The role of university-business interaction in knowledge system and its effect on growth

  • Linlin Yu

    Student thesis: Doctoral Thesis


    Knowledge is recognised as the driver of productivity and economic growth, and its role is still being developed. The development of growth theory results in the focus of knowledge shifting from knowledge investment to knowledge spillover. In the meantime, links between knowledge actors are the main consideration of the regional innovation system. Among these links, the interaction between University and business is particularly stressed. Another group of studies regarding the University paradigms show that modern Universities have complemented their basic function of teaching and research with knowledge outreach, and this results in the collaboration between modern Universities and local firms. These literatures from different fields form an overlap, which emphasises the role of knowledge spillover through University-industry in innovation. On the other hand, because of the geographical proximity, networks of University-business interaction are usually localised. There are still some areas not covered by the literature. According to these gaps in the literature, there is need of a framework and statistical evidence to identify the effect of University-business interaction in long-term and short-term growth of a region or nation. It also needs to illustrate the role and various University activities in the regional knowledge system, considering the difference in regional knowledge absorptive capacity and University specialty. Therefore, three research objectives are generated with the design of a particular study for each, including the OECD Study, the UK regional Study and the UK University Study. This research is based on the knowledge production function framework, and extends it with the factors regarding to University-business interaction. Model framework of this research is based on the extended production function. This research builds Structural Equation Modelling (SEM) with the utilisation of a quantitative approach and secondary data. There are two main analysis tools chosen in the data analysis. SmartPLS is dealing with Path Analysis and Structural Equation Modelling (SEM) analysis, while SPSS is dealing with the Linear Regression Analysis, Factor Analysis and Cluster Analysis. The results of this research not only support the contribution of University-business interaction to economic growth and technological progress, but also discovers that regional variety (in knowledge absorption capacity) and University variety (in speciality), matters to knowledge commercialisation. Accordingly, appropriate regional policy incentives are suggested to promote the networks of University-business interaction, taking into account those varieties between regions and Universities. This research contributes to the knowledge by defining a framework example of knowledge measurement by combining two types of knowledge, and three stages of knowledge, with a dynamic point of view. It develops the knowledge spillover theory and Triple Helix Model with not only proving dimensions of University-business interaction is the engine of regional growth, but also clarifying the relationships of Universities, and different nodes in the knowledge system. This research contributes to practice by recommending three policy directions to focus on: the University-business interaction whilst considering its long term effect and short term effect; University specialty including elite paradigm and outreach paradigm; and regional variety in knowledge absorption. This research also contributes to methodology with aspects in research design, analysis techniques, and statistical tools, since this research is designed with three layers of structural level studies with multi-objective tasks. This allows the studies to switch from the linear perspective to the network perspective. There are some limitations in each part of the study, mainly from the finding application, generalisation and data availability. Further research possibilities could choose target nations with similar knowledge infrastructure and systems to investigate. It could also consider applying a framework with more specific indicators of knowledge transfer. For the regional scale, further research could consider giving more details to possible activities and University types, when the data is available. It could also look at those regions with a similar capacity of knowledge absorption to analyse, to give a more accurate result.
    Date of Award10 Jun 2016
    Original languageEnglish
    SupervisorDavid Pickernell (Supervisor) & Rami Djebarni (Supervisor)


    • innovation systems
    • Knowledge Based Economy
    • Research-Industry interaction
    • Outreach University

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