Enhancing supply chain sustainability performance: the pivotal role of emerging technologies
Bibliographic record
Abstract
Purpose This study aims to investigate the impact of sustainable supply chain practices on sustainability performance in North American and Canadian firms in a business-to-business (B2B) context, specifically focusing on the mediating role of emerging technologies. It aims to deepen the understanding of this complex relationship, contributing to both theoretical knowledge and practical applications. Design/methodology/approach This study collected data from supply chain managers in the USA and Canada using a mixed-methods approach that includes partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA) and importance-performance map analysis (IPMA). PLS-SEM was utilized to model the relationships between sustainable practices, emerging technologies and sustainability performance. NCA identified the essential conditions required for sustainability performance, while IPMA was used to assess the importance and performance of different constructs, helping to pinpoint areas where the managerial focus can yield the most significant improvements. Findings This study reveals that sustainable supply chain practices (SSCP) alone do not directly lead to enhanced sustainability performance. SSCP includes product design, procurement, investment recovery and social sustainability. Sustainability performance includes economic, environmental and social performance. Instead, adopting specific emerging technologies, particularly artificial intelligence, wearable devices and virtual reality, is crucial. A significant threshold identified is these technologies’ 80% adoption rate for substantial performance improvements. Furthermore, this study distinguishes the varying impacts of different technologies on economic, social and environmental aspects of sustainability. Originality/value This research offers new insights by showing that emerging technologies fully mediate the relationship between SSCP and performance. It expands on existing literature by detailing the specific impacts of various technologies, moving beyond the generalized approach seen in prior research. Specific impacts of emerging digital technologies on SSCP and performance remain underexplored in a B2B environment, and this research aims to address this gap.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".