What Are the Performance Indicators for Successful New Product Development Projects in <scp>Small and Medium‐Sized Enterprises</scp>?*
Bibliographic record
Abstract
ABSTRACT New product development (NPD) has become essential for many small and medium‐sized enterprises (SMEs) to ensure their competitiveness and survival. However, NPD is fraught with pitfalls that can lead to project failure. To increase the likelihood of success, SMEs need to monitor the performance of their NPD projects using accurate indicators. The literature is underdeveloped when it comes to the indicators used by SMEs, with most research focusing on larger companies. Our research aims to fill this gap by taking a closer look at the indicators used by five Canadian SMEs that have successfully carried out NPD projects. Drawing on resource‐based view theory, we identify the stages and activities carried out and the indicators used at different points in the NPD process of SMEs characterized by some resource constraints. Situation awareness theory helps to select quality criteria in the identification of indicators such as measurability. Our results show that a wide variety of indicators are used by SMEs to measure different dimensions of performance. Many of these indicators have not been previously identified in the literature.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".