Big Data and the Computational Social Science of Entrepreneurship and Innovation

TL;DR

This paper explores how big data and machine learning can revolutionize research in entrepreneurship and innovation by enabling precise measurement and virtual experimentation of innovation processes.

Contribution

It introduces methods for leveraging diverse large-scale data types and AI models to advance theory and practice in entrepreneurship research.

Findings

Machine learning enables system-level observatories of innovation.

AI models create 'digital doubles' for virtual experimentation.

Big data enhances measurement and prediction of entrepreneurial phenomena.

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.