Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach. (November 2022)

• Record Type: Journal Article
• Authors: Filieri, Raffaele; Lin, Zhibin; Li, Yulei; Lu, Xiaoqian; Yang, Xingwei
• Other Names: Bagozzi Richard P. guest-editor.; Brady Michael K. guest-editor.; Huang Ming-Hui guest-editor.
• Is Part Of: Journal of service research. Volume 25:Number 4(2022)
• Page Start: 614
• Page End: 629
• Publication Date: 2022-11
• Subjects: emotion -- emotion detection -- online customer reviews -- machine learning -- thematic analysis -- service robot; Customer services -- Periodicals; Service industries -- Periodicals; 658.81205
• DOI: 10.1177/10946705221103937
• Languages: English
• ISSNs: 1094-6705

Abstract

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.