Quality management in research and development
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the nature of research topics and methodologies employed in existing studies of quality management (QM) in research and development (R&D). Design/methodology/approach Using a systematic review methodology (SRM), this paper analyzes the literature to identify major themes, shortcomings, and key management practices. Findings The literature review reveals that researchers have mainly explored only how to implement quality principles and practices in the R&D environment and made little effort to explore other aspects of QM. QM practices discussed in the literature consist of top management commitment, R&D workforce involvement, training, a process‐based approach, teamwork and cross‐functional teams, fact‐based measurement and feedback mechanisms, R&D client focus, and good communication with suppliers. The dominant methodology employed in existing studies is either a case study or conceptual approach. Originality/value The paper provides researchers with valuable information about how this research area has evolved, what main themes have been discussed in the literature, and what management practices are effective in pursuing quality efforts in R&D. This study also makes a contribution to the development of quality theory in R&D by pointing out significant gaps in the current literature and suggesting important areas for future study.
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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 | 0.190 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".