Empowering business research with ChatGPT: academic and student insights through the UTAUT lens

• Published in: Discover Computing Vol. 28; no. 1; pp. 179 - 20
• Main Author: Al-Hattami, Hamood Mohammed
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2025; Springer Nature B.V; Springer
• Subjects: Artificial intelligence; Author productivity; Behavior; Business education; Business research; Chatbots; ChatGPT; Computer Science; Data Mining and Knowledge Discovery; Data Structures and Information Theory; Ethical standards; Ethics; Fashion models; Higher education; Influence; Information Storage and Retrieval; Natural Language Processing (NLP); Pattern Recognition; Perceived risk; Privacy; Structural models; Students; Technology adoption; Trust; User experience; User satisfaction; UTAUT; Variables; Perceived risk; User satisfaction; Behavioral intention; ChatGPT; Business research; UTAUT; Technology acceptance; Trust
• ISSN: 2948-2992; 1386-4564; 2948-2992; 1573-7659
• DOI: 10.1007/s10791-025-09692-1

In an era where artificial intelligence (AI) is reshaping academic inquiry, ChatGPT has emerged as a transformative tool within the landscape of business research. This study explores the determinants of ChatGPT adoption among business academics and students by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) framework with three critical constructs: trust (T), perceived risk (PR), and user satisfaction (US). Data was collected from 332 participants, and the conceptual model was tested using SmartPLS 4. The structural model results indicate that performance expectancy (β = 0.361, t = 5.802, p  < 0.001), effort expectancy (β = 0.194, t = 4.035, p  < 0.001), social influence (β = 0.209, t = 3.738, p  < 0.001), and facilitating conditions (β = 0.185, t = 2.377, p  = 0.017) significantly predict behavioral intention (BI). In turn, behavioral intention (β = 0.292, t = 3.865, p  < 0.001), trust (β = 0.169, t = 2.223, p  = 0.026), and user satisfaction (β = 0.323, t = 5.880, p  < 0.001) positively influence actual usage behavior (AUB), while perceived risk exerts a significant negative influence on AUB (β =– 0.101, t = 2.157, p  = 0.031). The extended model accounts for 64.6% of the variance in behavioral intention (R 2  = 0.646) and 37.8% in actual usage behavior (R 2  = 0.378). Notably, user satisfaction emerged as the most influential predictor of actual usage, highlighting the necessity of delivering intuitive, secure, and rewarding user experiences. These insights offer valuable implications for AI developers, educators, and policymakers aiming to foster effective and ethical integration of ChatGPT into business research.