Outcomes of Environmental Management Systems: the Role of Motivations and Firms’ Characteristics
Bibliographic record
Abstract
Abstract This article analyzes the influence of the sources of motivation that lead companies to adopt environmental management systems (EMSs) on the outcomes of these systems. A set of hypotheses derived from an extensive review of the literature is analyzed using cluster analysis – in order to identify groups of companies – as well as correlation and regression analyses, with data obtained from a survey of 361 Spanish organizations that have environmental certification. The results reveal that, for the groups identified, companies from the holistic cluster (with high levels of both internal and external drivers) and from the internal focus cluster (with more intensive internal sources of motivation) secure greater benefits from the process of adopting an EMS. This article also sheds light on the influence on the outcomes of some variables that have been under‐researched, such as the economic resources invested in an EMS and whether or not the certified companies belong to a sector with high environmental pressure. The findings help to characterize the firms with environmental certification and may also help managers, policy makers and other stakeholders to anticipate the potential benefits of EMSs. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".