Discriminating Contexts and Project Management Best Practices on Innovative and Noninnovative Projects
Bibliographic record
Abstract
Managing an innovation project (i.e., a project that produces a new product or that involves a new concept or a new technology) is hypothesized as being different from managing projects that produce a standard product with low innovative content using few innovative technologies. If this hypothesis is true, different processes or more strict and extensive use of well-known practices will be required, and specific tools and techniques will be adopted to execute these processes. This article explores the use of 91 project management practices. The data set consists of 734 responses from experienced project managers and program directors. The article compares innovative project contexts and practices with low innovative environments. Best practices are identified by examining which practices and contexts discriminate between high- and low-performing organizations. This article reveals that maturity in project management processes is strongly associated with a high project success rate for the entire sample. The participation of the project manager or program director during the front end of the project is shown to be one of the principal factors discriminating high-performing organizations delivering innovation projects. Availability of competent personnel as well as practices that enhance project definition also discriminate between high and low performers on innovative projects.
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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 | 0.010 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| 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".