In the era of electronic word-of-mouth, hotels are under the pressure to respond to online reviews more effectively and strategically, to maintain and enhance hotel reputation and financial viability. Little research is done to guide organizations to effectively operate management response strategies. Using data mining techniques such as text mining, sentiment analysis, and Latent Dirichlet Allocation, guided by the affect theory, this study develops and evaluates an “AAAA” framework that classifies management responses to online hotel reviews into four categories: acknowledgment, account, action, and affect. A training sample of 29,606 review responses collected from three U.S cities on TripAdvisor.com are mined and analyzed. The results will be further tested by a longitudinal, econometric sales model, to identify the effectiveness of management response strategies for hotels. This research-in- progress will contribute to emerging research in management responses to online hotel reviews and provide managerial implications for managing responses to enhance hotel performance.
Deng, Tianjie; Lee, Young Jin; and Xie, Lijia, "Management Responses to Online Hotel Reviews: Text Mining to Lift Sales" (2018). PACIS 2018 Proceedings. 190.