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Consumers' perception-oriented product form design using multiple regression analysis and backpropagation neural network

Published online by Cambridge University Press:  29 April 2015

Hung-Yuan Chen  and
Hua-Cheng Chang
Hung-Yuan Chen*
Affiliation:
Department of Visual Communication Design, Southern Taiwan University of Science and Technology, Tainan City, Taiwan, Republic of China
Hua-Cheng Chang
Affiliation:
Department of Multimedia and Entertainment Science, Southern Taiwan University of Science and Technology, Tainan City, Taiwan, Republic of China
*
Reprint requests to: Hung-Yuan Chen, Department of Visual Communication Design, Southern Taiwan University of Science and Technology, No. 1, Nantai Street, Yongkang District, Tainan City 71005, Taiwan, Republic of China. E-mail: hungyuan@mail.stust.edu.tw
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Abstract

Consumers' psychological perceptions of a product are significantly influenced by its appearance aesthetics, and thus product form plays an essential role in determining the commercial success of a product. The evolution of a product's form during the design process is typically governed by the designer's individual preferences and creative instincts. As a consequence, there is a risk that the product form may fail to satisfy the consumers' expectations or may induce an unanticipated consumer response. This study commences developing an integrated design approach based on the numerical definition of product form. A series of evaluation trials are then performed to establish the correlation between the product form features and the consumers' perceptions of the product image. The results of the evaluation trials are used to construct three different types of mathematical model (a multiple regression analysis model, a backpropagation neural network model, and a multiple regression analysis with a backpropagation neural network model) to predict the likely consumer response to any arbitrary product form. The feasibility of an integrated design approach is demonstrated using a three-dimensional knife form. Although this study takes an example for illustration and verification purposes, the methodology proposed in the present study is equally applicable to any form of consumer product.

Keywords

Backpropagation Neural NetworkConsumers' PerceptionMultiple Regression AnalysisMultiple Regression Analysis Technique With a Backpropagation Neural NetworkProduct Form Design
Type
Regular Articles
Information
AI EDAM , Volume 30 , Issue 1 , February 2016 , pp. 64 - 77
Copyright
Copyright © Cambridge University Press 2015 

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