Information technology plays a critical role in modern business, especially regarding the accounting function. Thus, one might expect that information technology and accounting systems would be a major component of accounting research. This study seeks to answer the questions: To what extent does the accounting literature include technology and systems research, and is technology and systems research adequately addressed in the accounting literature? To answer these questions, this study examines the proportion of technology and systems articles in seven top-ranked accounting journals, four academic and three professional. Journals examined include: The Accounting Review, Journal of Accounting Research, Journal of Accounting and Economics, Contemporary Accounting Research, Journal of Accountancy, CPA Journal, and Strategic Finance (formerly Management Accounting). The findings indicate that while there has been a dramatic increase in the proportion of systems articles published in the professional journals, there were very, very few technology and systems articles published in the academic journals. This might suggest a lack of appropriate attention by academic accounting journals to this important area. Consequently, accounting information systems (AIS) professors who are doing research in systems have a disadvantage in publishing research in top-ranked academic journals. This may result in adverse career consequences for AIS professors if they are expected to publish in these particular academic journals. Further, lack of systems research published in academic journals is detrimental to the perceived and actual relevance of these journals, as technology and systems issues are vitally important to the accounting profession.
Decision-making in executive information systems (EIS) frequently involves scanning complex sets of multidimensional data, which can be difficult with traditional data portrayal techniques. Therefore, schematic faces may be useful in EIS. Schematic faces can represent up to 20 variables by mapping those variables to the salient facial features so that positive outcomes result in a happier looking face, thus simplifying the data scanning process. The composites of the variables, i.e. the faces, aid management by providing a sophisticated graphical interface between the data and the decision maker. An experiment was conducted to compare decision time and accuracy when basing decisions on tables, bar charts, and schematic faces. The gender and cognitive style of subjects were also considered. MANOVA results indicate that both decision speed and accuracy are better when viewing schematic faces. Cognitive style was also a significant factor in the model.
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