When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics

Forthcoming at Information Systems Research

64 Pages Posted: 17 Jul 2020 Last revised: 3 Mar 2023

See all articles by Yue Guan

Yue Guan

Communication University of China

Yong Tan

University of Washington - Michael G. Foster School of Business

Qiang Wei

Tsinghua University - School of Economics & Management

Guoqing Chen

Tsinghua University - School of Economics & Management

Date Written: March 3, 2023

Abstract

Customer-Generated Images (CGIs) on e-commerce platforms have been widely adopted as a promotional tool to persuade customers into purchases. Despite their prevalent applications, the effect of CGIs on customers’ post-purchase satisfaction has not been extensively examined. This study postulates that CGIs could cause expectation disconfirmation and reduce product uncertainty for customers, therefore making their effect on subsequent product ratings complex. We leverage multiple methods and datasets to gain a better understanding of this problem and underlying mechanisms. We employ a difference-in-differences model to empirically test our hypotheses and find that CGIs lead to a decline in subsequent ratings compared with product ratings not exposed to CGIs. Further heterogeneity analyses demonstrate that high CGI review rating and high aesthetic quality exacerbate the negative effect, while reviewer face disclosure could alleviate the negative effect. Through the cross-product analyses, we find that the negative effect is more prominent for experience goods (e.g., women dresses) than for search goods (e.g., lightning cables). Finally, the underlying mechanism is further validated through a lab experiment which shows that participants experience significantly higher expectation and more negative disconfirmation in the CGI group with high review ratings, whereas uncertainty reduction effect is insignificant, which collectively explains the decline of subsequent product ratings. These findings suggest that platforms and retailers should be aware of the potential negative effect of CGIs on the rating dynamics and take appropriate measures to circumvent it.

Keywords: Customer Generated Images; rating dynamics; image aesthetics; face disclosure; review ranking algorithm

JEL Classification: M10,M15

Suggested Citation

Guan, Yue and Tan, Yong and Wei, Qiang and Chen, Guoqing, When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics (March 3, 2023). Forthcoming at Information Systems Research, Available at SSRN: https://ssrn.com/abstract=3633590 or http://dx.doi.org/10.2139/ssrn.3633590

Yue Guan (Contact Author)

Communication University of China ( email )

1 Dingfuzhuang E Street
Chaoyang Qu
Beijing Shi
China

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

Qiang Wei

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62789824 (Phone)
+86-10-62771647 (Fax)

HOME PAGE: http://www.sem.tsinghua.edu.cn/en/weiq

Guoqing Chen

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62789925 (Phone)
+86-10-62789925 (Fax)

HOME PAGE: http://www.sem.tsinghua.edu.cn/en/chengq

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