Platform Loophole Exploitation, Recovery Measures, and User Engagement: A Quasi-Natural Experiment in Online Gaming

Author:

Chen Jianqing1ORCID,He Shu2ORCID,Yang Xue3ORCID

Affiliation:

1. Jindal School of Management, The University of Texas at Dallas, Richardson, Texas 75080;

2. Warrington College of Business, University of Florida, Gainesville, Florida 32611;

3. Business School, Nanjing University, Nanjing 210093, China

Abstract

Online platforms often encounter the challenge of system vulnerabilities, such as bugs, which can be exploited by certain users for illicit gains. These platforms face a dilemma when devising countermeasures, particularly in deciding whether to penalize rule breakers. Different countermeasures can lead to varying economic impacts, including subsequent user engagement. In this study, based on unique field data from a prominent online gaming platform, we discovered that the occurrence of bugs has a negative effect on the online duration and consumption of observing players. Surprisingly, although the platform is responsible for the bugs, not penalizing rule breakers results in more substantial reductions in platform engagement among observing players compared with penalizing them. This effect is particularly pronounced for observers who are directly affected by rule violations. Our findings emphasize the essential role of the platform in fairly punishing rule breakers. To ensure the long-term prosperity of an online platform and the overall welfare of its participants, it is crucial for the platform to maintain high-quality system control and implement effective governance mechanisms for rule enforcement, thereby restoring justice and order to the online community.

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems,Management Information Systems

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Process Mining for Game Analytics;2024 IEEE Conference on Games (CoG);2024-08-05

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