Epistemic plasticity and Innovation in Data-Driven Organizational Cultures



A long tradition in strategy and innovation research asserts that data-driven organizations—in which new-product investment decisions are based on quantitative evidence of market demand—excel at incremental innovation, but unintentionally allocate resources away from less-measurable breakthrough innovations. Questioning this reasoning, I distinguish the magnitude of an organization’s use of quantitative analysis from the epistemic plasticity of its organizational culture (the extent to which it uses non-quantitative analyses). Specifically, I argue and demonstrate that organizations that make substantial use of quantitative analysis will produce more breakthrough innovations—provided that they are plastic enough to use qualitative analysis liberally as well. Those with low epistemic plasticity (that rely solely on quantitative analysis) produce the fewest breakthroughs. I test this theory using

  • (1) a unique panel dataset of 61 U.S. consumer-packaged goods organizations from 2010 through 2016, measuring breakthrough innovation as outlier product sales, and
  • (2) measuring quantitative and qualitative analysis using a word embedding model on employee résumé data.

In support of my proposed theory, supplementary mixed-methods analyses demonstrate that the effect of epistemic plasticity on innovation is contingent upon the level of uncertainty in the market; the novelty of products launched; and the degree of methodological polarization between members of the organization. I also explore antecedents of data-driven culture and epistemic plasticity in organizations. This paper contributes to research on innovation in organizations, decision-making, and the link between organizational culture and strategic performance.

This event is open to faculty, doctoral students, academic researchers, and graduate students.

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