Research on factors influencing teaching staff’s continued use intention of AIGC tools: based on structural equation modeling and artificial neural networks

Previous studies have applied models such as UTAUT to investigate users' intentions to adopt new technological tools. However, these studies have primarily focused on initial usage intentions, paying limited attention to continuance intention. To address this gap and provide a more comprehensive understanding of HE teaching staff's continuance intention toward AIGC tools, this study innovatively integrates ECM and UTAUT to develop a novel framework for examining this continuance intention. Using a combined approach of Partial Least Squares Structural Equation Model (PLS-SEM) and Artificial Neural Network (ANN) models, data from 310 HE teaching staff in China were analyzed. The results show that: (1) perceived usefulness, satisfaction, effort expectancy, and performance expectancy have significant direct effects on teaching staff's continued intention to use; (2) social influence and perceived risk do not have significant effects on continued intention; (3) perceived usefulness and expectation confirmation have significant positive effects on continued intention through satisfaction: The ANN analysis further indicates that the relative importance of the predictors of continuance intention, in descending order, is as follows: performance expectancy, satisfaction, expectation confirmation, effort expectancy, and perceived usefulness; (4) the ANN analysis further indicates that the relative importance of the predictors of continuance intention, in descending order, is as follows: performance expectancy, satisfaction, expectation confirmation, effort expectancy, and perceived usefulness.