Transformational And Transactional Leadership Styles As Predictors Of Nurses’ Turnover Intention: Evidence From Saudi Governmental Hospitals

Abstract

Background: Effective leadership is essential for enhancing nurses' performance and job satisfaction. Among various leadership styles, transformational and transactional styles have a significant impact on nurse turnover intentions. Understanding this relationship helps in the development of measures for improving retention and ensuring a stable nursing workforce in Saudi government hospitals.

Aim: This study aims to determine the relationship between transformational and transactional leadership styles as predictors of nurses’ turnover intentions in a governmental hospital in Jeddah, Saudi Arabia.

Methods: A Descriptive correlational study design was conducted in a governmental hospital affiliated to Ministry of Health among 292 nurses using the Multifactorial Leadership Questionnaire and the Turnover Intention Scale to determine the relationship between leadership styles and nurses' turnover intention.

Results: Transformational leadership was the most frequently recognized style (M = 45.95 ± 7.80) and demonstrated a significant mild negative correlation with turnover intention (r = –0.192, p < .001). Transactional leadership showed a weaker but still significant negative correlation (r = –0.117, p = .047), whereas passive-avoidant leadership displayed no significant relationship (r = 0.027, p = .640).

Conclusion and Recommendations: These findings contribute to global evidence supporting transformational leadership as a key strategy for reducing nurse turnover and strengthening leadership development initiatives across healthcare organizations, resulting in improved job satisfaction. The study recommended that healthcare policymakers and nursing administrators in Saudi Arabia should prioritize leadership development programs with a focus on transformational and transactional approaches, alongside policies that strengthen workplace support, professional growth, and career pathways. Future research with larger, multi-center samples and mixed-method designs is recommended.