TQM and organizational performance using the balanced scorecard approach
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between the implementation of total quality management (TQM) and organizational performance, using the balanced scorecard (BSC) approach. Design/methodology/approach In order to investigate the relationship between TQM and BSC, a questionnaire was developed and distributed to 30 largest pharmaceutical distribution companies in Iran. Structural equation modeling was used to evaluate the measurement model and to test the research hypotheses using the data from 933 completed questionnaires. Findings The results supported the research model and revealed that TQM implementation can positively and significantly influence the BSC and its four perspectives. Practical implications Considering the strong association between TQM and all four perspectives of organizational performance (BSC), managers should strongly leverage the implementation of TQM practices in order to reach their strategic objectives. Originality/value This study is the first empirical study conducted on the association of TQM and BSC in the pharmaceutical industry. The findings of this study provide strong evidence supporting the implementation of TQM in the pharmaceutical context.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".