Impact of TQM on company's performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of total quality management (TQM) implementation on different dimensions of company performance. Design/methodology/approach The study investigates Canadian finalists (winners and certificates of merit) in the Total Quality category of the Canada Awards for Business Excellence. The data were collected either through in‐depth personal interviews or by mail/telephone using the questionnaire and then analyzed. Findings The data analysis confirmed the hypothesized positive impact of TQM on all investigated dimensions of company performance, i.e. employee relations (improved employee participation and morale), operating procedures (improved products and services quality, process and productivity, and reduced errors/defects), customer satisfaction (reduced number of customer complaints), and financial results (increased profitability). Research limitations/implications Small sample size limited the scope of statistical analysis. Also, the results of this study are only valid for TQM adopters and give an indication of what performance can be achieved by companies that undertake a successful TQM program. Practical implications The study provides useful insights into the performance improvement that can be achieved through TQM. Originality/value The study provides evidence on how different dimensions of performance are affected by TQM and gives insights into how long does it take to obtain these benefits.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".