3730 Walnut Street
561 Jon M. Huntsman Hall
Philadelphia, PA 19104
Research Interests: Big Data Analytics, Artificial Intelligence, Enterprise Social Media, Innovation, information Worker Productivity, Prediction, Viral Marketing
Lynn Wu is an assistant professor at the Wharton School. She is interested in studying how emerging information technologies, such as enterprise social media and big data analytics, impact innovation and productivity in organizations. Specifically, her work follows three streams. In the first stream, she studies how enterprise social media and information derived from online platforms affect individuals’ work performance and long-term career trajectories. In her second stream of research, she examines the role of investment in IT, especially the newer wave of technology advances in data analytics and artificial intelligence, affect firm innovation, labor demand, and productivity. In her third stream, 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, 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)
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.
bryan hong and Lynn Wu (Under Revision), Information Technology, Organizational Delayering, and Firm Productivity: Evidence from Canadian Microdata.
Abstract: Using novel data covering a sample of businesses representative of the Canadian economy, we show that information technology (IT) investment has led to the removal of managerial layers within organizational hierarchies, and that IT investments and organizational delayering are complementary in improving firm productivity. We find evidence that IT-based delayering has affected the employment of managers but not non-managerial employees, and leads to increased automation and process innovations. As a result, IT has replaced certain routine managerial functions within the firm. However, by analyzing detailed decision-making authority over a wide range of tasks, we find that managers remaining within the firm have also gained greater authority over a broader range of strategic activities critical to organizational performance that can complement IT investment. This phenomenon, which we consider an “IT management paradox,” is distinct from prior studies that have found that IT investments often lead to decreased decision authority for managers through centralization or decentralization.
Xitong Li and Lynn Wu (2017), 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 (2017), Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements, Management Science, 64 (7).
Lynn Wu and Gerald Kane (Under Revision), Network-biased Technical Change: How Information Management Tools Overcome Some Biases but Exacerbate Others.
Lynn Wu and C. Eesley (Under Revision), Entrepreneurial Adaptation and Social Networks: Evidence from a Randomized Experiment on a MOOC Platform.
Lynn Wu and Erik Brynjolfsson (2014), The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales, Economic Analysis of the Digital Economy .
Lynn Wu (2013), Social Network Effects on Productivity and Job Security: Evidence From the Adoption of a Social Networking Tool, Information Systems Research, 24, pp. 30-51.
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) and the business drivers of technology-related decisions in firms. We will be discussing some of the new and most disruptive technologies right now to stimulate thought on new applications for commerce and new ventures, as well as their implications to the tech industry as a whole. Topics include social media, online advertising, big data, and cloud computing. The course will take a layered approach (from network infrastructure) to data infrastructure to applications infrastructure, or direct enablers of commerce) to first, understanding and then, thinking about technology enablers. Network infrastructure layers include fundamentals of wired and wireless infrastructure technologies such as protocols for networking, broadband technologies - for last (DSL, Cable etc) and other miles (advances in optical networking) and digital cellular communications. Data infrastructure layers include usage tracking technologies, search technologies and data mining. Direct application layers include personalization technologies (CRM), design technologies for content and exchanges, software renting enablers, application service provision, agents and security mechanisms. Finally some emberging technology enablers (such as bluetooth, biometrics and virtual reality) are identified and discussed.
This course is about understanding emerging technology enablers with a goal of stimulating thinking on new applications for commerce. No prerequisite or technical background is assumed. 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, investement banking and venture capital in the tech sector.
The best publication of the year by the Association of Information Systems
Best published paper in 2013 for the Information System Research Journal
Twitter is like a megaphone, but business should see it more like a telephone, according to this opinion piece.Knowledge @ Wharton - 2018/08/9