Performance measurement in new product development projects: findings from successful small and medium enterprises
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
New product development projects enhance the competitiveness of small and medium enterprises but carry a high risk of failure. Monitoring the progress of these projects’ activities, using specific performance indicators, helps to reduce this risk. However, studies in small and medium enterprises are limited and they do not identify appropriate and useful indicators to help controlling the resources allocation . By mobilizing the literature on project management and innovation, we studied the processes adopted in five small and medium enterprises that have experienced success in new product development to identify the activities as well as the indicators used to make decisions about continuing or stopping the project. The results show that the activities and indicators are adapted to the context of each enterprise, such as the availability of certain resources and expertise and the proximity of the customers, and that taking these indicators into account ensures better management of new product development projects and reduces failure rates.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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