Transcript The resource

AEA / Nov. 3rd 2011
Successful factors of government supported
industry-university collaboration: An empirical study using
SEM
Young-Soo Ryu (KISTEP)
Contents
1.
Introduction
2.
Literature review and Hypotheses
3.
Methods
4.
Result of Analysis
5.
Discussions
6.
Conclusions
1. Introduction
Purpose
- It is a general perspective to be skeptical whether the government-led industry-university
collaboration is yielding intended outcomes as a strategy to reinforce national
competitiveness (Hyung-deuk Hong, 2003).
- The focus of this study is on the kinds of activities required for the success of
government supported industry-university collaboration.
- How networking and resource-input influence the collaboration performance in
mutual interactions between organizations.
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1. Introduction
Questions
- Which factors influence the performance of industry-university collaboration?
- What is the structural causal relationship between these factors?
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1. Introduction
Research Scope
- First, R&D performance was defined and the variables and hypotheses were framed
through literature reviews.
- Second, the analysis of the influential factors was conducted on the variables to provide
the result.
- And last, it reached the conclusions were reached through the discussions on the
empirical findings in order to present this study’s implications.
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2. Literature Review and Hypotheses
Factors for the success
Specific resources and management ability of the organization is the major success
factors of industry-university collaboration on the resource-based view.
- The role of networking is emphasized as a necessary tool for an organization’s
management ability.
- The organization’s interactive relationship is (found to be) the major factor in
continuing the industry-university collaboration (Geisler, 1995).
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2. Literature Review and Hypotheses
Factors for the success
Table 1 Success factors of industry-university collaboration
Type
Success factors
Physical and human resources
Resource-input
Jae-wuk Jeon(1999), Jung-hae Seo(2000), Schermerhorn(1975), Baker et al.(1983),
López-Martínez et al.(1994), Siegel et al.(2003)
Jeon(1999), Jung-hae Seo(2000), Hyun-bong Yang and Ji-seung
Will of top management executives Jae-wuk
Hong(2007), Hyun-hwang Lee (2008), Quinn(1979), Pinto and Slevin(1989)
Communication and system
Communication Establishment of networks
system
Education and training
Technology
marketing
Literature
Goldhor and Lund(1983), Ghoshal and Barlett(1988), Pinto and Slevin(1989),
Geisler(1995), Rebentisch and Ferretti(1995), Barnes et al.(2002), Stock and
Tatikonda(2000)
Jong-moo Park et al.(2000), Jung-hae Seo(2000), Jong-hwa Park and Chang-soo
Kim(2001), Hyun-hwang Lee(2008)
Jung-hae Seo(2000), Siegal et al.(2003), Guan et al.(2006)
Motivation
Hyung-deuk Hong(2003), Goldhor and Lund(1983), Siegal et al.(2003), Friedman
and Silberman(2003), Guan et al.(2006)
Provision of technical information
Riesenberger(1998), Athaide et al.(1996)
Active development
Jae-wuk Jeon(1999)
Partner development
Jae-wuk Jeon(1999), Hakanson(1993), Fontana et al.(2006), Choi and Lee(2000)
Partner selection Possessed technology
Collaboration will
Hyun-hwang Lee(2008)
Sugandhavanija et al.(2011)
2. Literature Review and Hypotheses
Relationship structure among performance, resource-input and networking
- Useful resource-input can bring strategies and operations that increase the organizations’
effectiveness and efficiency (Barney, 1991; Watjatrakul, 2005).
- Networking is also influenced by the resource-input since the organization’s strategies
and activities are dependent on its resource characteristics (López-Martínez et al., 1994;
Siegel et al., 2003).
- To expand the organization’s limited resources, the establishment of networks and
activities is required to reinforce the organizations’ competitiveness. (Hagedoom, 1996).
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2. Literature Review and Hypotheses
Relationship structure among performance, resource input and networking
- The resource-input and networking have on influence on the collaboration performance
directly.
- The resources invested for industry-university collaboration are linked to the influence of
the collaboration performance via networking in the organization’s mutual interactions.
Networking
(Interactive relationship of
the organization)
Resource-input
Collaboration performance
Figure 1 Relational structure of collaboration performance, resource-input and networking
2. Literature Review and Hypotheses
Relational structure within networking
- The communication system becomes the basis for mutual exchanges and contacts.
- Providing information and technology marketing are important processes for the
university [supplier]-industry [demander] relationship in order to make agreements
(Riesenberger, 1998).
- Selecting the right partner is a crucial factor influencing the performance of industryuniversity collaboration (Jae-wuk Jeon, 1999; Hakanson, 1993; Fontana et al., 2006; Choi
and Lee, 2000).
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2. Literature Review and Hypotheses
Relational structure within networking
- The communication system affects the technology marketing and partner selection.
- As a process for mutual interactions between partners, the technology marketing
influences the partner selection from which the industry-university collaboration
performance is attained.
Technology marketing
Partner selection
Communication system
Figure 2 Relational structure within networking
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2. Literature Review and Hypotheses
Establishment of hypotheses
H5
Communication
system
H1
Technology
marketing
Resource-input
H2
H6
H3
H8
Collaboration
performance
H7
H4
Partner
selection
Figure 3 Analysis model
H 1: The communication system has a positive (+) influence on technology marketing.
H 2: The communication system has a positive (+) influence on partner selection.
H 3: Technology marketing has a positive (+) influence on partner selection.
H 4: Partner selection has a positive (+) influence on the industry-university collaboration performance.
H 5: The resource-input has a positive (+) influence on the communication system.
H 6: The resource-input has a positive (+) influence on technology marketing.
H 7: The resource-input has a positive (+) influence on partner selection.
H 8: The resource-input has a positive (+) influence on the industry-university collaboration performance.
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3. Methods
Definition of variables
Table 2 Definition of variables
Type
Operational definition
Measurement variable
Measurement method
Communication Degree of establishing
system
communication systems
 Establishment of networks between industry and university Questionnaire
 Cooperation of technology-transfer associated organizations (Likert 7 point scale)
 Technology-transfer education and training
 Compensation for technology providers
 Compensation for technology-transfer experts
Technology
marketing
Degree of expansion and
adjustment of technology
information for demand match
 Provision of technology information
 Follow-up development for demand match
Partner
selection
Degree of finding partners that can  Finding of commercialization technology
absorb knowledge
 Finding of companies that use university technology
Resource-input Degree of sufficiency in human
resources and physical resources
 Cost of technology transfer programs
 TLO specialists
 Will of the top manager
Collaboration
performance
 Level of satisfaction in using the program
 Intention to participate again
 Level of expected performance achievement
Degree of satisfaction on the
government supported industryuniversity collaboration program
3. Methods
Data collection and measurement method
The analysis data was collected through a questionnaire given to professors of 18
universities and to industry personnel who have technology-transfer experience using the
Technology Licensing Organization supported by Connect Korea Program.
- The questionnaire was conducted from April 9th to 17th in 2009.
- List of the participants were set at 927 people, of which only 122 forms were returned.
Finally the 117 questionnaires were utilized for analysis.
Likert 7 point scale (negative ① ← neutral ④ → positive ⑦), and SPSS 19.0 and AMOS
19.0 were used for empirical analysis.
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4. Result of Analysis
Analysis of factors and reliability testing
Table 3 Result of factor analysis and reliability test
Factor
Item
 Establishment of networks between industry and
university
 Cooperation of technology-transfer associated
organizations
Communication
 Technology-transfer education and training
system
 Compensation for technology providers
Partner selection
3.90
1.163
0.688
0.62
0.43
0.04
0.34
3.99
1.079
0.739
0.78
0.14
0.33
0.02
3.62
1.195
0.733
0.63
0.30
0.05
0.49
3.95
1.364
0.726
0.84
-0.05
0.07
0.13
 Compensation for technology-transfer experts
3.59
1.205
0.701
0.70
0.23
0.29
0.27
 Finding of commercialization technology
3.49
1.330
0.858
0.14
0.90
0.16
0.08
 Finding of companies that use university technology
3.99
1.534
0.827
0.11
0.80
0.19
0.37
 Cost of technology transfer programs
4.27
1.424
0.730
0.45
0.33
0.62
0.18
4.03
3.61
1.263
1.273
0.811
0.815
0.27
0.09
0.54
0.04
0.67
0.81
-0.04
0.40
3.79
1.164
0.738
0.46
0.40
0.13
0.59
3.63
1.201
0.789
0.20
0.14
0.36
0.77
Resource -input  TLO specialists
Technology
marketing
Common Factor 1 Factor 2 Factor 3 Factor 4 Cronbach'
Average Standard
deviation ality
α
 Will of the top manager
 Provision of technology information
 Follow-up development for demand match
Distribution (%)
Cumulative distribution (%)
KMO / Bartlett
Significance level
26.243 19.849
15.706 14.496
26.243 46.092
61.798 76.294
KMO = 0.878, Bartlett = 783.394
Significance level = 0.000
0.864
0.855
0.785
0.705
0.907
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4. Result of Analysis
Analysis of factors and reliability testing
The value of Chai-square(χ2) 211.856 (df=80), p value 0.000, NFI (normed for index) 0.716, IFI
(incremental fit index) 0.877, CFI (comparative fit index) 0.874, and RMSEA (root mean square error of approximation)
0.119.
Table 4 Results of significance testing between latent variables and observational variables
Observational variable
X1. Establishment of networks between university and industry
X3. Technology-transfer education and training
X4. Compensation for technology providers
X5. Compensation for technology-transfer experts
X2. Cooperation of technology-transfer associated organizations
X8. Finding of commercialization technology
X9. Finding of companies that use university technology
X10. Cost of technology transfer programs
X11. TLO specialists
X12. Will of the top manager
X7. Follow-up development for demand match
X6. Provision of technology information
Y1. Level of satisfaction of using the program
Y2. Intention to participate again
Y3. Level of expected performance achievement
Unstandar Standar- Standard
-dized
dized
deviation
← Communication system
1.174
0.765
0.189
← Communication system
1.052
0.691
0.181
← Communication system
1.224
0.763
0.197
← Communication system
1.398
0.756
0.226
← Communication system
1.000
0.582
← Partner selection
1.097
0.827
0.118
← Partner selection
1.000
0.775
← Resource-input
1.123
0.666
0.163
← Resource-input
1.272
0.853
0.146
← Resource-input
1.000
0.749
← Technology marketing
0.842
0.667
0.114
← Technology marketing
1.000
0.816
← Collaboration performance
1.012
0.827
0.096
← Collaboration performance
0.912
0.776
0.094
← Collaboration performance
1.000
0.865
←
Latent variable
t value
p value
6.223
5.825
6.215
6.178
0.000
0.000
0.000
0.000
9.263
0.000
6.887
8.731
0.000
0.000
7.382
0.000
10.566
9.665
0.000
0.000
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4. Result of Analysis
Findings
The value of Chai-square(χ2) 171.855 (df=82), p value 0.000, NFI 0.850, IFI 0.916, CFI
0.913, and RMSEA 0.096.
Figure 4 Result of factor analysis and reliability testing
Hypothesis
Path
Hypothesis 1 Communication system → Technology marketing
Unstandardized Standardized
path coefficient path coefficient
Standard
deviation
t-value
p-value
0.000
Adoption
0.817
0.661
0.183
4.460
Adopted
Hypothesis 2 Communication system → Partner selection
-0.558
-0.438
0.491
-1.136
Hypothesis 3 Technology marketing → Partner selection
0.959
0.929
0.531
1.805
0.071
Adopted
Hypothesis 4 Partner selection → Collaboration performance
0.419
0.372
0.119
3.522
0.000
Adopted
Hypothesis 5 Resource-input → Communication system
0.468
0.735
0.076
6.191
0.000
Adopted
Hypothesis 6 Resource-input → Technology marketing
0.232
0.296
0.108
2.154
0,031
Adopted
Hypothesis 7 Resource-input → Partner selection
0.189
0.233
0.174
1.090
0.276 Not adopted
Hypothesis 8 Resource-input → Collaboration performance
0.497
0.543
0.105
4.714
0.000
0.256 Not adopted
Adopted
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4. Result of Analysis
Findings
Note) 1. The dotted lines represent the path that is discarded from the analysis model.
2. The path coefficient is indicated as the standardized coefficient.
3. *p<.10, **p<.05, ***p<.01
Figure 4 The results of analysis model
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4. Result of Analysis
Findings
The resource-input (0.780) had the highest influence on collaboration performance, next in
order of influence, was partner selection (0.372), the technology marketing (0.346), and the
communication system (0.066).
Table 5 Effects of latent variables
Type
Direct effect
Communication system Indirect effect
Technology marketing
Partner selection
Collaboration
performance
Analysis model
Communication
Technology
system
marketing
Resource-input
Partner selection
0.735
-
Total effect
0.735
Direct effect
0.296
0.661
Indirect effect
0.486
-
Total effect
0.782
0.661
Direct effect
0.233
-0.438
0.929
Indirect effect
0.405
0.614
-
Total effect
Direct effect
Indirect effect
Total effect
0.638
0.543
0.237
0.780
0.177
0.066
0.066
0.929
0.346
0.346
0.372
0.372
5. Discussions
The cause of partial discordance to the hypotheses can be found in the organizations’
characteristics as an external factor.
- As for partner selection, the mutual benefits achieved from industry-university
collaboration plays important roles (Bresson and Amesse, 1991; Dodgson, 1993).
- It is required to recognize that technology development cost can be reduced (Millson et
al., 1992; Littler et al., 1995).
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5. Discussions
- The technology marketing has the possibility of providing collaboration profits for both
parties - industry [demand] and university [supply].
- The fundamental factors of industry-university collaboration such as resource input and
communication system show that they do not provide direct motivations in partner
selection.
- The technology marketing can be considered a critical factor in the process of
choosing the right partners.
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6. Conclusions
Implications
The resource-input is a direct influence factor on the performance of government supported
industry-university collaboration and has a causal relationship that affects the collaboration
via networking.
In the relational structure within the networking, the communication system, technology
marketing and partner selection have successive influences on the collaboration
performance.
In order to increase the performance of government-supported industry-university
collaboration , it is required to actively manage the establishment of networks inside and
outside of the organization.
On the other hand, this research confirms that the technology marketing is a very important
factor for selecting appropriate partners. Universities and industries have to clarify their
collaboration purpose and contents for the mutual profits.
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6. Conclusions
Research limit and future works
This study empirically investigated the structural causal relations between the resourceinput and networking required for the success of government-supported industry-university
collaboration.
The collaboration for only direct technology transfer was chosen to suggest success factors
of the industry-university collaboration and examined the causal relations between each
factor.
Further studies on the empirical analysis of the multilateral factors such as external
environments are expected on the future.
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