Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia
by
,
Zhanna V. Gornostaeva
1,*,
Larisa V. Shabaltina
2,
Igor V. Denisov
2,
Aleksandra A. Musatkina
3 and
Nikolai G. Sinyavskiy
4 1
Faculty of Economics, Service and Entrepreneurship, Don State Technical University, 344000 Rostov-on-Don, Russia
2
Department of Management Theory and Business Technologies, Plekhanov Russian University of Economics, 109992 Moscow, Russia
3
Department of Constitutional and Administrative Law, Institute of Law, Togliatti State University, 445667 Togliatti, Russia
4
Department of Economic Security and Risk Management, Financial University under the Government of the Russian Federation, Leningradsky Ave., 49/2, 125167 Moscow, Russia
*
Author to whom correspondence should be addressed.
Risks 2024, 12(6), 101; https://doi.org/10.3390/risks12060101
Submission received: 9 April 2024
/
Revised: 4 May 2024
/
Accepted: 22 May 2024
/
Published: 20 June 2024
(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development II)
Abstract
:The purpose of this paper was to reveal the influence of the support of the sustainable development goals (SDGs) on the financial risks of responsible universities in Russia. This paper fills the gap in the literature that exists regarding the unknown consequences of SDGs’ support by responsible Russian universities concerning their financial risks. Based on the experience of the top 30 most responsible Russian universities in 2023, we used regression analysis to compile a model for their financial risk management. This model mathematically describes the cause-and-effect relationships of financial risk management in responsible Russian universities. This paper offers a new approach to financial risk management in responsible Russian universities. In it, financial risks to Russian universities are reduced due to universities accepting responsibility for state and private investors. A feature of the new approach is that the effective use of university funds is ensured not by cost savings but by the support of the SDGs. The potential for a reduction in financial risk in responsible universities in Russia through alternative approaches to financial risk management was disclosed. The proposed new approach can potentially raise (to a large extent) the aggregate incomes of responsible universities in Russia compared to the existing approach. The main conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures which risk burdening universities. This burden could be prevented with the newly developed approach to financial risk management in responsible universities in Russia through support of the SDGs. The theoretical significance lies in clarifying the specific list of the SDGs whose support makes the largest contribution to reducing financial risks for the universities—namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the new approach will allow for full disclosure of the potential reduction in financial risks in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is as follows: the proposed recommendations will allow improved financial risk management in Russian universities through optimization of the support of the SDGs.
1. Introduction
Financial risks have strong effects on the activities of modern organizations, and they have specific features in each sector of the economy. The specifics for the Russian higher education sector consist of the domination of state universities. From a financial perspective, this means that a large share of Russian universities’ resources is composed of state subsidies. Historically, Russian universities were created using the national budget, and their full state provision was planned.
Recent decades have seen a full-scale market reformation of the Russian economy, including the higher education sector. The formation of market relations in this sphere initiated the reconsideration of the strategy of financing Russian universities’ activities. To reduce the burden on the state budget and increase the total volume of financing for university activities, the inflow of private investments in the sphere of higher education has been stimulated in recent years in Russia. Private investments mainly take the form of payments for higher education services that are provided by universities and university innovations and technologies that are purchased by private businesses.
Financial risks to universities are treated as a reduction in the volume of financing of universities’ activities from various sources. The essence of financial risk management in universities consists in raising their attractiveness for state financing and private investments. The Decade of Action introduced uncertainty in universities’ financial risk management. The practice of achieving the Sustainable Development Goals (SDGs) gained significant popularity.
To support the UN (2024a) Global Initiative, respectable organizations, such as THE (2024), began compiling international rankings of universities using the criteria of how they support the SDGs. To preserve global competitiveness, universities had to conform to new criteria set by international university rankings, so they started supporting the SDGs. Universities that support the SDGs can be called “responsible universities” because they accept responsibility for the sustainable development of socioeconomic systems.
The problem is that while striving towards strengthening global competitiveness, universities may face the growth of financial risks. This is because support of the SDGs is connected to additional expenditures by universities. State regulators can potentially treat expenditures for the achievement of the SDGs as unplanned expenditures and ineffective use of the budget assets provided to universities. The reduction in the economic effectiveness of the management of responsible universities may become a reason for a decrease in their state financing and redistribution of assets in favor of more effective universities.
Private investors focus on price. Universities’ support of the SDGs can cause the growth of the cost of paid services of higher education, which are provided by universities, and increase the cost of university innovations and technologies, which are available for purchase by private businesses. This may cause a reduction in the pricing competitiveness of educational and research services that are provided by responsible universities, a reduction in demand, and a decrease in sales volume. Consumers of these services would then shift to alternative local and foreign suppliers of these services—universities from other countries.
Striving to solve these problems, this paper seeks to determine the influence of support of the SDGs on the financial risks of responsible universities in Russia. This goal is achieved with the help of the two following tasks. The first is to identify the cause-and-effect relationships of financial risk management in responsible Russian universities. The second is to identify the potential for a reduction in financial risks in responsible universities in Russia through alternative approaches to financial risk management. This paper fills a gap in the literature that is connected to the unknown influence of the support of the SDGs on financial risks to universities in Russia. The paper clarifies this contribution and offers a new approach to financial risk management for Russian universities—through support of the SDGs.
2. Literature Review
2.1. The Existing Approach to Financial Risk Management in Responsible Universities in Russia
The model of financing for state universities in Russia was changed in the market reformation of the Russian economy on the whole and the Russian system of higher education in particular. Several decades ago, only state universities existed in Russia, and all of them were fully funded from the state budget. Market reforms led to the emergence of private universities, and to the reduction in state financing of state universities (Zheleznov 2023). This created financial risks to state universities in Russia, for the capabilities and volume of state financing decreased, and the mechanisms of attracting private financing were not fully developed.
The Russian model of financing state universities is unique—it is completely different from the system of university financing in Europe, Asia, and the USA, where private universities dominate in developed countries (Kelchen et al. 2024). State financing of their activities is not performed directly, but indirectly—through provisions of grants for tuition and educational loans.
In Russia, as in many other dynamically developing countries, there exists state procurement to universities for training personnel. In the Russian model, the Ministry of Science and Higher Education determines which specialties are in the highest demand in each region and country on the whole and allocates state-funded places to universities, at which students’ tuition is financed from the federal budget. Together with this, there is paid education—paid for by students themselves and/or employers (targeted, corporate education) (Mao et al. 2024).
In this regard, the term “investor in higher education” is introduced. It is a private subject financing university activities, namely, students and employers who pay for higher education services, and companies that receive university innovations (Huňady et al. 2023). For comparison, the USA has private commercial institutions that rely on investors, but this is not a very popular practice (Blume-Kohout 2023).
This paper is based on the concept of financial risk management in universities, the provisions of which are given in the works by Bogoviz et al. (2018), Kato et al. (2024), and Zarova and Tursunov (2022). According to this concept, the financial risk to state universities, which is the research object in this paper, is the reduction in the volume of their financing (Dyrstad et al. 2024; Krieger 2024). However, the following should be differentiated:
- Risk of the reduction in the total financing of the activities of universities, whose structure is based on state financing from the national budget (Chairassamee and Hean 2023; Turginbayeva and Domalatov 2019);
- Risk of the reduction in extra-budgetary financing of universities’ activities from the funds of private investors: consumers (individual and corporate) services of higher education and B2B consumers of university innovations and technologies (Litvinova 2022; Moll 2023).
The existing approach to financial risk management in responsible universities of Russia involves maximization of the effectiveness of universities’ activities due to the following: an increase in the results in the sphere of education (Tovmasyan et al. 2022), research (Bogoviz and Mezhov 2015; Fukugawa 2023), and international activities (Petrenko and Stolyarov 2019), or in combination with the reduction in wages of representatives of academic staff for saving in the interests of the reduction in costs, or in combination with an increase in wages of academic staff as a means of maximization of the above results (Przhedetskaya and Borzenko 2019).
2.2. Support of the SDGs in Responsible Universities: International Practice and Russian Experience
Currently, international practice is dominated by responsible universities, which are treated as universities that accept responsibility for:
- The state for the effectiveness of spending the provided budget funds and for the economic, environmental, and social consequences of universities’ activities and practical implementation of the created innovations (Dyrstad et al. 2024; Thawesaengskulthai et al. 2024);
- Private investors for the quality and affordability of educational and research services that are provided by universities (Hahn et al. 2024; Khasanov et al. 2019).
Thus, a responsible university is a university that in the course of its activities supports the SDGs, publishes the corresponding reports, and, accordingly, is presented in university rankings, including international ones, which are connected with the achievement of the SDGs. Certain recent research, e.g., Athari et al. (2024), showed that national ESG is very important, although the most objective rankings are international university rankings, among which an important role belongs to the respectable ranking THE (2024).
Accordingly, an irresponsible university could be defined as a university that does not support the SDGs and/or does not publish the corresponding reports on sustainable development and is not in university rankings, including international ones, which are connected with the achievement of the SDGs. That is why we suggest using the presence and position in the THE “Impact Rankings 2023” (2024) as the criterion of differentiation of responsible and irresponsible universities.
Our literature review (Kyambade et al. 2024a; Ncube 2023; Preuss et al. 2023) revealed the high level of support of SDGs among Russian universities, which proves that responsible state universities dominate Russia’s higher education system. Also, the existing publications note the significant contribution of support of SDGs by universities for the growth of their global competitiveness and expansion of their international activities (Kyambade et al. 2024b; Marchigiani and Garofolo 2023; Zhao and Cheah 2023).
However, the consequences of responsible universities’ supporting the SDGs for their financial risks are insufficiently elaborated and largely unknown, which is a gap in the literature. This paper strives to fill the revealed gap, posing the following research question:
RQ: How does support of the SDGs by responsible universities in Russia influence their financial risks?
Certain literature sources—Bock et al. (2018) and Mántica (2022)—put forward an assumption that support of the SDGs by responsible universities can raise their financial risks, for it is connected with additional expenditures.
Contrary to them, Abankina et al. (2018) and Liu and Gao (2021) present their point of view that support of the SDGs by responsible universities can reduce their financial risks because it raises the loyalty of all interested parties: consumers (students and employers), business partners, and state regulators and employees (academic staff), whose labor efficiency and quality results increase. Based on this, the following hypothesis is proposed in this paper:
H: Support of the SDGs ensures the reduction in financial risks of responsible universities in Russia.
To check this hypothesis, we performed econometric modeling of the influence of the activity of SDG support as an innovative managerial practice, together with traditional managerial practices, on the financial risks to responsible universities in Russia.
3. Materials and Methods
This research sample contains the top 30 responsible Russian universities, which were selected by the criteria of their presence among the top 1000 universities in the world and most active support for the SDGs, in the THE “Impact Rankings 2023” (2024). The sample structure in the aspect of the position of responsible Russian universities in this ranking is shown in Figure 1.
As shown in Figure 1, 10% of the sample (3 universities) are in the category “201–300” in the considered ranking. The category “301–400” includes 13.3% of the sample (4 universities). The category “401–600” contains 26.7% of the sample (8 universities). The category “601–800” contains 33.3% of the sample (10 universities)—this is the largest category among responsible Russian universities. The category “801–1000” has 16.7% of the sample (5 universities).
The sample of the top 30 responsible Russian universities in 2023, which was studied in this paper, is presented in Table A1. Measures of financial risk management, implemented by the top 30 most responsible universities in Russia in 2023, are shown in Table A2. Financial risks of the top 30 most responsible Russian universities in 2023 are characterized in Table A3. The activity of the implementation of the SDGs by the top 30 most responsible universities in Russia in 2023 is indicated in Table A4.
To solve the first task, which involves revealing the cause-and-effect relationships of financial risk management in responsible Russian universities, we performed a factor analysis of this management. The task was solved with the help of regression analysis. This method was used to identify high-precision–regression–dependence of the indicators of financial risks—university’s revenues from all sources (Ufr1) and university’s revenues from extra-budgetary sources (Ufr2), according to MIREA, MIC (2024)—on the system of factors, which include, first, alternative financial risk management measures:
- Level of implementation of the SDGs (Resp), score 1–100 (according to THE 2024);
- Educational activity (Ex1): “Average score of the Unified State Examination of accepted students”, score 1–100 (according to MIREA, MIC 2024);
- Research activities (Ex2): volume of R&D per one member of academic staff, thousand rubles (according to MIREA, MIC 2024);
- International activities (Ex3): share of foreign students in the total number of students, % (according to MIREA, MIC 2024);
- Wages of academic staff (Ex4): academic staff wages/average wages in the region’s economy ratio, % (according to MIREA, MIC 2024).
Second, detailed practices of support of SDGs in universities, which include practices that are widespread among Russian responsible universities (share of universities that implement them exceeds 10% in the total sample according to THE 2024): level of support of SDG 4, SDG 5, SDG 8, SDG 9, SDG 11, and SDG 7 by responsible universities. Hypothesis H is deemed proven if the regression coefficient at the factor variable Resp is positive in regression equations for both resulting variables (Ufr1 and Ufr2). We also selected SDGs at which regression coefficients are positive in regression equations for both resulting variables (Ufr1 and Ufr2). The regression analysis results’ reliability was checked with the help of the F-test and t-test.
For the most complete consideration and the most correct reflection of the cause-and-effect relationships, we included the macro-level factors in the research model. Among the macro-factors that potentially influence the financial risks to universities, this paper considers the following: economic (EGl), social (SGl), and political (PGl) globalization (according to KOF 2024), as well as state financing of higher education (PETE).
To calculate the value of the indicator PETE, we calculated the product of “expenditure on tertiary education (% of government expenditure on education)” (World Bank 2024a) and “government expenditure on education, total (% of GDP)” (transferred into shares of 100, World Bank 2024a). Since the macro-level factors influence the system of higher education on the whole, we evaluated their effect not on specific universities but on the general position of the three leading Russian universities in the THE ranking (TopUTHE from the materials of the UN 2024b). The values of the selected indicators are presented in Table 1.
Since the period in the Russian economy for which the required statistical data are available is relatively short, the sample is eight years. Therefore, to find the connection between the financial risks to universities and the macro-environment of the Russian economy, we selected the correlation analysis method.
This method was used to identify Figure 2 the interconnection between TopUTHE and EGl, SGl, PGl, and PETE. The indicator TopUTHE was selected for this research because it reflects the involvement of universities in sustainable development and their interest in the achievement of the SDGs to improve their position in international university rankings. In this way, the connection between the development of responsible universities in Russia and financial risks to state universities and globalization in various directions was determined.
We also conducted a correlation analysis of the interdependence of PETE and EGl, SGl, and PGl. This demonstrates the connection between various directions of globalization and Russian state universities’ financial risks. The positive connection is demonstrated by positive values of the correlation coefficients, and the negative connection is demonstrated by negative values of the correlation coefficients.
For the systemic reflection of aggregate results of econometric research, we used the structural equations modeling (SEM) method. The research model has the following form (Figure 2):
Figure 2.
The research model. Source: Authors.
In the research model SEM in Figure 2, E is errors—a variation in the indicators’ values. To solve the second task, which involved identifying the potential of financial risk reduction in responsible Russian universities in case of alternative approaches to financial risk management, we used the obtained regression equations to forecast the consequences for resulting variables (Ufr1 and Ufr2), which reflect financial risks, from the following: (1) maximization of SDGs’ support by responsible universities (Resp = 100) and (2) maximization (achievement of the maximum values in the sample) of the values of control variables (Ex1–Ex4). We selected the optimal combination of the level of support for the selected top-priority SDGs for the maximization of the support of SDGs by Russian responsible universities.
4. Results
4.1. Factor Analysis of Financial Risk Management in Responsible Universities of Russia
To solve the first task, which involves determining the cause-and-effect relationships of financial risk management in responsible Russian universities, we conducted a factor analysis of this management in the top 30 most responsible Russian universities in 2023 (from Table A1). Regression analysis of the dependence of the indicators of financial risks on the alternative measures of financial risk management was conducted in Table 2.
The results obtained (Table 2) show that the risk of total financing reduction in Russian universities’ activities is 67.43% determined by the implementation of the considered measures of financial risk management. In turn, the risk of the reduction in extra-budgetary financing of universities’ activities from private investor funds in Russia is 71.32% determined by the implementation of the considered measures of financial risk management.
The F-test was passed in both cases at the level of significance of 0.01. Standard errors are relatively small, which proves the correctness of the regression analysis results. However, the t-test was passed at the resulting variable Ufr1 only for Resp (at the level of significance of 0.05) and Ex1 (at the level of significance of 0.15), and at the resulting variable Ufr2 for Resp (at the level of significance of 0.05) and Ex1 (at the level of significance of 0.10). Regression analysis assessed how financial risk indicators depend on detailed SDG support practices in universities, as shown in Table 3.
The results obtained (Table 3) show that the risk of total financing reduction in Russian universities’ activities is 69.72% determined by support for the SDGs in universities. In turn, the risk of extra-budgetary financing reduction in Russian universities’ activities from private investor funds is 70.29% determined by the support for the SDGs in universities. The F-test was passed at the level of significance of 0.05 for Ufr1 and at the level of significance of 0.01 for Ufr2. Standard errors are relatively small, which proves the regression analysis results are correct.
However, the t-test was passed at the resulting variable Ufr1 only for SDG 4 (at the level of significance of 0.05), SDG 8 (at the level of significance of 0.20), SDG 9 (at the level of significance of 0.01), and SDG 17 (at the level of significance of 0.20). At the resulting variable Ufr2, the t-test was passed only for SDG 4 (at the level of significance of 0.05), SDG 5 (at the level of significance of 0.10), SDG 8 (at the level of significance of 0.01), and SDG 11 (at the level of significance of 0.25).
The established regression dependencies allowed compiling a model of financial risk management in responsible Russian universities, which is the following system of equations of multiple linear regression:
According to model (1), growth of the level of implementation of the SDGs by Russian universities by 1 point leads to an increase in revenues of Russian universities from all sources by RUB 0.2421 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.1144 billion. An increase in the “average score of the Unified State Examination of accepted students” by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.2637 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.1357 billion.
An increase in the “volume of R&D per one member of academic staff” by RUB 1 thousand leads to Russian universities’ revenues from all sources by RUB 0.00001 billion and a decrease in Russian universities’ revenues from extra-budgetary sources by RUB 0.0003 billion. An increase in the “share of foreign students in the total number of students” by 1% leads to a decrease in Russian universities’ revenues from all sources by RUB 0.0370 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0470 billion.
An increase in “academic staff wages/average wages in the region’s economy ratio” by 1% leads to Russian universities’ revenues from all sources by RUB 0.0129 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0177 billion.
The results obtained mean that Russian universities’ research activities increase their financial risks, and Russian universities’ international activities have a contradictory effect on their financial risks.
The detailed analysis of the dependence of the financial risks of Russian universities on the implementation of concrete SDGs showed that the growth of the activity of Russian universities’ support for SDG 4 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0609 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0304 billion. Growth of the activity of Russian universities’ support for SDG 5 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0215 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0297 billion.
Growth of the activity of Russian universities’ support for SDG 8 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0306 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0277 billion. Growth of the activity of Russian universities’ support for SDG 9 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0649 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0306 billion.
Growth of the activity of Russian universities’ support for SDG 11 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0164 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0149 billion. Growth of the activity of Russian universities’ support for SDG 17 by 1 point leads to an increase in Russian universities’ revenues from all sources by RUB 0.0747 billion and an increase in Russian universities’ revenues from extra-budgetary sources by RUB 0.0184 billion.
Thus, regression coefficients at the factor variable Resp are positive in regression equations for both resulting variables (Ufr1 and Ufr2) and the connection between the variables is statistically significant. Therefore, hypothesis H is deemed proven. It was established that the connection between financial risks to Russian universities and alternative measures of the management of these risks is unstable—it is statistically significant only with educational activities, while the connection with other measures is statistically insignificant, contradictory, and even negative.
We also selected SDGs at which regression coefficients are positive in regression equations for both resulting variables (Ufr1 and Ufr2). These are SDG 4, SDG 8, and SDG 9. Thus, their support should be the focus on efforts of Russian responsible universities for an increase in the effectiveness of management of their financial risks.
For the most complete consideration and correct reflection of the cause-and-effect relationships, we took into account the macro-level factors from Table 1. The results of their correlation analysis are shown in Figure 3.
The results in Figure 3 show that universities’ involvement in sustainable development and their interest in the achievement of the SDGs to improve their position in international university rankings increased in the course of political globalization (correlation equals 0.4360), but reduced in the course of economic (correlation equals −0.1245) and social (correlation equals −0.2414) globalization, as well as in the course of growth of state budget financing of higher education (correlation equals −0.0141).
Financial risks to state universities in Russia (which are connected with the reduction in state budget higher education funding volume) are reduced due to economic (correlation is 0.3959), social (correlation is 0.0038), and political (correlation is 0.1286) globalization. For the systemic reflection of aggregate results of the econometric research, they were joined in one SEM model (Figure 4).
The SEM model systematized the results obtained and allowed for the following generalized conclusions: First, the support factors of the SDGs are much more differentiated and have a larger and non-contradictory influence on the reduction in financial risks to universities in Russia than alternative factors. Second, micro-level factors (support of the SDGs and alternative factors) determine the financial risks to universities in Russia to a larger extent. Third, among the macro-level factors, the largest influence on the development of responsible universities in Russia is performed by political globalization, and on the reduction in financial risks to Russian universities—economic globalization.
4.2. Potential of Financial Risk Reduction in Responsible Russian Universities in Case of Alternative Approaches to Financial Risk Management
To solve the second task, which involved determining the potential of the reduction in financial risks in responsible Russian universities in alternative approaches to financial risk management, we used the obtained regression equations to compile forecasts of the consequences for resulting variables (Ufr1 and Ufr2) in each of the approaches. The forecasts were compiled for the period of the Decade of Action, i.e., until 2030.
According to the economic modeling results, we propose a new approach to financial risk management in responsible Russian universities, based on the support for the SDGs. In this new approach, the responsible Russian universities’ financial risks are reduced due to their accepting responsibility to the state for the economic, environmental, and social consequences of the universities’ activities and the practical implementation of the created innovations, as well as accepting responsibility to private investors for the quality and affordability of educational and research services provided by universities.
A feature of the offered approach and its essential difference from the existing one is that in the new approach, the high effectiveness of spending of provided budgetary and extra-budgetary funds and, accordingly, universities’ high investment attractiveness is ensured not due to saving but to support of SDGs.
In the proposed approach, the focus is on responsible universities’ support for SDG 4 through an increase in the quality of higher education services and providing wide groups of the population with the opportunity for life-long learning; SDG 8 through the development of applied skills with students for successful employment in the specialty and career-building by university graduates; SDG 9 through the creation of breakthrough applied innovations for the Russian economy in support for the strengthening of strategic academic leadership and Russian technological sovereignty.
The perspective of improvement of financial risk management in responsible Russian universities through the optimization of SDGs’ support reflects the forecasted consequences for financial risks from the maximization of the support of SDGs by responsible universities (Resp = 100), which is shown in Figure 5.
As shown in Figure 5, growing the activity of Russian university’s support for SDGs by 42.93% (from 69.96 points in 2023 to 100.00 points by 2030) will lead to an increase in Russian responsible universities’ revenues from all sources by 106.91% (from RUB 6.80 billion in 2023 to RUB 14.07 billion by 2030 in 2023 constant prices). Revenues of responsible Russian universities from extra-budgetary sources will grow by 120.73% (from RUB 2.85 billion in 2023 to RUB 6.28 billion by 2030). For this to be implemented in practice, the following recommendations on the improvement of financial risk management in Russian universities through optimization of SDGs’ support are offered (Figure 6).
According to Figure 6, to improve financial risk management in Russian universities through the optimization of support of SDGs (achievement of the growth of universities’ revenues according to the control values from Figure 2), the following is recommended:
- Growth of the activity of support of SDG 4 by 70.98% (from 25.36 points in 2023 to 43.37 points by 2030);
- Growth of the activity of support of SDG 8 by 130.70% (from 40.90 points in 2023 to 94.36 points by 2030);
- Growth of the activity of support of SDG 9 by 233.56% (from 29.98 points in 2023 to 100.00 points by 2030).
For comparison, let us consider also the perspective of the reduction in financial risks in responsible Russian universities through the development of the potential of the existing approach to financial risk management. This involves maximization (achievement of maximum values in the sample) of the values of control variables (Ex1–Ex4). The forecast of financial risks to Russian universities at the maximization of the results of implementing the current managerial measures is shown in Figure 7.
As shown in Figure 7, maximization of the results of implementing the current managerial measures involves the following:
- An increase in the “average score of the Unified State Examination of accepted students” by 34.43% (from 72.25 points in 2023 to 97.13 points by 2030);
- An increase in “volume of R&D per one member of academic staff” by 480.22% (from RUB 1158.22 thousand in 2023 to RUB 6720.25 thousand by 2030);
- An increase in the “share of foreign students in the total number of students” by 127.26% (from 14.29% in 2023 to 32.48% by 2030);
- An increase in “academic staff wages/average wages in the region’s economy ratio” by 44.42% (from 218.79% in 2023 to 315.97% by 2030).
For these reasons, responsible Russian universities’ revenues from all sources will grow by 104.35% (from RUB 6.80 billion in 2023 to RUB 13.90 billion until 2030 in 2023 constant prices). Responsible Russian universities’ revenues from extra-budgetary sources will grow by 146.48% (from RUB 2.85 billion in 2023 to RUB 7.02 billion by 2030).
Thus, the newly developed approach, which involves an increase in SDGs’ support, has a much larger potential for financial risk reduction in responsible Russian universities than the alternative existing approach to financial risk management, because it increases universities’ revenues from all sources to a larget extent. Development of this potential in practice in the period until 2030 can be facilitated by the selected optimal combination of the activity of support for the selected top-priority SDGs for the maximization of SDGs’ support of SDGs by responsible Russian universities.
5. Discussion
This paper’s contribution to the literature consists of the development of the concept of financial risk management in universities through clarification of the consequences of SDGs’ support by responsible universities in Russia for their financial risks. This paper continues the scientific discussion by Bogoviz et al. (2018), Kato et al. (2024), and Zarova and Tursunov (2022). The influence of the measures of the management of universities’ management on their financial risks in Russia, which is estimated in the existing literature and which is specified in this paper, is shown in Table 4.
As shown in Table 4, to confirm the results of Tovmasyan et al. (2022), the results obtained in this paper proved that the development of educational activities of Russian universities does reduce their financial risks. However, the effectiveness of other measures of financial risk management, which conform to the existing approach to the management of these risks, turned out to be low. Thus, unlike Bogoviz and Mezhov (2015) and Fukugawa (2023), we have established that the development of research activities does not reduce but instead raises financial risks. This is demonstrated by the obtained negative values of regression coefficients at the factor variable Ex2 in the model (1).
Unlike Petrenko and Stolyarov (2019), we established that the development of international activities of Russian universities has a contradictory effect on their financial risks, reducing the risk of reduction in extra-budgetary financing of universities’ activities due to the funds of private investors, but raising the risk of reduction in total financing of universities’ activities. Unlike Przhedetskaya and Borzenko (2019), we proved that the change in wages of academic staff in Russian universities does not have a statistically significant effect on their financial risks (for this factor variable in Model (1), the t-test was not passed).
Unlike Bock et al. (2018) and Mántica (2022), we substantiated that support for the SDGs does not raise but reduces financial risks. This was the proof of hypothesis H, to confirm Abankina et al. (2018) and Liu and Gao (2021). Thus, support of the SDGs was set into the basis of this paper’s new approach to financial risk management in responsible universities in Russia, which is the foundation of the scientific novelty and originality of this research.
The scientific novelty and value of the authors’ results and recommendations in this paper consist in the development of a new approach to the management of financial risks to Russian universities. The essential difference between the newly offered approach and the existing approach is inclusion in the system of measures of financial risk management, which contains an increase in results in the sphere of educational, research, and international activities with wages for academic staff, of an additional factor—support of the SDGs. In this paper, the significant contribution of SDGs’ support to the reduction in financial risks in Russian universities was substantiated for the first time, and the necessity for active support of the SDGs by Russian universities to reduce their financial risks was justified.
6. Conclusions
The set goal was achieved: we revealed and proved the positive influence of support of the SDGs on financial risks to responsible universities in Russia, which is expressed in the reduction in these risks. Therefore, the obtained new scientific results filled the literature gap, which is connected with the unknown influence of support of the SDGs on financial risks to Russian universities. Given the revealed positive contribution, a new approach to financial risk management of Russian universities was offered—through support of the SDGs.
This paper’s main results are as follows:
First, we identified the cause-and-effect relationships of financial risk management in responsible universities in Russia. Based on the leading experience of the top 30 most responsible Russian universities in 2023, we compiled a model of financial risk management in responsible Russian universities, which mathematically described and quantitatively measured the influence of each managerial measure on the financial risks to Russian universities.
The model showed that among the measures of financial risk management, which are used within the existing approach, only the educational activity of universities ensures the reduction in their financial risks, while the consequences of research, international, and personnel activities of Russian universities for their financial risks are statistically insignificant, contradictory, and even negative. In contrast, support of the SDGs demonstrated a significant and statistically reliable contribution to the reduction in financial risks to responsible Russian universities.
Second, the potential of financial risk reduction in responsible Russian universities using alternative approaches to financial risk management was disclosed and compared. The considered alternatives showed that the existing approach can potentially increase the aggregate revenues of responsible universities in Russia by 104.35% and the new proposed approach by 106.91%. This is a scientific argument in favor of the new approach to raising the effectiveness of financial risk management in Russian universities.
The main authors’ conclusion is that the existing approach to financial risk management in Russian universities is based on low-efficiency managerial measures and causes a high risk of burden on universities, which can be reduced by the new approach to financial risk management in responsible universities in Russia through support for the SDGs.
The theoretical significance lies in the specification of the concrete narrow list of the SDGs the support for which contribute the most to the reduction in financial risks of responsible universities in Russia: namely, SDG 4, SDG 8, and SDG 9. The practical significance is that the newly developed approach will allow for the fullest development of the potential for financial risk reduction in responsible universities in Russia in the Decade of Action (2020–2030). The managerial significance is that the proposed author’s recommendations will allow financial risk management improvement in Russian universities through the optimization of SDGs’ support.
Author Contributions
Methodology, Larisa V. Shabaltina; Formal analysis, Nikolai G. Sinyavskiy; Resources, Igor V. Denisov; Writing—original draft, Zhanna V. Gornostaeva; Writing—review & editing, Aleksandra A. Musatkina. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
The data will be made available upon request.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Sampler of the top 30 most responsible universities of Russia in 2023.
| Rank | International Name of the University | Official Russian Name of the University According to Its Statute | Link to the Information on the University in the Materials of MIREA, MIC (2024), |
|---|---|---|---|
| 201–300 | Kazan Federal University | Federal State Autonomous Educational Institution of Higher Education “Kazan (Volga) Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1519 (accessed on 1 May 2024) |
| 201–300 | Peter the Great St Petersburg Polytechnic University | Federal State Autonomous Educational Institution of Higher Education “Peter the Great St. Petersburg Polytechnic University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=240 (accessed on 1 May 2024) |
| 201–300 | RUDN University | Federal State Autonomous Educational Institution of Higher Education “Patrice Lumumba Peoples’ Friendship University of Russia” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=222 (accessed on 1 May 2024) |
| 301–400 | Altai State University | Federal State Budget Educational Institution of Higher Education “Altai State University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=4 (accessed on 1 May 2024) |
| 301–400 | Bauman Moscow State Technical University | Federal State Budget Educational Institution of Higher Education “Bauman Moscow State Technical University (National Research University)” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=147 (accessed on 1 May 2024) |
| 301–400 | Irkutsk National Research Technical University | Federal State Budget Educational Institution of Higher Education “Irkutsk National Research Technical University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=82 (accessed on 1 May 2024) |
| 301–400 | National Research Nuclear University MEPhI | Federal State Autonomous Educational Institution of Higher Education “National Research Nuclear University MEPhI” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=165 (accessed on 1 May 2024) |
| 401–600 | Financial University under the Government of the Russian Federation | Federal State Budget Educational Institution of Higher Education “Financial University under the Government of the Russian Federation” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1767 (accessed on 1 May 2024) |
| 401–600 | Immanuel Kant Baltic Federal University | Federal State Autonomous Educational Institution of Higher Education “Immanuel Kant Baltic Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=217 (accessed on 1 May 2024) |
| 401–600 | ITMO University | Federal State Autonomous Educational Institution of Higher Education “National Research University ITMO” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=234 (accessed on 1 May 2024) |
| 401–600 | Kursk State Medical University | Federal State Budget Educational Institution of Higher Education “Kursk State Medical University” of the Ministry of Healthcare of the Russian Federation | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1786 (accessed on 1 May 2024) |
| 401–600 | Moscow Institute of Physics and Technology (MIPT) | Federal State Autonomous Educational Institution of Higher Education “Moscow Institute of Physics and Technology” (National Research University) | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=161 (accessed on 1 May 2024) |
| 401–600 | Russian Biotechnological University (BIOTECH) | Federal State Budget Educational Institution of Higher Education “Russian Biotechnological University” (ROSBIOTECH) | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=155 (accessed on 1 May 2024) |
| 401–600 | Plekhanov Russian University of Economics | Federal State Budget Educational Institution of Higher Education “Plekhanov Russian University of Economics” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=209 (accessed on 1 May 2024) |
| 401–600 | Siberian Federal University | Federal State Autonomous Educational Institution of Higher Education “Siberian Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1507 (accessed on 1 May 2024) |
| 601–800 | Industrial University of Tyumen | Federal State Budget Educational Institution of Higher Education “Industrial University of Tyumen” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=301 (accessed on 1 May 2024) |
| 601–800 | North-Caucasus Federal University | Federal State Autonomous Educational Institution of Higher Education “North-Caucasus Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=266 (accessed on 1 May 2024) |
| 601–800 | North-Eastern Federal University | Federal State Autonomous Educational Institution of Higher Education “Ammosov North-Eastern Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1527 (accessed on 1 May 2024) |
| 601–800 | Novosibirsk State Agrarian University | Federal State Budget Educational Institution of Higher Education “Novosibirsk State Agrarian University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1649 (accessed on 1 May 2024) |
| 601–800 | Perm State University | Federal State Autonomous Educational Institution of Higher Education “Perm State National Research University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=198 (accessed on 1 May 2024) |
| 601–800 | Russian State Agrarian University—Moscow Timiryazev Agricultural Academy | Federal State Budget Educational Institution of Higher Education “Russian State Agrarian University—Moscow Timiryazev Agricultural Academy” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1640 (accessed on 1 May 2024) |
| 601–800 | Southern Federal University | Federal State Autonomous Educational Institution of Higher Education “Southern Federal University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=337 (accessed on 1 May 2024) |
| 601–800 | South Ural State University | Federal State Autonomous Educational Institution of Higher Education “South Ural State University” (National Research University) | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=336 (accessed on 1 May 2024) |
| 601–800 | Tomsk Polytechnic University | Federal State Autonomous Educational Institution of Higher Education “National Research Tomsk Polytechnic University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=296 (accessed on 1 May 2024) |
| 601–800 | Volgograd State University | Federal State Autonomous Educational Institution of Higher Education “Volgograd State University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=36 (accessed on 1 May 2024) |
| 801–1000 | Bashkir State University | Federal State Budget Educational Institution of Higher Education “Ufa University of Science and Technology” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=666669204 (accessed on 1 May 2024) |
| 801–1000 | Bashkir State Medical University | Federal State Budget Educational Institution of Higher Education “Bashkir State Medical University” of the Ministry of Healthcare of the Russian Federation | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=1731 (accessed on 1 May 2024) |
| 801–1000 | Belgorod State National Research University | Federal State Autonomous Educational Institution of Higher Education “Belgorod State National Research University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=17 (accessed on 1 May 2024) |
| 801–1000 | Lobachevsky State University of Nizhni Novgorod | Federal State Autonomous Educational Institution of Higher Education “National Research State University of Nizhny Novgorod named after N.I. Lobachevsky” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=170 (accessed on 1 May 2024) |
| 801–1000 | Novosibirsk State Technical University | Federal State Budget Educational Institution of Higher Education “Novosibirsk State Technical University” | https://monitoring.miccedu.ru/iam/2023/_vpo/inst.php?id=177 (accessed on 1 May 2024) |
Source: Compiled by the authors based on materials of MIREA, MIC (2024); THE (2024).
Table A2.
Measures of financial risk management that are implemented by the top 30 most responsible universities in Russia in 2023.
Table A2.
Measures of financial risk management that are implemented by the top 30 most responsible universities in Russia in 2023.
| Rank | Name | The Activity of Support of SDGs, Score 1–100 | Educational Activities | Research Activities | International Activities | Wages of Academic Staff |
|---|---|---|---|---|---|---|
| 201–300 | Kazan Federal University | 82.1 | 74.94 | 759.06 | 20.29 | 236.42 |
| 201–300 | Peter the Great St Petersburg Polytechnic University | 82.1 | 79.23 | 1690.64 | 17.55 | 228.24 |
| 201–300 | RUDN University | 82.1 | 70.54 | 359.66 | 29.45 | 249.15 |
| 301–400 | Altai State University | 76.7 | 67.95 | 429.17 | 22.69 | 227.76 |
| 301–400 | Bauman Moscow State Technical University | 76.7 | 78.67 | 1919.01 | 5.71 | 200.59 |
| 301–400 | Irkutsk National Research Technical University | 76.7 | 63.84 | 614.90 | 9.86 | 203.18 |
| 301–400 | National Research Nuclear University MEPhI | 76.7 | 88.50 | 4507.56 | 21.38 | 222.09 |
| 401–600 | Financial University under the Government of the Russian Federation | 72.6 | 81.45 | 567.12 | 8.21 | 200.76 |
| 401–600 | Immanuel Kant Baltic Federal University | 72.6 | 75.30 | 953.41 | 14.50 | 231.32 |
| 401–600 | ITMO University | 72.6 | 77.28 | 3379.59 | 18.26 | 315.97 |
| 401–600 | Kursk State Medical University | 72.6 | 67.91 | 128.75 | 30.04 | 206.53 |
| 401–600 | Moscow Institute of Physics and Technology (MIPT) | 72.6 | 97.13 | 6720.25 | 14.18 | 222.02 |
| 401–600 | Russian Biotechnological University (BIOTECH) | 72.6 | 75.11 | 486.83 | 13.33 | 248.90 |
| 401–600 | Plekhanov Russian University of Economics | 72.6 | 74.98 | 313.87 | 9.23 | 206.21 |
| 401–600 | Siberian Federal University | 72.6 | 68.53 | 437.35 | 4.36 | 189.78 |
| 601–800 | Industrial University of Tyumen | 66.7 | 63.95 | 192.74 | 7.61 | 200.63 |
| 601–800 | North-Caucasus Federal University | 66.7 | 68.06 | 298.30 | 9.78 | 208.00 |
| 601–800 | North-Eastern Federal University | 66.7 | 62.97 | 376.13 | 4.55 | 201.14 |
| 601–800 | Novosibirsk State Agrarian University | 66.7 | 61.01 | 409.22 | 13.38 | 171.20 |
| 601–800 | Perm State University | 66.7 | 69.97 | 707.80 | 7.92 | 188.97 |
| 601–800 | Russian State Agrarian University—Moscow Timiryazev Agricultural Academy | 66.7 | 67.89 | 1284.13 | 4.89 | 201.59 |
| 601–800 | Southern Federal University | 66.7 | 74.08 | 745.48 | 12.44 | 212.42 |
| 601–800 | South Ural State University | 66.7 | 66.47 | 663.99 | 10.74 | 208.94 |
| 601–800 | Tomsk Polytechnic University | 66.7 | 73.69 | 2056.92 | 23.98 | 232.01 |
| 601–800 | Volgograd State University | 66.7 | 66.91 | 127.16 | 3.71 | 209.68 |
| 801–1000 | Bashkir State University | 59.6 | 70.00 | 513.18 | 7.09 | 212.51 |
| 801–1000 | Bashkir State Medical University | 59.6 | 72.81 | 611.64 | 32.48 | 260.13 |
| 801–1000 | Belgorod State National Research University | 59.6 | 68.21 | 1522.27 | 25.67 | 214.93 |
| 801–1000 | Lobachevsky State University of Nizhni Novgorod | 59.6 | 72.12 | 1036.46 | 10.21 | 237.75 |
| 801–1000 | Novosibirsk State Technical University | 59.6 | 68.03 | 933.94 | 15.27 | 214.95 |
| Maximum value in the sample, score 1–100 | 82.1 | 97.1 | 6720.3 | 32.5 | 316.0 | |
Source: Compiled by the authors based on materials of MIREA, MIC (2024); THE (2024).
Table A3.
Financial risks to the top 30 most responsible universities in Russia in 2023.
| Rank | Name | University’s Revenues from All Sources, Billion Rubles | University’s Revenues from Extra-Budgetary Sources, Billion Rubles | Share of University’s Revenues from Extra-Budgetary Sources, % |
|---|---|---|---|---|
| 201–300 | Kazan Federal University | 13.504122 | 6.844396 | 50.68 |
| 201–300 | Peter the Great St Petersburg Polytechnic University | 12.937710 | 5.717963 | 44.20 |
| 201–300 | RUDN University | 15.563008 | 10.132104 | 65.10 |
| 301–400 | Altai State University | 2.243975 | 1.045707 | 46.60 |
| 301–400 | Bauman Moscow State Technical University | 19.151485 | 4.325470 | 22.59 |
| 301–400 | Irkutsk National Research Technical University | 3.891520 | 1.142909 | 29.37 |
| 301–400 | National Research Nuclear University MEPhI | 7.859391 | 4.089563 | 52.03 |
| 401–600 | Financial University under the Government of the Russian Federation | 10.189828 | 4.764089 | 46.75 |
| 401–600 | Immanuel Kant Baltic Federal University | 3.189907 | 0.813055 | 25.49 |
| 401–600 | ITMO University | 9.717539 | 5.101974 | 52.50 |
| 401–600 | Kursk State Medical University | 1.636157 | 0.935208 | 57.16 |
| 401–600 | Moscow Institute of Physics and Technology (MIPT) | 13.760323 | 5.124797 | 37.24 |
| 401–600 | Russian Biotechnological University (BIOTECH) | 2.199905 | 0.690459 | 31.39 |
| 401–600 | Plekhanov Russian University of Economics | 9.037110 | 4.882867 | 54.03 |
| 401–600 | Siberian Federal University | 8.388832 | 2.001505 | 23.86 |
| 601–800 | Industrial University of Tyumen | 3.896312 | 1.546146 | 39.68 |
| 601–800 | North-Caucasus Federal University | 3.164272 | 1.436112 | 45.39 |
| 601–800 | North-Eastern Federal University | 6.644250 | 1.427638 | 21.49 |
| 601–800 | Novosibirsk State Agrarian University | 1.214777 | 0.399411 | 32.88 |
| 601–800 | Perm State University | 2.876025 | 1.285299 | 44.69 |
| 601–800 | Russian State Agrarian University—Moscow Timiryazev Agricultural Academy | 5.758056 | 2.045639 | 35.53 |
| 601–800 | Southern Federal University | 6.738630 | 2.702528 | 40.10 |
| 601–800 | South Ural State University | 4.808992 | 1.970243 | 40.97 |
| 601–800 | Tomsk Polytechnic University | 6.904136 | 2.247028 | 32.88 |
| 601–800 | Volgograd State University | 1.141863 | 0.404013 | 35.38 |
| 801–1000 | Bashkir State University | 5.025474 | 1.830321 | 36.42 |
| 801–1000 | Bashkir State Medical University | 6.866067 | 4.322318 | 62.95 |
| 801–1000 | Belgorod State National Research University | 5.661811 | 2.442763 | 43.14 |
| 801–1000 | Lobachevsky State University of Nizhni Novgorod | 5.492526 | 2.548206 | 46.39 |
| 801–1000 | Novosibirsk State Technical University | 4.559561 | 1.188434 | 26.06 |
| Maximum value in the sample, score 1–100 | 1.915148 | 1.013210 | 65.1 | |
Source: Compiled by the authors based on materials of MIREA, MIC (2024).
Table A4.
The activity of implementing the SDGs in the top 30 most responsible universities in Russia in 2023, score 1–100.
Table A4.
The activity of implementing the SDGs in the top 30 most responsible universities in Russia in 2023, score 1–100.
| Rank | Name | SDG 1 | SDG 2 | SDG 3 | SDG 4 | SDG 5 | SDG 6 | SDG 7 | SDG 8 | SDG 9 | SDG 10 | SDG 11 | SDG 12 | SDG 13 | SDG 14 | SDG 15 | SDG 16 | SDG 17 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 201–300 | Kazan Federal University | 0.0 | 0.0 | 0.0 | 74.1 | 0.0 | 0.0 | 0.0 | 74.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 81.7 |
| 201–300 | Peter the Great St Petersburg Polytechnic University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.5 | 88.9 | 0.0 | 76.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 81.7 |
| 201–300 | RUDN University | 0.0 | 0.0 | 0.0 | 84.5 | 67.7 | 0.0 | 0.0 | 71.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 301–400 | Altai State University | 0.0 | 0.0 | 0.0 | 78.7 | 0.0 | 0.0 | 0.0 | 71.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 301–400 | Bauman Moscow State Technical University | 0.0 | 0.0 | 0.0 | 81.1 | 0.0 | 0.0 | 0.0 | 0.0 | 87.2 | 0.0 | 0.0 | 0.0 | 66.3 | 0.0 | 0.0 | 0.0 | 70.5 |
| 301–400 | Irkutsk National Research Technical University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 67.8 | 66.3 | 0.0 | 0.0 | 0.0 | 66.7 | 0.0 | 0.0 | 0.0 | 0.0 | 75.5 |
| 301–400 | National Research Nuclear University MEPhI | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 87.2 | 73.3 | 0.0 | 76.6 | 0.0 | 0.0 | 0.0 | 0.0 | 81.7 |
| 401–600 | Financial University under the Government of the Russian Federation | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 56.1 | 62.2 | 87.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 401–600 | Immanuel Kant Baltic Federal University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 71.5 | 0.0 | 73.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 401–600 | ITMO University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 57.7 | 91.1 | 0.0 | 67.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 53.3 |
| 401–600 | Kursk State Medical University | 0.0 | 0.0 | 78.9 | 0.0 | 0.0 | 0.0 | 0.0 | 71.5 | 77.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 401–600 | Moscow Institute of Physics and Technology (MIPT) | 68.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 66.3 | 87.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 53.3 |
| 401–600 | Russian Biotechnological University (BIOTECH) | 0.0 | 68.5 | 0.0 | 66.5 | 61.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 401–600 | Plekhanov Russian University of Economics | 0.0 | 0.0 | 0.0 | 83.5 | 61.4 | 0.0 | 0.0 | 62.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 401–600 | Siberian Federal University | 0.0 | 67.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 60.0 | 0.0 | 0.0 | 0.0 | 72.2 | 0.0 | 75.5 |
| 601–800 | Industrial University of Tyumen | 0.0 | 0.0 | 0.0 | 66.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 65.1 | 67.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 601–800 | North-Caucasus Federal University | 60.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 62.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 601–800 | North-Eastern Federal University | 60.1 | 0.0 | 0.0 | 0.0 | 51.5 | 0.0 | 0.0 | 48.3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 601–800 | Novosibirsk State Agrarian University | 0.0 | 72.8 | 0.0 | 0.0 | 0.0 | 49.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 72.2 | 0.0 | 45.2 |
| 601–800 | Perm State University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 57.7 | 0.0 | 0.0 | 67.0 | 0.0 | 0.0 | 0.0 | 72.2 | 0.0 | 61.0 |
| 601–800 | Russian State Agrarian University—Moscow Timiryazev Agricultural Academy | 0.0 | 56.2 | 0.0 | 73.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 67.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 601–800 | Southern Federal University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 57.7 | 72.9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 70.5 |
| 601–800 | South Ural State University | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 72.5 | 0.0 | 0.0 | 67.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 70.5 |
| 601–800 | Tomsk Polytechnic University | 0.0 | 0.0 | 0.0 | 50.9 | 0.0 | 0.0 | 0.0 | 0.0 | 87.2 | 67.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 75.5 |
| 601–800 | Volgograd State University | 0.0 | 0.0 | 0.0 | 0.0 | 51.5 | 0.0 | 0.0 | 62.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 801–1000 | Bashkir State University | 0.0 | 0.0 | 0.0 | 58.6 | 51.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 75.5 |
| 801–1000 | Bashkir State Medical University | 0.0 | 0.0 | 73.4 | 43.5 | 51.5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| 801–1000 | Belgorod State National Research University | 0.0 | 0.0 | 63.8 | 0.0 | 0.0 | 0.0 | 0.0 | 62.2 | 60.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 61.0 |
| 801–1000 | Lobachevsky State University of Nizhni Novgorod | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 72.9 | 57.5 | 60.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 53.3 |
| 801–1000 | Novosibirsk State Technical University | 68.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 51.8 | 57.7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 45.2 |
| Maximum value in the sample, score 1–100 | 68.4 | 72.8 | 78.9 | 84.5 | 67.7 | 49.1 | 67.8 | 74.3 | 91.1 | 73.3 | 76.0 | 76.6 | 66.3 | - | 72.2 | - | 81.7 | |
| Number of non-zero values in the sample | 4 | 4 | 3 | 11 | 7 | 1 | 3 | 19 | 11 | 5 | 8 | 2 | 1 | 0 | 3 | 0 | 30 | |
| Share of non-zero values in the sample, % | 9.52 | 9.52 | 7.14 | 26.19 | 16.67 | 2.38 | 7.14 | 45.24 | 26.19 | 11.90 | 19.05 | 4.76 | 2.38 | 0.00 | 7.14 | 0.00 | 71.43 | |
Source: Compiled by the authors based on materials of MIREA, MIC (2024); THE (2024).
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Figure 1.
The sample structure, the number of responsible universities. Source: Compiled by the authors based on THE (2024) materials.
Figure 3.
Correlation between the financial risks to Russian universities and the macro-level factors in 2015–2022. Source: Authors.
Figure 3.
Correlation between the financial risks to Russian universities and the macro-level factors in 2015–2022. Source: Authors.
Figure 4.
Model SEM. Source: Authors.
Figure 5.
Forecast of financial risks to responsible universities in Russia in case of maximization of their support for the SDGs. Source: Authors.
Figure 5.
Forecast of financial risks to responsible universities in Russia in case of maximization of their support for the SDGs. Source: Authors.
Figure 6.
Recommendations for the improvement of financial risk management in Russian universities through optimization of support for SDGs. Source: Authors.
Figure 6.
Recommendations for the improvement of financial risk management in Russian universities through optimization of support for SDGs. Source: Authors.
Figure 7.
Forecast of financial risks to Russian universities at the maximization of the results of implementing the current managerial measures. Source: Authors.
Figure 7.
Forecast of financial risks to Russian universities at the maximization of the results of implementing the current managerial measures. Source: Authors.
Table 1.
Dynamics of the macro-level factors and position of Russian universities in the THE ranking in 2015–2022.
Table 1.
Dynamics of the macro-level factors and position of Russian universities in the THE ranking in 2015–2022.
| Year | The Times Higher Education Universities Ranking: Average Score of Top 3 Universities (Worst 0–100 Best) | Economic Globalization Overall Index, Score 0–100 | Social Globalization Overall Index, Score 0–100 | Political Globalization Overall Index, Score 0–100 | Government Expenditure on Education, Total (% of GDP) | Expenditure on Tertiary Education (% of Government Expenditure on Education) | Government Expenditure on Tertiary Education, % of GDP |
|---|---|---|---|---|---|---|---|
| 2015 | 46 | 49 | 72 | 92 | 3.83 | 21.14 | 0.81 |
| 2016 | 17 | 53 | 69 | 92 | 3.76 | 21.61 | 0.81 |
| 2017 | 44 | 55 | 69 | 92 | 4.69 | 21.61 | 1.01 |
| 2018 | 48 | 54 | 69 | 92 | 4.68 | 21.61 | 1.01 |
| 2019 | 49 | 54 | 69 | 92 | 3.51 | 21.61 | 0.76 |
| 2020 | 52 | 55 | 69 | 92 | 3.70 | 21.61 | 0.80 |
| 2021 | 53 | 53 | 68 | 92 | 3.70 | 21.61 | 0.80 |
| 2022 | 52 | 49 | 68 | 92 | 3.70 | 21.61 | 0.80 |
Source: Compiled and calculated by the authors based on (KOF 2024; UN 2024b; World Bank 2024a, 2024b).
Table 2.
Regression analysis of the dependence of the indicators of financial risks on the alternative measures of financial risk management.
Table 2.
Regression analysis of the dependence of the indicators of financial risks on the alternative measures of financial risk management.
| Analysis Sphere | Analysis Elements | Ufr1 | Ufr2 |
|---|---|---|---|
| Regression statistics | Multiple R | 0.6743 | 0.7132 |
| R-square | 0.4547 | 0.5086 | |
| Adjusted R-square | 0.3411 | 0.4063 | |
| Standard error | 3.6789 | 1.7320 | |
| Observations | 30 | 30 | |
| ANOVA and F-Stat | Significance F | 0.0088 | 0.0029 |
| Level of significance | 0.01 | 0.01 | |
| k1 = m | 5 | 5 | |
| k2 = n − m − 1 | 30 − 5 − 1 = 24 | 30 − 5 − 1 = 24 | |
| F table | 3.8951 | 3.8951 | |
| F observed | 4.0025 | 4.9686 | |
| F-test | passed | passed | |
| Regression coefficients | Y-intercept | −31.4630 | −19.1334 |
| Resp | 0.2421 | 0.1144 | |
| Ex1 | 0.2637 | 0.1357 | |
| Ex2 | 0.00001 | −0.0003 | |
| Ex3 | −0.0370 | 0.0470 | |
| Ex4 | 0.0129 | 0.0177 | |
| Standard error | Y-intercept | 11.8534 | 5.5806 |
| Resp | 0.1115 | 0.0525 | |
| Ex1 | 0.1679 | 0.0790 | |
| Ex2 | 0.0008 | 0.0004 | |
| Ex3 | 0.0971 | 0.0457 | |
| Ex4 | 0.0311 | 0.0146 | |
| t-Stat | Y-intercept | −2.6543 | −3.4285 |
| Resp | 2.1709 | 2.1799 | |
| Ex1 | 1.5705 | 1.7171 | |
| Ex2 | −0.0087 | −0.8070 | |
| Ex3 | −0.3812 | 1.0273 | |
| Ex4 | 0.4137 | 1.2071 |
Source: Calculated and compiled by the authors.
Table 3.
Regression analysis of the dependence of the indicators of financial risks on detailed practices of support of SDGs in universities.
Table 3.
Regression analysis of the dependence of the indicators of financial risks on detailed practices of support of SDGs in universities.
| Analysis Sphere | Analysis Elements | Ufr1 | Ufr2 |
|---|---|---|---|
| Regression statistics | Multiple R | 0.6972 | 0.7029 |
| R-square | 0.4861 | 0.4940 | |
| Adjusted R-square | 0.3521 | 0.3621 | |
| Standard error | 3.6481 | 1.7953 | |
| Observations | 30 | 30 | |
| ANOVA and F-Stat | Significance F | 0.0112 | 0.0096 |
| Level of significance | 0.05 | 0.01 | |
| k1 = m | 6 | 6 | |
| k2 = n − m − 1 | 30 − 6 − 1 = 23 | 30 − 6 − 1 = 23 | |
| F-table | 2.5277 | 3.7102 | |
| F-observed | 3.6263 | 3.7431 | |
| F-test | passed | passed | |
| Regression coefficients | Y-intercept | −3.0271 | −1.7401 |
| SDG 4 | 0.0609 | 0.0304 | |
| SDG 5 | 0.0215 | 0.0297 | |
| SDG 8 | 0.0306 | 0.0277 | |
| SDG 9 | 0.0649 | 0.0306 | |
| SDG 11 | 0.0164 | 0.0149 | |
| SDG 17 | 0.0747 | 0.0184 | |
| Standard error | Y-intercept | 3.4809 | 1.7130 |
| SDG 4 | 0.0226 | 0.0111 | |
| SDG 5 | 0.0341 | 0.0168 | |
| SDG 8 | 0.0224 | 0.0110 | |
| SDG 9 | 0.0192 | 0.0094 | |
| SDG 11 | 0.0246 | 0.0121 | |
| SDG 17 | 0.0542 | 0.0267 | |
| t-Stat | Y-intercept | −0.8696 | −1.0158 |
| SDG 4 | 2.6902 | 2.7283 | |
| SDG 5 | 0.6295 | 1.7718 | |
| SDG 8 | 1.3637 | 2.5124 | |
| SDG 9 | 3.3791 | 3.2388 | |
| SDG 11 | 0.6652 | 1.2291 | |
| SDG 17 | 1.3798 | 0.6890 |
Source: Calculated and compiled by the authors.
Table 4.
The influence of the measures of the management of universities on their financial risks in Russia, which is estimated in the literature and specified in this paper.
Table 4.
The influence of the measures of the management of universities on their financial risks in Russia, which is estimated in the literature and specified in this paper.
| Measures of University Management | Influence on Financial Risks to Universities | |
|---|---|---|
| Influence Estimated in the Literature | Influence Revealed in This Paper | |
| Development of educational activities | Reduces financial risks (Tovmasyan et al. 2022) | Reduces financial risks |
| Development of research activities | Reduces financial risks (Bogoviz and Mezhov 2015; Fukugawa 2023) | Increases financial risks |
| Development of international activities | Reduces financial risks (Petrenko and Stolyarov 2019) | Has a contradictory effect on financial risks |
| Changes in academic staff wages | Reduces financial risks (Przhedetskaya and Borzenko 2019) | Does not have a statistically significant effect on financial risks |
| SDGs support | Increases financial risks (Bock et al. 2018; Mántica 2022) | Reduces financial risks |
Source: Authors.
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MDPI and ACS Style
Gornostaeva, Z.V.; Shabaltina, L.V.; Denisov, I.V.; Musatkina, A.A.; Sinyavskiy, N.G. Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia. Risks 2024, 12, 101. https://doi.org/10.3390/risks12060101
AMA Style
Gornostaeva ZV, Shabaltina LV, Denisov IV, Musatkina AA, Sinyavskiy NG. Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia. Risks. 2024; 12(6):101. https://doi.org/10.3390/risks12060101
Chicago/Turabian StyleGornostaeva, Zhanna V., Larisa V. Shabaltina, Igor V. Denisov, Aleksandra A. Musatkina, and Nikolai G. Sinyavskiy. 2024. "Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia" Risks 12, no. 6: 101. https://doi.org/10.3390/risks12060101
APA StyleGornostaeva, Z. V., Shabaltina, L. V., Denisov, I. V., Musatkina, A. A., & Sinyavskiy, N. G. (2024). Support of the SDGs as a New Approach to Financial Risk Management in Responsible Universities in Russia. Risks, 12(6), 101. https://doi.org/10.3390/risks12060101
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