Online Diaries and Professional Service

Abstract

Online diaries, series of posts generated by consumers in chronological order to record their post-consumption experience over time, have recently emerged in professional services platforms. As a novel form of electronic word-of-mouth (eWOM), an online diary has an initiating post and some optional follow-up posts. Compared to conventional online reviews with a single post, the dynamic structure of online diaries may change the way consumers search and process information. Using a large dataset from an online platform of cosmetic procedures, this paper empirically investigates 1) whether providing follow-ups in online diaries affects the sales of professional services, and 2) how the impact of follow-ups is moderated by the perceived risk of professional services and the quality of service providers. We find that providing follow-ups in diaries has a positive effect on the sales of the respective cosmetic procedures. Moreover, the effect is weaker for high-quality providers than for low-quality providers, indicating that the quality of providers substitutes the effect of follow-ups. Interestingly, the effect of follow-ups is asymmetric for procedures with high and low perceived risks. For high-risk procedures, providing follow-ups increases sales regardless of the quality of providers. In contrast, for low-risk procedures, providing follow-ups substantially increases sales for low-quality providers, but not for high-quality providers. Finally, the substitution effect of the quality of providers over follow-ups is stronger for procedures with a lower perceived risk. Our findings provide important implications for both platform owners and service providers.

Hongfei Li

Assistant Professor

Hongfei Li is an assistant professor from the Department of Decision Sciences and Managerial Economics (DSME) of School of Business, at the Chinese University of Hong Kong. His current research focuses on business analytics in emerging online platforms, applications of artificial intelligence and machine Learning, and statistical methodology.