Received January 7, 2008 / Accepted March 3, 2007
THE IMPACT OF FINANCIAL SUPPORT SYSTEM ON TECHNOLOGY INNOV ATION: A CASE OF TECHNOLOGY GUARANTEE SYSTEM IN KOREA
∗
Woo-Seok Jang
Seoul National University, Seoul, Korea
∗∗
Woojin Chang
Seoul National University, Seoul, Korea
Abstract
We analyzed the impact of financial support system on technological innovation of small and medium manufacturing firms in Korea, with a special interest in technology guarantee system. This was done using a sample of 1,014 Korean manufacturing firms of which 43% were venture companies. Our study provides two important conclusions. First, the result of empirical analysis indicates that financial support systems have a significant influence on both product innovation and process innovation of SMEs in Korea. Second, a more important conclusion of this rearch is that technology guarantee system impacts on product innovation; however not on process innovation. This result implies that technology guarantee system attaches more importance to technological innovations related with product development than to tho related with process enhancement.
Keywords: Financial support system; Technology innovation; Technology guarantee
* Ph.D. Candidate. Graduate Program in Technology Management, Seoul National University, Seoul, Korea;
jangws21@snu.ac.kr ; Tel: +82-2-880-1380; Fax: +82-2-872-8359.
** Corresponding Author. Assistant Professor. Department of Industrial Engineering, College of Engineering, Seoul National University, Seoul, Korea; changw@snu.ac.kr ; Tel: +82-2-880-8335; Fax:
+82-2-889-8560.
1. Introduction
In thes days of vere struggle for technological innovation, various supporting systems for innovation are introduced and operated in the majority of nations in the world. The national supporting system for technological innovations of companies offers various components such as finance, human power, legislation, and technology information. In this paper, we focus on the financial support system among a variety of supporting systems.
The financial support system for technological innovation refers to financial policies that provide companies with funds to put through technical improvements. There have long been problems that entrepreneurial companies are not able to rai all the capital they need for technological innovation. Therefore, governments in many countries have tried to solve the problems by taking on the role of venture capital investor to support technological innovation of the firms. There are three kinds of financial policies with which the government supports companies for technological innovation: Loans, equities, and guarantees.
In Korea, many types of financial support systems have been developed by the government since th
e 1980s. And tho systems have offered a good sum of loans, equities and credit guarantees. However, there is a question that they really have had an effect on the technological innovation of the manufacturing firms. Moreover, the history of technology guarantee system is relatively short and the evaluation of the impact on technological innovation has scarcely accomplished until now. Therefore, the aim of this rearch is to answer to the questions. Do Korean small manufacturing firms really receive help from the financial support system to innovate product and process? Does technology guarantee that systems have an effect on the technological innovation? What kind of financial support system do Korean small manufacturing firms implement? 2. Financial support system for Technological innovation
The economics literature has identified at least two main rationales for governments to offer public subsidies for technological innovation of firms.
First, public finance theory emphasizes that subsidies are an appropriate respon to activities that generate positive externalities. Innovation and commercialization of new technology usually accompany high uncertainty and risk. Therefore, financial market and firms have a tendency to evade investment on technological innovation to step away from the potential loss. In such circumstances, the amount of investment on technological innovation would be lower than the social
optimal level. Mansfield et al. (1977) measured the social and private rate of returns from a sample of innovations. The results indicated that the private rates of return from the investments had been much lower than the social rate of return. Lerner (1999) examined the long-run performance of high-technology firms receiving public funds and found that the fund awardees enjoyed substantially greater employment and sales growth.
The social optimal level of R&D investment may be higher than the private optimal level due to the prence of R&D spillovers (Teece, 1986; Griliches, 1992; Jaffe, 1996). Firms may invest less than the social optimal level becau they could not defend and extract all of the rents from the innovation.
A cond rationale for public subsidies for technological innovation has pointed to the prence of important financial constraints of small firms. Informational asymmetries may make raising external capital expensive for entrepreneurs (Myers and Majluf, 1984; Greenwald, Stiglitz and Weiss, 1984). An important factor influencing the viability of small firms is capital requirements and there is some evidence that small and medium enterpris (SMEs) are more likely to be subject to liquidity constraints than larger firms (Acs and Audretsch, 1990). Oakey (1995) showed that access to and costs of finance are some of the most important factors, which affect the ability of a technology-ba
d firm to grow. Giudici and Paleari (2000), bad on an empirical analysis on a survey of 46 small high-tech Italian firms, argued that traditional financial sources are inadequate to finance innovative projects. And Oakey (2003) argued that a better integration of public and private ctor funding would be to the advantage of all funders, the recipients and the wider economies in which all tho involved co-exist.
However, other works argue that government involvement may be distorted becau of the interested parties to maximize their own benefits. The emphasize the distortion that may result from government subsidies and suggest a more skeptical view of such programs. Olson (1965) and Stigler (1971) argued that direct and indirect subsidies would be captured by groups standing to gain substantial benefits, and that even very small firms could be organized to benefit from public subsidies. And Peltzman (1976) and Becker (1983) formally modeled the theory of regulatory capture.
Nowadays, public subsidies are designed to minimize such distortions and to maximize social benefits. Technology guarantee system came into the world with this background. Technology is one of the most important asts, which determine the future earnings of the company. However it has hardly been accepted as collateral in the financial market, becau it is hard to estimate the monetar
y value of such intangible asts. Recently, technology guarantee system bad on technology evaluation is growing up in Korea. In this rearch, we investigate the effect of technology guarantee system on technological innovation.
3. The Structure and Implementation of Korean Technology Guarantee System
KIBO (which means “technology guarantee” in Korean) was founded in 1989 by the Korean Government as a non-profit guarantee institution under the special enactment, "Financial Assistance to New Technology Business Act". The mission of KIBO is to contribute to the national economy by providing credit guarantees to facilitate financing for new technology-bad enterpris while promoting the growth of technologically strong SMEs and venture business. As an institution specialized in technology financing, KIBO focus onto technology innovative enterpris. As shown in table 1, its total guarantee amount came up to US$ 11,335 million in 2006, and 97.6% of this sum was preferentially directed to the new technology-bad enterpris.
Table 1 Technology Guarantees of KIBO
2002 2003 2004 2005 2006
9,449 9,311 9,567 8,886 11,308
New Technology Business
(84.9%) (83.2%) (86.0%) (88.3%) (97.6%)
1,683 1,876 1,561 1,175 27
Others
(15.1%) (16.8%) (14.0%) (11.7%) (2.4%)
Total 11,132
11,187
11,128
10,060
11,335 Unit: US$ Million.
Source: KIBO technology fund (kr)
The general process of technology guarantee schemes of KIBO is described in figure 1. A small technology-bad company that cannot meet a bank's lending criteria (which usually means the company cannot provide tangible collateral) applies for technology guarantee. And KIBO investigates and evaluates the creditworthiness and the value of technology of the company. In most cas, the banks rely on the investigation and the approval by KIBO for their decision of the loan extension.
1. Application for Loans
2. Consultation and
Application for
Technology Guarantee
3. Credit Investigation and
Evaluation
4. Approval of
Technology Guarantee
5. Issuance of a Letter of
Guarantee
6. Provision of Loans Figure 1: The structure of technology guarantee system
Source: KIBO technology fund (kr)
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4. Empirical Analysis
4.1. Data and variables
This rearch is bad on a survey data. The sample consists of 1,014 Korean SMEs that are obrved over a 3-year period (2003-2005). They were all manufacturing firms, were established in 1980 or later, were independent at founding time, and had remained so up to the end of 2005. The number of employees of the sample firms ranged from 10 to 499. 70 companies were listed in the Korean stock market, 101 companies were listed in the KOSDAQ market, and the other 843 companies were not listed in the stock market. 436 (43%) companies were designated as “Venture Company” by the government, and the others were not. Table 2 reports the distribution of industry and size of sample firms.
Table 2 Distribution of sample firms according to the industry and the size.
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Employees
Industry
10-19 20-49 50-99 100-249 250-499
12星座顺序
Total
1. Food processing, Tobacco 8 9 13 11 13 54 5.3%
2. Textiles, Apparel, Leather 1
7
11
20
7
46
4.5%
3. Wood, Pulp, Paper 1 2 4 12 10 29 2.9%
4. Chemicals, Rubber, Plastic 19 27 36 57 35 174
17.2%
5.
Metal,
Nonmetal 12 17 13 23 17 82
8.1%
6.
Machinery 45 91 50 52 34 272
26.8%
7.
Computers,
Electronics,
Precision 42 69 51 66 37 265
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26.1%
8.
Automobile 4 11 14 24 24 77
7.6%
9.
Others 2 4 4 2 3 15
1.5%
134 237 196 267 180 1014 Total
娲的拼音13.2% 23.4% 19.3% 26.3% 17.8%
Table 3 shows the variables ud in this analysis. Dependent variable is binary: 1 indicates that the fi
rm is reported to succeed in innovation during 2003-2005 and 0, otherwi. According to the type of innovation, PRODUCT and PROCESS mean product innovation and process innovation, respectively.
In order to analyze the impact of the financial support system on innovation performance, we investigated five main public funds in Korea. They were MOST (Ministry of Science and Technology) fund, MOICE (Ministry of Commerce, Industry and Energy) fund, MIC (Ministry of Information and Communication) fund, SBC (Small Business Corporation) fund, and KIBO technology fund. 1 indicates that the firm received financial support from one of the funds during 2003-2005 and 0, otherwi.淡而无味
We ud four control variables: AGE, VENTURE, RDI and SIZE.
Rearchers have not shown any connsus of opinions about the influence of firm age on innovative performance. Shan et al. (1994) and Powell et al. (1996) argued that the influence of firm age on innovative performance was insignificant. However Stuart (2000) showed that firm age was significantly related to the sales growth rate in a high technology industry. We ud AGE, the age of the firm, as a control variable to examine the influence on technological innovation.
In Korea, the “Venture Company” means the enterpri certified by the government for the title. Technology-bad venture firms may be more innovative than non-venture firms (Lim et al., 2005; Lee and Oh, 2003). We ud a dummy variable, VENTURE, which reprents whether the firm was a venture enterpri.
R&D expenditure is one of the most significant factors affecting innovative performance of a firm (Cohen and Levinthal, 1989, 1990; AHUJA, 2000). We employed RDI, which reprents R&D intensity (the average R&D expenditure for three years divided by the average sales for the same period) to examine the impact of R&D expenditure on technological innovation.李达康是好是坏>手机qq闪退
The relation between firm size and innovation has been extensively investigated by many rearchers. Schumpeter (1942) argued that large firms were more innovative than small ones becau the former can cope with high R&D costs and can appropriately utilize the results of R&D. However, many rearchers have indicated that large firms with dominant market power are less innovative becau they are bureaucratic and not threatened. Small firms, on the other hand, can be more innovative due to organizational flexibility and quick decision making (Scherer, 1965; Kamien and Schwartz, 1982; Scherer and Ross, 1990). We controlled the size effect using a variable, SIZE, which reprents the number of employees.
Table 3 Variables and their descriptions
Dependent Variable
PRODUCT Product innovation
PROCESS Process innovation
Independent Variable
SUPPORT_ALL Whether the firm received at least one the financial support (dummy) SUPPORT_01 Financial support from MOST fund (dummy)
SUPPORT_02 Financial support from MOCIE fund (dummy)
SUPPORT_03 Financial support from MIC fund (dummy)
SUPPORT_04 Financial support from SBC fund (dummy)
SUPPORT_05 Financial support from KIBO Technology Fund (dummy)
AGE Age of firm
VENTURE Venture firm (dummy)
RDI R&D intensity = R&D investment / Sales
SIZE Number of employees
4.2. Analysis and results
We estimated the impact of independent variables on technological innovation using a logistic regression model. The model was divided into two categories bad on the type of innovation: product innovation and process innovation. In model 1 and model 3, we analyzed the total impact of the financial support systems. And in model 2 and model 4, we identified the impact of five main public funds in Korea.
Table 4 shows the results estimated by the logistic regression model. The results of model 1 and model 3 indicate that the financial support systems, as a whole, have a significant influence on both product innovation and process innovation.
Model 1 and model 2, which show the impact on product innovation, indicate that SUPPORT_05 (financial support from KIBO technology fund), AGE, VENTURE, SIZE have a significant influence o
n product innovation. The financial support from KIBO technology fund affects product innovation; however it does not affect process innovation as shown in model 4. This result can be explained in the following n. The KIBO technology fund ud to be granted to the companies with product-bad technology rather than tho with process-bad technology. Meanwhile, SUPPORT_2, which indicates MOICE (Ministry of Commerce, Industry and Energy) fund, has a significant influence on process innovation (model 4).
Table 4 Logistic regression of the likelihood of product innovation and process innovation
Product Innovation Process Innovation
Model 1 Model 2 Model 3 Model 4 Constant 0.035 (0.173) 0.061 (0.171) -0.282*(0.169) -0.251 (0.167) SUPPORT_ALL 0.374***(0.143) 0.381***(0.140)
SUPPORT_01 0.516 (0.388) -0.137 (0.331) SUPPORT_02 0.144 (0.208) 0.509***(0.199) SUPPORT_03 -0.354 (0.284) 0.209 (0.281) SUPPORT_04 0.065 (0.166) 0.150 (0.158) SUPPORT_05 0.353**(0.171) 0.199 (0.163) AGE 0.015**(0.006) 0.015**(0.006) -0.006 (0.005) -0.007 (0.005) VENTURE 0.519***(0.159) 0.514***(0.159) -0.048 (0.153) -0.082 (0.154) RDI -0.250 (0.428) -0.268 (0.429) -0.640 (0.674) -0.647 (0.690) SIZE -0.002**(0.001) -0.001**(0.001) 0.003***(0.
001) 0.003***(0.001) -2 Log Likelihood 1304.339 1299.721 1373.984 1366.262 N 1014 1014 1014 1014 Standard error in parenthesis
