Karim R. Lakhani

Why Hackers Do What They Do: Understanding Motivation and Effort in Free/Open Source Software Projects

In this paper we report on the results of a study of the effort and motivations of individuals to contributing to the creation of Free/Open Source software. We used a Web-based survey, administered to 684 software developers in 287 F/OSS projects, to learn what lies behind the effort put into such projects. Academic theorizing on individual motivations for participating in F/OSS projects has posited that external motivational factors in the form of extrinsic benefits (e.g.: better jobs, career advancement) are the main drivers of effort. We find in contrast, that enjoyment-based intrinsic motivation, namely how creative a person feels when working on the project, is the strongest and most pervasive driver. We also find that user need, intellectual stimulation derived from writing code, and improving programming skills are top motivators for project participation. A majority of our respondents are skilled and experienced professionals working in IT-related jobs, with approximately 40 percent being paid to participate in the F/OSS project.

Community, Joining, and Specialization in Open Source Software Innovation: A Case Study

This paper develops an inductive theory of the open source software (OSS) innovation process by focussing on the creation of Freenet, a project aimed at developing a decentralized and anonymous peer-to-peer electronic file sharing network. We are particularly interested in the strategies and processes by which new people join the existing community of software developers, and how they initially contribute code. Analyzing data from multiple sources on the Freenet software development process, we generate the constructs of “joining script”, “specialization”, “contribution barriers”, and “feature gifts”, and propose relationships among these. Implications for theory and research are discussed.

Marginality and Problem-Solving Effectiveness in Broadcast Search

We examine who the winners are in science problem-solving contests characterized by open broadcast of problem information, self-selection of external solvers to discrete problems from the laboratories of large research and development intensive companies, and blind review of solution submissions. Analyzing a unique data set of 166 science challenges involving over 12,000 scientists revealed that technical and social marginality, being a source of different perspectives and heuristics, plays an important role in explaining individual success in problem solving. The provision of a winning solution was positively related to increasing distance between the solvers field of technical expertise and the focal field of the problem. Female solvers---known to be in the “outer circle” of the scientific establishment---performed significantly better than men in developing successful solutions. Our findings contribute to the emerging literature on open and distributed innovation by demonstrating the value of openness, at least narrowly defined by disclosing problems, in removing barriers to entry to nonobvious individuals. We also contribute to the knowledge-based theory of the firm by showing the effectiveness of a market mechanism to draw out knowledge from diverse external sources to solve internal problems.

Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis

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. This paper was accepted by Christian Terwiesch, operations management.

Getting Clear About Communities in Open Innovation

Research on open source software, user innovation and open innovation have increasingly emphasized the role of communities in creating, shaping and disseminating innovations. However, the comparability of such studies has been hampered by the lack of a precise definition of the community construct. In this paper we review prior definitions (implicit and explicit) of the community construct, and other suggestions for future research.

The Principles of Distributed Innovation

Distributed innovation systems are an approach to organizing for innovation that seems to meet the challenge of accessing knowledge that resides outside the boundaries of any one organization. We provide an overview of distributed innovation systems that are achieving success in three different industries. We explore why people participate, the organizing principles of production, and the implications for intellectual property policy. Finally, the potential extensions and limitations of this alternative model of innovation are considered.

Prize-based contests can provide solutions to computational biology problems

Advances in biotechnology have fuelled the generation of unprecedented quantities of data across the life sciences. However, finding individuals who can address such “big data” problems effectively has become a significant research bottleneck. Historically, prize-based contests have had striking success in attracting unconventional individuals who can solve difficult challenges. To determine whether this approach could solve a real “big data” biologic algorithm problem, we used a complex immunogenomics problem as the basis for a two-week online contest broadcast to participants outside academia and biomedical disciplines. Participants in our contest generated over 600 submissions containing 89 novel computational approaches to the problem. Thirty submissions exceeded the benchmark performance of NIH’s MegaBLAST. The best achieved both greater accuracy and speed (x1000). Here we show the potential of using online prize-based contests to access individuals without domain-specific backgrounds to address big data challenges in life sciences.

Organizations in the Shadow of Communities

The concept of a community form is drawn upon in many subfields of organizational theory. Although there is not much convergence on a level of analysis, there is convergence on a mode of action that is increasingly relevant to a knowledge-based economy marked by porous and shifting organizational boundaries. We argue that communities play an underappreciated role in organizational theory – critical not only to occupational identity, knowledge transfer, sense-making, social support, innovation, problem-solving, and collective action but also, enabled by information technology, increasingly providing socioeconomic value – in areas once inhabited by organizations alone. Hence, we posit that organizations may be in the shadow of communities. Rather than push for a common definition, we link communities to an organizations evolution: its birth, growth, and death. We show that communities represent both opportunities and threats to organizations and conclude with a research agenda that more fully accounts for the potential of community forms to be a creator (and a possible destroyer) of value for organizations.

Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science

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.

'Open' Disclosure of Innovations, Incentives and Follow-on Reuse: Theory on Processes of Cumulative Innovation and a Field Experiment in Computational Biology

Recent calls for greater openness in our private and public innovation systems have particularly urged for more open disclosure and granting of access to intermediate works–early results, algorithms, materials, data and techniques–with the goals of enhancing overall research and development productivity and enhancing cumulative innovation. To make progress towards understanding implications of such policy changes we devised a large-scale field experiment in which 733 subjects were divided into matched independent subgroups to address a bioinformatics problem under either a regime of open disclosure of intermediate results or, alternatively, one of closed secrecy around intermediate solutions. We observe the cumulative innovation process in each regime with fine-grained measures and are able to derive inferences with a series of cross-sectional comparisons. Open disclosures led to lower participation and lower effort but nonetheless led to higher average problem-solving performance by concentrating these lesser efforts on the most performant technical approaches. Closed secrecy produced higher participation and higher effort, while producing less correlated choices of technical approaches that participants pursued, resulting in greater individual and collective experimentation and greater dispersion of performance. We discuss the implications of such changes to the ongoing theory, evidence and policy considerations with regards to cumulative innovation. (JEL O3, JO, D02) * Boudreau: London Business School and Institute of Quantitative Social Science at Harvard University, Regent’s Park, London, U.K. NW1 4SA, fax: +44 (0)20 7000 8701, telephone: +44 (0)20 7000 8455, e-mail: kboudreau@london.edu; Lakhani: Harvard Business School and Institute of Quantitative Social Science at Harvard University, email: klakhani@hbs.edu. We are grateful to members of the Harvard Medical School communities for their contribution of considerable attention and resources to this project, including Ramy Arnaout, Eva Guinan and Lee Nadler. We also thank managers at TopCoder, including Jack Hughes, Rob Hughes, Mike Lydon, and Ira Heffan, who provided invaluable assistance in carrying out all aspects of the experiment and in designing and implementing the experimental platform. We thank expert computation and data scientists Po-Ru Loh, Hernan Amiune and Xiaoshi Lu for careful technical evaluations of the data algorithms developed within the experiment. We would like to thank several people for their comments, including: Lee Branstetter, Wesley Cohen, Carliss Baldwin, Erik Brynjolfsson, Chaim Ferschtman, Rebecca Henderson, Nicola Lacetera, Alan McCormack, Petra Moser, Ramana Nanda, Richard Nelson, Catherine Tucker and seminar participants at London Business School, Tel Aviv University, the National Bureau of Economic Research (NBER), and the Roundtable for Engineering Entrepreneurship Research (REER). Onal Vural provided excellent research assistance. All errors are our own. Boudreau would like to acknowledge financial support from a London Business School Research and Materials Development Grant and the University of Toronto, Rotman School of Management. Lakhani would like to acknowledge the financial support of the HBS Division of Research and Faculty Development. A Google Faculty Research Grant supported both authors.