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TRUVACE RECORD VERSION
record: TRV-2026-2637
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T16:55:15.547124Z
status: published
lens: g_space
sector: business
headline: The contingent effects of AI transparency in service recovery: a moderated mediation model of perceived warmth and competence
dek: Purpose This research investigates how artificial intelligence (AI) transparency shapes consumer responses in service recovery. By revealing the reasoning process behind AI decisions, transparency functions as an anthropomorphic cue that humanizes the system's communication. Drawing on social cognitive theory, the research develops and tests a generalizable mechanism framework linking AI transparency, consumer cognition and recovery satisfaction, while identifying boundary conditions. Design/methodology/approach…
gain_title: In three scenario-based experiments with 98, 230 and 284 participants, AI that revealed its reasoning raised customers' satisfaction with service recovery, partly by making the system seem warmer and more competent.
problem_title: (none)
trace_subject: (none)
gain_reading: In three scenario-based experiments with 98, 230 and 284 participants, AI that revealed its reasoning raised customers' satisfaction with service recovery, partly by making the system seem warmer and more competent.
gain_evidence: AI transparency significantly improves recovery satisfaction, indirectly through perceived warmth and competence.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers ran three scenario-based experiments to test whether an AI system that shows the reasoning behind its decisions leaves customers more satisfied after a service failure. Transparency significantly improved recovery satisfaction, and the effect ran indirectly through how warm and how competent customers judged the AI to be.

The benefit depended on how bad the failure was. When the service failure was low in severity, transparency worked through both warmth and competence. When severity was high, those indirect effects were not significant, which the authors identify as a boundary condition for transparency.

The work treats transparency as a kind of anthropomorphism, a cue that makes the AI's communication feel more human, and applies social cognitive theory to AI-mediated service recovery. The findings come from experimental scenarios, not from observed customer interactions.
limitation: The indirect effects through warmth and competence were not significant when service failures were severe, and the evidence comes from scenario-based experiments rather than real customer complaints.
tag: Evidence-backed gain
key_points: Study 1 (N = 98) tested the direct effect of AI transparency on recovery satisfaction. | Study 2 (N = 230) examined perceived warmth and perceived competence as parallel mediators of that effect. | Study 3 (N = 284) tested service failure severity as a moderator and estimated the conditional indirect effects. | Severity moderated the effect of transparency on both warmth and competence. | Indirect effects through warmth and competence were significant under low severity but not under high severity.
rundown: The research was built in three steps. The first experiment, with 98 participants, tested whether AI transparency affects recovery satisfaction directly. The second, with 230, tested whether perceived warmth and perceived competence each carry part of the effect. The third, with 284, added service failure severity as a moderator.

Results show transparency improving recovery satisfaction indirectly through warmth and competence. Severity moderated the effects of transparency on both mediators, so the pathways held under low-severity failures and were not significant under high-severity ones.

The authors frame transparency as an anthropomorphic cue that exposes the AI's thought process, extending social cognitive theory to AI-mediated service recovery.
sources:
- peer_reviewed | Online Information Review | https://doi.org/10.1108/oir-08-2025-0646 | 2026-11-03
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