دقت تصمیم گیری چند متغیره و ارائه اطلاعات حسابداری

Since multivariate graphics provide spatial integration, summarization and comparison of information, they may provide the means for improving decision-making. This study tests for the incremental benefit of multivariate graphics over a tabular format, by comparing the outcomes for tabular–graphical combination formats with tabular-only formats in an experimental environment. This is an area where research has been sparse and where existing results are inconsistent. The study examines the interactive influence of presentation format and information complexity on multivariate decision accuracy, to determine the most effective presentation format for the performance of multivariate decision tasks of varying complexity. Results show a significant interaction between presentation format and information complexity to affect multivariate decision accuracy. When information complexity is low, presentation format has no impact on accuracy. However, when information complexity is high, the tabular-alone format shows the highest accuracy. The advantages of graphical and pictorial formats reported in earlier studies are not supported, a finding which has significant implications for the manner of disclosure of financial statements through graphical means.

The identification of potential business failure is important to many corporate managers, investors, bankers and auditors. If the warning signs exhibited ahead of failure can be identified in advance, then appropriate action can be undertaken hopefully to reverse the process or at least to minimise the possible damage. One potential way to improve such judgements is to improve the information presentation format (Davis, 1989; Kleinmuntz & Schkade, 1990; Libby, 1981 and Maines, 1995). Bankruptcy prediction is typically a multivariate decision requiring the consideration of multiple financial variables over multiple time periods. Since multivariate graphics provide spatial integration, summarization and comparison of financial variables, they may result in better decisions (Gibson & Schroeder, 1990). The traditional and more familiar tabular presentation of data is typically available to decision makers, though they can usually refer to specific data values before making a final decision. The potential for improved decision making exists if additional information can be gleaned from the presentation of data in alternative formats. Given that tabular presentations are familiar, useful and normally available, this study tests for the incremental benefit of multivariate graphics over tabular formats (i.e., by comparing tabular–graphical combination formats with a tabular-alone format), an area where research efforts have been sparse (excepting Benbasat & Dexter, 1986; DeSanctis & Jarvenpaa, 1989; Frownfelter, 1998 and Nibbelin et al., 1992; Wright, 1995). Even then results have been inconsistent (as detailed below). In addition to the more traditional multivariate graphic presentations of bar charts, this study includes the more innovative form of schematic faces developed by Chernoff (1973). By assigning variables and their values to facial features, changes in expression, can provide a quick indication of the relationship among variables. A single global judgement is promptly facilitated despite the novelty of the approach. Because task characteristics (essentially task type and task complexity) have been shown to be important in decision performance across different presentation formats (see reviews and meta-analyses: DeSanctis, 1984; Hwang & Wu, 1990; Jarvenpaa & Dickson, 1988; Montazemi & Wang, 1988–1989; and other works: Amer, 1991; Blocher, Moffie, & Zmud, 1986; Davis, 1989 and Hard and Vanecek, 1991; Umanath & Vessey, 1994; Wright, 1995) report format and task characteristics must be considered in an interactive manner in examining decision performance across different formats. The issue of task characteristics is, however, quite complex, given the variety of definition, interpretation and measurement, and the absence of a ready taxonomy of classification. Task characteristics have many dimensions, among them, those more commonly reported in the literature: task type and task complexity (DeSanctis, 1984; Libby & Lewis, 1982). Task complexity can be further distinguished into information complexity and job complexity (Liang, 1986). Such a distinction, however, is rarely made in the literature, further complicating this issue because of alternative ways of measuring information complexity and job complexity. The current study focuses on the multivariate decision task and on information complexity, because information complexity is more fundamental than job complexity and can be objectively assessed, independent of any particular task-doer, and unaffected by the presentation format (Campbell, 1988). The current study also initiates a novel method to measure information complexity, based on the internal consistency of the information, developed by So and Smith (2002) but originally suggested by Steinmann (1976). This is of particular relevance to the multivariate decision tasks of this study. This study seeks to determine the effective presentation format (tabular–graphical combination, tabular–facial combination, or tabular-alone format) for the performance of multivariate decision tasks of varying information complexity, through an experimental study which examines the interactive influence of presentation format and information complexity on multivariate decision performance.

The results of the study are disturbing and could have wide-reaching implications. They suggest that when decision makers are provided with graphical representations, complementing numerical financial data, that they will make use of these graphical representations, but that inferior outcomes will be generated as a result. The nature of this outcome has parallels with those reported by Smith and Taffler (1995), who attempt to improve decision making by providing users with chairman's narratives in conjunction with the corresponding financial statement data. On the contrary, they observed a deterioration in decision making performance, which they explained in terms of the decision making heuristics due to Kahnemann and Tversky (1972). They suggest that, faced with complex and extensive datasets, users will adopt the representative heuristic in order to focus on sub-sets of data: they will prefer ‘soft’ qualitative data to ‘hard’ quantitative data, even though the latter is more reliable and better specified. Such an explanation would fit the outcomes of the present study too: subjects may focus on soft data (graphical representations) in preference to hard data (financial ratio numbers) even though their processing skills may be better equipped for the latter. It would be no surprise then, where both datasets are provided, that decision performance is lower than that arising for tabular ratio data alone. The implications of such findings for financial reporting are serious; they suggest that without more education in the use of graphics, their continued provision may lead to a deteriorating decision performance. The results in this study are subject to limitations regarding the measurement and manipulation of experimental variables. The measure of information complexity in this study is a novel one, and is but one among a number of possible ways to measure information complexity. Also, the choice of only two information complexity levels, high and low, is simplistic. Accordingly, our inferences from the results are limited to the definition of task complexity chosen, and to the manipulation levels we have used. Although the experiment controls for work experience and cognitive style, there may be other relevant individual characteristics, for example, culture, personality, and tolerance for ambiguity. Future research needs to address these individual differences.