The paper focuses on assessment of the countries' competitiveness dependence on their level of the information infrastructure development. The aim of the research is to elicit existence and degree of a country's competitiveness dependence on the level of information infrastructure development. The method of statistical data processing, namely correlation-regression analysis, was used as a mathematical apparatus. Calculations were carried out with Microsoft Excel's "Data analysis" package. The Global Competitiveness Index of countries (ranking) was chosen as a result factor, with the following sign factors: number of mobile-cellular subscriptions, number of mobile-broadband subscriptions, number of fixed-broadband Internet subscriptions, number of fibre Internet subscriptions, number of Internet users. The regression model obtained reflects the linear dependence of competitiveness on three factors: the number of mobile broadband Internet subscribers, the number of fixed broadband Internet subscribers and the share of Internet users. The 126.44 constant reflects the influence of all other factors not taken into account in the model and, vice versa, negatively affects the ranking of the country. The regression model was tested on the Russian Federation. The model made it possible to determine the forecast level of the global competitiveness index in 2020.
Keywords: Information environmentglobal competitiveness indexmodeldependencyforecast
Digital, or information, infrastructure is a set of organizational structures that ensure functioning and development of a country's information space and means of information communication (Digital Economy of the Russian Federation Program, 2017). The information infrastructure includes information centres, databases and knowledge bases, communication systems, and everything that provides access to information resources for citizens of the country. A country's competitiveness is a set of institutions, policies, and factors that determine the level of productivity measured as the gross domestic product per capita (Lleo, 2018; Kotarski, 2018).
To study the relationship between competitiveness and the level of development of the country's information infrastructure, the following indicators can be taken: as a result factor, the growth rate of the gross domestic product, as indicators of the information infrastructure, indicators included in the calculation of information and communication technology development index (ICT Development Index - IDI) and the Global Innovation Index (Measuring the Information Society Report 2018, 2018; Rankings, 2019).
But in this study, a country's competitiveness is measured by the Global Competitiveness Index 4.0, published annually by the World Economic Forum (Schwab, 2018).
The country's Global Competitiveness Index describes its competitiveness according to 12 indices grouped into subindexes, such as "creating an enabling environment", "markets", "human capital" and "innovation ecosystem".
Each category has its own indicators. The "Enabling environment" subindex includes four subcategories, one of which is "ICT adoption".
"ICT adoption" subcategory includes the following indicators:
number of mobile-cellular subscriptions per 100 pop.;
number of mobile-broadband subscriptions per 100 pop.;
number of fixed-broadband Internet subscriptions per 100 pop.;
number of fibre Internet subscriptions per 100 pop.;
number of Internet users, %.
According to the 2018 WEF report (Schwab, 2018), Russia ranked 43rd place out of 140 in terms of competitiveness. According to the indicators of the "ICT adoption" subcategory, the places were distributed as follows:
number of mobile-cellular subscription – 11;
number of mobile-broadband subscriptions – 51;
number of fixed-broadband Internet subscriptions – 46;
number of fibre Internet subscription – 12;
Internet users – 49%.
In this subcategory, Russia ranked 25th place.
Given the rather high rank of Russia in the "ICT adoption" subcategory, as well as the positive dynamics of this subcategory, a mathematical model is required to predict the country's ranking depending on the category indicators forecast.
Despite the fact that the Global Competitiveness Index is a synthetic indicator based on the ratio of the 140 countries achievements in various areas, including the ICT adoption, we believe it is possible to carry out a correlation and regression analysis and identify those indicators from the subcategory "ICT adoption" that have a greater impact on the competitiveness of countries. Also, we believe it's possible to identify the regression model, which will provide a forecast of the country's competitiveness depending on the achievements in the field of ICT (Chernova, Starostin, Degtereva, & Andronova, 2019; Ivanova, Poltarykhin, Szromnik, & Aniehkina, 2019).
Purpose of the Study
The aim of the research is to elicit existence and degree of a country's competitiveness dependence on the level of information infrastructure development (Abdrahmanova, Vishnevskij, Volkova, & Gohberg, 2018; Annual report 2018-2019, 2019; Dutta, Lanvin, & Wunsch-Vincent, 2018). The choice of goal is due to the need to predict changes in the country's competitiveness under the influence of new information technologies, such as a digital industry platform.
Despite the fact that the Global Competitiveness Index is a synthetic indicator based on the ratio of the 140 countries achievements in various areas, including the ICT adoption, we believe it is possible to carry out a correlation and regression analysis and identify those indicators from the subcategory "ICT adoption" that have a greater impact on the competitiveness of countries. Also, we believe it's possible to identify the regression model, which will provide a forecast of the country's competitiveness depending on the achievements in the field of ICT (Chernova, Starostin, Degtereva, & Andronova, 2019; Ivanova, Poltarykhin, Szromnik, & Aniehkina, 2019; Fathian, Fakheri-Fard, Ouarda, Dinpashoh, & Nadoushani, 2019; Mutrakov, Zajnasheva, & Muxametzyanov, 2018; Tukaeva & Muxametzyanov, 2013).
The Global Competitiveness Index of countries (ranking) was chosen as a result factor (y). All markers under the subcategory "ICT adoption", are accepted as sign factors:
The correlation matrix with the following correlation coefficients is obtained: rx1y=-0,57; rx2y=-0,71; rx3y=-0,84; rx4y=-0,51; rx5y=-0,87. The closest correlation is observed between x2, х3, х5, and y. Factors with correlation coefficients greater than 0.7 per module are left for further analysis. The minus sign means inverse linear dependence. Feedback is explained by the fact that country ratings are accepted as Y. The higher the rating, the lower the number.
The obtained model of multiple linear regression has the following form:
The value of the R-square determination coefficient is 0.82, which indicates that the obtained dependence reflects the observed phenomenon with a sufficient degree of approximation. The significance of the determination coefficient is determined by Fisher's criterion. At the next stage, the significance of regression coefficients aj was checked using the Student's criterion. All factors turned out to be significant, the model did not require improvement.
Thus, the regression equation obtained puts the country's competitiveness in direct dependence on the number of mobile-broadband subscriptions, the number of fixed-broadband Internet subscriptions and the share of Internet users.
The regression equation obtained has the following meaning: the increase in the number of mobile-broadband Internet subscriptions per unit will lead to an increase in the rating by 0.22 points; an increase in the number of fixed-broadband Internet subscriptions per unit will lead to an increase in the rating by 1.28 points; an increase in the share of Internet users by 1% will be followed by an increase in the country ranking by 0.45 points. The constant of 126.44 reflects the influence of all other factors not taken into account in the model and, vice versa negatively affects the ranking of the country. Comparison of the calculated rating of Russia with the actual value allowed to determine the error of the model - 11%.
This model can be used to forecast the impact of changes in state ICT development on its competitiveness. For example, the obtained model allows predicting the change in the global competitiveness index of Russia for the 2020 countries ranking under the influence of only positive changes in the indicators of the "ICT adoption" subcategory, all other conditions being equal. Publicly available information base (Monitoring the development of the information society in the Russian Federation, 2018). Other equal conditions are understood to be the leveling of changes in the ratings of other countries. The forecasted value of the index (rating) of Russia's global competitiveness for 2020 on the basis of the forecasted dynamics of factors in the subcategory "ICT adoption" was 29.
The regression model obtained not only predicts the value of the resulting factor but also determines what ICT adoption efforts should be made to achieve the desired value of the global competitiveness index.
Like any other, this model has a number of limitations. Firstly, it is based on linear regression. Secondly, it does not take into account the achievements of other countries, which is defined above as "all things being equal". Thirdly, the model does not take into account the synergetic effect, which implies that the influence of a combination of factors, and not just those of the subcategory "ICT adoption", can lead to greater positive change.
Directions for further research
The next stage of the research will be the analysis and study of the possibilities of digital economy tools for the development of the Russian agrifood industry in the light of the foreign researchers' developments (Trivelli, Apicella, Chiarello, Rana, Fantoni, & Tarabella, 2019; Iticha & Takele, 2019). This stage includes a critical review of Russian and foreign information systems used at various levels of managing economic processes in agriculture, as well as a review of existing approaches to assessing the level of development of the information infrastructure of the region and the industry. The main research stages and directions are outlined in the authors' works (Musina, Yangirov, Nasyrova, & Haritonov 2019; Musina, Kharitonov, Turganov, & Nizamova, 2019).
The reported research was funded by the Russian Foundation for Basic Research and the government of the Bashkortostan Republic of the Russian Federation according to the research project №19-410-020028 р_а.
- Abdrahmanova, G., Vishnevskij, K., Volkova, G., & Gohberg, L. (2018). Indikatory cifrovoj ekonomiki: 2018. Statisticheskij sbornik.[Digital Economy Indicators: 2018. Statistical Digest]. Moscow: Higher School of Economics. [in Rus.]
- Annual report 2018-2019. (2019). World Economic Forum. Retrieved from https://www.weforum.org/reports/annual-report-2018-2019
- Chernova, V., Starostin, V., Degtereva, E., & Andronova, I. (2019). Study of sector-specific innovation efforts: the case from Russian economy. Entrepreneurship and sustainability issues, 7(1), 540-552. [in Rus.] https://doi.org/10.9770/jesi.2019.7.1(38)
- Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2018). Global Innovation Index 2018. World Intellectual Property Organization. Retrieved from https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2018
- Fathian, F., Fakheri-Fard, A., Ouarda, T. B. M. J., Dinpashoh, Y., & Nadoushani, S. S. M. (2019). Multiple streamflow time series modeling using VAR-MGARCH approach. Stochastic environmental research and risk assessment, 33(2), 407-425. https://doi.org/10.1007/s00477-019-01651-9
- Ivanova, V., Poltarykhin, A., Szromnik, A., & Aniehkina, O. (2019). Economic policy for country's digitalization: a case study. Entrepreneurship and sustainability issues, 7(1), 649-661. https://doi.org/10.9770/jesi.2019.7.1(46)
- Iticha, B., & Takele, C. (2019). Digital soil mapping for site-specific management of soils. Geoderma, 351, 85-91. https://doi.org/10.1016/j.geoderma.2019.05.026
- Kotarski, K. (2018). The Eurozone's crisis conundrum and the role of macroeconomic theory. World review of political economy, 9(4), 477-506. https://doi.org/10.13169/worlrevipoliecon.9.4.0477
- Lleo, S. (2018). Financial and Macroeconomic Connectedness. Quantitative finance, 18(12), 1967-1968. https://doi.org/10.1080/14697688.2018.1533080
- Measuring the Information Society Report 2018. (2018). Internatinal Telecommunications Union. Retrieved from https://www.itu.int/en/ITU-D/Statistics/Pages/publications/misr2018.aspx
- Monitoring razvitiya informacionnogo obshchestva v Rossijskoj Federacii [Monitoring the development of the information society in the Russian Federation] (2018). Retrieved from http://www.rosstat.gov.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/science_and_innovations/it_technology/# [in Rus.].
- Musina, D., Yangirov, A., Nasyrova, S., & Haritonov, S. (2019). Metodologicheskaya baza dlya proektirovaniya regional'noj otraslevoj cifrovoj platformy. [Methodological base for the design of a regional industrial digital platform]. Economics and Management: Scientific and Practical Journal, 4, 40-43. [in Rus.] Retrieved from https://elibrary.ru/download/elibrary_39163009_12920663.pdf
- Musina, D., Kharitonov, S., Turganov, A., & Nizamova, G. (2019). Digital Communication Platform for the Agro Industrial Complex. Advances in Social Science, Education and Humanities Research, 289, 89-92. https://doi.org/10.2991/csis-18.2019.18
- Mutrakov, O., Zajnasheva, Z., & Muxametzyanov, I. (2018). Faktornaya model` razvitiya avtoservisny`x uslug v regione. [Factor model of car service development in the region]. Upravlenie e`konomicheskimi sistemami: e`lektronny`j nauchny`j zhurnal, (12), 52-66. Retrieved from https://elibrary.ru/download/elibrary_36999878_19355904.pdf [in Rus.]
- Programma «Cifrovaya ekonomika Rossijskoj Federacii. (2017). [Digital Economy of the Russian Federation Program]. [in Rus.] Retrieved from http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf
- Rankings. (2019). Global innovation index. Retrieved from https://www.globalinnovationindex.org/gii-2019-report
- Schwab, K. (2018). The Global Competitiveness Report 2018. World Economic Forum. Retrieved from https://www.weforum.org/reports/the-global-competitveness-report-2018
- Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0 Unveiling technological connections in the agrifood sector. British food journal, (121), 8, 1730-1743. https://doi.org/10.1108/BFJ-11-2018-0747
- Tukaeva, Z., & Muxametzyanov, I. (2013). Model` prognozirovaniya nechetkix danny`x dlya resheniya biznes zadach predpriyatij TE`K. [Fuzzy data forecasting model for solving business problems of energy companies]. Upravlenie e`konomicheskimi sistemami: e`lektronny`j nauchny`j zhurnal, 8, 19-26. Retrieved from https://cyberleninka.ru/article/n/model-prognozirovaniya-nechetkih-dannyh-dlya-resheniya-biznes-zadach-predpriyatiy-tek [in Rus].
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
About this article
12 March 2020
Print ISBN (optional)
Information technology, communication studies, artificial intelligence
Cite this article as:
Musina*, D. R., Yangirov, A. V., & Nasyrova, S. I. (2020). Dependence Of A Country's Competitiveness On Its Information Infrastructure. In O. D. Shipunova, V. N. Volkova, A. Nordmann, & L. Moccozet (Eds.), Communicative Strategies of Information Society, vol 80. European Proceedings of Social and Behavioural Sciences (pp. 333-338). European Publisher. https://doi.org/10.15405/epsbs.2020.03.02.39