• Journal of Technology Innovation
• /
• v.16 no.1
• /
• pp.191-213
• /
• 2008

This study investigates the effect of technology import and R&D investment on the value of the firm in Korea. The result shows that the technology import announcement effect of firms with a low R&D investment is higher than that of firms with a high R&D investment. The evidence suggests that technology import can substitute the existing R&D capability of the firm. In addition, the result shows that there is an optimal level of technology import and R&D investment to maximize the value of the firm. In particular, firms with a low R&D investment and a large amount of technology import experience the highest announcement effect. The study concludes that an adequate allocation of fim's capital between R&D investment and technology import is needed for firm's optimal technology strategy.

• Journal of Technology Innovation
• /
• v.31 no.2
• /
• pp.59-82
• /
• 2023

The purpose of this research is to conduct the empirical analysis of the short- and long-term causal relationship between public R&D investment, corporate R&D investment, and university R&D investment on economic growth in Korea. To this end, based on the time series data from 1976 to 2020, a causality test was conducted through the unit root test, cointegration test, and vector error correction model (VECM). As a result, it was found that there is a long-run equilibrium relationship between economic growth in Korea, public R&D investment, corporate R&D investment, and university R&D investment, in which a causal relationship exists in the long run. Also, while public R&D investment has a short-term effect on economic growth, corporate and university R&D investment does not have a short-term effect on economic growth. In addition, the results shows that there is a bidirectional causal relationship between economic growth and public R&D investment, corporate R&D investment and public R&D investment, and university R&D investment and public R&D investment in the short term. Through this research, it was empirically found that a highly mutual relationship exists between public R&D investment, corporate R&D investment, university R&D investment and economic growth. In order to increase the ripple effect of R&D investment on economic growth in the future, R&D investment between universities and corporations should be mutually promoted, and R&D investment by corporations should have a positive effect on public R&D investment so that public R&D investment can contribute to future economic growth.

• Korean Journal of Organic Agriculture
• /
• v.27 no.4
• /
• pp.391-408
• /
• 2019

The purpose of this study is to analyze the spillover effect of R & D investment focusing on agriculture sector. Therefore, the Korean industry is divided into 18 industries and the data period including 1970 ~ 2014 is analyzed. In addition, the method is based on the analysis of the production function, the growth contribution of the production factor, and the spillover effect of other industries that affect the agricultural output. The results of this study are summarized as follows. First, R & D investment has a positive effect on the production of Korean agriculture. Second, the impact of high tech industry R & D investment on Korean agriculture is positive. Third, the R & D investment in the public sector is relatively higher than the R & D investment in the private sector. In the R & D stage, the R & D investment of the first level technology has a great influence on the production of agriculture. As a result of this study, governments should consider the above mentioned research results to determine resource priorities based on limited resources in relation to R & D investments that contribute to production and economic growth.

• Asia pacific journal of information systems
• /
• v.25 no.1
• /
• pp.61-81
• /
• 2015

Investment in research and development (R&D) is critical in the information technology (IT) firms, where newer and better technology is a quintessential goal that directly affects innovation and competitive advantage. This study investigates how R&D investment influences firm performance and value, and how the effect of R&D investment differs between IT hardware and software firms. We also analyze the relationship between firm age and R&D investment in order to identify learning effects on continuous R&D investment. The empirical investigation in this study, based on longitudinal archival data from 2001 to 2010, found a significant effect of R&D investment on firm performance in IT firms. Further, this study demonstrates causal relationship between firm age, and verifies that learning effects are present in R&D investment. Moreover, the results are found to differ between IT hardware and IT software firms.

• The Journal of the Korea Contents Association
• /
• v.22 no.8
• /
• pp.287-296
• /
• 2022

The purpose of this study is to verify the effect of government subsidies and R&D investment on the increase in profits of Chinese environmental protection companies. The research method for this was empirically analyzed using the Chinese CSMAR database for 161 Chinese environmental protection companies and using statistical data obtained from 805 samples. The analysis results are as follows. First, government subsidies had a positive effect on the increase in profits of Chinese environmental protection companies. Second, government subsidies had a positive effect on R&D investment by Chinese environmental protection companies. Third, R&D investment had a positive effect on the increase in profits of Chinese environmental protection companies. Fourth, R&D investment through government subsidies has a partial mediating effect on the effect on the increase in profits of Chinese environmental protection companies. In this study, based on the results of the study, a plan was proposed to promote the increase in profits of Chinese environmental protection companies by expanding government subsidies to Chinese environmental protection companies and focusing on R&D investment.

• Proceedings of the Korea Technology Innovation Society Conference
• /
• 2000.11a
• /
• pp.346-356
• /
• 2000

This paper aims to analyse the relationship between R＆D investment and economic growth in Korea. The analysis result shows 38 percent of average economic growth rate(7.1%) of Korea between 1976 and 1998 was achieved by the growth of Total Factor Productivity (TFP), and the R&D investment during the period contributed in achieving the economic growth rate by as much as 9.86 percent. In the process of the estimate, the rate of return of the R&D investment from both government and private was calculated as 47 percent. The relationship between private R&D investment, government R&D investment and the GDP was also investigated, and it was estimated that the private sector invested 2.0 percent of the GDP in R&D during the period, and was found that 1 won of government R&D investment induced 0.202 won of private sector's R&D investment. However, the time-lag effect, which is naturally believed to exist between the R&D investment and the economic growth, could not be analysed in a mathematical form, because of the lack of the data to establish this relationship. However, this paper believe that the time-lag effect in this relationship was included implicitly by using the data of 23 years.

• Journal of Technology Innovation
• /
• v.20 no.1
• /
• pp.45-73
• /
• 2012

In this paper, we analyse empirically the effects of financial characteristics on the relationship between R&D investment and market value of firms listed on Korea Exchange. The main results of this study can be summarized as follows. Firm size increase the market valuation of R&D investment because it provides economies of scale, easier access to capital market, and R&D cost spreading. Market share also positively effects the relationship between R&D investment and firm value. Alternatively, free cash flow has a negative effect on the relationship between R&D investment and firm value because firms with high free cash flow could be tempted to use the free cash flow to undertake negative NPV projects. The dependence on external finance is a handicap negatively assessed by the market when firms undertake R&D projects due to the higher information asymmetry associated with this kind of project. Labor intensity has a negative effect on the relationship between R&D investment and firm value because the abnormal profits arising from R&D investment are diluted among employees. Capital intensity also has a negative effect on the relationship between R&D investment and firm value due to the greater financial constraints faced by capital intensive firms. In conclusion, several financial characteristics(firm size and market share) positively effect the relationship between R&D investment and firm value, while others(free cash flow, dependence on external finance, labor intensity, and capital intensity) exert a negative effect. Therefore, we conclude that the effectiveness of R&D investment depends on these financial characteristics.

• Journal of Technology Innovation
• /
• v.24 no.3
• /
• pp.81-105
• /
• 2016

The purpose of this study is to estimate the cost-saving effect of R&D investment in the ICT industry. As is well known, the R&D investment induces both the product innovation and the process innovation, in turn leads the effect of creating profit and cutting cost. However, it appears that studies concerned with the cost-saving effect of R&D investment have been unproductive, while most existing studies concentrate on the topic involved with the creating profit of R&D investment. Therefore, we extend the effect of R&D investment to a framework of the cost-saving focusing on the ICT industry. To empirically analyze the effect, we built a simultaneous three-equation model comprising a translog cost function and two cost share equations, and employed the SUR analysis. As a result, we found out that the cost-saving effect on the total cost is statistically significant. In addition, we examined relationships between the R&D investment and each cost of production elements. The results show that on the one hand, the R&D investment and the intermediate good cost have the substitution relationship. On the other hand, the complementary relationship is observed between the R&D investment and each labor or capital cost.

• Clean Technology
• /
• v.17 no.2
• /
• pp.181-188
• /
• 2011

In this study, the complementary or crowding-out effects of public R&D subsidy on private R&D investment in the cleaner production were analysed between the effects and the major determinants (company size, R&D investment intensity, ratio of government investment, R&D manpower intensity). Among 207 firms' projects, the number of the complementary effect was 95 (45.9%) while that of crowding-out effect was 38 (18.4%). Resulting from logistic regression, the higher the R&D investment of sponsored companies is, the more complementary effect they show, responding to public R&D subsidy, and increase own R&D investment. The other determinants, however, showed no significant effects on firms' R&D investment. To heighten the effect of public cleaner production R&D, it is need to increase the priority of R&D investment intensity among the determinants. And to increase the performance of governmental R&D investment, further studies for the individual public R&D programs are necessary.

• Journal of Technology Innovation
• /
• v.22 no.1
• /
• pp.1-22
• /
• 2014

R&D investment also has a gestation period similar to other investments in economics. The gestation period originates from time lag effect of input and output. Thus it is necessary to consider time lag effects when analyzing the relationship between firms' R&D investment and R&D performance. The main objective of this research is to estimate the length of time lag effect of R&D investment. The Almon distribution lag model was applied to estimate the time lag effect. The firm level panel dataset was established from 2002 to 2009. The net value of R&D investment and the number of patent applications were used to measure R&D input and output, respectively. This method found the estimated time lag to be 1~2 years across all datasets. The same analyses were applied to chemical, metal, electronic, exact science, and machinery industries' data. And we found there were differences among sectors in regard to the time lag effect.