Ai Practices, Trust, And Cloud Accounting Adoption: A Moderation Analysis In Resource-Constrained Environments

Abstract

The rapid evolution of artificial intelligence (AI) and cloud-based accounting systems (CBAS) presents transformative opportunities for small and medium enterprises (SMEs), yet adoption remains inconsistent in developing economies due to resource constraints, infrastructural gaps, and cultural skepticism. This study examines how AI practices influence intentions to adopt CBAS among SME accountants and owners in Southern districts Khyber Pakhtunkhwa (KP) Province, Pakistan, with a focus on the moderating role of trust in AI. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and trust theory, the research proposes that AI practices (e.g., automation efficiency, predictive analytics) positively drive adoption intentions (H1), while trust in AI amplifies this relationship (H2). A cross-sectional survey of 401 SME professionals was conducted, utilizing validated scales to measure AI practices, trust in AI, and CBAS adoption intentions. Data analysis revealed a small but significant direct effect of AI practices on adoption intentions. Critically, trust in AI emerged as a significant moderator, strengthening the positive impact of AI practices on adoption decisions. This underscores trust’s role in mitigating perceived risks (e.g., data breaches, ethical concerns) and bridging the gap between technological utility and socio-cultural reservations in KP’s context. The findings extend UTAUT by integrating trust as a contextual moderator, offering a localized framework for AI adoption in high-stakes, resource-constrained environments. Practically, the study highlights the need for AI implementation strategies that prioritize transparency, ethical governance, and capacity-building initiatives tailored to SMEs’ socio-technical realities.

Keywords: Artificial intelligence, cloud-based accounting, trust, SME adoption, UTAUT, Pakistan.