Drivers of Generative AI Adoption in Higher Education: A fsQCA Study on Student Motivations and Technology Perceptions - Yuan, Rongjing (European Journal of AI, Computing & Informatics, 2026)
Generative Artificial Intelligence (GenAI) is reshaping higher education, yet the drivers of students’ continuance intention in academic contexts remain underexplored. Building on the Rich Intrinsic Motivation (RIM) framework and technology adoption theories, this study investigates the configurational effects of intrinsic motivations (accomplishment, knowledge, stimulation), extrinsic motivation (perceived usefulness), and technology characteristics (ease of use, novelty) on GenAI adoption. Using Fuzzy-Set Qualitative Comparative Analysis (fsQCA) on data from 238 university students, the study reveals that no single factor is necessary for adoption. Instead, four distinct sufficient configurations drive high continuance intention: (1) "Happy Achievers" (Hedonic-Mastery), (2) "Curious Explorers" (Hedonic-Knowledge), (3) "Conquerors" (Pure Mastery), and (4) "Determined Strivers" (Utilitarian-Striving). These findings highlight the complex interplay between motivational and technological factors, offering tailored insights for educators to foster sustainable GenAI integration in learning.