The purpose of this study was to investigate the saleability judgements of retail store buyers of women's and men's wear. A sample of 81 women's and men's wear buyers, representing two specialty stores and one mass merchandiser, was sent questionnaires. Principal Components Factor Analysis with Varimax Rotation was used to reduce the number of product, vendor and information source variables to eight factors. Three significant differences existed between the women's wear and men's wear buyers, verifying that not all retail buyers are alike. Results will benefit educators in preparing students to become more effective buyers, retail management can incorporate this same information into a buyer training program and apparel manufacturers can use the study in planning product strategies to retailers.

The purpose of this research was to determine if perception of fit in jeans will vary by target wearer, sex, educational background, political-social attitudes, and brand name. One hundred and sixteen usable questionnaires were completed by students enrolled in the 1987 spring semester at a university in North Texas. The questionnaire gathered data about perception of fit in jeans, social-political attitudes, clothing interest, and jeans consumption. No differences were found between perception of fit by sex or target wearer. Factors found to affect perception of fit were educational background, political-social attitudes, brand name, and clothing interest. Subjects indicated fit was the most important factor considered when purchasing jeans.

Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.