IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v15y2024i4d10.1007_s13132-024-01958-1.html
   My bibliography  Save this article

Data Mining Technology of Country’s Digital Development Level Assessing for Economic Development and Sustainable Growth: Multivariate Adaptive Regression Spline

Author

Listed:
  • Lyeonov Serhiy

    (Sumy State University
    Silesian University of Technology)

  • Yuriy Bilan

    (Czech University of Life Sciences Prague)

  • Koibichuk Vitaliia

    (Sumy State University)

  • Malyarets Lyudmyla

    (Simon Kuznets Kharkiv National University of Economics)

  • Ashfaq Ahmad

    (King Saud University)

  • Carmen Gabriela Secară

    (Național University of Science and Technology POLITEHNICA București, Pitești Universitary Centre, Faculty of Economics Science and Law, str. Târgu din Vale, no. 1, 110040)

Abstract

The article deals with an optimal multivariate adaptive regression spline that describes the dependence of the countries’ digital development on the national cyber security index and accessible business. It is based on predictive methods of intelligent data analysis. The objects of the study are 103 countries. The statistical data of the investigated indicators of these countries were used for 2021 according to the reports of the e-Governance Academy and the World Bank Doing Business Group. The originality of the research lies in the multivariate adaptive regression spline of countries’ digital development level developed by the authors. The research is implemented within the scope of execution of four stages. A multiple regression model was developed to compare the results of the classical regression model and the results obtained by the MARS model, characterizing the influence of the national cyber security index and ease of doing business on digital development. Its development was implemented in the third stage. Both independent variables positively and directly affect the dependent indicator. At the same time, the influence of the “ease of doing business” index is four times greater than the national cyber security index, caused by the mass use of digital technologies in all business areas and the digital economy, and that is a stimulating factor of economic development. The absence of multicollinearity in the predictor variables was proven using the variance inflation factor test, and the statistical significance of the model — using the Student’s test, coefficient of determination, standard error of estimate, mean absolute error, Fisher’s test, and ANOVA. The MARS model was developed using the Salford Predictive Modeler software. The quality of the model is based on the generalized cross-validation criterion. The obtained results made it possible to comprehensively determine the influence of the cyber security national index and ease of doing business on the country’s digital development under study and can be used by analytical departments of socio-economic objects (banks, financial institutions), national cyber police bodies, national cyber security coordination centers for economic development and sustainable growth.

Suggested Citation

  • Lyeonov Serhiy & Yuriy Bilan & Koibichuk Vitaliia & Malyarets Lyudmyla & Ashfaq Ahmad & Carmen Gabriela Secară, 2024. "Data Mining Technology of Country’s Digital Development Level Assessing for Economic Development and Sustainable Growth: Multivariate Adaptive Regression Spline," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 19866-19886, December.
    • Handle: RePEc:spr:jknowl:v:15:y:2024:i:4:d:10.1007_s13132-024-01958-1
    DOI: 10.1007/s13132-024-01958-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-01958-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13132-024-01958-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mihai Andronie & George Lãzãroiu & Roxana ?tefãnescu & Lumini?a Ionescu & Mãdãlina Coco?atu, 2021. "Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps," Oeconomia Copernicana, Institute of Economic Research, vol. 12(4), pages 1033-1062, December.
    2. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Annals of Operations Research, Springer, vol. 281(1), pages 27-64, October.
    3. Haili Zhang & Guohua Zou, 2020. "Cross-Validation Model Averaging for Generalized Functional Linear Model," Econometrics, MDPI, vol. 8(1), pages 1-35, February.
    4. Yuichi Takano & Ryuhei Miyashiro, 2020. "Best subset selection via cross-validation criterion," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 475-488, July.
    5. Liping Wu & Kai Hu & Oleksii Lyulyov & Tetyana Pimonenko & Ishfaq Hamid, 2022. "The Impact of Government Subsidies on Technological Innovation in Agribusiness: The Case for China," Sustainability, MDPI, vol. 14(21), pages 1-15, October.
    6. Ebenezer Boateng & Emmanuel Asafo-Adjei & John Gartchie Gatsi & ªtefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2022. "Multifrequency-based non-linear approach to analyzing implied volatility transmission across global financial markets," Oeconomia Copernicana, Institute of Economic Research, vol. 13(3), pages 699-743, September.
    7. Aleksandra Kuzior & Paulina Brożek & Olha Kuzmenko & Hanna Yarovenko & Tetyana Vasilyeva, 2022. "Countering Cybercrime Risks in Financial Institutions: Forecasting Information Trends," JRFM, MDPI, vol. 15(12), pages 1-22, December.
    8. Gurbanov Nijat & Yagublu Nargiz & Akbarli Narmin & Niftiyev Ibrahim, 2022. "Digitalization and the Covid-19-Led Public Crisis Management: An Evaluation of Financial Sustainability in the Azerbaijan Business Sector," SocioEconomic Challenges (SEC), Sciendo, vol. 6(3), pages 23-38, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Serrano, Breno & Minner, Stefan & Schiffer, Maximilian & Vidal, Thibaut, 2024. "Bilevel optimization for feature selection in the data-driven newsvendor problem," European Journal of Operational Research, Elsevier, vol. 315(2), pages 703-714.
    2. Svenja Damberg & Ulla A. Saari & Morgane Fritz & Vytaute Dlugoborskyte & Katerina Božič, 2024. "Consumers' purchase behavior of Cradle to Cradle Certified® products—The role of trust and supply chain transparency," Business Strategy and the Environment, Wiley Blackwell, vol. 33(8), pages 8280-8299, December.
    3. Shafiqul Islam & Mohammad Fakhrul Islam & Noor-E- Zannat, 2023. "Behavioral Intention to Use Online for Shopping in Bangladesh: A Technology Acceptance Model Analysis," SAGE Open, , vol. 13(3), pages 21582440231, September.
    4. P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020. "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 226-261, August.
    5. Balcerzak, Adam P. & Zinecker, Marek & Skalický, Roman & Rogalska, Elżbieta & Doubravský, Karel, 2023. "Technology-oriented start-ups and valuation: A novel approach based on specific contract terms," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    6. Andrés Gómez & Oleg A. Prokopyev, 2021. "A Mixed-Integer Fractional Optimization Approach to Best Subset Selection," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 551-565, May.
    7. Pop, Rebeka-Anna & Hlédik, Erika & Dabija, Dan-Cristian, 2023. "Predicting consumers' purchase intention through fast fashion mobile apps: The mediating role of attitude and the moderating role of COVID-19," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    8. Zhangchao & Wei Chien Ng & Yu Qing Soong, 2024. "Impact of Social Media Influencers on Consumers’ Well-Being and Purchase Intention. A Case Study on Douyin Platform," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(14), pages 355-370, December.
    9. Aaron Kinyu Hoshide, 2023. "Sustainable Development Agricultural Economics and Policy: Intensification versus Diversification," Sustainability, MDPI, vol. 15(12), pages 1-4, June.
    10. Tomokaze Shiratori & Ken Kobayashi & Yuichi Takano, 2020. "Prediction of hierarchical time series using structured regularization and its application to artificial neural networks," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-23, November.
    11. Yangyang Zhao & Bojun Gu & Xin Xu & Dingding Yang, 2025. "The Role of Green Patents in Innovation: An fsQCA Study of Chinese Listed Agricultural Enterprises," Sustainability, MDPI, vol. 17(5), pages 1-14, March.
    12. Halima Habuba Mohamed & Yongjun Li & Ataullah Kiani & Usman Ghani, 2024. "Raving Fans or Silent Critics? Brand Service Recovery’s Impact on Positive eWOM," SAGE Open, , vol. 14(1), pages 21582440231, January.
    13. Raluca Georgiana Robu & Ana-Maria Holobiuc & Alina Petronela Alexoaei & Valentin Cojanu & Dumitru Miron, 2023. "Regional Patterns of Pesticide Consumption Determinants in the European Union," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    14. Marek Vochozka & Jaromir Vrbka & Petr Suler, 2020. "Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    15. Stanca, Liana & Dabija, Dan-Cristian & Câmpian, Veronica, 2023. "Qualitative analysis of customer behavior in the retail industry during the COVID-19 pandemic: A word-cloud and sentiment analysis approach," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    16. Bo Yao & Fangbin Qiao, 2023. "The Heterogeneous Effects of Central and Local Subsidies on Firms’ Innovation," Sustainability, MDPI, vol. 15(2), pages 1-15, January.
    17. Dariusz Krawczyk & Viktoriya Martynets & Yuliia Opanasiuk & Ihor Rekunenko, 2023. "Socio-Economic Development of European Countries in Times of Crisis: Ups and Downs," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
    18. Oana-Alexandra SARCEA (MANEA), 2024. "AI & Cybersecurity-connection, impacts, way ahead," International Conference on Machine Intelligence & Security for Smart Cities (TRUST) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 1, pages 17-26, July.
    19. Foued Saâdaoui & Monjia Khalfi, 2024. "Revisiting Islamic banking efficiency using multivariate adaptive regression splines," Annals of Operations Research, Springer, vol. 334(1), pages 287-315, March.
    20. Hiroki Saishu & Kota Kudo & Yuichi Takano, 2024. "Cutting-plane algorithm for estimation of sparse Cox proportional hazards models," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 57-82, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jknowl:v:15:y:2024:i:4:d:10.1007_s13132-024-01958-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.