Embarrassed by Robots? How AI Types Affect Customer Experience

Details

Supervisors

Hazée, Simon

Faculty

Louvain School of Management

Degree label

Master [120] : ingénieur de gestion, à finalité spécialisée

Abstract

enIn an era where artificial intelligence (AI) is reshaping the way services are delivered, the integration of service robots presents both opportunities and challenges for managing customer experiences. This thesis therefore studies the underexplored influence of different types of artificial intelligence (mechanical AI, thinking AI, and feeling AI) embedded in service robots on customer embarrassment during service encounters. While prior studies have established that service robots can reduce social discomfort, and consequently embarrassment, compared to human employees, they have largely treated AI as a uniform category, overlooking distinctions between different types of AI intelligence. Addressing this gap through a comprehensive literature review and experimental research, this study employed a 3 (AI type: mechanical, thinking, feeling) by 2 (context sensitivity: high vs. low) between-subjects design with 210 participants, who were randomly exposed to one of six scenarios. Results reveal that more advanced AI (thinking and feeling) significantly increase embarrassment compared to mechanical AI, especially in high-sensitivity contexts. However, no significant difference was found between thinking and feeling AI, revealing that these two advanced AI types elicited similar levels of embarrassment. While higher AI intelligence resulted in stronger perceptions of automated social presence, this variable did not mediate the relationship between AI type and customer embarrassment. These findings offer both theoretical and managerial implications, suggesting that not all forms of AI are perceived equally and that their implementation in socially sensitive service contexts should be approached with careful consideration.