When Project Meets Innovation: “PRO-INNOVA Conceptual Model”
Bibliographic record
Abstract
At the macro and micro levels, governments, industries, and companies are constantly challenged by their stakeholders and customers to show relevance by adding a new value with innovative services, products, and solutions. The same stakeholders are simultaneously very demanding for the agile delivery of results with a high impact. Both competing and often contradictory demands can be challenging to be met by organizations. Innovating new and unique value often requires a different set of skill and environment (Reflective, creative process with the need for a reasonable time to experiment) than those required for delivering rapid projects (Time intensive and process-driven activity). This state of complexity is the main reason for the research study that is discussed in this article. A proposed conceptual framework to merge between some of the innovation and the project phases referred to here as “Pro-Innova” for short. It suggests a new theoretical model that integrates the innovation and project management activities, using some aspects of the design thinking and the system dynamics loops. It focuses on the complementary and shared aspects found in both areas to address the challenges, limitations, and contradictions as well as the complexity each area (Innovation and Project) has on its own.
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.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".