Does AI affect job burnout and insecurity differently by gender? A fuzzy-set qualitative comparative analysis (fsQCA) of 26 university faculty cases

With the development of artificial intelligence technology, university lecturers are experiencing a series of reactions that may occur after changing traditional teaching methods. This study uses 26 Chinese university lecturers as a sample, based on the grounded theory of qualitative research, and uses NVivo 14 and fsQCA 3.0 to explore the differentiated antecedent configuration pathways of job burnout and job insecurity among university lecturers of different genders under the influence of AI. The study found that male university lecturers with weaker adaptability to AI tend to develop stronger negative awareness of AI, making them more prone to job burnout. In contrast, female university lecturers with stronger adaptability to AI are more likely to develop positive awareness of AI, yet they also experience higher levels of job burnout. Male lecturers who are highly adaptable to AI but have negative awareness of it are more likely to feel insecure about their jobs for job insecurity. Nevertheless, the effect of female lecturers' adaptability and awareness of AI on their job insecurity seems minimal. Based on the different configuration pathways formed by the antecedent conditional variables, this study explains the combination of factors that significantly affect lecturers’ job insecurity and job burnout to help universities pay attention to and take effective strategies to alleviate the negative impact of these factors, reasonably allocate limited resources, and assist university lecturers of different genders to understand and manage job insecurity and burnout more rationally.