POTENTIAL ELECTRIC VEHICLE DRIVERS IN AUSTRIA By Alfons Priener; - - PowerPoint PPT Presentation
HOW TO TRIGGER MASS-MARKET ADOPTION FOR ELECTRIC VEHICLES? - AN ANALYSIS OF POTENTIAL ELECTRIC VEHICLE DRIVERS IN AUSTRIA By Alfons Priener; Robert Sposato; Nina Hampl Department for Sustainable Energy Management Institute for Operations,
By Alfons Prießner; Robert Sposato; Nina Hampl Department for Sustainable Energy Management Institute for Operations, Energy, and Environmental Management (OEE) Alpen-Adria University Klagenfurt
04 September 2017, IAEE 2017 - Vienna
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| 1% 1% 1% 2% 16% 17% 25% 37% 0% 10% 20% 30% 40% Bioethanol/Biodiesel Erdgas Altöl/Pflanzenöl Anderes Elektrisch (Batterie- Elektrofahrzeuge (BEV)… Hybrid Benzin Diesel
If I buy a car, I would chose the following...
(1.000 respondents – Oct 2016)
Can you imagine to purchase an electric vehicle ...
11% 28% 36% 25% Ja Eher ja Eher nein Nein
SOURCE: WU Wien, Deloitte, Wien Energie: „Erneuerbare Energien in Österreich 2016“
Who are these early and potential adopters?
49% of Austrian population are interested in purchase an electric vehicle
Yes Rather Yes Rather No No Petrol Electric (Battery Electric Vehicle (BEV) Others Used oil/plant oil Natural Gas
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Problem statement and research objective: Early-Electric Vehicle (EV) Adopters Predictors & Characteristics
Early-Adoption Predictors
Nayum et al., 2016), Germany (e.g., Plötz et al., 2014) Austria (Bahamonde-Birke & Hanappi, 2016).
Problem Statement Research Objectives
1. Test the influence of cultural worldviews of car drivers on the propensity to purchase an EV (Cherry et al., 2014 already tested their influence on adoption of other clean technologies) 2. Identify and characterize potential-adopter sub-segments via demographics, EV preferences and socio-psychological characteristics
Early-Adopters Predictors & Characteristics
SOURCE: Prießner, Sposato & Hampl 2017
Characteristics Sub-Segments:
demographics and car preferences
creating incentives that are more effectively accelerating EV diffusion Nayum et al. (2016)
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Based on existing literature hypothesis on the effect of socio- demographic, -psychological, worldviews and EV incentives were derived
H1: Socio- demo- graphic H2: Socio- psycho- logical H4: Context: EV incentives Category Hypothese effect on EV-adoption H3: Worldviews Variable Reference Education Nayum et al., 2016; Plötz et al., 2014; Tal & Nicolas, 2013 Income Axsen et al., 2016; Nayum et al., 2016; Plötz et al., 2014; Tal & Nicolas, 2013; Carley, Krause, Lane, & Graham, 2013 Age Hidrue, Parsons, Kempton, & Gardner, 2011; Nayum et al., 2016; Plötz et al., 2014 Dwelling density Plötz et al., 2014 # of cars per household Klöckner, Nayum, & Mehmetoglu, 2013; Nayum et al., 2016; Peters & Dütschke, 2014; Tal & Nicholas, 2013 Gender (to be male) Plötz et al., 2014 # of people per household Nayum et al., 2016 Pro-Environmental (a=.90) Carley et al., 2013; Hidrue et al., 2011; Wolf & Seebauer, 2014; Axsen et al., 2016 Pro-Technological (a=.80) Axsen et al., 2016, Wolf & Seebauer, 2014). Egbue and Long (2012 Individualism (a=.55) Cherry et al. (2014); Kahan et al., 2012 Hierarchical (a=.50) Cherry et al. (2014); Kahan et al., 2012 EV incentive sub-region e.g., Langbroek, Franklin, & Susilo, 2016; Mannberg, Jansson, Pettersson, Brännlund, & Lindgren, 2014; Sierzchula et al., 2014
SOURCE: Prießner, Sposato & Hampl 2017
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We conducted a nationally representative online survey and used a multi-nominal logistic regression and non-hierarchical cluster analysis
Survey Details Survey Participants Descriptive Methodology
the socio-demographic, socio-psychological (including cultural worldviews) and contextual characteristics (i.e. policy incentives) have an influence on the willingness to purchase EVs
some light on characteristics of potential adopter segments; their preferences for policy incentives were compared with ANOVAs
autumn 2016 (n=1.000).
towards EVs, their willingness to invest and related policy incentives
51% vs. 51% Sample Population
2,711 vs. 2,769
SOURCE: Prießner, Sposato & Hampl 2017
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Predictors for für early-/potential EV-adoption
weak predictors:
case of a car-purchase
etc. 32 51 100 17 Potential adopters2 Non- adopters3 Early adopters1 Total Adopter-segments e-cars Austria (%) N=1.000 status Q4 2016
1: Already own an e-car or want to buy an e-car as next car 2: Can imagine to buy a car in the near future, but not as their next car 3: No intention to replace his/her car against an e-car in the near future
strong predictors:
technological attitude
hierarchical worldviews
Socio-psychological variables are stronger predictors for an early- and potential EV-adoption than socio-demographic ones
SOURCE: Prießner, Sposato & Hampl 2017
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Evaluation purchase motives PRO EV per adopter segment: 1=non-relevant – 5=very relevant Emission-free Protection of the environment and climate Lower operation cost Ideal for short distance and city traffic High efficiency of electric engines More independence from energy suppliers Low driving noise by low speed The battery of the car can be also used as a buffer storage for the in-house photovoltaic system Charm of modern technologies Good experiences of friends or relatives Main Take-aways
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No big difference in valuation between early- and potential adopters
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Non-adopters see buying reasons for an electric car less relevant
▪
Electric cars are not seen as a static symbol, but as a green alternative with lower operating costs, well suited for city traffic
1 4 3 2 5
Status symbol
EV-purchase motives are evaluated significantly higher from early- and potential adopters
SOURCE: WU Wien, Deloitte, Wien Energie: „Erneuerbare Energien in Österreich 2016“
Potential adopters Non-adopters Early Adopters
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Evaluation EV Non-Purchase Motives / customer segment: 1=non-relevant – 5=very relevant
Range of EVs too low Too expensive Low availability of EV-charging stations (in Austria/abroad) EV batteries are rather short-lived Long charging duration No charging possibility near apartment/house The technology for electric cars is not yet fully developed EV are also a burden on the environment (e.g., battery production and disposal, electricity production) EVs are rather small and therefore e.g., not suitable as a family car Too small selections of models EV is only a transition technology Not safe enough High complexity A petrol- or diesel car is clean enough I do not need a car
Main Take-aways
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Range, price, e- charging infrastructure are still the most perceived e-car barriers
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The gap in structural E-car barriers between non-adopters and early adopters is not statistically significant, i.e., uncertainties as well as ignorance in every future adopter segment
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Attitude barriers stronger for non- adopters
General Non- Purchase Motives
1 4 3 2 5
SOURCE: WU Wien, Deloitte, Wien Energie: „Erneuerbare Energien in Österreich 2016“
Potential adopters Non-adopters Early Adopters
EV non-purchase motives are evaluated similar across all adopter segments
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Conservative Non-Techs (34%) Undiscerning Urbanites (16%) The Undecided Individualists (28%) EV Supporters (32%) Non- Purchase Motives2 High Low Low High Purchase Motives1
1 3 4 2
3 2 Less than average income, residence in the country, tendency for a more individualistic outlook 1 4 More likely male, live in urban area, above average income and age with strong environmental awareness and interest in digitization
Segments – characteristics
Rather feminine, better educated, live on the country-side and has higher income, more than 1 car / household More likely to be younger and better educated, living in urban space, little interest in the environment, hierarchies or digitalization, usually no car
1 Factors General EV Motives ((Low TOC, Less Co2 emissions, etc.) & Technological Motives (Charm of new technology, no noise, etc.) 2 Factors Structural Barriers (High Price, Little range, few charging stations, etc.) & Attitudinal Barriers (too complex, too small, etc)
Four potential adopter segments with different characteristics have been identified
SOURCE: Prießner, Sposato & Hampl 2017
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3 2 1 4
Conservative Non-Techs (34%) Undiscerning Urbanites (16%) The Undecided Individualists (28%) EV Supporters (32%) Non- Purchase Motives2 High Low Low High Purchase Motives1
1 3 4 2
Policy incentives preferences
Preference for purchasing incentives (e.g., purchase premium, tax benefits, etc.), less for toll / park / lane benefits High preference for any kind of e- car promotions, including, for regulation of internal combustion engines or number of loading infrastructure No real preference for specific e- mobility; Similar setting as the segment "non-buyer" Average preference for purchase- and service-oriented subsidies; No preference for regulation of combustion engines
These potential adopter segments also strongly vary in their preferences for policy incentives.
SOURCE: Prießner, Sposato & Hampl 2017
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Conclusion: how to trigger a mass-market-adoption of EVs in Austria?
First, potential future adopters are getting more heterogeneous. To achieve a transition towards electric mobility in our society, policy makers, marketers and research scholars need to get an even more granular understanding of preferences and characteristics (focus on socio-psychological) of the future EV adopters compared to early ones. 3 2 1 Third, policy incentives alone will not trigger enough EV sales to sufficiently contribute to GHG emissions reduction. Our findings underline the need to tailor policy incentives to meet the specific needs of different types of potential EV adopters Second, EV-related industries can increase acceptance of EVs with alternative tailored products and business models e.g., some ideas (to be researched): EV-Supporters: Smaller EV city cars, E-car-sharing Undiscerning Urbanites: E-hailing, E-car-sharing, E-busses The Undecided: E-car-pooling, types of hybrid-models Conservative Non-Techs: Awareness campaigns
SOURCE: Prießner, Sposato & Hampl 2017
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Questions?
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Backup
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Erste Welle: Besitzer EV &
Kaufintention (1-6 Jahre)
Zweite Welle: Kaufintention (7-15 Jahre)
Status and Luxury Enthusiasts High-end Käufer, die sich Luxus, differenziertes Design und Leistung erwarten Risk-Averse Greens „Early Adopters“ von grüner Technologie, die sich um die Umwelt sorgen, aber kein großes Preis-Premium zahlen möchten Urban-EV-Supporters Stadt-Pendler, eher männlich, älterer und umweltbedachter Autofahrer mit höherem Umweltbewusstsein und Bedarf Basis-Mobilitätslösung Durchschnittlich Präferenz für kauf- & nutzungsorientierte Anreize – ähnlich Kaufintention 1-6 Jahre Undiscerning Urbanites Junger Käufer und besser gebildet, lebt im urbanen Raum und hat ein höheres Umweltbewusstsein Keine wirkliche Präferenz für spezifische E-Mobilitätsanreize, ähnlich der Gruppe „Ohne Kaufintention“ Conservative Non-Techs Eher weiblich, besser gebildet, lebt auf dem Land und hat höheres Einkommen Präferenz für kostensenkende Anreize
Mehrheit der Käufer lebt im städtischen Bereich
The Undecided Schlechter gebildet, geringeres Einkommen, wohnt eher auf dem Land, hat eine individualistischere Weltanschauung Hohe Präferenz für jegliche Art von E-Mobilitätsanreize
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Kaufintention in nächster Dekade, andere Hälfte mit eher Skepsis und Ablehnung
für Informationskampagnen auf
Befragten mit Kaufintention als große Barriere eingestuft
in Einkommen, Alter, Ausbildung, Stadt-Land, Anzahl Autos, Haushaltsgröße im Vgl. zu Nichtkäufern daher weitere Segmentierung der Kundenbasis erforderlich
ein Hebel für eine höhere und schnellere Akzeptanz von Elektroautos darstellen
Mobilitätslösungen (z.B. Share-Economy, Öffentlicher Verkehr, etc.)
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Who owns an Electric Vehicle (EV) or plans to purchase one as his/her next car?
SOURCE: Website Tesla & Toyota; Umfrage WU Wien, Deloitte & Wien Energie Nov 2016 Österreich (n=1000)
an EV as their next car (Early Adopters) Every second car driver can imagine to purchase an EV (Early & Potential Adopters) But who are these early and potential adopters?
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Details on Variables Research Project 1
SOURCE: Prießner, Sposato & Hampl 2017 Variables Variable code Total Sample Early adopters Potential adopters Non-adopters
1,000 163 325 512 Willingness-to-purchase 3=Early Adopters 2=Potential Adopter 1=Potential Non-Adopter 1.56 3 2 1 Socio-demographic variables Gender 1=male, 49.0% 56.1% 48.4% 47.6% 2=female 51.0% 44.8% 52.5% 53.7% Age Years 45.0 45.01 43.8 45.8 Education 1=compulsory school 5.8% 5.6% 6.8% 5.1% 2=vocational training 44.1% 40.5% 39.7% 48.0% 2=high school 25.1% 24.5% 27.7% 26.8% 4=college 24.2% 29.4% 25.8% 20.1% Household size People range from 1-6 2.43 2.24 2.67 2.31 Income Net EUR per month per household 2,785 2,681 2,873 2,673 Number of cars per household 0=No car 17.1% 25.8% 18.2% 13.2% 1= One car 46.9% 44.2% 41.5% 51.2% 2= More than one cars 36.0% 30.1% 40.3% 35.2% Dwelling density 1=Municipal <10k, 30.2% 28.8% 30.5% 30.5% 2=Town 10-100k 32.9% 33.1% 30.1% 34.5% 3=City >100k 36.9% 38.0% 39.4% 35.0% Socio-psychological variables (see details on scales in Appendix) 1=disagree, 2=rather disagree, 3=rather agree, 4=agree Pro-technological attitude e.g., “I see the digitization as an opportunity for better networking.” 3.14 3.28 3.21 3.04 Pro-environmental attitude e.g., “I would say of myself that I am environmentally conscious.” 3.02 3.25 3.15 2.87 Individualism - Communitarianism e.g., “The government interferes far too much in our everyday lives.” 2.84 2.66 2.85 2.91 Egalitarianism- Hierarchism- e.g., “Our society would be better off if the distribution of wealth was more equal.” 3.11 3.30 3.17 3.01 Contextual variable EV policy incentives (provided in federal state) 0=No EV policy incentive 48.0% 42.9% 52.0% 47.1% 1=EV policy incentive 52.0% 57.1% 48.0% 52.9%
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Findings indicate that socio-psychological (incl. worldviews) in contrary to socio-demographic factors play a significant role in explaining differences between segments of potential adopters and non-adopters
SOURCE: Prießner, Sposato & Hampl 2017
Dependent variable = WTI Exp(B) Non- adopters 1,2 Exp(B) Potential- adopters 1,2 Gender (female) Dwelling density: Municipal=1 1.505 (0.304) 1.132 (0.311) Dwelling density: town=2 1.113 (0.231) 0.839 (0.240) Dwelling density: City=3 Hypothese: Evaluation H1: Socio- demographic H2: Socio- psychological H4: Context: EV incentives H3: Worldviews Hypothese effect on early EV-adoption Age 1.004 (0.007) 1.000 (0.008) Education 0.904 (0.113) 0.981 (0.116) Household-size 1.041 (0.098) 1.272† (0.099) Income 1.040 (0.032) 1.050 (0.023) Gender (male) 0.644 (0.202) 0.684 (0.208) # of cars per household=1 1.039 (0.241) 0.740 (0.248) Constant 5.645 (0.936) 1.770 (0.968) # of cars per household=0 0.376** (0.328) 0.524† (0.332) # of cars per household =2 Pro-technological attitude 0.700* (0.175) 0.898 (0.182) Pro-environment attitude 0.352*** (0.186) 0.742 (0.192) Individualistic Worldview 1.506*** (0.096) 1.313** (0.097) Egalitarian Worldview 0.735** (0.104) 0.845† (0.108) EV incentives = No 1.220 (0.235) 1.628* (0.242)
Rejected Accepted Partially accpeted
EV incentives = Yes
1 Standard errors in parentheses 2 EV adopters as reference. Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
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4 Potential Adopter segments were identified for the next wave of adoption
1 Factors General EV Motives ((Low TOC, Less Co2 emissions, etc.) & Technological Motives (Charm of new technology, no noise, etc.) 2 Factors Structural Barriers (High Price, Little range, few charging stations, etc.) & Attitudinal Barriers (too complex, too small, etc)
Conservative Non-Techs (34%) Undiscerning Urbanites (16%) The Undecided (28%) EV Supporters (32%) Non- Purchase Motives2 High Low Low High Purchase Motives1 1 3 4 2
3 Tend to be younger and more educated and live in an urban area, high pro-environmental attitude
at all 2 Less educated, earn below average income, inhabit more likely the countryside and has a more individualistic worldview
policy incentive 1 more likely to be female, better educated, living on the countryside and has a higher income
incentives 4 Tend to be a male, older and environmentally conscious car driver, who shows high pro-environmental attitude
purchase- and user-based incentives, similar to early adopters
SOURCE: Prießner, Sposato & Hampl 2017
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References (Part 1 of 5)
Åhman, M. 2006. Government policy and the development of electric vehicles in Japan. Energy Policy, 34(4): 433–443. Axsen, J., Goldberg, S., & Bailey, J. 2016. How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners? Transportation Research Part D: Transport and Environment, 47: 357–370. Bahamonde-Birke, F. J., & Hanappi, T. 2016. The potential of electromobility in Austria: Evidence from hybrid choice models under the presence of unreported information. Transportation Research Part A: Policy and Practice, 83: 30–41. Beggs, S. D., & Cardell, N. 1980. Choice of smallest car by multi-vehicle households and the demand for electric vehicles. Transportation Research Part A: General, 14(5-6): 389–404. Ben-Akiva, M., Bradley, M., Morikawa, T., Benjamin, J., Novak, T., Oppewal, H., & Rao, V. 1994. Combining revealed and stated preferences data. Marketing Letters, 5(4): 335–349. Bleijenberg, A., & Egenhofer, C. 2013. Pathways to low carbon transport in the EU: From possibility to reality: Report of the CEPS Task Force on Transport and Climate Change. Brussels: Centre for European Policy Studies. Brownstone, D., Bunch, D. S., & Train, K. 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Research Part B: Methodological, 34(5): 315–338. Bunch, D. S., Bradley, M., Golob, T. F., Kitamura, R., & Occhiuzzo, G. P. 1993. Demand for clean-fuel vehicles in California: A discrete- choice stated preference pilot project. Transportation Research Part A: Policy and Practice, 27(3): 237–253. Burgess, M., King, N., Harris, M., & Lewis, E. 2013. Electric vehicle drivers?: Reported interactions with the public: Driving stereotype change? Transportation Research Part F: Traffic Psychology and Behaviour, 17: 33–44. Carley, S., Krause, R. M., Lane, B. W., & Graham, J. D. 2013. Intent to purchase a plug-in electric vehicle: A survey of early impressions in large US cites. Transportation Research Part D: Transport and Environment, 18: 39–45. Carlin, B. P., & Louis, T. A. 2008. Bayesian Methods for Data Analysis (3rd ed.). Boca Raton, FL.: Chapman and Hall/CRC Press. Cherry, T. L., García, J. H., Kallbekken, S., & Torvanger, A. 2014. The development and deployment of low-carbon energy technologies: The role of economic interests and cultural worldviews on public support. Energy Policy, 68: 562–566. Cherubini, S., Iasevoli, G., & Michelini, L. 2015. Product-service systems in the electric car industry: Critical success factors in marketing. Journal of Cleaner Production, 97: 40–49.
Jun 2017. Dimitropoulos, A., Rietveld, P., & van Ommeren, J. N. 2013. Consumer valuation of changes in driving range: A meta-analysis. Transportation Research Part A: Policy and Practice, 55: 27–45. Egbue, O., & Long, S. 2012. Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Special Section: Frontiers of Sustainability, 48: 717–729.
Last Modified 04.09.2017 08:03 W. Europe Standard Time Printed
22
|
References (Part 2 of 5)
Electric Vehicle World Sales Database. Europe Plug-in Sales for 2016; http://www.ev-volumes.com/country/total-euefta-plug-in- vehicle-volumes-2/. Eppen, G. D., Hanson, W. A., & Martin R. Kipp 1991. Bundling - New Products, New Markets, Low Risk. Sloan Management Review, Ewing, G., & Sarigöllü, E. 2000. Assessing Consumer Preferences for Clean-Fuel Vehicles: A Discrete Choice Experiment. Journal of Public Policy & Marketing, 19 (1): 106–118. Fassnacht, M., Stallkamp, C., Lampl, L. L., & Rolfes, L. 2011. Betriebsformen im Automobilhandel ?: Resultate einer empirischen
Fojcik, T. M., & Proff, H. 2014. Accelerating market diffusion of battery electric vehicles through alternative mobility concepts. International Journal of Automotive Technology and Management, 14(3/4): 347. Godlevskaja, O., van Iwaarden, J., & van der Wiele, T. 2011. Moving from product‐based to service‐based business strategies. International Journal of Quality & Reliability Management, 28(1): 62–94. Golden, B. R. 1992. RESEARCH NOTES. THE PAST IS THE PAST--OR IS IT?: THE USE OF RETROSPECTIVE ACCOUNTS AS INDICATORS OF PAST STRATEGY. Academy of Management Journal, 35(4): 848–860. Graham-Rowe, E., Gardner, B., Abraham, C., Skippon, S., Dittmar, H., Hutchins, R., & Stannard, J. 2012. Mainstream consumers driving plug-in battery-electric and plug-in hybrid electric cars: A qualitative analysis of responses and evaluations. Transportation Research Part A: Policy and Practice, 46(1): 140–153. Granovskii, M., Dincer, I., & Rosen, M. A. 2006. Economic and environmental comparison of conventional, hybrid, electric and hydrogen fuel cell vehicles. Journal of Power Sources, 159(2): 1186–1193. Green, E. H., Skerlos, S. J., & Winebrake, J. J. 2014. Increasing electric vehicle policy efficiency and effectiveness by reducing mainstream market bias. Energy Policy, 65: 562–566. Green, P. E., & Krieger, A. M. 1995. Alternative approaches to cluster-based market segmentation. Journal of the Market Research Society, 37(3): 221–239. Green, P. E., & Srinivasan, V. 1990. Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice. Journal of Marketing, 54(4): 3. Hackbarth, A., & Madlener, R. 2016. Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany. Transportation Research Part A: Policy and Practice, 85: 89–111. Haddadian, G., Khodayar, M., & Shahidehpour, M. 2015. Accelerating the Global Adoption of Electric Vehicles: Barriers and Drivers. The Electricity Journal, 28(10): 53–68. Hedlund-de Witt, A. 2012. Exploring worldviews and their relationships to sustainable lifestyles: Towards a new conceptual and methodological approach. Ecological Economics, 84: 74–83. Hidrue, 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. Hinz, O., Schlereth, C., & Zhou, W. 2015. Fostering the adoption of electric vehicles by providing complementary mobility services: A two-step approach using Best–Worst Scaling and Dual Response. Journal of Business Economics, 85(8): 921–951.
Last Modified 04.09.2017 08:03 W. Europe Standard Time Printed
23
|
References (Part 3 of 5)
Hoen, A., & Koetse, M. J. 2014. A choice experiment on alternative fuel vehicle preferences of private car owners in the
IEA 2016a. Global EV Outlook 2016: Beyond one million electric cars. Paris, France: OECD/Interational Energy Agency. IEA 2016b. World Energy Outlook 2016. Paris, France: OECD/Interational Energy Agency. IPCC 2012. Renewable Energy Sources and Climate Change Mitigation: Intergovernmental Panel on Climate Change. IPCC 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC. Kahan, D. M., Jenkins-Smith, H., & Braman, D. 2011. Culture and Identity-Protective Cognition: Explaining the White-Male Effect in Risk Perception. Journal of Risk Research, 14(2). Kley, F., Wietschel, M., & Dallinger, D. 2013. Evaluation of European electric vehicle support. Frauenhofer-Institut. Lane, B., & Potter, S. 2007. The adoption of cleaner vehicles in the UK: Exploring the consumer attitude–action gap. Journal
Langbroek, J. H., Franklin, J. P., & Susilo, Y. O. 2016. The effect of policy incentives on electric vehicle adoption. Energy Policy, 94: 94–103. Larson, P. D., Viáfara, J., Parsons, R. V., & Elias, A. 2014. Consumer attitudes about electric cars: Pricing analysis and policy implications. Transportation Research Part A: Policy and Practice, 69: 299–314. Lieven, T. 2015. Policy measures to promote electric mobility – A global perspective. Transportation Research Part A: Policy and Practice, 82: 78–93. Lieven, T., Mühlmeier, S., Henkel, S., & Waller, J. F. 2011. Who will buy electric cars?: An empirical study in Germany. Transportation Research Part D: Transport and Environment, 16(3): 236–243. Louviere, J. J., Street, D., Burgess, L., Wasi, N., Islam, T., & Marley, A. A. 2008. Modeling the choices of individual decision- makers by combining efficient choice experiment designs with extra preference information. Journal of Choice Modelling, 1(1): 128–164. Lu, L., Han, X., Li, J., Hua, J., & Ouyang, M. 2013. A review on the key issues for lithium-ion battery management in electric
Mannberg, A., Jansson, J., Pettersson, T., Brännlund, R., & Lindgren, U. 2014. Do tax incentives affect households׳ adoption
McKinsey & Company 2017. Electrifying insights: How automakers can drive electrified vehicle sales and profitablity. Germany: McKinsey & Company. Mitchell, J., Borroni-Bird, C. E., & Burns, L. 2010. Re-inventing the Automobile: Urban Mobility for the 21st Century. Cambridge, MA: MIT Press. Mooi, E., & Sarstedt, M. 2011. A concise guide to market research: The process, data, and methods using IBM SPSS
Last Modified 04.09.2017 08:03 W. Europe Standard Time Printed
24
|
References (Part 4 of 5)
Nayum, A., & Klöckner, C. A. 2014. A comprehensive socio-psychological approach to car type choice. Journal of Environmental Psychology, 40: 401–411. Nayum, A., Klöckner, C. A., & Mehmetoglu, M. 2016. Comparison of socio-psychological characteristics of conventional and battery electric car buyers. Travel Behaviour and Society, 3: 8–20. Newman, D., Wells, P., Nieuwenhuis, P., Donovan, C., & Davies, H. 2014. Learning from Electric Cars as Socio-technical Mobility Experiments: Where Next? Transfers, 4(2). Parsons, G. R., Hidrue, M. K., Kempton, W., & Gardner, M. P. 2014. Willingness to pay for vehicle-to-grid (V2G) electric vehicles and their contract terms. Energy Economics, 42: 313–324. Peters, A., & Dütschke, E. 2014. How do Consumers Perceive Electric Vehicles?: A Comparison of German Consumer
Plötz, P., Schneider, U., Globisch, J., & Dütschke, E. 2014. Who will buy electric vehicles?: Identifying early adopters in
Punj, G., & Stewart, D. W. 1983. Cluster Analysis in Marketing Research: Review and Suggestions for Application. Journal
Reinders, M. J., Frambach, R. T., & Schoormans, J. P. L. 2010. Using Product Bundling to Facilitate the Adoption Process of Radical Innovations*. Journal of Product Innovation Management, 27(7): 1127–1140. Rezvani, Z., Jansson, J., & Bodin, J. 2015. Advances in consumer electric vehicle adoption research: A review and research
Rogers, E. M. 2003. Diffusion of innovations. New York: Free Press. Schuitema, G., Anable, J., Skippon, S., & Kinnear, N. 2013. The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice, 48: 39–49. Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. 2014. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68: 183–194. Simonin, B. L., & Ruth, J. A. 1995. Bundling as a strategy for new product introduction: Effects on consumers' reservation prices for the bundle, the new product, and its tie-in. Journal of Business Research, 33(3): 219–230.
http://www.spiegel.de/auto/aktuell/deutschland-umweltministerium-ermoeglicht-fahrverbote-fuer-dieselfahrzeuge-a-1126230.html, 17 Dec 2016, 17 Dec 2016. Stauss, B. (Ed.) 2009. Moving forward with service quality: [proceedings of the QUIS 11 - Services Conference, June 11-14, 2009, Wolfsburg, Germany] (1st ed.). Ingolstadt: Ingolstadt School of Management, Cath. Univ. Eichstätt-Ingolstadt. Steinhilber, S., Wells, P., & Thankappan, S. 2013. Socio-technical inertia: Understanding the barriers to electric vehicles. Energy Policy, 60: 531–539. Stern, P. C. 2000. New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior. Journal of Social Issues, 56(3): 407–424.
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References (Part 5 of 5)
Stremersch, S., & Tellis, G. J. 2002. Strategic Bundling of Products and Prices: A New Synthesis for Marketing. Journal of Marketing, 66(1): 55–72. Tal, G., & Nicolas, M. A. (Eds.) 2013. Studying the PEV market in california: Comparing the PEV, PHEV and hybrid markets: Barcelona, Spain : November 17-20, 2013. Piscataway, NJ: IEEE. Thiel, C., Schmidt, J., van Zyl, A., & Schmid, E. 2014. Cost and well-to-wheel implications of the vehicle fleet CO2 emission regulation in the European Union. Transportation Research Part A: Policy and Practice, 63: 25–42. Tie, S. F., & Tan, C. W. 2013. A review of energy sources and energy management system in electric vehicles. Renewable and Sustainable Energy Reviews, 20: 82–102. Train, K. E. 2003. Discrete Choice Methods with Simulation. Cambridge: Cambridge University Press.
http://newsroom.unfccc.int/media/521376/paris-electro-mobility-declaration.pdf, 20 Dec 2016. UNFCCC 2015b. Adoption of a protocol, another legal instrument, or an agreed outcome with legal force under the Convention applicable to all Partie. Paris, France. Wells, P., & Nieuwenhuis, P. 2014. EV Business Models in a Wider Context: Balancing Change and Continuity in the Automotive Industry. In D. Beeton & G. Meyer (Eds.), Electric vehicle business models: 3–16. New York NY: Springer Berlin Heidelberg. Wesche, J. P., Plötz, P., & Dütschke, E. 2016. How to trigger mass market adoptions in EVs? Factors predicting interest in electric vehicles in Germany. Wesseling, J. H., Niesten, E. M. M. I., Faber, J., & Hekkert, M. P. 2015. Business Strategies of Incumbents in the Market for Electric Vehicles: Opportunities and Incentives for Sustainable Innovation. Business Strategy and the Environment, 24(6): 518– 531. Wolf, A., & Seebauer, S. 2014. Technology adoption of electric bicycles: A survey among early adopters. Transportation Research Part A: Policy and Practice, 69: 196–211. Zhang, Y., Yu, Y., & Zou, B. 2011. Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV. Energy Policy, 39(11): 7015–7024