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					FACTORS INFLUENCING PERFORMANCE OF TECHNOLOGY
   TRANSFER – A STUDY ON TAIWAN’S UNIVERSITIES
                               Hsueh-Chiao Chiang,
 Graduate Institute of Technology and Innovation Management, National Chengchi
                                    University,
        19F., No.106, Ho-Ping E.Rd.,Sec.2, Taipei City 106, Taiwan ROC
                               hcchiang@nsc.gov.tw

                                 Chuan-Hung Wang,
    Graduate Institute of Business Administration, National Taiwan University
   2F., No.10, Lane 97, Sec. 5, Roosevelt Rd., Wunshan District, Taipei City 116,
                                   Taiwan ROC
                              d94741002@ntu.edu.tw


                                     ABSTRACT
     This study is to measure the relations between the involvement of university
resources and the achievement in innovation and commercialization, resulting from
academic research outcomes. Then “a quantitative method” was used to investigate
the relations among these indicators include research manpower, research funding,
industry university relation, research outcome, and the performance of technology
transfer. There are three main findings in this study. First, more research manpower
and research funding results in more research outcomes. Second, a stronger industry
university relation leads to a better technology transfer. Finally, research outcome
does not indicate any observable relation of itself to technology transfer.

Keyword: technology transfer, university-industry cooperation, structural equation
modeling

                               INTRODUCTION
As Taiwan continues to march further into the age of knowledge based economy era,
capitalization of knowledge has become an essential driving force for Taiwan’s
economic development, and universities are eagerly hoped to act as the key leader
behind the theme.
In the past two decades, The United States and Japan has been enthusiastically
encouraging technology innovation in universities. By means of technology transfer
and consultation service to industry, these universities are regarded as the
underpinning of industry competence. As technology innovation has been successfully
commercialized in the two countries, not only universities has benefited from
significant income on license fee, but also has industry provided hundreds of
thousands of jobs and made billions of U. S. dollars in profit.
In recent years, Taiwan government has been also actively encouraging universities to
manage their research capacity in an outward fashion to supporting industry. Since
1999, Taiwan has made the Fundamental Science and Technology Act in action,
which allows universities to retain their inventions derived from government-funded
research. Furthermore, eighty percent of any profit from their inventions is to be
shared between the inventer(s) and the university where the invention is funded. Thus,

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along with the Act being active, the “Science-government environment” mode where
universities often present to foster knowledge-based activities would gradually
transform into the “Science-economy environment” mode for better competency. In
the view of university as a knowledge-based economy contributor, the study focuses
on factors affecting technology transfer in university and their relations.


                        THEORY AND HYPOTHESES
Technology transfer performance: indicators and affecting factors
Although, in some American scholars’ opinions (Carlsson & Fridh, 2002; Thursby &
Kemp, 2002), the number of licensing can be considered as the sole evaluation
indicator to reflect the performance of technology transfer, the Association of
University Technology Managers (AUTM, 2004) suggests two more indicators, the
income of licensing and the number of spin-offs, for the evaluation process. The
AUTM’s suggestion is accepted in this study and discussed in the later sections.
According to literature, five factors affecting the technology transfer performance are
generally identified as follows.
a) University-corporate cooperation can enhance the success rate of technology
transfer (Lee & Win, 2004).
b) Technology transfer services can transform university inventions into profit
through licensing (Joseph & Jonathan, 2003).
c) Research capacity in terms of human resources is the determining factor to the
success of technology transfer (Thursby & Thursby, 2001).
d) The amount of university research funding is related to the commercialization of
inventions activities. More research funds indicates more profit from technology
transfer (Carlsson & Fridh, 2002).
e) Research outcomes, in terms of publications and patents, will increase the
opportunity of technology transfer. As patents normally contribute to the evaluation of
university’s academic performance (Azagra Caro, Fernandez de Lucio, & Gutierrez
Gracia, 2003; Meyer, Sinilainen, & Utecht, 2003; Carlsson & Fridh, 2002), and
publications with potential commercial applications, publicized by research professors,
would be identified much easier than others for technology transfer (Thursby &
Thursby, 2001), it is obvious that any increase for either of them, always encouraged
by university, would facilitate technology transfer.


Human resources, research funding, and technology transfer performance
Through exchange of innovative ideas and knowledge, university researchers are
often capable of turning knowledge into innovation, and furthermore into economic
strength. The end product of such process can be expected to strengthen with
sufficient research funds and research professors. Thus, when measuring university
input to technological innovation, the number of researchers involved and the amount
of research funds should be two significant factors considered (Griliches, 1990).
Carlsson and Fridh (2002) found that the profit from managing technology transfer
cases increases as the amount of research funds invested increases, in a study on 170
of the U. S. universities. The hypothesis in this section is:
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Hypothesis 1. The greater research capacity the university provides, the better
performance in technology transfer the university will achieve.


Inventions and technology transfer performance
Publication is a popular way for university professors to share new knowledge or
ideas with other people and for knowledge itself to evolve. It can also be indication of
research performance. Three indicators, the number of publications, the growth rate of
publication, and the cite, are often applied in research performance studies done by
research centers in European Union, Sweden, or the United Kingdom. In Taiwan, the
number of publications and the number of patents are used as the primary measuring
categories for both the Research Performance Evaluation on Nation-wide Universities
and National Statistical Survey on Research Performance (National Science Council,
2006). The hypothesis in this section is:
Hypothesis 2. The more research outcomes the university generates, the better
performance in technology transfer the university will achieve.


University-corporate cooperation and technology transfer performance
University-corporate cooperation, a presentation of university-corporate relationship,
relies on the mutual activities among university researchers, technology transfer
service staff, and corporate personnel (Etzkowitz & Leydesdorff, 2000). Thus, the
number of university-corporate cooperation cases indicates how the industry is thirsty
for technical support from university, and the university performance on technology
transfer can also be depicted by the number itself (Thursby & Kemp, 2002; Lee &
Win, 2004). The hypothesis in this section is:
Hypothesis 3. The more university-corporate cooperation, the better performance in
technology transfer the university will achieve.


                                     METHODS
Data
The data used for the study are drawn from the database of Taiwan Ministry of
Education and National Science Council. This database contains comprehensive
information related to the education in Taiwan. Our sample included all Taiwan
universities reported in the database in 2004. The final sample size is 93 universities.


Model and measure
A conceptual model which incorporates all of the latent variables displayed in Figure
1. The three hypotheses indicate that research capacity, research outcomes, and
university-industry cooperation all influence technology transfer performance
positively. The relationship between research capacity and research outcomes was
considered in the conceptual model for the model completeness, although it is not the
focus of this study and we didn’t set hypothesis for the relationship. As well as the
relationship for university-industry cooperation and research outcomes is.



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       Research                  H1
       capacity
        (RC)                    0.28†

                                        Research                        Technology
                       0.75**           outcomes            H2            transfer
                                          (RO)                          performance
                                                            0.01           (TTP)
     University-                                       H3
       industry           0.11
     cooperation                                       0.76**
        (UIC)



                            Figure 1. Structural equations model
 a
   Solid lines indicate significant paths. Dashed lines indicate nonsignificant paths.
 †p<0.10, *p<0.05, **p<0.01


The latent factors and the measured variables used in the study are displayed in Table
1. Each latent factor was measured by two measured variables. In order to reduce the
variance of data that may cause estimation bias, we use the transformation of the
natural logarithm to deal with the income of licensing (IL), the amount of research
funds (RF), and the amount of university-industry cooperation budget (UICB).

                    Table 1. Latent factors and measured variables

               Latent factors                         Measured variables
   Technology transfer performance         the income of licensing (IL)
   (TTP)                                   the number of licensing (NI)
   Research outcomes                       the number of publication (NPU)
   (RO)                                    the number of patents (NPA)
   Research capacity                       the number of research professors
   (RC)                                    (NRP)
                                           the amount of research funds (RF)
   University-industry cooperation         the number of university-industry
   (UIC)                                   cooperation (NUIC)
                                           the amount of university-industry
                                           cooperation budget (UICB)




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Structural equation modeling
We applied structural equation modeling (SEM) techniques to test our hypotheses via
path analysis. Using Lisrel 8.51, we estimated the parameters of our research model
suggested by the relationships shown in Figure 1. It is felt that the use of such an
approach allows for the estimation of latent (i.e. unmeasured) factors which underlie
measured (i.e. observable) variables. Second, Lisrel will allow us to model the many
relationships to be included in a broader context that includes technology transfer
performance, research outcomes, research capacity and university-industry
cooperation.


                                   RESULTS
Table 2 presents the descriptive statistics and correlations for all the variables
analyzed in this study.

                   Table 2. Means, standard deviations, and correlations

  N=93        1           2         3           4       5         6          7       8
1 IL        1.00
2 NI        0.86** 1.00
3 NPU       0.67** 0.80**       1.00
4 NPA       0.32**     0.32**   0.39**     1.00
5 NRP       0.59**     0.68**   0.74**     0.16      1.00
6 RF        0.64**     0.62**   0.67**     0.23*     0.62**     1.00
7 NUIC      0.83**     0.94**   0.69**     0.36**    0.54**     0.59** 1.00
8 UICB      0.71**     0.57**   0.42**     0.28**    0.35**     0.47** 0.63**      1.00
  Mean      5.66       9.45     254.46     24.08     352.43     4.49       10.32   7.38
   s.d.     1.39       12.50    240.22     46.39     226.20     1.55       11.97   2.29
†p<0.10, *p<0.05, **p<0.01


We assessed the overall fit of our research model using several fit index: the ratio of
chi-square and degree of freedom (χ 2/df), the goodness-of-fit index (GFI), the
normed fit index (NFI), the non-normed fit index (NNFI), and the comparative fit
index (CFI).
The GFI assesses the correspondence between the observed and hypothesized
covariances. A good GFI should be 0.90 or higher; our model’s GFI of 0.91 was good.
The NFI is a comparison of a proposed model to the null model (in which no
relationships among the variables are posited). Values greater than 0.80 are
considered indicative of good fit. Our model had an NFI of 0.94, which shows a very
good fit. Other fit indexes were represented on Table 3. In general, all these results
suggested that our model fit the data well.




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                      Table 3. Goodness-of fit measures of the SEM

                       Fit index        Hypothesized           Recommended
                                          Model                    value
                          χ2                 33.79                  -
                          df                   14                   -
                        (χ2/df)                2.41                <3
                         GFI                   0.91               >0.90
                         NFI                   0.94               >0.90
                         NNFI                  0.92               >0.90
                         CFI                   0.96               >0.90


Figure 1 also represents our research model with the maximum likelihood parameter
estimates. Two of the three predicted links were significant. Research capacity had a
significant, positive effect on technology transfer performance (p<0.10). Therefore,
Hypotheses 1 was supported. University-industry cooperation was found to be
positively associated with technology transfer performance (p<0.01). So Hypotheses 3
was supported. Contrary to our prediction in Hypotheses 2, no evidence supported a
direct effect of research outcomes on technology transfer performance. The more
information about parameter estimates for hypothesized model was represented on
Table 4.

                   Table 4. Parameter estimates for hypothesized model

            Path                   Parameter          Standard      t-value     Results
                                   estimates            error
       H1:RCTTP                    0.28†               0.18            1.70   Supported
          (γ11)
       H2:ROTTP                     0.01               0.09            0.13      Not
          (β12)                                                                supported
      H3:UICTTP                    0.76**              0.12            5.53   Supported
          (γ12)
†p<0.10, *p<0.05, **p<0.01


                                  DISCUSSION
With these hypotheses, a statistical analysis using Structural Equation Modeling
(SEM) is performed in this study on data collected from ninety-three universities in
Taiwan. Conclusions from the data analysis using SEM are shown as follows:
First, university research capacity is significantly related to technology transfer. For
those universities exhibiting strong research capacity and vigorous R&D activities,
better performance in technology transfer is suggested.


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Second, R&D output is of little influence on performance in technology transfer.
University research professors are generally more interested in publishing papers than
pursuing inventions with probable commercial applications. It could contribute to
lack of persuasive incentives or motivations. Furthermore, the lack of incentive is
also suggested to facilitate an unwillingness of filing on patent over research
outcomes, which is regarded as an obstacle for technology transfer.
Finally, university-corporate cooperation is found to be significantly influential to
performance in technology transfer. When university professors become reliable to
their cooperative partners in carrying out cooperative research projects, cooperative
partners tend to offer more cooperation opportunities and transfer more technological
research outcomes.
In this study, we provided some implications for the universities. First, universities
should reinforce the recruiting of outstanding researchers and offer more on R&D
expenditure. Second, government should provide a substantial subsidy to universities
for research projects. Third, evaluation of professor’s performance on R&D output by
universities should weigh more on the commercialization of R&D output.
Commercialization-oriented research projects and filing on patent should be
encouraged. Finally, universities should encourage university-corporate cooperation
by taking it as an important weighing factor in promotion and award measures.


                                REFERENCES
AUTM. 2004. AUTM US Licensing Survey.
Azagra Caro, J. M., Fernandez de Lucio, I. & Gutierrez Gracia, A. 2003. University
       Patents:Output and Input Indicators…of What?. Research Evaluation, 12(1),
       5-16.
Carlsson, B. & Fridh, A. 2002. Technology transfer in United States universities.
       Journal of Evolutionary Economics, 12, 199-232.
Etzkowitz, H. & Leydesdorff, L. 2000. The Dynamics of Innovation:From National
        Systems and “Mode 2” to a Triple Helix of University-Industry-Government
        Relations. Research Policy, 29(2/3), 109-123.
Griliches, Z. 1990. Patent Statistics as Economic Indicators: A Survey. Journal of
        Economic Literature, 92, 630-653.
Joseph, F. & Jonathan, S. 2003. University Technology Transfer: Do Incentives,
        Management, and Location Matter?. Journal of Technology Transfer, 28(1),
        17-30.
Lee, J. & Win, H. N. 2004. Technology transfer between university research centers
        and industry in Singapore. Technovation, 24(5), 433-442.
Meyer, M., Sinilainen, T. & Utecht, J. T. 2003. Towards Hybrid Triple Helix
        Indicators: A Study of University-Related Patents and a Survey of Academic
        Inventors. Scientometrics, 58(2), 321-350.
National Scinece Council. 2006. Indicators of Science and Technology Republic of
        Chian. National Scinece Council: printer.
Thursby, J. & Thursby, M. C. 2001. Industry Perspectives on Licensing University
        Technologies. Industry & Higher Education, 15(4), 289-294.
Thursby, J. G. & Kemp, S. 2002. Growth and productive efficiency of university
        intellectual property licensing. Research Policy, 31, 109-124.


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ursby, J. G. & Kemp, S. 2002. Growth and productive efficiency of university
        intellectual property licensing. Research Policy, 31, 109-124.


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