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Crowds, citizens, and science: a multi-dimensional framework and agenda for future research

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  • Chiara Franzoni
  • Marion Poetz
  • Henry Sauermann

Abstract

Research projects that actively involve ‘crowds’ or non-professional ‘citizen scientists’ are attracting growing attention. Such projects promise to increase scientific productivity while also connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on ‘Crowd Science’ and ‘Citizen Science’ investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature.

Suggested Citation

  • Chiara Franzoni & Marion Poetz & Henry Sauermann, 2022. "Crowds, citizens, and science: a multi-dimensional framework and agenda for future research," Industry and Innovation, Taylor & Francis Journals, vol. 29(2), pages 251-284, February.
  • Handle: RePEc:taf:indinn:v:29:y:2022:i:2:p:251-284
    DOI: 10.1080/13662716.2021.1976627
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    Cited by:

    1. Beck, Susanne & Brasseur, Tiare-Maria & Poetz, Marion & Sauermann, Henry, 2022. "Crowdsourcing research questions in science," Research Policy, Elsevier, vol. 51(4).
    2. Mindel, Vitali & Overstreet, Robert E. & Sternberg, Henrik & Mathiassen, Lars & Phillips, Nelson, 2024. "Digital activism to achieve meaningful institutional change: A bricolage of crowdsourcing, social media, and data analytics," Research Policy, Elsevier, vol. 53(3).
    3. Koehler, Maximilian & Sauermann, Henry, 2024. "Algorithmic management in scientific research," Research Policy, Elsevier, vol. 53(4).

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