The Role of Higher Education in Regional Development

Innovation is an indispensable element in any sphere of social life, offering new vision on the primary challenges in global and Russian development, particularly at the regional level. Numerous studies acknowledge the significant role of the regional innovation system as a crucial point of development of regional potential. Therefore, this study aimed to estimate the core role of universities in fostering innovative regional systems and establishing the link between universities and regional innovation. The correlation was identified by building a model, using the Data Envelopment Analysis (DEA). The results showed that the regions with the most active universities-driven innovation include the Moscow region, the Arkhangelsk region, St. Petersburg, the Republic of Mordovia, the Republic of Tatarstan, the Perm region, the Amur region, and the Magadan region.

Innovation; Influence; Region; Socioeconomic development; Universities

The development of innovative economy is currently a primary focus for the Russian regions (Karpenko, 2011). At the core of this development lies the transfer and management of knowledge, which is a significant task within university activity (Borovkov et al., 2020). An exploration of the effectiveness of higher educational institutions has showed a growing interest in this subject within the Russian Federation, specifically during the 1990s (Grebenyuk, 2012). To address efficiency concerns, it is essential to investigate various social and economic factors (Alamah, AlSoussy, and Fakih, 2023; Nauffal, 2019; Rivchun, 2010) (Figure 1).

In this study, a nonparametric method for measuring relative efficiency was proposed for achieving the objectives (Hanid et al., 2019), (Banker, Charnes, and Cooper, 1984).  Through the application of the Data Envelopment Analysis (DEA) method (Rabar, Rabar, and Pavletic, 2022; Glukhov, Gorin, and Raskovalov, 2020), the significance of the dependent and independent variables used to determine the adequacy of the model was verified. Additionally, the Structural Equation Modelling (SEM) method was used to validate the model, and a "path" diagram was conducted to identify the most significant independent variables from the general list selected, according to the timetable and data set. The novelty incorporated a specific number of variables, yielding results that are both adequate and closely in line with reality (Farrell, 1957). These simulations can guide the fostering of more effective interaction between universities and the regional innovationsystem, as well as the formation of a more optimal strategic development model for innovative activity (Zabala-Iturriagagoitia et al., 2007).

    For the study, a nonparametric method was selected to measure relative efficiency, with a focus on the DEA method which provided more information about the effect of local universities on the local economy.  
    Initial data were collected from statistical reports on regional activities in the field of higher education. All data were then standardized and checked for normal deviation; those data that significantly exceeded the limits were excluded. The results were then incorporated into a DEA model. 
    The method comprises of constructing an efficiency boundary and analyzing the positioning of the studied objects (Rudskaya and Rodionov, 2017). When the point of the object lies on the efficiency boundary, then the functioning is considered effective. Using the DEA method, regions with high innovative results relative to resource limitations were identified and considered effective (Liu and Wang, 2019).
    In the model, each object was referred to as a Decision-Making Unit (DMU) for transforming resources into outputs (Ellis, Christofides, and Panagiotis, 2015) (see Equation 1). Therefore, the aim is to determine a benchmark position of the region that optimally combines the effectiveness of the innovation system and regional resources (Rudskaya, 2017) (see Equation 2).

2.1. Size of Dataset
        The model aims to maximize the ratio of "results" to "resources." Initially, the traditional model invented by Cooper and Rhodes (Cooper, Seiford, and Tone, 2007), was adopted to estimate constant returns to scale (Lee, Lee, and Kim, 2009). 

        In this model, the combination of values such as (x; y) and (tx; ty) is also allowed. The obtained efficiency factors include wide combinations of resource and output indicators with any non-negative coefficients. Consequently, the result can be equated to global technical efficiency indicators (Ji and Lee, 2010).
        A composite indicator is derived from a set of resources (xi) and achieved results (yr) using ratios:

if X is a matrix of size (×), Y-matrix (×). 

The efficiency of each DMU is estimated and optimized, while, optimization problems are solved for the dimension of the matrices outlined in formula (see Equation 2). The estimated DMUj in each trial was denoted as DMUo (o = 1, 2, ..., n). To determine the optimal values of the resource weights (vi) (i = 1, …, m) and the results (ur) (r = 1, …, s), a linear fractional programming problem was solved:

        The limits in the following model control the ratio of "results" to "resources" ensuring a value not exceeding 1 (with the ratio = 1, DMU can be named technically efficient) (Putri et al., 2016). Therefore, the largest optimal value of  is 1(see Equation 4). A primary constraint of the model asserts that outputs and inputs are non-negative, showing the presence of some non-zero value (see Equations 5-7). While the limit may diverge from reality, it is removed when using more advanced models of data envelopment analysis (Wahid, and Ahmad, 2015).

2.2. Indicators as basic for the model

The deep description of fractional-linear programming is replaced by a linear programming problem in this part of the analysis (Liu et al., 2010). Further elucidation can be discovered in the dissertation study by Rudskaya I.A., titled "Formation and Development of Regional Innovation Systems in the Russian Economy" (Rodionov, Rudskaya, and Gorovoi, 2013). Table 1 shows indicators chosen as basic for the model (Rudskaia and Rodionov, 2018; Rodionov, Rudskaya, and Gorovoi 2013).

Table 1 Model inputs and outputs

Input (universities resources)	Output (results)
- The number of higher education institutions, their branches, the number of students enrolled in undergraduate, graduate, and specialty programs, and the release of bachelors, specialists, and masters - indicators characterizing the potential capacity of educational institutions in the region.	Gross regional product per capita
- The number of research analysts by region	The innovative activity of organizations
- The number of teachers for undergraduate, graduate, and specialist programs. Teachers are a conduit for the transfer of knowledge.	The number of innovative goods, works, and services represents the innovative productivity of the region's economy.
-  The value of expenditures on R&D. The indicator shows the availability of investments in R&D.	The amount of issued patents shows the efficiency of innovation processes in the region's economy.
	The niche of education in the sectoral structure of the GRP.

                
     According to the explored input and output data, the model test coefficients were assigned the following designations given in Table 2 (Rudskaia and Rodionov, 2018; Rodionov, Rudskaya, and Gorovoi, 2013).

Table 2 Input and output coefficients of the model

Input (2011)	Output (2018)
-  Amount of higher educational institutions, nHEI, units.	Gross regional product per capita, grp, million rubles
- Amount of branches of higher educational institutions, nbHEI, units.	Innovative activities of organizations, innact, share %
-  Number of teachers for bachelor's, master's, and specialist's programs, nteach, units	The volume of innovative products and services, volinn, million rubles
-  Quantity of students enrolled in bachelor's, master's, and specialist's programs, nstu, units.	Number of issued patents, patents, share%
-  Graduation of bachelor's, masters, and specialists, ngrad, units.	The niche of education in the sectoral structure of GRP, educ, share %
-  Amount of research analysts with a degree by region, study, and units.
-  Study and development expenditures, million rubles

3.1.  System Performance Benchmark

         The result of a comprehensive assessment of the innovation environment showed that most regions were not technically effective in evaluating the consistent creation and commercialization of new knowledge and technologies. The characteristics of technically efficient regions are described in Table 3 (Rudskaia and Rodionov, 2018; Rodionov, Rudskaya, and Gorovoi, 2013).
Table 3 Technically efficient regional innovative

        According to the chat, the blue line, which indicates overall efficiency, consistently remains below the red and green lines, representing effectiveness at stages 1 and 2. The result of the first stage serves as a resource for the second stage and operates as intermediate indicators (Mayo, Shoghli, and Morgan, 2020; Xi, Li, and Lin, 2013). An effective model aims to minimize the resources of the intermediate stage, thereby achieving minimal resource investment at the initial stage system (Gozali et al., 2020).

      The graph shows that the vast majority of regions were not technically efficient in developing innovation processes concerning the creation and commercialization of new knowledge and technologies (Figure 1 and 2).

Figure 1 Histograms for independent variables after logarithmization, adapted from (Velichenkova and Rodionov, 2020; Velichenkova, 2020).

Figure 2 Histograms for dependent variables after logarithmization, adapted from (Velichenkova and Rodionov, 2020; Velichenkova, 2020).

3.1.1.  Regional innovation rating result

         Based on the analysis of the histograms, the visualizations of the results are slightly shifted to the left, showing a left-sided asymmetry. The solid orange line in the graph is plotted using a normal distribution function. An abnormal distribution was observed in the variable "lnnHEI," attributed to the uneven distribution of universities across the country, Regions, such as Moscow, St. Petersburg, and Kazan, had a higher number of universities. Similarly, the variable "lnpatent" also falls outside the normal distribution, suggesting significant variations in patent grants across different regions.

      This disparity in innovative effectiveness across regions can be attributed to various factors. The absence of universities, small innovative enterprises, the lack of a state program supporting innovation, or a predominantly over-65 population, which is typical for certain constituent entities of the Russian Federation, all contribute to this variation.

       Prominent regions with significant innovative performance include The Lipetsk Region, The Tula Region, The Republic of Mordovia, The Udmurtian Republic, The Yamalo-Nenets Autonomous District, and The Tyumen Region. However, an exception to this trend is the "weak" Chukotka Autonomous Region. The weakness is attributed to the limited availability of innovative tools, suggesting that marginal improvement can enhance the overall innovative system and effectiveness.

        The aim is to provide a brief overview of another study opinion. Regions with the highest investment in innovation may not always use their potential effectively (Sergeev, Marikhina, and Velichenkova, 2017). This disparity in innovative effectiveness across regions can be attributed to various factors (International Monetary Fund, 2023). The absence of universities, small innovative enterprises, the lack of a state program supporting innovation, or a predominantly over-65 population, which is typical for certain constituent entities of the Russian Federation, all contribute to this variation (Panasenko, 2018).

       Prominent regions exhibiting notable innovative performance include the Lipetsk Region, the Tula Region, the Republic of Mordovia, the Udmurtian Republic, the Yamalo-Nenets Autonomous District, and the Tyumen Region. However, an exception to this trend is found in the Chukotka Autonomous Region, which is considered "weak" in terms of innovation. This weakness is primarily attributed to the limited availability of innovative tools, indicating that a marginal improvement in this aspect could significantly enhance the overall innovative system and effectiveness (Rodionov and Velichenkova, 2020).

         It's noteworthy that regions with the highest investment in innovation may not always utilize their potential effectively (Bogdanova and Karlik, 2020) as highlighted by Sergeev, Marikhina, and Velichenkova (2017). This overview aims to provide a brief synthesis of diverse opinions in the field (Zhogova, Zaborovskaia, and  Nadezhina, 2020).

         Based on the final result of the study, universities were identified as an effective tool for introducing innovation. The most effective region identified include the Moscow Region, Moscow, the Nenets Autonomous Region, the Arkhangelsk Region, St. Petersburg, the Republic of Adygea, the Republic of Crimea, Sevastopol, the Republic of Mordovia, the Republic of Tatarstan, Perm Territory, the Yamalo-Nenets Autonomous District, Amur Region, Magadan Region, and the Chukotka Autonomous Region.

         For regions with unconsidered effectiveness, the following explanations (*) were provided. The Republic of Crimea and Sevastopol were not taken into account, since they were not part of the Russian Federation at the beginning of the analysis in 2011. Furthermore, the Republic of Adygea, the Yamalo-Nenets Autonomous Okrug, and the Chukotka Autonomous Okrug lack universities or branches, which serve as key resources for regional innovative development in Russia, thereby necessitating their removal from the sample. 

Figure 3 The remoteness of Russian regions from the efficiency frontier, adapted from (Velichenkova and Rodionov, 2020; Velichenkova, 2020).

         In conclusion, the analysis covered different aspects of developing an innovative environment within the region. Firstly, it evaluated the technical efficiency of the resource base in promoting innovation. Secondly, the analysis identified the effectiveness of universities as part of the regional innovation environment. Both segments of the study yielded adequate and realistic results. The only identified trend was the lack of a well-functioning system for implementing the innovation process. However, a university-based innovation process was developed, showing significant results. The number of commercially successful innovation integrated into a real sector need to be augmented. This step was essential for enhancing economic efficiency and increasing innovation activity within universities to create a significant impact on the management. It is important to note that the selected variable is not exhaustive. The study had limited access to information about special programs in universities, which also had a significant effect on several regional economies. Therefore, new information about the pandemic period and its influence on the educational sector is needed for improvement purposes.

        The authors are grateful to the project "Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization" (FSEG-2023-0008), for funding this study.

Alamah, Z., AlSoussy, I., Fakih, A., 2023. The Role of International Research Collaboration and Faculty Related Factors in Publication Citations: Evidence from Lebanon. Economies, Volume 11(3), p. 90

Banker, R.D., Charnes, A., Cooper, W.W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, Volume 30(9),  pp. 1078–1092

Bogdanova, T., Karlik, M., 2020. Problems of Competitive Strategy Choice According to Industry and Regional Factors. International Journal of Technology, Volume 11(8), pp. 1478–1488

Borovkov, A.I., Bolsunovskaya, M.V., Gintciak, A.M., Kudryavtseva, T.J., 2020. Simulation Modelling Application for Balancing Epidemic and Economic Crisis in the Region. International Journal of Technology, Volume 11(8), pp. 1579–1588

Cooper, W.W., Seiford, L.M., Tone, K., 2007. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver Software. Second Edition. New York: Springer

Ellis, M., Christofides, Panagiotis, D., 2015. Real-time Economic Model Predictive Control of Nonlinear Process Systems. AIChE Journal, Volume 61(2), pp. 555–571

Farrell, M.J., 1957. The Measurement of Productive Efficiency. Journal of the Royal Statistical Society: Series A (General), Volume 120(3), pp. 253–281

Glukhov, V.V., Gorin E.A., Raskovalov V.L., 2020. The Quality of Engineering Education as the Basis for Innovative Activity and Socio-Economic Development. In: Materials III (VIII) All-Russian Full-Time and Part-Time Scientific and Practical Conference, pp. 41–46

Gozali, L., Masrom, M., Zagloel, T.Y.M., Haron, H.N., Garza-Reyes, J.A., Tjahjono, B., Irawan, A.P., Daywin, F.J., Syamas, A.F., Susanto, S., Aliwarga, H.K.K., Marie, I.A., 2020. Performance Factors for Successful Business Incubators in Indonesian Public Universities. International Journal of Technology, Volume 11(1), pp. 155–166

Grebenyuk, I.I., 2012. Analysis of Innovative Activities of Higher Educational Institutions in Russia. Academy of Natural Sciences, p. 307

Hanid, M., Mohamed, O., Othman, M., Danuri, M.S.M., Mei Ye, K., Berawi, M.A., 2019. Critical Success Factors (CSFS) In University-Industry Collaboration (UIC) Projects in Research Universities. International Journal of Technology, Volume 10(4), pp. 667–676

International Monetary Fund, 2023. World Economic Outlook: A Rocky Recovery. International Monetary Fund

Ji, Y.-B., Lee, C., 2010. Data Envelopment Analysis. The Stata Journal Promoting Communications on Statistics and Stata, Volume 10(2), pp. 267–280

Karpenko, O.M., 2011. Social Aspects of University Design in the Context of Global Problems of Higher Education. Sociology of Education, Volume 4, pp. 10–35

Lee, C., Lee, J., Kim, T., 2009. Defense Acquisition Innovation Policy and Dynamics of Productive Efficiency: A DEA Application to the Korean Defense Industry. Asian Journal of Technology Innovation, Volume 17(2), pp. 151–171

Liu, P., Wang, L.-F., 2019. Determination of Cross-efficiency Considering the Original Efficiency Value of DMUs in DEA Cross-evaluation. IAENG International Journal of Applied, Availabe online at: https://www.iaeng.org/IJAM/issues_v49/issue_2 /IJAM_49_2_06.pdf, Accessed on November 27, 2023

Liu, W.B., Meng, W., Li, X X., Zhang, D.Q., 2010. DEA Models with Undesirable Inputs and Outputs. Annals of Operations Research, Volume 173(1), pp. 177–194

Mayo, G., Shoghli, O., Morgan, T., 2020. Investigating Efficiency Utilizing Data Envelopment Analysis: Case Study of Shipyards. Journal of Infrastructure Systems, Volume 26(2), p. 04020013

Nauffal, D.I., 2019. Measuring the Economic Impact of Universities Operating in Unstable Environments: The Case of a Private Institution in Lebanon. Higher Education Quarterly, Volume 73(3), pp. 343–358

Panasenko, E.A., 2018. Formation of the Innovative Space of a Modern University (On the Example of the Priority Project of Ministry of Science and Higher Education of RF "Universities as Centers of Space for Creating Innovations"). Vectors of Well-being: Economy and Society, Volume 4(31) , pp. 31–40

Putri, E.P., Chetchotsak, D., Ruangchoenghum, P., Jani, M.A., Hastijanti, R., 2016. Performance Evaluation of Large and Medium Scale Manufacturing Industry Clusters in East Java Province, Indonesia. International Journal of Technology, Volume 7(7), pp.  1269–1279

Rabar, D., Rabar, D., Pavletic, D., 2022. Manufacturing Process Performance Measurement Model Using Categorical DEA Approach – a Case of Dry-Docking. International Journal of Technology, Volume 13(3), pp. 484–495

Rivchun, T.E., 2010. Scientific and Methodological Approaches to Assessing the Effectiveness of the University. Service Plus, Volume 3, pp. 137–146

Rodionov, D., Velichenkova, D., 2020. Relation Between Russian Universities and Regional Innovation Development. Journal of Open Innovation: Technology, Market, and Complexity, Volume 6(4), p. 118

Rodionov, D.G., Rudskaya, I.A., Gorovoi, A.A., 2013. On the Issue of the Methodology of Management of Regional Innovation Systems. Bulletin of the Leningrad State University Named After A.Pushkin, Volume 4(6)

Rudskaia, I., Rodionov, D., 2018. Comprehensive Evaluation of Russian Regional Innovation System Performance using a Two-stage Econometric Model. Revista Espacios, Volume 39(04)

Rudskaya, I., Rodionov, D., 2017. Econometric Modeling As a Tool for Evaluating the Performance of Regional Innovation Systems (With Regions of the Russian Federation as  the Example). Academy of Strategic Management Journal, Volume 16(S2)

Rudskaya, I.A., 2017. Formation and Development of Regional Innovation Systems in the Russian Economy. Thesis D.Sci.Econ.: 08.00.05. SpbPU, p. 382

Sergeev, D.A., Marikhina, V.V., Velichenkova, D.S., 2017. Innovative Financial Tools used for Hedging Russian Banks’ Investment Risks Under the Condi-tions of Unstable Economy. In: Proceedings of the 30^th International Business Information Management Association Conference, IBIMA 2017-Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, pp. 2133–2143

Velichenkova, D.S., 2020. Ensuring the Effectiveness of the Regional Innova-tion System Based on the Integration of Innovative Activities of Universi-ties. Ph.D thesis. Econ. Sciences: 08.00.05. –SPbPU, SPb, p. 214

Velichenkova, D.S., Rodionov, D.G., 2020. Methodology for Assessing the Effectiveness of a Regional Innovation System, Taking Into Account the Impact of University Complexes. Innovation and Investment,  Volume 8, pp. 67–82

Wahid, A., Ahmad, A., 2015. Min-Max Controller Output Configuration to Improve Multi-model Predictive Control when Dealing with Disturbance Rejection. International Journal of Technology. Volume 6(3), pp. 504–515

Xi, Y-G., Li, D-W., Lin, S., 2013. Model Predictive Control – Status and Challenges. Acta Automatica Sinica, Volume 39(3), pp. 222–236

Zabala-Iturriagagoitia, J.M., Voigt, P., Gutierrez-Gracia, A., Jimenez-Saez, F., 2007. Regional Innovation Systems: How to Assess Performance. Region-al Studies, Volume 41 (5), pp. 661–672

Zhogova, E., Zaborovskaia, O., Nadezhina, O., 2020. An analysis of the Indicators of Regional Economy Spatial Development in the Leningrad Region of Russia. International Journal of Technology, Volume 11(8), pp. 1509–1518