How to calculate privacy: privacy concerns and service robots’ use intention in hospitality

Abstract

Artificial intelligence in hospitality is increasingly transforming the way we travel. Service robots collect, store, analyze, and act upon a continuous stream of private information as a by-product of human-robot interaction. As such, they invade consumers’ virtual and physical space and raise privacy challenges in AI settings. We conduct a survey study(n = 576) and validate a mediating contextualized model of consumers’ adoption decisions on service robots in hospitality from privacy concerns. Our findings highlight the interplay between perceived risk and perceived benefit in shaping service robots’ adoption decisions is partially mediated by privacy concerns. Our findings also highlight the mechanism of privacy concerns, conceptualized as psychological constructs of collection, control and awareness of privacy practices, as an important addition to the established multiple chain mediating effect. The insights explain how consumers calculate privacy between perceived risk and benefit, and help reconcile a fundamental tension among consumers, how to avail benefits of privacy by improving the privacy awareness and control associated with the collection of private information in hospitality.