The next Entrepreneurship and Innovation Policy Research virtual seminar is Wednesday, October 18, from 11:00-12:15 ET. Ryan Allen (University of Washington) will present "Market Size Inversion: How Diffusion Dynamics Obfuscate the Potential Market Size of Novel Innovations" Click HERE to register for the 10/18 seminar (abstract is below). We hope you join us.
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- Tim Folta (UCONN), Maryann Feldman (ASU), and Supradeep Dutta (Rutgers U)
Abstract: I develop a model to explain what I call "market size inversion": why novel innovations often outperform initial pre-launch indications of market size, while conventional products underperform. In the model, potential adopters rely more on social endorsements when evaluating whether to adopt relatively novel innovations. Therefore, a larger portion of demand does not exist until after the innovation diffuses, implying that initial indications of market size for relatively novel innovations are downward biased. I use agent-based simulations to illustrate these dynamics, and to explore the implied optimal firm innovation selection strategies. The model suggests that novel innovations will achieve higher final adoption than non-novel counterparts with similar initial market size indications, which can lead to the observed "inversion" of expected vs. actual adoption. Given this dynamic, firms' optimal selection strategy is a balanced prioritization of market size indicators and novelty. I empirically substantiate these insights using data from approximately 33,000 consumer product launches. I also use a smaller subset of products linked to CPG firms' employees' resumes to proxy reliance on market size in innovation, to validate the firm-level insights. The paper contributes to the strategy literature in three ways: it complements the prevailing competition-centric theory with a demand-side theory of the value of novelty in innovation, elaborates the role of social diffusion as a source of uncertainty for nascent products and technologies, and articulates fundamental limits of experimentation and data-driven decision-making in innovation.