Next Article in Journal
Telepresence and Interactivity in Mobile Learning System: Its Relation with Open Innovation
Next Article in Special Issue
Mathematical Modeling for Financial Analysis of an Enterprise: Motivating of Not Open Innovation
Previous Article in Journal
Factors Affecting Customer Satisfaction and Loyalty in Online Food Delivery Service during the COVID-19 Pandemic: Its Relation with Open Innovation
Previous Article in Special Issue
Central Banks Digital Currency: Detection of Optimal Countries for the Implementation of a CBDC and the Implication for Payment Industry Open Innovation
Article

Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators

1
Financial Faculty, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
2
Department of Economics, Ogarev Mordovia State University, Saransk 430005, Russia
3
Department of Finance and Prices, Plekhanov Russian University of Economics, Moscow 117997, Russia
4
School of Business, Istanbul Medipol University, Istanbul 34083, Turkey
*
Author to whom correspondence should be addressed.
J. Open Innov. Technol. Mark. Complex. 2021, 7(1), 77; https://doi.org/10.3390/joitmc7010077
Received: 28 January 2021 / Revised: 19 February 2021 / Accepted: 22 February 2021 / Published: 27 February 2021
(This article belongs to the Special Issue Financial Open Innovations for Sustainable Economic Growth)
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. View Full-Text
Keywords: open innovation dynamics; GDP; inflation; unemployment; clustering algorithms; random forest; country classification open innovation dynamics; GDP; inflation; unemployment; clustering algorithms; random forest; country classification
Show Figures

Figure 1

MDPI and ACS Style

Baboshkin, P.; Yegina, N.; Zemskova, E.; Stepanova, D.; Yuksel, S. Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators. J. Open Innov. Technol. Mark. Complex. 2021, 7, 77. https://doi.org/10.3390/joitmc7010077

AMA Style

Baboshkin P, Yegina N, Zemskova E, Stepanova D, Yuksel S. Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(1):77. https://doi.org/10.3390/joitmc7010077

Chicago/Turabian Style

Baboshkin, Pavel, Natalia Yegina, Elena Zemskova, Diana Stepanova, and Serhat Yuksel. 2021. "Non-Classical Approach to Identifying Groups of Countries Based on Open Innovation Indicators" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1: 77. https://doi.org/10.3390/joitmc7010077

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop