An important decision faced by accounting information system designers concerns the amount of detail to be incorporated into each accounting report. Too little detail may result in relevant information being omitted, whereas too much detail may lead to user confusion or frustration. Thus, a misjudgment in either direction can cause decision quality to be reduced. The objective of this study is to explore the effect of data aggregation on decision quality in an accounting context based on an hypothesized curvilinear relationship. Previous empirical studies (e.g., Barefield [1972], Abdel-khalik [1973], Chervany and Dickson [1974], and Harvey, Rhode, and Merchant [1979]) have provided some weak evidence that higher or lower levels of aggregation can lead to either better or worse performance, depending on the circumstances. While this prior work is consistent with a curvilinear hypothesis, predictions of the effect of aggregation on decision quality clearly require more detailed specifications of the form of the relationship. Our results provide weak support for one such curvilinear relationship. Two commonly used models of the accounting aggregation process, communication theory (as developed by Lev [1968]) and information economics (as developed by Demski [1972] and Feltham [1977]), do not provide an explicit rationale for predicting a deterioration in decision quality when a user is faced with a great deal of data (low level of aggregation). However, a change in decision quality is often incorporated in models of human information processing. All three models are seen as complementary and can be used in conjunction to provide an adequate
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