0.431 0.007

Significance (bilateral)

0.008

Significance (bilateral) Correlation coefficient

0.637

Correlation coefficient

0.000

Significance (bilateral)

0.042 0.497

Significance (bilateral) Correlation coefficient

0.464

0.000

Correlation coefficient

0.372

Significance (bilateral)

0.032

Correlation coefficient

0.417

Significance (bilateral)

TNR

Correlation coefficient

Coefficient and testing

Note * indicates that this index has not reached the 0.05 significant level

Spearman coefficient

Kendall coefficient

Reposting

Pearson coefficient

Commenting

Information sharing behavior

Coefficient type

Table 4.6 Correlation coefficient values

0.000

0.376

0.004

0.479

0.000

0.419

0.004

0.597

0.007

0.431

0.000

0.514

TNC

0.026

0.363

0.001

0.342

0.096 *

0.616

0.000

0.427

0.000

0.326

0.041

0.409

NUFF

0.003

0.489

0.000

0.535

0.006

0.408

0.016

0.301

0.011

0.461

0.001

0.572

NUF

0.000

0.445

0.017

0.479

0.007

0.514

0.031

0.515

0.019

0.479

0.000

0.494

NSIFF

0.000

0.535

0.13 *

0.314

0.000

0.467

0.012

0.419

0.001

0.442

0.003

0.316

NSIF

0.024

0.549

0.000

0.545

0.024

0.319

0.000

0.437

0.043

0.596

0.006

0.458

ITD

4.5 Summary 149

150

4 Contextual Factors Affecting Brand Crisis Information …

Table 4.7 Values of partial correlation coefficients Information sharing behavior

Coefficient and its tests

TNR

TNC

NUFF

NUF

NSIFF

NSIF

ITD

Reposting

Partial correlation coefficient

0.381

0.443

0.518

0.368

0.654

0.359

0.414

Significance (bilateral)

0.001

0.000

0.000

0.006

0.046

0.032

0.000

Partial correlation coefficient

0.471

0.495

0.524

0.403

0.481

0.546

0.528

Significance (bilateral)

0.004

0.008

0.000

0.000

0.003

0.000

0.084 *

Commenting

Note * indicates that the coefficient has not reached the significant level of 0.05 Table 4.8 Results of random effects variable intercept model estimation Dependent Ind. variable variable

Coefficient Standard T test value error value

Probability R2 value Adjusted Model pvalue R2 value p (F) value

Number of Intercept 86.125 reposts (mean)

3.6812

14.231 0.000

TNR

0.312

0.0125

23.891 0.000

TNC

0.194

0.0206

3.129

0.003

NUFF

0.156

0.0173

18.469 0.000

NUF

0.174

0.0204

14.236 0.000

NSIFF

0.131

0.0362

2.856

37.805 0.000

0.946

0.931

0.001

0.962

0.937

0.000

0.007

NSIF

0.179

0.0146

ITD

-0.104

0.0191

2.961

Number of Intercept 69.827 comments (mean)

2.9469

46.371 0.000

TNR

0.247

0.0285

2.799

TNC

0.172

0.0107

35.468 0.000

0.005

0.008

NUFF

0.139

0.0149

20.394 0.000

NUF

0.104

0.0161

3.046

NSIFF

0.098

0.0124

39.126 0.000

NSIF

0.116

0.0295

3.262

ITD

− 0.075

0.0182

34.741 0.000

0.004 0.002

4.5 Summary

151

sub-environments and factors, all of which constitute a huge environmental complex which creates a social atmosphere causing users in this environment to have a spontaneous or accidental act of information sharing. It shows that the flow, transmission and utilization of information among users are affected by the information usage environment, which can encourage users to form information needs and drive them to actively search, inquire and utilize information. Based on the reasoning, induction and analysis of the findings of previous relevant researches, this chapter analyzes the influencing contextual factors of the information sharing behavior of Weibo users in brand crisis by Probit model and panel data analysis, and sorts out the static and dynamic contextual factors that have a significant impact on the user’s information sharing behavior. Thus, the research findings of this chapter have laid a theoretical foundation for the study of the information sharing behavior of Weibo users in brand crisis in Chaps. 5 and 6.

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Chapter 5

Static Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases

General information behavior theory holds that individuals are influenced by a variety of interference factors in the process of information seeking and utilization, which may enhance or hinder the user’s search effect and utilization behavior (Wilson, 1997). Information seeking behavior is influenced by many factors within various dynamic mechanism links. There are many kinds of intermediary variables that have an important influence on the information behavior and dynamic mechanism, including psychological characteristics, population statistics, social roles, interpersonal relationships, environmental characteristics and source information characteristics (Wilson, 1999). The theory of Information Use Environment (IUE) holds that the environment of information use can encourage users to have information demand and drive them to actively carry out information search, query and utilization behavior. It is the starting point of all information behaviors, such as information need, information search, evaluation and utilization (Taylor, 1986a). Various factors can have a significant impact on their information screening and selection. In short, the flow, transmission and utilization of information between users are affected by IUE, which can be used to determine the usefulness and the value of information. (Taylor, 1996). However, differences in the characteristics of information contexts in this environment or domain may cause users to take different information processing paths or methods, such as the central and peripheral information processing paths in the elaboration likelihood model (ELM) (Cacioppo & Petty, 1984), or heuristic and systemic approaches in the heuristic-systematic model (HSM) (Chaiken & Eagly, 1989). The central path emphasizes that people analyze and think carefully about the information after obtaining the information, identifies the relevant variables and carefully looks for the relevant clues. Users have a finer awareness of the information. The peripheral path refers to people’s identification and judgement of information content and viewpoints through perceptive cognition after obtaining information The degree of fineness of information processing is relatively low to enable a change of attitude more quickly and directly. The systemic approach is similar to the central path, while the heuristic approach is similar to the peripheral path of the ELM (Todorov

© Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_5

155

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5 Static Influencing Mechanism of Weibo Users’ …

et al., 2002). Therefore, in the user information behavior, the differences of information contextual characteristics may cause the user to take different information processing paths or methods, which in turn have different persuasion effect on people, thus affecting the user’s selection of information behavior. Based on Weibo information platform, this chapter studies the static influencing mechanism of user information sharing behavior in brand crisis. It begins with research hypothesis and research framework proposed on the basis of relevant theories and previous research results. Then, the questionnaire is used to collect relevant data to test the hypotheses and explore the specific influential paths as well as the differences in each path that IV, IS and IA have on the Weibo users’ information sharing behavior in brand crisis. On this basis, each sample group is fitted and estimated separately to analyze the differences in the influence path coefficients in different gender, age, education and occupational groups, thus revealing the different effects of static contextual factors on the reposting and commenting behavior of different types of user groups.

5.1 Introduction In recent years, the frequency of brand crises has increased. Such outbreaks are not only closely related to the management and brand operation ability of the enterprises concerned, but also with the arrival of the era of new media dominated by the Internet, which helps the spread of brand crisis information to gather more speed. In the new media, users can share and interact with the information instantly, so that the crisis information can be widely disseminated at a very fast speed until public opinions go viral. As a new media, Weibo is developing fast and harvesting popularity among the Internet users in China. It is now an important platform for people to obtain and share information and communicate with each other on a daily basis. When brand crisis outbreaks have become the norm today, Weibo is playing an important role in the rapid spread of crisis information. In the process of crisis information spread, the user information behavior on Weibo is influenced by individual factors such as users’ cognitive style and demographic characteristics and other factors such as user environment and information context. Different contextual environments can form different information fields, which in turn affect the characteristics of information sharing.1 Users may exhibit different information behaviors in different contexts for the same information. Therefore, to study the influence mechanism of information sharing behavior of Weibo users in brand crisis from the perspective of contextual factors is helpful to form a more comprehensive understanding of the mechanism. Scholars at home and abroad have been constantly exploring and researching such influence mechanism from different perspectives (Liu et al., 2012; Hansen et al., 2011; Yu, 2011; Zhang, 2013; Sun & Li, 2012). However, most of the relevant 1

Dey (2001).

5.1 Introduction

157

studies in the past have explored the relationship between independent variables and information sharing behavior through regression models (such as logistic regression) or variance analysis which regards the action mechanism between the variables as a dark-box process. These studies can reveal whether the influence of independent variables on dependent variables is significant or how big it is or the direction of the influence, but they cannot reveal the specific influence mechanism of the variables. They tend to know the hows but not the whys. Moreover, the conclusions are mainly based on the analysis of the total population data without considering the impact of user group differences (such as gender, age, education, occupation differences etc.) on the relationship between the variables. Past studies have shown that the persuasive effect of information and decision-making behavior of the information recipients are influenced by differences in demographic variables, mainly as different groups of information users will hold different attitudes and behavioral characteristics towards the same information (Al-Suqri, 2015); (Park, 2015). Therefore, past research conclusions may be weak in practical operation or targeting. With the rapid development of information technology and network technology, user information behavior is greatly influenced by the contextual environment, which requires a comprehensive and indepth understanding of the influence mechanism of information behavior, generating the need to explore and study the user information behavior from the perspective of contextual factors. Previous studies have touched upon the important influence of contextual factors on user information behavior, but specific study on the impact has not been found. Therefore, the following questions will be explored in this chapter: (1) (2)

(3)

what is the influence mechanism of static contextual factors on Weibo users’ information sharing behavior in brand crisis? Are there differences in the influence mechanism of static contextual factors among user groups of different genders, ages, educational levels and occupations? What are the specific differences of influence in user groups of different genders, ages, educational levels and occupations?

5.2 Hypothesis 5.2.1 Relationship Between Information Context and Physiological Stimulation 5.2.1.1

Information Visualization

Information visualization (IV) is the use of visualization technology in the field of non-spatial data, mainly by means of image processing, user interface, computer vision and graph, to process the data information into visual form by means of expression, modeling and so on. The content and attributes of data information can be displayed in three-dimensional, visual and animated ways to enable users to visually

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and quickly discover the content, potential features, relationships and patterns of information, thus to understand abstract data more quickly (Conati et al., 2015). Fluency reflects the subjective experience of ease or difficulty associated with information processing (Oppenheimer & Kelso, 2015). Perceptual fluency (PF) refers to the perception and physical form of the degree of difficulty in the process brought to the user by the surface attribute characteristics of the information when the individual is processing the information (Babel & McGuire, 2015). It is not about the ease or difficulty of information processing; it is a kind of feeling and experience about the degree of difficulty in information processing. Previous studies have analyzed the factors influencing people’s PF by changing the attribute characteristics of information or data, and found that the simplicity of information can have an important impact on individuals’ PF in information processing, and when information is more intuitive and simple, the speed of information processing is faster (Alter et al., 2007, 2013; Wanke & Hansen, 2015). Cognitive absorption (CA) refers to the state of a user’s involvement and engagement with a specific object in the cognitive process. It has two basic attributes. The first is engagement, which means that people’s attention is targeted on certain characteristics while other characteristics are excluded in their cognitive process The second is concentration, which refers to people’s degree of focus and cognitive intensity towards the targeted phenomenon or features. When people are highly cognitively focused on something, their feelings (including sight, hearing, taste, etc.) and perception (including consciousness and thought) will engage around the targeted object while excluding all other objects (Reychav & Wu, 2015; Zhang et al., 2006). In information processing, people’s subjective experience of the ease of information processing affects the weight assignment they give to different cues and the degree of attention to cues when processing information. Usually people’s weight assignment and attention to cues with high PF is higher than the weight assignment and attention to cues with low PF (Shah & Oppenheimer, 2007). When the display of information is more direct and vivid, it is perceived by the user that less effort is required for information processing so the user is more easily attracted, showing more engagement and absorption on the information (Freitas & Schirmer, 2015; Sengupta & Chang, 2013; Simola et al., 2014). It can be seen that information visualization enhances people’s perceptual fluency in information processing, and also improves people’s cognitive absorption on information. Based on this, the following hypotheses are proposed: H1a: There is a positive correlation between IV and PF. H1b: There is a positive correlation between the degree of IV and CA. 5.2.1.2

Sentiment

Information sentiment reflects the emotional appeal of information, which encodes information directly from the emotional or sensational aspects of the target audience, incorporating elements such as joy, fear, sadness, etc. (Agarwal et al., 2015). In the process of information dissemination, the emotional element of information acts

5.2 Hypothesis

159

directly on the user, injecting the emotional component into the user’s consciousness, so that the user can get the relevant emotional experience from the information or be moved by the situation described by the emotional element, which produces psychological resonance and impact, and finally stimulates the user to form certain emotional or sensational responses (Balahur & Jacquet, 2015; Vosoughi et al., 2015). Past studies have shown that information users are more willing and likely to fine-process information in negative sentimental situations. It is more likely for them to use central path or systematic information processing method (Schwarz & Clore, 1996, 2003). Sensory information theory also emphasizes that sadness or pessimistic emotions reflect the presence of problems in the environment, thus facilitating the high level information processing by information users (Mackie & Worth, 1989; Petty et al., 2015). Sadness or pessimistic emotions are often highly correlated with distrust, which make users more willing to process and think about the information to reduce the psychological tension and imbalances associated with uncertainty about the negative situation (Tiedens & Linton, 2001). Some scholars have studied users’ follows of political events on Weibo from the perspective of information sentiment, and found that information with positive or negative emotions is more likely to attract and retain user attention than neutral information (Stieglitz & Xuan, 2011). However, in the spread of crisis, the information is usually presented in a negative sentiment form, which is transmitted to the user and interacts with the user, resulting in greater cognitive absorption (Schwarz, 2000, 2002). Based on this, the following hypothesis is proposed: H2: There is a positive correlation between IS and CA. 5.2.1.3

Information Authority

Information source refers to the source of information acquisition by people out of their own needs. Authority means that someone or something has a legitimate influence on others, causing others to have a sense of trust or compliance, which is a spiritual force based on legitimate right that affects the object primarily through some kind of authority of the subject (Deimen et al., 2015). Information authority (IA) indicates that the source has certain convincing or prestigious characteristics, which makes users accept and obey the information voluntarily, and identify themselves with the content of the information. IA is mainly embodied in the three aspects of identity authority, channel authority and content authority. Identity authority is reflected in the authority of source provider in seniority. Channel authority is reflected in the authenticity and reliability of information in the means of dissemination. Content authority is reflected in the degree of authority of information content in professional, scientific and logical aspects (Rieh, 2002; Vieira, 2014; Wang & Zhang, 2011). Studies have shown that in the case of inadequate information evidence, information published by experts is regarded as being more trustworthy and reliable than information published by non-experts (Chaiken & Masheswaren, 1994); source channels of authorities at higher levels are more likely to generate more trust and less doubt among users about leads than low-authority source channels (Cosenza &

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5 Static Influencing Mechanism of Weibo Users’ …

Solomon, 2015; Reinhard & Sporer, 2015); and users are more likely to rely on highly specialized sources of information for information processing and understanding (Petty et al., 1983). It can be seen that high IA gives users more trust in the cues in the information and generates higher cue dependency (CD). Based on this, the following hypothesis is proposed: H3: There is a positive correlation between IA and CD.

5.2.2 Relationship Between Physiological Stimulation and Perceptual Attributes 5.2.2.1

Perceptual Fluency

PF refers to user’s perception and experience of the degree of difficulty in the information processing brought to the user by the surface attribute characteristics of the information. User’s subjective experience of the difficulty of information processing also affects the weight assignment to different cues and the degree of absorption during information processing (Claypool et al., 2015; Deckert, 2015). In general, users assign greater weight and more attention to information cues with high PF (Shah & Oppenheimer, 2007). Two approaches are used in information processing. One is analytical processing when information is analyzed and understood through careful thinking. The other is heuristic processing, which is a superficial analysis and cognition of information when relatively fewer attention and cognitive resources are invested. In addition to having a direct impact on user cognition through the surface attributes of information, PF indirectly affects the user’s cognition through different information processing methods (Alter and Oppenheimer 2007). In general, higher PF makes people more likely to choose heuristics processing, while lower PF makes people more likely to choose a systematic way of processing (Shah & Oppenheimer, 2008). Cognitive absorption (CA) means that when people perceive things, they focus on specific objects or features while excluding other objects or features, putting their feelings (including vision, hearing, taste, etc.) and perceptions (including consciousness, thinking, etc.) engaged on the targeted objects or features. Thus, PF can enable the user to give a higher weight to information cues and produce a higher degree of attention to reduce their information processing effort so that the user can quickly integrate and smoothly understand the information content without too much effort (Storme et al., 2015). Therefore, in the processing of crisis information, the negative meaning of information transmission and the described harmful situation can be presented coherently in users’ minds, thus strengthening their overall perception of the harm in crisis information. Based on this, the following hypotheses are proposed: H4: There is a positive correlation between PF and CA. H5a: There is a positive correlation between PF and PH.

5.2 Hypothesis

5.2.2.2

161

Cognitive Absorption

Cognition refers to the impression and viewpoint formed by people’s analysis and understanding of things, or the process of people’s knowing of things, i.e., the process of extracting, acquiring and processing the attributes of things. CA indicates that people’s attention is directed and focused on a particular object or feature in the cognitive process, and when there is CA on an object or feature, people perceive, think, remember, imagine, and experience the object to gain a clear, profound, and comprehensive understanding of the object (Leger et al., 2014; Oh & Sundar, 2015). Based on ELM persuasion theory, past studies have shown that different ways of processing information in crisis situations can have an important impact on people’s risk perception (Trumbo, 1999, 2002). Scholars have studied the effects of distraction on persuasion effects (Baron et al., 1973; Harkins & Petty, 1981; Jeong & Hwang, 2014) and found that users’ distraction in information processing reduces the likelihood that they will process the information using the central path, and the degree to which they process the information in fine and detailed ways, thereby reducing the persuasive effect of the information. In contrast, CA facilitates the formation of better information persuasion effects (Leong, 2011). Based on this, the following hypothesis is proposed: H5b: There is a positive correlation between CA and PH. 5.2.2.3

Cue Dependency

The concept of information cues (information scent) was first proposed in the theory of information foraging (Pirolli & Card, 1999) and is defined as any information that can draw the user’s attention, guide understanding, or prompt further action when information is acquired and accepted. When a user browses a web page, navigation, links, and captions or pictures related to the link can all be considered as information scents (Zhang, 2013). Dependence refers to an individual’s over-reliance on external forces or things as a result of their own weak sense of independence and self-control. It manifests as strong sensitization in emotion and cognition (Bristow et al., 2002; Yang et al., 2015). The heuristic systematic model (HSM) divides people’s cognition into heuristic approach and systemic approach according to the differences in people’s efforts in information processing. Their willingness and information processing ability have a decisive influence on their choice of information processing approach (Chaiken & Ledgerwood, 2011). In the systematic processing, people think comprehensively and carefully about the explicit and implicit information cues before forming an attitude response. Heuristic processing, on the other hand, takes simple and direct cues as the main reference basis for cognition, and identifies and understands the information from the surface cues of information, which lacks in-depth thinking and analysis. When people are too dependent on information cues, they tend to use heuristics approach to understand and process information (Ling & Raghubir, 2015; Wang et al., 2015). In heuristic information processing, people are more likely to trust

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the information cues they depend on (Priester & Petty, 1995) because of their low willingness to process and perceive information, resulting in better persuasion effect of the information. Based on this, the following hypothesis is proposed: H5c: There is a positive correlation between CD and PH.

5.2.3 Relationship Between Perceptual Attributes and Behavior Intention According to behavioral studies, people’s behavior is directly related to their intention and the degree of behavior willingness can be used as an important measure of behavior occurrence. When predicting human behavior, it is possible to conduct the prediction by analyzing and studying behavioral intention (Mullet, 1985; Armstrong & Morwitz, 2000; Kalat, 2015). Need is the source of all motivations and behaviors, which is a state where certain physiological conditions are missing, generating desires and motivations in the subject’s effort to acquire a certain external object or to achieve a certain purpose (Lewin, 2014; Weiner, 1972). When people have a need for something, they develop a psychological tension, which in turn causes physiological tension, thus causing the individual to lose mental balance. Mental imbalance is an important cause of motivation and behavior, in which physiological tension provides the driving force for the emergence of a certain mental motive or the formation of a certain need (Heider & Benesh-Weiner, 1988; Moore, 2006). In this case, people tend to eliminate or weaken the physiological tension and psychological discomfort by changing their attitudes or taking a certain action until their specific needs are met or a new context appears and their psychological balance is restored. In a brand crisis, when people perceive harm, an unpleasant or uncomfortable experience occurs, triggering the absence of certain physiological conditions and the formation of tension, followed by psychological imbalances (Heider, 2013; Vaidis, 2014). On Weibo, when users experience psychological imbalance caused by harm perception, they tend to ease their physical tension and psychological discomfort by means of various information behaviors such as reposting and commenting on the information, thus restoring the psychological equilibrium. Based on this, the following hypotheses are proposed: H6a: There is a positive correlation between PH and FI. H6b: There is a positive correlation between PH and CI.

5.2.4 Moderating Effect of Harm Relevance Harm relevance (HR) refers to the perception of the extent to which the event relates to oneself after a crisis, and how likely it is that it will cause injury and loss to one’s

5.2 Hypothesis

163

body, mind, emotions, and property. The closer the harm caused by a crisis event is to an individual, the more attention is paid to the crisis event, and the higher the involvement. Differences in involvement have an important impact on the persuasive effect of information (Johnson & Eagly, 1989; Petty & Cacioppo, 1984, 1990; Petty et al., 1981). Users with high levels of involvement tend to use the central route to process information so that it has a better persuasive effect and can form a more lasting attitude. In addition, when a crisis occurs, HR enables people to gain awareness of the injury from the vision, perception, and perception of the crisis context, forming varying degrees of information flow into the user’s brain, thus creating a crisis harm experience. Experience is an irrational process of psychological activity that is closely related to people’s intuition and can create functions for people’s inner feelings and emotions (Schmitt, 2011; Tao, 2014; Tynan et al., 2009). People acquire certain understanding of things and form certain impressions during the experience (Rahman et al., 2012; Wang & Zhou, 2010), which is finalized in the form of specific perceived states and cognitive outcomes (Christopher & Andre, 2007; Ismail, 2011). As a result, the closer people feel relevant to the harm, the more attention they will pay to the crisis, resulting in a more powerful harmful experience. It can be seen that in the influence paths of PF, CA, CD and PH, HR will have a moderating effect on each path through cognitive involvement and harm experience. Based on this, the following hypotheses are proposed: H7a: HR has a positive moderating effect on PF and PH. H7b: HR has a positive moderating effect on CA and PH. H7c: HR has a positive moderating effect on CD and PH.

5.2.5 Theoretical Framework for Research This book takes IV (information visualization), IS (information sentiment) and IA (information authority) as independent variables, FI (forwarding/reposting intention) and CI (commenting intention) as dependent variables, PF (perceptual fluency), CA (cognitive absorption), CD (cue dependence) and PH (perceived harm) as intermediary variables, and HR (harm relevance) as the moderating variable to build the theoretical framework of this study as shown in Fig. 5.1.

5.3 Research Design and Data Collection 5.3.1 Research Methods In the study of multiple variable relationships, the traditional method mainly uses correlation analysis and regression model, which requires that each concept be composed of a single measurement index or problem item, and it is difficult to

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Information

Physiological

Perceptual

Behavioral

Stimulation

Attributes

Intention

Context

HR PF IV FI

CA

IS

PH CI

IA

CD

Fig. 5.1 Theoretical framework

deal with the research framework of each construct involving multiple measurement dimensions or question items. The structural equation model (SEM) is a multiregression research method that can handle multiple measurement indicators or problem items involved in each construct simultaneously. SEM can not only estimate the errors in the measurement, but also be used to calculate the reliability and validity of the measurement results. It is not limited by the basic assumptions in classical measurement theory, and it is easy to detect the correlation between certain errors (Bollen, 2014). In this study, variables including IV, IS, IA, PF, CA, CD, PH, HR, FI and CI cannot be measured directly. Each construct can only be measured by means of questions representing different dimensions. Since this theoretical framework involves multiple dependent variables, multiple mediators and multiple sets of path relationships, using SEM model for data processing has greater advantages than other models. In data collection, the online questionnaire has the advantages of a wide range of investigation, timely information feedback, convenient execution and better anonymity, even sensitive questions that are not convenient to answer in field survey can be completed online without any worries. The questionnaire is assigned only to Weibo users who have participated in information reposting or commenting of brand crisis so that the quality of the investigation and the accuracy of the research conclusions are better ensured. Questions that are difficult to answer in field survey are completed without difficulty through the online questionnaire.

5.3.2 Design of Scale and Questionnaire In order to verify the theoretical framework, it is necessary to measure the relevant constructs through the scale and the questionnaire, and then use the structural

5.3 Research Design and Data Collection

165

equation model to estimate the paths. The design of the scale and the questionnaire mainly has drawn references from the research results of relevant classical scales and related literature in the past. Modifications are made in the light of the specific needs of this study. The constructs are: IV (information visualization), IS (information sentiment), IA (information authority), PF (perceptual fluency), CA (cognitive absorption), CD (cue dependence), PH (perceived harm), HR (harm relevance), FI (forwarding/reposting intention), and CI (commenting intention). All constructs are measured using the 5 Point Likert Scale, where 1 means very dissatisfied and 5 means very satisfied. The specific contents and structure of the scale are shown in Table 5.1. The questionnaire design is carried out according to the names of the constructs, measurement contents and item structure of the scale. The questionnaire mainly covers the following parts: Part I: including questions on the ten constructs related to brand crisis information, i.e., IV, IS, IA, PF, CA, CD, PH, HR, FI and CI. Part II: including questions on the demographic statistical characteristics, i.e., gender, age, educational level and occupation. In addition, a number of filtering and distracting items are set up in the questionnaire to improve the quality of the investigation. Appendix 2 in this book provides the full content of the Questionnaire.

5.3.3 Data Collection The data used in this study mainly consists of relevant user information from the official API and web crawlers. On this basis, random sampling of Weibo users who have crisis information reposing or commenting behaviors is conducted, and then questionnaire is used to get relevant data. In order to ensure the validity of the official survey results, the questionnaire questions are detected and purified by presurvey before the formal survey is carried out to ensure the validity and quality of the questionnaire design. In the pre-survey, 150 questionnaires have been randomly distributed at Shanghai Jiaotong University and 105 questionnaires are returned, of which 7 are invalid, with an effective recovery rate of 65.33%. Then, the reliability and validity of the pre-survey are analyzed. The statistical results show that the KMO value is 0.836, greater than the standard value of 0.70; the p value of Bartlett test is 0.006, which is below 0.01.The original hypothesis of “the correlation coefficient matrix is the unit matrix” is rejected, which indicates that all variables are correlated and there is significant correlation within the scale and the sample data, which is suitable for EFA analysis. EFA analysis is conducted on the whole scale and 10 factors are extracted, and their cumulative explanatory variance is 87.92%. The cumulative explanatory variances of each subscale are larger than 84.06%. Except for the factor load of item PH4 being 0.51 which is below 0.60, the factor load of all other items on the corresponding variables are above 0.60. Meanwhile, the Cronbach’s α value of each subscale are all above 0.70. In the analysis of Corrected Item Correlation Coefficient (CITC), except for the CITC index of item PH4 being 0.16, which is

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Table 5.1 Research scale design Concept

Measuring points

Items

References

IV

Information content and its attributes being transformed to a 3 dimensional image, especially its content

4

Card et al., (2009),

IS

How information varies due to certain emotional appeals e.g. pain, rage, anger, remorse

4

Agarwal et al., (2011)

IA

Finding its expression in the authorities of 4 identity, channeland profession

Fritch and Cromwell (2002)

PF

Perception on complexity during information processingMainly include: Back ground color contrast, font, clarity, presentation time. symmetry and simplicity

4

Oppenheimer (2008)

CA

Attention is paid to certain subjects in the cognitive process: user’s feeling(visual, hearing, taste) and consciousness(awareness, thinking) all point to them

4

Rouis (2012)

CD

Processing information mainly through cues or guidance rather than through systematic and careful analysis

4

Bristow et al., (2002)

PH

The extent to which the users feel that the crisis will cause physical, psychological, emotional and property damage or loss

4

Slovic (2000)

HR

The extent to which the event relates to the 4 users and users’ perception about potential damage or loss to themselves, their family, relatives, friends and other people concerned

Dawar (2009)

FI

The degree of users’ subjective tendency, probability, strength and duration of forwarding/reposting intentionafter reading the information

4

Lin (2006)

CI

The degree of users’ subjective tendency, probability, strength and duration of commenting intentionafter reading the information

4

Lin (2006)

below the reference value of 0.30, the remaining items are all above 0.30, so PH4 is to be deleted from the questionnaire while all other items are kept. After deleting item PH4, the reliability of the subscales and the overall scale is analyzed again. The results show that the Cronbach’s α value of the subscale from which item PH4 has been deleted has significantly improved, while the Cronbach’s α value of the other subscales are above 0.70. The Cronbach’s α value of the overall scale is also

5.3 Research Design and Data Collection

167

above 0.70. It can be concluded that the structural superiority of the questionnaire has improved after deleting item PH4, indicating that the deletion is reasonable. During the formal investigation, based on the relevant user information obtained from the official API and web crawlers, the users who have participated in reposting or commenting of brand crisis information are randomly sampled. All users who have participated in the sharing of crisis information are taken as the sampling population, and all user IDs obtained are taken as the sampling frame. The distribution characteristics of users’ gender, age, educational level and occupation in the 2015 SinaWeibo User Development Report issued by SinaWeiboare used as the reference standard for the questionnaire design.2 The online questionnaire survey has beens carried out to obtain relevant data. The investigation adopts the online questionnaire, E-mail and other network communication tools. Meanwhile, in order to improve the accuracy of the survey results and the rate of recovery of the questionnaires, before each survey, every participant is informed in advance that he or she would be paid RMB 7 yuan after completing and submitting the questionnaire. The payment can be made through mobile phone charges, WeChat Lucky Money, QQ coin, Alipay and online bankcard payment. The data collection process for this study has taken 3 months. 20,000 questionnaires have been distributed with 2407 returned, 315 of which are invalid. The effective questionnaire recovery rate is 10.46%. The distribution characteristics of demographic variables of effective sample data are shown in Table 5.2. Table 5.2 shows that the sample data cover the user groups of SinaWeibo with different genders, ages, educational levels and occupations. The sample distribution is similar to the distribution characteristics of user demographic variables in the 2015 SinaWeibo User Development Report, indicating that the sample data can well represent the overall characteristics of all SinaWeibo users.

5.4 Data Processing, Testing and Analyzing The number of valid samples in this study is 2092, which is larger than the minimum critical value of 150 (Rigdon 2000) required by structural equation model (SEM). By drawing the box graph of sample data, 21 singular values in 2092 samples are found which should be removed to ensure the accuracy and reliability of the research results.

2

Sina Weibo Data Center (2015).

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Table 5.2 Demographic characteristics of the sample (N = 2092)

Variable

Grouping

Number

Percentage(%)

Gender

Male

1285

61.42

Female

807

38.58

≤ 29 Years Old

983

46.99

30〜39 Years Old

682

32.60

40〜49 Years Old

319

15.25

≥ 50 Years Old

108

5.16

University & above

1306

62.43

Senior High School

362

17.30

Junior High School

278

13.29

Primary School and below

146

6.98

Government agency

359

17.16

Public institution 437

20.89

Enterprise

961

45.94

Self-employed

335

16.01

Age

Educational Level

Occupation

5.4.1 Reliability and Validity Analysis 5.4.1.1

Reliability Analysis

SPSS 22.0 is used to test the internal consistency of the item data. The results show that the Cronbach’s α values of IV, IS, IA, PF, CA, CD, PH, HR, FI and CI are 0.79, 0.86, 0.76, 0.73, 0.78, 0.81, 0.85, 0.76, 0.83, and 0.87. The total Cronbach’s α value of the whole scale is 0.84, that is, the Cronbach’s α value of each subscale and the whole scale are all above the standard value of 0.70, indicating that the reliability of the questionnaire design and sample data is good.

5.4.1.2

Validity Analysis

Construct Validity Before EFA analysis, KMO and Bartlett sphericity test are used to test whether the sample data are suitable for EFA analysis. The results show that KMO value is 0.814, which is greater than the standard value of 0.70; p value of Bartlett test is 0.000,

5.4 Data Processing, Testing and Analyzing

169

which is below 0.001, thus the original hypothesis that “correlation coefficient matrix is unit matrix” is rejected, indicating that there is correlation between variables, and correlation within the scale and sample data is significant. Thus, EFA analysis suitable. First, EFA analysis is conducted on the whole scale. Results show that 10 factors can be extracted, and the cumulative explained variance of these 10 factors is 91.47%. Secondly, the EFA analysis of each subscale show that the cumulative explanatory variance of each subscale are all above 82.19%. The factor load of item IV3 is 0.42 which is below 0.60. The factor load of all other items are above 0.60. So it is necessary to remove item IV3 from the sample data. Finally, a one-dimensional test is conducted to ensure that multiple items measuring the same construct can only be loaded on the same construct. The results show that the test values are greater than the standard value of 0.50, indicating that each construct meets the one-dimensional requirement and the scale has good construct validity.

Aggregate Validity On the basis of the above construct validity test, item IV3 is deleted from the data, and the data is analyzed by CFA. The results are shown in Table 5.3. The standard load coefficients between each measurement item and the potential variables measured are greater than the standard value of 0.60, and the t values of each corresponding significance test are greater than the critical value of 3.31 (p = 001), which indicates that the measured variables can be used to measure the latent variables effectively. Table 5.3 shows that the AVE value of each variable is greater than the standard value of 0.50, and the CR values are all greater than the standard value of 0.70, indicating that the measurement variables can effectively reflect the characteristics of each latent variable, and there is good consistency among the measurement indexes of each group, indicating that the scale and the sample data have good aggregate validity.

Discriminant Validity The correlation coefficients between the variables and the square root of the AVE are calculated, and the results are organized as shown in Table 5.4. It shows that the square root of AVE of all latent variables (i.e. the value on the diagonal in Table 5.4) in the scale is greater than the absolute value of all correlation coefficients corresponding to the variable and other variables, indicating that the scale does not have observational variables (i.e. items) that span multiple constructs, and the constructed measurement indexes all fall on the expected constructs. Therefore, the discriminant validity of the scale and sample data is good.

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Table 5.3 Results of confirmatory factor analysis Variable

Item

Standard load

t Value

Reliability

AVE

CR

IV

IV 1

0.67

N/A

0.79

0.69

0.84

IV 2

0.75

6.57

IV 4

0.83

4.49

IS1

0.75

N/A

0.86

0.67

0.75

IS2

0.75

8.61

IS3

0.67

7.26

IS 4

0.79

4.17

IA1

0.82

N/A

0.76

0.73

0.74

IA2

0.67

11.04

IA3

0.83

7.27 0.73

0.68

0.84

0.78

0.76

0.79

0.81

0.71

0.82

0.85

0.65

0.87

0.76

0.73

0.73

0.83

0.76

0.82

0.87

0.69

0.85

IS

IA

PF

CA

CD

PH

HR

FI

CI

IA4

0.77

4.94

PF1

0.83

N/A

PF2

0.71

6.42

PF3

0.78

7.12

PF4

0.76

8.64

CA1

0.84

N/A

CA2

0.73

9.39

CA3

0.72

8.96

CA4

0.87

6.24

CD1

0.79

N/A

CD2

0.67

6.91

CD3

0.69

5.42

CD4

0.78

7.12

PHI

0.65

N/A

PH2

0.75

5.83

PH3

0.72

8.39

HR1

0.74

N/A

HR2

0.68

6.85

HR3

0.87

9.95

HR4

0.78

7.25

FI1

0.76

N/A

FI2

0.67

8.18

FI3

0.77

4.12

FI4

0.65

5.83

CI1

0.84

N/A

CI2

0.78

4.34

CI3

0.85

6.85 (continued)

5.4 Data Processing, Testing and Analyzing

171

Table 5.3 (continued) Variable

Item

Standard load

t Value

CI4

0.83

5.94

Reliability

AVE

CR

Note: N/A is Not Available, indicating no value

Table 5.4 Results of discriminant validity analysis Variable

IV

IS

IA

PF

CA

CD

PH

HR

IV

0.83

IS

0.43

0.82

IA

0.61

0.51

0.85

PF

0.56

0.56

0.65

0.82

CA

0.42

0.47

0.57

0.58

0.87

CD

0.48

0.57

0.43*

0.49

0.41

0.84

PH

0.51

0.45

0.56

0.64

0.43

0.49

0.81

HR

0.57

0.58

0.56

0.56

0.56

0.65

0.56

0.85

FI

0.43

0.49

0.47

0.42

0.47

0.57*

0.47

0.57

CI

0.56

0.64

FI

CI

0.87

0.57 0.43 0.56 0.64 0.57 0.43 0.51 0.83 √ Note: the value on the diagonal is AV E, and the other values are the correlation coefficients;* indicates that the coefficient does not reach the significant level of 0.05

5.4.2 Hypothesis Testing In order to improve the stability of the model test results and the universality of the conclusion, the sample data are randomly divided into two equal groups of sub samples, namely, the first group n1 = 1,046, which is used for the model fitting and estimation; the second group n2 = 1,046, which is used for the validity test of the model fitting and estimation results. Through descriptive statistics of 10 latent variables, the absolute values of all kurtosis coefficients are between 0.811 and 6.321, which are less than the reference value of value 7; the absolute values of all skewness coefficients are between 0.797 and 2.184, which are less than the reference value of value 3. It can be considered that the sample data is approximately normal distribution (Huang, 2004), so the Maximum Likelihood Estimate is used to estimate the model.

5.4.2.1

Main Effects

Initial Model Based on the first group of sub sample data, AMOS 22.0 statistical software is applied to fit and estimate the theoretical model. The fitting results and correction indexes are shown in Figs. 5.2 and 5.3 respectively, and the fitness index is shown in Table

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Fig. 5.2 Results of initial model fitting

Fig. 5.3 Indication of the initial model modification indices

5.4 Data Processing, Testing and Analyzing

173

Table 5.5 Results of fitness analysis of the modified models Test Statistics

Fit Initial Model standard

First Modified Model Second Modified Model

Statistics Judgment Statistics Judgment Statistics Judgment judgmention Adaptation Adaptation standard judgment judgment Absolute fit index X2 value

p > 0.05 0.01

No

0.03

No

0.26

Yes

RMR value

< 0.05

0.01

Yes

0.08

No

0.01

Yes

RMSEA value

< 0.08

0.16

No

0.06

Yes

0.03

Yes

GFI value

> 0.90

0.95

Yes

0.68

No

0.96

Yes

AGFI value

> 0.90

0.67

No

0.94

Yes

0.92

Yes

NFI value

> 0.90

0.74

No

0.96

Yes

0.97

Yes

RFI value

> 0.90

0.92

Yes

0.86

No

0.95

Yes

IFI value

Incremental fit index

> 0.90

0.86

No

0.51

No

0.78

No

TLIvalue(NNFI > 0.90 value)

0.91

Yes

0.74

No

0.95

Yes

CFI value

> 0.90

0.75

No

0.91

Yes

0.98

Yes

> 0.50

0.23

No

0.68

Yes

0.54

Yes

Simplicity fit index PGFI value PNFK

> 0.50

0.62

Yes

0.17

No

0.89

Yes

PCFI value

> 0.50

0.46

No

0.43

No

0.38

No

CN value

> 200

253

Yes

324

Yes

516

Yes

X 2 /ldf

< 3.00

3.47

No

2.49

Yes

1.31

Yes

5.5. The results show that the X 2 value of the initial model fitness test is 124.726, the corresponding test p value is 0.000 < 0.05. The test result reaches the significant level of 0.05, and the original hypothesis is rejected, indicating that the initial model does not fit the sample data and should be modified. According to the output Modification Indices (MI), if the covariant relationship between the error variables of FI2 and CI3 is established, the maximum improvement of the fitness between the model and the sample data can be achieved, and the chi-square value can be reduced by at least 48.03. Therefore, the model is modified accordingly.

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Fig. 5.4 Fitting results of the first modified model

Firstmodified Model The covariance relationship between the error variables of FI2 and CI3 is established, and the modified model is estimated. The fitting results and modification indices are shown in Figs. 5.4 and 5.5 respectively, and the fitness indexes are shown in Table 5.5. The X 2 value of the firstmodified model fitness test is 54.375, and the corresponding test p value is 0.000 < 0.05. The test result reaches the significant level of 0.05. The original hypothesis is rejected, which indicates that the firstmodified model still does not fit the sample data and should be modified again. According to the modification model (MI), if the covariant relationship between the error variables of CD1 and PF4 is established, the maximum improvement of the fitness between the model and the sample data can be achieved, and the chi-square value can be reduced by at least 29.63. Therefore, the model is modified a second time accordingly.3

Second Modified Model The covariance relationship between the error variables of CD1 and PF4 is established, and the modified model is estimated. The fitting results and modification indices are shown in Figs. 5.6 and 5.7 respectively, and the fitness indexes are shown in Table 5.5. Among them, the X2 value of the second modification model is 11.135, and the corresponding test p value is 0.316 > 0.05. Therefore, the original hypothesis 3

Xue et al. (2015).

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175

Fig. 5.5 Indication of modification indices of the first modified model

is accepted, indicating that the second modified model can better fit the sample data. At the same time, almost all the fitness statistics meet the acceptable standard,4 and no correction parameters are provided in the MI column of the output results, which suggests that the second modified model is acceptable and there is no need to further modify the model (see Table 5.8 for the parameter estimation results of the second modified model).

5.4.2.2

Moderating Effect

After estimating the main effect path of the model, HR is added as a moderating variable to the relevant main effect path, and three linear regression equation models are constructed to test the moderating effect of HR. As all the relevant variables in the three models are measured by Likert five point scale, there is no dimensional difference between the variables, so it is not necessary to centralize the data of the relevant variables before estimating the model. In this study, the moderating effect test methods proposed by Baron and Kenny (1986) and James and Brett (1984) are adopted to conduct multiple stepwise regression on related variables to test whether the moderating effect of HR in each path is significant. The test results are shown in Table 5.6.

4

Wu (2013).

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Fig. 5.6 Fitting results of the second modified model

Fig. 5.7 Indication of modification indices of the second modified model

Model2 (Dependent variable: PH)

Model3 (Dependent variable: PH)

0.13*

PF × HR

Step 3

1.59

8.24

6.85 CA × HR

HR

CA

Note: *indicates that the coefficient does not reach the significant level of 0.05

0.24

HR

Step 2

0.39

PF

Step 1 0.46

0.41

0.52 6.75

2.38

7.91

CD × HR

HR

CD

0.54

0.47

0.56

2.82

10.25

5.93

Independent variable β coefficient t value Independent variable β coefficient t value Independent variable β coefficient t value

Regression steps Model1 (Dependent variable: PH)

Table 5.6 Moderating effect test

5.4 Data Processing, Testing and Analyzing 177

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5.4.3 Analysis of Results 5.4.3.1

Result Validity

In order to ensure that the research conclusions can be universally extended to the sampled population, the validity test of the model estimation results is very important (Faber & Kowalski, 1997). Therefore, based on the above-mentioned fitting and estimation of the theoretical model, it is necessary to test the second set of confirmatory subsamples to ensure that the estimated results of the model are not only applicable to a specific part of the sample data (Thakur et al., 2013). Among them, the output results of the fitness index for fitting the secondary correction model based on the estimating sub-sample and the confirming sub-sample are shown in Table 5.7. Table 5.7 shows that the fit index values for fitting based on the estimating subsample and the confirming sub-sample almost all meet the relevant standards, indicating that the model estimation results based on the estimating sub-sample data can be verified by the test sub-sample data. It shows that the model estimation results Table 5.7 Statistical values of model fit based on estimating and confirmatory samples Fit index

Acceptance criteria

Estimated subsample (n1 = 1046)

Confirmatory subsample (n2 = 1046)

Statistics

Statistics

Judgment

Judgment

Absolute fit index X2 value

p > 0.05

0.26

Yes

0.34

Yes

RMR value

< 0.05

0.01

Yes

0.03

Yes

RMSEA value

< 0.08

0.03

Yes

0.01

Yes

GFI value

> 0.90

0.95

Yes

0.84

No

AGFI value

> 0.90

0.97

Yes

0.92

Yes

NFI value

> 0.90

0.94

Yes

0.93

Yes

RFI value

> 0.90

0.92

Yes

0.97

Yes

IFI value

> 0.90

0.78

No

0.91

Yes

TLI value (NNFI value)

> 0.90

0.91

Yes

0.94

Yes

CFI value

> 0.90

0.95

Yes

0.99

Yes

PGFI
0.50

0.63

Yes

0.83

Yes

PNFI
0.50

0.82

Yes

0.14

No

PCFI
0.50

0.48

No

0.69

Yes

CN value

> 200

916

Yes

836

Yes

X2 /d/

< 3.00

1.31

Yes

2.06

Yes

Value-added fit index

Simple fit index

5.4 Data Processing, Testing and Analyzing

179

Table 5.8 Model estimation results based on estimating and confirmatory samples Hypothesis Estimating subsample (n1 = 1046) (n1 = 1046)

Path

Standardized t value Test coefficient result

Confirmatory subsample (n2 = 1046) (n2 = 1046) Standardized t value Test coefficient result

IV → PF

Hla: +

0.53

4.64

Support 0.46

5.84

Support

IV → PF

Hlb: +

0.62

7.52

Support 0.57

9.17

Support

IS → CA

H2: +

0.51

5.18

Support 0.46

3.62

Support

IA → CD

H3: +

0.59

9.23

Support 0.52

7.58

Support

PF → CA

H4: +

0.38

8.42

Support 0.34

2.81

Support

PF → PH

H5a: +

0.46

2.47

Support 0.48

4.63

Support

CA → PH

H5b: +

0.54

3.62

Support 0.49

7.93

Support

CD → PH

H5c: +

0.57

7.58

Support 0.42

9.23

Support

PH → FI

H6a: +

0.53

5.81

Support 0.44

6.42

Support

PH → CI

H6b: +

0.56

9.84

Support 0.51

13.95

Support

Moderating H7a:十 effect (1)

0.13

1.59

No

0.15

1.14

No

Moderating H7b: + effect (2)

0.46

6.75

Support 0.43

9.64

Support

Moderating H7c: + effect (3)

0.54

2.82

Support 0.57

4.59

Support

of this study are valid (Among them, the parameter estimation results based on the confirmatory sub-sample are shown in Table 5.8).

5.4.3.2

Testing Results

The results are shown in Table 5.8. It can be seen from Table 5.8 that in the main effect correlation hypothesis test, all path coefficients reach the 0.05 significant level, and the path coefficients are all between 0 and 1, indicating that the estimated values are all valid. At the same time, according to the positive and negative of each path coefficient, it shows that the ten hypotheses from H1 to H6b are all supported. In the correlation hypothesis test of the moderating effect, the absolute |t| value of the significance test of the interaction term coefficient in Model 1 is less than 1.96, which does not reach the significance level of 0.05, indicating that the moderating effect of HR between PF and PH is not significant, that is, hypothesis H7a is not supported. In Model 2 and Model 3, the |t| value of the significance test of each coefficient is greater than 1.96, reaching a significant level of 0.05. Combined with the positive and negative coefficients of each interaction term, it can be seen that HR has positive moderating effect between CA and PH and between CD and PH. Therefore, both H7b and H7c are supported.

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5.5 Cluster Analysis User information sharing behavior on Weibo always exhibits a habit effect, as being affected by the user’s living habits, thus presenting autocorrelation and periodic characteristics. However, people’s habits and characteristics will be different because of their gender, age, educational background, occupation and other individual differences. In addition, the persuasive effect and decision-making behavior of the information on the recipient will also be affected by the difference in demographic variables, which is manifested in that different information user groups will form different attitudes and behavior characteristics for the same information (e.g. Al-Suqri, 2015; Park, 2015). Therefore, in order to have a deeper understanding of the autocorrelation, periodicity, and differences of information behaviors of Weibo users, it is necessary to further understand the differences in user groups of different genders, ages, education backgrounds, and occupations based on the verification of the above theoretical framework, comparison and analysis.

5.5.1 Population Sample Based on the validated correct model, the validated model is estimated based on the overall sample data, which is used to analyze the overall path coefficients. Among them, the X 2 value of the model fit test is 8.102, and the corresponding test p value is 0.56 > 0.05, so the original hypothesis cannot be rejected, indicating that the modified model can better fit the overall sample data. At the same time, the output adaptability indicators are: X 2 value is 872.64, df value is 407 (X 2 /df = 2.14), NFI value is 0.91, GFI value is 0.96, AGFI value is 0.94, CN is 692, RMR value is 0.09 (Not up to the standard), the CFI value is 0.93, and the RMSEA value is 0.031. In all the fitness indicators, except for the RMR value that did not meet the standard, the other indicators all reached the acceptable reference value. At the same time, the output results did not provide any suggestive parameters that need to be corrected, indicating that the model as a whole can be better fit the overall sample data.5 The sample data is adopted without correction. The estimated results are shown in Fig. 5.8. Figure 5.8 shows that the total effect of IV, IS, and IA through various intermediary variables on users’ FI are: 0.22, 0.13, 0.23. The total effect of IV, IS, and IA through the mediating variables on the user’s CI are: 0.23, 0.14, and 0.24, respectively. It can be seen that, in the influence of various information contextual factors on the FI and CI, the order of the magnitude of the influence is: IS, IV, and IS. The influence of the contextual factors on CI is greater than the impact on FI. The moderating effect of HR is estimated based on the overall sample data, and the results are shown in Table 5.9 (because the moderating effect in Model 1 is not significant, it has been removed from the estimation). 5

Xue et al. (2015).

5.5 Cluster Analysis

181

Fig. 5.8 Coefficient values of the main effect path of the model

Table 5.9 Moderating effect Model 2 (Dependent variable: PH)

Model 3 (Dependent variable: PH)

Ind. variable

β coefficient

t value

Ind. variable

β coefficient

CA

0.56

9.47

CD

0.52

8.94

HR

0.42

2.63

HR

0.49

11.27

CA × HR

0.49

6.19

CD × HR

0.56

5.81

t value

Table 5.9 shows that the interaction coefficients in Model 2 and Model 3 are both significant, and the moderating effect of HR between CA and PH is smaller than the moderating effect between CD and PH.

5.5.2 Gender Group Using user gender as a nominal variable, the total sample data are put into a “male group” and a “female group” to estimate the validated model. According to the SEM multi-group model, the identifiable condition degree of freedom: df = [(p + q)(p + q + 1) × G/2] + K − t which is greater than or equal to 0. Here, p is the number of externally observed variables, q is the number of internally observed variables, G is the number of groups, and K is the average of all waiting to be estimated, and t is the sum of the number of free parameters of each group model. From this, the df of the gender group model can be calculated as df = 1016 > 0, indicating that the group model can be identified. Based on the gender group data, the main effect model and the moderating effect model are estimated, and the results are shown in Tables 5.10 and 5.11 respectively.

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Table 5.10 Fit test of the gender group model Group category

Fit index p(χ2)

X 2 /df

GFI

AGFI

RMR

RMSEA

NFI

CFI

CN

Fit standard

> 0.05

< 3.00

> 0.90

> 0.90

< 0.05

< 0.08

> 0.90

> 0.90

> 200

Male

0.21

0.46

0.95

0.87

0.01

0.06

0.82

0.91

483

Female

0.21

0.46

0.95

0.87

0.01

0.06

0.82

0.91

483

Table 5.11 Results of gender group analysis

Path

Standardization Coefficient Male

Female

IV → FI

0.24

0.20

IV → CI

0.26

0.21

IS → FI

0.15

0.12

IS → CI

0.16

0.12

IA → FI

0.26

0.21

IA → CI

0.27

0.23

Moderating effect 2 (CA → PH)

0.50

0.45

Moderating effect 3 (CD → PH)

0.57

0.54

Table 5.10 shows that, the NFI value of 0.82 and the AGFI value of 0.87 fall below the fit standard, while all other fit indexes meet the standard value. On the whole, the model can fit well with the sample data of each gender group. At the same time, Table 5.11 shows that the estimated value of each path coefficient is between 0 and 1, and the p value of the significance test of each coefficient reaches the significance level of 0.05, indicating that the model parameter estimation result is valid, and the modified model has cross gender validity.6 According to the absolute value of each path coefficient, it can be seen that IV, IS and IA have more influence on FI and CI in men than in women. As for the moderating effect of HR on CA and PH, it is greater with the male group than the female group.

5.5.3 Age Group Using user age as a nominal variable, the sample data is divided into four age groups: 29 years old and younger, 30–39 years old, 40–49 years old, and 50 years old and older. The degree of freedom of the age group model is calculated to be 2,032 > 0, indicating that the group model can be identified. The main effect model and 6

Xue et al. (2015).

5.5 Cluster Analysis

183

Table 5.12 Fit test of the age group model Group category

Fit index p(χ2)

X 2 /df

GFI

AGFI

RMR

RMSEA

NFI

CFI

CN

Fit standard

> 0.05

< 3.00

> 0.90

> 0.90

< 0.05

< 0.08

> 0.90

> 0.90

> 200

≤ 29

0.94

0.03

0.98

0.94

0.24

0.03

0.97

0.95

30 - 39

0.94

0.03

0.98

0.94

0.24

0.03

0.97

0.95

346

40- 49

0.94

0.03

0.98

0.94

0.24

0.03

0.97

0.95

346

≥ 50

0.94

0.03

0.98

0.94

0.24

0.03

0.97

0.95

346

346

Table 5.13 Results of age group analysis Path

Standardization Coefficient ≤ 29

30- 39

40- 49

≥ 50

IV → FI

0.21

0.25

0.23

0.19

IV → CI

0.23

0.26

0.25

0.21

IS → FI

0.13

0.18

0.15

0.11

IS → CI

0.15

0.19

0.18

0.13

IA → FI

0.22

0.23

0.24

0.21

IA → CI

0.25

0.28

0.26

0.22

Moderating effect 2 (CA → PH)

0.48

0.53

0.51

0.46

Moderating effect 3 (CD → PH)

0.55

0.59

0.57

0.52

moderating effect model are estimated based on age group data, and the results are shown in Tables 5.12 and 5.13 respectively. Table 5.12 shows that the RMR value of 0.24 in each group model does not meet the fit standard. All other fit indexes meet the standard value. On the whole, the model can fit well with the sample data of each age group. At the same time, Table 5.13 shows that the estimated value of each path coefficient is between 0 and 1, and the p value of the significance test of each coefficient reaches the significance level of 0.05, indicating that the model parameter estimation result is valid, and the modified model has a cross-age validity. According to the absolute value of each path coefficient, among the influences of IV, IS and IA on FI and CI, the “30~39 years old” user group has the greatest impact, followed by “40~49 years old” and “29 and under” age group. The last is the “50 years old and above” age group. The order of the moderating effect of HR on CA and PH and on CD and PH is: “30~39 years old”, “40~49 years old”, “29 years old and below” and “50 years old and above”.

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5.5.4 Education Group Using the user’s educational background as a nominal variable, the sample data is divided into four educational background groups: “university and above”, “high school or technical secondary school”, “junior high school”, and “primary school and below”. The degree of freedom of the educational background group model df is 2,032, which is greater than 0, indicating that the group model can be identified. The main effect model and the moderating effect model are estimated based on the education group data, and the results are shown in Tables 5.14 and 5.15 respectively. Table 5.14 shows the RMSEA value is 0.81 and the CFI value is 0.79 in each group model, which do not meet the fit standard. All other fit indexes meet the standard value. On the whole, the model can perform well. Adapt to the sample data of each academic group. At the same time, Table 5.15 shows that the estimated value of each path coefficient is between 0 and 1, and the p-value of the significance test of each coefficient reaches the significance level of 0.05, indicating that the model parameter estimation results are valid, and the modified model has cross-degree validity. According to the absolute value of each path coefficient, it can be seen the impact of IV, IS and IA on FI and CI on the “university and above” user group is the highest, followed by “senior high school” user group and “junior high school”, user group and the lowest is with the user group of “primary school and below”. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is the same.

5.5.5 Occupational Group Using user occupation as a nominal variable, the sample data is divided into four occupational groups: “enterprise”, “public institution”, “government agency”, and “self-employed”. The degree of freedom of the occupational group model is 2,032, which is greater than 0, indicating that the group model can be identified. The main effect model and the moderating effect model are estimated based on the occupational group data, and the results are shown in Tables 5.16 and 5.17 respectively. Table 5.16 shows that the GFI value in each group model is 0.65, which does not meet the fit standard. All other fit indicators meet the standard value. On the whole, the model fits each occupation group well. The estimated value of each path coefficient is between 0 and 1, and the p-value of the significance test of each coefficient reaches the significance level of 0.05, indicating that the model parameter estimation result is valid, and the modified model has cross-occupational validity. According to the absolute value of each path coefficient, it can be seen the impact of IV, IS and IA on FI and CI on the “enterprise” user group is the highest, followed by the “public institution” user group, and then the “government agency” user group. The “selfemployed” user group is the lowest. The order of the moderating effect of HR on CA

0.41 0.41 0.41 0.41

Senior high school or technical secondary school

Junior high school

Elementary school and below

> 0.05

p(χ2)

Fit index

University and above

Fit standard

Group category

Table 5.14 Fit test of the education group model

0.06

0.06

0.06

0.06

< 3.00

V/df

0.94

0.94

0.94

0.94

> 0.90

GFI

0.92

0.92

0.92

0.92

> 0.90

AGFI

0.02

0.02

0.02

0.02

< 0.05

RMR

0.81

0.81

0.81

0.81

< 0.08

RMSEA

0.95

0.95

0.95

0.95

> 0.90

NFI

0.79

0.79

0.79

0.79

> 0.90

CFI

268

268

268

268

> 200

CN

5.5 Cluster Analysis 185

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5 Static Influencing Mechanism of Weibo Users’ …

Table 5.15 Results of education group analysis Path

Standardization Coefficient University and above

Senior high school or technical secondary school

Junior high school

Elementary school and below

IV → FI

0.25

0.22

0.20

0.18

IV → CI

0.27

0.24

0.21

0.21

IS → FI

0.15

0.13

0.12

0.10

IS → CI

0.18

0.15

0.13

0.12

IA → FI

0.26

0.24

0.22

0.20

IA → CI

0.28

0.25

0.24

0.22

Moderating effect 2 (CA → PH)

0.52

0.50

0.48

0.46

Moderating Effect 0.59 3 (CD → PH)

0.58

0.55

0.53

Table 5.16 Fit test of the occupation group model Group category

Fit index p(χ2)

x 2 /df

GFI

AGFI

RMR

RMSEA NFI

CFI

Fit standard

> 0.05

< 3.00

> 0.90

> 0.90

< 0.05

< 0.08

> 0.90

> 0.90 > 200

Government agency

0.17

0.15

0.65

0.96

0.01

0.04

0.91

0.97 294

Public institution

0.17

0.15

0.65

0.96

0.01

0.04

0.91

0.97 294

CN

Enterprise

0.17

0.15

0.65

0.96

0.01

0.04

0.91

0.97 294

Self-employed

0.17

0.15

0.65

0.96

0.01

0.04

0.91

0.97 294

Table 5.17 Results of occupation group analysis Path

Standardization Coefficient Government agency

Public institution

Enterprise

Self-employed

IV → FI

0.21

0.23

0.24

0.20

IV → CI

0.22

0.24

0.25

0.21

IS → FI

0.12

0.14

0.15

0.11

IS → CI

0.13

0.15

0.16

0.12

IA → FI

0.22

0.24

0.25

0.21

IA → CI

0.23

0.25

0.26

0.22

Moderating effect 2 (CA → PH)

0.48

0.50

0.51

0.47

Moderating effect 3 (CD → PH)

0.55

0.57

0.58

0.55

5.5 Cluster Analysis

187

and PH and on CD and PH from high to low is: “enterprise”, “public institution”, “government agency”, and “ self-employed”.

5.6 Conclusion and Discussion 5.6.1 Conclusion The scale design and data collection process of this study have good reliability and validity. On this basis, the main effect hypothesis and the moderating effect hypothesis of the theoretical model are tested by means of the structural equation model and the multi-step regression. The following conclusions are drawn. First, the impact of IV on FI and CI through the three intermediate variables PF, CA and PH is significantly positive. The impact of IS on FI and CI through the two intermediate variables CA and PH is significantly positive. The impact of IA on FI and CI through the two intermediate variables CD and PH is significantly positive. Second, the positive moderating effect of HR on CA and PH and on CD and PH is significant; whereas, the positive moderating effect of HR on PF and PH is not significant. Third, the analysis of the overall sample data shows that the influence of each information contextual factor on FI and CI in order from big to small is: IA, IV, and IS. The contextual factors have greater impact on FI, compared with that of CI. The moderating effect of HR between CA and PH is less than that between CD and PH. Fourth, through the analysis of the relevant groups, it is found that the theoretical model has cross gender, cross age, cross educational level and cross occupation validity, which shows that the theoretical model is stable and there are differences in each influence path among different groups. Fifth, in terms of gender differences, IV, IS and IA have more influence on FI and CI among men than women. The moderating effect of HR between CA and PH and between CD and PH is also greater in men than in women. Sixth, in terms of age difference, IV, IS and IA have the greatest influence on FI and CI upon user group of “30 to 39 years old”. The second is the “40 to 49 years old” age group. The third is the “29 years and under” age group. The last is the “50 years and older” age group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “30 to 39 years old,” “40 to 49 years old,” “29 years and under,” and “50 years and older”. Seventh, in terms of educational differences, IV, IS and IA have the greatest influence on FI and CI upon the “university and above” education group. The second is the “senior high school or secondary technical school” education group. The third is the “junior high school” education group. The last is the “primary school and below” education group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “university and above” education group,

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“senior high school or secondary technical school” education group, “junior high school” education group and “primary school and below” education group. Eighth, in terms of occupational differences, IV, IS and IA have the greatest influence on FI and CI upon the “enterprise” group. The second is the “public institution” group. The third is the “government agency” group. The last is the “self-employed” group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “enterprise” group, “public institution” group, “government agency” group, and “self-employed” group. Through the combination and application of information processing theory (the ELM model and the HSM model) and information behavior theory in the field of information behavior research of social media users, this research has obtained some new discoveries and drawn certain conclusions of the static influence mechanism of user information behavior on Weibo. It can provide some guidance for the further study on user information behavior mechanism and for improving the prediction and theoretical system of user information behavior. It also helps to enhance the deepening and development of network user information behavior research guided by information processing theory and information behavior theory.

5.6.2 Discussion The influence mechanism of IV, IS and IA on FI and CI reflects the whole dynamic process from the formation of information cognition to the generation of behavior of Weibo users. First, different contextual factors have different physiological stimulation on users, which in turn affects their perceptual properties, and finally transmits to their behavior intention. In this influence mechanism, the information contextual factors directly act on the user’s psychological variables, which lead to user’s behavior, indicating that the information sharing behavior is influenced by specific contextual factors, and there are different dynamic mechanism processes in every link in information behavior (Niedzwiedzka, 2003). User information behavior always occurs in a specific time and space and is the result of the interaction of the user’s personal and environmental factors (Lewin, 1951). Contextual factors do not exist in isolation. They are made up of many sub-environments and multiple factors, all of which constitute a huge environmental complex, which creates a social atmosphere that enables users in the environment to develop a spontaneous or accidental information sharing behavior (Fisher, 2006). Through the study of the static influence mechanism of user information sharing behavior under the guidance of the information context theory and information processing theory, the author reveals the influence path of IV, IS and IA on Weibo users’ reposting and commenting behavior respectively, and discovers the specific action path and function size of the independent variables on the dependent variables, which reflects the whole process of user’s behavior from information receiving to information processing to changing of attitude. On this basis, the differences in influence effects between different groups of gender, age, educational level and occupation

5.6 Conclusion and Discussion

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are compared. The research shows that in the process of information search, information reading and information sharing by Weibo users, information in different forms, different content characteristics and different sources will lead to the difference of user’s intention and efforts to process information, thus exerting different persuasion effects on users through different physiological stimulation and perception paths. Both user information behavior and dynamic mechanism are affected by various factors, including psychological characteristics, demographic characteristics, information characteristics, environmental characteristics and information source characteristics. In all hypothesis tests, only H7a is not supported, i.e. the moderating effect of HR between PF and PH is not significant. This is because in a brand crisis, PH can be significantly affected by HR itself as an independent variable (Dawar & Lei, 2009). When PF and HR have an effect on PH, the two variables remain independent of each other and have an effect on PH. The significant effects come from the effect of HR as an independent variable on PH, rather than the effect of PF and HR. In the analysis of different user groups, the influence of IV, IS and IA on FI and CI is different among user groups of different demographic statistical features. This may be caused by the differences in the social characteristics and social roles possessed by different user groups. Of all the social factors, user-related factors are one of the most important ones affecting people’s information search and sharing behavior. The user’s individual characteristics usually include gender, age, occupation, education level, information literacy, personality characteristics and cognitive style (Fisher, 2005). Among them, the user’s occupation and work structure is the most fundamental factor that determines the user’s information needs and behavior. Occupation is an important factor for a person to survive and thrive in society, and people in different occupational fields are bound by different rules and behavior regulations. Occupation has different social functions and plays a differentiated social role. Occupation also reflects a particular interest relationship that people form in society, so that in the conduct of information behavior, due to certain explicit or implicit constraints or forced by a certain perspective of interest, people in different occupational groups tend to form different perceptions and attitudes towards the same thing, resulting in differentiated behavioral characteristics (Bayles, 1989; Camenisch, 1983; Durkheim, 2014; House, 1993). In terms of gender differences, there are physiological differences as well as cultural differences caused by different social roles. Men and women are different in social status and social roles, and society has different role expectations and evaluation of different genders, leading to a series of differences in behavior norms, social roles, gender stratification and so on between men and women in general. The physiological differences in the gender groups have formed different thinking patterns and behavior habits between men and women through the regulation of social norms and social system and the subtle influence of the gender culture over the years (Benedict, 1934; Mead, 1977; Modleski, 2014; Oakley, 2015). In terms of user age, it is usually closely related to a person’s physical and intellectual development, and it also reflects the richness of their social experience. Age may reflect the degree of maturity of the mind. Influenced by social norms, social cultures and customs, people of different ages play different social

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roles and assume different social responsibilities, all of which have important effects on their thinking patterns, cognitive patterns, and behavioral characteristics (Fraser et al., 2000; Jonler et al., 1995). Finally, in terms of user educational level, education and learning are the process by which people can change and reshape their thinking and cognitive ways. One’s educational level is represented by how much education one has received in society. Highly educated people have more scientific knowledge and more formal training. When compared with people of low educational level, they are often more prudent and more scientific in thinking and cognition. Thus, people of different educational levels show certain differences in cognition, attitude and behavior (Bourdieu & Passeron, 1990; Carret et al., 2003; Ottaway, 2013).

5.7 Summary The static influence mechanism of the information sharing behavior of Weibo users in brand crisis is studied in this chapter. Based on the relevant theories and related research results, the author first puts forward the research hypotheses and research framework. The research data are obtained by official APIs and web crawlers. Random sampling of users who have participated in brand crisis information reposting or commenting on Weibo is conducted, followed by the implementation of the online questionnaire. SPSS 22.0 and AMOS 22.0 are used to process and analyze the sample data and test the research hypotheses, in order to explore the influence path of IV, IS and IA on FI and CI and to discover the differences in the impact effect. On top of this, the sample data of each group are fitted and estimated respectively, and the different characteristics of each influence path coefficient in different gender, age, educational level and occupation groups are analyzed in order to reveal the different influence of static contextual factors on the reposting and commenting behavior of different types of user groups. The research findings can be applied to monitoring and managing Weibo users’ information sharing behavior in brand crisis. They can be used to identify what kind of contextual characteristics is more likely to lead to users’ reposting and commenting behavior, and which gender, age, education and occupation type of user group is more likely to repost and comment on crisis information. Thus, targeted information sharing behavior monitoring and response strategies can be formulated to improve the efficiency and effectiveness of brand crisis management on Weibo.

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Chapter 6

Dynamic Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases

According to general information behavior theory, user’s information behavior is an orderly cycle process, with information need as the starting point and information utilization as the end. Information need is the focus of the whole process of information behavior and, when they seek and utilize information, individuals will be affected by various interference factors, which may facilitate or hinder their search results and their utilization behavior (Wilson, 1999). What is more, the integrated model of information behavior holds that people’s information behavior rarely happens in a completely independent environment. Instead, it is usually accompanied by other people’s information behavior and its interaction and interrelationship with other people’s information behavior will in turn have significant impact and interference on the user’s own information behavior. The model also assumes that information behavior always occurs in certain situations, that is, users’ information behavior is the product of specific contexts; and there is an activating mechanism in all aspects of the process of information behavior. (Niedzwiedzka, 2003). However, information use environment (IUE) theory holds that the environment of information use can encourage users to develop information needs and drive them to actively carry out information search, query and utilization behaviors. It is the starting point of all information behaviors, such as generation of information needs, information search, evaluation and utilization (Taylor, 1986a). Through the analysis of IUE, combined with internal and external information, a series of activities such as the use of information resources, decision-making, proposal and improvement of measures can be implemented. In IUE, users seek and acquire information that is valuable to them at a specific time according to their own information needs. Various factors in turn can have a significant impact on the user’s information screening and selection. In other words, the flow, transmission and utilization of information among users are affected by IUE, which can be used to determine the usefulness and the value of information. (Taylor, 1996). Meanwhile, the theory of information horizons emphasizes three basic concepts: social network, situation and context, and holds that human information behavior is composed of individuals, situations, contexts and social networks and that individuals can perceive changes in © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_6

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their environment and then evaluate and react to the changes (Sonnenwald, 1999). Users usually seek, obtain and use information within their information horizon, and the information seeking process is one where individuals constantly adjust themselves to interact and coordinate with information resources (Trusina et al., 2004). In contrast, information ground theory assumes that the environment around people does not exist independently, but is a huge community composed of a variety of influencing factors, which affect and interact with each other. From the particular ground where people come together for a certain purpose emerges a social atmosphere that fosters the spontaneous sharing of information (Fisher, 1999); (Fisheret al., 2006). In that information ground, three major factors, namely people, place and information, have significant impact on information sharing behavior. Because the information ground is composed of all the explicit and implicit elements in the place, the physical and social attributes of the place will have an important influence on the direction and intensity of people’s information sharing behavior. The basic conditions of the place may promote or inhibit people’s information seeking and information sharing behavior, thus affecting the effect and willingness of people to exchange or share information. As for the information factors, information characteristics such as frequency of information reposting and commenting, subject content, topical issues, information source, information credibility, accessibility and familiarity all have an important impact on people’s information sharing behavior (Fisher & Julien, 2009; Ma & Wang, 2014). As for the microblog information platform Weibo, in general users’ information behavior concerns their use of the platform to obtain, disseminate and share information, principally the behavior of following, being followed, reposting and commenting. Users acquire information by following other people’s microblogs, spread their own information by being followed by others, and share information by reposting and commenting on other people’s microblogs (Zhao & Zhang, 2013a). In previous relevant studies, many scholars have studied and explored the influencing mechanisms of microblog users’ reposting and commenting behaviors from different perspectives, laying a foundation for further studies of users’ information sharing behavior and its influencing mechanisms. These studies generally can be divided into two categories. The first category mostly employs theories of complex networks or social networks to construct models of and analyze the reposting and commenting behaviors of microblog users. Studies under this category can clearly reveal the hidden network relationships, the key users, and the relationships between users in the information reposting and commenting behaviors, and can predict users’ information sharing behavior.1,2,3 However, currently the theoretical foundation for research on microblog users’ information behavior is not yet well-developed, so such studies, directly applying traditional theories to studying microblog users’ information behavior, lacks sufficient underpinning. The other category of research uses

1

Yan et al. (2013). Goncalves et al. (2011). 3 Zhao and Zhang (2013b). 2

6 Dynamic Influencing Mechanism of Weibo …

199

correlation analysis, variance analysis and regression models to explore the influencing factors, influencing paths and process characteristics of user reposting and commenting behavior. Studies in this category can generally only draw conclusions about the significance, size and direction of the influence of independent variables on dependent variables.4,5,6 However it is difficult for them to establish the specific path of the influence of the independent variables on dependent variables and the dynamic change process of path size; in other words, they get to know hows but not the whys. In these studies, because the corresponding independent variables and dependent variables exist in time series, their relationships and their positive and negative effects at different time points are constantly changing, that is to say, the relationship between variables is dynamic. It is difficult to come to a dynamic description of the influence of independent variables on dependent variables and grasp the dynamic influencing process between the variables through correlation analysis and regression analysis based only on a certain time point or on the sum of a certain point in time. Generally speaking, most of the past literature studies the influencing mechanisms of information behavior from a static perspective, while there is still a lack of literature on the influencing mechanisms from a dynamic perspective (the static perspective means that the relationship of variables analyzed does not change with time, and the dynamic perspective means that the relationship of variables analyzed changes over a certain period of time). In particular, this is lacking in studies of the dynamic influencing mechanisms of information sharing behavior from the perspective of contextual factors: what are the real-time dynamic response characteristics of information sharing behavior when affected by external factors; what are the hysteresis characteristics of the influence; and how are the dynamic changes of the contribution ratio of relevant factors to behavior fluctuation, etc. At present there is no literature on such research content. Therefore, research on the dynamic influencing mechanism of information behavior needs to be improved in both scope and depth. In this context, this chapter explores and studies the influencing mechanism of contextual factors of Weibo users’ information sharing behavior in brand crisis from the perspective of dynamic analysis, based on the collection of data, using time series analysis methods such as vector autoregression (VAR) model and state space model, constructing models in light of the objective characteristics of the data. The research framework of this chapter is shown in Fig. 6.1.

6.1 Impact of Total Number of Reposts and Comments In order to understand the characteristics of the relationship between the relevant variables so as to ensure the correctness of the VAR model construction and estimation, 4

Xiao and Zhu (2013). Che and Wang (2013). 6 Zhang and Zhao (2015). 5

200

6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.1 Structure of this chapter

it is necessary to build an exploratory model to test and identify the Granger causality between the variables and the hysteresis characteristics of the endogenous variables before formally constructing the VAR model. In the Granger causality test, different settings of time lags will have an important impact on the test results, so it is necessary, before carrying out the test, to analyze the correlation between the time series to ensure the accuracy of the test (Karlin et al., 2014). First, the cross-correlations between the total numbers of reposts and comments and the numbers of reposts and comments is analyzed, so as to understand the correlation between the endogenous variables and the time-lag characteristics of mutual impact, thus improving the effectiveness of the exploratory model. The corresponding cross-correlations spike diagrams are shown in Figs. 6.2, 6.3, 6.4 and 6.5. Figures 6.2, 6.3, 6.4 and 6.5 show that the correlation degree between the total number of reposts and comments and the number of reposts and comments respectively exhibits slow decline with the increase of time lag. The cross-correlations coefficients between the numbers of reposts and comments are greater than 0.50 within Lag 3. It can be preliminarily interpreted that the impact of the total numbers of reposts and comments on the behaviors of reposting and commenting was more obvious within Lag 3. According to the characteristic of hysteresis correlation between variables, the lag number can be set to three in the construction of exploratory models of the number of reposts and the number of comments to estimate the model, and then use that as a basis to identify and test the variable structure and lag standard in the formal VAR model.

6.1 Impact of Total Number of Reposts and Comments

201

Fig. 6.2 Cross-correlations between the number of reposts and the total number of reposts

Fig. 6.3 Cross-correlations between the number of reposts and the total number of comments

6.1.1 Causality Test The study identifies and tests the variable structure of the formal model, and analyzes whether there is significant Granger causality between the total numbers of reposts and comments and the number of reposts and the number of comments respectively, so as to determine whether it is reasonable to have a model structure setting where the two variables—the total number of reposts and the total number of comments—are

202

6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.4 Cross-correlations between the number of comments and the total number of reposts

Fig. 6.5 Cross-correlations between the number of comments and the total number of comments

introduced into the equation of the number of reposts and the number of comments. The corresponding Granger causality test results are shown in Table 6.1. As can be seen in Table 6.1, in the exclusion test of causality between the total numbers of reposts and comments and reposting behavior, the corresponding p values of the χ 2 test of the total number of reposts, the total number of comments and the combination of the two variables are less than the significant level of 0.05, indicating that the original hypothesis that there is no causality can be rejected. Therefore, the total number of reposts and the total number of comments cannot be excluded from

Commenting behavior model

Reposting behavior model

χ 2 value

13.035

11.099

28.059

Independent variable

Total number of reposts

Total number of comments

Both independent variables

15.178

30.050

Total number of comments

Both independent variables

6

3 0.000

0.002

0.000

24.296

Total number of reposts

3

Commenting behavior

0.000

0.011

0.005

P value

Dependent variable

6

3

3

df

Reposting behavior

Dependent variable

Causality exclusion test

Table 6.1 Granger causality test

38.159

20.745

8.278

χ 2 value

Both independent variables

Total number of comments

Commenting behavior

31.704

5.669

26.591

Total number of reposts

Both independent variables

Total number of comments

Reposting behavior

Independent variable

Total number of reposts

6

3

3

6

3

3

df

0.000

0.129

0.000

0.000

0.000

0.041

P value

18.926

10.362

12.667

χ 2 value

Both independent variables

Commenting behavior

Total number of reposts

10.407

29.016

6.265

Total number of comments

Both independent variables

Reposting behavior

Total number of reposts

Independent variable

Total number of comments

6

3

3

6

3

3

df

0.109

0.000

0.099

0.004

0.016

0.005

P value

6.1 Impact of Total Number of Reposts and Comments 203

204

6 Dynamic Influencing Mechanism of Weibo …

the equation related to reposting behavior, that is, the total number of reposts and the total number of comments are the Granger causes of reposting behavior. In the exclusion test of causality between the total numbers of reposts and comments and commenting behavior, the corresponding p values of χ 2 test of the total number of reposts, the total number of comments and the combination of the two variables are also less than the significant level of 0.05, indicating that the original hypothesis that there is no causality could be rejected. Therefore, the total number of reposts and the total number of comments can not be excluded from the equation related to the commenting behavior, that is, the total number of reposts and the total number of comments are the Granger causes of the commenting behavior. What’s more, the p values of other related tests are almost all less than the significant level of 0.05, so the selection of endogenous variables in the construction of the VAR model is effective.

6.1.2 VAR Model Construction Based on the Granger causality test of the VAR model, in order to further understand the length of lag time of the total numbers of reposts and comments on reposting behavior and commenting behavior, it is necessary to do a statistical analysis and determine the lag length on the VAR model. The corresponding statistical results are shown in Table 6.2. According to LR (likelihood ratio) test and the information minimization criterion of FPE, AIC, SC and HQ (Dhrymes, 2012), it can be seen from Table 6.2 that the optimal lag lengths of the VAR models for reposting behavior and commenting behavior are both 3. Based on the above causality tests and time lag analysis, the VAR models of reposting behavior and commenting behavior are set and estimated respectively. The corresponding model form is as follows: ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎤ b11 b12 b13 Behavior a1 Behavior ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎥⎢ ⎣ T otal Z ⎦ = ⎣ a2 ⎦ + ⎢ ⎣ b21 b22 b23 ⎦⎣ T otal Z ⎦ + · · · T otal P a3 T otal P b31 b32 b33 t t−1 ⎤⎡ ⎡ ⎤ ⎡ ⎤ σ11 σ12 σ13 Behavior ε1 ⎥⎢ ⎢ ⎥ ⎥ ⎢ ⎥ +⎢ ⎣ σ21 σ22 σ23 ⎦⎣ T otal Z ⎦ + ⎣ ε2 ⎦ T otal P ε3 σ31 σ32 σ33 t−k ⎡

(6.1)

Behavior means reposting and commenting behavior, TotalZ refers to total number of reposts, TotalP refers to total number of comments, k is lag order for endogenous variables, and εi is random error term. In order to determine the correctness of the VAR model construction and setting, it is necessary to test the stability of the model. The stability test usually compares the inverse roots of AR characteristic polynomial with the value 1. If it is less than

69.62954 84.22172 94.04574 103.0098 1066.901

2

3

4

5

34.32018

5

1

30.89475

4 8.394746

22.02593

3

0

27.72507

2

lag order selected by the criterion LR sequential modified LR test statistic (each test at 5% level) FPE final prediction error AIC Akaike information criterion SC Schwarz information criterion HQ Hannan-Quinn information criterion

* Indicates

Commenting behavior VAR model

24.78293

1

2.140894

0.00137

0

NA

4.64E-07

8.97e-08*

3.36152

1.54E-07

91.85219*

1.28E-07

0.000102

7.368017

16.4162

NA

0.00092

0.000467*

2.773466

0.0007

69.90830*

0.000564

0.060743

FPE

3.309905

3.790876

NA

−20.99456

0

Reposting behavior VAR model

LR

LogL

Lag

Model

Table 6.2 Selection criteria of lag length

− 6.888692

−7.902714

−6.557113 −6.118039

−8.005717 −8.001224

−125.0448*

−6.624251

−7.203693 −127.3626*

−0.477713

−0.742681

−1.611843

−0.529483

−1.713521*

−2.003242* −1.540022

−1.039619

−1.715634

−0.674343

2.970894 −1.364999

2.87432

SC

−1.847867

AIC

2.879265

−7.904789

−7.931537

− 127.2439*

−7.850788

−7.17402

−0.666925

−1.485623

−1.567335

− 1.988405*

−1.681016

−1.82314

HQ

6.1 Impact of Total Number of Reposts and Comments 205

206

6 Dynamic Influencing Mechanism of Weibo …

the value 1, it indicates that the set VAR model is stable; otherwise, it indicates that the set VAR model is unstable and needs to be reset and reconstructed (Asteriou & Hall, 2011). The stability test results of the model are shown in Figs. 6.6 and 6.7. The dots in the unit circle in Figs. 6.6 and 6.7 represent the modulus of inverse roots of AR characteristic polynomial. If these dots fall inside the unit circle, it indicates that the VAR model is stable; if some dots fall on the unit circle, it indicates that the model is unstable. Figures 6.6 and 6.7 show that, in the models of reposting behavior and commenting behavior, the dots representing the modulus of inverse roots of AR characteristic polynomial fall inside the unit circle, indicating that the constructed models meet the stability requirements, and that the set models are correct and need not be rebuilt. The estimated results of the corresponding models are shown in Table 6.3 (only the estimated values of parameters related to this study are listed below). Fig. 6.6 Reposting model inverse roots of AR characteristic polynomial and unit circle (left)

Fig. 6.7 Commenting model inverse roots of AR characteristic polynomial and unit circle (right)

6.1 Impact of Total Number of Reposts and Comments

207

Table 6.3 Parameter estimation and test results of VAR models Independent variable

Dependent variable Number of reposts

Number of comments

Coefficient

T test value

Coefficient

T test value

Number of reposts (−1)

0.1066

−3.2410

N/A

N/A

Number of reposts (−2)

0.1839

6.5434

N/A

N/A

Number of reposts (−3)

0.0039

8.2481

N/A

N/A

Number of comments (−1)

N/A

N/A

0.4089

−4.0285

Number of comments (−2)

N/A

N/A

0.0574

14.0374

Number of comments (−3)

N/A

N/A

−1.1292

−10.5573

Total number of reposts (−1)

0.1014

−3.2722

0.9242

6.2608

Total number of reposts (−2)

0.2610

−6.0038

0.6501

−9.3677

Total number of reposts (−3)

−0.0001

−7.0008

0.1431

4.1933

Total number of comments (−1)

0.2553

9.8459

0.1906

−6.1910

Total number of comments (−2)

0.0617

12.6211

0.1789

3.2287

Total number of comments (-3)

−0.0006

−5.0082

−0.1523

18.4301

C

5.4448

6.6952

2.4230

5.5152

R-squared

0.9977

0.9975

Adj. R-squared

0.9951

0.9947

Sum sq. resids

0.0012

0.0025

S. E. equation

0.0122

0.0179

F-statistic

387.7589

360.1606

Log likelihood

60.9803

54.1168

Akaike AIC

−5.6644

−4.9018

Schwarz SC

−5.1698

−4.4072

Mean dependent

12.0548

11.1554

S.D. dependent

0.1758

0.2482

It can be seen from Table 6.3 that the | t | values of the significance test of each coefficient in the reposting and commenting behavior models are all greater than the critical value of 1.96, that is, the t test of each coefficient reaches the significant level of 0.05. In addition, both the coefficients of determination–the R-squared and Adj. R-squared —have high values, which indicates that the VAR model constructed has a good fit with the sample data and that the model can be used in the related research and analysis of the dynamic impact of the total number of reposts and the total number of comments on reposting behavior and commenting behavior respectively.

208

6 Dynamic Influencing Mechanism of Weibo …

6.1.3 Impulse Responses Analysis In order to reveal the dynamic disturbance characteristics of the total numbers of information reposts and comments on reposting behavior and commenting behavior, impulse response analysis was conducted on reposting behavior and commenting behavior based on VAR model estimation. Impulse response function (IRF) is used to analyze the dynamic influence of an endogenous variable’ random disturbance term on the system. Here the generalized impulse function is used for the estimation and the function form is as follows:  ψT otal (q, δ j , t−1 ) = E(yT otal,t+q εT otal, jt  = δT otal, j , T otal,t−1 ) − E(yT otal,t+q T otal,t−1 )



A T otal,q T otal, j δT otal, j = √ √ σT otal, j j σT otal, j j q = 0, 1, 2, . . . , t = 1, 2, . . . , T

(6.2)

 In the function, σT otal, j j = ET otal (ε2jt ), T otal, j = E T otal (εt ε jt ) means the jth element of εt covariance matrix , and εt comes from the error term (column vector εT otal,t of yT otal,t = 1 yT otal,t−1 + · · · + 1 yT otal,t− p + εT otal,t i is coefficient matrix, and p is lag order. Corresponding analysis results are shown in Figs. 6.8 and 6.9. It can be seen from Fig. 6.8 that when the total number of reposts is impacted by a positive shock, the impact immediately leads to the users’ reposting behavior.

Fig. 6.8 Impulse response of the number of reposts

6.1 Impact of Total Number of Reposts and Comments

209

Fig. 6.9 Impulse response of the number of comments

The response value in the first lag is about 0.18, and then decreases slowly to about 0.14 in the twenty-first lag, which indicates that the total number of reposts has a same-direction impact on the reposting behavior and that its influence effect is large in the whole transmission process. When the total number of comments is affected by a positive shock, the impact also immediately leads to the users’ reposting behavior. The response value in the first lag is about 0.10, and then decreases slowly to about 0.60 in the twenty-first lag, which indicates that the total number of comments also has the same-direction impact on the reposting behavior and that its influence effect is large in the whole transmission process. It can be concluded that the user reposting behavior is significantly affected by the herd effect, which has an important impact on the whole transmission process and that the total number of reposts has a greater impact than the total number of comments on the reposting behavior. It can be seen from Fig. 6.9 that when the total number of comments is impacted by a positive shock, the impact immediately leads to the users’ commenting behavior. The response value in the first lag is about 0.67, then declines rapidly and after two weeks starts to slowly approach 0, which indicates that the total number of comments has a same-direction impact on the commenting behavior and that its influence effect is larger in the first 14 lags. When the total number of reposts is affected by a positive shock, the impact also immediately leads to the users’ commenting behavior. The response value in the first lag is about 0.48, then decreases rapidly and after two weeks starts to slowly approach 0, which indicates that the total number of reposts has a same-direction impact on the commenting behavior and that its influence effect is larger in the first 14 lags. It can be concluded that the user commenting behavior is significantly affected by the herd effect that lasts about 14 days, and that the total

210

6 Dynamic Influencing Mechanism of Weibo …

number of comments has a greater impact than the total number of reposts on the commenting behavior.

6.1.4 Marginal Influence On the basis of the impulse response analysis of information sharing behavior, in order to further understand the marginal influence of the total number of crisis information reposts and the total number of comments on the reposting behavior and commenting behavior respectively, the state space model is used here to analyze the change process of the marginal influence of relevant factors, so as to reveal the fluctuation process characteristics of the influence effect of the total number of reposts and the total number of comments on reposting behavior and commenting behavior respectively.7 The corresponding model forms are as follows: Observation equation: ln ybehavior,t = ctotal,t + atotal_z,t ln ytotal_z,t−i + btotal_ p,t ln ytotal_ p,t− j + u total,t i = 0, 1, 2, . . . , T − 1, j = 0, 1, 2, . . . , T − 1;

(6.3)

State equation:

atotal_z,t = atotal_z + γ atotal_z,t−1 + εtotal_z,t btotal_ p,t = btotal_ p + σ btotal_ p,t−1 + εtotal_ p,t

(6.4)

In these equations, ybehavior refers to the reposting or commenting behavior, ytotal_z,t−i refers to the total number of reposts of lagged order j that has a long-run equilibrium relationship after cointegration test, u total,t is the continuous uncorrelated error term for meeting mean value E(u total,t ) = 0 and covariance matrix var(u total,t ) = Htotal,t , εtotal_z,t is the continuous uncorrelated error term for meeting mean value E(εtotal_z,t ) = 0 and covariance matrix var(εtotal_z,t ) = Htotal_z,t , εtotal_ p,t is the continuous uncorrelated error term for meeting mean value E(εtotal_ p,t ) = 0 and covariance matrix var(εtotal_ p,t ) = Htotal_ p,t . The state space model analysis results are shown in Figs. 6.10 and 6.11. It can be seen from Fig. 6.10 that the marginal influence of the total number of reposts on reposting behavior is about 0.28 at the beginning of the crisis, and then it increases rapidly, reaching the maximum value of the whole transmission process around the third day. On the fourth day, there is a brief decline, and then on the fifth day, it begins to rise. From the sixth to the ninth day, the marginal influence stays at a relatively high level. On the tenth day, it begins to decline rapidly, and there is a slight positive fluctuation on the thirteenth, the fifteenth and the nineteenth day. 7

Durbin and Koopman (2012).

6.1 Impact of Total Number of Reposts and Comments

211

Fig. 6.10 Marginal influence on the number of reposts

Fig. 6.11 Marginal influence on the number of comments

The marginal influence of the total number of comments on reposting behavior is about 0.18 at the beginning of the crisis, and then rises to the first peak of 0.48 on the third day, followed by a decline and fluctuation process. It reaches the second peak of 0.55 on the eighth day, and then enters a continuous attenuation and small fluctuation process. In the whole process, the marginal influence of the total number

212

6 Dynamic Influencing Mechanism of Weibo …

of reposts on reposting behavior is greater than that of the total number of comments on reposting behavior. As can be seen from Fig. 6.11, the marginal influence of the total number of comments on commenting behavior is about 0.58 at the beginning of the crisis, and then it fluctuates upward, reaching the maximum value of the whole communication process around the fifth day at about 1.17. On the sixth day, there is a short period of decline and then on the seventh day, it starts to rebound. From the eighth day to the eleventh day, the marginal influence stays at a relatively high level. On the eleventh day, it began to decline and there was a slight positive fluctuation on the nineteenth day. The marginal influence of the total number of reposts on commenting behavior is about 0.49 at the beginning of the crisis and then there is a slight fluctuation till the tenth day, followed by a slow decline and fluctuation process, and on the seventeenth and eighteenth days there is a major positive fluctuation. Throughout the whole process, the marginal influence of the total number of comments on commenting behavior is greater than that of the total number of reposts on commenting behavior.

6.2 Impact of the Blogger’s Number of Follows and Followers In order to better understand the time lag characteristics of the correlation between the user’s own numbers of follows and followers and reposting and commenting behaviors and the impact of the former on the latter, the cross-correlation spike diagrams showing the relationship between the respective numbers of follows and followers and the respective numbers of reposts and comments are analyzed. The corresponding cross-correlation spike diagrams are shown in Figs. 6.12, 6.13, 6.14 and 6.15. Figures 6.12, 6.13, 6.14 and 6.15 show that the respective correlations between the numbers of follows and followers and the numbers of reposts and comments all show a slow decline trend as time lag increases. The cross-correlations coefficients with the number of reposts and comments are greater than 0.50 within three lags. It can be preliminarily concluded that the influence of the number of follows and the number of followers on reposting behavior and commenting behavior is more obvious within three lags. According to the lag correlation characteristics between variables, the lag number can be set at three for estimation in the construction of an exploratory model of the number of reposts and the number of comments. On this basis, the variable structure and lag standard in the formal VAR model can be identified and tested.

6.2 Impact of the Blogger’s Number of Follows and Followers

213

Fig. 6.12 Cross-correlations between the number of reposts and the number of followers

Fig. 6.13 Cross-correlations between the number of reposts and the number of follows

6.2.1 Causality Test The study identifies and tests the variable structure of the formal model, and analyzes whether there is significant Granger causality between the numbers of follows and followers and the number of reposts and the number of comments respectively, so as to determine whether it is reasonable to have a model structure setting where

214

6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.14 Cross-correlations between the number of comments and the number of followers

Fig. 6.15 Cross-correlations between the number of comments and the number of follows

the two variables—the number of follows and the number of followers—are introduced into the equation of the number of reposts and the number of comments. The corresponding Granger causality test results are shown in Table 6.4. As can be seen in Table 6.4, in the exclusion test of causality between the numbers of follows and followers and reposting behavior, the corresponding p values of the χ 2 test of the number of follows, the number of followers and the combination of the

Commenting behavior model

Reposting behavior model

24.083

32.764

Follows

Both independent variables

8.090

24.231

Follows

Both independent variables

6

3 0.001

0.044

0.006

12.554

Followers

3

Commenting behavior

0.000

0.000

Dependent variable

6

3

3

Both independent variables

Follows

Commenting behavior

Followers

Both independent variables

Follows

Reposting behavior

18.558

Followers

0.000

Independent variable

P value

χ 2 value

Independent variable

df

Followers

Reposting behavior

Dependent variable

Causality exclusion test

Table 6.4 Granger causality test

12.774

27.614

7.500

11.496

21.072

25.182

χ 2 value

6

3

3

6

3

3

df

0.047

0.000

0.058

0.074

0.000

0.000

P value

Both independent variables

Commenting behavior

Followers

Follows

Both independent variables

Reposting behavior

Followers

Independent variable

Follows

51.839

6.265

36.419

45.907

35.191

6.366

χ 2 value

6

3

3

6

3

3

df

0.000

0.099

0.000

0.000

0.000

0.095

P value

6.2 Impact of the Blogger’s Number of Follows and Followers 215

216

6 Dynamic Influencing Mechanism of Weibo …

two variables are less than the significant level of 0.05, indicating that the original hypothesis that there is no causality can be rejected. Therefore, the number of follows and the number of followers cannot be excluded from the equation related to reposting behavior, that is, the number of follows and the number of followers are the Granger causes of reposting behavior. In the exclusion test of causality between the numbers of follows and followers and commenting behavior, the corresponding p values of χ 2 test of the number of follows, the number of followers and the combination of the two variables are also less than the significant level of 0.05, indicating that the original hypothesis that there is no causality could be rejected. Therefore, the number of follows and the number of followers can not be excluded from the equation related to the commenting behavior, that is, the number of follows and the number of followers are the Granger causes of the commenting behavior. What’s more, the p values of other related tests are almost all less than the significant level of 0.05, so the selection of endogenous variables in the construction of the VAR model is effective.

6.2.2 VAR Model Construction Based on the Granger causality test of the VAR model, in order to further understand the length of lag time of the numbers of follows and followers on reposting behavior and commenting behaviors, it is necessary to do a statistical analysis and determine the lag length on the VAR model. The corresponding statistical results are shown in Table 6.5. It can be seen from Table 6.5 that the optimal lag lengths of the VAR models for reposting behavior and commenting behavior are both 3. Based on the above Table 6.5 Selection criteria of lag length Model

Lag LogL

Reposting behaviour VAR model

0

84.24548 NA

7.80E-09

−10.15568

− 10.01082

−10.14827

1

126.8491 5.147179

4.35E−10

− 14.35614

−13.77669

−14.32646

2

132.672

6.550817

2.10E−10

−13.959

−12.94498

−13.90708

3

139.5349

63.90541a

1.21e−10a

−122.7924a

−120.4747a

−122.6737a

4

161.7949 8.347477

2.99E−10

−15.34936

−13.46617

−15.25292

5

1030.339 0

NA

−13.69187

−12.24326

−13.61769

0

84.23307 NA

7.81E−09

−10.15413

− 10.00927

−10.14672

Commenting 1 behaviour 2 VAR model 3

131.5538 7.157534

1.83E−10

−14.94423

−14.36479

−14.91456

136.9325 6.05097

1.23E−10

−14.49156

−13.47754

−14.43963

146.4758 70.98112a 6.71e−lla

−121.5672a −119.2494a −121.4485a

4

170.3904 8.967975

1.02E−10

−16.42381

−14.54062

−16.32737

5

1020.537 0

NA

− 14.55948

−13.11088

− 14.4853

a indicates

LR

FPE

AIC

SC

that the value is significant according to the corresponding standard

HQ

6.2 Impact of the Blogger’s Number of Follows and Followers

217

causality tests and time lag analysis, the VAR models of reposting behavior and commenting behavior are set and estimated respectively. The corresponding model form is as follows: ⎤⎡ ⎤ ⎡ ⎤ ⎡ ⎡ ⎤ b11 b12 b13 Behavior a1 Behavior ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎣ Sel f _G ⎦t = ⎣ a2 ⎦ + ⎢ ⎣ b21 b22 b23 ⎦⎣ Sel f _G ⎦ + · · · Sel f _F

a3 ⎡ σ11 σ12 ⎢ ⎢ + ⎣ σ21 σ22 σ31 σ32

Sel f _F b31 b32 b33 t−1 ⎤⎡ ⎤ ⎡ ⎤ σ13 Behavior ε1 ⎥⎢ ⎥ ⎢ ⎥ ⎥ σ23 ⎦⎣ Sel f _G ⎦ + ⎣ ε2 ⎦ Sel f _F ε3 σ33 t−k

(6.5)

In the model, Behavior means reposting or commenting behavior, Self_G refers to the number of follows, Self_F refers to the number of followers, k is lag intervals for endogenous variables, and εi is random error term. In order to determine the correctness of the VAR model construction and setting, it is necessary to test the stability of the models. The stability test results of the models are shown in Figs. 6.16 and 6.17. Figures 6.16 and 6.17 show that, in the models of reposting behavior and commenting behavior, the dots representing the modulus of inverse roots of AR characteristic polynomial all fall inside the unit circle, indicating that the constructed models meet the stability requirements, and that the set models are all correct and need not be rebuilt. The estimated results of the corresponding models are shown in Table 6.6 (only the estimated values of parameters related to this study are listed below). It can be seen from Table 6.6 that the | t | values of the significance test of each coefficient in the reposting and commenting behavior models are all greater than the critical value of 1.96, that is, the t test of each coefficient reaches the significant level Fig. 6.16 Reposting model inverse roots of AR characteristic polynomial and unit circle

218

6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.17 Commenting model inverse roots of AR characteristic polynomial and unit circle

of 0.05. In addition, both the coefficients of determination—the R-squared and Adj. R-squared—have high values, which indicates that the VAR model constructed has a good fit with the sample data and that the model can be used in the related research and analysis of the dynamic impact of the number of follows and the number of followers on reposting behavior and commenting behavior respectively.

6.2.3 Impulse Responses Analysis In order to reveal the characteristics of the dynamic disturbance of the numbers of follows and followers on reposting behavior and commenting behavior, impulse response analysis was conducted on reposting behavior and commenting behavior respectively based on VAR model estimation. Here the generalized impulse function is used for the estimation and the function form is as follows:  ψ Sel f (q, δ j , t−1 ) = E(ySel f,t+q ε Sel f, jt = δ Sel f, j ,  Sel f,t−1 )



 A δ  Sel f,q Sel f, j Sel f, j − E(ySel f,t+q  Sel f,t−1 ) = √ √ σ Sel f, j j σ Sel f, j j q = 0, 1, 2Lim , t = 1, 2, . . . , T

(6.6)

 2 In the function, σ Sel f, j j = E Sel f (ε jt ), Sel f, j = E Sel f (εt ε jt ) means the jth element of εt covariance matrix , and εt comes from the error term (column vector) ε Sel f,t of ySel f,t = 1 ySel f,t−1 + · · · + 1 ySel f,t− p + ε Sel f,t . i is coefficient matrix, and p is lag order. Corresponding analysis results are shown in Figs. 6.18 and 6.19. It can be seen from Fig. 6.18 that when the number of followers is impacted by a positive shock, the impact immediately leads to users’ reposting behavior. The

6.2 Impact of the Blogger’s Number of Follows and Followers

219

Table 6.6 Parameter estimation and test results of VAR models Independent variable

Dependent variable Number of reposts

Number of comments

Coefficient

T value

Coefficient

T value

Number of reposts (−1) (−1)

0.3102

−11.5642

N/A

N/A

Number of reposts (−2) (−2)

0.238

−21.2029

N/A

N/A

Number of reposts (−3) (−3)

−0.0539

−10.2719

N/A

N/A

Number of comments (−1) (−1)

N/A

N/A

0.5796

−20.5730

Number of comments (−2) (−2)

N/A

N/A

15.7801

−15.6004

Number of comments (−3) (−3)

N/A

N/A

−0.8563

11.4204

Independent variable

Dependent variable Number of reposts

Number of comments

Coefficient

T value

Coefficient

T value

Number of follows ( (−1))

0.0802

−7.1243

0.5619

−21.5462

Number of follows (−2) (−2)

0.1444

4.1931

0.3640

−14.1892

Number of follows (−3) (−3)

−0.0303

−6.6479

−0.5322

−9.0709

Number of followers (−1) (−1)

0.0665

17.2576

0.4224

−7.0596

Number of followers (−2) (−2)

0.0715

16.1055

0.0226

−21.0583

Number of followers (−3) (−3)

0.0442

−10.0651

−0.1508

−15.3008

C

9.4486

−6.5173

2.7568

−13.1300

R-squared

0.9983

0.9756

Adj. R-squared

0.9964

0.9481

Sum sq. resids

0.0008

0.0219

S.E. equation

0.0105

0.0524

F-statistic

527.2774

35.5706

Log likelihood

63.7409

34.8285

Akaike AIC

−5.9712

−2.7587

Schwarz SC

−5.4765

−2.2640

Mean dependent

12.0548

10.9589

S.D. dependent

0.1758

0.2303

response value in the first lag is about 0.16, then decreases to 0.125 in the second lag, and started to climb back in the third lag to the maximum value of the entire disturbance process at about 0.18, followed by a rapid decline until the ninth lag to 0.06. From the tenth lag to the twenty-first lag, the drop gradually slows down to 0.03. It indicates that the number of followers has a same-direction impact on the reposting behavior, that is, users’ reposting behavior is significantly affected by followers’ following phenomenon, and the effect of the first ten lags is greater. When the number of follows is subject to a positive shock, the impact also immediately leads to users’ reposting behavior. The response value in the first lag is about 0.12,

220

6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.18 Impulse response of the number of reposts

Fig. 6.19 Impulse response of the number of comments

then decreases to 0.05 in the second lag, and remains at the same value in the third lag. After that, the response value starts to climb back and reaches 0.09 at the fifth and sixth lags, followed back a decline to 0.04 at the eleventh lag. Then the drop gradually slows down till the twenty-first lag at 0.02. It shows that the number of follows has the same-direction impact on the reposting behavior, that is, users’ reposting behavior

6.2 Impact of the Blogger’s Number of Follows and Followers

221

is significantly affected by the degree of one’s attention to other people’s Weibo and the effect of the first eleven lags is greater. The number of followers has a greater impact than the number of follows on the reposting behavior. It can be seen from Fig. 6.19 that when the number of followers is impacted by a positive shock, the impact immediately leads to the users’ commenting behavior. The response value in the first lag is about 0.015. A rapid decline is seen between the second lag and the fourteenth lag, reaching around 0.001, followed by a slow decrease to approach 0. It indicates that the number of followers has a same-direction impact on the commenting behavior, that is, users’ commenting behavior is significantly affected by followers’ following phenomenon, and the effect of the first ten lags is greater. When the number of follows is subject to a positive shock, the impact does not immediately lead to users’ commenting behavior. The response value only starts to soar at the second lag, reaching the peak of the entire disturbance process at 0.011 at the third lag, followed by a slow decline till the twenty-first lag, approaching 0. It indicates that the change in the number of follows does not immediately affect commenting behavior but starts to produce disturbance at the second lag and reaches a peak value at the third lag and that the influence effect is larger in the first 12 lags. What’s more, during the first three lags after the crisis, the number of followers has a greater impact than the number of follows on the commenting behavior and then, after the third lag, the number of follows has a greater impact than the number of followers on the commenting behavior.

6.2.4 Marginal Influence On the basis of the impulse response analysis of information sharing behavior, in order to further understand the marginal influence of the number of follows and the number of followers on reposting behavior and commenting behavior respectively, the state space model is used here to analyze the change process of the marginal influence of relevant factors, so as to reveal the fluctuation process characteristics of the influence effect of the number of follows and the number of followers on reposting behavior and commenting behavior respectively. The corresponding model forms are as follows: Observation equation: ln ybehavior,t = csel f,t + asel f _g,t ln ysel f _g,t−i + bsel f _ f,t ln ysel f _ f,t− j + u sel f,t i = 0, 1, 2, . . . , T − 1, j = 0, 1, 2h...μ T − 1

(6.7)

State equation:

asel f _g,t = asel f _g + γ asel f _g,t−1 + εsel f _g,t bsel f _ f,t = bsel f _ f + σ bsel f _ f,t−1 + εsel f _ f,t

(6.8)

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6 Dynamic Influencing Mechanism of Weibo …

Fig. 6.20 Marginal influence on the number of reposts

In these equations, ybehavior refers to the reposting or commenting behavior, ysel f _g,t−i refers to the number of follows of lagged order i that has a long-run equilibrium relationship after cointegration test, ysel f _ f,t− j refers to the number of followers of lagged order j that has a long-run equilibrium relationship after cointegration test, u sel f,t is the continuous uncorrelated error term for meeting mean value E(u sel f,t ) = 0 and covariance matrix var(u sel f,t ) = Hsel f,t , εsel f _g,t is the continuous uncorrelated error term for meeting mean value E(εsel f _g,t ) = 0 and covariance matrix var(εsel f _g,t ) = Hsel f _g,t , εsel f _ f,t is the continuous uncorrelated error term for meeting mean value E(εsel f _ f,t ) = 0 and covariance matrix var(εsel f _ f,t ) = Hsel f _ f,t . The analysis results of the state space model are shown in Figs. 6.20 and 6.21. It can be seen from Fig. 6.20 that the marginal influence of the number of followers on reposting behavior is about 0.42 at the beginning of the crisis, and then it increases rapidly, reaching two peaks on the second (0.67) and fourth (0.75) day respectively. After the fourth day, there is a dramatic decline until the value reaches around 0.21 on the ninth day, followed by a rebound until the twelfth and the thirteenth day where the value stabilizes at 0.42. And then there is a downward fluctuation till the value reaches 0.15 on the twenty-first day. The marginal influence of the number of follows on reposting behavior is about 0.29 at the beginning of the crisis, and then falls to 0.15 on the fourth day, followed by an upward fluctuation process until it reaches the peak value of 0.44 on the eighth day. After that there is an obvious decline followed by slight fluctuation until the value falls to around 0.02 at marginal influence level. Throughout the process, the marginal influence of the number of followers is greater than that of the number of follows on reposting behavior on all the days except from the ninth day to the eleventh and on the seventeenth day when the former is less than the latter.

6.2 Impact of the Blogger’s Number of Follows and Followers

223

Fig. 6.21 Marginal influence on the number of comments

As can be seen from Fig. 6.21, the marginal influence of the number of followers on commenting behavior is about 0.84 at the beginning of the crisis, and it reaches the peak value of 0.88 on the first day, followed by a fluctuant rapid decline until the fourth day at 0.21. Then there is a slow upward fluctuation process from the fifth to the eighth day when the value climbs up to 0.51. After that it keeps falling slowly, finally reaching 0.12 on the twenty-first day. The marginal influence of the number of follows on commenting behavior is about 0.48 at the beginning of the crisis and then there is a slight fluctuation till the fourth day, followed by a slow decline and fluctuation process. On the fifth day the influence starts to rise rapidly until it reaches the peak value at 0.95 on the ninth day. Then it fluctuates downward until the twentyfirst day, down to 0.22. Throughout the process, the marginal influence of the number of followers is greater than that of the number of follows on commenting behavior in the first three days of the crisis, but smaller for the rest of the time.

6.3 Impact of the Number of Follows and Followers of Source Information In order to better understand the time lag characteristics of the correlation between the numbers of follows and followers of information source (IS) on the reposting behavior and commenting behavior and the influence of the former on the latter, the cross-correlation spike diagrams showing the relationship between the respective number of IS follows and IS followers and the respective number of reposts and

224

6 Dynamic Influencing Mechanism of Weibo …

comments are analyzed. The corresponding cross-correlations spike diagrams are shown in Figs. 6.22, 6.23, 6.24 and 6.25. Figures 6.22, 6.23, 6.24 and 6.25 show that the respective correlation between the numbers of IS follows and IS followers and the numbers of reposts and comments shows a slow decline trend as time lag increases. The cross-correlations coefficients with the number of reposts and comments are greater than 0.50 within two lags. It can be preliminarily concluded that the influence of the number of IS follows and

Fig. 6.22 Cross-correlations between the number of reposts and the number of IS followers

Fig. 6.23 Cross-correlations between the number of reposts and the number of IS follows

6.3 Impact of the Number of Follows and Followers …

225

Fig. 6.24 Cross-correlations between the number of comments and the number of IS followers

Fig. 6.25 Cross-correlations between the number of comments and the number of IS follows

the number of IS followers on reposting behavior and commenting behavior is more obvious within two lags. According to the lag correlation characteristics between variables, the lag number can be set at two for estimation in the construction of an exploratory model of the number of reposts and the number of comments. On this basis, the variable structure and lag standard in the formal VAR model can be identified and tested.

226

6 Dynamic Influencing Mechanism of Weibo …

6.3.1 Causality Test The study identifies and tests the variable structure of the formal model, and analyzes whether there is significant Granger causality between the numbers of IS follows and IS followers and the numbers of reposts and comments respectively, so as to determine whether it is reasonable to have a model structure setting where the two variables– the number of IS follows and the number of IS followers—are introduced into the equation of the number of reposts and the number of comments. The corresponding Granger causality test results are shown in Table 6.7. As can be seen in Table 6.7, in the exclusion test of causality between the numbers of IS follows and IS followers and reposting behavior, the corresponding p values of the χ 2 test of the number of IS follows, the number of IS followers and the combination of the two variables are less than the significant level of 0.05, indicating that the original hypothesis that there is no causality can be rejected. Therefore, the number of IS follows and the number of IS followers cannot be excluded from the equation related to reposting behavior, that is to say, the number of IS follows and the number of IS followers are the Granger causes of reposting behavior. In the exclusion test of causality between the numbers of IS follows and IS followers and commenting behavior, the corresponding p values of χ 2 test of the number of IS follows, the number of IS followers and the combination of the two variables are also less than the significant level of 0.05, indicating that the original hypothesis that there is no causality could be rejected. Therefore, the number of IS follows and the number of IS followers can not be excluded from the equation related to the commenting behavior, that is to say, the number of IS follows and the number of IS followers are the Granger causes of the commenting behavior. What’s more, the p values of other related tests are almost all less than the significant level of 0.05, so the selection of endogenous variables in the construction of the VAR model is effective.

6.3.2 VAR Model Construction Based on the Granger causality test of the VAR model, in order to further understand the length of lag time of the numbers of IS follows and IS followers on reposting behavior and commenting behaviors, it is necessary to do a statistical analysis and determine the lag length on the VAR model. The corresponding statistical results are shown in Table 6.8. It can be seen from Table 6.8 that the optimal lag lengths of the VAR models for reposting behavior and commenting behavior are both 2. Based on the above causality tests and time lag analysis, the VAR models of reposting behavior and commenting behavior are set and estimated respectively. The corresponding model form is as follows:

Commenting behavior model

Reposting behavior model

12.045

28.972

IS followers

Both independent variables

19.006

57.585

IS followers

Both independent variables

4

2 0.000

0.000

0.000

52.682

IS follows

2

Commenting behavior

0.000

0.002

Dependent variable

4

2

2

Both independent variables

IS followers

Commenting behavior

IS follows

Both independent variables

IS followers

Reposting behavior

9.168

IS follows

0.010

Independent variable

P value

χ 2 value

Independent variable

df

IS follows

Reposting behavior

Dependent variable

Causality exclusion test

Table 6.7 Granger causality test

380.624

7.119

61,378

16.260

6.804

5.197

χ 2 value

4

2

2

4

2

2

df

0.000

0.068

0.000

0.003

0.033

0.074

P value

Both independent variables

Commenting behavior

IS follows

IS followers

Both independent variables

Reposting behavior

IS follows

Independent variable

IS followers

10.710

25.073

6.073

18.376

5.119

6.924

χ 2 value

4

2

2

4

2

2

df

0.030

0.000

0.048

0.001

0.077

0.031

P value

6.3 Impact of the Number of Follows and Followers … 227

228

6 Dynamic Influencing Mechanism of Weibo …

Table 6.8 Selection criteria of lag length Model

Lag LogL

FPE

AIC

SC

HQ

Reposting behaviour VAR Model

0

65.32733 NA

8.30E-08

−7.790916

−7.646056

−7.783498

1

108.8112 65.2258

1.15E−09

−12.1014

−11.52196

−12.07173

1.13e−13a

−113.7525a

−111.4347a

−113.6338a

37.67280a

2

115.3407

3

124.3435 6.752138

2.90E—09 −11.79294

−10.34434

−11.71876

4

224.8043 7.345661

1.83E−09

−11.79258

−10.77856

−11.74066

5

958.0197 0

NA

− 23.22554 − 21.34236 −23.12911

68.22438 NA

5.78E−08

−8.153048

− 8.008188 −8.14563

109.9032 62.51825

1.00E−09

−12.2379

−11.65846

Commenting 0 behaviour 1 VAR Model 2

a indicates

LR

−12.20823

117.2366 17.74009a 6.37e−lla

−127.3593a −125.0415a −127.2406a

3

126.8449 7.206193

2.12E−09

−12.10561

−10.65701

4

174.1518 8.250096

1.45E−09

−12.02958

−11.01556

−11.97765

5

1066.874 0

NA

−16.89398

−15.01079

−16.79754

−12.03143

that the value is significant according to the corresponding standard

⎤⎡ ⎤ ⎤ ⎡ ⎤ ⎡ b11 b12 b13 Behavior Behavior a1 ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ Sr ce_G ⎦ = ⎣ a2 ⎦ + ⎢ ⎣ b21 b22 b23 ⎦⎣ Sr ce_G ⎦ + · · · Sr ce_F a3 Sr ce_F b31 b32 b33 t t−1 ⎤⎡ ⎡ ⎤ ⎡ ⎤ σ11 σ12 σ13 Behavior ε1 ⎥⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ + ⎣ σ21 σ22 σ23 ⎦⎣ Sr ce_G ⎦ + ⎣ ε2 ⎦ Sr ce_F ε3 σ31 σ32 σ33 t−k ⎡

(6.9)

Here Behavior means reposting or commenting behavior, Srce_G refers to the number of follows, Srce_F refers to the number of followers, k is lag intervals for endogenous variables, and εi is random error term. In order to determine the correctness of the VAR model construction and setting, it is necessary to test the stability of the models. The stability test results of the models are shown in Figs. 6.26 and 6.27. Figures 6.26 and 6.27 show that, in the models of reposting behavior and commenting behavior, the dots representing the modulus of inverse roots of AR characteristic polynomial all fall inside the unit circle, indicating that the constructed models meet the stability requirements, and that the set models are all correct and need not be rebuilt. The estimated results of the corresponding models are shown in Table 6.9 (only the estimated values of parameters related to this study are listed below). It can be seen from Table 6.9 that the | t | values of the significance test of each coefficient in the reposting and commenting behavior models are all greater than the critical value of 1.96, that is, the t test of each coefficient reaches the significant level of 0.05. In addition, both the coefficients of determination—the R-squared and Adj.

6.3 Impact of the Number of Follows and Followers …

229

Fig. 6.26 Reposting model inverse roots of AR characteristic polynomial and unit circle

Fig. 6.27 Commenting model inverse roots of AR characteristic polynomial and unit circle

R-squared—have high values, which indicates that the VAR model constructed has a good fit with the sample data and that the model can be used in the related research and analysis of the dynamic impact of the number of IS follows and the number of IS followers on reposting behavior and commenting behavior respectively.

6.3.3 Impulse Responses Analysis In order to reveal the characteristics of the dynamic disturbance of the numbers of IS follows and IS followers on reposting behavior and commenting behavior, impulse response analysis was conducted on reposting behavior and commenting behavior respectively based on VAR model estimation. Here the generalized impulse function is used for the estimation and the function form is as follows:

230

6 Dynamic Influencing Mechanism of Weibo …

Table 6.9 Parameter estimation and test results of VAR models Independent variable

Dependent variable Number of reposts

Number of comments

Coefficient

T value

Coefficient

Number of reposts (−1)

0.5582

11.5068

N/A

N/A

Number of reposts (−2)

−0.2293

−10.6022

N/A

N/A

Number of comments (−1)

N/A

N/A

0.7007

9.4779

Number of comments (−2)

N/A

N/A

0.2222

−14.4658

Number of IS follows (−1)

0.0436

9.1204

0.4496

−20.0562

Number of IS follows (−2)

0.0689

7.3321

−0.1765

−17.2361

Number of IS followers (−1)

0.1337

11.2461

0.4901

−18.2997

Number of IS followers (−2)

−0.1192

15.2792

−0.1022

−14.4069

C

4.9064

−13.8895

6.7908

−10.4928

R-squared

0.9982

0.9881

Adj. R-squared

0.9974

0.9822

Sum sq. resids

0.0019

0.0526

S.E. equation

0.0126

0.0662

F-statistic

1169.2059

167.3383

Log likelihood

60.4588

28.9884

Akaike AIC

−5.6272

−2.3145

Schwarz SC

−5.2793

−1.9666

Mean dependent

11.9665

10.4242

S.D. dependent

0.2496

0.4974

T value

 ψ Sr ce (q, δ j , t−1 ) = E(ySr ce,t+q ε Sr ce, jt = δ Sr ce, j ,  Sr ce,t−1 )



 A Sr ce,q  Sr ce, j δ Sr ce, j  − E(ySr ce,t+q  Sr ce,t−1 ) = √ √ σ Sr ce, j j σ Sr ce, j j q = 0, 1, 2, . . . , t = 1, 2, . . . , T

(6.10)

 2 In the function, σ Sr ce, j j = E Sr ce (ε jt ), Sr ce, j = E Sr ce (εt ε jt ) means the jth element of εt covariance matrix , and εt comes from the error term (column vector) ε Sr ce,t of ySr ce,t = 1 ySr ce,t−1 + · · · + 1 ySr ce,t− p + ε Sr ce,t . i is coefficient matrix, and p is lag order. Corresponding analysis results are shown in Figs. 6.28 and 6.29. It can be seen from Fig. 6.28 that when the number of IS followers is impacted by a positive shock, the impact immediately leads to users’ reposting behavior. The response value in the first lag is about 0.21, then rises quickly to the maximum value of the entire process at 0.27 at the third lag. After that it plummets to about 0.18 at the eighth lag, followed by a slow and short rise until the twelfth lag to 0.07. Then a

6.3 Impact of the Number of Follows and Followers …

231

Fig. 6.28 Impulse response of the number of reposts

Fig. 6.29 Impulse response of the number of comments

slow decline till the twenty-first lag brings the value down to 0.04. It indicates that users’ reposting behavior is significantly affected by the number of IS followers, and the effect of the first eight lags is greater. When the number of IS follows is subject to a positive shock, the impact also immediately leads to users’ reposting behavior. The response value in the first lag is about 0.05 and then soars to 0.11 at the third lag,

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6 Dynamic Influencing Mechanism of Weibo …

reaching the peak value of the entire process. After that, it starts to drop sharply until the eighth lag at 0.01, followed by a slight fluctuation to 0.02 at the fourteenth lag. A slow decline is seen between the fifteenth lag and the twenty-first lag where the value drops to 0.01. It shows that users’ reposting behavior is significantly affected by the number of IS follows and the effect of the first eight lags is greater. Throughout the process, the number of IS followers has a greater impact than the number of IS follows on the reposting behavior. It can be seen from Fig. 6.29 that when the number of IS followers is impacted by a positive shock, the impact immediately leads to users’ commenting behavior. The response value at the first lag is about 0.021. A rapid decline is seen after that till the fourteenth lag, reaching around 0.002, followed by a slow decrease till the twentyfirst lag to nearly 0. It indicates that users’ commenting behavior is significantly affected by the number of IS followers and the effect of the first 14 lags is greater. When the number of IS follows is subject to a positive shock, the impact does not immediately lead to users’ commenting behavior. The response value only starts to soar at the second lag, reaching the peak of the entire process at 0.012 at the third and fourth lags, followed by a decline till the twenty-first lag, approaching 0. It indicates that users’ commenting behavior is significantly affected by the number of IS follows and that the influence effect is larger in the first 14 lags. What’s more, during the first three lags after the crisis, the number of IS followers has a greater impact than the number of IS follows on the commenting behavior, and for the rest of the lags, the impact is smaller.

6.3.4 Marginal Influence On the basis of the impulse response analysis of information sharing behavior, in order to further understand the marginal influence of the number of IS follows and the number of IS followers on reposting behavior and commenting behavior respectively, the state space model is used here to analyze the change process of the marginal influence of relevant factors, so as to reveal the fluctuation process characteristics of the influence effect of the number of IS follows and the number of IS followers on reposting behavior and commenting behavior respectively. The corresponding model forms are as follows: Observation equation: ln ybehavior,t = csr ce,t + asr ce_g,t ln ysr ce_g,t−i + bsr ce_ f,t ln ysr ce_ f,t− j + u sr ce,t i = 0, 1, 2, . . . , T − 1, i = 0, 1, 2, . . . , T − 1 State equation:

(6.11)

6.3 Impact of the Number of Follows and Followers …

asr ce_g,t = asr ce_g + γ asr ce_g,t−1 + εsr ce_g,t bsr ce_ f,t = bsr ce_ f + σ bsr ce_ f,t−1 + εsr ce_ f,t

233

(6.12)

In these equations, ybehavior refers to the reposting or commenting behavior, ysr ce_g,t−i refers to the number of IS follows of lagged order i that has a long-run equilibrium relationship after cointegration test, ysr ce_ f,t− j refers to the number of IS followers of lagged order j that has a long-run equilibrium relationship after cointegration test, u sr ce,t is the continuous uncorrelated error term for meeting mean value E(u sr ce,t ) = 0 and covariance matrix var(u sr ce,t ) = Hsel f,t , εsr ce_g,t is the continuous uncorrelated error term for meeting mean value E(εsr ce_g,t ) = 0 and covariance matrix var(εsr ce_g,t ) = Hsr ce_g,t , εsr ce_ f,t is the continuous uncorrelated error term for meeting mean value E(εsr ce_ f,t ) = 0 and covariance matrix var(εsr ce_ f,t ) = Hsr ce_ f,t . The analysis results of the state space model are shown in Figs. 6.30 and 6.31. It can be seen from Fig. 6.30 that the marginal influence of the number of IS followers on reposting behavior is about 0.44 at the beginning of the crisis, and then it increases rapidly on the first day to 0.52. On the second day it drops to 0.34, followed by a rapid increase on the third day to around 0.56, the peak value of the whole process. And then there is a downward fluctuation till the value reaches 0.13 on the twenty-first day. During the process on the fifteenth day, there is a positive fluctuation whose value is about 0.38. The marginal influence of the number of IS follows on reposting behavior is about 0.24 at the beginning of the crisis. It reaches the maximum value on both the first and the third day at around 0.26, followed by an slow downward fluctuation until the twenty-first day when the value is as low as 0.03. Throughout the process, the marginal influence of the number of IS followers is greater than that of the number of IS follows on reposting behavior.

Fig. 6.30 Marginal influence on the number of reposts

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Fig. 6.31 Marginal influence on the number of comments

As can be seen from Fig. 6.31, the marginal influence of the number of IS followers on commenting behavior is about 0.18 at the beginning of the crisis. It then quickly reaches the peak value of 0.46 on the first day and maintains a high influence level for the next two days at about 0.43. On the fourth day the value plummets to 0.28. From the sixth to the eighth day the influence stays at 0.31. Then there is a downward fluctuation process till the twenty-first day when the value decreases to 0.09. During the process on the eighteenth day, there is a slight positive fluctuation. The marginal influence of the number of IS follows on commenting behavior is about 0.14 at the beginning of the crisis and then there is an upward fluctuation till reaching the maximum value of the whole process at about 0.45 on the twelfth day. It then starts to decline rapidly till the eighteenth day when the value is 0.12, followed by a slight rebound until the twenty-first day, rising to 0.18. Throughout the process, the marginal influence of the number of IS followers is greater than that of the number of IS follows on commenting behavior in the first eight days, on the seventeenth day and the nineteenth day of the crisis, but smaller for the rest of the time.

6.4 The Impact of Information Temporal Distance In order to better understand the time lag characteristics of the correlation between the temporal distance of crisis information on reposting behavior and commenting behavior and the influence of the former on the latter, the spike cross-correlations diagrams showing the relationship between temporal distance and the respective

6.4 The Impact of Information Temporal Distance

235

number of reposts and comments are analyzed. The corresponding cross-correlations spike diagrams are shown in Figs. 6.32 and 6.33. Figures 6.32 and 6.33 show that the respective correlation between temporal distance and the numbers of reposts and comments shows a rapid decline trend as time lag increases. The cross-correlations coefficients with the numbers of reposts and comments are greater within two lags. It can be preliminarily concluded that the influence of temporal distance on reposting behavior and commenting behavior

Fig. 6.32 Cross-correlations between the number of reposts and temporal distance

Fig. 6.33 Cross-correlations between the number of comments and temporal distance

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is more obvious within two lags. According to the lag correlation characteristics between variables, the lag number can be set at two for estimation in the construction of an exploratory model of the number of reposts and the number of comments. On this basis, the variable structure and lag standard in the formal VAR model can be identified and tested.

6.4.1 Causality Test The study identifies and tests the variable structure of the formal model, and analyzes whether there is significant Granger causality between temporal distance of crisis information and the numbers of reposts and comments respectively, so as to determine whether it is reasonable to have a model structure setting where the variable of temporal distance is introduced into the equation of the number of reposts and the number of comments. The corresponding Granger causality test results are shown in Table 6.10. As can be seen in Table 6.10, in the exclusion test of causality between temporal distance and reposting behavior, the corresponding p values of the χ 2 test of temporal distance are less than the significant level of 0.05, indicating that the original hypothesis that there is no causality can be rejected. Therefore, temporal distance cannot be excluded from the equation related to reposting behavior, that is to say, temporal distance is the Granger cause of reposting behavior. In the exclusion test of causality between temporal distance and commenting behavior, the corresponding p values of χ 2 test of temporal distance are also less than the significant level of 0.05, indicating that the original hypothesis that there is no causality could be rejected. Therefore, Table 6.10 Granger causality test Reposting behavior model

Dependent variable

Reposting behavior

Temporal distance

Independent χ 2 value df variable

P value Independent variable

χ 2 value df

P value

Temporal distance

2

0.023

Reposting behavior

29.417

2

0.000

2

0.003

Both independent variables

12.034

2

0.002

7.544

Both 11.691 independent variables Commenting Dependent behavior variable model Temporal distance

Commenting behavior

Temporal distance

25.708

Both 8.326 independent variables

2

0.000

Commenting 6.914 behavior

2

0.032

2

0.016

Both independent variables

2

0.000

48.619

6.4 The Impact of Information Temporal Distance

237

temporal distance can not be excluded from the equation related to commenting behavior, that is to say, temporal distance is the Granger cause of the commenting behavior. What’s more, the p values of other related tests are almost all less than the significant level of 0.05, so the selection of endogenous variables in the construction of the VAR model is effective.

6.4.2 VAR Model Construction Based on the Granger causality test of the VAR model, in order to further understand the length of lag time of temporal distance on reposting behavior and commenting behavior, it is necessary to do a statistical analysis and determine the lag length on the VAR model. The corresponding statistical results are shown in Table 6.11. It can be seen from Table 6.11 that the optimal lag lengths of the VAR models for reposting behavior and commenting behavior are both 2. Based on the above causality tests and time lag analysis, the VAR models of reposting behavior and commenting behavior are set and estimated respectively. The corresponding model form is as follows:        b11 b12 a1 Behavior Behavior = + ··· + T imeDist a2 T imeDist b21 b22 t t−1      σ11 σ12 ε1 Behavior + (6.13) + T imeDist ε2 σ21 σ22 t−k In the model, Behavior means reposting or commenting behavior, TimeDist refers to temporal distance, k is lag intervals for endogenous variables, and εi is random error term. In order to determine the correctness of the VAR model construction and setting, it is necessary to test the stability of the models. The stability test results of the models are shown in Figs. 6.34 and 6.35. Figures 6.34 and 6.35 show that, in the models of reposting behavior and commenting behavior, the dots representing the modulus of inverse roots of AR characteristic polynomial all fall inside the unit circle, indicating that the constructed models meet the stability requirements, and that the set models are all correct and need not be rebuilt. The estimated results of the corresponding models are shown in Table 6.12 (only the estimated values of parameters related to this study are listed below). It can be seen from Table 6.12 that the | t | values of the significance test of each coefficient in the reposting and commenting behavior models are all greater than the critical value of 1.96, that is to say, the t test of each coefficient reaches the significant level of 0.05. In addition, both the coefficients of determination— the R-squared and Adj. R-squared—have high values, which indicates that the VAR

a indicates

101.7972 144.5835 1099.724

3

4

5

0

12.48141 NA

8.44E−08

4.86E−08

2.57e−09a

12.81649

9.37E−08

90.51865a

5.56E−05

NA

1.73E−07

16.04488

NA

0

13.33879

3.79E−07

1.16e−08a

17.68019a 4.790822

2.11E−07

0.000121

FPE

90.14354

NA

LR

that the value is significant according to the corresponding standard

84.7085

2

1029.028

5 73.61392

132.5078

4

1

85.36062

3

13.26815

78.97286

2

0

67.11615

1

Commenting behavior VAR model

7.020456

0

Repostingbehavior VAR model

LogL

Lag

Model

Table 6.11 Selection criteria of lag length

−7.122298 −129.1478a −7.526041 −6.94954 −11.31475

−131.4655a −8.974644 −7.963563 −13.19794

−6.232585

−7.246607

− 7.701739

−5.471474

−6.920078

−9.805288

−120.3108a

−122.6286a

−1.138658

− 6.310078

−6.889519

−11.68847

−0.357697

−0.502557

−1.283518

SC

AIC

−13.1015

−7.911636

−8.900464

−131.3468a

−7.672067

−1.2761

−11.59204

−7.194681

−6.845897

−122.5099a

−6.859847

−0.495139

HQ

238 6 Dynamic Influencing Mechanism of Weibo …

6.4 The Impact of Information Temporal Distance

239

Fig. 6.34 Reposting model inverse roots of AR characteristic polynomial and unit circle

Fig. 6.35 Commenting model inverse roots of AR characteristic polynomial and unit circle

model constructed has a good fit with the sample data and that the model can be used in the related research and analysis of the dynamic impact of temporal distance on reposting behavior and commenting behavior respectively.

6.4.3 Impulse Responses Analysis In order to reveal the characteristics of the dynamic disturbance of temporal distance on reposting behavior and commenting behavior, impulse response analysis was conducted on reposting behavior and commenting behavior respectively based on VAR model estimation. Here the generalized impulse function is used for the estimation and the function form is as follows:

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Table 6.12 Parameter estimation and test results of VAR models Independent variable

Dependent variable Number of reposts

Number of comments

Coefficient

T value

Coefficient

T value

Number of reposts (−1)

1.1195

14.5059

N/A

N/A

Number of reposts (−2)

−0.6815

−7.2069

N/A

N/A

Number of comments (−1)

N/A

N/A

0.0010

−17.0315

Number of comments (−2)

N/A

N/A

0.0027

−12.0271

Temporal distance (−1)

1.1261

− 20.2800

1.0120

−7.1065

Temporal distance (−2)

−0.8373

−16.3843

−0.1834

−5.0514

C

− 3.5400

8.3491

−2.1275

−10.7177

R-squared

0.9975

0.9919

Adj. R-squared

0.9962

0.9879

Independent variable

Dependent variable Number of reposts Coefficient

T value

Number of comments Coefficient

Sum sq. resids

0.0027

0.4171

S.E. equation

0.0152

0.1864

F-statistic

804.4295

247.2342

Log likelihood

56.9137

9.3182

Akaike AIC

−5.2540

−0.2440

Schwarz SC

−4.9061

0.1039

Mean dependent

11.9665

8.4513

S.D. dependent

0.2496

1.6994

T value

 ψT im Dt (q, δ j , t−1 ) = E(yT im Dt,t+q εT im Dt, jt = δT im Dt, j , T im Dt,t−1 )



 A δ  T im Dt,q T im Dt, j T im Dt, j − E(yT im Dt,t+q T im Dt,t−1 ) = √ √ σT im Dt, j j σT im Dt, j j q = 0, 1, 2, . . . , t = 1, 2, . . . T

(6.14)

 2 In the function, σT im Dt, j j = E T im Dt (ε jt ), T im Dt, j = E T im Dt (εt ε jt ) means the jth element of εt covariance matrix , and εt comes from the error term (column vector) εT im Dt,t of yT im Dt,t = 1 yT im Dt,t−1 + · · · + 1 yT im Dt,t− p + εT im Dt,t . i is coefficient matrix, and p is lag order. Corresponding analysis results are shown in Figs. 6.36 and 6.37. In Figs. 6.36 and 6.37, the broken line with solid small square is the impulse response curve, and the dashed line on both sides are the deviation bands of plus/minus double standard deviation (±2S.E). It can be seen from Fig. 6.36 that

6.4 The Impact of Information Temporal Distance

241

Fig. 6.36 Impulse response of the number of reposts

Fig. 6.37 Impulse response of the number of comments

when information temporal distance is impacted by a positive shock, the impact does not immediately lead to users’ reposting behavior. The response value waits till the second lag to soar to 0.045, and at the third lag it stays at the same level. Then it declines slowly till the twenty-first lag brings the value down to nearly 0. It shows that the impact of the first 12 lags is greater and the peak is reached at the second and the third lags. The response value gradually diminishes after the twelfth lag and the impact is lesser. It can be seen from Fig. 6.37 that when temporal distance is impacted by a positive shock, the impact does not immediately lead to users’ commenting behavior. The response value waits till the second lag to soar to

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6 Dynamic Influencing Mechanism of Weibo …

0.02. Then it drops to 0.01 at the third lag. After that it gradually declines till the twenty-first lag when the value approaches 0. It can be seen that the shock impact is greater in the first 10 lags and that the disturbance is the most obvious at the second and the third lags. After the tenth lag the response value is smaller, i.e. the impact is smaller.

6.4.4 Marginal Influence On the basis of the impulse response analysis of information sharing behavior, in order to further understand the marginal influence of temporal distance on reposting behavior and commenting behavior respectively, the state space model is used here to analyze the change process of the marginal influence of temporal distance, so as to reveal the fluctuation process characteristics of the influence effect of temporal distance on reposting behavior and commenting behavior respectively. The corresponding model form is as follows: Observation equation: ln ybehavior,t = ctimdt,t + atimdt,t ln ytimdt,t−i + u timdt,t i = 0, 1, 2, . . . , T − 1, j = 0, 1, 2, . . . , T − 1;

(6.15)

State equation: atimedt,t = atimedt + γ atimedt,t−1 + εtimedt,t

(6.16)

In these equations, ybehavior refers to the reposting or commenting behavior, ytimdt,t−i refers to the number of temporal distance of lagged order i that has a long-run equilibrium relationship after cointegration test, u timdt,t is the continuous uncorrelated error term for meeting mean value E(u timdt,t ) = 0 and covariance matrix var(u timdt,t ) = Htimdt,t , εtimdt_g,t is the continuous uncorrelated error term for meeting mean value E(εtimdt,t ) = 0 and covariance matrix var(εtimdt,t ) = Htimdt,t . The analysis results of the state space model are shown in Figs. 6.38 and 6.39. It can be seen from Fig. 6.38 that the marginal influence of temporal distance on reposting behavior is about 0.09 at the beginning of the crisis, and then it fluctuates upward rapidly to reach the peak value on the seventh day at about 0.78. After that the influence starts to fluctuate downward until the twelfth day at about 0.4, followed by a period of slight fluctuation between the thirteenth day and the eighteenth day when the value stays at around 0.42. But on the nineteenth day it starts to drop quickly until it reaches about 0.10 on the twenty-first day. As can be seen from Fig. 6.39, the marginal influence of temporal distance on commenting behavior is about 0.17 at the beginning of the crisis. It then fluctuates upward quickly to reach the peak value of 0.7 on the sixth day and then maintains a high influence level between the seventh

6.4 The Impact of Information Temporal Distance

243

Fig. 6.38 Marginal influence on the number of reposts

Fig. 6.39 Marginal influence on the number of comments

and the eleventh day at above 0.53. On the twelfth day the value plummets to 0.30. From the thirteenth to the seventeenth day the influence stays at about 0.35. Then there is sharp decline till the twenty-first day when the value decreases to about 0.05.

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6.5 Decomposition Analysis of Impact Contribution Ratio 6.5.1 Reposting Behavior Fluctuation In order to compare the disturbance effect of each dynamic contextual influencing factor on reposting behavior, so as to identify what important effects different influencing factors have on reposting behavior, it is necessary to do variance decomposition of the fluctuation contribution ratio of each influencing factor based on the above analysis of the dynamic disturbance characteristics and marginal influence of each influencing factor on reposting behavior (Lee & Lee, 2015). Variance decomposition is an effective method to analyze the contribution ratio of the structural impact of some related endogenous variables on specific endogenous variables in VAR models. The formula of the relative variance contribution ratio (RVC) is as follows: s−1 

RV C j→zhuan f a (s) =

q=0 k  j=1

 2 (q) azhuan f a, j σ j j

s−1 

q=0

 2 (q) azhuan f a, j σ j j



(6.17)

in which j refers to the corresponding influencing factors, s is the finite term value of (q)

q, and azhuωva, j =

  ∂ yzhuvya,t+q , q = 0, ·1, ·2, · · · , ·t = 1, ·2, · · · , ·T ; σ j j = E ε2jt ∂ y jt

The results of variance decomposition is shown in Fig. 6.40. As can be seen from Fig. 6.40, in addition to its own inertia effect, other contextual factors also have an impact on reposting behavior’s fluctuation. Their contribution ratios in order of size are: the total number of reposts, the total number of comments, the number of followers, information temporal distance, the number of IS followers, the number of follows and the number of IS follows.

6.5.2 Commenting Behavior Fluctuation In order to compare the disturbance effect of each dynamic contextual influencing factor on commenting behavior, so as to identify what important effects different influencing factors have on commenting behavior, it is necessary to do variance decomposition of the fluctuation contribution ratio of each influencing factor based on the above analysis of the dynamic disturbance characteristics and marginal influence of each influencing factor on commenting behavior. The formula of the relative variance contribution ratio (RVC) is as follows:

6.5 Decomposition Analysis of Impact Contribution Ratio

245

Fig. 6.40 Variance decomposition of the fluctuation of the number of reposts

2 s−1  (q) σjj q=0 a pinglun, j RV C j→ pinglun (s) = k 2   s−1  (q) σjj q=0 a pinglun, j

(6.18)

j=1

in which j refers to the corresponding influencing factors, s is the finite term value ∂y (q) of q, and a pinglun, j = pinglun,t+q , q = 0, 1, 2, …, t = 1, 2, …, T ; σ j j = E(ε2jt ). ∂ y jt The results of variance decomposition is shown in Fig. 6.41. As can be seen from Fig. 6.41, in addition to its own inertia effect, other contextual factors also have an impact on commenting behavior’s fluctuation. Their contribution ratios in order of size are: the number of follows, the total number of comments, the total number of reposts, the number of followers, information temporal distance, the number of IS followers and the number of IS follows.

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Fig. 6.41 Variance decomposition of the fluctuation of the number of comments

6.6 Summary Based on the data of the reposting and commenting behaviors of brand crisis information on Sina Weibo, this chapter uses the VAR model and the state space model to study the influencing mechanisms of dynamic contextual factors of Weibo information sharing behavior in brand crisis from the objective characteristics of the data. In other words, the chapter studies in detail the path process of the impact of relevant contextual factors on the reposting and commenting behaviors of brand crisis information on Weibo from a dynamic perspective. Firstly, the Granger causality test is used to test and establish the correctness of the VAR model variables. At the same time, it is further confirmed that the relevant dynamic contextual factors have a significant impact on information sharing behavior. Secondly, by analyzing the time lag of each endogenous variable in the models, the length of time lag of each corresponding VAR model is determined and selected, and the endogenous time lag characteristics of each influencing factor are revealed. On this basis, the corresponding VAR models are constructed, and impulse response analysis of the disturbance of reposting and commenting behaviors is carried out to reveal the characteristics of the dynamic

6.6 Summary

247

response process of information reposting and commenting behaviors after being impacted by various contextual factors. Thirdly, through the construction and analysis of the state space models, this study further reveals the dynamic change process of the marginal influence of various contextual factors on information reposting and commenting behavior in the process of crisis information dissemination. Finally, through the variance decomposition of the VAR models of the overall variables, the dynamic change process of the contribution ratio of each contextual factor to the fluctuation of information reposting and commenting behavior is analyzed, and the change process of the influence of each contextual factor on information reposting and commenting behavior at different time points is accurately analyzed. The research results show that the obtaining and disseminating information through the network by users is not simply information being transmitted among users, but an atmosphere is being created that can impact information exchange and interaction among users in the whole network context. The whole platform is composed of many sub-contexts such as the sub-context of the total numbers of reposts and comments, the sub-context of the numbers of followers and follows, the sub-context of the numbers of IS followers and follows, the sub-context of information temporal distance, and so on. Together these sub-contexts form a grand context, a platform where users come together for information communication activities, from which emerges a social atmosphere and synergistic environment that fosters the spontaneous sharing of information among users of the platform. In user information behavior, contextual factors directly act on the user’s psychological variables, resulting in behavior. It indicates that information sharing behavior is affected by specific contextual factors and that there are different activating mechanism processes in every aspect of information behavior. User information behavior always occurs in a specific time and space and is the result of the interaction between the user and the environment. The result always keeps changing, so it is a dynamic process. It always occurs within a context, so it is also the product of that specific context. This environment does not exist in isolation. It is always composed of many intertwining sub-environments and many factors. All these sub-environments and factors constitute a huge environment complex, which creates a certain social atmosphere that makes users in that environment spontaneously or accidentally carry out information sharing behavior. However, at present, with the rapid development and wide application of Internet technology, online media has become an important channel for people to obtain information and also an important venue for users to gather in a virtual environment. Social media used for information exchange and sharing is one of the new information fields. The flow, transmission and utilization of information among users are all impacted by IUE. That environment can cause users to develop information needs and drive them to actively search, inquire and utilize information, so that through the analysis of IUE, combined with the internal and external information characteristics, a range of activities can be realized such as utilizing information resources, making decisions, making proposals and improving measures. The conclusions of this chapter can be applied to the monitoring and management of Weibo users’ brand crisis informationsharing behavior, and specific and targeted monitoring strategies and management measures can be formulated according to the

248

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disturbance characteristics, marginal influence and contribution ratio of the dynamic contextual factors of information sharing behavior.

References Asteriou, D., & Hall, S. G. (2011). Applied econometrics. Palgrave Macmillan. Che, P., & Wang, S. (2013). Statistical analysis and modeling of Weibo user behavior in great events. Journal of Beijing University of Posts and Telecommunications (Social Sciences Edition) 6, 8–16 Dhrymes, P. J. (2012). Econometrics: Statistical foundations and applications. Springer. Durbin, J, & Koopman, S. J. (2012). Time series analysis by state space methods. Oxford University Press. Fisher, K. E., & Naumer, C. M. (2006). Information grounds: Theoretical basis and empirical findings on information flow in social settings (pp. 93–111). Springer. Fisher, K. E., & Julien, H. (2009). Information behavior. Annual Review of Information Science and Technology, 43(1), 1–73. Goncalves, B., & Perra, N., Vespignani, A. (2011). Modeling user’s activity on Twitter networks: Validation of dunbar’s number. Plos one, 6(8):2011. Karlin, S., Goodman, L. A., Anderson, T. W., et al. (2014). Studies in econometrics, time series, and multivariate statistics. Academic Press. Lee, C. F., & Lee, J. C. (2015). Introduction to Financial Econometrics and Statistics. Springer. Ma, Y., & Wang, M (2014). Guowai xinxichang lilun de fazhan yu yanjin yanjiu (Research on the Development and Evolution of the Information Grounds Theory). Library and Information 155(1), 105–110. [马岩,王锰.国外信息场理论的发展与演进研究.图书与情报, 2014,155(1):105–110]. Niedzwiedzka, B. (2003). A proposed general model of information behaviour. Information Research, 9(1), 9–1. Sonnenwald, D. H., & Iivonen, M. (1999). An integrated human information behavior research framework for information studies. Library and Information Science Research, 21(4), 429–457. Taylor, R. S. (1986). On the study of information use environments. In Proceedings of the 49th Annual Meeting of the American Society for Information Science (ASIS’86) (Vol. 23, pp. 331–334). IEEE. Taylor, R. S. (1996). Information use environments. In Managing Information for the Competitive Edge, pp. 93–135. Trusina, A., Rosvall, M., Sneppen, K. (2004). Information horizons in networks. Physical Review Letters, 94(23). Wilson, T. D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249–270. Xiao, Q., & Zhu, Q. (2013). On the features and types of Weibo user behavior. Modern Information, 12, 69–74. Yan, Q., Wu, L., Zheng, L. (2013). Social network based microblog user behavior analysis. Physica A: Statistical Mechanics and Its Applications, 392(7), 1712–1723. Zhang, J., & Zhao, L. (2015). Review of studies on Weibo user behavior. Information Science, 8, 27. [张静,赵玲.微博用户行为研究述评.情报科学,2015(8):27.] Zhao, L., & Zhang, J (2013a). Multi-dimentional analysis of microblog user behavior research. Information and Documentation Services, 34(5), 65–70. [赵玲, 张静.微博用户行为研究的多 维解析.情报资料工作, 2013, 34(5):65–70]. Zhao, L., & Zhang, J. (2013b). Analysis of Weibo user behavior based on complex network. Modern Information, 33(9), 35–43.

Chapter 7

Strategies for Monitoring Brand Crisis Information Sharing by Weibo Users

In recent years, business competition grows ever more fiercely with the continuous development of market economy. On top of that, consumers are becoming more aware of their rights. In the complex and changing market environment, brand crisis is happening more and more frequently. With Weibo rises and improves rapidly in China, it has become an important platform for people to obtain and share information and interact with each other. As crisis has become a social norm, Weibo is a force to be reckoned with in the dissemination of crisis information. This chapter, through studying the strategies for monitoring information sharing behavior on Weibo over brand crisis, may provide reference for enterprise managers for monitoring and managing the dissemination of brand crisis information, in the hope of improving their efficiency and effect in crisis response and handling. The study on the strategies for monitoring crisis information sharing on Weibo has always been the focus and a hot topic in the academic and business communities. Most of the past researches have only come to some macroscopic and general conclusions, but few researches look into specificity or operability. What’s more, they also fail to yield relevant empirical research conclusions to support the classification of warning indicators and the positioning of monitoring period, and there is also a lack of monitoring strategies related to information behavior from the perspectives of the fluctuation characteristics, the static contextual indicators and the dynamic contextual indicators. Based on the conclusions of Chaps. 3, 5 and 6 of this book and aimed at the inadequacies of previous relevant researches, this chapter proposes specific monitoring strategies for information sharing behavior on Weibo in brand crisis. This study, by giving a full exposition of the fluctuation characteristics of information sharing behavior and detailed analysis of the influencing mechanisms of contextual factors, may help business managers to predict the evolution process of information reposting and commenting behaviors, so as to help them see clearly the key periods in terms of autocorrelation, trend patterns, periodical patterns and cluster patterns in the management process of brand crisis information dissemination. With that knowledge, they can focus their crisis response strategies and public relations activities on © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_7

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Fig. 7.1 Structure of Chap. 7

the key times of the largest marginal growth rate of fluctuation, behavior peak and group cluster and at the same time see clearly which factors will have a significant impact on users’ information sharing behavior. These related contextual factors can be regarded as monitoring indicators of crisis information sharing behavior to be used to identify which contextual features of information are more likely to cause users’ reposting or commenting behaviors, as well as identify the differentiation effects of different gender, age, educational and professional groups. By classifying the corresponding dynamic factors, the effective time length of tracking the relevant monitoring indicators can be determined, and the disturbance effect of each factor on the sharing behavior at different times can be predicted, so as to accurately identify the key periods for tracking and monitoring the influencing factors. Finally, according to different time periods, different indicator levels and different user groups, targeted regulatory strategies can be adopted to achieve effective and efficient crisis management. The research framework of this chapter is shown in Fig. 7.1.

7.1 Positioning Monitoring Time The research conclusions of Chap. 3 titled “Fluctuation Features of Brand Crisis Information Sharing by Weibo Users” and the conclusions of the accurate analysis of the autocorrelation, trend characteristics, periodic characteristics and cluster characteristics of behavior fluctuation in reposting and commenting by Weibo users in brand crisis can help business managers to predict the evolution process of information reposting and commenting behaviors, so as to help them see clearly the key periods in the Weibo information communication management process in a brand crisis in terms of autocorrelation, trend patterns, cycle patterns and cluster patterns. With that knowledge, they can focus their crisis response strategies and

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public relations activities on the periods with the largest fluctuation marginal growth rate, behavior peak and group cluster. Thus they can accurately locate the timings for monitoring reposting and commenting behaviors of brand crisis information.

7.1.1 Monitoring Times for Reposting Behavior 7.1.1.1

Behavior Prediction and Autocorrelation Monitoring

The conclusions of relevant studies on the autocorrelation features of brand crisis information reposting in Chap. 3 show that the reposting behavior has obvious autocorrelation, and it is apparent in the range of lag period 2–3. In the estimation of ARIMA model, the actual values of the reposting logarithmic first-order difference fit well with the ARIMA (3,1,3) model estimates, and that all residual values are within 95% confidence interval, indicating that the model settings and model estimates are valid. In positioning monitoring times, the constructed ARIMA (3,1,3) model can be used to predict brand crisis information reposting behavior by Weibo users, in order to understand and grasp the development trend and characteristics of crisis information reposting behavior. In the study, the correlation of ARIMA (3,1,3) model and the number of significant lags show that users’ reposting behavior has an important impact on their own behavior within the Lag 3 period, that is, users’ past behavior of participating in reposting has a significant impact on their current one, and there is a significant dependence between current reposting behavior and past reposting behavior in the Lag 3 period. Each fluctuation of crisis information reposting, according to its autocorrelation, will have a greater impact on the information reposting behavior at the first, second, fourth and sixth lags, indicating that the corresponding lags after each fluctuation of reposting behavior should be the focus of monitoring, while the fluctuation at other lags is relatively small.

7.1.1.2

Monitoring of Trend Characteristics and Periodic Characteristics

According to the conclusions of relevant research in Chap. 3 on the decomposition of the trend and periodic characteristics of fluctuation in brand crisis information reposting behavior, the reposting trend of the whole communication process reaches the maximum value on the fourth and fifth days after the crisis, and the marginal growth rate stays positive until the fourth day. The one-week reposting trend is a rising process from Monday to Friday with periodic characteristics as follow: fast climb on Tuesday, slow increase on Wednesday and Thursday, and positive marginal growth rate from Monday to Friday. The one-day reposting behavior shows a rapid increase between 6:00 a.m. and 4:00 p.m. and between 8:00 p.m. and 10 p.m., and the periodic feature shows a rising trend between 8:00 a.m. and 10 p.m. and positive marginal growth rates between 6:00 a.m. and 12:00 p.m., 2:30 p.m. and 4:30 p.m., and 8:00

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p.m. and 11 p.m. In positioning monitoring time during the whole communication process, the trend characteristics show that the reposting behavior in the first five days after the crisis exhibits a rising trend, and the changes in marginal growth rate show that the reposting behavior in the first four days is on the rise, and that there is a slight increase in the adjacent places on the ninth day and the tenth day, making the corresponding times the focuses for monitoring reposting behavior. During the week, the trend characteristics, the periodic characteristics and the marginal growth rate of reposting behavior all show that the time between Tuesday and Friday is the key time of the week for monitoring. During the day, the key times for monitoring and management fall between 9:00 a.m. and 11 a.m., 2p.m. and 4 p.m., and 8:00 p.m. and 11 p.m. The irregular variables in the whole communication process show that in the first five days there is a rising trend, and a periodic peak value is seen on the fifth, eighth, twelfth and nineteenth days. During the week, irregular variables mostly reach a periodic peak value on Tuesday and Friday. During the day, a periodic peak is seen at 10:00 a.m., 2:00 p.m., 6:00 p.m. and 10:00 p.m. The corresponding times of the above irregular features can be used as the reference point for monitoring and management.

7.1.1.3

Monitoring of Cluster Features

According to the relevant research conclusions of the group features of crisis information reposting behavior in Chap. 3 of this study, there is ARCH effect in reposting behavior throughout the whole communication process, and fluctuation clustering phenomenon is seen on the second, the third and the seventh days after the crisis with an obvious clustering effect. The conditional variance is the largest from the second day to the seventh day after the crisis, and larger on the first day and from the eighth day to the tenth day. During the week, there is fluctuation clustering phenomenon on Wednesday, Thursday and Friday with an obvious clustering effect. The conditional variance is the largest on Thursday morning, followed by Wednesday morning, Friday afternoon, Tuesday morning and Saturday morning. During the day, fluctuation clustering phenomenon happens between 9:00 a.m. and 11:00 a.m., 3:00 p.m. and 5:00 p.m., and 9:00 p.m. and 11:00 p.m. with an obvious clustering effect. The conditional variance is the largest from 8:00 a.m. to 11:00 a.m. and 9:00 p.m. to 11:00 p.m., and larger from 3:00 p.m. to 4:00 p.m.. In positioning monitoring time during the whole communication process, it can be seen from the residual line chart that there is clustering phenomenon in the first four days and from the sixth day to the eighth day, and the corresponding conditional variance line chart shows that the reposting clustering phenomenon on the third day, the fourth day, the fifth day and the seventh day fluctuates greatly, making those days the key monitoring periods. During the week, the reposting clustering phenomenon is mainly concentrated between Wednesday and Friday, and the conditional variance diagram shows that the cluster fluctuation range is larger on Wednesday morning and Thursday morning, followed by Friday afternoon, making the corresponding time periods the focuses of monitoring. During the day, clustering phenomenon is seen from 9:00 a.m. to 11:00 a.m., 3:00 p.m. to 5:00

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p.m. and from 9:00 p.m. to 11:00 p.m. and the corresponding conditional variance line chart shows that the cluster fluctuation range is the most obvious from 9:00 a.m. to 11:00 a.m. and from 9:00 p.m. to 11:00 p.m., followed by from 3:00 p.m. to 4:00 p.m. making the corresponding times the key monitoring periods.

7.1.2 Monitoring Times for Commenting Behavior 7.1.2.1

Behavior Prediction and Autocorrelation Monitoring

The conclusions of relevant studies on the autocorrelation features of brand crisis information commenting in Chap. 3 show that there is significant autocorrelation in the number of comments sequence, and it is apparent in the range of lag period 3–4. In the estimation of ARIMA model, the actual values of the commenting logarithmic first-order difference fit well with the ARIMA (3,1,3) model estimates, and that all residual values are within 95% confidence interval, indicating that the model settings and model estimates are valid. In positioning monitoring times, the constructed ARIMA (3,1,3) model can be used to predict brand crisis information commenting behavior by Weibo users, in order to accurately grasp the development trend and characteristics of crisis information commenting behavior. In the study, the correlation of ARIMA (3,1,3) model and the number of significant lags show that users’ commenting behavior has an important impact on their own behavior within the Lag 3 period, that is, users’ past behavior of participating in commenting has a significant impact on their current behavior of participating in commenting, and there is a significant dependence between current commenting behavior and past commenting behavior in the Lag 3 period. Each fluctuation of crisis information commenting, according to its autocorrelation, has a greater impact on the information commenting behavior at the first, third, fourth and sixth lags, indicating that the corresponding lags after each fluctuation should be the focus of monitoring, while the fluctuation at other lags is relatively low.

7.1.2.2

Monitoring of Trend Characteristics and Periodic Characteristic

According to the conclusions of relevant research in Chap. 3 on the decomposition of the trend and periodic characteristics of information commenting behavior over brand crisis, the commenting trend of the whole communication process shows a rapid rise right after the crisis, and reaches the maximum value on the fourth and fifth days, and the marginal growth rate stays positive until the fourth day. The oneweek commenting trend shows a rapid rise from Monday to Friday and a maximum value reached on Friday. The periodic characteristics are as follow: fast climb on Tuesday and Wednesday, maximum periodic effect reached on Thursday, positive marginal growth rate from Monday to Friday with a larger growth rate seen between

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Wednesday and Friday. The one-day commenting behavior shows periodic peak value reached at 10 a.m., 4:00 p.m. and 10 p.m. with a large trend effect. The periodic characteristics shows periodic peak value reached at 12:00 p.m., 4:00 p.m. and 10 p.m. with a large periodic effect. Positive marginal growth rate is seen between 6:00 a.m. and 12:00 p.m., 2:00 p.m and 5 p.m., and 7:30 p.m and 11:00 p.m.. In positioning monitoring time during the whole communication process, the trend characteristics show that the commenting behavior in the first five days after the crisis exhibits a rising trend, and the marginal growth rate shows that the commenting behavior in the first four days is obviously on the rise, and that there is a slight increase in the adjacent places on the ninth day and the tenth day, making the corresponding times the focuses for monitoring commenting behavior. During the week, the trend characteristics, the periodic characteristics and the marginal growth rate of commenting behavior all show that the time between Wednesday and Friday is the key time of the week for monitoring. During the day, the key times for monitoring fall between 8:00 a.m. and 11 a.m., 2p.m. and 4 p.m., and 8:00 p.m. and 11 p.m. The irregular variables in the whole communication process show that in the first five days there is a rising trend, and a periodic peak value is seen on the second, fifth, ninth, eleventh, fourteenth, sixteenth and nineteenth days. During the week, irregular variables reach a periodic peak value on Tuesday and Thursday. During the day, a periodic peak is seen at 10:00 a.m., 2 p.m., 6 p.m. and 10 p.m. The corresponding times of the above irregular features can be used as the reference point for monitoring and management.

7.1.2.3

Monitoring of Cluster Features

According to the relevant research conclusions of the group features of crisis information commenting behavior in Chap. 3 of this study, there is ARCH effect in commenting behavior throughout the whole communication process, and fluctuation clustering phenomenon is seen from the first day to the forth day and on the seventh day after the crisis with an obvious clustering effect. The conditional variance is the largest on the second day and the third day after the crisis, followed by the first day and the seventh day. During the week, there is fluctuation clustering phenomenon on Wednesday, Thursday and Friday with an obvious clustering effect. The conditional variance is the largest on Friday afternoon, followed by Thursday morning, Wednesday morning, Saturday morning and Tuesday morning. During the day, obvious fluctuation clustering phenomenon happens between 8:00 a.m. and 11:00 a.m., 3:00 p.m. and 5:00 p.m., and 9:00 p.m. to 11:00 p.m. The conditional variance is the largest from 8:00 a.m. to 10:00 a.m. and 9:00 p.m. to 11:00 p.m., and larger from 3:00 p.m. to 4:00 p.m.. In positioning monitoring time during the whole communication process, it can be seen from the residual line chart of commenting behavior that there is clustering phenomenon in the first four days and from the sixth day to the eighth day, and the corresponding conditional variance line chart shows that the reposting clustering phenomenon on the first, the third, the fourth, and the seventh day fluctuates greatly, making those days the key monitoring periods. During the week, the commenting clustering phenomenon is mainly concentrated

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between Wednesday and Friday, and the conditional variance diagram shows that the cluster fluctuation range is larger on Wednesday morning, Thursday morning and Friday afternoon, followed by Tuesday morning and Saturday morning, making the corresponding time periods the focuses of monitoring. During the day, clustering phenomenon is seen from 8:00 a.m. to 11:00 a.m., 3:00 p.m. to 6:00 p.m. and from 9:00 p.m. to 11:00 p.m. and the cluster fluctuation range is greater from 8:30 a.m. to 9:30 a.m. and from 9:00 p.m. to 11:00 p.m., followed by 3:00 p.m. to 4:00 p.m., making the corresponding times the key monitoring periods.

7.2 Monitoring of Static Contextual Factors According to the research conclusion of Chap. 5 titled “Static influencing mechanisms of Weibo users’ information sharing of brand crisis cases”, there are specific impact paths of information visualization (IV), information sentiment (IS) and information authority (IA) upon reposting and commenting behaviors and variation concerning these paths exists among groups of different genders, ages, education backgrounds and occupations. Therefore, in managing brand crisis information communication on Weibo, relevant contextual factors can be regarded as monitoring indicators of crisis information sharing behavior to be used to identify which contextual features of information are more likely to cause users’ reposting or commenting behaviors and to adopt targeted regulatory strategies according to the differentiated characteristics of different gender, age, educational and professional groups.

7.2.1 Indicator of Information Visualization (IV) According to the research conclusions of the impact of IV on crisis information sharing behavior in Chap. 5, the total effect of IV on users’ forwarding intention (FI) through various mediating variables is 0.22, and 0.23 on users’ commenting intention (CI), and the impact on CI is greater than that on FI. All the path coefficients reach the 0.05 significant level, and the path coefficients are al between 0 and 1. It can be seen that the impact of IV on FI and CI is significantly positive. Therefore, in the monitoring and management of the sharing behavior of crisis information, relevant information can be screened and classified according to the degree of IV, and the information with a high degree of visualization can be the focus of monitoring the behavior of crisis information reposting and commenting so as to improve monitoring efficiency.

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7.2.2 Indicator of Information Sentiment (IS) According to the research conclusions of the impact of IS on crisis information sharing behavior in Chap. 5, the total effect of IS on user FI through various mediating variables is 0.13, and 0.14 respectively on user CI, and the impact on CI is greater than that on FI. All the path coefficients reach the 0.05 significant level, and the path coefficients are all between 0 and 1. It can be seen that the impact of IS on FI and CI is significantly positive. Therefore, in the monitoring and management of the sharing behavior of crisis information, relevant information can be screened and classified according to the degree of IS, and the information with a high degree of sentiment can be the focus of monitoring the behavior of crisis information reposting and commenting so as to improve monitoring efficiency.

7.2.3 Indicator of Information Authority (IA) According to the research conclusions of the impact of IA on crisis information sharing behavior in Chap. 5, the total effect of IA on user FI through various mediating variables is 0.23, and 0.24 on users CI, and the impact on CI is greater than that on FI. All the path coefficients reach the 0.05 significant level, and the path coefficients are all between 0 and 1. It can be seen that the impact of IA on FI and CI is significantly positive. Therefore, in the monitoring and management of the sharing behavior of crisis information, relevant information can be screened and classified according to the degree of IA, and the information with a high degree of IA can be the focus of monitoring the behavior of crisis information reposting and commenting so as to improve monitoring efficiency.

7.2.4 Indicator of Harm Relevance (HR) According to the research conclusions of the impact of harm relevance (HR) on crisis information sharing behavior in Chap. 5, in the moderating effect model of HR between perceptual fluency (PF) and perceived harm (PH), the absolute |t| value of the significance t test of the interaction term coefficient is less than 1.96, which does not reach the significance level of 0.05, indicating that the corresponding moderating effect is not significant. In the moderating effect model of HR between cognitive absorption (CA) and PH and that of HR between cue dependence (CD) and PH, both the absolute |t| values of the significance t test of the interaction term coefficient are greater than 1.96, which reach the significance level of 0.05, indicating that the corresponding moderating effects are vital. What’s more, the moderating effect of HR between CA and PH is smaller than that between CD and PH. Thus it can be concluded that HR has a significant moderating effect on FI and CI. Therefore, in

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the monitoring and management of the sharing behavior of crisis information, user groups can be screened and classified according to the degree of HR, and the groups with a high degree of HR can be the focus of monitoring the behavior of crisis information reposting and commenting so as to improve monitoring efficiency.

7.2.5 Indicator of Group Difference According to the research conclusions of group differences in the influencing mechanism of static contextual factors in Chap. 5, the impact of IV, IS and IA upon FI and CI is greater in men than in women, indicating that more attention should be paid to monitoring male users when it comes to the static contextual indicators. In terms of differences in age groups, the corresponding variables have the greatest impact on the “30~39 years old” user group, followed by “40~49 years old” and “29 and under” age group and the lowest is with the “50 years old and above” age group. It indicates that, when it comes to monitoring different age groups, the order of priority monitoring is: “30~39 years old”, “40~49 years old”, “29 years old and below” and “50 years old and above”. In terms of differences in education groups, the corresponding variables have the greatest impact on the “university and above” user group, followed by “senior high school” user group and “junior high school” user group and the lowest is with the user group of “primary school and below”. It shows that, when it comes to monitoring different education groups, the order of priority monitoring is: “university and above”, “senior high school”, “junior high school”, “primary school and below”. In terms of differences in occupational groups, the corresponding variables have the greatest impact on the “enterprise” user group, followed by the “public institution” user group, and then the “government agency” user group. The “self-employed” user group is the lowest. It indicates that, concerning to monitoring different occupational groups, the order of priority monitoring is: “enterprise”, “public institution”, “government agency”, and “ self-employed”.

7.3 Monitoring of Dynamic Contextual Factors According to the research conclusions of Chap. 6 titled “Dynamic Influencing Mechanism of Weibo Users’ Information Sharing of Brand Crisis Cases”, we can see from the respective dynamic change process and characteristics of the time lag features, impulse disturbance, marginal influence and contribution ratios of the impact of the total numbers of Weibo information reposts and comments, the numbers of follows and followers, the numbers of IS follows and followers, and information temporal distance on information sharing behavior in the management of brand crisis information communication on Weibo, relevant contextual factors can be regarded as the dynamic monitoring indicators of information sharing behavior, and the related indicators can be classified to determine the effective time length for tracking the

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relevant monitoring indicators and to predict the disturbance effect of each factor on the sharing behavior at different times so as to accurately locate the key times for tracking and monitoring the influencing factors.

7.3.1 Monitoring of Reposting Behavior 7.3.1.1

Classification of Monitoring Indicators

According to the variance decomposition results of crisis information reposting behavior in Chap. 6 of this study, the order of contribution ratio of each dynamic contextual indicator to the fluctuation of reposting behavior in terms of size is: the total number of reposts, the total number of comments, the number of followers, information temporal distance, the number of IS followers, the number of follows, the number of IS follows and The size of contribution ratio reflects the impact of different factors on reposting behavior. In the process of monitoring information reposting behavior, the importance of each dynamic contextual monitoring indicator can be graded according to its contribution ratio, so as to improve the efficiency and effect of reposting behavior monitoring.

7.3.1.2

Indicators of the Total Number of Reposts and the Total Number of Comments

According to the research on the influencing mechanism of the total numbers of reposts and comments in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the total number of reposts and reposting behavior, and between the total number of comments and reposting behavior, and each fluctuation of the total number of reposts and the total number of comments has an important impact on users’ information reposting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information reposting behavior. The analysis results of the lag length and impulse response of the impact show that, when the total number of reposts is impacted by a positive shock, the shock will immediately lead to users’ reposting behavior, and the first lag response value is about 0.18; when the total number of comments is impacted by a positive shock, the shock will also immediately lead to users’ reposting behavior, and the first lag response is about 0.10. The lag effect of both the total number of reposts and the total number of comments on reposting behavior is quite large in the whole communication process. At the same time, the marginal influence of the total number of reposts on reposting behavior is about 0.28 at the beginning of the crisis, and the marginal influence of the total number of comments on reposting behavior is about 0.18 The marginal influence of the total number of reposts on information reposting behavior is larger in the first eleven days after the crisis, and the marginal influence of the total number of comments on information reposting behavior is larger in the

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first eight days after the crisis, indicating that the first eleven days and the first eight days after the crisis are the two days when the total number of reposts and the total number of comments respectively have an important impact on reposting behavior, making both the key times for monitoring of the two variables.

7.3.1.3

Indicators of the Number of Followers and the Number of Follows

According to the research on the influencing mechanism of the numbers of followers and follows in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the number of followers and reposting behavior, and between the number of follows and reposting behavior, and each fluctuation of the numbers of followers and follows has an important impact on users’ information reposting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information reposting behavior. The analysis results of the lag length and impulse response of the impact show that, when the number of followers is impacted by a positive shock, the shock will immediately lead to users’ reposting behavior, and the first lag response value is about 0.16; when the number of follows is impacted by a positive shock, the shock will also immediately lead to users’ reposting behavior, and the first lag response is about 0.12. The lag effect of the number of followers and the number of follows on reposting behavior is quite large in the first eight days after the crisis. At the same time, the marginal influence of the number of followers on reposting behavior is about 0.42 at the beginning of the crisis, and the marginal influence of the number of follows on reposting behavior is about 0.29 at the beginning of the crisis. The marginal influence of the number of followers on information reposting behavior is larger in the first eight days after the crisis, and the marginal influence of the number of follows on information reposting behavior is larger between the sixth and the eighth days after the crisis, indicating that the corresponding periods are the times when the number of followers and the number of follows respectively have an important impact on reposting behavior, making them the key times for monitoring the two variables.

7.3.1.4

Indicators of the Number of IS Followers and the Number of IS Follows

According to the research on the influencing mechanism of the numbers of IS followers and IS follows in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the number of IS followers and reposting behavior, and between the number of IS follows and reposting behavior, and each fluctuation of these two numbers has an important impact on users’ information reposting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information reposting behavior. The analysis results of the lag length and impulse response of the impact show that, when the number of IS followers

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is impacted by a positive shock, the shock will immediately lead to users’ reposting behavior, and the first lag response value is about 0.21, followed by a rapid increase till the third lag when there is a maximum value of 0.27; when the number of IS follows is impacted by a positive shock, the shock will also immediately lead to users’ reposting behavior, and the first lag response value is about 0.05, followed by a rapid increase till the third lag when there is a maximum value of 0.11. The lag effect of the number of IS followers and the number of IS follows on reposting behavior is larger in the first seven days after the crisis. At the same time, the marginal influence of the number of IS followers on reposting behavior is about 0.44 at the beginning of the crisis, and the marginal influence of the number of IS follows on reposting behavior is about 0.24 at the beginning of the crisis. The marginal influence of the number of IS followers on information reposting behavior is larger in the first eight days and on the fifteenth day after the crisis, and the marginal influence of the number of IS follows on information reposting behavior is larger in the first eight days after the crisis, indicating that the corresponding periods are the times when the number of IS followers and the number of IS follows respectively have an important impact on reposting behavior, making them the key times for monitoring of the two variables.

7.3.1.5

Indicator of Information Temporal Distance

According to the research on the influencing mechanism of information temporal distance in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between information temporal distance and reposting behavior and each fluctuation of the temporal distance has an important impact on users’ information reposting behavior. Therefore, the variable can be used as a monitoring indicator of crisis information reposting behavior. The analysis results of the lag length and impulse response of the impact show that, when the temporal distance is impacted by a positive shock, the shock will not immediately lead to users’ reposting behavior. The response value waits till the second lag to soar to 0.045, and at the third lag it stays at the same level. The lag effect of the temporal distance on reposting behavior is larger in the first eight days after the crisis. At the same time, the marginal influence of the temporal distance on reposting behavior is about 0.09 at the beginning of the crisis, and then it fluctuates upward rapidly to reach the maximum value on the seventh day at about 0.78. During this process, the marginal influence of the temporal distance on information reposting behavior is larger between the fourth day and the tenth day after the crisis, indicating that the corresponding period is the time when the temporal distance has an important impact on reposting behavior, making it the key time for monitoring of the variable.

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7.3.2 Monitoring of Commenting Behavior 7.3.2.1

Classification of Monitoring Indicators

According to the variance decomposition results of crisis information commenting behavior in Chap. 6 of this study, the order of contribution ratio of each dynamic contextual indicator to the fluctuation of commenting behavior in terms of size is: the number of follows, the total number of comments, the total number of reposts, the number of followers, information temporal distance, the number of IS followers, the number of IS follows. The size of contribution ratio reflects how big the impact of different factors on commenting behavior is. In the process of monitoring information commenting behavior, the importance of each dynamic contextual monitoring indicator can be classified according to its contribution ratio, so as to improve the efficiency and effect of commenting behavior monitoring.

7.3.2.2

Indicators of the Total Number of Reposts and the Total Number of Comments

According to the research on the influencing mechanism of the total numbers of reposts and comments in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the total number of reposts and commenting behavior, and between the total number of comments and commenting behavior, and each fluctuation of the total number of reposts and the total number of comments has an important impact on users’ information commenting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information commenting behavior. The analysis results of the lag length and impulse response of the impact show that, when the total number of comments is impacted by a positive shock, the shock will immediately lead to users’ commenting behavior with the first lag response value at about 0.67; when the total number of reposts is impacted by a positive shock, the impact will also immediately lead to users’ commenting behavior with the first lag response at about 0.48. The lag effect of the total number of reposts and the total number of comments on commenting behavior is larger in the first 12 days after the crisis. At the same time, the marginal influence of the total number of comments on commenting behavior is about 0.58 at the beginning of the crisis, and the marginal influence of the total number of reposts on commenting behavior is about 0.49 at the beginning of the crisis. The marginal influence of the total number of reposts on information commenting behavior is larger in the first 10 days after the crisis, and the marginal influence of the total number of comments on information commenting behavior is larger in the first 12 days after the crisis, indicating that the corresponding two periods are the times when the total number of reposts and the total number of comments respectively have an important impact on commenting behavior, making both the key times for monitoring of the two variables.

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7 Strategies for Monitoring Brand Crisis Information …

Indicators of the Number of Followers and the Number of Follows

According to the research on the influencing mechanism of the numbers of followers and follows in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the number of followers and commenting behavior, and between the number of follows and commenting behavior, and each fluctuation of the numbers of followers and follows has an important impact on users’ information commenting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information commenting behavior. The analysis results of the lag length and impulse response of the impact show that, when the number of followers is impacted by a positive shock, the shock will immediately lead to users’ commenting behavior with the first lag response value at about 0.015; when the number of follows is impacted by a positive shock, the shock will not immediately lead to users’ commenting behavior. The response value starts to rise rapidly at the second lag and reaches a peak value of 0.011 at the third lag. The lag effect of both the number of followers and the number of follows on commenting behavior is larger in the first ten days after the crisis. At the same time, the marginal influence of the number of followers on commenting behavior is about 0.84 at the beginning of the crisis and reaches the maximum value of the whole process at 0.88 on the first day; the marginal influence of the number of follows on commenting behavior is about 0.48 at the beginning of the crisis, followed by a slight fluctuation until the fourth day. The marginal influence of the number of followers on information commenting behavior is larger in the first eight days after the crisis, and the marginal influence of the number of follows on information commenting behavior is larger between the sixth day and the thirteenth day after the crisis, indicating that the corresponding periods are the times when the number of followers and the number of follows respectively have an important impact on commenting behavior, making them the key times for monitoring of the two variables.

7.3.2.4

Indicators of the Number of IS Followers and the Number of IS Follows

According to the research on the influencing mechanism of the numbers of IS followers and IS follows in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between the number of IS followers and commenting behavior, and between the number of IS follows and commenting behavior, and each fluctuation of these two numbers has an important impact on users’ information commenting behavior. Therefore, these two variables can be used as monitoring indicators of crisis information commenting behavior. The analysis results of the lag length and impulse response of the impact show that, when the number of IS followers is impacted by a positive shock, the shock will immediately lead to users’ commenting behavior with the first lag response value at about 0.021; when the number of IS follows is impacted by a positive shock, the shock will not

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immediately lead to users’ commenting behavior. The response value starts to rise rapidly at the second lag and reaches a maximum value of the whole process at 0.012 at the third and fourth lags. The lag effect of both the number of IS followers and the number of IS follows on commenting behavior is larger in the first ten days after the crisis. At the same time, the marginal influence of the number of IS followers on commenting behavior is about 0.18 at the beginning of the crisis. Then it soars to the maximum value of the whole process at 0.46 on the first day and stays at a relatively high level on the second and the third days. The marginal influence of the number of IS follows on commenting behavior is about 0.14 at the beginning of the crisis. The marginal influence of the number of IS followers on information commenting behavior is larger in the first nine days after the crisis, and that of the number of IS follows on information commenting behavior is larger from the fifth day to the sixteenth day after the crisis, indicating that the corresponding periods are the times when the number of IS followers and the number of IS follows have an important impact on commenting behavior, making them the key times for monitoring the two variables.

7.3.2.5

Indicator of Information Temporal Distance

According to the research on the influencing mechanism of information temporal distance in Chap. 6 of this study, the Granger causality test shows that there is a significant causal relationship between information temporal distance and commenting behavior and that each fluctuation of thetemporal distance has an important impact on users’ information commenting behavior. Therefore, the variable can be used as a monitoring indicator of crisis information commenting behavior. The analysis results of the lag length and impulse response of the impact show that, when the temporal distance is impacted by a positive shock, the shock will not immediately lead to users’ commenting behavior. The response value waits till the second lag to soaringto 0.02. The lag effect of the temporal distance on commenting behavior is larger in the first five days after the crisis. At the same time, the marginal influence of the temporal distance on commenting behavior is about 0.17 at the beginning of the crisis, and then it fluctuates upward rapidly to reach the peak value of the whole process on the sixth day at about 0.67. During this process, the marginal influence of the temporal distance on information commenting behavior is larger between the fifth day and the eleventh day after the crisis, indicating that the corresponding period is the time when the temporal distance has an important impact on commenting behavior, making it the key time for monitoring of the variable.

7.4 Summary Based on the research conclusions of Chaps. 3, 5 and 6 and aimed at addressing the inadequacies of previous studies and the needs of this study, this chapter proposes

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specific monitoring strategies for information sharing behavior on Weibo in brand crisis. The main contents include: positioning monitoring times of user information sharing behavior, the whole communication process, key monitoring times in a week and in a day; monitoring of the static contextual indicators of user information sharing behavior, including selective monitoring of IV, IS and IA, and differential monitoring according to user demographic variables; monitoring of the dynamic contextual indicators of user information sharing behavior, including classification of dynamic contextual indicators and positioning of indicator monitoring times. The research on the specific monitoring strategies of Weibo users’ information sharing behavior in brand crisis may help to strengthen business managers’ ability to monitor and cope with brand crises, and improve their initiative to respond to and deal with crisis events, which is of great significance to the management of Weibo information dissemination in brand crisis.

Chapter 8

Conclusion and Suggestions

In todays’ complicated and changing market environment where brand crisis is happening ever more frequently, Weibo as an important place for information seeking and sharing as well as interactive exchanges has an undeniable impact on the dissemination of crisis information. This study has selected sixty-six much publicized cases of brand crisis from China’s Sina Weibo between 2010 and 2016. Data on dynamic mechanisms have been collected through API and web crawler. On this basis, online questionnaire has been conducted with sample users who have engaged in brand crisis information reposting or commenting behavior to obtain research data on static mechanisms. Statistical software such as SPSS 22.0, AMOS 22.0, Stata 13.0 and EViews 8.0 has been used and research methods including trend decomposition of time series, ARIMA model, autoregression conditional heteroscedasticity (ARCH), vector autoregression (VAR), structural equation model (SEM) and state space model have been employed to process and analyze data, so as to come to conclusions and put forward relevant monitoring suggestions. This chapter summarizes and analyzes the research results and limitations, and proposes suggestions for future research.

8.1 Research Findings 8.1.1 Accurate Analysis of the Fluctuation Characteristics of Brand Crisis Information Sharing by Weibo Users Decomposition analysis of the fluctuation characteristics of Weibo users’ information sharing behavior in brand crisis reveals that such behavior possesses autocorrelation characteristics and the fluctuation characteristics is further decomposed into trend characteristics, periodic characteristics, cluster characteristics and irregular characteristics. The whole process of reposting and commenting and its weekly © Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4_8

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and daily fluctuation features are accurately analyzed. It is found that reposting behavior shows significant autocorrelation characteristics in the first three lags, reaches a peak on about the third day after the crisis, and shows obvious clustering phenomenon and major fluctuation on the second to fourth and seventh days. During the week, reposting happens the least on Monday and Tuesday, followed by Saturday and Sunday, but from Wednesday to Friday its number rises rapidly and reaches a maximum value on Friday; clustering phenomenon is clearly seen on Wednesday, Thursday and Friday, and larger cluster fluctuation range is seen on Thursday morning, Wednesday morning and Friday afternoon. During the day, peak times of reposting behavior are from 10:00 a.m. to 12 a.m., 3:00 p.m. to 4:00 p.m. and 9:00 p.m. to 11:00 p.m.; what’s more, obvious clustering phenomenon and major fluctuation are seen from 9:00 a.m. to 11:00 a.m., 3:00 p.m. to 5:00 p.m. and 9:00 p.m. to 11:00 p.m. As for commenting behavior, it shows significant autocorrelation characteristics in the first four lags, reaches a peak on about the third day after the crisis, and shows obvious clustering phenomenon and major fluctuation on the second, third, seventh and eighth days. During the week, commenting happens the least on Monday and Tuesday, followed by Saturday and Sunday, but from Wednesday to Friday its number rises rapidly and reaches a maximum value on Friday; clustering phenomenon is clearly seen on Wednesday, Thursday and Friday, and larger fluctuation range is seen on Friday afternoon, Thursday morning and Wednesday morning. During the day, peak times of commenting behavior are from 10:00 a.m. to 12 a.m., 3:00 p.m. to 5:00 p.m. and 9:00 p.m. to 11:00 p.m.; what’s more, obvious clustering phenomenon is seen from 8:00 a.m. to 11:00 a.m., 3:00 p.m. to 5:00 p.m. and 9:00 p.m. to 11:00 p.m., and large fluctuation range is seen from 8:30 a.m. to 9:30 a.m., 3:00 p.m. to 4:00 p.m. and 9:00 p.m. to 11:00 p.m.

8.1.2 Identifying and Quantitative Analysis of the Contextual Influencing Factors of Information Sharing Behavior on Weibo in Brand Crisis The study, based on relevant theories and previous research results and through inference, induction and analysis, identifies the static and dynamic contextual factors that have an important impact on Weibo users’ information sharing behaviors in brand crisis. The static contextual influencing factors mainly include information visualization, information sentiment and information authority while the dynamic contextual influencing factors mainly include the total number of information reposts, the total number of information comments, the number of followers, the number of follows, the number of IS followers, the number of IS follows and information temporal distance. Probit model and panel data model are used for quantitative testing

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and analysis of the causality of the influencing factors. It is found that the corresponding contextual factors all have a significant impact on information reposting and commenting behaviors.

8.1.3 Study on the Influencing Mechanism of Static Contextual Factors of Weibo Users’ Information Sharing of Brand Crisis Cases The scale design and data collection process of this study have good reliability and validity. On this basis, the main effect hypothesis and the moderating effect hypothesis of the theoretical model are tested by means of the structural equation model and the multi-step regression. The following conclusions are drawn. First, the impact of IV on FI and CI through the three intermediate variables PF, CA and PH is significantly positive. The impact of IS on FI and CI through the two intermediate variables CA and PH is significantly positive. The impact of IA on FI and CI through the two intermediate variables CD and PH is significantly positive. Second, the positive moderating effect of HR on CA and PH and on CD and PH is significant; whereas, the positive moderating effect of HR on PF and PH is not significant. Third, the analysis of the overall sample data shows that the influence of each information contextual factor on FI and CI in order from big to small is: IA, IV, and IS. The contextual factors have greater impact on CI, compared with that of FI. The moderating effect of HR between CA and PH is less than that between CD and PH. Fourth, through the analysis of the relevant groups, it is found that the theoretical model has cross gender, cross age, cross educational level and cross occupation validity, which shows that the theoretical model is stable and there are differences in each influence path among different groups. Fifth, in terms of gender differences, IV, IS and IA have more influence on FI and CI among men than women. The moderating effect of HR between CA and PH and between CD and PH is also greater in men than in women. Sixth, in terms of age difference, IV, IS and IA have the greatest influence on FI and CI upon user group of “30 to 39 years old”. The second is the “40 to 49 years old” age group. The third is the “29 years and under” age group. The last is the “50 years and older” age group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “30 to 39 years old,” “40 to 49 years old,” “29 years and under,” and “50 years and older”. Seventh, in terms of educational differences, IV, IS and IA have the greatest influence on FI and CI upon the “university and above” education group. The second is the “senior high school or secondary technical school” education group. The third is the “junior high school” education group. The last is the “primary school and below” education group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “university and above” education group,

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“senior high school or secondary technical school” education group, “junior high school” education group and “primary school and below” education group. Eighth, in terms of occupational differences, IV, IS and IA have the greatest influence on FI and CI upon the “enterprise” group. The second is the “public institution” group. The third is the “government agency” group. The last is the “self-employed” group. The order of the moderating effect of HR on CA and PH and on CD and PH from high to low is “enterprise” group, “public institution” group, “government agency” group, and “self-employed” group.

8.1.4 Study on the Influencing Mechanism of Dynamic Contextual Factors of Brand Crisis Information Sharing by Weibo Users First, it is further confirmed that there is significant causal relationship between the total number of reposts, the total number of comments, the number of followers, the number of follows, the number of IS followers, the number of IS follows, the temporal distance and user’s information sharing behavior. The VAR model of the impact of the total number of reposts, the total number of comments, the number of followers and the number of follows on information reposting and commenting behaviors has a 3-lag endogenous structure. The VAR model of the impact of the number of IS followers, the number of IS follows, and the temporal distance on information reposting and commenting behaviors has a 2-lag endogenous structure. Second, the impact of each fluctuation of the total number of reposts and the total number of comments on reposting behavior is great in the whole lag process, and the impact on commenting behavior is greater from the first to the tenth lag; the marginal influence of the total number of reposts and the total number of comments on reposting behavior is larger between the second day and the eleventh day and between the second day and the eighth day after the crisis respectively, and the marginal influence on commenting behavior is larger in the first ten days and the first eleven days after the crisis respectively. Third, the impact of each fluctuation of the number of followers and the number of follows on reposting behavior is greater in the first ten lags, and the disturbance on commenting behavior is greater in the first nine lags; the marginal influence of the number of followers and the number of follows on reposting behavior is larger between the second day and the eighth day and between the six day and the thirteenth day after the crisis respectively, and the marginal influence on commenting behavior is larger in the first eight days and between the sixth day and the eleventh day after the crisis respectively. Fourth, the impact of each fluctuation of the number of IS followers and the number of IS follows on reposting behavior is greater in the first seven lags, and the disturbance on commenting behavior is greater in the first ten lags; the marginal influence of both the number of IS followers and the number of IS follows on reposting

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behavior is larger in the first eight days after the crisis, and the marginal influence on commenting behavior is larger in the first eleven days and between the seventh day and the thirteenth day after the crisis respectively. Fifth, the impact of each fluctuation of the temporal distance on reposting behavior is greater in the first eight lags, and the disturbance on commenting behavior is greater in the first five lags; the marginal influence of the temporal distance on reposting behavior is larger between the fourth day and the eleventh day after the crisis, and the marginal influence on commenting behavior is larger between the fifth day and the eleventh day after the crisis respectively. Sixth, in terms of the disturbance of each dynamic contextual factor on reposting behavior and commenting behavior, their contribution ratios to reposting behavior in order of size are: the total number of reposts, the total number of comments, the number of followers, information temporal distance, the number of IS followers, the number of follows and the number of IS follows. Their contribution ratios to commenting behavior in order of size are: the number of follows, the total number of comments, the total number of reposts, the number of followers, information temporal distance, the number of IS followers and the number of IS follows.

8.1.5 Study of the Strategies for Targeted Monitoring of Brand Crisis Information Sharing by Weibo Users Based on the conclusions of Chaps. 3, 5 and 6, specific monitoring strategies for information sharing behavior on Weibo in a brand crisis are proposed. They mainly include: monitoring period positioning of information sharing behavior that includes the whole communication process and the key monitoring times in a week and in a day; monitoring of static contextual indicators of information sharing behavior that includes selective monitoring of IV, IS and IA and differentiated monitoring based on the variables of the demographic characteristics of users; and monitoring of dynamic contextual indicators of information sharing behavior that includes classification of dynamic contextual monitoring indicators and positioning time of indicator monitoring.

8.2 Research Contributions This study combines communication, psychology, and sociology theories, collects data through official APIs, web crawler and questionnaire, processes and analyzes data using quantitative means such as time series analysis and structural equation models, and studies the influencing mechanisms of information sharing behavior of Weibo users over brand crisis from the information contextual perspective. In

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general, this study has the following theoretical contributions in research topics, research design and research findings: (1)

If only descriptive analysis is employed to analyze the fluctuation characteristics of information sharing behavior of Weibo users, then only fairly preliminary conclusions can be drawn and no accurate analysis of the trend characteristics, periodic characteristics and cluster characteristics can be carried out as the fluctuations are a combination of trend characteristics, periodic characteristics, cluster characteristics and irregular characteristics. In this study, through the time series ARIMA model, trend decomposition and autoregression conditional heteroscedasticity model, component variables including the autocorrelation, trend characteristic, periodic characteristic and cluster characteristic of information sharing behavior fluctuation are decomposed, based on which each characteristic is accurately analyzed. Built on that, the study then proceeds to analyze the influencing contextual factors of the information sharing behavior of Weibo users in brand crisis by Probit model and panel data analysis, and sorts out the static and dynamic contextual factors that have a significant impact on the user’s information sharing behavior. The author finds that users’ information behavior in the cyber world is influenced by many factors, which can usually be summed up in two major categories: one is individual and the other is environmental. Because of this influence, the fluctuation of information behavior exhibits autocorrelation, trend, periodical and cluster features. Moreover, the behavior of an individual in the virtual environment at any point in time is the result of the interaction between individual factors and the external environment, and a dynamic process because it keeps changing all the time. The behavior always happens in a certain context, that is, it is also the product of a specific context. In the past research, there have been many statements from classical theories of information behavior. For example, Wilson’s general information behavior theory regards user information behavior as an orderly circular process, with information need as the starting point and information utilization as the end (Wilson, 1999). Individuals in the process of information seeking and information utilization are affected by a variety of interference factors and there are various activating mechanism links, of which active retrieval is the key to individual information behavior. The author also integrates individual information need, activating mechanism, influencing factors and information response into the model, which makes the contextual factors, interference variables and the activating mechanisms in the process of information behavior more clearly visible. However, the theory does not give a detailed discussion on the specific composition of information behavior, but only provides a theoretical framework for the analysis and research of information behavior. Especially at present, due to the rapid development of network technology and information technology, the characteristics of people’s information behavior may be greatly different from those of the past, and become more complex in composition, which makes these studies or theories need to be constantly updated and improved in variable structure and structure analysis.

8.2 Research Contributions

(2)

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Through the combination and application of information behavior theory, field theory of psychology, information ground theory and information contextual theory in the field of information behavior research of social media users, this research has obtained some new discoveries and drawn certain conclusions of the characteristics and influencing factors of online information behavior. It can provide some reference and guidance for the further exploration of the accurate characteristics and patterns of other user information behaviors and for the construction of related theories. It also helps to deepen and develop network user information behavior research guided by information behavior theory, field theory of psychology, information ground theory and information contextual theory. Through studying the influencing mechanism of static contextual factors of Weibo users’ information sharing behavior in brand crisis, based on information contextual theory and information processing theory, this study takes IV, IS and IA as independent variables, PF, CA and CD as mediating variables, and constructs theoretical models with harm relevance as the moderating variable, so as to study the mechanism of static contextual factors on crisis information sharing behavior. The study reveals the influence path of IV, IS and IA on Weibo users’ reposting and commenting behavior respectively, and discovers the specific action path and function size of the independent variables on the dependent variables, which reflects the whole process of user’s behavior from information receiving to information processing to changing of attitude. On this basis, the differences in influence effects between different groups of gender, age, educational level and occupation are compared. The research shows that in the process of information search, information reading and information sharing by Weibo users, information in different forms, different content characteristics and different sources will lead to the difference in user’s intention and efforts to process information, thus exerting different persuasion effects on users through different physiological stimulation and perception paths. Generally speaking, the information of different contextual characteristics has different physiological stimulation on users, which in turn affects their perceptual properties, and finally transmits to their behavior intention. In this influence mechanism, the information contextual factors directly act on the user’s psychological variables, which lead to user’s behavior, indicating that there is a activating mechanism process in every step of information sharing behavior.

Previous research has provided numerous statements of classic information behavior theories, for example Wilson (1999) and Niedzwiedzka (2003) both think that the activating mechanism can occur at all stages of the information behavior process. However, the formation mechanism of people’s information behavior may have undergone tremendous changes due to the rapid development of network technology and information technology. For example, the era of traditional media was affected more by the characteristics of realistic interpersonal relationship, while the new media era may be impacted more by the network environment or contextual factors, or virtual interpersonal relationship is becoming increasingly important while

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the impact of realistic interpersonal characteristics is relatively weakened. In addition, the changes in how information is represented and presented and how it circulates in the context of new media may have led to differences in user’s information cognition from that in the era of traditional media, which makes the elaboration likelihood model and heuristic-systematic model in need of constant updating and improvement in terms of variables and structure. Through the combination and application of information processing theory (ELM and HSM) and information behavior theory in the field of information behavior research of social media users, this research has obtained some new discoveries and drawn certain conclusions of the static influencing mechanism of user information behavior on Weibo. It can provide some reference and guidance for the further study on user information behavior mechanism and for improving the prediction and theoretical system of network user information behavior. It also helps to deepen and develop network user information behavior research guided by information processing theory and information behavior theory. (3)

Through studying the influencing mechanism of the dynamic contextual factors of information sharing behavior by Weibo users in brand crisis, the study gives a dynamic decomposition of the dynamic influencing process of the time lags, the pulse disturbance, the marginal influence and the fluctuation contribution ratio of the respective impact of the total numbers of reposts and comments, the numbers of follows and followers, the number of IS follows and followers and temporal distance on information sharing behavior and comes to relevant conclusions on the characteristics of dynamic processes in which independent variables affect dependent variables and the size of the impact. It indicates that users’ obtaining and disseminating information through the network is not simply information being transmitted among users, but an atmosphere is being formed that can impact information exchange and interaction among users in the whole network context. The whole platform is composed of many subcontexts such as the sub-context of the total numbers of reposts and comments, the sub-context of the numbers of followers and follows, the sub-context of the numbers of IS followers and follows, the sub-context of information temporal distance, etc. and then together these sub-contexts form a grand context, a platform where users come together for information communication activities, from which emerges a social atmosphere and synergistic environment that fosters the spontaneous sharing of information among users in the platform.

Numerous statements of classic information behavior theories can be found in previous research: information ground theory holds that from a particular ground where people come together for a purpose emerges a social atmosphere that fosters the spontaneous sharing of information; the theory of information horizon put forward by Sonnenwald (1999) proposes that users usually go into information search, acquisition, utilization and other information activities within their own information horizon. User information search behavior is the process of individuals constantly adjusting their own behavior and maintaining interaction and coordination with information resources. In this information domain, the user will use the optimal scheme of information search, query, acquisition, and utilization according to their own conditions.

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The theory of information use environment (IUE) proposed by Taylor (1986a) holds that, in IUE, various factors can have a significant impact on information screening and selection. The flow, transmission and utilization of information between users are affected by IUE, which can be used to determine the usefulness and the value of the information and these factors to some extent lead to the different characteristics of user information behavior. However, with the rapid development and wide application of Internet technology, online media has become an important way for people to obtain information and an important gathering place in the virtual environment. The social media in which users exchange and share information have also become a new kind of information ground. It is because of the rapid development of network technology and information technology that information communication has developed a new information circulation pattern and interpersonal network space, which then caused tremendous changes in the formation mechanism of people’s information behavior. For example, the era of traditional media was affected more by the characteristics of realistic interpersonal relationship, while the new media era may be impacted more by the network environment or contextual factors, or virtual interpersonal relationship is becoming increasingly important while the impact of realistic interpersonal characteristics is relatively weakened. These changes may have brought about changes in the formation mechanism and activating mechanism of user information behavior, which renders the classical theories unfit to reflect the current production mechanism of user information behavior. Therefore, relevant theoretical models need to be constantly updated and improved in terms of variables and structure, such as integrating some new elements and paths. Overall, through the combination and application of information ground theory, IUE theory, theory of information horizons and information behavior theory in the field of information behavior research of social media users, this research has obtained some new discoveries and drawn certain conclusions of the dynamic influencing mechanism of network information behavior. It can provide some theoretical reference and support for the improvement of the theory of network information ground and for the construction of a monitoring indicator system and the classification of indicators for the sharing behavior of crisis information. It also helps to deepen and develop network user information behavior research guided by information ground theory, IUE theory, theory of information horizons and information behavior theory. (4)

In terms of research concept and means, this book uses related theories from communication, psychology, sociology and other disciplines, and employs quantitative means such as ARIMA model, autoregression conditional heteroscedasticity (ARCH) model, vector autoregression (VAR) model, structural equation model (SEM), state space model to study the fluctuation characteristics, static influencing mechanism and dynamic influencing mechanism of Weibo users’ information sharing behavior over brand crisis. Because the study integrates multidisciplinary theories and uses a variety of research methods, it further promotes the idea of interdisciplinary research on information behavior and also provides reference for further study of information behavior from the

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perspective of contextual factors. Through the comprehensive explanation of the fluctuation characteristics of information sharing behavior and the specific analysis of the influencing mechanism of contextual factors and by proposing targeted monitoring strategies for Weibo users’ information sharing behavior in brand crisis, the research conclusions not only enriches the research results on the information behavior of Weibo and other social media users and helps people to fully understand the objective laws and influencing mechanisms of the information sharing behavior of Weibo users over brand crisis, but also provides reference and theoretical basis for tracking, monitoring and managing information communication on Weibo over brand crisis.

8.3 Limitations and Suggestions Firstly, the study looks at the influencing mechanisms of Weibo users’ information sharing behavior in brand crisis from the perspective of contextual factors. Due to space limitation, only two kinds of information behavior influencing mechanisms, namely reposting and commenting, are examined. However, in addition to information reposting and commenting, there are also other information behaviors such as publishing, adding to favorite, following and being followed. Future research can consider to study the influencing mechanisms of Weibo users’ information behavior including information publishing, adding to favorite, following and being followed in brand crisis from the perspective of contextual factors, and explore the relevant contextual factors that have a significant impact on different information behaviors, as well as the differences in the influencing mechanisms of each information behavior. It will be helpful for enterprises or media to have a more comprehensive understanding of the influencing mechanisms of Weibo users’ information behavior in brand crisis, so as to develop more comprehensive strategies for monitoring information behavior. Secondly, in the research of the influencing mechanism of static contextual factors, this study summarizes three important factors, namely information visualization, information sentiment and information authority according to the relevant theories and previous research results. However, among the relevant static contextual factors, in addition to the three factors selected in this study, there are still other factors that may have a significant impact on the user’s information behavior, such as tags, URL, account registration time and authentication status. In the future, related research can consider exploring whether other static factors have an significant impact on user information sharing behavior and how different the influencing mechanisms of various factors are, to help enterprises or media to have a more comprehensive understanding of the influencing mechanisms of the contextual factors of Weibo users’ information behavior in brand crisis, so as to develop more comprehensive strategies for monitoring information behavior. Finally, in the research on the influencing mechanism of static situation factors, due to the limitation of space, the author did not further subdivide the age groups

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and occupational groups in the group analysis, thus unable to grasp the size difference of each influence path in the more specified groups or to monitor the user information sharing behavior more accurately. In addition, in the group analysis, the author only analyzed the four most important demographic variable groups: gender, age, education background and occupation. In fact, there are still other demographic variable user groups or groups divided by other criteria. Future research can build on this and analyze other demographic variable groups and other types of variable groups, in order to develop more detailed and comprehensive strategies for monitoring information sharing behavior.

References Niedzwiedzka, B. (2003). A proposed general model of information behaviour. Information Research, 9(1), 9–1. Sonnenwald, D. H. (1999). Evolving perspectives of human information behavior: Contexts, situations, social networks and information horizons. Exploring the contexts of information behavior. In Proceedings of the Second International Conference in Information Needs. Taylor Graham. Taylor, R. S. (1986). On the study of information use environments. In Proceedings of the 49th Annual Meeting of the American Society for Information Science (ASIS’86) (vol. 23, pp. 331–334) IEEE. Wilson, T. D. (1999). Models in information behaviour research. Journal of documentation, 55(3), 249–270.

Appendix A

Original Data Retrieval Formats

1.

Data file save format

2.

Format of original data variables Variable Table 1.

© Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4

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Variable Table 2.

Variable Table 3.

Appendix A: Original Data Retrieval Formats

Appendix B

Questionnaire

Questionnaire on Reposting and Commenting behavior by Weibo Users over Brand Crisis Dear Sir/Madam, We are from Shanghai Jiao Tong University and currently doing a research project on information behavior by Weibo users over brand crisis. We are now conducting a research survey for the project on information reposting and commenting by Weibo users in brand crisis. It will be much appreciated if you fill out the following questionnaire for us. Your support and comments will be valuable to our research. We promise to keep your response confidential and only use it for academic reference, so please write down your real thoughts. Thank you for your support! Rewards: To show our gratitude for your time and support, when you complete the questionnaire and submit it, you will gain a reward worth 7RMB. The reward is offered through different ways of online payment including mobile top-up, Wechat red packet, Alipay, Tencent QB, online bank payment. Feel free to choose whatever suits you.

© Xiamen University Press 2022 C. Yang, Sharing Behavior of Brand Crisis Information on Social Media, https://doi.org/10.1007/978-981-16-6667-4

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Appendix B: Questionnaire Part One (please check the letter of your choice) 1. Have you ever reposted or commented on information concerning brand crisis on Weibo? (please check two options) A. Reposted

B. Commented

C. Never reposted

D. Never commented

2. About Information Visualization (IV) IV1. To what extent do you think tables improve information visualization in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little IV2. To what extent do you think pictures improve information visualization in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little IV3. To what extent do you think videos improve information visualization in brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

IV4. To what extent do you think direct and vivid ways of expression improve information visualization in brand crisis? A. Greatly B. Much C. Moderately D. Slightly

E. Little

3. About Information Sentiment (IS) IS1. To what extent do you think the expression of pain shows information sentiment in brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

IS2. To what extent do you think the expression of anger shows information sentiment in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little IS3. To what extent do you think the expression of despair shows information sentiment in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little IS4. To what extent do you think the expression of hatred shows information sentiment in brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

4. Information Authority (IA) IA1. To what extentdo you think the official status of the information source shows information authority in the dissemination of brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

IA2. To what extent do you think the professional aspect of the information source shows information authority in the dissemination of brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little

Appendix B: Questionnaire

281

IA3. To what extent do you think the formal status of the informationsource shows information authority in the dissemination of brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

IA4. To what extent do you think the authentication status of the information source shows information authority in the dissemination of brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

5. Perceptual Fluency (PF) PF1. In brand crisis, to what extent do you think the clarity of the information influences your perception of the information as easy andfluent? A. Greatly B. Much C. Moderately D. Slightly E. Little PF2. In brand crisis, to what extent do you think the simplicity of the expression of the information influences your perception of the information as easy andfluent? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

PF3. In brand crisis, to what extent do you think the visibility of informationcoding influences your perception of the information as easy andfluent? A. Greatly B. Much C. Moderately D. Slightly E. Little PF4. In brand crisis, to what extent do you think the aesthetic quality of the information structure influences your perception of the information as easy andfluent? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

6. Cognitive Absorption (CA) CA1. To what extent do you give your attention to specific information exclusively when you process brand crisis information? A. Greatly B. Much C. Moderately D. Slightly E. Little CA2. To what extent do you have your senses (including vision, hearing, etc.) focused on specific information exclusively when you process brand crisis information? A. Greatly B. Much C. Moderately D. Slightly E. Little CA3. To what extent do you have your awareness (including consciousness, thinking, etc.) focused on specific information exclusively when you process brand crisis information? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

CA4. How attentive you are to the specific information that you are interested in when you process brand crisis information? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

7. Cue Dependence (CD) CD1. To what extent do you develop your understanding directly based on suggestive cues when you process brand crisis information? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

CD2. To what extent do you develop your understanding directly based on guiding cues when you process brand crisis information? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

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Appendix B: Questionnaire CD3. To what extent do specific properties of the information influence your trust in the entire information when you process brand crisis information? A. Greatly B. Much C. Moderately D. Slightly E. Little CD4. To what extent do you form instinctive understanding based on some cues without careful reasoning or analysis when you process brand crisis information? A. Greatly B. Much C. Moderately D. Slightly E. Little 8. Perceived Harm (PH) PH1. To what extent do you think the financial loss contributes to your perceived harm in brand crisis? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

PH2. To what extent do you think the physical harm contributes to your perceived harm in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little PH3. To what extent do you think the psychological harm contributes to your perceived harm in brand crisis? A. Greatly B. Much C. Moderately D. Slightly E. Little PH4. To what extent do you think the emotional harm contributes to your perceived harm in brand crisis? B. A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

9. Harm Relevance (HR) HR1. To what extent does the possibility of physical harm to yourself demonstrate the relevance of the brand crisis to you? A. Greatly B. Much C. Moderately D. Slightly E. Little HR2. To what extent does the possibility of physical harm to your family members demonstrate the relevance of the brand crisis to you? A. Greatly B. Much C. Moderately D. Slightly E. Little HR3. To what extent does the possibility of physical harm to your relatives demonstrate the relevance of the brand crisis to you? A. Greatly B. Much C. Moderately D. Slightly

E. Little

HR4. To what extent does the possibility of physical harm to your friends demonstrate the relevance of the brand crisis to you? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

10. Forward Intention (FI) FI1. How much do you intend to forward the information of brand crisis after reading it? A. Greatly B. Much C. Moderately D. Slightly E. Little

Appendix B: Questionnaire

283

FI2. What is the possibility of you forwarding the information of brand crisis after reading it? A. Great B. Much C. Moderate D. Slight E. Little FI3. How strong is your intention to forward the information of brand crisis after reading it? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

FI4. How long does your intention of forwarding the information of brand crisis last after reading it? A. Extremely long

B. Relatively long

C. Moderately long

D. Slightly long

E. Very short 11. Comment Intention (CI) CI1. How much do you intend to comment on the information of brand crisis after reading it? A. Greatly B. Much C. Moderately D. Slightly E. Little CI2. What is the possibility of you commenting on the information of brand crisis afterreading it? A. Great

B. Much

C. Moderate

D. Slight

E. Little

CI3. How strong is your intention to comment on the information of brand crisis after reading it? A. Greatly

B. Much

C. Moderately

D. Slightly

E. Little

CI4. How long does your intention of commenting on the information of brand crisis remain after reading it?

A. Extremely long

B. Relatively long

C. Moderately long

D. Slightly long

E. Very short Part Two (please check the letter of your choice) 1. How old are you? A. ≤29

B. 30 – 39

C. 40 – 49

D. ≥50

2. What is your occupational domain? A. Government agency

B. Public institution

C. Enterprise

D. Self-

employed 3. What is your educational background? A. University and above C. Junior highschool

B. Senior high school D. Primary school and below

4. What’s your gender? A. Male

B. Female

Thank you again for your support! Wish you happiness and good health!