Different agents, different trust: private information disclosure in hybrid service encounters

Purpose As artificial intelligence (AI)-enabled service technologies become more common, consumers increasingly disclose private information to both human and technological service agents. This study aims to examine how service agent type shapes private information disclosure in hospitality and tourism encounters. Design/methodology/approach Using an exploratory sequential mixed-methods design, Phase I explored disclosure experiences with human and technological service agents and informed the development of an agent-contingent conceptual framework. Phase II tested the relationships among multidimensional trust (competence, integrity and benevolence), perceived control (transparency, vulnerability and privacy empowerment) and willingness to disclose. Phase III experimentally examined the effects of service agent type and brand familiarity. Findings Across the studies, service agent type influenced why consumers trusted an agent more than whether they were willing to disclose. Technological agents drew trust more strongly from competence, whereas human agents benefited more from benevolence. In turn, trust, rather than perceived control, was the primary predictor of willingness to disclose private information. Brand familiarity provided an additional organizational assurance cue, increasing both trust and willingness to disclosure across service agent types. Practical implications Service firms should align disclosure practices with the trust cues associated with different service agents. AI-enabled interfaces can strengthen trust through clear data usage explanations and human support, while human agents can reinforce perceptions of competence through standardized privacy procedures. Originality/value This study develops an agent-contingent framework of private information disclosure in hybrid service encounters, showing how disclosure reflects evaluations of the information requester, disclosure process and organization responsible for the data.