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Organizations adopt specific decision-making models that do not consider the intrinsic strategic aspects of the process, such as culture, target audience, decision-makers, organizational environment, and strategic goals. This article aims to present a group decision model that links the structuring of the problem to strategic organizational goals to work with the uncertainties of the process, helping decision-makers search for information and direct decision-making, allowing actors a broad and systemic vision linked to organizational goals. This model was developed for application in credit analysis and granting in financial organizations. The study is proven by the possibility of applying organizational strategy to the decision process using a problem-structuring model and the multi-criteria decision model. The model ensures that the paths reflect the corporate reality, and the vision regarding decision problems adopted serves the process, classifying the decision into answers representing the decision-maker's perception following the strategic aspects.

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References

  1. Almeida-Dias, J., Figueira, J. R., Roy, B. (2010). Electre Tri-C: A multiple criteria sorting method based on characteristic reference actions. European Journal of Operational Research, 204(3), 565-580. https://doi.org/10.1016/j.ejor.2009.10.018.
     Google Scholar
  2. Atmaca, S., Karadaş, H. A. (2020). Decision making on financial investment in Turkey by using ARDL long-term coefficients and AHP. Financial Innovation, 6, 30. https://doi.org/10.1186/s40854-020-00196-z.
     Google Scholar
  3. Bacha, S., Azouzi, M. A. (2019). How gender and emotions bias the credit decision-making in banking firms. Journal of Behavioral and Experimental Finance, 22, 183-191. ISSN 2214-6350. https://doi.org/10.1016/j.jbef.2019.03.004.
     Google Scholar
  4. Baidoo, E., Natarajan, R. (2021). Profit-based credit models with lender’s attitude towards risk and loss. Journal of Behavioral and Experimental Finance, 32, 100578. https://doi.org/10.1016/j.jbef.2021.100578.
     Google Scholar
  5. Brelih, M., Rajkovič, U., Ružič, T., Rodič, B., & Kozelj, D. (2018). Modelling decision knowledge for the evaluation of water management investment projects. Central European Journal of Operations Research, 27(3), 759–781. https://doi.org/10.1007/s10100-018-0600-5.
     Google Scholar
  6. Cabrerizo, F. J., Viedma, E. H., Pedrycz,W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision-making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624-633. https://doi.org/10.1016/j.ejor.2013.04.046.
     Google Scholar
  7. Cailloux, O., Meyer, P., Mousseau V. (2012): Eliciting ELECTRE TRI category limits for a group decision makers. European Journal of Operational Research, 233(1):133–140. <10.1016/j.ejor.2012.05.032>.
     Google Scholar
  8. Chen, N., Xu, Z., Xia, M. (2015). The ELECTRE I Multi-Criteria Decision-Making Method Based on Hesitant Fuzzy Sets. International Journal of Information Technology & Decision Making, 14(3), 621-657. DOI: 10.1142/S0219622014500187.
     Google Scholar
  9. Colasante, A., & Riccetti, L. (2021). Financial and non-financial risk attitudes: What does it matter? Journal of Behavioral and Experimental Finance, 30, 100494. https://doi.org/10.1016/j.jbef.2021.100494.
     Google Scholar
  10. Daily, B., Whatley, A., Ash, S. R., Steiner, R. L. (1996). The effects of a group decision support system on culturally diverse and culturally homogeneous group decision making. Information & Management, 30(6), 281-289. https://doi.org/10.1016/S0378-7206(96)01062-2.
     Google Scholar
  11. de Almeida, A. T., Frej, E. A., Roselli, L. R. P. (2021). Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff. Cent Eur J Oper Res, 29, 7–47. https://doi.org/10.1007/s10100-020-00728-z.
     Google Scholar
  12. Diller, C., & Oberding, S. (2017). Der "Strategic Choice Approach": ein in Deutschland unterschätzter Methodenbaukasten für die Raumplanung, disP. The Planning Review, 53(2), 94-108, DOI: 10.1080/02513625.2017.1341200.
     Google Scholar
  13. Dirir, S. A. (2022). Performing a Quantile Regression to Explore the Financial Inclusion in Emerging Countries and Lessons African Countries Can Learn from Them. European Journal of Development Studies, 2(5), 1–9. https://doi.org/10.24018/ejdevelop.2022.2.5.153.
     Google Scholar
  14. Elgebeily, E., Guermat, C., Vendrame, V. (2021). Managerial optimism and investment decision in the UK. Journal of Behavioral and Experimental Finance, 31, 100519. https://doi.org/10.1016/j.jbef.2021.100519.
     Google Scholar
  15. Ewe, S. Y., Lee, C. K. C., Watabe, M. (2020). Prevention focus and prior investment failure in financial decision making. Journal of Behavioral and Experimental Finance, 26, 100321. https://doi.org/10.1016/j.jbef.2020.100321.
     Google Scholar
  16. Figueira, J., Roy, B. (2002). Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure. European Journal of Operational Research, 139(2.1), 317-326. 10.1016/S0377-2217(01)00370-8.
     Google Scholar
  17. Friend, J. K., Hickling, A. (1997). Planning under Pressure: The Strategic Choice Approach (2nd ed.). Pegamon, Oxford.
     Google Scholar
  18. Hermansson, C., & Jonsson, S. (2021). The impact of financial literacy and financial interest on risk tolerance. Journal of Behavioral and Experimental Finance, 29, 100450. https://doi.org/10.1016/j.jbef.2020.100450.
     Google Scholar
  19. Hu, D., Schwabe, G., Li, X. (2015). Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges. Financial Innovationü, 1, 2. https://doi.org/10.1186/s40854-015-0001-x.
     Google Scholar
  20. Karan, M.B., Ulucan, A. & Kaya, M. Credit risk estimation using payment history data: a comparative study of Turkish retail stores. Cent Eur J Oper Res, 21, 479–494 (2013). https://doi.org/10.1007/s10100-012-0242-y.
     Google Scholar
  21. Kumar, S., Rao, S., Goyal, K., & Goyal, N. (2022). Journal of Behavioral and Experimental Finance: A bibliometric overview. Journal of Behavioral and Experimental Finance, 34, 100652. https://doi.org/10.1016/j.jbef.2022.100652.
     Google Scholar
  22. Levino, N. D. A., & Morais, D. C. (2013). Applying Strategic Choice Approach for Decision Making of Watersheds Committees. IEEE International Conference on Systems, Man, and Cybernetics, Manchester, 2013, pp. 38-43, DOI: 10.1109/SMC.2013.14.
     Google Scholar
  23. Lim, T. S., Mail, R., Abd Karim, M. R., Ahmad Baharul Ulum, Z. K., Jaidi, J., & Noordin, R. (2018). A serial mediation model of financial knowledge on the intention to invest: The central role of risk perception and attitude. Journal of Behavioral and Experimental Finance, 20, 74–79. https://doi.org/10.1016/j.jbef.2018.08.001.
     Google Scholar
  24. Meng, Y., Wu, H., Zhao, W., Chen, W., Dinçer, H., & Yüksel, S. (2021). A hybrid heterogeneous Pythagorean fuzzy group decision modelling for crowdfunding development process pathways of fintech-based clean energy investment projects. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00250-4.
     Google Scholar
  25. Mensah, A. (2022). Institutional Innovations to Reduce High Transaction Costs and Risks in Smallholder Markets in Ghana. European Journal of Development Studies, 2(4), 79–84. https://doi.org/10.24018/ejdevelop.2022.2.4.147.
     Google Scholar
  26. Mousseau, V., Slowinski, R., Zielniewicz, P. (2000): A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support. Computers & Operations Research, 27(7–8), 757-777. https://doi.org/10.1016/S0305-0548 (99)00117-3.
     Google Scholar
  27. Rigopoulos, G., Askounis, D. Th., Metaxiotis, K. (2010). NeXCLass: A Decision Support System for Non-Ordered Multicriteria Classification. International Journal of Information Technology & Decision Making, 9(1), 53-79. DOI: 10.1142/S0219622010003622.
     Google Scholar
  28. Roy B. (1978). ELECTRE III: Un alghoritme de methode de classements fonde sur une representation floue des préférences em presence de critères multiples. Cahieres de CERO, 20(1), 3-24. Disponível em https://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=PASCAL7930086912.
     Google Scholar
  29. Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). Lausanne Presses Polytechniques et Universitaires Romandes, Disponível. Retrieved from: https://www.rairo-ro.org/articles/ro/pdf/1968/01/ro196802V100571.pdf.
     Google Scholar
  30. Roy, B. (1996). Multicriteria Methodology for Decision Aiding. Springer Publishing.
     Google Scholar
  31. Roy, B. M., & Skalka, J. (1985). ELECTRE IS: Aspécts methodologiques et guide d´utilization. Cahier du LAMSADE. Université de Paris–Dauphine. Retrieved from: https://www.scielo.br/scielo.php?script=sci_nlinks&ref=000264&pid=S0101-7438201300020000900051&lng=
     Google Scholar
  32. Roy, B., & Bouyssou D. (1993). Aide multicritère à la décision : Methodes et cas. Economica; Paris. Retrieved from: https://www.researchgate.net/profile/Denis_Bouyssou/publication/265441342_Aide_Multicritere_a_la_Decision_Methodes_et_Cas/links/5a9575e0aca27214056922b6/Aide-Multicritere-a-la-Decision-Methodes-et-Cas.pdf.
     Google Scholar
  33. Roy, B., & Bertier, P. (1971). La me´thode Electere II: Une me´thode de classesment en pre´sence de crite`res multiples. Direction scientifique, Note de travail, p. 142. SEMA (Metra International), Paris.
     Google Scholar
  34. Roy, B., & Hugonnard, J. C. (1981). Classement des prolongements de lignes de stations en banlieu parisienne. Cahiers u LAMSADE. Niversité Dauphine et RATP. Paris, 1981. Retrieved from: http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=PASCAL83X0134101.
     Google Scholar
  35. Roy, P. K., Shaw, K. (2021). A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS. Financial Innovation, 7, 77 https://doi.org/10.1186/s40854-021-00295-5.
     Google Scholar
  36. Schotten, P. C., de Sousa Pereira, L., Morais, D. C. (2022). Credit granting sorting model for financial organizations. Financial Innovation, 8, 10 https://doi.org/10.1186/s40854-021-00315-4.
     Google Scholar
  37. Schotten, P. C., Morais, D. C. (2019). A group decision model for credit granting in the financial market. Financial Innovation, 5, 6. https://doi.org/10.1186/s40854-019-0126-4.
     Google Scholar
  38. Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297. https://doi.org/10.1016/j.jbef.2020.100297.
     Google Scholar
  39. Singh, N., & Singh, S. (2017). A novel hybrid GWO-SCA approach for optimization problems. Engineering Science and Technology, an International Journal, 20(6), 1586–1601. https://doi.org/10.1016/j.jestch.2017.11.001.
     Google Scholar
  40. Todella, E., Lami, I. M., & Armando, A. (2018). Experimental Use of Strategic Choice Approach (SCA) by Individuals as an Architectural Design Tool. Group Decision and Negotiation, 27(5), 811–826. https://doi.org/10.1007/s10726-018-9567-9.
     Google Scholar
  41. Trzaskalik, T., Sitarz, S., & Dominiak, C. (2019). Bipolar method and its modifications. Central European Journal of Operations Research, 27(3), 625–651. https://doi.org/10.1007/s10100-019-00615-2.
     Google Scholar
  42. Wang, B., & Zhang, L. (2013). Calibrating low-rank correlation matrix problem: an SCA-based approach. Optimization Methods and Software, 29(3), 561–582. https://doi.org/10.1080/10556788.2013.829057.
     Google Scholar
  43. Wang, Z. J., & Li, K. W. (2015). A multi-step goal programming approach for group decision making with incomplete interval additive reciprocal comparison matrices. European Journal of Operational Research, 242(3), 890–900. https://doi.org/10.1016/j.ejor.2014.10.025.
     Google Scholar
  44. Wu, W., & Kou, G. (2016). A group consensus model for evaluating real estate investment alternatives. Financial Innovation, 2(1). https://doi.org/10.1186/s40854-016-0027-8.
     Google Scholar
  45. Xiao, F., & Ke, J. (2021). Pricing, management and decision-making of financial markets with artificial intelligence: introduction to the issue. Financial Innovation, 7(1). https://doi.org/10.1186/s40854-021-00302-9.
     Google Scholar
  46. Xu, L., Wu, L., Li, X., & Shen, F. (2020). Introduction to the special issue on analytical and decision-making technique innovation in financial market. Financial Innovation, 6(1). https://doi.org/10.1186/s40854-020-00215-z.
     Google Scholar
  47. Yu, W. (1992). ELECTRE TRI: Aspects Methodologiques et Guide d’Utilisation. Document du LAMSADE. Université de Paris Dauphine; Paris.
     Google Scholar
  48. Zhou, S., Xu, X., Zhou, Y., Chen, X. (2017). A Large Group Decision-Making Method Based on Fuzzy Preference Relation. International Journal of Information Technology & Decision Making, 16(3), 881-897. Doi: 10.1142/S021962201550039X.
     Google Scholar