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With the advent of the technological age, electronic word-of-mouth, commonly referred to as e-WOM, has gained a great deal of traction. What is of particular interest for the present study is the triggers behind pass-on behaviors in online users that make them repost, retweet, and otherwise share information. Studies on the subject matter such as the one by Phelps et al. (2004) date back to as early as 2004. Before the rise of social media, Phelps et al. (2004) studied the influence of viral advert on users’ willingness and intention to pass them on through emails. Fast forward to the 2010s, researchers’ focus shifted to social media platforms, and predominantly Twitter (Yang et al., 2018; Pressgrove et al., 2018).
While the dependent variables were by and far similar (e-WOM metrics), researchers selected diverse predictors. Some of them pertained to posts and messages themselves, studying the anatomy of what makes content viral (Yang et al., 2018; Pressgrove et al., 2018). Others sought to understand what role personality traits such as curiosity played in pass-on behaviors (Fang et al., 2018). Lastly, researchers such as Ruiz-Mafe et al. (2018) and Yang and Zhou (2011) put an emphasis on the social context and its elements such as social influence to gain a deeper insight into the nature of e-WOM.
It is clear that there is a need for further research that would combine different types of factors. Besides, not all of the past studies draw on coherent theoretical underpinnings and use a specific framework to inform their methodology. Among the employed theories and frameworks are social interaction utility framework, social learning theories, and theory of planned behavior. The present research will be based on the social cognitive theory and inquire the influence of different predictors – personal, environmental, and religious – on pass-on behaviors.
References
Fang, Y.-H., Tang, K., Li, C.-Y., & Wu, C.-C. (2018). On electronic word-of-mouth diffusion in social networks: curiosity and influence. International Journal of Advertising, 37(3), 360–384. Web.
Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44(4), 333–348. Web.
Pressgrove, G., McKeever, B. W., & Jang, S. M. (2018). What is Contagious? Exploring why content goes viral on Twitter: A case study of the ALS Ice Bucket Challenge. International Journal of Nonprofit & Voluntary Sector Marketing, 23(1), 1. Web.
Ruiz-Mafe, C., Bigne-Alcañiz, E., Sanz-Blas, S., & Tronch, J. (2018). Does social climate influence positive eWOM? A study of heavy-users of online communities. Business Research Quarterly, 21(1), 26–38. Web.
Yang, Q., Tufts, C., Ungar, L., Guntuku, S., & Merchant, R. (2018). To retweet or not to retweet: Understanding what features of cardiovascular tweets influence their retransmission. Journal of Health Communication, 23(12), 1026–1035. Web.
Yang, H., & Zhou, L. (2011). Extending TPB and TAM to mobile viral marketing: An exploratory study on American young consumers’ mobile viral marketing attitude, intent and behavior. Journal of Targeting, Measurement & Analysis for Marketing, 19(2), 85–98. Web.
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