Outcomes of Sustainable Practices: A Triple Bottom Line Approach to Evaluating Sustainable Performance of Manufacturing Firms in a Developing Nation in South Asia
Bibliographic record
Abstract
Maintaining sustainable operations has become a major responsibility of practitioners. Sustainable practices are executed to ensure sustainable performance. Many studies conducted to examine the outcomes of sustainable practices have focused either on the economic outcomes, social outcomes or environmental outcomes of such operations disregarding the Triple Bottom Line Approach to evaluating sustainable performance. Among them the majority have focused on environmental outcomes. Less focus is placed on developing countries or countries in South Asia. Against this background this paper aims to examine the outcomes of sustainable practices towards sustainable performance of manufacturing firms in a developing nation in South Asia. A study was conducted among 154 apparel manufacturing and exporting firms of Sri Lanka in relation to their sustainable practices and sustainable performance as members of supply chains. The sustainable practices were studied in relation to orientation, collaboration, continuity, risk management and pro-activity while sustainable performance was analyzed along economic performance, social performance and environmental performance of these firms. The findings were analyzed using Variance Based Structural Equation Modelling (Partial Least Squares) and it revealed that sustainable practices lead to sustainable performance even in the context of a developing nation in South Asia, highlighting the importance of the execution of sustainable practices irrespective of the level of development of a nation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".