Supply chain digitization and corporate digital innovation: evidence from China
Bibliographic record
Abstract
Purpose Supply chain digitization utilizes digital technologies to connect supply chain partners, enhancing the flow of information and collaboration among them. Drawing on the knowledge spillover theory and the institutional theory, this paper aims to empirically investigate the effect of supply chain digitization on corporate digital innovation. Design/methodology/approach This study employs a quasi-natural experimental design to examine the causal effect of supply chain digitization on corporate digital innovation. Utilizing China's 2018 Pilot Supply Chain Innovation and Application as an exogenous policy shock, it constructs a difference-in-differences model and uses Chinese A-share listed firms from 2012 to 2023 as research samples. Findings Supply chain digitization significantly enhances firms' digital innovation outputs. Specifically, supply chain digitization has a more positive effect in firms with a wider supply chain partner base and those with lower initial levels of digital transformation. Furthermore, the effect is stronger in state-owned enterprises, larger firms, capital-intensive industries, and regions with advanced digital infrastructure. Originality/value To the best of the authors' knowledge, this study is the first attempt to empirically identify the causal link between supply chain digitization and corporate digital innovation. It contributes to the literature on the economic consequences of supply chain digitization.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".