Investigating the Impact of Lean Management on Innovation in Vietnamese SMEs
Bibliographic record
Abstract
Internationalization in society and economy has fostered promptly the production volumes and product diversification due to the complexity in customers’ design. Lean management has been known as effective and efficient tools in the management of customer value by reducing cost of the resources necessary to achieve the needs of customers. Today innovation has been considered as driving force of business success in every industry in the context of high competition. The link between lean management and innovation capability has been recently proposed in some literatures, which show that some aspects of lean management may negatively affect and some may positively affect a company’s capability to be successful with certain types of innovations. This paper develops a framework to analyze the impact of lean management on innovation capability in Vietnamese small and medium enterprises. Five propositions has been presented and tested in the sample of 122 SMEs engaged in lean management. The findings suggest that due to implementation of lean management the changes of organizational structure and inter-departmental coordination have positive effect and the changes of human resources management and organizational culture have negative effect on the innovation capability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".