Concurrent engineering teams I: organizational determinants of usage
Bibliographic record
Abstract
Purpose This article is the first in a two‐part discussion of the determinants and performance consequences of concurrent engineering (CE) team usage in organizations. The purpose of this first article is to develop a model of the organizational factors that influence the extent that CE teams are used when developing new products. Design/methodology/approach To test the model, 2,500 questionnaires were mailed to new product development (NPD) managers from the machinery, computer product, electrical equipment, and transportation equipment manufacturing industries, of which 189 usable questionnaires were returned, for a usable response rate of 7.5 percent. The data were analyzed using structural equation modeling with partial least squares. Findings Results indicate that an innovative organizational climate and complex NPD activities both influence the extent that organizations support functional integration on NPD teams, and this support, in turn, influences the extent that organizations use CE teams. Analyzing the qualitative data using content analysis indicates additional factors influencing CE team usage. Research limitations/implications To researchers, this study examines in detail the extent of CE team usage, thus addressing a major gap in the research literature. This study also addresses the concerns of researchers by examining organizational contextual factors. Practical implications To NPD managers, this study highlights organizational precursor conditions needed in order for CE teams to be supported in the organizations, specifically complex NPD activities and an innovative organizational climate. By examining these two variables, NPD managers can gauge the likelihood that CE teams will be supported even before they are actually implemented.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".