Customer misbehavior in AI-enabled services

Purpose – When artificial intelligence (AI) enhances hospitality service efficiency and quality, it may also extend chances for customer misbehavior as commonly accepted social norms in traditional human-human services are altered. Unfortunately, there is very little theoretical reflection on this issue. This reflection paper proposes an integrative framework to understand the relationship between AI-enabled services and customer misbehavior for future research. Design/methodology/approach – This paper synthesized previous research on customer misbehavior in AI-enabled services and developed a conceptual framework. Findings – This reflection paper addresses three issues: (1) the distinction between customer misbehavior in AI-enabled versus human-provided services, (2) the underlying behavioral mechanisms, and (3) potential mitigation strategies. They propose that AI-enabled services amplify interactions between customer traits and service environments and lead to customer misbehavior through cognitive perceptions (e.g. perceived monitoring, social presence and perceived social judgment) and moral emotions (e.g. guilt or anticipatory guilt). Research limitations/implications – This paper proposes a conceptual agenda for understanding customer misbehavior in AI-enabled services. Future research should focus on minimizing unintended costs and negative side effects of integrating AI technologies into service encounters. Social implications – This study offers managerial and marketing implications to mitigate customer misbehavior in AI-enabled services. They also help prevent damage to service robots, thereby minimizing waste of resources from governments, enterprises and society. Originality/value – This paper provides an updated overview of customer misbehavior in AI-enabled services, complementing existing research by highlighting negative customer outcomes associated with AI technology-driven service encounters.