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dc.contributor.authorJensen, Matilde Bisballe
dc.contributor.authorSteinert, Martin
dc.date.accessioned2021-01-18T12:04:59Z
dc.date.available2021-01-18T12:04:59Z
dc.date.created2021-01-11T10:56:41Z
dc.date.issued2020
dc.identifier.citationArtificial intelligence for engineering design, analysis and manufacturing. 2020, 34 (3), 315-326.en_US
dc.identifier.issn0890-0604
dc.identifier.urihttps://hdl.handle.net/11250/2723467
dc.description.abstractThis paper sheds light on the new possibilities for user research activities facilitated by access to makerspaces. We present four case studies of user research conducted in two university-based makerspaces as examples of makerspace-driven user research. Further, by comparing the cases to three classical user research activities, namely observation, prototyping, and user journey mapping, we highlight the main aspects of this new context of user research. We find that accessibility to makerspaces enables user researchers to build low-fidelity yet high-functionality prototypes for exploring users’ preferences and motivations in controlled and repeatable ways. These prototypes fall into the category of experience prototypes, but they have greater functionality than the prototypes previously used in this field. Thus, a user researcher can explore a topic more systematically and in a more hypothesis-driven manner. In summary, this study encourages stakeholders in the early stages of product development to consider a makerspace as a resource for user-related requirement elicitation rather than for only specific product iteration.en_US
dc.language.isoengen_US
dc.publisherCambridge University Pressen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleUser research enabled by makerspaces: bringing functionality to classical experience prototypesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber315-326en_US
dc.source.volume34en_US
dc.source.journalArtificial intelligence for engineering design, analysis and manufacturingen_US
dc.source.issue3en_US
dc.identifier.doihttps://doi.org/10.1017/S089006042000013X
dc.identifier.cristin1868717
dc.description.localcode© Cambridge University Press 2020. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/ by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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