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TRUVACE RECORD VERSION
record: TRV-2026-2611
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T08:03:20.250425Z
status: published
lens: p_space
sector: labor
headline: Trust in AI vs human expert: investigating behavioral intention among employees
dek: 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 inte…
gain_title: (none)
problem_title: In five scenario-based survey experiments with hospitality employees, low-quality information was more likely to be trusted and followed when it came from an AI expert than from a human expert.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In five scenario-based survey experiments with hospitality employees, low-quality information was more likely to be trusted and followed when it came from an AI expert than from a human expert.
problem_evidence: Low-quality information is more likely to be trusted and followed when it comes from an AI expert rather than a human expert.
quick_read: Five scenario-based survey experiments with hospitality employees tested how information quality (high or low) and the source of the advice (an AI expert or a human expert) shape trust and behavioral intention. Employees were more likely to trust and follow low-quality information when an AI expert delivered it than when a human expert did.

The finding points to automation bias in workplaces that use AI: employees may defer to a machine source even when the content is poor. The study measured trust, perceived effectiveness and behavioral intention, so the result concerns what employees said they would do in scenarios.

The authors report that critical thinking skills and cognition-based trust may help reduce the effect. They say both information quality and the agent delivering it determine employees' psychological state and behavior where AI is integrated, and they offer guidance for managers who want to introduce AI ethically.
limitation: The evidence comes from scenario-based survey experiments, which measure stated trust and behavioral intention rather than actual workplace behavior, and the mitigating role of critical thinking is described only as something that "may help".
tag: Evidence-backed problem
key_points: The research used five scenario-based survey experiments rather than observation of real workplace decisions. | Participants were hospitality employees, and the experiments varied information quality (high versus low) and agent (AI expert versus human expert). | Outcomes measured were trust, perceived effectiveness and behavioral intention. | Low-quality information was more likely to be trusted and followed when attributed to an AI expert. | Critical thinking skills and cognition-based trust may help mitigate the unfavorable effect. | The work builds on automation bias and source credibility theory in hospitality.
rundown: The study, published in the International Journal of Contemporary Hospitality Management, set out to test the interactive effect of information quality and agent on hospitality employees. Across five scenario-based survey experiments, the researchers varied whether information was high or low quality and whether it came from an AI expert or a human expert.

Employees' responses covered trust, perceived effectiveness and behavioral intention. The central result is that weak information gained an advantage in trust and uptake when an AI expert, not a human expert, was its source.

The authors say critical thinking skills and cognition-based trust may help counter this effect. They frame the work as a contribution to automation bias and source credibility theory, and as practical guidance for managers who want to implement AI technologies ethically.
sources:
- peer_reviewed | International Journal of Contemporary Hospitality Management | https://doi.org/10.1108/ijchm-07-2025-1078 | 2026-06-09
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