A Fuzzy-Probabilistic Model for Interpretable Entrepreneurial Orientation Prediction
Authors
-
Sandeep Bhattacharjee
Assistant Professor (Corresponding Author), Amity University, Kolkata, Major Arterial Road, Action Area II, Kadampukur Village, Rajarhat, Newtown, Kolkata - 700 135, West Bengal
https://orcid.org/0000-0002-6686-3947
DOI:
https://doi.org/10.17010/amcije/2025/v8i2/175918Keywords:
AUC-ROC, entrepreneurial orientation, fuzzy logic, hybrid, interpretability, precision.JEL Classification Codes :C45, C63, D81, L26, M13
Publication Chronology: Paper Submission Date : November 17, 2025 ; Paper sent back for Revision : November 25, 2025 ; Paper Acceptance Date : November 30, 2025.
Abstract
Purpose : The present study is an effort to improve entrepreneurship research by shifting from traditional theory-based and socio-cultural explanations to a data-driven, interpretable decision-modeling framework. It demonstrated the complex behavioral patterns that influence entrepreneurship skills using simple and transparent analytical techniques.
Methodology : In this research, a new framework known as the hybrid fuzzy cognitive system–entrepreneurial decision model (FCSM-EDM) has been used. This framework used fuzzy logic to create rules based on entrepreneurship traits such as risk appetite, innovativeness, leadership skills, resilience, and business knowledge. It also utilized Shapley Additive exPlanations (SHAP)-based interpretability, Sankey visual reasoning, and explainable AI for drawing clear insights for entrepreneur decision-making.
Findings : Results from the working model revealed a fuzzy accuracy of 79.99%, an interpretability score of 0.88, a precision of 0.75, and a high interpretability-precision tradeoff score (IPTS) of 0.809. The model exhibited RUC-AUC of 68%, a bootstrapped mean accuracy of 0.66, and a tiny standard deviation of 0.1999, which shows that it was both stable and reliable.
Practical Implications : The model presented a precise and comprehensible decision-support system that allows educators, politicians, incubators, and financial institutions to evaluate entrepreneurial potential. It improved the design of evidence-based interventions, the identification of talent, and entrepreneurship training suited to various behavioral profiles.
Originality : This study introduced a novel integration of fuzzy decision modeling, SHAP-based interpretability, and Sankey visual analytics in the field of entrepreneurship research. It offered a scalable and interpretable alternative for entrepreneurial and corporate decision-making applications by outperforming Bayesian fuzzy systems, fuzzy-ANFIS hybrids, random forests, and SVM-RBF models.
Downloads
Published
How to Cite
Issue
Section
References
1) Arshi, T. A., Islam, S., & Gunupudi, N. (2021). Predicting the effect of entrepreneurial stressors and resultant strain on entrepreneurial behaviour: An SEM-based machine-learning
approach. International Journal of Entrepreneurial Behavior & Research, 27(7), 1819–1848. https://doi.org/10.1108/IJEBR-08-2020-0529
2) Baron, R. A. (2004). The cognitive perspective: A valuable tool for answering entrepreneurship's basic “why” questions. Journal of Business Venturing, 19(2), 221–239. https://doi.org/10.1016/S0883-9026(03)00008-9
3) Baron, R. A. (2007). Behavioral and cognitive factors in entrepreneurship: Entrepreneurs as the active element in new venture creation. Strategic Entrepreneurship Journal, 1(1–2), 167–182. https://doi.org/10.1002/sej.12
4) Bogachov, S., Kwilinski, A., Miethlich, B., Bartosova, V., & Gurnak, A. (2020). Artificial intelligence components and fuzzy regulators in entrepreneurship development. Entrepreneurship and Sustainability Issues, 8(2), 487–499. https://doi.org/10.9770/jesi.2020.8.2(29)
5) Boudreaux, C. J., Nikolaev, B. N., & Klein, P. (2019). Socio-cognitive traits and entrepreneurship: The moderating role of economic institutions. Journal of Business Venturing, 34(1), 178–196. https://doi.org/10.1016/j.jbusvent.2018.08.003
6) Chen, W., Zhang, S., Li, R., & Shahabi, H. (2018). Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Science of The Total Environment, 644, 1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
7) Douglas, E. J., Shepherd, D. A., & Prentice, C. (2020). Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship. Journal of Business Venturing, 35(1), Article ID 105970. https://doi.org/10.1016/j.jbusvent.2019.105970
8) Dzwigol, H., Aleinikova, O., Umanska, Y., Shmygol, N., & Pushak, Y. (2019). An entrepreneurship model for assessing the investment attractiveness of regions. Journal of Entrepreneurship Education, 22, 1–7. https://lib.iitta.gov.ua/id/eprint/718688/
9) García Márquez, F. P., & Peinado Gonzalo, A. (2022). A comprehensive review of artificial intelligence and wind energy. Archives of Computational Methods in Engineering, 29(5), 2935–2958. https://doi.org/10.1007/s11831-021-09678-4
10) Haynie, J. M., Shepherd, D., Mosakowski, E., & Earley, P. C. (2010). A situated metacognitive model of the entrepreneurial mindset. Journal of Business Venturing, 25(2), 217–229. https://doi.org/10.1016/j.jbusvent.2008.10.001
11) Haynie, M., & Shepherd, D. A. (2009). A measure of adaptive cognition for entrepreneurship research. Entrepreneurship Theory and Practice, 33(3), 695–714. https://doi.org/10.1111/j.1540-6520.2009.00322.x
12) Hockerts, K., & Wüstenhagen, R. (2010). Greening Goliaths versus emerging Davids-Theorizing about the role of incumbents and new entrants in sustainable entrepreneurship. Journal of Business Venturing, 25(5), 481–492. https://doi.org/10.1016/j.jbusvent.2009.07.005
13) Krueger, N. F. (2017). Entrepreneurial intentions are dead: Long live entrepreneurial intentions. In M. Brännback & A. L. Carsrud (eds.), Revisiting the entrepreneurial mind: Inside the black box: An expanded edition (Vol. 35, pp. 13–34). Springer. https://doi.org/10.1007/978-3-319-45544-0_2
14) Kumar, P., Arya, S. R., & Mistry, K. D. (2021). Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance. International Journal of Emerging Electric Power Systems, 22(4), 383–399. https://doi.org/10.1515/ijeeps-2020-0256
15) Ladeira, M. J., Ferreira, F. A., Ferreira, J. J., Fang, W., Falcão, P. F., & Rosa, Á. A. (2019). Exploring the determinants of digital entrepreneurship using fuzzy cognitive maps. International Entrepreneurship and Management Journal, 15, 1077–1101. https://doi.org/10.1007/s11365-019-00574-9
16) Laureiro-Martínez, D., & Brusoni, S. (2018). Cognitive flexibility and adaptive decision-making: Evidence from a laboratory study of expert decision makers. Strategic Management Journal, 39(4), 1031–1058. https://doi.org/10.1002/smj.2774
17) Lundberg, S., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. arXiv. https://doi.org/10.48550/arXiv.1705.07874
18) Mahajan, A., & Chowdhary, R. (2022). Entrepreneurial competencies of Indian women entrepreneurs in micro service enterprises. AMC Indian Journal of Entrepreneurship, 5(2), 30–48. https://doi.org/10.17010/amcije/2022/v5i2/171468
19) Mitchell, R. K., Busenitz, L., Lant, T., McDougall, P. P., Morse, E. A., & Smith, J. B. (2002). Toward a theory of entrepreneurial cognition: Rethinking the people side of entrepreneurship research. Entrepreneurship Theory and Practice, 27(2), 93–104. https://doi.org/10.1111/1540-8520.00001
20) Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepreneurship Theory and Practice, 41(6), 1029–1055. https://doi.org/10.1111/etap.12254
21) Nayak, V., & Hegde, K. P. (2023). Paryavarna Sakhi: Enabling women-led waste management and social entrepreneurship for sustainable development. AMC Indian Journal of Entrepreneurship, 6(4), 8–20. https://doi.org/10.17010/amcije/2023/v6i4/173569
22) Obschonka, M., Lee, N., Rodríguez-Pose, A., Eichstaedt, J. C., & Ebert, T. (2020). Big data methods, social media, and the psychology of entrepreneurial regions: Capturing cross-county personality traits and their impact on entrepreneurship in the USA. Small Business Economics, 55, 567–588. https://doi.org/10.1007/s11187-019-00204-2
23) Rao, N., Sankaran, K., & Praveen, S. (2022). Evolution of social entrepreneurship research in India: Bibliometric analysis of literature. AMC Indian Journal of Entrepreneurship, 5(3), 29–41. https://doi.org/10.17010/amcije/2022/v5i3/172441
24) Shepherd, D. A., & Majchrzak, A. (2022). Machines augmenting entrepreneurs: Opportunities (and threats) at the nexus of artificial intelligence and entrepreneurship. Journal of Business Venturing, 37(4), Article ID 106227. https://doi.org/10.1016/j.jbusvent.2022.106227
25) Shepherd, D. A., Williams, T. A., & Patzelt, H. (2014). Thinking about entrepreneurial decision making: Review and research agenda. Journal of Management, 41(1), 11–46. https://doi.org/10.1177/0149206314541153
26) Siu, W.-S., & Lo, E. (2013). Cultural contingency in the cognitive model of entrepreneurial intention. Entrepreneurship Theory and Practice, 37(2), 147–173. https://doi.org/10.1111/j.1540-6520.2011.00462.x
27) Su, X., Liu, S., Zhang, S., & Liu, L. (2020). To be happy: A case study of entrepreneurial motivation and entrepreneurial process from the perspective of positive psychology. Sustainability, 12(2), 584. https://doi.org/10.3390/su12020584
28) Tkachenko, V., Kuzior, A., & Kwilinski, A. (2019). Introduction of artificial intelligence tools into the training methods of entrepreneurship activities. Journal of Entrepreneurship Education, 22(6). 1–10. https://www.abacademies.org/articles/Introduction-of-artificial-intelligence-tools-1528-2651-22-6-477.pdf
29) Wang, L., Langari, R., & Yen, J. (1999). 8 – Identifying fuzzy rule-based models using orthogonal transformation and backpropagation. Fuzzy Theory Systems, 1, 187–204. https://doi.org/10.1016/B978-012443870-5.50010-1
30) Wei, Y., Lv, H., Chen, M., Wang, M., Heidari, A. A., Chen, H., & Li, C. (2020). Predicting entrepreneurial intention of students: An extreme learning machine with Gaussian barebone Harris hawks optimizer. IEEE Access, 8, 76841–76855. https://doi.org/10.1109/ACCESS.2020.2982796
31) William, P., Badholia, A., Patel, B., & Nigam, M. (2022). Hybrid machine learning technique for personality classification from online text using HEXACO model. In 2022 International
Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 253–259). IEEE. https://doi.org/10.1109/ICSCDS53736.2022.9760970
32) Williamson, A. J., Drencheva, A., & Battisti, M. (2020). Entrepreneurial disappointment: Let down and breaking down, a machine-learning study. Entrepreneurship Theory and Practice, 46(6), 1500–1533. https://doi.org/10.1177/1042258720964447
33) Yadav, J. (2021). Self-help groups and women entrepreneurship in India: Opportunities and challenges. AMC Indian Journal of Entrepreneurship, 4(1), 13–21. https://doi.org/10.17010/amcije/2021/v4i1/159225
34) Zadeh, L. A. (1968). Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications, 23(2), 421–427. https://doi.org/10.1016/0022-247X(68)90078-4
35) Zhang, Y., Ma, Z., Song, X., Wu, J., Liu, S., Chen, X., & Guo, X. (2022). Road surface defects detection based on IMU sensor. IEEE Sensors Journal, 22(3), 2711–2721. https://doi.org/10.1109/JSEN.2021.3135388
