Exploring the nexus between innovation orientation, green supply chain management, and organizational performance in e-retailing industry
Bibliographic record
Abstract
This study aims to evaluate the influence of green supply chain management (GSCM) on organizational performance, taking into account the mediating role of innovation orientation. This study serves as a crucial resource for the e-commerce sector, providing insights to identify operational gaps and to implement cutting-edge GSCM practices. The findings are valuable for organizations aiming to refine their processes and achieve their business goals in a competitive environment. Utilizing a quantitative research methodology, this study examines the online retail industry in the UAE. A convenience clustered sample of 165 companies in Dubai was analyzed using SmartPLS 4.0 to identify patterns and insights. The results indicate a significant positive correlation between GSCM and organizational performance. Innovation orientation emerges as a substantial mediating factor, highlighting its crucial role in enhancing organizational efficiency and effectiveness. This research paves the way for future studies to explore additional influential factors within the online retail sector. Investigating the roles of customer satisfaction and loyalty as independent variables, along with digitalization as a mediating factor, could provide comprehensive insights into their collective impact on organizational performance. For the online retail sector, the adoption of innovative GSCM practices, such as green purchasing and investment recovery, is essential to improve organizational performance. The expanding trend of e-commerce highlights the potential for organizations to examine various factors that contribute to sustainable competitive advantages.
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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 | 0.006 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".