How the Quality Management Systems Impacts the Organizational Effectiveness?: Application of PLS-SEM and fsQCA Approach
Bibliographic record
Abstract
The aim of this study was to examine the impact of Quality Management (QM) enablers, such as leadership, people management, policy & strategy, and QM processes (educational, administrative), on the organizational effectiveness (OE)”. The data was collected from 365 heads and faculty professionals of Indian engineering educational institutions (EEIs). SmartPLS and fsQCA 3.0 software was used to test the relationships between the variables. The results were based on fuzzy qualitative comparative analysis (fsQCA), which looked at different combinations that could be used to improve OE. Earlier studies have mostly tried to look at the direct link between QM enablers and organizational effectiveness. Our results show, on the other hand, that QM processes act as a bridge between QM enablers and organizational effectiveness. There is hardly any research that has looked into how quality management enablers, quality management processes, and organizational effectiveness are linked. This study tries to figure out how they work together in EEIs. By using both direct and configurational methods, the study helps improve the quality of organizational (engineering education) matters. Analysis shows the fsQCA calibration procedure value of 0.50 which represents a crossover point between the two extremes. The consistency scores vary from 0.60 to 0.69 and that the consistency values of the three causal conditions are more than 0.65. The overall constancy of the solution was 0.81, with the full solution and a portion of the complete solution having constancy greater than or equal to 0.80. Finally, the use of fsQCA shows that there are many ways to improve quality policies, leadership and top management commitment, and quality management processes that lead to more effective organizations.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".