Keywords
Environmental Scanning
Strategic Decision Support
Financial Software
Sustainable Competitive Advantage
Dynamic Capabilities
Financial Analytics
How to Cite
Abstract
Purpose: To examine how Competitive Intelligence (CI) can be operationalized within financial software to support environmental scanning, strategic signal identification, and sustainable decision-making under volatile conditions.
Methodology/Approach: A quantitative, design-oriented approach was applied to OpenFinData, comprising 1,500 records across 19 financial tasks mapped into 12 CI dimensions. The analysis combined data preprocessing, TF-IDF, dimensionality reduction, clustering, and exploratory indicators of information density, responsiveness, and strategic fit.
Originality/Relevance: The study integrates Competitive Intelligence, Financial Analytics, and Dynamic Capabilities, positioning financial software as enabling infrastructure for organizational intelligence processes while distinguishing computational capability from organizational CI capability.
Key Findings: The findings reveal different levels of coverage and alignment between analytical workloads and CI dimensions, with greater concentration in investment intelligence and risk and compliance intelligence. Workload structure shows potential to support organizational sensing and strategic decision support but should not be interpreted as a direct measure of organizational CI maturity.
Theoretical/Methodological Contributions: The study extends the interface among CI, Environmental Scanning, Financial Analytics, and Dynamic Capabilities and proposes a reproducible procedure for mapping analytical demands into CI dimensions and assessing their strategic alignment.
References
[1] M. L. Maluleka and B. Z. Chummun, "Competitive intelligence and strategy implementation: critical examination of present literature review," South African J. Inf. Manag., vol. 25, no. 1, a1610, 2023, doi: 10.4102/sajim.v25i1.1610.
[2] I. Tsuchimoto and Y. Kajikawa, "Competitive intelligence practices in Japanese companies: multicase studies," Aslib J. Inf. Manag., vol. 74, no. 4, pp. 631 to 651, 2022, doi: 10.1108/AJIM-05-2021-0133.
[3] K. K. Ledi, "Surviving black swan: competitive intelligence and frugal innovation as panaceas to SME value creation during crisis," Cogent Business and Management, vol. 11, no. 1, art. 2405056, 2024, doi: 10.1080/23311975.2024.2405056.
[4] J. Ranjan, C. Foropon, J. G. Sarkar, and A. Sarkar, "Corrigendum to Big data analytics in building the competitive intelligence of organizations," Int. J. Inf. Manag., vol. 65, art. 102512, 2022, doi: 10.1016/j.ijinfomgt.2022.102512.
[5] B. Harris and J. Brooker, "Environmental scanning: a look to the future," New Directions for Evaluation, vol. 2025, no. 185, pp. 33 to 41, 2025, doi: 10.1002/ev.20633.
[6] J. M. Odiachi, A. A. Sulaimon, and O. L. Kuye, "Succession management: a proficient resource in organisational sustainability," Management Dynamics in the Knowledge Economy, vol. 11, no. 2, p. 112, 2023, doi: 10.2478/mdke-2023-0008.
[7] K. Ragazou, I. Passas, A. Garefalakis, and C. Zopounidis, "Business intelligence model empowering SMEs to make better decisions and enhance their competitive advantage," Discov. Anal., vol. 1, art. 2, 2023, doi: 10.1007/s44257-022-00002-3.
[8] S. Rixin and M. Xinrui, "Cultivating competitive advantage for Chinese SMEs via big data analytics: the mediating role of data driven innovation and antecedents of data governance and data driven culture," PLoS ONE, vol. 20, no. 12, e0337324, 2025, doi: 10.1371/journal.pone.0337324.
[9] D. Korayim, V. Chotia, G. Jain, S. Hassan, and F. Paolone, "How big data analytics can create competitive advantage in high stake decision forecasting? The mediating role of organizational innovation," Technol. Forecast. Soc. Change, vol. 199, art. 123073, 2024, doi: 10.1016/j.techfore.2023.123073.
[10] A. Hassani and E. Mosconi, "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technol. Forecast. Soc. Change, vol. 175, art. 121416, 2022, doi: 10.1016/j.techfore.2021.121416.
[11] S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, and G. Mann, "BloombergGPT: a large language model for finance," arXiv:2303.17564, 2023, doi: 10.48550/arXiv.2303.17564.
[12] H. Yang, X.-Y. Liu, and C. D. Wang, "FinGPT: open source financial large language models," arXiv:2306.06031, 2023, doi: 10.48550/arXiv.2306.06031.
[13] N. Ashal and A. Morshed, "Balancing data driven insights and human judgment in supply chain management: the role of business intelligence, big data analytics, and artificial intelligence," J. Infrastructure, Policy and Development, vol. 8, no. 8, art. 3941, 2024, doi: 10.24294/jipd.v8i8.3941.
[14] K. Du, F. Xing, R. Mao, and E. Cambria, "Financial sentiment analysis: techniques and applications," ACM Comput. Surv., vol. 56, no. 9, art. 220, 2024, doi: 10.1145/3649451.
[15] M. Todd, J. Wibmer, and P. Zhang, "Text-based sentiment analysis in finance: synthesising the existing literature and exploring future directions," Intelligent Systems in Accounting, Finance and Management, vol. 31, no. 1, e1549, 2024, doi: 10.1002/isaf.1549.
[16] Q. Xie et al., "FinBen: a holistic financial benchmark for large language models," in Proc. NeurIPS 2024 Datasets and Benchmarks Track, 2024, doi: 10.48550/arXiv.2402.12659.
[17] J. Cernevicene and A. Kabasinskas, "Explainable artificial intelligence (XAI) in finance: a systematic literature review," Artif. Intell. Rev., vol. 57, no. 8, art. 216, 2024, doi: 10.1007/s10462-024-10854-8.
[18] L. Zhang et al., "FinEval: a Chinese financial domain knowledge evaluation benchmark for large language models," arXiv:2308.09975 v2, 2024, doi: 10.48550/arXiv.2308.09975.
[19] T. Lim, "Environmental, social, and governance (ESG) and artificial intelligence in finance: state of the art and research takeaways," Artif. Intell. Rev., vol. 57, no. 4, art. 76, 2024, doi: 10.1007/s10462-024-10708-3.
[20] Y. Nie et al., "CFinBench: a comprehensive Chinese financial benchmark for large language models," arXiv:2407.02301, 2024, doi: 10.48550/arXiv.2407.02301.
[21] A. Karanikola, G. Davrazos, C. M. Liapis, and S. Kotsiantis, "Financial sentiment analysis: classic methods vs. deep learning models," Intelligent Decision Technologies, vol. 17, no. 4, pp. 1093 to 1116, 2023, doi: 10.3233/IDT-230478.
[22] Y. Karulkar, A. Shah, and R. Naik, "From data to decisions: evaluating machine learning models for stock market forecasting," Vision, advance online, 2025, doi: 10.1177/09711023251349445.
[23] D. M. Teixeira and R. S. Barbosa, "Stock price prediction in the financial market using machine learning models," Computation, vol. 13, no. 1, art. 3, 2025, doi: 10.3390/computation13010003.
[24] Anonymous, "Hybrid machine learning models for long term stock market forecasting: integrating technical indicators," J. Risk Financial Manag., vol. 18, no. 4, art. 201, 2025, doi: 10.3390/jrfm18040201.
[25] A. Nagarathinam, A. Chellasamy, and S. Rangasamy, "Strategic data analytics for sustainable competitive advantage," in Data-Driven Decision Making, J. Poulose, V. Sharma, and C. Maheshkar, Eds. Singapore: Palgrave Macmillan, 2024, pp. 77 to 106, doi: 10.1007/978-981-97-2902-9_4.
[26] P. Held, T. Heubeck, and R. Meckl, "Boosting SMEs digital transformation: the role of dynamic capabilities in cultivating digital leadership and digital culture," Review of Managerial Science, advance online, 2025, doi: 10.1007/s11846-025-00919-5.
[27] A. Rohan, M. D. Hossen, M. N. Pranto, B. Hossain, A. M. Yoshi, and R. Islam, "Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting," Frontiers in Artificial Intelligence, vol. 8, art. 1696423, 2026, doi: 10.3389/frai.2025.1696423.
[28] Anonymous, "Stock market prediction using machine learning and deep learning techniques: a review," AppliedMath, vol. 5, no. 3, art. 76, 2025, doi: 10.3390/appliedmath5030076.
[29] Y. Wang, X. Sui, and Q. Zhang, "The impact of FinTech on corporate carbon emissions: towards green and sustainable development," Business Strategy and the Environment, vol. 33, no. 6, pp. 5523 to 5543, 2024, doi: 10.1002/bse.3778.
[30] F. A. Almaqtari, S. Rehman, S. Nigam, and M. Khan, "The impact of board structure, IT governance, and FinTech on green finance and sustainability: an integrated model," Strategic Change, vol. 34, no. 2, pp. 337 to 357, 2025, doi: 10.1002/jsc.2623.
[31] M. B. Chenguel and N. Mansour, "Green finance: between commitment and illusion," Competitiveness Review: An International Business Journal, vol. 34, no. 1, pp. 179 to 192, 2024, doi: 10.1108/CR-10-2022-0162.
[32] T. Maungwa and P. Laughton, "Exploring the approaches of competitive intelligence intermediaries and information service intermediaries in soliciting key intelligence and information needs," J. Intelligence Studies in Business, vol. 14, no. 2, pp. 6 to 22, 2024, doi: 10.37380/jisib.v14i2.1234.
[33] L. L. Moreira, S. S. Pinto, L. Costa, and N. Araujo, "Evaluating digital transformation in small and medium enterprises using the Alkire-Foster method," Heliyon, vol. 11, no. 2, e41838, 2025, doi: 10.1016/j.heliyon.2025.e41838.
[34] D. V. Hoang, N. T. Hien, H. V. Thang, P. N. T. Phuong, and T. T.-T. Duong, "Digital capabilities and sustainable competitive advantages: the case of emerging market manufacturing SMEs," SAGE Open, vol. 15, no. 2, 2025, doi: 10.1177/21582440251329967.
[35] Y. Yesuf, Z. Fields, A. Jain, and E. Kassa, "Artificial intelligence adoption as a driver of innovation and competitiveness in SMEs: a bibliometric and systematic review," F1000Research, vol. 14, art. 1248, 2025, doi: 10.12688/f1000research.171494.1.