Accounting Commons^™

Introductory Information Systems Course Redesign: Better Preparing Business Students, Gina Harden, Robert M. Crocker, Kelly Noe

Faculty Publications

Aim/Purpose The dynamic nature of the information systems (IS) field presents educators with the perpetual challenge of keeping course offerings current and relevant. This paper describes the process at a College of Business (COB) to redesign the introductory IS course to better prepare students for advanced business classes and equip them with interdisciplinary knowledge and skills demanded in today’s workplace.

Background The course was previously in the Computer Science (CSC) Department, itself within the COB. However, an administrative restructuring resulted in the CSC department’s removal from the COB and left the core course in limbo.

Methodology This paper presents a …

An Adaptive Learning Model Which Accommodates Asymmetric Error Costs And Choice-Based Samples, James V. Hansen, James B. Mcdonald, Rayman D. Meservy

Faculty Publications

This paper introduces an adaptive-learning model, EGB2, which optimizes over a parameter space to fit data to a family of models based on maximum-likelihood criteria. We also show how EGB2 can be modified to handle asymmetric costs of Type I and Type II errors, thereby minimizing misclassification costs. It has been shown that standard methods of computing maximum-likelihood estimators of qualitative-response models are generally inconsistent when applied to sample data with different proportions than found in the universe from which the sample is drawn. We investigate how a choice estimator, based on weighting each observation's contribution to the log-likelihood function, …

Learn Audit Selection Rules From Data: A Genetic Algorithms Approach, David P. Greene, Rayman D. Meservy, Stephen F. Smith

Faculty Publications

The construction of expert systems typically require the availability of expertise that can be modeled. However, there are many important problems where no expertise exists, yet there is a wealth of data indicating results in different situations. Machine learning algorithms attempt to discover rules which capture the regularities that exists in such data.

Case-Based Reasoning And Risk Assessment In Audit Judgment, Eric L. Denna, James V. Hansen, Rayman D. Meservy, Larry E. Wood

Faculty Publications

The purpose of this paper is to describe the results of an effort to utilize Case Based Reasoning (CBR) to model a specific audit judgment task. To date most efforts to develop computational models of audit judgment have used strictly rule-based representation methods. Some researchers have recently adopted more robust structures to model the auditor domain knowledge. Although these recent efforts to extend the representation methods appear to be more accurate descriptions of auditor reasoning and memory, they still lack a comprehensive. theory to guide the development of the model. A commonly encountered phenomenon in audit judgment is for an …

Investigating Expertise In Auditing, Paul E. Johnson, Andrew D. Bailey Jr, Rayman D. Meservy

Faculty Publications

Research on human expertise in auditing contexts is an important area of study. Our objective in this paper is to present an approach to conducting research on human expertise. We begin with some issues of terminology and then propose a strategy for inquiry into the phenomena of expertise. Finally, we attempt to illustrate this strategy with three examples of recently completed work. The examples include: two from the auditing literature that involve the analysis of internal control and a medical diagnosis case which illustrates the "garden path" problem that is of concern in any decision making context, including auditing.

Internal Control Evaluation: A Computational Model Of The Review Process, Rayman D. Meservy, Andrew D. Bailey Jr, Paul E. Johnson

Faculty Publications

This study investigated the strategies by which experienced auditors evaluate systems of internal accounting controls. The research method included: (1) observations, using concurrent protocols, of a small sample of practicing auditors performing the internal control evaluation task; (2) extensive interviews with one of the practicing auditors; (3) formalization of auditor processes as a computational model; and (4) validation of the model. The simulation model was Im· plemented as an expert system and tuned to one auditor. The model output consists of a trace of the model processing including: (1) rec• ommendatlons for specific controls to be compliance tested; and (2) …

Kass: A Knowledge-Based Auditor Support System, Ramayya Krishnan, Rayman D. Meservy, Vandana Gadh

Faculty Publications

This paper describes the design of a knowledge-based system to assist auditors in the evaluatation of internal accounting controls and focusses on the logic-based language AL that has been developed as a knowledge representation formalism. Interesting features of AL include a declarative approach to modeling accounting systems and the means to explicitly describe authority structures typically used to enforce internal controls.

A Mathematical Contraction And Automated Analysis Of Internal Controls, Andrew D. Bailey Jr, Gordon Leon Duke, James Gerlach, Chen-En Ko, Rayman D. Meservy, Andrew B. Whinton

Faculty Publications

This paper presents a precedent oriented computer assisted method of designing, analyzing and evaluating systems of internal controls. The ..!..nternal Control _!iodel, TICOM III, is fundamentally a design and analysis tool useful in the context of Automated Office Information Systems (AOIS's). Traditional analysis and evaluation tools, such as flowcharting, written narratives and questionnaires, are inefficient, if not insufficient, to support the design and control analysis of these new systems. The advantages of TICOM III over traditional methods such as narrative description, questionnaires and flowcharts are: (1) the evaluation can be more rigorous and exhaustive because of the speed, accuracy and …