Abstract
In today’s knowledge-based society, industry-university cooperation (IUC) is recognized as an effective tool for technological innovation. Many studies have shown that selecting the right partner is essential to the success of the IUC. Although there have been a lot of studies on the criteria for selecting a suitable partner for IUC or strategic alliances, there has been a problem of making decisions depending on the qualitative judgment of experts or staff. While related works using patent analysis enabled the quantitative analysis and comparison of potential research partners, they overlooked the fact that there are several sub-technologies in one specific technology domain and that the applicant’s research concentration and competency are not the same for every sub-technology. This study suggests a systematic methodology that combines the Latent Dirichlet Allocation (LDA) topic model and the clustering algorithm in order to classify the sub-technology categories of a particular technology domain, and identifies the best college partners in each category. In addition, a similar-patent density (SPD) index was proposed and utilized for an objective comparison of potential university partners. In order to investigate the practical applicability of the proposed methodology, we conducted experiments using real patent data on the electric vehicle domain obtained from the Korean Intellectual Property Office. As a result, we identified 10 research and development sectors wherein Hyundai Motor Company (HMC) focuses using LDA and clustering. The universities with the highest values of SPD for each sector were chosen to be the most suitable partners of HMC for collaborative research.
Highlights
In modern society, knowledge and technology play key roles in promoting national development and economic growth
In order to overcome these limitations, we propose a systematic methodology of research and development (R&D) partner selection for industry-university cooperation (IUC) based on patent analysis, which combined latent Dirichlet allocation (LDA) and a clustering algorithm
This study was conducted to propose an effective alternative to the selection of appropriate partners, which was identified as a key factor for the success of industry-university cooperation (IUC), especially industry-university joint research
Summary
Knowledge and technology play key roles in promoting national development and economic growth. It is natural that the competition between companies for the prior occupation of superior technologies has been intensifying. In this highly industrialized society, technological innovations for sustainable development are essential for businesses to compete in global markets. Industry-university cooperation (IUC), a form of open innovation, has gained much attention as an effective alternative to bring about technological innovation and growth [2,3,4,5,6]. A variety of industry-university cooperation (IUC) activities such as joint research and development (R&D), education and training, production support, knowledge or technology transfer, and the exchange of human resources and information, etc. A variety of industry-university cooperation (IUC) activities such as joint research and development (R&D), education and training, production support, knowledge or technology transfer, and the exchange of human resources and information, etc. have been carried out [9]
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