Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators
This article aims to highlight various methods and approaches to grouping countries, according 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 model...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-02-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2199-8531/7/1/77 |
| _version_ | 1797423419794391040 |
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| author | Pavel Baboshkin Natalia Yegina Elena Zemskova Diana Stepanova Serhat Yuksel |
| author_facet | Pavel Baboshkin Natalia Yegina Elena Zemskova Diana Stepanova Serhat Yuksel |
| author_sort | Pavel Baboshkin |
| collection | DOAJ |
| description | This article aims to highlight various methods and approaches to grouping countries, according 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 isolating 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 extensive 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 interrelationships between countries. |
| first_indexed | 2024-03-09T07:47:48Z |
| format | Article |
| id | doaj.art-55f5a528c43e45d98374b0db7092ed2e |
| institution | Directory Open Access Journal |
| issn | 2199-8531 |
| language | English |
| last_indexed | 2024-03-09T07:47:48Z |
| publishDate | 2021-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj.art-55f5a528c43e45d98374b0db7092ed2e2023-12-03T02:41:39ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312021-02-017777710.3390/joitmc7010077Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation IndicatorsPavel Baboshkin0Natalia Yegina1Elena Zemskova2Diana Stepanova3Serhat Yuksel4Financial Faculty, Financial University under the Government of the Russian Federation, Moscow 125167, RussiaDepartment of Economics, Ogarev Mordovia State University, Saransk 430005, RussiaDepartment of Economics, Ogarev Mordovia State University, Saransk 430005, RussiaDepartment of Finance and Prices, Plekhanov Russian University of Economics, Moscow 117997, RussiaSchool of Business, Istanbul Medipol University, Istanbul 34083, TurkeyThis article aims to highlight various methods and approaches to grouping countries, according 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 isolating 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 extensive 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 interrelationships between countries.https://www.mdpi.com/2199-8531/7/1/77open innovation dynamicsGDPinflationunemploymentclustering algorithmsrandom forest |
| spellingShingle | Pavel Baboshkin Natalia Yegina Elena Zemskova Diana Stepanova Serhat Yuksel Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators Journal of Open Innovation: Technology, Market and Complexity open innovation dynamics GDP inflation unemployment clustering algorithms random forest |
| title | Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators |
| title_full | Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators |
| title_fullStr | Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators |
| title_full_unstemmed | Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators |
| title_short | Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators |
| title_sort | non classical approach to identifying groups of countries based on open innovation indicators |
| topic | open innovation dynamics GDP inflation unemployment clustering algorithms random forest |
| url | https://www.mdpi.com/2199-8531/7/1/77 |
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