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Driven by Change: Commercial Drivers’ Acceptance and Perceived Efficiency of Using Light-Duty Electric Vehicles in Germany

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  • Wolff, Stefanie

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

In this paper, we examine to what extent commercial drivers accept the substitution of conventional cars with light-duty e-vehicles (LDEVs) by conducting a cross-sectional survey at Deutsche Post, a major German postal delivery service provider. Specifically, we explore drivers’ acceptance from two perspectives. First, we investigate whether drivers are more satisfied with the LDEVs than with the conventional vehicles. Second, we question whether the EVs increase drivers’ perceived efficiency. Combining these two perspectives, we show that the greater the drivers’ overall satisfaction with LDEVs, the higher is the drivers’ perceived efficiency. We prove this by means of latent measures, such as perceived usefulness and perceived ease of use, using adaptations of Davis’ Technology Acceptance Model and Rogers’ Diffusion of Innovation Theory to form our Unified Technology Acceptance Model. Findings suggest that, on average, drivers are slightly more satisfied with their assigned LDEVs than with the available conventional cars. If drivers were able to choose their preferred vehicles, the majority of them would favor LDEVs. We detect statistically significant patterns of latent measures affecting perceived usefulness and perceived ease of use of LDEVs. While this paper focuses on German delivery service employees, the methodology presented here could easily be applied to any enterprise in the growing logistics sector electrifying its car fleet. Hence, our contributions are valuable for transportation research, and more specifically, to all potential commercial EV drivers, e.g., our insights might be relevant for approximately 500,400 drivers employed in the German logistics sector alone.

Suggested Citation

  • Wolff, Stefanie & Madlener, Reinhard, 2018. "Driven by Change: Commercial Drivers’ Acceptance and Perceived Efficiency of Using Light-Duty Electric Vehicles in Germany," FCN Working Papers 11/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
  • Handle: RePEc:ris:fcnwpa:2018_011
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    References listed on IDEAS

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    1. Hidrue, Michael K. & Parsons, George R. & Kempton, Willett & Gardner, Meryl P., 2011. "Willingness to pay for electric vehicles and their attributes," Resource and Energy Economics, Elsevier, vol. 33(3), pages 686-705, September.
    2. Steinhilber, Simone & Wells, Peter & Thankappan, Samarthia, 2013. "Socio-technical inertia: Understanding the barriers to electric vehicles," Energy Policy, Elsevier, vol. 60(C), pages 531-539.
    3. Viswanath Venkatesh, 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research, INFORMS, vol. 11(4), pages 342-365, December.
    4. Kaplan, Sigal & Gruber, Johannes & Reinthaler, Martin & Klauenberg, Jens, 2016. "Intentions to introduce electric vehicles in the commercial sector: A model based on the theory of planned behaviour," Research in Transportation Economics, Elsevier, vol. 55(C), pages 12-19.
    5. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    6. Wikström, Martina & Hansson, Lisa & Alvfors, Per, 2014. "Socio-technical experiences from electric vehicle utilisation in commercial fleets," Applied Energy, Elsevier, vol. 123(C), pages 82-93.
    7. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    8. Globisch, Joachim & Dütschke, Elisabeth & Schleich, Joachim, 2018. "Acceptance of electric passenger cars in commercial fleets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 122-129.
    9. Ian T. Jolliffe, 1982. "A Note on the Use of Principal Components in Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(3), pages 300-303, November.
    10. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    11. Axsen, Jonn & Orlebar, Caroline & Skippon, Stephen, 2013. "Social influence and consumer preference formation for pro-environmental technology: The case of a U.K. workplace electric-vehicle study," Ecological Economics, Elsevier, vol. 95(C), pages 96-107.
    12. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    13. Dale L. Goodhue, 1995. "Understanding User Evaluations of Information Systems," Management Science, INFORMS, vol. 41(12), pages 1827-1844, December.
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    Cited by:

    1. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    2. Liu, Xueying & Madlener, Reinhard, 2019. "Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion," FCN Working Papers 15/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).

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    More about this item

    Keywords

    Electric vehicles (EVs); Driver acceptance; Commercial EV fleet; Perceived efficiency; Germany; Technology Acceptance Model;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • M50 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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