• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.129-134
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• 1997

The purpose of this study is to estimate the distribution rate of R&D input on R&D output in major manufacturing industrial sector. The distribution rate is estimated on time-series data for the period 1980 to 1996. The data used in this study can be divided into the two categories. 1) R&D output data (Patent, Utility) 2) R&D input data (R&D expenditure, R&D workers) The raw data of R&D expenditure is transformed into R&D stock. And the specific production function is used to represent the interaction between R&D input and output. The production function shows the maximum rate of R&D output that can be achieved by certain given, technologically possible, R&D input combinations. The main findings can be summarized as follows. 1) There was a diminishing return between R&D input and output$(\alpha+\beta<1). 2) R&D output growth was more affected by R&D expenditures than R&D workers. 3) R&D workers were more contributed highly to Patent granted than Utility model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.491-497
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• 2018

This study analyzes the relationship between R&D input and performance using panel data from the defense industry. A research model is established based on the R&D logic model, and the study sample consists of a strongly balanced panel data (n=351) empirically analyzed using panel linear regression. Results identified that defense improvement expenditure has a positive influence on the R&D input, and R&D input positively affected patents using a 5-year time lag. In addition, R&D input positively impacts economic performance, including sales and profit. Hence, the major finding includes R&D inputs have statistically significant effects on economic outcome and the R&D logic model featuring a time-lag.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.

• Journal of Technology Innovation
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• v.7 no.1
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• pp.176-186
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• 1999

This paper starts out by reviewing the literature that in different ways utilizes patent data as an output of Research ＆ Development (R&D) investment. The main focus, however, is an analysis of time-lag between industrial R&D input and its output. To achieve this research's purpose, the basic data associated with the industrial R&D input (expenditure, researchers) and output (applied patent and utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw input data were altered into real flow data (but stock data) using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag; between industrial R&D input and its output is within 1 to 3 years. b) The time-lag: of patents was longer than that of utility models. c) The time-lag: in electrical-electronic, chemical industry was longer than that of the mechanical industry.

• Journal of Technology Innovation
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• v.19 no.1
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• pp.1-27
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• 2011

This paper aims to measure and analyze R&D productivities and efficiencies of 17 major OECD countries including Korea over the 1984-2008 period by using the Malmquist Productivity Index and Data Envelopment Analysis, classifying R&D performance into an output and outcome aspects. It also searches the Korea's current status and characteristics in each R&D stage to enhance Total Factor Productivity (TFP) compared with other developed countries. Our major findings are the followings: (i) Korea's productivity index of R&D input vis-a-vis R&D output is very high (13.39% annual growth rate) compared with those of major advanced countries, whereas the annual average of efficiency index is very low (0.33), i.e. Korea's technical efficiency index has risen to 0.83 at the last time series started at 0.10 point and come up to the level of major advanced countries. (ii) the Korea's productivity index of R&D output vis-a-vis R&D outcome is very low (14.02% annual reduction rate) compared with those of major advanced countries, whereas the annual average of efficiency index is very high (0.22), i.e. Korea's integrated frontier technical efficiency index has dropped to 0.057 at the last time series started at 1.00 point and coming up to the level of major advanced countries. (iii) The productivity of R&D input vis-a-vis R&D outcome is positively correlated with that of R&D output vis-a-vis R&D outcome and the growth of R&D input factors. In a nutshell, it implicates that the effort to take advantage of R&D outputs, namely establishing the diffusion and commercialization system of technical knowledge to the level of developed countries, should be strengthened over that on the growth of R&D investment and output for enhancing R&D productivity and efficiency in Korea.

• Journal of Korea Technology Innovation Society
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• v.14 no.3
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• pp.552-577
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• 2011

We have analyzed technology transfer and commercialization process and factors affecting the outcomes of technology commercialization of public research institutes in Korea. A technology commercialization process model was presented as an input, intermediate outcomes/capabilities, output (outcome) structure using the structural equation model. Input variables include R&D input, technology commercialization strategy/support, collaboration, social capital. The model also includes R&D capabilities and technology commercialization performance as intermediate variable and output variable respectively. The technology commercialization performance was measured as the number of technology transfer and spin-off. We conducted survey and 88 institutes responded. Empirical results show that R&D input influence R&D capabilities and R&D capabilities influence the output of technology transfer and commercialization. Collaboration activities and social capital also appear to have a positive effect on the output. However, the effect of strategy and support on the output appear to be not statistically significant.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.560-562
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• 2002

The liquid crystal(LC) pixel capacitance Clc, which varies as a function of applied pixel voltage, is a main factor of pixel voltage errors on input gamma voltage, and therefore of the electro-optics(E-O) characteristics of LC pixel for a-Si TFT LCDs. The pixel voltage error(${\Delta}$Vp) for input gamma voltage was simulated for 14.1 inch diagonal XGA panel. An agreement between the experimental results and simulation was satisfactory for the gamma voltage compensation, ${\Delta}$Vp of the input gamma voltage. The proposed compensation method was successfully introduced to a 14.1 inch diagonal XGA panel, and a remarkable improvement of image sticking was achived.

• 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.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.160-171
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• 1997

This paper starts out by reviewing the literature that in different ways utilizes patent data as a output of R&D investment. The main focus, however, is an analysis of time-lag between R&D input and output. To achieve this research objective, the basic data associated with the R&D input(expenditure, researchers) and output(patent, utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw output data were altered it to objective data using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag between R&D input and output is from 1 to 4 years. This result is equal to the research conclusion of the existing foreign studies. b) It was found that the time-lag of patents was longer than of utility models. c) It was showed that the time-lag of electrical-electronic, mechanical industry was longer than the chemical one.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.839-866
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• 2005

This study addresses the effects on the economy of atomic sector's R&D by considering how much KAERI's R&D expenditures in 2000 affect on the production and value-added of each industry and the whole economy. This study answers to the question using competitive import input-output tables and both supply driven and demand driven IO models, which are frequently employed in evaluating economic impacts of R&D in both domestic and foreign academic areas.