Publications

Purpose: This article offers insight on how to effectively help incumbent organizations prepare for global business shifts to open source and digital business models.

Design/methodology/approach: Discussion related to observation, experience and case studies related to incumbent organizations and their efforts to adopt open source models and business tools.

Findings: Companies that let their old culture reject the new risk becoming obsolete if doing so inhibits their rethinking of their future using powerful tools like crowdsourcing, blockchain, customer experience-based connections, integrating workflows with artificial intelligence (AI), automated technologies and digital business platforms. These new ways of working affect how and where work is done, access to information, an organization’s capacity for work and its efficiency. As important as technological proficiency is, managing the cultural shift required to embrace transformative industry architecture – the key to innovating new business models – may be the bigger challenge.

Research limitations/implications: Findings are based on original research and case studies. Insights are theoretically, based on additional study, interviews, and research, but need to be tested through additional case studies. Practical implications: The goal is to make the transition more productive and less traumatic for incumbent firms by providing a language and tested methods to help senior leaders use innovative technologies to build on their core even as they explore new business models.

Social implications: This article provides insights that will lead to more effective ideas for helping organizations adapt. Originality/value: This article is based on original research and case experience. That research and experience has then been analyzed and viewed through the lens of models that have been known to work. The result is original insights and findings that can be applied in new ways to further adoption within incumbent organizations.

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.

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.

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.