An Investigation of Localization as an Element of Cognitive Fit in Accounting Model Representations

Abstract

Cognitive fit, a correspondence between task and data representation format, has been demonstrated to lead to superior task performance by individual users and has been posited as an explanatiton for performance differences among users of various problem representations such as tables, graphs, maps, and schematic faces. The current study extends cognitive fit to accounting models and integrates cognitive fit theory with the concept of localization to provide additional evidence for how cognitive fit works. Two accounting model representations are compared in this study, the traditional DCA (Debit-Credit-Account) accounting model and the REA (Resources-Events-Agents) accounting model. Results indicate that the localization of relevant objects or linkages is important in establishing cognitive fit.

Data architectures for an organizational memory information system

“…Likewise, as shown in the means Table , RDB and its data attributes provide the best fit for the high coordinative/low dynamic task complexity environment. These results are consistent with Dunn and Grabski () because the tasks they considered generally fall into the high coordinative/low dynamic level of complexity.…”

“…Data architectures represent the way that data are logically stored and organized in an IS. How data are represented, that is, the architecture, and data's correspondence with changing tasks are important to task performance (Dunn & Grabski, ). In the design processes, the objective is to select the appropriate data attributes and corresponding data architecture that best support the characteristics of a portfolio of tasks, i.e., a set of transaction processing, decision making, and managerial control tasks.…”

Agent-based Human-computer-interaction for Real-time Monitoring Systems in the Trucking Industry

“…The theory of cognitive fit has further been applied to explain differences in usefulness of tables versus graphics [33], geographic information systems [28] and accounting information systems [6]. We extend cognitive fit to human computer interaction in a real-time monitoring environment.…”

User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit

“…In addition to its use in studies of graphs and tables [91], CFT has been used to explain sense making in data-exploration tasks [92], efficient query building [93,94], online shopping [95,96], graphs and information load [97], graphs and uncertainty [98,99], geographical information systems [100][101][102], accounting visualization [88,[103][104][105], spreadsheet usage and error correction [106], expert-data and selforganizing maps [107], maps [108,109], model comprehension [110][111][112], efficient database views [113], visual interactive simulations [114], multi-attribute data presentation [115], software development [116][117][118], hypertext and knowledge type [119], executive support systems [120,121], judgment strategies [122], system acceptance [123], multimedia effectiveness [124], interface design [125], decision aid features [126], pattern recognition [127], virtual reality [128], knowledge transfer [129], decision tables, trees, and rules [130], expertise and online reviews [...…”