Winning Businesses in Product Development: The Critical Success Factors
Bibliographic record
Abstract
OVERVIEW:2007 is Research-Technology Management's 50th year of publication. To mark the occasion, each issue reprints one of RTM's six most frequently referenced articles. The articles were identified by N. Thongpapanl and Jonathan D. Linton in their 2004 study of technology innovation management journals, a citation-based study in which RTM ranked third out of 25 specialty journals in that field (see RTM, May–June 2004, pp. 5–6). The benchmarking study reprinted here was originally published in 1996 and has been updated with its author's reflections. Their study of 161 business units uncovered the key drivers of new product performance at the business unit level. Ten different performance measures were gauged, including percentage of sales by new products, profitability and success rate. The ten gauges were reduced to two key performance dimensions—profitability and impact—which defined the “performance map.” Nine possible drivers—including strategy, process, organizational design, and climate for innovation—were investigated, and four key drivers of performance were identified; namely, a high-quality new product process, the new product strategy for the business unit, resource availability, and R&D spending levels. Merely having a formal new product process had no impact.
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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 | 0.028 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".