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.

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Enterprise systems typically include constructs such as ledgers and journals with debit and credit entries as central pillars of the systems' architecture due in part to accountants and auditors who demand those constructs. At best, structuring systems with such constructs as base objects results in the storing the same data at multiple levels of aggregation, which creates inefficiencies in the database. At worst, basing systems on such constructs destroys details that are unnecessary for accounting but that may facilitate decision making by other enterprise functional areas. McCarthy (1982) proposed the resources-events-agents (REA) framework as an alternative structure for a shared data environment more than thirty years ago, and scholars have further developed it such that it is now a robust design theory. Despite this legacy, the broad IS community has not widely researched REA. In this paper, we discuss REA's genesis and primary constructs, provide a history of REA research, discuss REA's impact on practice, and speculate as to what the future may hold for REA-based enterprise systems. We invite IS researchers to consider integrating REA constructs with other theories and various emerging technologies to help advance the future of information systems and business research.

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This paper extends Sowa's (1997) Meaning Triangle to develop a framework for accounting information systems (AIS) research—the Research Pyramid. This framework identifies relationships between objects in economic reality, people's concepts of economic reality, symbols used to record and represent economic reality, and the resultant accounting information systems that capture and present data about economic reality. The Research Pyramid has two major uses. First, the paper illustrates how the Research Pyramid can be used to identify new research questions to extend existing research streams. To be used in this manner, existing AIS research is classified along each of the edges of the Research Pyramid. Once an area of the literature has been analyzed, the edges that have not been studied extensively reveal potential primitive mappings for future exploration. Second, each primitive mapping is evaluated to identify which of four research methodologies (design science, field studies, survey research, and laboratory experiments) are likely techniques for use in future studies. This analysis can help researchers with strong methodological training to identify new, interesting questions to be answered that capitalize on their research strengths. As such, the Research Pyramid is a tool to characterize existing AIS research, identify areas for future exploration, and provide guidance on appropriate methodologies to apply.