An examination of strategies employed for the integration of management systems
Bibliographic record
Abstract
Purpose This paper is an empirical study of the organisational approaches used for integration of management systems (MSs) and the comparative effectiveness of such approaches. Design/methodology/approach Research employed four case studies. Results are derived from the analysis of triangulated evidence obtained from in‐depth interviews, observations, internal documents analysis, archives, and short questionnaires. Findings Results identified two archetypes of integration strategies termed “systems approach” and “techno‐centric approach”. Maximum benefits are achieved by using a systems approach to integration of MSs, while using the techno‐centric approach leads to benefits mainly at the operational level. Research limitations/implications This research is qualitative and, as such, does not investigate the integration of MSs across a large number of organisations. The research does not investigate the causality between strategies employed for integration and their outcomes. Originality/value There is little empirical research to date on the strategies employed for integration of MSs and their effectiveness. This research contributes to both literature and practice by demonstrating that a systems approach gives rise to greater integration throughout various organisational levels and greater benefits as compared to other approaches.
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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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".