Human information processing research in auditing; A review and synthesis;

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Human Information Processing Research in Auditing: A Review and Synthesis
Robert H. Ashton
New York University
The importance of individual decision making to the audit process is increasingly being recognized. Decisions involving the collection, interpretation and integration of audit evidence are receiving attention from auditing firms concerned with improving the effectiveness and efficiency of audits. Concur­rent
with the profession's interest in audit decision making, a growing body of knowledge about decision making by practicing auditors is being generated by academic researchers. This body of knowledge, based on human information processing research, focuses on the understanding, evaluation and improve­ment
of audit decision making. It offers great potential for identifying shortcom­ings
of audit decision making, and for reducing or eliminating those shortcomings.
This paper reviews and synthesizes human information processing re­search
in auditing. Its purpose is to introduce this body of knowledge to readers who are relatively unfamiliar with it. Coverage of the topic is fairly broad, emphasizing the questions of why this research is conducted and what its implications are, and de-emphasizing methodological issues of experimental design and analysis.1 The discussion proceeds in four parts: (1) some background information on human information processing research in auditing, (2) an explanation of the reasons for conducting this type of research, (3) an overview of the research evidence, and (4) a consideration of some of its practical implications.
Introduction and Background
Human information processing research in auditing focuses on several decision-related activities of practicing auditors. Although a large audit may entail hundreds of judgments and decisions, it is useful for research purposes to abstract audit decision making to four basic types of decision-related activities: (1) evaluations or judgments of current information, (2) predictions of future outcomes, (3) assessments of the probability that particular outcomes will occur (and revisions of such probabilities), and (4) choices among alternative courses of action.
For example, auditors collect, interpret and combine various types of evidence in order to evaluate internal control system design, the materiality of an item, and the implications of sample outcomes. Auditors may predict error-rate levels in audit populations, or the future going-concern status of a client, or
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