The Impact of Ex post Risk-Reduction Mechanism on Online Sales: Evidence from Complications Insurance for Cosmetic Surgeries

Abstract

The perceived risk of professional services (e.g., education and surgeries) is typically much higher than that of commodity goods, due to the highly consequential nature of these services and the large information asymmetries between customers and service providers. Therefore, risk management (e.g., reducing the risks of prospective customers) is of the essence to professional services. In this paper, we study how the introduction of a risk-reduction strategy affects the demand for professional services in online platforms. In doing so, we leverage a natural experiment on an online platform for cosmetic procedures, which started to offer complications insurance, i.e., a type of add-on insurance covering the potential cost of surgical malpractice or complications, for a subset of cosmetic procedures in 2016. Our empirical study shows that this risk-reduction strategy has asymmetric effects on low-risk and high-risk procedures. Specifically, the introduction of insurance increases the sales of low-risk procedures, but has no significant effect on the sales of high-risk procedures. More importantly, the insurance has a negative spillover effect on uninsured competitors, regardless of their risk levels. The negative spillover effect of insurance on high-risk procedures is rather intriguing because it hurts the sales of uninsured procedures without increasing the sales of their insured competitors, and thus cannot be explained by demand cannibalization, the common explanation behind negative spillover effects. We discuss a possible explanation in the paper. Our findings have important implications for platforms to design and evaluate their risk-reduction strategies.

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.