Learning User Play-Then-Pay Behaviors in Digital Games: A Dynamic Perspective

• 成果类型: Article
• 署名作者: Guo, Mengzhuo; Li, Yijun; Xu, Xiangyang; Zhang, Qingpeng; Zeng, Daniel Dajun; Chen, Frank Youhua
• 署名单位: Sichuan University; University of Electronic Science & Technology of China; Tencent; University of Hong Kong; University of Hong Kong; Chinese Academy of Sciences; Institute of Automation, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; City University of Hong Kong
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251400467
• 发表日期: 2026
• 关键词: Hidden Markov model; online; engagement; support

摘要

The gaming industry has emerged as a critical force in the digital content economy, yet managing user behavior to drive sustained activity and monetization remains a complex operational challenge. In this study, we propose a two-layer hidden Markov model to capture users' gameplay and payment behaviors by constructing a play-then-pay chain that links user engagement to subsequent purchase intention dynamics. Drawing on a real-world dataset, we uncover three levels of engagement states measuring the degree of stickiness with the focal game, as well as two levels of purchase intention states describing one's willingness to pay. We find that a higher engagement state is associated with a volatile transition pattern and leads to a higher upward transition tendency in purchase intention, while low and medium engagement states tend to maintain a low purchase intention state. We also examine several factors that affect the transitions of these psychological states. The analysis reveals that user activity in same-type games enhances upward transitions only among users in the medium engagement state, without affecting users in the high engagement state, and exhibits no significant effect on purchase intentions. In contrast, user activity in different types of games has a negative effect on users in both low and high engagement states. Our state-dependent outcomes suggest that the managers' strategies are more effective when targeted toward users with low engagement and purchase intention states. Further experimental analysis supports the effectiveness of the proposed play-then-pay chain for predicting users' behaviors. Our policy simulation demonstrates that traffic subsidization effectively redirects user attention to the focal game, with interventions targeting different-type games yielding greater improvements in propensities for both gameplay and payment behavior compared to same-type games. Our work provides managerial implications for platform managers.