When change is all around: How dynamic network capability and generative NPD learning shape a firm’s capacity for major innovation
Bibliographic record
Abstract
Abstract How can firms shape their capacity to engage in major innovation when change is all around? Drawing on dynamic capability theory, we argue that a firm needs to be able to sense, seize, and transform network relationships for new product development (NPD). Referred to here as a “dynamic network capability,” this facilitates generative NPD learning, whereby the firm both (1) unlearns and (2) engages in exploratory new learning. In turn, we argue that generative NPD learning is strongly associated with a firm's capacity for major innovation. Our theorizing is supported by a study of 184 small‐ and medium‐sized, U.S. manufacturing firms. A moderated mediation analysis suggests that when external dynamism is high, generative NPD learning mediates the relationship between dynamic network capability and major innovation capacity. This indicates that the firm's ability to “relearn” is critical. This mediating effect is further strengthened when internal dynamism is also high. Our results provide empirical evidence that the higher‐order concept of a dynamic capability influences the reconfiguration of resources such as NPD knowledge. The findings also signal the combined influence of external (environmental) and internal (organizational) dynamism on this relationship.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".