Successful factors of government supported

Successful factors of government supportedindustry-university collaboration: An empirical study using SEM

Young-Soo Ryu (KISTEP)

AEA / Nov. 3rd 2011

1. Introduction

2. Literature review and Hypotheses

3. Methods

4. Result of Analysis

5. Discussions

6. Conclusions

Contents

1. Introduction 1

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.

1. Introduction 2

Questions

- Which factors influence the performance of industry-university collaboration?

- What is the structural causal relationship between these factors?

1. Introduction 3

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.

2. Literature Review and Hypotheses 4

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).


Factors for the success

Type Success factors Literature

Resource-inputPhysical and human resources Jae-wuk Jeon(1999), Jung-hae Seo(2000), Schermerhorn(1975), Baker et al.(1983),

López-Martínez et al.(1994), Siegel et al.(2003)

Will of top management executives Jae-wuk Jeon(1999), Jung-hae Seo(2000), Hyun-bong Yang and Ji-seung Hong(2007), Hyun-hwang Lee (2008), Quinn(1979), Pinto and Slevin(1989)

Communication system

Communication and systemGoldhor 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)

Establishment of networks Jong-moo Park et al.(2000), Jung-hae Seo(2000), Jong-hwa Park and Chang-soo Kim(2001), Hyun-hwang Lee(2008)

Education and training 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)

Technology marketing

Provision of technical information Riesenberger(1998), Athaide et al.(1996)

Active development Jae-wuk Jeon(1999)

Partner selection

Partner development Jae-wuk Jeon(1999), Hakanson(1993), Fontana et al.(2006), Choi and Lee(2000)

Possessed technology Hyun-hwang Lee(2008)

Collaboration will Sugandhavanija et al.(2011)

Table 1 Success factors of industry-university collaboration


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).


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.

Figure 1 Relational structure of collaboration performance, resource-input and networking

Networking(Interactive relationship of

the organization)

Resource-input Collaboration performance


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 industry-university collaboration (Jae-wuk Jeon, 1999; Hakanson, 1993; Fontana et al., 2006; Choi and Lee, 2000).


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.

Figure 2 Relational structure within networking


Communication system Partner selection


Establishment of hypotheses

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.

H5Communication system

Partner selection

Collaboration performance


Resource-input

H1 H2 H6

H3

H7

H4

H8

3. Methods 11

Definition of variables

Type Operational definition Measurement variable Measurement methodCommunication system

Degree of establishing communication systems

Establishment of networks between industry and university Cooperation of technology-transfer associated organizations Technology-transfer education and training Compensation for technology providers Compensation for technology-transfer experts

Questionnaire(Likert 7 point scale)


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 absorb knowledge

Finding of commercialization technology 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


Degree of satisfaction on the government supported industry-university collaboration program

Level of satisfaction in using the program Intention to participate again Level of expected performance achievement

Table 2 Definition of variables

3. Methods 12

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 ← neutral → positive ), and SPSS 19.0 and ① ④ ⑦ AMOS 19.0 were used for empirical analysis.

4. Result of Analysis 13

Analysis of factors and reliability testing

Factor Item Average Standard deviation

Commonality Factor 1 Factor 2 Factor 3 Factor 4 Cronbach'

α

Communication system

Establishment of networks between industry and university 3.90 1.163 0.688 0.62 0.43 0.04 0.34 0.864

Cooperation of technology-transfer associated organizations 3.99 1.079 0.739 0.78 0.14 0.33 0.02

Technology-transfer education and training 3.62 1.195 0.733 0.63 0.30 0.05 0.49 Compensation for technology providers 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

Partner selection Finding of commercialization technology 3.49 1.330 0.858 0.14 0.90 0.16 0.08 0.855

Finding of companies that use university technology 3.99 1.534 0.827 0.11 0.80 0.19 0.37

Resource -input

Cost of technology transfer programs 4.27 1.424 0.730 0.45 0.33 0.62 0.18 0.785 TLO specialists 4.03 1.263 0.811 0.27 0.54 0.67 -0.04 Will of the top manager 3.61 1.273 0.815 0.09 0.04 0.81 0.40


Provision of technology information 3.79 1.164 0.738 0.46 0.40 0.13 0.59 0.705

Follow-up development for demand match 3.63 1.201 0.789 0.20 0.14 0.36 0.77

Distribution (%) 　 26.243 19.849 15.706 14.496 Cumulative distribution (%) 　 26.243 46.092 61.798 76.294 KMO / BartlettSignificance level

KMO = 0.878, Bartlett = 783.394Significance level = 0.000 0.907

Table 3 Result of factor analysis and reliability test


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 ← Latent variable Unstandar-dized

Standar- dized

Standard deviation t value p value

X1. Establishment of networks between university and industry ← Communication system 1.174 0.765 0.189 6.223 0.000X3. Technology-transfer education and training ← Communication system 1.052 0.691 0.181 5.825 0.000X4. Compensation for technology providers ← Communication system 1.224 0.763 0.197 6.215 0.000X5. Compensation for technology-transfer experts ← Communication system 1.398 0.756 0.226 6.178 0.000X2. Cooperation of technology-transfer associated organizations ← Communication system 1.000 0.582X8. Finding of commercialization technology ← Partner selection 1.097 0.827 0.118 9.263 0.000X9. Finding of companies that use university technology ← Partner selection 1.000 0.775X10. Cost of technology transfer programs ← Resource-input 1.123 0.666 0.163 6.887 0.000X11. TLO specialists ← Resource-input 1.272 0.853 0.146 8.731 0.000X12. Will of the top manager ← Resource-input 1.000 0.749X7. Follow-up development for demand match ← Technology marketing 0.842 0.667 0.114 7.382 0.000X6. Provision of technology information ← Technology marketing 1.000 0.816Y1. Level of satisfaction of using the program ← Collaboration performance 1.012 0.827 0.096 10.566 0.000Y2. Intention to participate again ← Collaboration performance 0.912 0.776 0.094 9.665 0.000Y3. Level of expected performance achievement ← Collaboration performance 1.000 0.865


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 Unstandardized path coefficient

Standardized path coefficient

Standard deviation t-value p-value Adoption

Hypothesis 1 Communication system → Technology marketing 0.817 0.661 0.183 4.460 0.000 Adopted

Hypothesis 2 Communication system → Partner selection -0.558 -0.438 0.491 -1.136 0.256 Not adopted

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 Adopted


Findings

Figure 4 The results of analysis model

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


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).

TypeAnalysis model

Resource-input Communication system

Technology marketing Partner selection

Communication system Direct effect 0.735 Indirect effect - Total effect 0.735

Technology marketing Direct effect 0.296 0.661 Indirect effect 0.486 - Total effect 0.782 0.661

Partner selection Direct effect 0.233 -0.438 0.929 Indirect effect 0.405 0.614 - Total effect 0.638 0.177 0.929


Direct effect 0.543 - - 0.372 Indirect effect 0.237 0.066 0.346 - Total effect 0.780 0.066 0.346 0.372

Table 5 Effects of latent variables

5. Discussions 18

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).

5. Discussions 19

- 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.

6. Conclusions 20

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.

6. Conclusions 21

Research limit and future works

This study empirically investigated the structural causal relations between the resource-input 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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Successful factors of government supported

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