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An Agent-Based Simulation Using Conjoint Data: The Case of Electric Vehicles in Germany

  • Markus GüntherEmail author
  • Marvin Klein
  • Lars Lüpke
Conference paper
Part of the Operations Research Proceedings book series (ORP)

Abstract

Agent-based models are currently in wide use in innovation and technology diffusion research, as they are able to capture the inherent complexity arising from adoption processes and they allow the consideration of various influences of the underlying social systems. While they are sometimes criticized as “toy models”, agent-based models often do not reach their full potential if they lack an empirical foundation. Therefore, we present an agent-based simulation that addresses consumers’ adoption behavior of electric and plug-in hybrid electric vehicles in Germany using various empirical data sources for parametrization and validation. In particular, we conducted a focus group and a choice-based conjoint study. Additionally, our model is to our knowledge the first that takes into account explicitly and comprehensively the supply of home charging options.

References

  1. 1.
    Barabási, A.-L., Albert, R., & Jeong, H. (1999). Mean-field theory for scale-free random networks. Physica A, 272(1–2), 173–187.CrossRefGoogle Scholar
  2. 2.
    Ellen, P. S., Bearden, W. O., & Sharma, S. (1991). Resistance to technological innovations: An examination of the role of self-efficacy and performance satisfaction. Journal of the Academy of Marketing Science, 19(4), 297–307.CrossRefGoogle Scholar
  3. 3.
    Frenzel, I., Jarass, J., Trommer, S., & Lenz, B. (2015). Erstnutzer von Elektrofahrzeugen in Deutschland: Nutzerprofile, Anschaffung, Fahrzeugnutzung (First-time users of electric vehicles in Germany: user profiles, acquisition, vehicle use). Berlin: Deutsches Zentrum für Luft- und Raumfahrt e. V.Google Scholar
  4. 4.
    Garcia, R., & Jager, W. (2011). From the special issue editors: Agent-based modeling of innovation diffusion. Journal of Product Innovation Management, 28(2), 148–151.CrossRefGoogle Scholar
  5. 5.
    Götz, K., Sunderer, G., Birzle-Harder, B., & Deffner, J. (2012). Attraktivität und Akzeptanz von Elektroautos. Ergebnisse aus dem Projekt OPTUM - Optimierung der Umweltentlastungspotenziale von Elektrofahrzeugen (Attractiveness and acceptance of electric cars. Results from the OPTUM project - Optimizing the environmental impact potential of electric vehicles). ISOE-Studientexte, vol 18. ISOE - Institut für sozial-ökologische Forschung, Frankfurt am Main.Google Scholar
  6. 6.
    Günther, M., & Stummer, C. (in press). Simulating the diffusion of competing multi-generation technologies: An agent-based model and its application to the consumer computer market in Germany. In A. Fink, A. Fügenschuh & M.J. Geiger (Eds.), Operations Research Proceedings 2016.Google Scholar
  7. 7.
    Hidruea, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for electric vehicles and their attributes. Resource and Energy Economics, 33(3), 686–705.CrossRefGoogle Scholar
  8. 8.
    Kaiser, A. (2016). Warum Holland grün angemalten Spritschluckern 7000 Euro schenkt (Why Holland pays greenly sprinkled 7,000 euros), manager magazin online. Retrieved July 14, 2017, from http://www.manager-magazin.de/politik/europa/elektromobilitaet-so-setzt-der-elektroauto-boom-hollands-fiskus-zu-a-1072200.html.
  9. 9.
    Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), 183–230.CrossRefGoogle Scholar
  10. 10.
    Krupa, J. S., Rizzo, D. M., Eppstein, M. J., Lanute, B. D., Galeema, D. E., Lakkaraju, K., et al. (2014). Analysis of a consumer survey on plug-in hybrid electric vehicles. Transportation Research Part A: Policy and Practice, 64(14), 31.Google Scholar
  11. 11.
    Kraftfahrt Bundesamt. (2016). Fahrzeugzulassungen im Juni 2016 (Vehicle registrations in June 2016), 21/2016.Google Scholar
  12. 12.
    Morrissey, P., Weldon, P., & O’Mahony, M. (2016). Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour. Energy Policy, 89, 257–270.CrossRefGoogle Scholar
  13. 13.
    Noori, M., & Tatari, O. (2016). Development of an agent-based model for regional market penetration projections of electric vehicles in the United States. Energy, 96, 215–230.CrossRefGoogle Scholar
  14. 14.
    Orme, B. (2000). Hierarchical Bayes: Why All the Attention? Sawtooth Software, Research Paper.Google Scholar
  15. 15.
    Sawtooth. (2016) Lighthouse Studio v9.0. Sawtooth Software,Manual.Google Scholar
  16. 16.
    Silvia, C., & Krause, R. M. (2016). Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: An agent-based model. Energy Policy, 96, 105–118.CrossRefGoogle Scholar
  17. 17.
    Stummer, C., Kiesling, E., Günther, M., & Vetschera, R. (2015). Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach. European Journal of Operational Research, 245(1), 157–167.CrossRefGoogle Scholar
  18. 18.
    Zhang, T., Gensler, S., & Garcia, R. (2011). A study of the diffusion of alternative fuel vehicles: An agent-based modeling approach. Journal of Product Innovation Management, 28(2), 152–168.CrossRefGoogle Scholar
  19. 19.
    Zsifkovits, M., & Günther, M. (2015). Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles. Central European Journal of Operations Research, 23(2), 501–522.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Business Administration and EconomicsBielefeld UniversityBielefeldGermany

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