Abstract
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.
Highlights
Research information is often spread across different systems and tables within a research institution
Since this information is often stored in different systems, research information systems (RIS) are needed to structure the information and to simplify the preparation of reports or to enable value-added services
The coefficient of determination of this study suggests that the dependency between the two latent variables of data quality dimensions and success criteria of user acceptance was significant
Summary
Research information is often spread across different systems and tables within a research institution. The term research information includes all metadata that arise in connection with research activities, for example, information about persons, publications, project data, and patent data, etc. Since this information is often stored in different systems, research information systems (RIS) are needed to structure the information and to simplify the preparation of reports or to enable value-added services. RIS provides the institution with a comprehensive overview of the activities, specialist areas, and services of its researchers and saves researchers time and effort because RIS makes it easier to create, update, and correct research profiles by automatically retrieving publication lists from relevant internal and external databases
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