Neural networks for optimal form design of personal digital assistants

Abstract

This paper presents a neural network (NN) approach to determining the optimal form design of personal digital assistants (PDAs) that best matches a given set of product images perceived by consumers. 32 representative PDAs and 9 design form elements of PDAs are identified as samples in an experimental study to illustrate how the approach works. Four NN models are built with different hidden neurons in order to examine how a particular combination of PDA form elements matches the desirable product images. The performance evaluation result shows that the number of hidden neurons has no significant effect on the predictive ability of the four NN models. The NN models can be used to construct a form design database for supporting form design decisions in a new PDA product development process. © 2009 Springer Berlin Heidelberg.