A meta-analysis of the relationship between quality management and innovation in small and medium-sized enterprises
Bibliographic record
Abstract
This paper presents a comprehensive meta-analysis examining the relationship between Quality Management (QM) and innovation in Small and Medium-Sized Enterprises (SMEs). Through a statistical synthesis of the findings of 31 empirical studies published between 2008 and 2022, this meta-analysis reveals a significant positive correlation between QM and diverse innovation types in SMEs. More specifically, the results show that total quality management, soft and hard quality management practices and quality management systems all positively correlate with technological, non-technological and green innovations. Importantly, the results underscore the pivotal role of leadership styles – charismatic, team-oriented, participative and autonomous – in enhancing the QM-innovation relationship, while human-oriented and self-protective styles appear to diminish it. The findings offer strategic insights for SMEs managers to optimize innovation through tailored quality initiatives and leadership style.
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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 | 0.030 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.028 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".