Description
Although considerable research has been conducted to investigate how online reviews influence product sales, understanding of why consumers rely on online reviews and the effect of interactions between key metrics (volume, valence, and variance) on product sales is very limited. We develop a research framework by applying information economics and signaling theory to demonstrate that online reviews have influences on product sales because reviews act as market signals that contain information about products' quality. The characteristics of signals (intensity, valence, consistency, and clarity) help consumers in reducing search cost and improving evaluations on product quality. We propose that signal intensity and signal consistency moderate the relationship between online reviews and product sales. Regarding methodological contribution, we propose a multilevel text mining approach to analyze online reviews by considering nested structure of reviews and uniqueness of individual review. The results of a pilot study and discussions are presented as well.
Recommended Citation
Wang, Ying; Aguirre-Urreta, Miguel; and Song, Jaeki, "Online Customer Reviews and Product Sales: The Moderating Role of Signal Characteristics" (2017). AMCIS 2017 Proceedings. 7.
https://aisel.aisnet.org/amcis2017/eBusiness/Presentations/7
Online Customer Reviews and Product Sales: The Moderating Role of Signal Characteristics
Although considerable research has been conducted to investigate how online reviews influence product sales, understanding of why consumers rely on online reviews and the effect of interactions between key metrics (volume, valence, and variance) on product sales is very limited. We develop a research framework by applying information economics and signaling theory to demonstrate that online reviews have influences on product sales because reviews act as market signals that contain information about products' quality. The characteristics of signals (intensity, valence, consistency, and clarity) help consumers in reducing search cost and improving evaluations on product quality. We propose that signal intensity and signal consistency moderate the relationship between online reviews and product sales. Regarding methodological contribution, we propose a multilevel text mining approach to analyze online reviews by considering nested structure of reviews and uniqueness of individual review. The results of a pilot study and discussions are presented as well.