Quality of Research Information in RIS Databases: A Multidimensional Approach

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.

[1]  Michael Miller.  Coefficient alpha: A basic introduction from the perspectives of classical test theory and structural equation modeling , 1995 .

[2]  Gunter Saake,et al.  Data Quality Measures and Data Cleansing for Research Information Systems , 2019, ArXiv.

[3]  Felix Naumann,et al.  Data fusion , 2009, CSUR.

[4]  Lilly Suriani Affendey,et al.  A Framework to Construct Data Quality Dimensions Relationships , 2013 .

[5]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[7]  Larry P. English.  Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits , 1999 .

[8]  Gunter Saake,et al.  Analyzing data quality issues in research information systems via data profiling , 2018, Int. J. Inf. Manag..

[9]  Joachim Schöpfel,et al.  Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries , 2019, Publ..

[10]  Stuart E. Madnick,et al.  Overview and Framework for Data and Information Quality Research , 2009, JDIQ.

[11]  R. P. Srivastava,et al.  A conceptual framework and belief‐function approach to assessing overall information quality , 2003, Int. J. Intell. Syst..

[12]  Richard Y. Wang,et al.  Anchoring data quality dimensions in ontological foundations , 1996, CACM.

[13]  Mouzhi Ge,et al.  A Review of Information Quality Research - Develop a Research Agenda , 2007, ICIQ.

[14]  N. Schmitt.  Uses and abuses of coefficient alpha. , 1996 .

[15]  Danette McGilvray,et al.  Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information TM , 2008 .

[16]  Carlo Batini,et al.  Methodologies for data quality assessment and improvement , 2009, CSUR.

[17]  Gunter Saake,et al.  Data measurement in research information systems: metrics for the evaluation of data quality , 2018, Scientometrics.

[18]  Marcus Kaiser,et al.  How to Measure Data Quality? - A Metric-Based Approach , 2007, ICIS.

[19]  Olgica Nedic,et al.  Peer Review of Reviewers: The Author's Perspective , 2018, Publ..