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Browsing by Author "Szajnfarber, Zoe"

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    Does Open Innovation Open Doors for Underrepresented Groups to Contribute to Technology Innovation?: Evidence from a Space Robotics Challenge
    Topcu, Taylan G.; Zhang, Lihui Lydia; Szajnfarber, Zoe (Elsevier, 2023-05)
    Diversity, equity, and inclusion (DEI) are increasingly being recognized as important policy goals for organizations across government and the industry. Improved DEI has been linked to both substantive improvement in innovation performance and societal good. However, despite a stated emphasis on DEI, progress has not kept up with aspirations. One indirect policy approach that holds promise is wider adoption of Open Innovation (OI) as part of an innovation toolkit. Proponents contend that OI reduces barriers to entry and garners productive contributions from diverse contributors. While there is anecdotal support for the diversifying potential of OI, so far, there is a dearth of empirical evidence connecting OI to DEI with consideration of performance outcomes, beyond `winners´. To study this link directly, this article leverages data from a previously conducted unique field experiment that explicitly tracked the population of potential solvers and their performance on a National Aeronautics and Space Administration (NASA) space robotics problem. We found that while OI attracted different solvers than the reference internal workforce, there was important variation in both the extent and direction of the observed differences, with respect to attributes of DEI. For instance, OI attracted proportionally fewer female solvers than the already male-dominated space workforce; and that proportion decreased further among solvers providing quality solutions. On the other hand, OI proved effective at granting access to an international pool of young professionals with potentially novel perspectives. Overall, our findings suggest OI can be an effective tool for achieving some diversity policy goals, but it is not well-suited for achieving all stated aspects of diversity. Therefore, we suggest a more targeted approach to matching the opportunities for OI to achieve specific policy objectives.
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    Towards a solver-aware systems architecting framework: leveraging experts, specialists and the crowd to design innovative complex systems
    Szajnfarber, Zoe; Topcu, Taylan G.; Lifshitz-Assaf, Hila (Cambridge University Press, 2022-03-11)
    This article proposes the solver-aware system architecting framework for leveraging the combined strengths of experts, crowds and specialists to design innovative complex systems. Although system architecting theory has extensively explored the relationship between alternative architecture forms and performance under operational uncertainty, limited attention has been paid to differences due to who generates the solutions. The recent rise in alternative solving methods, from gig workers to crowdsourcing to novel contracting structures emphasises the need for deeper consideration of the link between architecting and solver-capability in the context of complex system innovation. We investigate these interactions through an abstract problem-solving simulation, representing alternative decompositions and solver archetypes of varying expertise, engaged through contractual structures that match their solving type. We find that the preferred architecture changes depending on which combinations of solvers are assigned. In addition, the best hybrid decomposition-solver combinations simultaneously improve performance and cost, while reducing expert reliance. To operationalise this new solver-aware framework, we induce two heuristics for decomposition-assignment pairs and demonstrate the scale of their value in the simulation. We also apply these two heuristics to reason about an example of a robotic manipulator design problem to demonstrate their relevance in realistic complex system settings.