TOPSIS approach to prioritize critical success factors of TQM
Why this work is in the frame
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Bibliographic record
Abstract
Purpose – The pharmaceutical industry has a critical impact on health promotion. It is essential to identify and prioritize the critical success factors (CSFs) within this industry to ensure successful implementation of total quality management (TQM). Therefore, the purpose of this paper is to identify and prioritize CSFs that affect TQM successful implementation in the pharmaceutical industry. Design/methodology/approach – Based on a thorough review of the literature and building on the earlier studies, a valid questionnaire was developed and sent to 320 managers in pharmaceutical sector. In total, 210 completed questionnaires were returned. The technique for order of preference by similarity to ideal solution (TOPSIS) was used to rank and prioritize CSFs. Findings – Results of the data analyses showed that information and analysis, management commitment, relationship with suppliers, and customer focus are the top four CSFs for the successful implementation of TQM in the pharmaceutical sector. Originality/value – Using TOPSIS approach, this is the first study that determines CSFs that have impact on successful implementation of TQM in the pharmaceutical sector. There have been limited studies investigating the CSFs in developing countries. The findings will be useful in helping manager to successfully implement TQM in emerging markets. The approach will help future studies to examine the impact of successful implementation of TQM on firm performance in other industries and in emerging markets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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