구매후기 정보 누적이 온라인 쇼핑몰 제품의 평점에 미치는 영향

This study derived an effective way to expose information on product reviews by analyzing how the accumulation of information on reviews of online shopping malls, which are receiving a lot of attention amid the rapid increase in non-face-to-face transactions with small and medium-sized venture companies with insufficient resources, affects product review ratings. Hypotheses were derived based on the main theory of behavioral economics and the theory of consumer expectation inconsistency, and for empirical research, the effect of the accumulation of information on product reviews were analyzed from a short and long-term perspective using Amazon's product reviews and seller information big data. For the empirical study, 9,092,480 reviews written for 378,411 products of Amazon were used, and the hypotheses were verified through hierarchical regression analysis. As a result of the analysis, it was found that the average rating decreased as the number of reviews increased. It was found that the product with a large number of recent reviews had a high rating. The characteristics of the product showed a moderating effect on these effects. This study will provide a new theoretical basis for research related to product review, and will help small and medium-sized venture companies that focus on sales through online shopping malls due to lack of resources to increase sales performance by appropriately utilizing review information. It will also provide empirical insights into effective product review information exposure measures for online shopping mall managers.