When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics
研究发现电商平台上的顾客生成图片会导致后续产品评分下降，高评分和高审美质量会加剧这一负面效应，而展示人脸则可缓解，对平台和零售商有警示意义。
Customer-generated images (CGIs) are images posted by customers on e-commerce platforms, and they usually appear in the review sections together with review text and ratings provided by customers having purchase experiences. Despite their prevalent adoption by e-commerce platforms, the effect of CGIs on customers’ postpurchase satisfaction remains unclear. We find that CGIs lead to a decline in subsequent ratings compared with product ratings not exposed to CGIs. Furthermore, high CGI review ratings and high aesthetic quality exacerbate the negative effect, whereas reviewers’ face disclosure in CGIs can alleviate the negative effect. Through cross-product analyses, we find that the negative effect is more prominent for experience goods (e.g., women’s dresses) than for search goods (e.g., lightning cables). Results from a laboratory experiment show that participants experience significantly higher expectation and negative disconfirmation when reading CGI reviews with high ratings, whereas the uncertainty reduction effect is insignificant, which collectively explains the decline of subsequent product ratings from a theoretical perspective. 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.