Abstract
In today's digital era severe smartphone usage is increased by several smartphone applications such as social networking services and instant messaging applications. In this study, we argue that use of social networking services and instant messaging applications may cause an individual's inevitable dependence to the smartphone which consequently leads to addiction. Thus, the current research examines use of both social networking services and instant messaging applications as predictors of smartphone addiction with some other antecedents of social networking services and instant messaging applications usage. Results show that use of social networking services and use of instant messaging applications are important predictors of smartphone addiction. Findings also represent that use of social networking services applications is affected by social networking services network size and use of instant messaging applications is affected by instant messaging network size and the number of instant messaging applications actively being used on smartphone. Moreover, social anxiety and peer influence are examined as the moderating effects between relationships of usage of social networking services and instant messaging applications and smartphone addictions. Results of these tests show that social anxiety moderates the relationship between use of social networking services applications and smartphone addiction and peer influence moderates the relationship between use of instant messaging applications and smartphone addiction. This study has contributions to the addiction research, and also implications to governments and health institutions pertaining to the effect of smartphone addiction on society and individual's health.
Cite
CITATION STYLE
Urmanov, B., & Hoyoung, S. (2021). AN EMPIRICAL INVESTIGATION ON THE FACTORS AFFECTING SMARTPHONE ADDICTION IN DIGITAL ERA: EFFECT OF SOCIAL NETWORKING SERVICES AND INSTANT MESSAGING APPLICATIONS USAGE. In ACM International Conference Proceeding Series (pp. 473–484). Association for Computing Machinery. https://doi.org/10.1145/3508072.3508169
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