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This paper presents a do-it-yourself algorithm to generate the historical GVKEY-CIK link table. The proposed algorithm features a technique of pre-classifying sample data into different treatment subgroups and utilizing historical firm information available from the source data to increase (reduce) matching efficiency (errors). Simulation results show that our algorithm is superior to applying only conventional name matching operations over the whole sample: 57.5 percent of the overall matching results are error free ex ante, and for the remaining 42.5 percent of data, records without Type I errors (with Type II errors) increase (decrease) by 34.0 percent (59.4 percent) when the optimal threshold is used. JEL Classifications: C89; M40; G10; G18. [ABSTRACT FROM AUTHOR]

Many auditing expert systems have been developed for individual components of the audit planning process such as inherent risk assessment and internal control evaluation, The audit planning process involves integrating judgments from each step in the process into subsequent steps. Based on a task analysis of the audit planning process, this paper presents a generalizable knowledge-based framework for the development of expert systems encompassing the entire audit planning process from assessment of overall audit risk to selection of appropriate audit procedures. The framework, which incorporates the audit risk model, suggests the use of knowledge-bases for the heuristic reasoning components of audit planning expertise and a database for the factual knowledge components. Knowledge of how to select appropriate audit procedures given a particular audit situation is best represented using a pattern matching algorithm. The paper also describes an attribute-based design approach which facilitates flexibility in the resulting expert system. [ABSTRACT FROM AUTHOR]

Controls must be changed when information systems are modified. Audit, assurance, and quality-control (AAQC) personnel must evaluate the reliability of controls in the new system versions. Based on Bunge (1977, 1979) and Wand and Weber (1989a, 1990), we describe a model and search-space algorithm that AAQC personnel can use to determine where required control changes are likely to be located in the new system version, thereby mitigating the need for an exhaustive evaluation of all controls. To use the model and algorithm, AAQC personnel must have (1) accurate and complete requirements specifications for the old and new versions of the system, (2) a controls specification for the old version that covers all errors and irregularities that might occur, (3) evidence to conclude all controls for the old version are in place, adequate, and working, and (4) specifications for the new version expressed as a level structure of systems and subsystems. [ABSTRACT FROM AUTHOR]

The article discusses the factors that might influence reliance on decision aids, including monetary incentives, feedback, expertise, experience, accountability, justification, competition and predictive ability. It focuses on the experiment, including the subjects and operationalization of decision aid explanation and framing. It addresses the development and testing of hypothesis. The authors recommend that knowledge of the algorithm used to develop the aid would increase confidence and should, therefore, increase reliance on the decision aid. The users should shift to greater reliance on the aid as the level of decision difficulty increases.

While behavioral research has consistently documented the advantages of using decision aids, decision makers have been reluctant to rely on them, generally with unfavorable results. We offer a model of aided decisions that incorporates both the level of reliance on the decision aid and the agreement of decisions with the decision aid's advice. In the model, the level of reliance is an attribute of the decision strategy adopted by the aided decision maker, while the extent of the decision maker's agreement with the decision aid is observed by comparing the decisions with the aid's advice. The model also includes a classification scheme for factors that affect decision strategy. The categories in this classification scheme are decision aid features, decision-maker characteristics, decision task characteristics and factors affecting the evaluation of decision strategies. In an experiment testing the sensitivity of decision aid reliance, neither the extent of algorithm revelation (revealed or bidden) nor decision framing (positive or negative) affected the level of reliance on the decision aid. In a comparison of aided and unaided decision makers, the decision aid had greater influence in the harder tasks than in the easier tasks. [ABSTRACT FROM AUTHOR]