SIG HIC - Human Computer Interaction
Loading...
Paper Type
Complete
Paper Number
1757
Description
Online reviews play a fundamental role in supporting purchase decisions and driving sales, but the sheer quantity and varying quality pose challenges for consumers to navigate. Using an unsupervised machine learning approach to extract latent topics from review texts, our paper demonstrates that shopping platforms can extract reviewers' original purchasing goals (profiles) varying in the degree of utility and hedonic orientations. These profiles significantly alter their review behaviors in terms of effort, complexity, sentiment, and rating decision. A follow-up experiment finds early evidence that future consumers perceive reviews that match their shopping orientation more favorably in terms of both argument quality and review helpfulness. The paper contributes a new approach to understanding reviewer behaviors and makes a practical recommendation to online shopping platforms to match reviewer and consumer purchasing orientations.
Recommended Citation
Nguyen, Long The and Sheffler, Zach, "Why Did You Buy It? A Text Mining Approach to Understanding Purchasing Goals and Review Behaviors" (2022). AMCIS 2022 Proceedings. 15.
https://aisel.aisnet.org/amcis2022/sig_hci/sig_hci/15
Why Did You Buy It? A Text Mining Approach to Understanding Purchasing Goals and Review Behaviors
Online reviews play a fundamental role in supporting purchase decisions and driving sales, but the sheer quantity and varying quality pose challenges for consumers to navigate. Using an unsupervised machine learning approach to extract latent topics from review texts, our paper demonstrates that shopping platforms can extract reviewers' original purchasing goals (profiles) varying in the degree of utility and hedonic orientations. These profiles significantly alter their review behaviors in terms of effort, complexity, sentiment, and rating decision. A follow-up experiment finds early evidence that future consumers perceive reviews that match their shopping orientation more favorably in terms of both argument quality and review helpfulness. The paper contributes a new approach to understanding reviewer behaviors and makes a practical recommendation to online shopping platforms to match reviewer and consumer purchasing orientations.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIG HCI