Concurrent engineering teams II: performance consequences of usage
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
Purpose This is the second paper in a two‐part discussion of the determinants and performance consequences of concurrent engineering (CE) team usage. In this paper, a model is developed outlining the relationship between the extent of CE team usage and three measures of performance, specifically NPD financial performance, NPD development performance, and communication quality. Design/methodology/approach To test the model, 2,500 questionnaires were mailed to NPD managers from the machinery, computer product, electrical equipment, and transportation equipment manufacturing industries. Of the 2,500 questionnaires mailed, 189 usable questionnaires were retuned for a usable response rate of 7.5 percent. Findings Results of performing partial least squares analysis indicate that the frequency of use of CE teams and functional involvement on CE teams influences communication quality, which in turn, influences both NPD financial and development performance. Research limitations/implications To researchers of NPD, the major implication of this study is that it highlights possible reasons (e.g. not considering the extent of usage or not including functional involvement or communication quality in their models) why they are obtaining such inconsistent results when examining the relationship between NPD practices and performance. The major limitation of this study is that only CE teams have been selected for investigation or risk the problems associated with developing a very long questionnaire. Originality/value To practicing NPD managers, the value of this research is that it highlights that CE teams which do little to improve communication quality will not lead to improvements in NPD performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it