I am currently the Zhang Jindong Professor of Operations, Information and Decisions at the University of Pennsylvania, Wharton School. My central research area is on the relationship between information technology and productivity and the factors that affect the value of IT investments. Most of my recent work has been the study of complementary factors, such as organizational design and human capital, on the value of IT. Most of this work is directed at firms in all industries, although I have become increasingly interested in IT deployment in healthcare. In recent years, we have been more extensively working on the role of the IT workforce and issues that affect the demand and wages of IT workers (such as offshoring and the H1-B visa program). I have also been extensively involved in electronic business research investigating the the nature of competition in electronic markets (such as on-line travel agents), the value proposition for alternative delivery systems (such as online retail banking), the role of switching costs in determining pricing and product strategy (as in online discount brokerage), and the effect of recommender systems on consumer behavior.
I teach undergraduate and graduate courses in information systems management and economics and data analysis. I also teach the undergraduate core class in OPIM in the Fall (off-season).
In my spare time, I also consult and conduct research into the design of IT outsourcing agreements, methods for evaluating IT investments, and other questions at the intersection of information systems, economics and econometrics. I also occasionally serve as an expert witness for information technology and consumer-related litigation (intellectual property, consumer behavior in computers and consumer electronics industries, enterprise software, and software project problems).
My current research focuses on the economics of IT labor mobility, contracting in enteprise software, the influence of recommender systems on consumer behavior, measuring intangible assets, and pricing information goods. See my personal site for more information.
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.
Lynn Wu, Lorin M. Hitt, Fujie Jin (2017), Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements, Management Science, 64 (7).
Abstract: This paper investigates the drivers behind Tesla's decision to make its patents freely available to other electric car manufacturers. The two sides of this market, car owners and potential charging stations, rely on each other to increase the value of their investment. We show under what conditions subsidizing the competitors can be profitable. By sharing technology, Tesla may be able improve the charging station network and increase it's own profit from car sales.
Prasanna Tambe (OPIM) and Lorin M. Hitt (2011), Now IT’s Personal: Offshoring and the Shifting Skill Composition of the US Information Technology Workforce, Management Science, (forthcoming).
XinXin Li (OPIM), Lorin M. Hitt, Z. John Zhang (MKTG) (2011), Product Reviews and Competition in Markets for Repeat Purchase Products, Journal of Management Information Systems, (forthcoming).
Prasanna Tambe (OPIM), Lorin M. Hitt, Erik Brynjolfsson (2011), The Extroverted Firm: How External Information Practices Affect Productivity, Management Science, (forthcoming).
I teach four courses:
OPIM101 – Introduction to OPIM (Fall only)
OPIM105 – Data Analysis in VBA and SQL (next offering Fall, 2015)
OPIM469 – Information Strategy and Economics (next offering unknown – maybe Fall, 2016)
OPIM955 – Doctoral Seminar in IS Economics (offered Spring, 2015)
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.
This course provides an introduction to the construction of data analysis tools that are commonly used for business applications, especially in consulting and finance. The course builds on the spreadsheet and analytical skills developed in OPIM101, providing a much more extensive treatment of spreadsheet application development and database management. The first portion of the course will focus on programming in VBA, the embedded programming language in the Microsoft Office suite of applications. This will be supplemented with discussion of industry best practice in software development, such as specification development, interface design, documentation, and testing. The second portion of the class will emphasize data access and analysis utilizing SQL, the industry standard language for interacting with database software.
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.
Twitter is like a megaphone, but business should see it more like a telephone, according to this opinion piece.Knowledge @ Wharton - 2018/08/9