Abstract
There is a substantial gap in research regarding the adoption of electric vehicles as a strategy to remedy the climate problem and reduce oil consumption by integrating complementary mobility services. To address this gap, we employ a two-step approach utilizing a hybrid stated preference method. Study 1 uses Best–Worst Scaling and identifies the top three complementary mobility services consumers would prefer with an electric vehicle. Study 2 applies Dual Response and analyzes the importance of these three services relative to other technological and economic factors of electric vehicles. Our results offer evidence that complementary mobility services may significantly foster electric vehicle adoption . Moreover, low purchase prices are less important than low recurring costs, such as electricity costs. Finally, a segmentation strategy may be fruitful because, e.g., men are more attracted by technological advantages than women and elderly consumers have a higher preference for services that offer convenience.
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Notes
See the subsequent Sect. 2.3.
See p. 34ff in Train (2009), especially Section 3.10, for the mathematical derivation of the algebraic manipulations as well as the underlying assumptions.
For a conceptual framework of Best–Worst Scaling, see Louviere et al. (2013). There are alternative methods with supplement “case 2” and “case 3”, in which respondents either choose the most and least preferred level of a product (case 2) or the most and least preferred alternative described by its attributes and levels (case 3).
See also Sect. 2.3.
As a robustness test, we use another status quo with a purchase price of 30,000€ and electricity costs of 3€ per 100 km. The general insights do not change, as reported in the “Appendix”.
References
Aftabuzzaman M, Mazloumi E (2011) Achieving sustainable urban transport mobility in post peak oil era. Transp Policy 18(5):695–702
Algesheimer R, Herrmann A, Dimpfel M (2006) Die Wirkung von brand communities auf die Markenloyalität—eine dynamische analyse im automobilmarkt. Zeitschrift für Betriebswirtschaft 76(9):933–958
Armstrong G, Cunningham MH, Kotler P (2010) Principles of marketing. Pearson Education, New Jersey, pp 63–65
Axsen J, Mountain DC, Jaccard M (2009) Combining stated and revealed choice research to simulate the neighbor effect: the case of hybrid-electric vehicles. Resour Energy Econ 31(3):221–238
Beggs S, Cardell S (1981) Assessing the potential demand for electric car. J Econom 17(1):1–19
Bettman JR, Johnson EJ, Payne JW (1990) A componential analysis of cognitive effort in choice. Organ Behav Hum Decis Process 45(1):111–139
Brazell JD, Diener CG, Karniouchina E, Moore WL, Séverin V, Uldry P-F (2006) The no-choice option and dual response choice designs. Mark Lett 17(4):255–268
Brigl S (2014) BMW at the consumer electronics show (CES) in Las Vegas 2014. https://www.press.bmwgroup.com/global/pressDetail.html?title=bmw-at-the-consumer-electronics-show-ces-in-las-vegas-2014&outputChannelId=6&id=T0162885EN&left_menu_item=node__5238. Accessed Dec 2014
Brownstone D, Bunch DS, Train K (2000) Joint mixed logit models of stated and revealed preferences for alternative-fuel. Transp Res Part B Methodol 34(5):315–338
Bunch DS, Bradley M, Golob TF, Kitamura R, Occhiuzzo GP (1993) Demand for clean-fuel vehicles in California: a discrete-choice stated preference pilot project. Transp Res Part A Policy Pract 27(3):237–253
Cooper RG, Kleinschmidt EJ (1987) New products: what separates winners from losers? J Prod Innov Manage 4(3):169–184
Dagsvik JK, Wennemo T, Wetterwald DG, Aaberge R (2002) Potential demand for alternative fuel vehicles. Transp Res Part B Methodol 36(4):361–384
De Wilde E, Cooke AD, Janiszewski C (2008) Attentional contrast during sequential judgments: a source of the number-of-levels effect. J Mark Res 45(4):437–449
Dhar R, Simonson I (2003) The effect of forced choice on choice. J Mark Res 40(2):146–160
Ewing GO, Sarigöllü E (1998) Car fuel-type choice under travel demand management and economic incentives. Transp Res Part D Transp Environ 3(6):429–444
Ewing G, Sarigöllü E (2000) Assessing consumer preferences for clean-fuel vehicles: a discrete choice experiment. J Public Policy Mark 19(1):106–118
Fadden S, Ververs PM, Wickens CD (1998) Costs and benefits of head-up display use: a meta-analytic approach. In: Proceedings of the human factors and ergonomics society annual meeting, vol 42, issue 1, 1998. SAGE Publications pp 16–20
Fassnacht M, Stallkamp C, Rolfes L (2011) Betriebsformen im automobilhandel—resultate einer empirischen untersuchung. Zeitschrift für Betriebswirtschaft 81(11):1181–1203
Finn A, Louviere JJ (1992) Determining the appropriate response to evidence of public concern: the case of food safety. J Public Policy Mark 11(2):12–25
Gärling A, Thøgersen J (2001) Marketing of electric vehicles. Bus Strategy Environ 10(1):53–65
Green PE, Krieger AM, Wind Y (2001) Thirty years of conjoint analysis: reflections and prospects. Interfaces 31(3):S56–S73
Hackbarth A, Madlener R (2013) Consumer preferences for alternative fuel vehicles: a discrete choice analysis. Transp Res Part D Transp Environ 25(1):5–17
Hidrue MK, Parsons GR, Kempton W, Gardner MP (2011) Willingness to pay for electric vehicles and their attributes. Resour Energy Econ 33(3):686–705
Hinz O, Schulze C, Takac C (2014) New product adoption in social networks: why direction matters. J Bus Res 67(1):2836–2844
International Council on Clean Transportation (2013) European vehicle market statistics pocketbook 2013. http://www.theicct.org/sites/default/files/publications/EU_vehiclemarket_pocketbook_2013_Web.pdf
Kempton W, Tomić J (2005) Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J Power Sources 144(1):268–279
Lee JA, Soutar G, Louviere J (2008) The Best–Worst Scaling approach: an alternative to Schwartz’s values survey. J Pers Assess 90(4):335–347
Lieven T, Mühlmeier S, Henkel S, Waller JF (2011) Who will buy electric cars? an empirical study in Germany. Transp Res Part D Transp Environ 16(3):236–243
Louviere JJ, Flynn TN, Carson RT (2010) Discrete choice experiments are not conjoint analysis. J Choice Model 3(3):57–72
Louviere J, Lings I, Islam T, Gudergan S, Flynn T (2013) An introduction to the application of (case 1) Best–Worst Scaling in marketing research. Int J Res Mark 30(3):292–303
Marley A, Flynn TN, Louviere J (2008) Probabilistic models of set-dependent and attribute-level Best–Worst choice. J Math Psychol 52(5):281–296
Mau P, Eyzaguirre J, Jaccard M, Collins-Dodd C, Tiedemann K (2008) The ‘Neighbor effect’: simulating dynamics in consumer preferences for new vehicle technologies. Ecol Econ 68(1):504–516
McFadden D (1973) Conditional logit analysis of qualitative choice behavior. Academic Press, New York, pp 105–142
McFadden D (2001) Disaggregate behavioral travel demand’s RUM side: A 30-year retrospective. Travel behaviour research: the leading edge. Pergamon, Amsterdam, pp 17–64
Mueller Loose S, Lockshin L (2013) Testing the robustness of Best Worst Scaling for cross-national segmentation with different numbers of choice sets. Food Qual Prefer 27(2):230–242
Olivier JGJ, Greet Janssens-Maenhout, Muntean M, Peters JAHW (2013) Trends in global Co2 emmissions: 2013 report. PBL Netherlands Environmental Assessment Agency
Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems and smart loads. Ind Inform IEEE Trans 7(3):381–388
Paromtchik I, Laugier C (1998) Automatic parallel parking and returning to traffic maneuvers. In: Video Proceedings of the IEEE international conference on robotics and automation, Citeseer, 1998
Peard E (2013) Electric cars slow to gain traction in Germany. Phys.org. http://phys.org/news/2013-05-electric-cars-gain-traction-germany.html. Accessed July 2014
Potoglou D, Kanaroglou PS (2007) Household demand and willingness to pay for clean vehicles. Transp Res Part D Transp Environ 12(4):264–274
Rao VR (2014) Applied conjoint analysis. Springer, New York, p 56
Schlereth C, Skiera B (2012) DISE: Dynamic Intelligent Survey Engine. In: Diamantopoulos A, Fritz W, Hildebrandt L (eds) Quantitative marketing and marketing management. Wiesbaden, Gabler Verlag, pp 225–243
Schlereth C, Skiera B, Wolk A (2011) Measuring consumers’ preferences for metered pricing of services. J Serv Res 11(4):443–459
Schlereth C, Eckert C, Schaaf R, Skiera B (2014) Measurement of preferences with self-explicated approaches: a classification and merge of trade-off-and non-trade-off-based evaluation types. Eur J Oper Res 238(1):185–198
Shepherd S, Bonsall P, Harrison G (2012) Factors affecting future demand for electric vehicles: a model based study. Transp Policy 20(1):62–74
Street DJ, Burgess L (2007) The construction of optimal stated choice experiments: theory and methods. Wiley, New Jersey, p 647
Swait J, Andrews RL (2003) Enriching scanner panel models with choice experiments. Mark Sci 22(4):442–460
Thurstone LL (1927) A law of comparative judgment. Psychol Rev 34(4):273
Tie SF, Tan CW (2013) A review of energy sources and energy management system in electric vehicles. Renew Sustain Energy Rev 20(1):82–102
Train K (2009) Discrete choice methods with simulation. Cambridge University Press, New York, p 34
Wesseling JH, Niesten EMMI, Faber J, Hekkert MP (2013) Business strategies of incumbents in the market for electric vehicles: opportunities and incentives for sustainable innovation. doi:10.1002/bse.1834
Acknowledgments
The authors gratefully thank Luigi Bianco for his assistance during the data collection in Study 1 & 2 and Dr. Donovan Pfaff from Bonpago for his financial support in study 2. We also thank Joséphine Süptitz, the two anonymous referees as well as the editor Günter Fandel for their valuable comments and excellent suggestions.
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Hinz, O., Schlereth, C. & Zhou, W. Fostering the adoption of electric vehicles by providing complementary mobility services: a two-step approach using Best–Worst Scaling and Dual Response. J Bus Econ 85, 921–951 (2015). https://doi.org/10.1007/s11573-015-0765-5
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DOI: https://doi.org/10.1007/s11573-015-0765-5