Quality of Research Information in RIS Databases: A Multidimensional Approach
| Autor: | Otmane Azeroual, Mohammad Abuosba, Joachim Schöpfel, Gunter Saake |
|---|---|
| Přispěvatelé: | German Center for Higher Education Research and Science Studies (DZHW), Otto-von-Guericke-Universität Magdeburg, University of Applied Sciences HTW Berlin (HTW), Groupe d'Études et de Recherche Interdisciplinaire en Information et COmmunication (GERiiCO) - EA 4073 (GERIICO ), Université de Lille, Groupe d'Études et de Recherche Interdisciplinaire en Information et COmmunication - ULR 4073 (GERIICO ) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Decision support system
Quantitative survey Correctness Computer science [SHS.INFO]Humanities and Social Sciences/Library and information sciences 05 social sciences 050905 science studies Data science Structural equation modeling [SHS]Humanities and Social Sciences Quality dimensions Data quality Research information [INFO]Computer Science [cs] 0509 other social sciences 050904 information & library sciences Completeness (statistics) ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353 Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353, pp.337-349, 2019 Business Information Systems Business Information Systems, pp.337-349, 2019, 22nd International Conference, BIS 2019, Seville, Spain, June 26–28, 2019, Proceedings, Part I, ⟨10.1007/978-3-030-20485-3_26⟩ Business Information Systems ISBN: 9783030204846 BIS (1) |
| DOI: | 10.1007/978-3-030-20485-3_26⟩ |
| Popis: | International audience; For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the research information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid research information. Because research information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of research information driven decision support. |
| Databáze: | OpenAIRE |
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