The gap between design intent and user response: identifying typical and novel car design elements among car brands for evaluating visual significance

Kyung Hoon Hyun, Ji-Hyun Lee () and Minki Kim
Additional contact information
Kyung Hoon Hyun: KAIST
Ji-Hyun Lee: KAIST
Minki Kim: KAIST

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 7, No 16, 1729-1741

Abstract: Abstract This paper identifies correlations of design intent and user response to stylistic recognition of 23 car brands, with an emphasis on visual aesthetics. By evaluating car exterior designs based on shape similarities, it is possible to find the distributions of the typical design elements and novel design elements. These can then be compared with looking probabilities on design elements observed from eye tracking experiments to conduct a Design Intent Analysis. We have identified that the participants’ viewing patterns are related to the degree of shape similarities of particular design elements such as the front bumper, side silhouette, and side front fender. We observed no significance in regard to subjects’ looking probabilities in relation to design intent of the other 16 design elements. Thus, the design intent of the car brands does not correlate with the user responses. The contribution of this paper is twofold: providing systematic measures and promoting practical possibilities for design quantification. The design field relies heavily on expert knowledge; an empirical understanding of designer intent and user response therefore can provide quantifiable insight to automobile companies. Based on our findings, companies could investigate how creating unique designs may not always be good strategies for improving design qualities, brand recognition or even purchase intent. Companies can efficiently and strategically manage design costs, which are directly related to the manufacturing cost.

Keywords: Product aesthetics; Design intent; Brand identity; Visual significance; Design strategy; Design management (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-015-1176-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:28:y:2017:i:7:d:10.1007_s10845-015-1176-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-015-1176-8

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().