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.

The topic of data integration from external data sources or independent IT-systems has received increasing attention recently in IT departments as well as at management level, in particular concerning data integration in federated database systems. An example of the latter are commercial research information systems (RIS), which regularly import, cleanse, transform and prepare the analysis research information of the institutions of a variety of databases. In addition, all these so-called steps must be provided in a secured quality. As several internal and external data sources are loaded for integration into the RIS, ensuring information quality is becoming increasingly challenging for the research institutions. Before the research information is transferred to a RIS, it must be checked and cleaned up. An important factor for successful or competent data integration is therefore always the data quality. The removal of data errors (such as duplicates and harmonization of the data structure, inconsistent data and outdated data, etc.) are essential tasks of data integration using extract, transform, and load (ETL) processes. Data is extracted from the source systems, transformed and loaded into the RIS. At this point conflicts between different data sources are controlled and solved, as well as data quality issues during data integration are eliminated. Against this background, our paper presents the process of data transformation in the context of RIS which gains an overview of the quality of research information in an institution’s internal and external data sources during its integration into RIS. In addition, the question of how to control and improve the quality issues during the integration process in RIS will be addressed.

Integrating data from a variety of heterogeneous internal and external data sources (e.g. CERIF and RCD data models with different modeling languages) in a federated database system such as "Research Information Management System (RIMS)" is becoming more challenging for (inter-)national universities and research institutions. Data quality is an important factor for successful integration and interpretation of research information and interoperability of various independent information systems. Before the data is loaded into RIMS, they should be reviewed during data integration process to resolve conflicts between the different data sources and clean the data quality issues. Poor data quality leads to distortion in data presentation, and thus to erroneous basis for decisions. It is ultimately a cost for scientific institutions and it starts with integrating research information into the RIMS. Therefore, the investment in the topic of information integration makes sense insofar, the achievement of a high data quality is of primary importance. This paper presents methods, processes and techniques of information integration in the context of research information management systems. In order to ensure the quality of research information in an institutions data sources during its integration into the RIMS. Numerous attempts have already been done by universities and research institutions to create techniques and solutions for this need.

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