Social commerce, as a digital shopping channel, encourages consumers to engage in online shopping through real-time interaction and information sharing. Although social commerce facilitates purchasing behavior, to date, few studies have explored the antecedents of switching behavior among social commerce users. This research aims to explore the antecedents that stimulate online consumers to develop information avoidance intentions and switching behavior in social commerce scenarios. Drawing on the theory of planned behaviour (TPB), a conceptual framework was developed to investigate the effects of perceived behavioral control, subjective norms, and information overload on social commerce consumers opt-out behaviors. The empirical testing of the survey sample (N=394) and the research hypotheses were executed using structural equation modeling. This study revealed that perceived behavioral control and attitudes towards information overload significantly increased social commerce consumers' information avoidance intentions, which in turn drove switching behavior. The mediating effect of information avoidance intention was significant, and it significantly mediated the relationship between attitudes towards information overload, perceived behavioral control, and switching behavior. However, the effect of subjective norms on information avoidance intention is insignificant, and there is no positive association between the two. This research extends the TPB theory by shedding light on the influencing factors of social commerce consumer switching behavior, and at the same time provides practical insights for the social commerce industry to gain knowledge about consumer information avoidance behavior.
Accenture. (2022). Why shopping's set for a social revolution. Available at: https://www.accenture.com/us-en/insights/software-platforms/why-shopping-set-social-revolution.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Arnold, M., Goldschmitt, M., & Rigotti, T. (2023). Dealing with information overload: a comprehensive review. Frontiers in psychology, 14, 1122200.
Asif, M. H., Zhongfu, T., Ahmad, B., Irfan, M., Razzaq, A., & Ameer, W. (2023). Influencing factors of consumers' buying intention of solar energy: a structural equation modeling approach. Environmental science and pollution research international, 30(11), 30017-30032.
Bansal, H. S., & Taylor, S. F. (1999). The service provider switching model (SPSM): a model of consumer switching behavior in the services industry. Journal of Service Research, 2(2), 200-218.
Deloitte. (2022). As seen in your feed: shopping goes social, trending past US$1 trillion in annual sales. Available at:
https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2023/social-commerce-rise-of-the-social-shopping-retail-market.html.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50.
Guo, Y. Y., Lu, Z. Z., Kuang, H. B., & Wang, C. Y. (2020). Information avoidance behavior on social network sites: information irrelevance, overload, and the moderating role of time pressure. International Journal of Information Management, 52, 102067.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review. 31(1), 2-24.
Han, H., Hsu, L.-T., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: testing the effect of environmental friendly activities. Tourism Management, 31(3), 325-334.
Hino, H. (2017). Does switching-intention result in a change in behaviour? exploring the actual behavioural shopping patterns of switching-intended customers. British Food Journal, 119(12), 2903-2917.
Kang, I. (2022). A study on switching behavior of social media: from a dynamic perspective. International Trade, Politics and Development, 6(3), 107-120.
Khan, A. G., Hasan, N., & Ali, Md. R. (2023). Unmasking the behavioural intention of social commerce in developing countries: integrating technology acceptance model. Global Business Review, 1-25.
Leong, L. Y., Hew, T. S., Ooi, K. B., Metri, B., & Dwivedi, Y. K. (2022). Extending the theory of planned behavior in the social commerce context: a meta-analytic SEM (MASEM) approach. Information Systems Frontiers, 25, 1847-1879.
Li, C., & Ku, Y. (2018). The power of a thumbs-up: will e-commerce switch to social commerce? Information & Management, 55(3), 340-357.
Li J. H. (2023). Information avoidance in the age of COVID-19: A meta-analysis. Information processing & management, 60(1), 103163.
Li, X. D., Wang, C., and Zhang, Y. P. (2020). The dilemma of social commerce: why customers avoid peer-generated advertisements in mobile social networks. Internet Research, 30(3), 1059-1080.
Liao, Y. W., Huang, Y. M., Huang, S. H., Chen, H. C., & Wei, C. W. (2019). Exploring the switching intention of learners on social network-based learning platforms: A perspective of the push-pull-mooring model. Eurasia Journal of Mathematics, Science & Technology Education, 15(9), 1-10.
Mingers, J., Standing, C. (2018). What is information? toward a theory of information as objective and veridical. Journal of Information Technology, 33, 85-104.
Rhodes, R. E., & Courneya, K. S. (2005). Threshold assessment of attitude, subjective norm, and perceived behavioral control for predicting exercise intention and behavior. Psychology of Sport and Exercise, 6, 349-361.
Shalender, K., & Sharma, N. (2020). Using extended theory of planned behaviour (TPB) to predict adoption intention of electric vehicles in India. Environment, Development and Sustainability, 23, 665-681.
Song, S., Yao, X., & Wen, N. (2021). What motivates Chinese consumers to avoid information about the COVID-19 pandemic?: the perspective of the stimulus-organism-response model. Information processing & management, 58(1), 102407.
Soroya, S. H., Farooq, A., Mahmood, K., Isoaho, J., & Zara, S. E. (2021). From information seeking to information avoidance: understanding the health information behavior during a global health crisis. Information processing & management, 58(2), 102440.
Statista. (2023). Social commerce - statistics & facts. Available at: https://www.statista.com/topics/8757/social-commerce/#topicOverview.
Sweeny, K., Melnyk, D., Miller, W., & Shepperd, J. A. (2010). Information avoidance: who, what, when, and why. Review of general psychology, 14(4), 340-353.
Wang, J., Vo?Thanh, T., Liu, Y., Dang?Van, T., & Nguyen, N. (2023). Information confusion as a driver of consumer switching intention on social commerce platforms: a multi-method quantitative approach. Information Technology & People, 37(1), 171-200.
Yang, Z. J., & Kahlor, L. (2013). What, me worry? the role of affect in information seeking and avoidance. Science Communication, 35(2), 189-212.
Ye, D.Y., Liu, F., Cho, D., & Jia, Z. (2022). Investigating switching intention of e-commerce live streaming users. Heliyon, 8, e11145.
Zhang, D., Zhang, Y., & Lou, S. (2023). What determines consumers’ purchasing behavioral intention on social commerce platforms: introducing consumer credit to TPB. Environment, Development and Sustainability, 1-21.
Zhao, X., Jr, J. G. L., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.
Zhou, T., & Xie, Y. (2023). Understanding social media users' information avoidance intention: a C-A-C perspective. Aslib Journal of Information Management.
