Prioritizing factors affecting the quality of managerial decision-making

Document Type : Original Article

Author

Department of Industrial Engineering and Management, Morvarid Intelligent Industrial Systems Research Group, Iran.

Abstract
Purpose:  Given the increasing complexity and uncertainty in today's organizational environments, making informed, timely, and flexible decisions is of paramount importance for ensuring the sustainability and long-term growth of organizations. Accordingly, the present study aims to identify and prioritize individual, group, organizational, technological, and environmental factors that influence the quality of managerial decision-making, thereby providing a practical framework for enhancing decision-making capacity and increasing organizational resilience.
Methodology:  This study is applied in nature and adopts a descriptive-analytical approach. Initially, a comprehensive set of factors was extracted through a systematic literature review. Then, a five-member expert panel was formed to identify the evaluation criteria, and the required data were collected using intuitionistic fuzzy numbers. The relative weights of the criteria were calculated using the FUCOM method, and the alternatives were finally prioritized using an aggregated decision matrix based on the multi-criteria WASPAS method.
Findings: Specialized knowledge is the most significant factor influencing the quality of managerial decision-making. It was followed by participative decision-making and data accuracy and reliability, which ranked second and third, respectively. In contrast, factors such as organizational structure and the legal, cultural, and social environment had the least impact. Overall, these results highlight the importance of focusing on internal and controllable factors to enhance organizational sustainability in dynamic environments.
Originality/Value: This research is among the first to provide a comprehensive prioritization of individual-to-environmental factors affecting the quality of managers' decisions by integrating a systematic literature review, intuitionistic fuzzy numbers, the FUCOM method, and the WASPAS method. The resulting framework not only enriches theoretical understanding but also serves as a practical guide for policymakers and managers in optimal resource allocation.

Keywords


[1]   Mandal, Dr. P. (2024). Strategic decision making in management. In Futuristic trends in management volume 3 book 4. Iterative international publisher, selfypage developers Pvt Ltd. https://www.doi.org/10.58532/V3BHMA4P1CH3
[2]   Saifudin, S., Maryanto, M., & Suharyat, Y. (2024). Teknik pengambilan keputusan dalam berorganisasi. Nusra: jurnal penelitian dan ilmu pendidikan, 5(1), 132–141. https://doi.org/10.55681/nusra.v5i1.1867
[3]   Mazorenko, o, KAITANSKYI, I., & BILLO, K. (2024). Adoption of strategic decisions at the enterprise. Modeling the development of the economic systems, (3), 152–158. http://dx.doi.org/10.31891/mdes/2024-13-20
[4]   Purnamawati, R. F. (2024). The role of cognitive bias in principal decision making: A narrative analysis of the literature. PPSDP international journal of education, 3(2), 213–219. https://doi.org/10.59175/pijed.v3i2.310
[5]   Mohanty, S., Sahoo, S. K., Sharma, I., Panigrahi, A., & Bosu, L. (2024). A study on overcoming cognitive biases in leadership decision-making. In Building organizational resilience with neuroleadership (pp. 159-182). IGI Global. https://B2n.ir/em4818
 [6] Owusu, S. P., & Laryea, E. (2023). The impact of anchoring bias on investment decision-making: Evidence from Ghana. Review of behavioral finance, 15(5), 729–749. https://doi.org/10.1108/RBF-09-2020-0223
[7]   Mueller-Saegebrecht, S. (2024). Business model innovation decisions: the role of group biases and risk willingness. Management decision, 62(13), 69–108. https://doi.org/10.1108/MD-05-2023-0726
[8]   Raghvendra, K. (2024). Emotional intelligence and leadership effectiveness: Navigating decision-making, team dynamics, and organizational success. International journal of science and research (IJSR), 13(9), 1411–1416. http://dx.doi.org/10.21275/SR24921211055
[9]   Raghav, S. V., & SM, P. (2024). Emotional intelligence in leadership: A cross-cultural analysis of employee engagement and retention. ShodhKosh: journal of visual and performing arts, 5(7), 338–349. https://doi.org/10.29121/shodhkosh.v5.i7.2024.2772
[10] Feeney, M. K., & Welch, E. W. (2013). Implementing information and communication technologies (ICT) in public organizations. Proceedings of the 14th annual international conference on digital government research (pp. 38–45). New York, NY, USA: ACM. https://doi.org/10.1145/2479724.2479734
[11] Li, Z., Gong, P., Wang, Y., & Qu, S. (2024). The impact of digital transformation on enterprise organizational structure. Highlights in business, economics and management, 41, 732–740. https://doi.org/10.54097/qt9jer93
[12] Ajeng, S. M., Nastiti, A. A., Nabilla, A. S., Hidayat N, R., & Kusumasari, I. R. (2025). The influence of macroeconomic factors on business decision making: Case study of PT kereta API indonesia (Persero). International journal of economics, accounting and management, 1(5), 238–247. http://dx.doi.org/10.60076/ijeam.v1i5.955
[13] Alawwad, A. (2024). The influence of external factors on corporate strategy and strategies for adaptation in light of the global challenges. International journal of financial, administrative, and economic sciences, 3(10), 91–125. https://doi.org/10.59992/IJFAES.2024.v3n10p3
[14] Lv, K., Zhu, S., Liang, R., & Zhao, Y. (2024). Environmental regulation, breakthrough technological innovation and total factor productivity of firms evidence from emission charges of China. Applied economics, 56(11), 1235–1249. https://doi.org/10.1080/00036846.2023.2175776
[15] Prorok, M., & Takács, I. (2024). The impacts of artificial intelligence and knowledge-based systems on corporate decision support. 2024 ieee 18th international symposium on applied computational intelligence and informatics (SACI) (pp. 000065–000070). IEEE. https://doi.org/10.1109/SACI60582.2024.10619820
[16] Acciarini, C., Brunetta, F., & Boccardelli, P. (2021). Cognitive biases and decision-making strategies in times of change: A systematic literature review. Management decision, 59(3), 638–652. http://dx.doi.org/10.1108/MD-07-2019-1006
[17] Gharbi, M., & Jarboui, A. (2024). The relationship between behavioral biases and strategic decision-making: Empirical evidence from emergent market. Journal of international  business and economy, 24(1), 1–22. http://dx.doi.org/10.51240/jibe.2023.1.1
[18] Bratianu, C., Paiuc, D., & Bejinaru, R. (2024). The impact of knowledge dynamics on multicultural leadership and the mediating role of cultural intelligence. European conference on knowledge management, 25(1), 103–108. https://doi.org/10.34190/eckm.25.1.2465
[19] Xie, Z. (2024). The influence of cultural backgrounds on team dynamics and decision making in multicultural environments. Transactions on economics, business and management research, 10, 139–145. http://dx.doi.org/10.62051/1nng6893
[20] Ruslaini, & Ekawahyu K. (2024). Integrasi IQ, EQ, penguasaan teknologi dan ketelitian pada kualitas keputusan organisasi. Journal of business, finance, and economics (JBFE), 5(1), 310–318. https://doi.org/10.32585/jbfe.v5i1.5617
[21] Gardi, B., Hamza, P. A., Sabir, B. Y., & Al-Kake, F. R. A. (2021). Investigating the effects of financial accounting reports on managerial decision making in small and medium-sized enterprise. Turkish journal of computer and mathematics education, 12(10), 2134–2142. https://dx.doi.org/10.2139/ssrn.3838226
[22] Rudnichenko, Y., Liubokhynets, L., Havlovska, N., Illiashenko, O., & Avanesova, N. (2021). Qualitative justification of strategic management decisions in choosing agile management methodologies. International journal for quality research, 15(1), 209–224. http://dx.doi.org/10.24874/IJQR15.01-12
[23] Dogan, M., Jacquillat, A., & Yildirim, P. (2024). Strategic automation and decision‐making authority. Journal of economics & management strategy, 33(1), 203–246. https://doi.org/10.1111/jems.12557
[24] Boyle, I. M., Duffy, A. H. B., Whitfield, R. I., & Liu, S. (2012). The impact of resources on decision making. Artificial intelligence for engineering design, analysis and manufacturing, 26(4), 407–423. https://doi.org/10.1017/S0890060412000273
[25] Van der Merwe, L., & Davey, C. (2024). The role of organisational culture and structure in data-driven green policy and decision-making. Environmental science & sustainable development, 9(2), 1–7. https://doi.org/10.21625/essd.v9i2.1085
[26] Kuzmin, O., Tsikalo, Y., Komarnytska, H., & Terlecka, V. (2024). Modelling of management decisions in the process of system integration at enterprises. International journal of services, economics and management, 15(2), 201–223. https://doi.org/10.1504/IJSEM.2024.137215
[27] Legerer, P., Pfeiffer, T., Schneider, G., & Wagner, J. (2009). Organizational structure and managerial decisions. International journal of the economics of business, 16(2), 147–159. https://doi.org/10.1080/13571510902917483
[28] Dluhopolska, T., Katola, T., & Khroponiuk, D. (2024). The influence of the external environment on the formation of the strategic behavior of the company. Innovation and sustainability, 4(2). 92–101. https://doi.org/10.31649/ins.2024.2.92.101
[29] Mircescu, G. D. (2023). Analysis of the external environment of the pharmaceutical organization. Logos universality mentality education novelty: Social sciences, 12(1), 67−76. https://doi.org/10.18662/lumenss/12.1/80
[30] Fomishyna, V., Plyaskina, A., & Fedorova, N. (2022). The influence of the external environment of host countries on managerial decisions on commodity export of business units. Scientific bulletin of kherson state university. series economic sciences, (45), 5–13. https://scispace.com/papers/the-influence-of-the-external-environment-of-host-countries-2vrh6adp
[31] Baets, W. R. J., & Van der Linden, G. (2003). The corporate environment. In Virtual corporate universities, integrated series in information systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0300-2_2
[32] Ebrahimpour Azbari, M., & Ghorbani Moghadam, S. (2025). Providing a decision-making model for the challenges of using social media in supply chain management using the metasynthesis and SWARA method. Journal of decisions and operations research, 10(1), 153–168. (In Persian). https://doi.org/10.22105/dmor.2025.495676.1898
[33] Chusi, T., Nicholaus, Q. S., Więckowski, J., Bouraima, M. B., & Qiu, Y. (2025). Revolutionizing Africa’s carbon footprint through innovative technology dissemination strategies for greenhouse gas emission reduction: An MCDM approach. Journal of fuzzy extension and applications, 6(2), 216–232.  https://doi.org/10.22105/jfea.2024.474476.1596
[34] Paul, A., Ghosh, S., Majumder, P., Pramanik, S., & Smarandache, F. (2025). Identification of influential parameters in soil liquefaction under seismic risk using a hybrid neutrosophic decision framework. Journal of applied research on industrial engineering, 12(1), 144–175.  https://doi.org/10.22105/jarie.2024.486149.1699
[35] Saeidi Aghdam, M., Komiak, S. Y., Amiri, M., & Bahiraie, A. (2025). Developing an E-commerce trust model in crowdfunding by integrating blockchain and edge computing using fuzzy technique. Journal of fuzzy extension and applications, 6(3), 424–447.  https://doi.org/10.22105/jfea.2025.481202.1654
[36] Bayatzadeh, S., & Amiri, M. (2025). Identifying and evaluating supplier selection criteria in Iran’s steel industry according to Industry 4.0 technologies, 5(3), 306-330. (In Persian). Innovation management and operational strategies. https://doi.org/10.22105/imos.2024.472776.1379
[37] Soltanifar, M., Zargar, S. M., & Aman, M. (2023). Improved WASPAS method for determining criteria priority and weights in solving MADM problems: a case study to determine leadership style in Covid-19 pandemic. Journal of decisions and operations research, 8(3), 749–770.
[38] Mousavi Arab, S. A., Homayounfar, M., & Ajalli, M. (2022). Balanced performance evaluation of B2C online stores with using a hybrid fuzzy ANP and fuzzy WASPAS approach. Journal of decisions and operations research, 6(Special Issue), 1–14. (In Persian). https://doi.org/10.22105/dmor.2021.287084.1403
[39] Mehrabi, M., Sorourkhah, A., & Edalatpanah, S. A. (2024). Decision-making regarding the granting of facilities to Sepah Bank loan applicants based on credit risk factors considering hesitant fuzzy sets. Financial and banking strategic studies, 1(3), 153–166. (In Persian). https://doi.org/10.22105/fbs.2023.181500
[40] Montazeri, F. Z., Sorourkhah, A., Marinković, D., & Lukovac, V. (2024). Robust-fuzzy-probabilistic optimization for a resilient, sustainable supply chain with an inventory management approach by the seller. Big data and computing visions, 4(2), 146–163. https://doi.org/10.22105/bdcv.2024.481945.1208
[41] Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37–45. https://doi.org/10.1016/0165-0114(89)90215-7
[42] Ejegwa, P., Augustine, Onyeke, I., Charles, & Adah, V. (2025). Recognition principle for course allocations in higher institutions based on intuitionistic fuzzy correlation coefficient. Journal of fuzzy extension and applications, 6(1), 94–108. https://doi.org/10.22105/jfea.2024.448400.1411
[43] Talaee, B., Sobhani, M., & Davvaz, B. (2024). Some properties of intuitionistic fuzzy modules. Journal of fuzzy extension and applications, 5(2), 190–198. https://doi.org/10.22105/jfea.2022.364946.1233
[44] Alijanzadeh, M. R., Shayannia, S. A., & Movahedi, M. M. (2024). Optimization of maintenance in supply chain process and risk-based critical failure situations (Case study: Iranian oil pipeline and telecommunication company, north district). Journal of applied research on industrial engineering, 11(1), 125–142. https://doi.org/10.22105/jarie.2022.322072.1419
[45] Daneshvar, M., Karimi Jafari, F., & Moradi Golriz, Z. (2024). Performance evaluation of organizational units with the hybrid approach of intuitionistic fuzzy DEMATEL-ELECTRE. Journal of decisions and operations research, 9(3), 584–606. (In Persian). https://doi.org/10.22105/dmor.2024.473271.1864
[46] Latifi, Z., & Pouyan, N. (2022). Structure of data envelopment analysis with intuitionistic fuzzy data (Case study: Evaluation of safety-based performance in construction projects). Journal of decisions and operations research, 7(4), 628–647. (In Persian). https://doi.org/10.22105/dmor.2021.296432.1452
[47] Sharma, P., & Kumar. (2024). Intuitionistic fuzzy lattice ordered G-modules. Journal of fuzzy extension and applications, 5(1), 141–158. https://doi.org/10.22105/jfea.2024.425751.1330
[48] Kumar, R., & Kumar, S. (2024). An extended combined compromise solution framework based on novel intuitionistic fuzzy distance measure and score function with applications in sustainable biomass crop selection. Expert systems with applications, 239, 122345. https://doi.org/10.1016/j.eswa.2023.122345
[49] Çodur, S., Erkayman, B., Alp, S. S., Özenir, O., Pamucar, D., Yıldız, G., … & Aktaş, S. (2024). Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements. Heliyon, 10(9), e30664. https://doi.org/10.1016/j.heliyon.2024.e30664
[50] Radomska-Zalas, A. (2023). Application of the WASPAS method in a selected technological process. Procedia computer science, 225, 177–187. https://doi.org/10.1016/j.procs.2023.10.002
[51]   Imeni, M., & Sorourkhah, A. (2023). Procrustes’ Bed Metaphor: Understanding the problem of creativity and innovation in the human resources of Iran’s state-owned enterprises. Innovation management and operational strategies, 4(3), 208–218. (In Persian). https://doi.org/10.22105/imos.2022.364479.1253