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Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation
This article presents a dataset of service design skills which service design experts value as important requirements for design team members. Purposive sampling and a chain referral approach were used to recruit appropriate experts to conduct questionnaire-based research. Using the analytical hiera...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203804/ http://dx.doi.org/10.1038/s41597-022-01421-3 |
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author | Nguyen, Hien Ngoc Lasa, Ganix Iriarte, Ion Atxa, Ariane Unamuno, Gorka Galfarsoro, Gurutz |
author_facet | Nguyen, Hien Ngoc Lasa, Ganix Iriarte, Ion Atxa, Ariane Unamuno, Gorka Galfarsoro, Gurutz |
author_sort | Nguyen, Hien Ngoc |
collection | PubMed |
description | This article presents a dataset of service design skills which service design experts value as important requirements for design team members. Purposive sampling and a chain referral approach were used to recruit appropriate experts to conduct questionnaire-based research. Using the analytical hierarchy process (AHP), pairwise skills-rating questionnaires were designed to elicit the experts’ responses. The resulting dataset was processed using AHP algorithms programmed in R programming language. The transparent data and available codes of the research may be reused by design practitioners and researchers for replication and further analysis. This paper offers a reproduceable research process and associated dataset for conducting multiple-criteria decision analysis with expert purposive sampling. |
format | Online Article Text |
id | pubmed-9203804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92038042022-06-18 Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation Nguyen, Hien Ngoc Lasa, Ganix Iriarte, Ion Atxa, Ariane Unamuno, Gorka Galfarsoro, Gurutz Sci Data Data Descriptor This article presents a dataset of service design skills which service design experts value as important requirements for design team members. Purposive sampling and a chain referral approach were used to recruit appropriate experts to conduct questionnaire-based research. Using the analytical hierarchy process (AHP), pairwise skills-rating questionnaires were designed to elicit the experts’ responses. The resulting dataset was processed using AHP algorithms programmed in R programming language. The transparent data and available codes of the research may be reused by design practitioners and researchers for replication and further analysis. This paper offers a reproduceable research process and associated dataset for conducting multiple-criteria decision analysis with expert purposive sampling. Nature Publishing Group UK 2022-06-16 /pmc/articles/PMC9203804/ http://dx.doi.org/10.1038/s41597-022-01421-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Nguyen, Hien Ngoc Lasa, Ganix Iriarte, Ion Atxa, Ariane Unamuno, Gorka Galfarsoro, Gurutz Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title | Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title_full | Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title_fullStr | Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title_full_unstemmed | Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title_short | Datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
title_sort | datasets of skills-rating questionnaires for advanced service design through expert knowledge elicitation |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203804/ http://dx.doi.org/10.1038/s41597-022-01421-3 |
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