527.8 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104
Research Interests: Artificial Intelligence, Digitization, Innovation, Natural Language Processing Application in Business, Network Science
Links: Personal Website
Bowen Lou is a fifth-year doctoral candidate in Operations, Information & Decisions Department of Wharton School, University of Pennsylvania, with a specific focus on information strategy and economics.
His research generally lies in economics of innovation and digitization. He studies the new waves of digitization spanning a wide spectrum of industry sectors by collaborating with leading companies that extensively track technology, labor and innovation trends. Recently he’s particularly interested in the role of artificial intelligence in transforming the development of innovation in the healthcare industry, hoping to make people live well.
Prior to joining Wharton, Bowen was 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.
Lynn Wu, Lorin M. Hitt, Bowen Lou (2020), Data Analytics Skills, Innovation and Firm Productivity, Management Science, 66 (5), pp. 1783-2290.
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: 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.
Abstract: We examine the role of data analytics in facilitating innovation in firms that have gone through an initial public offering (IPO). It has been documented that an IPO is associated with a decline in innovation despite the infusion of capital from the IPO that should have spurred innovation. Using patent data for over 2,000 firms, we find that firms that possess or acquire data analytics capability experience a smaller decline in innovation compared to similar firms that have not acquired that capability. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that either combine existing technologies into new ones or reuse existing innovations by applying them to new problem domains—both forms of innovation that are especially well-supported by analytics. Our results suggest that the increased deployment of analytics may reduce some of the innovation decline of IPOs, and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing newly acquired capital to the acquisition of analytics capabilities.
Abstract: We study how artificial intelligence can influence the drug discovery and development process in the global pharmaceutical industry. Despite an enormous amount of time and financial resources invested in developing drugs, pharmaceutical firms experience declines in novelty for drugs they produced. As AI becomes an important general purpose technology (GPT), it could be used to address some known challenges in the drug development process. Using a number of large-scale datasets that contain detailed historical records of global patents and job postings, as well as drug development, we identify AI-related patents and hiring strategies to approximate firms’ AI capabilities and construct a relatively new similarity-based metric to measure drug novelty based on their chemical structure. By differentiating multiple stages in the drug development process, we examine where AI is most effective in developing new drugs. We find that AI can primarily affect the earliest stage in drug discovery when tasks are heavily dependent on automatic data processing and reasoning. However, it may not necessarily help with the more expensive and risky clinical trial stages that require substantial human engagements and interventions. Additionally, AI can facilitate the development for drugs at the medium level of chemical novelty but is not helpful with drugs at the extreme ends of the spectrum, those that are entirely novel or incremental me-too drugs. Our study sheds light on the understanding of the roles and limitations modern technology can have on drug development, one of the most complex innovation processes in the world but have been paid less attention in information systems literature.
President Gutmann Leadership Award, 2019
Mack Institute Innovation Research Fellowship, 2019
Certificate in College and University Teaching, 2018
Paul R. Kleindorfer Scholar Award, 2018
Wharton George James Travel Award, 2017, 2018
Best Conference Paper Award Finalist, CIST, INFORMS, 2017
Graduate and Professional Student Assembly (GAPSA) Research Travel Grant, 2017
Wharton Risk Center Russell Ackoff Doctoral Student Fellowship, 2016
Wharton Doctoral Fellowship, 2015-Present