Prescriptive Analytics: A Bibliometric Analysis of Current Trends in Data - Driven Decision - Making

Kharismi Burhanudin, Zuraida Abal Abas, Norhazwani Md Yunos, Abdul Syukor Mohamad Jaya, Ahmad Fadzli Nizam Abdul Rahman, Muhammad Faheem Mohd Ezani, Mohamad Huzaimy Jusoh

Abstract

This study presents a bibliometric synthesis of prescriptive analytics with the aim of charting its intellectual structure, historical trajectory, and emerging frontiers in data-driven decision making. We delineated the review scope, implemented systematic searches in Scopus and Web of Science (WoS), extracted and harmonized records, and undertook descriptive trend analysis alongside thematic mapping. The corpus evidence marked growth in the field: Scopus records increased from a single item in 1970 to 39 in 2024 (reported as 19.4% of the total), while WoS expanded from one publication in 1976 to 107 in 2024 (reported as 10.7%). More than 83% of the outputs have appeared since 2013, accompanied by substantial citation accumulation. The analysis identified leading authors, institutions, and recurrent topics spanning “prescriptive analytics,” “decision science” and “decision intelligence.” It also surfaces conceptual inconsistencies and underexplored areas, motivating the development of adaptive, context-aware frameworks that reconcile competing objectives and enable practical, interdisciplinary AI solutions.
Given the rapid technological change and database coverage effects, the observed trends should be interpreted as time- and source-dependent. Overall, the bibliometric perspective clarifies temporal patterns, disciplinary distribution, and high-frequency keywords and delineates a research agenda to address the most salient gaps.

 

Keywords: Scopus; Web of Science (WoS); Prescriptive Analytics; Decision Intelligence; Decision Science.

 

DOI https://doi.org/10.55463/issn.1674-2974.52.9.6


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References


S. IEVA ET AL., “A Retrieval-Augmented Generation Approach for Data-Driven Energy Infrastructure Digital Twins,” Smart Cities, vol. 7, no. 6, pp. 3095–3120, Dec. 2024, doi: 10.3390/smartcities7060121.

A. CONSILVIO, G. VIGNOLA, P. LÓPEZ ARÉVALO, F. GALLO, M. BORINATO, AND C. CROVETTO, “A data-driven prioritisation framework to mitigate maintenance impact on passengers during metro line operation,” European Transport Research Review, vol. 16, no. 1, Dec. 2024, doi: 10.1186/s12544-023-00631-z.

A. OJEDA, J. VALERA, E. MEDINA, H. SAMADIAN, AND R. PADILLA, “AI implementation in big data: Shaping data analysis for business decisions,” Issues in Information Systems, vol. 25, no. 4, pp. 158–172, 2024, doi: 10.48009/4_iis_2024_113.

A. CELEPIJA, A. PALMERO APROSIO, B. LEPRI, AND R. KAZHAMIAKIN, “AI product cards: A framework for code-bound formal documentation cards in the public administration,” Data Policy, vol. 7, Jan. 2025, doi: 10.1017/dap.2024.55.

S. HERATH PATHIRANNEHELAGE, Y. R. SHRESTHA, AND G. VON KROGH, “Design principles for artificial intelligence-augmented decision making: An action design research study,” European Journal of Information Systems, 2024, doi: 10.1080/0960085X.2024.2330402.

C. SMYTH, D. DENNEHY, S. FOSSO WAMBA, M. SCOTT, AND A. HARFOUCHE, “Artificial intel-ligence and prescriptive analytics for supply chain resilience: a systematic literature re-view and research agenda,” 2024, Taylor and Francis Ltd. doi: 10.1080/00207543.2024.2341415.

S. AYDOĞAN, G. E. OKUDAN KREMER, AND D. AKAY, “Linguistic summarization to sup-port supply network decisions,” J Intell Manuf, vol. 32, no. 6, pp. 1573–1586, Aug. 2021, doi: 10.1007/s10845-020-01677-9.

A. DE VITO ET AL., “Assessing ChatGPT’s theoretical knowledge and prescriptive accu-racy in bacterial infections: a comparative study with infectious diseases residents and specialists,” Infection, 2024, doi: 10.1007/s15010-024-02350-6.

W. RAGHUPATHI and V. RAGHUPATHI, “Contemporary business analytics: An overview,” Aug. 01, 2021, MDPI. doi: 10.3390/data6080086.

G. GRANDER, L. F. DA SILVA, AND E. D. R. SANTIBAÑEZ GONZALEZ, “Big data as a value generator in decision support systems: a literature review,” Jul. 28, 2021, Emerald Group Holdings Ltd. doi: 10.1108/REGE-03-2020-0014.

D. CHAPELA-CAMPA AND M. DUMAS, “From process mining to augmented process execu-tion,” Softw Syst Model, vol. 22, no. 6, pp. 1977–1986, Dec. 2023, doi: 10.1007/s10270-023-01132-2.

A. TIRON-TUDOR AND D. DELIU, “Big Data’s Disruptive Effect on Job Profiles: Manage-ment Accountants’ Case Study,” Journal of Risk and Financial Management, vol. 14, no. 8, Aug. 2021, doi: 10.3390/jrfm14080376.

S. GANESAN AND S. GOPALSAMY, “Business intelligence and advanced analytics: Impact and behavior of business decision-making process,” International Journal of Recent Technology and Engineering, vol. 8, no. 3 Special Issue, pp. 375–379, Oct. 2019, doi: 10.35940/ijrte.C1080.1083S19.

T. SUSNJAK, “Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT,” Int J Artif Intell Educ, vol. 34, no. 2, pp. 452–482, Jun. 2024, doi: 10.1007/s40593-023-00336-3.

J. N. DE CARVALHO, F. R. DA SILVA, AND E. G. S. NASCIMENTO, “Challenges of the Bio-pharmaceutical Industry in the Application of Prescriptive Maintenance in the Industry 4.0 Context: A Comprehensive Literature Review,” Nov. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s24227163.

J. LOPES, M. FARIA, AND M. F. SANTOS, “Exploring trends and autonomy levels of adap-tive business intelligence in healthcare: A systematic review,” May 01, 2024, Public Library of Science. doi: 10.1371/journal.pone.0302697.

C. WISSUCHEK AND P. ZSCHECH, “Prescriptive analytics systems revised: a systematic lit-erature review from an information systems perspective,” Information Systems and e-Business Management, 2024, doi: 10.1007/s10257-024-00688-w.

S. ALAM, Z. DONG, I. KULARATNE, AND M. S. RASHID, “Exploring approaches to overcome challenges in adopting human resource analytics through stakeholder engagement,” Management Review Quarterly, 2025, doi: 10.1007/s11301-025-00491-y.

N. K. THAKRE, S. BALWANTRAO DESHMUKH, A. SHARMA, S. DASH, A. DHERE, AND T. V P, “Prescriptive Decision Making Model for Contextual Intelligence in Human Resource Analytics,” 2024. [Online]. Available at: www.nano-ntp. com

G. YADAV, “AI and analytics conundrum: unpacking the barriers in modern HR with ISM and MICMAC analysis,” 2025, Emerald Publishing. doi: 10.1108/IJOA-08-2024-4782.

S. KHAMIS ET AL., “Knowledge Visualization of Internet Usage Pattern to Improve Stu-dents’ Academic Performance Using Prescriptive Analytic,” Journal of Advanced Re-search in Applied Sciences and Engineering Technology, vol. 48, no. 1, pp. 283–298, Jun. 2025, doi: 10.37934/ARASET.48.1.283298.

M. KUMARI and M. S. KULKARNI, “Developing a prescriptive decision support system for shop floor control,” Industrial Management and Data Systems, vol. 122, no. 8, pp. 1853–1881, Aug. 2022, doi: 10.1108/IMDS-09-2021-0584.

M. STRAND AND A. SYBERFELDT, “Using external data in a BI solution to optimise waste management,” J Decis Syst, vol. 29, no. 1, pp. 53–68, Jan. 2020, doi: 10.1080/12460125.2020.1732174.

P. LI, S. WANG, and Y. CHEN, “Use of Real-World Evidence for Drug Regulatory Deci-sions in China: Current Status and Future Directions,” Nov. 01, 2023, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s43441-023-00555-9.

K. BURHANUDIN, M. H. JUSOH, Z. I. A. Latiff, M. H. Hashim, and N. D. K. Ashar, “The Estimation of the Geomagnetically Induced Current Based on Simulation and Meas-urement at the Power Network: A Bibliometric Analysis of 42 Years (1979-2021),” IEEE Access, vol. 10, pp. 56525–56549, 2022, doi: 10.1109/ACCESS.2022.3175882.

G. L. MALLEN, “Control Theory and Decision Making in Organisations A Reconnais-sance.”

S. SHAMS, N. M. MUBARAK, N. A. B. ISMAIL, M. M. H. KHAN, A. AL-MAMUN, AND A. AH-SAN, “Urban water supply risks assessment under tropical climate,” Sci Rep, vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-88922-4.

M. AVERY ET AL., “Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from two field experiments on recruitment in Tech. Does Artificial Intelligence help or hurt gender diversity? Evidence from Two Field Experiments on Recruitment in Tech Leibbrandt acknowledges the support from the Australian Research Council. We thank.”

I. BATTAS, H. BEHJA, AND M. EL OUAZGUITI, “A proposed real-time decision support platform for Moroccan fixed mining production systems,” Knowl Inf Syst, Feb. 2024, doi: 10.1007/s10115-024-02271-8.

Z. ALKALHA, L. JUM’A, S. ZIGHAN, AND M. ABUALQUMBOZ, “A multi-faceted approach for leveraging AI and intellectual capital for enhanced supply chain decision-making,” Journal of Intellectual Capital, 2025, doi: 10.1108/JIC-07-2024-0201.

S. KUSMARYANTO AND C. B. SANTOSO, “A scoping review of middle managers in the digital transformation era in public sector organizations: Are they still needed?,” 2025, Co-gent OA. doi: 10.1080/23311975.2025.2461734.

M. MASCARENHAS ET AL., “Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy,” Jan. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/jcm14020549.

F. ANSARI, R. GLAWAR, AND T. NEMETH, “PriMa: a prescriptive maintenance model for cyber-physical production systems,” Int J Comput Integr Manuf, vol. 32, no. 4–5, pp. 482–503, May 2019, doi: 10.1080/0951192X.2019.1571236.

J. D. C. López-Urbina, “Artificial intelligence in enhancing human talent and knowledge management in organizations: a systematic review in Scopus,” Jan. 20, 2025, Universidad Nacional de San Martin. doi: 10.51252/rcsi.v5i1.889.

V. MAHMOODIAN, H. CHARKHGARD, AND I. DAYARIAN, “Equitable Workload Allocation in Vehicle Routing Problem With Heterogeneous Drivers,” Prod Oper Manag, 2025, doi: 10.1177/10591478241305873.

M. M. FARD AND J. PINEAU, “Non-Deterministic Policies in Markovian Decision Pro-cesses,” 2011.

X. DONG, C. A. HINSCH, S. ZOU, AND H. FU, “The effect of market orientation dimen-sions on multinational SBU’s strategic performance: An empirical study,” International Marketing Review, vol. 30, no. 6, pp. 591–616, 2013, doi: 10.1108/IMR-12-2011-0284.

A. K. NOOR, “Potential of cognitive computing and cognitive systems,” Jan. 01, 2015, De Gruyter Open Ltd. doi: 10.1515/eng-2015-0008.

S. VAN POUCKE, M. THOMEER, J. HEATH, AND M. VUKICEVIC, “Are randomized controlled trials the (G)old standard? from clinical intelligence to prescriptive analytics,” 2016, JMIR Publications Inc. doi: 10.2196/jmir.5549.

K. DESTIGTER ET AL., “Optimizing integrated imaging service delivery by tier in low-resource health systems,” Insights Imaging, vol. 12, no. 1, Dec. 2021, doi: 10.1186/s13244-021-01073-8.

J. XU AND S. SEN, “Decision Intelligence for Nationwide Ventilator Allocation During the COVID-19 Pandemic,” SN Comput Sci, vol. 2, no. 6, Nov. 2021, doi: 10.1007/s42979-021-00810-6.

M. NUNES, J. BAGNJUK, A. ABREU, C. SARAIVA, E. NUNES, AND H. VIANA, “Achieving Competitive Sustainable Advantages (CSAs) by Applying a Heuristic-Collaborative Risk Model,” Sustainability (Switzerland), vol. 14, no. 6, Mar. 2022, doi: 10.3390/su14063234.

A. C. BĂROIU AND A. BÂRA, “A Descriptive-Predictive–Prescriptive Framework for the Social-Media–Cryptocurrencies Relationship,” Electronics (Switzerland), vol. 13, no. 7, Apr. 2024, doi: 10.3390/electronics13071277.

M. S. OTHMAN AND G. TAN, “A Prescriptive Simulation Framework with Realistic Be-havioural Modelling for Emergency Evacuations,” ACM Transactions on Modeling and Computer Simulation, vol. 34, no. 1, Jan. 2024, doi: 10.1145/3633330.

A. DEGAS ET AL., “A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajecto-ry,” Feb. 01, 2022, MDPI. doi: 10.3390/app12031295.

C. BUNTAIN AND J. GOLBECK, “Automatically Identifying Fake News in Popular Twitter Threads,” May 2017, doi: 10.1109/SmartCloud.2017.40.

R. F. REIER FORRADELLAS AND L. M. GARAY GALLASTEGUI, “Digital Transformation and Arti-ficial Intelligence Applied to Business: Legal Regulations, Economic Impact and Per-spective,” Laws, vol. 10, no. 3, Sep. 2021, doi: 10.3390/laws10030070.

M. SAVOIA ET AL., “European Nephrologists’ Attitudes toward the Application of Artifi-cial Intelligence in Clinical Practice: A Comprehensive Survey,” Blood Purif, vol. 53, no. 2, pp. 80–87, Feb. 2024, doi: 10.1159/000534604.

S. VOLKOVA ET AL., “Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal dis-covery methods,” Comput Math Organ Theory, vol. 29, no. 1, pp. 220–241, Mar. 2023, doi: 10.1007/s10588-021-09351-y.

N. R. LEWIS ET AL., “Forecasting of in situ electron energy loss spectroscopy,” NPJ Comput Mater, vol. 8, no. 1, Dec. 2022, doi: 10.1038/s41524-022-00940-2.

A. LEBIS, J. HUMEAU, A. FLEURY, F. LUCAS, AND M. VERMEULEN, “Fully Individualized Curriculum with Decaying Knowledge, a New Hard Problem: Investigation and Rec-ommendations,” Int J Artif Intell Educ, Sep. 2023, doi: 10.1007/s40593-023-00376-9.

R. RIEDL, “Is trust in artificial intelligence systems related to user personalities? Review of empirical evidence and future research directions,” Electronic Markets, vol. 32, no. 4, pp. 2021–2051, Dec. 2022, doi: 10.1007/s12525-022-00594-4.

R. RAY, Z. AGAR, P. DUTTA, S. GANGULY, P. SAH, AND D. ROY, “MenGO: A Novel Cloud-based Digital Healthcare Platform for Andrology Powered by Artificial Intelligence, Data Science & Analytics, Bioinformatics and Blockchain.” [Online]. Available: https://www.systemonsilicon.com.

H. ADAM, A. BALAGOPALAN, E. ALSENTZER, F. CHRISTIA, AND M. GHASSEMI, “Mitigating the impact of biased artificial intelligence in emergency decision-making,” Communica-tions Medicine, vol. 2, no. 1, Dec. 2022, doi: 10.1038/s43856-022-00214-4.

D. R. MANDEL AND D. IRWIN, “On measuring agreement with numerically bounded lin-guistic probability schemes: A reanalysis of data from Wintle, Fraser, Wills, Nicholson, and Fidler (2019),” PLoS One, vol. 16, no. 3 March, Mar. 2021, doi: 10.1371/journal.pone.0248424.

P. YE, X. WANG, W. ZHENG, Q. WEI, AND F. Y. WANG, “Parallel cognition: hybrid intelligence for human-machine interaction and management,” Frontiers of Information Technology and Electronic Engineering, vol. 23, no. 12, pp. 1765–1779, Dec. 2022, doi: 10.1631/FITEE.2100335.

N. L. RIDER ET AL., “PI prob: A risk prediction and clinical guidance system for evaluating patients with recurrent infections,” PLoS One, vol. 16, no. 2 February, Feb. 2021, doi: 10.1371/journal.pone.0237285.

M. RELICH, I. NIELSEN, AND A. GOLA, “Reducing the Total Product Cost at the Product Design Stage,” Applied Sciences (Switzerland), vol. 12, no. 4, Feb. 2022, doi: 10.3390/app12041921.

L. A. CANDIDO, G. A. G. COÊLHO, M. M. G. A. DE MORAES, AND L. FLORÊNCIO, “Review of Decision Support Systems and Allocation Models for Integrated Water Resources Management Focusing on Joint Water Quantity-Quality,” J Water Resour Plan Manag, vol. 148, no. 2, Feb. 2022, doi: 10.1061/(asce)wr.1943-5452.0001496.

G. GONÇALVES COSTA, W. J. D. NASCIMENTO JÚNIOR, M. N. MOMBELLI, AND G. GIROTTO JÚNIOR, “Revisiting a Teaching Sequence on the Topic of Electrolysis: A Comparative Study with the Use of Artificial Intelligence,” J Chem Educ, vol. 101, no. 8, pp. 3255–3263, Aug. 2024, doi: 10.1021/acs.jchemed.4c00247.

T. H. BUI and V. P. NGUYEN, “The Impact of Artificial Intelligence and Digital Econ-omy on Vietnam’s Legal System,” International Journal for the Semiotics of Law, vol. 36, no. 2, pp. 969–989, Apr. 2023, doi: 10.1007/s11196-022-09927-0.

M. RATIA, J. MYLLÄRNIEMI, AND N. HELANDER, “The potential beyond IC 4.0: the evolution of business intelligence towards advanced business analytics,” Measuring Business Ex-cellence, vol. 23, no. 4, pp. 396–410, Nov. 2019, doi: 10.1108/MBE-12-2018-0103.

A. L. VENGER AND V. M. DOZORTSEV, “Trust in Artificial Intelligence: Modeling the De-cision Making of Human Operators in Highly Dangerous Situations,” Mathematics, vol. 11, no. 24, Dec. 2023, doi: 10.3390/math11244956.

A. G. FADHIL, H. M. ALI, Z. A. KHALAF, M. AHMED, AND S. H. AHMED, “Volve Oil Field S-Wave Log Data Prediction Using GBR and MLPR,” Iraqi Journal of Science, vol. 65, no. 4, pp. 2264–2274, 2024, doi: 10.24996/ijs.2024.65.4.40.

A. K. SHUKLA, P. K. MUHURI, AND A. ABRAHAM, “A bibliometric analysis and cutting-edge overview on fuzzy techniques in Big Data,” Eng Appl Artif Intell, vol. 92, Jun. 2020, doi: 10.1016/j.engappai.2020.103625.

H. KENT BAKER, N. PANDEY, S. KUMAR, AND A. HALDAR, “A bibliometric analysis of board diversity: Current status, development, and future research directions,” J Bus Res, vol. 108, pp. 232–246, Jan. 2020, doi: 10.1016/j.jbusres.2019.11.025.

N. DONTHU, S. KUMAR, AND D. PATTNAIK, “Forty-five years of Journal of Business Re-search: A bibliometric analysis,” J Bus Res, vol. 109, pp. 1–14, Mar. 2020, doi: 10.1016/j.jbusres.2019.10.039.

A. DET UDOMSAP AND P. HALLINGER, “A bibliometric review of research on sustainable construction, 1994–2018,” May 01, 2020, Elsevier Ltd. doi: 10.1016/j.jclepro.2020.120073.

F. XING, “Designing Heterogeneous LLM Agents for Financial Sentiment Analysis,” ACM Trans Manag Inf Syst, Mar. 2024, doi: 10.1145/3688399.

A. MYSTAKIDIS, P. KOUKARAS, AND C. TJORTJIS, “Advances in Traffic Congestion Predic-tion: An Overview of Emerging Techniques and Methods,” Feb. 01, 2025, Multidisci-plinary Digital Publishing Institute (MDPI). doi: 10.3390/smartcities8010025.

M. AL AMIN, R. BALDACCI, AND V. KAYVANFAR, “A comprehensive review on operating room scheduling and optimization,” Mar. 01, 2025, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s12351-024-00884-z.

A. S. THAKUR, T. L. ALEX, AND A. NIGHOJKAR, “Artificial Intelligence in Maritime Anomaly Detection: A Decadal Bibliometric Analysis (2014–2024),” 2025, Springer. doi: 10.1007/s40032-025-01169-w.

T. FEHRER, L. MODER, AND M. RÖGLINGER, “A Taxonomy for Process Improvement and Innovation Systems,” Business and Information Systems Engineering, 2025, doi: 10.1007/s12599-025-00928-4.

C. C. DINULESCU, K. ALSHARE, AND V. PRYBUTOK, “Decoding business analytics: discover-ing the hidden core through a novel taxonomy,” Industrial Management and Data Sciences, Jan. 2024, doi: 10.1108/IMDS-03-2024-0255.

D. FARRUGIA, C. ZERAFA, T. CINI, B. KUASNEY, AND K. LIVORI, “A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry,” SN Comput Sci, vol. 2, no. 3, May 2021, doi: 10.1007/s42979-021-00623-7.


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