The Impact of AI-Powered Decision Support Systems on Decision-Making Effectiveness

Authors

DOI:

https://doi.org/10.26877/asset.v8i4.2432

Keywords:

AI-based tools, AI-Driven DSS, Decision Analytics, Decision Support System, Industry Comparison

Abstract

AI-powered Decision Support Systems (AI-DSS) enhance analytical capabilities through advanced data processing, predictive analytics, and intelligent decision support. This study investigates the impact of AI-powered DSS on decision-making effectiveness across multiple sectors. A quantitative research design was employed using a structured questionnaire distributed to 33 professionals and decision-makers experienced in AI-based DSS. Decision-making effectiveness was assessed through decision accuracy, decision-making speed, operational efficiency, and user satisfaction. Data were analyzed using descriptive statistics, Pearson correlation, and simple linear regression. The results show a moderate positive correlation between AI usage and decision-making effectiveness (r = 0.550, p < 0.001). Regression analysis further demonstrates that AI utilization significantly predicts decision-making effectiveness (B = 0.265, t = 3.664, p < 0.001). These findings indicate that greater utilization of AI-powered DSS is associated with improved decision outcomes, particularly in decision accuracy, response speed, and operational efficiency. The study also demonstrates the applicability of AI techniques, including machine learning, neural networks, fuzzy logic, and Bayesian modelling, across diverse industrial contexts. Overall, the findings provide empirical evidence that AI-powered DSS contribute significantly to organizational decision-making effectiveness across multiple sectors.

Author Biographies

  • Edi Priyanto, University of Technology Yogyakarta

    Doctorate Program of Management Sciences, University of Technology Yogyakarta

  • Nur Wening, University Technology of Yogyakarta

    Department of Management Science, University of Technology Yogyakarta

  • Rianto Rianto, University Technology of Yogyakarta

    Department of Data Science, University of Technology Yogyakarta

  • Tri Gunarsih, University Technology of Yogyakarta

    Department of Management Science, University of Technology Yogyakarta

References

[1] Sadeghi R. K, Ojha D, Kaur P, Mahto R V, Dhir A. Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems 2024;180:114194. https://doi.org/10.1016/j.dss.2024.114194.

[2] Suganthi V, Kalaiselvi K. Decision Support System in Healthcare Monitoring. Artificial Intelligence‐Based System Models in Healthcare 2024:55–78.

[3] Al Maruf A. A systematic review of ERP-integrated decision support systems for financial and operational optimization in global retails business. American Journal of Interdisciplinary Studies 2025;6:236–62.

[4] Mollá N, Heavin C, Rabasa A. Data-driven decision making: new opportunities for DSS in data stream contexts. Journal of Decision Systems 2022;31:255–69. https://doi.org/10.1080/12460125.2022.2071404.

[5] Pratt L, Bisson C, Warin T. Bringing advanced technology to strategic decision-making: The Decision Intelligence/Data Science (DI/DS) Integration framework. Futures 2023;152:103217. https://doi.org/10.1016/j.futures.2023.103217.

[6] Mamun MNH. Role of AI and Data Science in Data-Driven Decision-Making for it Business Intelligence: A Systematic Literature Review. Available at SSRN 5402976 2025.

[7] Alojail M, Alturki M, Bhatia Khan S. An Informed Decision Support Framework from a Strategic Perspective in the Health Sector. Information 2023;14:363. https://doi.org/10.3390/info14070363.

[8] Phillips-Wren G, Daly M, Burstein F. Support for cognition in decision support systems: an exploratory historical review. Journal of Decision Systems 2022;31:18–30. https://doi.org/10.1080/12460125.2022.2070946.

[9] Rainy TA, Goswami D, Rabbi MS, Al Maruf A. A Systematic Review of AI-Enhanced Decision Support Tools in Information Systems: Strategic Applications In Service-Oriented Enterprises And Enterprise Planning. Review of Applied Science and Technology 2023;2:26–52.

[10] Li F, Xu J. Revolutionizing AI-Enabled Information Systems Using Integrated Big Data Analytics and Multi-Modal Data Fusion. IEEE Access 2025;13:212316–40. https://doi.org/10.1109/ACCESS.2025.3552039.

[11] Ge Y, Han F, Wu F, Zhao Y, Li H, Tian Y, et al. Sustainable decision making based on systems integration and decision support system promoting endorheic basin sustainability. Decision Support Systems 2024;179:114169. https://doi.org/10.1016/j.dss.2024.114169.

[12] Kumari VS, Vijila J, Balu R. Decision Making Biomedical Support System. Artificial Intelligence‐Based System Models in Healthcare 2024:253–80.

[13] Gupta S, Modgil S, Bhattacharyya S, Bose I. Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research 2022;308:215–74. https://doi.org/10.1007/s10479-020-03856-6.

[14] Omar F, Nabot A, Alqirem R. Transforming Decision Support Systems Through Artificial Intelligence: Enhancing Analytics, Automation, and Interaction BT - Applied Artificial Intelligence in Business: Systems, Tools and Techniques. In: Yaseen SG, editor., Cham: Springer Nature Switzerland; 2025, p. 479–91. https://doi.org/10.1007/978-3-031-90271-0_34.

[15] De Oliveira FJB, Ferson S, Dyer R. A collaborative decision support system framework for vertical farming business developments. International Journal of Decision Support System Technology (IJDSST) 2021;13:1–33.

[16] Arulanthu P, Perumal E. An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction. International Journal of Imaging Systems and Technology 2020;30:815–27.

[17] Wang X, Schneider H, Walsh KR. A predictive analytics approach to building a decision support system for improving graduation rates at a four-year college. Journal of Organizational and End User Computing (JOEUC) 2020;32:43–62.

[18] Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Network Open 2022;5:e220269–e220269. https://doi.org/10.1001/jamanetworkopen.2022.0269.

[19] Tama BA, Lim S. A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems. Mathematics 2020;8:1814. https://doi.org/10.3390/math8101814.

[20] Lee MH, Siewiorek DP, Smailagic A, Bernardino A, Bermúdez i Badia S. Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment. Proceedings of the ACM on Human-Computer Interaction 2020;4:1–27.

[21] Huang Z, George MM, Tan Y-R, Natarajan K, Devasagayam E, Tay E, et al. Are physicians ready for precision antibiotic prescribing? A qualitative analysis of the acceptance of artificial intelligence-enabled clinical decision support systems in India and Singapore. Journal of Global Antimicrobial Resistance 2023;35:76–85. https://doi.org/10.1016/j.jgar.2023.08.016.

[22] Suntaranont B, Aramkul S, Kaewmoracharoen M, Champrasert P. Water Irrigation Decision Support System for Practical Weir Adjustment Using Artificial Intelligence and Machine Learning Techniques. Sustainability 2020;12:1763. https://doi.org/10.3390/su12051763.

[23] Du Y, McNestry C, Wei L, Antoniadi AM, McAuliffe FM, Mooney C. Machine learning-based clinical decision support systems for pregnancy care: A systematic review. International Journal of Medical Informatics 2023;173:105040. https://doi.org/10.1016/j.ijmedinf.2023.105040.

[24] Pitt E, Bradford N, Robertson E, Sansom-Daly UM, Alexander K. The effects of cancer clinical decision support systems on patient-reported outcomes: A systematic review. European Journal of Oncology Nursing 2023;66:102398. https://doi.org/10.1016/j.ejon.2023.102398.

[25] Kayvanfar V, Elomri A, Kerbache L, Vandchali HR, El Omri A. A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics 2024;6:100063. https://doi.org/10.1016/j.sca.2024.100063.

[26] Gil M, Wróbel K, Montewka J, Goerlandt F. A bibliometric analysis and systematic review of shipboard Decision Support Systems for accident prevention. Safety Science 2020;128:104717. https://doi.org/10.1016/j.ssci.2020.104717.

[27] Andargoli AE, Ulapane N, Nguyen TA, Shuakat N, Zelcer J, Wickramasinghe N. Intelligent decision support systems for dementia care: A scoping review. Artificial Intelligence in Medicine 2024;150:102815. https://doi.org/10.1016/j.artmed.2024.102815.

[28] Spoladore D, Tosi M, Lorenzini EC. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artificial Intelligence in Medicine 2024;151:102859. https://doi.org/10.1016/j.artmed.2024.102859.

[29] Mouazer A, Tsopra R, Sedki K, Letord C, Lamy J-B. Decision-support systems for managing polypharmacy in the elderly: A scoping review. Journal of Biomedical Informatics 2022;130:104074. https://doi.org/10.1016/j.jbi.2022.104074.

[30] Murali M, Ni M, Karbing DS, Rees SE, Komorowski M, Marshall D, et al. Clinical practice, decision-making, and use of clinical decision support systems in invasive mechanical ventilation: a narrative review. British Journal of Anaesthesia 2024;133:164–77. https://doi.org/10.1016/j.bja.2024.03.011.

[31] Fahd K, Miah SJ. Designing and evaluating a big data analytics approach for predicting students’ success factors. Journal of Big Data 2023;10:159. https://doi.org/10.1186/s40537-023-00835-z.

[32] Masood A, Sheng B, Yang P, Li P, Li H, Kim J, et al. Automated Decision Support System for Lung Cancer Detection and Classification via Enhanced RFCN With Multilayer Fusion RPN. IEEE Transactions on Industrial Informatics 2020;16:7791–801. https://doi.org/10.1109/TII.2020.2972918.

[33] Gupta S, Kumar P, Tekchandani R. A machine learning-based decision support system for temporal human cognitive state estimation during online education using wearable physiological monitoring devices. Decision Analytics Journal 2023;8:100280. https://doi.org/10.1016/j.dajour.2023.100280.

[34] Gallardo M, Elia A, Thompson RB. Decision support systems and models for aiding irrigation and nutrient management of vegetable crops. Agricultural Water Management 2020;240:106209. https://doi.org/10.1016/j.agwat.2020.106209.

[35] Lin J-S, Chen K-H. A novel decision support system based on computational intelligence and machine learning: Towards zero-defect manufacturing in injection molding. Journal of Industrial Information Integration 2024;40:100621. https://doi.org/10.1016/j.jii.2024.100621.

[36] Hendriks MP, Jager A, Ebben KCWJ, van Til JA, Siesling S. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: Composition of an implementation model based on a scoping review. Critical Reviews in Oncology/Hematology 2024;195:104267. https://doi.org/10.1016/j.critrevonc.2024.104267.

[37] Belhadi A, Kamble S, Fosso Wamba S, Queiroz MM. Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework. International Journal of Production Research 2022;60:4487–507. https://doi.org/10.1080/00207543.2021.1950935.

[38] Antomarioni S, Lucantoni L, Ciarapica FE, Bevilacqua M. Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study. Expert Systems with Applications 2021;185:115622. https://doi.org/10.1016/j.eswa.2021.115622.

[39] Coussement K, Benoit DF. Interpretable data science for decision making. Decision Support Systems 2021;150:113664. https://doi.org/10.1016/j.dss.2021.113664.

[40] Siciliani L, Taccardi V, Basile P, Di Ciano M, Lops P. AI-based decision support system for public procurement. Information Systems 2023;119:102284. https://doi.org/10.1016/j.is.2023.102284.

[41] Irawan Y. Decision Support System For Employee Bonus Determination With Web-Based Simple Additive Weighting (SAW) Method In PT. Mayatama Solusindo. Journal of Applied Engineering and Technological Science (JAETS) 2020;2:7–13. https://doi.org/10.37385/jaets.v2i1.162.

[42] Fahlepi R. Decision Support Systems Employee Discipline Identification Using The Simple Multi Attribute Rating Technique (SMART) Method. Journal of Applied Engineering and Technological Science (JAETS) 2020;1:103–112. https://doi.org/10.37385/jaets.v1i2.67.

[43] Arif M, Ismail A, Irfan S. AI-Powered Approaches for Sustainable Environmental Education in the Digital Age: A Study of Chongqing International Kindergarten. International Journal of Environment, Engineering and Education 2025;7:35–47. https://doi.org/10.55151/ijeedu.v7i1.184.

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Published

2026-09-18

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Vol. 8 No. 4 (2026): August-October

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