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

Author

Listed:
  • Martin Obschonka
  • Christian Fisch
  • 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))

  • Tharindu Fernando
  • Clinton Fookes

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.

Suggested Citation

  • Martin Obschonka & Christian Fisch & Tatiana Beliaeva & Sami Ben Jabeur & Tharindu Fernando & Clinton Fookes, 2026. "Visual artificial intelligence as a methodological frontier in entrepreneurship research: a roadmap and empirical demonstration," Post-Print hal-05649171, HAL.
    • Handle: RePEc:hal:journl:hal-05649171
    DOI: 10.1007/s11187-026-01221-8
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