DATA-DRIVEN MANAGEMENT AND BUSINESS INTELLIGENCE AS DETERMINANTS OF SUSTAINABLE GROWTH OF ENTERPRISES IN THE REPUBLIC OF SERBIA
DOI:
https://doi.org/10.61837/mbuir040126018aKeywords:
Data-driven management, business intelligence, sustainable growth, strategic decision making, digital transformation, competitiveness, Serbian enterprises.Abstract
In the context of digital transformation and increasing market uncertainty, data-driven management and business intelligence (BI) systems have become essential instruments for improving strategic decision-making and ensuring sustainable enterprise growth. The subject of this paper is the analysis of the role of data-driven management and business intelligence as determinants of sustainable growth of enterprises in the Republic of Serbia. The aim of the research is to examine the theoretical foundations of these concepts and to assess their significance for competitiveness and long-term business stability in a transitional economy. The research is based on a theoretical-analytical approach, including a review of relevant international and domestic literature, comparative analysis, and the methods of synthesis and deduction. The results indicate that the effective integration of BI tools into managerial processes enhances decision quality, reduces business risk, and strengthens competitive positioning. However, limited digital maturity and insufficient analytical culture remain significant challenges in Serbian enterprises. The paper concludes that systematic implementation of data-driven practices represents a strategic prerequisite for sustainable growth and long-term economic development.
References
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Boston: MIT Press.
Shollo, A., & Galliers, R. D. (2016). Towards an un derstanding of business intelligence systems success: Effects of maturity and culture on analytical deci sion making. Decision Support Systems, 89, 1-10. https://doi.org/10.1016/j.dss.2016.05.005
Popović, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on ana lytical decision making. Decision Support Systems, 54(1), 649-661, 729-739. https://doi.org/10.1016/j. dss.2012.08.017
Hurbean, L., Militaru, F., Munteanu, V. P., Danaiata, D., Fotache, D., & Muntean, M. (2024). Assessing the influence of business intelligence and analyt ics and data-driven culture on managerial perfor mance: Evidence from Romania. Systems, 13(1), https://doi.org/10.3390/systems13010002
Al-Daraba, K., Al-Shami, S. A., Rashid, N., & Qureshi, M. I. (2025). Systematic review of factors influencing adoption of business intelligence sys tems. Discover Sustainability, 6, 936. https://doi. org/10.1007/s43621-025-01876-5
Turban, E., Sharda, R., Delen, D., & King, D. (2018). Business Intelligence and Analytics: Systems for Decision Support (10th ed.). Boston: Pearson.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
Lönnqvist, A., & Pirttimäki, V. (2006). The mea surement of business intelligence. Information Systems Management, 23(1), 32-40.
Wixom, B. H., & Watson, H. J. (2010). The BI based organization. International Journal of Business Intelligence Research, 1(1), 13-28.
Islam, S., Hossain, E., Rahman, M. S., Rahman, M. J., Khan, S. I., & Ashik, A. A. M. (2023). Digital transformation in SMEs: Unlocking competi tive advantage through business intelligence and data analytics adoption. Journal of Business and Management Studies, 5(6), 177-186. https://doi. org/10.32996/jbms.2023.5.6.14
Solanki, A., Jain, K., & Jadiga, S. (2024). Building a data-driven culture: Empowering organizations with business intelligence. International Journal of Computer Trends and Technology, 72(2), 46–55. [Online]
Bindeeba, D. S., Tukamushaba, E. K., & Bakashaba, R. (2025). Digital transformation and its mul tidimensional impact on sustainable business performance: Evidence from a meta-analytic re view. Future Business Journal, 11, 90. https://doi. org/10.1186/s43093-025-00511-z
Duong, V. (2024). Big data analytics and busi ness intelligence in business marketing: A review. International Journal of Information Technology and Computer Science Applications, 2(3), 139 146. https://doi.org/10.58776/ijitcsa.v2i3.162
Jie, C., Harcharanjit S. M., Singh, Yi., Shan-Yong, W. (2023). The impact of business intelligence and big data analytics on sustainability performance. Journal of Cleaner Production, 429, 139410. https:// doi.org/10.1016/j.jclepro.2023.139410
Mazic, M., Ristic, Z., Aleksic, M. (2024). Customer relationship management (crm) - a model for im proving the market performance of companies in the textile industry, SCIENCE International jour nal, 3(3), 89-95. doi: 10.35120/sciencej0303089m UDK: 334.72:677]:005.332.1:005.42-057.187 https://scienceij.com/index.php/sij/article/ view/193/158
Kumar, M. (2019). Data-Driven Decision Making: How Organizations Use Analytics to Transform their Strategies. International Journal for Multidisciplinary Research, 1(2). https://doi. org/10.36948/ijfmr.2019.v01i02.20554
Diván, M. J. (2017). Data-driven decision making. Proceedings of the International Conference on Infocom Technologies and Unmanned Systems. https://doi.org/10.1109/ICTUS.2017.8285973
IBM. (2024). Data-driven decision making. Preuzeto sa https://www.ibm.com/think/topics/ data-driven-decision-making
Li, X., Cheng, Y., Xia, X., & Møller, C. (2024). Data governance and data management in operations and supply chain: A literature review. https://arxiv. org/abs/2407.06199
Lemon, K. N., Verhoef, P. C. (2016). Understanding customer experience throughout the customer jour ney. Journal of Marketing, 80(6), 69–96. https:// doi.org/10.1509/jm.15.0420
Begenau, J., Farboodi, M., Veldkamp, L. (2018). Big data in finance and the growth of large firms. Journal of Monetary Economics, 97, 71–87.
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https:// doi.org/10.1016/j.ejor.2017.02.023
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.103169
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., Childe, S. J. (2017). Big data an alytics and firm performance. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j. jbusres.2016.08.009
OECD. (2021). The Digital Transformation of SMEs. Paris: OECD Publishing. https://doi. org/10.1787/bdb9256a-en
Bag, S., Gupta, S., & Kumar, A. (2021). Role of business intelligence and analytics in sustainabili ty. Journal of Cleaner Production, 283, 124566. https://doi.org/10.1016/j.jclepro.2020.124566
Republički zavod za statistiku (2023). Upotreba informaciono-komunikacionih tehnologija u Republici Srbiji. Beograd: RZS, 81.
Delias, P., & Kitsios, F. C. (2023). Operational research and business intelligence as drivers for digital transformation. Operational Research International Journal, 23, 45. https://doi. org/10.1007/s12351-023-00784-8
https://unglobalcompact.org/what-is-gc/partici pants/1101-Banca-Intesa-Beograd?
https://www.bancaintesa.rs/en/stanovnistvo/na sa-grupa/ko-smo-mi.html?
Nwaimo, C. S., Adegbola, A. E., & Adegbola, M. D. (2024). Sustainable business intelligence solu tions: Integrating advanced tools for long-term business growth. GSC Advanced Research and Reviews, 19(3), 31-39. https://doi.org/10.30574/ gscarr.2024.19.3.0190
Popov, B. (2022). Arhitektura sistema poslovne inteligencije. Zbornik radova Fakulteta tehničkih nauka u Novom Sadu. https://zbornik.ftn.uns. ac.rs/index.php/zbornik/article/view/2420
Wedel, M., Kannan, P. K. (2016). Marketing an alytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/ jm.15.0413
Delhaize Serbia (2024). About the company. Taken from: https://www.delhaizeserbia.rs (april 2026)
