Green intellectual capital and environmental management accounting: Natural resource orchestration in favor of environmental performance
Bibliographic record
Abstract
Abstract Taking inspiration from the natural resource‐based view of the firm and resource orchestration theory, we propose a new approach, that is, natural resource orchestration, to investigate how green intellectual capital and environmental management accounting stimulate environmental performance. Using survey data collected from 106 chief financial officers (CFOs) of publicly listed companies in Iran, findings show that the elements of green intellectual capital (green human capital, green structural capital, and green relational capital) are positively associated with both environmental management accounting and environmental performance. In addition, findings support the hypothesis that the use of environmental management accounting mediates the relationship between green intellectual capital and environmental performance. This study provides fresh insights into how an organization deals with the effective alignment (i.e., orchestration) of various green resources, for example, green intellectual capital and environmental management accounting, to promote environmental performance. This is the first study ever to introduce the natural resource orchestration approach for examining how environmental management accounting appears to play a role in translating green intellectual capital into enhanced environmental performance.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".