Sincere or Falsehearted? Investigation of Online Reviews and Merchant Response

ABSTRACT

Academia and industry widely believe that merchants’ customized response is an indispensable tool for handling online reviews, particularly negative reviews caused by service failures. However, whether merchants actually fulfill the promises they make in their responses remains unclear. On the basis of the literature on online reviews and service recovery, this study utilizes a series of textual features of online reviews and merchant responses via text analysis, including review topics, review sentiments, and response pertinence, to construct a novel indicator. This indicator, the consistency between merchants’ words and deeds (hereafter referred to as CWD), can be used to infer the degree of merchant response fulfillment. In particular, this study first proposes two indicators, namely, response and action levels, and then measures their difference to evaluate the CWD level of merchants. CWD can reflect the effort exerted by merchants to achieve service recovery. This study significantly contributes to the literature on service recovery and online reviews. The research findings derived from this study can help urge merchants to provide consumers with improved products and services. They can also be applied to the online ranking system to enhance platform fairness and protect the long-term interests of consumers and other stakeholders.

KEYWORDS AND PHRASES:

• Online review
• review response
• topic model
• online sentiment
• service recovery
• words–deeds consistency

Acknowledgments

The authors thank the editor and the anonymous reviewers for their constructive comments and suggestions throughout the review process. This work was supported by the National Natural Science Foundation of China (grant numbers 61773199, 71902086, and 71732002), and the Key Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province (grant number 2017SJB0006).

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

¹ https://www.traveldaily.cn/article/133180

Additional information

Notes on contributors

Xiaolin Li

Xiaolin Li ([email protected]) is a full professor at Department of Marketing and Electronic Business, School of Business, Nanjing University, Jiangsu, China. She received her Ph.D. in computer science from School of Computer Science and Technology, Jilin University, China. Dr. Li’s research interests include data mining, business intelligence, and general decision making. Her work has been published in such journals as IEEE Transactions on Knowledge Discovery and Engineering, Decision Support Systems, Electronic Commerce Research and Applications, Asia-Pacific Journal of Accounting & Economics, Information Sciences, and others.

Li Ma

Li Ma ([email protected]) received her master’s degree in electronic commerce and marketing from the School of Business, Nanjing University, Jiangsu, China. Her research interests focus on online reviews and data mining.

Benjiang Lu

Benjiang Lu ([email protected]; corresponding author) is an assistant professor in the Department of Marketing and Electronic Business, School of Business, Nanjing University, Jiangsu, China. He received his Ph.D. in management science and engineering from Tsinghua University, China. Dr. Lu’s research interests include intra- and extra-organizational online knowledge sharing communities, employee performance and creativity, and e-commerce customer behaviors. His work has been published in such journals as Journal of Management Information Systems, Information & Management, Decision Support Systems, Computers in Human Behaviors, Journal of Theoretical and Applied Electronic Commerce Research, and others.

Kexin Huang

Kexin Huang ([email protected]) is a master’s student in the Department of Electronic Commerce and Marketing at the School of Business, Nanjing University, Jiangsu, China. Her research interests focus on online reviews and data mining.