전통산업 고도화를 위한 데이터 기반 R&D 지원정책

Abstract

The traditional industries have played an important role in the South Korean economies in the past several decades, but it currently faces the limitations of growth potential and profitability due to saturated world market and rapid changes in the global industrial ecosystem. Central and local governments have suggested solutions for the industry, but they are just temporary prescriptions. There are rarely any studies taking strategic approaches to the problem. If governments can support the companies that are likely to increase their value-added selectively, it will break through the current situation more effectively. Therefore, this paper studied the data-based improved plan for the governmental funding policy of SMEs to support innovative activities. In particular, we have been studied in data-based support methods to induce traditional industries to transform them into high-value-added through SME R&D funding policy. And the fishery industry, which is one of the traditional industries centered in the Southeast area in Korea, was selected as the target industry. An exploratory study was conducted to examine the applicability of other traditional industries by constructing a data-driven method that induces an industrial transformation for the fishery industry. Firstly, This paper introduces a study on the selection method utilizing data envelopment analysis (DEA) to find SMEs with the potentials to increase profits and growth. We suggest selecting SMEs with high management efficiency and the ability to utilize intangible assets as the target companies. We also suggest policy objectives for SMEs in the domestic fisheries industry based on the results of DEA analysis and propose a data-based method for the policy decisions. Secondly, for resource allocation based on evidence to induce effective and efficient innovation, this research examined the patent patterns in major fisheries industry countries and the inter-relationship and convergence between technologies. We obtained patent information from Espacenet DB. We applied the association rule mining to IPC code to examine innovation trends and convergence patterns to understand the convergence characteristics of the technology. Through reviewing the effects of the association rule mining, they propose consequences for fisheries R&D strategies to improve Korean fisheries' competitiveness. Based on the research results, a practical and strategic method for flexibly converting to a new industry based on the infrastructure of the traditional fisheries industry through efficient investment by the government was suggested, and the applicability and feasibility of other traditional industries were explored.