Alan S. Levitan

Personal and Institutional Characteristics Affecting Research Productivity of Academic Accountants

Abstract Accounting research and publication have taken on a role far beyond the advancement and dissemination of knowledge. Academics and institutions now focus on publications in major journals for funding, prestige, merit, and tenure decisions. This article attempts to identify institutional and individual variables that correlate with accounting research productivity. The results could benefit new PhDs, current faculty, and institutions wishing to foster more favorable research environments. Authors publishing a main article in The Journal of Accounting Research or The Accounting Review during 1984 through 1988 were surveyed to ascertain factors associated with their success. These factors were compared with the results of a survey of a randomly selected control group of accounting educators. The data reveal significant differences in work profiles for the two groups. Critical factors include hours of teaching per year compared with hours of research, reasons for topic selection, and choices of journa...

Using Genetic Algorithms to Optimize the Selection of Cost Drivers in Activity-based Costing

In this paper, we address a cost-drivers optimization (CDO) problem in which two separate but interrelated decisions (i.e. the number of cost drivers needed and which cost drivers to use) are considered. It is desirable to have (1) an optimal selection of cost drivers in order to provide better indication of product costs and (2) an optimal number of cost drivers in order to avoid excessive control costs and to minimize information costs associated with data collection, storage and processing. The objective of the CDO problem is to balance savings in information costs with loss of accuracy. We propose an heuristic procedure based on genetic algorithms as an alternative with the potential to address more generalized objective functions. Genetic algorithms represent an innovative and promising heuristic approach which does produce results superior to published alternatives. The development and implementation of the algorithm is supported with the literature review and comparative analysis. We also comment on the complexity and experimental design issues for addressing large and practical problems.

How AIS can progress along with ontology research in IS

Recent years have witnessed a strong and growing interest in the computer science (CS) and information systems (IS) disciplines in applying and extending ontological principles to various CS/IS domains such as knowledge representation, natural language processing, conceptual modeling, and IS development. Similar interest and work have also been observed in accounting information systems (AIS) research. Though ontology research in AIS has enjoyed sustained interest and produced some significant results, there is relatively little incorporation of recent developments in CS/IS ontology research into AIS. This paper provides an overview of some leading areas of ontology research in CS/IS and AIS in an attempt to bridge this gap. The main objectives of this paper are to (1) introduce CS/IS ontology research, (2) highlight areas of future research in AIS where CS/IS ontology research developments can be used to address important and pressing issues, and (3) broaden an area of research where AIS can make unique contributions to distinguish itself.

Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction, with a Real Estate Illustration

Drawing useful predictions from vast accumulations of data is becoming critical to the success of an enterprise. Organizations’ databases grow exponentially from transactions with external stakeholders in addition to their own internal activities. An important organizational computing issue is that, as they grow, the databases become potentially more valuable and also more difficult to analyze. One example is predicting the value of residential real estate based on past comparable sales transactions. This is critical to several important sectors of the US economy including the mortgage finance industry and local governments that collect property taxes. The common methodology for dealing with such property valuation is based on multiple regression, although this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative, but the results are very mixed. This article introduces a novel approach for improving predictions using an adaptive, neuro-fuzzy inference model, and illustrates its application to real estate property price prediction through the use of comparable properties. Although neuro-fuzzy–based approaches have been found to be effective for classification and estimation in many fields, there is very little existing work that investigates their potential in a real estate context. In addition, this article addresses several common problems in existing studies, such as small sample size, lack of rigorous data sampling, and poor model validation and testing. Our model is tested with real sales data from the assessment office in a large US city. The results show that the neuro-fuzzy model is superior in all of the test scenarios. The article also discusses and refines a unique technique to defining comparable properties to improve accuracy. Test results show very promising potential for this technique in mass appraisal in real estate and similar contexts when used with the neuro-fuzzy model.

Text Mining Using Latent Semantic Analysis: An Illustration through Examination of 30 Years of Research at JIS

ABSTRACT: Big Data presents a tremendous challenge for the accounting profession today. This challenge is characterized by, among other things, the explosive growth of unstructured data, such as text. In recent years, new text-mining methods have emerged to turn unstructured textual data into actionable information. A critical role of accounting information systems (AIS) research is to help the accounting profession assess and utilize these methodologies in an accounting context. This paper introduces the latent semantic analysis (LSA), a text-mining approach that discovers latent structures in unstructured textual data, to the AIS research community. An LSA-based approach is used to analyze AIS research as published in the Journal of Information Systems (JIS) over the last 30 years. JIS research serves as an appropriate domain of analysis because of a perceived need to contextualize the scope of AIS research. The research themes and trends resulting from this analysis contribute to a better understanding...

Modeling an Object-Oriented Accounting System with Computer-Aided Software Engineering.

ABSTRACT: The purpose of this case is, first, to provide students with an experience in systems modeling, using facts gathered through interviews with employees who may not be skilled in presenting their responsibilities in a systematic, logical, sequential manner. Second, students will gain actual hands‐on experience learning and using a leading modeling language, the Unified Modeling Language (UML), through a popular Computer‐Aided Software Engineering (C.A.S.E.) tool. Finally, the students will be using those interview facts to model an object‐oriented system for processing cash receipts. In that effort, they will learn and apply the unique documentation techniques used in analyzing and designing object‐oriented systems with design features such as use cases, class diagrams with inheritance, and sequence diagrams.