The effects of ambiguity on project task structure in new product development
Bibliographic record
Abstract
New product development (NPD) projects are characterised by task ambiguity, whereby the set of tasks necessary for project completion and the relationships between tasks are initially unknown and only emerge as the development process unfolds. This paper uses interview data from NPD project managers in a large telecom firm to examine the influence of product requirements ambiguity on NPD task structures. The findings are used to propose a taxonomy outlining four generic patterns by which NPD task structures change during the product development process as a result of requirements ambiguity—task expansion, contraction, substitution and combination. The results also highlight in general terms the role of communication, coordination, knowledge and problem solving as distributed NPD project teams struggle to resolve ambiguity. Knowledge of how NPD project task structures evolve can lead to improved strategies for managing projects with ambiguous requirements. Two general types of strategies are suggested, decomposition of project tasks to minimize interdependence between tasks and the flexible adaptation of NPD task structures as new forms of task interdependence are recognised during the development process. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.023 | 0.240 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".