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Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models

Author

Listed:
  • Sami Ben Jabeur

    (ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University), UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University))

  • Houssein Ballouk

    (CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)

  • Salma Mefteh-Wali

    (ESSCA - ESSCA – École supérieure des sciences commerciales d'Angers = ESSCA Business School)

  • Anis Omri

    (UCAR - Université de Carthage (Tunisie) = University of Carthage)

Abstract

To date, entrepreneurship researchers have tended to avoid state-of-the-art artificial intelligence techniques; in this paper, we fill that gap. Based on eclectic entrepreneurship theory, we present an original work that uses artificial intelligence to forecast the macrolevel determinants of entrepreneurial opportunity. Modern artificial intelligence could open new areas for future research opportunities in entrepreneurship and help close the gap between theory and practice. Our empirical analysis offers two major results by using a panel dataset of 149 countries covering 2007–2018 and six machine-learning models. First, entrepreneurs prefer to exploit opportunities in countries with stable economic governance that provide high education standards, health, social capital, and a safe, natural environment. Second, CatBoost regression performs better in predicting entrepreneurial opportunity compared to linear regression and more advanced machine-learning models. Recommendations for policy-makers and managers and directions for future studies are also discussed.

Suggested Citation

  • Sami Ben Jabeur & Houssein Ballouk & Salma Mefteh-Wali & Anis Omri, 2021. "Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models," Post-Print hal-03442122, HAL.
    • Handle: RePEc:hal:journl:hal-03442122
    DOI: 10.1016/j.techfore.2021.121353
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    Cited by:

    1. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    2. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    3. Shetewy, Nsreen & Shahin, Ahmed Ismail & Omri, Anis & Dai, Kuizao, 2022. "Impact of financial development and internet use on export growth: New evidence from machine learning models," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Byron Graham & Karolis Matikonis, 2025. "SME crisis management and performance: leveraging algorithm supported induction to unravel complexity," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-33, August.
    5. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    7. Graham, Byron & Bonner, Karen, 2024. "The role of institutions in early-stage entrepreneurship: An explainable artificial intelligence approach," Journal of Business Research, Elsevier, vol. 175(C).
    8. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    9. Stef, Nicolae & Başağaoğlu, Hakan & Chakraborty, Debaditya & Ben Jabeur, Sami, 2023. "Does institutional quality affect CO2 emissions? Evidence from explainable artificial intelligence models," Energy Economics, Elsevier, vol. 124(C).
    10. Cui, Lijuan & Xu, Yekun, 2025. "Technological change and entrepreneurial activities: Evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 72(C), pages 330-346.
    11. Qin, Weiwei, 2024. "How to unleash frugal innovation through internet of things and artificial intelligence: Moderating role of entrepreneurial knowledge and future challenges," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    12. Ben Jabeur, Sami & Bakkar, Yassine & Cepni, Oguzhan, 2025. "Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models," Energy Economics, Elsevier, vol. 141(C).
    13. Deng, Yanru & Nepal, Rabindra & Shao, Xuefeng & Ding, Chante Jian & Wu, Zhan, 2024. "Zooming in or zooming out: Energy strategy, developmental parity and regional entrepreneurial dynamism," Energy Economics, Elsevier, vol. 140(C).
    14. Nayef Shaie Alotaibi & Awad Hajran Alshehri, 2023. "Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes," Sustainability, MDPI, vol. 15(13), pages 1-18, July.
    15. Chang, Victor & Hahm, Nattareya & Xu, Qianwen Ariel & Vijayakumar, P. & Liu, Ling, 2024. "Towards data and analytics driven B2B-banking for green finance: A cross-selling use case study," Technological Forecasting and Social Change, Elsevier, vol. 206(C).

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