A Systematic Method for Selecting Critical Product Form Features Regarding Consumers´ Image Perceptions

This study proposes a product form feature (PFF) selection method based on a numerical definition-based approach (NDA) and the consumers´ image perceptions (CIPs). In the proposed approach, NDA is used to generate an explicit numerical definition of the product form design and the corresponding CIPs are determined by means of a semantic differential experiment. A CIP prediction model is constructed using support vector regression (SVR) techniques. Finally, the feature selection method, namely SVR with support vector machine recursive feature elimination (SVM-RFE), is used to identify the critical form features. The validity of the feature selection method is demonstrated using a knife design for illustration purposes. The result shows that the proposed method provides product designers with a powerful tool for systematically selecting the critical form features and evaluating their respective effects on the consumers´ image perceptions.