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Big Data and the Computational Social Science of Entrepreneurship and Innovation

arXiv 2505.08706 ↗First posted 13 May 2025econ.GN cs.AI cs.CYcited by 0

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Abstract (arXiv)

As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate 'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 17 of its 107 references that are in the map. First posted on arXiv 13 May 2025.

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References · 17

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5 more
  1. 2024A semantic embedding space based on large language models for modelling human beliefscited by 9
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