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.
This case presents the logic and execution underlying the launch of Data.gov, an instantiation of President Obama's initiative for transparency and open government. The process used by Vivek Kundra, the federal CIO, and his team to rapidly develop the website and to make available high-value data sets for reuse is highlighted. The case recounts Kundra's experience at the state and local government levels in developing open data initiatives and the application of that experience to the federal government. The case demonstrates the benefits of making government data available in terms of both engaged citizens and the potential for new innovations from the private sector. Potential drawbacks of open access including security and privacy issues are illustrated. Issues related to the role of government in releasing data and the balance between accountability and private-sector innovation are explored.
Karim R. Lakhani, Patrick Ferguson, Sarah Fleischer, Jin H. Paik, and Steven Randazzo. 2019. Kangatech. Harvard Business School Case. Harvard Business School. Publisher's VersionAbstract
On a warm January afternoon in 2019, Steve Saunders, Dave Scerri, Carl Dilena, and Nick Haslam (see Exhibit 1 for biographies), co-founders of KangaTech, wrapped up the latest round of discussions about the future direction of their sports-technology start-up. Focused on injury prediction and prevention in elite sport, the Melbourne, Australia-based KangaTech prepared to launch a new model of their core product, an integrated exercise frame and software system that used strength exercises to identify and mitigate the risk of soft-tissue and ligament injuries (see Exhibit 2 for overview of product). The team was excited about the new product and was confident that it improved upon many of the features of the previous model. However, Saunders and his co-founders couldn’t help but think about the long-term strategy of the company. Spun off in 2015 out of an internal R&D initiative at the North Melbourne Football Club, KangaTech spent the past four years squarely focused on product development and gaining early traction in the elite sports markets in the U.S., the U.K., and Australia (see Exhibit 3 for company timeline). As of 2019, KangaTech had users across 15 different sites, including professional teams in the National Basketball Association, the English Premier League, and the Australian Football League. The company also underwent a successful round of financing recently, and the proceeds of which were used to fund the new version of the KangaTech product. Off the back of this recent success, the co-founders were focused on how they might be able to navigate the future ahead of them. Dilena explained, “We are going through a pretty robust strategy discussion at the moment. It is one of those decision points for us as to how we best proceed.” Dilena continued, “We’ve been largely product-based and product-development-based until now. How do we scale up? How do we take that next quantum leap as an organization? So part of that has been looking at where do we see the market opportunities?” Specifically, KangaTech weighed up three options for unlocking the full commercial value of the company’s technology: 1) Going deeper into the sports market; 2) Expanding into the allied health market; or, 3) Pursuing contracts in the defense industry. Evaluating the merits of each of these options was not clear. Which market had the greatest upside? Which market would expose the firm to the greatest risk? Which of these opportunities held the most promise for KangaTech?
Frontline staff are well positioned to conceive improvement opportunities based on first-hand knowledge of what works and does not work. The innovation contest may be a relevant and useful vehicle to elicit staff ideas. However, the success of the contest likely depends on perceived organizational support for learning; when staff believe that support for learning-oriented culture, practices, and leadership is low, they may be less willing or able to share ideas. Purpose: We examined how staff perception of organizational support for learning affected contest participation, which comprised ideation and evaluation of submitted ideas. Methodology/Approach: The contest held in a hospital cardiac center invited all clinicians and support staff (n = 1,400) to participate. We used the 27-item Learning Organization Survey to measure staff perception of learning-oriented environment, practices and processes, and leadership. Results: Seventy-two frontline staff submitted 138 ideas addressing wide-ranging issues including patient experience, cost of care, workflow, utilization, and access. Two hundred forty-five participated in evaluation. Supportive learning environment predicted participation in ideation and idea evaluation. Perceptions of insufficient experimentation with new ways of working also predicted participation. Conclusion: The contest enabled frontline staff to share input and assess input shared by other staff. Our findings indicate that the contest may serve as a fruitful outlet through which frontline staff can share and learn new ideas, especially for those who feel safe to speak up and believe that new ideas are not tested frequently enough. Practice Implications: The contest’s potential to decentralize innovation may be greater under stronger learning orientation. A highly visible intervention, like the innovation contest, has both benefits and risks. Our findings suggest benefits such as increased engagement with work and community as well as risks such as discontent that could arise if staff suggestions are not acted upon or if there is no desired change after the contest.
Open innovation processes promise to enhance creative output, yet we have heard little about successful launches of new technologies, products, or services arising from these approaches. Certainly, crowdsourcing platforms (among other open innovation methods) have yielded striking solutions to hard scientific and technological problems—prominent examples being the Netflix predictive recommendation algorithm and the approach to reducing the weight of GE jet engine brackets. But most R&D organizations are still struggling to reap the very real rewards of open innovation. We believe we’ve hit on an important hidden factor for this failure and that it holds the key to a successful integration and execution of open innovation methods.
We study group contests where group sizes are stochastic and unobservable to participants at the time of investment. When the joint distribution of group sizes is symmetric, with expected group size , the symmetric equilibrium aggregate investment is lower than in a symmetric group contest with commonly known fixed group size . A similar result holds for two groups with asymmetric distributions of sizes. For the symmetric case, the reduction in individual and aggregate investment due to group size uncertainty increases with the variance in relative group impacts. When group sizes are independent conditional on a common shock, a stochastic increase in the common shock mitigates the effect of group size uncertainty unless the common and idiosyncratic components of group size are strong complements. Finally, group size uncertainty undermines the robustness of the group size paradox otherwise present in the model.
James “Hondo” Geurts, the Acquisition Executive for U.S. Special Operations Command was in the middle of his Senate confirmation hearing in 2017 to become Assistant Secretary of the Navy for Research, Development and Acquisition. The questions had a common theme: how would Geurts’s experience running an innovative procurement effort for U.S. Special Forces units enable him to change a much larger—and much more rigid—organization like the U.S. Navy? In one of the most secretive parts of the U.S. military, Geurts founded an open platform called SOFWERX to speed the rate of ideas to Navy SEALs, Army Special Forces, and the like. His team even sourced the idea for a hoverboard from a YouTube video. But how should things like SOFWERX and protypes like the EZ-Fly find a place within the Navy writ large?
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.
Distributed ledger technologies — collectively known as blockchain — have burst onto the business scene, accompanied by a significant amount of hype.1 They are widely expected to disrupt existing industries and lead to the creation of new types of companies.
Some of the excitement may indeed be warranted, but only if organizations focus on how these technologies can be used to support their strategy. Without that lens, companies risk making large investments in initiatives that don’t create meaningful value.
However, with careful planning, businesses can use blockchain to gain an edge over rivals in a number of ways. It can provide a foundation for powerful applications that will streamline core operations. Distributed ledger technologies can lower transaction costs and make intellectual property ownership and payments more transparent, seamless, and automated. But companies should resist jumping on the bandwagon until they first understand what specific problems they can solve with blockchain — and for whom. How will it help them reach new customers? How can it improve efficiency or transparency in their supply chains? And most important, what will blockchain enable them to do that competitors and new entrants can’t do? Answering these sorts of practical, targeted questions will allow businesses to cut through the hype and create a blockchain strategy that makes sense for them.
To begin, it’s critical to understand the basic uses and functionalities of blockchains, which tend to get lost in the buzz. So we will provide a quick primer on digital ledgers before discussing how companies should build powerful problem-solving applications that are uniquely configured to their own strategies.
The global economy is coalescing around a few digital superpowers. We see unmistakable evidence that a winner-take-all world is emerging in which a small number of “hub firms”—including Alibaba, Alphabet/Google, Amazon, Apple, Baidu, Facebook, Microsoft, and Tencent—occupy central positions. While creating real value for users, these companies are also capturing a disproportionate and expanding share of the value, and that’s shaping our collective economic future. The very same technologies that promised to democratize business are now threatening to make it more monopolistic.
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 Received: 30 September 2016; Revised: 3 December 2016; Accepted: 18 December 2016 C The Author 2017. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 1 2 Hill et al. 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.
Contracts, transactions, and the records of them are among the defining structures in our economic, legal, and political systems. They protect assets and set organizational boundaries. They establish and verify identities and chronicle events. They govern interactions among nations, organizations, communities, and individuals. They guide managerial and social action. And yet these critical tools and the bureaucracies formed to manage them have not kept up with the economy’s digital transformation. They’re like a rush-hour gridlock trapping a Formula 1 race car. In a digital world, the way we regulate and maintain administrative control has to change.
Scientists typically self-organize into teams, matching with others to collaborate in the production of new knowledge. We present the results of a field experiment conducted at Harvard Medical School to understand the extent to which search costs affect matching among scientific collaborators. We generated exogenous variation in search costs for pairs of potential collaborators by randomly assigning individuals to 90-minute structured information-sharing sessions as part of a grant funding opportunity for biomedical researchers. We estimate that the treatment increases the baseline probability of grant co-application of a given pair of researchers by 75% (increasing the likelihood of a pair collaborating from 0.16 percent to 0.28 percent), with effects higher among those in the same specialization. The findings indicate that matching between scientists is subject to considerable frictions, even in the case of geographically-proximate scientists working in the same institutional context with ample access to common information and funding opportunities.
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.
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.
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.