The Influence of the Eco-Control Package on Environmental and Economic Performance: A Natural Resource-Based Approach
Bibliographic record
Abstract
ABSTRACT While a growing body of literature has examined and demonstrated the influence of eco-control on organizational performance, little is known about how this influence occurs within the organization. Building on a natural resource-based view, the aim of this study is to investigate the extent to which the eco-control package supports environmental capabilities that, in turn, contribute to an organization's environmental and economic performance. Using survey data from a sample of Canadian manufacturing firms, the results of this study suggest that eco-control may constitute a mechanism that can support environmental capabilities in order to contribute to a firm's environmental and economic performance. More specifically, these results suggest that the eco-control package fosters eco-learning, continuous environmental innovation, stakeholder integration, and shared environmental vision capabilities that can, in turn, contribute both directly to the firm's environmental performance and indirectly to economic performance. Also, some evidence suggests that different eco-control practices support different environmental capabilities and that the simultaneous use of several eco-control practices seems to be necessary to support the implementation of a complete set of environmental capabilities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".