Artificial Intelligence and Human Resource Performance in Higher Education: An Integrated UTAUT-TTF-TOE Model with Nonlinear Adoption Effects

Abstract

This study investigates how artificial intelligence (AI) affects human resource (HR) performance in Chinese higher education institutions by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT), Task-Technology Fit (TTF), and Technology-Organization-Environment (TOE) frameworks. It examines the inverted U-shaped moderating effect of AI adoption intensity on the usage–performance relationship. A cross-sectional quantitative design was employed, with data collected through structured questionnaires from 385 respondents across 32 higher education institutions in Guangdong Province, China. Structural equation modeling (SEM) tested the hypothesized relationships, while polynomial regression with quadratic interaction terms examined the curvilinear moderation. All hypothesized paths were supported. Performance Expectancy, Effort Expectancy, and Task-Technology Fit significantly predicted Behavioral Intention, while Technology Characteristics, Task Characteristics, Organizational Support, and Environmental Factors significantly predicted TTF. Actual AI Usage and TTF independently predicted HR Performance. AI adoption intensity showed an inverted U-shaped moderating effect, indicating that moderate rather than maximum adoption produces the strongest performance gains. The study contributes by integrating UTAUT, TTF, and TOE; extending TTF by positioning organizational and environmental factors as antecedents of fit; providing evidence of nonlinear AI adoption effects in higher education; and demonstrating differential AI effectiveness across HR functions, with structured, data-intensive functions showing stronger technology–performance relationships.