Supply chain digitization and corporate digital innovation: evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it