Professor · Economics & Strategy of Innovation
Kevin Boudreau
I study how to harness massively-scaled innovation, knowledge creation, and economic experimentation — focusing on platforms, digital infrastructure and AI, and science and discovery.
I use field experiments and other methods, often working with engineering teams to design and deploy the underlying infrastructure, while collecting data to derive lessons and optimize systems.
Northeastern · NBER · Harvard IQSS · Associate Editor, Management Science · former Chief Economist, NASA Tournament Lab
k.boudreau@northeastern.edu Publications CV
Industry, leadership & advisory: Qualcomm · Economist Intelligence Unit · Deloitte · Nikean Wireless · Babbage · Canadian Space Agency · data, AI & platform startups
About
Kevin Boudreau is a Professor at Northeastern University's D'Amore-McKim School of Business, in the Department of Entrepreneurship & Innovation, with courtesy appointments in the College of Computer Science and the College of Social Sciences & Humanities. His work sits at the intersection of strategy, innovation, and the economics of knowledge and science, with a particular focus on platforms, digital organization, and how new ideas and technologies are produced and evaluated.
A defining feature of his research is the use of large-scale randomized field experiments — run with partners including NASA, industry platforms, and a strategic scientific grants office — to study questions long thought impossible to test cleanly: how platforms attract and govern complementors, how competition and incentives shape innovation, how scientific collaborations form, how knowledge moves across disciplinary frontiers, and how a scientific funder can run experiments to optimize toward its own goals. As Chief Economist of the NASA Tournament Lab (2011–2015), he helped build the infrastructure for running such experiments at scale. This draws on a career in industry, where he led M&A and large-deal digital-infrastructure teams at QUALCOMM, built a European telecoms consulting and advisory practice at the Economist Intelligence Unit, and advised on strategy at Deloitte. It continues today in hands-on technical work: he assembles engineering teams to build platforms and scale their adoption through the IoT Open Innovation Lab, NUgig, the Cyber-Physical Marketing Challenge, and Digital Scientific Twins, both to prototype for industry partners and to generate the data behind his research. He was also part of the founding faculty team of Northeastern's Wireless Internet Research Center. His papers appear in Management Science, Strategic Management Journal, Organization Science, Research Policy, the RAND Journal of Economics, the Review of Economics and Statistics, Science, and Nature Biotechnology.
He is a Research Associate at the NBER and a Faculty Research Fellow at Harvard's Institute for Quantitative Social Science, an Associate Editor at Management Science, and a former Fulbright Research Scholar (2022–2023). His research has been supported by more than $1.5 million in funding — from the Kauffman, Sloan, Google, and Microsoft programs, NASA, GE, and Northeastern's RIELS, among others — and his work has earned multiple best-paper honors — two Copeland Awards, and recognition from INFORMS, Management Science, and Harvard Business Review. He earned his PhD from the MIT Sloan School (as an MIT Presidential Fellow), an MA in Economics from the University of Toronto, and a BASc in Engineering (summa cum laude) from the University of Waterloo.
Approach
Building Real Systems to Run Real Experiments
My research draws on a broad range of methods — structural and reduced-form causal econometrics, machine learning, and historical and qualitative analysis, alongside economic theory. What sets a large share of it apart is that the work requires running large-scale field experiments with live organizations and platforms — and, often, building the very systems I study. In collaboration with industry partners, scientific organizations, and grant-making bodies — and, where needed, by assembling my own engineering and data-science teams — I design and deploy real digital infrastructure, prototype platforms, and scale their adoption in the field, then run explicit experiments that test, measure, and optimize these systems while they operate. The aim is two-sided — to advance fundamental knowledge and to improve the systems themselves — pairing hands-on engineering and the scientific management of live operations with economic, business, and quantitative analysis.
Platforms and partnerships have included the NASA Tournament Lab, the IoT Open Innovation Lab, NUgig, the Cyber-Physical Marketing Challenge, and Digital Scientific Twins — alongside collaborations with scientific grant-making offices to run experiments that help optimize toward their goals.
Recent Research Spotlight
AI, Strategy & Innovation
Artificial intelligence is the next great platform technology, and I study it as one — how firms compete and design business models on top of AI, and how AI is reshaping innovation, work, and science itself. As across my research, I both run field experiments and help build the AI platforms that generate the data behind them.
AI in Business & Platforms
- Artificial Intelligence as a Platform Technology
- AI-Enabled Job Markets & Market Participation
- Properly Platforming AI as a GPT
- NuGig AI Platform
AI in Science & Discovery
- This Time Is Different: Three Waves of AI Adoption in Science
- Digital Scientific Twins: Prototyping a Platform
- AI in Scientific Collaboration
Research Affiliate, Harvard Business School AI Institute
Research & Publications
Publications
One question runs through all of this: how do you harness innovation and knowledge creation at massive scale — when it takes large numbers of loosely-organized people to produce something genuinely new? I pursue it across two settings that turn out to be the same problem — digital platforms (increasingly AI platforms) and science itself — using theory, large-scale data, and field experiments. The work is grouped by program below.
Current Projects & Working Papers
Management & Strategy of AI
- AI-Enabled Job Markets & Market Participation
- AI in Science
- Properly Platforming AI as a GPT
Platform Strategy & Innovation
- Managing Your Long Tail Innovation
Science & the Innovation Workforce
- Field Experiment on Interdisciplinary Science
- Development of Engineers & STEM Workers
Platform Strategy, Entrepreneurship & Innovation
How open should a platform be? Who should control its complements? What makes participation take off? This stream develops a set of ideas — granting access vs. devolving control, platform boundaries, complementor incentives, self-fulfilling expectations — from early conceptual work to recent experiments at the platform edge and on AI platforms.
-
Artificial Intelligence as a Platform Technology: Strategic Implications of Competing on Top of an AI Platform
The strategy of competing on top of someone else's AI platform — who captures value, and how, when AI is the platform everyone builds on.
Abstract
Artificial Intelligence is a transformative technology, but there exists considerable uncertainty about its evolution and impact on firms, society, and the economy. The development of Generative Artificial Intelligence and Large Language Models in many ways parallels the structure of other technology platforms and ecosystems. In this chapter, we discuss the development of Artificial Intelligence technologies (particularly Generative Transformer-Based Artificial Intelligence such as Large Language Models) and explore the similarities to platform ecosystems. We then discuss how learnings from research in platforms can apply to Artificial Intelligence and implications for competitive strategies of firms, innovation by firms, and broader implications of these technologies diffusing across the economy.
-
Free(mium) Strategies for Digital Goods
Abstract
Freemium strategies (offering both a free and paid version) are a commonly used approach to sell digital goods. Academic research into freemium strategies is distributed across disciplines, and each study typically focuses on a single type of freemium strategy within a specific context. This chapter provides a review of the literature on freemium strategies, highlighting the central insights across these studies. The goal of this review is to provide a resource that researchers can use to gain an overview of research into this topic. We then discuss areas where there are opportunities for further exploration, and where the anecdotes that are often used to discuss freemium strategies and the reality of the academic literature do not align. We conclude by highlighting areas for future research into this domain.
-
Gender Differences in Response to Competitive Organization: Evidence from a Product Development Platform
A field experiment on an IoT platform finding men and women respond differently to competitively-organized work, with the gap varying across STEM and non-STEM domains.
Abstract
Prior research, primarily based on lab experiments, suggests that females might be more averse to competition than males and could be more inclined towards collaboration, instead. Were these findings to generalize to adults across the workforce, there could be profound implications for organizational design and personnel management. We report on a field experiment in which 97,678 adults from a wide range of fields and ages were invited to join a product development opportunity. Individuals were randomly assigned to treatments framing the opportunity as either involving competitive or collaborative interactions with other participants. Among those outside of science, technology, engineering, and math fields (STEM), we find significant gender differences in willingness to participate under competition. Among those in STEM fields, we detect no statistical gender differences. These results and broader patterns documented in the study are consistent with significant heterogeneity in competitiveness across both men and women, with field and career sorting resulting in differences (in gender differences) across fields.
-
Competing on Freemium: Digital Competition with Network Effects
How “freemium” pricing interacts with network effects to shape digital competition — when giving the product away free is the winning move.
Abstract
"Freemium" product strategies—where a free basic version of a product is offered alongside a full "premium" paid version—are often used by companies to attempt to increase the size of their user base and benefit from network effects. However, there is limited empirical evidence of how using freemium strategies impacts firm revenues. We empirically investigate how the strengthening of network effects on the Apple App Store influenced product sales of firms using freemium strategies, contrasting the impacts on both market leaders and followers. We find that stronger network effects did not on their own lead to greater revenues for market leaders with respect to followers. However, in settings where freemium strategies were used, network effects greatly amplified the advantage of leaders over followers.
-
Profiting from Digital Innovation: Patents, Copyright, and Performance
When formal IP (patents, copyright) actually improves app developers’ performance — and when, in fast-moving digital markets, it doesn’t.
Abstract
It is yet unclear whether patents and copyright are effective at protecting digital innovations. In this paper, we investigate this question using novel product-level data on mobile apps, in which we relate the use of both patents and copyright to (i) revenue performance and (ii) IP licensing. We theorize that these relationships depend on differences in product-level characteristics and that apps differentiated by their design are more likely and effectively to be protected by patents; apps combining elements of differentiated content are more likely and effectively protected by copyright. Our results support these predictions that product characteristics shape the appropriate contingent use of patent and copyright protection in digital products. These patterns are especially relevant to industries where digital products combine elements of differentiated design and differentiated digital content.
-
Crowdfunding as Donations to Entrepreneurial Firms
Many crowdfunding backers behave more like donors than investors or buyers — funding firms for reasons beyond expected returns.
Abstract
The bulk of today's ("preorder-," "reward-," "gift-," and "donation-based") crowdfunding raises funds for small, private entrepreneurial ventures without granting funders private claims to the projects' income or the ability to guarantee the realization and delivery of project outcomes. We theorize and show empirically – via a mixed-method approach applied to a representative and remarkably informative case – that the payoff structure for crowdfunders, akin to a public good contribution problem, leads to the tangible value of main project outputs exerting little influence on contributions to crowdfunding. This then raises the question of which funder motivations fund seekers may have to address to crowdfund their projects. We demonstrate the especially large role of non-pecuniary motivations and pinpoint three particular motivations that profit-seeking entrepreneurs may stimulate to be financed through crowdfunding. The findings hold important implications for entrepreneurs' crowdfunding strategies, platform design, and our understanding of how this funding institution works in general. The study also adds to emerging research on the implications of the public good nature of crowdfunding.
-
Promoting Platform Takeoff and Self-Fulfilling Expectations: Field Experimental Evidence
A field experiment showing platform takeoff is partly self-fulfilling: shifting participants’ expectations of success measurably increased their participation.
Abstract
A platform might have the potential to bring enormous value to its users. However, without a well-orchestrated launch strategy that coordinates a sufficient number of users onto the platform, this potential will not be realized. The theoretical literature predicts that one approach to coordinating platform take-off is to influence the market's subjective focal expectations of the future installed base of users. This paper reports on a field experiment investigating the causal role of subjective expectations in the launch of a new platform venture, in which invitations to join a newly launched platform were sent to 16,349 individuals. The invitations included randomized statements regarding the size of the future expected installed base (along with disclosures of the current installed base). I find that simple, subjective, uncommitted, and relatively costless statements broadcasted by the platform with the goal of influencing market expectations were indeed able to influence platform takeoff and overcome an initial chicken-and-egg problem. These broadcasted subjective statements regarding future installed base had a larger influence on adoption rates than did disclosures of the true current installed base during early adoption. However, these subjective statements of expected future installed base ceased to have any effect once the true current installed base grew large. I discuss implications for the promotion, marketing, and evangelism of new platform ventures.
-
Protecting Digital Assets: The Use of Formal and Informal Appropriability Strategies by App Developers
How app developers actually protect what they build — leaning on informal strategies (speed, complexity, secrecy) as much as formal IP.
Abstract
Innovation and product development by complementors on a digital platform, differ from traditional and analog industries in ways that may influence the use of appropriability strategies—including the low costs of development, high uncertainty of outcomes, and the nature of digital technologies themselves. Taken together, these characteristics lead digital platforms to be populated predominantly by a "long tail" of very small firms. Here, we study the use of appropriability strategies (formal and informal) by third-party developers on a platform and consider whether smaller firms use such appropriability strategies differently from the larger firms that have typically been studied. In data on 809 developers on the Apple App Store representing 9,152 titles ("reweighed" to estimate population distributions), we find an overwhelming majority of firms attempt to protect their innovations (70%). Tiny firms—the vast majority of all developers—use only informal protections. Larger firms, in contrast, rely on formal intellectual property rights and use copyright, patents, and trademarks as commonly as do firms in traditional industries, but generally do so together with informal strategies. Several strategies distinct to digital platforms are documented. We interpret the uses of appropriability strategies in relation to digital competition faced by heterogenous innovators.
-
"Crowds" of Amateurs and Professional Entrepreneurs in Marketplaces
Distinguishes amateur hobbyists from professional entrepreneurs among platform complementors, and shows they contribute innovation in different ways.
Abstract
Platforms often have "crowds" of amateurs working on them as complementors, in other cases professional entrepreneurs—or both. What can a platform owner do to implement these outcomes? I document evidence on mobile app developers showing that just small, incremental changes in platform design—related to the bare minimum costs required to build an app and factors affecting non-pecuniary payoffs—can lead the "bottom-to-fall-out" of the market to amateurs. Where the bottom-falls-out, there is a flood of lowest-quality developers who nonetheless are long-lived on the platform and engage in relatively high development activity. I find no evidence that amateurs crowd-out development activity of top developers in this context. Moreover, the bottom-falling-out is associated with the generation of significantly greater numbers of highest-quality products. I discuss several interpretations.
-
Platform Boundary Choices and Governance: Entrepreneurship, Innovation, and Growth in Mobile Computing
A framework for where platforms draw their boundaries — how to open up to outside innovators while still coordinating and orchestrating the whole.
Abstract
Rather than organize as traditional firms, many of today's companies organize as platforms that sit at the nexus of multiple exchange and production relationships. This chapter considers a most basic question of organization in platform contexts: the choice of boundaries. Herein, I investigate how classical economic theories of firm boundaries apply to platform-based organization and empirically study how executives made boundary choices in response to changing market and technical challenges in the early mobile computing industry (the predecessor to today's smartphones). Rather than a strict or unavoidable tradeoff between "openness-versus-control," most successful platform owners chose their boundaries in a way to simultaneously open-up to outside developers while maintaining coordination across the entire system.
-
Performance Responses to Competition Across Skill Levels in Rank-Order Tournaments
More competition in a tournament motivates some skill levels and discourages others — so contest design must account for who is competing.
Abstract
Tournaments are widely used in the economy to organize production and innovation. We study individual data on 2775 contestants in 755 software algorithm development contests with random assignment. The performance response to added contestants varies nonmonotonically across contestants of different abilities, precisely conforming to theoretical predictions. Most participants respond negatively, whereas the highest-skilled contestants respond positively. In counterfactual simulations, we interpret a number of tournament design policies (number of competitors, prize allocation and structure, number of divisions, open entry) and assess their effectiveness in shaping optimal tournament outcomes for a designer.
-
Unpaid Platform Complementors and the Network Effect Mirage
A platform’s network-effect “value” can be a mirage: many complementors are unpaid amateurs, and adding more can crowd each other out rather than add value.
Abstract
Platforms have evolved beyond just being organized as multi-sided markets with complementors selling to users. Complementors are often unpaid, working outside of a price system and driven by heterogeneous sources of motivation—which should affect how they respond to platform growth. Does reliance on network effects and strategies to attract large numbers of complementors remain advisable in such contexts? We test hypotheses related to these issues using data from 85 online multi-player game platforms with unpaid complementors. We find that complementor development responds to platform growth even without sales incentives, but that attracting complementors has a net zero effect on on-going development and fails to stimulate network effects. We discuss conditions under which a strategy of using unpaid crowd complementors remains advantageous.
-
Let a Thousand Flowers Bloom? An Early Look at Large Numbers of Software App Developers and Patterns of Innovation
Bigger, more varied crowds of app developers generate more — and more novel — innovation, up to a point, with crowding effects.
Abstract
In this paper, I study the effect of adding large numbers of producers of application software programs ("apps") to leading handheld computer platforms, from 1999 to 2004. To isolate causal effects, I exploit changes in the software labor market. Consistent with past theory, I find a tight link between the number of producers on platform and the number of software varieties that were generated. The patterns indicate the link is closely related to the diversity and distinct specializations of producers. Also highlighting the role of heterogeneity and nonrandom entry and sorting, later cohorts generated less compelling software than earlier cohorts. Adding producers to a platform also shaped investment incentives in ways that were consistent with a tension between network effects and competitive crowding, alternately increasing or decreasing innovation incentives depending on whether apps were differentiated or close substitutes. The crowding of similar apps dominated in this case; the average effect of adding producers on innovation incentives was negative. Overall, adding large numbers of producers led innovation to become more dependent on population-level diversity, variation, and experimentation—while drawing less on the heroic efforts of any one individual innovator.
-
The Confederacy of Heterogeneous Software Organizations and Heterogeneous Developers: Field Experimental Evidence on Sorting and Worker Effort
Abstract
Software development occurs in a patchwork or "confederacy" of different types of institutions (universities, small start-ups, multinational enterprises, government agencies, etc.) utilizing varied work approaches. Here we speculate on one possible explanation for this organizational heterogeneity: it may reflect inherent heterogeneity of the software workforce, in terms of which kinds of organizations individual workers prefer to work within ("institutional preference"). We take very preliminary steps towards investigating this possibility by devising a novel 10-day field experiment to estimate the differences in behavior that are created by sorting workers into their preferred institutional regimes versus having them unsorted. The experiment involved assigning 1040 elite software developers to either a competitive or a cooperative work regime to create software for NASA's Space Life Sciences Directorate. Half of the subjects—the "sorted" group—were assigned according to their institutional preferences; the other half—the "unsorted" group—were assigned without regard to their preferences. Assignment was done in a manner that sorted and unsorted groups had identical distributions of raw problem-solving ability. We find a remarkably large effect of institutional preference-based sorting on the effort exerted. Sorting on institutional preferences roughly doubled effort within the competitive regime and increased effort by roughly half in the cooperative regime, while accounting for incentives. Our experimental approach and results indicate the importance of accounting for worker preferences in creative activities that drive the rate and direction of inventive activity in the economy.
-
Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis
Adding competitors to an innovation contest lowers each entrant’s effort but raises the odds of a breakthrough solution — and which effect wins depends on how uncertain the problem is.
Abstract
Contests are a historically important and increasingly popular mechanism for encouraging innovation. A central concern in designing innovation contests is how many competitors to admit. Using a unique data set of 9,661 software contests, we provide evidence of two coexisting and opposing forces that operate when the number of competitors increases. Greater rivalry reduces the incentives of all competitors in a contest to exert effort and make investments. At the same time, adding competitors increases the likelihood that at least one competitor will find an extreme-value solution. We show that the effort-reducing effect of greater rivalry dominates for less uncertain problems, whereas the effect on the extreme value prevails for more uncertain problems. Adding competitors thus systematically increases overall contest performance for high-uncertainty problems. We also find that higher uncertainty reduces the negative effect of added competitors on incentives. Thus, uncertainty and the nature of the problem should be explicitly considered in the design of innovation tournaments. We explore the implications of our findings for the theory and practice of innovation contests.
-
Open Platform Strategies and Innovation: Granting Access vs. Devolving Control
“Opening” a platform isn’t one thing: granting outside developers more access sharply raised the rate of innovation, while devolving control did not — distinct levers with different effects.
Abstract
This paper distinguishes two fundamentally distinct approaches to opening a technology platform, and their different impacts on innovation. One approach is to grant access to a platform and thereby open-up markets for complementary components around the platform. Another approach is to go further and give up control over the platform itself. Using data on 21 systems from handheld computing systems (1990-2004), I find that granting greater levels of access to independent hardware developer firms related to up to a fivefold acceleration in new handheld device development, depending on the precise degree of access and how this was implemented. Where operating system platform owners went further to give up control (beyond just granting access to their platforms) the incremental effect on new device development was still positive but an order of magnitude smaller. The evidence from the industry and theoretical arguments both suggest that distinct economic mechanisms were set in motion by these two approaches to opening.
-
Platform Rules: Multi-Sided Platforms as Regulators
Platform owners act as private regulators, governing their ecosystems through rules, not just prices.
Management of Knowledge, Science & Discovery
The same levers — openness, rules, incentives, the cost of search — shape how science produces knowledge. This stream studies how scientific work is organized, evaluated, and recombined: peer review as an institution, how new collaborations form, who does multidisciplinary science, and how ideas move across the knowledge frontier.
-
Scientific Peer Review: Institutional Workings and Performance
-
Field Experiments in the Science of Science: Lessons from Peer Review and the Evaluation of New Knowledge
Abstract
Scientific evaluation and peer review govern the allocation of resources and certification of knowledge in science, yet have been subjected to limited causal investigation. This chapter synthesizes randomized experiments embedded in live peer-evaluation systems at journals, conferences, and funding agencies, restricting attention to published studies. I organize this evidence using a Q–A–R–S framework that decomposes peer review into attributes of submissions (Q), authors (A), reviewers (R), and evaluation systems (S), and interpret outcomes through a view of the core problem of scientific evaluation as assessing new knowledge using the existing stock of knowledge.
The chapter treats experimental design choices as objects of analysis, assessing what existing interventions can—and cannot—identify given their designs and settings, the institutional constraints they face, and opportunities for higher-leverage experimentation. I show that randomized experimentation embedded in peer review spans the full Q–A–R–S space, albeit sparsely, and yields uneven but informative insights across different margins.
Based on the full body of evidence, I advance several novel claims: (1) system interventions often affect participant behavior with little impact on core evaluative judgments; (2) core evaluations are most clearly shaped by who reviews and their expertise; and (3) peer review functions more reliably as a "filter" of poor submissions than as a fine-grained "ranker" of acceptable submissions. Overall, the evidence points to a functioning institution operating under binding epistemic and organizational constraints, rather than to systemic failure. I identify channels for improving the speed, cost, and reliability of scientific evaluation institutions.
Substantial scope remains to redesign embedded experiments to increase inferential power, generalizability, and cumulative insight, while reducing disruption and more tightly linking to institutional innovation and policy changes.
-
This Time Is Different: A Comparison of Three Waves of AI Adoption in Science
Abstract
This study examines the diffusion of artificial intelligence within the scientific enterprise to understand whether the recent wave of generative AI represents a fundamental departure from previous technological paradigms. We undertake a large-scale quantitative analysis of 4.8 million academic publications from 1950 to 2024, systematically mapping the adoption patterns of three distinct AI waves: deep learning, natural language processing, and GenAI. Our analysis reveals that GenAI has diffused with unprecedented speed and breadth across scientific disciplines. We find that GenAI adoption is associated with markedly lower concentration levels and is correlated with the formation of smaller research teams, suggesting a reversal of long-standing trends toward greater specialization and larger team sizes in science. These findings indicate that GenAI constitutes a significant shift in the relationship between AI and scientific discovery.
-
From Theory to Practice: Field Experimental Evidence on Early Exposure of Engineering Majors to Professional Work
Early exposure to real professional engineering work (e.g., co-op) measurably shapes students’ later trajectories.
Abstract
Young workers typically enter the professional labor market only after completing higher education. We investigate how earlier professional work experience affects skilled worker development. In a field experiment, 1,787 Engineering majors were randomly assigned to 6-month work terms to begin either in the second or third year of studies. Early exposure caused systematic differences in inclination to take Engineering elective courses, choice of major, and the probability of persisting in Engineering years later—consistent with engagement, retention, and sorting effects. Early exposure notably increased academic and professional outcomes of lower-income students.
-
Randomized Insights: Field Experiments in Understanding Knowledge Production in the Sciences
-
A Field Experiment on Search Costs and the Formation of Scientific Collaborations
A field experiment showing that simply lowering the cost of finding each other significantly increased the formation of new scientific collaborations.
Abstract
We present the results of a field experiment conducted at Harvard Medical School to understand the extent to which search costs affect matching among scientific collaborators. We generated exogenous variation in search costs for pairs of potential collaborators by randomly assigning individuals to 90-minute structured information-sharing sessions as part of a grant funding opportunity. We estimate that the treatment increases the probability of grant co-application of a given pair of researchers by 75%. The findings suggest that matching between scientists is subject to considerable friction, even in the case of geographically proximate scientists working in the same institutional context.
-
Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science
Evaluating new science is shaped by bounded rationality: the more expert an evaluator is in a proposal’s area, the lower they tend to score it — expertise surfaces more information cues (more potential problems) — and highly novel proposals get discounted too. Both distort how research resources are allocated.
Abstract
Selecting among alternative projects is a core management task in all innovating organizations. In this paper, we focus on the evaluation of frontier scientific research projects. We argue that the "intellectual distance" between the knowledge embodied in research proposals and an evaluator's own expertise systematically relates to the evaluations given. To estimate relationships, we designed and executed a grant proposal process at a leading research university in which we randomized the assignment of evaluators and proposals to generate 2,130 evaluator–proposal pairs. We find that evaluators systematically give lower scores to research proposals that are closer to their own areas of expertise and to those that are highly novel. The patterns are consistent with biases associated with boundedly rational evaluation of new ideas. The patterns are inconsistent with intellectual distance simply contributing "noise" or being associated with private interests of evaluators. We discuss implications for policy, managerial intervention, and allocation of resources in the ongoing accumulation of scientific knowledge.
-
Innovation Experiments: Researching Technical Advance, Knowledge Production, and the Design of Supporting Institutions
An agenda for using field experiments to study how technical advance and knowledge get produced — and to design the institutions that support them.
Abstract
This paper discusses several challenges in designing field experiments to better understand how organizational and institutional design shapes innovation outcomes and the production of knowledge. We proceed to describe the field experimental research program carried out by our Crowd Innovation Laboratory at Harvard University to clarify how we have attempted to address these research design challenges. This program has simultaneously solved important practical innovation problems for partner organizations, like NASA and Harvard Medical School, while contributing research advances, particularly in relation to innovation contests and tournaments.
-
"Open" Disclosure of Innovations, Incentives, and Follow-on Reuse: Theory and a Field Experiment in Computational Biology
A field experiment in computational biology testing how openly disclosing intermediate results changes follow-on reuse and the pace of cumulative innovation.
Abstract
Most of society's innovation systems – academic science, the patent system, open source, etc. – are "open" in the sense that they are designed to facilitate knowledge disclosure among innovators. An essential difference across innovation systems is whether disclosure is of intermediate progress and solutions or of completed innovations. We theorize and present experimental evidence linking intermediate versus final disclosure to an 'incentives-versus-reuse' tradeoff and to a transformation of the innovation search process. We find intermediate disclosure has the advantage of efficiently steering development towards improving existing solution approaches, but also has the effect of limiting experimentation and narrowing technological search. We discuss the comparative advantages of intermediate versus final disclosure policies in fostering innovation.
-
Prize-Based Contests Can Provide Solutions to Computational Biology Problems
An open prize contest produced computational-biology solutions that beat the established benchmark — evidence that opening a hard problem to a crowd can outperform the experts.
Translation to Practice
Harvard Business Review · MIT Sloan Management Review · HBS
-
A Short Guide to Strategy for Entrepreneurs
-
Designing Your Company: Creating, Delivering, and Capturing Value
Abstract
These notes set out a step-by-step process for designing—or equally, analyzing and evaluating—a company's design and strategy. They apply as much to newly conceived entrepreneurial ventures as to established, continually evolving enterprises.
At the core, three minimum conditions must be met to sustain an economically viable enterprise. On a practical level, the notes provide a field-tested process for articulating and refining strategy. At the same time, they integrate and distill what are often treated as disparate economic concepts and frameworks into a single, cohesive guide. The result is a roughly 100-page synthesis of what is known on this topic.
Strategy, entrepreneurship, business model design, innovation, and competitive dynamics are often taught through fragmented frameworks and separate courses. These notes aim instead to summarize the core concepts and their interconnections within an integrated "framework-of-frameworks" and a series of design steps. The goal is to provide clarity and synthesis without resorting to artificial simplification.
Taken together, these notes serve as a field guide, teaching tool, and research map, designed to complement standard textbooks and articles in the areas of strategy, business model design, entrepreneurship, and innovation.
-
Using the Crowd as an Innovation Partner
-
Experiments in Open Innovation at the Harvard Medical School
-
How to Manage Outside Innovation: Competitive Markets or Collaborative Communities?
Teaching Cases
-
Digital Twins Platform
-
NuGig AI Platform
-
The Economist Corporate Network Platform
-
Fubles: The Birth of a Social Sport Sharing Platform
Abstract
This case study considers early progress and strategic issues faced in the design of Fubles.com, as it grows and expands across geographies.
-
Space Industry: EADS Astrium
No public link
-
Debate on Licensing the Palm OS Platform
Honors & Funding
More than $1.5 million in competitive research funding over the years — from federal agencies, foundations, and industry — alongside multiple best-paper honors.
Selected Honors & Awards
- Copeland Best Paper Award — Promoting Platform Takeoff and Self-Fulfilling Expectations (2022)
- INFORMS TIM Best Paper Runner-Up — Looking Across and Looking Beyond the Knowledge Frontier (2020)
- IoT Colleges & Universities Innovator Organization Award (2018)
- Copeland Best Paper Award — Looking Across & Looking Beyond the Knowledge Frontier (2017)
- INFORMS TIM Best Paper — Let a Thousand Flowers Bloom? (2017)
- Management Science Distinguished Service Award (2015)
- HBR / McKinsey & Co. Award Runner-Up — Using the Crowd as an Innovation Partner (2014)
- Management Science Best Paper Finalist (2013)
- Management Science Meritorious Service Award (2013)
Selected Funding
- Platform Innovation & AI — $50k
- Advancing NUgig's AI, DMSB — $35k
- Fulbright Scholarship — $50k
- IoT Open Innovation Collaborative Platform Build — Kauffman $200k · DMSB $50k · GE Corp $15k
- NUgig, Kauffman Foundation — $50k
- CIGREF Grant — $120k
- Sloan Foundation Research Grant — $60k
- NASA Tournament Lab, with K. Lakhani — $600k
- Google Faculty Research Grant — $150k
- London Management Lab Research Award — $75k
- Microsoft Research Faculty Award — $60k
Professional Background & Service
Professional & Industry Experience
- Founding President, Babbage Innovation & Data Analytics — acquired by Wipro
- Founding team, Nikean Wireless — acquired by Nextel
- Director — built the Western European technology advisory practice, Economist Intelligence Unit
- Senior Manager — led in-country M&A teams across Latin America, QUALCOMM
- Strategy Consultant — Deloitte
- Strategy & advisory — Canadian Space Agency
- Advisor — data, AI & platform startups
- Earlier: Braxton Strategy Consulting · Nortel / Bell-Northern Research
Labs, Research & Industry Collaborations
- RPDIES — Research Program on Digital Innovation, Entrepreneurship & Strategy
- IoT Open Innovation Lab
- Wireless Internet of Things Research Center — founding faculty
- NUgig — experiential-education venture
- Digital Scientific Twins
- Cyber-Physical Marketing Challenge
- RIELS — Research Institute for Experiential Learning Science, Northeastern
- Harvard Institute for Quantitative Social Science — Faculty Research Fellow
- Harvard Business School AI Institute — Research Affiliate
- NASA Tournament Lab — former Chief Economist
Editorial & Professional Service
- Management Science — Associate Editor, 2011–present
- Strategic Management Journal — Editorial Board, 2016–present
- Boston University Digital Management — Scientific Committee, 2023–present
- Journal of Economics & Management Strategy — co-Editor, 2022–2023
- Academy of Management Journal — Editorial Board, 2012–2014
- AOM Doctoral Consortium Co-Leader & Organizer (BPS / TIM)
- Reviewer: ASQ, Management Science, SMJ, Org Science, Research Policy, AMJ, AMR, Science, PNAS, NSF, ISR, MISQ, AER, QJE, JPE
Education
- PhD, Behavioral & Policy Sciences (Technology, Innovation, Entrepreneurship & Strategy), MIT Sloan School, 2006. MIT Presidential Fellow. Dissertation: "How Open Should an Open System Be?"
- MA, Economics, University of Toronto, 1998 — focus on industrial & organizational economics and econometrics
- BASc, Engineering (minor: Management Science), University of Waterloo, 1996. Summa cum laude; Canada Scholarship. Designed and ran experimental projects and built statistical tools in microelectronics.