Assessing organizational commitment and organizational citizenship behavior in ensuring the smoothness of the supply chain for medical hospital needs towards a green hospital: Evidence from Indonesia
Bibliographic record
Abstract
This research examined the relationships between Organizational Commitment, Organizational Citizenship Behavior (OCB), the Supply Chain, and Green Hospital practices in a healthcare setting. A cross-sectional study was conducted using self-reported data from healthcare employees. Structural equation modeling was employed to analyze the relationships and mediation effects. The study confirmed that Organizational Commitment positively impacts the Supply Chain and Green Hospital practices. Similarly, Organizational Citizenship Behavior significantly influences the Supply Chain and Green Hospital initiatives. The Supply Chain was found to have a positive impact on Green Hospital practices and served as a mediator in the relationships between Organizational Commitment and Green Hospital and Organizational Citizenship Behavior and Green Hospital. The research provides valuable insights for healthcare organizations seeking to enhance their sustainability efforts. Fostering Organizational Commitment and Organizational Citizenship Behavior among employees can contribute to more efficient supply chain operations and environmentally responsible practices. The study underscores the crucial role of supply chain management in translating commitment and proactive behaviors into tangible sustainability initiatives. The research is context-specific, and using self-reported data and a cross-sectional design may limit generalizability. Future studies should explore these relationships in diverse settings and consider longitudinal or mixed-method approaches.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".