Automated Enforcement and Traffic Safety

Zhi (Aaron) Cheng
Zhi (Aaron) Cheng
[email protected]
https://orcid.org/0000-0002-2070-3761
Department of Management, London School of Economics, London WC2A 2AE, United Kingdom
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,
Zhanyu Dong
Corresponding Author
Zhanyu Dong
[email protected]
https://orcid.org/0009-0001-4574-2847
Business School, Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China; and Research Center for Innovation, Entrepreneurship, and Technology Finance, Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China
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Min-Seok Pang
Min-Seok Pang
[email protected]
https://orcid.org/0000-0001-9010-6260
Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706
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Department of Management, London School of Economics, London WC2A 2AE, United Kingdom

Corresponding Author

Zhanyu Dong

Business School, Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China; and Research Center for Innovation, Entrepreneurship, and Technology Finance, Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China

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Min-Seok Pang

[email protected]

https://orcid.org/0000-0001-9010-6260

Wisconsin School of Business, University of Wisconsin-Madison, Madison, Wisconsin 53706

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Published Online:15 Apr 2025https://doi.org/10.1287/mnsc.2023.00575

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Volume 71, Issue 12

December 2025

Pages vii-x, 9869-10753, iv-vi

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Received:February 21, 2023
Accepted:February 02, 2025
Published Online:April 15, 2025

Cite as

Zhi (Aaron) Cheng, Zhanyu Dong, Min-Seok Pang (2025) Automated Enforcement and Traffic Safety. Management Science 71(12):10067-10087.

https://doi.org/10.1287/mnsc.2023.00575

Keywords

Acknowledgments

The authors thank D.J. Wu (department editor), the anonymous associate editor, and three reviewers for constructive feedback that significantly improved the article. This study also benefited from insights shared by colleagues and seminar participants at American University, Arizona State University, Hanyang University, London School of Economics and Political Science, Sun Yat-sen University, Temple University, University of Florida, University of Georgia, University of Hong Kong, University of Memphis, University of Michigan, University of Minnesota, University of Rochester, and University of Wisconsin-Madison. Additionally, the work was enriched by discussions with conference and workshop participants at the Workshop on Information Systems and Economics, the Conference on Information Systems and Technology, the Statistical Challenges in Electronic Commerce Research, the AI and the Future of Work Conference, and the Conference on Health IT and Analytics, among others. The authors also thank Paul A. Pavlou, Ling Xue, Sunil Wattal, Ting Li, and Michael Barzelay for valuable comments on earlier versions of this work. Finally, the authors acknowledge Prof. Yingwei Huang, who sadly passed away, for invaluable contributions in facilitating access to the data source, as well as generous guidance and support during the early stages of this research. All remaining errors are the authors’ own.

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