Revolutionizing Innovation: Users, Communities, and Open Innovation

Dietmar  Harhoff; Karim R. Lakhani

Book | 2016

Revolutionizing Innovation: Users, Communities, and Open Innovation

Abstract

The last two decades have witnessed an extraordinary growth of new models of managing and organizing the innovation process, which emphasize users over producers. Large parts of the knowledge economy now routinely rely on users, communities, and open innovation approaches to solve important technological and organizational problems. This view of innovation, pioneered by the economist Eric von Hippel, counters the dominant paradigm, which casts the profit-seeking incentives of firms as the main driver of technical change. In a series of influential writings, von Hippel and colleagues found empirical evidence that flatly contradicted the producer-centered model of innovation. Since then, the study of user-driven innovation has continued and expanded, with further empirical exploration of a distributed model of innovation that includes communities and platforms in a variety of contexts and with the development of theory to explain the economic underpinnings of this still emerging paradigm. This volume provides a comprehensive and multidisciplinary view of the field of user and open innovation, reflecting advances in the field over the last several decades. The contributors—including many colleagues of Eric von Hippel—offer both theoretical and empirical perspectives from such diverse fields as economics, the history of science and technology, law, management, and policy. The empirical contexts for their studies range from household goods to financial services. After discussing the fundamentals of user innovation, the contributors cover communities and innovation; legal aspects of user and community innovation; new roles for user innovators; user interactions with firms; and user innovation in practice, describing experiments, toolkits, and crowdsourcing and crowdfunding.

Keywords: Innovation and Management; Transformation; Collaborative Innovation and Invention;

Format: Print

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Harhoff, Dietmar and Karim R. Lakhani, eds. Revolutionizing Innovation: Users, Communities, and Open Innovation. Cambridge, MA: MIT Press, 2016.

Working Paper | HBS Working Paper Series | 2019

Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review

Misha Teplitskiy, Hardeep Ranu, Gary Gray, Michael Menietti, Eva Guinan and Karim R. Lakhani

Organizations in science and elsewhere often rely on committees of experts to make important decisions, such as evaluating early-stage projects and ideas. However, very little is known about how experts influence each other’s opinions and how that influence affects final evaluations. Here, we use a field experiment in scientific peer review to examine experts’ susceptibility to the opinions of others. We recruited 277 faculty members at seven U.S. medical schools to evaluate 47 early stage research proposals in biomedicine. In our experiment, evaluators (1) completed independent reviews of research ideas, (2) received (artificial) scores attributed to anonymous “other reviewers” from the same or a different discipline, and (3) decided whether to update their initial scores. Evaluators did not meet in person and were not otherwise aware of each other. We find that, even in a completely anonymous setting and controlling for a range of career factors, women updated their scores 13% more often than men, while very highly cited “superstar” reviewers updated 24% less often than others. Women in male-dominated subfields were particularly likely to update, updating 8% more for every 10% decrease in subfield representation. Very low scores were particularly “sticky” and seldom updated upward, suggesting a possible source of conservatism in evaluation. These systematic differences in how world-class experts respond to external opinions can lead to substantial gender and status disparities in whose opinion ultimately matters in collective expert judgment.

Keywords: social influence; expert judgment; evaluation; Performance Evaluation; Judgments; Gender; Status and Position; Power and Influence;

Citation:

Teplitskiy, Misha, Hardeep Ranu, Gary Gray, Michael Menietti, Eva Guinan, and Karim R. Lakhani. "Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review." Harvard Business School Working Paper, No. 19-107, April 2019. View Details

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Understanding why employees go the extra mile at work is a key problem for many organizations. We conduct a field experiment at a medical organization to study motivations for employees to submit project proposals for organizational improvement. In total, we analyze 1,237 employees, 118 proposals, and quality evaluations for more than 12,000 evaluator-proposal pairs. The analysis shows that solicitations offering a personal reward for top submissions boost participation rates without affecting submission quality. We show that this is due to workers partially internalizing the positive effects of their submissions on the other individuals in the workplace. We also find that offering employees project funding to implement their own proposals potentially backfires, undermining participation. And solicitations emphasizing mission-oriented goals, like improving patient care, are sensitive to the solicited person’s gender, with women responding more than men. These results shed light on the factors that drive employees’ engagement in organizational tasks, beyond regular duties, and provide insights on how to design incentives to foster contributions to public goods inside organizations.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include studying algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

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Revolutionizing Innovation: Users, Communities, and Open Innovation

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