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 capabilities and innovation using detailed firm-level data. To measure innovation, we first utilize a survey to capture two types of innovation practices, process improvement and new technology development for 331 firms. We then use patent data to further analyze new technology development for a broader sample of more than 2,000 publicly-traded 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 create new technologies by combining a diverse set of existing technologies than they are in firms that are focused on generating entirely new technologies. These results are consistent with the theory that data analytics are complementary to certain types of innovation because they enable firms to expand the search space of existing knowledge to combine into new technologies, as well as prior theoretical arguments that data analytics support incremental process improvements. Data analytics appear less effective for developing entirely new technologies or creating combinations involving a few areas of knowledge, innovative approaches where there is either limited data or limited value in integrating diverse knowledge. Overall, our results suggest firms that have historically focused in specific types of innovation—process innovation and innovation by diverse recombination—may become the leading investors in data analytics and receive the most benefits from it.
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.