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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. 

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 research at the dawn of automating work systems. 

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. 

Bots and humans can combine in multiple ways in the service of knowledge production. Designers make choices about the purpose of the bots, their technical architecture, and their initiative. That is, they decide about functions, mechanisms, and interfaces. Together these dimensions suggest a design space for systems of bots and humans. These systems are evaluated along several criteria. One criterion is productivity. Another is their effects on human editors, especially newcomers. A third is sustainability: how they persist in the face of change. Design and evaluation spaces are described as part of an analysis of Wiki-related bots: two bots and their effects are discussed in detail, and an agenda for further research is suggested. 

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.