Abstract
PurposeWidespread concerns about excessive use of mobile instant messaging (MIM) have inspired research in different disciplines. However, the focus thus far tends to be on diagnostics and cognitive predictors. There is little understanding from the socio-technical perspective to capture the drivers of excessive use of MIM. To address this research gap, the authors aim to enrich existing literature by adopting a social structure and social learning model (SSSLM) to understand the excessive use of MIM. The authors argue that excessive MIM use is developed and reinforced in highly interactive online communication, through a social learning process.Design/methodology/approachThe authors conduct a cross-sectional online survey to validate our proposed research model on excessive use of mobile instant messaging (MIM). 368 valid responses are obtained from active MIM users in China.FindingsThe results suggest that highly interactive MIM creates a technology-based social structure that facilitates the social learning process of excessive technology use. The influence of perceived interactivity of MIM on excessive MIM use is mediated by a series of contextualized social learning factors. Furthermore, the influences of perceived interactivity on social learning factors are moderated by MIM use experience.Originality/valueThe authors contribute to literature in related fields by highlighting the crucial role of social learning in facilitating excessive technology use. The authors contribute to the social structure and social learning model by contextualizing it into the context of excessive MIM use. Design guidelines are provided with a purpose to inhibit excessive use of MIM.
Subject
Library and Information Sciences,Computer Science Applications,Information Systems
Reference91 articles.
1. The empirical status of social learning theory of crime and deviance: the past, present, and future,2017
2. Social learning and deviant behavior: a specific test of a general theory;American Sociological Review,1979
3. Social research 2.0: virtual snowball sampling method using Facebook;Internet Research,2012
Cited by
8 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