• International Journal of Technology (IJTech)
  • Vol 17, No 4 (2026)

Methodology for Determining the Optimal Strategy for Sustainable Development of Regional Economic Systems in the Context of Overcoming the Strategic Gap and Balancing the Spatial Distribution of Resources Based on a Generalized Approach Analysis

Methodology for Determining the Optimal Strategy for Sustainable Development of Regional Economic Systems in the Context of Overcoming the Strategic Gap and Balancing the Spatial Distribution of Resources Based on a Generalized Approach Analysis

Title: Methodology for Determining the Optimal Strategy for Sustainable Development of Regional Economic Systems in the Context of Overcoming the Strategic Gap and Balancing the Spatial Distribution of Resources Based on a Generalized Approach Analysis
Dmitriy Rodionov, Irina Eremina, Vladimir Vallask, Mariam Voskanyan

Corresponding email:


Cite this article as:
Rodionov, D., Eremina, I., Vallask, V., & Voskanyan, M. (2026). Methodology for determining the optimal strategy for sustainable development of regional economic systems in the context of overcoming the strategic gap and balancing the spatial distribution of resources based on a generalized approach analysis. International Journal of Technology, 17 (4), 1451–1466


9
Downloads
Dmitriy Rodionov Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Polytechnicheskaya, 29, 195251, Russia
Irina Eremina Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Polytechnicheskaya, 29, 195251, Russia
Vladimir Vallask Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Polytechnicheskaya, 29, 195251, Russia
Mariam Voskanyan Russian-Armenian University, H. Emin 123, 0054, Yerevan, Armenia
Email to Corresponding Author

Abstract
Methodology for Determining the Optimal Strategy for Sustainable Development of Regional Economic Systems in the Context of Overcoming the Strategic Gap and Balancing the Spatial Distribution of Resources Based on a Generalized Approach Analysis

This study develops and validates an integrated six-dimensional generalized approach (GAP) measurement system for strategic gaps in regional development. The proposed methodology quantitatively assesses disparities between current and target regional states across six resource domains: informational, technical-technological, financial, personnel, organizational, and marketing. The model was tested on 15 Russian regions using correlation and regression modeling to identify statistically significant gaps. The greatest influence is exerted by: the sectoral structure of the economy (=0.32; p<0.01), the quality of the institutional environment (=-0.38; p<0.01) and the level of production diversification (=-0.45; p<0.001). The negative cumulative effect in monospecialized regions is highlighted. Scenario forecasting demonstrates that comprehensive programs combining economic diversification, human capital development, and institutional improvement can reduce strategic gaps by 25%-35% in the medium term (5-7 years). The results provide a foundation for the development of differentiated regional policy. The results of the study allow moving from unified approaches to differentiated regional development regulation and provide a scientific basis for making informed management decisions.

Disproportions; GAP analysis; Regional development; Spatial development; Strategic gaps

References

Berawi, M. A., Sari, M., Amiri, N. Y. A., Susilowati, S. I., Utami, S. R., & Kulachinskaya, A. (2025). Developing a machine learning model to improve the accuracy of owner estimate cost in the capital expenditure procurement process. International Journal of Technology, 16(4), 1179–1189. https://doi.org/10.14716/ijtech.v16i4.7409

Brown, A., & Miller, S. (2021). Bridging the divide: Gap-analysis for regional policy. Journal of Regional Studies, 45(1), 112–129.

Chung, K., Garcia, M., & Thompson, L. (2018). Beyond gdp: Multidimensional frameworks for sustainable development. Sustainability Review, 10(4), 225. https://doi.org/10.2139/SSRN.5337044

Davis, P., Roberts, J., & Chen, X. (2022). Optimizing spatial resource allocation: An mcdm model for regional planning. Computers, Environment and Urban Systems, 91, 101728.

Dmitriev, N., Zaytsev, A., Faizullin, R., & Bunkovsky, D. (2022). The instrumental apparatus of the innovative potential audit of the enterprise in the implementation of project activities. International Journal of Technology, 13(7), 1484–1494. https://doi.org/10.14716/ijtech.v13i7.6212

Egorov, N., Babkin, A., Babkin, I., & Yarygina, A. (2021). Innovative development in northern russia assessed by triple helix model. International Journal of Technology, 12(7), 1387–1396. https://doi.org/10.14716/ijtech.v12i7.5355

Eroshkin, S. Y., Viktorovna, D. M., & Vladimirovich, T. P. (2017). Analysis of the main economic indicators and opportunities for managing business in russia in 2016-2017. International Journal of Economic Research, 14(9), 221–239. https://serialsjournals.com/index.php?route=product/product&product%20id=364

Habel, C. (2009). Academic self-efficacy in all: Capacity-building through self-belief. Journal of Academic Language and Learning, 3(2), 94–104.

Hao, Y. (2022). Effect of economic indicators, renewable energy consumption and human development on climate change: An empirical analysis based on panel data of selected countries. Frontiers in Energy Research, 10, 841497. https://doi.org/10.3389/fenrg.2022.841497

Johnson, B., & Lee, H. (2019). Economic growth strategies for regions. Oxford University Press.

Kredina, A., Nurymova, S., Satybaldin, A., & Kireyeva, A. (2022). Assessing the relationship between non-cash payments and various economic indicators. Banks and Bank Systems, 17(1), 67–79. https://doi.org/10.21511/bbs.17(1).2022.06

Kushnarev, N. G., Rogozhnik, N. N., & Tsybulnik, L. V. (2022). Questions of assessing the level of state support to industry and its impact on economic indicators of development in regional integration associations. Studies on Russian Economic Development, 33(3), 249–256. https://doi.org/10.1134/S107570072203008X

Lemeshko, B. Y., & Lemeshko, S. B. (2005). Extending the application of grubbs-type tests in rejecting anomalous measurements. Measurement Techniques, 48(6), 536–547.

Li, Y., Cheng, S., & Cui, J. (2022). Mining of the association rules between socio-economic development indicators and rural harmless sanitary toilet penetration rate to inform sanitation improvement in china. Frontiers in Environmental Science, 10, 817655. https://doi.org/10.3389/fenvs.2022.817655

Lundaeva, K. A., & Gintciak, A. M. (2025). The impact of patent regulation features on the innovative activities of enterprises. International Journal of Technology, 16(4), 1093–1103. https://doi.org/10.14716/ijtech.v16i4.7388

Narula, S., Tamvada, J. P., Kumar, A., Puppala, H., & Gupta, N. (2024). Putting digital technologies at the forefront of industry 5.0 for the implementation of a circular economy in manufacturing industries. IEEE Transactions on Engineering Management, 71, 3363–3374. https://doi.org/10.1109/TEM.2023.3344373

Nazarychev, D. V., Marinin, S. A., & Shamin, A. E. (2022). Topical issues of determining the threshold values of indicators of economic security for integrated industrial enterprises. In Lecture Notes in Networks and Systems (Vol. 372, pp. 751–760). https://doi.org/10.1007/978-3-030-93155-1_81

Orel, Y., Kulinich, O., Dziuba, H., Krasnostanova, N., & Bakhaiev, R. (2025). Multilevel strategic planning for sustainable development in public administration. International Journal of Technology, 16(4), 1104–1123. https://doi.org/10.14716/ijtech.v16i4.7595

Rehman, A., & Umar, T. (2024). Literature review: Industry 5.0. leveraging technologies for environmental, social and governance advancement in corporate settings. Corporate Governance: The International Journal of Business in Society, 25(2), 229–251. https://doi.org/10.1108/CG-11-2023-0502

Rodionov, D., & Velichenkova, D. (2020). Relation between russian universities and regional innovation development. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 1–26. https://doi.org/10.3390/joitmc6040118

Rusanov, M. A., Abbazov, V. R., & Baluev, V. A. (2022). On the approach to forecasting indicators of socio-economic development of the region based on indirect indicators. Modeling, Optimization and Information Technology, 10(3(38)), 2–3. https://doi.org/10.26102/2310-6018/2022.38.3.004

Sandler, D., & Gladyrev, D. (2020). Analysis of the relations between scientometric and economic indicators of russian universities’ performance. Business, Management and Education, 18(2), 331–343. https://doi.org/10.3846/bme.2020.12955

Sargsyan, S. A., Hakobyan, P. M., & Shushanyan, R. A. (2022). The role of socio-economic and scientometric indicators in the cancer mortality rate. The Manager, 13(4), 54–68. https://doi.org/10.29141/2218-5003-2022-13-4-5

Saroji, G., Berawi, M. A., Sari, M., Madyaningarum, N., Socaningrum, J. F., Susantono, B., & Woodhead, R. (2022). Optimizing the development of power generation to increase the utilization of renewable energy sources. International Journal of Technology, 13(7), 1422–1431. https://doi.org/10.14716/ijtech.v13i7.6189

Shang, C., Jiang, J., Zhu, L., & Saeidi, P. (2023). A decision support model for evaluating risks in the digital economy transformation of the manufacturing industry. Journal of Innovation & Knowledge, 8(3), 100393. https://doi.org/10.1016/j.jik.2023.100393

Shaposhnykov, K., Filyppova, S., Krylov, D., Ozarko, K., Yudin, M., & Biliaze, O. (2023). Innovative development of enterprises in the context of digital transformations of the institutional environment of the national economy. Management Theory and Studies for Rural Business and Infrastructure Development, 45(3), 233–241. https://doi.org/10.15544/mts.2023.23

Smith, J. (2020). Traditional metrics in regional economics. In K. Peterson (Ed.), Assessing Regional Development (pp. 25–45). Springer.

Sutriadi, R., Hadicahyono, D. A., & Drestalita, N. C. (2025). Understanding a smart sustainable city theme: A case of urban innovation performance in bandung city, indonesia. International Journal of Technology, 16(4), 1143–1153. https://doi.org/10.14716/ijtech.v16i4.5531

Talipova, L., Radaev, A., Morozova, E., Skhvediani, A., & Efremov, A. (2025). Methodology for substantiating the characteristics of safety indicators dependency on the parameters of urban environment infrastructure. International Journal of Technology, 16(4), 1154–1166. https://doi.org/10.14716/ijtech.v16i4.7414

Tashenova, L., Babkin, A., Mamrayeva, D., & Babkin, I. (2020). Method for evaluating the digital potential of a backbone innovative active industrial cluster. International Journal of Technology, 11(8), 1499–1508. https://doi.org/10.14716/ijtech.v11i8.4537

Tubis, A. A. (2023). Digital maturity assessment model for the organizational and process dimensions. Sustainability, 15(20), 15122. https://doi.org/10.3390/su152015122

Visvizi, A., Malik, R., Guazzo, G. M., & C¸ ekani, V. (2024). The industry 5.0 (i50) paradigm, blockchain-based applications and the smart city. European Journal of Innovation Management, 28(1), 5–26. https://doi.org/10.1108/EJIM-09-2023-0826

Voronkova, V., Nikitenko, V., & Vasyl’chuk, G. (2023). Foreign experience in implementing digital education in the context of digital economy transformation. Baltic Journal of Economic Studies, 9(3), 56–65. https://doi.org/10.30525/2256-0742/2023-9-3-56-65

Wilson, T. (2017). From diagnosis to prescription: Advancing gap-analysis methodology. Public Sector Strategy Journal, 8(2), 55–70.

Zainy, M. L. S., Pratama, G. B., Kurnianto, R. R., & Iridiastadi, H. (2023). Fatigue among indonesian commercial vehicle drivers: A study examining changes in subjective responses and ocular indicators. International Journal of Technology, 14(5), 1039–1048. https://doi.org/10.14716/ijtech.v14i5.4856

Zaitseva, I. V., Malafeev, O. A., Skvortsova, O. I., & Bondar, V. V. (2021). Model of distribution of labor resources among regions in order to improve their economic indicators. Components of Scientific and Technological Progress, 6(60), 21–24.

Zaytsev, A., Dmitriev, N., Rodionov, D., & Magradze, T. (2021). Assessment of the innovative potential of alternative energy in the context of the transition to the circular economy. International Journal of Technology, 12(7), 1328–1338. https://doi.org/10.14716/ijtech.v12i7.5357

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, 11(8), 1509–1518. https://doi.org/10.14716/ijtech.v11i8.4539

Zubkova, D. A., Rakova, V. V., Burlutskaya, Z. V., & Gintciak, A. M. (2022). Automatic calibration of sociotechnical systems simulation models on the example of the infection spread model. International Journal of Technology, 13(7), 1452–1462. https://doi.org/10.14716/ijtech.v13i7.6180