Bowen Lou

• Lynn Wu, Bowen Lou, Lorin M. Hitt (2024), Innovation Strategy After IPO: How Data Analytics Mitigates the Post-IPO Decline in Innovation, Management Science. 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.

• Yi Liu, Xinyi Zhao, Bowen Lou, XinXin Li, The Coin of AI has Two Sides: Matching Enhancement and Information Revelation Effects of AI on Gig-Economy Platforms. Abstract

  Artificial intelligence (AI) has been increasingly integrated in the process of matching between workers and employers requesting job tasks on a gig-economy platform. Unlike the conventional wisdom that adopting AI in the matching process always benefits the platform by assigning better-matched jobs (employers) to workers, we, however, discover unintended but possible revenue-decreasing consequences for the AI-adopting platform. We build a stylized game-theoretical model that considers gig workers’ strategic participation behavior. We find that while the matching enhancement effect of AI can increase the platform’s revenue by improving matching quality, AI-assigned jobs can also reveal information about the uncertain labor demand to workers and thus change workers’ participation decision in an unfavorable way, resulting in revenue loss for the platform. We extend our model to the cases where (1) the share of revenue between workers and platform is endogenous and (2) the workers compete for the job tasks, and find consistent results. Furthermore, we examine two approaches to mitigate the potential negative effect of AI-enabled matching for the platform and find that under certain conditions, the AI-adopting platform can be better off by truthfully revealing the labor demand or competition information to workers. Our results shed light on both the intended positive and unintended negative roles of utilizing AI to facilitate matching, and highlight the importance for thoughtful development, management, and application of AI in the gig economy.

• Bowen Lou and Lynn Wu (2021), AI on Drugs: Can Artificial Intelligence Accelerate Drug Development? Evidence from a Large-scale Examination of Bio-pharma Firms, MISQ, 45 (3). Abstract

  Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. Using a resource-based view, we develop an AI innovation capability and find it to help firms identify new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “me-too” drugs. Examining AI skills, a key component of AI innovation capabilities, we find that a main effect of AI innovation capabilities come from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and improving AI tools is an iterative process requiring synthesizing inputs from both AI and domain experts. Taken together, our study sheds light on both the advantages and the limitations of using AI in drug discovery and how to effectively manage AI resources for drug development.

• 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.

• Lynn Wu, Bowen Lou, Lorin M. Hitt (2019), Data Analytics Supports Decentralized Innovation, Management Science, 65 (10), pp. 4863-4877. 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.

• Aaron Gerow, Bowen Lou, Eamon Duede, James Evans (2015), Proposing Ties in a Dense Hypergraph of Academics, Social Informatics. 10.1007/978-3-319-27433-1_15

Instructor

• Wharton Tech Camp (PhD), Summer 2018

Teaching Assistant

• OIDD 314/662 Enabling Technologies (Undergraduate & MBA), Fall 2015, Fall 2016, Fall 2017
• OIDD 101 Introduction to Operations, Information & Decisions (Undergraduate), Spring 2019