• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.698-705
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• 2014

R&D investment has rised in recent years. Korea's R&D budget is 43.8 trillion won which is 3.74% adjusted gross domestic product in 2010. Technological advances and technical inovation will bring productivity growth to Firm and Firm's productivity growth will increase GDP in sequence. Therefore the importance of study on the Firm's R&D industry is getting growing. In this study we attempt to analyse the economic impact of the Firm's R&D industry through several years using an inter-industry analysis. Specifically, this study analyze production-inducing effect, value added inducing effect, and employ-inducing effect based on demand-driven model. The analysed results of year from 1995 to 2009, the Firm's R&D investment increases production-inducing effect, value added inducing effect, and employ-inducing effect with the course of time. This means that influence of the Firm's R&D industry has increased.

• Journal of Technology Innovation
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• v.5 no.1
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• pp.15-43
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• 1997

This paper investigates empirically the relationship between various business portfolio properties (particularly technological properties) and chaebol's performance using data on the 50largest chaebols in Korea. In addition to the traditional indexes to measure diversification such as entropy index, we calculated inter-industry technological similarity using R'||'&'||'D expenditure data by industry and 1990 Input-output Table in korea, and obtained chaebol-level technological relatedness and internal transaction proportion from chaebols' business profile, inter-inustry technological similarity and 1990 input-output table. We applied factor analysis on 13 business portfolio property indexes and showed that they could be grouped into 3 dimensions. diversification scope, inter-business relatedness and degree of vertical integration. In this paper, using 50 largest chaebols' financial data (1989-1994), we analyzed empirically the effect of business portfolio properties on ROS(Return On Sales) which is conventional index for firm performance and on TFP(Total Factor Productivity) growth which is a pure measure of firm performance. To utilize the advantage of panel data, FEM(Fixed Effect Model) and REM(Random Effect Model) were used. The empirical result shows that the entropy index as a measurement of inter-business relatedness in not significant but technological relatedness index is significant. OLS estimates on pooled data were considerably different from FEM or REM estimates on panel data. By introducing interaction effect among the three variables for business portfolio properties, we obtained three findings. First, only VI(Vertical integration) has a significant positive correlation with ROS. Second, when using TFP growth as an dependent variable, both TR(Technological Relatedness) and VI are significant and positively related to the dependent variable. Third, the interaction term between TR and VI is significant and negatively affects TFP growth, meaning that TR and VI are substitutes. These results suggest strategic directions on restructuring business portfolio. As VI is increased, chaebols will get more profit. A higher level of either TR or VI will increase TFP growth rate, but increase in both TR and VI will have a negative effect on TFP growth. To summarize, certain business portfolio properties such as VI and TR can be considered "resources" themselves since they can affect profit rate and productivity growth. VI and TR have a synergy effect of change in profit rate and productivity growth. VI increases ROS and productivity growth, while TR increases productivity growth representing a technological synergy effect.t.

• 한국벤처창업학회:학술대회논문집
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• 2006.04a
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• pp.77-104
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• 2006

Utilizing the patent application data between 1997 and 2002, this study focuses on analysing the impact of patents on firm value. Especially we attempt to examine the difference of patents between venture firms and general firms. This paper first shows that the number of the patent applications of general firms listed on the securities market are more than those of venture firms listed on KOSDAQ. It is thought that this result is originated from facts that the size of firms of the securities market is usually bigger than the firms of KOSDAQ and that these large firms could manage R&D more efficiently. Second, this paper reports that there is no difference in the ratio of patent maintenance between venture firms and general firms. Both venture firms and non-venture firms would do their best to keep their patents after patent regisration. Third, in the regression of patent index and firm growth, we find that the excellence of patent and the number of patents per employee would have an impact on the growth of firms. Fourth, the regression of patent index and profitability shows that the excellence and the number of patents per employee might have an effect on the profitability of firms.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.369-387
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• 2012

Based on the survey data, this study focused venture firms examines how organizational resources and capabilities along with its external environmental conditions have an effect on its strategy and performance. In particular, this article attempts, by performing a binary logistic regression analysis, to identify the venture-specific importance and priority of the factors that may influence firms' strategy patterns, with multiple regression analysis on the relationships between some variables included in the model. The survey was conducted from October 1, 2010 through October 30, 2010. The results of this study are the following. First, the more firms are exposed to high industry growth and low competitive intensity, the higher chance they get to pursuit aggressive strategy. And then a firm seeks aggressive strategy, when it has more technological resources and human resources. Third, environmental uncertainty, industry growth, technological resources, human resources, financial resources and marketing capabilities have positive effects on firm's performance. But, competitive intensity has no direct influence firm's performance. Finally, CEO competence directly influences firm's performance, but the interaction. of CEO competence with other variables is not significant.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2001.11a
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• pp.307-337
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• 2001

In this paper, we propose the valuation frame of the IT(Information Technology) ventures using ROV(Real Options Valuation) model. Generally, ROV can comprises the traditional valuation method such as DCF(Discounted Cash Flow), which can measure only the tangible value of a firm from the expected future earnings, in that ROV can additionally measure the intangible value such as the strategic value of a firm in the uncertain environment. We set up the hypothetic IT venture future investment plan and assume that there are a growth option and a switching option consequently along the investment time horizon, which are caused by each characteristics of ventures and IT technologies, especially modularity. In the case that there are several embedded real options in the firm's investment plan in a row, we should apply the compound option pricing model as a real option valuation model in order to consider the value interaction between real options. In an addition, we present the results of optimal investment timing analysis using real options approach and compare them. with those of the original assumed investment timing.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.43-65
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• 2008

This study investigates the effects of business environment on the e-business strategy and performance of venture firms. The development of the research model is based on the empirical studies on the strategy literature. The data from the survey was analyzed using Partial Least Squares(PLS). For Daedeok Valley Venture Firms, product innovation differentiation strategy is affected by environmental uncertainty. And, cost leadership strategy tend to be influence by environmental uncertainty. Finally, venture firm's performance is effected by cost leadership strategy and marketing differentiation strategy. However, for in Hsinchu Science Park Venture Firms, product innovation differentiation strategy is affected by environmental uncertainty and heterogeneity. And, marketing differentiation strategy is enhanced by environment uncertainty and industry growth. In addition, cost leadership strategy tend to be influence by environmental uncertainty and heterogeneity. Finally, venture firm's performance is effected by cost leadership strategy and product innovation differentiation strategy.

• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.431-457
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• 2007

The main purpose of this study is to analyze the determinants of innovation in the medium core firms that belong to components and materials industry. For this purpose, we introduce the Schumpeterian hypothesis as a theoretical background at first. According to the Schumpeterian hypothesis, large firms in concentrated markets are likely to have more innovative activities. That means, firm site and market structure are the main determinant of innovation. Then, we propose other economic factors that have been considered to have effects on firms' innovative activities in previous studies. Those factors are export, profit, growth rate, R&D expenditure and capital intensity. In order to analyze the determinants of innovation, we estimate whether firm size, market structure, export, profit, growth rate, R&D expenditure and capital intensity affect to the possibility of creating innovation in medium core firms. In order to do this, our study uses survey data from 'Korean Innovation Survey(2005)' conducted by STEPI as well as utilizes the probit model as an analytical method. According to the empirical results, firm size has a positive relationship with innovative activities of medium core firms but market concentration does not. We find the negative correlation between market concentration and innovative activities in this study. Thus, was have to say that we do not fully support the Schumpeterian hypothesis in this case. Among other variables, profit and R&D expenditure are estimated to have positive relationship with innovative activities, while export and capital intensity are estimated to have negative relationship with innovative activities. In case of growth rate, we do not find any significant relationship with innovative activities. In conclusion, larger firm size, higher market competition, more access to the financial market and additional R&D investment would facilitate innovative activities of medium core firms. However, we have to review the relationship between export and innovative activities that has been estimated in this study. While the estimated effect of export on innovative activities can be explained by the own characteristics of medium core firms that produce and supply capital goods to final manufacturer, we have address this issue in the future.

• Information Systems Review
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• v.24 no.4
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• pp.41-54
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• 2022

Despite the recent rapid advancement of science and technology, we have been experiencing the decline in productivity since the 2000s. This study aims to investigate the decline at both industry and firm levels, by looking at the emergence and growth of large firms such as Amazon, Alphabet, and Apple and M&A trends. Following the results of previous studies, our results show that productivity at industry level has decreased since the 2000s. Particularly, in the period after 2011, the deterioration of allocative efficiency due to the large firms and the decline in the growth rate of surviving firms in the industry with low ratio of large firms contributed to the productivity decline. On the other hand, our analysis at firm level demonstrates that the productivity of firms that acquired IT firms improved over the entire period. While M&As have a positive impact on productivity, M&As with a demand-side motive such as market penetration and expansion of channels have a relatively larger impact than the ones for production or operation efficiency. Our results also suggest that the higher the proportion of large firms in a specific industry, the lower the productivity of individual firms in the same industry. Overall, given that the industry's structural changes for digital transformation tends to strengthen the growth of large firms, our findings have significant implications by empirically identifying the relationships of the emergence of large firms, the acquisition of IT/Non-IT firms, and motivations for M&As to firm/industry productivity.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.91-100
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• 2018

Small and medium enterprises (SMEs) endeavor to overcome the adverse resource conditions and secure competitive advantage through technological innovation capability. Prior studies have focused on the overall or specific dimensions of technological innovation capability, and examined their performance impact. However, there has been less scholarly attention on the dynamic characteristics such as the relative importance of technological innovation capability or its performance impact at the different growth stages of a firm. In this vein, this study investigates the relationship between SME innovation capability and innovation performance at each growth stages of a firm. Based on the empirical analysis of manufacturing SMEs in Korea, we found that all dimensions of technological innovation capability had positive effects on innovation performance. However, each dimension of technological innovation capability had different effect on innovation performance by the growth stages. The planning capability can improve innovation performance at the growth and maturity stages. Manufacturing capability can have positive effect on innovation performance at the maturity stage. Both of new product development capability and commercialization capability contribute to innovation performance at all of the growth stages. This study suggests the guidelines for enhancing technological innovation capability at the different growth stages of SMEs. It also provides policy implications for the design and operation of growth-stage specific programs. Finally, the limitations of the research and future research directions are presented.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.