“Better Than I Expected” Is Not As Informative As You Might Expect: When and Why Comparative Reviews Are (Un)Helpful
Guest Speaker: Dr. Y. Charles Zhang
(University of California, Riverside)
Time/Date: 10 am Thursday 25th, May 2023
Classroom: Room 2101, Tongji Building A
ABSTRACT:
Comparatives, such as more, better, and faster, mark the use of comparative language. Communicators often use comparative language when conveying their evaluations about a target, which is particularly frequent in customer reviews. Archival data analyses of 333,801 Amazon and Yelp reviews show that reviews that include comparative language are perceived by readers as more helpful than reviews that do not, controlling for length and valence of the review (Study 1). However, reviews that compare the target to the reviewer’s own expectations, experiences, or belongings, one of the most common types of comparison that reviewers use, are perceived as less helpful. Five lab experiments confirm the causality (Studies 2a & 2b) and investigate the mechanism: comparing to the reviewer’s own expectations, experiences, or belongings are considered less helpful because these referents are only accessible to the reviewer, not to the readers (Study 3). Evidence further shows that comparisons with accessible referents are perceived as helpful because accessible referents offer readers an anchor to predict their consumption experience with the target (study 4). Finally, we proposed and tested an intervention method, namely shifting reviewers’ focus from sharing their own experience to considering the readers’ decision needs, which is contrary to the instructions many customer review platforms currently offer to the reviewers (study 5).
Guest Bio
Y. Charles Zhang is an Assistant Professor of Marketing at UC Riverside, School of Business. His main research interest is judgment and decision making with an emphasis on numerical judgment and inference. Some of his published work is focused on how the granularity of communicated numbers conveys information that goes beyond the magnitude of the numbers. Professor Zhang also has some work on metaphor and embodiment.
Professor Zhang graduated from the University of Michigan with a PhD in Marketing. At UCR, he teaches Marketing Research to both MBAs and undergraduates, which he enjoys a lot owing to his earlier training in statistics and survey methodology as well as his industrial experience from Young & Rubicam and eBay. Prior to joining UCR in 2014, Professor Zhang taught at Boston College. In his leisure time, Professor Zhang enjoys classical music and board games.
