The value of strategy and flexibility in new product development
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the relationship between strategy and flexibility in new product development, and the operational and financial performance in the supply chain context. The motives for conducting this research are to introduce the supply chain strategies and new product development flexibility (NPDF) as constructs that could have the potential to contribute to the success of supply chain performance. Based on the relational view of the firm, the authors propose that supply chain strategy is an antecedent of NPDF and can create value for the buying firm in terms of better financial and non-financial performance. Design/methodology/approach – The structural equation modeling approach was used to evaluate the proposed model and analyze hypothesized relationships. The analysis, based on data collected from 175 small- and medium-sized (SME) Canadian manufacturing companies. Findings – The analysis shows that there are direct positive effects from strategy on NPDF. The findings indicate also a direct positive association between NPDF and performance and showed that the total effect (direct and indirect) positively influenced performance. Originality/value – The literature did not reveal any study which attempted to examine strategy, NPDF, and performance in the supply chain context of SMEs. The current study fills this important gap in the literature.
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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 | 0.018 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".