Predicting the Impact of Psychological Strain on Turnover Intentions of Early Career Academics: A SEM-based Machine-learning-assisted Study

Limited empirical data exists on academic-job-related stressors that cause psychological strain and drive turnover intentions of early career academics (ECAs). Drawing on the Challenge Hindrance Model, the study posited several challenges and hindrances that create psychological strain leading to turnover intentions (TI). In this quantitative cross-lagged study, data was collected through a two-wave questionnaire survey at six-month intervals from two hundred seventy-five ECAs working in Sudanese higher education institutions. The structural equation modeling test results showed a significant effect of challenge and hindrance stressors on the psychological strain and TI. At the same time, the machine learning model predicted psychological strain's most decisive impact on the belief-desire state of intentionality. Furthermore, the study found that job embeddedness can mitigate the adverse effects of academic job-specific stressors on the psychological strain. This study is one of the first to analyze the TI of ECAs in Sudanese higher education through casual, reversed causal, and reciprocal path models. Keywords: psychological strain, turnover intentions, early career academics, higher education, structural equation modeling, machine learning.