Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints

T. Ravichandran () and Chaoqun Deng ()
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T. Ravichandran: Lally School of Management, Rensselaer Polytechnic Institute, Troy, New York 12180
Chaoqun Deng: Paul H. Chook Department of Information Systems and Statistics, Zicklin School of Business, Baruch College, City University of New York, New York, New York 10010

Information Systems Research, 2023, vol. 34, issue 1, 319-341

Abstract: There is limited systematic research on managerial response strategies to online customer complaints and negative reviews. In this paper, we synthesize justice theory and service recovery literature to develop a model that explores the mechanisms through which appropriate managerial responses to customer complaints influence aggregate future review valence and complaints. We test our model using data from TripAdvisor.com, a leading travel review platform. Using text analysis (e.g., natural language processing and deep learning), we extract and code the variables in our model from the reviews and the managerial responses to these reviews. Key findings indicate that responding to customer reviews—in particular, negative reviews—will have a positive influence on future review valence. Moreover, responses with more rational cues than emotional cues to customer complaints about procedural unfairness will have a positive influence on future review valence. However, responses with more rational cues than emotional cues to customer complaints about interactional unfairness will have a negative influence on future review valence. Moreover, we find that when reviews have both distributive and interactional unfairness, responses with more rational cues matter. In addition, we find that both rational cues and emotional cues in responses to distributive unfairness and rational cues in responses to procedural unfairness are effective to decrease the future occurrence of similar complaints. We interpret and discuss the implication of these findings for theory and practice.

Keywords: managerial response; negative reviews; customer complaints; future review valence; text analysis; deep learning (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orisre:v:34:y:2023:i:1:p:319-341

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