On the relationship between supplier integration and time‐to‐market
Bibliographic record
Abstract
Abstract Recent operations management and innovation management research emphasizes the importance of supplier integration. However, the empirical results as to the relationship between supplier integration and time‐to‐market are ambivalent. To understand this important relationship, we incorporate two major recent developments. First, the literature has started to redefine supplier integration into two dimensions, supplier product integration and supplier process integration. Second, recent research has begun to examine spillover effects that extend beyond the direct costs and benefits of the supplier contract. Using survey data of 116 firms in the industrials, health care, and information technology industries, the results confirm our hypotheses and show that supplier product integration decelerates time‐to‐market while supplier process integration accelerates time‐to‐market. The results also show a positive relationship between supplier integration and the adoption of external technologies, which either decelerates or accelerates time‐to‐market depending on the level of internal exploration activities. Our research, thus, helps to open the ‘black‐box’ of the relationship between supplier integration and time‐to‐market, and provides a theoretically grounded explanation to the apparent contradictory results in prior research about the influence of supplier integration on time‐to‐market. In addition, we contribute to research on spillover effects by emphasizing that information technology adoption and assimilation is an important spillover effect of supplier integration.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".