Attitudes and motivation influence how participants engage in different scientific activities in the online community of a citizen science project

Datos Bibliográficos

ID21452438
AutoresTill Bruckermann (0000-0002-8789-8276, Leibniz University Hannover, autor de correspondencia), Denise Bock (0000-0002-8116-1628, IPN – Leibniz Institute for Science and Mathematics Education), Hannah Greving (0000-0001-5487-9506, Leibniz University Hannover), Anke Schumann (Leibniz Institute for Zoo and Wildlife Research), Milena Stillfried (Leibniz Institute for Zoo and Wildlife Research), Konstantin Börner (Leibniz Institute for Zoo and Wildlife Research), Robert Hagen (0000-0002-4342-9216, Leibniz Institute for Zoo and Wildlife Research), Sophia E Kimmig (0000-0002-4140-6002, Leibniz Institute for Zoo and Wildlife Research), Miriam Brandt (0000-0002-0490-9823, Leibniz Institute for Zoo and Wildlife Research), Ute Harms (0000-0001-6284-9219, IPN – Leibniz Institute for Science and Mathematics Education)
Año2026
Páginas1-18
Fecha de publicación2026-03-13
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaBehaviour and Information Technology (JOURNAL)
Identificadores de la revistaISSN: 0144-929X • E-ISSN: 1362-3001
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/0144929x.2026.2641601
OpenAlexW7135186030
IdiomaEN
Referencias citadas79

Information technology facilitates participation in various scientific tasks in the online communities of citizen science (CS) projects. Previous research suggests that the production of scientific knowledge is more robust when participants are engaged not only in the scientific activities of data collection, but also in data analysis. However, only few participants engage in data analysis. Although their motivation and attitudes might influence participants’ engagement, little is known about how motivation and attitudes are related to engagement in different scientific activities. Using latent profiles of engagement and multinomial logistic regression analysis of motivation and attitudes, we show that the influence of motivation and attitudes differs between the activities of data collection and data analysis. Intrinsic motivation and positive attitudes promoted active engagement in data collection, but had opposite effects in data analysis. These findings suggest that citizens’ engagement levels and the driving factors vary between different scientific activities. Implications highlight the need for different support strategies to enhance citizen participation in full scientific processes, and point to potential structural adjustments in CS project designs. This research underscores the importance of tailored motivational and support mechanisms to enhance citizen engagement in data analysis for better learning and a more robust knowledge production

Citizen science · Online community · Online participation · Public participation · The Internet · Work (physics) · Scientific Computing and Data Management · Species Distribution and Climate Change · Team Dynamics and Performance