Data quality assessment in context: A cognitive perspective
In organizations today, the risk of poor information quality is becoming increasingly high as larger and more complex information resources are being collected and managed. To mitigate this risk, decision makers assess the quality of the information provided by their IS systems in order to make effective decisions based on it. To do so, they may rely on quality metadata: objective quality measurements tagged by data managers onto the information used by decision makers. Decision makers may also gauge information quality on their own, subjectively and contextually assessing the usefulness of the information for solving the specific task at hand. Although information quality has been defined as fitness for use, models of information quality assessment have thus far tended to ignore the impact of contextual quality on information use and decision outcomes. Contextual assessments can be as important as objective quality indicators because they can affect which information gets used for decision making tasks. This research offers a theoretical model for understanding users' contextual information quality assessment processes. The model is grounded in dual-process theories of human cognition, which enable simultaneous evaluation of both objective and contextual information quality attributes. Findings of an exploratory laboratory experiment suggest that the theoretical model provides an avenue for understanding contextual aspects of information quality assessment in concert with objective ones. The model offers guidance for the design of information environments that can improve performance by integrating both objective and subjective aspect of users' quality assessments.
cognition quality metadata dual-process theory information quality management information quality dimensions decision support