<b>BARRIERS</b><b> </b><b>AND</b><b> </b><b>ENABLERS</b><b> </b><b>OF</b><b> </b><b>CHATGPT</b><b> </b><b>ADOPTION</b><b> </b><b>AMONG UNIVERSITY FACULTY: A CROSS-SECTIONAL STUDY</b>

Abstract

Background: The rapid emergence of generative artificial intelligence (AI) tools such as ChatGPT has transformed higher education, offering new opportunities for research, teaching, and academic productivity. However, adoption among university faculty remains uneven, shaped by both technological and psychological factors. Purpose: This study examined the barriers and enablers influencing ChatGPT adoption among university faculty using an integrated framework combining the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), extended with AI-specific constructs such as ethical concerns and AI anxiety. Methods: A cross-sectional quantitative survey was administered to 372 faculty members from diverse disciplines. Data were collected using a validated online questionnaire comprising seven constructs: performance expectancy, effort expectancy, facilitating conditions, institutional support, ethical concerns, AI anxiety, and behavioral intention. Statistical analyses included descriptive statistics, correlation, multiple regression, and ANOVA. Results: Findings revealed that performance expectancy (β = .34, p < .001), effort expectancy (β = .21, p < .01), facilitating conditions (β = .18, p < .05), and institutional support (β = .15, p < .05) significantly predicted behavioral intention to adopt ChatGPT, collectively explaining 72.4% of variance (R² = .724). Conversely, AI anxiety (β = –.19, p < .01) and ethical concerns (β = –.14, p < .05) negatively affected adoption. STEM and business faculty exhibited higher adoption intentions than humanities faculty. Conclusion: Faculty attitudes toward ChatGPT are largely positive, driven by perceived usefulness, ease of use, and institutional facilitation. Nonetheless, ethical apprehensions and anxiety remain key obstacles. Universities should address these barriers through ethical guidelines, professional development, and supportive infrastructure to ensure responsible and sustainable integration of generative AI in academia.

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