An empirical study of the relationship between quality practices and business performance excellence in Central Canada
Bibliographic record
Abstract
Overall excellence of business performance can be defined and measured through various factors. In an attempt to operationalise measurement of performance excellence, a stratified sample of 280 firms in the Canadian provinces of Ontario and Quebec is studied. Using the structural equation modeling methodology, this study empirically demonstrates that business performance may be measured by 24 variables grouped into seven correlated factors labelled as financial performance, product quality effectiveness, process quality, the role of customer, of employee, of supplier, and stakeholder behaviour. It is also shown that these 7 factors can be grouped further, to form a higher order factor, which may be labelled as business performance excellence. In order to identify variables that can affect performance excellence, two sets of variables are considered. First, the effect of different levels of quality initiatives on business performance is studied. Among the several quality initiatives investigated are the national quality award programs, the six-sigma program, and ISO 9000 certification program. Moreover, the effect of using these quality initiatives in combination is studied. Second, the relationship between firm characteristics and performance excellence is evaluated. The study reveals that, regarding quality initiatives, only ISO 9000 certification combined with initiatives of the highest level such as a national quality awards program assessment has a highly significant affect on perceived performance excellence. Also, the assessment reveals that a firm's location, industry sector, organization size, or whether the firm is private or publicly listed are not significant factors for perceived overall performance excellence. The upshot of this research is a holistic scorecard for the measurement of business performance excellence. Furthermore, this work offers continuing positive support for the future of total quality management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".