Kaizen transferability in non-Japanese cultures: a combined approach of total interpretive structural modeling and analytic network process
Bibliographic record
Abstract
Purpose The literature on Kaizen transferability to non-Japanese culture is still evolving. The results suggest that the relevant research is still at a descriptive and explanatory stage. This study aims to identify and prioritize the importance of significant Kaizen transferability factors in a non-Japanese culture. Design/methodology/approach A decision theory-based prescriptive analysis methodology was used to analyze identified Kaizen transferability success factors. Firstly, a list of Kaizen transferability factors was devised from the literature using a systematic literature review. Secondly, an integrated interpretative structure modeling and analytic network process approach were applied to generate preference among factors. Findings A framework with a prioritized Kaizen transferability success factors included, in ascending order, organization culture, employee participation, employee discipline, employee personal initiative, top management commitment, management enforcement, employee eagerness, management support and national culture and traditions. Research limitations/implications Managers and decision-makers would better understand where to direct their effort and attention to implement the Kaizen management philosophy to improve firm-level productivity. Although the factors studied in this research considered the Indonesian context, the proposed framework could be replicated and extended to include other cultures. Originality/value The present work contributes to the limited studies and documentation on Kaizen activities' transferability challenges and the Kaizen body of knowledge in developing countries. This study should help organizations in other developing countries, assimilate how to adopt and manage the Kaizen philosophy implementation by following the framework created in this research.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".