Original Research

The impact of psychological strain on early-career academics’ turnover intentions: A structural equation modeling and machine learning study

Abdelfatah Arman, Tahseen Arshi, Khalid Khan, Nouha Almahmoud
SA Journal of Human Resource Management | Vol 24 | a3576 | DOI: https://doi.org/10.4102/sajhrm.v24i0.3576 | © 2026 Abdelfatah Arman, Tahseen Arshi, Khalid Khan, Nouha Almahmoud | This work is licensed under CC Attribution 4.0
Submitted: 18 January 2026 | Published: 18 June 2026

About the author(s)

Abdelfatah Arman, Department of Management, Faculty of Business Administration, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates
Tahseen Arshi, Department of Management, Faculty of Business Administration, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates
Khalid Khan, Department of Management, Faculty of Business Administration, American University of Ras Al Khaimah, Ras Al Khaimah, United Arab Emirates
Nouha Almahmoud, Department of Management, School of Business Administration, Al Yamamah University, Riyadh, Saudi Arabia

Abstract

Orientation: In 2016, the Sudanese government reported that 12 149 faculty members had resigned from higher education institutions (HEIs). This study focuses on education-sector-specific psychological drivers that trigger early-career academics’ (ECAs) turnover intention (TI) in developing economies.
Research purpose: This study examines academic job-specific drivers that psychologically strain ECAs’ TI in developing economies such as Sudan, given the specific challenges ECAs face.
Motivation for the study: The rationale for this research stems from evident gaps in ECA’s TI. Existing studies do not provide sufficient insight into education-sector-specific psychological drivers that trigger ECA’s TI in developing economies.
Research approach/design and method: In this quantitative cross-lagged study, data were collected via a two-wave questionnaire administered at 6-month intervals to N = 275 ECAs working in Sudanese HEIs. Structural equation modeling (SEM) and machine learning were used in this study.
Main findings: The study found that academic job stressors significantly increase psychological strain, which, in turn, leads to TI. Structural equation modeling and machine learning analyses show that workload, limited growth opportunities, technology, and challenges in educational quality significantly predict psychological strain, while job embeddedness moderates the relationship.
Practical/managerial implications: One of this study’s significant contributions to professional practice is that job embeddedness can serve as an antidote to challenges and hindrances related to TI.
Contribution/value-add: The study grounds the research in the challenge–hindrance model of stress (CHM) to understand the challenges and hindrances specific to the higher education sector. Contrary to some CHM assumptions that certain challenges can be motivational; this was not found to be true in the study.


Keywords

psychological strain; turnover intentions; early-career academics; higher education; structural equation modeling; machine learning

JEL Codes

D63: Equity, Justice, Inequality, and Other Normative Criteria and Measurement

Sustainable Development Goal

Goal 8: Decent work and economic growth

Metrics

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