Adapting to Data Protection Regulation: The Role of IS Architecture and Organizational Structure
Abstract: Prior research shows that the proliferation of data analytics and AI/ML technologies creates business value and affords competitive advantages. Inherent tension arises between organizations’ increasingly malleable structure due to the generativity of data and the top-down forces of IS architecture standards and governance. However, the influence of IS architecture and organizational structure on business value has not been explored in the context of heightened regulatory uncertainty. I assemble a panel dataset of large U.S. financial services corporations from 2014Q1 to 2020Q4 to examine the sources of firms’ heterogeneous adaptation to the European Union’s General Data Protection Regulation (GDPR). This regulation entailed requirements for changing business practices and uncertainty regarding the interpretation and implementation of these requirements. Drawing on theoretical insights from the information processing view of organization design, we propose that centralized data governance mechanisms reduced firms’ adaptation performance, while temporarily decentralized team structures improved firms’ adaptation performance. We find that a 1% increase in the pre-shock share of European consumer business revenue led to firms experiencing up to 1.4% more negative revenue response when they implemented centralized data architecture and governance, and a 1.1% less negative revenue response when they utilized decentralized data teams and self-organizing agile teams. These effects lasted through the end of the sample time period. This study is among the first to establish an empirical link between firms’ IS architecture and their ability to adapt to rising regulatory standards and uncertainty. A key contribution of this work is to demonstrate the importance of IS architecture factors in influencing firms’ adaptation performance and to provide evidence of the theorized impact of organization design under dynamic environmental change.
Local Labor Market Frictions and Platform-Dependent Entrepreneurship (with Y. Lyu)
Abstract: This paper examines the relationship between local labor market conditions and platform-based entrepreneurs' performance. Drawing on the labor market frictions perspective, we hypothesize that local job scarcity induces high-ability individuals to sort into self-employment, increasing the share of high-quality entrepreneurs entering digital platform-based entrepreneurship. Combining data from a large online marketplace with official labor market statistics across U.S. states, we show that entrepreneurial businesses entering the platform in low vacancy rate locations achieve superior revenue performance. We also show that the vacancy rate positively moderates the relationship between local wages and entrepreneurial revenues. Finally, those entering platform-based entrepreneurship in low vacancy labor markets are more likely to multi-home and have a business partner, consistent with choosing founding strategies associated with being higher quality.
Sampling Bias in Entrepreneurial Experiments (with R. Koning and R. Nanda)
Abstract: Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and persistent impact on the venture’s success. Specifically, we show that products with a female-focused target market launching on a typical day, when nine in ten users on this platform are men, experience 45% less growth a year after launch than those for whom the target market is more male-focused. By isolating exogenous variation in the composition of beta testers unrelated to the characteristics of launched products on that day, we find that on days when there are unexpectedly more women beta testers on the platform—reducing the amount of sampling bias for female-focused products—the gender-performance gap shrinks towards zero. Our results highlight how sampling bias can lead to fewer successfully commercialized innovations for consumers who are underrepresented among early users.
The Economic Effects of Social Networks: Evidence from the Housing Market (with M. Bailey, T. Kuchler, and J. Stroebel)
Journal of Political Economy, 126(6): 2224-2276, December 2018.
Measuring Social Connectedness (with M. Bailey, T. Kuchler, J. Stroebel, and A. Wong)
Journal of Economic Perspectives, 32(3): 259-80, Summer 2018.