Methodological Pluralism and Innovation in Data-Driven Organizations

Abstract

Prior research on data-driven innovation, which assumes quantitative analysis as the default, suggests a tradeoff: Organizations that rely heavily on data-driven analysis tend to produce familiar, incremental innovations with moderate commercial potential, at the expense of risky, novel breakthroughs or hit products. We argue that this tradeoff does not hold when quantitative and qualitative analysis are used together. Organizations that substantially rely on both types of analysis in the new-product innovation process will benefit by triangulating quantifiably verifiable demand (which prompts more moderate successes but fewer hits) with qualitatively discernible potential (which prompts more novelty but more flops). Although relying primarily on either type of analysis has little impact on overall new-product sales due to the countervailing strengths and weaknesses inherent in each, together they have a complementary positive effect on new-product sales as each compensates for the weaknesses of the other. Drawing on a unique dataset of 3,768 new-product innovations from NielsenIQ linked to employee résumé job descriptions from 55 consumer-product firms, we find support for our hypothesis. The highest sales and number of hits were observed in organizations that demonstrated methodological pluralism: substantial reliance on both types of analyses. Further mixed-method research examining related outcomes—hits, flops, and novelty—corroborates our theory and confirms its underlying mechanisms.

Keywords: Innovation, Opportunity Identification, Data-Driven Decision Making, Search, Product Innovation

JEL Classification: O31, O32, D83, M1, M31, L2