Exploring the influence of environmental and social standards in integrated management systems on economic performance of firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze the impact of the integration of management systems that include economic, social and environmental standards on economic performance. Design/methodology/approach The methodology consists of analyzing reports of certified companies and secondary data on economic performance indicators. Two sample groups of companies were compared against each other. The core group is composed of companies that have integration of certification on each triple bottom line (TBL) dimensions, economic, environmental and social (ISO 9001 and ISO 14001 and OHSAS 18001). The control group is composed of companies of the same size and sector (mirror sample) but without standards related to social and environmental dimensions. The comparative analysis of both core and control groups was performed based on non-parametric methods, such as the mood median test and structural equation modeling. Findings Several economic performance indicators of both groups were statistically analyzed and compared. The results show that companies with integrated management systems (IMS) (core group) on a TBL perspective showed better economic performance compared to other companies of the control group. Moreover, this study shows that the industry sector influences this relation, particularly in the energy, chemical and petrochemicals, services and transportation sectors. Practical implications For executives and managers, the results suggest that the amount invested in IMS in a TBL perspective increases the economic performance of companies, resulting in profitability, increased equity and sales growth. It reinforces the win–win perspective on sustainability in companies instead of the mindset on negative trade-offs on economics. Originality/value This research sheds light on controversies, discussed in the literature, concerning the positive vs negative effects on the economic performance of IMS, with social and environmental standards. The results show that economic performance is improved in companies of the core group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".