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Quantification of relationships between artificial intelligence adoption in entrepreneurial enterprises and green innovation in European Union countries

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dc.title Quantification of relationships between artificial intelligence adoption in entrepreneurial enterprises and green innovation in European Union countries en
dc.contributor.author Gavurová, Beáta
dc.contributor.author Ribeiro-Navarrete, Samuel
dc.contributor.author Rigelský, Martin
dc.contributor.author Pimenta-Alonso, Andrea Mara
dc.relation.ispartof International Journal of Entrepreneurial Behaviour and Research
dc.identifier.issn 1355-2554 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 1758-6534 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2026
utb.relation.volume 32
utb.relation.issue 11
dc.citation.spage 135
dc.citation.epage 163
dc.type article
dc.language.iso en
dc.publisher Emerald Publishing
dc.identifier.doi 10.1108/IJEBR-06-2025-0836
dc.relation.uri https://www.emerald.com/ijebr/article/32/11/135/1364812/Quantification-of-relationships-between-artificial
dc.relation.uri https://www.emerald.com/ijebr/article-pdf/32/11/135/11583237/ijebr-06-2025-0836en.pdf
dc.subject artificial intelligence en
dc.subject digital transformation en
dc.subject eco-innovation en
dc.subject enterprise size en
dc.subject European Union en
dc.subject regression analysis en
dc.subject cluster analysis en
dc.description.abstract Purpose – The goal of the study was to quantify the relationships between AI adaption in enterprises regarding their size and the green innovation outcomes represented by the eco-innovation index in the European Union countries. Achieving this goal was supported by seven research questions based on the outcomes of available research studies and the defined research gap. Design/methodology/approach – The data for the year 2021 was examined for all the EU countries (n = 27). The seven areas of AI use were processed in enterprises classified by size into small, medium and large entities. The Eco-innovation Index was applied as part of eco-innovation. Regression and cluster analysis (CA) were used for analytical processes. The results of the regression models revealed the existence of significant relationships, but these relationships were closest, especially in large enterprises. Findings – Medium-sized enterprises demonstrated a lower level of interconnection between AI and eco-innovation outputs; the least significant interconnections were found in small enterprises. The results of the CA pointed to the position of the EU countries. It was found that Bulgaria, Poland, Hungary, Romania and Slovakia were among the countries with the lowest outcomes in the field of AI use in the business sector and eco-innovation, and the Nordic countries, such as Denmark, Finland and Sweden, were among the countries with the most positive outcomes. Practical implications – The study outcomes will be significant for policymakers dealing with the digital transformation processes, for regulators and creators of strategic development plans at the regional and national levels. They will also support the creation of international databases for the preparation of comparative analyses, the implementation of complementary qualitative research and experiments revealing the barriers to the adoption of digital transformation technologies and their impact on the eco-innovation outputs of enterprises. Originality/value – The study outcomes will be significant for policymakers dealing with the digital transformation processes, for regulators and creators of strategic development plans at the regional and national levels. en
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1012869
utb.identifier.obdid 43887342
utb.identifier.scopus 2-s2.0-105039398508
utb.identifier.wok 001758938600001
utb.source j-scopus
dc.date.accessioned 2026-10-23T12:00:27Z
dc.date.available 2026-10-23T12:00:27Z
dc.description.sponsorship Vedecka Grantova Agentura MSVVaS SR a SAV [1/0554/24]
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.ou Center for Applied Economic Research
utb.contributor.internalauthor Gavurová, Beáta
utb.fulltext.references 1/0554/24
utb.fulltext.sponsorship Funding: This work was supported by Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (award no. 1/0554/24).

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Attribution 4.0 International Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution 4.0 International