Topic Features in Negative Customer Reviews: Evidence Based on Text Data Mining
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Several studies have focused on the effects of online negative customer reviews on sales, especially pertaining to Internet shopping and e-retailing. However, there is mixed evidence and the theoretical studies have mainly focused on the volume and valence. To understand the effects of negative customer reviews on sales, the present study uses text data mining techniques to investigate how three factors, namely “content topic, proportion, and consistency,” bout the textual content of negative customer reviews influence online sales. Relevant data were collected from a large-scale online shopping platform. The results of content association and topic extraction reveal four topics—product quality, delivery service, cost performance, and taste. A new econometric model proposed in this study shows that different topics have different effects on sales. Negative customer reviews with a higher percentage or consistency about these four topics significantly jeopardize product sales. Theoretical and managerial implications and future research directions are also presented.
KeywordsInternet shopping Negative customer reviews Online sales Text data mining Content topic Proportion Consistency
We thank the editor and the anonymous reviewers for their thoughtful reviews and constructive suggestions during the review process. This work was supported by JSPS KAKENHI Grant numbers JP15H06747, JP17K18152, and the National Natural Science Foundation of China NSFC71572065.
- 12.Fowler, G. A., & Avila, J. D. (2009). On the internet, everyone’s a critic but they’re not very critical. The Wall Street Journal.Google Scholar
- 13.Chatterjee, P. (2001). Online reviews: Do consumers use them? Advances in Consumer Research, 28, 129–133.Google Scholar
- 34.Chen, P.Y., Dhanasobhon, S., & Smith, M. D. (2008). All reviews are not created equal: The disaggregate impact of reviews and reviewers at amazon. com. Working Paper. https://doi.org/10.2139/ssrn.918083.
- 39.Taylor, J. W. (1974). The role of risk in consumer behavior. The Journal of Marketing, 38(2), 54–60.Google Scholar
- 42.Ye, Q., Xu, M., Kiang, M., Wu, W., & Sun, F. (2013). In-depth analysis of the seller reputation and price premium relationship: A comparison between eBay US and TaoBao China. Journal of Electronic Commerce Research, 14(1), 1–10.Google Scholar
- 49.Moscovici, S. (1985). Social Influence and Conformity. Handbook of Social Psychology.Google Scholar
- 50.Turner, J. C. (1991). Social Influence. Pacific Grove: Thomson Brooks/Cole Publishing Co.Google Scholar
- 55.Zhu, M., & Lai, S. (2009). A study about the WOM influence on tourism destination choice. In 2009 International conference on electronic commerce and business intelligence, pp. 120–124.Google Scholar
- 56.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.Google Scholar
- 57.Chuang, J., Manning, C. D., & Heer, J. (2012). Termite: Visualization techniques for assessing textual topic models. In Proceedings of the international working conference on advanced visual interfaces (pp. 74–77). ACM.Google Scholar
- 58.Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces, pp. 63–70.Google Scholar