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Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time

Author

Listed:
  • Katharina Keller

    (Goethe University Frankfurt)

  • Christian Schlereth

    (WHU – Otto Beisheim School of Management)

  • Oliver Hinz

    (Goethe University Frankfurt)

Abstract

Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.

Suggested Citation

  • Katharina Keller & Christian Schlereth & Oliver Hinz, 2021. "Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 482-500, May.
    • Handle: RePEc:spr:joamsc:v:49:y:2021:i:3:d:10.1007_s11747-020-00758-8
    DOI: 10.1007/s11747-020-00758-8
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    as
    1. Gensler, Sonja & Hinz, Oliver & Skiera, Bernd & Theysohn, Sven, 2012. "Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs," European Journal of Operational Research, Elsevier, vol. 219(2), pages 368-378.
    2. Louviere, Jordan J. & Islam, Towhidul, 2008. "A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling," Journal of Business Research, Elsevier, vol. 61(9), pages 903-911, September.
    3. Jeffrey Meyer & Venkatesh Shankar & Leonard L. Berry, 2018. "Pricing hybrid bundles by understanding the drivers of willingness to pay," Journal of the Academy of Marketing Science, Springer, vol. 46(3), pages 497-515, May.
    4. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 2(1), pages 19-40, March.
    5. Qing Liu & Angela Dean & David Bakken & Greg Allenby, 2009. "Studying the level-effect in conjoint analysis: An application of efficient experimental designs for hyper-parameter estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(1), pages 69-93, March.
    6. Christian Schlereth & Bernd Skiera, 2012. "DISE: Dynamic Intelligent Survey Engine," Springer Books, in: Adamantios Diamantopoulos & Wolfgang Fritz & Lutz Hildebrandt (ed.), Quantitative Marketing and Marketing Management, edition 127, chapter 11, pages 225-243, Springer.
    7. Hinz, Oliver & Schlereth, Christian & Zhou, Wenyan, 2015. "Fostering the Adoption of Electric Vehicles by Providing Complementary Mobility Services: A Two-Step Approach using Best-Worst Scaling and Dual Response," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77138, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    9. Hackbarth, André & Madlener, Reinhard, 2016. "Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 89-111.
    10. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304, October.
    11. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    12. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, October.
    13. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    14. Anders Jensen & Elisabetta Cherchi & Juan Dios Ortúzar, 2014. "A long panel survey to elicit variation in preferences and attitudes in the choice of electric vehicles," Transportation, Springer, vol. 41(5), pages 973-993, September.
    15. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    16. Schlereth, Christian & Skiera, Bernd & Schulz, Fabian, 2018. "Why do consumers prefer static instead of dynamic pricing plans? An empirical study for a better understanding of the low preferences for time-variant pricing plans," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1165-1179.
    17. Jeff Brazell & Christopher Diener & Ekaterina Karniouchina & William Moore & Válerie Séverin & Pierre-Francois Uldry, 2006. "The no-choice option and dual response choice designs," Marketing Letters, Springer, vol. 17(4), pages 255-268, December.
    18. Christian Schlereth & Bernd Skiera, 2017. "Two New Features in Discrete Choice Experiments to Improve Willingness-to-Pay Estimation That Result in SDR and SADR: Separated (Adaptive) Dual Response," Management Science, INFORMS, vol. 63(3), pages 829-842, March.
    19. Joffre Swait & Rick L. Andrews, 2003. "Enriching Scanner Panel Models with Choice Experiments," Marketing Science, INFORMS, vol. 22(4), pages 442-460, September.
    20. Meeran, Sheik & Jahanbin, Semco & Goodwin, Paul & Quariguasi Frota Neto, Joao, 2017. "When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?," European Journal of Operational Research, Elsevier, vol. 258(2), pages 512-524.
    21. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
    22. Gilbride, Timothy J. & Currim, Imran S. & Mintz, Ofer & Siddarth, S., 2016. "A Model for Inferring Market Preferences from Online Retail Product Information Matrices," Journal of Retailing, Elsevier, vol. 92(4), pages 470-485.
    23. Coupey, Eloise & Irwin, Julie R & Payne, John W, 1998. "Product Category Familiarity and Preference Construction," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(4), pages 459-468, March.
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