Digital Transformation in Russia: Problems, Challenges and Prospects

Digital transformation is a concept that introduces the state and companies to the application of technologies and processes that include data digitization, cloud computing. An assessment of how the digital conversion program is being implemented in RF.

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National Research University Higher School of Economics

Institute for Statistical Studies and Economics of Knowledge

Digital Transformation in Russia: Problems, Challenges and Prospects

Student Arina Vorvul

Moscow, 2019

Table of Contents

Introduction

Chapter 1. Theoretical part

Chapter 2. Methodology and Approach

2.1 Data

2.2 Factor analysis

2.3 Cluster analysis

2.4 Findings

2.5 Limitations

Chapter 3. International practices

3.1 General information

3.2 United States of America

3.3 China

3.4 Germany

3.5 Results

Chapter 4. Russian practice

4.1 Programs of digital transformation

4.2 Implementation

4.3 Budget

4.4 Formation

Conclusion

References

List of Abbreviations

CRM

Customer-Relationship Management

EAEU

Eurasian Economic Union

EC

European Commission

ERP

Enterprise Resource Planning

GDP

Gross Domestic Product

IANA

Internet Assigned Numbers Authority

ICANN

The Internet Corporation for Assigned Names and Numbers

ICT

Information and Communications Technology

I-DESI

The International Digital Economy and Society Index

IoT

Internet of Things

IT

Information Technology

NIST

National Institute of Standards and Technology

NRI

Network Readiness Index

NTIA

National Telecommunications and Information Administration

OECD

Organization for Economic Co-operation and Development

PC

Personal Computer

PRC

People's Republic of China

SMEs

Small and Medium-Sized Enterprises

UNCTAD

United Nations Conference on Trade and Development

USPTO

United States Patent and Trademark Office

Summary

Digital transformation is a concept that represents to the state and companies the application of technologies and processes, which include digitizing data, cloud computing, the Internet of things and big data to gain competitive advantages in the domestic and global markets. Assessing how a digital transformation program is being implemented is a difficult task, given that there is no closed definition of the term and that the collection of information is not specifically aimed at the concept of digital transformation. Acknowledging these difficulties, the present study measures the presence of factors that characterize the digital transformation in the world. The analysis suggests that the existence of a digital infrastructure, combined with analytical capabilities for working with big data, manifests itself in two dimensions, which show readiness for the introduction of digital transformation in each country. Five homogeneous groups of countries were found, showing large differences between countries. The analysis and comparison of countries that fell into the cluster group with Russia and recommendations for improving the program of digital transformation in Russia based on international practices were made. digital transformation cloud

The widespread adoption of the information and communication technology (ICT) is one of the most important conditions for the development of national economies of all countries. Along with the positive effects of the digital economy, new challenges have emerged, the response to which should be determined at the state level.

The study of international experience is of great importance for the development of the digital economy in Russia and the implementation of the digital transformation program. The study was conducted on the basis of the US programs "Digital Economy Agenda", the Chinese Internet Plus Action Plan and the Made in China 2025 Program, the Single Digital Market Strategy for Europe and the German Industry 4 Program, the Russian Digital Economy Program and "Strategy of development of the information society of the Russian Federation for 2017-2030"

Keywords:

Digital transformation; digital economy; digitalization directions; information and communication technology.

Introduction

Nowadays digital transformation is accelerating in almost all of industries and sectors: manufacturing, financial, service sector, social and educational sphere, infrastructure etc. The implementation of digital transformation itself and its core principles are considered the main solution of the one of global economic problems - steady industrial development of all sectors of the economy, the elimination of "distortions" in one or another direction.

The proof to the mentioned above might be the fact that in 2017-2030 The Russian government is going to implement the digital economy program that was personally supported and approved by President Vladimir Putin. The main objective of the program is creation and development of the digital environment, which will facilitate the resolution of the problems of competitiveness and national security of the Russian Federation. At the same time, an integral part of the digital transformation is the digital economy that will be also discovered in details in this work. Therefore, it is worth mentioning that the digital economy is an economy of innovations that is developing due to the effective introduction of new technologies. The number of Internet users in such a system grows exponentially, information technologies (including software, hardware and digital services) are becoming a part of ordinary life, so the digital revolution occurs. The IT industry itself develops very fast, grows faster than any other and its penetration in our lives not only the biggest ever, but also continues to accelerate. We can state that the Internet and digital technologies has really changed the course of our lives. What is more important, digital technologies and so called "connectivity of everything" became a disruptor that changes established business processes. That is why digital transformation is important subject and require detailed research.

Digital transformation changes established business models, production processes, and corporate governance and in some cases even allows to create new ones that never existed before. Over the past decade, there have been numerous breakthrough improvements in the information and communication technology (ICT) infrastructure and analytical capabilities through a large flow of innovations at all levels of the business (Bleicher & Stanley, 2016; Grover & Kohli, 2013). However, the scale of digital transformation is enormously low, and the extent to which this occurs is the result of a combination of several elements: enterprise information infrastructure resources (Ashrafi & Mueller, 2015), industry environment (Mithas, Tafti, Mitchell, 2013; Brettel, et al., 2014), and politics (Xu & Li, 2018). Changes in the corporate world provide new opportunities for retaining the competitive position of companies. This includes interaction with its employees and customers (Dery, Sebastian, Meulen, 2017) and positioning itself in the market. The rapid pace of technological improvements require a high rate of adaptation. The most innovative companies were able to understand at an early stage how to benefit and get value from new digital tools and influence their business model (OECD, 2017). Within the framework of various aspects of digital transformation, politicians, scientists and managers, attract attention to: the opportunities that arise as a result of applying digitization to processes, what is now called digital transformation (Smit, et al., 2016). The term digital transformation is widely used to describe the concept of the digital environment: fully automated processes, where human intervention is minimized, which is necessary (Smit, et al., 2016; Hofmann & Rьsch, 2017; Brettel, et al., 2014). Even if it is a somewhat new concept (born in 2011) (Qin, Liu, Grosvenor, 2016), the number of scientific studies covering it is growing. However, the available data on the digitization of processes is still scarce, even if they are developing rapidly. In the framework of the Single Digital Market Strategy (European Commission, 2015), the European Commission (EC) is working to identify and collect data that allows to measure and characterize a digital society (European Commission, 2015), including data related to the digitization of all processes. However, there are no studies has been presented regarding a horizontal representation of ??the digital transformation development degree in the world. Moreover, at the moment there are several works by American researchers such as, for example, Nicholas Negroponte, Chris Meyer, Mohanbir Sawhney, Daniel Spulber, Don Tapscott, Steve Jurvetson, Patricia Seybold, and others, who tried to characterize new features of modern digital transformation and define restrictions that governments may face based on international practices, but there are very limited number of works related to Russian practices. Thus, there is a gap in the scientific field that this study is trying to fill, at least partially, by answering the following research questions: What limitations can Russia encounter while implementing a digital transformation program based on international practices taking into account Russian specifics?

For the purposes of this study, digital transformation represents a set of technologies, devices, and processes that allow you to create self-contained models that are integrated at several stages of the process and at several levels with minimal human intervention. Scientists can interpret these concepts (self-contained production, integrated operations, decentralized solutions, minimal human intervention) in different ways, and there is a wide discussion in the academic and corporate environment about their practical implementation. The study does not discuss the driving forces or consequences of digital transformation, but it tries to provide an appropriate contribution regarding the presence of elements that characterize digital transformation in the world. Thus, a set of variables was collected from the literature (that were considered relevant) for evaluation of digital transformation. Subsequently, factor and cluster analysis were used to improve data interpretation and to draw the main conclusion: which countries should become benchmark for further comparison with the Russian Federation, taking into account the level of digitalization of infrastructure and Big Data Maturity.

The study is organized as follows:

Chapter 1 provides general information about digital transformation, its definition and context, and includes a review of the literature and its components;

Chapter 2 discusses the nature and rationale of the variables that were used to measure the implementation degree of digital transformation, describes the whole methodology based on cluster and factor analyses, presents quantitative information, discusses the results, findings and limitations of the study (see Figure 1);

Figure 1: Structure of Chapter 2

Chapter 3 describes in detail the international practice of countries that have been selected in cluster analysis and describes the programs created by governments for digital transformation;

Chapter 4 describes the Russian practice: programs, their implementation, their budgeting and formation.

Finally, conclusions include recommendations on the digital transformation programs in Russia, which should be taken into account during the implementation process.

Chapter 1. Theoretical part

In the beginning of 2015, President of the Russian Federation V.V. Putin prioritized the digital economy development. In order for the economy to be transformed into a digital one properly, it is necessary to define the concept of "digital economy" itself. Lots of researchers fairly believe that such a concept as "digital economy" appeared in the 90s of the twentieth century. One of the best definitions of the digital economy concept ideology was made by famous IT researcher N. Negroponte. He presented digital economy as the following metaphor: "the transition from atomic movement to movement of bits" (Alexeev, 2016).

There are obviously other opinions on this subject that exist in scientific community. Chairman of the Board of Directors of the Information Society Development Institute Y.A. Khokhlov, one of the experts on the topic in Russian Federation, noted that the term "Digital Economy" first began to be widely used by Don Tapscott, who wrote the monograph "Digital Society", which was publically presented in 1994 (InvestFuture, 2017).

P. Gribov, associate professor of the Economic Security Department of the RANEPA, notes that governmental discussions and President's prioritization has a linear correlation with the fact that nowadays the very concept of "digital" economics has not finally been defined and interpreted. P. Gribov believes that today almost everything could be covered by the concept of the digital economy, for example, such areas as hardware manufacturing, software development, computer technologies, as well as many other things, such as e-services, Internet-based products, instant messengers, etc. (Glaziev, 2017).

Russian economist V. Katasonov believes that the excitement around the digital economy topic in the Russian Federation is the usual "community talks" (Prasolov, 2017), which is just another "trendy" development direction. He notes that the digital economy itself was invented in the United States, believing that not only "economic futurologists" are responsible for that, but also representatives of American intelligence services might have taken part in it (which could be quite fair considering the fact, that digitalization, first of all, supplied us with enormous amounts of data, allowing 3rd parties know us better than ourselves).

Also, V. Katasonov states that the euphoria and statements that "digital industry will pull Russia out of the economic crisis" is not well validated by real data (InvestFuture, 2017).

However, it should be noted that such a phenomenon as "digital economy" is very serious. The transition to a digital economy has become one of the main priorities and development vectors for Russian Federation. According to experts, the level of digital economy implementation will be the key definer of country's competitiveness within the new technological order that is establishing nowadays. In this case, we can say that the digital economy acts as the "oil" of the future.

The formation of the digital economy is one of the key aspects of national security and independence in the future, as stated by V.V. Putin during the meeting of the Strategic Development Board. According to the President of the Russian Federation, the development of the digital economy can be compared with railway construction in the XIX century, or electrification in the XX century. President Putin stated following: "Digital economy is not a separate industry, in fact, it is the basis for creating qualitatively new business, trade, logistics, and production models, changing the format of education, health care, public administration, communication between people, and therefore, sets a new paradigm of development of the state, the economy and the whole society" (Plugotarenko, 2017).

In this case, it makes sense to agree with the Russian leader that there is no well-established and commonly used international regulation in this area, however, we need to understand that the main problem of the digital economy is the question of exactly where are its borders. In connection with the foregoing, we can assume in this study that the digital economy is a complex of such economic, social and cultural relations that are based on the application of digital ICT.

For example, experts confirm that, thanks to the presence of excellent schools of mathematics and theoretical physics, the Russian Federation has the opportunity to seek leadership in a number of areas within the framework of the new economy, and first of all - the digital one. IT-companies of the Russian Federation, without any doubt, are very competitive. Specialists from the Russian Federation offer simply the best and unique software solutions, and, in fact, create new areas of knowledge and new environments for the development of the economy and life (Prasolov, 2017).

O. Shibanov, who is a professor of finance and deputy first vice-rector of the Russian Economic School, is confident that the economy of the Russian Federation is open, and for this reason, it is very important for the country that it has the most access to technology and the market. . It would be strange to assume that in the very near future the share of the digital economy in the Russian Federation will be over 50% of GDP, however, the active introduction of the digital economy in all sectors currently serves as one of the fundamental moments for all industries (Kovalenko, 2017).

It is impossible to imagine the development of such areas as trade, logistics, certain types of industrial production, housing and communal services, transport and other sectors of the economy without accelerated development of the digital economy. However, the cost of information technology is very significant. As noted by Russian President V.V. Putin, in 2017, about 200 billion rubles will be spent on digitalization. As for A. Kudrin, he has offered to spend on the "digital revolution" till 2024 enormous amount of 185 trillion rubles, which is about 30% of GDP per year (Alexeev, 2016).

The experience of many countries makes it possible to conclude that the accelerated development of the digital economy is not always associated with significant financial costs. In this case, the matter is largely related to mental potential level of people and to how quickly the process of changing paradigms of thinking in the business environment is going. The state itself, of course, plays a very important role in this process, and that is especially important for developing countries. However, business continues to remain as a key agent, since it is the main actor to introduce all these digital innovations.

If we look closer at the banking system of the Russian Federation, we can see a large number of skilled workers in both the IT sphere and risk management, many of whom are engaged in introducing innovations without significant financial costs. If the information that is supposed to be saved with the use of the "blockchain" technology is secretive, then in this case it is possible that questions arise. However, this problem has a solution, since this kind of data can always be encrypted (InvestFuture, 2017).

Thus, it is possible to make a general conclusion that the digital revolution, which is taking place nowadays, is quite long process, the unfolding of which takes place over several years or even decades. Digital technologies already dominate in most of spheres of life in Russia: informational, financial and significant part in the manufacturing and social spheres of the Russian economy. Moreover, such technologies increase their penetration level in both households and business. At the same time, it should be noted that the monopolistic use of digital technologies in the interests of particular individuals, companies or countries might become a threat to the national security of other countries. But we have to remember that neutralization of such threats should be performed only by using legal methods.

Paying more attention to the analysis of new phenomenons and trends in the economy and relying on the papers of American researchers such as Nicholas Negroponte, Chris Meyer, Mohanbir Sawhney, Daniel Spulber, Don Tapscott, Steve Jurvetson, Patricia Seybold, etc., we can detect the desire of the authors to characterize new features of the modern economy using such terms as "New Economy", "Economy 2000", "Internet Economy", "Net Economy", "Web Economy", "Digital Economy", "E-commerce" (E-business), "intangible economy", "non-substances Renaissance economy ", etc.

These terms are often used as synonyms when new phenomena in economics is being discovered. It might be caused by the formation of a global electronic network, the global spread of personal computers (PC), the creation and continuous improvement of software, the development of information and digital technologies, the production of products and services by IT companies.

The founders and leading specialists in research and popularization of the theory of "information society", in particular "information economy", include U. Beck, Z. Brzezinski, R. Katz, M. Castells, M. McLuhan, I. Masuda, M. Porat, D. Tapscott, T. Stounier, E. Tofler, T. Umesao, E. Fukuyama, and others. The works of F. Machlup and M. Porat (1962) first noted the conceptual foundations of the information economy.

Classically, "digital economy" is an activity in which key production factor is digital data and its use, which can significantly increase efficiency / productivity in various types of economic activity. In addition, "digital economy" is the economy that uses digital technologies and services (Kapranova, 2018). The terms "data economics", "internet economics", "new economics", or "web economics" are often being used as the synonyms to the terms above.

Supporters of the information economy associate the formation of the information type of US society with the development of the fourth sector of the economy. F. Machlup includes into that sector: law, education, publishing, media, computer manufacturing.

Canadian scientist M. McLuhan contributed to the development of information theory as well. The author claimed that telecommunication technologies are the basis for the formation of socio-economic systems, the factor that determines material and spiritual progress. He calls such a society "Electronic", where telecommunications influence the subconscious of the masses, networks of such a nervous system are being formed, producing the effect of a "global embrace". Japanese scientist I. Masuda in his work describes the emerging information society as a high post-industrial stage. He points out that the development of information technologies will affect the structure of the economy, where the leading industry will be "intellectual production", while information and knowledge will play the role of the product. ICT itself will become productive force, displacing industrial machine technologies. He expresses the opinion that new production forces will influence the society and lead it to a classless society devoid of the contradictions of the industrial age.

The American-Spanish economist M. Castells makes the great contribution to science along with others. As the most important features of the information society, analysts point out the "network character". So, M. Castells asserts that the new social morphology is composed precisely by networks, and the spread of network logic greatly affects the course and results of processes related to production, daily life, culture and power. The author builds a theory of a network society with interacting, intertwining networks that form a network space.

The Japanese scientist K. Imai characterizes the network form of organization as a system of units between which more or less permanent links are being maintained within certain relations. Thus, in the basis of such an economy are new forms of socio-economic activity of individuals, forming a new model of social relations. Mass availability and involvement of economic agents in electronic networks, integration of economic activities into the Internet space in the late 90s of the XX century gave scientists a reason to build their concepts on the basis of the concept of "virtual economy". In that area stand out: A. Bulle, A. Crocker, M. Weinstein. In their opinion, society is moving to a new phase of capitalism, where the industrial system is being transformed under the influence of ICT.

In such conditions, a person is alienated from the flesh in the process of interaction with ICT and turns it into information flows that form virtual capital. Russian economists like A.A. Auzan, A.V. Buzgalin, A.P. Dobrynin, S.N. Evtushenko, A.A. Domrachev, Yu.N. Andriyashin, V.P. Kupriyanovsky, Avdeenko T.V. made a great contribution to the "digital economy" theory as well

Now there is no single definition of the concept of "digital economy". According to analysts Gartner, the digital economy - is the creation, consumption and management of value associated with digital products, services and assets in organizations. Analysts at Boston Consulting Group believe that the digital economy is the use of online-based innovative digital technologies by all participants in the economic system, from individuals to large companies and states. "Markets based on digital technologies that facilitate trade in goods and services through e-commerce," - the definition given by the OECD (Organization for Economic Cooperation and Development). The World Bank considers that "the digital economy is a new paradigm of accelerated economic development."

The governmental program "is based on the premise that the digital economy is an economic activity, which key production factor is data in digital form." There are no other analogs or generally accepted definitions, despite a sufficient data array and official documents on the digital economy that define trends, forms, methods and mechanisms of management. Most often, the digital economy is perceived as an object of strategic management. "The Development Strategy of the Information Society in the Russian Federation for 2017-2030", which is the basement for government's program for the digitalization of the economy, provides a close but more detailed definition: "The digital economy is an economic activity, in which key factor of production is data in digital form. Its processed large volumes and usage of the results of analysis, compared to traditional forms of management, can significantly improve the efficiency of various types of production, technology, equipment, storage, sale, delivery of goods and services " (Digital Transformation Program, 2016).

In some works, it is noted that the digital economy is one of the evolutionary forms of manifestation of the new economy. Consequently, as a "form of the form", the digital economy contains not only a set of signs of a new economy, but also contains a number of distinctive aspects that characterize the qualitative certainty of the digital economy. Along with the emergence of new laws and trends that did not occur in the "industrial" economy, new content of traditional economic postulates drawn attention. It is combined and interconnected with digital technologies, manifest themselves in a new way. Under the influence of scientific, technical and economic progress, there are significant changes in the seemingly canonical rules of a market economy, business rules, in new manifestations of traditional economic principles and laws. For example, the emergence and development of global electronic networks, computers, software, digital technologies, electronic products and services radically change the content, ratio and value of the following concepts in the new economy: material (material) and non-material (non-material), geography and distance, space and time, use value (utility) and cost, quantity and quality, competition and consumer preference, mediation and logistics, human capital and ethics and business, transactions and performance evaluation, sellers and buyers behavior, new relations of producers and consumers, marketing and sales technologies, etc.

It is also worth to be noted that together with creation and rapid development of Internet companies in developed countries, and especially in the USA, a new market of Internet services, products, services, service providers, etc., is emerging and transforms traditional spheres of the economy. Therefore, it is important to distinguish the Internet economy and the digital economy in the narrow sense: the digital economy is the economy of digital technologies, products and services of Internet companies and firms in the information society. In the broad sense of the word: digital economy is a new information economy, an economy of enterprises of any industry, operating in a global electronic network using digital technology format, and having a number of distinctive features compared to the so-called "industrial" economy, mainly corresponding to the 3rd, 4th technological structure.

The advantages of the digital economy, in the opinion of N. ??Negroponte, could be: the lack of physical weight of products, which is replaced by information volume, lower resource costs for the production of electronic goods, a much smaller area occupied by products, as well as almost instant movement of goods through the Internet (Umaev, 2017).

Figure 2: Definition of digital transformation

Based on the available research results in the topic, we can offer the following definition of the subject area of the digital transformation: the digital transformation is a system set of economic relations regarding the production, distribution, exchange and consumption of goods and services of the techno-digital form of existence. Digital transformation defines the maximum satisfaction of the needs of individuals through the use of information technology and digital infrastructure (see Figure 2). A feature of the digital economy is to increase the efficiency of interaction between all participants in the process of creating, distributing and exchanging goods and services. The basis of this transformation should be a digital environment that meets the needs of producers and consumers in their interaction. The purpose of these changes is the steady development of all sectors of the economy and competitive improvement of companies, and improvement of the quality of life.

Chapter 2. Methodology and Approach

2.1 Data

Data collected from the Eurostat and the Russtat databases. They are the results of responses to a questionnaire on the use of ICT and e-commerce in enterprises (Eurostat, 2017). All data refer exclusively to enterprises of the production sector in accordance with the statistical classification of economic activities in the European Community (NACE - Nomenclature statistical information on economic activities of the European Community, Rev.2) (Eurostat, 2008) for companies with 10 or more employees. According to NACE Rev.2, manufacturing involves a wide range of activities, including the production of food, textiles, pharmaceuticals, equipment, etc. The measure is the proportion of these enterprises that recognized the presence of the element. Information for 2017 was available for all variables used, with the exception of enterprises that have an ERP software package for exchanging information between different functional areas and enterprises. If the particular variable was missing for a country, the proxy server was determined by means of hierarchical cluster analysis. Polish information was used to supplement Bulgaria's missing information on high-level CC services procurement (accounting applications, CRM software, computing power) and analyze big data from portable device geolocation (bd_geo) and Croatia's missing information on enterprises with ERP software package for information exchange between different functional areas (ERP). The missing value in Germany for purchasing services with high CC level (accounting applications, CRM software, computing power) (advcloud) has been replaced by Croatian. For countries where information was missing for more than two variables, no changes were made. As a result, for Belgium there is no information on any of the variables, and there is no information on the use of big data for Ireland, Greece, Cyprus, Latvia and Austria. Therefore, these six countries were excluded from the analysis.

A summary of the variables used is provided below:

mobInt - mobile connection to the internet for business use to use dedicated business software applications (Kagerman, Wahlster, Helbig, 2013; Hermann, Pentek, Otto, 2016)

speed - the maximum contracted download speed of the fastest fixed internet connection is at least 100 Mb/s (Smit, Kreutzer, Moeller, Carlberg, 2016; Hermann, et.al, 2016)

erp - enterprises who have ERP software package to share information between different functional areas (Smit, et al., 2016; Brettel, Friederichsen, Keller, Rosenberg, 2014)

intBP - enterprises whose business processes are automatically linked to those of their suppliers and/or customers (Hofmann & Rьsch, 2017; Brettel, et al., 2014)

tInvS - enterprises sending only paper invoices B2BG (Mithas, Tafti, Mitchell, 2013; Brettel, et al., 2014)

advcloud - buy high CC services (accounting software applications, CRM software, computing power) (Smit, et al., 2016; hermann, et al., 2016; Brettel, et al., 2014; Moeuf, Pellerin, Lamouri, Tamayo-Giraldo, Barbaray, 2017; Pedone & Mezgar, 2018; Wang, Zhang, Liu, Li, Tang, 2017)

bd_anysrc - enterprises analyzing big data from any data source (Hermann, et al., 2016; Santos, Oliveira, Andrade, Lima, Costa, Martinho, 2017; Wang, Wan, Zhang, Li, 2016)

bd_sensors - analyze own big data from enterprise's smart devices or sensors (Hermann, et al., 2016; Santos, Oliveira, Andrade, Lima, Costa, Martinho, 2017; Wang, Wan, Zhang, Li, 2016)

bd_geo - analyze big data from geolocation of portable devices

The final data set is presented in table 1.

Table 1: Values of data

Countries

mobInt

speed

erp

intBP

tInvS

advcloud

bd_anysrc

bd_sensors

bd_geo

Bulgaria

12.00

8.00

26.00

16.00

41.00

3.00

6.00

3.00

3.00

China

45.00

15.00

43.00

17.00

20.00

7.00

5.00

2.00

2.00

Czech Rep.

39.00

8.00

35.00

19.00

3.00

6.00

7.00

4.00

3.00

Denmark

56.00

28.00

52.00

26.00

2.00

19.00

7.00

4.00

2.00

Estonia

26.00

12.00

20.00

14.00

3.00

13.00

8.00

5.00

3.00

Finland

53.00

21.00

60.00

25.00

4.00

29.00

13.00

9.00

4.00

France

31.00

6.00

48.00

14.00

26.00

7.00

7.00

3.00

3.00

Germany

32.00

9.00

53.00

28.00

34.00

9.00

5.00

3.00

2.00

Hungary

16.00

8.00

20.00

10.00

65.00

4.00

5.00

2.00

3.00

Italy

22.00

4.00

45.00

13.00

13.00

5.00

7.00

3.00

2.00

Lithuania

23.00

19.00

38.00

26.00

19.00

8.00

11.00

6.00

7.00

Luxembourg

35.00

25.00

47.00

19.00

22.00

9.00

12.00

6.00

3.00

Malta

28.00

6.00

33.00

17.00

23.00

7.00

15.00

8.00

3.00

Netherlands

44.00

18.00

58.00

20.00

9.00

15.00

15.00

10.00

4.00

Poland

25.00

9.00

25.00

17.00

49.00

3.00

5.00

2.00

3.00

Portugal

30.00

19.00

38.00

15.00

32.00

6.00

10.00

4.00

4.00

Romania

13.00

10.00

19.00

8.00

36.00

4.00

9.00

3.00

7.00

Russia

38.00

4.00

53.00

24.00

38.00

9.00

7.00

3.00

1.00

Slovakia

27.00

7.00

30.00

23.00

31.00

8.00

9.00

5.00

4.00

Slovenia

24.00

14.00

39.00

11.00

33.00

10.00

9.00

6.00

2.00

Sweden

43.00

27.00

60.00

12.00

10.00

18.00

7.00

4.00

2.00

U.S.A

53.00

29.00

51.00

23.00

4.00

17.00

5.00

3.00

2.00

U.K.

25.00

8.00

23.00

13.00

15.00

13.00

12.00

5.00

4.00

Source: Eurostat, Russtat

During the collection of data was observed a large data scatter. For example, in Bulgaria only 12% of industrial enterprises have mobile access to the Internet for business purposes, while in Denmark mobile access to the Internet in enterprises is 56%. We would also like to note that in more than half of the countries less than 10% of industrial enterprises have a download speed of over 100 MB under the contract, but in USA, Denmark, Sweden, Finland and Luxembourg the level is above 20%. In addition, in most countries a low percentage of CC services. With regard to the use of ERP, the level of use of this system is quite high from a minimum of 11% in Latvia to a maximum of 60% in Finland and Sweden, with seven countries representing more than 50%. Regarding variables related to the processing and use of big data, the use of this type of analytics is usually quite low (less than 10%) among manufacturing companies, but when collecting data it was noted that in some countries more than 10% (but less than 20%) of enterprises work with big data from any source. Only in the Netherlands, 10% use big data collected from smart devices, and in all other countries less than 10% of manufacturers work with this technology to get information from mobile or geolocation devices.

2.2 Factor analysis

Factor analysis was used to measure the presence of digitalization and information infrastructure. This will highlight trends among countries and their industries, which, in return, make it possible to recognize patterns. If the variables correlate, this correlation may indicate the presence of at least one common pattern, a hidden dimension, which ultimately can explain the variables themselves. The quality of information that can be obtained from factor analysis will depend on the existing correlation structure among the data set, and the following specific steps have been taken to determine its capabilities:

First, the correlation between the variables was calculated and based on the correlation matrix. If the correlation is high, this means that the selected variables measure the same phenomena; the low correlation between the two variables suggests that other elements explain their behavior.

Secondly, a suitability test was performed. One of the most common for this kind of analysis is the Kaiser-Meier-Olkin test. It helps to check how much the correlation between pairs can be explained by other variables.

Thirdly, if suitability was confirmed, then the next step was a factor analysis, for which several factors were determined, ensuring that they retain a large percentage of the variance of the total data. Factors were interpreted based on the context from the studied variables that most contributed to them.

Fourthly, if they became representative, the factors showed hidden dimensions that cannot be obtained directly from a simple analysis of the variables.

Fifthly, after the factors for each country are determined, all the results were plotted on a two-dimensional graph and, if possible, they developed homogeneous groups of countries to create clusters.

It is worth noting that cluster analysis is useful for quantifying and characterizing measurements that perfectly explain differences between countries, even if it does not provide an explanation by itself. However, by understanding clusters, one can get general explanations based on understanding of the context in each country.

As a result, at the first stage correlations were calculated for all two pairs of variables, the results are shown in Table 2. All calculations were carried out within the first six variables (mobint, speed, erp, intBP tlnvS and advcloud), with each value that is not less than 0.51. On the other, not very high value, but permissible, to indicate the possibility of using a common force.

Table 2: Correlation

mobInt

speed

erp

intBP

tInvS

advcloud

bd_anysrc

bd_sensors

bd_geo

mobInt

1

0,59

0,77

0,54

-0,63

0,74

0,16

0,34

-0,35

speed

1

0,48

0,22

-0,44

0,61

0,26

0,35

0,09

erp

1

0,51

-0,44

0,59

0,15

0,32

-0,37

intBP

1

-0,26

0,37

0,11

0,27

-0,06

tInvS

1

-0,64

-0,39

-0,49

0,01

advcloud

1

0,4

0,59

-0,08

bd_snysrc

1

0,91

0,43

bd_sensors

1

0,27

bd_geo

1

Source: calculations of this study

It is assumed that there is a negative correlation between the variable tlnvS (enterprises that produce only paper bills) and all others. MobInt shows a consistently higher level of correlation with variables of this set (minimum correlation 0.59) than with any of the others. In this subset, there are no correlation levels above 0.8, which implies some independence between the variables. As for the variables associated with big data, they show a low correlation with variables from the first subset: cloud services have a correlation of 0.59 with big data from smart devices, but the correlations between large data variables and the rest are equal to or below 0.4. There is a high correlation (0.91) between the percentage of manufacturing companies that analyze big data from any source and those doing the same with help of smart devices or enterprise sensors. It allows suggestion that smart devices or sensors are an important source of information for big data analysis. The correlation is lower (0.43) between the percentage of manufacturing enterprises that analyze large data from any source, and those reporting that they do it from the geolocation of portable devices. In addition, the correlation between bd_smart and bd_geo is low (0.27) as well.

After obtaining the correlation matrix, we applied an analysis of the main components. The screen plot and the Kaiser Method (Peres-Neto, Jackson, Somers, 2005) point out two factors. The application of the Kaiser-Meier-Olkin test for the adequacy of the sample showed a value of 0.70, not very high, but within the limits of meaningfulness. This value indicates that the data is suitable for further analysis. If the value were less than 0.5, then the factor analysis would not be applicable to this sample.

Finally, taking into account the contribution of each variable to the two factors we considered two dimensions:

1. The first dimension is called "Digital Infrastructure". It includes interconnectivity, interoperability and virtualization, which should form a digital and information infrastructure. Since none of the variables was collected for the specific purpose of measuring the digital infrastructure, it is their combination and the simultaneous occurrence that indicate the possibility of presence or readiness for development in the digital direction.

2. The second dimension was called the "Big Data Maturity". It expresses the ability to process information generated by the digital infrastructure (informational transparency).

These two dimensions explain 69% of the total variance, a value high enough so that explanatory power can be explained to them (Table 3).

Table 3: Factor Loading

Variable

% of manufacturing companies

Digital Infrastructure

Big Data Maturity

mobInt

mobile connection to the internet for business use to use dedicated business software applications

0,95

-0,04

erp

enterprises who have ERP software package to share information between different functional areas

0,87

-0,10

advcloud

buy high CC services (accounting software applications. CRM software. computing power)

0,81

0,33

speed

the maximum contracted download speed of the fastest

fixed internet connection is at least 100 Mb/s

0,64

0,26

intBP

enterprises whose business processes are automatically linked to those of their suppliers and/or customers

0,60

0,00

tInvS

enterprises sending only paper invoices B2BG

-0,67

-0,35

bd_anysrc

enterprises analyzing big data from any data source

0,21

0,91

bd_sensors

analyze own big data from enterprise's smart devices or sensors

0,43

0,82

bd_geo

analyze big data from geolocation of portable devices

-0,37

0,72

Variance explained

3,89

2,32

Variance explained (%)

43%

26%

Total variance explained (%)

43%

69%

Source: calculations of this study

The coefficients for each country are shown in Figure 3. Horizontal axis represents the first factor, digital infrastructure, and vertical axis represents the second factor, Big Data Maturity. In countries to the right of the vertical axis, digital infrastructure is above average, while countries located above the horizontal axis show a higher than average ability to process information from the Big Data Maturity.

2.3 Cluster analysis

Cluster analysis was applied across countries plotted on the graph in two dimensions to determine homogeneous groups with respect to the factors considered. Since there was no intention to determine the number of clusters in advance, a hierarchical methodology was developed first. The Single, Complete, Average, Centroid, and Ward algorithms were used to measure the distance between each pair of observations (and clusters) to create homogeneous groups. To determine the initial number of clusters in each method, the R-squared and dendrogram were calculated and evaluated. This analysis showed that Complete and Ward's methods were the ones that obtained better results. Ward was the final method chosen; since it showed higher levels of R-Squared in all cluster capabilities (see Fig. A1), with a choice of five clusters (see Fig. A2). The clusters generated by the Ward's method were used subsequently. This methodological option tends to give better results (Cruz-Jesus, et al., 2012; Sharma, 1996). The result of this approach and the restructuration (i.e., an optimized model taking into account small discrepancies) helped to form five clusters of countries in the two measurements found. Within each cluster, countries tend to be homogeneous for each factor - digital infrastructure or big data maturity - that makes it possible to characterize clusters based on the prevalence of measurements. Fig. 3 helps to better imagine how countries are grouped into clusters.

Source: results of this study

Finland and the Netherlands, which are countries where digital infrastructure and maturity of big data simultaneously exhibit a more significant positive deviation from the average, form a cluster called Leaders. The second cluster includes Russia, China, Germany, USA, Sweden and Denmark - all of them with a relatively high level of digital infrastructure, but a low level of maturity of big data. In this cluster, USA, Denmark and Sweden are significantly different from the other three countries in terms of both factors, which indicates a higher propensity to reach the first quadrant. In general, this cluster was called "Digital Infrastructure". Romania, the UK, Malta and Lithuania form the third cluster, where the maturity of big data is above average, but the digital infrastructure is below average, with Romania showing significantly lower levels of digital infrastructure than in the other three countries. This group is a "Big Data Maturity" cluster. The fourth cluster includes Hungary, Poland and Bulgaria, three countries that are below the average for both factors. They were called "laggards." Finally, all other countries - Luxembourg, the Czech Republic, Estonia, Portugal, Slovakia, Slovenia, France and Italy - unite the fifth cluster, where both levels of factors are close to the average for the entire group. All quadrants are represented in this cluster, which has been designated as "Average".

2.4 Findings

Two dimensions that seem to explain the prevalence of digitalization in different countries point to two main elements related to how digitization affects innovation in the context of manufacturing firms: (i) digital infrastructure and (ii) big data maturity. The first dimension refers to infrastructure that goes beyond the classic concepts of hardware and cables and includes information, communication and communication technologies that, within the digital paradigm, change the way companies develop their business strategies (Bharadwaj, Sawy, Pavlov, Venkatraman, 2013). As for the second dimension, Big Data Maturity, it concerns the ability to process information generated by the infrastructure. The ability to extract data and interpret information obtained from production processes and supply chain processes has a high added value, as it increases predictive power and facilitates error management (Santos, et al., 2017; Wang, et al., 2016).

The system characteristics of interconnection, interoperability and virtualization allow you to generate and store large amounts of granular data obtained at several stages of the processes and with the help of several devices. Processing this data using big data methods turns it into valuable information. A country that demonstrates the high value of digital information infrastructure has a large percentage of enterprises using communications networks and platforms. The high maturity of big data in a particular country indicates the availability of analytical capabilities. The simultaneous presence of both elements provides the possibility of interconnectivity, interoperability, virtualization and information transparency - all elements that should be present in the digital infrastructure. The high level of both dimensions does not indicate the existence of an already formed digital infrastructure inside the country as such, but it indicates the possibility of its formation.

The data show significant differences between European countries and the Russian Federation, but this is not surprising. This transformation is a fairly new direction, which is actively developing in all spheres and countries, both in government and in companies. Everyone adapts and to it, but since it develops quickly enough, it requires a lot of attention and flexibility to introduce new technologies. These differences can be easily seen in Figure 3.

In general, the Scandinavian countries demonstrate a high level of acceptance both in digital and in terms of the maturity of big data, even if this level of acceptance is not homogeneous among them: Finland acts as a leader, while Denmark and Sweden, although included in the digital set. infrastructure, represent higher values in both dimensions than their cluster counterparts. The Netherlands and Luxembourg are two countries along with Finland, where both the digital infrastructure and the big data maturity have higher level than average. In fact, past research has shown that at the level of the European Union and the Russian Federation, these countries are at the forefront of bridging the digital divide (Cruz-Jesus, et al., 2012; Vicente & Lopez, 2011), which should indicate a strong inclination, including from the corporate sector, to make decisions oriented to a higher degree of digitization, which, in turn, is a necessary condition for the existence of a digital information infrastructure. As for other countries, the very broad significance of the topic of the digital divide does not allow any analogies, taking into account the peculiarities of the concept of digital transformation.

For Germany, China and Russia, data show the need for further development in the use of big data analytics in order to reach the EU average in this dimension. Surprisingly, France and Italy, two of the largest economies in the EU, show lower than average values in both dimensions. Moreover, the UK, despite its strong position in terms of big data maturity, is significantly lower than the average in digital infrastructure.


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