The Effects of Commercialization Capability in Small and Medium-sized Businesses on Business Performances: Focused on Moderating Effects of Open Innovation
Bibliographic record
Abstract
This study aims to set the effects of technology commercializing capabilities (Acquisition / Internalization, market-oriented innovation, and exploitational innovation) on business performances (financial performance, non-financial performance, and innovational performance) as a primary model; examine the moderating effects of open innovation; build up the foundation to promote small and medium enterprises located in industrial complexes in Daegu; and lay groundwork for regional industrial strategies and national policy projects. We examined the relations between variables by conducting correlation measurement with only those variables that went through the above process. And hierarchical regression analysis was done to confirm our research model and hypothesis test.The empirical analysis results of the research are as follows: First, we found that acquisition/internalization affected greatgly the firm’s financial performance and innovation performance (the speed of commercialization, the number of new product developments). Second, technological exploitation has positive effects on on their financial performance and innovation performance (the speed of commercialization, and the number of new product developments). Third, market exploitation also influenced strongly financial and innovative performances. This is because small and medium-sized companies in Korea produce and deliver products that higher level companies order rather than they develop their own products and improve the management performance by selling them to the market. Fourth, small and medium-sized firms seek to overcome the drawbacks coming from geographic proximity by means of open innovation during the process of commercializing the goods with their transferred techniques.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".