skip to main content

Search for: All records

Award ID contains: 1717473

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  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. Autonomous, intelligent tools are reshaping all sorts of work practices, including innovative design work. These tools generate outcomes with little or no user intervention and produce designs of unprecedented complexity and originality, ushering profound changes to how organizations will design and innovate in future. In this paper, we formulate conceptual foundations to analyze the impact of autonomous design tools on design work. We proceed in two steps. First, we conceptualize autonomous design tools as ‘rational’ agents which will participate in the design process. We show that such agency can be realized through two separate approaches of information processing: symbolic and connectionist. Second, we adopt control theory to unpack the relationships between the autonomous design tools, human actors involved in the design, and the environment in which the tools operate. The proposed conceptual framework lays a foundation for studying the new kind of material agency of autonomous design tools in organizational contexts. We illustrate the analytical value of the proposed framework by drawing on two examples from the development of Ubisoft’s Ghost Recon Wildlands video game, which relied on such tools. We conclude this essay by constructing a tentative research agenda for the research into autonomous design tools and design work.
  3. 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
  4. 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
  5. 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.
  6. 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.
  7. The collective intelligence of online communities often depends on implicit forms of coordination, given the fluidity of membership and the lack of traditional hierarchies and associated incentive structures. This coordination drives knowledge production. Studying temporal dynamics may help elucidate how coordination happens. Specifically, the rate of interaction with an artifact such as a Wikipedia page can function as a signal that affects future interactions. Many activities can be characterized as bursty, meaning activity is not evenly spread or random, but is instead concentrated. This study analyzes 3,260 Wikipedia articles and shows that the coordination pattern in the Wikipedia community is mostly bursty. More importantly, the extent of burstiness affects article quality. This work highlights the important role temporal dynamics can play in the coordination process in online communities, and how it can affect the quality of knowledge production.