Big Data and the Computational Social Science of Entrepreneurship and Innovation
Journal:
arXiv
Published Date:
May 13, 2025
Abstract
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.
