ORCID
Document Type
Article
Date
11-2025
Keywords
generative AI, crisis communication, machine heuristics, relational tone, forgiveness intentions, sincerity
Disciplines
Communication Technology and New Media | Mass Communication | Public Relations and Advertising
Description/Abstract
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.
Recommended Citation
Lim, J. S., Hong, N., & Schneider, E. (2025). How warm-versus competent-toned AI apologies affect trust and forgiveness through emotions and perceived sincerity. Computers in Human Behavior, 172, 108761. https://doi.org/10.1016/j.chb.2025.108761
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Communication Technology and New Media Commons, Mass Communication Commons, Public Relations and Advertising Commons
