How warm-versus competent-toned AI apologies affect trust and forgiveness through emotions and perceived sincerity
As generative artificial intelligence (GenAI) becomes more integrated into corporate communication, its role in crisis messaging raises critical questions about audience perception and trust. Drawing on theories of machine heuristics, this study explores how relational cues in AI-authored crisis apo...
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| Published in: | Computers in human behavior Vol. 172; p. 108761 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.11.2025
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| Subjects: | |
| ISSN: | 0747-5632 |
| Online Access: | Get full text |
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| Summary: | As generative artificial intelligence (GenAI) becomes more integrated into corporate communication, its role in crisis messaging raises critical questions about audience perception and trust. Drawing on theories of machine heuristics, this study explores how relational cues in AI-authored crisis apologies shape emotional and cognitive responses that ultimately influence trust and forgiveness. A 3 (authorship attribution: AI vs. human vs. control) x 2 (relational tone: warmth vs. competence) between-subjects factorial design with 464 participants was conducted to assess if and how incorporating a warm tone into AI-generated apologies can help overcome AI's inherent limitations associated with machine heuristics. Results show that human-authored apologies are perceived as more sincere, with warmth enhancing their positive impact. AI authorship elicited more negative emotions and reduced perceived sincerity compared to human authorship; however, relational tone was found to moderate the indirect effects of authorship on trust and forgiveness through negative emotions and perceived sincerity. These findings highlight the importance of both emotional and cognitive mechanisms in AI-mediated communication. This research advances an understanding of AI-mediated communication, identifying relational tone as a critical moderator of machine heuristic effects in crisis communication contexts. By integrating both emotional (negative affect) and cognitive (perceived sincerity) mediators into the model, this research provides a deeper understanding of how audiences evaluate and respond to AI-generated apologies in crisis contexts. Additionally, it offers a novel application of machine heuristic theory, extending its relevance to reputational management and organizational transparency.
•AI-authored apologies generated more negative emotions than human-authored ones.•Relational tone (warmth vs. competence) moderates trust and forgiveness.•Warmth mitigated the negative effects of AI-authored apologies.•Emotional and cognitive mechanisms impact audience responses to AI apologies.•Study extends machine heuristic theory to reputational management and transparency. |
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| ISSN: | 0747-5632 |
| DOI: | 10.1016/j.chb.2025.108761 |