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Articles | Applied Psychology and Cognitive Science | Volume 1 - Issue 1 - 2026 | 14−26 |
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Factors Influencing Public Trust in AI Decision Support Systems: A Cognitive and Behavioral Perspective

Author(s): Dimas Ardhana Prasetyo 1 , Raka Mahendra Wibisono 2
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1 Universitas Hasanuddin, Jl. Perintis Kemerdekaan Km. 10, Tamalanrea, Makassar, Sulawesi Selatan 90245, Indonesia
2 Universitas Udayana, Jl. P.B. Sudirman, Dauh Puri Klod, Denpasar, Bali 80234, Indonesia

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Applied Psychology and Cognitive Science

Volume 1 - Issue 1 - 2026

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Public trust represents a critical prerequisite for the responsible adoption of artificial intelligence based decision support systems (AI DSS), particularly in high stakes contexts where algorithmic recommendations may substantially influence consequential human decisions. Despite increasing reliance on AI DSS, the cognitive and behavioral mechanisms underlying public trust remain insufficiently understood, particularly regarding how transparency interacts with individual resistance to algorithmic decision making. This study examined the cognitive and behavioral determinants of trust in AI DSS using a cross sectional survey of 412 adults. Perceived transparency, algorithm aversion, anthropomorphism, need for cognition, general attitudes toward AI, perceived risk, and prior AI experience were examined as potential determinants of trust. Hierarchical multiple regression and moderation analyses were conducted to identify significant predictors and conditional relationships, while analysis of variance was used to assess differences in trust according to prior AI experience. The cognitive and behavioral factors collectively explained approximately 50% of the variance in trust. Perceived transparency emerged as the strongest positive predictor, followed by favorable attitudes toward AI, whereas perceived risk and algorithm aversion significantly reduced trust. Importantly, algorithm aversion significantly moderated the relationship between transparency and trust, such that the positive effect of transparency weakened as algorithm aversion increased. Trust also differed significantly according to prior AI experience, with highly experienced participants reporting greater trust than those with limited experience. These findings demonstrate that transparency, although important, is insufficient as a standalone mechanism for establishing trust in AI DSS. The study advances understanding of AI trust by demonstrating that the effectiveness of transparency depends partly on users’ preexisting cognitive and behavioral dispositions toward algorithmic systems. Consequently, responsible trust building strategies should integrate understandable and context appropriate explanations, effective risk communication, meaningful user experience, and explicit consideration of individual differences in attitudes toward algorithmic decision making.

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SUBMITTED: 11 August 2026
ACCEPTED: 02 October 2026
PUBLISHED: 26 October 2026
SUBMITTED to ACCEPTED: 53 days

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