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Prelims

Di Wang (Wuhan University, China)
Deborah Richards (Macquarie University, Australia)
Ayse Aysin Bilgin (Macquarie University, Australia)
Chuanfu Chen (Wuhan University, China)

The Development of Open Government Data

ISBN: 978-1-80262-316-1, eISBN: 978-1-80262-315-4

Publication date: 15 July 2022

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Citation

Wang, D., Richards, D., Bilgin, A.A. and Chen, C. (2022), "Prelims", The Development of Open Government Data, Emerald Publishing Limited, Leeds, pp. i-xix. https://doi.org/10.1108/978-1-80262-315-420221013

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Emerald Publishing Limited

Copyright © 2022 Di Wang, Deborah Richards, Ayse Aysin Bilgin and Chuanfu Chen. Published under exclusive licence by Emerald Publishing Limited

Half Title Page

The Development of Open Government Data

Title Page

The Development of Open Government Data: Connecting Supply and Demand Through Portals

By

Di Wang

Wuhan University, China

Deborah Richards

Macquarie University, Australia

Ayse Aysin Bilgin

Macquarie University, Australia

And

Chuanfu Chen

Wuhan University, China

United Kingdom – North America – Japan – India – Malaysia – China

Copyright Page

Emerald Publishing Limited

Howard House, Wagon Lane, Bingley BD16 1WA, UK

First edition 2022

Copyright © 2022 Di Wang, Deborah Richards, Ayse Aysin Bilgin and Chuanfu Chen. Published under exclusive licence by Emerald Publishing Limited.

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British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN: 978-1-80262-316-1 (Print)

ISBN: 978-1-80262-315-4 (Online)

ISBN: 978-1-80262-317-8 (Epub)

List of Figures and Tables

Figure 1. Relationship between Open Data, OGD, and PSI.
Figure 2. Research Roadmap.
Figure 3. Structure and Flow of the Selected Literature Review Topics.
Figure 4. Open Government Data Lifecycle.
Figure 5. Roadmap of Concepts in Chapter 2 and Later Studies.
Figure 6. AHP Hierarchy Structure (See Table 7 for A1–A15 Descriptions).
Figure 7. Comparison of Evaluation Scores by Portal.
Figure 8. Number of Different Format Datasets provided by Portals.
Figure 9. Research Model.
Figure 10. Citizens' Acceptance of Data-Acquisition Methods.
Figure 11. Reported Usage Frequency and Preferences of Keyword Search and Browse.
Figure 12. Differences in Acceptance of Regular Recommendations between Genders.
Figure 13. Frequency and Percentage of Help Functions.
Figure 14. Frequency of People's Preference of Virtual Agents' Appearance.
Figure 15. Distribution of Different Gender Participants' Preferences of Virtual Agent's Role.
Figure 16. Distribution of Different Aged Participants' Preferences of Virtual Agent's Appearance.
Figure 17. Distribution of Different Education Background Participants' Preferences of Virtual Agent's Gender.
Figure 18. The Refined Research Model.
Figure 19. Modified DOI Research Model for Connecting OGD Demand and Supply.
Figure 20. Modified Research Model With Conclusions.
Figure 21. Screenshot of the Shanghai OGD Portal.
Figure 22. Integrated Research Model.
Figure 23. Experimental Design.
Figure 24. Home Page of the Imitated Portal.
Figure 25. Interface for the Conversational Virtual Agent.
Figure 26. Experimental Procedure.
Figure 27. Homepage of the Imitated Portal With the Virtual Agent.
Figure 28. Methods of Knowing About OGD and OGD Portals.
Figure 29. Types of Data Used.
Figure 30. Usage Frequency of OGD and OGD Portals.
Figure 31. Purposes of Use of OGD.
Figure 32. Frequency Distribution Histogram of Playing Computer Games.
Figure 33. Process for Building DOI Models.
Figure 34. Relationship Map of the Variables in DOI Models.
Figure 35. Process for Building Model to Predict Citizens' Possibility of Using the OGD Portal.
Figure 36. Model for Predicting Citizens' Possibility of Using the OGD Portal.
Figure A1. An Example of an OGD Portal.
Table 1. Structure of Objectives and Research Questions.
Table 2. Comparison of Technology Acceptance Models and Theories.
Table 3. Comparison of OGD Portal Evaluation Frameworks.
Table 4. Evaluation Aspects of Frameworks and Principles.
Table 5. Exclusion of Evaluation Elements.
Table 6. Categories for Data Collection.
Table 7. Local OGD Portal Evaluation Framework.
Table 8. Province-Level OGD Portals in China Included for Evaluation.
Table 9. Evaluation Results.
Table 10. Data Accessibility by Category.
Table 11. Data Quality by Category.
Table 12. Correlation Analysis for Contexts and Number of Datasets.
Table 13. Descriptions of OGD Attributes.
Table 14. Socio-Demographic Characteristics.
Table 15. Likelihood Ratio Tests of Citizens' Knowledge of OGD.
Table 16. Reliability and Adequacy Tests of all Scales.
Table 17. Descriptive Analysis of Citizens' Demands for OGD Subjects.
Table 18. T-Tests of Citizens' Knowledge of OGD and Their Demands of OGD Subjects.
Table 19. One-Way ANOVA of Age, Education, Occupation, and OGD Subjects.
Table 20. Descriptive Statistics of OGD Attributes.
Table 21. One-Way ANOVA of Age, Education, Occupation, and OGD Attributes.
Table 22. T-Tests of Citizens' Knowledge of OGD and OGD Attributes.
Table 23. Chi-Square Tests of OGD Utilization Purpose.
Table 24. Chi-Square Tests of Citizens' Preferences of Virtual Agents' Appearance.
Table 25. T-Tests of OGD Utilization Purposes and Demands for OGD Subjects.
Table 26. T-Tests of Data-Acquisition Methods and OGD Attributes.
Table 27. Pearson Correlation Analysis of Data Acquisition and OGD Attributes.
Table 28. Pearson Correlation Analysis of Need of Help and OGD Attributes.
Table 29. Comparison Model of OGD's Supply Side and Demand Side.
Table 30. Description of Cases for Supply Side.
Table 31. Reliability and Adequacy Tests of the Model.
Table 32. Online Visibility of OGD Portals.
Table 33. Comparative Results for Supply Side and Demand Side in Relative Advantage.
Table 34. Comparative Results for Supply Side and Demand Side in Compatibility.
Table 35. Comparative Results for Supply Side and Demand Side in Complexity.
Table 36. Correlations Between Observability, Relative Advantage, and Compatibility on Demand Side.
Table 37. Correlation Analysis of Relative Advantage and Compatibility.
Table 38. List of Selected OGD Portals.
Table 39. Selection of Usability Principles.
Table 40. Criteria for Usability Principles.
Table 41. Fleiss's Kappa for Principles and Criteria.
Table 42. Average Values for Principles and Criteria.
Table 43. Kruskal–Wallis H Test of OGD Portals.
Table 44. Portals' Average Scores by Principle and One-Way ANOVA Results.
Table 45. Portals' Average Scores by Criterion and One-Way ANOVA Results.
Table 46. Socio-Demographics of the Participants.
Table 47. Participants' Knowledge of OGD and OGD Portals.
Table 48. Description of Big Five Personality Dimensions and Trust Tendency.
Table 49. Reliability and Adequacy Tests of Scales.
Table 50. Results of Shapiro–Wilk Normality Test.
Table 51. Descriptive Analysis of Completion of Tasks.
Table 52. Mann–Whitney U Test of Completion of Tasks Between the Two Groups.
Table 53. Descriptive Analysis of TAM Importance Scale.
Table 54. Descriptive Analysis of TAM Perceived Ease of Use Scale.
Table 55. Analysis of PEOU and Socio-Demographic Variables.
Table 56. Analysis of PEOU and OGD and OGD Portal Experience Variables.
Table 57. Descriptive Analysis of TAM Perceived Usefulness Scale.
Table 58. Pearson's Correlation Analysis of Importance and PEOU.
Table 59. Pearson's Correlation Analysis of Importance and PU.
Table 60. Pearson's Correlation Analysis of PEOU and PU.
Table 61. Descriptive Analysis of DOI for OGD Portal.
Table 62. Analysis of Demographic Background and DOI.
Table 63. Descriptive Analysis of DOI for the Virtual Agent.
Table 64. Analysis of Demographic Background and DOI.
Table 65. Analysis of Participants' Past Experience With OGD and Trustworthiness.
Table 66. Descriptive Analysis of Rapport.
Table 67. Analysis of Socio-Demographic Background and Rapport.
Table 68. Chi-Square Tests of Demographic Background and Two-Task Accuracy Rate.
Table 69. T-Tests of TAM Scales and Two-Task Accuracy Rate.
Table 70. Mann–Whitney U Tests of DOI and Trustworthiness Scales and Two-Task Accuracy Rate.
Table 71. T-Tests of Rapport Scales and Two-Task Accuracy Rate.
Table 72. Mann–Whitney U Tests of DOI for Virtual Agent and Two-Task Accuracy Rate.
Table 73. Cutoff Points and Categorization of DOI Variables.
Table 74. Accuracy Rates of Models for DOI Variables.
Table 75. Models for DOI Variables.
Table 76. Research Objectives and Research Questions of This Book.
Table B1. Chinese Dialogue CSV Files for Introduction.
Table B2. Chinese Dialogue CSV Files for FAQ.
Table B3. Chinese Dialogue CSV Files for Data Application.
Table B4. Chinese Dialogue CSV Files for Feedback.

About the Authors

Di Wang is a Postdoc Researcher from School of Information Management, Wuhan University. She received her PhD in Computing from Macquarie University and PhD in Information Resources Management from Wuhan University. Her research interests include big data analytics, open data policy, open data resources, and data services. She published in journals including Government Information Quarterly and Library Hi Tech and presented at conferences including JCDL, ICADL, iConference, etc. She has been awarded several research grants from the National Science Foundation of China, the National Social Science Foundation of China, and China Postdoctoral Science Foundation.

Deborah Richards is a Professor in the Department of Computing at Macquarie University. Following 20 years in the IT industry during which she completed a BBus (Comp and MIS) and MAppSc (InfoStudies), she completed a PhD in Artificial Intelligence on the Reuse of Knowledge at the University of New South Wales and joined academia in 1999. While she continues to work on solutions to assist decision-making and knowledge acquisition, for the past decade, her focus has been on intelligent systems, agent technologies, and virtual worlds to support human learning, health, and well-being. She has over 300 refereed publications and been awarded numerous research and teaching grants from the Australian Research Council and other research funders.

Ayse Aysin Bilgin is an Associate Professor, active in the statistical analysis and design of studies in health and statistics education. Her research in health developed to include adolescent health and recently studies related to breast cancer, MS, and Alzheimer's disease. She is one of the pioneers on applying machine learning to health data sets. She was awarded “Excellence in research: Five Future-shaping Priorities (Healthy People)” by Macquarie University. Ayse has won several learning and teaching awards for her outstanding contributions to student learning such as ALTC citation for “Outstanding Contributions to Student Learning.” Her pedagogical research interests are focused on statistics education, such as learning approaches in statistics, impact of learning spaces to students' learning. She is the President of International Association for Statistical Education (IASE) (July 2021–July 2023).

Chuanfu Chen is a Distinguished Research Professor and Dean of the Graduate School, Wuhan University. From 2005 to 2012, he was Dean of the School of Information Management of Wuhan University and was the Deputy President of Library Society of China from 2004 to 2012. He earned his MLS in 1986, and his PhD in Law in 2001, both from Wuhan University. His research focuses on library development, public access to information resources, big data governance, and copyright. He has published in many journals including ASLIB Proceedings, College & Research Libraries, Electronic Library, Journal of Library Science of China, and Scientometrics, among others. He has also been awarded numerous research grants from the National Social Science Foundation and the Natural Science Foundation of China.

Abbreviations

AHP

Analytic Hierarchy Process

API

Application programming interface

CR

Consistency ratio

DOI

Diffusion of Innovation theory

FAQ

Frequently asked question

GDP

Gross domestic product

HCI

Human–computer interaction

ICL

Intention – complexity

ICP

Intention – compatibility

ICT

Information and communication technology

IOB

Intention – observability

IRA

Intention – relative advantage

ITR

Intention – trialability

MM

Motivational model

OGD

Open government data

PBC

Perceived behavioral control

PEOU

Perceived ease of use

PSI

Public sector information

PU

Perceived usefulness

RQ

Research question

TAM

Technology acceptance model

TIPI

Ten-item personality inventory

TPB

Theory of planned behavior

TRA

Theory of reasoned action

TTS

Text to speech

UTAUT

Unified theory of acceptance and use of technology

Preface

Open government data (OGD) have developed rapidly in these years due to various benefits that can be derived through transparency and public access. However, researchers emphasize lack of use instead of lack of disclosure as a key problem in OGD's present development. Previous studies look into this issue either from the supply side, focusing on data quantity and quality, or from the demand side, focusing on factors that affect users' acceptance of OGD, but seldom consider both sides at the same time. This book makes a comparison of the supply and demand sides of OGD and explores possible directions for the future development of OGD portals based on the discovered mismatches lying between the two sides.

The core purpose of this book is to improve OGD utilization by balancing the supply side and demand side of OGD according to the demands of citizens through the development of OGD portals. To achieve this objective, four connected studies were designed and carried out. The first study built an evaluation framework for understanding the development of the supply side of OGD by evaluating existing Chinese province-level OGD portals. Sequentially, with citizens as primary users on the demand side and the major beneficiaries of OGD, the second study focused on a survey conducted to analyze citizens' awareness and utilization of OGD portals. A third study compared the supply and demand sides of OGD based on Diffusion of Innovation (DOI) theory, using the data collected in the previous two studies. A final study tested the proposed usability criteria for building an OGD portal in helping users to use the data on the portal by carrying out a between-subjects experiment. All case studies in these four parts were carried out in China.

This research finds that Chinese OGD portals are in an early stage of development. Citizens have limited awareness of OGD and OGD portals. Significant correlations are recognized among citizens, and their demands and utilization of OGD. Mismatches lie between the supply and demand sides of OGD. Following the proposed usability criteria for building an OGD portal could improve citizens' proper utilization of OGD. Future directions for developing OGD are identified.

The key contribution of this book to the present literature is the theoretical and practical understanding of OGD and its user, as well as proposing directions for OGD portals' future development in order to encourage citizens' OGD utilization.

This book originates from the PhD research study of Dr Di Wang in Macquarie University, Australia, under the supervision of Prof. Deborah Richards, Assoc. Prof. Ayse Aysin Bilgin, and Prof. Chuanfu Chen.

Acknowledgments

First and foremost, I would like to express my great gratitude to my supervisor, Prof. Deborah Richards. Your immense knowledge and research experiences guided me through my whole research. This book would not be completed without your support and advice.

I would also like to express my sincere appreciation to my associate supervisor, Assoc. Prof. Ayse Aysin Bilgin, and my adjunct supervisor, Prof. Chuanfu Chen, for your support, motivation, and suggestions during all the time of my research and writing of this book.

I would like to acknowledge the contributions of my parents, Mr Lin Wang and Mrs Lifen Ma for your continuous loving support. I could not have completed this work without your encouragement.

Besides my supervisors and parents, I would like to thank the people who have offered generous assistance with my studies, particularly Ms Meredith Taylor, who helped me with building the virtual agent for the experiment, and Ms Xu Chen, who helped me with setting OGD portals for the experiment.

I extend my thanks to Macquarie University, which provided the MQ Research Training Program (MQRTP), and the China Scholarship Council, which has provided financial assistance during my research. I would also like to express my appreciation for the kind support of the School of Information Management, Wuhan University, that has allowed me to complete this book.

Finally, I would like to thank everybody who was important and supportive to the successful realization of my first book, as well as expressing my apology that I could not mention each personally one by one.

Di Wang

Wuhan, China, December 2021

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