Auditors' causal inference judgments during audit planning: A model of reasoning and judgments.

摘要

This research presents a model of the causal inferences made during audit planning. Appropriate causal inference is critical for successful audit performance. From a practical perspective, an incorrect causal inference may lead to audit ineffectiveness and/or inefficiency. From a research perspective, causal inference can be difficult because the task is semi-structured, contextually complex, information intensive, and interrelated with other complex audit planning judgments.; Using the five-step process of knowledge elicitation suggested by Peters, Lewis, and Dhar (1989), judgment-process data was obtained and a reasoning model of the entire sequence of audit planning judgments was constructed. The model, implemented as a computer program, reveals the knowledge and reasoning that is applied, including subtle linkages among interrelated judgments. The model reaches a specific conclusion for each judgment (i.e., going-concern evaluation, audit risk assessments, materiality judgments), and links these subgoal. conclusions to other interrelated judgments; the process continues until a final conclusion is reached (i.e., a causal conclusion). The model emphasizes the contextual richness necessary for each audit situation. In addition, similar to use of decision aids in practice, the model includes the decision aids that the auditors would use, including aids that use archival data.; To test the appropriateness of the model's causal conclusions, eleven highly contextual audit cases were adapted from both the professional and auditing literatures. The model was tested against three different sources of judgments/conclusions: (1) the expert who assisted in development of the model, (2) results from actual audit engagements, and (3) five highly experienced auditors who were not involved in the model development. High consistency between the model's conclusions and the three sources of judgments criteria is evidenced (i.e., 89%, 100%, and 78%, respectively).; In sum, the current research demonstrates the feasibility of researching simultaneously the entire cognitive process of interrelated complex judgments for audit planning. Also, given the optimal integration of knowledge, reasoning, and decision aids, the model could be the basis for a decision support tool for audit planning, as well as for training and educational purposes.