The Role of Sustainability for Enhancing Third-Party Logistics Management Performance
Bibliographic record
Abstract
Technological development and globalization made the supply chain more complex in today’s business environment. In competitive market conditions, shippers tend to outsource most of their logistical activities to Third-Party Logistics (3PL) service providers. These activities have drawn attention of decision makers regarding sustainability concerns. This study examines sustainability initiatives which have been implemented particularly for the 3PL functions namely; transportation, warehousing and packaging services and their influence on performances. Empirical data have been collected through a worldwide online survey which has been sent to industrial experts working in logistics and supply chain management fields. The results were analyzed through the Structural Equation Modelling (SEM). The analysis indicated that, the 3PL functions significantly affect environmental, economic, social and operational performance, except packaging which had no significant impact on economic, operational and social performance, in addition to transportation which had no significant impact on social performance. Regarding the performance outcome and its impact on logistics efficiency, logistics effectiveness and competitiveness, empirical results indicated that there is no significant impact between the variables except, social performance which had a significant impact on logistics efficiency and competitiveness, operational performance which had a significant impact on logistics efficiency, logistics effectiveness and competitiveness. The proposed model and hypotheses developed give further understanding regarding 3PL industries thereby help decision makers in solving the problems related to 3PL sustainability initiatives.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".