The combined effects of user schemas and degree of cognitive fit on data retrieval performance

Cheryl L. Dunn, Gregory J. Gerard and Severin V. Grabski

International Journal of Accounting Information Systems, 2017, vol. 26, issue C, 46-67

Abstract: Given the massive and accelerating amount of data modern organizations are collecting, it is imperative that employees possess the skills to navigate various data structures and develop sound data manipulation and retrieval strategies. This research investigates whether and how user schemas and the degree of cognitive fit combine to affect data retrieval task performance. We measured two types of user schemas associated with debit-credit-accounting (DCA) and resource-event-agent (REA) accounting systems. All participants completed tasks that were either facilitated by DCA or REA. Degree of cognitive fit was manipulated as high (when the task was facilitated by the system) or low (when the task was not facilitated by the system). Results show that the positive association between users' schemas and data retrieval performance is enhanced when the degree of cognitive fit is high, but is attenuated when the degree of cognitive fit is low. The findings that participants with equivalent amounts of training on the accounting models had varying schema strengths for those models provide prima facie evidence that one should not assume schemas' existence based on experience. Of particular importance is the finding that cognitive fit is even more important than schemas, as this provides guidance for companies interested in facilitating data retrieval to focus their decision support efforts first on providing interfaces that match the tasks to be performed and second on training their decision makers to develop schemas consistent with the interfaces. While the combination of strong user schemas and high cognitive fit will yield the best results, if a company must choose due to limited resources, the provision of high cognitive fit with the interface-task match is more important than developing strong user schemas.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:26:y:2017:i:c:p:46-67

DOI: 10.1016/j.accinf.2017.06.001

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