The Use of Deep Feature and Surface Feature Information in the Performance of Complex Audit Tasks

This study examines auditor performance with respect to complex tasks. Previous research in both psychology and auditing suggests that expert/novice differences are likely to occur during the problem representation phase of problem solving. An experiment was designed to demonstrate that more experienced auditors exhibit superior performance with respect to a real estate valuation task, and that this superior performance is due to the use of underlying patterns of information about individual properties during the representation phase of problem solving.The design of the experiment was a 2 X 2 X 2 X 2 full-factorial design with one between-subjects factor (expertise), and three within-subjects factors (deep features, surface features, and property type). The results indicate that the more expert participants outperformed novice participants overall. Predictions regarding the use of underlying patterns of individuating information by experts were partially supported. It was also predicted that novices would perform worse than experienced auditors because of their tendency to overweight surface feature information, and this prediction was also partially supported. An unexpected result was that experienced auditors exhibited a tendency to commit Type II errors in performing the task. The implications of these findings will be discussed