Abstract
As more and more AI-powered tools and platforms are adopted in organizations to automate routine tasks, support decision-making, and improve efficiency, adoption is often lopsided, with employees embracing the system while also wondering whether it can effectively perform work-critical tasks. This study examines how organizations adopt AI platforms by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) in the context of user experience (UX) and competence trust, two AI-salient concepts. Competence trust is the extent to which employees believe an AI platform will reliably produce accurate, dependable, and work-relevant outputs. An exploratory sequential mixed-methods design was employed to generate and confirm inductive analysis-level explanations of trust formation and acceptance, grounded in insights gained through observation. The first phase involves semi-structured interviews with 15–25 organizational users. In this phase, the study maps the path from the UX stage to trust or distrust and acceptance, identifies important incidents that affect people's confidence in the platform, and gathers users’ trust-related language to help fine-tune constructs and measurement criteria. In Phase 2, a survey instrument is developed from issues identified in Phase 1 and established scales. CFA and SEM test a longer UTAUT model in which factors affecting performance expectancy, effort expectancy, social influence, and facilitating conditions are used to quantify levels of competence, trust and behavioral intention to use the AI platform. Where the sample size allows, multi-group comparisons can be made by user intensity or job function. Phase 3 combines qualitative themes and quantitative path findings through a combined display to extract converging data, identify contradictions, and provide expanded interpretations, ultimately deriving actionable suggestions that will be applied. Contribution of the study. The study contributes theoretically by framing competence trust as an integral mechanism linking UX to adoption within a broader UTAUT framework, and by providing a better explanation of why perceiving usefulness and ease of use alone may be inadequate in AI settings. Moreover, it has practical design and governance implications for organizations to foster sustainable adoption by incorporating UX features that signal reliability (e.g., stability, clear guidance, robust error handling) and by reinforcing social and organizational support to enhance trust.