Frank Nagle is an assistant professor in the Strategy Unit at Harvard Business School. He studies the economics of IT and digitization with a focus on the value of crowdsourcing, and how these topics relate to the future of work. His research interests include free digital goods, cybersecurity, and generating strategic predictions from unstructured big data. His work utilizes large datasets derived from online social networks, financial market information, and surveys of enterprise IT usage. Professor Nagle’s work has been published in Management Science, Organization Science, MIT Sloan Management Review, and Research Policy. He has won awards and grants from AOM, NBER, SMS, INFORMS, and EURAM.
Professor Nagle serves on the advisory board at Nexleaf Analytics and advises other big data analytics startups. He currently advises the OECD Working Party on Innovation and Technology Policy. He has consulted for The World Bank, the U.S. Treasury Department, the Social Security Administration, and various companies in the technology, defense, and energy sectors.
Prior to his academic career, Frank worked at a number of startups and large companies in the information security and technology consulting industries. In these roles, he researched a variety of topics related to social network privacy and the economics of IT, spoke at numerous conferences, and developed and taught a two-week course that all FBI cyber agents must pass before entering the field.
Prior to joining HBS, he was an assistant professor in the Management & Organization Department at the Marshall School of Business at the University of Southern California, where he also served as co-director of Marshall Digitopolis, and as a faculty affiliate of the Lloyd Greif Center for Entrepreneurial Studies and the Annenberg Research Network on International Communication. Frank earned his DBA in Technology and Operations Management from Harvard Business School. He also earned a BS and MS in Computer Science from Georgetown University and an MS in International Business Economics from City University, London.
As open source software (OSS) is increasingly used as a key input by firms, understanding its impact on productivity becomes critical. This study measures the firm-level productivity impact of nonpecuniary (free) OSS and finds a positive and significant value-added return for firms that have an ecosystem of complementary capabilities. There is no such impact for firms without this ecosystem of complements. Dynamic panel analysis, instrumental variables, and a variety of robustness checks are used to address measurement error concerns and to add support for a more causal interpretation of the results. For firms with an ecosystem of complements, a 1% increase in the use of nonpecuniary OSS leads to an increase in value-added productivity of between 0.002% and 0.008%. This effect is smaller for larger firms, and the results indicate that prior research underestimates the amount of IT firms use.
As the economy becomes more information based, firms are increasingly using crowdsourced public goods as inputs for innovation and production. Counterintuitively, some firms pay their employees to contribute to the creation of these goods, which can be used freely by their competitors. This study argues that such firms learn by contributing as they receive feedback from the crowd of more experienced users and are therefore able to better capture value from using the goods. Data on firm contributions to open source software (OSS), an important crowdsourced public good, is used to test the theoretical predictions. Using matching and panel data methods to help address endogeneity concerns, this study shows that contributing firms capture up to 100% more productive value from usage of OSS than their free-riding peers. Furthermore, this paper examines what types of contributions are most beneficial and in what technological environments such learning can best be applied.
Researchers have long hypothesized that research outputs from government, university, and private company R&D contribute to economic growth, but these contributions may be difficult to measure when they take a non-pecuniary form. The growth of networking devices and the Internet in the 1990s and 2000s magnified these challenges, as illustrated by the deployment of the descendent of the NCSA HTTPd server, otherwise known as Apache. This study asks whether this experience could produce measurement issues in standard productivity analysis, specifically, omission and attribution issues, and, if so, whether the magnitude is large enough to matter. The study develops and analyzes a novel data set consisting of a 1% sample of all outward-facing web servers used in the United States. We find that use of Apache potentially accounts for a mismeasurement of somewhere between $2 billion and $12 billion, which equates to between 1.3% and 8.7% of the stock of prepackaged software in private fixed investment in the United States and a very high rate of return to the original federal investment in the Internet. We argue that these findings point to a large potential undercounting of the rate of return from IT spillovers from the invention of the Internet. The findings also suggest a large potential undercounting of "digital dark matter" in general.
On the 50th anniversary of Garrett Hardin’s “The Tragedy of the Commons”, this article considers the benefits and potential downsides of the digital commons, which emerged well after Hardin wrote his seminal article. Unlike the physical world Hardin wrote about, the digital world is essentially infinitely abundant, which leads to a very different tragedy, and many new opportunities.
As firms increasingly rely on crowdsourced digital goods, understanding their impact on productivity becomes critical. This study measures the firm-level productivity impact of one such good, non-pecuniary (free) open source software (OSS). The results show a previously unmeasured positive and significant return to the usage of non-pecuniary OSS that is not solely due to cost savings. Inverse probability weighting, instrumental variables, firm fixed effects, and management quality data add support for a causal interpretation. Across firms, a 1% increase in non-pecuniary OSS leads to a .073% increase in productivity or a $1.35 million increase in value-added production for the average firm in the sample. This effect is greater for larger firms and for firms in the services industry. These findings indicate that existing studies underestimate the amount of IT firms use and suggest that firms assuming the risks associated with non-pecuniary OSS gain benefits from collective intelligence and labor spillovers.
As crowdsourced digital goods become more widely available and more frequently used as key inputs by firms, understanding the impact they have on productivity becomes of critical importance. This study measures the firm-level productivity impact of one such good, non-pecuniary (free) open source software (OSS). The results show a positive and significant return to the usage of non-pecuniary OSS that has gone unmeasured in prior studies of the economics of IT. The study addresses the endogeneity issues inherent in productivity studies by using inverse probability weighting, an instrumental variable approach, and firm fixed effects to add support for a causal interpretation. Across firms, a 1% increase in the amount of non-pecuniary OSS used by a firm leads to a .073% increase in productivity. This translates to a $1.38 million increase in value-added
production for the average firm in the sample. This effect is greater for larger firms and for firms in the services industry. These findings show that firms willing to take on the risks associated with non-pecuniary OSS reap benefits from collective intelligence and labor spillovers. Further, the results suggest that existing studies underestimate the amount of IT at the firm, and therefore underestimate the importance of IT to firm
productivity.
Studies of online word of mouth have frequently posited―but never systematically conceptualized and explored―that the level of disagreement between existing product reviews can impact the volume and the valence of future reviews. In this study we develop a theoretical framework of disagreement in online WOM and test our predictions in a dataset of nearly 300,000 online reviews for 425 movies over three years. This framework highlights that rather than thinking of disagreement as dispersion of opinions around a mean, high levels of disagreement can be better conceptualized as opposing opinion poles. Such a conceptualization has important implications for how disagreement can be measured and how results can be interpreted. We theoretically develop, validate, and apply a novel statistical measure of disagreement that can be used alongside existing alternative approaches such as standard deviation. We find that only high levels of disagreement―with opposing opinion poles―influence future reviews while simple dispersion does not. We show that high levels of disagreement among previously posted reviews lead to more future product reviews, a relationship that is moderated by informational content such that higher informational content amplifies the effect. Further, we show that increased disagreement leads to future reviews of lower valence. Our findings highlight that an important role for research on big data in information systems is to examine how existing measurement approaches and interpretations can be improved by fully leveraging the richness that digital trace data offers.
Researchers have long hypothesized that spillovers from government, university, and private company R&D contribute to economic growth, but these contributions may be difficult to measure when they take a non-pecuniary form. The growth of networking devices and the Internet in the 1990s and 2000s magnified these challenges, as illustrated by the deployment of the descendent of the NCSA HTTPd server, otherwise known as Apache. This study asks whether this experience could produce measurement issues in standard productivity analysis, specifically omission and attribution issues, and, if so, whether the magnitude is large enough to matter. The study develops and analyzes a novel data set consisting of a 1% sample of all outward-facing web servers used in the United States. We find that use of Apache potentially accounts for a mismeasurement of somewhere between $2 billion and $12 billion, which equates to between 1.3% and 8.7% of the stock of prepackaged software in private fixed investment in the United States. We argue that these findings point to a large potential undercounting of "digital dark matter" and related IT spillovers from university and federal funding.
Elizabeth J. Altman, Frank Nagle and Michael Tushman
Innovation has traditionally taken place within an organization's boundaries and/or with selected partners. This Chandlerian approach to innovation has been rooted in transaction costs, organizational boundaries, and information processing challenges associated with distant search. Information processing, storage, and communication costs have long been an important constraint on innovation and a reason for innovative activities to take place inside the boundaries of an organization. However, exponential technological progress has led to a dramatic decrease in information constraints. In a range of contexts, information costs approach zero. In this chapter, we discuss how sharply reduced information costs enable organizations to engage with communities of developers, professionals, and users for core innovative activities, frequently through platform-based businesses and ecosystems and by incorporating user innovation. We then examine how this ease of external engagement impacts the organization and its strategic activities. Specifically, we consider how this shift in information processing costs affects organization boundaries, business models, interdependence, leadership, identity, search, and intellectual property. We suggest that much of the received wisdom in these areas of organization theory requires revisiting. We then discuss the implications for an organization's management of innovation and conclude with research opportunities.
Elizabeth J. Altman, Frank Nagle and Michael Tushman
The goal of this annotated bibliography on technology and innovation is to organize and present the most important literature relevant to a scholar seeking to understand and advance the field. It includes articles that are highly-cited and foundational pieces, as well as recent articles that help give the reader a sense of where the field is headed and where likely opportunities for future research lie. This article seeks to strike an equilibrium among the variety of perspectives that exist in technology and innovation literature, balancing new and old research as well as economic, organizational, and cross-disciplinary methodologies. The innovative process is broadly considered here, as well as the technologies that result from it, including business model innovation, service-level innovation, and product innovation, highlighting articles that utilize diverse levels of analysis.
Altman, Elizabeth J., Frank Nagle, and Michael Tushman. "Technology and Innovation Management." In Oxford Bibliographies: Management, edited by Ricky W. Griffin. New York: Oxford University Press, 2013.
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Nagle, Frank. "Privacy Breach Analysis in Social Networks." In Mining Social Networks and Security Informatics, edited by Tansel Ozyer, Zeki Erdem, Jon Rokne, and Suheil Khoury, 63–78. Springer Science + Business Media, 2013.
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Frank Nagle, Lisa Singh and Aris Gkoulalas-Divanis
Citation:
Nagle, Frank, Lisa Singh, and Aris Gkoulalas-Divanis. "EWNI: Efficient Anonymization of Vulnerable Individuals in Social Networks." Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) (2012): 359–370.
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Aditi Ramachandran, Lisa Singh, Edward Porter and Frank Nagle
While re-identification of sensitive data has been studied extensively, with the emergence of online social networks and the popularity of digital communications, the ability to use public data for re-identification has increased. This work begins by presenting two different cases studies for sensitive data reidentification. We conclude that targeted re-identification using traditional variables is not only possible, but fairly straightforward given the large amount of public data available. However, our first case study also indicates that large-scale re-identification is less likely. We then consider methods for agencies such as the Census Bureau to identify variables that cause individuals to be vulnerable without testing all combinations of variables. We show the effectiveness of different strategies on a Census Bureau data set and on a synthetic data set.
Ramachandran, Aditi, Lisa Singh, Edward Porter, and Frank Nagle. "Exploring Re-Identification Risks in Public Domains." Proceedings of the Annual International Conference on Privacy, Security, and Trust (2012).
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Nagle, Frank, and Michael Sutton. "Economic Models for Software Vulnerability Research." Chap. 1 in Cyber Fraud: Tactics, Techniques, and Procedures. 2nd ed. Auerbach Publications, in press.
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Nagle, Frank, and Lisa Singh. "Privacy in Online Social Networks: Empirical Evidence from Facebook." Proceedings of the International Conference on Advances in Social Network Analysis and Mining (2009).
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Nagle, Frank, and Michael Sutton. "Emerging Economic Models for Vulnerability Research." Proceedings of the Workshop on the Economics of Information Security (2006).
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the American Economic Association Annual Meeting, Boston, MA, January 2015.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the INFORMS Conference on Information Systems and Technology (CIST), Institute for Operations Research and the Management Sciences (INFORMS), San Francisco, CA, November 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the INFORMS Annual Meeting, San Francisco, CA, November 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." NBER Productivity Lunch Seminar, National Bureau of Economic Research, Cambridge, MA, October 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Strategic Management Society Annual International Conference, Madrid, Spain, September 2014. (Nominated for Best Conference Paper.)
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Thematic Conference on Knowledge Commons, Engelberg Center on Innovation Law & Policy and International Association for the Study of the Commons, New York, NY, September 5–6, 2014.
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Nagle, Frank. "Online Word of Mouth and Product Quality Disagreement." Paper presented at the Academy of Management Annual Meeting, Philadelphia, PA, August 2014. (Selected for Best Paper Proceedings.)
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Academy of Management Annual Meeting, Philadelphia, PA, August 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Open and User Innovation Conference, Boston, MA, July 28–30, 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the DRUID Society Conference, Copenhagen, Denmark, June 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the ZEW Conference on the Economics of Information and Communication Technologies, Center for European Economic Research (ZEW), Mannheim, Germany, June 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Charles River Distinguished Speaker and Doctoral Student Conference on Technology and Innovation, Harvard Business School and MIT Sloan School of Management, Boston, MA, May 15, 2014.
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Nagle, Frank. "Crowdsourced Digital Goods and Firm Productivity." Paper presented at the Consortium on Competitiveness and Cooperation, Boston, MA, April 2014.
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Nagle, Frank. "Stock Market Prediction via Social Media: The Importance of Competitors." Paper presented at the Academy of Management Annual Meeting, Lake Buena Vista, FL, August 10–13, 2013.
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Nagle, Frank. "Stock Market Prediction via Social Media: The Importance of Competitors." Paper presented at the 11th ZEW Conference on the Economics of Information and Communication Technologies, Center for European Economic Research (ZEW), Mannheim, Germany, June 21–22, 2013.
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Nagle, Frank. "Stock Market Prediction via Social Media: The Importance of Competitors." Paper presented at the Trans-Atlantic Doctoral Conference, London Business School, London, UK, May 9–11, 2013.
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Nagle, Frank, and Christoph Riedl. "Online Word of Mouth and Product Quality Disagreement." Paper presented at the Workshop on Information Systems and Economics, Association of Information Systems, Orlando, FL, USA, December 15–16, 2012.
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Nagle, Frank, and Christoph Riedl. "Online Word of Mouth and Product Quality Disagreement." Paper presented at the Workshop on Information in Networks, Leonard N. Stern School of Business, New York, September 28, 2012.
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