Investigating the role of environmental management systems standards and management systems software in enhancing organizational learning
Bibliographic record
Abstract
The purpose of this research was: to explore the organizational learning capabilities of the ISO 14001 standard and environmental management systems software (EMS-based software); to investigate whether EMS-based software has the necessary features to assist organizations with the implementation and maintenance of their environmental management system/standard; and to gain an understanding as to the functionality of the standard and software based on the experience of users. In order to achieve the objectives of this study, a questionnaire was used to survey individuals within organizations in Canada and the United States of America that were registered to the ISO 14001:2004 standard and that used EMS-based software. The results of this study suggest that the ISO 14001:2004 standard may have certain capabilities that may have the effect of enhancing organizational learning. The results also highlight some of the strengths as well as challenges users may have regarding the requirements/elements of the standard. The study also provides preliminary insights and observations as to the functionality, performance and learning capabilities of EMS-based software. However, due to the low response rate, and the use of non-probability sampling, generalizations of the results of this study will have to be made with caution. Nevertheless, this study furthers organizational learning-oriented research and understanding concerning standards and software in a preliminary and practical way in certain respects. First, the study provides scholars and organizations with a broader view regarding the application of the standard and software to environmental management - that is, as a means to improve learning - which arguably could enable them to better understand and improve the processes of learning within organizations. This could translate to an improvement in an organization’s competitive advantage and an improvement in environmental performance and thus enhanced environmental protection. Second, the study offers insights regarding possible improvements that could be made to the standard and software, which would be of benefit to the organizations that use the standard and software and to the developers of EMS-based software. Finally, the study contributes to the relatively limited available research on the learning capabilities associated with and functionality of environmental management standards and software.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".