Supply chain partnership and innovation performance of manufacturing firms: Mediating effect of knowledge sharing and moderating effect of knowledge distance
Bibliographic record
Abstract
Affected by complicated issues, such as regional conflicts, trade wars, and the COVID-19 pandemic, manufacturing firms face enormous challenges in reconstructing the global supply chain landscape to form new cooperative innovation mechanisms. This study investigates the relationship between supply chain partnerships (SCP) and innovation performance (IP) from a knowledge-management perspective. A multi-factor conceptual model of this relationship was proposed, considering the mediating effect of knowledge sharing (KS) and the moderating effect of knowledge distance (KD). SCP is measured in three dimensions: trust relationship (TR), commitment relationship (CMR), and contractual relationship (CTR). IP is measured in two aspects: technological innovation performance and management innovation performance. An empirical study was conducted to test the hypotheses using data from 417 valid questionnaires. Confirmatory factor analysis and structural equation modeling were applied to test the hypotheses. The results demonstrate that (1) KS plays a significant mediating role in how SCP impacts IP and that the indirect effect of TR through KS on IP is greater than that of CTR or CMR. (2) KD between supply chain partners plays a significant negative moderating role between KS and IP; that is, the smaller the KD, the higher the IP achievable through sufficient KS. These findings shed new light on building collaborative innovation mechanisms for supply chain management.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".