Critical linkages among TQM factors and business results
Bibliographic record
Abstract
Purpose To empirically investigate the relationships among critical TQM factors and business results. Design/methodology/approach Twenty‐three hypotheses regarding the relationships among TQM factors and business results have been developed through literature review and tested using structural equation modeling (SEM). The study utilized survey data obtained from US manufacturing companies. Findings Provides information about the results of each hypothesis, their implications, and how these findings compare to previous studies. Pays special attention to the relationships between TQM factors and business results and discusses findings in this area by offering insights from 22 previous studies that analyzed TQM‐performance relationships. Research limitations/implications Researchers could use the results of this study to explore various related hypotheses in more detail and improve the accuracy of future empirical quality management studies. The study makes specific recommendations for such future studies. There were also some research limitations. For instance, the data were obtained through mail survey and relied on the perceptions of the respondents. Practical implications The results of this study can be used by managers to prioritize the implementation of TQM practices. For instance, those practices that are found to have a positive impact on business results can be recommended to managers so that they can allocate resources to improve these practices to get the best results. Originality/value This study conducts a comprehensive review of the literature to develop factors of critical TQM practices and business results, and unlike most previous studies, it uses multiple, distinct indicators for each factor to test an elaborate SEM model of the relationships among these factors.
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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 | 0.019 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".