Effect of teamwork culture on NPD team’s capability in Indian engineering manufacturing sector
Bibliographic record
Abstract
Teamwork, for competitive advantage, involving techno-socio-cultural aspects of organizations for engineering developments, is gaining importance rather rapidly due to advent of globalization.The purpose of this study is to identify the importance of teamwork culture for new product development (NPD) success through enriching the NPD team's capability in Indian manufacturing industries.It accumulates the teamwork culture dynamics and practices, their interrelationships and their combined impact on NPD team's capability in terms of technological developments for NPD success.This practical analysis collects primary data from 263 design and development experts from Indian engineering manufacturing companies.Structural equation modeling (SEM) approach is applied to investigate the interrelationship among associated variables of teamwork culture and NPD team's capability for NPD success.Concurrent engineering team (CET), communication infrastructure (CI), system integration (SI) and result orientation (RO) have been recognized as allied factors of teamwork culture.Successful adoption of associated variables ensures NPD success through influencing NPD team's capability which in turn articulated by technological developments.The realization of combined impact of teamwork and its allied variables escalates technological developments which in turn enriches NPD team's capability assuring organizational success.The result indicates that both CI and SI support CET for accelerating NPD team's capability through technological developments.Besides, CET motivates innovation orientation most precisely referred as RO for escalating NPD team's capability through technological developments for innovation.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".