The Moderating Effect of Environmental Uncertainty on New Product Development and Time Efficiency<sup>*</sup>
Bibliographic record
Abstract
This empirical study examines the influence of environmental uncertainty on industrial product innovation. The present study addresses what is believed to be a shortcoming in the new product development literature and explores potential effects of environmental uncertainty on the development process, project organization, and on project timeliness with a sample of development projects in two countries, Canada and Australia. When looking at the combined sample of 182 completed projects, this study finds that the perceived market‐related project environment has a direct and positive impact on time efficiency. Further, this research finds that a higher degree of technological uncertainty moderates the relationship between development process, project organization and time efficiency. Consequently, innovating companies may benefit by adapting some of their development approaches to different environmental conditions and to varying degrees of uncertainty. However, when examining country‐specific effects, the results change quite significantly. In particular, the findings indicate that environmental uncertainty in the Canadian sample neither directly impacts time efficiency, nor does it have any moderating effect. Instead, technical proficiency in the development process, project team organization, and process compression appear to be viable strategies to increase time‐efficient development. In contrast, the results of the Australian study suggest that perceived market and technological uncertainty impact time efficiency. In particular, under conditions of technological unpredictability, project team organization increases time efficiency, whereas process compression appears to decrease time‐efficient product development. However, process compression seems to be a viable strategy in environments characterized by lower technological uncertainty. The results also point to the importance of disaggregating data when studying product development processes across countries.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".