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Abstract
risks Article Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies 1 , 2 , 3 Elena G. Popkova * and Bruno S. Sergi Chair “Economic Policy and Public-Private Partnership”, Moscow State Institute of International Relations (MGIMO University), 119454 Moscow, Russia Center for International Development, Harvard University, Cambridge, MA 02138, USA; bsergi@fas.harvard.edu Department of Economics, University of Messina, 98122 Messina, Italy * Correspondence: elenapopkova@yahoo.com Abstract: The relevance of this study lies in the fact that financial risk is a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. The purpose of this article is to clarify specific aspects of financing factors and financial risk related to social entrepreneurship in developing countries (in comparison with the standard financial risk related to commercial entrepreneurship) in order to analyze the influence of the financing factors of social entrepreneurship on sustainable development, as well as to determine the potential for the development of social entrepreneurship through financial risk management. To achieve this goal, this article uses the methodology of econometrics—dataset modelling of financial risk management in social entrepreneurship to achieve sustainable development in emerging economies. On the basis of the results of this study, firstly, it is substantiated that the financial risks entailed by social entrepreneurship differ from the standard financial risk present in commercial entrepreneurship. Specific factors of the financing of sustainable Citation: Popkova, Elena G., and development in emerging economies are determined and, on the basis of this, financial risks specific Bruno S. Sergi. 2021. Dataset to social entrepreneurship in emerging economies are identified as follows: (1) reduced stimulus to Modelling of the Financial Risk use financial resources in long-term investments, which disrupts the stability and decreases inclusion; Management of Social (2) joint public–private investments and decreased investment in R&D; and (3) expanded investment Entrepreneurship in Emerging in the skills required for jobs and “markets of tomorrow”. Secondly, a contradictory influence of Economies. Risks 9: 211. https:// financing factors on sustainable development is demonstrated. Thirdly, a large potential for the doi.org/10.3390/risks9120211 development of social entrepreneurship by means of financial risk management (maximum reduction) Academic Editor: Ajay Subramanian was identified. With the minimization of financial risk, social entrepreneurship would demonstrate substantial progress, with an increase of 99.61% (more than 50%) from 45.18 points to 90.18 points. A Received: 3 October 2021 novel contribution of this paper to the extant literature consists of the specification of the essence and Accepted: 17 November 2021 specifics of social entrepreneurship in emerging economies through the identification of financial Published: 26 November 2021 risks and the provision of recommendations for their management. Publisher’s Note: MDPI stays neutral Keywords: financial risk; risk management; dataset modelling; social entrepreneurship; sustainable with regard to jurisdictional claims in development; emerging economies published maps and institutional affil- iations. 1. Introduction Social entrepreneurship is a special type of business that incorporates the individual Copyright: © 2021 by the authors. or simultaneous implementation of the following directions of activity: (1) corporate social Licensee MDPI, Basel, Switzerland. responsibility; (2) corporate ecological responsibility; (3) non-commercial activities (includ- This article is an open access article ing charity) towards the provision of public and socially important benefits. Financial distributed under the terms and risks are a serious obstacle to the development of social entrepreneurship, preventing the conditions of the Creative Commons implementation of potential support for sustainable development goals in business. This is Attribution (CC BY) license (https:// the problem addressed by this research. The following issues hinder the development of creativecommons.org/licenses/by/ solutions to this problem. 4.0/). Risks 2021, 9, 211. https://doi.org/10.3390/risks9120211 https://www.mdpi.com/journal/risks Risks 2021, 9, 211 2 of 20 The first issue is that the way in which social entrepreneurship is financed is quite different from the manner in which commercial business is. The investment climate is het- erogeneous, and there could be a situation in practice in which, in case of a favourable—on the whole—climate, there could be high investment attractiveness for commercial projects but low investment attractiveness and investment defecits for sustainable development. There are no special statistics on the investment attractiveness of social entrepreneurship, resulting in uncertainty with respect to specific financial risks related to it. The orienta- tion of the general investment climate in an investment system can lead to imprecise and distorted evaluations of the financial risks entailed by social entrepreneurship. The second issue is that, unlike commercial entrepreneurship, where financial risk management constitutes one of its main activities, financial risk management takes a background role in social entrepreneurship activities. Social entrepreneurship has limited capabilities in the sphere of financial risk management, requiring the implementation of risk management at the level of state regulators. However, despite the active development of social entrepreneurship around the world and the adoption of the SDGs in national strategies, not enough attention has been paid to financial risk management in social entrepreneurship at the national level due to the inflexibility of institutions. The third issue is global inequality, due to which the financial risks entailed by social entrepreneurship in emerging economies are large and cannot be easily overcome. This is because emerging economies are peculiar in that they posses the largest and most chronic financial resource deficits and less favourable—on the whole—investment climates (as compared to advanced economies). In addition to this, the effectiveness of institutions in emerging economies is also low, making them less flexible and hindering government support in managing the financial risks entailed by social entrepreneurship (Kliestik et al. 2018; Kovacova et al. 2019). The purpose of this article is to clarify specific financing factors and financial risks related to social entrepreneurship in developing countries (in comparison with the standard financial risks entailed by commercial entrepreneurship), to analyze the influence of the financing factors related to social entrepreneurship on sustainable development, and to determine the potential for social entrepreneurship development through financial risk management. To achieve this goal, the article performs dataset modelling of financial risk man- agement in social entrepreneurship for sustainable development in emerging economies. The subject of this research is the social entrepreneurship index, standard financial factors related to commercial entrepreneurship, and those specific factors of financing sustainable development in developing countries, for which statistics of specific factors of financing sustainable development are available. The study is based on data for 2021 (at the end of 2020). This paper ’s originality consists of implementing not traditional but dataset modelling of financial risk management in social entrepreneurship. This allows (not due to the use of the report but the use of the dataset on social entrepreneurship) for the systemic (com- prehensive and complex) consideration of all manifestations of social entrepreneurship— corporate responsibility (social and ecological) and non-commercial activities. This allows us to fill the gap in the statistical data, which are given fragmentarily (only in one of three directions) in the existing reports. The novelty of this paper consists of considering the specifics of social entrepreneur- ship during the identification of its special financial risks. This allows us to avoid the association of social entrepreneurship with commercial entrepreneurship during the study of financial risks and incorrect results. Instead, we obtain precise results that are correct for social entrepreneurship. This paper ’s uniqueness consists of the consideration of the emerging economies’ experience and the specifics of the manifestation of financial risks of social entrepreneurship in them. This introduction is followed by the materials (literature review and gap analysis) and methods (methodology and logic of testing the offered hypotheses, the empirical basis of Risks 2021, 9, 211 3 of 20 the research). Then, the research results are given, which are followed by the conclusions of our research. 2. Materials and Methods 2.1. Theoretical Basis, Literature Review and Gap Analysis The theoretical basis of this research is the concept of financial risk and risk manage- ment, sustainable development, and social entrepreneurship (in which it is opposed to commercial entrepreneurship). The concept of categorising countries by the criterion of the level of income and level of markets’ development, according to which emerging and advanced economies are distinguished. This research is based on the following work in the sphere of financial risk manage- ment in entrepreneurship: Bakos and Dumitrascu (2021), Bouri et al. (2021), Dalwai and Salehi (2021), Duygun et al. (2020), Elkhal (2019), Lasloom (2021), Locurcio et al. (2021), Sabău et al. (2021), and Syed and Bawazir (2021). We also use the materials of the following works on the topic of the contribution of social entrepreneurship to sustainable development: Al-Omoush et al. (2021), Cardella et al. (2021), Chandra et al. (2021), Fhiri et al. (2021), Fridhi (2021), Méndez-Picazo et al. (2021), Popkova et al. (2020), Sahrakorpi and Bandi (2021), Setiawan et al. (2021), Suseno and Abbott (2021), and Thörnqvist and Kilstam (2021). We also use the published materials on the topic of sustainable development and implementation of the SDGs in emerging economies of such researchers as Alam et al. (2021), Galindo-Martín et al. (2021), Hassani et al. (2021), Sebestyén and Abonyi (2021), Tabares (2021), Tang et al. (2021), and Ullah et al. (2021). The literature review on this research problem has shown a high level of elaboration and a lack of solutions due to specific research gaps. The first gap is the incompleteness of the current statistics in social entrepreneurship, which does not allow for its precise and correct measurement. The second gap is the uncertainty surrounding the specifics of social entrepreneurship’s financial risks. The third gap is the poor elaboration of the experience and insufficient information on the specifics of social entrepreneurship and its financial risks in emerging economies. These gaps predetermine the three following research questions (RQ). RQ1: What (exactly) are the financial risks of social entrepreneurship? Based on the specifics of investments in sustainable development, which is noted and emphasised in the works (Azmat et al. 2021; Chen 2021; Staszkiewicz and Werner 2021), we offer Hypothesis 1: Hypothesis 1 (H1). The financial risks of social entrepreneurship differ from the standard financial risks of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency). RQ2: What is the correct way of managing the financial risks of social entrepreneur- ship? Based on the existing publications (He and Yan 2020; Lee 2020; Zhang and Wang 2021), which note the contradictory impact of the factors of financing on sustainable development, we offer Hypothesis 2: Hypothesis 2 (H2). The financial risk management of social entrepreneurship should be flexible and take into account the multidirectional impact of the factors of financing on it (stimulating some factors and restraining other factors). RQ3: What is the potential of social entrepreneurship’s development by means of management (maximum reduction) of financial risks? Risks 2021, 9, 211 4 of 20 According to the accumulated scientific knowledge in the sphere of market economies, given in the works (Graafland and Wells 2021; Shao et al. 2021; Wut et al. 2021), we also offer Hypothesis 3: Hypothesis 3 (H3). Despite its non-commercial nature, social entrepreneurship faces large fi- nancial risks and largely depends on their overcoming—that’s why social entrepreneurship would demonstrate significant progress with minimal financial risks. This paper aims to perform the dataset modelling of the financial risk management in social entrepreneurship for sustainable development in emerging economies. 2.2. Methodology and Empirical Basis of the Research To check Hypothesis 1, we use the method of regression analysis. We determine the regression dependence of the social entrepreneurship index on the standard financial factors of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency—given in Table 1) and on the specific factors of financing sustainable development (given in Table 2). Table 1. Standard financial factors of commercial entrepreneurship in emerging economies of the sample in 2021 (as a result of 2020), position (the higher, the better). Source: Compiled by the authors based on the World Bank (2021). Protection of Minority Solution to Receipt of Credits Taxation Investors Non-Solvency Country Category Country Fr Fr Fr Fr 1 2 3 4 Argentina 104 61 170 111 Brazil 104 61 184 77 Chile 94 51 86 53 China 80 28 105 51 India 25 13 115 52 Indonesia 48 37 81 38 Mexico 11 61 120 33 Russia 25 72 58 57 Slovakia 48 88 55 46 South Africa 80 13 54 68 Turkey 37 21 26 120 Finland 132 72 95 65 New Zealand 1 3 9 36 Sweden 80 28 31 17 Austria 94 37 44 22 Japan 94 57 51 3 Denmark 48 28 8 6 France 104 45 61 26 Ireland 48 13 4 19 Israel 48 18 13 29 Belgium 67 45 63 9 Australia 4 57 28 20 Estonia 48 79 12 54 Netherlands 119 79 22 7 Developed countries Developing countries Risks 2021, 9, 211 5 of 20 Table 1. Cont. Protection of Minority Solution to Receipt of Credits Taxation Investors Non-Solvency Country Category Country Fr Fr Fr Fr 1 2 3 4 Italy 119 51 128 21 Germany 48 61 46 4 Republic of 67 25 21 11 Korea Canada 15 7 19 13 UK 37 7 27 14 Greece 119 37 72 72 Portugal 119 61 43 15 Poland 37 51 77 25 Spain 80 28 35 18 Switzerland 67 105 20 49 Czech Republic 48 61 53 16 Hungary 37 97 56 66 USA 4 36 25 2 Table 2. Specific factors of financing of sustainable development in emerging economies of the sample in 2021 (as a result of 2020), points 1–100. Increase Incentives Facilitate the Incentivize and Update Education to Direct Financial Creation of Expand Patient Curricula and Resources towards “Markets of Investments in Expand Investment Long-Term Tomorrow”, Research, Innovation Country in the Skills Needed Country Investments, Especially in Areas and Invention That Category for Jobs and the Strengthen Stability that Require Can Create the New “Markets of and Expand Public-Private “Markets of Tomorrow” Inclusion Collaboration Tomorrow” msr msr msr msr 1 2 3 4 Argentina 32.8 34.3 31.9 46.9 Brazil 60.3 38.0 36.2 39.5 Chile 57.5 39.7 31.7 52.1 China 72.8 49.7 50.0 67.0 India 54.5 40.2 32.5 43.5 Indonesia 59.7 45.0 45.6 49.0 Mexico 49.0 35.7 27.2 43.3 Russia 55.3 - 35.6 44.9 Slovakia 54.7 39.3 31.3 46.5 South Africa 48.6 35.6 31.7 42.6 Turkey 49.8 38.5 28.9 39.8 Developing countries Risks 2021, 9, 211 6 of 20 Table 2. Cont. Increase Incentives Facilitate the Incentivize and Update Education to Direct Financial Creation of Expand Patient Curricula and Resources towards “Markets of Investments in Expand Investment Long-Term Tomorrow”, Research, Innovation Country in the Skills Needed Country Investments, Especially in Areas and Invention That Category for Jobs and the Strengthen Stability that Require Can Create the New “Markets of and Expand Public-Private “Markets of Tomorrow” Inclusion Collaboration Tomorrow” msr msr msr msr 1 2 3 4 Finland 95.4 59.5 53.4 75.3 New Zealand 93.2 45.0 45.2 63.4 Sweden 89.0 52.2 50.8 69.4 Austria 88.3 47.3 38.8 60.6 Japan 84.7 53.5 54.7 51.3 Denmark 84.6 46.7 41.7 71.5 France 83.0 50.1 50.8 56.8 Ireland 81.9 46.6 36.1 59.5 Israel 81.77 51.2 53.1 66.6 Belgium 81.2 49.3 47.8 65.8 Australia 81.2 44.0 42.9 63.5 Estonia 81.1 44.9 43.4 56.8 Netherlands 79.9 50.4 48.3 71.8 Italy 79.8 43.0 36.9 40.7 Germany 79.3 48.1 49.2 61.4 Republic of 78.3 46.7 53.4 60.0 Korea Canada 75.1 49.5 42.8 65.3 UK 72.4 46.1 40.9 59.7 Greece 68.3 36.0 25.2 38.7 Portugal 67.1 44.6 42.2 49.8 Poland 62.7 37.5 32.1 41.9 Spain 59.7 44.4 40.4 51.4 Switzerland 59.2 50.8 51.6 70.8 Czech Republic 58.2 41.9 40.2 48.5 Hungary 52.0 39.4 36.7 40.8 USA 47.8 57.7 57.3 68.2 Source: Compiled by the authors based on data from the World Economic Forum (2021). To check the reliability of the regression models, we perform an F test (by F criterion). The research sample includes all (11) emerging economies for which the statistics on the specific factors of financing sustainable development are available in the materials of the World Economic Forum (2021). To form a sufficient volume of panel data, statistical data on 26 developed countries included in the World Economic Forum (2021) rating are also collected and used in this article. The logic of testing the offered hypothesis is as follows: the regression dependence of social entrepreneurship on the specific factors of financing sustainable development alone Developed countries Risks 2021, 9, x FOR PEER REVIEW 6 of 18 Denmark 84.6 46.7 41.7 71.5 France 83.0 50.1 50.8 56.8 Ireland 81.9 46.6 36.1 59.5 Israel 81.77 51.2 53.1 66.6 Belgium 81.2 49.3 47.8 65.8 Australia 81.2 44.0 42.9 63.5 Estonia 81.1 44.9 43.4 56.8 Netherlands 79.9 50.4 48.3 71.8 Italy 79.8 43.0 36.9 40.7 Germany 79.3 48.1 49.2 61.4 Republic of 78.3 46.7 53.4 60.0 Korea Canada 75.1 49.5 42.8 65.3 UK 72.4 46.1 40.9 59.7 Greece 68.3 36.0 25.2 38.7 Portugal 67.1 44.6 42.2 49.8 Poland 62.7 37.5 32.1 41.9 Spain 59.7 44.4 40.4 51.4 Switzerland 59.2 50.8 51.6 70.8 Czech Re- 58.2 41.9 40.2 48.5 Risks 2021, 9, 211 7 of 20 public Hungary 52.0 39.4 36.7 40.8 USA 47.8 57.7 57.3 68.2 Source: Compiled by the authors based on data from the World Economic Forum (2021). should be observed, and for the standard financial factors of commercial entrepreneurship, the F test must not be passed (the model has to be insufficiently reliable). The source of the data on social entrepreneurship is the dataset “Social entrepre- The source of the data on social entrepreneurship is the dataset “Social entrepreneur- neurship in the global economy: from virtual scores to big data”. We perform dataset ship in the global economy: from virtual scores to big data”. We perform dataset modelling, modelling, the advantage of which is the fullest consideration of the manifestations of the advantage of which is the fullest consideration of the manifestations of social en- social entrepreneurship. The relevant statistics are given in the dataset and the calculat- trepreneurship. The relevant statistics are given in the dataset and the calculated integral Risks 2021, 9, x FOR PEER REVIEW 7 of 18 ed integral index is given (Figures 1 and 2). index is given (Figures 1 and 2). 70.00 61.15 54.09 As shown in Figure 2, the highest level of development of social entrepreneurship 60.00 49.03 46.69 46.88 45.16 in developed economi 4e 2s .8 o 3f the sample in 2021 (as a result of 2020) is observed in New 50.00 41.27 40.60 34.61 34.70 Zealand (84.173 points), and the lowest in Hungary (34.302 points). 40.00 To test Hypothesis 2, we use comparative and logical methods; we determine and 30.00 compare the impact of financing factors on social entrepreneurship. The economic and 20.00 mathematical sense of the offered hypothesis is as follows: the regression coefficients in 10.00 the model of social entrepreneurship’s dependence on the specific factors of sustainable 0.00 development financing must have positive and negative signs. To test Hypothesis 3, we use the substitution method. We put the optimal values of the financial factors in the obtained regression models (the reliability of which has been confirmed). We also use the trend analysis method to determine the social entrepreneur- ship index’s growth (dynamics of change) compared to the current level (2021). The hy- Figure 1. Social entrepreneurship index (SEPR) in emerging economies of the sample in 2021 (as a Figure 1. Social entrepreneurship index (SEPR) in emerging economies of the sample in 2021 (as a pothesis is deemed proven if financial risk management leads to an increase in the social result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of entrepreneurship index of more than 50%. Scientific Communications (2021). Scientific Communications (2021). As shown in Figure 1, the highest level of development of social entrepreneurship 73.238 Hungary in emerging economies of the 3 4 s.a 3m 02ple in 2021 (as a result of 2020) is observed in Russia 38.401 (61.15 points), and the lowest in Slovakia (34.70 points) and Argentina (34.61 points). Switzerland 62.699 50.326 Poland 46.651 38.13 Greece 38.108 70.496 Canada 70.452 59.327 Germany 61.14 57.568 Netherlands 67.478 42.042 Australia 64.166 45.775 Israel 47.086 57.565 France 55.341 55.713 Japan 57.793 51.643 Sweden 60.923 84.173 Finland 53.698 0 10 20 30 40 50 60 70 80 90 Figure 2. Social entrepreneurship index (SEPR) in developed economies of the sample in 2021 (as a result of 2020), points Figure 2. Social entrepreneurship index (SEPR) in developed economies of the sample in 2021 (as a result of 2020), points 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021). 1–100. Source: Compiled by the authors based on data from the Institute of Scientific Communications (2021). As shown in Figure 1, the highest level of development of social entrepreneurship Additionally, this article uses the dynamic research method to get more accurate re- in emerging economies of the sample in 2021 (as a result of 2020) is observed in Russia sults. Dynamic modelling of the impact of financial risks on social entrepreneurship is (61.15 points), and the lowest in Slovakia (34.70 points) and Argentina (34.61 points). carried out using the example of Russia as a vivid example of a developing country (part of BRICS). The Sustainable Development Vector Index (MRSV) calculated by the Mos- cow Exchange (2021a) is used as an indicator of social entrepreneurship. The Index of the Moscow Exchange (IMOEX), also calculated by the Moscow Exchange (2021b), serves as an indicator of financial risk. The Sustainable Development Vector Index (MRSV) is relatively new—it has been calculated since 21 September 2020. Therefore, to obtain a large enough sample, this arti- cle uses monthly data from both indices. The dynamics of the values of these indices from 21 September 2020 to 11 November 2021 is given in the Supplementary Materials to this article (since the data table contains 292 observations—it is too large to be included Risks 2021, 9, 211 8 of 20 As shown in Figure 2, the highest level of development of social entrepreneurship in developed economies of the sample in 2021 (as a result of 2020) is observed in New Zealand (84.173 points), and the lowest in Hungary (34.302 points). To test Hypothesis 2, we use comparative and logical methods; we determine and compare the impact of financing factors on social entrepreneurship. The economic and mathematical sense of the offered hypothesis is as follows: the regression coefficients in the model of social entrepreneurship’s dependence on the specific factors of sustainable development financing must have positive and negative signs. To test Hypothesis 3, we use the substitution method. We put the optimal values of the financial factors in the obtained regression models (the reliability of which has been confirmed). We also use the trend analysis method to determine the social entrepreneur- ship index’s growth (dynamics of change) compared to the current level (2021). The hypothesis is deemed proven if financial risk management leads to an increase in the social entrepreneurship index of more than 50%. Additionally, this article uses the dynamic research method to get more accurate results. Dynamic modelling of the impact of financial risks on social entrepreneurship is carried out using the example of Russia as a vivid example of a developing country (part of BRICS). The Sustainable Development Vector Index (MRSV) calculated by the Moscow Exchange (2021a) is used as an indicator of social entrepreneurship. The Index of the Moscow Exchange (IMOEX), also calculated by the Moscow Exchange (2021b), serves as an indicator of financial risk. The Sustainable Development Vector Index (MRSV) is relatively new—it has been calculated since 21 September 2020. Therefore, to obtain a large enough sample, this article uses monthly data from both indices. The dynamics of the values of these indices from 21 September 2020 to 11 November 2021 is given in the Supplementary Materials to this article (since the data table contains 292 observations—it is too large to be included in the text of the article). If a negative dependence is revealed in the function IMOEX = F (MRSV), indicating the negative impact of financial risk on social entrepreneurship in Russia, this will provide additional confirmation of the hypothesis put forward. 3. Results For dataset modelling of the impact of the financial factors on social entrepreneurship in emerging economies, let us consider regression analysis results. Before modelling, we will analyze the multicollinearity of the variables selected for the study. For this, their cross-correlation is calculated in Table 3. Table 3. Dataset modelling of the impact of the standard financial factors of commercial entrepreneur- ship on social entrepreneurship. SEPR Fr Fr Fr Fr msr msr msr msr 1 2 3 4 1 2 3 4 SEPR 1 - - - - - - - - Fr 0.30 1 - - - - - - - Fr 0.34 0.24 1 - - - - - - Fr 0.42 0.37 0.21 1 - - - - - Fr 0.49 0.15 0.15 0.47 1 - - - - msr 0.47 0.18 0.20 0.41 0.53 1 - - - msr 0.27 0.18 0.15 0.28 0.43 0.51 1 - - msr 0.51 0.05 0.00 0.39 0.51 0.54 0.67 1 - msr 0.57 0.03 0.10 0.51 0.49 0.62 0.65 0.76 1 Source: Obtained by the authors automatically with the help of the “Correlation” function in Microsoft Excel. The results of the correlation analysis from Table 3 indicate the absence of multi- collinearity. First, let us consider the dependence of social entrepreneurship on the standard financial factors of commercial entrepreneurship (Table 4). Risks 2021, 9, 211 9 of 20 Table 4. Dataset modelling of the impact of the standard financial factors of commercial entrepreneurship on social entrepreneurship. Regression Statistics Multiple R 0.6029 R-square 0.3635 Adjusted R-square 0.2840 Standard error 10.1788 Observations 37 Dispersion analysis df SS MS F Significance F Regression 4 1893.7802 473.4450 4.5696 0.0049 Residue 32 3315.4357 103.6074 Total 36 5209.2159 Coefficients Standard Error t-Statistics p-Value Lower 95% Upper 95% Constant 67.9504 4.3443 15.6412 0.0000 59.1013 76.7996 Coefficient at Fr 0.0456 0.0499 0.9137 0.3677 0.1472 0.0560 Coefficient at Fr 0.1029 0.0675 1.5254 0.1370 0.2403 0.0345 Coefficient at Fr 0.0417 0.0467 0.8921 0.3790 0.1368 0.0535 Coefficient at Fr 0.1515 0.0663 2.2859 0.0290 0.2865 0.0165 Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. The correlation of the indicators (60.9%) is moderate. Estimate F equals 4.5696. Table F at 11 observations and 4 factor variables (k = m = 4, k = n m 1 = 37 4 1 = 32) at 1 2 the significance level = 0.05 equals 2.14. Since table F exceeds estimate F (4.5696 > 2.14), the F test is not passed, and the regression equation is insufficiently reliable at the set . The influence of all considered factors on social entrepreneurship turns out to be negative—this is evidenced by the negative values of the coefficients. Let us carry out a correspondence analysis—for this, we estimate the linearity (Figure 3) and diagnose the model assumptions (Figure 4). Risks 2021, 9, x FOR PEER REVIEW 9 of 18 The correlation of the indicators (60.9%) is moderate. Estimate F equals 4.5696. Ta- ble F at 11 observations and 4 factor variables (k1 = m = 4, k2 = n − m − 1 = 37 − 4 − 1 = 32) at the significance level α = 0.05 equals 2.14. Since table F exceeds estimate F (4.5696 > 2.14), the F test is not passed, and the regression equation is insufficiently reliable at the set α. The influence of all considered factors on social entrepreneurship turns out to be Risks 2021, 9, 211 10 of 20 negative—this is evidenced by the negative values of the coefficients. Let us carry out a correspondence analysis—for this, we estimate the linearity (Figure 3) and diagnose the model assumptions (Figure 4). Figure 3. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Figure 3. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. Microsoft Excel. The graphs in Figure 3 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression. Risks 2021, 9, 211 11 of 20 Risks 2021, 9, x FOR PEER REVIEW 10 of 18 Fr1 Residual graph Fr2 Residual graph 0 50 100 150 0 50 100 150 -10 -10 -20 -20 Fr1 Fr2 Fr3 Residual graph Fr4 Residual graph 25 25 10 10 5 5 0 0 0 50 100 150 200 0 50 100 150 -5 -5 -10 -10 -15 -15 -20 -20 Fr3 Fr4 Figure 4. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Figure 4. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. Microsoft Excel. The graphs in Figure 4 indicate the homogeneity of the dispersion of the residues. The graphs in Figure 3 indicate that the relationship of the resulting variable with all Now let us consider the dependence of social entrepreneurship on the specific factors of factorial variables is quite reliably described by linear regression. sustainable development financing (Table 5). The graphs in Figure 4 indicate the homogeneity of the dispersion of the residues. Now let us consider the dependence of social entrepreneurship on the specific factors of Table 5. Dataset modelling the impact of the specific factors of sustainable development financing on social entrepre- sustainable development financing (Table 5). neurship. Regression Statistics Multiple R 0.6310 R-square 0.3981 Adjusted R-square 0.3229 Standard error 9.8983 Observations 37 Dispersion analysis Significance df SS MS F F Residuals Residuals Residuals Residuals Risks 2021, 9, 211 12 of 20 Table 5. Dataset modelling the impact of the specific factors of sustainable development financing on social entrepreneurship. Regression Statistics Multiple R 0.6310 R-square 0.3981 Adjusted R-square 0.3229 Standard error 9.8983 Observations 37 Dispersion analysis df SS MS F Significance F Regression 4 2073.9920 518.4980 5.2921 0.0022 Residue 32 3135.2239 97.9757 Total 36 5209.2159 Coefficients Standard Error t-Statistics p-Value Lower 95% Upper 95% Constant 16.2665 9.3239 1.7446 0.0907 2.7258 35.2587 Coefficient at msr 0.1693 0.1398 1.2112 0.2347 0.1154 0.4540 Coefficient at msr 0.3647 0.2427 1.5031 0.1426 0.8590 0.1295 Coefficient at msr 0.3685 0.3192 1.1544 0.2569 0.2817 1.0187 Coefficient at msr 0.4548 0.2528 1.7990 0.0815 0.0602 0.9698 Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. The correlation of the indicators (63.10%) is moderate. Estimate F equals 5.2921. Table F at 11 observations and 4 factor variables (k = m = 4, k = n m 1 = 11 4 1 = 6) at 1 2 the significance level = 0.05 equals 0.14. Since estimate F exceeds the table (5.2921 > 2.14), the F test is passed, and the regression equation is reliable at the set . Let us carry out a correspondence analysis. For this, we estimate the linearity (Figure 5) and diagnose the model assumptions (Figure 6). The graphs in Figure 5 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression. The graphs in Figure 6 indicate the homogeneity of the dispersion of the residues. This allows for the compilation of a regression model of social entrepreneurship’s dependence on the specific factors of sustainable development financing in emerging economies: SEPR = 1,602,665 + 0.1693 msr 0.3647 msr + 0.3685 msr 0.4548 msr (1) 1 2 3 1 According to this regression model, social entrepreneurship in emerging economies is peculiar for the following financial risks: reduction in stimuli for using financial resources in long-term investments, which disrupts stability and decreases inclusion: an increase in msr1 of 1 point leads to an increase in the social entrepreneurship index of 0.1693 points; joint public–private investments; reduction in investments in R&D: an increase in msr of 1 point leads to a decrease in the social entrepreneurship index of 0.3647 points; decrease in investment in R&D: increase in msr of 1 point leads to an increase in the social entrepreneurship index of 0.3685 points; expand investment in the skills needed for jobs and “markets of tomorrow”: in- crease in msr of 1 point leads to an increase in the social entrepreneurship index of 0.4548 points. Risks 2021, 9, x FOR PEER REVIEW 11 of 18 Regression 4 2073.9920 518.4980 5.2921 0.0022 Residue 32 3135.2239 97.9757 Total 36 5209.2159 Upper Coefficients Standard Error t-Statistics P-Value Lower 95% 95% Constant 16.2665 9.3239 1.7446 0.0907 −2.7258 35.2587 Coefficient at msr1 0.1693 0.1398 1.2112 0.2347 −0.1154 0.4540 Coefficient at msr2 −0.3647 0.2427 −1.5031 0.1426 −0.8590 0.1295 Coefficient at msr3 0.3685 0.3192 1.1544 0.2569 −0.2817 1.0187 Coefficient at msr4 0.4548 0.2528 1.7990 0.0815 −0.0602 0.9698 Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. The correlation of the indicators (63.10%) is moderate. Estimate F equals 5.2921. Ta- ble F at 11 observations and 4 factor variables (k1 = m = 4, k2 = n − m − 1 = 11 − 4 − 1 = 6) at the significance level α = 0.05 equals 0.14. Since estimate F exceeds the table (5.2921 > Risks 2021, 9, 211 13 of 20 2.14), the F test is passed, and the regression equation is reliable at the set α. Let us carry out a correspondence analysis. For this, we estimate the linearity (Figure 5) and diagnose the model assumptions (Figure 6). FFigure igure 55. . RResidual esidual ggraphs. raphs. So Sour urce: ce: Ob Obtained tained by by th the e au authors thors au automatically tomatically wwith ith th the e hhelp elp of of th the e ““Regr Regres ession” sion” fu function nction in in Microsoft Excel. Microsoft Excel. Risks 2021, 9, x FOR PEER REVIEW 12 of 18 Risks 2021, 9, 211 14 of 20 The graphs in Figure 5 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression. msr1 Residual graph msr2 Residual graph 25 25 20 20 15 15 10 10 5 5 0 0 0.0 50.0 100.0 150.0 0.0 20.0 40.0 60.0 80.0 -5 -5 -10 -10 -15 -15 -20 -20 msr1 msr2 msr3 Residual graph msr4 Residual graph 25 25 20 20 15 15 10 10 5 5 0 0 0.0 20.0 40.0 60.0 80.0 0.0 20.0 40.0 60.0 80.0 -5 -5 -10 -10 -15 -15 -20 -20 msr3 msr4 Figure 6. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Figure 6. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. Microsoft Excel. Let us insert the optimal values of the financial factors into the obtained regression The graphs in Figure 6 indicate the homogeneity of the dispersion of the residues. model (1). The method of trend analysis is used to determine the growth (dynamics of This allows for the compilation of a regression model of social entrepreneurship’s de- change) of the indicators as compared to the current level (2021) (Figure 7). pendence on the specific factors of sustainable development financing in emerging Figure 3 shows that managing the financial factors that negatively influence social economies: entrepreneurship (facilitate the creation of “markets of tomorrow”, especially in areas that require public–private collaboration; expand investment in the skills needed for jobs and SEPR=1,602,665 + 0.1693 msr1 − 0.3647 msr2 + 0.3685 msr3 − 0.4548 msr1 (1) “markets of tomorrow”) prevented the growth of the impact (to prevent the risk increase). According to this regression model, social entrepreneurship in emerging economies This fact explains that support for social entrepreneurship in sustainable development is peculiar for the following financial risks: is not the only priority of emerging economies. Thus, they cannot adapt their financial systems only to this priority at the expense of other preferences. reduction in stimuli for using financial resources in long-term investments, which Increase the stimuli for using financial resources in long-term investments, which disrupts stability and decreases inclusion: an increase in msr1 of 1 point leads to an disrupts the stability and decreases inclusion by 84.87% (up to the maximum 100 points). increase in the social entrepreneurship index of 0.1693 points; An increase in investments in R&D of 187.5% (also to 100 points) leads to a rise in the social entrepreneurship index of 99.61% (up to 90.18 points). Resiaduals Residuals Residuals Residuals Risks 2021, 9, x FOR PEER REVIEW 13 of 18 joint public–private investments; reduction in investments in R&D: an increase in msr2 of 1 point leads to a decrease in the social entrepreneurship index of 0.3647 points; decrease in investment in R&D: increase in msr3 of 1 point leads to an increase in the social entrepreneurship index of 0.3685 points; expand investment in the skills needed for jobs and “markets of tomorrow”: in- crease in msr1 of 1 point leads to an increase in the social entrepreneurship index of 0.4548 points. Let us insert the optimal values of the financial factors into the obtained regression Risks 2021, 9, 211 15 of 20 model (1). The method of trend analysis is used to determine the growth (dynamics of change) of the indicators as compared to the current level (2021) (Figure 7). 120.00 200.00 100.00 100.00 180.00 90.18 100.00 160.00 187.51 140.00 80.00 99.61 120.00 84.87 54.09 60.00 45.18 100.00 46.83 36.00 80.00 34.78 40.00 60.00 40.00 20.00 20.00 0.00 0.00 0.00 0.00 Increase incentives to Facilitate the creation of Incentivize and expand Social entrepreneurship Update education direct financial "markets of tomorrow", patient investments in index, points 1-100 curricula and expand resources towards long- especially in areas that research, innovation and investment in the skills term investments, require public-private invention that can create needed for jobs and strengthen stability and collaboration new "markets of “markets of tomorrow” expand inclusion tomorrow" At the current level of financial risks (2021), points 1-100 At the minimum level of financial risks, points 1-100 Growth by means of reduction of financial risks, % Figure 7. The perspective of social entrepreneurship’s development in emerging economies through the reduction of Figure 7. The perspective of social entrepreneurship’s development in emerging economies through the reduction of fi- financial risk. Source: Authors. nancial risk. Source: Authors. Additionally, we constructed a dynamic regression model of the impact of financial Figure 3 shows that managing the financial factors that negatively influence social risks on social entrepreneurship in Russia. The dependence of the Sustainable Development entrepreneurship (facilitate the creation of “markets of tomorrow”, especially in areas Vector Index (MRSV) on the Index of the Moscow Exchange (IMOEX) is given in Table 6. that require public–private collaboration; expand investment in the skills needed for jobs and “markets of tomorrow”) prevented the growth of the impact (to prevent the risk in- Table 6. Dataset modelling of the impact of the Index of the Moscow Exchange (IMOEX) on the Sustainable Development crease). This fact explains that support for social entrepreneurship in sustainable devel- Vector Index (MRSV) in Russia from 21 September 2020 to 11 November 2021. opment is not the only priority of emerging economies. Thus, they cannot adapt their fi- nancial systems only to this priority at the expense of other preferences. Regression Statistics Increase the stimuli for using financial resources in long-term investments, which Multiple R 0.9944 disrupts the stability and decreases inclusion by 84.87% (up to the maximum 100 points). R-square 0.9888 An increase in investments in R&D of 187.5% (also to 100 points) leads to a rise in the so- Adjusted R-square 0.9887 Standard error ci 5.82 al entr 10 epreneurship index of 99.61% (up to 90.18 points). Observations 292 Additionally, we constructed a dynamic regression model of the impact of financial Dispersion analysis risks on social entrepreneurship in Russia. The dependence of the Sustainable Develop- df SS MS F Significance F ment Vector Index (MRSV) on the Index of the Moscow Exchange (IMOEX) is given in 27 27 284 Table 6. Regression 1 8.64 10 8.64 10 25,506.1466 1.1 10 25 23 Residue 290 9.83 10 3.39 10 Total 291 8.74 10 Coefficients Standard Error t-Statistics P-Value Lower 95% Upper 95% 11 11 12 11 Constant 3.58439 0.0004 9.6 10 2.68 10 1.5 10 4.3 10 Coefficient at IMOEX 2.3668 0.0148 159.7064 1.11 10 2.3377 2.3960 Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. The correlation of the indicators (99.44%) is very high; however, the variables do not duplicate each other (multicollinearity is absent). Estimate F equals 25,506.1466. Table F at 292 observations and 1 factor variable (k = m = 1, k = n – m 1 = 292 – 1 1=290) at 1 2 the significance level = 0.05 equals 3.92. Since estimate F exceeds the table (25,506.1466 > 3.92), the F test is passed, and the regression equation is reliable at the set . Let us carry out a correspondence analysis—for this, we estimate the linearity and diagnose the model assumptions (Figure 8). Risks 2021, 9, x FOR PEER REVIEW 14 of 18 Table 6. Dataset modelling of the impact of the Index of the Moscow Exchange (IMOEX) on the Sustainable Develop- ment Vector Index (MRSV) in Russia from 21 September 2020 to 11 November 2021. Regression Statistics Multiple R 0.9944 R-square 0.9888 Adjusted R-square 0.9887 Standard error 5.82 × 10 Observations 292 Dispersion analysis df SS MS F Significance F 27 27 −284 Regression 1 8.64 × 10 8.64 × 10 25,506.1466 1.1 × 10 25 23 Residue 290 9.83 × 10 3.39 × 10 Total 291 8.74 × 10 Coefficients Standard Error t-Statistics P-Value Lower 95% Upper 95% 11 11 12 11 Constant −9.6 × 10 2.68 × 10 −3.58439 0.0004 −1.5 × 10 −4.3 × 10 −284 Coefficient at IMOEX 2.3668 0.0148 159.7064 1.11 × 10 2.3377 2.3960 Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. The correlation of the indicators (99.44%) is very high; however, the variables do not duplicate each other (multicollinearity is absent). Estimate F equals 25,506.1466. Ta- ble F at 292 observations and 1 factor variable (k1 = m = 1, k2 = n – m − 1 = 292 – 1 − 1=290) at the significance level α = 0.05 equals 3.92. Since estimate F exceeds the table (25,506.1466 > 3.92), the F test is passed, and the regression equation is reliable at the set Risks 2021, 9, 211 16 of 20 α. Let us carry out a correspondence analysis—for this, we estimate the linearity and di- agnose the model assumptions (Figure 8). Figure 8. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Figure 8. Residual graphs. Source: Obtained by the authors automatically with the help of the “Regression” function in Microsoft Excel. Microsoft Excel. The graphs in Figure 8 indicate that the relationship of the resulting variable with The graphs in Figure 8 indicate that the relationship of the resulting variable with all factorial variables is quite reliably described by linear regression. The graphs also all factorial variables is quite reliably described by linear regression. The graphs also in- indicate the homogeneity of the dispersion of the residues. This allows us to compile dicate the homogeneity of the dispersion of the residues. This allows us to compile a re- a regression model of social entrepreneurship’s dependence on the specific factors of gression model of social entrepreneurship’s dependence on the specific factors of sus- sustainable development financing in emerging economies: tainable development financing in emerging economies: MRSV = 960,277,188,684.289 IMOEX + 2.37 (2) MRSV = −960,277,188,684.289 IMOEX + 2.37 (2) According to this regression model (2), with an increase in the Index of the Moscow According to this regression model (2), with an increase in the Index of the Moscow Exchange (IMOEX) of 1 RUB, the Sustainable Development Vector Index (MRSV) is down Exchange (IMOEX) of 1 RUB, the Sustainable Development Vector Index (MRSV) is 960.28 billion rubles. The considered example of Russia is indicative—it demonstrates down 960.28 billion rubles. The considered example of Russia is indicative—it demon- that social entrepreneurship is indeed largely determined by financial risks. The Russian strates that social entrepreneurship is indeed largely determined by financial risks. The experience can be extended to other developing countries. Russian experience can be extended to other developing countries. 4. Discussion Thus, the performed dataset modelling of financial risks management in social en- trepreneurship for sustainable development in emerging economies has shown that the financial risks of social entrepreneurship differ from the standard financial risks of com- mercial entrepreneurship. The standard financial factors of commercial entrepreneurship (receipt of credits, protection of minority investors, taxation, and solution to non-solvency) have not demonstrated either high (correlation equals 43%, which is low) or statistically significant connection with social entrepreneurship in emerging economies (Hypothesis 1 has been proven). We determined specific factors of sustainable development financing in emerging economies, which have shown a stronger impact (correlation—83%) on social entrepreneur- ship, and the regression model of their dependence is reliable. This allows us to identify the special financial risks of social entrepreneurship in emerging economies: reduction of stimuli for using financial resources in long-term investments, which disrupts the stability and decreases inclusion; joint public–private investments; decrease in investments in R&D; expand investment in the skills needed for jobs and the “markets of tomorrow”. A contradictory influence of the factors of financing on sustainable development is substantiated. Increased incentives to direct financial resources towards long-term investments strengthen stability, expand inclusion (factor 1), and incentivize and expand patient investments in research, innovation, and invention (factor 3), which have positively impacted social entrepreneurship in emerging economies. The influence of support for the creation of the “markets of tomorrow”, especially in areas that require public–private collaboration (factor 2) and expansion of investments in skills for the “markets of tomorrow” (factor 4), on social entrepreneurship in emerging economies was negative (Hypothesis 2 has been proven). Therefore, the financial risk man- Risks 2021, 9, 211 17 of 20 agement of social entrepreneurship should be flexible in considering the multidirectional financing factors (stimulating some factors and restraining other factors). Additionally, the large potential of social entrepreneurship’s development by means of management (maximum reduction) of financial risks has been determined. Despite its non-commercial nature, social entrepreneurship in emerging economies faces large financial risks and largely depends on their overcoming. That is why with minimum financial risk—in the case of an increase in risks for using financial resources in long-term investments, which disrupts the stability and decreases inclusion by 84.87% and increase of investments in research, innovation, and investments that could create new “markets of tomorrow” by 187.5%—social entrepreneurship will demonstrate large substantial progress: an increase of 99.61% (more than 50%), from 45.18 points to 90.18 points (Hypothesis 3 has been proven). The results obtained, firstly, clarified the specifics of investments in sustainable de- velopment noted in the works (Azmat et al. 2021; Chen 2021; Staszkiewicz and Werner 2021). Our results also identified unique factors in financing social entrepreneurship for (1) increasing incentives for directing the financial resources into long-term investments, strengthening stability and increasing inclusiveness; (2) promoting the creation of “mar- kets of tomorrow”, especially in areas where public–private cooperation is required; (3) encouraging and expanding investment in research, innovation and inventions that can create new “markets of tomorrow”; (4) updating curricula and increasing investment in skills for work and the “markets of tomorrow”. This showed for the first time that invest- ment in sustainable development needs to take into account both the specifics of social entrepreneurship and developing countries. Unlike in past studies, it has been proven that the standard financial risks of commercial entrepreneurship (obtaining loans, protecting minority investors, taxation, and resolving insolvency) are not universal—they do not apply to social entrepreneurship in developing countries. Secondly, the article expands on the scientific background of existing work (He and Yan 2020; Lee 2020; Zhang and Wang 2021) on the conflicting influence of financing factors on sustainable development. For the first time, funding factors were shown to have both an enabling and a disincentive effect on social entrepreneurship in developing countries. Based on this, the article proposes flexible management of financial risks of social entrepreneurship (to stimulate some factors and restrain others). Thirdly, in contrast to existing publications in the field of market economics (Graafland and Wells 2021; Shao et al. 2021; Wut et al. 2021), this article argues that despite the difference in goals (commercial/non-commercial) and funding factors, both commercial and social entrepreneurship face high financial risks and need financial risk management. This conclusion requires a revision of the existing approach to social entrepreneurship management in favour of greater attention to financial risk management. 5. Conclusions This paper ’s contribution to the literature consists of specifying the essence and features of social entrepreneurship in emerging economies by determining unique financial risks and developing recommendations for their management. The scientific significance of this paper consists of the substantiation of the fact that commercial factors (in the form of financial risks) have a more critical role in social entrepreneurship’s development than was previously (in the existing literature) believed. The potential of social entrepreneurship’s development in emerging economies is implemented (in 2021) only by 50% because of the restraining influence of financial risks. In case of the optimal management of financial risks (their minimisation), the level of social entrepreneurship’s development in emerging economies will approach a maximum, which will reduce their underrun from developed countries in the sphere of implementing SDGs and support global sustainable development. It should be concluded that a specific limitation of this work’s results is the study of the experience of only emerging economies. The absence of standard financial risks of Risks 2021, 9, 211 18 of 20 commercial entrepreneurship with social entrepreneurship could be predetermined by the specifics of emerging economies—underdevelopment of market relations and reduced effectiveness of institutions. Future studies should deal with this limitation and pay attention to the experience of advanced economies and compare it to the experience of emerging economies. It must also be recognized that the sample of developing countries is limited (due to the unavailability of data for many developing countries) and the variables are measured by indices (due to the lack of more accurate statistics on the topic of social entrepreneurship), which can cause bias in empirical results. The inclusion of developed countries in the sample did not allow for the full consideration of the characteristics of developing countries. To overcome this limitation, further research is recommended to support the develop- ment of global statistics on social entrepreneurship with high empirical specifications and full coverage of developing countries. After more accurate statistics become available, it is advisable to continue the dataset modelling and refine the results obtained in this article. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/risks9120211/s1, Supplementary Materials: data table. Author Contributions: Conceptualization, E.G.P.; methodology, E.G.P.; investigation, E.G.P.; writing— original draft preparation, E.G.P.; writing—review and editing, B.S.S.; supervision, B.S.S.; project administration, B.S.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. References Alam, Shahriar Tanvir, Sayem Ahmed, Syed Mithun Ali, Sudipa Sarker, Golam Kabir, and Asif Ul-Islam. 2021. Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics 239: 108193. [CrossRef] [PubMed] Al-Omoush, Khaled Saleh, VIrginia Simón-Moya, Mohammad Atwah Al-ma’aitah, and Javier Sendra-García. 2021. The determinants of social CRM entrepreneurship: An institutional perspective. Journal of Business Research 132: 21–31. [CrossRef] Azmat, Fara, Ameeta Jain, and Fabienne Michaux. 2021. Strengthening impact integrity in investment decision-making for sustainable development. Sustainability Accounting, Management and Policy Journal. [CrossRef] Bakos, Levente, and Danut Dumitrascu Dumitrascu. 2021. Decentralized enterprise risk management issues under rapidly changing environments. Risks 9: 165. [CrossRef] Bouri, Elie, Riza Demirer, Rangan Gupta, and Jacobus Nel. 2021. Covid-19 pandemic and investor herding in international stock markets. Risks 9: 168. [CrossRef] Cardella, Giuseppina Maria, Brizeida Raquel Hernández-Sánchez, Alcides Almeida Monteiro, and Jose Carlos Sánchez-García. 2021. Social entrepreneurship research: Intellectual structures and future perspectives. Sustainability 13: 7532. [CrossRef] Chandra, Yanto, Fandy Tjiptono, and Andhy Setyawan. 2021. The promise of entrepreneurial passion to advance social entrepreneur- ship research. Journal of Business Venturing Insights 16: e00270. [CrossRef] Chen, Bingyao. 2021. Public-private partnership infrastructure investment and sustainable economic development: An empirical study based on efficiency evaluation and spatial spillover in china. Sustainability 13: 8146. [CrossRef] Dalwai, Tamanna, and Mahdi Salehi. 2021. A business strategy, intellectual capital, firm performance, and bankruptcy risk: Evidence from Oman’s non-financial sector companies. Asian Review of Accounting 29: 474–504. [CrossRef] Duygun, Meryem, Daniel Ladley, and Mohamed Shaban. 2020. Challenges to global financial stability: Interconnections, credit risk, business cycle and the role of market participants. Journal of Banking and Finance 112: 105735. [CrossRef] Elkhal, Khaled. 2019. Business uncertainty and financial leverage: Should the firm double up on risk? Managerial Finance 45: 536–44. [CrossRef] Fhiri, Nur Suriaty Daud, Shuhairimi Abdullah, Yasmin Ahmad, Noor Salwani Hussin, Jamsari Jamaluddin, and Abdul Jalil Ramli. 2021. Social entrepreneurship: Environmental sustainability. AIP Conference Proceedings 2339: 020237. [CrossRef] Fridhi, Bechir. 2021. Social entrepreneurship and social enterprise phenomenon: Toward a collective approach to social innovation in Tunisia. Journal of Innovation and Entrepreneurship 10: 14. [CrossRef] Galindo-Martín, Miguel-Ángel, Maria-Soledad Castaño-Martínez, and Maria-Teresa Méndez-Picazo. 2021. Effects of the pandemic crisis on entrepreneurship and sustainable development. Journal of Business Research 137: 345–53. [CrossRef] [PubMed] Risks 2021, 9, 211 19 of 20 Graafland, Johan, and Thomas R. Wells. 2021. In Adam Smith’s Own Words: The Role of Virtues in the Relationship Between Free Market Economies and Societal Flourishing, A Semantic Network Data-Mining Approach. Journal of Business Ethics 172: 31–42. [CrossRef] Hassani, Hossein, Xu Huang, Steve MacFeely, and Mohammad Reza Entezarian. 2021. Big data and the united nations sustainable development goals (UN SDGs) at a glance. Big Data and Cognitive Computing 5: 28. [CrossRef] He, Chen, and Gujun Yan. 2020. Path selections for sustainable development of green finance in developed coastal areas of China. Journal of Coastal Research 104 (Suppl. 1): 77–81. [CrossRef] Institute of Scientific Communications. 2021. Dataset “Social Entrepreneurship in the Global Economy: From Virtual Scores to Big Data”. Available online: https://iscvolga.ru/dataset-social-predprinim (accessed on 9 August 2021). Kliestik, Tomas, Maria Misankova, Katarina Valaskova, and Lucia Svabova. 2018. Bankruptcy prevention: New effort to reflect on legal and social changes. Science and Engineering Ethics 24: 791–803. [CrossRef] Kovacova, Maria, Tomas Kliestik, Katarina Valaskova, Pavol Durana, and Zuzana Juhaszova. 2019. Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana 10: 743–72. [CrossRef] Lasloom, Nasser Mohammed. 2021. The Inevitability of Financial Risks in Businesses and How to Overcome Them: Saudi Arabia in Focus. Smart Innovation, Systems and Technologies 227: 815–24. [CrossRef] Lee, Jung Wan. 2020. Green finance and sustainable development goals: The case of China. Journal of Asian Finance, Economics and Business 7: 577–86. [CrossRef] Locurcio, Marco, Francesco Tajani, Pierluigi Morano, Debora Anelli, and Benedetto Manganelli. 2021. Credit risk management of property investments through multi-criteria indicators. Risks 9: 106. [CrossRef] Méndez-Picazo, Maria-Teresa, Miguel-Angel Galindo-Martín, and Maria-Soledad Castaño-Martínez. 2021. Effects of sociocultural and economic factors on social entrepreneurship and sustainable development. Journal of Innovation and Knowledge 6: 69–77. [CrossRef] Moscow Exchange. 2021a. Sustainable Development Vector Index (MRSV). Available online: https://www.moex.com/ru/index/ MRSV/archive/#/from=2019-10-01&till=2021-11-11&sort=TRADEDATE&order=desc (accessed on 9 August 2021). Moscow Exchange. 2021b. Moscow Exchange Index (IMOEX). Available online: https://www.moex.com/ru/index/IMOEX/archive/ #/from=2020-09-21&till=2021-11-11&sort=TRADEDATE&order=desc (accessed on 9 August 2021). Popkova, Elena, Piper DeLo, and Bruno Sergi. 2020. Corporate Social Responsibility Amid Social Distancing During the COVID-19 Crisis: BRICS vs. OECD Countries. Research in International Business and Finance 55: 101315. [CrossRef] [PubMed] Sabău, Andrada-Ioanna, Codruta Mare, and Ioanna Lavinia Safta. 2021. A statistical model of fraud risk in financial statements. Case for Romania companies. Risks 9: 116. [CrossRef] Sahrakorpi, Tiia, and Venkata Bandi. 2021. Empowerment or employment? Uncovering the paradoxes of social entrepreneurship for women via Husk Power Systems in rural North India. Energy Research and Social Science 79: 102153. [CrossRef] Sebestyén, Viktor, and Janos Abonyi. 2021. Data-driven comparative analysis of national adaptation pathways for Sustainable Development Goals. Journal of Cleaner Production 319: 128657. [CrossRef] Setiawan, Hari Harjanto, Mu’man Nuryana, Badrun Susantyo, Agus Budi Purwanto, and Muhammad Belanawane Sulubere. 2021. Social entrepreneurship for beneficiaries of the Program Keluarga Harapan (PKH) toward sustainable development. IOP Conference Series: Earth and Environmental Science 739: 012053. [CrossRef] Shao, Jinhua, Brayan Tillaguango, Rafael Alvarado, Santiago Ochoa-moreno, and Johanna Alvarado-Espejo. 2021. Environmental impact of the shadow economy, globalisation, trade and market size: Evidence using linear and non-linear methods. Sustainability 13: 6539. [CrossRef] Staszkiewicz, Piotr, and Alaksander Werner. 2021. Reporting and disclosure of investments in sustainable development. Sustainability 13: 908. [CrossRef] Suseno, Yuliani, and Ling Abbott. 2021. Women entrepreneurs’ digital social innovation: Linking gender, entrepreneurship, social innovation and information systems. Information Systems Journal 31: 717–44. [CrossRef] Syed, Ali Murad, and Hana Saeed Bawazir. 2021. Recent trends in business financial risk—A bibliometric analysis. Cogent Economics and Finance 9: 1913877. [CrossRef] Tabares, Sabrina. 2021. Certified B corporations: An approach to tensions of sustainable-driven hybrid business models in an emerging economy. Journal of Cleaner Production 317: 128380. [CrossRef] Tang, Min, Peihan Liu, Xiangrui Chao, and Zhenglin Han. 2021. The performativity of city resilience for sustainable development of poor and disaster-prone regions: A case study from China. Technological Forecasting and Social Change 173: 121130. [CrossRef] Thörnqvist, Christer, and Jonna Kilstam. 2021. Aligning Corporate Social Responsibility with the United Nations’ Sustainability Goals: Trickier than it Seems? A Study of Social Entrepreneurship in Sweden. Economics 9: 161–77. [CrossRef] Ullah, Hafeez, Zhuquan Wang, Shahid Bashir, Abdul Razzaq Khan, Madiha Riaz, and Nausheen Syed. 2021. Nexus between IT capability and green intellectual capital on sustainable businesses: Evidence from emerging economies. Environmental Science and Pollution Research 28: 27825–43. [CrossRef] [PubMed] World Bank. 2021. Doing Business: Rating of Countries 2020. Available online: https://russian.doingbusiness.org/ru/rankings (accessed on 9 August 2021). Risks 2021, 9, 211 20 of 20 World Economic Forum. 2021. Global Competitiveness Report Special Edition 2020: How Countries are performing on the Road to Recovery. Available online: https://www.weforum.org/reports/the-global-competitiveness-report-2020 (accessed on 9 August 2021). Wut, Tai-Ming, Wai-Tung Chan, and Stephanie W. Lee. 2021. Unconventional entrepreneurship: Women handicraft entrepreneurs in a market-driven economy. Sustainability 13: 7261. [CrossRef] Zhang, Bufan, and Yifeng Wang. 2021. The Effect of Green Finance on Energy Sustainable Development: A Case Study in China. Emerging Markets Finance and Trade 57: 3435–54. [CrossRef]
Journal
Risks – Multidisciplinary Digital Publishing Institute
Published: Nov 26, 2021
Keywords: financial risk; risk management; dataset modelling; social entrepreneurship; sustainable development; emerging economies