Perceived GenAI competency and university teachers’ intention to continue using generative artificial intelligence: Extending the technology acceptance model

Generative artificial intelligence (GenAI) is reshaping higher education, and understanding teachers' intention to continue using GenAI is increasingly important in this context. Grounded in the technology acceptance model (TAM), this study examines how different dimensions of teachers' perceived GenAI competency are associated with perceived ease of use (PEOU), perceived usefulness (PU), and GenAI use continuance intention (GenAI-UCI). Using survey data from 615 university teachers in Qingdao, China, structural equation modeling was employed to examine the hypothesized relationships among perceived GenAI competency dimensions, TAM-based beliefs, and continuance intention. Results showed that perceived GenAI competency dimensions related to teaching, research, and professional engagement were positively associated with PU, whereas perceived basic understanding showed a small and non-robust association with PEOU. PU was positively associated with GenAI-UCI, whereas PEOU showed no significant direct association with GenAI-UCI. Overall, the findings suggest that perceived GenAI competency is not a homogeneous capability; rather, different competency dimensions were associated with different TAM-based beliefs within the proposed model. The findings highlight the importance of teachers' perceptions of GenAI usefulness in their professional practices for understanding continuance intention in higher education.