Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants According to the AI-UTAUT Model

Abstract

This study aims to explore trust perceptions and the adoption experience of Artificial Intelligence (AI) within Human Resource Information Systems (HRIS) in the Palestinian private sector, utilizing the AI-UTAUT model as an analytical reference framework. An exploratory qualitative approach grounded in Grounded Theory principles was adopted, conducting semi-structured interviews with a purposive sample of (16) HR managers, IT managers, and decision-makers. The findings reveal that AI adoption does not follow a linear technical trajectory driven solely by performance expectancy or effort expectancy. Instead, it emerges as a dynamic and evolving "negotiated adoption" process shaped by the interplay of organizational, contextual, and sovereign factors. The study further uncovers that multidimensional trust (technical, organizational, and sovereign) functions as a critical "governing filter" that reshapes the efficacy of traditional behavioral acceptance determinants within the AI-UTAUT model. Consequently, the study developed an emergent explanatory framework: the Multidimensional Trust-Governed Negotiated Adoption Framework (MTG-Adopt). This framework reframes AI adoption as a trust-based digital governance system rather than a mere response to utility or ease of use. The study contributes theoretically by redefining technology acceptance logic in non-Western, resource-constrained contexts; methodologically by highlighting the value of Grounded Theory in generating emergent explanatory frameworks; and practically by emphasizing the priority of building institutional and sovereign trust systems as a prerequisite for the responsible and sustainable adoption of AI technologies.