Institutional Learning and Sustainable Performance in Public Healthcare Systems: A Moderated Mediation Analysis from Indonesia’s National Health Insurance Context
Bibliographic record
Abstract
This study investigates how institutional learning capacity contributes to the sustainable performance of public healthcare services through two workforce-related mechanismsorganizational commitment and organizational citizenship behavior (OCB)-and whether governance-related leadership practices condition these relationships.Using a cross-sectional survey of nurses from seven healthcare institutions operating under Indonesia's National Health Insurance System (BPJS), 176 valid responses were analyzed using partial least squares structural equation modeling (PLS-SEM).The results indicate that institutional learning exerts a significant direct effect on workforce performance and indirectly enhances service sustainability through strengthened commitment and citizenship behavior.Organizational commitment and OCB both function as mediating mechanisms that translate learning into improved performance outcomes.Transformational leadership strengthens the effect of commitment on performance but weakens the contribution of OCB, suggesting that highly salient managerial interventions may reduce the voluntary and self-initiated nature of citizenship behaviors.The model explains a substantial proportion of variance in performance (R² = 0.845) and demonstrates predictive relevance.From a planning and policy perspective, the findings highlight the importance of investing in institutional learning systems, leadership development, and governance design to support sustainable public service delivery in healthcare.The study contributes to the sustainable development literature by demonstrating how organizational learning and human capital mechanisms interact to shape the long-term effectiveness and resilience of public healthcare systems.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".