Innovate or perish : success factors and sources of failures
Bibliographic record
Abstract
This article has three objectives: 1) identify the determinants of success that may affect new product development projects (NPD); (2) illustrate the impact of the types of risk on the success rate of NPD projects; and 3) make suggestions to better understand the issues and challenges faced by firms when they engage in projects of NPD. Its major contribution to the advancement of knowledge is twofold. Firstly, it incorporates the contributions of the three main trends of literature dedicated to the management of NPD: 1) research that is interested in the determinants of performance in the context of NPD projects management; 2) that which is related to the identification of the success and failure factors of the NPD projects; and 3) that which deals with the identification and management of risk in NPD projects. Secondly, this article considers, as its unit of analysis, SMEs that are rarely empirically studied in the literature on innovation management. \nThe results of this study are based on a survey by questionnaire of 158 innovative manufacturing firms in the region of Quebec and Chaudière-Appalaches (Canada). They are based on the estimation of a model of binary logistic regression linking the propensity of SMEs to NPD failure, and several explanatory variables derived from these three streams of literature. The results of the estimation of this model showed that the propensity of SMEs to fail in their projects of NPD grows with the increase of the importance attached by firms to success factors related to human resources, to the match between clients and products, to the framework of the NPD project, and to organizational climate and support. However, this probability decreases with the increase of the importance attached by firms to success factors related to the escalation of commitment of the project leader and his team, to the risks related to the NPD projects including those related to the underestimation of resources and communication within the project team, to the degree of novelty of the products developed by the firm, to the percentage of sales made to the three major clients, and to its size.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".