Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.

了解机器人与客户相遇并通过在线评论分享的消费者情绪对于预测消费者采用服务机器人的意图至关重要。定性分析具有从数据中产生丰富见解的优势，但它需要大量的手动工作。学者们强调了使用算法识别和区分情绪的好处。本研究通过采用混合机器-人类智能方法，批判性地解决了定性分析和机器学习方法的优缺点。我们从两个主要社交媒体平台（携程和 TripAdvisor）中提取了 9707 条客户评论样本，包括 8 个国家的 412 家酒店。结果表明，服务机器人的客户体验非常积极，揭示与机器人互动会引发喜悦、爱、惊喜、兴趣和兴奋等情绪。不满主要表现在客户因故障无法使用服务机器人时。服务机器人在移动时会引发更多情绪。研究结果进一步揭示了文化对客户对服务机器人的情绪反应的潜在调节作用。该研究强调，混合方法可以利用机器学习算法的可扩展性和效率，同时克服其缺点，例如解释能力差和情感类别有限。研究结果进一步揭示了文化对客户对服务机器人的情绪反应的潜在调节作用。该研究强调，混合方法可以利用机器学习算法的可扩展性和效率，同时克服其缺点，例如解释能力差和情感类别有限。研究结果进一步揭示了文化对客户对服务机器人的情绪反应的潜在调节作用。该研究强调，混合方法可以利用机器学习算法的可扩展性和效率，同时克服其缺点，例如解释能力差和情感类别有限。