Crowdsourcing & Open Innovation

The Laboratory of Innovation Science at Harvard is currently working on a number of studies, experiments, and projects that center around Crowdsourcing & Open Innovation. Such initiatives allow for the optimization of research for solutions to complex problems by calling on the crowd, instead of limiting knowledge to what is available within an organization.

Listed below are some examples of research which have greatly benefited from utilizing exterior knowledge and not limiting resources to those employed in-house, including a number of crowdsourcing challenges from our partners at NASA, the Broad Institute, and others. Browse LISH’s Crowdsourcing & Open Innovation projects and papers below.

If you are looking to run a crowdsourcing challenge, LISH's Find A Crowdsourcing Platform can help you find a platform that meets your needs.


Philip Brookins, John P. Lightle, and Dmitry Ryvkin. 2018. “Sorting and communication in weak-link group contests.” Journal of Economic Behavior & Organization, 152, Pp. 64-80. Publisher's VersionAbstract
We experimentally study the effects of sorting and communication in contests between groups of heterogeneous players whose within-group efforts are perfect complements. Contrary to the common wisdom that competitive balance bolsters performance in contests, in this setting theory predicts that aggregate output increases in the variation in abilities between groups, i.e., it is maximized by the most unbalanced sorting of players. However, the data does not support this prediction. In the absence of communication, we find no effect of sorting on aggregate output, while in the presence of within-group communication aggregate output is 33% higher under the balanced sorting as compared to the unbalanced sorting. This reversal of the prediction is in line with the competitive balance heuristic. The results have implications for the design of optimal groups in organizations using relative performance pay.
Karim R. Lakhani, Andrew Hill, Po-Ru Loh, Ragu B. Bharadwaj, Pascal Pons, Jingbo Shang, Eva C. Guinan, Iain Kilty, and Scott Jelinsky. 2017. “Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.” GigaScience, 6, 5, Pp. 1-10. Publisher's VersionAbstract

BACKGROUND: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets.

RESULTS: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project.

CONCLUSIONS: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics.

Teppo Felin, Karim R. Lakhani, and Michael L. Tushman. 2017. “Firms, Crowds, and Innovation.” Strategic Organization, 15:2, Special Issue on Organizing Crowds and Innovation, Pp. 119-140. Publisher's VersionAbstract

The purpose of this article is to suggest a (preliminary) taxonomy and research agenda for the topic of “firms, crowds, and innovation” and to provide an introduction to the associated special issue. We specifically discuss how various crowd-related phenomena and practices—for example, crowdsourcing, crowdfunding, user innovation, and peer production—relate to theories of the firm, with particular attention on “sociality” in firms and markets. We first briefly review extant theories of the firm and then discuss three theoretical aspects of sociality related to crowds in the context of strategy, organizations, and innovation: (1) the functions of sociality (sociality as extension of rationality, sociality as sensing and signaling, sociality as matching and identity); (2) the forms of sociality (independent/aggregate and interacting/emergent forms of sociality); and (3) the failures of sociality (misattribution and misapplication). We conclude with an outline of future research directions and introduce the special issue papers and essays.

Karim R. Lakhani and Akiko Kanno. 2017. Weathernews. Harvard Business School Case. Harvard Business School. Publisher's VersionAbstract

Tomohiro Ishibashi (Bashi), chief executive officer for B to S, and Julia Foote LeStage, chief innovation officer of Weathernews Inc., were addressing a panel at the HBS Digital Summit on creative uses of big data. They told the summit attendees about how the Sakura (cherry blossoms) Project, where the company asked users in Japan to report about how cherry blossoms were blooming near them day by day, had opened up opportunities for the company's consumer business in Japan. The project ultimately garnered positive publicity and became a foothold to building the company's crowdsourcing weather-forecasting service in Japan. It changed the face of weather forecasting in Japan. Bashi and LeStage wondered whether the experience could be applied to the U.S. market.

Olivia Jung, Andrea Blasco, and Karim R. Lakhani. 2017. “Perceived Organizational Support For Learning and Contribution to Improvement by Frontline Staff.” Academy of Management Proceedings, 2017, 1. Publisher's VersionAbstract

Utilizing suggestions from clinicians and administrative staff is associated with process and quality improvement, organizational climate that promotes patient safety, and added capacity for learning. However, realizing improvement through innovative ideas from staff depends on their ability and decision to contribute. We hypothesized that staff perception of whether the organization promotes learning is positively associated with their likelihood to engage in problem solving and speaking up. We conducted our study in a cardiology unit in an academic hospital that hosted an ideation contest that solicited frontline staff to suggest ideas to resolve issues encountered at work. Our primary dependent variable was staff participation in ideation. The independent variables measuring perception of support for learning were collected using the validated 27-item Learning Organization Survey (LOS). To examine the relationships between these variables, we used analysis of variance, logistic regression, and predicted probabilities. We also interviewed 16 contest participants to explain our quantitative results. The study sample consisted of 30% of cardiology unit staff (n=354) that completed the LOS. In total, 72 staff submitted 138 ideas, addressing a range of issues including patient experience, cost of care, workflow, utilization, and access. Figuring out the cost of procedures in the catheterization laboratory and creating a smartphone application that aids patients to navigate through appointments and connect with providers were two of the ideas that won the most number of votes and funding to be implemented in the following year. Participation in ideation was positively associated with staff perception of supportive learning environment. For example, one standard deviation increase in perceived welcome for differences in opinions was associated with a 43% increase in the odds of participating in ideation (OR=1.43, p=0.04) and 55% increase in the odds of suggesting more than one idea (OR=1.55, p=0.09). Experimentation, a practice that supports learning, was negatively associated with ideation (OR=0.36, p=0.02), and leadership that reinforces learning was not associated with ideation. The perception that new ideas are not sufficiently considered or experimented could have motivated staff to participate, as the ideation contest enables experimentation and learning. Interviews with ideation participants revealed that the contest enabled systematic bottom-up contribution to quality improvement, promoted a sense of community, facilitated organizational exchange of ideas, and spread a problem-solving oriented mindset. Enabling frontline staff to feel that their ideas are welcome and that making mistakes is permissible may increase their likelihood to engage in problem solving and speaking up, contributing to organizational improvement.