Dimensions of uncertainty and their moderating effect on new product development project performance
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we measure the dimensions of uncertainty, starting from the definitions constructed for and generally used in innovation projects. We then evaluate their direct and indirect effects on the performance of product and service development projects. Four dimensions of uncertainty are delimited with satisfactory validity and reliability, suggesting a differential moderating effect of the four types of uncertainty (technical and project uncertainty, market uncertainty, fuzziness and complexity) depending on the performance dimension (effectiveness and efficiency) and co‐moderator (project methods and human resource adequacy). Of the four dimensions explored, technical and project, and market uncertainty are true moderators and have the largest interactive effect, fuzziness has a strong direct effect on both performance dimensions whereas complexity weakly directly influences effectiveness. The latter two also influence the relations between performance and the factors related to human resources and project management methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it