Quality management in research and development
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 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.
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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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