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Open AccessArticle

Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems

1
German Center for Higher Education Research and Science Studies (DZHW), Schützenstraße 6A, 10117 Berlin, Germany
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Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany
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University of Applied Sciences HTW Berlin, Wilhelminenhofstraße 75 A, 12459 Berlin, Germany
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GERiiCO-Labor, University of Lille, 59650 Villeneuve-d’Ascq, France
*
Author to whom correspondence should be addressed.
Received: 17 March 2020 / Revised: 1 April 2020 / Accepted: 3 April 2020 / Published: 6 April 2020
(This article belongs to the Special Issue Data Reuse for Sustainable Development Goals)
In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried out. So far, just the importance of data quality in RIS, the investigation of its dimensions and techniques for measuring, improving, and increasing data quality in RIS (such as data profiling, data cleansing, data wrangling, and text data mining) has been focused. With this work, we try to derive an answer to the question of the impact of data quality on the success of RIS user acceptance. An acceptance of RIS users is achieved when the research institutions decide to replace the RIS and replace it with a new one. The result is a statement about the extent to which data quality influences the success of users’ acceptance of RIS. View Full-Text
Keywords: research information systems (RIS); RIS implementation; research information quality; system quality; user acceptance; case study research information systems (RIS); RIS implementation; research information quality; system quality; user acceptance; case study
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MDPI and ACS Style

Azeroual, O.; Saake, G.; Abuosba, M.; Schöpfel, J. Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems. Data 2020, 5, 35.

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