The impact of e-commerce development on the supply chain competitiveness of a-share listed companies in China
Bibliographic record
Abstract
In China’s modern economic system, e-commerce has become a crucial driver for the development of businesses and supply chains. To clarify the impact and mechanisms of e-commerce development on the competitiveness of corporate supply chains, this study conducts an empirical analysis using data from A-share listed companies between 2010 and 2022. The results indicate that: (1) The development of e-commerce significantly enhances the competitiveness of corporate supply chains, with results remaining robust after endogenous and robustness tests; (2) The level of e-commerce development can further promote the improvement of supply chain competitiveness by improving resource integration, alleviating financing constraints, and increasing R&D investment; (3) The effects of e-commerce development exhibit heterogeneity: in state-owned enterprises and firms with lower competition, the influence of e-commerce on supply chains is more significant; in regions with high-speed rail (HSR) connectivity, e-commerce not only stabilizes customer relationships but also promotes corporate innovation, whereas in areas without HSR, e-commerce development may significantly mitigate issues related to capital occupation. The core innovation of this paper lies in the construction of a “e-commerce—resource integration/financing constraints/innovation investment—supply chain competitiveness” three-dimensional driving framework, establishing a multi-indicator evaluation system, and conducting heterogeneity and mechanism analyses from multiple perspectives. This provides systematic empirical evidence for the role of e-commerce in enhancing supply chain resilience and competitive advantage, offering policy recommendations for businesses to effectively improve their competitiveness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".