Dorothy M. Fisher

An extended self-organizing map network for market segmentation: a telecommunication example

Kohonens self-organizing map (SOM) network is an unsupervised learning neural network that maps an n-dimensional input data to a lower dimensional output map while maintaining the original topological relations. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. In this research effort, we applied this extended version of SOM networks to a consumer data set from American Telephone and Telegraph Company (AT&T). Results using the AT&T data indicate that the extended SOM network performs better than the two-step procedure that combines factor analysis and K-means cluster analysis in uncovering market segments.

Selecting the right MBA schools - An application of self-organizing map networks

The self-organizing map (SOM) network, an unsupervised neural computing network, is a categorization network developed by Kohonen. The SOM network was designed for solving problems that involve tasks such as clustering, visualization, and abstraction. In this study, we apply the clustering and visualization capabilities of SOM to group and plot the top 79 MBA schools as ranked by US News and World Report (USN&WR) into a two-dimensional map with four segments. The map should assist prospective students in searching for the MBA programs that best meet their personal requirements. Comparative analysis with the outputs from two popular clustering techniques K-means analysis and a two-step Factor analysis/K-means procedure are also included.

The application of SOM as a decision support tool to identify AACSB peer schools

For a business school, the selection of its peer schools is an important component of its International Association for Management Education (AACSB) (re)accreditation process. A school typically compares itself with other institutions having similar structural and identity-based attributes. The identification of peer schools is critical and can have a significant impact on a business schools accreditation efforts. For many schools the selection of comparable peer schools is a judgmental process. This study offers an alternative means for selection; a quantitative technique called Kohonens Self-Organizing Map (SOM) network for clustering. In this research, we first demonstrate the capability of SOM as a clustering tool to visually uncover the relationships among AACSB-accredited schools. The results suggest that SOM is an effective and robust clustering method. Then, we compare the results of SOM with that of other clustering methods, such as K-means, Factor/K-means analysis, and kth nearest neighbor procedure. The objective of this study is to demonstrate that a two-dimensional SOM map can be used to integrate the results of various clustering methods and, thus, act as a visual decision support tool.

The effect of sample size on the extended self-organizing map network-A market segmentation application

Kohonens self-organizing map (SOM) network maps input data to a lower dimensional output map. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. Kiang, Hu and Fisher used the extended SOM network for market segmentation and showed that the extended SOM provides better results than the statistical approach that reduces the dimensionality of the problem via factor analysis and then forms segments with cluster analysis. In this study, we examined the effect of sample size on the extended SOM compared to that on the factor/cluster approach. Two sampling schemes, one with random sampling and the other one with proportionate sampling were used. Comparisons were made using the correct classification rates between the two approaches at various sample sizes. Unlike statistical models, neural networks are not dependent on statistical assumptions. Thus, the results for neural network models are stable across sample sizes but sensitive to initial weights and model specifications.

The Effect of Sample Size on the Extended Self-Organizing Map Network for Market Segmentation

Kohonens Self-Organizing Map (SOM) network maps input data to a lower dimensional output map. The extended SOM network further groups the nodes on the output map into a user specified number of clusters. Kiang, Hu and Fisher used the extended SOM network for market segmentation and showed that the extended SOM provides better results than the statistical approach that reduces the dimensionality of the problem via factor analysis and then forms segments with cluster analysis. In this study we examine the effect of sample size on the extended SOM compared to that on the factor/cluster approach. Comparisons will be made using the correct classification rates between the two approaches at various sample sizes. Unlike statistical models, neural networks are not dependent on statistical assumptions. Thus we expect the results for neural network models to be stable across sample sizes but may be sensitive to initial weights and model specifications.

Understand Corporate Rationales for Engaging in Reverse Stock Splits - A Data Mining Application

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and reverse stock splits. However, there is little research into the relationship between reverse stock splits and corporate performance as well as bankruptcies. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups, those declaring bankruptcy within 2 years and those remaining solvent. The apparent rationales for engaging in reverse splits differ between the two groups, i.e., weak firms attempting to increase their stock price while solid firms seeking to reposition their stock in the market. Two alternative approaches, Altmans Z-scores and artificial neural networks, are used for classifying reverse splitting firms into the two groups. A comparison is then made of the relative success of Z-scores and neural networks in the classification. This study should generate an understanding of corporate rationale for engaging in reverse splits and the relative success of Z-scores and artificial neural networks in forecasting the two groups.

Web Accessibility and Information Systems Curriculum

The Web has great potential as the content on the Web can be presented in different formats to make information available to individuals with disabilities. However, the potential of the Web is still largely unrealized. Most of the students in the information systems program are unaware of accessibility issues. Few information systems departments have incorporated universal design and Web accessibility in their curriculum. The objective of the research is to examine WAI (Web accessibility initiative) guidelines for Web accessibility so as to incorporate Web accessibility in information systems curriculum

Determining the value of undergraduate business programs from market vs academic perspectives

Purpose The purpose of this paper is to generate an understanding of the value-added to students enrolled in selected undergraduate business programs from an academic and market perspectives. Although there are numerous studies that rank undergraduate colleges and universities, the selection of the “best value” undergraduate business program is a formidable task for prospective students. This study uses data envelopment analysis (DEA), a linear programming-based tool, to evaluate undergraduate business administration programs. The DEA model connects costs (inputs) with benefits (outputs) to evaluate the value-added to students by undergraduate business programs from a market as well as academic perspectives. The study’s findings should assist prospective students in selecting business programs that provide the best value from their individual perspectives. The results can also help schools to identify their corresponding market niche and allocate their recourses more effectively. Design/methodology/approach Use DEA method. DEA was developed by Charnes et al. (1979) to evaluate the performance of multi-input and -output production operations. The analytical and computational capacities of DEA are firmly based on mathematical theory. Findings This study takes a different approach toward the ranking of college programs. Most studies rank-order programs (universities) based on arbitrary weightings of attributes of quality and provide a general ranking of programs that is said meet the needs of many different constituencies including students, parents, donors, administrators’ faculty and alumni. Originality/value This is an original research using DEA and The Bloomberg/Businessweek online data for business school ranking.

Do reverse stock splits indicate future poor stock performance

There has been much written on the individual topics of bankruptcy prediction, corporate performance, and forward/reverse stock splits. However, there is little research into the relationship between reverse stock splits and subsequent corporate performance and the potential for bankruptcy. Previous research suggested there is a negative drift in stock prices following reverse splits. The purpose of this study is to provide and empirically support rationales for reverse splits by classifying reverse splitting firms into two groups. The presumed rationales for engaging in reverse splits would differ between the two groups, so do the subsequent stock performance. Our results show that both neural networks and Z-scores can successfully distinguish the two groups of firms while neural networks outperforms Z-scores in finding the firms with best performing stocks.