Disruptive Technologies and Sustainable Supply Chain Management: A Review and Cross-Case Analysis
Bibliographic record
Abstract
This study examines the relationship between disruptive technologies and their potential impacts on sustainable supply chain management (SSCM), with a focus on the following technologies: Big Data Analytics / Artificial Intelligence / Machine Learning, Blockchain, Industry 4.0 / Internet of Things (IoT), 3D Printing / Additive Manufacturing, and P2P / Sharing Economy. Based on a comprehensive literature review on both theoretical and practical roles of these disruptive technologies in SSCM, we conduct a cross-case study to analyze the impacts of disruptive technologies on sustainability performance. From 100 application cases of 41 companies in key supply chain management and sustainability journals, we develop a classification scheme based on implementation complexity and sustainability performance of disruptive technologies. The implementation complexity and sustainability performance matrix show that all the cases examined have a positive overall sustainability performance score which indicates that investment in disruptive technologies improves the sustainability performance of firms. However, the impact of each disruptive technology on sustainability performance varies with the types of technology and sustainability dimensions. We also utilize the cases to illustrate how disruptive technologies are applied to key areas of SSCM and identify opportunities for future research.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".