A replication to validate and improve a measurement instrument for Deming's 14 Points
Bibliographic record
Abstract
Purpose The success of implementing Deming's management method depends on the ability of managers to operationally define and measure Deming's 14 Points. Tamimi et al. developed a set of operational measures for these 14 Points. They tested the validity and reliability of their instrument using a sample of firms that were involved in implementing TQM practices from one to five years out from implementation. This paper aims to examine this issue. Design/methodology/approach In this study, which retested their measurement items, data were collected from over 100 manufacturing and service companies of all sizes across the USA and Canada. The data were analyzed using similar statistical analysis procedures and comparisons were made with the results of Tamimi et al.'s study. Findings The results replicated the study by Tamimi et al. and supported their operational definitions with two exceptions. The scales for “Eliminating slogans and targets”, and “Taking action to accomplish the transformation”, were not found to be reliable in either the original or the current study. These two scales need to be modified and new questions are suggested in the paper. Research limitations/implications The response rate for this study was high. However, self‐selection to participate and self‐reported responses could lead to some bias in responses. Originality/value The resulting operational definitions should prove useful to organizations interested in adopting Deming's management method.
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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 | 0.068 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".