In this paper, we show a "Strategic Diagram" of the robot technology by applying the co-word analysis to the metadata of Korean related national R&D projects in 2001. The strategic diagram shows the evolutionary trends of the specific R&D domain and relational patterns between subdomains. We may use this strategic diagram to support both the strategic planning and the R&D Program. IntroductionThe rapid development of Information Communication Technology (ICT) has enabled us to access any sort of information without limitation on time and place. However, such a benefit of ICT revolutions also put us into a dilemma of selection in a flood of information. Information accessing and collecting is not an issue any more. Now, the problem is how to classify and analyze the contents of information.The technological change mentioned above brings not only opportunities but also challenges in managing national R&D program. Abundant data and low cost of processing large database enable us to make informed decision in traditional R&D management process, such as planning, selection, and assessment of R&D projects. LEE & JEONG: Mapping Korea's national R&D domain of robot technology 4 Scientometrics 77 (2008)Newly introduced text-mining technology gives us new knowledge, such as technological domains and interaction among sub-domains, which help us to make strategic decision and to formulate better R&D policy. It is quite important and challenging for practitioners to discover new knowledge from the large technological database.Many techniques and applications have been introduced in advanced countries in Europe and North America. However, few applications are reported in non-English speaking Asian countries. With little infrastructure for text mining their own language, it's very hard for practitioners to apply text mining technique in their own languages.In this paper, as an application of co-ward analysis on Korean(non-English) technological database, we show a strategic diagram for the robot technology domain from the Korean national R&D Project metadata in 2001. The strategic diagram shows the overall evolutionary trends of the specific R&D domain in a two-dimensional diagram. The strategic diagram shown in this study can be used for the analysis of the development trends of a specific technology domain.

Abstract:Global competition has increased the importance of patents as a means to protect and strengthen technology and competitiveness. The purposes of our study were to identify what industries in South Korea are strong or weak in terms of patent applications and to identify some strategies to enable weak industries to become strong. For this, we gathered statistics on seven variables as follows: number of businesses, number of employees, research and development investment, number of full-time equivalent researchers, number of research institutions, domestic market size, and number of patent applications. Especially, to compare the ratio of patent applications and the ratio of domestic market size across industries, the industries were classified into the following three categories: strong-, weak-, and no-patent. Furthermore, data envelopment analysis (DEA) suggested some strategies to strengthen patent applications for each industry. In the DEA analysis, the number of patent applications was used as the output variable and the other six variables were used as input variables. Our study will particularly assist industries where protection by patents is an important aspect of their businesses.

In this research, we studied the relation of research and development (R&D) investment to turnover and number of listed companies by using the financial information of publicly listed enterprises in all industrial fields of the world from 2007 to 2015. First of all, the present condition (as of 2017) of number and distribution of publicly listed enterprises was investigated. Secondly, the industrial areas having top 10 average turnovers and R&D expenses during 9 years (2007~2015) were analyzed by using their descriptive statistics and CAGR values. Finally, the analyses of correlation and linear regression were performed by using average R&D expense (independent variable) and average turnover or the number of listed enterprises (dependent variables). In other words, two models with different combination of independent and dependent variables (Model A: R&D expense and turnover, Model B: R&D expense and number of listed firms) were developed for the statistical analyses. As a result, it was confirmed that both the turnover and the number of listed companies were influenced by the R&D investment because the coefficients of determination for Model A and Model B were 0.686 and 0.612, respectively (both pvalues < 2.2 × 10 − 16 ). From the results of this study, it is expected that the unlisted firms (e.g., start-up companies) can build the basis of their growth and innovation when they invest in R&D higher inducing the increases in (1) turnover and (2) probability of becoming a listed firm. Thus, the financial information of enterprises can be utilized effectively as the quantitative evidence in order to develop the research model and methodology related to their growth and innovation.

In this study, the investigation into basic methodology for selecting the industrial areas suitable to the small and medium-sized enterprises (SMEs) in Korea was performed by using the statistical data about the corporations (2010~2012) as the quantitative evidences containing the number of companies, the number of workers, the annual sales, and the indices of market concentration and growth potential. From the Statistics Korea and the KISTI Market Analysis and Prediction System (K-MAPS), the statistical data organized by the Korean Standard Industrial Classification (KSIC) were obtained to conduct this research through the following procedure. First of all, the numbers of enterprises and employees and the annual sales of all industries were investigated and the largest number of workers and the highest annual sales were found in the sector of manufacturing among all sectors of KSIC. Secondly, the top three divisions with the highest annual sales in all divisions of manufacturing sector were selected. Thirdly, the subclasses having high values of annual sales and SMEs proportions among all subclasses in the top three divisions of the previous step were chosen as the candidates of SMEs-recommendable fields. Fourthly, the degree of market concentration was analyzed by using three-firm concentration ratio (CR3) and Herfindahl-Hirschman index (HHI) of the selected subclasses. Finally, the study for growth potential of chosen subclasses was performed through the analysis of compound annual growth rate (CAGR). After the overall process of this study was carried out with the synthetic consideration of the above-mentioned factors, the three subclasses of KSIC as industrial areas suitable to the SMEs could be found: (1) Manufacture of printed circuit boards, (2) Manufacture of parts and accessories for motor engines, and (3) Manufacture of parts and accessories for motor vehicle body. From this result, it was found that the values of annual sales, CR3, HHI, and CAGR can be very useful factors to discover the recommendable industry fields to the SMEs.