Integrated Data-Driven and Multi-Criteria Decision-Making in Management: A Strategic Framework for Complex Organizational Choices
Abstract
Decision-making in management has evolved from intuition-dominated judgment toward more structured, evidence-based, and analytics-supported processes. Yet many organizations still struggle to integrate strategic judgment, quantitative evaluation, uncertainty treatment, and governance considerations into a single coherent decision architecture. This paper develops an integrated framework for managerial decision-making that combines bounded rationality, evidence-based management, Multi-Criteria Decision-Making (MCDM), decision support systems, and human–AI collaboration. The study first synthesizes the theoretical foundations of managerial decision-making and then proposes a professional five-stage framework for screening, weighting, scoring, validating, and governing strategic alternatives. To demonstrate the logic of the framework, an illustrative management case is presented for project portfolio selection under financial, operational, strategic, and risk criteria. The model employs normalized criterion scores, weighted utility aggregation, evidence credibility assessment, and governance alignment adjustments. The numerical illustration shows that alternatives with the highest standalone financial attractiveness are not always optimal once implementation time, strategic fit, evidence quality, and risk exposure are jointly considered. The paper contributes to management literature in three ways: 1) it bridges classical and contemporary decision theories into a unified managerial structure, 2) it offers a transparent mathematical model that managers can adapt to real organizational settings, and 3) it demonstrates how human judgment and algorithmic support can be combined without fully delegating strategic control to automated systems. The findings suggest that effective managerial decision-making depends not only on analytical sophistication but also on process quality, evidence integrity, and organizational alignment. These insights are particularly relevant for senior managers facing high-stakes choices in dynamic and uncertain environments. Real references below were verified from publisher or scholarly records.
Keywords:
Managerial decision-making, strategic decision processes, multi-criteria decision-making, evidence-based management, decision support systemsReferences
- [1] Simon, H. A. (1979). Rational decision making in business organizations. American economic review, 69(4), 493–513. https://www.nobelprize.org/uploads/2018/06/simon-lecture.pdf
- [2] March, J. G., & Simon, H. A. (1958). Organizations. Wiley. https://books.google.com/books/about/Organizations.html?id=FbBJEAAAQBAJ
- [3] Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Prentice-Hall. https://books.google.com/books/about/A_behavioral_theory_of_the_firm.html?id=0R5VAAAAMAAJ
- [4] Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
- [5] Mintzberg, H., Raisinghani, D., & Théorêt, A. (1976). The structure of “unstructured” decision processes. Administrative science quarterly, 21(2), 246–275. https://mintzberg.org/articles/structure-unstructured-decision-processes
- [6] Nutt, P. C. (1976). Models for decision making in organizations and some contextual variables which stipulate optimal use. Academy of management review, 1(2), 84–98. https://doi.org/10.5465/amr.1976.4408670
- [7] March, J. G. (1991). Exploration and exploitation in organizational learning. Organization science, 2(1), 71–87. https://doi.org/10.1287/orsc.2.1.71
- [8] Eisenhardt, K. M., & Zbaracki, M. J. (1992). Strategic decision making. Strategic management journal, 13(S2), 17–37. https://doi.org/10.1002/smj.4250130904
- [9] Eisenhardt, K. M. (1989). Making fast strategic decisions in high-velocity environments. Academy of management journal, 32(3), 543–576. https://doi.org/10.5465/256434
- [10] Rajagopalan, N., Rasheed, A. M. A., & Datta, D. K. (1993). Strategic decision processes: Critical review and future directions. Journal of management, 19(2), 349–384. https://doi.org/10.1016/0149-2063(93)90057-T
- [11] Dean, J. W., Jr., & Sharfman, M. P. (1996). Does decision process matter? A study of strategic decision-making effectiveness. Academy of management journal, 39(2), 368–392. https://doi.org/10.2307/256784
- [12] Elbanna, S. (2006). Strategic decision-making: Process perspectives. International journal of management reviews, 8(1), 1–20. https://doi.org/10.1111/j.1468-2370.2006.00118.x
- [13] Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. Wiley. https://doi.org/10.1017/CBO9781139174084
- [14] Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill. https://openlibrary.org/books/OL4425444M/The_analytic_hierarchy_process
- [15] Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer. https://books.google.com/books/about/Multiple_attribute_decision_making.html?id=X-wYAQAAIAAJ
- [16] Goodwin, P., & Wright, G. (2014). Decision analysis for management judgment. Wiley. https://books.google.com/books/about/Decision_Analysis_for_Management_Judgmen.html?id=eenRAwAAQBAJ
- [17] Rousseau, D. M. (2006). Is there such a thing as “evidence-based management”? Academy of management review, 31(2), 256–269. https://cebma.org/assets/Uploads/Rousseau-Is-there-such-thing-as-evidence-based-management-v2.pdf
- [18] Power, D. J. (2008). Decision support systems: A historical overview. In Handbook on decision support systems 1: Basic themes (pp. 121–140). Springer. https://doi.org/10.1007/978-3-540-48713-5_7
- [19] Burstein, F., & Holsapple, C. W. (2008). Handbook on decision support systems 1: Basic themes. Springer. https://doi.org/10.1007/978-3-540-48713-5
- [20] Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
- [21] Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American economic review, 106(5), 133–139. https://doi.org/10.1257/aer.p20161016
- [22] Davenport, T. H. (2006). Competing on analytics. Harvard business review, 84(1), 98–107, 134. https://hbr.org/2006/01/competing-on-analytics
- [23] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
- [24] Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
- [25] Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S., & Ebel, P. (2019). The future of human-AI collaboration: A taxonomy of design knowledge for hybrid intelligence systems. In proceedings of the 52nd Hawaii international conference on system sciences (pp. 1–10). https://doi.org/10.24251/HICSS.2019.034