Sustainable Supply Chain Management and Organizational Performance: The Intermediary Role of Competitive Advantage
Bibliographic record
Abstract
Sustainability issues have been on the rise due to negative impacts of organizational practices on the environment. The logistics sector has been known as a major contributor in polluting and consuming enormous amount of resources. This study therefore aims to provide insight into how sustainable supply chain management (SSCM) influences performance of organizations operating in the logistics sector. This study went further to focus on the intermediary function of competitive advantage in the SSCM and organizational performance relationship. Questionnaires were distributed to solicit information from 190 logistics managers. Data were analysed using partial least square method of structural equation modelling. Analysis of the data indicates that SSCM significantly and positively influence competitive advantage and organizational performance. In addition, competitive advantage also proved to significantly influence organizational performance. Competitive advantage indirectly has a significant impact on the SSCM and organizational performance relationship. The findings of the study provide key information to managers and academics in understanding the essence of integrating sustainability in supply chain management (SCM) and how the integration influences organizational performance in the current business and industrial setting.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".