Organization & Processes

The Laboratory for Innovation Science at Harvard (LISH) conducts research on how labs operate, including the process researchers take in developing new products and ideas and how best to capitalize on successes and bring solutions out of the lab and into commercial use.

Key Questions

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What are the drivers of productivity in science and engineering laboratories?

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How can crowds be integrated with traditional R&D functions in companies and academic labs?

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What are the biases in the processes of evaluating innovative ideas? How can they be overcome?

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What are the predictors of breakthrough success for innovative scientific ideas?

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How can technology commercialization be accelerated from academic and government labs?

 

Projects in this research track are most directly associated with the Managing R&D Labs & Organizations and Technology Translation areas of application, which include experiments around grant applications and scientific awards, the development of a massive open online course on technology translation, and the integration of crowds into academic labs. See below for more information on each of the individual projects in this research track.

Related Publications

Hannah Mayer. 7/2020. “AI in Enterprise: AI Product Management.” Edited by Jin H. Paik, Jenny Hoffman, and Steven Randazzo.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

Moderated by HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani, the July virtual session featured Peter Skomoroch from DataWrangling and formerly at LinkedIn. Together, they discussed what differentiates AI product management from managing other tech products and how to adapt to the uncertainty in the AI product lifecycle.

Misha Teplitskiy, Hardeep Ranu, Gary Gray, Michael Menietti, Eva Guinan, and Karim Lakhani. Working Paper. “Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review.” HBS Working Paper Series. Publisher's VersionAbstract
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.
Karim R. Lakhani, Anne-Laure Fayard, Manos Gkeredakis, and Jin Hyun Paik. 10/5/2020. “OpenIDEO (B)”. Publisher's VersionAbstract
In the midst of 2020, as the coronavirus pandemic was unfolding, OpenIDEO - an online open innovation platform focused on design-driven solutions to social issues - rapidly launched a new challenge to improve access to health information, empower communities to stay safe during the COVID-19 crisis, and inspire global leaders to communicate effectively. OpenIDEO was particularly suited to challenges which required cross-system or sector-wide collaboration due to its focus on social impact and ecosystem design, but its leadership pondered how they could continue to improve virtual collaboration and to share their insights from nearly a decade of running online challenges. Conceived as an exercise of disruptive digital innovation, OpenIDEO successfully created a strong open innovation community, but how could they sustain - or even improve - their support to community members and increase the social impact of their online challenges in the coming years?
Hannah Mayer. 10/2020. “Data Science is the New Accounting.” Edited by Jin H. Paik and Jenny Hoffman.Abstract

In the October session of the AI in Enterprise series, HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani and Roger Magoulas (Data Science Advisor) delved into O'Reilly's most recent survey of AI adoption in larger companies. The discussion explored common risk factors, techniques, tools, as well as the data governance and data conditioning that large companies are using to build and scale their AI practices. 

 

Read Hannah Mayer's recap of the event to learn more about what senior managers in enterprises need to know about AI - particularly, if they want to adopt at scale. 

 

Hannah Mayer. 9/2020. “AI in Enterprise: In Tech We Trust.. Maybe Too Much?Edited by Jin H. Paik and Jenny Hoffman.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

In the September session of the AI in Enterprise series, HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani spoke with Latanya Sweeney about algorithmic bias, data privacy, and the way forward for enterprises adopting AI. They explored how AI and ML can impact society in unexpected ways and what senior enterprise leaders can do to avoid negative externalities. Professor of the Practice of Government and Technology at the Harvard Kennedy School and in the Harvard Faculty of Arts and Sciences, director and founder of the Data Privacy Lab, and former Chief Technology Officer at the U.S. Federal Trade Commission, Latanya Sweeney pioneered the field known as data privacy and launched the emerging area known as algorithmic fairness.

Hannah Mayer, Jin H. Paik, Timothy DeStefano, and Jenny Hoffman. 8/2020. “From Craft to Commodity: The Evolution of AI in Pharma and Beyond”.Abstract

While there are dispersed resources to learn more about artificial intelligence, there remains a need to cultivate a community of practitioners for cyclical exposure and knowledge sharing of best practices in the enterprise. That is why Laboratory for Innovation Science at Harvard launched the AI in the Enterprise series, which exposes managers and executives to interesting applications of AI and the decisions behind developing such tools. 

Moderated by HBS Professor and co-author of Competing in the Age of AI, Karim R. Lakhani, the August virtual session featured Reza Olfati-Saber, an experienced academic researcher currently managing teams of data scientists and life scientists across the globe for Sanofi. Together, they discussed the evolution of AI in life science experimentation and how it may become the determining factor for R&D success in pharma and other industries.

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