• Knowledge Management Research
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• v.19 no.2
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• pp.1-19
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• 2018

This study aims to identify the causal relationship among public technology transfer, technological performance, and research and development (R&D) productivity. Using the impulse-response function(IRF) of a panel vector autoregressive model (panel VAR), this study suggests the results of how long the factors such as technological performance (patent), public technology transfer, and R&D productivity takes and lasts if a one-unit shock of standard deviation occurs. As a result, first, the increase of public technology transfer activities has no power to increase the technology performance but improve the R&D productivity. If the public institute increases its technology transfer activities by one unit, the R&D productivity will increase within five years. Second, the impact of increasing technological performance on improvement of public technology transfer and R&D productivity is an insignificant. Third, the effect of R&D productivity on the public technology transfer creates a substantial reaction after a current time. Considering the structural relationships among public technology transfer, technological performance, and R&D productivity, if policy makers intend to construct the active R&D circumstance, technology suppliers should be motivated to run the active R&D mechanism because they achieve gains.

• 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 Technology Innovation
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• v.11 no.2
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• pp.151-173
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• 2003

This paper analyses the efficiency and productivity of R&D system across time (1991${\sim}$2000) and 16 OECD countries using multi-output and multi-input non-parametric frontier methods such as DEA (data envelopement analysis) and Malmquist productivity indexes. Malmquist productivity indexes are decomposed into two components measures, namely technical change and efficiency change. To calculate R&D efficiency and productivity, we used R&D stock and the number or researchers as R&D input proxies and the number of adjusted SCI papers and U.S. patent applications as R&D output proxies. Empirical result shows that Switzerland, Canada, U.S., Australia's R&D efficiencies are the highest and Korea's R&D productivity growth is the highest in the sample for the period. Technical efficiency growth was a more important source of productivity growth than technological innovation.

• Journal of Korea Technology Innovation Society
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• v.21 no.3
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• pp.1077-1100
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• 2018

The present study analyzed the effect of firms' external research and development (R&D) on corporate productivity, while investigating the moderating effect of firm size on the external R&D-productivity nexus. In the empirical analysis, we estimated South Korean manufacturing firms' total factor productivity (TFP) using the firm level data drawn from the Survey of Business Activities (Korea National Statistical Office) for the years 2006-2015. Thereafter, focusing on the role of external R&D and its interaction with the firm size in determining firms' TFP, the productivity function was estimated as well. To this end, we used ordinary least squares (OLS) and quantile regression to highlight the heterogeneous impacts of external R&D by companies' productivity level. Empirical results confirmed that firms' external R&D significantly enhanced corporate productivity in all manufacturing industries, from high-tech to low-tech. The moderating effect of firm size in determining the productivity effect of external R&D was not as prominent as in the case for internal R&D, which exhibited some degree of the size premium in the productivity-enhancing effect. These results suggest that regardless of the firm size, external R&D can be an important channel for corporate productivity improvement, and can be a particularly effective strategy for SMEs with relatively limited internal R&D capacities.

• STI Policy Review
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• v.3 no.1
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• pp.80-95
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• 2012

Few studies have focused on the impact of R&D outsourcing on technological innovation and productivity despite the increased importance of R&D outsourcing. This study analyzes the productivity effects of investment in R&D outsourcing with a sample of Korean manufacturing industries from 2001 to 2009. The estimation results show a nonlinear U-shaped relationship between productivity and the share of R&D outsourcing capital for total R&D capital. This implies that the cost of R&D outsourcing outweighs its benefits in the early stages of R&D outsourcing. The U-shaped relationship is particularly pronounced in high-technology industries.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.728-749
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• 2022

The information and communication technology (ICT) value creation process is inherently unobservable. In addition to the direct effect of ICT on productivity, some information or knowledge can create value through other knowledge activities. In this paper, we study the impact of ICT on productivity through R&D. We tested the mediating effect of R&D between ICT and productivity using panel data from 47 US industries from 1987 to 2013 from the Bureau of Labor Statistics. The results show that R&D partially mediates ICT and productivity. That is, ICT directly increases productivity, and some of its effects can be realized through R&D. Recipients who acquire knowledge through ICT have to interpret codified ideas and apply them to practice. The increased absorptive capacity that can be developed through R&D improves interpretation ability, allowing employees to share more complex ideas. Thus, ICT helps people to effectively communicate, but some information and knowledge can be realized and applied through R&D knowledge activities. This is the first study empirically examining the process of ICT value creation through R&D. It also provides practical guidelines for knowledge management, such as making decisions about ICT and R&D investments that are better done concurrently rather than individually to maximize their impact on productivity.

• 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.

• Korea Trade Review
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• v.42 no.2
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• pp.1-27
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• 2017

The purpose of this paper is to examine how industry-level R&D investments increase exports in Korean manufacturing industries through the strengthening of product competitiveness and cost competitiveness. We developed a research model indicating that R&D investments positively affect product competitiveness and cost competitiveness, in which investments in R&D will finally lead to increases in exports in manufacturing industries. Product competitiveness is divided into new product innovation capability and product quality competitiveness, while cost competitive advantage is divided into labor productivity and capital productivity. We have collected data from 20 manufacturing industries between 2004 and 2014, and analyzed them through path analysis. Empirical results of this study are as follows. First, R&D investment in the manufacturing industry positively affects new product innovation capability, product quality competitiveness, labor productivity and capital productivity of the industries. Second, increased product quality competitiveness, labor productivity and capital productivity positively affects exports of Korean manufacturing industries. Thus, we can conclude that R&D investments in Korean manufacturing industries positively influence exports through increases in product quality competitiveness, labor productivity and capital productivity.

• Environmental and Resource Economics Review
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• v.22 no.1
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• pp.53-76
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• 2013

This paper analyzed on the relationship between R&D productivity of renewable energy and the Emissions Trading Scheme using OECD's country-specific patents and R & D input data. We empirically tested whether this R & D productivity has been substantially improved before and after the implementation of the emissions trading scheme and whether emission trading scheme has been promoted technology progress of renewable energy. Analytical methods used in this study, Negative Binomial Models which was proposed by Hausman et al. (1984). According to the results of this analysis, the R & D productivity of renewable energy was improved by emissions trading scheme, which was statistically significant at the 99% confidence interval [CI]. The R&D productivity of renewable energy was higher in Annex I countries. This research is significant in that R&D productivity was analyzed in associated with the emission trading scheme rather than it was analyzed by simply comparing R&D productivity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.41
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• pp.33-40
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• 1997

The main objective of this study is to analysis the relationship between productivity measurement model which describe and explain R&D(Research & Development) productivity based on production function of Cobb-Douglas and the structure of the R&D investment. The model focuses on the variables related to R&D investment in order to measure the efficiency of R&D largely. So, the proposed model describe the relationship between output(or / input) and factors of production such as capital cost, labor cost and R&D expense, etc. These factors are associated with a signigicant positive correlation between productivity and R&D investment.