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An Experimental Investigation of Audit Decision-Making: An Evaluation Using System-Mediated Mental Models

This paper has two purposes. The first is to present hypotheses generated by joining two foundation theories of decision-making. The first foundation theory is the mental model theory, which has its roots in cognitive psychology (Johnson-Laird 1983). The theory posits that individuals make inferences by constructing and integrating mental models, which are internal representations of external items. Decision-makers facing complex problems make predictable errors by failing to fully integrate their mental models of the inference task. The second foundation theory is the general systems theory, which argues that decision-makers need to understand the system as a whole in order to lessen the chances of overlooking important properties of the system (Bertalanffy 1968). Joining these two theories, we formulate a set of hypotheses referred to as System-Mediated Mental Model (SMMM) hypotheses. The SMMM hypotheses provide insights as to how systems thinking can lead to improved decision-making. In short, the SMMM hypothesis posits that systems knowledge will help DMs integrate multiple mental models more accurately, thus reducing systematic inference errors. The second purpose of this paper is to investigate the hypotheses using a laboratory experiment that compares “bottom-up data immersion” approach to decision-making (i.e., settings with no guideline for developing a framework to solve the problem) with “top-down big picture” approach to decision-making (i.e., settings with a guideline for developing a framework). Our results are consistent with the predictions of the SMMM hypotheses and indicate that a top-down systems approach can enhance the effectiveness of decision-making by reducing systematic errors that arise from not fully understanding the system underlying the problem. These findings have implications for the systems audit approach developed by Bell et al. (1997).