Performance measurement by TQM adopters
Bibliographic record
Abstract
Purpose Organizations wishing to implement TQM face unavoidable profound changes in performance measurement and are in need of guidance and better understanding of the role of different performance measurement methods and systems. The objective of this paper is therefore to provide guidance for future TQM adopters through investigation of existing practices implemented by a group of finalists in the total quality category of Canada Awards for Business Excellence. Especially the usage and perceived appropriateness of different methods are of interest. Design/methodology/approach A sample of finalists in the total quality category of Canada Awards for Business Excellence was surveyed. The data were collected either through in‐depth personal interviews or by mail/phone using a questionnaire. Next, descriptive statistical techniques were used to analyze the data. T‐statistic tests were performed in order to determine the significance of the results. Findings Regarding the extent of use and appropriateness of the traditional and TQM‐related performance measures (PMs) and systems/methods (PMS) found in TQM environment, the findings reported that PMs and PMS, used and considered appropriate by TQM adopters, are predominantly process‐oriented (process sequence flow charts, Pareto chart, cause and effect diagram), long‐term‐oriented (market research/customer survey, percentage of sales from new products and absolute market share), and customer‐oriented (number of complaints, percentage on‐time delivery, overall customer satisfaction). Research limitations/implications The small sample limited exclusively to finalists in the total quality category of Canada Awards for Business Excellence may be a limitation. Practical implications This research provides guidance for companies considering implementation of TQM or in the process of adopting TQM with regard to the design of a performance measurement system that would support their TQM efforts successfully. Originality/value This research is looking at extent of use of performance measures and methods and at their perceived appropriateness by TQM adopters at the same time. Thanks to this approach it provides valuable insights into performance measurement in TQM both for academics and for practitioners.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".