Individual difference factors and men's attraction to female body features /

Existing research on attraction to body features has suggested that men show general preferences for women with lower waist-to-hip ratios (WHR), larger breasts, and slender body weights. The present study intended to expand on this research by investigating several individual difference factors and their potential contribution to variation in what men find attractive in female body features. Two hundred and seventy-three men were assessed for sex-role identity, 2D:4D digit ratios (a possible marker of prenatal exposure to androgens, and thus masculinization), physical attractiveness, early sexual experiences (as indices of early sexual conditioning), and early family attitudes toward body features, as well as their current preferences for WHR, breast size, weight, and height in women. For WHR, as predicted, physical attractiveness, early sexual experiences, and lower (more masculine) right-hand 2D:4D ratios significantly predicted current preferences for more feminine (lower) WHR. Early sexual experiences significantly predicted later preferences for breast size; in addition, more masculine occupational preferences and lower (more masculine) left-hand 2D:4D ratios predicted preferences for larger breasts. Participants' height, education level, Unmitigated Agency (masculinity) scores, and early sexual experiences significantly predicted current preferences for height. Finally, early sexual experiences significantly predicted current preferences for weight. The results suggest that variation in preferences for women's bodily features can be uniquely accounted for by a number of individual difference factors. Strengths and weaknesses of the study, along with implications for future research, are discussed.

Is more information always good? : investigating the impact of website interface features on e-retailer's sales performance

A number of frameworks have been suggested for online retailing, but still there exists little consensus among researchers and practitioners regarding the appropriate amount of information critical and essential to the improvement of customers' satisfaction and their purchase intention. Against this backdrop, this study contributes to the current practical and theoretical discussions and conversations about how information search and perceived risk theories can be applied to the management of online retailer website features. This paper examines the moderating role of website personalization in studying the relationship between information content provided on the top US retailers' websites, and customer satisfaction and purchase intention. The study also explores the role played by customer satisfaction and purchase intention in studying the relationship between information that is personalized to the needs of individual customers and online retailers' sales performance. Results indicate that the extent of information content features presented to online customers alone is not enough for companies looking to satisfy and motivate customers to purchase. However, information that is targeted to an individual customer influences customer satisfaction and purchase intention, and customer satisfaction in tum serves as a driver to the retailer's online sales performance.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.