A Study on the Effects of Innovation Competency on the Management Quality Activities Based on Malcolm Baldrige Model
Bibliographic record
Abstract
Purpose: This study was designed to identify whether organizational characteristics of Korean companies can make differences in innovative capability and Malcolm Baldridge management quality standard. In addition, based on the results and by verifying the influence of the innovative capability to management quality activity, it was to investigate the relations between the two factors. Methods: The subjects of this study were workers at major companies and small-and-medium sized companies. T-test was used to identify differences in innovative capabilities of industrial and age, position classifications, and regression analysis was employed to verify the influences of the innovative capability to the management quality activity. Results: The size of company caused some differences in market innovative sector regarding innovative capability. Management quality activity showed differences because of the sizes of companies and their supply types, while innovative capability influenced on all sectors of management quality activity. Conclusion: In this study Malcolm Baldridge management quality standard was applied to Korean companies. The results verified the meaningful influence of innovative capability to management quality activity. This means that the management quality activity can make a better performance when the innovative capability is good enough. Thus, the enhancement of management quality activity requires the boost of innovative capabilities of organization members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".