Bowen Lou is a fourth-year doctoral candidate in the Operations, Information and Decisions department of the Wharton School, with specialization in Information Systems.
Now he’s working on the topics of Artificial Intelligence, Innovation & Economics. Specifically he studies the relationship among big data investment, innovation, and firm performance. To do so, he delves into large-scale texts about individuals and organizations from social media, digital publications and the World Wide Web. Bowen is passionate about proposing and applying eclectic but robust solutions from statistical natural language processing and network science in order to understand latent patterns and extract managerial insights.
Prior to joining Wharton, Bowen worked as a research programmer at Knowledge Lab in Computation Institute and a research assistant at Booth School of Business for most of his time in Chicago. He also has worked in technology and banking corporations including Intel and China Guangfa Bank. Bowen received BEng in Information Security from Shanghai Jiao Tong University, and MS in Computer Science from University of Chicago.
Abstract: We examine the relationship between data analytics and innovation, focusing on how the benefits of analytics may differ depending on how firms organize their innovative activities. Our analysis draws on prior work that has measured firm analytics capability using detailed employee-level data and matches these data to metrics on innovation structure that are constructed by analyzing intra-firm inventor networks. We apply community detection algorithms to these inventor networks to determine whether a firm has a centralized or decentralized innovation structure. In a panel of large firms from the years 1988 to 2013, we find that firms with a decentralized innovation structure have a greater demand for analytics skills and receive greater productivity benefits from their analytics capabilities, consistent with a complementarity between analytics and decentralized innovation. Furthermore, we find the complementarity is strongest for innovation involving the recombination of existing technologies. This suggests that data analytics can alleviate a weakness in decentralized innovation structures by allowing firms to search for knowledge broadly and link distant ideas to create new ones, an advantage previously available primarily to centralized innovation.
Abstract: We examine the relationship between data analytics capabilities and innovation using detailed firm-level data. To measure innovative activity, we utilize a survey on process- and innovation- oriented business practices, and we use patent data to analyze the innovative output and characteristics of firms. We find that data analytics capabilities are more likely to be present and are more valuable in firms that are oriented around process improvement and that innovate by recombining existing technologies; data analytics skills have no effect on or are possibly negatively related to value in firms that focus on generating creative and truly novel innovations. We interpret these findings as consistent with data analytics skills being complementary to the exploitation rather than exploration strategies as described in the technology strategy literature.