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Antecedents and consequences of excessive online social gaming: a social learning perspective

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Drawing on the social learning theory, the purpose of this paper is to examine the antecedents and consequences of users’ excessive online social gaming. Specifically, the authors develop a model to propose that observational learning and reinforcement learning mechanisms together determine excessive online social gaming, which further foster adverse consequences.


The model is empirically validated by a longitudinal survey among users of a popular online social game: Arena of Valor. The empirical data are analyzed using component-based structural equation modeling approach.


The empirical results offer two key findings. First, excessive online social gaming is determined by observational learning factors, i.e. social frequency and social norm, and reinforcement learning factors, i.e. perceived enjoyment and perceived escapism. Second, excessive online social gaming leads to three categories of adverse consequences: technology-family conflict, technology-work conflict and technology-person conflict. Meanwhile, technology-family conflict and technology-work conflict further foster technology-person conflict.


This study contributes to the literature by developing a nomological framework of excessive online social gaming and by extending the social learning theory to excessive technology use.
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Keywords: Computer games; Excessive online social gaming; Excessive technology use; Internet addiction; Longitudinal data; Observational learning; Reinforcement learning; Social learning theory; Structural equation modelling; Virtual community

Document Type: Research Article

Affiliations: 1: School of Management, Xi’an Jiaotong University, Xi’an, China 2: International School of Business & Finance, Sun Yat-sen University, Guangzhou, China 3: Dongwu Business School, Soochow University, Taipei, Taiwan 4: Department of Finance and Decision Sciences, School of Business, Hong Kong Baptist University, Hong Kong 5: Department of Information Systems, City University of Hong Kong, Hong Kong

Publication date: August 23, 2019

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