Multi-dimensional drivers and ethical synergy in student GenAI adoption: A TAM-NAM-based configurational analysis

The rapid proliferation of generative artificial intelligence (GenAI) in higher education has triggered profound transformations in learning models, yet it raises significant concerns regarding academic integrity and ethical governance. Addressing the scarcity of systematic empirical research, this study moves beyond traditional adoption paradigms by integrating the Technology Acceptance Model (TAM) and Norm Activation Model (NAM). Based on survey data from 1,456 Chinese university students, this research employs Partial Least Squares Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine technological, ethical, individual, and social drivers. The results demonstrate that perceived usefulness, enjoyment, and subjective norms significantly drive adoption. Crucially, awareness of consequences indirectly influences intention through the mediation of personal norms. Furthermore, fsQCA uncovers multiple configurational pathways, revealing the synergistic mechanisms underlying adoption. This study enriches the theoretical understanding of ethical AI adoption and provides actionable insights for policymakers to foster responsible GenAI governance.