Exploring the Complex Configurations of Factors Influencing Chinese College Students’ Intention to Adopt ChatGPT

ChatGPT enhances learner performance, efficiency, and experience. While previous studies have explored the intention to adopt ChatGPT for educational purposes, they have often relied on multiple regression analyses or partial least squares methods without addressing the intricate relationships among multiple factors. This study employed the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate the complex configuration of antecedent conditions influencing the intention to adopt ChatGPT. Data were collected from 376 Chinese college students and analyzed using a hybrid approach that combined fuzzy-set qualitative comparative analysis (fsQCA) and necessary condition analysis (NCA). The results revealed that performance expectations, effort expectations, social influence, and facilitating conditions are not essential for a high intention to adopt ChatGPT. Two types of configurations were identified as drivers of high adoption intention: intrinsic value-driven and external environment-dependent. By contrast, three configurations were associated with low adoption intention. This study, through a configurational perspective, provides novel insights into the intentions of Chinese college students to adopt ChatGPT, thereby complementing existing qualitative and theoretical frameworks.