Trust in AI vs human expert: investigating behavioral intention among employees
通过五个情景实验，研究了信息质量高低和信息来源（AI专家或人类专家）如何共同影响酒店员工的信任和行为意向，发现低质量信息来自AI时更易被信任，但批判性思维可缓解此效应。
Purpose This study aims to investigate the interactive effect between information quality (high vs low) and agent (artificial intelligence [AI] expert vs human expert) on hospitality employees’ trust and behavioral intention. Design/methodology/approach Five scenario-based survey experiments were conducted, systematically varying the combinations of information quality and agent. Data were collected from hospitality employees to examine their responses regarding trust, perceived effectiveness and behavioral intention. Findings Low-quality information is more likely to be trusted and followed when it comes from an AI expert rather than a human expert. However, critical thinking skills and cognition-based trust may help mitigate this unfavorable effect. The findings demonstrate that both the information quality and the agent delivering it are critical determinants of employees’ psychological state and behavior in workplace settings where AI is integrated. Originality/value This study contributes to automation bias and source credibility theory in hospitality by investigating the interplay between information quality and agent. It enriches the literature on AI’s integration in workplace settings and provides actionable insights for managers seeking to ethically implement AI technologies.