Effects of Managerial Response to Negative Reviews on Future Review Valence and Complaints - Ravichandran, T.; Deng, Chaoqun (Information Systems Research, 2023)
Online reviews are very instrumental in driving customer behaviors. This coupled with the fact that negative reviews seem to have a stronger effect on customer behaviors raises the stakes for managers to effectively respond to such reviews in order to protect their brand. However, given the exponential growth in the volume of reviews, a strategic approach that enables managers to focus their efforts in responding to negative reviews is needed. This paper develops a framework to classify negative reviews and managerial responses and examines how the fit between the nature of review and the nature of managerial response impacts the customers’ complaining behavior in the future. We focus on the mix of rational and emotional cues in exploring the appropriateness of managerial responses to negative reviews. Using text analysis (e.g., natural language processing and deep learning) and using large sale review and response data from TripAdvisor, we extract and code the variables in our model. The findings provide specific and actionable guidelines for responding to negative reviews in online forums. First, managers should respond to negative reviews in order to safeguard the brand and improve firm reputation. Second, managers should be aware that they can respond both rationally and emotionally to negative reviews. Whereas emotional responses have been the preferred mode in most firms, our theorizing and findings clearly indicate response with rational cues is also particularly important in dealing with complaints. When complaints pertain to primarily the procedures in the service delivery process such as speed and flexibility, managers should respond with rational cues that explain the reasons for the service failure and the steps taken to address such failures and reinforce the value of the service provided by the firm. When customers complain only about the nature of their interactions with the hotel or also file grievances about the services not aligning with their needs, managers should respond with more emotional cues such as apologizing or appreciating the customer for patronage and being attentive to the empathy and emotional gratification needs of customers. When customers complain that they were discriminated against, they were not getting what they deserve, or the service did not meet their requirements, managers should respond with both rational cues that explain the discrepancy between actions and expected outcomes and providing some compensation and emotional cues that satisfy the customers’ need for emotional gratification. Such customized and calibrated responses that are appropriate for the nature of the complaint would be critical in shaping the views of other customers in the online review forum. Firms, in their efforts to deal with the growing volume of reviews, have increasingly automated the response process using template responses. Our findings suggest that a more deliberate approach of carefully tailoring the responses to negative reviews is likely to be beneficial in online review forums. Firms could use a data-driven approach of extracting and classifying the nature of complaints according to our proposed framework. Recent advances in machine learning algorithms allow for such classification with greater precision. Instead of drafting each response from scratch, managers can use machine-written skeletons in their responses to target some specific reviews. Firms could then generate responses that are tailored to the nature of the complaints. Such approaches to generate tailored responses could allow firms to deal with the large review volumes in a more effective manner.