Testing turnover intentions and organizational justice on servant leadership: a validation of the servant leadership survey scale in sub-Saharan Africa
Bibliographic record
Abstract
Purpose Servant leadership, a form of holistic leadership, has been primarily explored in Western cultures. Consequently, there is a lack of understanding on whether servant leadership style is transferable to other cultures, posing a scientific but also ethical challenge in the leadership literature. The purpose of this study is to validate a servant leadership scale (Van Dierendonck and Nuijten, 2011) in the sub-Saharan region (specifically in Uganda) in the health care context. Furthermore, with the aim of improving quality of patient care, it explores the impact servant leadership has on organizational justice and employees’ turnover intentions. Design/methodology/approach The cross-sectional study gathered data from 13 public Regional Referral Hospitals in Uganda. After ethical approval, self-administered surveys were distributed in the 13 survey locations. The survey used standardized scales to measure servant leadership, organizational justice and employee turnover intentions. A total of 355 respondents completed the survey. Findings Results of the confirmatory factor analysis supported six of the eight factors of the servant leadership scale. Furthermore, regression analysis showed a significant relation between servant leadership and organizational justice (r = 0.678; p < 0.01) and a negative relation between servant leadership and employee turnover intentions (r = −0.139; p < 0.01). Originality/value This study brings an important contribution to the functionality of the servant leadership scale in a non-Western context. It also provides insight into the positive impact servant leadership style can have on health workers and patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".