Description
Focusing on social network sites (SNS), this study proposes a research model for investigating the individuals’ inertia toward the SNS and their online social well-being. In viewing inertia as a manifestation of the status quo bias, this study draws upon a set of theoretical explanations of the status quo bias to understand the individual’s continuing preference for the same SNS being used. To understand the antecedents of inertia in depth, this study utilizes switching barriers and habit to be the conscious and unconscious antecedents on an individual’s inertia toward the SNS. This study hypothesizes that switching barriers and habit increase the individual’s inertia in using SNS, and thereby increase the individual’s online social well-being. Data collected from 350 Facebook users provides support for all the hypotheses and the research model explains 44.1% of the variance of the individual’s online social well-being.
Recommended Citation
Huang, Hsin-Yi and Shih, Sheng-Pao, "Exploring an individual’s inertia toward the social network sites" (2017). AMCIS 2017 Proceedings. 4.
https://aisel.aisnet.org/amcis2017/eBusiness/Presentations/4
Exploring an individual’s inertia toward the social network sites
Focusing on social network sites (SNS), this study proposes a research model for investigating the individuals’ inertia toward the SNS and their online social well-being. In viewing inertia as a manifestation of the status quo bias, this study draws upon a set of theoretical explanations of the status quo bias to understand the individual’s continuing preference for the same SNS being used. To understand the antecedents of inertia in depth, this study utilizes switching barriers and habit to be the conscious and unconscious antecedents on an individual’s inertia toward the SNS. This study hypothesizes that switching barriers and habit increase the individual’s inertia in using SNS, and thereby increase the individual’s online social well-being. Data collected from 350 Facebook users provides support for all the hypotheses and the research model explains 44.1% of the variance of the individual’s online social well-being.