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AEA / Nov. 3rd 2011 Successful factors of government supported industry-university collaboration: An empirical study using SEM Young-SooRyu (KISTEP)
Introduction • Literature review and Hypotheses • Methods • Result of Analysis • Discussions • Conclusions Contents
1 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 performancein mutual interactions between organizations.
2 1. Introduction • Questions • - Which factors influence the performance of industry-university collaboration? • - What is the structural causal relationship between these factors?
3 1. Introduction • Research Scope • - First, R&D performance was defined and the variables and hypotheseswere 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.
4 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).
5 2. Literature Review and Hypotheses Factors for the success Table 1 Success factors of industry-university collaboration
6 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). - Networkingis 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).
7 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 networkingin 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
8 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 industry-university collaboration (Jae-wuk Jeon, 1999; Hakanson, 1993; Fontana et al., 2006; Choi and Lee, 2000).
9 2. Literature Review and Hypotheses Relational structure within networking - The communication systemaffects 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 Technology marketing Communication system Partner selection
Communication system H5 Resource-input H8 Collaboration performance H1 H2 H6 H7 H4 Technology marketing H3 Partner selection 10 2. Literature Review and Hypotheses 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.
11 3. Methods Definition of variables Table 2 Definition of variables
12 3. Methods Data collection and measurement method The analysis data was collected through a questionnaire given to professors of 18 universities and to industry personnelwho 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.
13 4. Result of Analysis Analysis of factors and reliability testing Table 3 Result of factor analysis and reliability test
14 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
15 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
16 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
17 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
18 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).
19 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.
20 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.
21 6. Conclusions 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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