Collaboration in Online Communities: Information Processing and Decision Making

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    Monitoring Collective Intelligence in Lithuania’s Online Communities
    ( 2022-01-04) Skarzauskiene, Aelita
    This paper presents the findings of a systematic survey that evaluated the potential of online communities (or Civic Tech) in Lithuania to co-create collective intelligence. Traditional approaches to public engagement remain relevant, notwithstanding, our enquiry is more interested in the growing potential of digital-enabled citizens to increase efficient collective performance. Civic intelligence is a form of collective intelligence exercised by a group’s capacity to perceive societal problems and its ability to address them effectively. The subject of the research is “bottom up” digital-enabled online platforms initiated by Lithuanian public organizations, civic movements and/or business entities. This scientific project advances our understanding about the basic preconditions in online communities through which collective intelligence is being systematically co-created. By monitoring the performance of Civic Tech platforms, the scientific question was examined, what are the socio-technological conditions that led the communities to become more intelligent. The results of web-based monitoring were obtained by applying Collective intelligence Monitoring technique and Pearson correlation analysis. This provided information about the potential and limits of online communities, and what changes may be needed to overcome such limitations.
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    Exploring Machine-based Idea Landscapes – The Impact of Granularity
    ( 2022-01-04) Wahl, Julian ; Stroehle, Thomas ; Füller, Johann ; Hutter, Katja
    Effective exploration of a landscape full of crowdsourced ideas depends on the right search strategy, as well as the level of granularity in the representation. To categorize similar ideas on different granularity levels modern natural language processing methods and clustering algorithms can be usefully applied. However, the value of machine-based categorizations is dependent on their comprehensibility and coherence with human similarity perceptions. We find that machine-based and human similarity allocations are more likely to converge when comparing ideas across more distant solution clusters than within closely related ones. Our exploratory study contributes to research on the navigability of idea landscapes, by pointing out the impact of granularity on the exploration of crowdsourced knowledge. For practitioners, we provide insights on how to organize the search for the best possible solutions and control the cognitive demand of searchers.
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    An Empirical Examination of Peer vs. Expert Advice in Online Forums
    ( 2022-01-04) Fadel, Kelly ; Jensen, Matthew ; Matthews, Michael ; Meservy, Tom
    Online discussion forums sponsored by electronic networks of practice offer a popular platform for a variety of participants to share their knowledge and provide feedback, including subject matter experts and a larger body of “peer” forum users with no particular expertise. Although prior research suggests that both expert and peer forum contributions can influence information seekers, current literature offers little guidance that explains how and when one is more influential than the other. This paper reports the results of two studies, a structured survey and a choice-based conjoint study, conducted to empirically validate a previously derived conceptual framework of 16 situational characteristics related to peer and expert advice seeking on forums. The results of our work offer theoretical and practical guidance for ongoing work in this area.
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