Article Sidebar

Download
PDF
Statistic
Read Counter : 170 Download : 67

Main Article Content

Abstract

This investigation outlines a new intelligent system to assist in decision-making for enterprise organisational changes in the context of the digital economy. The innovations of this study are threefold: First, the creation of a multi-dimensional decision model defined by the real-time indicators from the digital economy, as well as traditional metrics of organisational change for structural evolution. Second, the application of a hybrid intelligent algorithm that incorporates deep learning with knowledge graphs enables the processing of both structured and unstructured data at the enterprise level, thereby offering broader decision-making support than standard systems. Third, the development of a system that provides optimised decision recommendations based on what happens after the decision is implemented, thus closing the gap between system design and reality. Results from practical tests conducted in several enterprises substantiate that the proposed system has 35% greater efficiency in making decisions and 42% lower risks in implementing organisational changes than the traditional methods. This development has a considerable impact on the teaching and practice of intelligent decision support in enterprise digital transformation, posing a new approach to managing organisational changes in the digital economy.

Keywords

Digital economy Intelligent algorithm Multi-dimensional decision model Deep learning Enterprise level

Article Details

How to Cite
Zhang, K. (2025). Research on an intelligent decision support system for enterprise organizational change in the digital economy environment. Future Technology, 4(4), 128–137. Retrieved from https://fupubco.com/futech/article/view/440
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. Entezami, M., Basirat, S., Moghaddami, B., Bazmandeh, D., & Charkhian, D. (2025). Examining the Importance of AI-Based Criteria in the Development of the Digital Economy: A Multi-Criteria Decision-Making Approach. Journal of Soft Computing and Decision Analytics, 3(1), 72-95. DOI: https://doi.org/10.31181/jscda31202555.
  2. Shahi, C., & Sinha, M. (2021). Digital transformation: challenges faced by organizations and their potential solutions. International Journal of Innovation Science, 13(1), 17-33. DOI: https://doi.org/10.1111/caim.12414.
  3. Onwujekwe, G., & Weistroffer, H. R. (2025). Intelligent Decision Support Systems: An Analysis of the Literature and a Framework for Development. Information Systems Frontiers, 1-32. DOI: https://doi.org/10.1007/s10796-024-10571-1.
  4. Mohammed-Shittu, N. (2025). Artificial Intelligence (AI)-Driven Decision Support Systems for Sustainable Administration of Public Universities in Rivers State, Nigeria. International Journal of Educational Management, Rivers State University., 1(2), 157-169. DOI: https://ijedm.com/index.php/ijedm/article/view/52.
  5. Shknai, O. S., Nechyporuk, O., Nalapko, O., Buyalo, O., & Lyashenko, A. (2025). A set of methods for enhancing the efficiency of information processing in intelligent decision support systems. D29 Authors: Edited by Svitlana Kashkevich, 62. DOI: 10.15587/978-617-8360-13-9.CH3.
  6. Li, T., Zheng, M., & Zhou, Y. (2025). LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting: Deep Learning for Renewable Energy Demand Prediction. Journal of Organizational and End User Computing (JOEUC), 37(1), 1-29. DOI: 10.4018/JOEUC.370005.
  7. Majnoor, N., & Vinayagam, K. (2023). The ascendency of the paradigm shift from organizational change management to change agility. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(4), 19. DOI: https://doi.org/10.26668/businessreview/2023.v8i4.1151.
  8. Passiante, G., & Ruggiero, G. (2025). An Innovative Management in the Digital Economy: The CNR Case Study. In Digital Innovation Management: People, Process, Platforms and Policy (pp. 1-20). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-80426-7_1.
  9. Shah, N., Zehri, A. W., Saraih, U. N., Abdelwahed, N. A. A., & Soomro, B. A. (2024). The role of digital technology and digital innovation towards firm performance in a digital economy. Kybernetes, 53(2), 620-644. DOI: https://doi.org/10.1108/K-01-2023-0124.
  10. Silva, D. C., Ferreira, F. A., Milici, A., Ferreira, J. J., & Ferreira, N. C. (2025). Business transformation processes and Society 5.0: opportunities and challenges. Management Decision. DOI: https://doi.org/10.1108/MD-05-2024-1209.
  11. Lv, B., Deng, Y., Meng, W., Wang, Z., & Tang, T. (2024). Research on digital intelligence business model based on artificial intelligence in post-epidemic era. Management Decision, 62(9), 2937-2957. DOI: https://doi.org/10.1108/MD-11-2022-1548.
  12. Leong, L. Y., Hew, T. S., Ooi, K. B., & Chau, P. Y. (2024). “To share or not to share?”–A hybrid SEM-ANN-NCA study of the enablers and enhancers for mobile sharing economy. Decision Support Systems, 180, 114185. DOI: https://doi.org/10.1016/j.dss.2024.114185.
  13. Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H. R., & El Omri, A. (2024). A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics, 100063. DOI: https://doi.org/10.1016/j.sca.2024.100063.
  14. Waqar, A. (2024). Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches. Expert Systems with Applications, 249, 123503. DOI: https://doi.org/10.1016/j.eswa.2024.123503.
  15. Ataei, P., Takhtravan, A., Gheibi, M., Chahkandi, B., Faramarz, M. G., Wacławek, S., ... & Behzadian, K. (2024). An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls. Heliyon, 10(3). DOI: 10.1016/j.heliyon.2024.e25036 External Link.
  16. Ge, Y., Xia, Y., & Wang, T. (2024). Digital economy, data resources and enterprise green technology innovation: Evidence from A-listed Chinese Firms. Resources Policy, 92, 105035. DOI: https://doi.org/10.1016/j.resourpol.2024.105035.
  17. Raihan, A. (2024). A review of the potential opportunities and challenges of the digital economy for sustainability. Innovation and Green Development, 3(4), 100174. DOI: https://doi.org/10.1016/j.igd.2024.100174.
  18. Javaid, M., Haleem, A., Singh, R. P., & Sinha, A. K. (2024). Digital economy to improve the culture of industry 4.0: A study on features, implementation and challenges. Green Technologies and Sustainability, 100083. DOI: https://doi.org/10.1016/j.grets.2024.100083.
  19. Sadeghi, K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180, 114194. DOI: https://doi.org/10.1016/j.dss.2024.114194.
  20. Poszler, F., & Lange, B. (2024). The impact of intelligent decision-support systems on humans' ethical decision-making: A systematic literature review and an integrated framework. Technological Forecasting and Social Change, 204, 123403. DOI: https://doi.org/10.1016/j.techfore.2024.123403.