Integrated management systems as a driver of sustainability performance: exploring evidence from multiple-case studies
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the impact of integrated management systems (IMSs) on sustainability (based on the triple bottom line (TBL) concept). To accomplish this objective, this paper seeks to answer the following research questions: How can IMS impact organizational sustainability performance? And, how the key challenges of IMS can influence companies in practice? Design/methodology/approach A case-based approach is used based on the following four cases from different sectors: an electric power distributor; an environmental consulting firm; a public transport firm; and a firm with a broad portfolio of equipment, products and provisions for industrial services in different markets. Findings The results show that the integration of management systems was driven by the companies' strategies toward sustainability. The stakeholders' perception is that a firm's image as a sustainable company also enhances environmental and social performance. The economic performance was not emphasized. Companies noted that the main challenge was motivating and engaging human resources. Originality/value This paper shows that sustainability was not a motivation for implementing an IMS. But, implementing an IMS was a driver of sustainability performance. Also, the relationship between IMS and organizational performance can be presented based on TBL perspectives, and implementing an IMS can be challenging in practice.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".