{"title":"A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research","authors":"Martin Obschonka, Moren Levesque","doi":"arxiv-2409.08890","DOIUrl":null,"url":null,"abstract":"The rapid expansion of AI adoption (e.g., using machine learning, deep\nlearning, or large language models as research methods) and the increasing\navailability of big data have the potential to bring about the most significant\ntransformation in entrepreneurship scholarship the field has ever witnessed.\nThis article makes a pressing meta-contribution by highlighting a significant\nrisk of unproductive knowledge exchanges in entrepreneurship research amid the\nAI revolution. It offers strategies to mitigate this risk and provides guidance\nfor future AI-based studies to enhance their collective impact and relevance.\nDrawing on Akerlof's renowned market-for-lemons concept, we identify the\npotential for significant knowledge asymmetries emerging from the field's\nevolution into its current landscape (e.g., complexities around construct\nvalidity, theory building, and research relevance). Such asymmetries are\nparticularly deeply ingrained due to what we term the double-black-box puzzle,\nwhere the widely recognized black box nature of AI methods intersects with the\nblack box nature of the entrepreneurship phenomenon driven by inherent\nuncertainty. As a result, these asymmetries could lead to an increase in\nsuboptimal research products that go undetected, collectively creating a market\nfor lemons that undermines the field's well-being, reputation, and impact.\nHowever, importantly, if these risks can be mitigated, the AI revolution could\nherald a new golden era for entrepreneurship research. We discuss the necessary\nactions to elevate the field to a higher level of AI resilience while\nsteadfastly maintaining its foundational principles and core values.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"118 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid expansion of AI adoption (e.g., using machine learning, deep
learning, or large language models as research methods) and the increasing
availability of big data have the potential to bring about the most significant
transformation in entrepreneurship scholarship the field has ever witnessed.
This article makes a pressing meta-contribution by highlighting a significant
risk of unproductive knowledge exchanges in entrepreneurship research amid the
AI revolution. It offers strategies to mitigate this risk and provides guidance
for future AI-based studies to enhance their collective impact and relevance.
Drawing on Akerlof's renowned market-for-lemons concept, we identify the
potential for significant knowledge asymmetries emerging from the field's
evolution into its current landscape (e.g., complexities around construct
validity, theory building, and research relevance). Such asymmetries are
particularly deeply ingrained due to what we term the double-black-box puzzle,
where the widely recognized black box nature of AI methods intersects with the
black box nature of the entrepreneurship phenomenon driven by inherent
uncertainty. As a result, these asymmetries could lead to an increase in
suboptimal research products that go undetected, collectively creating a market
for lemons that undermines the field's well-being, reputation, and impact.
However, importantly, if these risks can be mitigated, the AI revolution could
herald a new golden era for entrepreneurship research. We discuss the necessary
actions to elevate the field to a higher level of AI resilience while
steadfastly maintaining its foundational principles and core values.