Method of improving the performance of public-private innovation networks by linking heterogeneous DBs
Prediction using ensemble and PPDM models
Bibliographic Data
| ID | 21404396 |
|---|---|
| Authors | Seung‐pyo Jun (0000-0002-4203-7990, Korea Institute of Science & Technology Information, corresponding author), Jaeseong Lee (0000-0002-3311-3891, Korea Institute of Science & Technology Information), Jae-Seong Lee, Juyeon Lee (0000-0001-6235-6043, Korea University of Science and Technology) |
| Year | 2020 |
| Volume | 161 |
| Pages | 120258 |
| Publication date | 2020-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technological Forecasting and Social Change (JOURNAL) |
| Journal identifiers | ISSN: 0040-1625 • E-ISSN: 1873-5509 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techfore.2020.120258 |
| OpenAlex | W3080125519 |
| Language | EN |
| Citations received | 2 |
| References cited | 39 |
Due to the complexity of the innovation process and intensifying competition, small and medium enterprises (SMEs) have increased their participation in public-private innovation networks (PPINs). This study used both inference and prediction models by linking two heterogeneous databases (DBs), consisting of the responses of 1,439 manufacturing SMEs to the Korean Innovation Survey and the financial information of approximately 119,890 companies. In the inference model, we analyzed the determinants that affect the business performance and R&D investment performance of SMEs in PPINs, using generalized linear models. The prediction model utilized a machine learning based ensemble model and the method of linking heterogeneous DBs based on privacy-preserving data mining (PPDM). The findings of this study indicate that while PPINs do not have a significant effect on business performance, they do have a positive correlation to R&D investment. This study also proposes two prediction models for forecasting increases in R&D investment by SMEs, which is considered to be an indicator of PPIN performance. These two models can be respectively used in cases where the features of the companies targeted for prediction can be known in advance and in cases where the features are unknown
Business · Competition (biology) · Econometrics · Economics · Ensemble forecasting · Inference · Investment (military) · Machine learning · Politics · Predictive modelling · Artificial Intelligence · Computer Science · Energy, Environment, Economic Growth · Innovation Diffusion and Forecasting · Innovation Policy and RD
Classification And Regression Trees
National Systems of Innovation
Links and Impacts
Generalized Linear Models
Big data analytics
Cooperative R&D and firm performance
Relationship between cooperation networks and innovation performance of SMEs
Toward the next generation of recommender systems
National Innovation Systems—Analytical Concept and Development Tool
Random Forests
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2020 - 2022 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |