Adaptive leadership, organizational culture and patient-perceived service quality in private healthcare: evidence from Addis Ababa
Bibliographic record
Abstract
Purpose This study investigates how adaptive leadership and organizational culture influence patient-perceived service quality in private healthcare institutions in Addis Ababa, Ethiopia. It further examines the mediating role of organizational culture in the relationship between leadership and service quality. Design/methodology/approach A quantitative, cross-sectional survey design was employed with data collected from 355 patients across 14 private hospitals. Established scales for adaptive leadership, organizational culture and service quality (SERVQUAL) were used. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct, indirect and mediating effects. Findings The results show that adaptive leadership has a significant positive effect on both organizational culture and patient-perceived service quality. Organizational culture also directly enhances service quality and partially mediates the relationship between adaptive leadership and service quality. The model explained 45.4% of the variance in service quality, highlighting the critical role of intangible organizational factors in shaping patient experiences. Practical implications The findings suggest that private healthcare providers should invest in leadership development programs that strengthen adaptive leadership capabilities and foster supportive, patient-centered cultures. Policymakers and managers should recognize that improving service quality requires not only technical and infrastructural investments but also attention to leadership and cultural dynamics. Originality/value This study contributes to the literature by extending adaptive leadership and organizational culture research into the underexplored context of Sub-Saharan Africa’s private healthcare sector. It provides empirical evidence of the mediating role of culture, offering theoretical and practical insights into how organizational resources shape patient experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".