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.

中文翻译：

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如何战略性地回应在线酒店评论：一种具有战略意识的深度学习方法


在线评论对消费者的购买行为产生相当大的影响，但对于正面和负面评论的最有效的管理响应策略仍然存在模糊性。为了解决这一差距，我们的研究引入了一种战略感知、基于深度学习的自然语言处理 (Sa-DLNLP) 模型，旨在优化企业响应。所提出的模型通过人工编码研究进行了严格评估，随后通过单独的用户响应研究进行了验证。我们的研究结果表明，积极的建设性反应显着增强了正面评论的影响，而被动建设性的策略在减轻负面评论的损害方面更有效。此外，该研究强调了全面简洁、个性化和及时响应的重要性。有趣的是，在处理负面评论时，过度解释、过度同理心或挑战客户的反应被发现会适得其反。这项研究不仅揭开了在线评论管理艺术的神秘面纱，而且还提供了一种先进的深度学习方法，可以直接使信息系统和管理学科受益。