Problems In Other Domains Research Articles

Comparative performance of the FSCL neural net and K-means algorithm for market segmentation

Given the success of neural networks in a variety of applications in engineering, such as speech and image quantization, it is natural to consider its application to similar problems in other domains. A related problem that arises in business is market segmentation for which clustering techniques are used. In this paper, we explore the ability of a specific neural network, namely the Frequency-Sensitive Competitive Learning Algorithm (FSCL), to cluster data for developing strategic marketing decisions. To this end, we investigate the comparative performance of FSCL vis-à-vis the K-means clustering technique. A cluster analysis conducted on brand choice data for the coffee category revealed that the two methodologies resulted in widely differing cluster solutions. In an effort to address the dispute over the appropriate methodology, a comparative performance investigation was undertaken using simulated data with known cluster solutions in a fairly large experimental design to mimic varying data quality to reflect data collection and measurement error. Based on the results of these studies, it is observed that a combination of the two methodologies, wherein the results of the FSCL network are input as seeds to the K-means, seems to provide more managerially insightful segmentation schemes.

Implicit memory: A commentary

This commentary about implicit memory research deals with four issues: terminology, its underlying logic, cognate areas, and theoretical issues. First, although appropriate terms are still being developed to describe the phenomena of interest in this area, I advocate the explicit/implicit distinction for the different forms of memory and types of tests and argue that the term remembering should only be applied to explicit retention. Second, although discovering functional dissociations between two retention tests are a useful research tactic, I believe a better strategy is to use converging operations for theoretical constructs in the form of multiple tests. Third, implicit memory research should be informed by research on similar problems in other domains (e.g., motor skill learning, social cognition). Finally, if we accept the interpretation of explicit/implicit memory research in terms of memory systems, it appears that 5 major systems and 20 or so subsystems may be required. Historical analogues to this situation are briefly considered.

On the Plausibility and Scope of Expert Systems in Management

Over the last decade there have been several efforts at building knowl- edge-based systems, mostly in the scientific and medical arenas. Despite the fact that almost all such systems are in their experimental stages, designers are optimistic about their eventual success. In the last few years, there have been many references to the possibility of expert systems in the management literature. Howev- er, what is lacking is a clear theoretical perspective on how various management problems differ in nature from problems in other domains and the implications of these differences for knowledge-based decision support systems for management. In this paper, I examine some of these differences, what they suggest in terms of the functionality that a computer-based system must have in order to support organiza- tional decision making, and the scope of such a system as a decision aid. The discussion is grounded in the context of a computer-based system called planet that exhibits some of the desired functionality.