Online Diaries and Risky Products

Image credit: Grand Canyon National Park, Arizona

Abstract

Recently, as eWOM expands into the medical service area, such as cosmetic surgery, new advances have been developed to accommodate to health-related characteristics. One such advance is online diaries, which are series of follow-up posts recording a patient’s recovery progress after a surgery. Different from single-post reviews studied in the prior literature, the unique structure of online diaries and information dynamics embedded may fundamentally change how consumers gather and interpret information. Moreover, due to potential physical harm and permanent damages, perceived risk of a surgery and perceived quality of hospitals are important for consumers’ decision. This study empirically investigates the impact of online diaries on sales of cosmetic surgeries and the moderating role of perceived risk of a surgery and perceived quality of hospitals. We show that online diaries with follow-up posts significantly drive up sales. Interestingly, the impact of diaries with follow-up posts is stronger for surgeries with lower perceived risk provided by hospitals with lower perceived quality. In contrast, the impact of diaries with follow-up posts is stronger for surgeries with higher perceived risk provided by hospitals with higher perceived quality. These results deepen our understanding of online diaries, a new format of eWOM, and their interplay with perceived risk and perceived quality. This study also provides important practical implications for online review platforms.

Date
Nov 3, 2018 9:25 AM
Event
CIST 2018
Location
Phoenix Convention Center
100 North Third Street, Phoenix, Arizona, 85004, USA
Avatar
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.