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<p>This paper investigates the dynamics of Technology Entrepreneurship (TE) across the EU member-states over a ten-year period (2013–2022). Recognizing the lack of comprehensive quantitative measures for TE, it proposes a novel composite indicator of TE, incorporating specific distinct national-level variables accrued from open panel data sources. The initial step of the methodology initiated in the paper foresees the normalization, weighting, and aggregation of the component indicators, to provide a consistent measurement of TE across countries and time. The next methodological step involves k-means clustering and principal component analysis (PCA) to group countries into three clusters, based on their TE performance. The key research contributions of the paper are the initiation of TE as a distinct measurable indicator and a statistically solid measurement of TE across the 28 EU member-states, revealing significant disparities in structural and dynamic TE capacity. The framework developed in this paper addresses a significant gap in TE quantization and offers a robust, evidence-based tool for policymakers and stakeholders seeking to foster more vibrant technology entrepreneurship ecosystems within the EU member-states.</p>

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Measuring Technology Entrepreneurship at the EU Level

  • 期刊论文

  • 发表日期：

    2025年8月29日

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  • Pavlos Kilintzis,
  • Elpida Samara,
  • Giorgos Avlogiaris,
  • Zoi Kaleli

摘要

This paper investigates the dynamics of Technology Entrepreneurship (TE) across the EU member-states over a ten-year period (2013–2022). Recognizing the lack of comprehensive quantitative measures for TE, it proposes a novel composite indicator of TE, incorporating specific distinct national-level variables accrued from open panel data sources. The initial step of the methodology initiated in the paper foresees the normalization, weighting, and aggregation of the component indicators, to provide a consistent measurement of TE across countries and time. The next methodological step involves k-means clustering and principal component analysis (PCA) to group countries into three clusters, based on their TE performance. The key research contributions of the paper are the initiation of TE as a distinct measurable indicator and a statistically solid measurement of TE across the 28 EU member-states, revealing significant disparities in structural and dynamic TE capacity. The framework developed in this paper addresses a significant gap in TE quantization and offers a robust, evidence-based tool for policymakers and stakeholders seeking to foster more vibrant technology entrepreneurship ecosystems within the EU member-states.