Examining the effects of contextual factors on TQM and performance through the lens of organizational theories: An empirical study
Bibliographic record
Abstract
Abstract Although much has been written about TQM, little attention has been paid to the potential effects of contextual factors on TQM and TQM–performance relationships. The use of organizational theory to formulate propositions regarding the effects of such factors is especially scarce in the TQM literature. This study uses institutional theory and contingency theory as the basis to test a number of such propositions. First, a model of TQM and organizational performance is developed. Then using survey data, the effects of five contextual factors – three institutional factors and two contingency factors – on the implementation of TQM practices and on the impact of TQM on key organizational performance measures are analyzed within a TQM–performance relationships model framework. The three institutional factors include TQM implementation, ISO 9000 registration, and country of origin, and the two contingency factors include company size and scope of operations. The results show that the implementation of all TQM practices is similar across subgroups of companies within each contextual factor. In addition, the effects of TQM on four performance measures, as well as the relationships among these measures, are generally similar across subgroup companies. Thus, for the five contextual factors analyzed, the overall findings do not provide support for the argument that TQM and TQM–performance relationships are context‐dependent. The implications of the study for managers and researchers, as well as study limitations, are also discussed.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".