User segmentation in human–robot interactions: insights from sports betting patrons using diffusion of innovation theory

ABSTRACT

Previous research has assumed uniformity in user response to service robots regardless of user profile. This study sought to understand the nature of human–robot interactions in a competitive setting by applying the diffusion of innovation theory. Through a survey of 447 sports betting patrons, we used unsupervised learning to cluster the patrons based on human/robot interaction, relational and psychological variables, usage intention, and socioeconomic information. Four clusters were identified: Tech laggards, Optimists, Skeptics, and Enthusiasts. Trust was a key determining factor of cluster membership, especially among frequent betters. Further, the study examined each cluster through the lens of psychological theories and ethical perspectives, identified associated risks, and offered mitigation strategies. Practical implications are discussed.

KEYWORDS:

• Risks and barriers of artificial intelligence
• Casino
• Gaming
• sports betting
• robot
• human–robot interaction
• artificial intelligence
• trust

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Internatioanl Center for Gaming Regulation at UNLV.