Green intellectual capital for sustainable healthcare: evidence from Iraq
Bibliographic record
Abstract
Purpose This research aims to examine the role of green intellectual capital (GIC) dimensions in promoting sustainable healthcare as reflected by sustainable performance. The mediating effect of green absorptive capacity (GAC) and moderating role of environmental turbulence were also explored. Design/methodology/approach Structural equation modeling was utilized for hypotheses testing of a survey data set of 387 at healthcare organizations operating in Iraq. The data were collected using purposive sampling with expert judgment from senior managers and professionals. Findings Contrary to previous studies, the findings showed that only green human and relational capitals predict green performance and only green human capital predicted economic performance. GAC was related to green human capital, green structural capital and performance, and played a significant mediating role on the relationships. Research limitations/implications Even though the research was limited to one region of a single country, Iraq, GAC can be modified by managers to enhance GIC for sustainable healthcare performance. This action must be viewed in terms of the future timing of the impact while managers display strong conviction for sustainability commitment. Managers will find GRC least associated with performance, but that GIC dimensions work best in unison. Originality/value The examination of GIC with GAC as moderated by environmental turbulence contributes nascent theoretical insights in sustainable healthcare.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".