Discriminating Contexts and Project Management Best Practices on Innovative and Noninnovative Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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