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)
Xitong Li and Lynn Wu (2017), Herding and Social-Network Word-of-Mouth: Evidence from Groupon, MISQ, Forthcoming.
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, Forthcoming.
Lynn Wu and Gerald Kane (Under Revision), Network-biased Technical Change: How Information Management Tools Overcome Some Biases but Exacerbate Others.
Lynn Wu, Lorin M. Hitt, Bowen Lou (Under Review), Data Analytics Skills, Innovation and Firm Productivity.
Abstract: We examine the relationship between data analytics capabilities and innovation using detailed firm-level data. To measure innovative activity, we utilize a survey on process- and innovation- oriented business practices, and we use patent data to analyze the innovative output and characteristics of 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 innovate by recombining existing technologies; data analytics skills have no effect on or are possibly negatively related to value in firms that focus on generating creative and truly novel innovations. We interpret these findings as consistent with data analytics skills being complementary to the exploitation rather than exploration strategies as described in the technology strategy literature.
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.
Chingyung Lin, Lynn Wu, Zhen Wen, Honghong Tong, Vicky Griffiths-Fisher, David Lubinsky (2012), Social Network Analysis in Enterprise, Proceedings of IEEE, 100 (9).
Sinan Aral, Erik Brynjolfsson, Lynn Wu (2012), Testing Three-Way Complementarities: Incentives, Monitoring and Information Technology, Management Science, 58, pp. 913-931.
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.
Cost-optimization strategies must include IT and business initiatives to make sure investments are maximized for long-term growth and profits. In this effort, next-generation technology becomes a critical partner.Knowledge @ Wharton - 2017/02/13