Browsing Articles by Subject "Data Quality"
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A multidimensional analysis of data quality for credit risk management: new insights and challengesInterest in group moods as an emergent phenomenon of group members’ interactions has significantly increased over the past two decades (Barsade & Gibson, 2007). Most studies focused particularly on understanding the effects of group moods on group processes (Barsade, 2001, Baartel & Saavedra, 2000, Barsade, Ward, Turner & Sonnenfled, 2000, Chiayu Tu, 2009) and group performance (Seung -Yoon Ree, 2006, Jordan, Lawrence & Troth, 2006). However, research investigating the antecedents of group moods is still scant. The current study fills this gap by focusing on the affective potential of group conflict. In this sense, group conflict focuses on how differences of opinion (task conflict) and person-related disagreements (relationship conflict) trigger group moods that differ in their valence (positive and negative) and level of activation (activated and unactivated) (Baartel & Saavedra, 2000). In this context, the group’s ability to define and understand its moods, their cause, evolution and relations between them - ability known as group emotional intelligence (Salovey & Mayer, 1990) - is expected to buffer the relation between conflict and group moods. By studying group moods in relation to group conflict, the current study extends previous research by considering group moods’ antecedents and not only their consequences. This contributes to a better understanding of group affect dynamics. In addition, the current study investigates different nuances of group moods given by different types of conflict. Whether an affect has a positive or negative valence, or whether it is activated or inactivated, has implications upon the further group dynamics.
Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes — An exploratory studyDecision making processes and their outcomes can be affected by a number of factors. Among them, the quality of the data is critical. Poor quality data cause poor decisions. Although this fact is widely known, data quality (DQ) is still a critical issue in organizations because of the huge data volumes available in their systems. Therefore, literature suggests that communicating the DQ level of a specific data set to decision makers in the form of DQ metadata (DQM) is essential. However, the presence of DQM may overload or demand cognitive resources beyond decision makers' capacities, which can adversely impact the decision outcomes. To address this issue, we have conducted an experiment to explore the impact of DQM on decision outcomes, to identify different groups of decision makers who benefit from DQM and to explore different factors which enhance or otherwise hinder the use of DQM. Findings of a statistical analysis suggest that the use of DQM can be enhanced by data quality training or education. Decision makers with a certain level of data quality awareness used DQM more to solve a decision task than those with no data quality awareness. Moreover, those with data quality awareness reached a higher decision accuracy. However, the efficiency of decision makers suffers when DQM is used. Our suggestion would be that DQM can have a positive impact on decision outcomes if it is associated with some characteristics of decision makers, such as a high data quality knowledge. However, the results do not confirm that DQM should be included in data warehouses as a general business practice, instead organizations should first investigate the use and impact of DQM in their setting before maintaining DQM in data warehouses.