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Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach

Malcolm J. Beynon, Paul Jones Orcid Logo, David Pickernell Orcid Logo

Journal of Business Research, Volume: 159, Start page: 113703

Swansea University Authors: Paul Jones Orcid Logo, David Pickernell Orcid Logo

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DOI (Published version): 10.1016/j.jbusres.2023.113703

Abstract

Innovation is a component of economic development, with an emphasis on entrepreneurship and small business activity. This longitudinal study evaluates innovation readiness drivers across European regions. The following research questions are posed: What are the sets of innovation drivers in Europe?...

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Published in: Journal of Business Research
ISSN: 0148-2963
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62687
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first_indexed 2023-02-19T09:25:52Z
last_indexed 2023-03-09T04:17:16Z
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spelling v2 62687 2023-02-19 Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach 21e2660aaa102fe36fc981880dd9e082 0000-0003-0417-9143 Paul Jones Paul Jones true false 913bd73da00d7df4f5038f6f144b235e 0000-0003-0912-095X David Pickernell David Pickernell true false 2023-02-19 BBU Innovation is a component of economic development, with an emphasis on entrepreneurship and small business activity. This longitudinal study evaluates innovation readiness drivers across European regions. The following research questions are posed: What are the sets of innovation drivers in Europe? What is their relative importance? How do they differ between regions over time? The study uses principal component analysis (PCA) and the constellation graph index approach. Three principal component drivers of innovation are identified: innovation system, absorptive capacity, and IP protection. The aggregation of component details reveals an innovation readiness dimension for each European region in a specific year. Variations in results for different years are discussed. The study covers a large number of countries. Innovation readiness across regions is examined. The results show that constituent innovation driver variables contribute to performance over time. Different patterns are revealed for high and low innovation regions. The relationships between innovation drivers are evaluated. Journal Article Journal of Business Research 159 113703 Elsevier BV 0148-2963 EU-Region level; innovation readiness; longitudinal; principal component analysis; constellation graph index 1 4 2023 2023-04-01 10.1016/j.jbusres.2023.113703 COLLEGE NANME Business COLLEGE CODE BBU Swansea University Not Required 2023-06-05T16:46:36.1378114 2023-02-19T09:10:18.2035578 Faculty of Humanities and Social Sciences School of Management - Business Management Malcolm J. Beynon 1 Paul Jones 0000-0003-0417-9143 2 David Pickernell 0000-0003-0912-095X 3
title Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
spellingShingle Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
Paul Jones
David Pickernell
title_short Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
title_full Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
title_fullStr Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
title_full_unstemmed Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
title_sort Evaluating EU-Region level innovation readiness: A longitudinal analysis using principal component analysis and a constellation graph index approach
author_id_str_mv 21e2660aaa102fe36fc981880dd9e082
913bd73da00d7df4f5038f6f144b235e
author_id_fullname_str_mv 21e2660aaa102fe36fc981880dd9e082_***_Paul Jones
913bd73da00d7df4f5038f6f144b235e_***_David Pickernell
author Paul Jones
David Pickernell
author2 Malcolm J. Beynon
Paul Jones
David Pickernell
format Journal article
container_title Journal of Business Research
container_volume 159
container_start_page 113703
publishDate 2023
institution Swansea University
issn 0148-2963
doi_str_mv 10.1016/j.jbusres.2023.113703
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description Innovation is a component of economic development, with an emphasis on entrepreneurship and small business activity. This longitudinal study evaluates innovation readiness drivers across European regions. The following research questions are posed: What are the sets of innovation drivers in Europe? What is their relative importance? How do they differ between regions over time? The study uses principal component analysis (PCA) and the constellation graph index approach. Three principal component drivers of innovation are identified: innovation system, absorptive capacity, and IP protection. The aggregation of component details reveals an innovation readiness dimension for each European region in a specific year. Variations in results for different years are discussed. The study covers a large number of countries. Innovation readiness across regions is examined. The results show that constituent innovation driver variables contribute to performance over time. Different patterns are revealed for high and low innovation regions. The relationships between innovation drivers are evaluated.
published_date 2023-04-01T16:46:34Z
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score 11.012678

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