Do the Benefits of Innovations Spill Over from Suppliers to Customers?
Bibliographic record
Abstract
Prior research has extensively examined customer-supplier relationships and documented the dominant roles that customers play in firms' financial and investment decisions. Although the nature of the relationship between customers and suppliers is bilateral, the literature has predominantly examined the relationship through the lens of customers, overlooking the impact that suppliers have on customers. Do the benefits of innovation spillover from suppliers affect customers along supply chains? The answer remains unknown. Accordingly, our study explores the benefits of innovation spillovers from suppliers to customers along the supply chain, namely the impact of suppliers' innovation activities on their customers' profitability. We find a positive association between suppliers' innovation activities and customers' profitability, consistent with the innovation spillover from suppliers to customers along supply chains. We also find that this relationship has become more pronounced in recent years, implying the importance of technology and employee mobility in spillover effects along the supply chain. Our additional analysis supports the robustness of this result. Our paper sheds light on the studies and practices of supply chain management by offering a holistic view of suppliers' roles in corporate innovation along supply chains.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".