• 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.

• Proceedings of the Technology Innovation Conference
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• 2006.02a
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• pp.5-20
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• 2006

An efficient and productive R&D operation is a major source of competitive advantage in today's economy, and a lot of efforts are made to raise R&D productivity. A prerequisite for making R&D more efficient and productive is to be able to measure it. Hence, a number of studies have attempted to measure R&D productivity. R&D productivity, in the previous studies, was measured with patents at the firm or industry level. However, most previous studies considered only a quantitative aspect, not a quantitative aspect of patents. In this study, various dimensions of patent quality as well as patent quantity were considered for the measurement of R&D performance. The differences in R&D performance across sectors were examined, and it was found that electrical/electronic industry shows higher R&D performance than mechanic and chemical industries. Discriminant analysis based on inputs and outputs for R&D shows' that there exist a strong discriminatory power across industries. The results of this research can provide the directions 'for the firm's R&D policy.

• STI Policy Review
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• v.8 no.1
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• pp.1-22
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• 2017

There has been a pronounced increase in research and development (R&D) expenditure in Singapore over the last two decades, with government spending accounting for a sizeable share. This increase has been spurred by public policy emphasis on research and innovation as engines of economic growth. This paper analyses the impact of R&D on economic performance in Singapore from 1978 to 2012 through the use of time series analysis. The Cobb-Douglas based analysis shows a long-run equilibrium relationship between Total Factor Productivity (TFP) and R&D investments. We found that the short-run productivity of R&D in Singapore is comparable to smaller advanced economies in the Organisation for Economic Co-operation and Development (OECD). However, in terms of long-run R&D productivity, Singapore lags slightly behind the smaller OECD nations and far behind the G7 countries. This suggests leakage of value capture and low absorptive capacity in local firms. Possibility of productivity improvements induced by policy changes in the 1990s was considered, but no evidence of significant structural breaks was found. Lastly, Granger causality analysis reveals that public sector R&D augments private sector R&D capital, thus playing an important role in generating externalities and spillover effects. Policy implications and lessons for other middle-income countries are discussed.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.321-329
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• 2016

The purpose of this study is to analyze the impact of R&D investment on productivity growth of the Korean Economy in the New Normal Era. To be specific, this study focuses on the impact of R&D capital, other capitals, and total factor productivity(TFP) on the labor productivity during the three periods: 1970-2014, 1970-1997, and 1999-2014. We found out that the change of the intensity in the R&D capital and other capitals significantly impacted on the change of the labor productivity in Korea. In particular, the estimated coefficients of these variables are higher after the period of the IMF financial crisis than before the crisis. We also estimated the marginal productivity of R&D capital investment in terms of the TFP growth. The estimated coefficients of the variables showed stronger effects after the period of the IMF financial crisis than before the crisis. As a result, the increase of R&D investment has been greatly impacted on the growth of the total factor productivity(TFP) after the IMF financial crisis in Korea.

• Journal of Korea Technology Innovation Society
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• v.11 no.3
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• pp.400-429
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• 2008

Global R&D environment is entering on more complicated aspect gradually and tends to increase sharply in a quantitative manner. Technology competition is getting higher. This study intends to raise recognition of a necessity of productivity in a rapidly-changing R&D field and to suggest alternatives for improving its research productivity. For it, the research productivity was analyzed by utilizing two processes of patent analysis and paper analysis. According to the analysis result, it can be confirmed that Korea has the low research productivity compared to advanced countries such as USA and Japan. Government-supported research institute showed the relatively low research productivity, compared to foreign research institutes. It is confirmed that the company research institute has better research productivity than the government-supported research institute. Analyzing the present situation of research productivity through such the patent and paper analysis, seems to contribute to suggest how the research must be done in order to identify the factor of hampering the competitiveness acquisition and to improve the future R&D productivity.

• Journal of Technology Innovation
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• v.2 no.1
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• pp.191-218
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• 1994

This paper aims to find out the R&D personnel management strategies which enhance the productivity of researchers. The R&D personnel management practices are examined focusing on career development and compensation scheme. As proxies for higher motivation and/or productivity, job satisfaction and propensity to leave of researchers are analyzed to evaluate the management practices. Using the survey data from researchers in the Korean automobile industry. this paper suggests the management strategies to raise job satisfaction and thus lower propensity to leave of R&D personnel.

• Journal of the Korean Professional Engineers Association
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• v.32 no.6
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• pp.40-43
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• 1999

The R&D productivity has the same meaning as that of general meaning. Its input includes budget, facility, schedule, idea and target, and its output includes patent, paper, technological innovation and revenue increase and technology accumulation influenced by these factors. The choice of R&D item which will have the large effectiveness after customization, resource input of proper level, application of efficient development schema, effective usage of development output, objective appraisal for the R&D output and extension for the future project are the main factors for which is suggested to enhance the R&D productivity. The management's expectation for R&D can be represented as the innovativeness of output and potential influence to outsiders. The speedy output, even if incremental investment more than planning happens, will eventually result in the productivity enhancement of the enterprise, and managers must not encounter the opportunity loss in return for the excessive investment control.

• Journal of Korea Technology Innovation Society
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• v.11 no.1
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• pp.72-90
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• 2008

What has been the contribution of industrial innovation to economic growth? Typically, the issue has been approached with growth-accounting methods augmented to include a "stock of knowledge". An independent estimate of the rate of return to R&D is found in order to impute patents granted to the accumulation of knowledge. Griliches(1973) then uses a regression approach to assess the effect of an R&D variable on the computed TFP growth rate. The regression coefficient on the R&D variable would provide an estimate of the social rate of return to R&D. The related studies tend to show high social rates of return to R&D, typically in a range of 20 to 40 % per year. We need to provide multiple equation dynamic system for productivity and innovation in Korean economy in state space form. A wide range of time series models, including the classical linear regression model, can be written and estimated as special cases of a state space specification. State space models have been applied in the econometrics literature to model unobserved variables like productivity. Estimation produces the following results. Considering the goodness of fit, we can see that the evidence is strongly in favor of the range $0.120{\sim}0.135$ for the elasticity of TFP to R&D stock in the period between 1970's and the early 2000's.

• Journal of Energy Engineering
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• v.29 no.3
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• pp.1-6
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• 2020

A portfolio matrix analysis was conducted to improve R&D productivity of the government-funded R&D projects in the energy sector. 27 projects (42 detailed technologies) in 2018 were evaluated on a 5-point scale in terms of availability and technology competitiveness, and portfolio matrix analysis was conducted twice. The results of the portfolio matrix analysis could provide the landscape of on-going R&D projects at a time and could be utilized as feedback data to establish development strategies for individual projects, while establishing differentiated management directions to improve R&D productivity in each of the four areas of the portfolio matrix.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.63-73
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• 2019

The paper aims to investigate relationships between technology and innovation management, total factor productivity and economic growth in China. By comparing the trends in total factor productivity growth of industrialized economies (i.e. OECD), this study intends to showcase the importance of total factor productivity progress in the Chinese economy. The study employs time series data of an annual basis for the period from 1977 to 2016 retrieved from the World Development Indicator. The study employs unit root test, cointegration test, fully modified least squares estimation method, canonical cointegrating regression and dynamic least squares estimation method to test the hypotheses. The results of the cointegrating regression analysis show that manufacturing growth leads to an increase of total factor productivity in the short-run in China. The findings of the study suggest that manufacturing (i.e. technology and product innovation) is positively related to the increase of total factor productivity in the short-run and total output growth in the long-run. The findings suggest that promoting technology and innovation management and supporting R&D subsidies may reduce the marginal cost of conducting R&D and increase the rate of technology and innovation management and R&D activity and therefore, the total factor productivity growth rate.