IT use in supporting TQM initiatives: an empirical investigation
Bibliographic record
Abstract
Purpose To provide insights into current IT and total quality management (TQM) theory and practice on operational and quality performance, in particular the use of IT in supporting TQM policies and practices. Design/methodology/approach Hypotheses derived from the key features of TQM and IT presented by previous authors are tested using structural equation modelling through field research on a sample of 234 manufacturing companies in Spain. Findings The results indicate that the sampled firms make considerable use of IT to support their TQM initiatives and that overall such efforts generate significant positive gains on operational and quality performance. The few exceptions to this are noted and discussed. Research limitations/implications The implications and limitations of the survey together with suggestions for further research are fully discussed. Practical implications A survey of IT in support of TQM initiatives on operational and quality performance in manufacturing suggests how firms and other organisations should focus their IT investments to improve performance. Originality/value Both information technology and TQM have had, and continue to have, a significant impact on most organizations. Although each paradigm has been widely researched there is little empirical research on the relationship between the two and how they both relate to business performance.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".