3730 Walnut Street
571 Jon M. Huntsman Hall
Philadelphia, PA 19104
Research Interests: applied econometrics, economics of electronic commerce, information systems and organization, information technology and productivity, intangible assets
Links: CV, Personal Website
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.
Prasanna Tambe, Lorin M. Hitt, Daniel Rock, Erik Brynjolfsson (Working), Digital Capital and Superstar Firms.
Abstract: General purpose technologies like information technology typically require complementary firm-specific human and organizational investments to create value. These complementary investments produce a form of capital, which we call Information technology-related intangible capital ("ITIC''). An understanding of how the accumulation of ITIC contributes to economic growth and differences among firms has been hindered by the lack of measures of the stock of ITIC. We use a new, extended firm-level panel on IT labor investments along with Hall’s Quantity Revelation Theorem to construct measures of both the prices and quantities of ITIC over the last thirty years. We find that 1) prices vary significantly for ITIC, 2) significant quantities of ITIC have been accumulating since the 1990s, with ITIC accounting for at least 25% of firms’ assets by the end of our panel, 3) that it has disproportionately accumulated in small subset of high-value, superstar firms, and 4) that the accumulation of ITIC predicts future productivity.
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.
Lynn Wu, Bowen Lou, Lorin M. Hitt, Innovation Strategy After IPO: How Data Analytics Mitigates the Post-IPO Decline in Innovation.
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.
Lynn Wu, Lorin M. Hitt, Fujie Jin (2018), Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements, Management Science, 64 (7).
Hessam Bavafa, Lorin M. Hitt, Christian Terwiesch (2018), The Impact of e-Visits on Visit Frequencies and Patient Health: Evidence from Primary Care, Management Science.
Lorin M. Hitt, Ruben Lobel, Ozge Yapar (Work In Progress), Technology sharing in two-sided markets.
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.
Lorin M. Hitt, Fujie Jin, Lynn Wu (Under Revision), Data Skills and Corporate Value of Social Media.
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).
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)
This course introduces the construction and use of data analysis tools that are commonly used for business analysis. The course builds on the spreadsheet and analytical skills developed in OIDD1010, providing a much more extensive treatment of spreadsheet application development (using Excel Visual Basic for Applications). In addition, we will cover best practices in programming and analytics generally which can carry over to other tools and languages. Time permitting, we will do an introduction to some advanced analytical methods that show up in complex data analysis tasks and provide a foundation for further study. In prior years, this course was a 1 cu offering combining the content described here with the content of what is now OIDD3150: Databases for Analytics (0.5 cu). Students seeking this experience can take this course along with OIDD3150 either sequentially or concurrently.
Relational databases are the primary way in which business data is stored and processed. This course focuses on the analysis of data in databases and the development of databases to support analytical tasks. Over the course of the semester, students will learn the database language SQL and use this language to perform analytical tasks on existing and self-created databases. In addition, we will cover database scripting languages and extensions. The course is intended as students with little or no database background and does not presume prior computer science or coding experience. This course is nearly all hands-on coding. Students interested in more conceptual discussions of technology should consider other OIDD offerings.
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 devoted to the study of the strategic use of information and the related role of information technology. It is designed for students who want to manage and compete in technology-intensive businesses. Heavy emphasis is placed on applying information economics principles and theoretical rigor to analyze businesses in information-intensive industries using both qualitative and quantitative techniques. We will study information-based industries like digital media, social networks, financial services, and online retail as well as traditional businesses that are being changed by new digital capabilities. There are four broad themes for the course: the economics of information goods and services, information and consumer behavior, markets and market design, and network economics. Each day we will discuss a core topic in one or more of these themes, with an emphasis on bridging theoretical ideas to real world applications. Application topics might include applying artificial intelligence, platform economics, and cryptocurrencies. Technology skills are not required, although a background in information technology management, strategic management, data science, or managerial economics is helpful.
Relational databases are the primary way in which business data is stored and processed. This course focuses on the analysis of data in databases and the development of databases to support analytical tasks. Over the course of the semester, students will learn the database language SQL and use this language to perform analytical tasks on existing and self-created databases. In addition, we will cover database scripting languages and extensions. The course is intended as students with little or no database background and does not presume prior computer science or coding experience. This course is nearly all hands-on coding. Students interested in more conceptual discussions of technology should consider other OIDD offerings such as OIDD 662.
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.
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