The role of project target clarity in an uncertain project environment
Bibliographic record
Abstract
Product development is recognized as cross‐functional teamwork that has become important in the fast‐paced, globally competitive environment. Despite an extant body of knowledge on the importance of fuzzy front‐end planning and functions of goals in the management literature, the impact of uncertain project environment and goal setting mechanisms in front‐end planning is not fully understood. Product development literature presents numerous case studies or conceptual papers that emphasize the importance of upfront planning and a need for team building; however, large‐scale empirical studies are rare. This paper presents a model linking uncertain project environment, project target clarity, teamwork and its outcome measures (i.e. a product's value to customer and time to market). The data were analyzed from 205 product development projects of firms from the USA and Canada. Valid and reliable instruments were developed to assess the nature and impact of inter‐relationships of these variables. Results from structural model tests indicate that uncertain project environment influences the nature of project targets which in turn affects the level of teamwork. Teamwork is an important process outcome for enhancing value to customer and time to market. Management implications are discussed as well.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".