Revisiting UTAUT for the Age of AI: Understanding Employees AI Adoption and Usage Patterns Through an Extended UTAUT Framework

• URL: http://arxiv.org/abs/2510.15142v1
• Date: Thu, 16 Oct 2025 21:01:41 GMT
• Title: Revisiting UTAUT for the Age of AI: Understanding Employees AI Adoption and Usage Patterns Through an Extended UTAUT Framework
• Authors: Diana Wolfe, Matt Price, Alice Choe, Fergus Kidd, Hannah Wagner,
• Abstract summary: This study investigates whether demographic factors shape adoption and attitudes among employees toward artificial intelligence (AI) technologies at work.<n>We surveyed 2,257 professionals across global regions and organizational levels within a multinational consulting firm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study investigates whether demographic factors shape adoption and attitudes among employees toward artificial intelligence (AI) technologies at work. Building on an extended Unified Theory of Acceptance and Use of Technology (UTAUT), which reintroduces affective dimensions such as attitude, self-efficacy, and anxiety, we surveyed 2,257 professionals across global regions and organizational levels within a multinational consulting firm. Non-parametric tests examined whether three demographic factors (i.e., years of experience, hierarchical level in the organization, and geographic region) were associated with AI adoption, usage intensity, and eight UTAUT constructs. Organizational level significantly predicted AI adoption, with senior employees showing higher usage rates, while experience and region were unrelated to adoption. Among AI users (n = 1,256), frequency and duration of use showed minimal demographic variation. However, omnibus tests revealed small but consistent group differences across several UTAUT constructs, particularly anxiety, performance expectancy, and behavioral intention, suggesting that emotional and cognitive responses to AI vary modestly across contexts. These findings highlight that demographic factors explain limited variance in AI acceptance but remain relevant for understanding contextual nuances in technology-related attitudes. The results underscore the need to integrate affective and organizational factors into models of technology acceptance to support equitable, confident, and sustainable engagement with AI in modern workplaces.

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