Investigating the mediating role of environmental efficiency in the impact of data privacy practices on enhancing reverse logistics: Evidence from the automotive engineering sector
Bibliographic record
Abstract
This research investigated how data privacy practices may impact reverse logistics in the automotive engineering sector, particularly by examining whether environmental efficiency plays a mediating role. This research uses the Resource-Based View (RBV) and Dynamic Capabilities Theory (DCT) to understand how data privacy-driven processes support environmental practices and reverse logistics optimization. Primary research is done through structured questionnaires of automotive engineering professionals. The partial least squares structural equation modeling (PLS-SEM) approach tested the relationships amongst data privacy, environmental efficiency, and reverse logistics. However, the results further clarify how key intermediate outcomes, after all, improved environmental efficiency, affected by robust data privacy practices, may enhance reverse logistics processes. The nexus of data privacy and environmental efficiency highlights the critical need to embed respect for private sector information into logistics strategies that achieve superior business performance and also protect corporate sustainability. The findings suggested that environmental consequences must be considered in the flexibility of data-privacy measures with important strategic implications for firms operating in a complex and more environmentally conscious market. This study makes a novel contribution to the extant literature by empirically detecting how environmental efficiency mediates data privacy practices and reverse logistics. These findings will be useful for industry practitioners to use data privacy to enable sustainable logistics management of business operations within automotive engineering.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".