Abstract

Car styling and features play an important role in an automobile’s design and how consumers perceive it. The goal of this study is to propose a methodology to assist automotive designers in understanding consumers’ affective responses to green vehicles' form design, in order to further develop the shape and styling of green cars. Given the increase of environmental awareness among consumers and the current fierce competition in the automotive industry, it is very important for automobile makers to effectively consider consumer desires for the form features of green vehicles. To achieve this, we performed Support Vector Regression (SVR) to incorporate the psychological response of customers into the design of shape variables and to establish prediction models to fulfill customers’ demands for green cars. In this paper, a systematic evaluation method is presented as follows. First, semantic differential (SD) evaluation was used to measure the feeling associated with certain adjectives describing green vehicle design to obtain customers' baseline attitudes, and factor analysis (FA) was used to extract representative affective dimensions. Next, automotive form features were determined by a numerical definition-based system approach (NDSA). Finally, a SVR-based model was constructed for predicting customer’s affective responses. The results of this experiment can provide a basis for future green technology vehicle design to support automotive makers in bringing visual expectations of automotive styling to life, therefore satisfying consumer needs.

Keywords

form features, semantic differential (SD), factor analysis (FA), numerical definition-based system approach (NDSA), support vector regression (SVR)

COinS
 
Jul 1st, 12:00 AM

Predicting Affective Responses to Green Technology Vehicles Using Support Vector Regression

Car styling and features play an important role in an automobile’s design and how consumers perceive it. The goal of this study is to propose a methodology to assist automotive designers in understanding consumers’ affective responses to green vehicles' form design, in order to further develop the shape and styling of green cars. Given the increase of environmental awareness among consumers and the current fierce competition in the automotive industry, it is very important for automobile makers to effectively consider consumer desires for the form features of green vehicles. To achieve this, we performed Support Vector Regression (SVR) to incorporate the psychological response of customers into the design of shape variables and to establish prediction models to fulfill customers’ demands for green cars. In this paper, a systematic evaluation method is presented as follows. First, semantic differential (SD) evaluation was used to measure the feeling associated with certain adjectives describing green vehicle design to obtain customers' baseline attitudes, and factor analysis (FA) was used to extract representative affective dimensions. Next, automotive form features were determined by a numerical definition-based system approach (NDSA). Finally, a SVR-based model was constructed for predicting customer’s affective responses. The results of this experiment can provide a basis for future green technology vehicle design to support automotive makers in bringing visual expectations of automotive styling to life, therefore satisfying consumer needs.

 

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