Organizational culture, quality improvement tools and methodologies, and business performance of a supply chain
Bibliographic record
Abstract
Unlike previous studies that have revealed a link between quality improvement programs and organizational culture typologies in individual companies, this study describes organizational culture dimensions that affect the use of quality improvement tools and methodologies and how both affect supply chain company performance. Structural equation modeling methods are applied to a sample of 200 organizations in the supply chain of a Canadian multinational company. The results show that employee promotion and investment constitutes the most influential cultural dimension. Organizational objectives and an employee reward system individually affect Kaizen. When the level of formalization in an organization is high, Kaizen and total quality management tools are used more intensively. When the level of formalization is low, lean manufacturing and internal audits are used more intensively. Superior communication in an organization causes plan–do–check–act approaches, lean manufacturing methods, corrective actions and internal audits to be used less intensively. Generally speaking, most quality improvement tools and methodologies positively influence business performance. These results suggest that organizations can improve business performance levels by selecting appropriate quality improvement programs depending on existing organizational culture dimensions and may thereby develop an organizational culture that enables successful quality improvements in a supply chain context.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".