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Advice by Algorithms: Dual Dimensions of Trust in AI Financial Service Adoption

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  • Chang, Chia-Yang (Mac)
  • Park, Sungjun (Steven)
  • Dinh, Minh Cong

Abstract

Artificial intelligence (AI) is reshaping financial services, replacing human advisors with algorithm-driven interfaces that offer personalized guidance at scale. While these systems deliver efficiency, their adoption depends on consumer trust—a barrier that becomes especially salient when advisory services are machines rather than humans. Despite its importance, little is known about how trust in AI develops and shapes consumer behavior in financial contexts. Drawing on Social Cognitive Theory (SCT), this research examines how two dimensions of trust—credibility-based (perceived competence) and benevolence-based (perceived goodwill)—shape vicarious learning, strengthen financial self-efficacy, and drive adoption of AI-enabled financial services. Data from 361 U.S. consumers analyzed via structural equation modeling reveal that trust is a key foundation to vicarious learning in human-AI interactions. To provide additional insights, results show that pandemic-induced fear moderates trust formation. These findings extend SCT to AI-mediated contexts, highlight the dual role of trust in technology adoption, and offer practical guidance for designing trustworthy robo-advisors in financial retailing.

Suggested Citation

  • Chang, Chia-Yang (Mac) & Park, Sungjun (Steven) & Dinh, Minh Cong, 2026. "Advice by Algorithms: Dual Dimensions of Trust in AI Financial Service Adoption," Journal of Retailing and Consumer Services, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:joreco:v:88:y:2026:i:c:s0969698925003169
    DOI: 10.1016/j.jretconser.2025.104537
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