Integrating Management Systems: A dynamic study of Spanish firms
Bibliographic record
Abstract
Management systems standards (MSSs) have developed in an unprecedented manner in the \nlast few years. The impact generated by quality, environmental and other MSSs is \ndemonstrated by the importance of such standards worldwide, ISO 9001 and ISO 14001 (ISO, \n2010). In particular, ISO 9001 accounts for 1,064,785 registered companies in more than 170 \ncountries and ISO 14001 for 223,149 in about 150 countries (ISO, 2010). From 2006 to the \nend of 2009, the number of certifications has increased with 167856 ISO 9001 certificates and \n94938 ISO 14001 certificates. During the last four years, both this proliferation and the increasing importance of MSSs have \nbeen demonstrated (ISO, 2010). Traditionally, organizations have focused on establishing \nMSs that comply with each MSS requirements individually, often in isolation from each other \nand sometimes even in conflict. However, Integrated Management Systems (IMS) that address organizations’ objectives \njointly are becoming more and more popular as they aim to satisfy the needs of several MSs \nwhile running a business. Achieving this can be beneficial to the \norganization’s efficiency and effectiveness, as well as reducing the cost of managing each \nsystem individually. First, a review of the literature on the IMS is presented. We subsequently develop the \nmethodology used in this study, which involves a quantitative analysis of the implementation \nof MSs, the extent of their integration, as well as the difficulties of integration. The last part \nof the article includes empirical results of the investigation and a concluding section.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".