Managerial responses to negative reviews could be easily understood as a brand-safeguarding strategy by firms because negative reviews can damage a company’s reputation. However, it is unclear if managers should respond to positive reviews and if so, if such action helps or hurts the firm. We develop a theoretical framework to explicate the mechanisms underlying the effects of managerial responses to positive reviews on user reviewing behaviors in online platforms. We classify positive reviews into four types: one-sided affective reviews, two-sided affective reviews, one-sided instrumental reviews, and two-sided instrumental reviews. We classify managerial responses as tailored and template responses. Using natural language processing and deep learning algorithms, we extract information presented in the texts in the reviews and responses. We theorize and test which kinds of managerial responses to positive reviews are helpful and which of them are harmful. Overall, we find that a tailored response is more appropriate when responding to two-sided instrumental positive reviews and one-sided affective positive reviews, whereas template responses work for one-sided instrumental positive reviews and two-sided affective positive reviews. Not responding would be an effective strategy for mixed positive reviews.

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.

An important theme in the alliance research has been the study of how prior alliance experience translates into value gains from alliances. Despite the strong theoretical argument regarding the value-enhancing role of alliance experience, past research has reported mixed results. In an attempt to resolve the inconclusive findings, we provide a more fine-grained view of alliance experience by examining characteristics such as relatedness and diversity, which are defined based on the functional focus and the industry of the partner. Furthermore, we argue that since leveraging alliance experience is a learning process, a firm’s knowledge integration capabilities enabled by information technology (IT) should influence the extent to which the firm benefits from alliance experience. Using data from 1,030 alliances made by 89 firms across 11 industries, we test the effects of relatedness and diversity on abnormal returns following alliance announcements. We find that functionally related experience is positively related to abnormal returns, whereas partner industry-based related experience affects the expected value negatively. We also find that a firm’s IT-enabled knowledge integration positively moderates the effects of both related and diverse experience on abnormal returns. Our findings highlight that although knowledge gained through prior experience is important, complementary capabilities that enable firms to leverage and utilize such knowledge are also necessary for ex ante value creation in alliances. We interpret these findings and discuss their implications for research in both strategic management and information systems.