The Interplay between Digital Entrepreneurship and Sustainable Development in the Context of the EU Digital Economy: A Multivariate Analysis

Abstract

A real challenge for the EU economy and society is to achieve both green and digital transitions in order to tackle the major economic, social and environmental issues faced by EU member states. In this context, digital entrepreneurship, which lies at the intersection of digital technologies and entrepreneurship, has recently benefited from increasing attention both in theoretical and empirical research and in strategic policies. Given these aspects, the aim of this article was to investigate the interrelationship between digital entrepreneurship and productive and innovative entrepreneurship and its impact on the achievement of the Sustainable Development Goals (SDGs) in EU countries. The results of correlation and regression analysis revealed that digital entrepreneurship, which implies productive and innovative entrepreneurial activities, is positively influenced by the degree of a country’s digitalization and, in turn, has a positive impact on the achievement of the SDGs (Total SDGs, SDG 8 and SDG 9). Furthermore, the findings of the principal component analysis and cluster analysis emphasize that there are differences and common features between EU countries in terms of the interrelationship between digital entrepreneurship, digitalization, economic development, national competitiveness and achievement of the SDGs. Therefore, specific measures should be implemented to boost digital entrepreneurship (especially in some central and eastern EU countries) so that this will be the key driver for sustainable development.

1. Introduction

The “Transforming our World: the 2030 Agenda for Sustainable Development” resolution adopted by the United Nations (UN) General Assembly [1] in 2015 as a comprehensive policy blueprint sets 17 Sustainable Development Goals (SDGs), which represent the global priorities for development by 2030 and address the major economic, social and environmental challenges faced by global and national communities. The UN 2030 Agenda attempts to restore harmony between progress and sustainability by creating a sustainable world which includes all countries [2] and achieve the kind of “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [3] (p. 43). At the European Union (EU) level, all SDGs are an intrinsic part of EU strategies and are regarded as essential for the formulation of policies either for internal and external action in all sectors [4]. Moreover, the European Commission [4] recognizes that the full accomplishment of the UN 2030 Agenda plays a pivotal role in boosting resilience and making sure that economies can recover from future shocks as the world faces the twin green and digital transitions.

Entrepreneurship and innovation have been recognized by the UN [5] as key drivers for sustainable development, addressing all three challenges of sustainability (economic, social and environmental) in the context of the 2030 Agenda. Researchers around the world have highlighted the role of entrepreneurship in sustainable development by focusing more on the economic and social dimensions of sustainability. Thus, entrepreneurship is seen as a core factor in generating wealth [6], driving economic growth and development, creating jobs and reducing unemployment and poverty, improving well-being and people’s standard of living [7,8,9,10,11]. Furthermore, empirical research [12,13,14,15] proves that a high level of quality of entrepreneurship (productive, innovative and opportunity-driven entrepreneurship) is more important than a high quantity of entrepreneurship for producing a positive effect on sustainable development. Even though the entrepreneurship literature has increasingly recognized entrepreneurship as an effective solution to various social, economic and environmental challenges of sustainable development [10,16,17], a research gap has been identified in terms of the holistic approach to the simultaneous role of the entrepreneurial activity that could address all three challenges [18,19].

Over the last decade, on the Fourth Industrial Revolution’s background, entrepreneurship has been significantly transformed due to the influence of digitalization on the economy and society [20,21,22,23]. Therefore, in recent years, digital entrepreneurship, which according to the EC has made use of novel digital technologies to shape existing businesses and to impact new ones [24], has received increasing attention both in theoretical and empirical research [7,25,26].

The interplay between digitalization and sustainability is highly emphasized by several research studies [27,28,29], which recognize that digitalization can be the right solution for research gaps and societal problems, while sustainability is a prerequisite for a responsible digital transformation [2]. Digitalization may be defined as the “sociotechnical process of applying digitizing techniques to broader social and institutional contexts that render digital technologies infrastructural” [30] (p. 749), as well as “the process by which economy and society are evolved around digital technologies providing new added-value opportunities” [2] (p. 15).

Deep integration of digital technologies in the economy has a high potential to contribute to sustainable development [31]. Empirical studies [32,33] highlight that the success of digital transformation of businesses in any sector of the economy (from the manufacturing sector to the education sector) depends on multiple factors, such as external support for digitalization, digitalization readiness preassessment and information and digital technology readiness [32]. In the current environment, one of the biggest challenges that any business or society faces, identified by Jafari-Sadeghi et al. [20], is the way in which new digital technologies are adopted, integrated and exploited.

As one of the connecting mechanisms between the multiple dimensions (technological, economic, social and ecological) of the socioeconomic system, digitalization can be a serious source of challenges to the resilience of this system [34,35], providing both new opportunities and new risks with unpredictable consequences. Such challenges need to be managed in a sustainable way, relying on the principles of sustainable development [35]. Moreover, at the EU level, the new growth strategies (e.g., The European Green Deal (2020) and Shaping Europe’s Digital Future (2020)) acknowledge the need for a holistic approach to the nexus between digitalization and sustainability to achieve the twin EU transitions. Therefore, emphasis is placed on that approach to investigate the multiple roles of digital entrepreneurship in the economic, social and economic dimensions of sustainable development.

Bearing this context in mind, the aim of this article is to highlight the interrelationship between digital entrepreneurship and productive and innovative entrepreneurship and its impact on the achievement of the SDGs (Total SDGs, SDG 8 and SDG 9) in the EU member states for the 2018–2019 period. Additionally, this research highlights the specific measures that should be taken to improve digital entrepreneurship in order to increase its effect on the SDGs. The research objectives focused on (1) analyzing the link between productive and innovative entrepreneurship on the one hand and economic development, national competitiveness and the degree of digitalization of an economy and society on the other, (2) exploring the interlink between digital entrepreneurship, productive entrepreneurship and innovative entrepreneurship, (3) investigating the nexus between digital entrepreneurships and the degree of digitalization of an economy and society, (4) analyzing the impact of digital entrepreneurship on the achievement of the SDGs, and (5) identifying the differences and common features between EU countries based on their interrelationship between digital, innovative and productive entrepreneurship, digitalization, economic development, national competitiveness and achievement of the SDGs.

Although several studies address the influence of digital technologies and/or entrepreneurship on achieving sustainable development, there is a lack of empirical research on the effects of digital entrepreneurship on sustainable development. In this research paper, as opposed to the existing empirical studies, we investigate the impact of digital entrepreneurship as the combination of digital technologies and entrepreneurship on the simultaneous attainment of the economic, social and environmental goals of sustainable development. Thus, this research contributes to filling the gap of empirical studies that highlighted the impact of digital entrepreneurship on sustainable development through a holistic and integrative approach.

The next section discusses the theoretical framework and the related literature, as well as the research hypotheses. Section three outlines the empirical data and statistical methods used. The fourth section provides the results of the research, the discussion and their main implications. The last section presents the article’s conclusions as well as its limitations and the further possible research.

2. Theoretical Background and Research Hypotheses

2.1. The Impact of Productive and Innovative Entrepreneurship on the Economic Dimension of Sustainable Development

Scholars have acknowledged a major impact of entrepreneurship on sustainable development, but they tend to focus more on the economic dimension of sustainability. Thus, entrepreneurship and its potential impact on macroeconomic performance have been largely studied over the last two decades [10,36,37], and both topics still continue to be of interest. Most of the empirical studies highlighted that entrepreneurship positively impacts the national economy in terms of economic growth, economic development and national competitiveness [7,11,15,38,39], and they also underlined that the degree of this impact in turn depends on a variety of influencing factors. At the same time, there are other empirical studies [10,17,40] which concluded that this macroeconomic impact of entrepreneurship is negative. These contradictory results can have multiple and interrelated explanations, such as the complexity of the link between entrepreneurship and economic growth [9], the level of economic development, the country’s development stage [12], the country’s innovativeness [38,41], the institutional and cultural settings [38], the motivations of entrepreneurs [8,42], the definition and measurement of entrepreneurship [15,36,40] and different forms of entrepreneurship [43,44].

As empirical research [14,36,40] has shown, the influence of entrepreneurship on economic welfare depends on how it is defined and measured. Thus, entrepreneurship measured by indicators which reflect a high level of innovativeness and quality (e.g., the Global Entrepreneurship Index, innovative SMEs, innovative and high-growth firms and opportunity-driven entrepreneurship) has a positive macroeconomic impact [8,13,15], and this underlines that the quality of entrepreneurship matters more than the quantity. In the same vein, Szerb et al. [15], analysing the relationship between entrepreneurship and regional performance at the level of the 121 EU regions in the 2012–2014 period, pointed out that the regional performance was negatively impacted by quantity entrepreneurship, while in the case of quality (Schumpeterian) entrepreneurship, this impact was positive. Opportunity-driven entrepreneurship is positively related to job growth and economic growth and development [8,12,38], as well as to technological and innovation progress [45]. On the contrary, it was found that necessity entrepreneurship is inversely linked to country-level innovation [45] and economic development [38]. Moreover, Dhahri et al. [46], examining the impact of opportunity and necessity-driven entrepreneurship on all three dimensions of sustainable development based on data for 20 developing countries, highlighted the positive impact of opportunity-driven entrepreneurship on all three dimensions of sustainable development (SD). Thus, it is claimed that opportunity entrepreneurs have a key role in achieving the SDGs. It is also shown that necessity-driven entrepreneurship only has a negative effect on the environmental sustainability dimension.

According to Baumol [47], entrepreneurship can be “productive” (contributing to an economy’s productivity growth), “unproductive” and even “destructive” due to the division of entrepreneurial activities into productive (based on innovation) and unproductive activities. This division is mainly determined by national institutional settings (rule of game) which can modify the incentive structures that prompt people to choose productive entrepreneurship, which fosters growth [48]. The systematic literature review conducted by Urbano et al. [37] claims that formal as well as informal institutions influence the nexus between entrepreneurship and economic growth. Moreover, from a holistic perspective, entrepreneurial activity emerges from the interaction between institutions, stakeholders and entrepreneurs themselves [25,49,50]. In the same light, Cao and Shi [51] found that structural gaps, resource scarcities and institutional voids are some of the main challenges countries face in adoption of the entrepreneurial ecosystems model from advanced economies to emerging economies. In the systemic and integrative approach, the entrepreneurship ecosystem is defined by the researchers of the Global Entrepreneurship and Development Institute as “the dynamic, institutionally embedded interaction between entrepreneurial attitudes, entrepreneurial abilities, and entrepreneurial aspirations by individuals, which drives the allocation of resources through the creation and operation of new ventures” [52] (p. 15). Based on this definition, the same researchers measured the entrepreneurship ecosystem through the Global Entrepreneurship Index (GEI), which reflects both opportunity-driven entrepreneurship and productive entrepreneurship that generates wealth [52].

Taking into consideration the above-mentioned perspectives, in the current paper, the Global Entrepreneurship Index was used to analyze the interlink between productive entrepreneurship and sustainable development.

Innovative entrepreneurship is considered one of the most “productive” forms of entrepreneurship [43,47]. Significant economic growth and a high level of economic development and national competitiveness are mostly generated by a small number of new firms or ventures, especially innovative new firms (innovative SMEs) and enterprises with high growth expectations [10,15,36,38]. Entrepreneurship, defined as a process of opportunity recognition and exploitation as well as a process of creation of new goods and services, is inseparable from innovation [16]. Thus, both innovation and entrepreneurship are two complementary concepts which are positively linked to each other [36,53,54]. Moreover, both concepts have been intertwined with economic development, as stated by Schumpeter [55]. Innovative entrepreneurs, according to Schumpeter [55], recognize and exploit opportunities, providing new combinations (innovations) as new methods of production and organization, new products, new markets, etc. Therefore, through innovation, the old structure is incessantly destroyed, and a new one is created (“creative destruction”), which means the creation of new ways to meet demands.

Entrepreneurs make a substantial contribution to growth, productivity and social welfare by using their knowledge and putting forth revolutionary innovations that serve our society [48]. Innovation and entrepreneurship are key factors of national competitiveness [56,57,58], alongside other influencing factors such as digitalization, institutions, infrastructure, information and communication technology (ICT) adoption, health and education [57,59]. Pradhan et al. [9] investigated the short- and long-term impacts of innovation and entrepreneurship on economic growth in the case of Eurozone countries and found that these three variables are intricately intertwined. In a sample of 64 countries worldwide (factor-driven, efficiency-driven and innovation-driven economies), Du and O’Connor [14] showed that improvement-driven opportunity entrepreneurship significantly contributes to increasing the national level of efficiency (expressed by GDP/labor). Ivanović-Đukić et al. [43], investigating the effect of various forms of innovative entrepreneurship (new technology development entrepreneurship, new product entrepreneurship and high growth expectation entrepreneurship) in 21 EU countries, found that all types of innovative entrepreneurship have positively influenced economic growth, but the effect is different depending on the degree of digitalization of a country. Digitalization is seen as a source of innovation on the one hand and, on the other, an outcome by restructuring business patterns in all sectors of economy, while the entrepreneurs are both the drivers and the affected agents of digital transformations [35].

Based on these premises, we developed the following hypotheses:

Hypothesis 1.

In the EU countries with higher economic development and national competitiveness, productive and innovative entrepreneurship is higher.

Hypothesis 2.

Productive and innovative entrepreneurship is higher in the EU countries with a high degree of digitalization of the economy and society.

2.2. From Productive and Innovative Entrepreneurship to Digital Entrepreneurship

Although digitalization affects more and more aspects of our lives, it mainly determines the transformation of the entrepreneurial process and business models in various sectors as a response to the change in a society’s needs [35]. For businesses, the digital transformation, induced by increased use of digital technologies, contributes to changes in products or services, business models and the way in which products or services are manufactured and delivered [20,60,61]. According to the European Commission [24], the distinguishing feature of digital enterprises is their high digital intensity (using new digital technologies such as mobile and cloud solutions, big data analytics and social media), which improves business operations, generates new business models and creates growth and jobs. Enterprises can benefit from the adoption of digital technologies in multiple ways, such as lower operational costs, higher annual turnover and productivity, more competitive advantages and new business model opportunities [7,60,61,62,63,64]. Martin-Rojas et al. [65] pointed out that corporate entrepreneurship is positively influenced by the acquisition and integration of technology and infrastructure, and in turn, it positively influences organizational performance in terms of profitability and growth. Moreover, researchers highlight that digital technologies generate a myriad of sources of opportunities for entrepreneurs to create a new generation of start-ups or new ventures [21,62,63,66], as well as a range of challenges to business owners and their firms [7]. These new business ventures and digital start-ups make novel technologies an integral part of their business models and operations [67]. Nambisan et al. [68] emphasized that both open innovation, which implies more open and distributed innovation models, and increasing digital platforms as a path to value generation have changed the nature of entrepreneurship throughout industries and have generated new opportunities for entrepreneurs and their firms.

As Jafari-Sadeghi et al. [20] underlined, over the last decade, entrepreneurship has been significantly transformed due to the interaction between digital technologies, platforms and infrastructures and its influence on value creation. The same empirical study proved that the technology readiness factors (ICT investment and ICT usage by businesses) had a positive impact on technology-driven entrepreneurship in European countries in the 2009–2015 period.

Taking into account that new digital technologies have transformed the entrepreneurial processes and outcomes, digital entrepreneurship is the result of the intersection of digital technologies and entrepreneurship [23,66]. Studies [66,67] highlighted that the core of digital entrepreneurship is the identification and pursuit of entrepreneurial opportunities presented by digital technologies. Digital entrepreneurship has not been clearly defined yet, and according to Kraus et al. [25], the body of research on digital entrepreneurship is still in a developmental stage.

In the current study, according to the European Commission’s definition [24] of digital entrepreneurship, and in line with other authors [25,69,70,71], digital entrepreneurship is seen as a subcategory of entrepreneurship which entails the digitalization of some or all physical aspects of a traditional business. It can also be seen “as the reconciliation of traditional entrepreneurship with the new way of creating and doing business in the digital era” [72] (p. 1). Taking into consideration that digital entrepreneurship is a multilevel phenomenon [7,63,66], theoretical and empirical approaches to digital entrepreneurship have spread across multiple levels, from characteristics of digital entrepreneurs and digital firms to communities as a whole ecosystem [21,23,66,73]. Researchers have shown that the success of a digital start-up depends on the characteristics of the entrepreneurs and internal operations of an enterprise, as well as the characteristics of the community to which it belongs [21,63]. Moreover, the integration of digital technologies in the business process does not rely solely on the internal changes of entrepreneurial processes and organizational management but also on external system conditions (institutional role, digital infrastructure and digital marketplace tendencies) and social attitudes (digital skills, digital trust, technology adoption, etc.) [35]. Other studies [23,62] revealed that digital entrepreneurship distinguishes itself by less bounded and more networked processes and outcomes which vary according to space and time [7]. Therefore, recent studies [66,67,74] focused on the digital entrepreneurship ecosystem which takes into account a multilevel perspective of digital entrepreneurship and captures the interaction between a larger number of actors (entrepreneurs, stakeholders, institutions, etc.). In the context of the current digital era, Autio et al. [21] suggest that an entrepreneurial ecosystem is a digital economy that utilizes the digital technologies and infrastructures available to make the pursuit of entrepreneurial opportunities easier with the help of new ventures by rethinking the business model. As Susan and Acs [44] showed, the digital entrepreneurial ecosystem lies at the intersection of the entrepreneurial ecosystem (agents (entrepreneurs) and institutions) and digital ecosystem (users and digital infrastructure). Autio et al. [26], in order to elaborate upon the European Index of Digital Entrepreneurship Systems (EIDES), took into account that an entrepreneurial ecosystem is a community of different stakeholders such as entrepreneurs, accelerators and advisors that use specialized resources to support entrepreneurial stand-up, start-up and scale-up through digitally upgraded business models. Empirical evidence at the EU level (27 EU countries and the United Kingdom) shows that the EIDES is positively correlated with economic development (GDP/capita) and national competitiveness (Global Competitiveness Index) [26]. In the current study, we take into consideration this perspective of the digital entrepreneurial ecosystem, and we use the EIDES to analyze digital entrepreneurship and its relationship with sustainable development.

This study explores how digital entrepreneurship is interlinked with productive and innovative entrepreneurship and investigates to what extent digital entrepreneurship can be influenced by the degree of digitalization of an economy and society in the case of EU countries. Thus, we developed the following hypotheses:

Hypothesis 3.

Digital entrepreneurship interrelates with productive and innovative entrepreneurship in EU countries.

Hypothesis 4.

The degree of digitalization of an economy and society in EU countries positively influences digital entrepreneurship.

2.3. Digital, Productive and Innovative Entrepreneurship for the SDGs’ Achievement

Dhahri and Omri [18] emphasized that there is a research gap in the holistic approach which assesses the impact of the entrepreneurial activity on the simultaneous reaching of the economic, social and environmental goals of sustainable development. Thus, the impact of entrepreneurship on the social and environmental dimensions of sustainable development is less known and researched than its economic impact [12]. Several researchers [6,19,75,76] recognized entrepreneurship as an efficient solution to environmental degradation and social inequality more than a potential cause of them. Moreover, empirical studies [77,78] have analyzed economic growth and its impact on poverty reduction through entrepreneurship, innovation and the development of new technology. In the same vein, Si et al. [77] stated that in recent years, inclusive entrepreneurship which incorporates more and more digital technology is seen as an effective solution to the alleviation of poverty and thus the reduction of social inequality.

Another empirical study [18], based on a sample of 20 developing countries in the 2001–2012 period, claims a positive contribution to the economic and social dimensions of sustainable development but a negative effect of entrepreneurship on the environmental dimension. Youssef et al. [79] found that entrepreneurship can contribute to all dimensions of sustainable development but only under specific circumstances related to innovation and institutional quality. Thus, despite the positive effect on economic growth, the authors proved, in the case of 17 African countries, that formal and informal entrepreneurship contribute to environmental degradation (CO₂ emissions), which is much higher in the case of informal entrepreneurship. The same authors [79] also proved that high levels of innovation and institutional quality enhance the strongly positive impacts of (formal and informal) entrepreneurship on all dimensions of sustainability.

In line with this, according to the Global Entrepreneurship Monitor Report [12], entrepreneurial activity is a key driver of the achievement of multiple SDGs, such as SDG 1 (“End poverty in all its forms everywhere”), SDG 8 (“Promote inclusive and sustainable economic growth, employment and decent work for all”) and SDG 10 (“Reduce inequality within and among countries”). Moreover, the UN [5] has recognized the key role of entrepreneurship in achieving all three dimensions of SD. Thus, the UN [5] has acknowledged entrepreneurship and innovation as key drivers to harness the national economic potential for achieving the SDGs. Regarding the contribution to the economic dimension of sustainability, the UN recognizes that entrepreneurship generates economic growth and creates jobs, fostering decent work and innovation. As for the social dimension of sustainability, it has been recognized that entrepreneurship can positively contribute to reducing inequalities, stimulate social cohesion and enlarge opportunities for all [5]. The contribution of entrepreneurship to the environmental dimension is also recognized by the UN [5], which highlights that entrepreneurship can respond to environmental challenges through promoting eco-friendly consumption patterns and sustainable practices, as well as through advocating for the implementation of novel digital technologies and resilience policies.

Although entrepreneurship is directly or indirectly related to all 17 SDGs, we focused in this paper on SDG 8 and SDG 9, the goals which highlight the interlink between economic growth, entrepreneurship, enterprises and innovation technology and its impact on sustainable development. Thus, SDG 8 (“Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”) emphasizes the importance of sustained economic growth in order to generate well-paid jobs and decent work, which improve the quality of life as well as eco-efficiency through resource efficiency in consumption and production [1,6]. The EU Report [80] underlines that the prosperity of European countries as well as the well-being of individuals are ensured by inclusive green economic growth and decent and productive employment. Moreover, Target 8.3 of SDG 8 reflects the importance of the closed interrelation between entrepreneurship and innovation in order to ensure both green and sustainable economic growth for all [1]. SDG 9 (“Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”) posits resilient and sustainable infrastructure, inclusive and sustainable industrialization and research and innovation as solutions to economic, social and environmental challenges [1]. In addition, this goal emphasizes the major role that innovation and technology play in reaching the SDGs. In this context, entrepreneurship, as both the engine of technological innovation and the answer to it, can generate economic growth, labor productivity and income, competitiveness, resource efficiency and job creation, as well as improve health, education and well-being for all [4,6,80].

Moreover, recent studies and national and international reports [2,27,28,81,82] acknowledged the essential role of technological innovation and digital technology in achieving every one of the 17 SDGs. For example, based on a sample of 75 low-, middle- and high-income countries, Omri [81] found that the effect of technological innovation on the Sustainable Development Goals depends on the stages of economic development. Thus, technological innovation simultaneously contributes to the economic, environmental and social dimensions of sustainable development only in high-income countries. In the case of middle-income countries, technological innovation only influences the economic and environmental dimensions, and in the case of low-income countries, no effects on these dimensions were identified. Several studies [27,70,83] have highlighted that the optimization of resources through the adequate adoption and use of digital technology can be enhanced, and businesses and entrepreneurship can become more sustainable. Furthermore, they pointed out that the best solution to achieve the SDGs is (digital) entrepreneurship, based on the principles of cutting-edge technologies, open innovation and social business.

Thus, although several empirical studies explore the role of digital technologies and entrepreneurship in achieving sustainable development, there is a lack of empirical research on the effects of digital entrepreneurship on the SDGs. Therefore, this study attempts to fill this research gap by exploring to what extent digital entrepreneurship can be a key factor for the achievement of the SDGs in the EU member states. Although this paper focuses more on the impact of digital entrepreneurship on SDG 8 and SDG 9, it offers an overall picture of the extent to which each country fulfils all the SDGs. As well as assessing the impact of digital entrepreneurship on the simultaneous attainment of the economic, social and environmental goals of sustainable development, we also take into consideration the total SDGs as the result of the average of all 17 SDGs. Given these aspects, we developed the following hypotheses:

Hypothesis 5.

Digital entrepreneurship has a positive impact on the achievement of the SDGs (Total SDGs, SDG 8 and SDG 9).

Hypothesis 6.

There are differences and common features between the EU countries based on their interrelations between (digital, innovative and productive) entrepreneurship, digitalization, economic development, national competitiveness and achievement of the SDGs (Total SDGs, SDG 8 and SDG 9).

3. Research Data and Methods

3.1. Data and Sample

In order to test the research hypotheses and thus achieve the aim of this study, we analyzed the variables described in Table 1. The data analysis was performed for the 2018–2019 period. The limitation of the analysis to this period was determined by the fact that some composite indexes (EIDES, SDGs Index, GCI 4.0. and NRI) were calculated only for the last 2 or 3 years. Because of time lags in data creation and release, the impact of the COVID-19 pandemic was not identified.

Our sample consisted of 25 countries from the EU without Luxembourg (an outlier in several variables) and Malta (the country for which some statistical data were unavailable). It is known that the EU member states adopted all 17 SDGs of the 2030 Agenda for Sustainable Development [1] and have implemented, since 2015, the main strategic measures related to digitalization that aimed to build the EU digital society (e.g., “A Digital Single Market Strategy for Europe) [84]. In addition, in order to highlight the progress made by EU countries regarding digital performance [85], the Digital Economy and Society Index (DESI) has been calculated since 2015. Therefore, the 2018–2019 period allowed the illustration of the entrepreneurship ecosystem in the context of digitalization of an economy and society and its effect on the achievement of the SDGs at the level of EU countries.

The statistical data on the analyzed variables were collected from the Eurostat Database [86], European Innovation Scoreboard Database [87], Global Entrepreneurship and Development Institute Database [88], DESI Datasets [89], Autio et al. [26], World Economic Forum Reports [59,90], Portulans Institute Reports [82,91] and SDSN and IEEP Reports [92,93]. The results of the statistical descriptions of the analyzed variables—mean, maximum and minimum values and standard deviation—are presented in Table 1.

Table 1. Descriptive statistics (n = 25).

Variables	Minimum	Maximum	Mean	Std. Deviation
Digital, innovative and productive entrepreneurship
SMEs introducing product innovations (SMEs_p_innov) 1	6.19 (RO)	38.41 (EL)	26.178	9.377
SMEs introducing business process innovations (SMEs_b_innov) 1	7.73 (RO)	51.97 (EL)	36.888	12.556
Global Entrepreneurship Index (GEI) score (0–100 values)	28.94 (BG)	76.80 (DK)	52.899	13.912
European Index of Digital Entrepreneurship Systems (EIDES) score (0–100 values)	25.30 (BG)	73.95 (DK)	45.080	15.256
Digitalization, competitiveness and economic development
Digital Economy and Society Index (DESI) score (0–100 values)	35.11 (BG)	70.22 (FI)	51.336	9.878
Network Readiness Index (NRI) score (0–100 values)	54.82 (RO)	82.70 (SE)	67.344	8.773
Global Competitiveness Index 4.0 (GCI 4.0) score (0–100 values)	61.00 (HR)	82.40 (NL)	71.754	6.686
GDP per capita in PPS * (as a percentage of EU-27 average = 100%)	52.00 (BG)	192.00 (IE)	95.640	30.450
Sustainable development goals
Total SDGs Index 2 score (0–100 values)	56.45 (BG)	80.23 (SE)	68.895	6.807
SDG 8 3 score (0–100 values)	48.02 (EL)	89.50 (DE)	74.778	10.928
SDG 9 4 score (0–100 values)	21.92 (BG)	91.66 (SE)	56.742	22.768

Note: ¹ Share of total SMEs (%). ² Total SDGs Index scores = average of all 17 SDGs scores. ³ “Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”. ⁴ “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”. * PPS = purchasing power standards. BG—Bulgaria; DE—Germany; DK—Denmark; EL—Greece; FI—Finland; HR—Croatia; IE—Ireland; NL—Netherland; RO—Romania; SE—Sweden. Source: own calculations based on [26,59,82,86,87,88,89,90,91,92,93].

Table 1. Descriptive statistics (n = 25).

Variables	Minimum	Maximum	Mean	Std. Deviation
Digital, innovative and productive entrepreneurship
SMEs introducing product innovations (SMEs_p_innov) 1	6.19 (RO)	38.41 (EL)	26.178	9.377
SMEs introducing business process innovations (SMEs_b_innov) 1	7.73 (RO)	51.97 (EL)	36.888	12.556
Global Entrepreneurship Index (GEI) score (0–100 values)	28.94 (BG)	76.80 (DK)	52.899	13.912
European Index of Digital Entrepreneurship Systems (EIDES) score (0–100 values)	25.30 (BG)	73.95 (DK)	45.080	15.256
Digitalization, competitiveness and economic development
Digital Economy and Society Index (DESI) score (0–100 values)	35.11 (BG)	70.22 (FI)	51.336	9.878
Network Readiness Index (NRI) score (0–100 values)	54.82 (RO)	82.70 (SE)	67.344	8.773
Global Competitiveness Index 4.0 (GCI 4.0) score (0–100 values)	61.00 (HR)	82.40 (NL)	71.754	6.686
GDP per capita in PPS * (as a percentage of EU-27 average = 100%)	52.00 (BG)	192.00 (IE)	95.640	30.450
Sustainable development goals
Total SDGs Index 2 score (0–100 values)	56.45 (BG)	80.23 (SE)	68.895	6.807
SDG 8 3 score (0–100 values)	48.02 (EL)	89.50 (DE)	74.778	10.928
SDG 9 4 score (0–100 values)	21.92 (BG)	91.66 (SE)	56.742	22.768

Note: ¹ Share of total SMEs (%). ² Total SDGs Index scores = average of all 17 SDGs scores. ³ “Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”. ⁴ “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”. * PPS = purchasing power standards. BG—Bulgaria; DE—Germany; DK—Denmark; EL—Greece; FI—Finland; HR—Croatia; IE—Ireland; NL—Netherland; RO—Romania; SE—Sweden. Source: own calculations based on [26,59,82,86,87,88,89,90,91,92,93].

3.2. Measures

3.2.1. Digital, Innovative and Productive Entrepreneurship

In order to highlight the multidimensional nature of entrepreneurship in the analyzed countries, this study used three complex indicators: the Global Entrepreneurship Index (GEI) for productive entrepreneurship, EIDES for digital entrepreneurship and the innovative SMEs for innovative entrepreneurship.

The quality of entrepreneurship in a country as well as the level of depth of the entrepreneurial ecosystem which fosters it were measured by the Global Entrepreneurship Index (GEI) [52]. Thus, the GEI is a composite index which includes 3 sub-indices (entrepreneurial abilities, attitudes and aspirations) and 14 pillars of the entrepreneurship ecosystem [52], which include both an individual and institutional component that can be connected to the micro- and the macro-level aspects of entrepreneurship. An overall GEI score close to 100% reflects highly productive entrepreneurship and a healthy entrepreneurship ecosystem which is characterized by the productive use of resources and, consequently, an increase in an economy’s ability to generate wealth and jobs [94]. According to the statistical data for the 2018–2019 period (Table 1), at the EU-25 level, Denmark was the leader with a GEI maximum value of 75.9%. The lowest value (28.9%) was recorded by Bulgaria, which reflects that this country has an extremely low level of productive entrepreneurship and is very far from a healthy entrepreneurship ecosystem.

For analyzing digital entrepreneurship, we used the EIDES, provided by Autio et al. [26]. This composite index assesses the physical and digital prerequisites for stand-up, start-up and scale-up ventures in the EU member states, and it can provide a useful in-depth description of a country’s systems of entrepreneurship [22] as an instrument that can help comprehend and evaluate the degree of the digital entrepreneurial ecosystem. The EIDES was developed based on the general and systemic framework conditions at the national level [26]. The values of the overall EIDES score are on a scale from 0 (lowest) to 100 (highest). The average of the EIDES scores for the 2018–2019 period at the EU-25 level was of 45.08 points, which was 28.87 points less than Denmark, the best EIDES performer in the EU. The minimum value was recorded by Bulgaria (Table 1).

Other complex indicators that were used in order to capture entrepreneurship, especially the innovative type, were SMEs introducing product innovations and SMEs introducing business process innovations as a share of the total SMEs. According to the EC Report [95], these indicators form one of the twelve innovation dimensions (the innovators dimension) of the European Innovation Scoreboard, which assesses the innovation performance of the EU member states. Statistical data were obtained from the EIS Database [87] for the 2018–2019 period. It is noticeable that the mean value of the share of SMEs introducing business process innovations (which combine process, marketing and organizational innovations) was higher than the mean value of the share of SMEs introducing product innovations in EU-25 countries (36.88% against 26.17%, respectively; Table 1). As can be seen in Table 1, the mean values of these indicators indicate large differences between the EU-25 countries. Greece was the leader of both types of innovative SMEs (52% and 36%, respectively). Romania recorded the lowest values on both indicators (7.73% and 6.19%, respectively).

3.2.2. Digitalization, Competitiveness and Economic Development

The degree of digitalization of the economies and societies at the EU level was analyzed based on two complex indexes: the DESI and the Network Readiness Index (NRI). The DESI, measured by the European Commission [85] since 2015, combines 37 indicators into 5 main interconnected dimensions: connectivity, use of the internet, human capital, integration of digital technology (business digitization and e-commerce) and digital public services. The DESI scores are on a scale from 0 (lowest) to 100 (highest). The statistical data for the 2018–2019 period (Table 1) show that the highest score was recorded by Finland (70.02), while Bulgaria recorded the weakest score (35.1). The NRI was initially launched by the World Economic Forum in 2002 in order to evaluate how information and communication technologies (ICTs) influence the development and competitiveness of nations [91]. Starting with 2019, the NRI was redesigned based on a renewed methodology by the Portulans Institute’s researchers to reflect how digital technology and people can be used in an efficient governance system so that they influence our society, economy and the environment in a positive way [91], thus accelerating the achievement of the SDGs. Therefore, in our analysis, we used the NRI scores for the 2018–2019 period. The NRI integrates 60 indicators into 4 fundamental dimensions: technology, people, governance and impact (economy, quality of life and SDG contribution) [82]. The NRI scores are based on a 0–100 scale, where 0 is the worst and 100 is the best. The average of the NRI scores for the 2018–2019 period at the EU-25 level was 67.34 points, being 15.36 points less than Sweden, the best NRI performer in the EU. Romania scored the lowest in terms of network readiness (54.82) at the EU level (see Table 1).

The economic development and the level of competitiveness of the national economy were analyzed based on two main indicators: GDP per capita in PPS (as a percentage of the EU-27 average = 100%) and Global Competitiveness Index 4.0 (GCI 4.0). The statistical data (Table 1) show the significant differences between EU countries in terms of the level of GDP per capita as proxies for economic development, which ranged from 52%, in Bulgaria, to 192%, in Ireland (EU-28 = 100%).

The GCI 4.0 was introduced by the World Economic Forum in 2018 and gives a comprehensive map of the driving forces which generate economic growth, high productivity and human development in the Fourth Industrial Revolution era [59]. This complex index is calculated based on 103 individual indicators which are organized into 12 pillars: infrastructure, institutions, macroeconomic stability, health, skills, labor market, product market, market size, financial system, business dynamism, ICT adoption and innovation capability [59]. A country’s performance in terms of competitiveness is reported as a progress score on a scale from 0 to 100. The maximum value (100) is “the frontier”, the case in which an issue no longer hampers productivity growth, and consequently, every country should set moving closer to the frontier as a key objective in the overall GCI 4.0 as well as for each of its components. An average GCI 4.0 score of 71.75 for the whole EU-25 countries and for the 2018–2019 period (see Table 1) reflects the EU competitiveness deficit. The Netherlands, the best performer (82.4), still fell 17.6 points short of the frontier. The worst performance in the GCI 4.0 was produced by Croatia (61 points).

3.2.3. Sustainable Development Goals

Achievements in sustainable development at the EU level were emphasized based on three complex indicators (Total SDG Index score (Total SDGs), Sustainable Development Goal 8 Index score (SDG 8) and Sustainable Development Goal 9 Index score (SDG 9)) calculated by the Sustainable Development Solutions Network (SDSN) in cooperation with IEEP [92,93] for the last 2 years (2018–2019). We mention that at EU level, Eurostat has released an SDG dataset and “Sustainable development in the European Union” reports annually since 2016 (e.g., [80]), but this report does not review the overall EU performance based on time-bound targets and does not predict how much “distance to targets” individual EU member states still need to cover to reach the SDGs [96]. Therefore, we used the Total SDGs Index score, SDG 8 score and SDG 9 score in order to highlight each country’s performance on a scale from 0 to 100 [92]. These indicators can show the percentages toward achievement of the SDGs (for the 17 goals overall and each goal).

The Total SDG Index score for each EU country is the result of the average of all 17 SDG scores. Every SDG score has an equal weight, underlining the commitment of policymakers to treat all SDGs both equally and indivisibly [93]. The score of SDG 8, “Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”, is grounded in eight indicators which are mainly related to employment, unemployment, disposable income, quality of work and in-work poverty. The score of SDG 9, “Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation”, is based on eight indicators which mainly cover R&D expenditure and personnel, digital skills, internet connectivity, digital infrastructure, etc. [92].

The average scores for Total SDGs, SDG 8 and SDG 9 for the 2018–2019 period showed that the EU-25 countries achieved 74.78% for SDG 8, 68.9% for Total SDGs and only 56.74% for SDG 9 (see Table 1). As can be seen in Table 1, there are significant differences among the EU-25 countries in terms of all SDG scores analyzed. Sweden tops both the Total SDG Index and SDG 9 with maximum values of 80.23% and 91.66%, respetively. As for SDG 8, Germany was leader in the EU-25 countries, while Greece was the worst performer. The largest discrepancies among EU countries were recorded in terms of SDG 9, with a minimum value of 21.92 (Bulgaria) and a maximum value of 91.66 (Sweden). Thus, Bulgaria is very far from achieving this SDG and also in terms of Total SDGs (minimum value of 56.45%).

3.3. Statistical Methods

We used descriptive statistics, correlation and regression analysis to test the first five research hypotheses (H1–H5). The intensity of the link between the analyzed variables was evaluated based on the Pearson correlation coefficient (r). The calculation of the Pearson correlation coefficient is given in Equation (1):

$${\mathrm{r}}_{\mathrm{x},\mathrm{y}} {}_{=}\frac{\mathrm{S}\left(\mathrm{x},\mathrm{y}\right)}{\mathrm{Sx}·\mathrm{Sy}}=\frac{{\sum }_{\mathrm{i}}^{\mathrm{n}}\left(\mathrm{xi}- \overline{\mathrm{x}}\right)·\left(\mathrm{yi}- \overline{\mathrm{y}}\right)}{\sqrt{{\sum }_{\mathrm{i}}^{\mathrm{n}}{\left(\mathrm{xi}- \overline{\mathrm{x}}\right)}^2}·\sqrt{{\sum }_{\mathrm{i}}^{\mathrm{n}}{\left(\mathrm{yi}- \overline{\mathrm{y}}\right)}^2}}$$

(1)

where S(x,y) is the sample covariance, Sx·Sy is the product of the sample standard deviations of X and Y, respectively, and $\overline{\mathrm{x}} \text{and} \overline{\mathrm{y}}$ are the sample means of X and Y, respectively.

A value of this coefficient closer to −1 or 1 means a stronger negative or positive correlation [97,98].

As opposed to correlation analysis, which indicates only the strength of association between the analyzed variables, regression analysis shows the difference between the dependent and independent variables [99]. Moreover, it reflects the impact of the independent variable on the dependent variable. Therefore, to identify a functional relation between the analyzed variables (DESI, NRI, EIDES, SDG 8, SDG 9, Total SDGs, etc.), we used simple linear regression analysis according to Equation (2):

Y = α + β × X + ε

(2)

where Y is the dependent variable, X is the explanatory variable, α and β are regression coefficients and ε is the residual or error.

The least squares method was used to estimate the regression coefficients. The validity of the regression model was assessed based on the Fisher–Snedecor (F) statistic. For checking if the errors (residual) of the regression model were affected by autocorrelation, the Durbin–Watson test was applied. A lack of autocorrelation between errors was identified when the values of the Durbin–Watson (DW) test ranged from 1.5 to 2.5. The coefficient of determination (R²), based on which the quality of the prediction is assessed, indicates to what extend the variance in the dependent variable is explained by the independent variables [99].

Principal component analysis (PCA) and cluster analysis (CA) were employed to test H6. In the first stage, considering the main advantage of the PCA being reducing the dimensionality of a dataset which includes a large number of interrelated variables (in our case, 11 variables; see Table 1) to a few principal components or factors that reflect the whole information comprised in the original data [99,100,101], we used this multivariate technique (PCA with Varimax rotation and Kaiser normalization). The number of principal components was determined based on multiple criteria: Catell’s scree plot criterion, the Kaiser criterion or eigenvalue-greater-than-one rule and the percentage of cumulative variance, which retains only those components that indicate a large percentage (between 70 and 90%) of the total variation of the initial variables [99,100].

The choice of using the PCA in this research was based first on the main objective of this method: to reduce the dimensionality of a set of data with many variables while preserving as much statistical information as possible and to create an uncorrelated “new” dataset (a linear combination of variables) for the subsequent multivariate analysis (such as cluster analysis). In the same vein, Kovács et al. [98] compared the PCA “to a shadow game, where the shadow image of a complex spatial shape is projected onto a planar surface so that the characteristic properties of the figure are lost as little as possible” [98] (p. 8). Compared with other data reduction techniques (e.g., factor analysis), PCA is more efficient, as it allowed us to find the components that maximized the variance, which in turn were used in the cluster analysis. Secondly, it was taken into consideration that this multivariate statistical method can contribute to solving the drawbacks generated by the use of different measurement units for the initial variables and the high variations of the covariance coefficients [99,100,102]. Last but not least, PCA and cluster analysis have been widely used by researchers to explore the social and economic differences and similarities between different nations [13,16,103].

In the second stage, the principal components obtained were used in the cluster analysis to identify the relatively homogenous groups of EU countries. The number of the cluster was determined based on hierarchical cluster analysis. Ward’s method and the Euclidian distance were employed. Then, the structure of the clusters was based on k-means cluster analysis [102,103]. Thus, these complex statistical methods of data analysis were applied to classify the EU-25 countries and provide a comparative view of the interplay between all 11 variables (see Table 1).

For data processing, IBM SPSS Statistics 26.0 (IBM, Armonk, NY, USA) was used.

4. Results and Discussion

As shown in the previous section, based on descriptive analysis (Table 1), there were large differences between the EU-25 countries in terms of each of the variables analyzed (GEI, EIDES, SMEs_b_innov, SMEs_p_innov, DESI, NRI, GCI 4.0, GDP/capita, Total SDGs, SDG 8 and SDG 9), with the eastern and southern countries generally lagging behind the western and northern countries. Bearing this empirical context in mind, we focused on the statistical analysis of interrelations between (productive, innovative and digital) entrepreneurship and economic development as well as competitiveness and sustainable development in the framework of a digital economy and society in order to find out if these gaps could be explained by the impact of entrepreneurship.

The results of the correlation analysis (

Table 2. Correlation matrix.

Pearson Correlation (r)	1	2	3	4	5	6	7	8	9	10	11
1. SMEs_p_innov	1
2. SMEs_b_innov	0.951 **	1
3. GEI	0.502 *	0.454 *	1
4. EIDES	0.500 *	0.429 *	0.940 **	1
5. DESI	0.449 *	0.378	0.846 **	0.928 **	1
6. NRI	0.538 **	0.465 *	0.947 **	0.985 **	0.913 **
7. GCI 4.0	0.449 *	0.400 *	0.911 **	0.945 **	0.827 **	0.969 **	1
8. GDP/capita	0.447 *	0.464 *	0.853 **	0.779 **	0.687 **	0.770 **	0.765 **	1
9. Total SDGs	0.391	0.338	0.789 **	0.816 **	0.794 **	0.850 **	0.806 **	0.560 **	1
10. SDG 8	0.356	0.269	0.767 **	0.802 **	0.784 **	0.805 **	0.731 **	0.572 **	0.733 **	1
11. SDG 9	0.586 **	0.543 **	0.890 **	0.923 **	0.809 **	0.945 **	0.936 **	0.749 **	0.848 **	0.724 **	1

Note: ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Source: own calculations based on [26,59,82,86,87,88,89,90,91,92,93].

) highlighted that in all EU-25 member states, productive entrepreneurship (measured by the GEI) was strongly positively correlated with both the GDP per capita (r = 0.853) and GCI 4.0 (r = 0.911). The same positive correlation (but of a lower intensity) was set between innovative entrepreneurship (SMEs introducing product innovations and SMEs introducing business process innovations as a share of the total SMEs) on the one hand and, on the other hand, GDP per capita (r = 0.447 and r = 0.464, respectively) and GCI 4.0 (r = 0.449 and r = 0.400, respectively). Additionally, a positive link was identified between innovative entrepreneurship and productive entrepreneurship (r = 0.502 and r = 0.454, respectively), confirming that innovation and entrepreneurship are interlinked [35,53,54]. Therefore, in the EU countries where productive and innovative entrepreneurship was higher (especially the advanced economies of the EU), the level of economic development and competitiveness was also higher, and vice versa. This confirms Hypothesis 1. These findings were sustained by other studies [39,56,57,94], which have highlighted the positive influence of entrepreneurship, characterized by a high level of innovativeness and quality, on macroeconomic performance (expressed by GDP/capita, GCI, total factor productivity, employment, etc.).

The results from Table 2 and Figure 1 illustrate that productive entrepreneurship (GEI) was very strongly positively associated with the DESI (r = 0.846) and NRI (r = 0.947). SMEs introducing product innovations were moderately linked both with the DESI (r = 0.449) and NRI (r = 0.538). In the case of SMEs introducing business process innovations, a moderately positive and statistically significant link was identified only with the NRI (r = 0.465). Therefore, Hypothesis 2—productive and innovative entrepreneurship is higher in the EU countries with a high degree of digitalization of economy and society—was confirmed and supported by other empirical studies [26,29].

Moreover, the GDP per capita and GCI 4.0 had a positive correlation with the DESI as well as with the NRI (Table 2), which means that high economic development and high national competitiveness go hand in hand with highly digitalized countries. Therefore, it is confirmed that the economic performance of countries has been significantly influenced by developments in digital technologies and their adoption [29,64].

Corroborating the positive bivariate link between productive entrepreneurship and innovative entrepreneurship with the positive bivariate link between the EIDES and GEI (r = 0.940) on the one hand and the EIDES and innovative SMEs (r = 0.500 and r = 0.429, respectively) on the other hand proves that, in EU member states, digital entrepreneurship is interrelated with productive and innovative entrepreneurship. Therefore, Hypothesis 3 was supported, and it is in line with [26], which showed a strong positive link between the EIDES and GEI in the context of European economies.

In order to test Hypothesis 4, simple linear regression analysis (Figure 2) was applied. The results pointed out that in the EU countries, digital entrepreneurship (measured by the EIDES) was positively influenced by the DESI (β = 0.928). The regression model was statistically significant (F (1, 23) = 142.803; p = 0.000; Durbin–Watson statistic = 2.148) and accounted for 86.1% of the variance in digital entrepreneurship (R² = 0.861, adjusted R² = 0.855).

We also conducted an analysis of the impact of the NRI on digital entrepreneurship (Figure 2). The estimated regression model was statistically significant (F (1, 23) = 757.596; p = 0.000; Durbin–Watson statistic = 2.281) and accounted for 97.1% of the variance in digital entrepreneurship (R² = 0.971, adjusted R² = 0.969). Therefore, it was found that the NRI positively influenced digital entrepreneurship (β = 0.985).

These findings emphasize that in the EU countries where the DESI and NRI are higher, the level of digital entrepreneurship is also high, and consequently, the degree of digitalization of the economy and society was identified as a key driver of digital entrepreneurship, confirming Hypothesis 4. Our results confirmed previous research findings in the context of European economies [20], which highlighted a positive impact of the technology readiness factors (ICT investment and ICT usage by businesses) on technology-driven entrepreneurship. This fact reflects the need to increase the access to and use of digital technologies for both people and business through a more effective governance structure to create an adequate framework for the development of digital entrepreneurship.

As noted in Table 2, two of the four variables which reflect the level of entrepreneurship (the GEI and EIDES) were significantly and positively correlated with variables specific to the achievement of SDGs (Total SDGs, SDG 8 and SDG 9). In the case of innovative SMEs, only SMEs_p_innov was significantly positively correlated with SDG 9. In addition, a higher intensity for the EIDES–SDGs link than both the GEI–SDGs link and SMEs_p_innov–SDGs link can be noticed (Table 2).

If we focus on the EIDES–SDGs link (Table 2 and Figure 3), the results of the correlation analysis highlight that the EIDES was very strongly correlated with SDG 9 (r = 0.923), Total SDGs (r = 0.816) and SDG 8 (r = 0.802). Furthermore, the regression models (models 1–3,

Table 3. Regression results: the impact of digital entrepreneurship (EIDES) on the achievement of SDGs (Total SDGs, SDG 8 and SDG 9).

Models		Unstandardized Coefficients		Standardized Coefficients	t-Statistics	Sig.
		B	Std. Error	Beta
Model 1 1 EIDES–Total SDGs	Constant	52.485	2.555		20.542	0.000
	EIDES	0.364	0.054	0.816	6.767	0.000
Model 2 2 EIDES–SDG 8	Constant	48.884	4.238		11.534	0.000
	EIDES	0.574	0.089	0.802	6.437	0.000
Model 3 3 EIDES–SDG 9	Constant	−5.331	5.700		−0.935	0.359
	EIDES	1.377	0.120	0.923	11.473	0.000

¹ Dependent variable: Total SDGs; R² = 0.666, adjusted R² = 0.651; std. error of the estimate = 4.020787; Durbin–Watson statistic = 2.266; F (1, 23) = 45.787, p < 0.001. ² Dependent variable: SDG 8; R² = 0.643, adjusted R² = 0.628; std. error of the estimate = 6.66952; Durbin–Watson statistic = 1.637; F (1, 23) = 41.434, p < 0.001. ³ Dependent variable: SDG 9; R² = 0.851, adjusted R² = 0.845; std. error of the estimate = 8.96971; Durbin–Watson statistic = 2.236; F (1, 23) = 131.639, p < 0.001. Source: own calculations based on [26,93,92].

) pointed out a significantly positive impact by digital entrepreneurship on sustainability (Total SDGs, SDG 8 and SDG 9). All models were statistically significant (F (1, 23) = 45.787, p = 0.000, R² = 0.666 for model 1; F (1, 23) = 41.434, p = 0.000, R² = 0.643 for model 2; F (1, 23) = 131.639, p = 0.000, R² = 0.851; see Table 3). The EIDES accounted for 66.6% of the variance in Total SDGs, 64.3% of the variance for SDG 8, and 85.1% of the variance for SDG 9. Thus, the high level of sustainable development in the EU member states can be explained by the existence of a high level of digital entrepreneurship. Based on these results, Hypothesis H5—digital entrepreneurship has a positive impact on the achievement of SDGs (Total SDGs, SDG 8 and SDG 9)—is confirmed.

In order to find out if there were differences and common features between the EU countries based on their interrelations between (digital, innovative and productive) entrepreneurship, digitalization, economic development, national competitiveness and the Sustainable Development Goals (Hypothesis 6), we took into account the cumulative influence of all 11 variables (GEI, EIDES, SMEs_b_innov, SMEs_p_innov, DESI, NRI, GCI 4.0, GDP/capita, Total SDGs, SDG 8 and SDG 9; see Table 1 and Table 2), and we employed complex statistical methods of data analysis (PCA and CA). As shown above, statistically significant correlations were identified between the initial variables included in the PCA (see correlation matrix, Table 2).

The overall Measure of Sampling Adequacy (MSA) was checked based on the Kaiser–Meyer–Olkin (KMO) indicator, whose value of 0.828 exceeded the minimum requirement of 0.50 [102]. Bartlett’s test of sphericity (approximate. chi-square = 423.574, p-value = 0.000) supported the overall MSA results, a fact which indicates the suitability of the variables set for the principal component analysis.

Based on the PCA with the Varimax rotation method with Kaiser normalization (rotation converged in 3 iterations), the 11 initial variables were divided into two components which explained 87.615% of the total variance (

Table 4. Total variance and eigenvalues explained.

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	8.202	74.567	74.567	8.202	74.567	74.567
2	1.435	13.048	87.615	1.435	13.048	87.615
…	…	…	…
11	0.003	0.028	100.000

Note: The extraction method was principal component analysis.

).

The first principal component (PC1) was strongly and positively correlated with 9 of the original variables and accounted for 74.567% of the total variance in the observed variables (Table 4 and

Table 5. Principal components for EU-25 countries (rotated component matrix).

Initial Variables	PC1	PC2
European Index of Digital Entrepreneurship Systems (EIDES)	0.948	0.258
Network Readiness Index (NRI)	0.947	0.294
Global Competitiveness Index 4.0 (GCI 4.0)	0.927	0.230
Global Entrepreneurship Index (GEI)	0.914	0.290
Digital Economy and Society Index (DESI)	0.898	0.202
SDG 9	0.875	0.386
Total SDGs	0.863	0.153
SDG 8	0.846	0.087
GDP/capita	0.743	0.337
SMEs introducing business process innovations (SMEs_b_innov)	0.197	0.969
SMEs introducing product innovations (SMEs_p_innov)	0.267	0.943

Note: The extraction method was PCA, and the rotation method was Varimax with Kaiser normalization. The rotation converged in 3 iterations.

). Thus, all nine variables had a positive contribution in the formation of this principal component and reflected the level of digital and productive entrepreneurship (EIDES and GEI), degree of digitalization (DESI and NRI), national competitiveness (GCI 4.0), economic development (GDP/capita) and Sustainable Development Goals (Total SDGs, SDG 8 and SDG 9). The second principal component (PC2), which explained 13.048% of the total variance (Table 4), was strongly positively correlated with two variables (Table 5), reflecting the level of innovative entrepreneurship (SMEs_p_innov and SMEs_b_innov).

In the next step, the two principal components (PC1 and PC2) were used in the cluster analysis to classify the EU-25 member states. The 3 formed clusters were statistically significant according to the results of the ANOVA analysis (for PC1, F (3, 21) = 23.319, p < 0.001; for PC2, F (3, 21) = 66.709, p < 0.001; Figure 4 and

Table 6. The results of the cluster analysis: final cluster centers and ANOVA.

Final Cluster Centers				ANOVA
	Cluster 1	Cluster 2	Cluster 3	Cluster		Error		F	Sig.
				Mean Square	df	Mean Square	df
PC 1	0.807	−0.455	−1.084	8.154	2	0.350	22	23.319	0.000
PC 2	0.331	−1.397	0.968	10.301	2	0.154	22	66.709	0.000

).

Cluster 1 was powerfully correlated with PC1 (0.807, Table 6) and included 12 countries (Denmark, Finland, Ireland, the Netherlands, Sweden, Austria, Belgium, Germany, France, the Czech Republic, Estonia and Slovenia). All countries (except the Czech Republic, Estonia and Slovenia) are “old EU member states”, being characterized by a high level of digitalization of the economy and society (DESI and NRI) and of national competitiveness (GCI 4.0). Compared with the other clusters, this cluster’s scores for digital entrepreneurship (EIDES) and productive entrepreneurship (GEI) were the highest. Additionally, the economic development (GDP/capita) and sustainable development goals (Total SDGs, SDG 8 and SDG 9) scored the highest (Figure 5, Figure 6 and Figure 7). These results emphasize that a favorable (digital and non-digital) infrastructure and institutions specific to the countries in this cluster (particularly in the northern countries of the EU) stimulated both productive and digital entrepreneurship, which in turn generated inclusive and green economic growth and development. As displayed in Figure 7, SDG 8 and SDG 9 recorded higher values related to the overall SDGs (Total SDGs), a fact which shows that the countries in this cluster performed better in terms of socioeconomic goals, but they have to address major challenges in achieving other SDGs linked to climate action, responsible consumption and production and biodiversity. In spite of these achievements, our results show that this cluster displayed an EIDES average value per cluster of 57.8% (of 100%, “the frontier”) and an attainment value of around 74% of the Total SDGs established for 2030. These findings suggest that there is room for improvement both in terms of digital entrepreneurship and the SDGs. To achieve all SDGs in these countries, digital technologies should be adopted, integrated and exploited by business and society so that they ensure a dynamic balance between the economic, social and ecological dimensions of sustainability.

Moreover, the countries in this cluster showed high heterogeneity. Inclusion of the Czech Republic, Estonia and Slovenia in this cluster was due to higher values for indicators specific to the digitalization of the economy (DESI and NRI), digital entrepreneurship (EIDES) and sustainable development compared with countries in other clusters, but they were smaller compared with the peer countries in the cluster.

Cluster 2 was very strongly and negatively correlated with PC 2 (−1.397, Table 6) and weakly and negatively correlated with PC 1 (−0.455, Table 6), thus being placed in the third quadrant (except Spain). This cluster contained seven countries (Bulgaria, Poland, Hungary, Romania, Slovakia, Latvia and Spain) mainly characterized by their lowest values in terms of innovativeness in business (SMEs introducing product innovations as well as SMEs introducing business process innovations) and SDG 9 (Figure 5, Figure 6 and Figure 7).

Spain’s position in the fourth quadrant (Figure 4) was due to the best value of the EIDES, DESI, NRI, GCI 4.0, GDP/capita, and SDG 9 (all variables defining PC1) compared with other countries in the same cluster. Excluding Spain, this cluster included former centrally planned economies and new EU member states, which are still largely lagging behind the EU’s mature economy markets. These countries had the lowest level of GDP/capita, which means that they had fewer resources and would be less able to better invest in innovation and in the kinds of institutions and infrastructure that are included in the EIDES and GEI.

Bulgaria and Romania were the countries with the worst values of all 11 variables, both related to peer countries in the cluster and to all EU-25 countries. These large gaps between EU countries and the fragmentation itself can be seen as an obstacle to the adoption and use of EU digital solutions on a large scale. The lowest score for SDG 9 (of 36.4 points) reflects that this cluster obtained 47.83% SDG 9 performance in cluster 1 (best performer) (Figure 7). The poorest performance of this cluster in terms of SDG 9 (Industry, Innovation and Infrastructure), and the existence of very large gaps related to other clusters underlines a necessity to enhance productivity and innovation to boost convergence across EU countries, as also emphasized by the EU report [92].

Cluster 3 included six countries (Croatia, Cyprus, Greece, Italy, Lithuania and Portugal). This cluster was strongly negatively correlated with PC1 (−1.084, Table 6) and strongly positively correlated with PC2 (0.968, Table 6). Thus, some of the indicators which defined PC1 such as the GCI 4.0, Total SDGs and SDG 8 had the lowest values. Among the three clusters, this ranked first in terms of all indicators which defined PC2 (cluster 2 ranked last), and therefore, it was characterized by the highest level of innovativeness of SMEs (Figure 5, Figure 6 and Figure 7). In spite of this, the countries in this cluster performed worse compared with cluster 1 in terms of national competitiveness, economic development and achievement of Sustainable Development Goals due to resource constraints and a limited capacity to benefit from the economies of scale of SMEs. Between clusters 3 and 2, there were insignificant differences (below 1 point) regarding the indicators which reflect digital entrepreneurship (EIDES), the degree of digitalization of an economy and society (DESI) and national competitiveness (GCI 4.0).

As for the achievement of the overall SDGs (Total SDGs) and SDG 8 (“Decent Work and Economic Growth”), it was noticed that cluster 3 ranked last. The most recent statistical data [86] show that the countries in cluster 3, especially Greece and Italy, had high in-work at-risk-of-poverty rates (15.1% and 15.6%, respectively), high shares of people at risk of poverty or social exclusion in the total population (29% and 21.6%, respectively) and high unemployment rates (17.3% and 10%, respectively), facts which explain the lowest performance of cluster 3 for SDG 8. In these countries, innovative and digital entrepreneurship should be boosted to generate better jobs and higher labor incomes. Consequently, workers would escape poverty, improve their quality of life and respond to ecological challenges through environmentally responsible consumption and production.

The findings above prove that there are differences and common features between EU countries based on their interrelation between (digital, innovative and productive) entrepreneurship, digitalization, economic development, national competitiveness and SDGs. Thus, Hypothesis H6 is confirmed. Therefore, different and specific steps are required to support both a high level of sustainable development and convergence across EU countries.

In order to identify some potential policies for increasing the positive impact of digital entrepreneurship on sustainable development at the EU country level, especially in the case of countries included in cluster 2, we analyzed the pillars based on which the EIDES rests [26]. To obtain a clearer picture of the weakest pillars of the EIDES for the analyzed countries (the pillars of general and systemic framework conditions at the national level for both the digital and non-digital versions), these are centralized in

Table 7. The weakest pillars of EIDES.

Countries	EIDES SCORE/ “Efficiency” *	General Framework Conditions		Systemic Framework Conditions
		Non-Digital Score	Digital Score	Non-Digital Score	Digital Score
Bulgaria	26.9/ 34.35%	Physical infrastructure	Market conditions	Knowledge creation and dissemination	Human capital
Romania	29.5/ 37.67%	Physical infrastructure	Market conditions	Finance	Human capital
Slovakia	33.1/ 42.27%	Formal institutions, regulation, taxation	Formal institution, regulation, taxation	Human capital	Networking and support
Hungary	34.3/ 43.81%	Culture, informal institutions	Culture, informal institutions	Networking and support	Finance
Latvia	34.3/ 43.81%	Physical infrastructure	Market conditions	Knowledge creation and dissemination	Knowledge creation and dissemination
Poland	37.9/ 48.4%	Formal institution, regulation, taxation	Formal institution, regulation, taxation	Knowledge creation and dissemination	Human capital

Note: * Efficiency of EIDES relative to the EU leader (Denmark’s EIDES = 78.3) Source: based on [26].

based on data from the last EIDES report [26]. According to the data from this report on the EIDES [26], all countries in cluster 2 (except Spain) were included in the “laggards” group. According to the data from Table 7, these countries’ EIDES efficiencies relative to the EU leader (Denmark) ranged from 34.35% (Bulgaria) to 48.4% (Poland), which emphasizes the need for policies in order to improve the EIDES score and its impact on the SDGs.

Bulgaria and Romania need to improve their EIDES scores, with both countries displaying EIDES efficiencies relative to the EU leader (Denmark) below 40% (Table 7). The weakest pillars of the EIDES which need to be improved are digital market conditions and non-digital physical infrastructure, as well as digital human capital. As for the latter, statistical data for 2019 (

Table 8. Barriers to EIDES.

Countries	Digital Skills 1	GERD *		Enterprises with E-Commerce Sales 3	Ease of Doing Business Score 4 and Rank 5
		Business Enterprise Sector 2	Government Sector 2
Bulgaria	29	0.56	0.21	11	72.0 (61)
Romania	31	0.28	0.15	12	73.3 (55)
Slovakia	54	0.45	0.17	15	75.6 (45)
Hungary	49	1.11	0.15	15	73.4 (52)
Latvia	43	0.17	0.12	14	80.3 (19)
Poland	44	0.83	0.02	16	76.4 (40)
EU	56	1.46	0.25	20
Denmark	70	1.82	0.08	34	85.3 (4)

Note: * GERD = gross domestic expenditure on R&D. ¹ Individuals who have basic or above basic overall digital skills (% of individuals). ² Percent of gross domestic product (GDP). ³ Percent of all enterprises without the financial sector (10 persons employed or more). ⁴ “An economy’s ‘ease of doing business’ score is reflected on a scale from 0 to 100, where 0 represents the lowest and 100 represents the best performance”. ⁵ Economy’s position relative to that of other economies (1–191 countries). Source: based on [86,104].

) proves that Bulgaria and Romania had the lowest levels in the EU in terms of individuals with at least basic digital skills (29.4% and 31% of total individuals, respectively) compared with the EU average of 56.1% and the best EU performer (the Netherlands: 79.4%). These data show that all countries in cluster 2 (especially Romania and Bulgaria) as well as the whole of the EU are very far from meeting the target of 70% in 2025 (according to The European Skills Agenda). Digital skills are recognized as a critical value for working, social interaction and learning on the one side and, on the other side, a key factor for the growth of digital businesses [7,26,80]. Given the aspects above, it is necessary to fill the digital skills gap so that both digital entrepreneurship and sustainable development increase in the analyzed countries.

The weakest digital market conditions pillars in Romania, Bulgaria and Latvia can be explained by low and very low shares of enterprises with e-commerce sales (compared with the EU-25 average and EIDES performer) (Table 8). Moreover, according to the DESI report [85], in these countries, there is a low and very low level for the Digital Intensity Index, which assesses the degree of using different digital technologies (0–12 digital technologies) at the enterprise level, highlighting that over 55% of businesses invested very little in digital technologies.

As exhibited in Table 7, in Romania, the weakest non-digital systemic framework condition was “finance”, which refers to financing SMEs and domestic credit to the private sector. Therefore, Romania must act in order to improve its access to finance for all entrepreneurs. In the case of Bulgaria, the “knowledge creation and dissemination” non-digital pillar was the weakest, highlighting the need to create a favorable framework, which should assure entrepreneurs’ access to the essential knowledge that drives their business ventures [26]. Moreover, it is necessary to boost the absorptive capacity of business firms to integrate knowledge inputs into new products and services. The “knowledge creation and dissemination” non-digital pillar was also the weakest in the cases of Poland and Latvia (Table 7). One of the main drawbacks of this pillar is the very low level of investment in R&D (GERD) in both the private and public sectors, as shown in Table 8. This result is congruent with other research [9,50,54], which proved that the level of R&D expenditure is often associated with innovation and entrepreneurial performance.

The weakest of the EIDES results within the general framework conditions recorded by Slovakia and Poland were digital and non-digital “formal institutions, regulation, taxation”. Therefore, these countries need to improve the efficiency and quality of their formal institutions and regulations. As seen in Table 8, the statistical data for the ease of doing business (EDB) score and rank point out a large gap between the analyzed countries from cluster 2 and Denmark (the best EIDES performer). Thus, a critical way to improve the institutional framework to harness the productive and digital entrepreneurship and its impact on sustainable development is through the widespread use of electronic systems (electronic tax filing platforms, online business incorporation processes and online procedures related to property transfers), which is, according to the Doing Business Report [104], a common feature of economies that score highest for EDB.

The weakest pillar for Hungary was “culture, informal institutions”, which highlights the need to increase the positive cultural and social norms and practices to enhance productive entrepreneurship by making careers in entrepreneurship more attractive and promoting risk taking for business growth [26]. Cultural values, social norms and practices as well as other informal institutions have rapidly been shaped and changed by digitalization, but this impact depends on the capacity of individuals and businesses to harness a country’s digital infrastructure. “Networking and support” were the weakest pillars of digital and non-digital framework conditions in Slovakia and Hungary, and therefore these countries need to boost a positive and supportive attitude toward entrepreneurs to national and international networks as well as formal and informal access to resources though social networks, including virtual social networks.

For everyone to benefit from digital technologies and to reduce the gaps between countries, ICT and digital investments are required, but “analog complements” [64] also have to be improved by creating regulations and institutions more favorable to business and entrepreneurship, as well as by improving workers’ skills, especially digital skills.

These results are in line with other research papers [37,49,51,105] that highlighted that there is a high heterogeneity of environmental conditions of entrepreneurship and proved the absence of an ideal context. Furthermore, the critical impact of these conditions on the type of entrepreneurship (including digital entrepreneurship) and its effect on the economy and society are stressed. Therefore, it is imperative to turn the barriers to (digital or non-digital) entrepreneurship into strong triggers and driving forces.

5. Conclusions and Main Implications

The fundamental research question of this paper was as follows: can digital entrepreneurship contribute to achievement of the SDGs in the context of the EU digital economy and to what extent? Therefore, this study has shed light on the interrelationship between digital entrepreneurship and productive and innovative entrepreneurship and its impact on achievement of the SDGs in the context of the digital economy in the EU member states for the 2018–2019 period. Moreover, we consider that it is essential to assess the level of digital entrepreneurship in the EU countries and its impact on sustainable development as well as identify both key opportunities and barriers in order to generate more and better digital entrepreneurship as a key driver for sustainable development.

The results of the correlation analysis show that in the EU-25 countries in the 2018–2019 period, a positive link was identified between the productive and innovative entrepreneurship (GEI and innovative SMEs) and macroeconomic performance (GDP/capita and GCI 4.0) on the one hand and between the productive and innovative entrepreneurship and the degree of digitalization of the economy and society (DESI and NRI) on the other hand. Thus, the EU countries (especially the more developed countries) with higher productive and innovative entrepreneurship were characterized by higher economic development and national competitiveness and were also highly digitalized. These findings clearly show that countries with better economic, competitive and digital backgrounds are more likely to be innovative, productive and digital entrepreneurial countries. Through corroborating these results with the strong and positive correlation identified between the GDP/capita, GCI 4.0, DESI and NRI, we conclude that a high level of economic development and national competitiveness goes hand in hand with a high level of digitalization of the economy and society and in turn goes hand in hand with productive and innovative entrepreneurship.

Moreover, our findings prove that at the level of the EU-25 countries, digital entrepreneurship (EIDES) was strongly positively interlinked with productive and innovative entrepreneurship as well as with the degree of digitalization. Therefore, it is argued that digital entrepreneurship is the result of the intersection of digital technologies and entrepreneurship [23,66] and fosters productive and innovative entrepreneurial activities. Our analysis showed that low digital and productive entrepreneurship are predominantly located in the less-digitalized countries, mainly among new EU member states. This result highlights the need to improve the digital infrastructure and the access to and use of digital technologies for both entrepreneurs and businesses in these countries.

Furthermore, the regression analysis results highlighted that the achievement of the Sustainable Development Goals (Total SDGs, SDG 8 and SDG 9) was positively influenced by the level of digital entrepreneurship. Thus, the gaps in the achievement of SDGs between the EU countries can be explained by the level and characteristics of digital entrepreneurship, a fact which highlights the need to implement specific policies to boost digital, innovative and productive entrepreneurship.

Based on the findings of the PCA and cluster analysis, the EU-25 countries were classified into three clusters which confirmed the differences and common features between these countries in terms of the interrelation between (digital, innovative and productive) entrepreneurship, digitalization, economic development, national competitiveness and achievement of the SDGs. Cluster 1, which mainly included the EU-15 countries (except the Czech Republic, Estonia and Slovenia), was the best performer in terms of all 11 analyzed variables (except the innovative SMEs). It is worth mentioning that even if the countries in cluster 1 are at an advantage when it comes to benefiting from the next stage of digital transformation, proactive actions are needed on multiple levels to foster the development and implementation of high-potential technologies, keeping in mind that such a transition requires caution and awareness of the potential risks [106].

The countries included in cluster 3 (Croatia, Cyprus, Greece, Italy, Lithuania and Portugal) were mainly characterized by a high level of innovativeness in their SMEs but were very far from achieving SDG 8 (65.8% of SDG 8 established for 2030) and the Total SDGs (63.2% of the Total SDGs established for 2030). Thus, in these countries, more efficient integration of SMEs into the value chain and market is required. There is a growing need to increase the resources and capacities of the SMEs so they benefit from economies of scale and become more productive. To achieve all SDGs by 2030, especially SDG 8 (“Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all”), as this cluster was the worst performer in terms of the attainment of SDG 8, these countries need to strengthen the link between (digital and non-digital) entrepreneurship, productive activities and decent job creation. Moreover, they also need to respond to ecological challenges.

The poorest performance was obtained by cluster 2 (Bulgaria, Poland, Hungary, Romania, Slovakia, Latvia and Spain), characterized by the lowest level of innovativeness of business, productive entrepreneurship, digitalization of the economy and society with negative consequences on economic development, national competitiveness and achievement in the SDGs. Based on the analysis of the weakest pillars of digital entrepreneurship (EIDES), some specific measures were identified in order to improve digital entrepreneurship and its impact on achievement of the SDGs. Thus, the countries in cluster 2—but not only these countries—need to invest more in R&D and digital technologies to support access to finance for all entrepreneurs and to improve the efficiency and quality of formal institutions and regulations. It is noteworthy that whether and to what extent the EU countries meet the SDGs by 2030 critically depends on how adaptive countries are to future technological changes [107]. Moreover, an improvement in digital skills is a prerequisite, as (digital) human capital lies at the heart of the EU’s twin (green and digital) transitions.

Given the results achieved in this work and the significant potential that digital entrepreneurship can have in the attainment of sustainable development, we consider that EU policies should encourage entrepreneurs to adopt, integrate and exploit digital technologies in their businesses so that these become economically viable, socially responsible and environmentally friendly.

Our study contributes to both the entrepreneurship and sustainability literature by providing an empirical approach, which demonstrates not only the positive impact of digital entrepreneurship on the achievement of the SDGs (Total SDGs, SDG 8 and SDG9) but also confirms the assumption that the latter are inextricably intertwined. Furthermore, the findings of this study can be useful for decision makers to formulate policies that stimulate digital entrepreneurship to reach the SDGs.

Limitations and Future Research

First, our analysis was restricted to the period before COVID-19 because of time lags in data creation and release. Moreover, because of the unavailability of statistical data for all 11 variables used in the principal component analysis and cluster analysis, our study was limited to the 2018–2019 period. Therefore, further research should investigate to what extent the COVID-19 pandemic affected the level and dynamic of digital entrepreneurship and its impact on the achievement of the SDGs by 2030. Furthermore, our analysis used only one variable for digital entrepreneurship provided by secondary datasets [26], and therefore future research should focus on taking into consideration more variables on the one side and on directions to build primary information for digital entrepreneurship on the other side. Secondly, in order to assess achievement of the SDGs at the EU level, our analysis was limited to only three indices: Total SDGs, SDG 8 and SDG 9. Therefore, this research paper could be extended in future research by taking into account many other indices related to education (SDG 4), health (SDG 3), climate change (SDG 13) and reduction of inequalities (SDG 10), among others.