TY - JOUR
T1 - Antecedents and consequences of excessive online social gaming
T2 - a social learning perspective
AU - Gong, Xiang
AU - Zhang, Kem Z.K.
AU - Chen, Chongyang
AU - Cheung, Christy M.K.
AU - Lee, Matthew K.O.
N1 - Funding Information:
The authors would like to thank the Department of Tourism, University of Otago (New Zealand) as well as the Diane Campbell Memorial award for supporting this research. The research was part of a doctoral dissertation funded through a University of Otago Doctoral scholarship.
PY - 2020/3/9
Y1 - 2020/3/9
N2 - Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.
AB - Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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.
KW - Computer games
KW - Excessive online social gaming
KW - Excessive technology use
KW - Internet addiction
KW - Longitudinal data
KW - Observational learning
KW - Reinforcement learning
KW - Social learning theory
KW - Structural equation modelling
KW - Virtual community
UR - http://www.scopus.com/inward/record.url?scp=85073996320&partnerID=8YFLogxK
U2 - 10.1108/ITP-03-2018-0138
DO - 10.1108/ITP-03-2018-0138
M3 - Journal article
AN - SCOPUS:85073996320
SN - 0959-3845
VL - 33
SP - 657
EP - 688
JO - Information Technology and People
JF - Information Technology and People
IS - 2
ER -