Investigating the Effects of Image-Seeking Behavior at Social Network Sites on Purchase Behavior: A Text Mining and Econometric Approach

Abstract

The emergence and rapid growth of social media platforms, and particularly social-media based brand communities have spurred significant popular interest in recent times. However, despite the growing economic importance of brand presence on social media, little is understood about whether and how user engagement with these brand communities benefits product sales in brick-and-mortar stores - the online-to-offline (O2O) conversion problem. In this study, we combine two large real-world datasets from a popular social network site (SNS) and the loyalty card dataset from a brick-and-mortar Asian fashion retailer to study the offline purchase behavior of the SNS brand page members. We present evidence that individuals upon joining the brand page reduce their offline purchase expenditure on average. However, using a combination of text mining and statistical approaches, we show that this reduction is significantly attenuated for some individuals who self-present more than others on the SNS. The findings from our study not only illuminate our understanding of the offline economic impacts of online self-presentation, but also present newer ways of performing behavioral targeting of online users.