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dc.contributor.authorBaboshkin, Pavel
dc.contributor.authorYegina, Natalya A.
dc.contributor.authorZemskova, E. S.
dc.contributor.authorStepanova, Diana I.
dc.contributor.authorYüksel, Serhat
dc.date.accessioned2021-05-24T06:39:22Z
dc.date.available2021-05-24T06:39:22Z
dc.date.issued2021en_US
dc.identifier.citationBaboshkin, P., Yegina, N. A., Zemskova, E. S., Stepanova, D. I. ve Yüksel, S. (2021). Non-classical approach to identifying groups of countries based on open innovation indicators. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 1-27. https://dx.doi.org/10.3390/joitmc7010077en_US
dc.identifier.issn2199-8531
dc.identifier.urihttps://dx.doi.org/10.3390/joitmc7010077
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6888
dc.description.abstractThis article aims to highlight various methods and approaches to grouping countries, ac-cording to the behavior of their open innovation indicators. GDP, inflation and unemployment are the most important indicators of the economic and social policies of states, allowing them to be evaluated and models built. To find the relationships between open innovation indicators the paper uses marginal analysis and feature reduction, as well as machine learning methods (shift to the mean, agglomerative clustering and random forest methods). The results showed that, after isolat-ing all groups, the importance of the signs was established and the patterns of behavior of indicators for each group were compared and open innovation dynamics was analyzed. The conclusions showed that it is obvious that increasing the number of variables in the model and using more ex-tensive indicators can greatly increase the accuracy, in contrast to the generally accepted simple classifications. This approach makes it possible to more accurately find the connections between sectors of the economy or between state economies in general. An accompanying result of the study was the clarification of the equality of open innovation indicators for the analysis of their interrela-tionships between countries.en_US
dc.language.isoengen_US
dc.publisherMDPI AGen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectOpen Innovation Dynamicsen_US
dc.subjectGDPen_US
dc.subjectInflationen_US
dc.subjectUnemploymenten_US
dc.subjectClustering Algorithmsen_US
dc.subjectRandom Foresten_US
dc.subjectCountry Classificationen_US
dc.titleNon-classical approach to identifying groups of countries based on open innovation indicatorsen_US
dc.typearticleen_US
dc.relation.journalJournal of Open Innovation: Technology, Market, and Complexityen_US
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Uluslararası Ticaret ve Finansman Bölümüen_US
dc.authorid0000-0002-9858-1266en_US
dc.identifier.volume7en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.endpage27en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3390/joitmc7010077en_US


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