International Journal of Information Quality http://www.inderscience.com/browse/index.php?journalID=204&year=2012&vol=3&issue=1 Inderscience Publishers Ltd en-uk support@inderscience.com International Journal of Information Quality 1751-0457 1751-0465 © 2013 Inderscience Enterprises Ltd. © 2012 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijiq_scoverijiq.jpg http://www.inderscience.com/browse/index.php?journalID=204&year=2012&vol=3&issue=1 http://www.inderscience.com/link.php?id=50036 Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as 'fitness for use'. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions. A total data quality management for credit risk: new insights and challenges
Helen-Tadesse Moges; Karel Dejaeger; Wilfried Lemahieu; Bart Baesens
International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 1 - 27
Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as 'fitness for use'. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions.

]]> 10.1504/IJIQ.2012.050036 International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 1 - 27 Helen-Tadesse Moges; Karel Dejaeger; Wilfried Lemahieu; Bart Baesens Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium. ' Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium. ' Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium. ' Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium; School of Management, University of Southampton, Southampton, SO17 1BJ, UK; Vlerick Leuven Gent Management School, Vlamingenstraat 83, B-3000 Leuven, Belgium data quality management information quality credit risk data definition 2012-10-25T23:20:50-05:00 © 2013 Inderscience Enterprises Ltd. 3 1 1 27 2012-10-25T23:20:50-05:00 http://www.inderscience.com/link.php?id=50043 This research focuses on developing a quality model for semantic information system (IS) standards. A lot of semantic IS standards are available in different industries. Often these standards are developed by a dedicated organisation. While these organisations have the goal of increasing interoperability, there is no way to determine the quality of such a standard. This research will provide quality attributes relevant to semantic IS standards. A theoretical grounded model is created and validated by 19 experts through a survey. Based on these findings, a quality model to assess the quality of semantic IS standards has been constructed. Towards a quality model for semantic IS standards
Erwin Folmer; Joris Van Soest
International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 28 - 48
This research focuses on developing a quality model for semantic information system (IS) standards. A lot of semantic IS standards are available in different industries. Often these standards are developed by a dedicated organisation. While these organisations have the goal of increasing interoperability, there is no way to determine the quality of such a standard. This research will provide quality attributes relevant to semantic IS standards. A theoretical grounded model is created and validated by 19 experts through a survey. Based on these findings, a quality model to assess the quality of semantic IS standards has been constructed.

]]> 10.1504/IJIQ.2012.050043 International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 28 - 48 Erwin Folmer; Joris Van Soest University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands; TNO, Capitool 10, 7521 PL Enschede, The Netherlands. ' University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands quality models semantic information systems information system standards interoperability information quality modelling semantics 2012-10-25T23:20:50-05:00 © 2013 Inderscience Enterprises Ltd. 3 1 28 48 2012-10-25T23:20:50-05:00 http://www.inderscience.com/link.php?id=50049 Information quality assessment of technical documentation is an integral part of quality management of products and services. Technical documentation is usually assessed using questionnaires, checklists, and reviews. This is cumbersome, costly and prone to errors. Acknowledging the fact that only people can assess certain quality aspects, we suggest complementing these with software-supported automatic quality assessment. The many different encodings and representations of documentation, e.g., various XML dialects and XML Schemas/DTDs, is one problem. We present a system, a software infrastructure, where abstraction and meta modelling are used to define reusable analyses and visualisations that are independent of specific encodings and representations. We show how this system is implemented and how it: 1) reads information from documentations; 2) performs analyses on this information; 3) visualises the results to help stakeholders understand quality issues. We introduce the system, the architecture and implementation, its adaptation to different formats of documentations and types of analyses, along with a number of real world cases exemplifying the feasibility and benefits of our approach. Altogether, our approach contributes to more efficient information quality assessments. The design and implementation of a software infrastructure for IQ assessment
Morgan Ericsson; Anna Wingkvist; Welf Löwe
International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 49 - 70
Information quality assessment of technical documentation is an integral part of quality management of products and services. Technical documentation is usually assessed using questionnaires, checklists, and reviews. This is cumbersome, costly and prone to errors. Acknowledging the fact that only people can assess certain quality aspects, we suggest complementing these with software-supported automatic quality assessment. The many different encodings and representations of documentation, e.g., various XML dialects and XML Schemas/DTDs, is one problem. We present a system, a software infrastructure, where abstraction and meta modelling are used to define reusable analyses and visualisations that are independent of specific encodings and representations. We show how this system is implemented and how it: 1) reads information from documentations; 2) performs analyses on this information; 3) visualises the results to help stakeholders understand quality issues. We introduce the system, the architecture and implementation, its adaptation to different formats of documentations and types of analyses, along with a number of real world cases exemplifying the feasibility and benefits of our approach. Altogether, our approach contributes to more efficient information quality assessments.

]]> 10.1504/IJIQ.2012.050049 International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 49 - 70 Morgan Ericsson; Anna Wingkvist; Welf Löwe School of Computer Science, Physics, and Mathematics, Linnaeus University, 351 95 Växjö, Sweden. ' School of Computer Science, Physics, and Mathematics, Linnaeus University, 351 95 Växjö, Sweden. ' School of Computer Science, Physics, and Mathematics, Linnaeus University, 351 95 Växjö, Sweden information quality assessment software-based analysis technical documentation software infrastructure quality management abstraction metamodelling reusable analysis visualisation 2012-10-25T23:20:50-05:00 © 2013 Inderscience Enterprises Ltd. 3 1 49 70 2012-10-25T23:20:50-05:00 http://www.inderscience.com/link.php?id=50038 Believability is one of the major information quality dimensions that plays a role in the operational fitness and sound decision making. This paper presents an empirical evaluation of how people perceive believability of data shown through visual and textual representations. Integration of text and images is also studied with respect to believability. The subjective assessment exhibits variation for different types of data sources: textual, image, and both. The manner in which believability varies appears to be heavily dependent on task. Some tasks are more believable when text is integrated with images, others do not benefit from the combination. The results may be influenced by possible bias towards particular data. The data is the result of scientific research into the process of incubation of the bone cells with gold nanoparticles. This research was selected for our study because it alleviates the effect of the accuracy dimension on the assessment of believability. These results are complemented by previous studies on subjective perception of accuracy, and show a non-linear perception of information quality. Task-dependence of subjective believability in integration of scientific data
Ahmed Abuhalimeh; M. Eduard Tudoreanu; Thikra Mustafa
International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 71 - 84
Believability is one of the major information quality dimensions that plays a role in the operational fitness and sound decision making. This paper presents an empirical evaluation of how people perceive believability of data shown through visual and textual representations. Integration of text and images is also studied with respect to believability. The subjective assessment exhibits variation for different types of data sources: textual, image, and both. The manner in which believability varies appears to be heavily dependent on task. Some tasks are more believable when text is integrated with images, others do not benefit from the combination. The results may be influenced by possible bias towards particular data. The data is the result of scientific research into the process of incubation of the bone cells with gold nanoparticles. This research was selected for our study because it alleviates the effect of the accuracy dimension on the assessment of believability. These results are complemented by previous studies on subjective perception of accuracy, and show a non-linear perception of information quality.

]]> 10.1504/IJIQ.2012.050038 International Journal of Information Quality, Vol. 3, No. 1 (2012) pp. 71 - 84 Ahmed Abuhalimeh; M. Eduard Tudoreanu; Thikra Mustafa IQ Programme, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204, USA. ' Information Science, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204, USA. ' Nanotechnology Centre, University of Arkansas at Little Rock, 2801 South University Avenue, Little Rock, AR 72204, USA believability subjective quality data quality information quality visualisation integration data integration scientific data data perception text images incubation bone cells gold nanoparticles nanotechnology biomedical engineering bioengineering task dependence 2012-10-25T23:20:50-05:00 © 2013 Inderscience Enterprises Ltd. 3 1 71 84 2012-10-25T23:20:50-05:00