How does big data change the search for innovation? Data-driven predictions can identify promising opportunities even when their underlying mechanisms are not understood. This has raised concerns that decoupling innovation from theoretical understanding weakens incentives to develop new theory and yields findings whose consequences are poorly understood. In this paper, I argue that big data can instead catalyze the generation of new theory. Data-driven search broadens the space of combinations explored and increases the variability in outcomes relative to the filtering provided by existing theory. As a result, this search strategy uncovers more empirical anomalies that stimulate, rather than substitute, new theorizing. I test these ideas in the domain of human genetics, where genome-wide association studies (GWAS) operate as a data-driven search for the genetic roots of disease. Compared with traditional theory-based approaches, GWAS introduce gene–disease combinations that span a wider portion of the genetic landscape, more frequently fall at both extremes of scientific quality, and often defy expectations drawn from prior knowledge. Rather than crowding out theory generation, GWAS findings are followed by a surge of research aimed at clarifying their causal mechanisms. Together, the results reveal a complementarity between theory and data in search, suggesting that big data can fuel virtuous cycles of theorizing by accelerating the identification of anomalies.
Ansprechpartner: Daehyun Kim
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