3730 Walnut Street
561 Jon M. Huntsman Hall
Philadelphia, PA 19104
Lynn Wu is an associate professor (with tenure) at the Wharton School. She teaches MBA, undergraduate and PhD classes about the use and impact of emerging technologies on business.
Her research examines how emerging information technologies, such as artificial intelligence and analytics, affect innovation, business strategy, and productivity. Specifically, her work follows three streams. In the first stream, she examines how data analytics and artificial intelligence affect firm innovation, business strategy, labor demand, and productivity for both large firms and startups. In her second stream, she studies enterprise social media and information derived from online platforms affect individuals’ work performance, career trajectories, entrepreneurship, and examine new type of biases that arise from using technologies. In her third stream of research, Lynn leverages fine-grained nanodata available through online digital traces to predict economic indicators such as real estate trends, labor trends and product adoption.
Lynn has published articles in economics, management and computer science. Her work has been featured by the Wall Street Journal, Businessweek, New York Times, Forbes, and The Economist.
Lynn received her undergraduate degrees from MIT (Finance and Computer Science), her master’s degree from MIT (Computer Science) and her Ph.D. from MIT Sloan School of Management (Management Science). Lynn has experiences working with a variety of firms in the technology industry (e.g. IBM, SAP, Google, Facebook etc), government agencies and think tanks (e.g the World Bank, the Russel Sage Foundation). She has also consulted and advised several startups. Prior to academia, she was a software engineer and a research scientist at MIT AI lab and IBM.
Jay Dixon, bryan hong, Lynn Wu (2021), The Robot Revolution: Managerial and Employment Consequences for Firms, Management Science, Forthcoming.
Abstract: As a new general-purpose technology, robots have the potential to radically transform industries and affect employment. Preliminary empirical studies using industry and geographic region-level data have shown that robots differ from prior general-purpose technologies and predict substantial negative effects on employment. Using novel firm-level data, we show that investments in robotics are associated with increased employee turnover, but also an increase in total employment within the firm. Examining changes in labor composition, we find that manager headcount has decreased but non-managerial employee headcount has increased, suggesting that robots displace managerial work that in prior waves of technology adoption was considered more difficult to replace. However, we also find that firms are more likely to hire managers from outside the firm and invest in additional training, suggesting that firms require different employee skills as the nature of work changes with robot investment. We also provide additional evidence that robot investments are not generally motivated by the desire to reduce labor costs but are instead related to an increased focus on improving product and service quality. With respect to changes in the way work is organized within the firm, we find that robot adoption predicts organizational changes in ways that differ from prior technologies. While information technology has generally been found to decentralize decision-making authority within organizational hierarchies, we find that robots can either centralize or decentralize decision-making, depending on the task. Overall, our results suggest that the impact of robots on employment is more nuanced than prior studies have shown.
Lynn Wu and Gerald Kane (2021), Network-biased Technical Change: How Information Management Tools Overcome Some Biases but Exacerbate Others, Organization Science, 32 (2), pp. 273-292.
Abstract: Organizations have long sought to improve employee performance by managing knowledge more effectively. In this paper, we test whether the adoption of digital tools for expertise search and access within an organization, often referred to as a support to an organization’s transactive memory system (TMS), improves employee performance. Using three years of data from more than 1,000 employees at a large professional services firm, we find that adopting an expertise search tool improves employee performance on financial dimensions, which results from improvements in network connections and information diversity. However, it does not affect all employees equally. We find that two types of employees appear to benefit from adoption more than others. First, traditionally information-disadvantaged employees (junior employees and women) appear to gain more from the adoption of Digital TMS tools (DTMS) because the tool overcomes the institutional barriers to resource access that these employees face in searching for knowledge. Second, employees with greater structural capital at the time of adoption also benefit more, because the tool eliminates natural networking barriers present in traditional offline interpersonal networks, allowing these employees to network more strategically. We also find that communication volume increases more for junior employees and women and increases it less for people with strong social networks, suggesting the mechanisms that benefit people with strong networks differ from those for women and junior employees, a finding consistent with our theoretical mechanisms. Taken together, an important implication of these findings is that implementing and adopting expert search tools for TMS has the potential to shift organizational sources of power and influence away from demographic-based characteristics and toward network-based ones—a characteristic we call “network-biased technical change.”
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, Forthcoming.
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.
C. Eesley and Lynn Wu (2020), For Startups, Adaptability and Mentor Network Diversity can be Pivotal: Evidence from a Randomized Experiment on a MOOC Platform, MISQ, 44 (5), pp. 661-697.
Abstract: Entrepreneurs leading digital ventures are often advised to be adaptable. However, research on how to pursue adaptable strategies and whether such strategies improve short- or long-term digital venture outcomes is sparse. By utilizing the ability to control content presentation and to measure outcomes through a course using a MOOC platform, we can introduce exogenous variation in strategies and mentorship characteristics, and link these attributes to venture outcomes over time. Contrary to expectations, we find that minimizing adaptability by adhering to a strong, persistent vision often results in better short-term outcomes as measured by quality of the pitch in digital startups. It also however results in worse long-term outcomes as measured by revenue, funding, and pivoting to a new venture. A more adaptable approach, when combined with a mentor who can facilitate this strategy by providing access to a structurally diverse social network, can offer the best combination of short- and long-run outcomes. The results suggest that guidance on mentor selection—especially selecting for the mentor’s social network attributes—is important over time for reaping the benefits of an adaptable strategy, particularly for digital ventures at their early-stage.
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: Startups are increasingly using social media to signal quality and provide information for potential investors. However, the effectiveness of social media is likely to be heterogeneous between different demographic and network characteristics. In this paper, we examine whether social media use can alleviate disadvantages experienced by firms with female founders and by firms at the periphery of the investor network. Using social media activity data on Twitter.com and venture capital investment data of startups on Crunchbase.com, we show that social media can mitigate some biases experienced in firms with women founders and in firms with low social capital in the investor network. This is because social media can serve as new channels for information access and quality signals, allowing disadvantaged companies to share information with investors about products and services. We find this effect is particularly salient in competitive markets where venture financing is harder to obtain, and stronger for new entrants than serial entrepreneurs.
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.
Xitong Li and Lynn Wu (2018), Herding and Social-Network Word-of-Mouth: Evidence from Groupon, MISQ, 42 (4).
Abstract: Modern online retailing practices provide consumers with new types of real-time information that potentially increase demand. In particular, showing past product sales information can reduce uncertainty about product quality, leading consumers to herd. This effect could be particularly strong for experience goods due to their inherent high uncertainty about product quality. Social media word-of-mouth (WOM) can increase product awareness as product information spreads via social media, not only increasing demand directly, but also amplifying existing quality signals such as past sales. This study examines the mechanisms behind the strategy of facilitating herding and the strategy of integrating social media platforms to understand the potential complementarities between the two strategies. We conduct empirical analysis using data from Groupon.com which sells goods in a fast cycle format of “daily deals.” We find that facilitating herding and integrating social media platforms are complements in generating sales, supporting that it is beneficial to combine the two strategies together on social media-driven platforms. Furthermore, we find that herding is more salient for experience goods, consistent with our hypothesized mechanisms, while the effect social media WOM does not differ between experience goods and search goods.
Lynn Wu, Lorin M. Hitt, Fujie Jin (2018), Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements, Management Science, 64 (7).
The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real world problem related to the career goals of the student. The one-semester project may be completed in either the fall or sprong term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor, and the student's faculty advisor, to the Office of Academic Programs two weeks prior to the start of the term.
Conducting business in a networked economy invariably involves interplay with technology. The purpose of this course is to improve understanding of technology (what it can or cannot enable), the business drivers of technology-related decisions in firms, and to stimulate thought on new applications for commerce (including disruptive technologies). The class provides a comprehensive overview of various emerging technology enablers and culminates in discussion of potential business impact of these technologies in the near future. No prior technical background is assumed and hence every effort is made to build most of the lectures from the basics. However, the Fall semester class will assume basic understanding of statistics and will focus more on big data analytics. Some assignments in the fall will involve data analytics using Python or R.
This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.
This course is about understanding emerging technology enablers with a goal of stimulating thinking on new applications for commerce. The class is self-contained (mainly lecture-based) and will culminate in a class-driven identification of novel businesses that exploit these enablers. No prerequisite or technical background is assumed. Students with little prior technical background can use the course to become more technologically informed. Those with moderate to advanced technical background may find the course a useful survey of emerging technologies. The course is recommended for students interested in careers in consulting, investment banking and venture capital in the tech sector.
This course provides an overview of some of the key Information Systems literature from the perspective of Insormation Strategy and Economics (ISE) and Information Decision Technologies (IDT). This course is intended to provide an introduction for first year OIDD doctoral students, as well as other Wharton doctoral students, to important core research topics and methods in ISE and IDT in order for students to do research in the field of Information Systems. While it is intended as a "first course" for OPIM doctoral students in ISE and IDT, it may also be useful for students who are engaged in research or plan to perform information technology related research in other disciplines.
The Sandy Slaughter Early Career Award recognizes and honors early career individuals who are on a path towards making outstanding intellectual contributions to the information systems discipline.
The AIS Early Career Award recognizes individuals in the early stages of their careers who have already made outstanding research, teaching, and/or service contributions to the field of information systems.
The best publication of the year by the Association of Information Systems
Best published paper in 2013 for the Information System Research Journal
Forget the dystopian vision of robots replacing humans at work. A new study co-authored by Wharton’s Lynn Wu shows how automation is increasing the demand for workers, even though it’s driving down the need for managers.Knowledge @ Wharton - 6/21/2021