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Perceived Quality Estimation by the Design of Discrete-Choice Experiment and Best–Worst Scaling Data: An Automotive Industry Case

  • Konstantinos StylidisEmail author
  • Serena Striegel
  • Monica Rossi
  • Casper Wickman
  • Rikard Söderberg
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 134)

Abstract

“Which product attributes do engineers have to focus on to receive the highest level of a customer’s appreciation?” In other words, can we design for high perceived quality? In this paper, discrete-choice experiment design is presented with the combination of best–worst scaling method to evaluate the perceived quality of the complete vehicle in application to the premium automotive industry. The application of Perceived Quality Framework (PQF) and Perceived Quality Attributes Importance Ranking (PQAIR) method to measure the importance of perceived quality attributes for the automotive engineers and customers depicted commonalities and differences in perception. This information and approach can significantly improve engineering practices regarding the perceived quality of cars.

Keywords

Perceived quality Product development Automotive Cars Premium Best–worst scaling Discrete choice Design for x Design Maxdiff Conjoint 

Nomenclature

PQ

Perceived Quality

PQF

Perceived Quality Framework

PQAIR

Perceived Quality Attributes Importance Ranking

DCE

Discrete-Choice Experiment

BWS

Best–Worst Scaling

OEM

Original Equipment Manufacturer

RD

Robust Design

GA

Ground Attribute

References

  1. 1.
    Crilly, N., Moultrie, J., Clarkson, P.J.: Seeing things: consumer response to the visual domain in product design. Des. Stud. 25(6), 547–577 (2004)CrossRefGoogle Scholar
  2. 2.
    Crilly, N., Maier, A.M., Clarkson, P.J.: Representing artefacts as media: modelling the relationship between designer intent and consumer experience. Int. J. Des. 2(3), 15–27 (2008)Google Scholar
  3. 3.
    Forslund, K., Dagman, A., Söderberg, R.: Visual sensitivity: communicating poor quality (2006)Google Scholar
  4. 4.
    Monö, RG., Knight, M., Monö, R.: Design for Product Understanding: The Aesthetics of Design from a Semiotic Approach. Liber (1997)Google Scholar
  5. 5.
    Akerlof, GA.: The market for“ lemons”: Quality uncertainty and the market mechanism. Q. J. Econ. JSTOR; 488–500 (1970)Google Scholar
  6. 6.
    Stiglitz, JE.: The contributions of the economics of information to twentieth century economics. Q. J. Econ. MIT Press. 115(4), 1441–1478 (2000)CrossRefGoogle Scholar
  7. 7.
    Stylidis, K., Wickman, C., Söderberg, R.: Perceived quality attributes framework and tanking method. EngrXiv. (2018)Google Scholar
  8. 8.
    Olson, J.C., Jacoby, J.: Cue utilization in the quality perception process. (1972)Google Scholar
  9. 9.
    Gilmore, H.L.: Product conformance cost. Quality progress. 7(5), 16–19 (1974)Google Scholar
  10. 10.
    Crosby, PB.: Quality is Free: The Art of Making Quality Certain. Signet (1980)Google Scholar
  11. 11.
    Garvin, D.A.: Product quality: an important strategic weapon. Bus. Horiz. 27(3), 40–43 (1984)CrossRefGoogle Scholar
  12. 12.
    Zeithaml, VA.: Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. J. Mark. 2–22 (1988)Google Scholar
  13. 13.
    Steenkamp, J-BE.: Conceptual model of the quality perception process. J. Bus. Res. Elsevier. 21(4):309–33 (1990)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Reeves, CA., Bednar, DA.: Defining quality: alternatives and implications. Acad Manag. Rev. Academy of Management 19(3), 419–445 (1994)CrossRefGoogle Scholar
  15. 15.
    Mitra, D., Golder, P.N.: How does objective quality affect perceived quality? Short-term effects, long-term effects, and asymmetries. Marketing Science. 25(3), 230–247 (2006)CrossRefGoogle Scholar
  16. 16.
    Aaker, DA.: Managing Brand Equity. Simon and Schuster (2009)Google Scholar
  17. 17.
    Taguchi, G.: Introduction to quality engineering: designing quality into products and processes (1986)Google Scholar
  18. 18.
    Taguchi, G., Chowdhury, S., Wu, Y.: Taguchi’s Quality Engineering Handbook, Wiley; 2005Google Scholar
  19. 19.
    Söderberg, R., Lindkvist, L.: Computer aided assembly robustness evaluation. J. Eng. Des. Taylor & Francis, 10(2), 165–181 (1999)CrossRefGoogle Scholar
  20. 20.
    Wickman, C., Söderberg, R.: Perception of gap and flush in virtual environments. J. Eng. Des. Taylor & Francis, 18(2), 175–193 (2007)CrossRefGoogle Scholar
  21. 21.
    Wagersten, O., Forslund, K., Wickman, C., Söderberg, R.: A Framework for Non-nominal visualization and perceived quality evaluation. Am. Soc. Mech. Eng pp. 739–48 (2011.)Google Scholar
  22. 22.
    Wickman, C., Wagersten, O., Forslund, K., Söderberg, R.: Influence of rigid and non-rigid variation simulations when assessing perceived quality of split-lines. J. Eng. Des. Taylor & Francis,25(1–3), 1–24 (2014)CrossRefGoogle Scholar
  23. 23.
    Söderberg, R., Lindkvist, L., Carlson, J.: Virtual geometry assurance for effective product realization. pp. 25–6 (2006)Google Scholar
  24. 24.
    Pedersen, S.N., Christensen, M.E., Howard, T.J.: Robust design requirements specification: a quantitative method for requirements development using quality loss functions. J. Eng. Des. Taylor & Francis, 27(8), 544–567 (2016)CrossRefGoogle Scholar
  25. 25.
    Pedersen, S.N.: Perceptual Robust Design. Technical University of Denmark (DTU) (2017)Google Scholar
  26. 26.
    Howard, T.J., Eifler, T., Pedersen, S.N., Göhler, S.M., Boorla, S.M., Christensen, M.E.: The variation management framework (VMF): A unifying graphical representation of robust design. Qual. Eng. Taylor & Francis, 1–10 (2017)Google Scholar
  27. 27.
    Schleich, B., Wartzack, S.: Challenges of Geometrical Variations Modelling in Virtual Product Realization. Procedia CIRP. Elsevier 60, 116–121 (2017)CrossRefGoogle Scholar
  28. 28.
    Burnap, A., Hartley, J., Pan, Y., Gonzalez, R., Papalambros, P.Y.: Balancing design freedom and brand recognition in the evolution of automotive brand styling. Am. Soc. Mech. Eng. pp. V007T06A047–7 (2015)Google Scholar
  29. 29.
    Pan, Y., Burnap, A., Liu, Y., Lee, H., Gonzalez, R., Papalambros, P.A.: Quantitative model for identifying regions of design visual attraction and application to automobile styling. (2016)Google Scholar
  30. 30.
    Striegel, S., Schleich, B., Zielinski, D., Wartzack, S.: Automotive premium quality improvement by high-end-visualization. Am. Soc. Mech Eng. pp. V002T02A108–8 (2017)Google Scholar
  31. 31.
    Louviere, J.J., Woodworth, G.: Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. J. Mark. Res. JSTOR, 350–67 (1983)CrossRefGoogle Scholar
  32. 32.
    Louviere, JJ.: The best-worst or maximum difference measurement model: applications to behavioral research in marketing. (1993)Google Scholar
  33. 33.
    Marley, A.A., Louviere, J.J.: Some probabilistic models of best, worst, and best–worst choices. J. Math. Psychol. Elsevier, 49(6), 464–480 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Konstantinos Stylidis
    • 1
    Email author
  • Serena Striegel
    • 2
  • Monica Rossi
    • 3
  • Casper Wickman
    • 1
    • 4
  • Rikard Söderberg
    • 1
  1. 1.Department of Industrial and Materials ScienceChalmers University of TechnologyGöteborgSweden
  2. 2.BMW Group, Total Vehicle ValidationMunichGermany
  3. 3.Department of Management, Economics and Industrial EngineeringPolitecnico di MilanoMilanItaly
  4. 4.Volvo Car Corporation, Customer Experience & Quality CentreGothenburgSweden

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