Understanding the effects of uncertainty on NPD speed: a temporal perspective
Bibliographic record
Abstract
Purpose Our goal is to propose an effective adaptive strategy to address the challenges of uncertainty stemming from deglobalization. Design/methodology/approach Using data from 248 firms across diverse industries, we investigate how environmental uncertainty influences new product development (NPD) speed and explore the moderating role of external network information – sourced from trade associations (TAs) and non-governmental organizations – in mitigating or amplifying the impact of uncertainty. Findings Our findings reveal that both technological uncertainty and market uncertainty positively influence NPD speed. Additionally, the embeddedness of TAs positively moderates the relationship between market uncertainty and NPD speed but negatively moderates the relationship between technological uncertainty and NPD speed. In contrast, the embeddedness of non-profit organizations and non-government organizations positively moderates the relationship between technological uncertainty and NPD speed while negatively moderating the relationship between market uncertainty and NPD speed. Originality/value Deglobalization has heightened environmental uncertainty, particularly in the areas of market dynamics and technological advancements. Existing research highlights a wide array of adaptive strategies to mitigate such uncertainty, often attributed to insufficient information for reliable forecasting. However, a critical yet less examined aspect of environmental uncertainty is the rapid pace of change. We propose that accelerating the speed of NPD represents an innovative strategy to navigate these challenges effectively.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".