Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-25T06:22:00.708Z Has data issue: false hasContentIssue false
  • English
  • Français

Perceived Quality Evaluation with the Use of Extended Reality

Part of: ICT for Design

Published online by Cambridge University Press:  26 July 2019

Kostas Stylidis ,
Andreas Dagman ,
Håkan Almius ,
Liang Gong  and
Rikard Söderberg

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.

If designers want to communicate quality aspects of the product, there is a need to bring these characteristics into the measurable space of perceived quality (PQ) attributes. To illustrate the solution for designers' dilemma of the “best design choice” in this study we applied the PQ attributes importance ranking (PQAIR) method, with the example of a bread toaster. We choose for evaluation three PQ attributes which can significantly influence visual quality of a product: Gap, Flush and Parallelism. We performed the experiment measuring subjective preferences over the toaster designs of two respondent's groups - “Designers” and “Customers.” We used sequentially: (i) web-survey (still images); (ii) desktop system; and (iii) fully immersive head-mounted display system (Virtual Reality).

Consequently, we conducted a post-experiment survey regarding subjective preferences, related to the PQ communication channels that have been implemented during the study. Our results indicate advantages and drawbacks for each PQ communication method that we applied in this experiment and encourage further research in the area of products' perceived quality assessment

Keywords

Perceived qualityVirtual realityNew product developmentRobust designIndustrial design
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1993 - 2002
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) 2019

References

Aaker, D.A. (2009), Managing brand equity, Simon and Schuster.Google Scholar
Alias Autostudio. [Computer program]. Available at https://www.autodesk.com/education/free-software/alias-autostudio. (Accessed 14 December 2018)Google Scholar
Amini, P., Falk, B., Hoth, N.C. and Schmitt, R.H. (2016), Statistical Analysis of Consumer Perceived Value Deviation. Procedia CIRP, Vol. 51, pp. 1–6.Google Scholar
Bjørke, Ø (1989), Computer-Aided Tolerancing, 2nd edition. ASME Press, New York.Google Scholar
Crosby, P.B. (1980), Quality is free: The art of making quality certain, SignetGoogle Scholar
Forslund, K. and Söderberg, R. (2010), “Aesthetic consequences of making car exteriors visually robust to geometrical variation”, J. Des. Res. Vol. 8 No. 3, pp. 252–271.Google Scholar
Forslund, K., Karlsson, M. and Söderberg, R. (2013), “Impacts of geometrical manufacturing quality on the visual product experience”, Int. J. Des. Vol. 7 No. 1, pp. 69–84Google Scholar
Garvin, D.A. (1984), “Product quality: an important strategic weapon”, Business horizons, Vol. 27 No. 3, pp. 40–43.Google Scholar
Gilmore, H.L. (1974), “Product conformance cost”, Quality progress, Vol. 7 No. 5, pp. 16–19.Google Scholar
Golder, P.N., Mitra, D. & Moorman, C. (2012), “What is quality? An integrative framework of processes and states”, The Journal of Marketing, Vol. 76 No. 4, pp. 1–23.Google Scholar
Heragu, S. S. (2016), Facility Design. 4th edn. Boston: CRC Press.Google Scholar
Hoffenson, S., Dagman, A. and Söderberg, R. (2015), “Visual quality and sustainability considerations in tolerance optimization: A market-based approach”, International Journal of Production Economics, Vol. 38, p. 168. https://doi.org/10.1016/j.ijpe.2015.06.023Google Scholar
Howard, T.J., et al. (2017), “The variation management framework (VMF): A unifying graphical representation of robust design”, Quality Engineering, pp. 1–10.Google Scholar
Louviere, J.J. (1993), “The best-worst or maximum difference measurement model: Applications to behavioral research in marketing”. In The American Marketing Association's Behavioral Research Conference Phoenix, Arizona.Google Scholar
Lööf, J., Hermansson, T. and Söderberg, R. (2007), “An efficient solution to the discrete least-cost tolerance allocation problem with general loss functions”. In: Davidson, J.K. (Ed.), Models for Computer Aided Tolerancing in Design and Manufacturing. Springer, Dordrecht, pp. 115–124.Google Scholar
Marley, A.A. & Louviere, J.J. (2005), “Some probabilistic models of best, worst, and best–worst choices”, Journal of Mathematical Psychology, Vol. 49 No. 6, pp. 464–480.Google Scholar
Maxfield, J., et al. (2002), “A virtual environment for aesthetic quality assessment of flexible assemblies in the automotive design process”, SAE Technical Papers. http://doi.org/0.4271/2002-01-0464Google Scholar
Mitra, D. & Golder, P.N. (2006), “How does objective quality affect perceived quality?” Short-term effects, long-term effects, and asymmetries. Marketing Science, Vol. 25 No. 3, pp. 230–247.Google Scholar
Morse, E., Dantan, J-Y., Anwer, N., Söderberg, R., Moroni, G., Qureshi, A. J., Jiang, X. and Matheieu, L. (2018), “Tolerancing: managing uncertainty from conceptual design to final product”, CIRP Annals, Vol. 68 No. 2, pp. 695–717.Google Scholar
Olson, J.C. & Jacoby, J. (1972), “Cue utilization in the quality perception process”. In SV-proceedings of the third annual conference of the association for consumer research.Google Scholar
Pedersen, S.N., Howard, T.J. and Eifler, T. (2017), Perceptual Robust Design.Google Scholar
Quattelbaum, B., Knispel, J., Falk, B. and Schmitt, R. (2013), “Tolerancing subjective and uncertain customer requirements regarding perceived product quality”, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 227 No. 5, pp. 702–708.Google Scholar
RD&T. [Computer program]. Available at http://www.rdnt.se/tool.html. (Accessed 14 December 2018)Google Scholar
Reeves, C.A. & Bednar, D.A. (1994), “Defining quality: alternatives and implications”, Academy of management Review, Vol. 19 No. 3, pp. 419–445.Google Scholar
Reuding, T. and Meil, P. (2004), “Predictive value of assessing vehicle interior design ergonomics in a virtual environment”, Journal of Computing and Information Science in Engineering, Vol. 4 No. 2, pp. 109–113.Google Scholar
Sawtooth Software [Computer program]. Available at https://www.sawtoothsoftware.com (Accessed 14 December 2018)Google Scholar
Shah, J.J., Ameta, G., Shen, Z. and Davidson, J. (2007), “Navigating the tolerance analysis maze”, Computer Aided Design Appications, Vol. 4 No. 5, pp. 705–718.Google Scholar
Smith, R. P. and Heim, J. A. (1999), “Virtual facility layout design: the value of an interactive three-dimensional representation”, International Journal of Production Research, Vol. 37 No. 17), pp. 3941–3957.Google Scholar
Söderberg, R. and Lindkvist, L. (1999), “Computer aided assembly robustness evaluation”, Journal of Engineering Design, Vol. 10 No. 2, pp. 165–181.Google Scholar
Söderberg, R., Wärmefjord, K., Carlson, J. S. and Lindkvist, L. (2017), “Toward a Digital Twin for real-time geometry assurance in individualized production”, CIRP Annals, Vol. 66 No. 1, pp. 137–140Google Scholar
Steenkamp, J.-B.E. (1990), “Conceptual model of the quality perception process”, Journal of Business Research, Vol. 21 No. 4, pp. 309–333.Google Scholar
Stylidis, K., Wickman, C. and Söderberg, R. (2015), “Defining Perceived Quality in the Automotive Industry: An Engineering Approach”, Procedia CIRP, Vol. 36, pp. 165–170.Google Scholar
Stylidis, K., Wickman, C. and Söderberg, R. (2018), “Perceived Quality Attributes Framework and Ranking Method”, engrXiv, Vol. 27.Google Scholar
Sutherland, I. E. (1965), The ultimate display, Multimedia: From Wagner to virtual reality, pp. 506–508Google Scholar
Tseng, M. M., Jiao, J. and Su, C.-J. (1997), “Framework of virtual design for product customization”. In IEEE Symposium on Emerging Technologies & Factory Automation, ETFA, pp. 7–14.Google Scholar
Wagersten, O., Forslund, K., Wickman, C. and Söderberg, R. (2011), “A framework for non-nominal visualization and perceived quality evaluation”. In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 739–748). American Society of Mechanical Engineers.Google Scholar
Wickman, C. & Söderberg, R. (2007), “Perception of gap and flush in virtual environments”, Journal of Engineering Design, Vol. 18 No. 2, pp. 175–193.Google Scholar
Zeithaml, V.A. (1988), “Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence”, The Journal of Marketing, pp. 2–22.Google Scholar
Zwingmann, X., et al. (2002), “Product reliability assessment using virtual samples”. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 624–629.Google Scholar