how to trigger mass-market adoption for electric …

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


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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


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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


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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


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


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


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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.


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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


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Many thanks for your

attention!!

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Questions?

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Backup

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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


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


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


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