HOW TO TRIGGER MASS-MARKET ADOPTION
FOR ELECTRIC VEHICLES? - AN ANALYSIS OF
POTENTIAL ELECTRIC VEHICLE DRIVERS IN
AUSTRIA
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
Research on predictors for early EV adoption North America (e.g., Axsen et al., 2016), Norway (e.g.,
Nayum et al., 2016), Germany (e.g., Plötz et al., 2014) Austria (Bahamonde-Birke & Hanappi, 2016).
Certain socio-demographic and socio-psychological predictors identified
The influence of cultural worldviews on the propensity to purchase an EV not research yetProblem
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:
A more granular understanding of potential-adopter sub-segments needed (Cherubini et al., 2015).
E.g., McKinsey (2017) sees three sub-segments of near-term potential adopters based on
demographics and car preferences
Most market segmentations are not focusing on socio-psychological factors despite their need in
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 We applied a multinomial logistic regression to examine whether
the socio-demographic, socio-psychological (including cultural
worldviews) and contextual characteristics (i.e. policy incentives)
have an influence on the willingness to purchase EVs
By applying a non-hierarchical cluster analysis, we aim to shed
some light on characteristics of potential adopter segments; their
preferences for policy incentives were compared with ANOVAs
A nationally representative online survey in Austria was conducted in
autumn 2016 (n=1.000).
The data was collected by an external market research company
A subsection of the questionnaire focused on participants’ attitudes
towards EVs, their willingness to invest and related policy incentives
Gender (share women): 51% vs. 51%
Sample Population
Income (EUR) 2,711 vs. 2,769
Federal Distribution & Age
Education
SOURCE: Prießner, Sposato & Hampl 2017
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Predictors for für early-/potential EV-adoption
Socio-demographic characteristics
weak predictors:
Men are more willing to buy an EV
People without a car have a preference for EVs in
case of a car-purchase
No significant effect: age, income, education,
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
Socio-psychological characteristics
strong predictors:
Early adopters: strong pro-environment and pro-
technological attitude
Non-adopters: strong individualistic and
hierarchical worldviews
EV policy incentives: mixed predictors, i.e.,
Early adopters: significant effect
Non-adopters: non-significant effect
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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SEITE 7
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
▪ No big difference in
valuation between
early- and potential
adopters
▪ 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
0 1 432 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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SEITE 8
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
▪ Range, price, e-
charging infrastructure
are still the most
perceived e-car
barriers
▪ 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
▪ Attitude barriers
stronger for non-
adopters
General Non-
Purchase Motives
0 1 432 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
2Policy 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
infrastructureNo 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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Many thanks for your
attention!!
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SEITE 15
Insgesamt wurden 6 Segmente von
potentiellen E-Autokäufern identifiziert
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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SEITE 16
Erste Schlüsse aus den weiteren
Analysen zu E-Mobilität in Österreich
Österreich ist zweigeteilt beim Thema Elektroautos: Hälfte mit
Kaufintention in nächster Dekade, andere Hälfte mit eher Skepsis
und Ablehnung
Unwissenheit über Vor-/Nachteile über Elektroautos zeigt Bedarf
für Informationskampagnen auf
Kosten, Reichweite und Ladeinfrastruktur werden auch von
Befragten mit Kaufintention als große Barriere eingestuft
Kaufinteressenten sind nicht stat. signifikant unterschiedlich
in Einkommen, Alter, Ausbildung, Stadt-Land, Anzahl Autos,
Haushaltsgröße im Vgl. zu Nichtkäufern daher weitere
Segmentierung der Kundenbasis erforderlich
Entwicklung gezielter Anreizbündel für Kundensegmente können
ein Hebel für eine höhere und schnellere Akzeptanz von
Elektroautos darstellen
Wichtig ist die Kombination von Elektromobilität mit anderen
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)
17% of the car
owners plan to purchase
an EV as their next car
(Early Adopters)
Every secondcar 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
No. of respondents 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
No real preference for incentives
at all
2 Less educated, earn below average
income, inhabit more likely the
countryside and has a more
individualistic worldview
High preference for any kind of
policy incentive
1 more likely to be female, better
educated, living on the countryside
and has a higher income
Preference for purchase-based
incentives
4 Tend to be a male, older and
environmentally conscious car driver,
who shows high pro-environmental
attitude
Decent high preference for
purchase- and user-based
incentives, similar to early
adopters
SOURCE: Prießner, Sposato & Hampl 2017
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