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Topic Features in Negative Customer Reviews: Evidence Based on Text Data Mining

  • Zhen Li
  • Fangzhou Li
  • Jing XiaoEmail author
  • Zhi Yang
Article
  • 55 Downloads

Abstract

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.

Keywords

Internet shopping Negative customer reviews Online sales Text data mining Content topic Proportion Consistency 

Notes

Acknowledgements

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.

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Copyright information

© Springer Japan KK, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Business AdministrationToyo UniversityTokyoJapan
  2. 2.Graduate School of Business AdministrationKobe UniversityKobeJapan
  3. 3.School of ManagementHuazhong University of Science and TechnologyWuhanChina

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