How to strategically respond to online hotel reviews: A strategy-aware deep learning approach
研究提出一种策略感知的深度学习模型，帮助酒店管理者优化对正面和负面评论的回应策略，发现主动建设性回应增强正面评论效果，而被动建设性策略更有效减轻负面评论损害。
Online reviews exert a considerable influence on consumer purchase behavior, yet there remains ambiguity about the most effective managerial response strategies for positive and negative reviews. Addressing this gap, our study introduces a Strategy-Aware, Deep Learning-Based Natural Language Processing (Sa-DLNLP) model designed to optimize firm responses. The proposed model underwent rigorous evaluation through a human-coded study and was subsequently validated by a separate user response study. Our findings reveal that active-constructive responses significantly enhance the impact of positive reviews, whereas passive-constructive strategies are more effective in mitigating the damage from negative reviews. Additionally, the study underscores the importance of concise, personalized, and prompt responses across the board. Interestingly, responses that are overly explanatory, excessively empathetic, or challenge customers were found to be counterproductive when dealing with negative reviews. This study not only demystifies the art of managing online reviews but also offers an advanced deep learning methodology that can directly benefit the disciplines of Information Systems and Management.