Bibliometric Analysis AI-Based Decision Analytics
DOI:
https://doi.org/10.58812/wsist.v4i02.3096Keywords:
Artificial Intelligence, AI-Based Decision Analytics, Machine Learning, Decision Support Systems, Deep LearningAbstract
The rapid advancement of artificial intelligence (AI) has transformed decision-making processes across various domains by enabling data-driven insights, predictive capabilities, and intelligent automation. This study aims to examine the development, intellectual structure, and emerging trends of research on AI-based decision analytics through a bibliometric analysis approach. Data were collected from the Scopus database using relevant search terms related to artificial intelligence and decision analytics. The study applies bibliometric techniques, including performance analysis, citation analysis, co-authorship analysis, keyword co-occurrence analysis, thematic evolution analysis, and density visualization using VOSviewer. The findings indicate that artificial intelligence, machine learning, deep learning, and clinical decision support systems represent the dominant research themes shaping this field. Highly cited studies demonstrate increasing scholarly attention toward ethical considerations, explainability, transparency, and trust in AI-driven decision systems. The collaboration analysis reveals that research development is supported by extensive international networks, with countries such as Germany, the United States, India, and China serving as influential contributors. Furthermore, the temporal analysis indicates a shift from algorithm-focused research toward human-centered and responsible AI applications. This study contributes to the literature by providing a comprehensive mapping of AI-based decision analytics research and identifying future directions related to explainable AI, trustworthy decision systems, and interdisciplinary applications across healthcare, business, and other complex decision environments.
References
[1] L. Lei, H. Feng, and J. Ren, “Artificial intelligence, human capital and firm-level total factor productivity,” Financ. Res. Lett., vol. 85, 2025, doi: 10.1016/j.frl.2025.107897.
[2] S. Si, S. Kagai, M. Zhuang, Y. Liu, F. N. Vladimir, and Y. Wei, “Research Progress of Artificial Intelligence in Research and Development of Renewable Energy Materials,” Xiyou Jinshu/Chinese J. Rare Met., vol. 47, no. 4, pp. 570–586, 2023, doi: 10.13373/j.cnki.cjrm.XY22050038.
[3] A. Sharma, A. Sharma, K. N. Raju, I. Keshta, and K. Guo, “Synergistic Integration of 5G-Enabled Cloud Native Infrastructures With Advanced Technologies for Urban Safety Enhancement,” IEEE Trans. Consum. Electron., vol. 71, no. 2, pp. 5321–5334, 2025, doi: 10.1109/TCE.2025.3560133.
[4] P. Korycki, M. Stojke, and K. Kępa, “Explainable Deep Learning for RF Fingerprinting Using Convolutional Neural Networks,” in Lecture Notes in Networks and Systems, Z. W., M. J., S. J., W. T., and K. J., Eds., Wrocław University of Science and Technology, Wrocław, Poland: Springer Science and Business Media Deutschland GmbH, 2026, pp. 218–231. doi: 10.1007/978-3-032-27927-9_21.
[5] S. Krakowski, J. Luger, and S. Raisch, “Artificial intelligence and the changing sources of competitive advantage,” Strateg. Manag. J., vol. 44, no. 6, pp. 1425–1452, 2023, doi: 10.1002/smj.3387.
[6] T. Eriksson, A. Bigi, and M. Bonera, “Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation,” TQM J., vol. 32, no. 4, pp. 795–814, 2020, doi: 10.1108/TQM-12-2019-0303.
[7] H. Saidi, Y. Chnigri, and F. Z. Madhat, “Artificial Intelligence and HRM in the Public Sector: An Integrative Literature Review,” in Advances in Science, Technology and Innovation, A. A. E.A., M. M., and N. A., Eds., Industrial Technologies and Services Laboratory (LTSI), Superior School of Technology of Fez, Sidi Mohamed Ben Abdellah University, Fez, Morocco: Springer Nature, 2026, pp. 85–89. doi: 10.1007/978-3-031-97825-8_16.
[8] N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, 2021.
[9] A. Čartolovni, A. Tomičić, and E. Lazić Mosler, “Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review,” Int. J. Med. Inform., vol. 161, 2022, doi: 10.1016/j.ijmedinf.2022.104738.
[10] M. Braun, P. Hummel, S. Beck, and P. Dabrock, “Primer on an ethics of AI-based decision support systems in the clinic,” J. Med. Ethics, vol. 47, no. 12, p. E3, 2021, doi: 10.1136/medethics-2019-105860.
[11] A. Al-Surmi, M. Bashiri, and I. Koliousis, “AI based decision making: combining strategies to improve operational performance,” Int. J. Prod. Res., vol. 60, no. 14, pp. 4464–4486, 2022, doi: 10.1080/00207543.2021.1966540.
[12] S. Tutun et al., “An AI-based Decision Support System for Predicting Mental Health Disorders,” Inf. Syst. Front., vol. 25, no. 3, pp. 1261–1276, 2023, doi: 10.1007/s10796-022-10282-5.
[13] C. Keding and P. Meissner, “Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions,” Technol. Forecast. Soc. Change, vol. 171, 2021, doi: 10.1016/j.techfore.2021.120970.
[14] C. Panigutti, A. Beretta, F. Giannotti, and D. Pedreschi, “Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems,” in Conference on Human Factors in Computing Systems - Proceedings, Universita di Pisa, Pisa, Italy: Association for Computing Machinery, 2022. doi: 10.1145/3491102.3502104.
[15] S. Sharma, N. Islam, G. Singh, and A. Dhir, “Why Do Retail Customers Adopt Artificial Intelligence (AI) Based Autonomous Decision-Making Systems?,” IEEE Trans. Eng. Manag., vol. 71, pp. 1846–1861, 2024, doi: 10.1109/TEM.2022.3157976.
[16] C. Barata et al., “A reinforcement learning model for AI-based decision support in skin cancer,” Nat. Med., vol. 29, no. 8, pp. 1941–1946, 2023, doi: 10.1038/s41591-023-02475-5.
[17] R. L. Pierce, W. Van Biesen, D. Van Cauwenberge, J. Decruyenaere, and S. Sterckx, “Explainability in medicine in an era of AI-based clinical decision support systems,” Front. Genet., vol. 13, 2022, doi: 10.3389/fgene.2022.903600.
[18] C. Meske and E. Bunde, “Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection,” Inf. Syst. Front., vol. 25, no. 2, pp. 743–773, 2023, doi: 10.1007/s10796-021-10234-5.
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