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 or is forthcoming in Management Science, Organization Science, Strategic Management Journal, Harvard Business Review, MIT Sloan Management Review, Research Policy, and Strategic Management Review. He has won awards and grants from AOM, NBER, SMS, INFORMS, and EURAM. At HBS, he is a faculty affiliate of the Digitial Initiative, the Managing the Future of Work Project, and the Laboratory for Innovation Science.
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, conducted cybersecurity assessments and breach investigations, 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.
We consider the role of individual-level diversification as a mechanism through which skilled researchers engage in successful exploration—recognizing and integrating new knowledge external to one’s domains of expertise. To approach an ideal experiment, we (1) employ a matching procedure and (2) exploit the unexpected adoption of Microsoft Kinect as a motion-sensing technology in research. We evaluate the impact of Kinect and its embodiment of new knowledge on a set of ability-matched, diversity-varying researchers without prior experience in motion sensing and find that diversified researchers explore more successfully than their more specialized peers. We also examine the role of personal preferences and professional incentives as antecedents of diversification and find that culture, age, and intellectual freedom are positively associated with the propensity to diversify successfully.
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.
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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User communities represent a unique organizing structure for the exchange of ideas and knowledge. They are organizations composed primarily of users working collaboratively, voluntarily, and with minimal oversight to freely and openly develop and exchange knowledge around a common artifact. The prevalence of user communities appears to be on the rise, as evidenced by communities across a variety of fields ranging from software to Legos to sports equipment. In this essay, we discuss how firms can benefit from working with user communities––that is, we discuss the opportunities for firms to leverage user communities as a source of open innovation. We theorize the conditions under which user communities will emerge and function and discuss the benefits that user communities can provide and the challenges they can create for firms, thereby illustrating the relevance and import of user communities to firms and the strategic management literature.
This study seeks to better understand the impact that government technology procurement regulations have on social value and national competitiveness. To do this, it examines the impact of a change in France’s technology procurement policy that required government agencies to favor open source software (OSS) over proprietary software in an attempt to reduce costs creating an unexpected demand shock for OSS. Analysis using the rest of the EU as controls via difference-in-differences and synthetic control frameworks shows that this policy change led to an increase of nearly 600,000 OSS contributions per year from France, creating social value by increasing the availability and quality of free and open source software. Estimates indicate this would have cost paid software developers roughly $20 million per year to replicate. However, the open nature of such goods means that any country can reap the benefits of these efforts. Therefore, additional economic outcomes that enhance France’s competitiveness are also considered. The results show that within France, the regulation led to a 0.6%–5.4% yearly increase in companies that use OSS, a 9%–18% yearly increase in the number of IT-related startups, a 6.6%–14% yearly increase in the number of individuals employed in IT related jobs, and a 5%–16% yearly decrease in software-related patents. All of these outcomes help to increase productivity and competitiveness at the national level. In aggregate, these results show that changes in government technology policy that favor OSS can have a positive impact on both global social value and domestic national competitiveness.
Nagle, Frank, Robert Seamans, and Steve Tadelis. "Transaction Cost Economics in the Digital Economy: A Research Agenda." Working Paper, February 2019.
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Nagle, Frank. "Us and Them: Predicting Firm Stock Performance via Social Media Sentiment about Competitors." Working Paper, November 2018.
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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.
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.
Nagle, Frank, and Christoph Riedl. "Drivers and Dynamics of Online Word of Mouth." Academy of Management Best Paper Proceedings (2014): 1326–1331.
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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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When organizations place machine learning systems at the center of their businesses, they introduce the risk of failures that could lead to a data breach, brand damage, property damage, business interruption, and in some cases, bodily harm. Even when companies are empowered to address AI failure modes, it is important to recognize that it is harder to be the defender than the attacker since the former needs to guard against all possible scenarios, while the latter only needs to find a single weakness to exploit. Enter AI insurance. The authors believe that businesses can expect stringent requirements when AI insurance is introduced to limit the insurance provider’s liability with rates cooling off as the AI insurance market matures. They offer a guide on preparing for the introduction of these insurance plans.
