The effect of R&D, technology commercialization capabilities and innovation performance/Moksliniu tyrimu ir eksperimentines pletros bei technologiju komercializavimo galimybes itaka inovaciju efektyvumui.
Kim, Seo Kyun ; Lee, Bong Gyou ; Park, Beom Soo 等
1. Introduction
1.1. Research background
A number of recent studies have investigated companies'
internal characteristics which may influence their technological
innovation performance (Lichtenthaler, Ernst 2007; David 2001; Tsai,
Wang 2005; Lin et al. 2006). However, most of these studies on the
innovation performance or innovative capabilities of IT SMEs have been
too broad in their scope to meaningfully contribute to the understanding
of the subject. They are oftentimes concerned either with the industry
as a whole or an entire industry sector (service sector, manufacturing
sector, etc.). Few of them deal with a specific subject related to IT
SMEs, as does this study (investigation of technology innovation
capabilities among IT SMEs that are direct and indirect beneficiaries of
public R&D funding, including R&D capabilities and technology
commercialization capabilities). On the other hand, there have been
quite a few studies comparing economic performance between companies
that receive government R&D subsidies and those that do not (Shin
2005; Kwon, Ko 2004; Lee 2004; David 2001; Lach 2002; Robson 2001).
These studies, however, report conflicting results (mutually
complementary effect vs substitutive effect). One of the reasons why
past research on the relationship between R&D and technological
innovation has failed to yield consistent results is that these studies
were most often overly focused on one of the determinants of R&D
innovation; namely, the independent effect of R&D, and largely
overlooked the effect of moderating or mediating factors which could
influence this same relationship (Kim 2003; O'Regan et al. 2006;
Lach 2002). Also rare are studies investigating the effect of the many
internal resources of firms (technology, manpower, organization,
capital, etc.) which could potentially influence their innovation
performance. Inquiries into the effect of R&D capabilities and
technology commercialization capabilities required for commercializing
the results of R&D on technology innovation performance have been
especially rare (Yam et al. 2004; Lin et al. 2006). All in all, at a
time when government R&D funding toward technology innovation among
IT SMEs are continuously on the rise, the stock of research is
surprisingly meager concerning the efficacy of such investment (David et
al. 2000). There is, therefore, an urgent need to develop a clear
understanding of how government funding affects the mechanism whereby
R&D activities among IT SMEs lead to actual innovation performance.
Currently, in Korea, IT SMEs are both directly and indirectly
beneficiaries of government R&D funding. Some of them are direct
recipients of government R&D grants, awarded toward independent
technology development programs. Others benefit indirectly from public
funding, as recipients of technologies developed in, and transferred
from, government-sponsored or other publicly-funded research
institutions, or as participants of joint R&D programs with these
institutions.
1.2. Research objectives
This study borrows the seven capability dimensions proposed by Yam
et al. (2004), namely, learning, R&D, resources allocation,
manufacturing, marketing, organizing and strategy planning. Unlike the
original model by Yam et al. (2004), in which the relationship between
capabilities and innovation performance is examined by setting all of
the seven dimensions as independent variables and innovation performance
as the dependent variable, in this study, manufacturing and marketing
are considered mediators in the correlation between R&D and
innovation performance, as this relationship is analyzed as an input,
process and output cycle.
The objectives of this study, in concrete, are to understand how
the R&D and technology commercialization capabilities of IT SMEs
that are direct or indirect beneficiaries of public funding, impact
their technology innovation performance; what factors influence the
technology innovation performance of these firms more than others; and
whether there is a difference between the indirect and direct recipients
of government R&D grants with regard to innovation performance and
capabilities influencing this performance. Another aim of this study is
to determine whether the technology commercialization capabilities of
these firms serve as the mediator in the relationship between R&D
and innovation performance. Finally, this paper presents research and
policy implications drawn from the results of the above inquiry.
To sum up, the objectives of this study are three: First, cast
light on the influence of two of the many potential factors affecting
technology innovation in IT SMEs, namely, R&D capabilities (learning
function, R&D function and external networking function) and
technology commercialization capabilities (manufacturing function and
marketing function), on their technological innovation performance.
Second, determine whether technology commercialization capabilities play
a mediating role between R&D capabilities and technological
innovation performance, within the process of innovation, conceived in
this study as a chain linking input (R&D capabilities), process
(technology commercialization capabilities) and output (innovation
performance). Third, investigate whether there is a difference between
companies that are direct and indirect beneficiaries of public R&D
funding in terms of how R&D capabilities and technology
commercialization capabilities influence technological innovation
performance.
2. Literature review
2.1. R&D capabilities and performance
R&D capability is generally understood as a dynamic capability
related to the creation and use of knowledge, enabling a company to
acquire and maintain its competitive advantage (Zahra, Gerald 2002). The
importance of R&D capability has been confirmed by empirical studies
attesting to the positive influence of direct efforts made by companies
toward technological innovation (R&D spending per employee, ratio of
R&D spending to sales, ratio of R&D employees to total
employees, etc.) on their technological innovation performance (product
innovation, number of patents, innovation indicators) (Romijin,
Albaladejo 2002).
Meanwhile, for IT SMEs to enhance their technological capabilities,
they must tap external knowledge resources to augment their internal
capabilities with knowledge acquired from external sources. In the case
of IT SMEs in catching-up countries, they need to absorb technology from
more advanced countries. Obtaining external knowledge is more difficult,
when the knowledge in question is tacit knowledge. Tacit knowledge is
harder to capture than other forms of knowledge and requires a level of
absorption capability on the part of learners (Zahra, Gerald 2002).
Research has found that the capability of absorption, coupled with
internal R&D investment, can effectively help accelerate
technological cooperation and magnify the performance-enhancing effect
of external know-how and technology, as well as strengthen external
technological cooperation (Miotti, Sachwald 2003). Britton (1993) and
Hagedoorn (1993) report that, in the case of IT SMEs, the ability to
access external partners matter more than independent internal
capabilities, and that inter-firm cooperation in the forms of joint
R&D, patent sharing, collaborative development, technology transfer
and joint venture have a positive influence on their innovation
performance, as it complements internal technological capabilities and
infrastructure.
Cassiman and Veugelers (2006) found that increase in internal
R&D investment and technological cooperation with external
organizations has a positive effect on the internal R&D capabilities
of companies. Studies have also reported that technological development
efforts are bound to be limited among IT SMEs, due to their lack of time
and resources devoted to R&D and high-quality manpower, and because
their technological knowledge is generally less than comprehensive,
especially compared to large corporations, often confined to a few
specific areas; for this reason, it is especially important for these
firms to engage in partnerships or joint R&D programs to acquire
external technology resources (Lee 2004; Kaufmann, Todtling 2002).
Lerner (1999) in his study comparing the performance of 1,435 US firms
receiving support from the SBIR with that of other firms benefiting from
no such support, reports that companies that are recipients of
government R&D grants grow at a faster rate, suggesting that public
R&D funding plays an important role in enhancing companies'
performance.
As for studies dealing with basic R&D capabilities of
companies, they can be classified into three categories according to
their focus: the focus in the research of the first category is the
influence of R&D intensity at the level of simple R&D investment
(input) on company performance. Studies of the second category second
are principally concerned with the effect of strengthening R&D
through inter-firm technological cooperation on the business performance
and organizational performance of companies. The aim in the research of
the third and last categories is to understand how R&D, understood
as a learning-by-doing process, affects the performance of companies.
2.2. Technology commercialization capability and performance
Technology commercialization capability refers to the ability to
absorb and re-adapt a new technology for use in production and
marketing; in other words, the ability to integrate technology in
concrete production and marketing activities (Jolly 1997). Nevens et al.
(1990) define technology commercialization capability as the ability to
rise above competitors and gain competitive advantage through cost
reduction, quality improvement and the absorption of new technologies.
Chen (2009) in his study investigating factors influencing the
performance of young ventures from a resource-based perspective,
measures the effects of incubator programs, venture capital support and
technology commercialization capabilities and reports that the role of
technology commercialization is chiefly that of a mediator in the
relationship between the organizational resources and innovative
capabilities of companies, and their performance. As for Lin et al.
(2006) and Lockett and Wright (2005), they analyzed technology
commercialization capabilities of companies quantitatively, by
calculating the ratio of marketing cost to sales, assuming that
technology commercialization can be translated into the amount of
company resources or efforts invested toward marketing technologies
resulting from R&D in the form of a product or service. Meanwhile,
Zahra and Nielsen (2002) in their resource-based view (RBV) study of
companies, singled out among multiple dimensions involved in the
commercialization of a technology, internal human resources and
technology-based manufacturing sources as having a positive influence on
the success of commercialization.
As for Schroeder et al. (2002) who investigated manufacturing
companies' capabilities and resources from a resource-based view
found that competitive advantage in manufacturing was influenced by the
appropriateness of processes and equipment, and that external and
internal learning played a highly significant role in a company's
ability to gain or maintain a competitive advantage. Dutta et al. (1999)
distinguished companies' capabilities into three types: marketing
capability, R&D capability and operations capability, and analyzed
how these three capabilities interacted with each other. The interaction
between marketing and R&D capabilities, he found, had an important
influence on a company's ability to develop new products and its
overall performance. Meanwhile, marketing capabilities of companies, he
reports, have the greatest influence of all on their innovative output.
For companies in high-tech markets, long-term innovative capabilities
and the ability to successfully commercialize their innovations
(developing customer-oriented products) matter most particularly, he
relates.
To sum up, findings by previous studies indicate that for
technology-based IT SMEs, marketing capabilities, in other words,
capabilities for successfully commercializing R&D results into
competitive products, and manufacturing capabilities are nearly as
important as R&D capabilities.
3. Research design
3.1. Survey
In this study investigating the relationship between R&D
capabilities and technology commercialization capabilities of IT SMEs
and their innovation performance, we narrowed our focus to IT SMEs that
are direct recipients or public R&D funding and their indirect
beneficiaries receiving technology support from government-sponsored
R&D institutions. A month-long survey was conducted in August 2008,
by email and fax, on 546 IT SMEs. 280 of them were direct recipients of
government R&D grants at least at some point in the two-year period
between 2005 and 2007, and 266 others, indirect beneficiaries, having
benefited from R&D programs at government-sponsored institutions,
either through simple technology transfer or as a participant of the
programs, over the same period.
The goal being assessing the R&D capabilities, technology
commercialization capabilities and innovation performance of IT SMEs,
the survey was addressed to members of surveyed companies who by virtue
of their official capacity possessed comprehensive knowledge of their
organization's technology-related capabilities, such as the CEO,
director of the R&D lab or the chief technology officer.
The response rate was 46%, with 262 out of a total sample
population of 546 companies returning complete responses. After
discarding eight of the 262 responses containing an excessive number of
missing values, 254 responses were retained for analysis.
3.2. Research model
The research model draws on the seven capability dimension model
proposed by Yam et al. (2004)--learning, R&D, Resources allocation,
manufacturing, marketing, organizing, strategy planning--which was
expanded with additional variables and appropriately modified to suit
the purpose of this study.
[ILLUSTRATION OMITTED]
In Yam et al. s (2004) original model, the relationship between the
seven capability dimensions and performance is examined by setting all
seven dimensions as independent variables, and performance as the
independent variable. We propose a new method as a research model (Fig.
1). In this study, consistent with the framework of innovation viewed as
the cycle of input-process-output, manufacturing and marketing, the two
technology commercialization capability variables, were treated as
mediators of the influence of R&D on performance. Meanwhile, to take
into consideration access to external resources, a potentially important
influence factor for the innovative capabilities of IT SMEs, the model
was further refined by adding the 'external networking
function' as one of the R&D capability factors.
3.3. Definition of variables and survey measurement
The goal of this study is to analyze the relationship between
R&D and technology commercialization capabilities of IT SMEs and
their technological innovation performance. R&D capabilities, the
independent variable, refers to all capabilities required for a company
to develop innovative products, including those related to the
acquisition, use and practical application of technology and knowledge.
We set up survey measurement as a presented Table 1. Three R&D
capability-related measured variables were defined: learning function
(function related to exploration, absorption and integration of external
technology and knowledge / three items), R&D function (R&D
workforce and the relative size of R&D investment / two items), and
external networking function (function related to active external
technology cooperation/ three items).
Technology commercialization capabilities, the independent variable
as well as the mediator, comprehensively refer to all capabilities
related to manufacturing and marketing, in other words, capabilities
required in the process of modifying and re-adapting a technology for
application in production, manufacturing and distribution of resulting
products.
Two technology commercialization-related variables were defined:
manufacturing function (function relevant to manufacturing products
corresponding to market demand/ four items) and marketing function
(function relevant to assessing customers' needs in a competitive
environment and marketing products accordingly). Technological
innovation performance, the dependent variable, is the indicator of
innovation resulting from technological innovation capabilities or
activities, and is measured in this study by product competitiveness
(companies' self-assessment on their product performance / three
items).
Except the two R&D function-related items, which were questions
requiring the respondents to choose a percentage range, all other items
were measured using a 7-point scale (1: not at all, 7: very much so).
4. Results of empirical analysis
4.1. Sample profile
As presented in Table 2, the sample surveyed totally 254 companies
including direct company (government R&D fund recipient IT SMEs) and
indirect company (ETRI Technology transfer & cooperation research IT
SMEs). The sample comprised 119 companies that were direct recipients of
government R&D grants during the period evaluated. The average
number of years in operation among these companies was 6 years, average
sales 830 million won, average capital 340 million won, and the average
number of full-time employees 13.
The number of companies that were indirect beneficiaries of
government R&D funding making up the sample was 135. The average
number of years in operation among these companies was 9 years, average
sales 2.5 billion won, average capital 530 million won, and the average
number of full-time employees 26. Companies that were indirect
beneficiaries of government R&D funding were, therefore, somewhat
larger in size than companies that were direct recipients of government
grants, as well as superior to the latter in terms of sales performance.
4.2. Reliability and validity analysis
A regression analysis and a covariance structure analysis were
performed on the data using SPSS 12.0 and AMOS 7.0. As can be seen in
Table 3, all factors exceeded 1.0 in Eigen value, and the factor
loadings of all measurement instruments were greater than the threshold
of 0.7, confirming their convergence validity. Finally, to test the
reliability of the data, Cronbach alpha values were calculated for each
category of measurement items. The Cronbach alphas of the manufacturing
function (MFF), external networking function (ENF), marketing function
(MKF), learning function (LF) and the R&D function (RDF) were 0.896,
0.924, 0.828, 0.747, and 0.730, respectively, indicating a good level of
reliability and validity.
4.3. Correlation analysis
A correlation analysis was performed to determine whether a
relationship exists between each of the factors.
As shown in Table 4, the analysis found that the R&D function
(RDF) had a significantly positive correlation only with product
performance, and that all other functions, namely, learning function
(LF), external networking function (ENF), manufacturing function (MFF)
and marketing function (MKF), also had a positive correlation with
innovation performance (product competitiveness: PC).
4.4. Results of model test
The above-described research model was tested for structural
goodness-of-fit (Table 5). The results obtained were RMR = 0.069, RMSEA
= 0.069, TLI = 0.928, CFI = 0.943, NFI = 0.9. These results suggest that
the model's goodness-of-it is very close to the optimal level.
[FIGURE 2 OMITTED]
The aim of this study is to explore the causal relationship that
may exist between the R&D and technology commercialization
capabilities of IT SMEs, and their innovation performance, and determine
what factors influence innovation performance. The results of analysis
are listed in Fig. 2 and Table 6.
First, the learning function (LF), one of the R&D
capability-related factors, proved to have a significant positive
influence on the manufacturing function (MF) and marketing function
(MKF), the two technology commercialization capability-related factors,
as well as on innovation performance (product competitiveness: PC).
Second, the R&D function (RDF), another R&D capability-related
factor, while it had no significant relationship with manufacturing
function (MFF) or marketing function (MKF), had a significantly positive
influence on innovation performance (product competitiveness: PC).
Third, the external networking function (ENF), the last of the
three R&D capability-related factors, had a significant positive
influence on the manufacturing function (MFF), but not on the marketing
function (MKF) or innovation performance (product competitiveness: PC).
Fourth, among the technology commercialization capability-related
factors, whilst the manufacturing function (MFF) did not have a
significant relationship with the marketing function (MKF), both the
former and latter had a significant positive influence on innovation
performance (product competitiveness: PC).
Meanwhile, the regression equation expressing innovation
performance (PC) as a function of R&D and technology
commercialization capabilities (PC = [alpha] + [[beta].sub.1] R&D
capabilities + (32 technology commercialization capabilities +s)
resulted in [beta] values which suggested a significant relationship to
innovation performance (PC) for both R&D capabilities (0.269) and
technology commercialization capabilities (0.444). But, the [beta] value
of technology commercialization capabilities largely exceeded that of
R&D capabilities, suggesting the existence of the mediating effect
by the former.
Furthermore, the results of the regression equation consisting of
low-level items of R&D and technology commercialization capabilities
(PC = [alpha] + [[beta].sub.1] LF + [[beta].sub.2] RDF + [[beta].sub.3]
ENF + [[beta].sub.4] MMF + [[beta].sub.5] MKF + [epsilon]) indicated
that all items except ENF were significant. However, the MMF (0.272) and
MKF (0.261) had a greater influence on innovation performance than LF
(0.191) and RDF (0.237); hence the mediators.
4.5. Comparison of regression analysis results between direct and
indirect recipients of government R&D funds
Table 7 lists the results of the regression analysis, showing
differences between IT SMEs that were direct recipients of public
R&D funding (internally carrying out pubic-funded R&D projects)
during the period studied, and those that are their indirect
beneficiaries (technology transfer from ETRI or participation in joint
research projects) in terms of how R&D and technology
commercialization capabilities influence their innovation performance.
The results indicate that among companies that were direct
recipients of government grants, only one of the R&D
capability-related factors, namely, the learning function (LF), yields a
moderate positive influence on innovation, with other factors such as
the R&D function (RDF) and external networking function (ENF) having
no significant influence. As for technology commercialization
capabilities, both the manufacturing function (MFF) and marketing
function (MKF) had a significant positive relationship to innovation
performance (product competitiveness: PC). Concerning the relationship
between R&D capabilities and technology commercialization
capabilities, only the external networking function and learning
function (LF) had a significant positive influence on the manufacturing
function (MFF) and marketing function (MKF), respectively. Among firms
that were indirect beneficiaries of public R&D funding, two R&D
capability-related factors, namely, the learning function (LF) and
R&D function, showed a significant positive correlation with
innovation performance (product competitiveness: PC). In terms of the
relationship between technology commercialization capabilities and
innovation performance, both the manufacturing function and marketing
function (MKF) exerted a significant positive influence on innovation
performance. Concerning the relationship between R&D capabilities
and technology commercialization capabilities, we found that the
learning function (LF) and external networking function (ENF) produced a
significant positive influence on technology commercialization
capabilities, with the R&D function having no significant
correlation.
Notably, both the learning function (LF) and external networking
function (ENF) had a significant positive correlation with the marketing
function, whereas the R&D function showed a significant negative
correlation with the marketing function. Meanwhile, the regression model
was tested for possible multicollinearity, and the results indicated no
presence of multicollinearity.
We were further able to compare the direct recipients of government
R&D grants and their indirect beneficiaries, in terms of the
explanatory power of variables, using coefficients of determination, and
determine what factors had more explanatory power than others.
As can be seen in (Fig. 3), the explanatory power of the
relationship between R&D capabilities and innovation performance
stood at 10.3% for companies that are direct recipients of government
funds, and 32.5% for companies that are their indirect beneficiaries.
With regard to the relationship between technology commercialization
capabilities and innovation performance (product competitiveness: PC),
its explanatory power was also substantially higher with companies that
are indirect beneficiaries of government R&D funding (37.2%) than
with companies that are the direct recipients of it (21%). The disparity
in explanatory power remained as wide between the two groups, also
concerning the relationship between R&D capabilities and the
manufacturing function (MFF): 10.2% for companies that are direct
recipients of government funds and 27.8% for those that are their
indirect beneficiaries. The same pattern persisted with the relationship
between R&D capabilities and the marketing function (MKF), whose
explanatory power was dramatically higher among the indirect
beneficiaries of public funding (27.6%) than the direct recipients of
government funds (9.5%). To sum up, the explanatory power of the
relationship between R&D capabilities, technology commercialization
capabilities and innovation performance, measured by coefficients of
determination, was far greater with IT SMEs that are indirect
beneficiaries of government R&D funding than those that were direct
recipients. The principal implication of these results is that indirect
investment through government-sponsored research institutions is
measurably more effective in enhancing the technological innovation
performance of IT SMEs, than direct investment in the form of funding
companies' internal R&D projects.
[FIGURE 3 OMITTED]
5. Conclusion and implications
The main findings of this study are as follows: First, in the
relationship between R&D capabilities (LF, RDF, ENF), technology
commercialization capabilities (MFF, MKF) and technological innovation
performance (PC), the learning function (LF) had a significant influence
on both technology commercialization capabilities and innovation
performance, playing a crucial role in the ability of IT SMEs to
effectively commercialize R&D results as well as in their overall
innovation performance. On the other hand, the R&D function (RDF)
only influenced innovation performance, and technology commercialization
capabilities proved to have no significant correlation with innovation
performance. As for the external networking function (ENF), this factor
had a measurable influence only on the manufacturing function (MFF), and
not on innovation performance or the marketing function.
The results, therefore, suggest that technological cooperation with
external organizations does not influence the innovation performance or
marketing performance of companies, and this may be explained by the
fact that external cooperation activities are generally centered on
R&D exchange and exchange of product manufacturing technologies.
Meanwhile, the manufacturing function (MFF) and marketing function
(MKF), the two technology commercialization capability-related factors
appear to have a highly significant influence on the innovation
performance of IT SMEs. The manufacturing function (MFF) and marketing
function (MKF) also showed a direct correlation with each other.
These results point to the importance of considering a
comprehensive set of R&D capabilities, when measuring the influence
of R&D on the innovation performance of companies, including
learning and external networking functions, and not just focusing on
R&D intensity, as is the case with much of the existing literature.
Second, technology commercialization capabilities played the role
of a mediator between R&D capabilities and innovation performance
(product competitiveness: PC). As shown in Table 5 above, the
goodness-of-fit of the research model used in this study is very close
to the optimal level, confirming that the technology commercialization
capability-related variables are mediators in the relationship studied.
The [beta] value in the regression analysis was also significantly
higher for technology commercialization capabilities (0.444) than for
R&D capabilities (.269). The same was true with the [beta] values of
low-level technology commercialization capability variables, attesting
to the fact that technology commercialization capabilities exert a far
greater influence on the innovation performance of IT SMEs than do
R&D capabilities.
The practical implication of the mediating role played by
technology commercialization capabilities between R&D capabilities
and innovation performance is that companies, in their technology
development efforts should not narrowly focus on R&D, but consider
also commercialization-related factors, so that resulting technologies
can effectively lead to concrete enhancement of performance.
Third, the correlations between R&D capabilities, technology
commercialization capabilities and innovation performance differed
substantially between companies that are direct recipients of government
funds, and those companies that are their indirect beneficiaries. R2,
the measure of the explanatory power of independent variables was
dramatically higher among indirect beneficiaries of public R&D
funding than direct recipients of government grants, concerning all
three variables including R&D capabilities, technology
commercialization capabilities and innovation performance. The policy
implication of this finding would be that government can more
effectively bring forth the enhancement of national technological
competitiveness through indirect investment in IT SMEs, in other words,
by channeling public R&D funding toward government-sponsored
research institutions, than through direct investment.
Currently, in South Korea, government support for R&D
activities in IT SMEs is provided in two forms: direct disbursement of
funds to companies on their internal development projects, and indirect
support, giving them access to technologies developed by government
research institutions. Based on the findings of this study, transferring
technologies developed by research organizations possessing high-quality
manpower and equipment to IT SMEs, in a state ready for
commercialization and with greater value-added, is a better way of
assisting them in gaining technological competitiveness.
http://dx.doi. org/10.3846/20294913.2011.603481
Received 12 February 2010; accepted 16 March 2011
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Seo Kyun Kim (1), Bong Gyou Lee (2), Beom Soo Park (3), Kyoung Seok
Oh (4)
(1,3,4) ETRI, 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, Korea
(2) Yonsei University, 262 Shinchon-dong, Seodaemun-gu, Seoul, 120-749,
Korea E-mails:
[email protected];
[email protected]; (3)
[email protected] (corresponding author);
[email protected]
Seo Kyun KIM is a Ph.D., Principal Researcher in ETRI. His research
areas are management of technology and Commercialization in IT and IT
Fusion industry. He got a Ph.D in management of technology by the Yonsei
University in Korea. Now He works at the ETRI(Electronics and
Telecommunications Research Institute; world best IT institutes) in
Korea as a SME Cooperation Team member.
Bong Gyou LEE is the Associate Dean and a Professor in the Graduate
School of Information at Yonsei University. He received his BA Degree
from Yonsei in 1988 and his MS Degree in 1992 and PhD in 1994 from
Cornell University. He was a commissioner of the Korea Communications
Commission in 2007 and 2008.
Beom Soo PARK is a Ph.D., SME Team Leader in ETRI. His research
areas are International Management and Commercialization of IT SMEs. He
got a Ph.D in international management by the Korea University in Korea.
Now He works at the ETRI in Korea as a SME Cooperation Team Leader.
Kyoung Seok OH is a Ph.D. Student at GSI, Yonsei University. He
received his M.S in management from Chungnam National University, Korea.
His research interests include Technology Strategy and Regulation
Policy.
Table 1. Survey measurement
Construct Variable Measurement
R&D Learning Monitoring about trends of R&D
Capability Function
(LF) Absorption ability of External
Knowledge
Importance of Tacit Knowledge
R&D function Relative size of R&D investment
(RDF)
Relative size of R&D workforce
External New market entry through
networking external Tech cooperation
function (ENF)
Synergy creation through
external Tech cooperation
Substantial help through
external Tech cooperation
Manufacturing process
reflection of R&D
Technology Manufacturing Continuous improvement of
Commercialization function (MFF) manufacturing system
Capability
Control of Product quality
Chief manufacturing cost
through new process
Marketing Knowledg holding for market
function (MKF) segmentation
Marketing ability of Sales man
Sales ability of new product
Technology Product Predominately at the cost side
Innovation Competitiveness
Performance (PC) Competitveness of market
Unique predominance of
technology product
Table 2. Sample profile
Division Direct Company Indirect Company
Company Number 119 (46%) 135 (54%)
Average Company age 6 years 9 years
H/W Company 77 (65%) 79 (58%)
S/W Company 42 (35%) 56 (42%)
2007 yrs sales average 830 million 2.5 billion
Average capital 340 million 530 million
Average employee 13 26
* Direct company: Government R&D Fund recipient IT SMEs
* Indirect company: ETRI Technoloy transfer & cooperation research
IT SMEs
Table 3. Factor analysis
Factors
Var Measurement 1 2 3
MFF Continuous improvement of 0.865 0.120 0.233
manufacturing system
Manufacturing process 0.830 0.196 0.089
reflection of R&D
Control of Product quality 0.823 0.141 0.255
Chief manufacturing cost 0.784 0.046 0.276
Synergy creation 0.168 0.926 0.094
ENF Substantial help 0.151 0.911 0.068
New market entry 0.095 0.873 0.180
MKF Marketing ability 0.218 0.106 0.882
Knowledg holding 0.248 0.088 0.770
Sales ability 0.295 0.156 0.743
LF Monitoring about trends of 0.163 0.187 0.171
R&D
Absorption ability 0.176 0.180 0.225
Tacit Knowledge 0.082 0.059 -0.002
RDF R&D Investment -0.030 -0.006 -0.015
R&D employee 0.043 0.043 -0.030
Eigen value 3.1 2.6 2.3
% of Variance 20.4 17.6 15.0
Factors
Var Measurement 4 5 [alpha]
MFF Continuous improvement of 0.156 -0.046 0.896
manufacturing system
Manufacturing process 0.228 0.088
reflection of R&D
Control of Product quality 0.084 0.006
Chief manufacturing cost 0.075 -0.021
Synergy creation 0.178 0.022
ENF Substantial help 0.097 -0.015 0.924
New market entry 0.158 0.044
MKF Marketing ability 0.050 -0.056 0.828
Knowledg holding 0.272 -0.026
Sales ability 0.090 0.024
LF Monitoring about trends of 0.800 0.082 0.747
R&D
Absorption ability 0.790 0.069
Tacit Knowledge 0.731 0.016
RDF R&D Investment 0.052 0.894 0.730
R&D employee 0.073 0.887
Eigen value 2.1 1.6
% of Variance 13.6 10.8
Table 4. Correlation analysis
Var Mens SD LF RDF
LF 5.75 0.85 1
RDF 3.39 0.77 0.133(*) 1
ENF 4.91 1.33 0.384(**) 0.045
MFF 5.04 1.07 0.401(**) 0.016
MKF 4.91 1.08 0.379(**) -0.032
PC 5.47 0.95 0.441(**) 0.259(**)
Var ENF MFF MKF PC
LF
RDF
ENF 1
MFF 0.351(**) 1
MKF 0.325(**) 0.553(**) 1
PC 0.285(**) 0.507(**) 0.484(**) 1
Table 5. Goodness-of-fit result
Structural goodness-of-fit Criterion Result
Absolute Fit Measures RMR 0.05-0.08 0.069
RMSEA 0.1under 0.069
Incremental Fit Measures TLI 0.9 over 0.928
CFI 0.9 over 0.943
NFI 0.9 over 0.9
Table 6. Path analysis result
Path Path SD t p
Coefficient
LF [right arrow] MFF 0.440 0.093 4.742 0.000
LF [right arrow] MKF 0.279 0.089 3.135 0.002
LF [right arrow] PC 0.149 0.068 2.178 0.029
RDF [right arrow] MFF -0.026 0.109 -0.237 0.812
RDF [right arrow] MKF -0.154 0.100 -1.543 0.123
RDF [right arrow] PC 0.275 0.082 3.334 0.000
ENF [right arrow] MFF 0.211 0.062 3.417 0.000
ENF [right arrow] MKF 0.035 0.056 0.637 0.524
ENF [right arrow] PC 0.016 0.041 0.381 0.703
MFF [right arrow] MKF 0.396 0.069 5.712 0.000
MFF [right arrow] PC 0.196 0.056 3.470 0.000
MKF [right arrow] PC 0.263 0.069 3.810 0.000
Table 7. Direct/indirect regression result
Dependent Independent Direct SMEs (N = 119)
PC LF 0.200 * F = 4.333 ** R = 0.321
RDF 0.159 [R.sup.2] = 0.103
ENF 0.129 Ajut [R.sup.2] = 0.079
PC MFF 0.256 ** F = 15.433 R = 0.458
MKF 0.290 ** [R.sup.2] = 0.210
Ajut [R.sup.2]= 0.197
MFF LF 0.138 F = 4.257 ** R = 0.319
RDF -0.090 [R.sup.2] = 0.102
ENF 0.246 ** Ajut [R.sup.2] = 0.078
MKF LF 0.221 * F = 3.975 ** R = 0.309
RDF 0-.018 [R.sup.2] = 0.095
ENF 0.167 Ajut [R.sup.2] = 0.071
Dependent Independent Indirect SMEs (N = 135)
PC LF 0.415 ** F = 20.700 ** R = 0.570
RDF 0.191 ** [R.sup.2] = 0.325
ENF 0.163 Ajut [R.sup.2] = 0.309
PC MFF 0.384 ** F = 39.133 ** R = 0.610
MKF 0.285 ** [R.sup.2] = 0.372
Ajut [R.sup.2] = 0.363
MFF LF 0.390 ** F = 16.591 * R = 0.528
RDF -0.024 [R.sup.2] = .0278
ENF 0.216 * Ajut [R.sup.2] = 0.262
MKF LF 0.321 ** F = 16.357 ** R = 0.525
RDF -0.166 * [R.sup.2] = 0.276
ENF 0.279 ** Ajut [R.sup.2] = 0.259
* P < 0.05, ** P < 0.01
