527.8 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104
Research Interests: Artificial Intelligence, Innovation, Natural Language Processing Application in Business, Social Networks
Links: Personal Website
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: Data analytics technology can accelerate the innovation process by enabling existing knowledge to be identified, accessed, combined and deployed to address new problem domains. However, like prior advances in information technology, the ability of firms to exploit these opportunities depends on a variety of complementary human capital and organizational capabilities. We focus on whether analytics is more valuable in firms where innovation within a firm has decentralized groups of inventors or centralized ones. Our analysis draws on prior work measuring firm analytics capability using detailed employee-level data and matches these data to metrics on intra-firm inventor networks that reveal whether a firm’s innovation structure is centralized or decentralized. In a panel of 1,864 publicly-traded 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. We also find that analytics helps decentralized structures to create new combinations and reuse of existing technologies, consistent with the ability of analytics to link knowledge across diverse domains and to integrate external knowledge into the firm. Furthermore, the effect primarily comes from the analytics capabilities of the non-inventor employees as opposed to inventors themselves. These results show that the benefit of analytics on innovation depends on existing organizational structures. Similar to the IT-productivity paradox, these results can help explain a contemporary analytics-innovation paradox—the apparent slowdown in innovation despite the recent increase in analytics investments.
Lynn Wu, Lorin M. Hitt, Bowen Lou (2018), Data Analytics Skills, Innovation and Firm Productivity, Management Science, Forthcoming.
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.