Volume 12, Issue 2 (February 2025), Pages: 220-229

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 Original Research Paper

The impact of big data analytics on financial market integration and efficiency in Saudi Arabia

 Author(s): 

 Taha Khairy Taha Ibrahim *

 Affiliation(s):

 Department of Accounting, Al-Jouf University, Sakakah, Saudi Arabia

 Full text

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0009-0000-9188-9455

 Digital Object Identifier (DOI)

 https://doi.org/10.21833/ijaas.2025.02.024

 Abstract

This study investigates the impact of Big Data Analytics (BDA) on market integration and efficiency in Saudi Arabia's financial sector. Using data from 182 participants in financial institutions, it examines key BDA dimensions—data volume, variety, velocity, accuracy, and analytical tools—and their effects on market performance. A structured survey and structural equation modeling (SEM) were used for data analysis. Findings highlight that data accuracy and advanced analytical tools significantly enhance market efficiency, while data volume and variety have a limited impact. The results emphasize prioritizing data quality and effective interpretation over quantity and diversity, with artificial intelligence (AI) playing a key role in decision-making and market efficiency. Despite regional and cross-sectional limitations, the study provides valuable insights for practitioners and policymakers. Future research should expand the scope and explore the long-term effects of BDA on financial markets.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Big data analytics, Market integration, Financial efficiency, Data accuracy, Artificial intelligence

 Article history

 Received 13 September 2024, Received in revised form 17 January 2025, Accepted 10 February 2025

 Acknowledgment

This work was funded by the Deanship of Graduate Studies and Scientific Research at Jou University under grant No. (DGSSR-2023-03-02181).

 Compliance with ethical standards

 Ethical considerations

Informed consent was obtained from all participants, ensuring they were aware of the study’s purpose and their rights, including confidentiality.

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Ibrahim TKT (2025). The impact of big data analytics on financial market integration and efficiency in Saudi Arabia. International Journal of Advanced and Applied Sciences, 12(2): 220-229

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 Figures

 Fig. 1 Fig. 2

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 

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 References (27)

1. Adesina AA, Iyelolu TV, and Paul PO (2024). Leveraging predictive analytics for strategic decision-making: Enhancing business performance through data-driven insights. World Journal of Advanced Research and Reviews, 22(3): 1927-1934. https://doi.org/10.30574/wjarr.2024.22.3.1961   [Google Scholar]
2. Alaskar TH, Mezghani K, and Alsadi AK (2021). Examining the adoption of big data analytics in supply chain management under competitive pressure: Evidence from Saudi Arabia. Journal of Decision Systems, 30(2-3): 300-320. https://doi.org/10.1080/12460125.2020.1859714   [Google Scholar]
3. Aljumah AI, Nuseir MT, and Alam MM (2021). Traditional marketing analytics, big data analytics and big data system quality and the success of new product development. Business Process Management Journal, 27(4): 1108-1125. https://doi.org/10.1108/BPMJ-11-2020-0527   [Google Scholar]
4. Almawishir NFS and Benlaria H (2024). Innovation and dimensions of sustainable development: Mediating role of ICTs. Cogent Business and Management, 11(1): 2336306. https://doi.org/10.1080/23311975.2024.2336306   [Google Scholar]
5. Bello OA (2024). The role of data analytics in enhancing financial inclusion in emerging economies. International Journal of Developing and Emerging Economies, 11(3): 90-112.   [Google Scholar]
6. Benlaria A, Sadaoui N, Almawishir NFS, and Benlaria H (2024). Navigating the oil-environment nexus: Saudi Arabia's challenge in sustainable development. International Journal of Energy Economics and Policy, 14(5): 292-300. https://doi.org/10.32479/ijeep.16647   [Google Scholar]
7. Bose S, Dey SK, and Bhattacharjee S (2023). Big data, data analytics and artificial intelligence in accounting: An overview. In: Akter S and Wamba SF (Eds.), Handbook of big data research methods: 32-51. Edward Elgar Publishing, Cheltenham, UK. https://doi.org/10.4337/9781800888555.00007   [Google Scholar]
8. Hair Jr JF, Sarstedt M, Hopkins L, and Kuppelwieser VG (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2): 106-121. https://doi.org/10.1108/EBR-10-2013-0128   [Google Scholar]
9. Htun HH, Biehl M, and Petkov N (2023). Survey of feature selection and extraction techniques for stock market prediction. Financial Innovation, 9: 26. https://doi.org/10.1186/s40854-022-00441-7   [Google Scholar] PMid:36687795 PMCid:PMC9834034
10. Hussien BSA, Benlaria H, Sadaoui N, Ahmed SAK, Lzabat LZ, and Badreldin BMAA (2024). Sustainable innovation and business success: The mediating roles of information technology capability and knowledge management. International Journal of Advanced and Applied Sciences, 11(5): 166-176. https://doi.org/10.21833/ijaas.2024.05.018   [Google Scholar]
11. Inamdar Z, Raut R, Narwane VS, Gardas B, Narkhede B, and Sagnak M (2021). A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. Journal of Enterprise Information Management, 34(1): 101-139. https://doi.org/10.1108/JEIM-09-2019-0267   [Google Scholar]
12. Javaid HA (2024). AI-driven predictive analytics in finance: Transforming risk assessment and decision-making. Advances in Computer Sciences, 7(1): 1-9.   [Google Scholar]
13. Kumar S, Sharma D, Rao S, Lim WM, and Mangla SK (2022). Past, present, and future of sustainable finance: Insights from big data analytics through machine learning of scholarly research. Annals of Operations Research, 332: 1199–1205. https://doi.org/10.1007/s10479-022-04535-4   [Google Scholar]
14. Li L, Lin J, Ouyang Y, and Luo XR (2022). Evaluating the impact of big data analytics usage on the decision-making quality of organizations. Technological Forecasting and Social Change, 175: 121355. https://doi.org/10.1016/j.techfore.2021.121355   [Google Scholar]
15. Lutfi A, Alsyouf A, Almaiah MA, Alrawad M, Abdo AAK, Al-Khasawneh AL, Ibrahim N, and Saad M (2022). Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability, 14(3): 1802. https://doi.org/10.3390/su14031802   [Google Scholar]
16. Maheshwari S, Gautam P, and Jaggi CK (2021). Role of big data analytics in supply chain management: Current trends and future perspectives. International Journal of Production Research, 59(6): 1875-1900. https://doi.org/10.1080/00207543.2020.1793011   [Google Scholar]
17. Martin IW and Nagel S (2022). Market efficiency in the age of big data. Journal of Financial Economics, 145(1): 154-177. https://doi.org/10.1016/j.jfineco.2021.10.006   [Google Scholar]
18. Nguyen DK, Sermpinis G, and Stasinakis C (2023). Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology. European Financial Management, 29(2): 517-548. https://doi.org/10.1111/eufm.12365   [Google Scholar]
19. Nisar QA, Nasir N, Jamshed S, Naz S, Ali M, and Ali S (2021). Big data management and environmental performance: Role of big data decision-making capabilities and decision-making quality. Journal of Enterprise Information Management, 34(4): 1061-1096. https://doi.org/10.1108/JEIM-04-2020-0137   [Google Scholar]
20. Niu Y, Ying L, Yang J, Bao M, and Sivaparthipan CB (2021). Organizational business intelligence and decision making using big data analytics. Information Processing and Management, 58(6): 102725. https://doi.org/10.1016/j.ipm.2021.102725   [Google Scholar]
21. Nnaji UO, Benjamin LB, Eyo-Udo NL, and Augustine E (2024). A review of strategic decision-making in marketing through big data and analytics. Magna Scientia Advanced Research and Reviews, 11(1): 084-091. https://doi.org/10.30574/msarr.2024.11.1.0077   [Google Scholar]
22. Ren S (2022). Optimization of enterprise financial management and decision‐making systems based on big data. Journal of Mathematics, 2022: 1708506. https://doi.org/10.1155/2022/1708506   [Google Scholar]
23. Ringle CM, Sarstedt M, Sinkovics N, and Sinkovics RR (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48: 109074. https://doi.org/10.1016/j.dib.2023.109074   [Google Scholar] PMid:37066088 PMCid:PMC10090253
24. Sarstedt M and Liu Y (2024). Advanced marketing analytics using partial least squares structural equation modeling (PLS-SEM). Journal of Marketing Analytics, 12: 1-5. https://doi.org/10.1057/s41270-023-00279-7   [Google Scholar]
25. Sarstedt M, Ringle CM, and Hair JF (2021). Partial least squares structural equation modeling. In: Homburg C, Klarmann M, and Vomberg A (Eds.), Handbook of market research: 587-632. Springer, Cham, Switzerland. https://doi.org/10.1007/978-3-319-57413-4_15   [Google Scholar]
26. Xu K, Wu Y, Li Z, Zhang R, and Feng Z (2024). Investigating financial risk behavior prediction using deep learning and big data. International Journal of Innovative Research in Engineering and Management, 11(3): 77-81. https://doi.org/10.55524/ijirem.2024.11.3.12   [Google Scholar]
27. Zabat L, Sadaoui N, Benlaria H, Ahmed SAK, Hussien BSA, and Abdulrahman BMA (2024). The impact of RegTech on compliance costs and risk management from the perspective of Saudi banks' employees. WSEAS Transactions on Business and Economics, 21: 1200-1216. https://doi.org/10.37394/23207.2024.21.98   [Google Scholar]