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Expert Opinions as a Method of Validating Ideas: Applied to Making GDPR Usable
Norwegian University of Science and Technology, Norway.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-6938-4466
2023 (English)In: Human Factors in Privacy Research: Nina Gerber, Alina Stöver, Karola Marky / [ed] Nina Gerber, Alina Stöver, Karola Marky, Springer, 2023, p. 137-152Chapter in book (Refereed)
Abstract [en]

This chapter presents two contributions; the first is a method and the second is the results of applying this method to usable privacy. First, we introduce a general method for validating ideas based on expert opinions. We adapt techniques that normally are used for validating data and apply them instead to analyze the expert opinions on the ideas under study. Since usually the expert opinions are varied, example-rich, and forward-looking, applying our method of ideas validation has the side effect of also identifying, in the process, open problems where the original studied ideas function as a foundation for further developments. Second, we employ a critical qualitative research, using theory triangulation to analyze the opinions coming from three groups of experts, categorized as “certifications,” “law,” and “usability.” These took part in a study where we thoroughly applied the method that we present here in order to validate five different types of ideas previously published under the collective title “Making GDPR Usable.” We will thus show how to validate ideas that come in the form of: a model, a definition, a prescriptive list, a set of criteria, and a form of rather general research idea as those usually appearing in position papers, namely “the need for evaluations and measuring of usability of privacy.”

Place, publisher, year, edition, pages
Springer, 2023. p. 137-152
Keywords [en]
Usable privacy, Expert opinions, Validation, GDPR, Certification, Qualitative research
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-98989DOI: 10.1007/978-3-031-28643-8_7Scopus ID: 2-s2.0-85195347276ISBN: 978-3-031-28642-1 (print)ISBN: 978-3-031-28643-8 (electronic)OAI: oai:DiVA.org:kau-98989DiVA, id: diva2:1846434
Available from: 2024-03-22 Created: 2024-03-22 Last updated: 2025-10-16Bibliographically approved

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Fischer-Hübner, Simone

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CiteExportLink to record
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Citation style
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