Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data
Ashish Kumar, Jari Salo, and Hongxiu Li
International Journal of Electronic Commerce,
Volume 23, Number 2, 2019, pp. 179-211.
Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users’ online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users’ incidence and time spent. Drawing from the PageRank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend; holiday; time of day; and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users’ incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers’ path navigational clickstream data from the parent social commerce platform.
Key Words and Phrases: Clickstream data, Dijkstra’s shortest path algorithm, hierarchical Bayesian method, multivariate type-2 Tobit, online communities, online platforms, online shopping, PageRank algorithm, social commerce platforms.