Paper ID
1349
Paper Type
short
Description
Customer Generated Image (CGI) on e-commerce platforms has been widely recognized as a marketing tool to persuade customers into purchases. Despite its persuasive power, the effect of CGI on post purchase satisfaction has seldom been examined. This study draws upon Elaboration Likelihood Model and proposes that the affective cues in CGI could distract consumer’s cognitive information processing and lead to unsatisfactory purchases with a larger probability. To empirically test our hypothesis, we employed a difference-in-differences model with propensity score weighting method and deep learning based face detection algorithm and found that CGI could cause subsequent review ratings 0.12 stars lower compared with those not exposed to CGI. Additional analysis indicated that this negative effect could be attenuated if the CGI contains human faces or the image review has a low rating. These findings have important implications for online platforms to better leverage user generated rich media content.
Recommended Citation
Guan, Yue; Tan, Yong; Wei, Qiang; and Chen, Guoqing, "The Dark Side of Images: Effect of Customer Generated Images on Product Assessment" (2019). ICIS 2019 Proceedings. 3.
https://aisel.aisnet.org/icis2019/human_computer_interact/human_computer_interact/3
The Dark Side of Images: Effect of Customer Generated Images on Product Assessment
Customer Generated Image (CGI) on e-commerce platforms has been widely recognized as a marketing tool to persuade customers into purchases. Despite its persuasive power, the effect of CGI on post purchase satisfaction has seldom been examined. This study draws upon Elaboration Likelihood Model and proposes that the affective cues in CGI could distract consumer’s cognitive information processing and lead to unsatisfactory purchases with a larger probability. To empirically test our hypothesis, we employed a difference-in-differences model with propensity score weighting method and deep learning based face detection algorithm and found that CGI could cause subsequent review ratings 0.12 stars lower compared with those not exposed to CGI. Additional analysis indicated that this negative effect could be attenuated if the CGI contains human faces or the image review has a low rating. These findings have important implications for online platforms to better leverage user generated rich media content.