• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.101-110
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• 2018

This study aims to objectively measure the efficiency of nanotechnology R&D programs by systematically evaluating the inputs and outputs of nanotechnology R&D activities and to find implications for improving the efficiency of nanotechnology R&D programs. Data on input factors such as R&D investment, R&D manpower, R&D period, and output factors such as paper, patent, and commercialization for R&D projects which started from 2008 or afterwards and ended by 2011 are gathered through National Science and Technology Knowledge Information Service, which are used for efficiency evaluation. In this study, we analyzed R&D efficiency in detailed technology units in depth. The process taken in this study is as follows. First, the basic statistics of input and output factors to compare and analyze R&D investment, R&D manpower, R&D period, paper, patent, and commercialization status by technology unit are analyzed. Next, DEA models are utilized to derive the overall efficiency, pure technology efficiency, and scale efficiency by conducting the efficiency evaluation for each technology unit, from which implications for strategic budget allocation are derived. In addition, partial efficiency evaluation is conducted to identify advantages and disadvantages of each technology unit. In turn, cluster analysis is performed to identify similar technology units, from which implications for efficiency improvement are derived.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.115-125
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• 2018

In order to examine the ways to improve the efficiency of R&D investment, this paper presents analysis on both quantitative and qualitative efficiency of R&D projects. As Korea's R&D investment has significantly increased in recent years, the efficiency of R&D investment has attracted attention. In this paper, a Data Envelopment Analysis(DEA) method is used to construct models for quantitative efficiency and qualitative efficiency analysis. Based on a cases of agricultural R&D projects of Korea, the efficiency of national R&D projects were analyzed and their quantitative and qualitative efficiencies are compared. As a result, statistically significant difference between quantitative and qualitative efficiency was found. Also, characteristics of Decision Making Units(DMUs) which can influence both quantitative and qualitative efficiency were identified. In particular, the stage of a R&D project has significant impact on R&D efficiency. This study suggests that in order to enhance R&D efficiency both quantitative and qualitative nature of outputs should be considered when measuring R&D efficiency.

• Journal of Technology Innovation
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• v.13 no.2
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• pp.207-222
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• 2005

A prerequisite for making R&D more productive is to able to measure its productivity. Most of the previous studies on this topic have attempted to measure R&D productivity at the firm or industry levels. In this study, however, R&D productivity is measured at the national level to provide R&D policy implications, particularly for Asian countries. Contrary to the previous studies where total factor productivity was adopted, this study employs the data envelopment analysis (DEA) approach to measure R&D productivity. DEA is a multi-factor productivity analysis model for measuring the relative efficiency of each Decision Making Unit (DMU). In addition to the basic DEA model that includes all inputs and outputs, five additional models are constructed by combining single input with all outputs and single output with all inputs in order to measure specialized R&D efficiency. In this study, the twenty-seven countries are classified into four clusters based on the output-specialized R&D efficiency: inventors, merchandisers, academicians, and duds. Then, the characteristics of the Asian countries with respect to R&D efficiency are identified. It is found that Singapore ranks high in total efficiency, and Japan in patent-oriented efficiency. Meanwhile, China, Korea, and Taiwan are found to be relatively inefficient in R&D. We expect that the findings from this study will be able to provide directions for R&D policy-making of the Asian countries.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.87-94
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• 2020

In this study, the factors affecting the efficiency of 48 projects of private R&D institutes were analyzed using the Tobit model. Influencing factors were selected as open R&D network size, IT industry, interaction between R&D network size and IT industry, and type of R&D network cooperation. As a result of Tobit analysis, the R&D network size, the IT industry, and the type of R&D network cooperation were found to be significant. The larger the open R&D network size, the lower the efficiency, and the IT industry showed lower R&D efficiency than other industries. In addition, cooperation with universities and research institutes showed lower R&D efficiency than cooperation with companies. As a result of these studies, companies will be able to select and focus on cooperation with the outside in relations and investment allocation.

• Korea Trade Review
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• v.45 no.1
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• pp.47-64
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• 2020

For the past few years, the Korean government has been consistently expanding its national research & development budget to accelerate economic growth through technology innovation and the enhancement of international competitiveness in global markets. The objective of this paper is to define the concept and analyze the current status of national R&D programs by measuring R&D efficiencies. We determine R&D efficiency by reflecting inputs and outcomes of the five main agricultural R&D programs between 2010 to 2015, and by categorizing and regrouping figures that may affect R&D performance. Among 1,128 targeted projects, 832 projects with patents and thesis were selected for analysis in terms of measuring technology efficiency, pure technology efficiency, and efficiency of scale. Also, the Kruskal-Wallis test was also utilized as well. As a result of empirical analysis, figures that affected the efficiency level of national R&D programs included differences in research resources, research management levels and skills, and research field. This study can be utilized as a reference for re-establishing national agricultural R&D policies, such as enhancement of national technology competitiveness in the global market environment, improving and adapting to new agricultural conditions, market expansion, national agricultural R&D efficiency, aging rural population, agricultural management cost increase, and climate change mitigation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.248-261
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• 2021

Scientific and technological performances (e.g., patents and publications) made through R&D play a pivotal role for national economic growth. National governments encourage academia-industry cooperation and thereby pursue continuous development of science technology and innovation. Increasing R&D-related investments and manpower are crucial for national industrial development, but evidence of poor performance in business performance, efficiency, and effectiveness has recently been found in Korea. This study evaluates performance efficiency of the 6T sector (Information Technology, Bio Technology, Nano Technology, Space Technology, Environment Technology, Culture Technology), which is considered a high-potential promising industry for the next generation growth and currently occupies two thirds of the national R&D projects. The study measures the relative efficiency of R&D in a comparative perspective by employing the Data Envelopment Analysis (DEA) method. The result reveals overall low efficiency in basic R&D (0.2112), applied R&D (0.2083), development R&D (0.2638), and others (0.0641), confirming that economic performance and efficiency were relatively poor compared to production efficiency. Efficient R&D needs policy makers to create strategies that can increase overall efficiency by improving productivity performance and quality while increasing economic performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.4
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• pp.125-136
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• 2016

The external R&D, which includes the adoption of the external technology and knowledge in addition to the internal R&D, is one of important factors for the innovation. Especially for small and medium-sized enterprises (SMEs), the external R&D has been considered as a key factor to carry out the innovation more efficiently due to the limitations of their resources and capacities. However, most of extant studies related to external R&D have focused on analyzing the influence of external R&D on innovation outputs or outcomes. Only a few studies have explored the impact of external R&D on the innovation efficiency. This study therefore investigates whether the external R&D effects the industry's innovation efficiency and productivity. On this study, we used Korean manufacturing industry data of SMEs from 2012 to 2014 and employed a global Malmquist productivity analysis technique, which is based on the Data Envelopment Analysis (DEA), to assess the innovation efficiency and productivity. Innovation performances of external R&D group and internal R&D group are compared. Then, the sectoral patterns of both innovation efficiency and productivity are analyzed with respect to the technological intensity, which is introduced by OECD. The results show that the gap of innovation efficiency between external and internal R&D groups has gradually decreased because of the continuous improvement of the external R&D group's performance, while the external R&D group lag behind the internal R&D group. In addition, patterns of the innovation efficiency and productivity change were different depending on the technological intensity, which means that the higher the technological intensity, the greater the effect of external R&D.

• Journal of Technology Innovation
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• v.16 no.2
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• pp.41-63
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• 2008

Employing firm-level data during the period of 1980-2006, this paper analyzes the R&D efficiency of IT SMEs. We focus on comparing the R&D efficiency of IT SMEs with that of large-sized IT firms and non-IT SMEs. The results are summarized as follows. First, the R&D investment of IT SMEs has not been increased since 2000. In IT manufacturing industry, the portion of the R&D investment of IT SMEs is no more than 10.6% in 2005. Second, we analyze the innovation capacity of SMEs with the number of the patent application. The result is similar with the trend of R&D investment. The portion of the patent application of SMEs has not been increased since 2000. Third, the R&D efficiency of non-IT firms is higher than that of IT SMEs regardless of the firm size. The R&D efficiency of non-IT SMEs is over three times as large as that of IT SMEs. Meanwhile, The R&D efficiency of the large-sized non IT firms is 1.86 times as large as that of IT large-sized firms. Finally, we estimate the R&D elasticity and compare between IT manufacturing and service industry. The result shows that the R&D elasticity of IT service industry is higher than that of IT manufacturing industry, regardless of firm size.

• Journal of Korea Technology Innovation Society
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• v.21 no.4
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• pp.1268-1295
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• 2018

This study examines the effects of policy mix on R&D efficiency in by using data from 2014 and 2016 Korean Innovation Survey. The DEA-Tobit analysis is used to estimate the impact of policy mix on relative R&D efficiency. As a result of the DEA analysis, the R&D efficiency of the Korean manufacturing industry firms is low, because the R&D investment has not been used effectively. According to the Tobit model, policy mix have a positive effect on R&D efficiency. In particular, the combination of market-oriented, market supply-oriented, and supply demand-oriented policy mix showed a positive relationship with R&D efficiency. R&D portfolio is necessary to improve R&D efficiencies and government has to facilitate a policy mix in view of the nature of firms and Consistency of policy tools.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.82-88
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• 2014

The government expands its investment on R&D programs for economic growth, thus there is growing attention on the result of R&D Programs. This study proposes more improved measuring method for efficiency when the number of R&D programs is not enough to be for measuring efficiency analysis. It provides more various application method of factors on efficiency analysis. This study analyzes the influence of each input factor on efficiency by using partial efficiency concept. And it also determines input factors in similar influence throughout Spearman correlation coefficient. Finally, it suggests new method to improve discriminatory power of efficiency analysis by determining representative factors. Also, the proposed method can be practiced not only for national R&D programs, but also for other fields of research.