Crowdsourcing & Open Innovation

Free and Open Source Software (FOSS) has become a critical part of the modern economy. There are tens of millions of FOSS projects, many of which are built into software and products we use every day. However, it is difficult to fully understand the health, economic value, and security of FOSS because it is produced in a decentralized and distributed manner. This distributed development approach makes it unclear how much FOSS, and precisely what FOSS projects, are most widely used. This lack of understanding is a critical problem faced by those who want to help enhance the security of FOSS (e.g., companies, governments, individuals), yet do not know what projects to start with. This problem has garnered widespread attention with the Heartbleed and log4shell vulnerabilities that resulted in the susceptibility of hundreds of millions of devices to exploitation.

This report, Census II, is the second investigation into the widespread use of FOSS and aggregates data from over half a million observations of FOSS libraries used in production applications at thousands of companies, which aims to shed light on the most commonly used FOSS packages at the application library level. This effort builds on the Census I report that focused on the lower level critical operating system libraries and utilities, improving our understanding of the FOSS packages that software applications rely on. Such insights will help to identify critical FOSS packages to allow for resource prioritization to address security issues in this widely used software.

The Census II effort utilizes data from partner Software Composition Analysis (SCA) companies including Snyk, the Synopsys Cybersecurity Research Center (CyRC), and FOSSA, which partnered with Harvard to advance the state of open source research. Our goal is to not only identify the most widely used FOSS, but to also provide an example of how the distributed nature of FOSS requires a multi-party effort to fully understand the value and security of the FOSS ecosystem. Only through data-sharing, coordination, and investment will the value of this critical component of the digital economy be preserved for generations to come.

In addition to the detailed results on FOSS usage provided in the report, we identified five high-level findings: 1) the need for a standardized naming schema for software components, 2) the complexities associated with package versions, 3) much of the most widely used FOSS is developed by only a handful of contributors, 4) the increasing importance of individual developer account security, and 5) the persistence of legacy software in the open source space.

This paper presents NASA’s experience using a Center of Excellence (CoE) to scale and sustain an open innovation program as an effective problem-solving tool and includes strategic management recommendations for other organizations based on lessons learned.

This paper defines four phases of implementing an open innovation program: Learn, Pilot, Scale and Sustain. It provides guidance on the time required for each phase and recommendations for how to utilize a CoE to succeed. Recommendations are based upon the experience of NASA’s Human Health and Performance Directorate, and experience at the Laboratory for Innovation Science at Harvard running hundreds of challenges with research and development organizations.

Lessons learned include the importance of grounding innovation initiatives in the business strategy, assessing the portfolio of work to select problems most amenable to solving via crowdsourcing methodology, framing problems that external parties can solve, thinking strategically about early wins, selecting the right platforms, developing criteria for evaluation, and advancing a culture of innovation. Establishing a CoE provides an effective infrastructure to address both technical and cultural issues.

The NASA experience spanned more than seven years from initial learnings about open innovation concepts to the successful scaling and sustaining of an open innovation program; this paper provides recommendations on how to decrease this timeline to three years.

Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial training and is subject to significant interobserver variation.

To determine whether crowd innovation could be used to rapidly produce artificial intelligence (AI) solutions that replicate the accuracy of an expert radiation oncologist in segmenting lung tumors for RT targeting.

We conducted a 10-week, prize-based, online, 3-phase challenge (prizes totaled $55 000). A well-curated data set, including computed tomographic (CT) scans and lung tumor segmentations generated by an expert for clinical care, was used for the contest (CT scans from 461 patients; median 157 images per scan; 77 942 images in total; 8144 images with tumor present). Contestants were provided a training set of 229 CT scans with accompanying expert contours to develop their algorithms and given feedback on their performance throughout the contest, including from the expert clinician.

Main Outcomes and Measures  The AI algorithms generated by contestants were automatically scored on an independent data set that was withheld from contestants, and performance ranked using quantitative metrics that evaluated overlap of each algorithm’s automated segmentations with the expert’s segmentations. Performance was further benchmarked against human expert interobserver and intraobserver variation.

A total of 564 contestants from 62 countries registered for this challenge, and 34 (6%) submitted algorithms. The automated segmentations produced by the top 5 AI algorithms, when combined using an ensemble model, had an accuracy (Dice coefficient = 0.79) that was within the benchmark of mean interobserver variation measured between 6 human experts. For phase 1, the top 7 algorithms had average custom segmentation scores (S scores) on the holdout data set ranging from 0.15 to 0.38, and suboptimal performance using relative measures of error. The average S scores for phase 2 increased to 0.53 to 0.57, with a similar improvement in other performance metrics. In phase 3, performance of the top algorithm increased by an additional 9%. Combining the top 5 algorithms from phase 2 and phase 3 using an ensemble model, yielded an additional 9% to 12% improvement in performance with a final S score reaching 0.68.

A combined crowd innovation and AI approach rapidly produced automated algorithms that replicated the skills of a highly trained physician for a critical task in radiation therapy. These AI algorithms could improve cancer care globally by transferring the skills of expert clinicians to under-resourced health care settings.

We report results of a natural field experiment conducted at a medical organization that sought contribution of public goods (i.e., projects for organizational improvement) from its 1200 employees. Offering a prize for winning submissions boosted participation by 85 percent without affecting the quality of the submissions. The effect was consistent across gender and job type. We posit that the allure of a prize, in combination with mission-oriented preferences, drove participation. Using a simple model, we estimate that these preferences explain about a third of the magnitude of the effect. We also find that these results were sensitive to the solicited person’s gender.

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.

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.