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  1. Design artifacts in online innovation communities are increasingly becoming a primary source of innovation for organizations. A distinguishing feature of such communities is that they are organized around design artifacts, not around people. The search for novel innovations thus equates to a search for novel designs. This is not a trivial problem since the novelty of a design is a function of its relationship to other designs, and this relationship changes as each design is added. These relations between artifacts affect both consumption and production. Moreover, these relations form a landscape whose structure affects the emergence of novelty. We find evidence for our theorizing using an analysis of over 35,000 Thingiverse design artifacts. This work identifies the differential effects of different forms of novelty, visual and verbal, on subsequent innovation, and identifies the differential effects of different degrees of structure in the landscape on novelty.
    Free, publicly-accessible full text available September 1, 2023
  2. Metahuman systems are new, emergent, sociotechnical systems where machines that learn join human learning and create original systemic capabilities. Metahuman systems will change many facets of the way we think about organizations and work. They will push information systems research in new directions that may involve a revision of the field’s research goals, methods and theorizing. Information systems researchers can look beyond the capabilities and constraints of human learning toward hybrid human/machine learning systems that exhibit major differences in scale, scope and speed. We review how these changes influence organization design and goals. We identify four organizational level generic functions critical to organize metahuman systems properly: delegating, monitoring, cultivating, and reflecting. We show how each function raises new research questions for the field. We conclude by noting that improved understanding of metahuman systems will primarily come from learning-by-doing as information systems scholars try out new forms of hybrid learning in multiple settings to generate novel, generalizable, impactful designs. Such trials will result in improved understanding of metahuman systems. This need for large-scale experimentation will push many scholars out from their comfort zone, because it calls for the revitalization of action research programs that informed the first wave of socio-technical researchmore »at the dawn of automating work systems.« less
  3. Societal challenges can be addressed not only by experts, but also by crowds. Crowdsourcing provides a way to engage the general crowd to contribute to the solutions of the biggest challenges of our times: how to cut our carbon footprint, how to address worldwide epidemic of chronic disease, and how to achieve sustainable development. Isolated crowd-based solutions in online communities are not always creative and innovative. Hence, remixing has been developed as a way to enable idea evolution and integration, and to harness reusable innovative solutions. Understanding the generativity of remixing is essential to leveraging the wisdom of the crowd to solve societal challenges. At its best, remixing can promote online community engagement, as well as support comprehensive and innovative solution generation. Organizers can maintain an active online community; community members can collectively innovate and learn; and as a result, society may find new ways to solve important problems. What affects the generativity of a remix? We address this by revisiting the knowledge reuse process for innovation model. We analyze the reuse of proposals in an online innovation community which aims to address global climate change issues, Climate CoLab. We apply several analytical methods to study factors that may contributemore »to the generativity of a remix and uncover that remixes that include prevalent topics and integration metaknowledge are more generative. Our findings suggest strategies and tools that can help online communities to better harness collective intelligence for addressing societal challenges.« less
  4. As people increasingly innovate outside of formal R&D departments, individuals take on the responsibility of attracting, managing, and protecting social, financial, human, and information capital. With internet technology playing a central role in how individuals work together to produce something that they could not produce alone, it is necessary to understand how technologies are shaping the innovation process from start to finish. We bring together human-computer interaction researchers and industry leaders who have worked with people and platforms designed to support collective innovation across diverse domains. We will discuss the current and future research on the role of platforms in collective innovation, including topics in social computing, crowdsourcing, peer production, online communities, gig economy, & online marketplaces.
  5. Remixing, a method to harness collective intelligence, is used in many online innovation communities. It is also an important form of online engagement. What actions lead to a remix that is generative? This paper addresses this question by using a knowledge reuse process model previously applied in offline settings as the basis for a series of hypotheses about online communities. An empirical study is performed to examine the relationship between three major actions in the knowledge reuse process model and the generativity of the remix created. An analysis of the reuse of proposals in an online innovation community, Climate CoLab, shows that those including prevalent topics and metaknowledge about integration are more generative. These findings provide insights to strategies and tools that can support knowledge reuse for innovation in online communities.