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E-Government Use, Perceived Transparency, Public Knowledge of Government Performance, and Satisfaction with Government: An Analysis of Mediating, Moderating, and Framing Mechanisms Based on the COVID-19 Outbreak Control Survey Data from China

Author

Listed:
  • Edward Gu

    (Zhejiang University)

  • Tianguang Meng

    (Tsinghua University)

  • Hongying Wang

    (University of Waterloo)

  • Alexander Zhang

    (University of Pittsburgh)

Abstract

According to the existing literature on public management, many factors affect popular satisfaction with the government (referred to as “government satisfaction”), one of which is the use of e-government. Particularly, many e-government proponents regard increasing government transparency as an important way to improve government satisfaction. To test the validity of this assertion, this paper uses an Ordinary Least Squares (OLS) regression model to analyze survey data on individuals’ e-government usage frequencies in China and their satisfaction with the local government during the COVID-19 pandemic. We find that people can rationally and consciously evaluate governments and make decisions by using the government performance information that they know about. This conscious and rational process is also mixed with the impact of some irrational and emotional factors, which is consistent with the concept of the “framing effect.” This paper identifies local government transparency as the mediating variable in the relationship between the use of e-government and government satisfaction and public knowledge of local government performance in COVID-19 outbreak control as the moderating variable in the relationship between local government transparency and government satisfaction. Since the mediating effect of local government transparency is significantly different from those of overall and central government transparency, government transparency at different levels plays a role of the above-mentioned “frame.”

Suggested Citation

  • Edward Gu & Tianguang Meng & Hongying Wang & Alexander Zhang, 2023. "E-Government Use, Perceived Transparency, Public Knowledge of Government Performance, and Satisfaction with Government: An Analysis of Mediating, Moderating, and Framing Mechanisms Based on the COVID-," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 169(1), pages 79-124, September.
    • Handle: RePEc:spr:soinre:v:169:y:2023:i:1:d:10.1007_s11205-023-03135-4
    DOI: 10.1007/s11205-023-03135-4
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    1. Christopher Hood, 2007. "What happens when transparency meets blame-avoidance?," Public Management Review, Taylor & Francis Journals, vol. 9(2), pages 191-210, June.
    2. Richard Breen & Kristian Bernt Karlson & Anders Holm, 2013. "Total, Direct, and Indirect Effects in Logit and Probit Models," Sociological Methods & Research, , vol. 42(2), pages 164-191, May.
    3. Marc Oliver Rieger & Mei Wang, 2022. "Trust in Government Actions During the COVID-19 Crisis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(3), pages 967-989, February.
    4. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    5. Keith Nicholls & J. Steven Picou, 2013. "The Impact of Hurricane Katrina on Trust in Government," Social Science Quarterly, Southwestern Social Science Association, vol. 94(2), pages 344-361, June.
    6. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    7. Bruno S. Frey & Fabian Homberg & Margit Osterloh, 2013. "Organizational Control Systems and Pay-for-Performance in the Public Service," CREMA Working Paper Series 2013-11, Center for Research in Economics, Management and the Arts (CREMA).
    8. Chun-Pin Tseng & Cheng-Wu Chen & Yu-Ping Tu, 2011. "A new viewpoint on risk control decision models for natural disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 59(3), pages 1715-1733, December.
    9. Viswanath Venkatesh & James Y. L. Thong & Frank K. Y. Chan & Paul J. H. Hu, 2016. "Managing Citizens’ Uncertainty in E-Government Services: The Mediating and Moderating Roles of Transparency and Trust," Information Systems Research, INFORMS, vol. 27(1), pages 87-111, March.
    10. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    11. Veljko Jovanović & Milica Lazić, 2020. "Is Longer Always Better? A Comparison of the Validity of Single-item Versus Multiple-item Measures of Life Satisfaction," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 15(3), pages 675-692, July.
    12. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    13. Xiajuan Guo & Wenyan Tu, 2017. "Corruption tolerance and its influencing factors—the case of China’s civil servants," Journal of Chinese Governance, Taylor & Francis Journals, vol. 2(3), pages 307-328, July.
    14. Michael M. Bechtel & Jens Hainmueller, 2011. "How Lasting Is Voter Gratitude? An Analysis of the Short‐ and Long‐Term Electoral Returns to Beneficial Policy," American Journal of Political Science, John Wiley & Sons, vol. 55(4), pages 852-868, October.
    15. Roumeen Islam, 2006. "Does More Transparency Go Along With Better Governance?," Economics and Politics, Wiley Blackwell, vol. 18(2), pages 121-167, July.
    16. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, vol. 65(3), pages 557-586, May.
    17. James G. March, 1978. "Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 587-608, Autumn.
    18. Gerring, John & Thacker, Strom C., 2004. "Political Institutions and Corruption: The Role of Unitarism and Parliamentarism," British Journal of Political Science, Cambridge University Press, vol. 34(2), pages 295-330, April.
    19. Richard Forgette & Marvin King & Bryan Dettrey, 2008. "Race, Hurricane Katrina, and Government Satisfaction: Examining the Role of Race in Assessing Blame," Publius: The Journal of Federalism, CSF Associates Inc., vol. 38(4), pages 671-691, Fall.
    20. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
    21. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
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