Quantifying Perception-Based Attributes in Design: A Case Study on the Perceived Environmental Friendliness of Vehicle Silhouettes.

Abstract

Design optimization problems have traditionally used engineering functionality attributes to inform the design of products and systems. However, the quantification and inclusion of subjective attributes has become a necessary part of the product design process. Previous research has assessed the aesthetics, emotional appeal, and expressiveness of "concept-based" product attributes (such as luxury or sportiness) in products. Environmental friendliness is a new product attribute that has emerged in prominence as consumers and manufacturers become more concerned with issuesof sustainability and the "footprint" on the environment.

In the automotive industry, there is an increased interest not only in making more fuel efficient vehicles, but also in making them visually appealing in a way that conveys environmental consciousness. Present day trends already show an increase in the number of fuel efficient and alternative powertrain vehicles being introduced in the market; it is expected that in the next few years there will be a large number of different hybrid powertrain vehicles on the market. Depending on market trends and government regulations, fuel economy may not be the only driver for the purchase of fuel efficient vehicles when the price premium paid for the new technology does not result in a timely payback in fuel cost savings. Previous research has shown that people used subjective reasoning, including styling, as a determining factor for purchasing hybrid vehicles.

Using methods from psychology and engineering, this dissertation presents a methodology to quantify subjective attributes for inclusion in design optimization models. A demonstration case study addresses the quantification of a perceptual design attribute named perceived environmental friendliness (PEF). A modeling framework that consists of stimuli development using design of experiments, survey design, and statistical analysis of data is presented. The model derived is included in a design optimization framework that considers how variables that influence PEF tradeoff with those that impact fuel economy. Results indicate that under certain conditions, there is a tradeoff between PEF and fuel economy; as PEF increases, the fuel economy decreases.