Factors Influencing Academic Staff Satisfaction and Intention to Use Generative AI

Abstract

Generative Artificial Intelligence (GenAI) is now used across many higher-education activities, yet academic staff do not experience it in the same way. This study investigated the factors associated with staff satisfaction with GenAI and their intention to use it at public universities in Hebei Province, China. A cross-sectional survey produced 450 valid responses through stratified multistage sampling. Confirmatory factor analysis and structural equation modelling were used to test a ten-construct model. Motivation recorded the highest mean. Emotional intelligence, performance expectancy, expectancy value, and teaching style were significant predictors of staff performance development. Satisfaction was predicted by staff performance development and motivation, whereas intention was predicted by satisfaction, motivation, facilitating conditions, and social influence. Motivation had the largest direct effect on intention. Staff performance development fully mediated the teaching style-satisfaction relationship, while satisfaction partially mediated the relationship between motivation and intention. The results suggest that staff responses to GenAI reflect a combination of personal, pedagogical, developmental, motivational, and contextual influences. Universities should support responsible GenAI integration through targeted professional and institutional measures.