Data-driven corporate growth: A dynamic financial modelling framework for strategic agility
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Abstract
This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application
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How to Cite
Sihotang, H. T., Vinsensia, D., Riandari, F., & Chandra, S. (2024). Data-driven corporate growth: A dynamic financial modelling framework for strategic agility. International Journal of Basic and Applied Science, 13(2), 84–95. https://doi.org/10.35335/ijobas.v13i2.485
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L. Li, J. Lin, Y. Ouyang, and X. R. Luo, “Evaluating the impact of big data analytics usage on the decision-making quality of organizations,” Technol. Forecast. Soc. Change, vol. 175, no. 2, p. 121355, 2022, doi: https://doi.org/10.1016/j.techfore.2021.121355.
Q. A. Nisar, N. Nasir, S. Jamshed, S. Naz, M. Ali, and S. Ali, “Big data management and environmental performance: role of big data decision-making capabilities and decision-making quality,” J. Enterp. Inf. Manag., vol. 34, no. 4, pp. 1061–1096, 2021, doi: https://doi.org/10.1108/JEIM-04-2020-0137.
S. Singh, S. S. Rajest, S. Hadoussa, A. J. Obaid, and R. Regin, Data-driven decision making for long-term business success. IGI Global, 2023.
A. E. Artene, A. E. Domil, and L. Ivascu, “Unlocking Business Value: Integrating AI-Driven Decision-Making in Financial Reporting Systems.,” Electron., vol. 13, no. 15, pp. 30–69, 2024, doi: 10.3390/electronics13153069.
M. Al-Okaily and A. Al-Okaily, “Financial data modeling: an analysis of factors influencing big data analytics-driven financial decision quality,” Emerald Publishing Limited, 2024. doi: https://doi.org/10.1108/JM2-08-2023-0183.
D. Bechtsis, N. Tsolakis, E. Iakovou, and D. Vlachos, “Data-driven secure, resilient and sustainable supply chains: gaps, opportunities, and a new generalised data sharing and data monetisation framework,” Int. J. Prod. Res., vol. 60, no. 14, pp. 4397–4417, 2022, doi: 10.1080/00207543.2021.1957506.
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R. Dahiya, S. Le, J. K. Ring, and K. Watson, “Big data analytics and competitive advantage: the strategic role of firm-specific knowledge,” J. Strateg. Manag., vol. 15, no. 2, pp. 175–193, 2022, doi: https://doi.org/10.1108/JSMA-08-2020-0203.
S. Akter, A. Gunasekaran, S. F. Wamba, M. M. Babu, and U. Hani, “Reshaping competitive advantages with analytics capabilities in service systems,” Technol. Forecast. Soc. Change, vol. 159, no. 10, p. 120180, 2020, doi: https://doi.org/10.1016/j.techfore.2020.120180.
A. Garg and D. P. Goyal, “Sustained business competitive advantage with data analytics,” Int. J. Bus. Data Anal., vol. 1, no. 1, pp. 4–15, 2019, doi: https://doi.org/10.1504/IJBDA.2019.098829.
D. J. Teece, G. Pisano, and A. Shuen, “Dynamic capabilities and strategic management,” Strateg. Manag. J., vol. 18, no. 7, pp. 509–533, 1997, doi: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z.
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M. P. Ferreira, F. R. Serra, B. K. Costa, and M. Almeida, “A bibliometric study of the resource-based view (RBV) in international business research using barney (1991) as a key marker,” Innovar, vol. 26, no. 61, pp. 131–144, 2016.
J. Barney, “Firm resources and sustained competitive advantage,” J. Manage., vol. 17, no. 1, pp. 99–120, 1991, doi: https://doi.org/10.1177/014920639101700108.
J. Barney, “Special theory forum the resource-based model of the firm: origins, implications, and prospects,” J. Manage., vol. 17, no. 1, pp. 97–98, 1991, doi: https://doi.org/10.1177/014920639101700107.
V. Grover, R. H. L. Chiang, T.-P. Liang, and D. Zhang, “Creating strategic business value from big data analytics: A research framework,” J. Manag. Inf. Syst., vol. 35, no. 2, pp. 388–423, 2018, doi: https://doi.org/10.1080/07421222.2018.1451951.
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