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Responsible AI governance, privacy concerns and the adoption of financial AI: an extension of UTAUT2 and communication privacy management

Longfei Zhang et al.

Information and Computer Security2026https://doi.org/10.1108/ics-10-2025-0432article
AJG 1ABDC B
Weight
0.50

Abstract

Purpose This study aims to examine consumer intention to adopt privacy preserving artificial intelligence in credit management. The focus is on how financial institutions that implement privacy protection and responsible artificial intelligence (AI) governance in advance can shape trust and technology acceptance, which addresses the gap concerning proactive corporate privacy governance. Design/methodology/approach An integrative model that combines the unified theory of acceptance and use of technology version two (UTAUT2) with communication privacy management is proposed. Privacy concerns are specified as a mediator and responsible AI governance is specified as a moderator. Survey data are analyzed using partial least squares structural equation modeling. Findings Most UTAUT2 factors significantly shape privacy concerns. Performance expectancy increases privacy concerns, whereas effort expectancy and social influence reduce them. Privacy concerns, trust and responsible AI governance directly and positively predict adoption intention. The moderating effect of responsible AI governance on the path from privacy concerns to intention is not significant. Originality/value This study extends UTAUT2 through the integration of communication privacy management and the explicit inclusion of responsible AI governance. It provides a privacy-centered account of technology acceptance in sensitive financial settings and offers actionable guidance for governance that precedes formal regulatory enforcement.

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https://doi.org/https://doi.org/10.1108/ics-10-2025-0432

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@article{longfei2026,
  title        = {{Responsible AI governance, privacy concerns and the adoption of financial AI: an extension of UTAUT2 and communication privacy management}},
  author       = {Longfei Zhang et al.},
  journal      = {Information and Computer Security},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/ics-10-2025-0432},
}

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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