Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-27T07:41:43.436Z Has data issue: false hasContentIssue false
  • English
  • Français

THE AGE OF DESIGN - HOW USERS PERCEIVE THE CHRONOLOGICAL ORDER WITHIN AUTOMOBILE GENERATIONS

Published online by Cambridge University Press:  27 July 2021

Jonathan Max Kiessling ,
Franziska Kern ,
Florian Reichelt ,
Daniel Holder  and
Thomas Maier
Jonathan Max Kiessling*
Affiliation:
Institute for Engineering Design and Industrial Design, University of Stuttgart
Franziska Kern
Affiliation:
Institute for Engineering Design and Industrial Design, University of Stuttgart
Florian Reichelt
Affiliation:
Institute for Engineering Design and Industrial Design, University of Stuttgart
Daniel Holder
Affiliation:
Institute for Engineering Design and Industrial Design, University of Stuttgart
Thomas Maier
Affiliation:
Institute for Engineering Design and Industrial Design, University of Stuttgart
*
Kiessling, Jonathan Max, University of Stuttgart, IKTD, Germany, jonathan.kiessling@iktd.uni-stuttgart.de

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The vehicle exterior design conveys a variety of visual information. Among these are the brand identity, assumed characteristics, and the vehicle's age or newness. While previous research focusses mainly on the first two attributes, we broaden the perspective by examining the age perception for vehicle model portfolios across brands.

Information of age is embedded not only in branding but also in the entirety of a vehicle's exterior design features. Therefore, this paper examines how participants of a self-reported study perceive individual models inside successive product portfolios without typical branding. The stimulus patterns were derived from 12 different series of BMW, Mercedes-Benz and Audi and edited accordingly. A total of 67 models from the years 1968 to 2019 were presented and evaluated in terms of perceived age, model and brand recognition.

The results show that most vehicles are perceived as newer than their actual age, successive model generations are clearly distinguishable and participants were able to sort all models in their correct chronological order. Finally, design-related age perception and knowledge-based age perception are introduced as possible underlying concepts of the visual perception of product age.

Keywords

Product perceptionDesign generationsProduct familiesIndustrial designPlatform strategies
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 2981 - 2990
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

References

Briggs, R. W., & Goldberg, J. H. (1995), “Battlefield recognition of armored vehicles”, Human factors, Vol. 37 No. 3, pp. 596–610. https://doi.org/10.1518/001872095779049381CrossRefGoogle Scholar
Catalano, C. E., Giannini, F., Monti, M., & Ucelli, G. (2007), “A framework for the automatic annotation of car aesthetics”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AI EDAM, Vol. 21 No. 1, pp. 73–90. https://doi.org/10.1017/S0890060407070151.CrossRefGoogle Scholar
Chen, L. L., Kang, H. C., & Hung, W. K. (2007), “Effects of design features on automobile styling perceptions”, IASDR 07, The Hongkong Polytech University, 12.11.2007-15.11.2007, pp. 16.Google Scholar
Coughlan, P., & Mashman, R. (1999), “Once is not enough: repeated exposure to and aesthetic evaluation of an automobile design prototype”, Design Studies, Vol. 20 No. 6, pp. 553–563. https://doi.org/10.1016/S0142-694X(99)00007-1.CrossRefGoogle Scholar
European Commission (2002), Distribution and Servicing of Motor Vehicles in the European Union - Explanatory Brochure of Commission Regulation (EC) No. 1400/2002 of 31 July 2002. [online] Available at: https://ec.europa.eu/competition/sectors/motor_vehicles/legislation/explanatory_brochure_en.pdf (25.11.2020).Google Scholar
Holder, D. (2016), Gefallensurteil und Blickanalyse zum Fahrzeugdesign zukünftiger Aufbaugestalten anhand einer technischen Prognose, Stuttgart, Universität Stuttgart.Google Scholar
Holder, D., Inkermann, D., Krasteva, P., Vietor, T., Maier, T. (2018), “Integrated Product Gestalt Design Method for the Analysis and Definition of Interface Elements Regarding Exterior and Interior”, Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Cham, Switzerland, Springer, pp. 888–897. http://doi.org/10.18419/opus-9045.CrossRefGoogle Scholar
Hyun, K. H., Lee, J. H., & Kim, M. (2017), “The gap between design intent and user response: identifying typical and novel car design elements among car brands for evaluating visual significance”, Journal of Intelligent Manufacturing, Vol. 28 No. 7, pp. 1729–1741. https://doi.org/10.1007/s10845-015-1176-8.CrossRefGoogle Scholar
Karjalainen, T. M., & Snelders, D. (2010), “Designing visual recognition for the brand”, Journal of Product Innovation Management, Vol. 27 No. 1, pp. 6–22. https://doi.org/10.1111/j.1540-5885.2009.00696.x.CrossRefGoogle Scholar
Kato, T., & Tsuda, K. (2016), “Study of sensitivity knowledge for quantitative evaluations to the car exterior design”, Procedia Computer Science, Vol. 96, pp. 1106–1111. https://doi.org/10.1016/j.procs.2016.08.152.Google Scholar
Kern, F. & Maier, T. (2020), “Eine Frage der Zeit – Alterswahrnehmung in der Mensch-Produkt-Interaktion”, GfA Frühjahrskongress 2020: Digitaler Wandel, digitale Arbeit, digitaler Mensch?, Berlin, 16.03.2020-18.03.2020, GfA-Press, Dortmund, pp. 6.Google Scholar
Kuhlenkasper, T., & Handl, A. (2019), Einführung in die statistische Auswertung von Experimenten: Theorie und Praxis mit R, Springer Spektrum, Berlin. https://doi.org/10.1007/978-3-662-59054-6.CrossRefGoogle Scholar
Li, B., Dong, Y., Wen, Z., Liu, M., Yang, L., & Song, M. (2018), “A machine learning–based framework for analyzing car brand styling”, Advances in Mechanical Engineering, Vol. 10 No. 7, pp. 1–17. https://doi.org/10.1177/1687814018784429.Google Scholar
McCormack, J. P., Cagan, J., & Vogel, C. M. (2004), “Speaking the Buick language: capturing, understanding, and exploring brand identity with shape grammars”, Design studies, Vol. 25 No. 1, pp. 1–29. https://doi.org/10.1016/S0142-694X(03)00023-1.CrossRefGoogle Scholar
Netcarshow (2020), Volvo 2020 XC40 Recharge. [online] Available at: https://www.netcarshow.com/volvo/2020-xc40_recharge/1280x960/wallpaper_0a.html (24.11.2020).Google Scholar
Ostrosi, E., Bluntzer, J. B., Zhang, Z., & Stjepandić, J. (2019), “Car style-holon recognition in computer-aided design”, Journal of Computational Design and Engineering, Vol. 6 No. 4, pp. 719–738. https://doi.org/10.1016/j.jcde.2018.10.005.CrossRefGoogle Scholar
Ranscombe, C., Hicks, B., Mullineux, G., & Singh, B. (2012), “Visually decomposing vehicle images: Exploring the influence of different aesthetic features on consumer perception of brand”, Design Studies, Vol. 33 No. 4, pp. 319–341. https://doi.org/10.1016/j.destud.2011.06.006.CrossRefGoogle Scholar
Schwarz, J. (2020), Methodenberatung. [online] Universität Zürich,. Available at: https://www.methodenberatung.uzh.ch/de.html (02.06.2020).Google Scholar
Seeger, H. (2014), Basiswissen Transportation-Design: Anforderungen - Lösungen - Bewertungen, Springer Fachmedien, Wiesbaden. https://doi.org/10.1007/978-3-658-04449-7CrossRefGoogle Scholar
Shepard, R. N., & Metzler, J. (1971), “Mental rotation of three-dimensional objects”, Science, Vol. 171 No. 3972, pp. 701–703. https://doi.org/10.1126/science.171.3972.701.CrossRefGoogle ScholarPubMed
Siebertz, K., van Bebber, D., Hochkirchen, T. (2017), Statistische Versuchsplanung – Design of Experiments (DoE), Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55743-3.Google Scholar