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Factors influencing disinformation avoidance behavior among generative artificial intelligence users: A
Tingyong Zhang1, Junping Qiu2, Zhongyang Xu3
1School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China; Department of Chinese Academy of Science and Education Evaluation, Hangzhou Dianzi University, Hangzhou, 310018, China; École de bibliothéconomie et des sciences de l'information, Université de Montréal, Montréal, Quebec, H3T 1J4, Canada.
Acta Psychologica
|April 24, 2026
Summary
Understanding generative artificial intelligence (AI) disinformation avoidance is key. Perceived risks and AI hallucinations increase anxiety, while user confidence and efficacy reduce avoidance, promoting sustainable AI use.
Area of Science:
- Human-computer interaction
- Artificial intelligence ethics
- Cognitive psychology
Background:
- Generative artificial intelligence (AI) adoption presents challenges with disinformation.
- Understanding user avoidance behaviors is crucial for sustainable AI development.
- Existing research often overlooks defensive user strategies.
Purpose of the Study:
- To investigate the psychological mechanisms behind disinformation avoidance behavior in generative AI users.
- To apply the cognition-affect-conation framework and heuristic-systematic model to AI disinformation.
- To identify factors influencing user trust and promote sustainable AI use.
Main Methods:
- Covariance-based structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed.
- Data from 769 valid user responses were analyzed.
- The study integrated cognitive and affective processes using established theoretical frameworks.
Main Results:
- Perceived risk and AI information hallucination increased AI disinformation anxiety.
- Perceived mind and performance efficacy boosted affective commitment, reducing avoidance.
- Systematic cues (hallucination, efficacy) were more impactful than heuristic cues (risk, mind).
- Psychological resilience moderated anxiety and commitment effects on avoidance.
- Four distinct pathways to avoidance behavior were identified, showing substitutability effects.
Conclusions:
- Generative AI disinformation anxiety drives avoidance, while affective commitment mitigates it.
- User psychological resilience plays a significant moderating role.
- Findings offer practical insights for AI developers to enhance user trust and mitigate avoidance behaviors.
- The study advances AI user behavior research by focusing on defensive perspectives and integrating cognitive-affective models.
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