Influence of the Time Perspective on New Product Development Success Indicators
Bibliographic record
Abstract
Understanding the underlying reasons for new product development success is central to effective new product management.However, difficulties related to estimating to what extent the objectives are being fulfilled and assessing the trade-offs between different project goals makes the new product development process challenging and risky.It is hence crucial for companies to be able to effectively measure their success.Much conceptual and empirical research has been carried out to identify the critical success indicators of the NPD processes.However, these success indicators might be dynamic as they change depending on where a product is in its lifecycle.The influence of this time perspective on success indicators of new product developments has not been explored very extensively.In this paper, we investigate the success criteria during different phases of the product lifecycle.The goal of this research is to determine the appropriate sets of metrics to be used for assessing success during each phase of a product lifecycle.A practical case study was carried out by investigating 28 companies from Canadian and Danish industries.The companies are various industrial sectors.The data collection was carried out through the use of a survey and interviews with relevant product development managers.The outcomes of this research showed that managers do perceive the success of new product development differently depending on the time perspective.A summary of specific metrics for measuring success during each product lifecycle phase is given.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".