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Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship

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  • Shepherd, Dean A.
  • Majchrzak, Ann

Abstract

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.

Suggested Citation

  • Shepherd, Dean A. & Majchrzak, Ann, 2022. "Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship," Journal of Business Venturing, Elsevier, vol. 37(4).
    • Handle: RePEc:eee:jbvent:v:37:y:2022:i:4:s0883902622000398
    DOI: 10.1016/j.jbusvent.2022.106227
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    1. Marc Schmitt, 2024. "Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering," Papers 2402.03806, arXiv.org.
    2. Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
    3. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
    4. Adelaida Ojeda-Beltrán & Andrés Solano-Barliza & Wilson Arrubla-Hoyos & Danny Daniel Ortega & Dora Cama-Pinto & Juan Antonio Holgado-Terriza & Miguel Damas & Gilberto Toscano-Vanegas & Alejandro Cama-, 2023. "Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning," Sustainability, MDPI, vol. 15(13), pages 1-19, June.
    5. Lupp, Daniel, 2023. "Effectuation, causation, and machine learning in co-creating entrepreneurial opportunities," Journal of Business Venturing Insights, Elsevier, vol. 19(C).

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