Kathryn A. Szabat

Concept Mapping—an Empirical Study in Introductory Financial Accounting

The AICPAs Core Competency Framework advocates a skills-based curriculum, based on the rapidly expanding body of knowledge in accounting and developing preferred skills for succeeding in the accounting profession. To address this concern, concept mapping is experimentally used in an introductory financial accounting course. Concept mapping is a learning tool involving a process of externalizing, through drawing and diagrams, the mental connections and association patterns students make on knowledge learned. Widely used in other disciplines, prior research suggests concept mapping develops more complete and organized knowledge structures, leading to more meaningful learning and higher-order thinking skills. This study tests the hypothesis that student learning in an introductory financial accounting course increases, as measured by examination scores, when traditional methods of instruction are supplemented by concept mapping activities. Extraneous variables, such as gender, SAT scores, major, and extra-credit work are tested validating no differences in the control and experimental groups potentially contributing to differences in learning outcomes. The results of the study show no statistically significant evidence supporting the stated hypothesis. A survey administered to the experimental group shows that concept mapping provides a positive student experience and is a useful learning tool. Two significant results arise from the survey response: (1) concept mapping is rated as a valuable learning tool by good concept map creators; and (2) better students indicate a preference for mapping software rather than creating maps manually.

The Current Landscape of Teaching Analytics to Business Students at Institutions of Higher Education: Who is Teaching What?

ABSTRACT Business analytics continues to become increasingly important in business and therefore in business education. We surveyed faculty who teach statistics or whose institutions offer statistics to business students and conducted web searches of business analytics and data science programs that are offered by these faculties associated with schools of business. The intent of the survey and web searches was to gain insight on the current landscape of business analytics and how it may work synergistically with data science at institutions of higher education, as well as inform the role that statistics education plays in the era of big data. The study presents an analysis of subject areas (Statistics, Operations Research, Management Information Systems, Data Analytics, and Soft Skills) covered in courses offered by institutions with undergraduate degrees in business analytics or data science influencing statistics taught to business students. Given the notable contribution of statistics to the study of business analytics and data science and the importance of knowledge and skills acquired in statistics-based courses not only for students pursuing a major or minor in the discipline, but also for all business majors entering the current data-centric business environment, we present findings about who is teaching what in business statistics education.

Prediction Analysis of Cross‐Classifications

Prediction analysis is an approach to the analysis of cross-classified, cross-sectional or longitudinal data. It is a method for predicting events based on specification of predicted relations among qualitative variables and includes techniques for both stating and evaluating those event predictions. This article presents the basic methodology employed in prediction analysis and highlights approaches developed by Hildebrand, Laing, and Rosenthal (6), and by von Eye, Brandtstadter & Rovine, (12, 13). The Hildebrand et al. approach involves measuring prediction success of a set of predictions via a proportionate reduction in error (PRE) measure, known as del. The von Eye et al. approach models the prediction hypothesis via nonstandard log-linear modeling and evaluates the extent to which the model adequately describes the data. Both approaches to prediction analysis provide a viable, valuable data analysis tool that researchers in the social and behavioral sciences can use in a wide range of research settings. Keywords: prediction analysis; prediction success; qualitative variables; cross-classified data; del; log-linear modeling

Application of single depot multiple travelling salesman method to cell formation problems

The cell formation problem (CFP) determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families. In this paper, the CFP is formulated as a single depot multiple travelling salesman problem (SDmTSP). Computational results comparing the model with a set of group technology (GT) problems available in the literature are presented. The approach produced solutions with better grouping efficacy than is reported in the literature.

An Investigation in How Six Sigma Project Teams Should Make Rational Decisions in Shared Leadership Environments

It is proposed that shared leadership environments have an effect on six sigma team decision-making as well as the methods used to make decisions. It is also proposed that the consensus decision-making method is the technique that is the most effective and related to the fundamental conditions of a shared leadership environment. Furthermore, it is believed that a model of shared leadership decision-making can be developed from the results of these hypotheses. Research has found that a strong relationship exists between shared leadership and team decision-making principles and approaches. As a six sigma team becomes more involved in the shared leadership model, the methods utilized to make decisions change in such a way that the power is more distributed among members; the team moves from a single individual decision method, towards a shared decision method. Review of the relationship between shared leadership and team decision-making techniques has shown that the consensus approach is the most effective in satisfying the fundamental conditions and requirements of the shared leadership model. A model of decision-making in shared leadership environments is proposed for use by teams to determine the type of decision-making method that should be employed as a teams level of shared leadership increases.

Futurescope 2020: Global Management Support Systems in the Knowledge Age

The rapid evolution of information technology (IT) has drastically changed organizational problem-solving and decision-making. Innovation and the free flow of information are considered the primary drivers of the accelerating pace of change in the global business environment, an environment characterized by great uncertainties ignited by opportunities and threats. The environmental forces of change have altered the rules of management to utilize information and knowledge in lieu of data in problem-solving and decision-making.1 Managers face challenges as they strive to add value to their organizations’ bottom lines. The gut instinct management style is history. Today’s business leaders must acquire real-time information across many time zones, adopt algorithmic decision-making techniques and use highly sophisticated technology to run their organizations effectively and efficiently.