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Artificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME.
Azer et al. (Mon,) studied this question.