TY - GEN
T1 - Online customer reviews and product sales
AU - Wang, Ying
AU - Aguirre-Urreta, Miguel
AU - Song, Jaeki
N1 - Publisher Copyright:
© 2017 AIS/ICIS Administrative Office. All Rights Reserved.
PY - 2017
Y1 - 2017
N2 - 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 an impact on product sales because reviews act as market signals that contain information about the quality of products. 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 moderates 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.
AB - 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 an impact on product sales because reviews act as market signals that contain information about the quality of products. 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 moderates 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.
KW - Information economics
KW - Latent Dirichlet allocation
KW - Multilevel modeling
KW - Online review
KW - Signaling theory
UR - http://www.scopus.com/inward/record.url?scp=85048528920&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048528920
T3 - AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation
BT - AMCIS 2017 - America's Conference on Information Systems
PB - Americas Conference on Information Systems
Y2 - 10 August 2017 through 12 August 2017
ER -