Knowledge creation, knowledge impact and knowledge diffusion: how do they connect with higher education?
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DOIhttp://dx.doi.org/10.21511/kpm.07(1).2023.07
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Article InfoVolume 7 2023, Issue #1, pp. 91-103
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Knowledge-based economy causes changes in the higher education system: university graduates must have the ability to constantly learn and improve their skills, generate and disseminate new knowledge, form and multiply the knowledge capital of business. This paper aims to investigate a pairwise interconnection between higher education indicators and sets of parameters characterizing knowledge creation, impact, and diffusion. The following higher education indicators are used: expenditure on education, tertiary enrollment, graduates in science and engineering, tertiary inbound mobility, researcher, gross expenditure on R&D, top 3 global corporate R&D investors, top 3QS university ranking. Knowledge creation indicators are patents by origin, PCT patents by origin, utility models by origin, scientific and technical articles, citable documents, H-index. Knowledge impact is characterized through labor productivity growth, new businesses, software spending, ISO 9001 quality certificates, high-tech manufacturing. Knowledge diffusion parameters include intellectual property receipts, production and export complexity, high-tech exports, ICT services exports. The information base of the study is the data of the Global Innovation Index Report from the World Intellectual Property Organization for 40 European countries (selected depending on the availability of statistics) for 2022, research method – Canonical Correlation Analysis. The strongest positive correlation was found between higher education indicators and knowledge creation parameters. The second position takes connection between higher education indicators and knowledge diffusion parameters, the third – between higher education indicators and knowledge impact indicators. Among the higher education indicators, the most significant were gross expenditure on R&D, top 3 global corporate R&D investors, top 3 QS university ranking.
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JEL Classification (Paper profile tab)I23, O30, D83
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References62
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Tables6
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Figures2
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- Figure 1. Simplified scheme of the canonical correlation model for the first canonical function for U-variables and knowledge creation V-variables
- Figure 2. Simplified scheme of the canonical correlation model for the second canonical function for U-variables and knowledge creation V-variables
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- Table 1. Input data description
- Table 2. Testing U-variables for normal distribution
- Table 3. Determination of raw coefficients and the optimal number of canonical functions for U-variables and knowledge creation of V-variables
- Table 4. Verification of the significance of standardized canonical coefficients for U-variables and knowledge creation of V-variables
- Table 5. Correlation matrices within and between U-variables and knowledge creation V-variables in the form of heatmaps
- Table 6. CCA results for U-variables and knowledge impact and knowledge diffusion V-variables
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