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Aligning Crowdworker Perspectives and Feedback Outcomes in Crowd-Feedback System Design

Haug, Saskia ORCID iD icon 1; Benke, Ivo 1; Maedche, Alexander 1
1 Institut für Wirtschaftsinformatik und Marketing (IISM), Karlsruher Institut für Technologie (KIT)

Abstract:

Leveraging crowdsourcing in software development has received growing attention in research and practice.
Crowd feedback offers a scalable and flexible way to evaluate software design solutions and the potential of
crowd-feedback systems has been demonstrated in different contexts by existing research studies. However,
previous research lacks a deep understanding of the effects of individual design features of crowd-feedback
systems on feedback quality and quantity. Additionally, existing studies primarily focused on understanding
the requirements of feedback requesters but have not fully explored the qualitative perspectives of crowd-based
feedback providers. In this paper, we address these research gaps with two research studies. In study 1, we
conducted a feature analysis (N=10) and concluded that from a user perspective, a crowd-feedback system
should have five core features (scenario, speech-to-text, markers, categories, and star rating). In the second
study, we analyzed the effects of the design features on crowdworkers’ perceptions and feedback outcomes
(N=210). We learned that offering feedback providers scenarios as the context of use is perceived as most
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Postprint §
DOI: 10.5445/IR/1000156005
Veröffentlicht am 15.03.2023
Originalveröffentlichung
DOI: 10.1145/3579456
Scopus
Zitationen: 2
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 04.2023
Sprache Englisch
Identifikator ISSN: 2573-0142
KITopen-ID: 1000156005
HGF-Programm 46.24.01 (POF IV, LK 01) Applied TA: Digitalizat. & Automat. Socio-Technical Change
Erschienen in Proceedings of the ACM on human-computer interaction
Verlag Association for Computing Machinery (ACM)
Band 7
Heft CSCW1
Seiten Art.-Nr.: 23
Schlagwörter crowdsourcing, feedback, crowd-feedback system, design, experimental, study, qualitative interviews
Nachgewiesen in Dimensions
Scopus
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