SKK GSB 이나윤 교수의 논문이 Journal of Personality and Social Psychology 게재되다
- SKKGSB
- Hit7255
- 2022-08-25
SKK GSB 신임 교원인 이나윤 교수의 논문 “Vertical versus Horizontal Variance in Online Reviews and Their Impact on Demand”가 Journal of Marketing Research에 게재 수락 되었습니다. 이나윤 교수는 Duke University에서 마케팅 박사 학위를 취득하였으며 주요 연구 주제는 소비자가 텍스트 리뷰에서 정보를 처리하는 과정과 이 정보가 기업의 수요, 결정, 경쟁에 어떻게 영향을 미치는지 이해하는 것을 목표로 합니다. 이번 게재 수락된 논문의 주요 내용은 다음과 같습니다.
Abstract as below:
This paper examines the differential impact of variances in the quality and taste comments found in online customer reviews on firm sales. Using an analytic model, we show that although increased variance in consumer reviews about taste mismatch normally decreases subsequent demand, it can increase demand when mean ratings are low and/or quality variance is high. In contrast, increased variance in quality always decreases subsequent demand, although this effect is moderated by the amount of variance in tastes. Since these theoretical demand effects are predicated on the assumption that consumers can differentiate between the two sources of variation in ratings, we conduct a survey that demonstrates that subjects are indeed able to reliably distinguish quality from taste evaluations from two subsets of reviews of size 5,000 taken from our larger datasets of reviews for 4,305 restaurants and 3,460 hotels. We use these responses to construct sets of reviews that we use in a controlled laboratory experiment on restaurant choice, finding strong support for our theoretical predictions. These responses are also used to train classifiers using a bag-of-words model to predict the degree to which each review in the larger datasets relates to quality and/or taste allowing us to estimate the two types of review variances. Finally, we estimate the effects of these variances in overall ratings on establishment sales, again finding support for our theoretical results.
Keywords:
review variance, vertical and horizontal content, text analysis, machine learning, quality and taste variance, crowd-sourced data