Visual artificial intelligence as a methodological frontier in entrepreneurship research: a roadmap and empirical demonstration

Martin Obschonka, Christian Fisch, Tatiana Beliaeva (), Sami Ben Jabeur (), Tharindu Fernando and Clinton Fookes
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Tatiana Beliaeva: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)
Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

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Abstract: Visual artificial intelligence (AI) is beginning to enter entrepreneurship research but adoption remains scarce, even as recent advances in vision-language models and AI-assisted coding are substantially lowering the technical barriers to entry. Combined with the abundance of visual data now available, visual AI opens new opportunities to study questions previously out of reach, while also raising methodological and ethical challenges. This paper provides a structured roadmap for using visual AI in entrepreneurship research: We map visual data sources, outline visual AI approaches, identify research insights, and propose an ethics audit. Ultimately, we illustrate the application of this visual AI pipeline with an example study: a facial image classification model distinguished between entrepreneur and non-entrepreneur portrait images in a platform-specific dataset with 79.5% accuracy, far outperforming human experts, raising important methodological and ethical questions.

Keywords: artificial intelligence (AI); visual data; machine learning; ethical AI; entrepreneurship; entrepreneuriat; IA éthique; apprentissage automatique; données visuelles; intelligence artificielle (IA) (search for similar items in EconPapers)
Date: 2026-05
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Published in Small Business Economics, 2026, pp.1-28. ⟨10.1007/s11187-026-01221-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05649171

DOI: 10.1007/s11187-026-01221-8

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