exploring predictors of electric vehicle adoption and

Alfons Prießner MSc.

Exploring predictors of electric vehicle adoption and

preferences for electric vehicle product bundles

DISSERTATION

submitted in fulfilment of the requirements for the degree of

Doctorate in Social Sciences and Economics

Alpen-Adria-Universität Klagenfurt

Faculty of Management and Economics

Supervisor

Univ.-Prof. Dr. Nina Hampl


Department of Operations, Energy, and Environmental Management

First Evaluator

Univ.-Prof. Dr. Nina Hampl


Department of Operations, Energy, and Environmental Management

Second Evaluator

Univ.-Prof. Dr. Rolf Wüstenhagen

University of St. Gallen

Institute for Economy and the Environment

Klagenfurt am Wörthersee, January 2019

Affidavit I

AFFIDAVIT

I hereby declare in lieu of an oath that

• the submitted academic paper is entirely my own work and that no auxiliary materials

have been used other than those indicated,

• I have fully disclosed all assistance received from third parties during the process of

writing the thesis, including any significant advice from supervisors,

• any contents taken from the works of third parties or my own works that have been

included either literally or in spirit have been appropriately marked and the respective

source of the information has been clearly identified with precise bibliographical

references (e.g. in footnotes),

• to date, I have not submitted this paper to an examining authority either in Austria or

abroad and that

• when passing on copies of the academic thesis (e.g. in bound, printed or digital form),

I will ensure that each copy is fully consistent with the submitted digital version.

I understand that the digital version of the academic thesis submitted will be used for the

purpose of conducting a plagiarism assessment.

I am aware that a declaration contrary to the facts will have legal consequences.

Eidesstattliche Erklärung

Ich versichere an Eides statt, dass ich

• die eingereichte wissenschaftliche Arbeit selbstständig verfasst und keine anderen als

die angegebenen Hilfsmittel benutzt habe,

• die während des Arbeitsvorganges von dritter Seite erfahrene Unterstützung,

einschließlich signifikanter Betreuungshinweise, vollständig offengelegt habe,

• die Inhalte, die ich aus Werken Dritter oder eigenen Werken wortwörtlich oder

sinngemäß übernommen habe, in geeigneter Form gekennzeichnet und den Ursprung

der Information durch möglichst exakte Quellenangaben (z.B. in Fußnoten) ersichtlich

gemacht habe,

• die eingereichte wissenschaftliche Arbeit bisher weder im Inland noch im Ausland

einer Prüfungsbehörde vorgelegt habe und

• bei der Weitergabe jedes Exemplars (z.B. in gebundener, gedruckter oder digitaler

Form) der wissenschaftlichen Arbeit sicherstelle, dass diese mit der eingereichten

digitalen Version übereinstimmt.

Mir ist bekannt, dass die digitale Version der eingereichten wissenschaftlichen Arbeit zur

Plagiatskontrolle herangezogen wird.

Ich bin mir bewusst, dass eine tatsachenwidrige Erklärung rechtliche Folgen haben wird.

Alfons Prießner, e.h. __________________________________

Klagenfurt am Wörthersee, Januar 2019

Für meine

Frau und beste Freundin

Talitha,

meine

Eltern

und

meinen Bruder

Martin

Danksagung III

DANKSAGUNG

Das Verfassen dieser Doktorarbeit war eine lange und prägende, aber auch sehr schöne Reise,

die ich ohne die Unterstützung einiger Personen nicht geschafft hätte. Diesen Personen möchte

ich hiermit meinen DANK aussprechen.

Besonders möchte ich meiner Doktormutter, Professor Nina Hampl, danken. Ihr stets offenes

Ohr für meine Ideen und Anliegen, ihr wertvolles Feedback zu meinen wissenschaftlichen

Artikeln und ihre ständige Förderung meiner Forschungsarbeit haben mir beim Verfassen dieser

Doktorarbeit sehr geholfen: Danke für dein Vertrauen und deine unermüdliche Unterstützung,

die mir meine ersten Schritte in der Wissenschaft ermöglicht haben! Mein Dank gilt auch

Andrea, Christian, Kristian, Markus, Paula und Robert für ein großartiges Arbeitsumfeld an der

NEMtastischen Abteilung. Auch wenn ich als externer Dissertant nur unregelmäßig an der

Universität war, habe ich mich dank euch am Institut immer heimisch gefühlt.

Ohne die finanzielle und inhaltliche Unterstützung der KELAG wäre die Doktorarbeit in dieser

Form nicht möglich gewesen. Hier gilt mein Dank besonders Bernd Aichwalder, Marlene

Ehrenstein, Esther Fellinger, Günther Kotschnig und Stefan Wakonig für den fachlichen Input

und die vielen Stunden des Austausches. Dank gebührt auch dem Unternehmen Sawtooth

Software, welches mir mit einem Stipendium Zugang zu ihrer Conjoint-Software gewährt hat.

Ohne diese Software hätten gewisse Fragestellungen dieser Dissertation nur unter erheblichem

Mehraufwand erforscht werden können.

Ein spezieller Dank gilt meinen langjährigen Freunden und Mentoren Wolfgang Ratheiser und

Wolf-Heinrich Reuter, die mich seit Beginn meines Studiums immer gefördert und zu Schritten

des beruflichen Wachstums ermutigt haben. Danke euch und alle weiteren Personen (aus

meinem Familien-/Freundeskreis sowie meinem Arbeitsumfeld bei McKinsey), die mich auf

meiner bisherigen Reise begleitet haben. Voller Freude blicke ich in die Zukunft und freue mich

auf weitere Reisen mit euch!

Ich hatte das große Glück, familiär die idealen Voraussetzungen für die Absolvierung eines

Doktoratsstudiums mitzubekommen. Mein aufrichtiger Dank gilt meinen Eltern, Alfons und

Sigrid, die mir eine akademische Laufbahn ermöglicht haben und mir immer mit Rat und Tat

zur Seite stehen. Es ist ein großes Privileg, Eltern wie euch zu haben. Dank euch weiß ich,

woher ich komme. Eure Erziehung hat mir erlaubt, meinen Weg zu finden und diesen auch zu

gehen. Eurer Unterstützung und Liebe habe ich es zu verdanken, dass ich heute hier stehe und

einer glücklichen Zukunft entgegenblicke.

Meinem Bruder Martin auch ein großes Dankeschön! Nur wenige Menschen haben das Glück,

in ihrem „kleinen“ Bruder ein Vorbild, einen Mentor und besten Freund zu finden. Danke, dass

du seit dem ersten Tag deines Lebens in jeglicher Hisicht mein Bro bist!

Last, but certainly not least, möchte ich aus tiefstem Herzen meiner Frau und besten Freundin

Talitha danken. Du bist meine Nummer 1, meine Kraftquelle und Inspiration, mein zu Hause.

Danke, dass du mich durch die Höhen und Tiefen dieser Doktorarbeit begleitet hast! Ohne deine

bedingungslose Liebe und deinen kompromisslosen Glauben an mich, hätte ich diese Arbeit

nicht geschafft.

Table of contents IV

TABLE OF CONTENTS

AFFIDAVIT ............................................................................................................................... I

DANKSAGUNG .................................................................................................................... III

TABLE OF CONTENTS ....................................................................................................... IV

LIST OF ABBREVIATIONS ................................................................................................ VI

REFEREED PUBLICATIONS ........................................................................................... VII

ABSTRACT ......................................................................................................................... VIII

ZUSAMMENFASSUNG ....................................................................................................... IX

1. INTRODUCTORY CHAPTER ...................................................................................... 1

1.1 BACKGROUND AND PROBLEM STATEMENT ........................................................................ 1

1.2 THEORETICAL BACKGROUND AND RESEARCH QUESTIONS ................................................. 4

1.3 OVERVIEW RESEARCH PAPERS AND OBJECTIVES ................................................................ 8

1.4 METHODOLOGY AND DATA ............................................................................................. 12

1.5 CONCEPTIONAL FRAMEWORK .......................................................................................... 16

1.6 OVERALL FINDINGS AND CONCLUSIONS........................................................................... 17

REFERENCES ....................................................................................................................... 27

PAPER 1: PREDICTORS OF ELECTRIC VEHICLE ADOPTION: AN ANALYSIS

OF POTENTIAL ELECTRIC VEHICLE DRIVERS IN AUSTRIA ............................... 33

ABSTRACT ............................................................................................................................ 33

1. INTRODUCTION .......................................................................................................... 34

2. THEORY AND HYPOTHESES ................................................................................... 36

2.1 SOCIO-DEMOGRAPHIC CHARACTERISTICS ........................................................................ 36

2.2 PSYCHOLOGICAL CHARACTERISTICS ................................................................................ 37

2.3 CONTEXTUAL FACTORS: EV POLICY INCENTIVES ............................................................ 39

3. METHODOLOGY AND DATA ................................................................................... 40

3.1 SAMPLE ........................................................................................................................... 40

3.2 QUESTIONNAIRE AND MEASURES ..................................................................................... 41

3.3 DATA ANALYSIS .............................................................................................................. 44

4. RESULTS ........................................................................................................................ 48

4.1 PREDICTORS OF EV ADOPTION ........................................................................................ 48

4.2 SUB-SEGMENTS OF POTENTIAL EV ADOPTERS ................................................................. 52

5. DISCUSSION AND CONCLUSIONS ......................................................................... 54

5.1 DISCUSSION ..................................................................................................................... 54

5.2 CONCLUSIONS AND POLICY IMPLICATIONS ...................................................................... 57

REFERENCES ....................................................................................................................... 59

APPENDIX ............................................................................................................................. 63

PAPER: 2: CAN PRODUCT BUNDLING INCREASE THE JOINT ADOPTION OF

ELECTRIC VEHICLES, SOLAR PANELS AND BATTERY STORAGE?

EXPLORATIVE EVIDENCE FROM A CHOICE-BASED CONJOINT STUDY IN

AUSTRIA ................................................................................................................................ 69

Table of contents V

ABSTRACT ............................................................................................................................ 69

1. INTRODUCTION .......................................................................................................... 70

2. LITERATURE REVIEW .............................................................................................. 72

2.1 DEFINITION AND TYPOLOGY OF BUNDLING STRATEGIES .................................................. 72

2.2 CONSUMER BENEFITS FROM PRODUCT BUNDLING ............................................................ 73

2.3 PRODUCT BUNDLING IN THE CONTEXT OF CLEAN TECHNOLOGIES .................................... 75

3. METHODS AND DATA ............................................................................................... 76

3.1 CHOICE-BASED CONJOINT ................................................................................................ 76

3.2 SURVEY AND EXPERIMENTAL DESIGN .............................................................................. 78

3.3 DATA COLLECTION AND SAMPLE ..................................................................................... 79

4. RESULTS ........................................................................................................................ 81

4.1 RELATIVE IMPORTANCE OF ATTRIBUTES .......................................................................... 81

4.2 PART-WORTH UTILITIES PER ATTRIBUTE LEVEL ............................................................... 82

4.3 LATENT CLASS ANALYSIS ................................................................................................ 84

5. DISCUSSION AND CONCLUSIONS ......................................................................... 91

5.1 DISCUSSION OF STUDY FINDINGS AND IMPLICATIONS ...................................................... 91

5.2 LIMITATIONS AND FUTURE RESEARCH ............................................................................. 94

REFERENCES ....................................................................................................................... 96

APPENDIX ........................................................................................................................... 101

PAPER 3: EXPLORING CONSUMER HETEROGENEITY IN WILLINGNESS TO

PAY FOR ELECTRIC VEHICLE PRODUCT BUNDLES ............................................ 104

ABSTRACT .......................................................................................................................... 104

1. INTRODUCTION ........................................................................................................ 105

2. METHODOLOGY AND DATA ................................................................................. 109

2.1 CONJOINT ANALYSIS ...................................................................................................... 109

2.2 MEASUREMENT OF SOCIO-DEMOGRAPHIC AND PSYCHOLOGICAL PARAMETERS ............. 115

2.3 SAMPLE ......................................................................................................................... 116

3. RESULTS AND DISCUSSION ................................................................................... 118

3.1 RELATIVE IMPORTANCE OF CONJOINT ATTRIBUTES ....................................................... 118

3.2 PART-WORTH UTILITIES OF ATTRIBUTE LEVELS ............................................................. 119

3.3 WILLINGNESS TO PAY FOR ATTRIBUTE LEVELS .............................................................. 121

3.4 IMPACT OF SOCIO-DEMOGRAPHIC AND PSYCHOLOGICAL CHARACTERISTICS ON PART-

WORTH UTILITIES AND WTP ................................................................................................ 125

4. CONCLUSION ............................................................................................................. 132

4.1 IMPLICATIONS ............................................................................................................... 132

4.2 LIMITATIONS AND FURTHER RESEARCH ......................................................................... 135

REFERENCES ..................................................................................................................... 137

APPENDIX ........................................................................................................................... 144

CURRICULUM VITAE ...................................................................................................... 149

Abbreviations VI

LIST OF ABBREVIATIONS

AAEE Austrian Association for Energy Economics

ANOVA Analysis of variance

BDM Becker-DeGroot-Marschak method

BEV Battery electric vehicle

BS Battery storage

CBC Choice-based conjoint

CEO Chief executive officer

CO2 Carbon dioxide

GHG Greenhouse gases

EEA European Environment Agency

EU European Union

EV Electric vehicle (summarizing PHEV and BEV)

EVS Electric Vehicle Symposium

HB Hierarchical Bayes

HEV Hybrid electric vehicle

ICE Internal combustion engine

IEA International Energy Agency

IEWT Internationale Energiewirtschaftstagung

IPCC Intergovernmental Panel on Climate Change

MNL/MLR Multinomial logistic/Multinomial logistic regression

N/A Not applicable

OEM Original equipment manufacturer

PHEV Plug-in hybrid electric vehicle

PV Photovoltaics

RLH Root likelihood

SD Standard deviation

UNFCCC United Nations Framework Convention on Climate Change

US(A) United States (of America)

V2G Vehicle-to-grid

VHB Verband Hochschullehrer für Betriebswirtschaft

WTP Willingness to pay

Referred publications VII

REFEREED PUBLICATIONS

The three papers that are the core of this dissertation, have been published or are under

review in peer-reviewed journals, and have been presented at conferences and doctoral

seminars. The following table provides an overview of all papers included in this thesis, also

giving their publication status. The chapter structure, reference formatting, and English

conventions followed in each paper are as they were submitted, and in compliance with each

target journal. If references are made to particular sections, the section numbers within each

paper are used.

TABLE 1: OVERVIEW OF THE THREE PAPERS OF THIS CUMULATIVE DISSERTATION

No. Title Author (s) Publication

Status

1 Predictors of electric vehicle adoption: an

analysis of potential electric vehicle drivers in

Austria

Priessner,

Alfons; a,c

Sposato,

Robert; a

Hampl, Nina a,b

Published in

Energy Policy

Volume 122,

2018: 701-714

2 Can product bundling increase the joint

adoption of electric vehicles, solar panels and

battery storages? Explorative evidence from a

choice-based conjoint study in Austria”

Priessner,

Alfons; a,d

Hampl, Nina a,b

Reviewed with

outcome Revise

and Resubmit by

Ecological

Economics,

28.12.2018

3 Exploring consumer heterogeneity in

willingness to pay for electric vehicles product

bundles

Priessner,

Alfons; a,d

Hampl, Nina a,b

Under review at

Transportation

Research Part A,

15.11.2018

a Alpen-Adria-Universität Klagenfurt, Department of Operations, Energy, and Environmental Management

b Vienna University of Economics and Business, Institute for Strategic Management

c The first author is the main author of this research paper, i.e., the first author developed the research questions,

analyzed the data, and wrote nearly the entire manuscript on his own. The co-authors collected the data in their

study “Erneuerbare Energien in Österreich 2016” (cf. Hampl and Sposato, 2017), provided coaching in

developing the research questions and analyzing the data, and they provided feedback during the development of

the manuscript.

d The first author is the main author of this research paper, i.e., the first author developed the research questions,

collected and analyzed the data, and wrote nearly the entire manuscript on his own. The co-author provided

coaching in developing the research questions and analyzing the data, and provided feedback on each section, as

well as input for the concluding section of each paper.

Abstract VIII

ABSTRACT

Climate change is one of the biggest challenges humankind is currently facing. Replacing

conventional internal combustion engines with electric ones combined with decarbonized

energy production could be one lever to reduce greenhouse gas emissions that cause global

warming. Despite recent advances in technology and strong policy support, electric vehicle

(EV) sales figures have fallen short of industry expectations. In addition, the carbon footprint

of EVs is also a point of critical discussion. Thus, the question of how consumer adoption of

more sustainable (i.e., emission-free) EVs can be increased, is a subject of lively discussion in

various fields, and it is far from being resolved.

This dissertation aims to add novel insights to this discourse by focusing on two sub-research

fields that, to some extent, have been limitedly investigated, namely the identification and

evaluation of (A) predictors of EV adoption, and (B) consumer preferences for bundling EVs

with photovoltaics (PV) and battery storage (BS), i.e., for EV-PV-BS product bundles. To

address the research objectives, two survey data sets (N = 1,000 for subject (A) and N = 1,251

for subject (B)) collected for the Austrian EV market were analyzed. The derived results have

been presented in three papers that form the core of this dissertation.

Paper 1 analyses the predictors of EV adoption, showing that psychological characteristics,

which include cultural worldviews, are stronger predictors than socio-demographics. Further,

Paper 1 shows that policy incentives are a relevant predictor of early EV adoption. Moreover,

four potential adopter segments with various characteristics and preferences for EV policy

incentive types are identified and described. Paper 2 focuses on the consumer preferences for

EV-PV-BS product bundles and shows that a significant share of potential EV drivers prefer

purchasing an EV in a bundle (with e.g., PV and BS) to buying it as a standalone product. The

study also identifies four sub-segments of potential adopters and evaluates their preference

differences along purchase price, ownership, and product specifications. Paper 3 estimates the

willingness to pay (WTP) figures for the EV-PV-BS product bundle attributes and points out a

gap between the WTP for EV add-on products and their current market prices. Additionally,

the paper shows that psychological factors, more than the socio-demographic ones, seem to

impact the utility of and WTP for an EV-PV-BS product bundle.

Overall, this dissertation draws four main conclusions: First, the intention to purchase an EV or

an EV-PV-BS product bundle is influenced more by people’s psychological than their socio-

demographic characteristics. Second, the bundling of an EV with PV and BS could have a

significant market potential, but certain purchase criteria need to be met before this market

potential will materialize. Such criteria include cost curve effects and innovative ownership

models. Third, the group of potential EV and EV-PV-BS bundle adopters seem to be quite

heterogeneous regarding either their characteristics and/or preferences toward the products.

This implies the need for more tailored product offerings and marketing efforts. Fourth, policy

incentives could help to increase EV adoption, but they should be offered in a mix of purchase

and use-based incentives for EVs.

Keywords: Electric vehicles; potential adopters; cultural worldviews; policy incentives;

photovoltaics; battery storage; product bundling; choice-based conjoint; cluster analysis;

consumer preference; willingness to pay; Austria

Abstract IX

ZUSAMMENFASSUNG

Herkömmliche Verbrennungsmotoren tragen wesentlich zur globalen Erwärmung und damit zu

einer der größten Herausforderungen des 21. Jahrhunderts bei. Die Ersetzung solcher Motoren

durch elektrische Motoren in Verbindung mit einer CO2-freien Stromerzeugung stellt eine

Möglichkeit dar, Treibhausgasemissionen zu verringern. Trotz des technologischen Fortschritts

auf diesem Sektor und intensiver Unterstützung aus der Politik blieb der Absatz von

Elektrofahrzeugen (E-Autos) in den letzten Jahren hinter den Erwartungen der Branche zurück.

Hinzu kommt, dass der CO2-Fußabdruck von E-Autos auch kritisch diskutiert wird. Daher ist

die Frage, wie die Kundenakzeptanz von nachhaltigeren (d.h. emissionsfreien) E-Autos erhöht

werden kann, Gegenstand zahlreicher Diskussionen in verschiedenen wissenschaftlichen

Disziplinen. Eine abschließende Lösung dieser Frage ist bislang nicht in Sicht.

Ziel dieser Dissertation ist es, zu diesem Diskurs beizutragen. Sie konzentriert sich dafür auf

zwei – bislang vergleichsweise wenig erforschte – Teilbereiche: Identifikation und Evaluierung

von (A) Prädiktoren für den Kauf von E-Autos und (B) Konsumentenpräferenzen bei der

Bündelung von E-Autos mit Photovoltaik (PV) und Batteriespeicher (BS) (d.h. E-Auto-PV-BS-

Produktbündel). Zur Erreichung der Forschungsziele wurden zwei Umfragedatensätze (N =

1.000 für Thema (A) und N = 1.251 für Thema (B)) für den österreichischen E-Auto-Markt

erhoben und analysiert. Die abgeleiteten Ergebnisse wurden in drei wissenschaftlichen Artikeln

zusammengefasst, die den Kern dieser Dissertation bilden.

Artikel 1 analysiert die Prädiktoren für den Kauf von E-Autos. Er zeigt, dass psychologische

Faktoren (wie z.B. kulturelle Weltanschauungen) die Kaufentscheidung stärker als

soziodemografische Charakteristika beeinflussen. Der Beitrag belegt außerdem, dass politische

Anreize einen frühen Kauf von E-Autos fördern. Überdies identifiziert und beschreibt er vier

Segmente potentieller KäuferInnen mit verschiedenen Merkmalen und Präferenzen für

politische Anreize. Artikel 2 konzentriert sich auf die Konsumentenpräferenzen von E-Auto-

PV-BS-Produktbündeln. Demnach würde es ein erheblicher Teil potenzieller E-Auto-

FahrerInnen vorziehen, ein E-Auto in einem Bündel (mit z.B. PV und BS) statt ohne

Zusatzprodukte zu kaufen. In diesem Beitrag werden außerdem vier Segmente potentieller

KäuferInnen identifiziert und deren Präferenzunterschiede hinsichtlich Kaufpreis, Eigentum

und Produktspezifikationen evaluiert. Artikel 3 untersucht die Zahlungsbereitschaft für die

Attribute eines E-Auto-PV-BS-Produktbündels. Er zeigt eine Lücke zwischen der

Zahlungsbereitschaft für E-Auto-Zusatzprodukte und deren aktuellen Marktpreisen auf.

Außerdem offenbart der Artikel, dass psychologische Faktoren den Nutzen und die

Zahlungsbereitschaft für ein E-Auto-PV-BS-Produktbündel stärker beeinflussen als

soziodemografische Faktoren.

Die Dissertation kommt zu vier wesentlichen Ergebnissen: Erstens scheint die Absicht, ein E-

Auto oder ein E-Auto-PV-BS-Produktbündel zu erwerben, stärker von psychologischen

Aspekten als von soziodemografischen Merkmalen der potentiellen KäuferInnen abzuhängen.

Zweitens hätte die Bündelung von einem E-Auto mit PV und BS – bei Vorliegen bestimmter

Voraussetzungen (z.B. Kostenkurveneffekte, innovative Eigentumsmodelle) – ein erhebliches

Marktpotenzial. Drittens scheinen die potenziellen KäuferInnen von E-Autos und E-Auto-PV-

BS-Produktbündeln hinsichtlich ihrer Charakteristika und/oder Präferenzen recht heterogen zu

sein. Daher sind individuellere Produktangebote und Marketingmaßnahmen notwendig, um

diese Segmente potentieller KäuferInnen zu erreichen. Viertens können politische Anreize zur

Erhöhung der Akzeptanz von E-Autos beitragen. Sie sollten jedoch in einer Mischung aus Kauf-

und Nutzungsanreizen für E-Autos angeboten werden.

Schlagwörter: Elektrofahrzeuge; potenzielle KäuferInnen; kulturelle Weltanschauungen;

politische Förderanreize; Photovoltaik; Batteriespeicher; Produktbündelung;

Conjointanalyse; Clusteranalyse; Konsumentenpräferenzen; Zahlungsbereitschaft; Österreich

Introductory Chapter 1

1. INTRODUCTORY CHAPTER1

“When Henry Ford made cheap, reliable cars, people said, 'Nah, what's wrong with a horse?'

That was a huge bet he made.“

Elon Musk, CEO Tesla Motors

1.1 Background and problem statement

There is almost unequivocal agreement among scientists that the climate of our earth is

changing and that mankind plays a critical role in this process (Anderegg et al., 2010). The

latest report from the Intergovernmental Panel on Climate Change (IPCC) (2014) concludes,

clearer than ever, that climate change is caused by the release of greenhouse gas (GHG)

emissions triggered by activities of the human population. Particularly the burning of fossil

fuels causes the release of CO2 emissions, the major GHG, into the atmosphere. Almost 80%

of the GHG emissions in the earth’s atmosphere have occurred since the 1970s, mainly driven

by population and economic growth (Archer and Rahmstorf, 2010). This increase in GHG

emissions is the most likely reason for the rise in temperature wich is causally associated with

several challenges such as droughts, higher sea-levels, etc. (Dow and Downing, 2011).

The IPCC argues that humanity is at risk if global warming were to continue at the

current rate, and calls for counter measures (IPCC, 2014). Former US president Obama made

the urgency of this topic very clear in his State of the Union Address in 2015, saying: “No

challenge poses a greater threat to future generations than climate change” (CNN, 2015).

Hence, in 2015 the members of the United Nations Framework Convention on Climate Change

(UNFCCC) set an ambitious target (also referred to as the Paris Agreement) to combat climate

change by limiting the global average temperature increase to well below the most noted 2

degrees Celsius target. They agreed to implement measures and policies in various GHG

producing industries to prevent temperature from increasing more than 1.5 degrees Celsius

above pre-industrial levels (UNFCCC, 2015). The International Energy Agency (IEA) claims

that to achieve this, an even more ambitious target for every short-term reduction of CO2

emissions is required, which would mean that we should employ “every known technological,

societal and regulatory decarbonization option” (IEA, 2016b: 5).

1 Please note that if specific notes are made to the papers, they are referred to as Paper 1 (Priessner, Sposato and

Hampl, 2018), Paper 2 (Priessner and Hampl, 2018a) and Paper 3 (Priessner and Hampl, 2018b); several sentences

of this chapter are drawn from Paper 1, Paper 2, and Paper 3 without explicit citation.


One promising pathway to achieve this UNFCCC goal is to decarbonize the transport

sector which is responsible for almost 25 percent of global energy-related GHG emissions

causing global warming (IEA, 2016a). To reduce the carbon footprint of transportation climate

experts and politicians pursue a variety of possible actions, such as incremental technology

improvements for internal combustion engines (ICEs), hybridization, biofuels, shifting to mass

transportation systems (e.g., urban rail, bus systems, etc.), reducing trip distances, and vehicle

sharing concepts (IEA, 2016a, 2009). Currently they pin their expectations largely on replacing

the predominant use of conventional ICEs dependent on fossil fuels with electric ones (IEA,

2016a; IPCC, 2014; UNFCCC, 2015).

These electric vehicles (EVs)2 could have a zero-emission level during usage if coupled

with decarbonized energy production (e.g., Bleijenberg and Egenhofer, 2013; Granovskii et al.,

2006; IEA, 2016a; Sims et al., 2014). Hence, various studies emphasize the need for a major

uptake of EVs within the next three decades to reduce the transportation sector’s impact on

climate change, and to meet GHG reduction targets (EEA, 2018; IEA, 2016a, 2009; IPCC,

2014; Sims et al., 2014; UNFCCC, 2015). For instance, IEA modelling forecasts that by 2030

we would need 35% of the global vehicle sales to be electric (comprising battery electric, plug-

in hybrid, and fuel cell vehicles ranging from two-wheelers to light-commercial vans or trucks)3

to achieve the UNFCCC target. Translated, this means that 150 million EVs fueled by

renewable energy should be in the market by 2030, which in the interim requires a sizable

growth of EV stock and a major deployment of EVs during the 2020s (IEA, 2016a).

Consequently, over the past decade an interest in EVs has resurfaced among

practitioners, consumers, policy makers, and also researchers. Players in the automotive and

the battery industry have ongoingly been working on new and improved technologies (e.g., in

the field of battery storage) to overcome some of the motives for not purchasing an EV (e.g.,

range anxiety, purchase price, etc.) (Wesseling et al., 2015). Consumer preferences are

changing toward more sustainable mobility solutions which also include EVs with more

efficient and zero-carbon emission electric engines (Thiel et al., 2014). Policy makers and

governments around the world (Sierzchula et al., 2014), ranging from China (Zhang et al., 2013)

to the USA (Diamond, 2009), and to members of the European Union (Kley et al., 2011; Lieven,

2015; Mannberg et al., 2014; Sierzchula et al., 2014) have introduced policies and subsidies

2 In this dissertation, the author will focus on EVs which are battery electric (BEV) or plug-in hybrid electric

(PHEV) vehicles, which can be charged from an external source of electricity. Hybrid electric vehicles (HEVs)

are thus excluded from this definition. Further, fuel cell electric vehicles powered by hydrogen are not in the scope

of this definition. 3 This dissertation only focuses on EVs in the light-duty segment used for regular passenger transport (IEA, 2018).


that promote electromobility. One regularly quoted example of successful policy support in EV

sales growth, comes from Norway (Langbroek et al., 2016), which is the third largest market

for electrically chargeable vehicles, after the US and China. Further, Norway is the undisputed

leader in new registration of EVs, meaning every third newly bought car in Norway is an EV

(Electric Vehicle World Sales Database, 2017). Moreover, research has been dealing with EVs

from various angles, e.g., in economical, engineering, or transportation literature (Granovskii

et al., 2006; Holland et al., 2015; Newman et al., 2014; Niesten and Alkemade, 2016; Rezvani

et al., 2015; Steinhilber et al., 2013; Zivin et al., 2012). Therefore, the transition to

electromobility has gradually been gathering pace, so that by 2016 EV sales had passed the one

million unit target (IEA, 2018).

However, despite all these efforts, sales growth across the entire EV market (besides

Norway and China) has fallen short of the growth rates required to achieve the targets of the

Paris Agreement (IEA, 2018, 2016a). Possible reasons are manifold, ranging from customer

concerns (e.g., Egbue and Long, 2012), technical problems (e.g., Lu et al., 2013), and economic

challenges (e.g., Dimitropoulos et al., 2013), to lock-in effects (e.g., Steinhilber et al., 2013) or

status-quo bias (Newman et al., 2014). Particularly, according to the literature, user concerns

about driving range, purchase price, and charging time are the most citied barriers to adoption

since the early 1980s (e.g., Beggs and Cardell, 1980; Bunch et al., 1993; Egbue and Long, 2012;

Schuitema et al., 2013). Additionally, consumers perceive EVs as cars of the future (Burgess

et al., 2013) or a work in progress (Graham-Rowe et al., 2012) due to technological and

infrastructural developments being unpredictable, which again has a negative impact on the

intention to purchase an EV.

On top of that, EVs’ effectiveness in combatting climate change has been also disputed

in the literature (Ellingsen et al., 2016; Sandy Thomas, 2012; Zivin et al., 2012). Experts argue

that, in production, depending on the size of the battery, power mix in production, location of

production, etc., EV’s CO2 footprint seems to be not superior or even worse than that of an ICE

(Ellingsen et al., 2016). Further, the energy requirements for raw material extraction and

processing are significantly higher than for ICEs (EEA, 2018). Several environmentalists also

argue that despite ICEs being replaced by EVs, certain regions hardly lower their GHG

emission at all, due to their current power supply mainly being produced by CO2-emissioning

resources (Holland et al., 2015; Zivin et al., 2012).

Nevertheless, there seems to be considerable agreement that over the entire life-cycle of

an EV (from raw material extraction to vehicle recycling) the CO2 footprint is below an ICE’s

when it is fueled by power from renewable energy sources (e.g., Bleijenberg and Egenhofer,


2013; EEA, 2018; Ellingsen et al., 2016; Granovskii et al., 2006; IEA, 2016a; Sims et al., 2014),

because the “largest potential reduction in GHG emissions between a BEV and an ICE occurs

in the in-use phase, which can more than offset the higher impact of the raw materials extraction

and production phases” (EEA, 2018: 7). Hawkins et al. (2012) already projected this in arguing

that, assuming the current electricity mix in Europe, across its life-cycle an EV produces 17-

21% less GHG emissions than comparable diesel fueled vehicles, and 26-30% less than petrol

vehicles 4 . The benefits of moving to EVs could be even larger if we could increase the

development of renewable energy and the circular economy, including vehicle sharing and

product design, reuse and recycling (EEA, 2018).

To summarize, the question of how to increase consumer adoption for (greener5) EVs

is subject to lively discussion in various fields (e.g., academic research, industry, politics), but

has still not been exhaustively answered. This doctoral thesis aims to contribute some novel

insights to this discourse by focusing on two relevant sub-research fields. First, scholars argue

that it is very important to understand predictors of EV adoption to increase the possible role of

EVs in the global transportation system (Axsen et al., 2016). Second, to benefit from EVs in

countering climate change, research often suggests the bundling of EVs with, e.g., photovoltaics

(PV) and battery storage (BS) (Delmas et al., 2017), but very little has been researched from a

consumer preference perspective (Cherubini et al., 2015). Therefore, this thesis aims to

contribute to the following two overall research questions:

A) What are the major predictors of an EV adoption?

B) What are the consumer preferences for EV-PV-BS product bundles?

1.2 Theoretical background and research questions

There is quite an extensive literature on the adoption of EVs, and across the past decade

research interest has been focused on various topics (e.g., consumer preference, EV adoption

barriers, EV policy effectiveness, characteristics of early EV users, environmental impact of

EVs) to promote its acceptance and adoption rate (cf., Liao et al., 2017; Rezvani et al., 2015).

The author of this doctoral thesis focuses on two aspects in the field which appeared to require

additional research, namely: (A) a better understanding of the predictors of EV adoption (Axsen

4 A Nissan LEAF was compared to an ICE (Mercedes A 170 (petrol) and Mercedes A 160 (diesel)), assuming a

total lifetime mileage of 150,000 km. All vehicles are comparable in size, mass, and performance characteristics. 5 In this thesis the term greener will be used in referring to a more sustainable and environmentally friendly usage

of an EV (i.e. zero-emission) which can be ensured when EV’s power is supplied purely by renewable energy

sources.


et al., 2016) and (B) the preferences for EV add-on products such PV and BS when they are

offered in the form of EV product6 bundles (Cherubini et al., 2015). In this chapter some

theoretical background on the need for research in these two fields, and derived more specific

research questions, will be provided.

(A) Predictors of EV adoption

Several recent research papers argue that a stronger focus on early-adopter customer

segments in product development and EV policy implementation is needed to gain a mass

market movement toward purchasing EVs (Green et al., 2014; Larson et al., 2014; Wesche et

al., 2016). Therefore, stakeholders and scientists have a pronounced desire to learn more about

early7 and potential8 EV adopters and the related EV adoption predictors. To date, predictors of

EV adoption/non-adoption have been researched widely, from North America (Axsen et al.,

2016) to Europe, which includes Norway (Nayum et al., 2016) and Germany (Plötz et al., 2014).

Initial insights of these studies suggest that socio-demographic and psychological

factors, but also policy incentives, significantly differentiate between EV owners, potential EV

adopters, and non-adopters not willing to purchase an EV in the near future. These findings are

in line with Stern (2000) who argues that personal characteristics (e.g., socio-economic),

attitudinal factors (e.g., several psychological parameters), and contextual forces (e.g., policy

incentives or regulations) trigger pro-environmental behavior and eventually could facilitate the

adoption of high-priced products such as greener cars. Nayum and Klöckner (2014)

interestingly added to this topic that the predictive power of socio-demographics for purchase

intention of environmentally friendly vehicles (such as EVs) might be lower when

psychological variables are included in the analysis of individual-related predictors. Based on

the relevant literature, this dissertation will research socio-demographic, psychological, and EV

policy incentives as predictors of EV adoption to gain a comprehensive impression of the

Austrian market.

In addition to assessing the impact of EV predictors on the Austrian market, this

dissertation aims to address two research gaps. First, existing literature has largely neglected to

6 In general, the term “product” refers to both goods and services (Stremersch and Tellis, 2002). In this dissertation

the author focuses on the products PV and BS, which are offered as EV add-on products and are referred to as EV-

PV-BS product bundle. 7 Early adopters are defined by the author as the people who have a positive attitude towards EVs and/or are

willing to purchase an EV as their next car or already own an EV. This category corresponds to the Innovators and

Early-Adopters segments identified by Rogers (2003) (approx. 15% of population). 8 Potential adopters are defined by the author as the people who generally have a positive attitude toward EVs

and/or are willing to purchase an EV, but not as their next car. This category corresponds to the Early Majority

segment identified by Rogers (2003) (approx. 34% of population).


consider the influence the psychological variable cultural worldview9 has on the propensity to

purchase an EV. Previous research has shown that cultural worldviews could be predictive of

people’s attitudes toward climate change (Kahan et al., 2007), or of their acceptance of climate

change mitigation policies (Hart and Nisbet, 2011) or clean technologies (Cherry et al., 2014;

Sposato and Hampl, 2018).

Second, there is a broad discussion ongoing in the literature and among stakeholders,

on the effectiveness of policy measures to increase the EV adoption. On the one hand, scholars

argue that policy incentives increase market penetration of EVs (e.g., Langbroek et al., 2016;

Mannberg et al., 2014; Sierzchula et al., 2014). On the other hand, practitioners as well as

experts criticize the use of purely financially related incentives as not being effective in

convincing non-adopters (e.g., Egbue and Long, 2012; Spiegel, 2017).

Therefore, the derived specific research questions which this doctoral thesis aims to

answer are:

• What are the major predictors (socio-economic and psychological) of EV adoption?

• How do the potential adopter’s cultural worldviews (individualism and egalitarianism)

impact the adoption of EVs?

• How do EV related policy incentives affect the adoption of EVs?

(B) Consumer preferences for EV-PV-BS product bundles

Bundling products with additional products or services is a well-known and well-

researched tool for increasing consumers’ acceptance and willingness to pay (WTP) (e.g.,

Eppen et al., 1991; Stremersch and Tellis, 2002). It has been shown that bundling positively

influences the launch of new products (Simonin and Ruth, 1995), improves consumers’

valuation of innovative products, and increases the consumer’s purchase intention regarding

these products (Reinders et al., 2010).

In the automotive industry, add-on services or products have a long history and are

considered crucial for customer satisfaction and acceptance. They range from car maintenance

and repair services, financing, leasing, insurance, banking, and many different entertainment

services offered to ensure exclusive customer experience (e.g., Godlevskaja et al., 2011). Add-

on services or products have become not only key to customer satisfaction, but also to

promoting new products in the vehicles market (Fassnacht et al., 2011). Therefore, scholars

argue that the add-on offerings need to be consciously reviewed and adopted according to

9 Cultural worldview is defined as “a general perspective from which a person sees and interprets the world”

(Cherry et al., 2014: 563).


changing customer needs or new technologies (Stauss, 2009). Cherubini et al. (2015) even

claimed that add-on components are relevant factors in purchasing decisions for EVs and

proposed product bundles or product-service bundles, as a key success factor to create a critical

mass of adopters. They specifically called for more research in this field.

However, to date, only the preferences for single add-on services (such as intelligent

parking or navigation packages) and not for EV product bundles (such as EV-PV or EV-PV-

BS) have been research in clean technology. Prior research focused on add-on services such as

navigation packages for charging services or intelligent parking and paying, for mobility

guarantees, or battery leasing (Fojcik and Proff, 2014; Hinz et al., 2015). Such research

evaluated the preferences and effects of electricity charging stations being available (e.g.,

Brownstone et al., 2000), or of the vehicle-to-grid (V2G) services on the acceptance of EVs

(Parsons et al., 2014). In contrast, EV add-on products such as PV and BS (in form of an EV-

PV-BS product bundle) have not been researched from a consumer preference perspective,

despite the high interest of automotive and electric utility players, as well as the ability to reduce

the EV’s CO2 emission (Delmas et al., 2017). Moreover, since consumer preferences are quite

diverse in the field of clean technologies (cf. Kaufmann et al., 2013; Salm et al., 2016; Tabi et

al., 2014), the identification of potential adopter segments and the description of their

preferences is an additional research gap to which this thesis tries to contribute.

Further, from a WTP perspective about EV product bundles, there is scant literature.

There is a broad literature stream on the WTP for EV attribute improvements (e.g., Ewing and

Sarigöllü, 2000; Hackbarth and Madlener, 2016; Tanaka et al., 2014). However, only a few

studies analyze the WTP for an EV if offered in a bundle with add-on services or warranties

(cf., Ensslen et al., 2018; Fojcik and Proff, 2014). For EV product bundles (e.g., PV-BS add-on

products), there are, to the author’s best knowledge, no extant WTP studies on the attributes of

such possible bundles.

Another angle which requires research, is the influence of individual-related

characteristics on the evaluation of EV product bundle attributes and the related WTP. Liao et

al. (2017) quite recently argued in their comprehensive literature review on EV consumer

preference studies that besides EV related attributes, individual-related characteristics, such as

socio-demographic or psychological factors, are major parameters influencing or moderating

EV utility. A literature review has identified a lack of research on the influence of (potential)

EV drivers’ characteristics on EV-PV-BS product bundle preferences and on the WTP (cf., Liao

et al. 2017 or Li et al., 2017 for details).


Overall, this dissertation aims to close the above-mentioned gaps by trying to answer

the following research questions:

• What are the preferences for the EV-PV-BS product bundles and how do these

preferences differ across various adopter segments?

• What is the WTP for attributes of EV-PV-BS product bundles?

• How do individual-related attributes influence the preferences and WTP for EV-PV-BS

product bundles?

1.3 Overview research papers and objectives

The objective of this doctoral thesis is to shed some light on the research gaps and

questions described above, by means of presenting three research papers. The research

objectives and publication status of each of these three papers are described below in more

detail.

Paper 1 of this thesis is titled “Predictors of electric vehicle adoption: an analysis of

potential electric vehicle drivers in Austria.” Considering the different effects of socio-

demographic and psychological variables, it investigates distinctions between early EV

adopters, potential EV adopters, and non-adopters regarding EV purchases or EV purchase

intention. In doing so, this paper is the first to test the predictive effect of cultural worldviews

(Cherry et al., 2014; Kahan et al., 2007) on the adoption/non-adoption of EVs. Further, Paper

1 contributes to the discussion of policy incentives by evaluating whether EV-related policy

incentives have a positive effect on EV adoption (cf. Egbue and Long, 2012). Based on an

Austrian wide representative sample of 1,000 respondents, this research paper discusses

interesting novel insights on the main predictors, and the role of cultural worldviews and policy

incentives in the acceptance of EVs. Additionally, for practitioners, the paper identifies and

characterizes various potential adopter sub-segments, shedding some light on the topic of EV

adopter heterogeneity and how the preferences for policy incentives differ among potential EV

adopter sub-segments (cf. Langbroek et al. 2016). Paper 1 is co-authored by Robert Sposato

and Nina Hampl10 and published in Energy Policy, Volume 122 in 2018 (5-Year Impact Factor:

5.038/VHB: B). Furthermore, earlier versions of this paper have been presented in 2017 at the

10. Internationale Energiewirtschaftstagung (IEWT) in Vienna/Austria, 5. Energiekonzept

Kongress in St.Gallen/Switzerland and at the Electric Vehicle Symposium (EVS) 30 in

Stuttgart/Germany.

10 The data analysis, as well the writing of the paper, was almost entirely done by the lead author (Alfons Priessner).


Paper 2, titled “Can product bundling increase the joint adoption of electric vehicles,

solar panels and battery storages? Explorative evidence from a choice-based conjoint study in

Austria,” takes a closer look at product bundling, in particular the bundling of EVs with

renewable power supply systems (PV with/without BS) in Austria. Since this type of EV-PV-

BS product bundle is not offered on the market yet, this study is, to our knowledge, the first to

analyze the preferences and the market potential of such EV-PV-BS product bundles. Based on

a conjoint experiment with 39311 potential EV drivers in Austria, the paper took a closer look

at different product bundle attributes and the preferences of potential EV drivers. Further, this

paper aims to provide a more finely grained view on consumer preferences, and hence,

identifies sub-segments of potential adopters, characterizing them via socio-demographic and

psychological factors, and identifying various product preferences across the segments by

running market simulations. Another objective of this paper, particularly relevant for

practitioners, is to evaluate the market potential of EV-PV-BS product bundles, as well as to

describe the relation between bundling complementary clean technologies and the increase in

intended adoption rate of such technologies. This paper is co-authored by Nina Hampl12 and

has received an invitation for “revise and resubmit” from Ecological Economics (5-Year Impact

Factor: 4.803/VHB: B). Moreover, this paper was presented at the 1. Advanced Demand

Modelling Workshop for Electromobility and at the 6th International PhD Day of the Austrian

Association of Energy Economics (AAEE) in 2018 in Vienna/Austria. In May 2019 this paper

will be also presented at the largest EV conference worldwide, the EVS 32 in Lyon/France.

Paper 3 also deals with EV-PV-BS product bundles, specifically from a willingness to

pay (WTP) angle. Hence, this paper is titled “Exploring consumer heterogeneity in willingness

to pay for electric vehicles product bundles.” The paper again used data from the Austrian-

wide conjoint experiment with 61611 potential EV adopters to arrive at the following research

objectives. First, the paper aims to estimate the WTP for the specific product bundle attributes

and related policy incentives. Second, the paper has the objective of evaluating the influence of

socio-demographic and psychological parameters, as well as by EV experience on potential

adopters’ preferences and their WTP for EV-PV-BS product bundles. This paper is co-authored

by Nina Hampl13. It has been submitted to Transportation Research Part A (5-Year Impact

11 Details about filtering the sample for the conjoint experiment (from the total sample of N=1,251) will be

provided in the next sub-section (Methodology and data), and in the dedicated sections in Paper 2 and Paper 3. 12 The data gathering and analysis, as well as the writing of the paper, was almost entirely done by the lead author

(Alfons Priessner). 13 The data gathering and analysis, as well as the writing of the paper, was almost entirely done by the lead author

(Alfons Priessner).


Factor: 3.809/VHB: B) and is currently under review. Further, this paper has been accepted for

the 11. IEWT conference, which takes place in February 2019 in Vienna/Austria.

Table 1 provides an overview of all three papers. It summarizes the title, authorship,

research objectives, as well as the theoretical foundations (see details above Theoretical

background and research questions), methodologies applied and sample size (see details below

in Methodology and data). Also, it comprises information on the current publication status of

each paper.

TABLE 1. OVERVIEW OF RESEARCH PAPERS THAT MAKE UP THIS DISSERTATION

No. Author (s) Title Research Objectives Theoretical

Background Methodology Sample Publication Status

1 Priessner,

Alfons; a,c

Sposato,

Robert; a

Hampl, Nina

a,b

Predictors of electric

vehicle adoption: an

analysis of potential

electric vehicle

drivers in Austria

1) Predictors of EV adoption (incl.

cultural worldviews)

2) Effectiveness of EV policy

incentives on EV adoption

3) Heterogeneity of potential EV

adopters in characteristics and

EV policy preferences

EV adoption

literature,

cultural

worldview

literature, EV

policy literature

Multinomial logistic

regression, cluster

analysis (hierarchical

and k-means

clustering), factor

analysis, ANOVA

and chi-square tests

N=1,000 Published in Energy

Policy Volume 122,

2018: 701-714

5-Year Impact Factor:

5.038/VHB: B

2 Priessner,

Alfons; a,d

Hampl Nina

a, b

Can product bundling

increase the joint

adoption of electric

vehicles, solar panels

and battery storages?

Explorative evidence

from a choice-based

conjoint study in

Austria

1) Preferences for bundling EV

with add-on products (PV

with/without BS) and market

potential

2) Consumer heterogeneity for EV-

PV-BS product bundling

Bundling

literature, EV

and clean

technology

consumer

preferences

literature

Conjoint analysis

(choice-based

conjoint (CBC)

approach), latent

class cluster analysis,

share of

preference/market

simulations

N=393 Reviewed with

outcome Revise and

Resubmit by Ecological

Economics, 28.12.2018


4.803/VHB: B

3 Priessner,

Alfons; a,d

Hampl Nina

a,b

Exploring consumer

heterogeneity in

willingness to pay for

electric vehicles’

product bundles

1) WTP for EV-PV-BS product

bundle attributes

2) Influence of socio-demographic

and psychological variables on

the WTP/preferences for EV-PV-

BS bundles

EV consumer

preferences and

WTP literature,

EV adoption

literature

Conjoint analysis

(CBC approach),

WTP calculations,

HB model with

covariates

N=616 Under review at

Transportation

Research Part A,

15.11.2018


3.809/VHB: B

a Alpen-Adria-Universität Klagenfurt, Department of Operations, Energy, and Environmental Management b Vienna University of Economics and Business, Institute for Strategic Management c The first author is main author of this research paper; i.e., the first author developed the research questions, analyzed the data and wrote nearly the entire manuscript on his own. The

co-authors collected the data in their study “Erneuerbare Energien in Österreich 2016” (cf. Hampl and Sposato, 2017), provided coaching in developing the research questions and

analyzing the data as well as provided feedback during the development of the manuscript. d The first author is main author of this research paper; i.e., the first author developed the research questions, collected and analyzed the data, and wrote nearly the entire manuscript on

his own. The co-author provided coaching in developing the research questions and analyzing the data and provided feedback on each section and input for the conclusion section of

each paper.


1.4 Methodology and data

All three papers of this doctoral thesis are reports on empirical studies that employed

quantitative research methods14. The target market of this dissertation is Austria, which had the

highest growth rate (128%) and the highest share of new registrations (1.2%) of EVs in the

European Union in 201615 (Electric Vehicle World Sales Database, 2017). Further, Hampl and

Sposato (2017) have shown that in Austria almost every second car driver could imagine

purchasing an EV. Hence, the Austrian population was considered as fairly suitable for analyzing

(A) predictors for EV adoption and (B) EV-PV-BS product bundle consumer preferences. Such a

population would be suitable for providing novel insights for countries at the beginning of EV

diffusion. Depending on the respective research questions and research context, different

methodologies and datasets were used, which are briefly described below, and given in more detail

in each paper.

In Paper 1 an Austrian-wide survey16 was conducted in 2016 to measure car drivers’

attitudes toward EVs and related policy incentives, and on their willingness to purchase. Further,

data on socio-demographic and psychological scales was collected (for further data details see

Paper 1). A market research company (meinungsraum.at) recruited a national-representative

sample of 1,000 respondents. Deloitte Austria and Wien Energie supported the survey with

expertise and funding. Other results of this survey have been published by Hampl and Sposato

(2017) in Erneuerbare Energien in Österreich 2016 (i.e., Renewable Energies in Austria 2016) and

in Sposato and Hampl (2018).

For analyzing the predictors of EV adoption, the author applied a multinomial logistic

regression (Backhaus et al., 2016). In addition, the author took a two-step approach to cluster

respondents into groups with minimized intra-cluster variance: hierarchical cluster analysis was

used to determine the optimal number of clusters, which served as input variables for the k-means

cluster analysis (Punj and Stewart, 1983). Finally, by applying ANOVA and chi-squared tests

14 Preparing for the quantitative research methods, the author conducted qualitative interviews (>10 interviews) with

experts or lead users (for more details see the Methods section in each paper) for each study. 15 The research proposal for this dissertation was accepted in June 2017. Hence the sales figures of 2016 formed the

most recent reference point for the decision regarding which country to focus on. 16 This survey is an annual survey covering several clean technology acceptance topics, i.e., public perceptions,

preferences, and willingness to invest in renewable energy and other low-carbon technologies across the Austrian

population. A sub-section of the survey deals with EV-related questions and topics, which were used for Paper 1.


differences between the segments were identified along a set of socio-demographic and

psychological parameters, and the author compared respondents’ preferences for policy incentives.

Paper 2 and Paper 3 are both based on the same survey, including a choice-based conjoint

(CBC) experiment. A market research company called market recruited a representative sample of

1,251 respondents from Austria. The participants, who successfully and properly filled out the

survey, were financially rewarded, which is considered common practice in the market research

industry (cf. Gamel et al., 2016; Salm et al., 2016). Flatliners or speeders were reported back to the

market research company, and did not receive any reward. An additional benefit using participants

from a panel pool is their experience with longer surveys and with choice experiments which could

temper Jaeger et al.’s (2001) criticism that CBC gets more accurate results, if participants are

already used to CBCs due to certain training effects (for details see the respective sections in Paper

2 and Paper 3).

The final samples of the two papers differ due to different filtering questions. For Paper 2,

393 17 respondents passed the filtering questions for the final sample. For Paper 3, 616 18

respondents made up the sample. Both samples comprised future EV drivers with varying purchase

horizons aligned to different research objectives in each paper (for details see the respective

sections in Paper 2 and Paper 3). Sawtooth Software Lighthouse, the standard application for CBC

experiments in marketing research, was used to design, administer, conduct and analyze the survey.

Conjoint analysis is a frequently used market-research method, which aims at explaining

purchasing behavior even if the analyzed product does not exist on the market yet (e.g., Louviere

et al., 2008). In market research, conjoint studies are considered superior to directly asking

consumers about their decision criteria, because people have little insight into their decision-

making rationale, and they often have a recall bias or other information recovery failures (Golden,

1992). Even so, a number of scholars have discussed the methodological challenges of conjoint

analysis (e.g., Ben-Akiva et al., 1994; Louviere et al., 2000), and this has led to ongoing

advancement (e.g., Orme and Chrzan, 2017; Train, 2003).

Due to its benefits in studying decision-making, also regarding products which do not exist

yet, conjoint analysis has already successfully been applied to the context of EVs for decades (e.g.,

17 Filter criteria: Potential EV driver with a positive attitude toward EVs, willingness to purchase an EV as their next

car within the next 5 years. 18 Filter criteria: Potential EV driver with a positive attitude toward EVs, willingness to purchase an EV within the

next 10 years.


Beggs and Cardell, 1980; Brownstone et al., 2000; Bunch et al., 1993; Ewing and Sarigöllü, 2000;

Hackbarth and Madlener, 2016; Hackbarth and Madlener, 2013; Hidrue et al., 2011; Hoen and

Koetse, 2014) and also for EV add-on studies (e.g., Hinz et al., 2015; Parsons et al., 2014). Table

2 provides an overview of the attributes of these studies and the methodology the author used in

each. Considering these earlier studies, the author also considers conjoint experiments as well

suited to investigate EV-PV-BS product bundle attributes in relation to each other from the

perspective of early/potential adopters.

Conjoint analysis uses an indirect questioning approach which divides the decision-making

process and products into underlying response preferences for particular attributes (referred to as a

decompositional approach) (Green and Srinivasan, 1990). This means that in a conjoint setting, a

respondent in the survey needs to make a decision (dependent variable) within several choice tasks

(i.e., between hypothetical but potential products, in this study EV-PV-BS product bundles). The

presented options vary along pre-defined attributes in their specific attribute levels (independent

variables) (e.g., Gustafsson et al., 2013; Louviere et al., 2000).

From the decisions made in the choice tasks, preferences for the attribute levels can be

derived in the form of average part-worth utilities and relative importance weights for each of the

attributes (e.g., Green and Srinivasan, 1990). Further, the part-worth data can be used to run latent

class cluster analysis (Sawtooth Software, 2004), share of preference market simulations with

varying attribute settings (Orme and Chrzan, 2017), WTP calculations (Orme, 2001) and

estimations on the covariates’ influence on the utility model (Orme and Howell, 2009). All these

methods have been applied in either Paper 2 and/or Paper 3.

Choosing the most relevant attributes and levels is a very critical task for the success of a

conjoint analysis study (Bergmann et al., 2006). Therefore, the author selected an elaborated

iterative process comprising a literature review, sales conversations with EV, PV, and BS retailers,

interviews with EV, PV, and BS lead users, and expert interviews. The survey development was

supported by the Austrian utility company KELAG with expertise and funding. Before going live

with the survey, the author conducted a pre-test with 45 EV supporters to verify the interpretation

of the attributes and test the relations among them. For further details about this applied iterative

process, please see details in the respective chapters in Paper 2 and Paper 3.

15

TABLE 2. OVERVIEW CONJOINT STUDIES ON ELECTRIC VEHICLES19

Source Attributes Number of choice tasks,

attributes, and levels

Respon-

dents

Analysis model

Hackbath and

Madlener

(2013)/(2016)

Purchase price, fuel type, fuel cost per 100 km, CO2 emission reduction, driving range,

fuel availability, refuel/recharging time, policy incentive

15 choice tasks / 8

attributes / 3 levels

711 Multinomial / Mixed Logit

Model (2013) / Latent Class

Model (2016)

Hinz et al.

(2015)

Range, charging time, motor power, purchase price, electricity cost per 100 km and 3

complementary services (IT-based parking space and payment, intelligent charging

station, augmented reality services)

14 choices tasks / 8

attributes / 2-4 levels

150 Hierarchical Bayes Model

Parsons et al.

(2014)

Price relative to favorite ICE, annual cash payback (V2G), required plug-in time per

day, minimum guaranteed driving range; (charging time, driving range, fuel saving,

performance, reduction in pollution kept at the same level);

18 choice tasks / 4 + 4

constant attributes / 4-8

levels

3,029 Latent Class Model

Hoen et al.

(2014)

Car type, purchase price, monthly cost, driving range, refuel/recharging time,

additional detour-time, number of available brands, policy measures

8 choice tasks / 8


1,903 Hierarchical Bayes Model/

Latent Class Model

Hidure et. al.

(2011)

Charging time, driving range, fuel saving, acceleration, reduction in pollution, price

compared to ICE

18 choice tasks / 6


3,029 Latent Class Model

Ewing and

Sarigollu (2000)

Price, fuel cost, repair and maintenance cost, commuting time, acceleration, range,

charging time

9 choices tasks / 7


881 Multinomial Logit Model

Brownstone et

al. (2000)

Fuel type, purchase price, vehicle range, home refueling time, home refueling cost,

service station refueling time, service station fuel cost, service station availability,

acceleration time, top speed, tailpipe emissions, vehicle size, body types, luggage space



7,287 Multinomial and Mixed

Logit Model

Bunch et al.

(1993)

Price, fuel cost, range, acceleration, fuel availability, emission reduction, dedicated

versus multi-fuel capability

5 choices tasks / 7


700 Multinomial and Nested

Logit Model

Beggs et al.

(1981)

Price, fuel cost, range, top speed, number of seats, warranty, acceleration, air

conditioning


attributes / NA

193 Ranked Logit

19 This table does not claim to be fully exhaustive, but it includes several relevant conjoint studies from peer-reviewed journals.


1.5 Conceptional framework

FIGURE 1. CONCEPTIONAL FRAMEWORK OF THIS DISSERTATION

a Paper 3 also includes the individual-related variable EV experience.

The overall conceptional framework, which constitutes the foundation of all three

papers of this doctoral thesis, is illustrated in Figure 1. This model illustrates the relationship

of (A) the purchase intention of an EV (Paper 1), and (B) an EV-PV-BS product bundle (Paper

2) (dependent variable) and the individual-related, context-related, and product-related

variables (independent variables). Further, socio-demographic and psychological

characteristics are also treated as moderators that either increase or decrease the product-

related/context-related attributes’ utility, and hence, participants’ intention to purchase an EV-

PV-BS product bundle in Paper 3.

Consequently, Paper 1 is operationalized differently to Paper 2 and Paper 3. Paper 1

includes socio-demographic characteristics (i.e., gender, age, income, current number of cars

per household, dwelling dispersion, and household size) and psychological characteristics (pro-

environmental attitude, pro-technological attitude, cultural worldviews), as well as policy

incentives as variables in predicting EV adoption/non-adoption. At the time of the survey,

Austria had different policy incentives for EVs at the regional level, i.e., per Bundesland. The

intention to purchase was operationalized with three categories, namely early EV adopters,


potential EV adopters and non-adopters. Paper 2 and Paper 3, in contrast, focus on the intention

to purchase an EV in a bundle. Hence, they assessed the influence of EV add-on products’

attributes (i.e., PV system with/without BS) and policy attributes on the purchase intention of

an EV in a product bundle. To describe the add-on products, the author used the following six

attributes with sub-levels: PV/BS add-on (ownership), self-sufficiency rate, amortization

period, provider, policy incentive, and purchase price. Paper 3 additionally assessed the impact

of individual-related characteristics (i.e., socio-demographics, psychological characteristics,

and EV experience) on the product-related and context-related variables and eventually the

intention to purchase an EV-PV-BS product bundle.

The target group of all three papers in focus in both the survey and the conducted semi-

structured interviews were early and potential EV adopters20. This target group is made up of

people who usually are the final decision makers in EV purchases, even though the adoption of

EVs is influenced by several stakeholders, such as car retailers, politicians, media, peer-groups,

and neighbors. Since peer-effect (Kahn, 2007) or threshold effect (Eppstein et al., 2011) are not

in focus of this study, the author focuses solely on the main decision maker in the EV purchase.

Further, by understanding early/potential adopters’ preferences and characteristics,

implications for practitioners and policy makers could be derived (Axsen et al., 2016).

1.6 Overall findings and conclusions

The overarching research aims of the dissertation are to investigate predictors that

influence the adoption of EVs, as well as the consumer preferences regarding EV-PV-BS

product bundles. Hence, each of the three papers of this doctoral thesis, building on prior

literature and on various data sources, provides answers to selected aspects of this overall

mission. The subsequent sections summarize the main insights, discuss the contributions these

studies make to the academic discourse, and then draw some implications for both practitioners

and policy makers. Then, several research limitations are discussed from a general to a paper-

specific level, all of which could be starting points of future scientific investigation. At the end

of this chapter, the author summarizes the main conclusions of this dissertation.

1.6.1 Main findings and theoretical implications

Paper 1 indicates that socio-demographic factors (such as income, age, and education)

have limited power in predicting adoption/non-adoption of EVs. In contrast, psychological

20 Paper 1 also analyzed the non-adopter segment in addition to the early and potential EV adopter segment.


characteristics play quite a significant role in explaining the (non-)intention to purchase an EV.

For instance, people with a strong individualistic and less egalitarian worldview are more likely

to be non-adopters of EVs. Further, early/potential EV adopters have a stronger pro-

technological and pro-environmental attitude than non-adopters. The analysis also shows that

the concept of EV policy incentives could be a relevant indicator of early adoption. Moreover,

Paper 1 identifies four groups of potential EV adopters (Rural Non-Techs, Undecided

Individualists, Undiscerning Urbanites, Urban EV Supporters) by applying a cluster analysis

based on their evaluation of EV purchasing and non-purchasing motives. These segments differ

considerably in their socio-demographic and psychological characteristics. Furthermore, the

identified clusters have heterogenous preferences for different types of policy incentives.

These findings advance the current understanding of early and potential adopters and

have several theoretical implications. First, the relatively low predictive power of socio-

demographic characteristics the author identified for EV adoption is contradicting with several

research outcomes on EV adoption (e.g., Axsen et al., 2016; Plötz et al., 2014). But, on the

other hand, these insights are in line with the general literature on environmental behaviors

(e.g., Kilbourne and Beckmann, 1998; Leonidou et al., 2010). Nayum and Klöckner (2014)

even argued that including psychological variables will lower the effect of socio-demographics

in explaining consumers’ purchase intention for environmentally friendly automobiles.

Relating to these perspectives, this dissertation concludes in line with Nayum and Klöckner

(2014) that further studies evaluating the factors that influence consumers’ intention to purchase

an EV should include several individual-related parameters (cf. Li et al., 2017; Liao et al., 2017)

instead of focusing on socio-demographics alone.

Second, Paper 1 adds first insights to EV literature on the effect of cultural worldviews

on EV adoption. For instance, the propensity to purchase an EV decreases among people with

a strong individualistic worldview. This result is in line with the general tendency of people

with individualistic worldviews to be skeptical regarding climate change (Kahan et al., 2007),

or exhibit less positive attitudes toward renewable technologies (Cherry et al., 2014). Overall,

these novel findings regarding cultural worldviews are a very promising starting point for future

studies in this field.

Third, Paper 1 contributes to the ongoing discussion on the effectiveness of policy

incentives by proving that policy incentives do influence early EV adoption. In addition, the

paper confirms a position already taken by Lane and Potter (2007), that policy incentives are

relatively ineffective if respondents do not already have an intention to purchase an EV.

Furthermore, this study finds that there are heterogenous incentive preferences which are


oriented to either purchasing an EV or using an EV, considering the identified potential

customer segments’ preferences. This finding emphasizes the importance of both the purchase-

based and use-based incentive types (cf., Mannberg et al., 2014; Sierzchula et al., 2014). It also

serves as a starting point for further research into conceptualizing proper and effective incentive

packages for different target groups.

Paper 2 follows a call for research put out by Cherubini et al. (2015). It assesses the

consumer preference regarding purchasing EVs in a bundle with PV-BS systems. The most

important decision criterion for an EV-PV-BS product bundle is the purchase price (31.1%).

Further criteria were identified as PV/BS add-on (ownership) (18.7%), and amortization period

(16.1%). The paper also shows in applied market simulations a best case-scenario (i.e., a

product bundle with the most preferred features) would bring up to 77.4% of the respondents

to purchase an EV in a bundle with PV and BS. Another analysis points out that the purchase

intention for a PV and BS is significantly lower when purchased standalone compared to being

purchased in an EV-PV-BS bundle. In other words, EV-PV-BS product bundles are highly

likely to accelerate the adoption of small-scale PV/PV-BS systems through cross-selling with

EVs. Moreover, the paper suggests that policy incentives for clean technologies are more

effective when price labels for product bundles are marked with the amount remaining after

subsidy reduction. Further, by applying a latent class segmentation analysis, the study identified

four distinct potential customer segments. These were labelled as Energy Self-Sufficient

Owners, Price-Sensitive Non-Owners, Economically Rational Owners, and Likely Non-

Adopters, which are quite homogenous in their socio-demographic and psychological

characteristics but differ along their product preferences.

Paper 2 contributes to academic research with findings on clean technology adoption,

by being the first to investigate the consumer preferences and market potential of EVs combined

with PV and BS in the form of a product bundle. To date, researchers have studied bundling of

an EV with add-on services (e.g., V2G) (Hinz et al., 2015), or the joint adoption of EVs and PV

(Delmas et al., 2017), or of PV and BS (Agnew and Dargusch, 2017). Paper 2 contributes to

clean technology literature by investigating the consumer preferences regarding EV-PV-BS

product attributes and policy incentives. Further, the identified clusters differ according to price

sensitivity, preferences related to specific bundle components, or their ownership-options.

These findings could be a starting point for future academic research.

Paper 2 also contributes to bundling literature by confirming a positive effect of product

bundles on the intention to purchase complementary products together with an EV. This is in

line with findings of other bundling literature (cf. Stremersch and Tellis, 2002). Further, due to


the broad heterogeneity in preferences regarding the items included in product bundles, the

paper proposes a mixed-bundling strategy for EV-PV-BS product bundles. Thus, retailers

should offer the products in a bundle as well as standalone, as a best practice in approaching

distinct consumers (e.g., Schmalensee, 1984). Future studies could build on this proposition

and focus on consumer preferences for additional specific features of the underlying bundle

products. Such features could include the EV model or brand, and the type of PV or BS system.

Overall, these research findings form an interesting basis for further in-depth studies at the

intersection of bundling and acceptance of clean technologies.

Paper 3 reports results that show the implicit WTP for EV add-on products (PV and BS)

is still below current market prices. Further, consumers are only willing to pay a small premium

for the convenience of being served by an all-in-one provider. Moreover, the higher the level

of incentives provided, the lower the subsidies are valued. The study also reveals that socio-

demographic variables (e.g., income, age, and education) have relatively minor effects on the

potential EV drivers’ bundle preferences and their WTP. Psychological variables, in contrast,

show a stronger effect. For instance, potential EV adopters seem to have a higher WTP for EV-

PV-BS product bundles when they have a pro-environmental attitude, a more technologically

oriented mindset, or more EV experience. This, once again, underlines the importance of

comprehensively evaluating the potential adopters’ characteristics.

Paper 3 is, to the author’s best knowledge, the first one to calculate the WTP for EV

add-on products PV and BS. Considering the identified mismatch between purchase price and

potential customers WTP, the WTP values could be an input for further studies simulating the

potential increases in market share due to predicted cost degression effects. Further, although

several studies analyze the socio-demographic characteristics, psychological motives, and

experiences of EV lead users, as well as potential EV adopters (cf. Axsen et al., 2016; Nayum

and Klöckner, 2014; Plötz et al., 2014), to date there is no analysis of how potential EV adaptor

characteristics and EV experiences can influence consumer preferences and their WTP for EV

product bundles. Therefore, this research paper’s findings present interesting incentives for

further research in the field. For example, they could investigate the effect of education and

awareness measures on supporting households’ investment decisions related to EVs and add-

on products; or they could inquire about the effect experience related to PV and BS could have

on the willingness to adopt EV-PV-BS product bundles.


1.6.2 Practical implications

Each paper of this doctoral thesis has specific implications for practitioners and policy

makers. The major ones are discussed below referring to each paper successively.

Paper 1 points out the need to focus marketing efforts on potential adopters’ interest in

state-of-the-art technology and/or their environmental concerns, rather than simply targeting

better educated and more affluent men in multi-person households in rural areas (cf. Plötz et

al., 2014; Tal and Nicolas, 2013). Further, the identified diverse subsegments of potential EV

drivers emphasize Hardman et al.’s (2016) proposition that future EV drivers are likely to be

very heterogenous in their socio-demographic and psychological characteristics. This, again,

triggers a need to address these segments differently with bespoke EV enlightenment

campaigns, products, or even new business models connected to EVs, such as e-hailing or e-

car sharing (e.g., McKinsey, 2017).

For policy makers, Paper 1 suggests that in the short-term future public authorities

should contribute to the expansion of green EVs with tailored EV policy mixes, for several

reasons. First, this study proves a number of positive effects policy incentives have on potential

early EV adoption. This is in agreement with EV literature which argues that policy incentives

increase EV’s market penetration (e.g., Sierzchula et al., 2014; Zhang et al., 2013). However,

the data of this paper also suggests that policy incentives are less effective for non-adopters

because they do not perceive EVs as equivalent to ICEs in terms of price, convenience, and

performance. This could imply that policy makers should transpose or keep policy incentives

until EVs are perceived as almost equal to ICEs. In addition, this paper points out that different

potential customer segments have heterogeneous policy incentive preferences (use-based or

purchase incentives). The effectiveness of incentives could be increased if offered in target

packages tailored to the preferences of certain potential adopters (e.g., Urban EV Supporters),

or if incentives are provided for e-car-sharing or e-hailing companies, as has already been

suggested by Green et al. (2014).

For marketers, the results of Paper 2 provide insights into the market potential of EV-

PV-BS product bundles. The author finds a strong customer interest in such types of offers in

that up to almost 78 percent of the respondents would choose the bundle in the best-case

scenario. PV and BS are twice as likely to be purchased in a bundle than as standalone products,

which is expected to convert into actual sales over the next couple of years. This implies that

car dealers could extend their range of offers with complementary products, such as PV and BS

(see e.g., Tesla’s solar roof systems and BS (Tesla Motors, 2018), or power utilities could offer

EVs together with PV/BS systems or other EV add-on products (e.g., TEAG AutoPaket (TEAG,

2018)). The identified subsegment of potential adopters of EV-PV-BS product bundles in Paper


2, are quite homogenous in personal-related characteristics, but very diverse in their product

bundle preferences. This requires customizing product offerings for certain sub-segments along

the dimensions of product (e.g., premium vs. low-cost) or ownership (owner vs. leaser). For

instance, subscription-based offers seem to be auspicious for such types of product bundles, as

certain sub-segments indicated a distinct preference for non-ownership models with monthly

payments and thus low upfront investment. Further, advertising product bundles with purchase

prices reduced by subsidies, is recommended. Evidence in Paper 2 suggests that potential

adopters prefer products labelled at net-cost (i.e., where the purchase price excludes potential

subsidies) to a product with a higher purchase price but high policy incentive, even though both

products have nearly the same ultimate cost.

Paper 3 points out that current market offers do not match the current WTP for EV add-

on products. Therefore, in addition to the ongoing cost degression of clean-tech products in

focus (Seba, 2014), marketers should consider developing new product offers with leasing

options that reduce the financial burden and closes the gap in WTP and purchase price. Further,

Paper 3 analyses provide evidence that there is a need for all-in-one providers, but respondents

are only willing to pay a small premium for being served by a one-stop provider, and they do

not distinguish particularly between the providers (i.e., whether they are served by a car dealer,

a utility company, or a specialty dealer). Nevertheless, providers could take the opportunity to

offer these EV-PV-BS products jointly in the form of integrated products. By applying this

approach these players generate a lock-in effect and long-term profitability in the same way as

Apple Inc. did it with their platform approach (Gawer and Cusumano, 2014). In addition, the

Paper 3 analyses suggest that certain potential EV driver characteristics influence the

evaluation of EV-PV-BS product bundles. Hence, by promoting EV product offerings to certain

sub-segments with specific characteristics, for instance pro-environmental and pro-

technological middle-aged men, or by providing information and testing opportunities, the

penetration rate of EV product bundles can be increased. Finally, from an input-output

perspective, Paper 3 suggests that government subsidies should not be too high, because

consumers’ evaluation of the subsidy decreases as the subsidy level increases. In other words,

the WTP for policy incentives is lower than the real value of the incentives, the higher the

subsidy granted. This implies that policy incentives should be kept relatively low. Nevertheless,

this doctoral thesis argues in line with Bauner and Crago (2015) that to reduce delays in

potential adopters’ investment timing, policy incentives are crucial as success in markets with

high uncertainty regarding technological development and price forecasts (such as the EV, PV,

BS market), do depend on such incentives.


1.6.3 Limitations and future research

As with any research, this doctoral thesis is subject to specific limitations, which offer

avenues of future research. Since all three papers are empirical studies based on survey data,

there are limitations to how generalizable the results are with respect to, e.g., location,

methodology, or context-issues. All papers share some limitations regarding the data collection,

and while Paper 2 and Paper 3 have some concerns regarding the conjoint methodology. In the

following paragraphs, the most noteworthy ones are summarized. For further details, see the

respective sections in the papers below.

First, even though the Austrian EV consumer preferences are of high interest due to

strong growth in numbers of new registrations in the EU (Electric Vehicle World Sales

Database, 2017), the results of these studies can only, if at all, be broadly generalized and should

be applied to other countries with caution. The country in focus, has particular topographies

with a large proportion of the population living in rural areas. Furthermore, the inhabitants are

considered comparatively pro-environmental (Liobikienė et al., 2017). Therefore, it would be

interesting to see whether further research into the acceptance of EV and EV product bundles

in other national contexts lead to similar results.

Second, it has to be pointed out that the cross-sectional surveys, which this dissertation

relies on, also comprise several limitations. First, the data collection was subcontracted to

market research companies. Since these companies provide answers by inviting their panel

members to participate in a survey via email, a certain non-response bias or self-selection bias

could not be excluded (Groves, 2006). This normally causes a difference between the people

responding to a survey and those who did not, risking that results become non-representative.

Before applying filtering questions, the samples of this dissertation seemed to be representative

of the Austrian population in terms of socio-demographics such as age or gender. Nonetheless,

the results and implications of this dissertation need to be interpreted with due caution. Another

bias researches often face in case studies investigating ethically correct behavior, is a social

desirability bias (Diekmann, 2017). This bias is referred to as the propensity of people to answer

questions in a way that could be seen as favorable by society, which can distort the result of

surveys (Lakitsch, 2009). Moreover, for the filter questions, the author used self-reported and

not verifiable answers to gauge respondents’ attitudes towards EVs, or their intention to

purchase an EV. This social-desirability bias and self-reported bias could be avoided by

applying indirect questioning such as conjoint methods do (Gustafsson et al., 2013). Further,

Haan and Kuckartz (1996) argued that self-reported intentions have been established as useful

indicators in previous studies.


Third, since the EV market in Austria is still a niche market and the EV product bundles

have to date only been piloted, but not sold in bundles, all three papers had to study mostly

potential adopters and their intention to purchase an EV. Hence, this dissertation’s results

assume purchase intention to be a certain antecedent of actual behavior (Ajzen, 2006).

However, literature suggests a certain intention-behavior gap, since intentions can be

influenced by several intervening events (Ajzen and Fisbbein, 1974). Nevertheless, this

approach has proven to be quite valuable for clean technologies research (Axsen et al., 2016;

Kaufmann et al., 2013; Peters and Dütschke, 2014; Salm et al., 2016). Future research could

overcome this limitation by complementing this dissertation’s results, for instance by

integrating longitudinal data to investigate actual behavior or change in opinions. By doing so,

future research could investigate how well purchase intention could predict actual behavior in

the context of EV purchases, which would be a valuable step in the validation of this doctoral

thesis findings.

Fourth, the findings in Paper 1 and Paper 3 on cultural worldviews are quite novel and

they contribute to the relatively new and growing literature on clean technologies and cultural

worldviews (Cherry et al., 2014; Sposato and Hampl, 2018). However, in both cases despite

choosing the cultural worldview measurement scale applied by Cherry et al. (2014), the

Cronbach-Alpha scores were a bit below the expected threshold (Backhaus et al., 2016). This

could be explained by a limited applicability of the selected cultural worldview items in the

Austrian culture. This observation agrees with results from other scholars that applied the

cultural cognition scale in their work in a non-US context (e.g., Capstick and Pidgeon, 2014).

Therefore, further studies could apply a sub-set of the cultural worldview scale which is more

suitable for the Austrian setting, or they could even develop cultural worldview scales more

applicable to the European cultural setting.

Further, Paper 2 and Paper 3 draw insights from data gathered by means of a conjoint

approach, which aims to mimic a purchase decision on a market in an experimental setup. This

approach was chosen due to its high suitability for testing hypothetical products not on the

market yet (as is the case for the EV product bundle in focus of this dissertation). However,

real-life purchase decisions are much more complex, involving a broad range of criteria which

cannot all be experimentally depicted. Therefore, in CBC experiments, only the most relevant

attributes based on a literature review and qualitative research phase could be analyzed.

Numerous product related and non-related aspects that could certainly have affected the result,

had to be excluded. Consequently, there are plenty of options for further research involving

different and unobserved sets of PV-BS add-on criteria or different types of EVs, since the


author considered only one type of EV or did not incorporate technical details of PVs or BS.

Moreover, one needs to keep in mind when working with conjoint experiments, that the CBC

related results such as the part-worth utilities of attribute levels or the relative importance scores

of attributes are dependent on the other attributes and attribute levels included in the

experiment. Therefore, future research should look into these aspects and investigate the

influence of other attributes or attribute levels on the purchase intention.

Paper 3 estimated several implicit WTP figures for the EV product bundle and its

parameter improvements, which are distinct and novel to the relevant literature. However,

despite it being quite useful to estimate WTPs based on CBC data (Gustafsson et al., 2013),

these figures comprise a hypothetical bias (List et al., 2006; Orme, 2001), meaning that due to

the experimental setting of the CBC, respondents did not actually have to pay their own money.

This could cause a gap between the hypothetical and the real WTP. Therefore, WTP figures

should be interpreted cautiously as upper limits. Further studies could either apply different

WTP calculations such as BDM (e.g., Wertenbroch and Skiera, 2002), or conduct studies which

allow observing actual purchase behavior rather than online-based survey reported behaviors.

1.6.4 Overall conclusions

Overall, the author draws four main conclusions in this dissertation. First, an EV

purchase or an EV product bundle purchase is more related to people’s mental state (i.e.,

psychological factors) than to their socio-demographics. In more detail, the findings show that

people with pro-technological and pro-environmental attitudes, as well as certain worldviews

(less individualistic and more egalitarian), are more willing to purchase an EV (Paper 1) and

they evaluate EV product bundles more favourably, thus leading to a higher WTP (Paper 3).

The dissertation argues that future research analyzing EV predictors or consumer preferences

and WTP for EV product bundling should strive to comprehensively evaluate potential driver

characteristics, and not only focus on a single dimension, such as socio-demographics only or

psychological characteristics only.

Second, this dissertation identifies a significant market potential for EV-PV-BS bundles

(Paper 2). However, since the WTP for EV-PV-BS bundles does not match the actual pricing

of these products yet, innovative ownership models (e.g. subscription-models) and all-in-one

providers are required in addition to further cost degression for market diffusion (Paper 3).

Nevertheless, bundling EV with PV and BS has the potential to decrease the carbon footprint

of EVs, while at the same time it could increase the diffusion of PV-BS systems.


Third, the dissertation finds the group of potential EV and EV-PV-BS bundle adopters

to be quite heterogenous in their attitudes and characteristics toward EVs or their varying

preferences for EV product bundles. Therefore, the thesis has identified different sub-segments

for EV-adopters (Paper 1) and EV-PV-BS adopters (Paper 2), which need to be targeted with

more tailored product offers and marketing efforts.

Fourth, regarding policy incentives, one needs to deal separately with the conclusions

related to effectiveness and preferences concerning EV policy incentives vs. EV-PV-BS policy

incentives. Regarding EV policy incentives, early EV adopters are more willing to purchase an

EV if supported by policy incentives. The opposite is true for non-adopters, who will not be

convinced in favor of EV adoption with policy incentives until they consider an EV to be

technologically similar to an ICE. Moreover, this dissertation argues that policy makers could

increase the effectiveness of incentives by targeting specific potential adopters (e.g., Urban EV

Supporters) with incentive packages tailored to their preference (Paper 1). Regarding EV-PV-

BS policy incentives, their value decreases when subsidy levels increase (Paper 3). Then, to

impress the savings advantage on possible adopters, the subsidy amount should be subtracted

from the labelled purchase price of the EV product bundle at an early stage (Paper 2).

Overall, these derived findings could help researchers, as well as practitioners and

policy makers, to better exploit the benefits of EVs and consequently can contribute to a more

carbon-free transportation system and a higher integration of EVs and renewable energies.


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PAPER 1: PREDICTORS OF ELECTRIC VEHICLE ADOPTION: AN ANALYSIS OF

POTENTIAL ELECTRIC VEHICLE DRIVERS IN AUSTRIA21

Priessner, Alfons*; Sposato, Robert*; Hampl, Nina*,#

ABSTRACT

Although barriers to the adoption of electric vehicles (EVs), such as purchase price, range

limitation, and charging infrastructure are diminishing, sales growth has still fallen short of

industry expectations. As industry and governments have an interest in counteracting this negative

trend by incentivizing EV purchasing, a better understanding of potential EV adopters and drivers

of early adoption becomes paramount for designing effective and efficient incentive schemes.

Therefore, drawing on a representative survey of Austrian citizens, this study analyzes early

adopters, potential adopters, and non-adopters of EVs. Findings indicate that psychological and,

to a lesser extent, socio-demographic factors play a significant role in predicting EV adoption.

Non-adopters are more likely to have an individualistic and less egalitarian worldview, and also,

compared to early adopters, to fall short in terms of pro-environmental and pro-technological

attitude. Further, early adopters are inclined to live in regions with EV policy incentives. Using

cluster analysis, this study identifies four groups of potential EV adopters based on their evaluation

of EV purchasing and non-purchasing motives. The potential-adopter segments differ considerably

in their socio-demographic and psychological characteristics, as well as in their preferences for

policy incentives. We discuss implications of our findings for the design of effective policy

schemes and marketing measures.

Keywords: Electric vehicles, willingness to purchase, potential adopters, cultural worldviews,

policy incentives

Highlights:

• Cultural worldviews are important predictors of early electric vehicle adoption.

• Potential adopters differ in their demographic and psychological characteristics.

• Heterogeneous segments call for tailored policy incentives and marketing measures.

21 Early versions of this paper have been presented at the 10. IEWT 2017, from 15.02.-17.02.2017, Vienna/Austria; at

5. Energiekonzept Kongress 11.05-12.05.2017 in St. Gallen/Switzerland and at the EVS 30, from 09.10.-11.10, 2017,

Stuttgart/Germany and has been published in Energy Policy under Priessner et. al. (2018):

https://doi.org/10.1016/j.enpol.2018.07.058

* Department of Operations, Energy, and Environmental Management, Alpen-Adria-Universität Klagenfurt

# Vienna University of Economics and Business Institute for Strategic Management

https://doi.org/10.1016/j.enpol.2018.07.058

Paper 1 34

1. INTRODUCTION

In December 2015, the Paris Agreement signed by the parties of the United Nations

Framework Convention on Climate Change set a clear, but ambitious target to combat climate

change by keeping the global average temperature increase below 2°C above pre-industrial levels

(UNFCCC, 2015b). The transportation sector contributes almost a quarter of all global energy-

related greenhouse gas (GHG) emissions (IEA, 2016b). One promising pathway to curb emissions

from fossil fuels in this sector is to replace conventional internal combustion engines (ICE) with

electric ones, coupled with decarbonized energy production (e.g. Bleijenberg and Egenhofer,

2013). Various studies have emphasized the necessity of a major uptake of electric vehicles (EV),

i.e., of battery electric vehicles (BEVs) or plug-in hybrid electric vehicles (PHEVs), within the

next three decades to meet greenhouse gas reduction targets (IEA, 2016a; IPCC, 2012; UNFCCC,

2015a). Recent advances in technology (e.g. higher battery energy density) have extended the

range of available EVs (IEA, 2016a) and, with specific policy support, have lowered vehicle costs

in a number of countries (Sierzchula et al., 2014), thereby reducing major consumer barriers.

Nevertheless, sales growth has fallen short of industry expectations (IEA, 2016a).

While industry stakeholders and policy makers are looking to accelerate the trend toward

investment in EVs, we need a better understanding of early EV adoption predictors and profiles of

potential EV adopters (i.e., people who can imagine to purchase an EV) to create more growth in

the market for EVs (Wesche et al., 2016). Larson et al. (2014) ascribe the slow EV rollout mainly

to the lacking insight stakeholders have of potential EV customers. Nayum and Klöckner (2014)

and Nayum et al. (2016) argue that policy makers should consider (potential) adopters’

multifaceted attitudes and psychological characteristics in creating incentives to accelerate EV

diffusion more effectively.

Despite this evident need to learn more about (early) adopters and potential EV adopters,

to date, only a small body of literature reports on studies of these adopter segments (Axsen et al.,

2016; Nayum et al., 2016; Nayum and Klöckner, 2014; Peters and Dütschke, 2014; Plötz et al.,

2014). Prior work has shown that certain socio-demographic and psychological differences (Axsen

et al., 2016; Nayum et al., 2016; Nayum and Klöckner, 2014; Peters and Dütschke, 2014)

distinguish between actual EV owners and potential EV adopters currently using ICE vehicles.

Our first research objective is to investigate a range of variables as possible predictors of EV

adoption, including one particular group of psychological variables, namely cultural worldviews.

Existing literature so far has largely neglected the influence of cultural worldviews on the

propensity to purchase an EV. Cultural worldviews can be described as “a general perspective

Paper 1 35

from which a person sees and interprets the world” (Cherry et al., 2014: 563), and it is

conceptualized as an overarching sense-making system for complex questions (Hedlund-de Witt,

2012). Previous research has shown that cultural worldviews are predictive of people’s acceptance,

attitudes, and also behavioral intentions regarding a variety of socially contested issues such as

nuclear waste, national security, possession of weapons, public health, climate change (Kahan et

al., 2007; Kahan et al., 2011; Kahan et al., 2012), climate change mitigation policies (Hart and

Nisbet, 2011), and clean technologies (Cherry et al., 2014; Sposato and Hampl, 2018). Given such

evidence, and in addition to previously studied variables, we introduce this concept here as we

expect to find that, depending on their cultural worldviews, respondents will be more or less likely

to adopt clean technology vehicles such as EVs. To the best of our knowledge no study has

attempted to analyze the particular relationship between cultural worldviews and EV adoption yet.

Our second research objective is to contribute to the current discussion in literature and

among stakeholders on the effectiveness of policy measures in the diffusion of EVs. On the one

hand, research argues that policy incentives increase market penetration of EVs (e.g., Langbroek

et al., 2016; Mannberg et al., 2014; Sierzchula et al., 2014), and public institutions such as the

International Energy Agency (IEA) consistently demand additional policy support to achieve their

widespread adoption and deployment (IEA, 2016a). On the other hand, leaders of global

automotive companies like Dieter Zetsche, Daimler CEO, argue that financial purchase incentives

are the wrong means for grasping customers’ attention and improving their acceptance of EVs

(Spiegel, 2017). Recent studies, in fact, also support this opinion (e.g., Egbue and Long, 2012;

Green et al., 2014). Our study aims to contribute to this ongoing debate by evaluating preferences

of different potential-adopter segments regarding a variety of policy measures that have been

proposed, as well as implemented. Specifically, the study pays attention to how these policy

measures influence the behavior of EV adopters.

To achieve these objectives, our study examines the joint effect of different socio-

demographic characteristics, psychological characteristics, as well as EV policy incentives on the

adoption of EVs. Further, we conduct cluster analysis to identify different sub-groups of potential

adopters according to distinct socio-demographic and psychological profiles. We use data

collected from a sample of 1,000 respondents representative of the Austrian population during the

final months of 2016. Our findings suggest that psychological factors, in contrast to socio-

demographic ones, play a significant role in explaining differences between different adopter

segments and are, hence, the better predictors of early EV adoption. Furthermore, we argue that

policy incentives reduce the amount of time it takes potential EV adopters to actually purchase an

Paper 1 36

EV, but do not similarly influence ICE fans. In addition, we find four main segments of potential

adopters of EVs, distinguished on the basis of significant differences in their attitude towards EV

purchasing and non-purchasing motives, as well as their policy incentive preferences.

2. THEORY AND HYPOTHESES

In the past few years, a growing body of literature has focused on people’s general

perception of EVs (Egbue and Long, 2012; Krupa et al., 2014; Peters and Dütschke, 2014; Plötz

et al., 2014; Schuitema et al., 2013). More recently, researchers have also begun to analyze the

profiles of actual adopters of EVs (Axsen et al., 2016; Hardman et al., 2016; Tal and Nicolas,

2013) who still represent a very small group (<0.1%) of the total car owner population globally

(IEA, 2016b). Only a few studies have compared early adopters and other potential EV adopter

groups to identify differences between the groups, and predictors of early EV adoption. Initial

insights suggest that socio-demographic and psychological factors, as well as policy incentives

significantly influence EV adoption (Axsen et al., 2016; Nayum et al., 2016; Sierzchula et al.,

2014). In his seminal work, Stern (2000) argues that personal characteristics (e.g. socio-

demographic characteristics), attitudinal factors (e.g. various psychological factors) or habits, and

contextual forces (e.g. government-implemented regulations or incentives) induce pro-

environmental behavior and eventually facilitate the adoption of high-cost products. Therefore, in

the following, we review the relevant literature in more detail and derive hypotheses regarding

socio-demographic, psychological, and contextual variables such as EV policy incentives that

function as predictors for EV adoption.

2.1 Socio-demographic characteristics

Studies consistently find that early EV adopters have demographic and socio-economic

characteristics that clearly distinguish them from potential EV adopters or non-adopters (i.e., from

ICE car buyers). At present, the literature suggests that actual or early adopters (depending on the

definition) typically (1) are highly educated (Nayum et al., 2016; Plötz et al., 2014; Tal and

Nicolas, 2013), (2) have higher incomes (Axsen et al., 2016; Carley et al., 2013; Nayum et al.,

2016; Plötz et al., 2014; Tal and Nicolas, 2013), (3) are young to middle aged (Hidrue et al., 2011;

Nayum et al., 2016; Plötz et al., 2014) (4) live in multi-car households (Klöckner et al., 2013;

Nayum et al., 2016; Peters and Dütschke, 2014; Tal and Nicolas, 2013), (5) live in larger

Paper 1 37

households (Nayum et al., 2016), (6) are predominantly male (Plötz et al., 2014), and (7) live in

small- to medium-sized municipalities (Plötz et al., 2014).

However, as predictors of EV purchasing these socio-demographic features need to be

interpreted with caution, as contradictory findings exist. For instance, Hidrue et al. (2011) were

not able to confirm higher income and multiple car ownership as key characteristics of early EV

adopters. Further, including additional variables such as psychological ones, the predictive value

of socio-demographic features on consumers’ intention to purchase environmentally friendly cars

has been found to be significantly lower (Nayum and Klöckner, 2014). These findings are

corroborated by general research in the field of environmental behavior, with several authors

finding socio-demographic variables to have minimal explanatory value for most environmental

behaviors (Kilbourne and Beckmann, 1998; Leonidou et al., 2010). Even so, in order to develop a

comprehensive theoretical model, we also include socio-demographic variables and thus posit the

following hypothesis:

Hypothesis 1. The socio-demographic characteristics (a) gender (being male), (b)

education, (c) income, (d) household size, (e) number of cars per household, and (f)

dwelling dispersion in the area of residency are positively related, and (g) age is negatively

related to the adoption of EVs.

2.2 Psychological characteristics

As mentioned above, some research has presented evidence pointing to psychological

variables (e.g. values, attitudes, norms, etc.) as important determinants of consumers’ uptake of

cars with emergent, cleaner technologies (Heffner et al., 2007; Jansson et al., 2011; Kahn, 2007;

Lane and Potter, 2007; Ozaki and Sevastyanova, 2011). A recent study by Nayum et al. (2016)

tests the relevance of psychological variables as predictors of consumers purchasing more

environmentally friendly vehicles such as EVs. In their paper, they build on Klöckner and

Blöbaum’s (2010) “comprehensive action determination model,” which incorporates intentional,

normative, situational, and habitual influences on environmentally friendly behavior. Overall, they

conclude that “for the design of effective measures to promote cars with emergent technologies,

psychological characteristics of different target groups need to be addressed alongside economic

factors” (Nayum et al., 2016: 8). Regarding psychological factors predicting early adoption of

EVs, these scholars were able to confirm an environmentally friendly attitude, a positive attitude

toward EVs, and high behavioral control as significant EV purchase predictors. The effect of social

Paper 1 38

and personal norms was found to be non-significant (Nayum et al., 2016). Other studies also found

that early adopters of EVs are more environmentally concerned (Carley et al., 2013), follow a

“green lifestyle” (Hidrue et al., 2011), have strong pro-environmental attitudes (Wolf and

Seebauer, 2014), and are highly engaged in an environment-oriented lifestyle (Axsen et al., 2016).

Concerning attitudes towards EVs, it is argued that people with an interest in new technologies

and living a technology-oriented lifestyle have a more positive attitude towards EVs (Axsen et al.,

2016; Wolf and Seebauer, 2014). Egbue and Long (2012) further show that people with a high

interest in technology development and who are aware of the differences between EVs and cars

with ICEs, are more likely to be early adopters of EVs. Based on these insights, we posit the

following:

Hypothesis 2. (a) Pro-environmental and (b) pro-technological attitudes are positively

related to the adoption of EVs.

Existing EV adotpion literature has, to date, neglected one set of psychological variables,

notably those referred to as “cultural worldviews.” Thus, their effect on the intention to purchase

an EV, has similarly received limited attention. Cultural worldviews can be described as

overarching guidelines in a sense-making system (Hedlund-de Witt, 2012). Theorizing in this

scholarly domain builds on the cultural theory of risk (Douglas and Wildavsky, 1983). The central

idea in literature on cultural worldviews is that people form their preferences for complex topics

through cultural cognition, which serves to maintain their cultural worldviews. In essence this

argumentation suggests that individuals use biased forms of information processing to promote

their “interests in forming and maintaining beliefs that signify their loyalty to important affinity

groups” (Kahan, 2013: 407).

Kahan et al. (2011) distinguishes four cultural worldview typologies categorized on two

distinct dimensions, namely hierarchism vs. egalitarianism, and individualism vs.

communitarianism. A person identified as egalitarian would perceive risks with regard to new

technologies to be greater than the benefits. Hierarchism and individualism, by contrast, are

positively correlated with lower risk perception regarding personal risk (e.g. car driving), or

environmental threats, and are similarly positively associated with technological risk-taking

(Wildavsky and Dake, 1990). Previous research has shown that devotion to one or another cultural

worldview predicts opposition to some and acceptance of other issues which include the disposal

of nuclear waste, national security, possession of weapons, public health, and climate change

Paper 1 39

(Kahan et al., 2007; Kahan et al., 2011). The concept of cultural worldviews has proven to be

highly suitable for predicting individual differences regarding environmental attitudes, sustainable

lifestyle choices, climate change perceptions, and the acceptance of renewable energy technologies

(Corner et al., 2012; Hedlund-de Witt, 2012; Kahan et al., 2012; Kahan, 2013; Sposato and Hampl,

2018). For instance, studies have demonstrated that individuals adjust their perceptions of climate

change (e.g. Kahan et al., 2012) and even climate change mitigation policies (Hart and Nisbet,

2012) in accordance with their worldviews.

There is, however, scant information on the links between cultural worldviews and the

acceptance of clean, low-carbon transportation technologies. Cherry et al. (2014) studied the

impact hierarchical and individualist worldviews have on the acceptance of government-sponsored

research projects on clean technologies (e.g. wind power and carbon capture/storage). They found

that both hierarchism and high individualism are associated with less support for research on low-

carbon technologies.

Considering the effect of cultural worldviews on topics related to climate change, as well

as on the acceptance of clean technologies as described above, the present study aims to investigate

whether cultural worldviews can serve as a predictor of respondents’ EV purchase intentions. We

expect to find:

Hypothesis 3. (a) Individualistic and hierarchical worldviews are negatively associated,

and (b) communitarian and egalitarian worldviews are positively associated with the

adoption of EVs.

2.3 Contextual factors: EV policy incentives

A wide array of different policy measures have been designed to increase the appeal of

EVs (e.g. Lieven, 2015; Sierzchula et al., 2014). In this context, both purchase-based and use-

based incentives are employed. Examples of the former incentives are subsidies granted upon

purchasing the EV, or tax rebates that come into effect when the EV is registered. Examples of

use-based incentives include privileges for EV drivers, such as waiving parking fees or emission

charges, or permission to use bus lanes. Both of these policy incentive types have been proven to

support the sales-uptake of EVs (Langbroek et al., 2016; Mannberg et al., 2014; Sierzchula et al.,

2014). Langbroek et al. (2016) even argue that there are no unambiguous preferences for either

purchasing or user-oriented incentives among potential EV customer segments.

Paper 1 40

However, other scholars argue that incentives do little to influence potential or non-

adopters who lack conviction with regard to the technology (Egbue and Long, 2012; Green et al.,

2014) or who have not perceived a satisfactory number of EVs in their environment (a threshold

effect) yet (Eppstein et al., 2011). Zhang et al. (2013) in fact confirm a weak influence of monetary

policy incentives on potential EV adopters’ willingness to buy EVs. Others, like Frey and Stutzer

(2006), observe that extrinsic incentives provided by the government eliminate the “intrinsic

motivation” associated with purchasing an eco-friendly vehicle. Nayum et al. (2016) also tend to

attribute potential EV buyers’ low level of social and personal norms to government incentives

and policies. However, they also add that policies and regulations related to environmental

behavior caused positive changes in general public attitudes toward purchasing more fuel-efficient

cars. Turcksin et al. (2013) concluded that a strong government policy is necessary to facilitate the

proliferation of cars with emergent technologies due to the generally low inherent motivation of

car buyers. Although the existing literature reports mixed findings, we expect to find a positive

effect of policy incentives on EV adoption and therefore formulate the following hypothesis:

Hypothesis 4. EV policy incentives have a positive effect on the adoption of EVs.

3. METHODOLOGY AND DATA

3.1 Sample

An online survey of public perceptions, preferences, and willingness to invest regarding

renewable energy and other low-carbon technologies across the Austrian population, was

conducted in October/November 2016 (n=1,000). The data was collected by an external market

research company (meinungsraum.at). In 2016 Austria had the highest growth rate (128%) and the

highest share of new registrations (1.2%) of EVs in the European Union (Electric Vehicle World

Sales Database, 2017). A study on attitudes to and perceptions of EVs therefore represents a highly

interesting case. Thus, a sub-section of the questionnaire focused on participants’ attitudes towards

EVs and related policy incentives, and on their willingness to purchase EVs. In terms of gender,

age, reported net income per household, and distribution of citizens across the Austrian federal

states the sample we collected matches the Austrian population. Our sample deviates from the

Austrian population overall only in terms of educational and household income level, in that our

respondents show a slightly higher education level and lower household income than the Austrian

average (Statistik Austria, 2017b) (see Table A.1 in the appendix).

Paper 1 41

3.2 Questionnaire and measures

The survey instrument contained a variety of items and measurement scales, of which only

the ones relevant to the study presented here will be described in more detail below (see Table 1

for an overview of all predictor variables across the three adopter segments).

The survey asked participants for details on their current car ownership (e.g. type, age,

company car or not, used or new car, type of engine, etc.), their general preference to purchase an

EV as their next car (yes/no), and, in case of no such immediate preference, their general intention

to purchase an EV on a 4-point Likert-type scale ranging from 1 (not likely at all) to 4 (very likely).

Based on this information, respondents were categorized into three groups (willingness-to-

purchase, WTP; dependent variable):

• Early Adopters (16%): already purchased an EV or intend to buy an EV as their

next car. 22

• Potential Adopters (33%): stated an interest in purchasing an EV, but not as their

next car.

• Non-Adopters (51%): stated preference for an ICE car to an EV and, at least at the

time of our survey, had no intention to purchase an EV.

Regarding this categorization, it should be noted that 17% of the respondents at the time

did not own a car, but two thirds of this sub-group were in the group of early and potential adopters

(at equal proportions per group of approx. 23%). We included these in the analysis since

respondents had the option to indicate “Other – No need for a car”, but chose an EV as potential

future car instead. Further, this group of non-car owners is on average almost 4.1 years (for early

adopters) and 3.9 years (for potential adopters) younger than those in the same segments currently

owning a car. Their younger age could additionally indicate that these respondents currently do

not need or cannot afford a car, but might intend to purchase an EV in the future.

Using purchase intentions as segmentation criteria carries some limitations (e.g.,

Carrington et al., 2010). However, since EV penetration is still relatively low, with approx. 1% of

all newly registered cars being EVs (Statistik Austria, 2017a), an analysis of actual EV buyers is

practically impossible relying on a sample like the one collected here. We consider the stated

22 In line with the representative character of the sample we collected, we only identified 1% as current EV and hybrid

EV owners. Since the size of this sub-sample does not allow for a meaningful statistical evaluation, we combined this

group with the people intending to purchase an EV as their next car and labeled them “Early Adopters,” following

Rogers (2003) who referred to the first approx. 16% of a population opting for a new technology as “Early Adopters”

(including 1-3% of Innovators).

Paper 1 42

purchase intention or preference for an EV as an acceptable starting point to study EV adoption

on a population level. This is common practice in studying the acceptance of new products on the

market (cf. Kim et al., 2015; Plötz et al., 2014; Schmalfuß et al., 2017; Schuitema et al., 2013).

Nevertheless, we do reflect on these concerns in discussing the study results and their limitations

in section 5.1.

To allow a comparison between the different EV adopter segments, our survey collected

information on socio-demographic variables such as gender, age, educational level, income,

number of people per household, dwelling dispersion in respondents’ post code area, and the

number of cars per household. The contextual variable EV policy incentives was included in the

model on the Austrian federal state level and was determined based on the participants’ postcode

. The survey further asked questions designed to measure the respondents’ cultural worldviews,

pro-environmental, and pro-technological attitudes.

Cultural worldviews wereere measured building on work by Kahan et al. (2007, 2011)

using a reduced scale, which included six items that were chosen based on their applicability to an

Austrian cultural context. The chosen items included statements participants were expected to

respond by selecting options on a 4-point Likert-type scale ranging from 1 (strongly disagree) to

4 (strongly agree). These statements were of the following kind: “The government interferes far

too much in our everyday lives.” (individualism-communitarianism); “Our society would be better

off if the distribution of wealth were more equal.” (egalitarianism-hierarchism). Responses to the

various items were averaged, to indicate that e.g., a higher score on the individualism-

communitarianism questions expressed a more individualistic cultural worldview, or a higher

score on the egalitarianism-hierarchy questions expressed a more egalitarian cultural worldview.

Reliability of the scale for individualism-communitarianism (“Individualistic worldview”) was α

= .55 and for egalitarianism-hierarchism (“Egalitarian worldview”) α = .50. 23.

Pro-environmental attitude (α = .90) was measured as environmental identity relying on

three items suggested by Whitmarsh and O'Neill (2010). The scale includes items in the form of

statements such as “Being environmentally friendly is an important part of my personality” which

had to be rated on a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly

agree).

23 These relatively low Cronbach-Alpha scores might be interpreted as an indication that the particular cultural

worldview items are limitedly applicable in the Austrian cultural context. Yet, these values are in line with results

from other scholars that applied the cultural cognition scale in their work in a non-US context (Capstick and Pidgeon,

2014). In any case, the results of cultural worldview variables need to be interpreted cautiously. We discuss this

drawback in more detail in section 5.1.

Paper 1 43

Pro-technological attitude (α = .80) was operationalized as participants’ agreement or not

with seven statements, again on a 4-point Likert-type scale ranging from 1 (strongly disagree) to

4 (strongly agree). The statements, generated in expert discussions on a general attitude toward

digitization, were ones such as “I see digitization as an opportunity to better connect objects of

daily life.” The responses for this variable, as well as for the pro-environmental attitude, were

averaged and can be interpreted with the same logic as for cultural worldviews (see Table A.8 in

the appendix for a summary and details on the psychological variables).

Additionally, the questionnaire included questions which asked respondents to evaluate

motives for or against an EV purchase. These responses were used as input for the cluster analysis

(see Tables A.2 and A.3 in the appendix). Purchasing and non-purchasing motives for EVs were

measured with items derived from the literature (e.g. Axsen et al., 2013; Carley et al., 2013; Egbue

and Long, 2012; J.D. Power & Associates, 2010; Schuitema et al., 2013) and tailored to the specific

local context. Respondents were asked to rate the importance of various EV purchasing motives

such as an EV being “emission-free” and “a status symbol”, as well as purchasing barriers such as

“range of the electric vehicle is too low” and “EVs are too expensive.” We used a 5-point Likert-

type scale ranging from 1 (not important at all) to 5 (very important). Further, we asked

respondents to rate the attractiveness of different EV policy measures on a scale ranging from 1

(not attractive at all) to 5 (very attractive). The EV policy incentives such as “purchase premium

as a subsidy to the acquisition costs of an electric vehicle,” “exemption from certain maturity

payments,” “free parking in downtown area,” etc. were derived from literature (Langbroek et al.,

2016; Sierzchula et al., 2014) and industry reports (see Table A.7 in the appendix for an overview).

TABLE 1: DESCRIPTION OF VARIABLES (IN MEANS OR PERCENTAGES) FOR TOTAL SAMPLE AND

ADOPTER GROUPS

Variables Variable code

Total

sample

Early

adopters

Potential

adopters

Non-

adopters

No. of respondents 1,000 163 325 512

Willingness-to-purchase 1=early adopter

2=potential adopter

3=non-adopter

1.56 1 2 3

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%

3=high school 25.1% 24.5% 27.7% 26.8%

4=university 24.2% 29.4% 25.8% 20.1%

Paper 1 44

Household size Number of people per

household

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 car 36.0% 30.1% 40.3% 35.2%

Dwelling dispersion 1=city >100k 36.9% 38.0% 39.4% 35.0%

2=town 10-100k 32.9% 33.1% 30.1% 34.5%

3=municipality <10k 30.2% 28.8% 30.5% 30.5%

Psychological variables

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

Individualistic

worldview

e.g., “The government

interferes far too much in

our everyday lives.”

2.84 2.66 2.85 2.91

Egalitarian

worldview

e.g., “Our society would

be better off if the

distribution of wealth

were more equal.”

3.11 3.30 3.17 3.01

Contextual variable

EV policy incentive1 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%

1 Percentage of respondents that live in federal states with/without EV policy incentives.

3.3 Data analysis

We performed a multinomial logistic regression (MLR) to test whether the socio-

demographic, psychological, and context variables (as predictors) are related to the willingness to

purchase an EV (dependent variable). Our choice for this statistical analysis was determined by

the ability of MLR to handle categorical variables for both dependent as well as independent

variables (Backhaus et al., 2016).

In essence, MLR is used to calculate the likelihood of a certain event (in our case

purchase/intention to purchase an EV) happening by fitting survey data to a logistic curve. The

probability of occurrence πg (xk) is then determined by xk (xk = x1k, x2k,…; xjk, = is the number

of values of j independent variables/predictors) and can be expressed as follows (Backhaus et al.,

2016):

𝜋𝑔 (𝑥𝑘) = 𝑃𝑟𝑜𝑏 (𝑌𝑘 = 𝑔|𝑥𝑘) (g=1,….G)

Paper 1 45

The model of the MLR can be simplified to be formulated as:

𝜋𝑔 (𝑥) =𝑒𝛼𝑔+𝛽𝑔1𝑥1+⋯+𝛽𝑔𝐽𝑥𝐽

∑ 𝑒𝛼ℎ+𝛽ℎ1𝑥1+⋯+𝛽ℎ𝐽𝑥𝐽𝐺ℎ=1

To measure the robustness of this MLR-model, three criteria were used (Field, 2009). As

first criterion we considered the fit of the model (i.e., whether the current model is significantly

better at predicting than the baseline model, which is a model with no predictor variables and

solely the intercept term) as measured by the chi-square statistics of a likelihood ratio test. As

second criterion we used the goodness of fit for the data as measured e.g. through Pearson and

Deviance statistics. This measure tests whether the predicted values differ significantly from the

observed values. Third, instead of using the R-squared value for explaining the proportion of the

variance by the model which is used in linear regression, a so-called pseudo R-squared value (Cox

and Snell; and Nagelkerke) is used as criterion in MLR.

In addition to this logistic regression analysis, we used an exploratory cluster analysis to

identify the extent to which potential adopters can be segmented. There are multiple clustering

approaches, which all aim to combine different cases in similar groups (Backhaus et al., 2016;

Bühl, 2014). For this study, we followed a two-step approach to cluster cases into groups with

minimized intra-cluster variance. First, we applied an exploratory factor analysis to the high

number of different EV purchasing and non-purchasing motives using the sub-sample of potential

adopters (n = 325). To determine the number of factors, we used several criteria such as the Kaiser

criterion with eigenvalue greater than 1, the KMO index, a measure of explained variance, and a

scree plot (Field, 2009; Michelsen and Madlener, 2012).

Then, these factors served as input for the cluster analysis which was conducted in two

steps as proposed by Punj and Stewart (1983). First, a hierarchical cluster analysis was performed

to determine the optimal number of clusters including its cluster centroid means based on a

dendrogram analysis and the agglomeration schedule. Next, these two points of information served

as input variables for the k-means cluster analysis. This approach was recommended, because k-

means clustering creates solid cluster solutions, if the starting position is known and the cluster

number is fixed (Punj and Stewart, 1983). Furthermore, k-means clustering has the advantage of

being able to manage large sample sizes (i.e., N > 100), it creates relatively homogenous groups,

and hence presents a more robust alternative to the other hierarchical clustering methods (Punj and

Stewart, 1983).

To cross-check the optimal number of clusters derived from the hierarchical approach, a

range of additional number of cluster solutions were calculated in the k-means approach and then

Paper 1 46

analyzed using the following set of criteria: interpreting the solutions clearly, avoiding solutions

with disproportionally large classes (e.g. greater than 50% of the sample), avoiding solutions with

very small classes (e.g. less than 10% of the sample), avoiding solutions where two or more classes

are essentially identical (Mooi and Sarstedt, 2011), and comparing Kappa-values across the range

of cluster solutions (McIntyre and Blashfield, 1980).

Additionally for validation purposes, clusters were validated by creating two sub-samples

(randomly splitting the sample) and then comparing two cluster solutions for consistency with

respect to the number of clusters and cluster profiles (Mooi and Sarstedt, 2011). Finally, segments

were characterized along a set of socio-demographic and psychological variables and compared

along respondents’ preferences for policy incentives using ANOVA and Chi-Squared tests.

47

TABLE 2 CORRELATION OF VARIABLES

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13

1. WTP 1

2. Gender -.05b 1

3. Age -.04b -.17** b 1

4. Education .07* b .01 b -.09** b 1

5. Dwelling dispersion .03 b -.00 b .02 b -.06 b 1

6. Income -.01 b -.17** b -.04 a .20** b .08* b 1

7. # of cars per household -.05* b -.05 b -.08* b .04b .39** b .41** b 1

8. Household size .03 b -.07* b -.04 a .01 b .17** b .29** a .37** b 1

9. Pro-technological attitude .16** b -.01 b -.04 a .08* b -.04 b .02 a -.05 b .02 a 1

10. Pro-environmental attitude .21** b .08* b -.06* a .03 b .05 b .02 a -.07* b -.02 a .12** a 1

11. Individualistic worldview -.11** b .03 b .05 a -.19** b .04 b -.04 a .01 b .02 a .09** a .06 a 1

12. Egalitarian worldview .17** b .11** b .02 a -.01 b .03 b .08** a -.11** b -.03 a .21** a -.32** a -.09** a 1

13. EV policy incentive .00 b -.03 b .05 b -.04 b .52** b .04 b .24** b -.07** b -0.3 b -.02 b .01 b .01 a 1

Note: N = 1,000; a=Pearson Correlation, b=Spearman Correlation.

* Correlation significant at p < 0.05 (two sided); ** Correlation significant at p < 0.01 (two sided).

Paper 1 48

4. RESULTS

4.1 Predictors of EV adoption

As starting point for the data analysis, we checked the correlation matrix of all variables

included in the MLR (Table 2). In contrast to most socio-demographic variables, psychological

variables (9-12) show significant correlations with the dependent variable willingness to

purchase (1).

Next, we ran MLR to predict each respondent’s membership of one of the adopter

segments early adopters, potential adopters, and non-adopters of EVs by using socio-

demographic, psychological, and context characteristics of the respondents. Several statistical

measures for the fit of the MLR model (Field, 2009), as well as the model’s ability to predict the

dependent variable, are summarized in Table 3. In a nutshell, these figures illustrate that the

empirical data fit the model reasonably well and that the MLR model predicts observed

classifications of respondents considerably better than pure chance would. Hence, we concluded

that the factors selected for this MLR model are among the relevant explanatory factors for

predicting actual or intended EV adoption.

TABLE 3: MEASURES FOR MODEL FIT AND ABILITY TO PREDICT DEPENDENT VARIABLES

Model fit measures Value Note

Model fitting information Chi2 = 165.65

d.f. =28; p =.00

In case of significance (p< 0.05), the current

model is better at predicting than the baseline

model (i.e., a model with no predictor variables

and solely the intercept term (Fields, 2009)).

Goodness of fit

In case of non-significance (p> 0.05), the

Pearson or Deviance test indicates that a proper

model fit exists (Fields, 2009).

Pearson Chi2 = 2059.33

d.f. = 1970; p =.08

Deviance Chi2 = 1841.76

d.f. = 1970; p =.98

Pseudo R-Square Estimate is comparable to R-Square for linear

regression, but cannot be interpreted as actual

share of variance explained.

Cox and Snell .153

Nagelkerke .176

Classification accuracy .562

Model performs better than chance.

Paper 1 49

Table 4 and Table 5 summarize the results of the MLR analysis. For each variable the

logistic regression coefficients (b), the standard error (SE), the significance level (p), and the

odds ratio with confidence interval (CI) are presented. As a reference category to distinguish

between the three segments in our study, we chose the early adopters group. The predictive power

and strength of the variables in the model can be assessed by the odds-ratio. This measure is an

indicator for the change in likelihood triggered by the alteration of one unit in one of the

independent variables (Backhaus et al., 2016; Urban, 1993). We discuss the interpretation of

Tables 4 and 5 in the next couple of paragraphs.

Socio-demographic factors seem to play a marginal role in predicting the willingness to

purchase, i.e., the EV adopter segments. We do not find evidence in our sample that better

education (H1b) (b = -0.11, Wald Chi2 (1) = 1.54, p >.05), higher income (H1c) (b = 0.03, Wald

Chi2 (1) = 0.65, p >.05), younger age (H1g) (b = 0.02, Wald Chi2 (1) = 0.42, p >.05), or living

in a small to medium-sized municipality (H1f) (b = 0.41, Wald Chi2 (1) = 1.02, p >.05/b = -0.12,

Wald Chi2 (1) = 1.35, p >.05) distinguishes between non-adopters and early adopters, nor

between potential and early adopters of EVs (see Table 5 for statistical details).

However, the other socio-demographic variables (gender, household size, number of cars)

do have some predictive power. First, the number of people living in a household seems to have

a positive impact as predictor of potential EV adoption compared to early EV adoption (b = 0.24,

Wald Chi2 (1) = 5.89, p <.05), but not as indicator that distinguishes between non-adopters and

early adopters (b = 0.04, Wald Chi2 (1) = 0.08, p >.05). If the number of people in a household

increases, the odds that an individual from this household will be a potential adopter rather than

an early adopter, increase by 27%. In other words, people living in a smaller household are more

likely to be early EV adopters than potential EV adopters. This finding is interesting due to its

predictive power, but contradicts our hypothesis that early adopters live in larger households

(H1d not supported). Second, the number of cars owned by a household also predicts adopter

segment membership, but in a less intuitive way, which contradicts current literature (e.g.,

Nayum et al., 2016b). Our results suggest that a person who currently lives in a household with

no car, is significantly less likely to be a non-adopter (b = -0.97, Wald Chi2 (1) = 5.79, p <.05)

or potential adopter (b = -0.66, Wald Chi2 (1) = 2.98, p <.05) than an early adopter. In other

words, current non-car-owners are in favor of electric cars, and ownership of more than one car

is not a significant predictor of early adoption. Hence, the hypothesis that early adopters have

more than one car and use EVs as a second car is also not confirmed (H1e not supported). Third,

our analysis confirms that men are less likely to be non-adopters (b = -0.41, Wald Chi2 (1) =

Paper 1 50

7.82, p <.05) or potential adopters (b = -0.36, Wald Chi2 (1) = 3.87, p <.05) than early adopters

(H1a supported).

Psychological variables seem to be better predictors of respondents’ willingness to

purchase an EV in the future. Concerning a person’s cultural worldview, more individualistic (b

= 0.79, Wald Chi2 (1) = 18.38, p <.001) and less egalitarian (b = -0.35, Wald Chi2 (1) = 4.11, p

<.01) perspectives are significant predictors of non-adoption rather than of early adoption.

Similarly, car users that are less pro-environmental (b = -1.04, Wald Chi2 (1) = 19.70, p <.001)

and less convinced of the benefits of new technologies (b = -0.40, Wald Chi2 (1) = 11.91, p <.01)

are more likely to be non-adopters than early EV adopters. Our analysis also shows that

psychological characteristics are less useful in predicting membership of potential adopter versus

early adopter segments. People of both these segments show no significant difference in their

pro-technological attitudes (b = -0.13, Wald Chi2 (1) = 1.07, p >.05) and egalitarian worldviews

(b = -0.06, Wald Chi2 (1) = 0.07, p >.05). However, a higher pro-environmental attitude (b = -

0.32, Wald Chi2 (1) = 4.55, p <.05) and less individualistic worldview (b = 0.46, Wald Chi2 (1)

= 6.12, p<.01) increase the odds of being an early EV adopter compared to a potential adopter.

Overall, we can thus confirm hypotheses 2a (pro-environmental attitudes), 2b (pro-technological

attitudes) and hypotheses 3a and 3b (cultural worldviews).

Regarding the effect of existing EV policy incentives, our analysis shows mixed results.

On the one hand, a person living in a region offering no subsidies is 63% more likely to be a

potential than an early adopter (b = 0.49, Wald Chi2 (1) = 4.54, p <.05). On the other hand, this

context variable is non-significant for differentiating between non-adopters and early adopters (b

= 0.19, Wald Chi2 (1) = 0.93, p >.05). This finding thus only partially confirms our hypothesis

4, which assumes that policy incentives have a positive effect on the adoption of EVs.

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TABLE 4: MULTINOMIAL LOGISTIC REGRESSION OUTPUT: NON-ADOPTERS VS. EARLY ADOPTERS

95% CI for Odds Ratio

Non-Adopters vs. Early Adopters b (SE) Lower

Odds

Ratio Upper

Constant 4.65 (1.10)

Age .02 (.01) .99 1.00 1.01

Education -.11 (.11) .72 .90 1.12

Household size .04 (.10) .86 1.04 1.26

Income .03 (.02) .98 1.00 1.02

Gender (male) -.41 (.20) * .45 .50 .98

# of cars per household = 0 -.97 (.32) * .20 .38 .72

# of cars per household = 1 .03 (.24) .65 1.03 1.66

Dwelling dispersion: city = 1 .41 (.30) .83 1.50 2.72

Dwelling dispersion: town = 2 -.12 (.23) .71 1.12 1.76

Pro-technological attitude -.40 (.17) ** .48 .67 .94

Pro-environmental attitude -1.04 (.19) *** .25 .35 .51

Individualistic worldview .79 (.18) *** 1.54 2.21 3.16

Egalitarian worldview -.35 (.17) ** .50 .71 .99

EV policy incentives=0 .19 (.24) .76 1.21 1.91

Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.

TABLE 5: MULTINOMIAL LOGISTIC REGRESSION OUTPUT: POTENTIAL ADOPTERS VS. EARLY

ADOPTERS

95% CI for Odds Ratio

Potential Adopters vs. Early Adopters b (SE) Lower

Odds

Ratio Upper

Constant .84 (1.16)

Age -.01 (.01) .98 1.00 1.01

Education -.04 (.11) .77 .97 1.20

Household size .24 (.10) * 1.05 1.27 1.55

Income .05 (.02) .94 .99 1.05

Gender (male) -.36 (.21) * .47 .70 .98

# of cars per household = 0 -.66 (.33) * .27 .52 .99

# of cars per household = 1 -.25 (.25) .48 .78 1.26

Dwelling dispersion: city = 1 .12 (.31) .62 1.13 2.08

Dwelling dispersion: town = 2 -.16 (.24) .53 .85 1.35

Pro-technological attitude -.13 (.18) .62 .88 1.25

Pro-environmental attitude -.32 (.19) * .50 .73 .99

Individualistic worldview .46 (.19) ** 1.10 1.59 2.28

Egalitarian worldview -.06 (.18) .66 .94 1.33

EV policy incentive=0 .49 (.24) * 1.02 1.63 2.62

Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.

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4.2 Sub-segments of potential EV adopters

As described in section 3.3, we followed Punj and Stewart (1983) in applying two

consecutively linked cluster analyses (hierarchical and k-means clustering), using standardized

factor scores of purchasing motives and non-purchasing motives for EVs (see Tables A.2 and

A.3 in the appendix) as input variables (Green and Krieger, 1995).

The hierarchical clustering approach identified four clusters as the optimal solution based

on an explorative dendrogram and agglomeration schedule analysis which we then used as input

for the k-means clustering approach. The Kappa-value of 83% (see Table 6) confirms the validity

of the four-cluster solution, which reaches its maximum at four clusters and decreases for a higher

number of clusters (McIntyre and Blashfield, 1980). Further, solutions with more than four

clusters resulted in clusters with smaller cluster sizes (i.e., less than 10%) which is not advisable

for statistical testing (Mooi and Sarstedt, 2011).

TABLE 6: KAPPA-VALUES FOR K-MEANS CLUSTER SOLUTIONS

Number of clusters 2 3 4 5 6

Kappa-value 0.32 0.68 0.83 0.81 0.43

Hence, potential adopters (n=325) were grouped into four segments. Table 7 qualitatively

summarizes these profiles, depending on the potential adopters’ attitudes to purchase and non-

purchase arguments (the cluster centroid means are presented in Table A.4 in the appendix).

Furthermore, we applied ANOVAs and Chi-Squared tests to test for statistically significant

differences between these groups regarding socio-demographic characteristics, psychological

characteristics, and preferences for policy incentives (see Tables A.5, A.6, and A.7. in the

appendix).

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TABLE 7: FOUR PROFILES OF POTENTIAL ADOPTERS BASED ON CLUSTER ANALYSIS A

Low purchase motives High purchase motives

High non-

purchase

motives

Rural Non-Techs (35%): More likely to be better

educated, live in rural areas, and have a higher

income, as well as more than one car.

Preference for purchase-based incentives.

Undecided Individualists (27%): Lower

educational level, more likely older and from

rural areas, and have a more individualistic

worldview.

High preference for any kind of policy incentive.

Low non-

purchase

motives

Undiscerning Urbanites (16%): More likely to

live in an urban area without a car, tend to be

younger and better educated, and show a less

strong environmental identity, as well as

technological interest.

No real preference for any incentives.

Urban EV Supporters (22%): More likely male

and better earning car drivers in the city.

Moderate preference for purchase and use-

based incentives, similar to early adopters.

a The profile characteristics are derived from statistical testing between the segments and are summarized in

simplified form in this table. For full comparison details see the description in section 4.2 or Tables A5, A6, A7 in

the Appendix.

Rural Non-Techs (n=113): This sub-group is the largest of the four. It is composed of

individuals who apparently respond strongly to structural product barriers such as price, driving

range, etc., and are less fascinated by the technological charm of EVs. Members of this potential

adopter sub-group are more likely better educated than the Undecided Individualists and Urban

EV Supporters and fall in a higher income bracket compared to the segments Undecided

individualists and Undiscerning Urbanites. In addition, they tend to have more than one car, and

are more likely to live in a rural area compared to the other three segments. In terms of

enticement, they prefer purchase-based incentives such as purchase subsidies, tax and fee cuts to

use-orientated incentives.

Undecided Individualists (n=89): Members of this potential adopter sub-segment achieve

high scores in their evaluation of purchase and non-purchase arguments. They exhibit an above-

average evaluation of general benefits, as well as technological benefits of an EV. However, this

is slightly overshadowed by the perception that EVs face structural and attitudinal barriers, which

unsurprisingly, is accompanied by skepticism. This group seems undecided about whether the

drawbacks of EVs outweigh their benefits. Considering the statistically significant differences in

individuals’ characteristics, we discovered that potential adopters in this sub-segment are less

educated, but older than Undiscerning Urbanites, are more likely to live in a rural area compared

to Undiscerning Urbanites/Urban EV Supporters, and have a more individualistic worldview

than the other three segments. In agreement with their attitudes, regarding purchasing and non-

Paper 1 54

purchasing motives these respondents are highly interested in any kind of incentive (purchase-

and use-oriented).

Undiscerning Urbanites (n=52): This sub-segment of potential adopters values the

environmental and operating benefits of EVs less than the other potential adopter segments, and

has below-average passion for the technological benefits of EVs. On the other hand, they are not

perturbed by structural and product-related barriers, and their perception of attitudinal barriers

conforms to the average of the potential adopter segment. Their attitude could be explained by

the fact that many of these adopters do not own a car yet, tend to be younger than the EV Urban

Supporters and Undecided Individualists, are more educated than Undecided Individualists, and

live in more urban areas than Rural Non-Techs and Undecided Individualists. Also, they have a

less egalitarian worldview than the other segments. Their low interest in EVs is also reflected in

a low pro-environmental attitude and low technological interests compared to the other three

segments. Further, they perceive EV benefits which are related to environmental and operational

benefits as less attractive. Their interest in policy incentives is very low, irrespective of the type

of incentive (purchase-/use-based) and comparable to the evaluation level of non-adopters.

Urban EV Supporters (n=71): This segment is very positive about the general and

technological benefits of an EV, and they evaluate EV structural and attitudinal barriers as low.

This group’s members are more likely to live in urban areas compared to Rural Non-Techs and

Undecided Individualists. The profile accounts for 22% of the potential-adopter segment and,

compared to Undecided Individualists and Undiscerning Urbanites, it describes predominantly

male and better earning car drivers who most likely own one car already. This segment is the

closest to the early-adopters group regarding attitudes. Hence, similar to the early adopters, these

members are highly interested in either kind of incentive.

5. DISCUSSION AND CONCLUSIONS

5.1 Discussion

Many people exhibit positive attitudes toward EVs, but only a small percentage of them

has already bought one. To close this gap by promoting the diffusion of this low-carbon

technology in individual transportation the identification and characterization of early adopters

is highly relevant. Furthermore, knowledge of the growing segment of potential EV adopters is

essential for effectively designing marketing programs, novel and successful business models,

and effective policy measures. Our study contributes to research on EV adoption by advancing

our understanding of early and potential EV adopters. To achieve this, we conducted a

Paper 1 55

multinomial logistic regression analysis to examine whether socio-demographic, psychological

(including cultural worldviews), and contextual characteristics (i.e., policy incentives) are related

to the willingness or intention to purchase EVs based on a representative sample of Austrian

citizens (n=1,000). Additionally, we aimed to shed light on the socio-demographic and

psychological characteristics of potential adopter segments and their preferences regarding

policy incentives by applying cluster analysis and by statistically testing for differences between

groups.

Our study reveals that psychological and, less so, socio-demographic factors play a role

in predicting membership of the defined adoption segments. This finding agrees with literature

on environmental behaviors, which found equally low explanatory power for socio-demographic

variables (e.g., Kilbourne and Beckmann, 1998; Leonidou et al., 2010), but it disagrees with

several research papers on early EV adoptions (Axsen et al., 2016; Nayum et al., 2016; Nayum

and Klöckner, 2014; Plötz et al., 2014). Our study fully supports the conclusion that using

psychological variables creates a comprehensive picture of customer profiles. This corresponds

with Nayum and Klöckner (2014) who already introduced the idea that including psychological

variables will lower the effect of socio-demographics in explaining consumers’ purchase

intention for environmentally friendly cars. This result has implications that could be relevant to

marketing practitioners, in that focusing on people’s attitudes toward the environment and their

interest in state-of-the-art technologies could be more effective in marketing than approaches that

target only middle-aged, high-income, better educated men with families, who live in rural or

suburban areas in multi-person households, and who own more than one car (c.f. Plötz et al.,

2014; Tal and Nicolas, 2013).

Our study similarly contributes to the literature by providing first insights regarding the

effect of cultural worldviews as EV purchase predictor. Particularly, individualism appears to

differentiate significantly between the three (non-)adopter segments. The more individualistic a

person in our sample, the less likely he or she is to purchase an electric car. This result conforms

to the general tendency of this ideological group who tends to have a less positive stance toward

renewable technologies (Cherry et al., 2014), is more skeptical regarding climate change (Kahan

et al., 2011; Kahan et al., 2007), and perceives environmental threats as overrated (Wildavsky

and Dake, 1990). Our findings on the relation between cultural worldviews and the acceptance

of EVs are a first step toward a new research field. However, these findings need to be interpreted

cautiously. First, we could not use the complete worldview measurement scale due to a length

restriction on the questionnaire. Second, the low Cronbach-Alpha scores we achieved could,

despite our special efforts in choosing items with an assumed high ‘cultural fit,’ indicate weak

Paper 1 56

applicability of the applied cultural cognition items to an Austrian cultural context. For future

studies in this field we would recommend that researchers develop a dedicated measurement

instrument tailored to a European cultural context.

The four identified potential-adopter clusters (Urban EV Supporters, Undecided

Individualists, Rural Non-Techs, and Undiscerning Urbanites) that vary in their attitudes and

characteristics, emphasize the need to target these segments differently with tailored products,

information campaigns, mobility concepts, and incentive packages. Compared to the early-

adopter group, the Urban EV Supporter segment yields similar characteristics, but is more likely

to live in urban areas. EVs tailored to city needs (e.g., smaller size) or to car-sharing practices

could be one logical lever for gaining market share in this segment (McKinsey & Company,

2017). Respondents that are part of the Undecided Individualists segment exhibited a particularly

interesting profile. Members of this cluster share a more individualistic worldview and tend to

live in the countryside. They could be targeted with tailored information campaigns, ancillary

services, and offers to decrease investment barriers and concerns, as well as to strengthen their

general positive attitude toward EVs. The Rural Non-Techs seem to be interested in EVs, but

according to how they evaluate purchasing/non-purchasing motives they seem not to be well

informed (e.g., they assess “ICE is clean enough” more highly than the other segments), and are

less aware of recent developments in the EV market. Axsen et al. (2016) also found that current

ICE car drivers typically know less about EVs and recent developments in vehicle and battery

technology, available models, etc., and therefore encouraged specific awareness campaigns.

Further, Bühler et al. (2014) propose real-life EV experience as a promising measure to increase

awareness and to decrease prejudice and perceived barriers, as they found that consumers are

significantly more interested in buying an EV after driving it. Undiscerning Urbanites find the

incentives and purchasing motives, as well as non-purchasing motives less attractive than the

other clusters do. This could be attributed to the fact that on average they are younger, more

likely to live in a larger city, thus more likely to rely on public transport, and not to own a car.

Their intention to purchase an EV, however, could also indicate a preference for using EVs

provided by taxis, car-sharing companies, or other public transportation such as e-buses.

In general, our study provides interesting new insight into potential EV adopters in the

Austrian market, which had the highest growth rate and highest share of new registrations of EVs

in the European Union in 2016 (Electric Vehicle World Sales Database, 2017). However, a

downside of this focus on a single market is the limited generalizability of our findings. Austria

has distinct geographical and topographical features with a large proportion of the population

living in rural areas, which could have influenced our survey results. Further, national cultures

Paper 1 57

differ in aspects such as pro-environmental attitudes. Austria, e.g., has a very long tradition of

recycling (Liobikienė et al., 2017). We would encourage scholars to conduct similar studies in

other countries with different national cultures and topographical settings, to verify our findings

and to gain new insight by comparing different markets.

5.2 Conclusions and policy implications

Regarding the effectiveness of policy incentives, our analysis provides two main insights.

First, early adopters are more likely than potential adopters to live in regions that have EV policy

incentives. Our study thus provides some empirical evidence for the efficacy of policy incentives

regarding early EV adoption. This is in line with Zhang et al. (2013), who have already shown a

significant, albeit small effect of government policies on users’ purchase intention, as well as on

public awareness of EVs. Second, EV policy incentives were found not to distinguish between

early adopters and non-adopters, which means that non-adopters and early adopters are equally

likely to live in regions with strong EV government policies. Lane and Potter (2007), among

others, argue that incentives are only effective among individuals who consider EVs and ICEs to

be technologically similar. Our data suggests that non-adopters, particularly, do not perceive EVs

as equivalent to ICEs in terms of performance, convenience, and price. One implication of these

insights for policy makers could be that they should implement or retain policy incentives to the

point where EVs are perceived as almost equal to ICEs in most relevant dimensions. This would

be a particularly effective and efficient use of subsidies considering that only those willing to

purchase an EV will be convinced to actually purchase one, and with the necessary incentives

will take this last step in the purchasing process, sooner. Overall, in line with other findings in

the literature (e.g., Nayum and Klöckner, 2014, Turcksin et al., 2013), we suggest that a strong

government policy can facilitate the wider diffusion of cars with emergent technologies (in our

case electric engines).

Regarding the heterogeneous nature of potential future EV adopters in terms of their

characteristics and preferences, the following conclusion and implications can be drawn. First,

as already pointed out by Hardman et al. (2016) future potential EV adopters are no longer the

stereotypical green and high-income car drivers; rather, they are individuals with diverse socio-

demographic and psychological characteristics. For instance, many of the potential EV adopters

currently do not even own or regularly need a car. This triggers a need for new business models

connected to EVs, such as e-car sharing, e-hailing, or peer-to-peer e-car rental (e.g., McKinsey

& Company, 2017). Second, our study also finds heterogeneity in policy incentive preferences

(specifically related to use-based and purchase incentives) among potential customer segments.

Paper 1 58

Hence, we presume differences in the preferences of use-oriented and purchase incentives among

potential customer segments. Policy makers can thus increase the effectiveness and efficiency of

incentives by targeting specific potential adopters (e.g., Urban EV Supporters) with incentive

packages according to their preference, or by providing incentives to special service providers

such as e-car-sharing or e-hailing companies, as Green et al. (2014) proposed earlier. In

conclusion, our findings regarding different potential-adopter sub-groups’ incentive preferences

can serve as a starting point for further research focusing on the identification of proper incentive

packages for different target groups.

Funding

This study received funding for data collection from Deloitte Austria and Wien Energie.

The sponsors, however, did not influence the study design, data collection methods, analysis or

interpretation, all of which were undertaken by the authors.

Paper 1 59

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Paper 1 63

APPENDIX

TABLE A.1 SAMPLE CHARACTERISTICS AND REPRESENTATIVENESS


Sample

mean

Austrian

population mean

Gender Male 49.0% 48.9%

Chi2 = 0.80, d.f. = 1, p=.777 Female 51.0% 51.1%

Age 18-19 years 1.4% 3.3%

Chi2 = 1.25, d.f. = 6, p = .869 20-29 years 18.0% 18.6%

30-39 years 18.0% 18.4%

40-49 years 23.9% 21.9%

50-59 years 20.0% 21.1%

60-69 years 19.0% 14.9%

70 years 0.6% 1.4%

Education Compulsory school 6.0 % 19.8%

Chi2 = 14.27, d.f. = 3, p < .001 Vocational training 44.0% 49.8%

High school 25.1% 14.8%

University 24.2% 13.6%

Net household income/month (EUR) 25% percentile 1,800 1,717

Chi2 = 34.95, d.f.= 2, p < .001 50% percentile 2,700 2,769

75% percentile 3,500 4,179

Federal state Burgenland 4.1% 3.4%

Chi2 = .464, d.f. = 8, p =.998 Carinthia 6.0% 6.5%

Lower Austria 17.9% 19.0%

Upper Austria 17.8% 16.8%

Salzburg 6.3% 6.3%

Styria 13.7% 14.3%

Tyrol 7.9% 8.5%

Vorarlberg 4.8% 4.4%

Vienna 21.5% 20.9%

Paper 1 64

TABLE A.2 RESULTS OF THE FACTOR ANALYSIS: PURCHASE MOTIVES

Components

General EV

motives

Technological

motives

Free of emissions .876

Protection of the environment and the climate .859

Low operating costs .768

Ideal for short journeys and city traffic .710

High efficiency of the electric motor .612

Charm of modern technologies .790

Lower driving noise at low speed .701

The battery of the car can also be used as a buffer storage for the in-

house photovoltaic system .688

Note: Extraction method: principal component analysis; rotation method: oblimin with Kaiser normalization. The

purchase motives load on two factors explaining 59% of the variance, yielding a Kaiser Meyer-Olkin Mesaure (KMO)

sampling adequacy value between .81-.85; Barlett’s test of Sphericity is significant at p < .001.

Note: Extraction method: principal component analysis; rotation method: oblimin with Kaiser normalization. The

non-purchase motives load on two factors explaining 58% of the variance, yielding a KMO sampling adequacy value

between .82-.88; Barlett’s test of Sphericity is significant at p < .001.

TABLE A.4 FINAL CLUSTER MEANS (AFTER ITERATIONS) FOR K-MEANS CLUSTER ANALYSIS

Centroid means

Rural Non-

Techs

Undecided

Individualists

Undiscerning

Urbanites

Urban EV

Supporters

Structural barriers .36 .60 -1.38 -.31

Attitudinal barriers -.11 .95 .01 -1.00

General EV motives .07 .34 -1.67 .68

Technological motives -.84 .79 -.44 .65

No. of respondents 113 89 52 71

TABLE A.3 RESULTS OF THE FACTOR ANALYSIS: NON-PURCHASE MOTIVES

Components

Structural barriers Attitudinal barriers

Low availability of charging stations (in Austria and abroad) .848

Range of the electric cars too low .779

Too expensive .772

Batteries are rather short-lived .689

No charing possible near the apartment/house .654

Long charging duration .631

A petrol or diesel vehicle is clean enough .817

EV is not save enough .746

High complexity .707

The electric car is only a transition technology .637

Electric cars are rather small and therefore, e.g., not suitable for a

family car .543

Electric vehicles are a burden to the environment (e.g., battery

production and disposal, electricity production) .480

65

Dependent variable = WTP

Rural Non-Techs

(Mean; SD)

Undecided Individualists

(Mean; SD)

Undiscerning Urbanites

(Mean; SD)

Urban EV Supporters

(Mean; SD) F-value p-value

Age d, f 43.14 (13.74) 44.63 (13.87) 38.17 (13.09) 47.79 (13.68) 5.15 0.00

Household size 2.72 (1.23) 2.65 (1.24) 2.48 (1.15) 2.45 (1.41) 0.87 0.46

Income a, b, f 3155.03 (1418.64) 2517.18 (1262.61) 2644.23 (1344.51) 3058.54 (1751.67) 3.61 0.01

Pro-technological attitude b, d, f 3.28 (0.53) 3.30 (0.51) 2.85 (0.58) 3.29 (0.56) 9.65 0.00

Pro-environmental attitude b, d, f 3.12 (0.60) 3.32 (0.55) 2.73 (0.55) 3.34 (0.58) 11.91 0.00

Individualistic worldview a, d, e 2.67 (0.65) 3.21 (0.56) 2.72 (0.49) 2.77 (0.74) 16.85 0.00

Egalitarian worldview b, d, f 3.19 (0.65) 3.28 (0.66) 2.86 (0.59) 3.23 (0.67) 5.18 0.00

a Rural Non-Techs vs. Undecided Individualists – p < 0.05, b Rural Non-Techs vs. Undiscerning Urbanites – p < 0.05, c Rural Non-Techs vs. Urban EV Supporters

– p < 0.05, d Undecided Individualists vs. Undiscerning Urbanites – p < 0.05, e Undecided Individualists vs. Urban EV Supporters – p < 0.05, f Undiscerning Urbanites

vs. Urban EV Supporters – p < 0.05

TABLE A.5 ANOVA SOCIO-DEMOGRAPHIC AND PSYCHOLOGICAL DIFFERENCES BETWEEN POTENTIAL ADOPTER CLUSTERS

66

Dependent variable = WTP Rural Non-Techs Undecided Individualists Undiscerning Urbanites Urban EV Supporters Chi2 p-value

Gender c, e, f

8.83 0.03

Male 42.3% 47.3% 42.3% 63.4%

Female 57.7% 52.7% 57.7% 36.6%

Education a, c, d

23.26 0.03

Compulsory school 10.8% 9.9% 3.8% 7.0%

Vocational training 31.5% 49.5% 30.8% 45.1%

High school 26.1% 23.1% 32.7% 29.6%

University 31.5% 17.6% 32.7% 18.3%

Dwelling dispersion a, b, c, d, e

48.28 0.00

City 14.5% 25.3% 50.0% 52.1%

Town 27.9% 39.6% 26.9% 22.5%

Municipal 57.7% 35.2% 23.1% 25.4%

# of cars per household a, b, c, d, f

24.85 0.00

No car 17.1% 16.5% 42.2% 11.3%

One car 28.8% 47.3% 30.4% 64.9%

More than one car 54.1% 36.3% 26.9% 23.3%

EV policy incentive d

5.02 0.17

No EV policy incentive 48.6% 46.2% 63.5% 56.3%

EV policy incentive 51.4% 53.8% 26.5% 43.7%

a Rural Non-Techs vs. Undecided Individualists – p < 0.05, b Rural Non-Techs vs. Undiscerning Urbanites – p < 0.05, c Rural Non-Techs vs. Urban EV Supporters –

p < 0.05, d Undecided Individualists vs. Undiscerning Urbanites – p < 0.05, e Undecided Individualists vs. Urban EV Supporters – p < 0.05, f Undiscerning Urbanites

vs. Urban EV Supporters – p < 0.05

TABLE A.6 CHI-SQUARED TEST SOCIO-DEMOGRAPHIC DIFFERENCES BETWEEN POTENTIAL ADOPTER CLUSTERS

67

Dependent variable = WTP

Rural Non-Techs

(Mean; SD)

Undecided Individualists

(Mean; SD)

Undiscerning Urbanites

(Mean; SD)

Urban EV Supporters

(Mean; SD) F-value p-value

Purchase subsidies b, c, d, f 4.41(0.72) 4.59 (0.54) 3.29 (0.91) 4.70 (0.52) 54.37 0.00

Exemption toll payment a, b, c, d, f 3.86 (0.96) 4.48 (0.72) 3.48 (0.78) 4.31 (0.89) 19.54 0.00

Free parking a, b, c, d, f 4.01 (1.08) 4.52 (0.74) 3.52 (0.75) 4.44 (0.87) 16.87 0.00

Scrapping premium a, b, c, d, f 3.86 (1.12) 4.41 (0.82) 3.11 (0.78) 4.24 (0.95) 19.55 0.00

Tax benefits company cars a, d, f 3.59 (1.21) 4.26 (0.95) 3.27 (0.77) 3.85 (1.35) 9.27 0.00

(Partially) deductibility of purchase price

in income tax return a, b, c, d, f 4.17 (0.94) 4.63 (0.57) 3.31(0.70) 4.55 (0.75) 36.64 0.00

Exemption standard fuel consumption and

car tax a, b, c, d, f 3.59 (1.21) 4.26 (0.95) 3.37 (0.77) 3.85 (1.35) 52.88 0.00

Bus lane usage a, c, d, f 3.27 (1.22) 3.80 (1.14) 3.13 (0.77) 3.90 (1.20) 8.39 0.00

Reserved special parking lots a, b, d, f 3.86 (1.05) 4.29 (0.90) 3.31 (0.83) 4.14 (0.83) 12.02 0.00

No speed limits a, d, f 3.31(1.23) 4.10 (1.02) 3.21 (0.89) 3.73 (1.36) 10.27 0.00

Free public charging a, b, d, f 4.56 (0.71) 4.79 (0.44) 3.40 (0.85) 4.73 (0.48) 63.63 0.00

Legally prescribed number of public

charging stations a, b, d, e, f 4.06 (0.88) 4.52 (0.66) 3.38 (0.72) 4.10 (0.87) 23.76 0.00

Regulation of internal combustion engines

a, d, e, f 3.14 (1.11) 3.84 (1.14) 3.25 (0.88) 3.10 (1.19) 9.32 0.00

a Rural Non-Techs vs. Undecided Individualists – p < 0.05, b Rural Non-Techs vs. Undiscerning Urbanites – p < 0.05, c Rural Non-Techs vs. Urban EV Supporters – p < 0.05, d Undecided Individualists vs. Undiscerning Urbanites – p < 0.05, e Undecided Individualists vs. Urban EV Supporters – p < 0.05, f Undiscerning Urbanites vs. Urban EV

Supporters – p < 0.05.

TABLE A.7 ANOVA POLICY INCENTIVES SUPPORT BETWEEN POTENTIAL ADOPTER CLUSTERS

Paper 1 68

Scale/Dimension Items Source(s)

Cultural worldview:

individualism-

communitarianism

The government interferes far too much in our

everyday lives.

Kahan et al. (2007, 2011);

Cherry et al. (2014)

Free markets – not government programs – are

the best way to supply people with the things

they need.

The government should do more to advance

society’s goals, even if that means limiting the

freedom and choices of individuals. (Recoded)

Cultural worldview:

egalitarianism- hierarchy

We have gone too far in pushing equal rights in

this country. (Recoded)

Kahan et al. (2007, 2011);

Cherry et al. (2014)

Our society would be better off if the

distribution of wealth were more equal.

Discrimination against minorities is still a very

serious problem in our society.

Pro-environmental

attitude

I would say of myself that I am environmentally

conscious.

Whitmarsh and O'Neill

(2010)

Being environmentally friendly is an important

part of my personality.

I would describe myself as someone who cares

about the environment.

Pro-technological

attitude

I see the digitization as ...

... opportunity for better networking of objects

of daily life.

... possibility of networking with people

worldwide.

... essential facilitation of communication and

the handling of everyday things.

... possibility of access to fast, up-to-date, and

extensive information and knowledge.

... danger to the privacy of the individual ("glass

man"). (Recoded)

… problematic with regard to hacker attacks.

(Recoded)

... predominantly negative development

regarding the safety of people. (Recoded)

TABLE A.8. PSYCHOLOGICAL MEASUREMENT SCALES USED IN SURVEY

69

PAPER: 2: CAN PRODUCT BUNDLING INCREASE THE JOINT ADOPTION OF

ELECTRIC VEHICLES, SOLAR PANELS AND BATTERY STORAGE?

EXPLORATIVE EVIDENCE FROM A CHOICE-BASED CONJOINT STUDY IN

AUSTRIA24

Priessner, Alfons*; Hampl, Nina*,#

ABSTRACT

Although electric vehicle (EV) sales have been increasing recently, EVs can only contribute to

mitigating climate change if their required power is generated from renewable energy sources.

Hence, a product bundle of EVs with photovoltaic (PV) solar panels in combination with battery

storage (BS) for households could be instrumental in improving EV adoption rates and thus

also their carbon footprint. We conducted a choice-based conjoint experiment with 393

respondents in Austria to investigate the effect of EV-PV-BS product bundles on purchase

intention. Our data show that a majority of potential EV drivers, given the choice, would prefer

to purchase an EV in such a bundle. Further, the purchase intention for a PV and BS is twice as

high in a bundle with an EV than standalone. Segmentation analysis identified four potential

customer segments, which we labelled “Price-Sensitive Non-Owners”, “Energy Self-Sufficient

Owners”, “Economically Rational Owners” and “Likely Non-Adopters”. The segments

specifically differ in their product preferences, which highlights a need for designing

customized bundle offerings. Moreover, we show that policy incentives are more effective

when product bundles are labelled with prices tags already discounted by subsidies. We draw

implications for practitioners and policymakers, as well as proposing areas of further research.

Keywords: Electric vehicle; photovoltaic solar panel; battery storage; product bundling;

choice-based conjoint; latent class analysis

Highlights:

• A significant share of EV drivers prefer purchasing an EV in a bundle to standalone

• Product bundling increases the adoption rate of complementary new technologies

• Heterogeneity in bundle preferences requires variety in bundle offerings

• Product bundles should be advertised with purchase prices reduced by subsidies

24 Early version of this paper has been presented at the 1. Advanced Demand Modeling Workshop for

Electromobility at 16.03.2018 and at 6th International PhD Day of the Austrian Association of Energy Economics

at 07.09.2018 in Vienna/Austria. Furthermore, this paper is accepted for the EVS 32 in Lyon/France from 19.05.-

22.05.2019 and has received an invitation for “Revise and Resubmit” from Ecological Economics at 28.12.2018



Paper 2 70

1. INTRODUCTION

Sales of electric vehicles (EV), i.e. battery-driven electric (BEV) or plug-in hybrid

electric (PHEV) vehicles, have been increasing over the last couple of years for several reasons

(IEA, 2018). First, climate change combating organizations have long been arguing for low-

carbon emission modes of transportation such as EVs to meet greenhouse gas reduction targets

(IPCC, 2014; UNFCCC, 2015). Second, after years of lip service, European car manufacturers

have committed to adjusting their engines and platforms towards electrical fleets within the

next decade (e.g. VW, Mercedes, BMW). Thus they are following EV pioneers such as Tesla

(Bloomberg, 2018). Third, as from 2025 a few countries (e.g. Norway) will implement a ban

on internal combustion engine sales (IEA, 2018).Global EV registrations reached the one

million threshold in 2016 (IEA, 2016a) and they are growing exponentially (IEA, 2018).

However, critics of EVs emphasize that the shift from fossil-fuelled to power-fuelled

engines only combats climate change if coupled with decarbonized electricity production (e.g.

Bleijenberg and Egenhofer, 2013). Several experts even predict a rise in greenhouse gas

emissions in certain regions due to EVs replacing fossil-fuel cars (Holland et al., 2015; Zivin et

al., 2012). Producing power from renewable energy sources such as wind and solar is a growing

trend which many policy makers encourage (e.g. European Commission, 2014). However, the

larger part of power globally is still generated from fossil energy sources (e.g. coal, gas) (IPCC,

2012).

Hence, the proportion of renewable power used by EVs needs to be increased to reduce

their carbon footprint. A possible solution is to offer EVs in combination with photovoltaic

(PV) solar panels and battery storage (BS) for producing and storing renewable energy at

residential sites. Such product bundles would help to reduce the CO2 emission of EVs. Delmas

et al. (2017: 235) already argue that “households that invest in both solar panels and electric

vehicles, and size their solar system to offset the additional electricity used by their vehicle, can

eliminate their carbon footprint from household and transportation activities”.

Besides this environmental benefit, EV-PV-BS product bundling could also be an

incentive that will increase EV adoption, since as a niche market EVs are mostly purchased by

technologically or environmentally discerning people (Axsen et al., 2016; Hidrue et al., 2011).

Also, PV and BS are still fairly new products on the market and despite high growth rates they

still suffer from a relatively low level of adoption for several reasons (IEA, 2016b; IPCC, 2012).

One reason remains the high installation cost, but another is the limited knowledge among

consumers which coincides with a higher level of perceived risk and potentially also a less

Paper 2 71

positive attitude towards these products. This is quite a common circumstance for many newly

launched products (cf., Jhang et al., 2012; Reinders et al., 2010; Schilke and Wirtz, 2012). For

a customer to adopt a new product, the risk-adjusted value must exceed the purchase price

(Kalish, 1985) or, in other words, the benefits have to outweigh the costs (Wang et al., 2008).

In marketing literature, one strategy often proposed to decrease the perceived risk and

consequently to increase customers’ willingness to purchase (new) products, is product

bundling (Eppen et al., 1991; Reinders et al., 2010; Simonin and Ruth, 1995), which entails

packing various products into one offering. Due to decreased search and assembly costs, such

bundling also increases consumer convenience (Harris and Blair, 2006; Kim et al., 2008).

Moreover, according to Reinders et al. (2010) consumers specifically value product bundles

with high fit, i.e. the bundle has products that are either complementary or, at least, related.

Consumers view the products in focus here (EVs and PVs (with/without BS)) as

complements that together generate higher customer value than separately (Agnew and

Dargusch, 2017; Delmas et al., 2017). Thus, it seems prudent to offer these products in a bundle.

Cherubini et al. (2015) already identify (product/product-service) bundles as one of five key

success factors in accelerating the diffusion of EVs, and call for further research to develop

such bundles and test their effectiveness. To date, research has covered only the impact of single

add-on services and not how multiple product or service add-ons affect the preference for EVs

(Fojcik and Proff, 2014; Hinz et al., 2015).

Against this background, our paper has the following research objectives. First,

following calls of Cherubini et al. (2015) to study the effect of multiple add-ons, we aim to

understand whether bundling EVs with renewable power supply systems (PV with/without BS)

has a positive impact on the joint adoption of these three products, thus constituting an effective

strategy to increase the share of “greener” EVs on the road. Investigating the combination of

the three products (EV, PV, BS) and their related consumer preferences are considered a novel

contribution to the literature in this field. Second, building on previous research evidence that

consumer preferences in various clean technology fields are quite heterogeneous (Kaufmann et

al., 2013; Salm et al., 2016; Tabi et al., 2014), our paper aims to contribute to extant literature

by identifying different customer segments and by investigating how they differ in their

characteristics (socio-demographic and psychographic variables) and preferences regarding

product attributes and policy incentives. To achieve these research goals, we conducted a survey

including a choice-based conjoint (CBC) experiment with a unique sample of potential EV

drivers in Austria (n = 393). With the derived part-worth utilities we performed a latent class

Paper 2 72

analysis to identify customer segments with distinct preferences related to EV-PV-BS product

bundles. Our findings show that a significant share of potential EV drivers prefer purchasing

an EV bundled with complementary clean technologies to a standalone product. However, the

results also highlight the necessity of customizing product bundles to the preferences of

different customer segments.

This paper is structured as follows: in Section 2, we give information on product

bundling and its typologies. We also provide a brief literature review on the benefits product

bundling and bundling in the context of clean technologies have for consumers. In Section 3,

we introduce our methodological approach and dataset. Section 4 presents the results of our

survey and choice experiment, including the latent class analysis. Section 5 concludes the paper

and discusses implications for marketers and policy makers, as well as limitations and areas for

further research.

2. LITERATURE REVIEW

2.1 Definition and typology of bundling strategies

The concept of ‘bundling’ was first introduced by Adams and Yellen (1976) in the mid-

seventies and has since then been applied and analysed in the field of economics and marketing

(cf., Guiltinan, 1987; Stremersch and Tellis, 2002; Yadav, 1995). A multitude of definitions

exist (Chiambaretto and Dumez, 2012); however, the one most widely used defines bundling

as “the sale of two or more separate products in one package”25 (Stremersch and Tellis 2002:

57).

Stremersch and Tellis (2002) introduced a standard typology that summarizes all

characteristics bundles can have in a single framework using two dimensions, namely bundle

form and bundle focus. Regarding bundle form, the literature distinguishes mainly between

pure, mixed and unbundling strategies (Adams and Yellen, 1976; Guiltinan, 1987;

Schmalensee, 1984; Stremersch and Tellis, 2002). In a pure bundle the offered products are

sold only in a bundle and are not even available separately. This bundling strategy enables

companies to reduce customer heterogeneity (Schmalensee, 1984). Completely opposite is the

unbundling strategy in which all associated products are sold as separate items. This strategy

suits products with strong homogenous customer preference, thus these customers typically

25 In general, the term “product” refers to both goods and services (Stremersch and Tellis, 2002). However, in the

empirical study of this paper we focus on goods only.

Paper 2 73

prefer one product and not necessarily another associated one (Schmalensee, 1984). The mixed

bundling strategy has features of two extreme bundle forms, thus it makes products available

in a bundle and separately. This strategy is often considered the optimal strategy if consumers

have heterogeneous preferences (Guiltinan, 1987; Schmalensee, 1984; Stremersch and Tellis,

2002). Mixed bundling reduces the heterogeneity in consumers’ reservation prices (i.e., the

maximum a buyer is willing to pay) and hence taps into consumers’ surpluses more efficiently

(Schmalensee, 1984). However, the most optimal bundling strategy depends on various factors

and needs to be evaluated separately for each bundle (Arora, 2011; Lee and O'Connor, 2003).

Regarding the second framework dimension, bundle focus, the literature distinguishes

between price bundling and product bundling (Guiltinan, 1987; Reinders et al., 2010;

Stremersch and Tellis, 2002). Price bundling involves selling two or more products in a

discounted package, regardless of the integration level of the products in a bundle (Guiltinana,

1987; (Simonin and Ruth, 1995). In contrast, product bundling involves integrating and selling

the products in a package at any price, but generating value by adding complementary products.

Hence, the main items theoretically do not need to be specially discounted (Stremersch and

Tellis, 2002), even if most consumers expect bundled products to cost less than the products

sold separately (Heeler et al., 2007; Tanford et al., 2011). Overall, the literature argues that

consumers tend to purchase products separately if a bundle of products is not at least discounted

or offered with value-adding complementarity (Harris and Blair, 2006; Reinders et al., 2010).

Therefore, the differentiation between price and product bundling is of high managerial

relevance triggering different strategic choices depending on the particular company’s

objectives. Price bundling is a pricing and promotional tool which can be applied by the

marketing department for a brief time period at short notice. In contrast, product bundling is

deployed more strategically and at longer term to distinguish themselves from competition.

This requires a more holistic and collaborative approach along the company value chain

(Stremersch and Tellis, 2002). EV, PV and BS are highly complementary, but they are relatively

new and complex products (Agnew and Dargusch, 2017; Delmas et al., 2017). Therefore, a

product-bundling strategy seems to be more appropriate in this context and we, thus, focus on

product bundles in the remainder of our paper.

2.2 Consumer benefits from product bundling

In the last few decades, product bundling got substantial research attention (cf.,

Stremersch and Tellis, 2002). Previous studies identified three major consumer benefits of

Paper 2 74

product bundling: (1) increased value through complementarity, (2) a decrease in (perceived)

risk, and (3) increased convenience.

Complementarity among bundle products is a prerequisite for successful product

bundling (Harris and Blair, 2006; Stremersch and Tellis, 2002). If the qualities of the products

in a bundle are unknown or unrelated to consumers, bundling might not be considered superior

to unbundling (Choi, 2003). In general, complementarity enhances the utility of one or more

jointly used products, which leads to a more positive evaluation of bundles (e.g. Reinders et al.,

2010; Simonin and Ruth, 1995; Stremersch and Tellis, 2002). For instance, Reinders et al.

(2010) argue that compared to a moderate fit, a high degree of fit between the products in a

bundle has a positive impact on the evaluation of those products and the intention to purchase

them in a bundle. Similarly, regarding complementary products, Simonin and Ruth (1995)

identify a moderating effect on the relationship between the prior attitudes to the individual

products and attitudes to the bundle. Stremersch and Tellis (2002) claim that well-integrated

products create value for consumers via, for instance, improved performance (e.g. workout

program and personalized dieting), seamless interaction (e.g. PC systems) or interconnectivity

(e.g. telecom systems).

Depending on the industry and individual characteristics, consumers consider

purchasing bundled products to be less risky than unbundled products. In industries, which lack

formalised technology standards, e.g., consumers perceive purchasing bundled products as

safer than unbundled ones (Lawless, 1991). A significant number of consumers who do not

know the products (knowledge uncertainty) or have less confidence in their ability to make a

prudent purchase choice (choice uncertainty), prefer pre-defined product bundles (Guiltinan,

1987; Urbany et al., 1989). In addition, researchers argue that product bundles increase

consumer acceptance by reducing customers’ perceived risks due to product spillover effects

(e.g. Choi 2003, Reinders et al., 2010, Simonin and Ruth 1995). For instance, Choi (2003)

developed a rationale for quality transfer from existing experience goods onto new experience

goods in a bundle based on the information leverage theory. He suggests that the use of a

product with established quality will benefit the new product in the bundle by overcoming the

asymmetry of information in the market.

Deciding to purchase more than one product in a single purchasing event is often

convenient to the customer (Stremersch and Tellis, 2002). This convenience benefit could be

influenced by the usefulness and ease of using the bundle. Schilke and Wirtz (2012), e.g., have

shown that consumers perceive bundled broadband services which include internet access, an

Paper 2 75

internet-linked telephone, and internet television as more favourable, if they also rate the

products’ usefulness and ease of use highly. Moreover, scholars argue that by purchasing

product bundles consumers will gain convenience benefits due to reduced search and assembly

efforts (Guiltinan, 1987; Harris and Blair, 2006; Kim et al., 2008). Kim et al. (2008) claim that

to avoid the complex purchase-decision process and to decrease search time, consumers choose

product bundles that online travel agents offer rather than themselves purchasing each product

separately. The more complex the product or the more unfamiliar the potential consumer is with

the product, the higher the perceived value of a reduction in search costs through integrated

product bundles (Harlam et al., 1995).

2.3 Product bundling in the context of clean technologies

To date very little has been published on product bundling in the context of clean

technologies, specifically those related to EV, PV and BS bundling. Several studies analyse the

EV bundled with add-on services. For instance, Hinz et al. (2015) or Fojcik and Proff (2014)

evaluated the impact of single add-on services (e.g. mobility guarantee, vehicle-to-grid (V2G),

IT-based parking, intelligent charging system, charging station finder, etc.) on the acceptance

of EVs. Their consensus is that add-on services could increase the purchase intention and

willingness to pay (WTP) for EVs depending on which services and mobility concepts are

added to the bundle. Particularly, V2G is a promising add-on service to EVs. By storing the

peak loads from renewable energy sources, an EV with V2G technology can contribute to the

balance of power grids (Sovacool et al., 2017). Parsons et al. (2014) show that EVs offered with

V2G services could increase the market acceptance if upfront payments or pay-as-you-go is

available for the V2G service. However, Hidrue and Parsons (2015) argue, that the WTP of

V2G-EVs (special type of EV that returns power to the grid) is still very low in relation to the

current and future cost of V2G-EVs. However, fast changing technology that could lower V2G-

EVs’ cost in the midterm future, could change this. Overall, bundling EVs with add-on services

is becoming increasingly important for e-mobility success (Laurischkat et al., 2016). Cherubini

et al. (2015) have even indicated product/product-service bundles as a key factor for increasing

EV acceptance, and called for it to be further researched.

Only a few studies have investigated the consumer preference related to PV and BS

bundling. Oberst and Madlener (2015), for instance, investigated German households’

preferences regarding prosumerism and their willingness to adopt renewable energy based

micro‐generation technologies. They find that households consider becoming prosumers by

Paper 2 76

investing in PV-BS systems with a high level of electricity self-supply, and that this is a more

important purchase driver than green electricity or a profitable investment. Galassi and

Madlener (2016) show that Italian PV owners and potential PV owners prefer a PV system with

a BS facility which is owned, controlled and maintained by an external company, e.g. a utility.

Hence, they argue that consumers prefer a “rent-your-roof” solution to a “plug-and-play” one.

Agnew and Dargusch (2017) also recently showed that accepting PVs increases when they are

bundled with a BS. But additionally, they pointed out that some safety, quality and knowledge

issues remain unresolved in the market, and call for instituting consumer “energy literacy”

measures. This would require a better understanding of consumers, their preferences and

expertise.

EV-PV combinations have been researched from different angles. For example, Klör et

al. (2017) considered how used EV batteries can be repurposed for storing power from

renewable sources (e.g. PV), and how add-on services could overcome consumers’ information

asymmetries in used EV battery purchases. Ida et al. (2014), contrastively, focused on consumer

preferences and conducted a stated preference analysis for i.a. PV and EVs. They derived

estimates for the penetration rates, potential reduction of greenhouse gas, and WTP in order to

foster the diffusion of such clean equipment in Japan. Although, they did not investigate

consumer preferences of these two products in a bundle, Delmas et al. (2017) did so quite

recently when they investigated the preference for jointly purchasing EVs with PVs in

California. They claim that such joint purchasing will continue to grow due to the products’

complementarity. Further, they argue that household sector emissions will decline if more

households drive EVs and own solar panels. In spite of consensus on the benefits and market

outlook regarding combining EVs with PV systems, there has to date not been much research

on EV-PV bundling from a consumer preference perspective.

3. METHODS AND DATA

3.1 Choice-based conjoint

We investigated potential EV drivers’ willingness and preferences to purchase EVs

bundled with PV and BS by applying a stated preference approach. More specifically, we

performed a choice-based conjoint (CBC) experiment (Orme and Chrzan, 2017). In contrast to

a revealed preference approach (dealing with actual behaviour), a stated preference approach is

based on behavioural intentions and responses to hypothetical choice situations (Adamowicz et

al., 1994; Ben-Akiva et al., 1994). This offers the advantage of running choice experiments for

Paper 2 77

products or situations that appear in the market only limitedly or not at all (Adamowicz et al.,

1994; Louviere et al., 2000). That the EV, PV and BS markets are still in an early stage of

diffusion, and that product EV-PV-BS bundles are currently not even available in the market,

have motivated our choice to conduct a CBC experiment.

However, the stated preference method comprises some hypothetical bias, meaning that

the stated and actual behaviours of the respondents are most likely not a perfect fit. Still, the

more familiar the respondents are with the survey setting, the lower the hypothetical bias

(Schläpfer and Fischhoff, 2012). Hence, the novelty of this product bundle (EV + PV + BS) to

an average Austrian consumer was managed by focusing the experimental design on a car (i.e.

EV) purchase to which we simply added a PV with/without a BS without going into

complicated technical details about these products. In addition, our study focused on

respondents who are very likely to become early adopters of EVs on the basis of positive

attitudes towards EVs which is a strong indicator of future EV purchase (Nayum et al., 2016).

Further, the phenomenon of hypothetical bias could be partially overcome by applying indirect

enquiring practices such as CBC experiments (Schläpfer and Fischhoff, 2012).

CBC is a well-established method in marketing research (Green and Srinivasan, 1990)

that has also been applied in the context of EVs (Ewing and Sarigöllü, 2000; Hackbarth and

Madlener, 2016; Hidrue et al., 2011) and small-scale renewable energy technology (Gamel et

al., 2016; Salm et al., 2016). CBC can be used for measuring consumer preferences for products

and services, and for simulating potential market sizes or defining post-hoc customer segments

based on consumer preferences (Gustafsson et al., 2013; Orme and Chrzan, 2017). This can be

achieved by simulating a buying situation, in which respondents need to choose the most

preferred option from several alternatives with varying attribute levels. By redoing this several

times the underlying preference for the attribute levels can be effectively elicited in the form of

average part-worth utilities for attribute levels and relative importance weights for each of the

attributes (Green and Rao, 1971; Green and Srinivasan, 1990; Gustafsson et al., 2013). The

theoretical foundation for these analyses is the classical utility theory which assumes that every

individual has a certain utility maximization attitude. Moreover, every product has a certain

utility for each individual, which can be defined as the sum of the part-worth utilities for the

various attributes of that product (Lancaster, 1966).

Paper 2 78

3.2 Survey and experimental design

We used Sawtooth Software Lighthouse, the standard application for CBC experiments

in marketing research, to design, administer, conduct and analyse the survey. The overall

questionnaire consisted of two parts. In the first part, we checked the respondents’ interests in

and attitude towards EVs, their willingness to purchase an EV including the time-horizon plus

a few first demographics (age, gender, federal state) for representativeness cross-checks in the

sampling process. Then respondents needed to answer psychographic questions about their

worldviews, technological affinity, and environmental identity (see Table A.1 in the appendix).

Next, they were asked to indicate their purchase intentions related to standalone PV and BS

systems as well as their WTP regarding their preferred EV.

The second part included the CBC experiment. In an introduction we provided

background details about the imagined context of purchasing an EV with the opportunity to buy

a PV and BS in a bundle with the car, and about the different options of PV and BS systems

ownership (owner or leaser with ownership option). Before starting the conjoint experiment,

we provided a sample choice task with all the variables including detailed descriptions of the

attributes. The selected attributes and attribute levels for the EV-PV-BS product bundles were

defined based on twelve interviews with lead users of at least one of these products and on sales

discussions with retailers. In addition, we conducted a pre-test of the CBC experiment with 45

potential EV drivers and several experts from the car and utility industry to confirm the

relevance and suitability of the chosen attributes and levels.26 Table 1 provides an overview of

the six attributes, a detailed description, and the corresponding attribute levels finally used in

the CBC experiment.

In the CBC experiment the respondents were shown a series of 12 choice tasks. Each of

the choice tasks presented three different product bundle alternatives and a non-option (in which

the person would prefer the EV without the power-supply add-on products). From this set of

four options the respondents had to choose their preferred option. The EV in each choice task

was fixed at 400 km range, 150 horse power, 40-60 minutes per full-charging27 and PV and BS

add-on products were added with varying characteristics. A full profile method was used, which

means that all attributes were presented for each set of alternatives (Orme and Chrzan, 2017).

26 The chosen attributes/levels are considered the most relevant for the purchase decision of the bundle in focus. 27 The car characteristics were taken from the Nissan Leaf 2.0, which was released at the beginning of 2018. The

Nissan Leaf model was the world’s best-selling electric car in 2017 (Bloomberg (2017).

Paper 2 79

After finishing the CBC experiment a few last demographics such as income, education,

household size, etc. were interrogated, which are used to characterize the customer segments in

the results section below.

TABLE 1: ATTRIBUTES AND ATTRIBUTE LEVELS IN THE CHOICE-BASED CONJOINT DESIGN

Attributes Attribute description Attribute levels

PV/BS add-on

(ownership)

The PV with/without BS can be purchased as owner or

leaser with ownership option. As a leaser no investment

costs incur, but you conclude a power purchase

agreement to receive power from the operator of this

system at very good price for 15 years. After this

period, the devices will become your property and you

will be able to get your electricity for free from your

own facility.

PV + BS owner (no monthly payment)

PV owner (no monthly payment)

PV + BS leaser with ownership option

(monthly payment)

PV leaser with ownership option

(monthly payment)

Self-

sufficiency

rate

The PV with/without BS can produce/procure a certain

percentage (25-100%) of the electricity demand itself,

and consequently save a percentage of the electricity

costs, similar to the total annual household cost.

Up to max. 25%

Up to max. 50%

Up to max. 75%

Up to max. 100%

Amortization

period

Period during which the investment costs for the PV

with/without BS will be returned to the owner/operator.

8 years

12 years

16 years

20 years

Provider The product bundle can by either purchased by an all-

in-one provider or by a set of dealers.

All-in-one car dealer/OEM

All-in-one utility

All-in-one specialist dealer

Diverse specialist dealers

Policy

incentive

The government of Austria can subsidise investments

in EVs, PV systems and BS.

0%

Up to max. 10%

Up to max. 20%

Up to max. 30%

Purchase price From this purchase price (which includes the EV and in

the case of the “owner” option, investment in a PV

system with/without BS), the state subsidy must still be

deducted to arrive at the final total price. Monthly

payments in the case of “leaser with ownership option”

are not included in this price.

EUR 25,000

EUR 30,000

EUR 35,000

EUR 40,000

EUR 45,000

3.3 Data collection and sample

The survey respondents were recruited by the market research company market in

February/March 2018 from their online panel pool of more than 20,000 active users in Austria.

The participants who succeeded to fill in the survey form properly, were financially rewarded,

as is currently common practice in market research (cf. Gamel et al., 2016; Salm et al., 2016).

A total sample of 1,251 respondents were invited based on quota sampling in order to ensure

Paper 2 80

representativeness, considering the distribution of gender, population by federal state and age

(see Table A.2 in the appendix).

From our total sample (N =1,251) we decided to remove four subgroups (Figure 1

illustrates the filter funnel). First, 438 respondents in the total sample who showed a negative

attitude towards EVs, were excluded. According to Nayum et al. (2016) a positive attitude

towards EVs, besides other factors, is an important predictor for purchasing an EV. Second, a

sub-segment of 189 respondents who were not willing to purchase an EV as their next car

despite a positive attitude towards EVs, were excluded. The remaining group of people with

positive attitudes towards EVs and willing to purchase one, (n= 624) represented approximately

50% of the total sample, which is in line with other studies in Austria (cf., Hampl and Sposato,

2017). These respondents could be further segmented according to their planned EV purchase

timeframe. Thus, third, since we aim to draw policy and marketing relevant implications from

this study, we focused only on the group of people willing to purchase an EV within the next 5

years (n = 431). Fourth, the focus sample was cleaned by removing 38 speeders 28 and

flatliners29, leaving a final sample of 393 respondents. These respondents provided 4716 choice

observations (12 choices completed by each of the 393 respondents), which is considered more

than sufficient for further analyses (Gustafsson et al., 2013).

As a final step, we applied statistical tests to investigate the representativeness of our

sample (see Table A.2 in the appendix). The results indicate that our final sample of 393

respondents only slightly differs from the Austrian population profile generally by being better

educated and having a higher average household income.

28 Respondents who were among the 10% that read the instructions of the CBC experiment the fastest, i.e. in less

than 18 seconds, while the mean was 60 seconds, and who completed the choice tasks among the 5% fastest

respondents, i.e. in less than 74 seconds, while the mean was 151 seconds, were excluded. 29 The average root likelihood (RLH) can be used as a measure of fit to assess data quality. In this study, as each

choice task presented four options, it would be predicted that each alternative would be chosen with a probability

of 25% (corresponding RLH of 0.25). All answer sheets scoring below 0.25 were removed.

Paper 2 81

FIGURE 1: FILTER LOGIC FROM TOTAL SAMPLE (N = 1,251) TO FINAL SAMPLE (N= 393)

4. RESULTS

4.1 Relative importance of attributes

As a first analysis of the CBC experiment, we report the relative importance scores of

the different attributes in Table 2. These scores describe the size of each attribute’s influence

on the purchase decision. The higher this importance score, the larger the difference between

each attribute’s highest and lowest part-worth utility, and the larger the contribution of this

attribute to the overall utility of, in our case, the product bundle. Due to standardization the

importance scores sum up to 100% across all attributes, which enables comparisons of effects

between attributes (Gustafsson et al., 2013). Unsurprisingly, the purchase price is ranked first

(31.1%) as the most important attribute for purchase decision. Ranked a close second and third

place are a PV/BS add-on (ownership) (18.7%) and a power self-sufficiency rate (16.1%). These

are followed by the amortization period (14.5%). The difference between the purchase price

and the other parameters confirms that price is the most important purchase driver, therefore

the adoption success of such product bundles are highly dependent on the cost-curve

development of EV, PV and BS (Seba, 2014). Interestingly, the attributes policy incentive

(10.6%) and provider (9.1%) are of minor importance. Further, the standard deviation of the

Paper 2 82

policy incentive attribute is relatively high, which points to some heterogeneity among survey

participants related to the attribute levels.

4.2 Part-worth utilities per attribute level

We chose the Hierarchical Bayes (HB) procedure, regarded the most advanced and

commonly used estimation method in recent CBC studies (cf., Hille et al., 2018; Hinnen et al.,

2017; Salm et al., 2016), to estimate the part-worth utilities per attribute level. The HB

procedure estimates part-worth utilities at the individual level for each respondent, which

enables a better hit ratio in predicting consumer choices as well as statistically more accurate

results (Orme and Chrzan, 2017) than traditional aggregate models (e.g. multinomial logit

analysis (McFadden, 1986)). The latter are criticized for losing a great deal of information by

aggregating data for all individuals (Gustafsson et al., 2013; Rossi and Allenby, 2003).

Table 2 gives the average part-worth utility for each attribute level of the CBC

experiment with the corresponding standard deviation and confidence interval. The utility

scores reflect a certain relative desirability of an attribute level compared to other levels of the

same attribute. The higher the utility score, the stronger the positive influence of the specific

attribute level on the potential EV driver’s decision to choose the bundled product. Negative

values indicate a lower desirability and a decrease in overall utility. All utilities are interval-

scaled and zero-centred, so that the sum of all utilities per attribute is zero. Further, the

magnitude of the utilities is highly dependent on the selected range of attribute levels.

Therefore, to compare utility values across attributes is not meaningful, while comparison

solely between different levels of a given attribute, is (Gustafsson et al., 2013).

The results unsurprisingly revealed that potential EV drivers most prefer lower prices,

shorter amortization periods and high sufficiency rates. The tipping point from positive to

negative part-worth utilities for the attribute amortization period lies between 12 and 16 years,

and for self-sufficiency rate between 50 and 75%. The results also reveal a negative preference

for product options with no policy incentive. Interestingly, there is a slight preference for

purchasing the product from one instead of multiple providers, but without a clear preference

for the type of provider. Furthermore, purchasing PV and BS in a bundle is preferred along with

ownership rather than renting.

Paper 2 83

TABLE 2: HIERARCHICAL BAYES MODEL ESTIMATION OF MEAN UTILITY VALUES AND MEAN

RELATIVE IMPORTANCE SCORES

(n = 4716 choices made by 393 respondents) a

Attributes and attribute levels b Mean Standard

deviation

Lower 95%

CI c

Upper 95%

CI

PV/BS add-on (ownership) (m = 18.71%; SD =

9.65)

PV + BS owner (no monthly payment) 33.39 46.49 24.20 42.12

PV owner (no monthly payment) 8.41 48.39 -0.83 17.09

PV + BS leaser with ownership option (monthly

payment)

-14.56 42.72 -23.88 -5.11

PV leaser with ownership option (monthly

payment)

-27.24 49.47 -35.81 -18.88

Self-sufficiency rate (m = 16.05%; SD = 8.12)

Up to max. 100% 38.76 33.81 28.62 42.58

Up to max. 75% 17.68 23.77 12.00 23.66

Up to max. 50% -16.19 23.54 -21.40 -11.20

Up to max. 25% -40.25 36.17 -48.04 -32.12

Amortization period (m = 14.50%; SD = 6.12)

8 years 32.50 27.17 25.16 39.58

12 years 15.29 19.87 5.08 22.96

16 years -10.31 25.35 -19.40 -1.09

20 years -37.48 28.25 -42.38 -25.00

Provider (m = 9.05%; SD = 4.05)

All-in-one car dealer/OEM 3.64 22.34 -1.25 8.83

All-in-one utility 6.66 20.28 0.16 13.77

All-in-one specialist dealer 10.86 16.43 3.72 17.60

Diverse specialist dealers -21.17 17.73 -24.58 -14.18

Policy incentive (m = 10.55%; SD = 5.52)

Up to max. 30% 21.41 23.53 13.08 25.68

Up to max. 20% 7.00 23.66 0.71 13.62

Up to max. 10% -7.08 19.00 -14.37 -0.11

0% -21.32 26.09 -27.37 -15.31

Purchase price (m = 31.14%; SD = 12.52)

EUR 25,000 77.16 56.40 65.09 87.83

EUR 30,000 38.97 29.63 30.17 48.35

EUR 35,000 16.87 23.43 10.61 23.56

EUR 40,000 -41.67 36.55 -48.71 -28.99

EUR 45,000 -91.28 48.34 -95.22 -69.98

None -31.93 316.58 -43.31 14.01

a We use the average root likelihood (RLH) as an indicator for data quality and measure of fit, which is the

geometric mean of all predicted probabilities. Its acceptance threshold depends on the number of options per choice

task. Since we offered our respondents four options, the predicted probability of randomly choosing one of these

options is 25%. Thus, our RLH threshold is 0.25. Our RLH value is relatively high (0.647), which indicates that

our predictions are about 2.6 or 2.7 times better than the random chance level. b Mean relative importance scores per attribute (m) and corresponding standard deviation (SD) in parentheses. The

importance scores sum up to 100%. c Confidence interval.

Paper 2 84

Further analyses revealed that in a best-case scenario, where all attributes are set to the

most preferred level, 77.4%30 of our sample would purchase an EV with energy-supplying and

storing facilities (see Table A.3 in the appendix). And even in a base case scenario, with the

status-quo of product prices and specifications of February 2018, almost 40.6% of potential EV

adopters would prefer purchasing an EV with PV and BS (see Table A.4 in the appendix). In

addition, EV-PV-BS bundling increases the acceptance of PV and BS significantly: today,

40.6% of all respondents are willing to purchase PV and BS in a bundle with an EV, rather than

purchasing the PV as a standalone (25.3%31; p = 0.0332) or the BS as a standalone (18.7%7; p =

0.018).

4.3 Latent class analysis

4.3.1 Identification of the number of segments

To investigate potential heterogeneity in the EV drivers’ preferences, we performed a

segmentation analysis based on the final sample (n = 393). Latent class analysis is the most

frequently used procedure to cluster respondents based on preferences elicited in a CBC

experiment (Campbell et al., 2011; Garrod et al., 2012; Hille et al., 2017; Morey and Thiene,

2017; Tabi et al., 2014). It is also considered superior to other segmentation techniques

(Desarbo et al., 1995) because of its higher reproducibility and ability to create groups of similar

size (Sawtooth Software, 2004b).

The only downside of the latent class approach is its use of different starting points at

each computation. This can be overcome with two cross-checks. First, by re-running the model

several times. Second, by randomly splitting the sample into two parts and performing the

analysis separately on each segment (Sawtooth Software, 2004). Applying these to our case we

examined whether comparable segments emerged, which we confirmed. Each time the model

estimated the solutions from 2 to 6 segments, and we kept the solution with the highest chi-

square for our further analyses.

To determine the best model we used a subset of the recommended main criteria: percent

certainty, Consistent Akaike Information Criterion (CAIC), and chi-square (Desarbo et al.,

30 We applied a Randomized First Choice Model (Sawtooth Software Market Simulator) to estimate the share of

preference for the product bundle scenarios. For further details see for instance Orme and Chrzan (2017) and Hille

et al. (2018). 31 Purchase intention figures derived from direct questioning in part of the survey. 32 The test of significance was a chi-square statistic.

Paper 2 85

1995; Sawtooth Software, 2004b). Percent certainty is used to reveal how much better an

identified solution (number of segments) is compared to no segments; in other words, to check

how well the proposed segmentation solution fits the data. Therefore, the higher this figure, the

better the model. However, adding segments constantly increases it, hence we further used

CAIC (Sawtooth Software, 2004) developed by Bozdogan (1987) and adjusted from

Ramaswamy et al. (1993).

CAIC = -2 Log Likelihood + (nk + k - 1) x (ln N +1)

k = the number of groups

n = the number of independent parameters estimated per group

N = the total number of choice tasks in the data set

CAIC is most frequently used for determining the number of segments. In contrast to

percent certainty, CAIC indicates the best solution when researching its minimum (Sawtooth

Software, 2004b). Chi-square, like percent certainty, indicates whether a segmentation solution

is significantly better than the null solution. The measure is calculated by subtracting twice the

log-likelihood value of a null solution from two times the log-likelihood of the respective

grouping solution (Sawtooth Software, 2004).

TABLE 3: SUMMARY OF BEST REPLICATIONS LATENT CLASS ANALYSIS

Groups Percent certainty CAIC Chi-square

2 28.37 9754.12 3709.22

3 32.58 9401.88 4260.08

4 34.61 9335.56 4525.04

5 35.70 9391.60 4667.63

6 36.68 9461.46 4796.41

Instead of looking only at the highest and lowest number of these three variables, we

consider it helpful to look at the differences between the levels as well, as Sawtooth Software

(2004) has recommended. In all three cases we identified a slower increase for percent certainty

and chi-square and a soft re-increase in CAIC as described above from the solution of four to

five groups. Hence, the model we finally chose is the 4-group solution with a percent certainty

of 34.61, a CAIC of 9335.56, and chi-square of 4525.04 (see Table 3).

Paper 2 86

4.3.2 Description of the identified segments

TABLE 4: HIERARCHICAL BAYES MODEL ESTIMATION OF MEAN UTILITY VALUES PER SEGMENT

Segments

Segment 1:

Price Sensitive

Non-Owners

Segment 2:

Energy Self-

Sufficient

Owners

Segment 3:

Economically

Rational Owners

Segment 4:

Likely Non-

Adopters

Segment size n = 140 n = 83 n = 87 n = 83

PV/BS add-on (ownership)

PV + BS owner (no monthly

payment) -30.98 (-2.01) a 114.83 (14.50) 46.72 (7.15) -9.10 (-0.28)

PV owner (no monthly payment) -15.56 (-3.89) 75.10 (9.99) 20.07 (3.00) -33.24 (-0.94)

PV + BS leaser with ownership

option

(monthly payment)

7.04 (5.32) -64.98 (-7.18) -35.72 (4.74) 8.99 (0.29)

PV leaser with ownership option

(monthly payment) 39.50 (0.92) -124.95 (-11.74) -31.07 (4.21) 33.36 (1.17)

Self-sufficiency rate

Up to max. 25% -33.21 (-4.18) -74.58 (-8.27) -50.07 (-6.29) -28.72 (-0.80)

Up to max. 50% -6.69 (-0.87) -50.50 (-5.87) -23.12 (-3.17) -15.64 (-0.46)

Up to max. 75% 11.59 (1.51) 34.95 (4.34) 26.57 (4.02) -3.52 (-0.10)

Up to max. 100% 28.31 (3.78) 90.13 (11.50) 46.62 (6.97) 47.88 (1.76)

Amortization period

8 years 48.92 (6.54) 19.15 (2.36) 39.21 (5.97) 28.22 (0.99)

12 years 18.33 (2.41) 12.49 (1.55) 17.99 (2.65) 1.08 (0.03)

16 years -28.43 (-3.61) 0.34 (0.04) -4.52 (-0.64) -28.30 (-0.84)

20 years -38.82 (-4.83) -31.98 (-3.81) -52.69 (-6.61) -1.01 (-0.03)

Provider

All-in-one car dealer/OEM 17.21 (2.28) -4.86 (-0.60) 5.51 (0.80) 49.34 (1.75)

All-in-one utility 11.44 (1.49) 9.02 (1.10) -6.98 (-1.01) 28.84 (0.96)

All-in-one specialist dealer 14.73 (1.96) 8.73 (1.09) 20.77 (3.11) -67.46 (-1.68)

Diverse specialist dealers -43.38 (-5.38) -12.88 (-1.53) -19.30 (-2.63) -10.71 (-0.31)

Policy incentive

0% -30.53 (-3.84) -20.37 (-2.46) -28.43 (-3.79) -27.32 (-0.82)

Up to max. 10% -32.44 (-4.07) -16.82 (-2.02) -2.90 (-0.42) 27.82 (0.94)

Up to max. 20% 15.48 (2.04) 8.36 (1.04) 10.04 (1.47) -5.39 (-0.16)

Up to max. 30% 47.49 (6.41) 28.82 (3.64) 21.30 (3.22) 4.90 (0.16)

Purchase price

EUR 25,000 114.09 (13.54) 32.81 (3.50) 111.29 (15.11) 137.50 (4.62)

EUR 30,000 70.25 (8.29) 15.84 (1.65) 50.51 (6.49) -22.74 (-0.49)

EUR 35,000 13.47 (1.53) 14.68 (1.58) 24.40 (3.03) 18.19 (0.45)

EUR 40,000 -72.15 (-7.27) -22.84 (-2.33) -58.34 (-5.66) -42.10 (-0.85)

EUR 45,000 -125.65 (-11.47) -40.49 (4.01) -127.87 (-9.24) -90.85 (-1.51)

None -497.7 (14.43) -210.4 (-13.75) 94.7 (19.32) 501.8 (-10.88) a T-ratios are shown in parentheses.

Paper 2 87

The four segments differ regarding the part-worth utilities and importance of several

attributes (Tables 4 and 5). Three of the four main profiles identified can be described as

potential adopters based on their clear preference for the product bundle. The fourth segment is

labelled Likely Non-Adopters (n = 83, 21.1% of the respondents) due to the high part-worth

utility of the none option (501.8). The other three segments are described in detail below.

TABLE 5: ATTRIBUTE IMPORTANCE SCORES PER SEGMENT

Segment

Segment 1:

Price Sensitive

Non-Owners

Segment 2:

Energy Self-

Sufficient Owners

Segment 3:

Economically

Rational Owners

Segment 4:

Likely Non-

Adopters

Segment size n = 140 n = 83 n = 87 n = 83

PV/BS ownership 11.7 40.0 13.7 11.1

Self-sufficiency rate 10.3 27.5 16.1 12.8

Amortization period 14.6 8.5 15.3 9.4

Provider 10.1 3.6 6.7 19.5

Policy incentive 13.3 8.2 8.3 9.2

Purchase price 40.0 12.2 39.9 38.1

Total 100% 100% 100% 100%

The first segment, which we called Price-Sensitive Non-Owners (n = 140, 35.6% of the

respondents), was characterized by a higher importance of the attributes purchase price and

policy incentive. They clearly preferred the lowest purchase price and the highest subsidy level

compared to the other segments. Interestingly, this segment is the only one which prefers not

to own a PV and BS. We argue that due to their cost consciousness these consumers prefer to

lease the add-on products over a certain period rather than making a higher upfront investment.

Conversely, they put relatively low emphasis on the self-sufficiency rate. Original equipment

manufacturers (OEM), as all-in-one providers, are clearly their preferred provider option.

The second segment can be labelled Energy Self-Sufficient Owners (n = 83, 21.1% of

the respondents). These group members identified PV/BS ownership as most important.

Specifically, they prefer both owning the add-on products and bundling the PV with a BS.

Further, customers within this segment assign a significantly higher value to self-sufficiency

rates compared to other segments, particularly at a level above 75%. This agrees with their

strong preference for an EV-PV-BS bundle (BS being a prerequisite for a self-sufficiency rate

over 50%). Their expressed preferences for the attributes amortization period, policy incentive,

provider and purchase price are distinctly less pronounced than those of the other two potential

adopter segments. Moreover, they have a relatively strong non-preference for OEMs as all-in-

Paper 2 88

one providers, favouring energy utilities or specialty dealers instead. However, the relative

importance of the attribute provider is the lowest across all segments.

The third segment is labelled the Economically Rational Owners (n = 87, 21.9% of the

respondents). They identified purchase price as the most important attribute. The second and

third most important product attributes for them are self-sufficiency rate and amortization

period. In contrast to the other two potential adopter segments, they have a strong preference

for low amortization periods. Despite the purchase price being highly important, they still

evaluate a purchase price of EUR 35,000 with positive part-worth utility. Depending on the

product specification, respondents from this customer segment can be adopters or non-adopters.

By applying several chi-square and t-tests, we further analysed whether the four

identified segments differ significantly in terms of demographic and psychographic

characteristics. The results showed that the four segments did not differ significantly with

respect to gender, education, monthly net household income, electricity cost per household and

household size, nor did they differ on most psychographic variables, such as technology or

environmental attitude and egalitarian worldviews (see Table A.1 appendix). However, they

differed quite significantly regarding age, house/apartment ownership, individualistic

worldview and willingness to pay for an EV. For instance, survey participants in the Likely

Non-Adopter segment were significantly older and had a more individualistic worldview than

those in the potential adopter segments. Also notable is that the Energy Self-Sufficient Owners

had a significantly higher (EUR 6-7k) willingness to pay for their preferred EVs than the other

segments. Moreover, the owner segments (Energy Self-Sufficient Owners and Economically

Rational Owners) were statistically significantly more likely than the other two segments to

own a house or an apartment.

4.3.3 Scenario analyses of different product types across segments

To further investigate the segments’ preferences for EV-PV-BS product bundle

characteristics we ran three simulations with the Sawtooth Software Market Simulator. Each of

the following three simulations tested certain product features and revealed consumer

preferences per segment regarding product design and policy subsidies (see Figure 2 for

details).

Paper 2 89

Simulation 1: Low-cost/economic product vs. premium/autarkic product

For our first product simulation we defined a product with low-cost and high-economic

rationale, and a second with some autarkic and premium characteristics. The two products

differed in all relevant dimensions. The first product bundle comprised a PV without BS, a self-

sufficiency level of 25%, but an amortization period of 8 years at a price of EUR 30,000 with

a subsidy of 20% and the products offered by different providers. The second, a premium

product, had a high self-sufficiency rate provided by a PV with BS at the highest price (EUR

45,000) and came with a policy incentive of 10%. The results of the simulation for consumer

segments are ambiguous. Segment 1 tended to prefer the low-cost/economic product, whereas

segment 2 had a stronger tendency towards the premium/autarkic product. For segments 3 and

4, both products seemed to be rather unattractive, either in terms of price or product features.

Simulation 2: Ownership vs. non-ownership product

For the second simulation we kept a few product specifications stable as in the self-

sufficiency rate at 75%, amortization period 20 years, different providers, and a policy incentive

of 10%. But we varied the product attribute ownership and the related price component.

Whereas with the first product bundle potential customers could purchase a PV with BS at EUR

40,000 and own it, the non-ownership product offered an EV at a purchase price of EUR 35,000

bundled with a PV and BS for rent. We hypothesized that potential customers would prefer not

to own a PV to reduce the high upfront investment and to avoid bearing any technical risks. But

the groups show significantly different results. Segment 2 strongly prefers owning a product

(71.8%) and segment 1 largely prefers a non-ownership bundle (71.6%). For groups 3 and 4 the

simulated products were not really interesting due to the high purchase price (83%/100% of the

respondents would choose the none option).

Simulation 3: Policy incentive vs. no policy incentive product

In the third simulation we designed the product bundles to evaluate the effect of policy

incentives. For this purpose, we ran two simulations. In the first (Part 1) we compared two

identical products (as in Simulation 2 offering a PV and BS with the ownership option at a price

of EUR 45,000), except for one being heavily incentivized (30%) and the other one having no

policy support (0%). Unsurprisingly, a majority across all three potential adopter segments

preferred the option with subsidies. However, almost a quarter of the respondents in segments

Paper 2 90

1 and 2 preferred the unsubsidized option, which seems counterintuitive. In the second

simulation (Part 2) we used the same products as before, excepting that this time the first

product was priced at EUR 45,000 and subsidized by 30%, and the second product was priced

at EUR 30,000 and 0% subsidy. Both products’ purchase price, after subsidies, was

approximately the same. Nevertheless, there was a clear preference for the cheaper product

without policy incentive, which might have clear implications for the price communication of

such product bundles. Particularly, segments 1 and 3 were very price sensitive and preferred

options with lower upfront costs to product options with a high purchase price and subsidies.

FIGURE 2: SUMMARY OF SIMULATION RESULTS PER SEGMENT

74

3522

0

23

60

803 4

70

100

0 %

25 %

50 %

75 %

100 %

Segment 1 Segment 2 Segment 3 Segment 4

Low-cost/economic product vs. premium/autarkic product

Low-cost/eco Premium/autarkic No bundle

20

72

100

72

187

08 10

83

100

0 %

25 %

50 %

75 %

100 %


Ownership vs. non-ownership product

Owner Non-Owner No Bundle

2641

3 0

6754

24

07 6

74

100

0 %

25 %

50 %

75 %

100 %


Policy incentive vs. no policy incentive product (Part 1)

Policy Incentive No Policy Incentive No Bundle

60 61

90

2127

4 0

2012

87100

0%

25%

50%

75%

100%


Policy incentive vs. no policy incentive product (Part 2)

Policy Incentive No Policy Incentive No Bundle

Paper 2 91

5. DISCUSSION AND CONCLUSIONS

EVs are gaining importance in the passenger car industry. To ensure that these vehicles

have a positive impact on the environment, they need to be powered by green electricity, e.g.

generated from residential PV systems. Consequently, to promote the diffusion of “greener”

EVs in individual transportation by effectively bundling an EV with PV and potentially BS on

the market, a proper understanding of the bundling process is highly relevant. The main goal of

this study was to analyse the effect of different characteristics of product bundles combining

EVs and renewable power generation facilities (PV with/without BS) on purchase intention.

Further, our study aimed to shed light on the characteristics of different customer segments and

their preferences for different features of such EV-PV-BS product bundles and related policy

incentives. We built our analyses on a unique dataset of 4716 experimental choices of 393

respondents in Austria who already had a positive attitude and willingness to purchase an EV

within the next 5 years.

5.1 Discussion of study findings and implications

Our results show that purchase price (31.1%) is the most important attribute for the

purchase decision, followed by PV/BS add-on (ownership) (18.7%), amortization period

(16.1%), power self-sufficiency rate (14.5%), policy incentive (10.6%) and provider (9.1%).

The results reveal a high interest in EV-PV-BS product bundles among Austrian citizens. Of

the respondents, 77.4% would purchase a product bundle with the most preferred features (best-

case scenario) rather than purchasing a standalone EV. Additional analyses identified four

distinct segments of respondents that differ in their preferences for particular attributes and

attribute levels. The most distinguishing characteristics among the segments are the

respondents’ general interest in an EV-PV-BS product bundle and their preference for specific

bundle components (e.g. just a PV or a PV with BS), their preferences related to the

ownership/non-ownership options of the PV/BS add-on systems, and their willingness to pay

for such a product bundle (price sensitivity). Further, the segments differ in terms of

participants’ age, individualistic worldview, willingness to pay for preferred EV and current

home ownership. For instance, the Likely Non-Adopters segment is significantly older and more

individualistic than the average respondent of the other three segments.

With these findings, our study contributes to literature on clean technology adoption by

providing first insights regarding the effect of product bundles on the intention to purchase

complementary products together with an EV. Our results indicate a high potential to decrease

Paper 2 92

the carbon footprint of EVs through cross-selling of PV and BS systems and at the same time

to accelerate the diffusion of small-scale PV as a renewable energy technology in Austria. So

far, scholars have only studied the effects of single add-on services, such as a mobility

guarantee, V2G, intelligent charging system, charging station finder, etc. on EV adoption

(Fojcik and Proff, 2014; Hinz et al., 2015), or they investigated the joint adoption of PV and

BS (Agnew and Dargusch, 2017), and EVs and PV (Delmas et al., 2017). Thus, the combined

investigation of complementary products to EVs, such as PV and BS, is a novel contribution to

literature and provides several avenues for further research in this field.

Our study shows that potential customers significantly differ in their preferences for the

attributes and attribute levels included in our CBC design. Our results highlight the purchase

price as the most important decision criterion. Two of the four identified customer segments

are specifically price sensitive (Price Sensitive Non-Owners, Economically Rational Owners).

Thus, despite the benefits of product bundles (e.g. increased consumer value through

complementary products, reduced search costs, decreased risk of incompatibility), our results

suggest that price discounts for such bundle offerings could be an effective approach to increase

purchase intention among the identified types of customers. These findings are in line with

existing literature on product bundles. For instance, Heeler et al. (2007) show that certain

customers would even request discounts for purchasing new products in a bundle even if it

offers a high product fit. Thus, further research should investigate the effect of price bundles,

separately or in combination with product bundles, on (potential) customers’ intention to

purchase joint offerings of EVs and other clean technologies, such as PV and BS.

Besides differences in price sensitivity and varying preferences regarding e.g. the

ownership/non-ownership option or policy incentives, we have identified heterogeneous

preferences regarding the items included in the product bundles. Some identified customer

segments prefer product bundles including only an EV and a PV, others prefer offerings

comprising all three types of products, i.e. an EV, PV and BS. Therefore, and also in line with

the literature, we suggest that a mixed-bundling strategy, i.e. offering both the products in a

bundle and standalone, might be the best to approach distinct consumers (Guiltinan, 1987;

Simonin and Ruth, 1995; Schmalensee, 1984, Stremersch and Tellis, 2002). Future studies

could build on these findings and focus on consumer preference for specific features of the

underlying bundle products, i.e. the model, brand and specifications of the EV, the type of PV

(e.g. roof-top or building integrated) or the type of BS system (e.g. the EV battery itself through

a Vehicle-to-Home function, a reused EV battery, a dedicated home BS system or a virtual

Paper 2 93

storage solution). Further, it could be interesting to investigate whether preferences for specific

product items in a bundle are merely driven by cost considerations or whether other reasons are

at play, such as limited knowledge regarding certain technologies (e.g., regarding BS our results

show that the increase in overall utility for adding a BS to the product bundle is lower than the

effect of increasing the self-sufficiency rate).

The effectiveness of policy incentives related to EV-PV-BS product bundles is another

promising avenue for further research. In our experiment we offered a purchase-based

incentive, i.e. a subsidy granted upon the purchase of the entire product bundle. Some scholars

argue, that providing subsidies or discounts on the entire bundle has a higher impact on

perceived value than savings on the item level (Yadav and Monroe, 1993). Johnson, Hermann,

and Bauer (1999), on the other hand, suggest that savings should rather be specified for each

item than for the entire bundle. Yadav (1995) adds that price reductions are more effective if

the most dominant product item in the bundle in terms of price is subsidised or discounted (in

our case, the EV). These questions require further research. Future studies could also investigate

what types of policy incentives, other than purchase incentives (e.g. tax rebates), have the

highest impact on purchase intention or the actual purchase decision for this type of product

bundle.

Our findings have several implications for practitioners and policymakers. For

marketers, our results provide valuable insight regarding the market potential of EV-PV-BS

product bundles. We find a strong interest in such types of offerings (up to three quarters of the

respondents would choose the bundle in the best-case scenario). However, our findings indicate

that for the average customer the type of provider is of relatively low importance (9.1%), as

long as the product bundle is offered “all-in-one”. As product prices for EVs, PV and BS

continue to decline33, new EV models will enter the market so that charging infrastructure will

constantly expand. We expect that a large share of this market potential will turn into actual

product bundle sales within the next couple of years.

Our results also provide some interesting conclusions related to new business models.

Car dealers can extend their offerings with complementary products such as PV and BS (see

e.g. Tesla’s offer of EVs, solar roof systems and BS), or electric utilities could offer EVs in a

subscription package (see e.g. TEAG-E-Car AutoPaket) together with PV and BS systems.

33 The cost of battery per kWh decreased from USD 1,000 in 2010 to approx. USD 200 in 2017, and will most

likely reach the USD 100 per kWh threshold by 2030 (McKinsey (2017). Similar effects have been registered for

PV in that USD per watt has improved by 11.4% per year since 1970, thus from about USD 100 per watt in 1970

to about 33 cents per watt in 2017 (Seba (2014).

Paper 2 94

Especially, subscription-based offerings seem to be promising for such types of product bundles

as more than half of the respondents in our sample indicated a clear preference for non-

ownership models with monthly instalments and thus low upfront costs.

The potential adopters’ segments we identified in our study are quite homogenous from

a socio-demographic and psychographic point of view. However, there is significant

heterogeneity in preferences for product bundle features. Therefore, our analysis underlines the

need for customized or at least segment-specific product bundle offerings, also in order to

increase the average willingness to pay.

For policymakers our results highlight that financial incentives can provide additional

leverage to increase the share of preference for EVs and complementary products. However,

the relative effect on purchase intention is relatively low (10.6%). Further, product simulations

show that 20 to 25% of the potential customers in our sample would prefer a product without

policy incentives compared to the same product with policy incentives. We find evidence that

potential adopters would prefer a product with a lower price tag (i.e. where the price excludes

potential subsidies) to a product with a higher purchase price but with high policy incentive,

even if the ultimate cost for both product bundles were the same. Future studies should

investigate whether this effect is caused by a tendency to avoid subsidies or by a cognitive bias

in the decision-making process, as individuals tend to use mental shortcuts to save effort

(Tversky and Kahneman, 1974).

5.2 Limitations and future research

To our best knowledge, this study is the first that evaluates the impact of product

bundling on the joint adoption of EVs, PV and BS. As with any research, the results are subject

to some limitations. First, our results are based on stated preference data. As described in section

3, this approach has several advantages, specifically in our case where the products in focus are

in an early market diffusion stage or do not even exist on the market yet (i.e. limited or no actual

users nor purchase decision data). Although our objective was to create a choice setting that is

as realistic as possible based on conversations with experts and a pre-study with potential EV

drivers, an online choice experiment is not directly comparable to real-life purchase decisions.

Therefore, future research should further explore the possibility of comparing our findings to

revealed preference data. Such research could test and confirm our findings in a real purchasing

context and compare the results from a conjoint experiment with additional data from the field.

Paper 2 95

Second, we carefully derived our attributes and attribute levels based on an extensive

literature review and qualitative expert interviews; even so, variables not included in our design

could also have an impact on the intention to purchase an EV-PV-BS product bundle. For

instance, potentially relevant decision parameters such as product warranty, (minimum)

contract period for non-ownership options, etc. could be considered in future work. In addition,

to reduce decision complexity we considered only one type of EV in our CBC experiment. This

comes with the drawback, that the potential EV drivers could differ in their purchase decisions

along different EV types. Further, future studies could provide details about the intended type

of PV and BS, as this study did not give that.

Third, whereas the findings of this study have delivered suggestions for marketers and

policymakers, our results only focus on Austrian potential EV drivers. The Austrian market has

one of the highest relative growth rates in EV car registrations in Europe (Electric Vehicle

World Sales Database, 2017) and Austrian citizens are considered as relatively pro-

environmental in global rankings (Liobikienė et al., 2017). Thus, conclusions and comparisons

with other countries should be done carefully. Future studies should consider detailed analyses

regarding similarities and differences in EV-PV-BS product bundle preferences between

different cultural settings.

Acknowledgment

The authors would like to acknowledge the financial support of the company KELAG,

the expertise they made available to us, as well as the helpful feedback we received from all

our interviewees during the pre-test phase of this survey.

Paper 2 96

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Bureau of Economic Research, Cambridge, MA.

101

APPENDIX

TABLE A.1. CHARACTERISTICS OF THE FINAL SAMPLE AND SEGMENTS (MEANS AND STANDARD DEVIATION OR PERCENTAGES) AND CHI-SQUARE OR T-

TESTS BETWEEN THE SEGMENTS


Final sample

(n = 393)

Price Sensitive

Non-Owners

Energy Self-

Sufficient Owners

Likely Non-

Adopters

Economically

Rational Owners

Gender % Male 55.0% 56.4% 51.8% 57.8% 52.9%

Age b, d, e Years 48.25 (14.5) 47.58 (14.4) 44.40 (13.48) 55.34 (14.27) 46.26 (13.98)

Education 1=compulsory school 2.3% 2.9% 2.4% 2.4% 1.1%

2=vocational training 35.9% 34.2% 31.3% 44.6% 29.8%

3=high school 26.7% 27.9% 32.5% 22.9% 23.0%

4=university 36.1% 35.0% 33.7% 30.1% 46.1%

Electricity bill EUR per month 81.1 (66.0) 74.8 (41.3) 80.5 (46.5) 88.3 (92.1) 85.5 (82.4)

Household size Number of people per household 2.54 (1.15) 2.45 (1.1) 2.75 (1.3) 2.45 (1.2) 2.59 (1.1)

Income Net EUR per month per household 3,295 (2,184) 3,294 (3,169) 3,231 (1,147) 3,223 (1,653) 3,427 (1,283)

Willingness to pay

for preferred EV a, d, e

EUR 26,597 (13,038) 25,335 (12,449) 31,662 (16,470) 25,710 (11,478) 24,643 (10,397)

Home ownership a, c % Homeowner 69.9% 60.0% 77.1% 69.9% 77.1%

Pro-technological

attitude

e.g. “New technologies contribute to a better

quality of life.” (Parasuraman and Colby, 2015)

2.47 (0.71) 2.45 (0.68) 2.47 (0.76) 2.49 (0.71) 2.51 (0.75)

Pro-environmental

attitude

e.g. “I think of myself as an environmentally-

friendly consumer.” (Whitmarsh and O'Neill,

2010)

1.85 (0.66) 1.93 (0.69) 1.84 (0.63) 1.89 (0.74) 1.70 (0.54)

Individualistic

worldview b

e.g. “The government interferes far too much in

our everyday lives.” (Cherry et al., 2014;

Kahan et al., 2007)

2.54 (0.79) 2.69 (0.81) 2.50 (0.74) 2.36 (0.79) 2.52 (0.83)

Egalitarian

worldview

e.g. “Our society would be better off if the

distribution of wealth were more equal.”

(Cherry et al., 2014; Kahan et al., 2007)

3.50 (0.74) 3.53 (0.70) 3.49 (0.77) 3.38 (0.77) 3.60 (0.74)

a Price Sensitive Non-Owners vs. Energy Self-Sufficient Owners: p < 0.05. b Price Sensitive Non-Owners vs. Likely Non-Adopters: p < 0.05. c Price Sensitive Non-Owners vs.

Economically Rational Owners: p < 0.05. d Energy Self-Sufficient Owners vs. Likely Non-Adopters: p < 0.05. e Energy Self-Sufficient Owners vs. Economically Rational

Owners: p < 0.05. f Likely Non-Adopters vs. Economically Rational Owners: p < 0.05.

Paper 2 102

TABLE A.2. CHARACTERISTICS OF THE TOTAL AND FINAL SAMPLE IN COMPARISON TO THE

AUSTRIAN POPULATION

Variables

Total sample

(N = 1,251; in %)

Final sample

(n = 393; in %)

Austrian population

(in %) b

Gender

(x2 = 0.124, d.f. = 1,

p = 0.725) a

(x2 = 0.712, d.f. = 1,

p = 0.396)

Female 48.5 45.0 50.8

Male 51.5 55.0 49.2

Age

(x2 = 0.988, d.f. = 3,

p = 0.804)

(x2 = 1.852, d.f. = 3,

p = 0.582)

18-29 years 14.1 11.7 19.3

30-44 years 25.7 30.3 25.5

45-59 years 30.7 31.3 29.4

60-80 years 29.3 26.7 25.8

Education

(x2 = 36.996, d.f. = 3,

p = 0.000)

Compulsory school 2.3% 26.9%

Vocational training 35.9% 45.8%

High school 26.7% 14.6%

University 36.1% 12.6%

Household income/month

(x2 = 130.323, d.f. = 2,

p = 0.000)

25% percentile 2,500 1,601

50% percentile 3,000 2,611

75% percentile 4,000 3,995

Federal state

(x2 = 4.125, d.f. = 8,

p = 0.843)

(x2 = 0.667, d.f. = 8,

p = 0.999)

Burgenland 7.9% 2.5% 3.3%

Carinthia 5.8% 5.9% 6.4%

Lower Austria 19.9% 21.6% 18.9%

Upper Austria 20.8% 17.3% 16.7%

Salzburg 4.8% 5.3% 6.3%

Styria 14.3% 14.2% 14.1%

Tyrol 6.9% 6.9% 8.5%

Vorarlberg 5.0% 4.8% 4.4%

Vienna 14.5% 21.4% 21.4%

a The results from chi-square tests are included in parentheses, which show whether significant differences could

be identified between the total/final study sample and the Austrian population. b Source: STATISTIK AUSTRIA, 2018b).

Paper 2 103

TABLE A.3. BEST-CASE SCENARIO FOR THE SENSITIVITY ANALYSES

Best-case scenario

Option 1 Option 2 Option 3

PV/BS add-on (ownership) PV + BS

(Ownership)

PV + BS

(Non-ownership)

Would not choose any

of those options

Amortization period 8 Years 8 Years

Self-sufficiency rate 100% 100%

Provider All-in-one provider All-in-one provider

Policy incentive 30% 30%

Purchase price EUR 25,000 EUR 25,000

Individual simulated share of

preference for product

bundle options

48.2% 29.3% 22.6%

Aggregated simulated share

of preference for product

bundle options

77.4% 22.6%

TABLE A.4. BASE CASE SCENARIO FOR THE SENSITIVITY ANALYSES

Base case scenario

Option 1 Option 2 Option 3

PV/BS add-on (ownership) PV + BS

(Ownership)

PV + BS

(Non-ownership)

Would not choose any

of those options

Amortization period 20 years 20 years

Self-sufficiency rate 25% 25%

Provider Different Different

Policy incentive 0% 0%

Purchase price EUR 45,000 EUR 45,000

Individual simulated share of

preference for product

bundle options

23.4% 17.2% 59.4%

Aggregated simulated share

of preference for product

bundle options

40.6% 59.4%

PAPER 3: EXPLORING CONSUMER HETEROGENEITY IN WILLINGNESS TO

PAY FOR ELECTRIC VEHICLE PRODUCT BUNDLES34

Priessner, Alfons*; Hampl, Nina*,#

ABSTRACT

Electric vehicles (EV) are one major lever in decarbonizing road transportation, particularly if

they are coupled with renewable power. Bundling EVs with photovoltaic (PV) systems and

battery storage (BS) provides a possible solution, but consumer preferences and willingness to

pay (WTP) for such bundles have been limitedly researched. Therefore, we conducted a choice-

based conjoint study with 616 respondents in Austria who have a positive attitude towards EVs

and a purchase intention. Our data shows that the WTP for EV add-on products is still

significantly below the current market price. Further, consumers are willing to pay only a small

premium for the convenience of being served by an all-in-one provider. Moreover, higher EV

subsidies, appear generally to be less valued. Socio-demographic variables have a significant,

but rather small effect on the respondents’ preferences and WTP. Psychological variables, in

contrast, show a significant impact. For instance, technology-minded people are willing to pay

more for EV-PV-BS bundles, and environmentally-conscious respondents are more willing

than non-environmentalists to accept longer amortization periods and lower self-sufficiency

rates; also, they are less sensitive to higher purchase prices and valuable products without a

subsidy incentive. These findings have important implications for marketers and policy makers,

as well as for further research in this field.

Keywords: Electric vehicle, product bundling, renewable energy, conjoint analysis, customer

preference, willingness to pay

Highlights:

• WTP for EV add-on products (PV and BS) is significantly below current market price

• WTP a small premium for EV product bundles offered by an all-in-one provider

• Importance of subsidies decreases by increasing level of incentives provided

• Socio-demographics affect WTP for EV product bundle less than psychological

features

34 This paper is accepted for the 11. IEWT in Vienna, Austria from 13.02.-15.02.2018 and is currently under

revision in Transportation Research: Part A



Paper 3 105

1. INTRODUCTION

Electric vehicles (EVs) experienced their first hype more than a century ago (New York

Times, 1911), but their rise was prevented mainly by the success of Henry Ford’s Model T

(Kirsch, 2000). The true revival of interest in the EV came at the start of the 21st century when

Toyota launched its first mass-produced hybrid EV, and in Silicon Valley Tesla Motors started

up, producing luxury electric sport cars (Fialka, 2015). Today, EVs that replace vehicles with

fossil-fuel internal combustion engines (IPCC, 2014) are considered one possible lever for

reducing greenhouse gas (GHG) emissions in transportation, which is significant as the

transportation sector in the EU-28 causes almost 26% of all GHG emissions (EEA, 2017).

Although the electromobility transition is gathering pace (IEA, 2018), EVs’

effectiveness in combatting climate change is disputed in the literature (Sandy Thomas, 2012;

Zivin et al., 2012). Some experts argue that certain regions will not experience GHG emission

reduction despite EVs replacing fossil-fuel cars, due to their current non-sustainably produced

power supply (Holland et al., 2015; Zivin et al., 2012). Other regions are predicted to face a

significant long-term increase in demand for “green” power due to the surge in electromobility,

which requires investments additional to existing expansion plans in power generation from

renewable energy sources. For example, by 2030 EVs will account for 6% of all power

consumption in Germany, and by 2050 this share could increase up to 25% (Hacker et al., 2014).

Either way, the proportion of electricity from renewable energy sources used by EVs needs to

be increased to achieve the desired GHG emission reduction (e.g., Bleijenberg and Egenhofer,

2013; Holland et al., 2015).

One possible solution to this sustainability challenge is to purchase EVs in combination

with photovoltaic (PV) solar panels and battery storage (BS) for producing and storing

renewable energy at residential sites. Such product bundles could have a twofold benefit. On

the one hand, these EV-PV-BS product bundles support the reduction of EV GHG emissions

(Delmas, 2018). On the other hand, they could increase EV acceptance (Cherubini et al., 2015)

due to the complementarity of these bundle products (Reinders et al., 2010), which in turn

decrease consumers’ (perceived) risk (Choi, 2003) and increase their convenience (Stremersch

and Tellis, 2002).

Therefore, car manufacturers such as Tesla or Porsche have recently started to sell PV

systems or energy storages (cf. Porsche Holding, 2018; Tesla Motors, 2018). Also, a study

published by the German Federal Association for Solar Economy (Bundesverband für

Solarwirtschaft) in 2018 suggests that nine out of ten potential EV drivers living in a house

Paper 3 106

consider purchasing a PV system once they purchase, or even before they purchase, an EV

(BSW Solar, 2018). These findings emphasize the relevance of products producing renewable

energy for EV stakeholders. Moreover, they reveal the importance of better understanding

potential EV drivers’ preferences and their willingness to pay (WTP) for EV-PV-BS product

bundles.

In fact, a growing literature stream has already been studying the adoption of EVs and

related consumer preferences from different angles (Liao et al., 2017; Rezvani et al., 2015).

Liao et al. (2017) quite recently reviewed EV consumer preference studies and concluded that

(1) EV related attributes, such as infrastructure (e.g., charging points), policy incentives (e.g.,

purchase subsidies), financial attributes (e.g., purchase price), and technical attributes (e.g.,

range, charging time), as well as (2) potential EV drivers’ characteristics, such as socio-

demographic and psychological factors, EV experience, and social influences are the major

factors influencing or moderating EV utility, and hence the intention to purchase an EV.

However, in contrast to research on consumer preferences for EV related attributes (e.g., Axsen

et al., 2016; Bailey and Axsen, 2015; Brownstone et al., 2000; Bunch et al., 1993; Ewing and

Sarigöllü, 2000; Hackbarth and Madlener, 2016; Hoen and Koetse, 2014) and WTP for EV

attribute improvements (e.g., Axsen et al., 2016; Ewing and Sarigöllü, 2000; Hackbarth and

Madlener, 2016; Ida et al., 2014; Tanaka et al., 2014), literature on preferences for EV product

bundle attributes and related WTP is quite scattered, and hence needs further research

(Cherubini et al., 2015).

On an attribute preference level, the current EV product bundling literature seems to

agree that specific products, services, and infrastructure bundled in EV sales could improve the

evaluation of an EV and hence its acceptance. In this respect, Hinz et al. (2015) monitored how

single add-on services, such as intelligent charging systems, vehicle-to-grid (V2G) systems, IT-

based parking, or charging station finders, affect the acceptance of EVs. Fojcik and Proff (2014)

similarly added alternative mobility concepts to an EV, e.g., tickets for using other modes of

transport such as busses or trains, the possibility of renting a traditional internal combustion

engine vehicle for trips longer than 150 km, or the opportunity to use an EV via sharing instead

of owning one. They also tested for an increase in EV acceptance across different adopter

segments. Both papers concluded that such add-on services or products could increase clients’

EV purchase intention. Other studies bundled EVs with home charging stations (e.g., Tanaka

et al., 2014), interconnected charging infrastructure, and charging platforms that include other

e-mobility services (Ensslen et al., 2018) or EV charging infrastructure in general (Brownstone

et al., 2000; Potoglou and Kanaroglou, 2007). Some have argued that the willingness to

Paper 3 107

purchase an EV increases if the availability of charging infrastructure increases, or given the

possibility of charging at home. More recent research focused on combining EVs with V2G

add-on services (Hidrue and Parsons, 2015; Parsons et al., 2014; Sovacool et al., 2017). Parsons

et al. (2014) show that the availability of upfront payments or pay-as-you-go for V2G services

linked to EVs could increase the market acceptance of EVs. Then, Hidrue and Parsons (2015)

argue that the uptake of V2G-EVs will depend on further cost degression effects. Related to

bundling an EV with renewable energy production systems, Delmas et al. (2017) investigated

the demand for EVs combined with PVs in California, forecasting a significant increase in such

EV-PV bundle purchases due to these products’ complementarity. To accelerate its diffusion

rate, they also called for further research on these types of bundles.

On a WTP for attribute level, the (admittedly scarce) EV product bundling literature

argues that WTP for an EV seems to increase if offered in a bundle. The studies of Fojcik and

Proff (2014) and Mau et al. (2008) estimated WTP for add-on services, also proving a higher

WTP for EVs offered in a bundle with supplementary services or warranties. Similar WTP

effects were shown for V2G services (Parsons et al., 2014). Ensslen et al. (2018) quite recently

tested whether EV product service systems (i.e., EVs are used with an interconnected charging

infrastructure and charging platform) support EV adoption in corporate environments. They

found a high WTP for EV product service systems, but WTP for “e-mobility charging

infrastructure and services alone is currently not sufficient to cover corresponding actual costs”

(Ensslen et al., 2018: in press).

A review of literature on the influence (potential) EV drivers’ characteristics have on

EV preferences and WTP identifies a similar lack of research in the area of EV product bundles.

The general characteristics of early and potential EV adopters have been quite widely analyzed

in different countries (Liao et al., 2017), and literature eventually suggests that socio-

demographics, psychological motives, and experiences with EVs have some influence on EV

adoption and WTP (see Li et al. (2017) for details). For instance, Nayum et al. (2016) argue

that early adopters of EVs are highly educated, have above average income, tend to be young

to middle-aged, and live in a multi-car household. Plötz et al. (2014) confirm this, adding that

early adopters are predominately male and live in small to medium-sized municipalities.

Priessner et al.’s (2018) study on early and potential adopters in Austria found socio-

demographic variables to be relatively weak predictors for EV purchase intention (only gender,

household size, and number of cars per household had predictive power), but against that, they

find psychological characteristics, such as pro-technological and pro-environmental attitude, as

well as an egalitarian worldview to have a positive impact on EV adoption. Several other

Paper 3 108

researchers in the field, similarly found that people with an environmental or technology-

oriented lifestyle are more likely to purchase EVs, as well as have a higher WTP for EVs (Axsen

et al., 2016; Carley et al., 2013; Hidrue et al., 2011; Nayum et al., 2016). An additional and

often researched lever for early EV adoption and higher WTP is EV experience (Bühler et al.,

2014; Carley et al., 2013; Schmalfuß et al., 2017; Skippon and Garwood, 2011). Bühler et al.

(2014) concluded that EVs are perceived and evaluated more positively by people who have

already driven an EV. Moreover, they concluded that the willingness to pay a premium for an

EV also increases slightly with EV driving experience.

Our literature review, therefore, points out two gaps in research which we aim to address

with this study. First, although EV bundling with add-on products or services has increasingly

become important for the success of electromobility (Laurischkat et al., 2016), no study so far

has analyzed (potential) adopters’ preferences, as well as WTP for EV-PV-BS product bundles.

Cherubini et al. (2015) already called for research on EV product bundles, since they suggested

bundling as one lever to increase EV adoption. Our paper follows this research call and

contributes to a better understanding of potential EV adopters’ product bundle preferences and

WTP. Second, although several studies analyze the socio-demographic characteristics,

psychological motives and experiences of EV lead users and potential EV adopters, to date

there is no analysis of how potential EV adaptor characteristics and EV experiences can

influence consumer preferences and their WTP for EV product bundles. Such knowledge is

essential for properly growing the EV market (Axsen et al., 2016). Building on the EV literature

described above, we hypothesize that socio-demographic characteristics (age, gender,

educational level, income, housing situation), psychological characteristics (cultural

worldview, pro-environmental attitude, and technology readiness), and EV experience

influence the preferences and WTP for EVs purchased in a bundle with PV or PV and BS.

Against this background, our paper addresses the following research questions: (1) What

is the WTP for specific EV product bundle attributes, and (2) to what extent are consumer

preferences and WTP for EV product bundles influenced by socio-demographic and

psychological parameters, as well as by EV experience? To answer our research questions, we

conducted a web-based survey and conjoint experiment with 616 potential EV drivers in

Austria. Based on this data we could determine customer preferences and the importance of

individual product attributes in consumer choice. We then calculated the WTP for attribute

levels. In addition, we modelled the impact of socio-demographic characteristics, psychological

characteristics, and EV experience on consumer preferences and WTP for EV product bundles.

Paper 3 109

Our paper is structured as follows: in section 2, we give information on our

methodological approach and dataset. Section 3 presents and discusses the results of our survey

and choice experiment, including WTP calculations. Section 4 concludes the paper and

discusses implications for research, marketers, and policy makers, as well as limitations and

areas for further research.

2. METHODOLOGY AND DATA

2.1 Conjoint analysis

Since the objective of our study is to investigate consumer preferences and WTP, we

used conjoint analysis as methodology (cf. Hinnen et al., 2017; Kaenzig et al., 2013). This

method is well suited to evaluate individuals’ preferences for hypothetical, but still realistic

purchase decisions. Developed and introduced by Luce and Tukey (1964) in mathematical

psychology in the mid-sixties of the last century, it has recently gained more and more

importance in a variety of research fields, such as marketing (Green and Srinivasan, 1990) or

entrepreneurship (Brundin et al., 2008). Moreover, conjoint analysis have been widely used in

recent studies investigating preferences for EVs (Beggs and Cardell, 1980; Brownstone et al.,

2000; Bunch et al., 1993; Ewing and Sarigöllü, 2000; Hoen and Koetse, 2014), WTP for EVs

(Hackbarth and Madlener, 2016; Hidrue et al., 2011; Parsons et al., 2014), for EV product

bundles (Fojcik and Proff, 2014; Hinz et al., 2015), or for clean technology product bundles

(Agnew and Dargusch, 2017; Galassi and Madlener, 2016; Ida et al., 2014; Oberst and

Madlener, 2015a).

The most widely applied conjoint design in research and practice is choice-based

conjoint (CBC) (Orme, 2009; Orme and Chrzan, 2017). The theoretical foundation for such

analyses is the classical utility theory which has two assumptions. First, every individual has a

certain utility maximization attitude. Second, every product or service has a certain utility for

each individual, which can be defined as the sum of the part-worth utilities for the various

attributes of this product or service (Lancaster, 1966; McFadden, 1986). Based on this theory,

products can be described by their most important attributes, and individual preferences for

attributes can be indirectly revealed in CBC experiments. In these experiments respondents

have to select their preferred option (dependent variable), from a range of choice objects (in

this study hypothetical EV-PV-BS bundles). Since the respondent repeats such a choice task

several times with varying attribute levels (independent variables), he or she needs to make

trade-offs between desired attributes. From the decisions made in the choice tasks the

Paper 3 110

underlying preference can effectively be elicited in the form of average part-worth utilities for

the attribute levels and relative importance weights for each of the attributes (Green and Rao,

1971; Green and Srinivasan, 1990; Gustafsson et al., 2013; Orme and Chrzan, 2017).

In market research, conjoint analysis studies are considered superior to simply asking

for consumer decision criteria, because people have little insight into their decision-making

rationale, or answers might be influenced by recall bias or other information recovery failures

(Golden, 1992). Additionally, direct answers related to preferences are often biased by social

desirability issues (Gustafsson et al., 2013). Another benefit of this approach is the opportunity

to simulate various product attributes in a controlled experimental setting in order to anticipate

and simulate specific choice contexts. This allows us to distill a number of implications for

policy makers and marketers (Ben-Akiva et al., 1994). Further, conjoint analysis is particularly

useful in immature markets for improving the product design or offering to best satisfy market

demand (Gustafsson et al., 2013; Louviere et al., 2000). Our choice to conduct a conjoint

experiment, was strengthened by our consideration that the products on which our study

focusses are still at the beginning of their diffusion process and currently not even sold in

bundles. Also, the methodological challenges that scholars have raised (e.g., Jaeger et al., 2001;

Louviere et al., 2008; McFadden, 1986), are constantly improving (e.g., Chapman et al., 2009;

Jaeger et al., 2001). Some limitations still assure the mainly exploratory nature of conjoint

analysis, which makes it well suited to investigating our identified research gaps and so

contributing in the area of EV-PV-BS product bundles.

2.1.1 Selection and description of conjoint attributes and levels

Selecting the relevant attributes and levels is the most critical part in a conjoint analysis

and hence must fulfil certain criteria. According to Bergmann et al. (2006) the attributes need

to be (1) relevant to the problem definition, (2) genuine and plausible, (3) understandable to

each respondent, and (4) provided with informative context.

Hence, we choose an elaborated iterative process to identify the most relevant attributes

and levels. To start with, we reviewed literature on EV product bundles (Delmas et al., 2017;

Ensslen et al., 2018; Fojcik and Proff, 2014; Hinz et al., 2015). Delmas et al. (2017) investigated

the joint offering of EVs and PVs, and argued that four parameters (i.e., price reduction, quality

improvements, innovative financing models, and policy subsidies) might positively influence

future demand for such product bundles. We took these suggested parameters together with

other criteria derived from literature on consumer preferences for PV and BS as a point of

departure (Agnew and Dargusch, 2017; Ida et al., 2014; Oberst and Madlener, 2015a). Next,

Paper 3 111

we conducted a qualitative analysis of product offerings on web-pages of major car

manufacturers and PV/BS producers and retailers in the German speaking area. Additionally,

we researched the policy incentive levels for EVs, PV and BS in Austria (BMVIT, 2017;

BMWFW, 2017; Photovoltaic Austria, 2018) 35 . As a next step, we conducted sales

conversations with EV, PV, and BS sellers to get more insight on the extant technological

standards and product benefits. Moreover, in semi-structured interviews with lead users of these

products, we talked about their purchase decision criteria and their WTP for each product

separately and in bundles. These conversations and interviews were conducted between August

and November 2017. Based on this qualitative data we identified a list of relevant attributes and

levels for the CBC design, which we refined once more, and then shortlisted in four expert

discussions with one car retailer, two utility company representatives focused on PV and BS

products, and one consultant focused on renewable energies and future of mobility. Finally, we

verified the interpretation of the attributes and levels in a pre-study with 45 respondents. A

subsample of these were also briefly interviewed to get additional feedback after they had

completed the survey.

To reduce the complexity of the choice experiment with three products (EV, PV, and

BS) for respondents, we decided to ask our interviewees whether they would be interested in

purchasing a PV system with or without BS as add-on, bundled with an EV36. This would draw

attention more to the bundle, than to the parameters of an EV. The latter have already been

researched quite comprehensively (cf. Liao et al., 2017). Further, the CBC literature suggests

limiting the number of independent variables to no more than six (Green and Srinivasan, 1990;

Orme, 2009). Hence, we finally selected six attributes for the CBC experiment, namely PV/BS

add-on (ownership), self-sufficiency rate, amortization period, policy incentive, provider, and

purchase price (see Table 1 for overview). To avoid a number-of-levels effect, the conjoint

design is symmetric with four levels per attribute, except for five levels for the pricing attribute

(Chapman et al., 2009).

The attribute PV/BS add-on (ownership) comprised two parameters. EVs could be either

bundled with a PV standalone or in a bundle with BS (Agnew and Dargusch, 2017). Further,

we included two financing options (Delmas et al., 2017), the first with ownership and no further

payments, and the second a non-ownership/leaser model with further payments , but no initial

35 At the time of the survey, Austria had different financial incentives for BS at the regional level. Since then, at

the federal level, a supplementary investment subsidy has been introduced for BS combined with a PV. In addition,

a national and several regional incentive programs incentivize the purchase of an EV. 36 The car of choice had the same characteristics as the Nissan Leaf 2.0, which was released at the beginning of

2018. The Nissan Leaf model was the world’s best-selling electric car in 2017 (Bloomberg (2017).

Paper 3 112

investment costs, as is reflected in the purchase price. If respondents were not interested in such

add-on products at all, they could still choose a none option, i.e., to purchase an EV only. This

attribute allowed us to assess whether potential EV adopters are interested in PV and BS add-

on products, whether they prefer PVs in a bundle with BS, and which ownership-/financing

option they find most preferable.

TABLE 1. ATTRIBUTES AND ATTRIBUTE LEVELS IN THE CHOICE-BASED CONJOINT DESIGN

Attributes Level 1 Level 2 Level 3 Level 4

PV/BS add-on

(ownership)

PV + BS owner (no

monthly payment)

PV owner (no

monthly payment)

PV + BS leaser with

ownership option

(monthly payment)

PV leaser with

ownership option

(monthly

payment)

Self-sufficiency rate Up to max. 25% Up to max. 50% Up to max. 75% Up to max. 100%

Amortization period 8 years 12 years 16 years 20 years

Provider All-in-one car

dealer/OEMb

All-in-one utility All-in-one specialist

dealer

Diverse specialist

dealers

Policy incentive 0% Up to max. 10% Up to max. 20% Up to max. 30%

Purchase pricea EUR 25,000 EUR 30,000 EUR 35,000 EUR 40,000

a Purchase price has a 5th level at EUR 45,000. b Original equipment manufacturer.

The levels of the attribute power self-sufficiency rate reflect a range from 25% to 100%,

i.e., off-grid with full self-supply of power (Ida et al., 2014; Oberst and Madlener, 2015). Very

high self-sufficiency rates (> 50%) are only feasible with BS (Agnew and Dargusch, 2017).

However, we included the option to install a PV system with a power self-sufficiency rate of

up to 100%, which comprises the possibility of selling excess power back to the grid and so

gaining credit for future power purchases, or of storing the electricity in virtual power storages

provided by, e.g., utility companies. Such offerings are already available in the Austrian market

(KELAG, 2018; Wien Energie, 2018). This attribute allows us to evaluate which level of power

self-supply will be needed to increase acceptance of such a type of product bundle.

The attribute amortization period comprises four levels 8, 12, 16, and 20 years (Oberst

and Madlener, 2015a). Galassi and Madlener (2016), in contrast, fixed this level at 20 years.

With the current market prices for PV and BS, and an electricity price in the midfield of the

European Union (EUROSTAT, 2017), the current average amortization period for PV and BS

add-on products in Austria lies between 15 and 20 years, depending on the level of subsidies

received (cf. KELAG, 2018; Wien Energie, 2018). A survey by Hampl and Sposato (2018)

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indicates an on average preferred amortization period of 10 years for PV systems in Austrian

households. With this variable we can test what amortization period potential EV adopters

would request for entire product bundles compared to purchasing individual products.

The levels of the attribute provider reflect the range of possible suppliers, from all-in-

one solutions provided by a car retailer, a utility company, or a specialty dealer to a set of

separate dealers providing these products. Richter (2013) already claimed that particularly for

small-scale renewable energy technologies such as PV or BS, an all-in-one business model is

recommendable for successful commercialization over scattered purchases. Such a product

bundle is not on the market yet, except for some pilot offerings by Tesla in the US (2018). But

since all three products are at the beginning of their diffusion curve and there is no clarity on

customer preferences regarding providers, our study specifically aims to assess the importance

of all-in-one providers for EV-PV-BS products. Further, the results will provide insight on the

type of company that the respondents prefer as a one-stop provider.

Policy incentives play a crucial role in EV acceptance (Lieven, 2015; Sierzchula et al.,

2014), and hence also in EV product bundle acceptance. Currently, the Austrian government

offers a broad range of incentives for EVs, PVs, and BS (BMVIT, 2017; BMWFW, 2017;

Photovoltaic Austria, 2018), with some regional differences. Hence, for simplicity, the levels

of this attribute range from 0% to 30% of total subsidies on the purchase price, corresponding

roughly to the subsidy offering in Austria at the time of the survey. By including the attribute

policy incentive, we can determine whether customers are willing to accept higher prices if the

EV-PV-BS product bundle is subsidized, what level of subsidies they consider to be sufficient,

or whether policy incentives are needed at all.

The levels of the attribute purchase price range from EUR 25,000 to EUR 45,000 and

are based on current list prices of the products included in the bundle (KELAG, 2018; Nissan,

2018; Sonnen, 2018). Previous research has shown that the purchase price is considered the

most important decision criterion (Agnew and Dargusch, 2017; Galassi and Madlener, 2016;

Hackbarth and Madlener, 2016; Hidrue et al., 2011; Oberst and Madlener, 2015a). The attribute

allows for estimating customers’ implicit WTP for the different levels of the other product

features, and makes market share forecasts possible in case of further EV, PV, and BS cost

decreases (Orme and Chrzan, 2017).

For this study, our respondents were invited to compare and show preferences in a series

of 12 choice tasks. We created a full-profile design using Sawtooth Software37, thus showing

37 One of the frequently used conjoint analysis software solutions in marketing research (cf. Hinnen et al., 2017;

Kaenzig et al., 2013; Kaufmann et al., 2013; Salm et al., 2016).

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all attributes at the same time for each product in each choice task. Each of the choice tasks

presented three different product bundle alternatives and a none option (if the person were to

prefer an EV38 without the power-supply add-on products) from which the respondents had to

choose their preferred option. An example of a choice task is illustrated in Figure 1.

FIGURE 1. SAMPLE CHOICE TASK

2.1.2 Estimation algorithm

For data analysis we estimated individual part-worth utilities using a Hierarchical Bayes

(HB) model (Rossi and Allenby, 2003) implemented in Sawtooth Software. Recent studies

show that the results from an HB and traditional mixed-logit model are very similar (Chassot

et al., 2014; Hampl and Loock, 2013; Salm et al., 2016). The HB model has the advantage of

measuring preferences both on an individual level and, as is traditional, on an aggregated level.

By doing so, HB acknowledges the heterogeneity in consumer preferences. This is possible due

to the HB algorithm’s “hierarchical” nature, which means that HB consists of (1) a lower and

(2) an upper level (cf. Gamel et al., 2016; Kaenzig et al., 2013; Sawtooth Software, 2009). At

the lower (i.e., individual) level, the general assumption is that the probability of the ith

38 The parameters of the EV in each choice task were fixed at a 400 km range, 150 horse power, 40-60 minutes

per full-charging, which are the characteristics of the Nissan Leaf 2.0 (Nissan (2018).

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individual choosing one option (k) among a set of options (j) is determined by a multinomial

logit model which can be described as follows (Sawtooth Software, 2009):

𝑝𝑘 = exp(𝑥′

𝑘𝛽𝑖)

∑ exp(𝑥′𝑗𝛽𝑖)𝑗

pk … probability that an individual i chooses the kth alternative in a given choice task.

xj … a vector of values describing the jth alternative in that choice task.

ßi … a vector of part worths for the ith individual.

At the upper level, the individual responses are pooled by assuming that the individuals’

part worths are described by the multivariate normal distribution ßi ~ Normal(α, D), where the

part-worth utilities (ßi) of the ith respondent are distributed with a vector of means α and a matrix

D of variances and covariances of the distribution of part worths across individuals (Sawtooth

Software, 2009).

The model parameters are derived from an iterative process applying a Monte Carlo

Markov Chain algorithm (cf. Gamel et al., 2016; Kaenzig et al., 2013; Sawtooth Software,

2009). To ensure convergence of the parameters we followed the approach proposed by

Sawtooth Software that recommends deleting the first 10,000 draws as burn-in of a total of

20,000 draws per respondent (Sawtooth Software, 2009). For a more detailed description of the

iterative estimation process of the parameters see (Sawtooth Software, 2009).

2.2 Measurement of socio-demographic and psychological parameters

In order to test the influence of socio-demographic and psychological characteristics on

private individuals’ preferences for EV-PV-BS product bundles, a set of variables (Table A.1

in Appendix) were used which were derived from the literature review in section 1. We

measured the variables in the course of the questionnaire accompanying the CBC experiment,

also using Sawtooth Software. On the one hand, socio-demographic variables such as gender,

age, educational level, income, and housing situation (apartment vs. house) were elicited. In

addition, we requested the respondents to indicate their experience with EVs. Answers were

given on a 4-point Likert scale with values ranging from (1) I own an electric car / owned an

electric car to (4) I have no EV experience at all.39

39 This variable was recoded in the covariate model to assess the impact of more EV experience.

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On the other hand, the survey comprised statements designed to measure each

respondent’s cultural worldview, pro-environmental attitude, and technology readiness (cf.

Priessner et al., 2018). Following Cherry et al. (2014), we used eight items to measure the

cultural worldviews. This abridged version of the scale, which was originally developed by

Kahan et al. (2007), includes statements such as “The government should do more to pursue

social goals, even if it means restricting the freedom and choice of the individual”

(individualism-communitarianism) or “Our society would be better off if the distribution of

wealth were more equal” (hierarchism-egalitarianism). Answer options were presented on a 5-

point Likert scale ranging from (1) strongly disagree to (5) strongly agree. The scale had a

reliability score of α = 0.70 (“communitarian worldview”) and α = 0.56 40 (“egalitarian

worldview”). To test the influence of pro-environmental attitude (α = 0.69) we applied the scale

of Whitmarsh and O'Neill (2010) relying on four items. The scale includes items such as “Being

environmentally friendly is an important part of my personality”, which had to be rated on a 5-

point Likert scale ranging from (1) strongly disagree to (5) strongly agree. Technology

readiness (α = 0.85) was operationalized as participants’ agreement on a 5-point Likert scale,

ranging from (1) strongly disagree to (5) strongly agree), using eight statements from the

Technology Readiness Index (Parasuraman, 2000; Parasuraman and Colby, 2015) related to a

general attitude toward technology, such as “Technology gives people more control over their

daily lives.” Responses to the items of the cultural worldviews scale were averaged per

dimension so that, e.g., a higher score on the individualism-communitarianism questions

indicates a more communitarian worldview. The aggregation of responses for pro-

environmental attitude and technology readiness follows a similar logic, i.e., respondents with

higher values are perceived to have a more positive environmental attitude and a higher

technology readiness (see Table A.3 in the appendix for a summary and details on the

psychological variables).

2.3 Sample

The target population of this survey consisted of Austrians aged between 18 and 75

years who indicated an intention to purchase an EV within the next decade. Therefore, in our

sampling process we applied two filter questions at the beginning of the questionnaire. First,

respondents needed to indicate their attitude towards EVs on a scale from (1) very negative to

40 The egalitarian worldview was measured in the survey but was not included in the final model due to minimal

effect on potential EV adopters’ preferences and its low reliability score in the Austrian setting.

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(4) very positive.41 A positive attitude ((3) positive and (4) very positive) is a good predictor of

both an EV purchase (Nayum et al., 2016) and the general acceptance of innovative products

(Jhang et al., 2012).

Second, the respondents needed to indicate the timeframe in which they intended to buy

an EV. Several research papers filtered potential EV drivers on the intention to purchase an EV

as their next car, but did not qualify the purchase intention related to the time horizon (Axsen

et al., 2015; Hackbarth and Madlener, 2013; Mabit and Fosgerau, 2011). However, the EV

market is still a very small niche in that 1.6% of all cars newly registered in the first half of

2018 in Austria were EVs (STATISTIK AUSTRIA, 2018a), and the uptake rates in Europe are

increasing, but slower than expected (IEA, 2018). Further, the average car age in Austria is 9.1

years (European Car Manufacturer Association, 2016). Therefore, we considered people willing

to purchase an EV in more than 10 years or without planning possibly to purchase an EV, as

not likely to purchase an EV as their next car. Hence, we excluded them from our target group

sample.

The respondents for the survey were recruited by the professional market research

company market in Spring 2018. They used their online panel pool of more than 20,000 active

users in Austria to invite interviewees via e-mail. Using participants from a panel pool has the

benefit of the pool’s experience with longer surveys and with choice experiments. Hence, we

could ameliorate Jaeger et al.’s (2001) criticism that CBCs get more accurate results if

participants are accustomed to CBCs due to training effects. The sample was drawn by quota

sampling, considering the distribution of gender, target population by federal state, and age. A

total sample of 1,251 survey participants were invited, of which 660 fulfilled both selection

criteria. We cleaned the sample by removing 44 speeders42 and flatliners43 (see filter funnel in

Figure 2).

Given the fact, that the market research company performed an iterative process in

recruiting respondents to ensure that the sample fulfills the predefined criteria for

representativeness, we cannot report a response rate, i.e., a ratio of participants of the survey

over the total number of potential interviewees approached. Consequently, the data might

41 For those indicating no attitude or preference towards EVs, we included an additional answer option: “I do not

know / I cannot say.” 42 Respondents who were among the fastest 10 percent in reading the instructions of the CBC experiment (less

than 20 seconds, mean 65 seconds), and who completed the CBC experiment among the fastest 5 percent of

respondents (less than 77 seconds, mean 158 seconds). 43 The average root likelihood (RLH) can be used as a measure of fit to assess data quality. In this study, as each

choice task presented four alternatives, the RLH predicts that each alternative would be chosen with a probability

of 25% (corresponding RLH of 0.25). All answers below 0.25 counted as “flatliners.”

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comprise some non-response bias. Nevertheless, as described in Table A.2 in the Appendix, the

total sample represents the average Austrian population quite well in terms of gender, age, and

federal state. Only the final sample used for data analysis differed in income distribution, having

a higher proportion of better earning respondents. Further, people with a university education

seem to be somewhat over-represented in the sample, but similar differences with respect to

education level have been found in other EV studies (Axsen et al., 2016).

FIGURE 2. FILTER LOGIC FROM TOTAL SAMPLE (N = 1,251) TO FINAL SAMPLE (N = 616)

3. RESULTS AND DISCUSSION

In this section, we discuss the results of the conjoint analysis: (1) the relative importance

scores of each attribute, and (2) the part-worth utilities per attribute level. Subsequently, we

estimate the WTP for the features of an EV-PV-BS product bundle. In the last sub-section, we

present and discuss the results of a model (part-worth utilities and WTP) comprising covariates,

i.e., socio-demographic and psychological variables, as well as EV experience.

3.1 Relative importance of conjoint attributes

Our results are based on data from 616 future EV drivers with a positive attitude toward

EVs and an intention to purchase an EV within the next ten years. Each respondent conducted

12 choice tasks which leads to a total of 7,392 choices. One result from the CBC analysis gives

the relative importance scores of the different attributes, describing the size of each attribute’s

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influence on the purchase decision (in our case, the decision to purchase an EV-PV-BS product

bundle). The relative importance scores are calculated by subtracting the highest and the lowest

part-worth utility within each attribute, and then standardizing these values to a sum of 100%

across attributes.44 Table 2 displays the average importance scores.

The most important purchase decision criterion is the purchase price at 30.6%, which

is in line with other EV, PV, or BS studies (Agnew and Dargusch, 2017; Galassi and Madlener,

2016; Hackbarth and Madlener, 2016; Hidrue et al., 2011; Oberst and Madlener, 2015a).

Ranked second, is the PV/BS add-on ownership model at 18.7%. Shih and Chou (2011) already

argued that the higher people’s concerns about an investment in renewable energy technologies

(e.g., regarding reliability, policy subsidy, electricity price, development of new technologies),

the more they value short-term, expensive non-ownership (i.e., leasing) contracts. The criterion

in third position (power self-sufficiency) measured at 16.4% ranked closely to the fourth

(amortization period) which measured at 15.0%. This order could suggest, that becoming a

prosumer (i.e., producing and self-consuming power) is slightly more important to potential

adopters than making a fast amortizing investment, as has also been indicated by Oberst and

Madlener (2015). Interestingly, policy incentives and the type of provider are of minor

importance at respectively 11.2% and 8.1%. This result allows the conclusion that lead users of

an EV product bundle find who provides their products to be negligible. Galassi and Madlener

(2016) already showed that the sales channel is of least importance in the decision to purchase

PV and BS bundles. Further, policy incentives seem to be no major purchase motive, as other

studies on clean technologies have also shown (Zhang et al., 2013). Additionally, Sierzchula et

al. (2014) pointed out that although policy incentives are correlated with the increase in market

share of EVs, they cannot ensure high EV adoption rates. Therefore, for them governmental

incentives are important in the early stage of the diffusion curve.

3.2 Part-worth utilities of attribute levels

The analysis of CBC data provides information on the average impact a particular

attribute level can have on the respondent’s decision to purchase an EV-PV-BS product bundle.

Table 2 displays the average raw utilities (coefficient estimates) of the HB model with the

corresponding standard deviations and confidence intervals. Each value represents the change

in utility of the total product when altering one of the attribute levels while keeping all others

44 The derived importance scores are dependent on the selected attributes and the definition of the attribute levels

(Orme and Chrzan (2017). For instance, illustrating policy incentives in exact EURs instead of relative cost saving

percentages may increase the importance of policy incentives (cf. Kaenzig et al. (2013).

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equal. All values are zero-centered; thus, they sum up to zero within each attribute. This implies

that a positive coefficient increases and a negative coefficient decreases the utility of that

particular attribute level. The higher the value, the stronger the influence of the specific attribute

level on the purchase decision. The utility values are highly dependent on the selected range of

attribute levels. Therefore, it is only meaningful to compare utility values between different

levels of a given attribute (Orme, 2009). By converting part-worth utilities into monetary values

(see section 3.3. below on WTP) this scale effect can be eliminated, which then enables cross

attribute comparison (Orme, 2001). The none-option score stands for the utility potential EV

drivers gain if they do not choose any of the product bundles shown to the them. Hence, it can

be read as an investment threshold which needs to be exceeded by the sum of the utility values

for the attribute levels of the respective product bundle to trigger a potential purchase (Orme

and Chrzan, 2017).

As an indicator of data quality and measure of fit we used the average root likelihood

(RLH) (Orme and Chrzan, 2017). In this study, we assume that each alternative would be

chosen with a probability of one quarter (i.e., the RLH threshold is 0.25). The RLH was 0.65,

which indicates a good model fit (i.e., our model is 2.6 times better than the random chance

level).

TABLE 2. PART-WORTH UTILITIES OF THE DIFFERENT ATTRIBUTE LEVELS FOR THE DECISION TO

PURCHASE AN EV-PV-BS BUNDLE

(n = 7,392 choices made by 616 respondents)

Attributes and attribute levelsa Mean

Standard

deviation

Lower

95% CIc

Upper

95% CIc

PV/BS add-on (ownership) (m = 18.75%; SD

= 10.19)b

PV owner (no monthly payment) 0.32 1.05 0.24 0.40

PV + BS owner (no monthly payment) 0.75 1.11 0.66 0.83

PV leaser with ownership option (monthly

payment)

-0.65 1.06 -0.73 -0.57

PV + BS leaser with ownership option (monthly

payment)

-0.42 1.10 -0.51 -0.33

Self-sufficiency rate (m = 16.00%; SD = 7.73)

Up to max. 25% -0.99 0.82 -1.05 -0.93

Up to max. 50% -0.27 0.52 -0.31 -0.23

Up to max. 75% 0.38 0.51 0.34 0.42

Up to max. 100% 0.87 0.87 0.81 0.94

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Amortization period (m = 15.00%; SD =

6.65)

8 years 0.82 0.67 0.76 0.87

12 years 0.31 0.39 0.28 0.35

16 years -0.24 0.56 -0.28 -0.19

20 years -0.89 0.65 -0.94 -0.84

Provider (m = 8.08%; SD = 4.32)

All-in-one car dealer/OEMd 0.03 0.42 -0.00 0.06

All-in-one utility 0.10 0.49 0.06 0.14

All-in-one specialist dealer 0.17 0.37 0.14 0.20

Diverse specialist dealers -0.30 0.38 -0.33 -0.27

Policy incentive (m = 11.15%; SD = 5.21)

0% -0.66 0.55 -0.70 -0.61

Up to max. 10% -0.03 0.40 -0.11 -0.05

Up to max. 20% 0.22 0.37 0.19 0.25

Up to max. 30% 0.51 0.55 0.47 0.56

Purchase price (m = 30.62%; SD = 12.39)

EUR 25,000 1.82 1.37 1.71 1.92

EUR 30,000 0.93 0.65 0.88 0.98

EUR 35,000 0.28 0.41 0.25 0.32

EUR 40,000 -0.92 0.83 -0.98 -0.85

EUR 45,000 -2.11 1.14 -2.20 -2.02

None option -0.73 6.66 -1.26 -0.20

a Coefficient estimates are equal to the posterior population means across the saved draws, interval-scaled and

zero-centered within attributes. b Mean relative importance scores per attribute (m) and corresponding standard deviation (SD) in parentheses.

The importance scores sum up to 100%. c Confidence interval.

3.3 Willingness to pay for attribute levels

Conjoint analysis also enables conversion of the part-worth utilities to aggregated

monetary WTP values (Green and Srinivasan, 1990; Orme, 2010). This approach is commonly

applied in clean technology research with slightly different calculation methods and denotations

(Hackbarth and Madlener, 2016; Ida et al., 2014; Kaufmann et al., 2013; Salm et al., 2016).

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In this study, we describe the WTP relative to a product bundle default option (i.e., EV

+ PV non-ownership, 25% sufficiency rate, 20 years amortization period, diverse specialist

dealers, 0% policy incentive). Our calculations aim to illustrate the willingness to pay a

premium for more desired product features. Thus, the WTP formula is determined as follows

(cf. approach in Salm et al., 2016):

𝑊𝑇𝑃 (𝑢𝑖𝑗) = (𝑢𝑖𝑗 − 𝑢𝑖𝑗 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 ) ∗ 𝑝𝑚𝑎𝑥 − 𝑝𝑚𝑖𝑛

𝑢𝑝𝑗 𝑚𝑎𝑥 − 𝑢𝑝𝑗 𝑚𝑖𝑛

This approach involves calculating the difference between the part-worth utility (uij) of one

attribute level (j) (e.g., 8 years) and the default part-worth utility (uij Default) (i.e., 20 years) within

the same attribute (i) (e.g., amortization period). This difference is then multiplied by the price

of one utility unit (i.e., difference between the highest (pmax) and lowest (pmin) possible price)

divided by the utility difference between the highest and lowest price (upj max – upj min) (Orme,

2010). The results of these WTP calculations are displayed in Figure 3.

Before interpreting the WTP results, we want to refer to the explorative nature of our

analysis. Our study did not aim to calculate precise WTP values. We merely intended to test

the joint effects of different attributes and levels in an experimental setting which would allow

us to derive WTP estimates. Compared to a direct questioning approach, the indirect preference

measurement of a conjoint analysis has the advantage of overcoming biases such as social

desirability, which occur particularly commonly in decisions related to environmental issues

(Diekmann, 2017). Moreover, CBC is considered very suitable for testing preferences and WTP

for hypothetical products (Gustafsson et al., 2013). Still, a CBC design remains experimental

in that the respondent does not actually have to pay the price he or she indicates to be willing

to accept. Such a setting results in a gap between hypothetical and real WTP, referred to as a

“hypothetical bias” (List et al., 2006; Orme, 2001). In addition, actual WTP in real life depends

on several other aspects, such as status-quo bias (Samuelson and Zeckhauser, 1988) or

competition (Orme, 2001).

Considering the above, some scholars argue for complementing conjoint-based WTP

with additional data from incentive-compatible procedures, such as the Becker-DeGroot-

Marschak method (BDM) or actual point-of-purchase contexts that would reveal more accurate

WTP values (Wertenbroch and Skiera, 2002). However, despite being of high managerial and

research interest, EV-PV-BS product bundles are not available on the market yet. Therefore, in

the absence of sufficient BDM or actual purchase data, we have to rely on conjoint data. Further,

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Miller et al. (2011:172) found that despite hypothetical bias, WTP values calculated on the basis

of conjoint data “still lead to the right demand curves and right pricing decisions.” Overall, the

WTP values presented here should be interpreted as upper boundaries and hence should be used

carefully.

One major insight this analysis brought, relates to the gap between WTP for EV add-on

products and current market prices (reference date October 2018). We noted an average WTP

for an increased self-sufficient energy supply (from 25% to 100%) at approximately EUR

9,500. In comparing the estimated WTP figures to current market prices for PV and BS in the

Austrian market(KELAG, 2018; Wien Energie, 2018)45, we identified a potential gap of 15 to

30%46. Nevertheless, assuming further cost curve effects of battery price47 toward USD 190 by

the end of this decade (and below the desiderated level of USD 100 per kWh by 2030)

(McKinsey, 2017) and further cost decreases for PV systems (the cost per watt has reduced by

11.4% per year, on average, since 197048 (Seba, 2014)), the market for EVs and their add-on

products is bound to become economically viable in the next decade (Seba, 2014).

Another interesting insight relates to the linearity of the WTP regarding improvements

in the self-sufficiency rate and amortization period. Following a clear order, the shortest

amortization period (i.e., 8 years) and the highest self-sufficiency level (i.e., 100%) have the

highest monetary value. Further, our WTP analysis reveals a similar WTP for the fastest

amortization and highest self-sufficiency attributes. Oberst and Madlener (2015) similarly

found that customers desire the highest self-sufficiency rate, as well as the fastest payback

periods for PV systems, as did Agnew and Dargusch (2017) for BS. Both the latter papers,

however, identified slightly diminished utilities toward the optimum value, which our research

did not. However, this is not considered unusual when conjoint analysis is applied in product

design research (Orme, 2010). Interestingly, there is a conflict of interest between these two

attributes, because with the current product offering one cannot achieve both optima; this is

only possible if virtual product storages are implemented (KELAG, 2018; Sonnen, 2018).

Regarding policy subsidy, potential investors assigned the highest WTP to the maximum

subsidy level, which is unsurprising. However, irrespective of the subsidy level, we noted that

the indicated WTP amounts are below the expected value of the incentive levels (e.g., 30% of

45 On average the PV + BS cost for a household with an annual electricity consumption of 5,000 to 7,000 kWh lies

between EUR 15,000 and EUR 19,450 (reference date October 2018). 46 This WTP gap is the “best case” since the calculated WTP figures should be interpreted as upper limits. 47 “From 2010 to 2016, battery pack prices fell roughly 80% from ~$1,000/kWh to ~$227/kWh” (McKinsey, 2017:

p.10) 48 In 1970 the cost per watt generated by a PV system was USD 100, which decreased to about 33 cents per watt

in 2014 (Seba, 2014).

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EUR 45,000 = EUR 13,500 vs. EUR 6,011 WTP, equals 45% of real subsidy value; or 10% of

EUR 45,000 = EUR 4,500 vs. EUR 2,960 WTP, equals 66% of real subsidy value). This shows,

that with lower subsidy levels (e.g., 10%) the WTP is closer to the expected value of the subsidy

than with higher subsidy levels (e.g., 30%). Several studies in clean technology literature that

refer to, e.g., solar boilers, PV, energy conservation, co-generation of heat and power, and EVs,

indicate that policy subsidies hardly influence potential adopter decisions, and hence are often

under-valued (Kemp, 2000; Zhang et al., 2013). Fischer and Newell (2008) ranked the

effectiveness of subsidies for renewables as the second least effective measure out of six policy

options, but also added, that “an optimal portfolio of policies achieves emissions reductions at

a significantly lower cost than any single policy” (p. 142). Therefore, in line with Fischer and

Newell (2008), we conclude that policy makers should use some level of subsidy for products

that do not have an existing market, but then embedded in a set of policy incentives (including

taxes or regulations).

Concerning preferences regarding the provider, we find that switching from different

specialty dealers to certain all-in-one providers results in an increase in the WTP of between

EUR 1,693 and EUR 2,536. This suggests that respondents see value in purchasing all products

from one provider, but are not willing to pay a high premium for it. The results also indicate

that future EV drivers are largely indifferent about particularly who provides their product

bundle. While car manufacturers, utilities, and specialty dealers might all be interested in

offering such product bundles, the difference in WTP between these potential all-in-one

providers is rather low. The implications of this finding will be discussed in detail in section

4.2.

Lastly, we drew two specific conclusions from the findings on WTP for the attribute PV

+ BS add-on ownership model. First, our WTP data suggests that consumers would preferably

purchase PV and BS in a bundle, irrespective of whether they own or lease the bundle. This

seems to confirm the theory that bundling complementary products generates benefits for which

users, to a certain extent, are also willing to pay a premium (Reinders et al., 2010). Second,

respondents seem to differentiate between owning and leasing the add-on products in their

WTP. We notice a higher WTP for owning the PV and BS than for the non-ownership option.

An explanation here could be that people who prefer the non-ownership model experience

strong uncertainty regarding investment parameters such as price, product reliability,

technological development, or subsidy level, and hence are willing only to pay less for such

investments in total, and vice versa (Shih and Chou, 2011).

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Note: Attribute levels of the default product (PV leaser with ownership option, 25% sufficiency rate, 20 years amortization

period, diverse specialist dealers, 0% policy incentive) are marked with an asterisk (*).

FIGURE 3. WTP FOR ATTRIBUTE LEVELS OF EV-PV-BS PRODUCT BUNDLE (RELATIVE TO

DEFAULT)

3.4 Impact of socio-demographic and psychological characteristics on part-

worth utilities and WTP

One of our research objectives is to test the influence of future EV drivers’ socio-

demographic and psychological characteristics and EV experience on their preferences and

WTP for EV-PV-BS product bundle attributes. Hence, we included five socio-demographic

variables (age, gender, income, education, and housing situation), three psychological variables

(communitarian cultural worldview, pro-environmental attitude, technology readiness), and

EV experience as covariates in the model (see Table 3 for details). A similar approach has been

successfully applied by Gamel et al. (2016) who analyzed the impact of age, asset valuation,

and environmental attitude on private individual wind power investment preferences.

According to Orme and Howell (2009), models including covariates could provide

additional information about respondents’ preferences, and hence could improve the share of

preference predictions through more accurate parameter estimates. Our RLH score improved

from 0.65 (cf. base model in section 3.2) to 0.73 (covariates model), which is considered quite

substantial (cf. Orme and Howell, 2009).

Paper 3 126

As recommended in the literature and to facilitate interpretation of the parameter

estimates, we zero-centered the covariates before adding them to the HB model (Orme and

Howell, 2009). Consequently, the mean value of each variable is zero and positive values

indicate an above-average score (e.g., income) or above-average agreement with the statements

measuring psychological constructs (e.g., cultural worldviews).

The first column in Table 3 displays the intercept, which equals the part-worth utility

value of an attribute level when all parameters included in the model are set to the mean (i.e.,

zero in our case). The formula for calculating the individual part-worth utility Betax for any

attribute level x follows the following logic (cf. Gamel et. al, 2016):

Betax = Interceptx + Parameterx × ExpressionCovariate

The part-worth utility values of the intercept term and covariate levels presented in

Table 3 were further converted into monetary figures by multiplying these utility values with

the price of one utility point as described in detail in section 3.349. As all WTP calculations, this

one is also prone to drawbacks which we already pointed out in section 3.3. In section 4.2 we

will discuss further research opportunities by applying alternative WTP methods to our research

question. Nevertheless, our paper extends Gamel et al.’s (2016) approach by adding WTP

calculations considering covariates which offer additional genuine insights, as displayed in

Table A.4 in the Appendix.

Comparing the WTP values across the covariates, we find that the variables gender and

housing have the strongest impact on consumer preferences of EV-PV-BS product bundles of

all the socio-economic characteristics included in the analysis. The non-purchase utility

decreases if, compared to a woman, a man intends to purchase our clean technology product

bundle. Additionally, we find that men are willing to pay more for higher self-sufficiency rates,

faster amortization periods, non-ownership models, and utilities’ all-in-one solutions. Further,

men are less willing to accept low subsidy levels. The effect of gender on the (intended)

acceptance of new and/or clean technologies, i.e., of men being more likely accepters, has been

shown in several studies (Claudy et al., 2010; Cockburn and Ormrod, 1993; Li et al., 2017;

Mostafa, 2006; Plötz et al., 2014; Priessner et al., 2018). However, the effect of gender related

to environmental behavior and concerns has been controversially discussed in literature for

49 We used the utility values of the intercept (see Table 3) to calculate the price of one utility point.

Paper 3 127

decades, without having reached a definite conclusion (Diamantopoulos et al., 2003;

MacDonald and Hara, 1994; Zelezny et al., 2002).

People who live in apartments commit to higher utility/higher WTP EV-PV-BS

solutions if they are offered via leasing/non-ownership models, at a lower price, and with a

medium subsidy level. Further, their non-purchase threshold is significantly higher than that of

people living in houses. This result is quite intuitive, since until recently, people living in a

residential complex were not able to own a PV system in Austria (except for single PV panels

for balconies). However, since the beginning of 2018, Austrian legislation permits joint

generating plants in residential buildings where residents can also consume their self-generated

electricity (BMWFW, 2017). Related to the impact of the housing situation on investment

intention in Austria, researchers have presented evidence that owning instead of renting an

apartment or a house increases the likelihood of the owner being a potential investor in

community renewable energy projects, even though such projects would allow investment of

both home owners and lessees (Ebers Broughel and Hampl, 2018).

Related to the educational level, we find that more educated people are more likely to

opt for ownership models related to EV-PV-BS product bundles. Further, as educational levels

improve, the part-worth utility score and the WTP for a 100% self-sufficiency rate decreases,

and the value for a 50% self-sufficiency level increases. We observe similar effects for the

utilities of the attribute amortization period (i.e., less WTP for 20 years, and more for 16 years).

This could indicate that more educated respondents are more knowledgeable about the

feasibility of self-sufficiency rates and amortization periods with reference to PV and BS

products currently on the market. Hence, they value the extreme levels less, but are willing to

pay more for the medium levels of these attributes. Other researchers (e.g., Islam and Meade,

2013) have shown a positive effect of technology awareness on the adoption of PV systems.

Contrary to literature on EV adoption, which indicates that more educated people are more

likely to purchase an EV (e.g., Nayum et al., 2016; Li et al., 2017), we find that with increasing

educational level the investment threshold (i.e., the none option) for EV-PV-BS product

bundles increases slightly, even though this coefficient is not statistically significant.

The variables age and income do statistically significantly influence the preferences of

the product bundle in focus, but the effects are quite small. On the one hand, with increasing

age, the investment threshold for EV-PV-BS product bundles increases significantly. This

suggests that older people tend to have less preference for purchasing such EV product bundles.

Generally, other research also suggests that age correlates negatively to the (intended) purchase

of new and environmentally preserving technologies (Diamantopoulos et al., 2003; Gamel et

Paper 3 128

al., 2016; Plötz et al., 2014). On the other hand, more affluent people seem to prefer products

with higher self-sufficiency rates, faster amortization periods and at least medium-levels of

subsidies. This finding also agrees with existing literature which suggests that the higher

individuals’ income, the more significant the direct effect on their choosing “greener”

technologies (Axsen et al., 2016; Nayum and Klöckner, 2014; Schaffer and Brun, 2015; Tal

and Nicolas, 2013). However, there is also literature contesting the effect of age or income on

the preference for clean technologies (Hidrue et al., 2011; Priessner et al., 2018; Sposato and

Hampl, 2018). This emphasizes that socio-demographic findings should always be interpreted

with caution (cf. Kilbourne and Beckmann, 1998).

In contrast to the impact of socio-demographic covariates as described above, the

psychological variables, as well as the EV experience variable, have stronger effects on

potential EV drivers’ product bundle preferences and WTP. People with a stronger pro-

environmental attitude, seem to ascribe significantly more utility to lower levels of self-

sufficiency and less utility to faster amortization. In monetary terms, they are willing to pay

more for e.g., a 25% self-sufficiency rate or a 20-year amortization period. In addition, as

environmental attitude rises, the utility of subsidies decreases, indicating that pro-

environmental individuals are more willing to purchases clean technologies, even if no

subsidies are offered. With a stronger pro-environmental attitude, the utilities for higher

purchase prices increase, and vice versa. All these findings underline that pro-environmental

attitude and life-style is a strong indicator of pro-environmental behavior (Roberts and

Straughan, 1999; Whitmarsh and O'Neill, 2010) and also of adopting clean technologies (Axsen

et al., 2012; Carley et al., 2013; Chen et al., 2016; Priessner et al., 2018).

With a more communitarian worldview, the utilities and WTP at a higher self-

sufficiency rate and a higher amortization level increase significantly. Moreover, respondents

who value a stronger governmental role in day-to-day life, more strongly support governmental

subsidies. This finding agrees with Rissman et al. (2017), who argue that cultural worldviews

are strong predictors of policy support. Other studies confirmed similar effects, holding that

people with a more communitarian worldview are more likely than those with an individualistic

worldview to perceive other governmental interventions such as nudge policies (related to

private as well as social welfare) (Hagman et al., 2015), climate mitigation policies (Hart and

Nisbet, 2011), or environmental regulations and taxes (Rissman et al., 2017) as largely

acceptable.

With an increase in EV experience, the utilities for higher purchase prices, as well as

higher self-sufficiency rates, increase significantly. This could imply that people with more EV

Paper 3 129

experience request EV add-on products at a higher prosumer level, but are also willing to pay

more than the average respondent for this benefit. This agrees with Bühler et al. (2014) who

argue that people with EV experience evaluate EVs more favorably and are willing to pay more.

Similarly, Agnew and Dargusch (2017) claim that households already owning a PV system are

more likely to purchase a BS. Further, other studies claim that knowledge and awareness of

renewable technologies increase their acceptance (Agnew and Dargusch, 2017; Islam and

Meade, 2013). Therefore, people who already own an EV or a PV system seem likely to adopt

other clean technologies as well.

Lastly, we found people’s technology readiness to have relatively little influence on the

preference of EV-PV-BS product bundles. Only the price that respondents are willing to pay

for the bundle in focus is significantly higher for those with a higher technological affinity. The

literature also confirms this for EVs alone (Axsen et al., 2015; Egbue and Long, 2012).

Moreover, the analysis shows that a more positive technology readiness influences the utility

of non-ownership financing options.

Summing up, our study shows that socio-demographic and psychological

characteristics, as well as EV experience, each influence the preference for an EV product

bundle to a different extent. The impact of most socio-demographics is relatively smaller

compared to that of psychological characteristics or the experience factor. This again confirms

other studies that have found socio-demographic variables to have low explanatory value for

most environmentally concerned behaviors (Leonidou et al., 2010). The finding also confirms

that psychological characteristics and EV experience are stronger predictors of clean

technologies acceptance (Bühler et al., 2014; Nayum et al., 2016; Priessner et al., 2018; Roberts

and Straughan, 1999; Sposato and Hampl, 2018).

130

TABLE 3. RESULTS OF THE PARAMETER ESTIMATION WITH USE OF COVARIATES

Attributes and attribute

levels

Intercept Age Education Income

Gender

(male)

Housing

(apartment)

EV

experience

Communi-

tarian

worldview

Pro-

environmental

attitude

Technology

readiness


PV owner (no monthly

payment)

0.86* -0.00 0.05 -0.00 -0.44* -0.48* -0.06 0.07 0.12 -0.21*


payment)

1.21* -0.02* 0.12† -0.01 0.00 -0.89* 0.17* -0.24* -0.16† -0.05

PV + BS leaser with

ownership option (monthly

payment)

-1.11* 0.01† -0.10 0.01† 0.10 0.76* -0.01 0.07 0.26* 0.02

PV leaser with ownership

option (monthly payment)

-0.95* 0.01† -0.06 -0.01 0.33* 0.61* -0.10 0.09 -0.22* 0.24*


Up to max. 25% -1.07* 0.02* -0.00 -0.01* -0.22 0.15 -0.11 -0.26* 0.34* 0.02

Up to max. 50% -0.06 0.02* 0.14* -0.01† -0.28* -0.14 -0.34* 0.10 0.00 0.04

Up to max. 75% 0.24* -0.01* -0.01 0.01* 0.25* 0.11 0.16* 0.13† -0.05 -0.08

Up to max. 100% 0.89* -0.03* -0.12* 0.01 0.25† -0.11 0.29* 0.22* -0.29* 0.02

Amortization period

8 years 0.68* 0.00 0.02 0.00 0.29† 0.16 -0.01 -0.05 -0.23* -0.03

12 years 0.31* 0.00 -0.03 0.02* 0.09 0.11 0.05 -0.10 -0.20* 0.10

16 years -0.16 -0.01 0.12* -0.00 -0.07 -0.18 -0.04 0.19* 0.11 -0.06

20 years -0.83* 0.00 -0.11† -0.02* -0.31* -0.09 0.01 0.12 0.32* -0.02

131

Provider

All-in-one car dealer/OEM 0.19 -0.01* -0.06 0.00 -0.32* 0.04 -0.01 0.02 -0.03 -0.05

All-in-one utility 0.05 0.02* 0.03 -0.01* 0.34* -0.33* 0.04 0.19* 0.09 0.11

All-in-one specialist dealer 0.23* 0.01 0.07 0.01* -0.09 0.09 -0.05 -0.14* -0.09 -0.03

Diverse specialist dealers -0.47* -0.02* -0.03 -0.01 0.08 0.20† 0.01 -0.07 0.02 -0.02

Policy incentive

0% -0.67* 0.01* -0.05 -0.01† -0.02 -0.09 -0.04 -0.22* 0.44* -0.01

Up to max. 10% 0.05 0.01† 0.03 -0.01* -0.26* 0.05 0.13 0.09 0.02 0.07

Up to max. 20% 0.07 -0.01* -0.00 0.01* 0.09 0.23† -0.05 -0.01 0.01 -0.15†

Up to max. 30% 0.55* -0.01* 0.02 0.01 0.19 -0.19† -0.04 0.18† -0.46* 0.08

Purchase price

EUR 25,000 1.95* 0.01 0.04 -0.00 -0.18 0.43* -0.01 0.19 -0.37* -0.36*

EUR 30,000 1.01* 0.00 0.09 -0.00 -0.17 0.46* -0.03 0.06 -0.16 -0.21*

EUR 35,000 0.43* -0.01 0.07 0.00 -0.05 -0.20 -0.32* -0.04 -0.11 -0.13

EUR 40,000 -1.24* 0.01 -0.04 0.00 0.45* -0.22† 0.10 -0.15 0.28* 0.35*

EUR 45,000 -2.15* 0.00 -0.16* 0.00 -0.05 -0.47* 0.24* -0.06 0.37* 0.35*

None option -0.78* 0.10* 0.33 0.01 -1.38* 1.56* 0.34 0.37 -0.05 -0.02

*Significant at the 0.05 level (parameter estimates are significantly positive/negative if more than 95% of the estimated parameter values in each iteration of the algorithm are positive/negative). † Significant at the 0.1 level (parameter estimates are significantly positive/negative if more than 90% of the estimated parameter values in each iteration of the algorithm are positive/negative).

Paper 3 132

4. CONCLUSION

To contribute to the global targets for reduced carbon emission, EVs need to be powered

with electricity produced from renewable energy sources. Consequently, a better grasp of EVs

bundled with PV and BS systems is highly relevant for promoting the diffusion of EVs in

individual transportation in a way that will limit environmental harm. This paper has aimed to

advance our current understanding of such EV-PV-BS product bundles by investigating private

individuals’ preferences and their WTP for such bundles. Further, our study aimed at shedding

light on how customers’ assessing values are influenced by socio-demographic and

psychological variables, as well as by self-assessed EV experience. We built our analyses on a

unique dataset of 7392 experimental choices of 616 respondents in Austria.

Our results show that the expressed WTP for EV add-on products (PV and BS) is still

fixed on amounts below current market prices. At the same time, the attribute purchase price

(30.6%) is the most important in directing a purchase decision, followed by PV/BS add-on

ownership (18.8%), and self-sufficiency rate (16.0%). Moreover, potential EV drivers have

some willingness to pay a premium for purchasing an EV-PV-BS bundle from an all-in-one

provider. Our study found influencing effects of socio-demographic and psychological

variables, and of self-assessed EV experience on product bundle preferences, as well as on the

WTP. However, our analysis showed that socio-demographic characteristics have relatively

minor effects on the potential EV drivers’ bundle preferences and their WTP, whereas the

psychological variables have a stronger effect. These findings have implications for further

research, as well as for practitioners and policy makers, which we discuss below.

4.1 Implications

Our study makes a two-fold contribution to EV literature and provides several avenues

for further research. First, following a call for EV product bundling research by Cherubini et al.

(2015), our paper is the first to analyze the WTP for the EV add-on products PV and BS in a

product bundle. In doing so, we show that the prices of current market PV and BS offerings do

not match the WTP of potential customers. However, if we assume an ongoing decrease in the

PV and BS cost curve, as described in section 3.3, we are likely to achieve grid parity within

the decade (Seba, 2014), which will make these add-on product offerings more appealing for

future EV drivers. Further studies can build on our insights and try to simulate the uptake in

market share by cost degression of EV, PV, or BS. Scholars could also investigate innovative

ownership models (Galassi and Madlener, 2016) in order to decrease initial investments. Even

though the average respondent in our sample prefers the ownership instead of the leasing option

Paper 3 133

related to PV and BS systems, we note some heterogeneity in preferences depending on the

socio-demographic and psychological profiles of the respondents. This finding could be of

interest in more detailed analyses.

Second, our study is the first to show that socio-demographic, psychological, and

experiential variables influence potential consumers’ preferences and WTP for EV product

bundles and their features. This is in line with findings in earlier EV literature (Bühler et al.,

2014; Nayum et al., 2016; Plötz et al., 2014). Related to socio-demographic variables, our

results indicate that more educated people have a better sense for currently feasible self-

sufficiency rates and amortization periods for PV and BS systems. One often discussed measure

for increasing awareness of renewable energy technology and investment criteria refers to

information and education campaigns (e.g., Islam, 2014; Islam and Meade, 2013). However,

the question remains whether such campaigns are effective in the context of the kind of complex

product bundles on which our study focuses. Thus, future research could investigate the effect

different measures have in supporting households’ investment decisions related to EVs and add-

on products. Such measures could range from awareness campaigns to more sophisticated

instruments such as online tools and apps that inform about relevant investment parameters.

Further, we find that the price people seem willing to pay for EV-PV-BS product

bundles is higher, when they have more EV experience, a pro-environmental attitude, or a more

technologically-ready mindset. Respondents with more EV experience are not only willing to

pay more for such product bundles, but also demand higher self-sufficiency rates from the add-

on products in focus, to facilitate their roles as prosumers. Future research could further

investigate the effect of experience, not only related to EVs but also related to PV and BS

systems (Agnew and Dargusch, 2017), on the willingness to adopt EV-PV-BS product bundles

or other EV add-on products. Another interesting finding of our analysis is that people with a

strong pro-environmental attitude accept lower self-sufficiency rates. Thus, future studies

would do well to further investigate the role of experiential and psychological factors in

explaining people’s preference for different prosumer and self-sufficiency levels. Overall, our

study suggests that future research attending to consumer preferences and WTP in EV product

bundling literature, should strive to comprehensively evaluate potential driver characteristics,

and not only focus on a single dimension, such as socio-demographics only or psychological

characteristics only.

For marketers, our analysis puts forward evidence that the provider of the product

bundle is of least importance in the purchase decision, at 8.1%. However, respondents are

willing to pay a small premium for being served by a one-stop provider, even if they do not

Paper 3 134

have a particular preference for the types of providers, whether they are car dealers, utility

companies, or specialty dealers. This is especially interesting, since many major car

manufacturers and utility companies are planning to position themselves as all-in-one players

(cf. Tesla, Porsche, EnBW) by creating a platform strategy similar to Apple’s (cf. Gawer and

Cusumano, 2014). Hence, the role of brands and customer loyalty in positioning the firm as an

all-in-one provider related to EV-PV-BS product bundles suggests a promising avenue for

further research.

Further, our results provide valuable insight regarding the market potential of EV-PV-

BS product bundles. Since the current market offering does not match the WTP for EV add-on

products, marketers can consider developing new product offerings with leasing options that

reduce the financial burden, as Cherubini et al., 2015) proposed for EVs, or Galassi and

Madlener (2014) for PVs. In addition, EV sharing or subscription-based concepts (Kley et al.,

2011) could be applied to EV-PV-BS product bundles. However, as our analysis additionally

reveals that the average respondent in our sample prefers owning to leasing EV add-on

products, the conception of such models, as well as their effect on the adoption of EVs or

renewable energy technologies, needs to be researched in more detail.

Our WTP analysis additionally reveals a similar WTP for the fastest suggested

amortization (i.e., 8 years) and highest self-sufficiency (i.e., 100%) attributes. However, the

desire for a higher self-sufficiency rate goes against a faster amortization period, because higher

self-sufficiency usually requires a higher investment. Such higher investment typically leads to

a longer amortization period while holding power prices constant despite an increasing share of

self-consumed power. Nevertheless, this finding suggests an opportunity for virtual energy

storage offerings, either as a stand-alone product or as add-on service to a stationary BS. Such

offering is cheaper than a large stationary BS unit, and hence can amortize more quickly, while

still providing the benefits of being almost energy-autarkic.

Moreover, our analysis suggests that certain EV driver characteristics influence the

evaluation of an EV product bundle. For example, people with higher pro-environmental

attitudes show a higher tolerance level for higher purchase prices while being more willing to

accept lower self-sufficiency rates and subsidy levels. Also, technologically-minded people are

willing to pay more, and men are more willing than women to accept longer amortization and

lower self-sufficiency rates. By promoting EV product offerings toward, for instance, pro-

environmental and pro-technological middle-aged men and by providing information and

testing opportunities, the penetration rate of “green” EVs might be increased, thus making a

major contribution to a decentralized energy supply.

Paper 3 135

For policy makers, our study suggests that in the near future public authorities should

contribute to the further expansion of “green” EVs. Since investment in EV product bundles is

not perceived as economically attractive yet, governments need to continue incentivizing

investments in clean technology, but only on a short-term basis until power produced by these

technologies approaches grid parity (Hoppmann et al., 2014). This could be done by providing

favorable conditions, not necessarily in the form of direct subsidies as tested in this study, but

also via indirect subsidies, such as guaranteed feed-in tariffs (cf. Fischer and Newell, 2008).

Similarly, due to car buyers’ inherent motivation to purchase an EV generally being low,

governmental support is still necessary to facilitate the proliferation of cars with emergent

technologies (Turcksin et al., 2013). We recommend using a mix of policies for all clean

technologies, to achieve significant emission reduction (Fischer and Newell, 2008).

Still, our study has pointed out that consumers’ evaluation of subsidies decreases as the

subsidy level increases. That means the WTP for policy incentives is lower than the real value

of the incentives. This implies that potential EV users appreciate some level of policy incentive,

but from an input-output perspective government subsidy should not be too high. Also

interesting in this context, is our finding that more environmentally friendly respondents prefer

policy incentives at very low levels, if any at all. Nevertheless, we agree with Bauner and Crago

(2015) that policy incentives should be maintained in markets with high uncertainty regarding

technological development and price forecasts (such as the EV, PV, and BS market), to reduce

delays in potential adopters’ investment timing. New research could further investigate which

types of incentives for promoting EV product bundles are most effective, and at what level.

4.2 Limitations and further research

Despite carefully designing our study according to expert interviews and conducting a

pre-test, our results are subject to several limitations. First, our study is a first step toward

estimating the WTP in relation to EV-PV-BS product bundles, but it is mainly exploratory due

to its experimental set-up using conjoint analysis. Being experimental, CBC tries to reduce real-

world complexity by isolating and focusing on the most important aspects. This of course means

that several product related and non-related aspects, that potentially also impact on future EV

drivers’ choices, have been excluded. For instance, decision making can be influenced by

certain group dynamics, such as herding effects. Further, we only considered one type of EV,

and we did not consider technical details of PVs or BS. Although, after conducting attribute

selection in cooperation with industry experts we are confident that we indeed selected the most

relevant attributes for the choice experiment, all doubt has not been removed. An EV purchase

Paper 3 136

with add-ons can differ across different EVs or different add-on product attributes. Future

research should look into these aspects and incorporate different EVs or different PV-BS

attributes not included in our study, to investigate the influence these attributes have. Further,

besides the respondent characteristics included in our analysis, literature discusses other

individual and context-related factors, such as societal influence (peer effects), mobility

patterns, or spatial factors that can impact the decision to adopt EV add-on products (see e.g.,

Liao et al., 2017; Li et al., 2017). These should be subjects of future research in this field.

Second, as already pointed out in section 3.3, our WTP figures should be considered as

upper limits since they are derived from hypothetical scenarios in which people do not need to

pay actual money. Therefore, using other research methods such as BDM (e.g., Wertenbroch

and Skiera, 2002; Miller et al., 2011), or conducting the study in a different experimental setup

which enables us to observe actual behavior rather than an online-based virtual behavior, would

be interesting. Since the product bundles we discuss are not on the market yet, purchase data

could be generated in pilot runs at large car or utility companies. Such an approach would also

allow researchers to combine stated and revealed preferences with WTP.

Third, even though our study has important implications to be added to the EV literature,

they might not be generalizable to countries other than Austria. There are different levels of EV

acceptance, government support, weather conditions, etc. in every country. Therefore, future

studies should take our research questions into the context of different countries, to enable a

detailed analysis of similarities and differences in EV-PV-BS preferences across different

national contexts. Further, what needs to be kept in mind when our results are interpreted, is

that the respondents in our sample have a positive attitude and willingness to purchase an EV

within the next decade. Hence, they are part of the potential EV early adopters segment (cf.

Priessner et al., 2018) and do not represent the entire Austrian population’s preferences.

Acknowledgment

The authors would like to acknowledge the financial support of the company KELAG,

the expertise they made available to us, as well as the helpful feedback we received from all

our interviewees during the pre-test phase of this survey.

Paper 3 137

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APPENDIX

TABLE A.1. DESCRIPTION OF VARIABLES AND CHARACTERISTICS OF THE FINAL SAMPLE (IN

MEANS AND PERCENTAGES)


Final sample

(n = 616)

Socio-demographic variables

Gender 1 = male 55.7%

2 = female 44.3%

Age Years 48.28

Education 1 = compulsory school 2.8%

2 = vocational training 33.9%

3 = high school 28.7%

4 = university 34.6%

Income Net EUR per month per household 3,225

Housing situation 1 = apartment 44.0%

2 = house 56.0%

EV experience Scale from 1-4 (1 = low, 4 = high) 2.98

Psychological variables (scale from 1 = strongly disagree to 5 = strongly agree)

Technology readiness e.g., “Technology gives people more control over their

daily lives.”

3.44

Pro-environmental

attitude

e.g., “I think of myself as an environmentally-friendly

consumer.”

4.11

Communitarian

worldview

e.g., “The government should do more to advance

society’s goals, even if that means limiting the freedom

and choices of individuals.”

2.54

Egalitarian

worldview

e.g., “Our society would be better off if the distribution of

wealth were more equal.”

3.48

Paper 3 145

TABLE A.2. CHARACTERISTICS OF THE TOTAL AND FINAL SAMPLE IN COMPARISON TO THE

AUSTRIAN POPULATION

Variables Total sample

(N = 1,251; in %)

Final sample

(n = 616; in %)

Austrian population

(in %) b

Gender

(x2 = 0.124, d.f. = 1,

p = 0.725) a

(x2 = 0.982, d.f. = 1,

p = 0.322)

Female 48.5 44.3 50.8

Male 51.5 55.7 49.2

Age

(x2 = 0.988, d.f. = 3,

p = 0.804)

(x2 = 1.908, d.f. = 3,

p = 0.592)

18-29 years 14.1 12.7 19.3

30-44 years 25.7 28.4 25.5

45-59 years 30.7 32.8 29.4

60-75 years 29.3 26.1 25.8

Education

(x2 = 35.538, d.f. = 3,

p = 0.000)

Compulsory school 2.8 26.9

Vocational training 33.9 45.8

High school 28.7 14.6

University 34.6 12.6

Household income/month

(x2 = 130.323, d.f. = 2,

p = 0.000)

25% percentile 2,500 1,601

50% percentile 3,000 2,611

75% percentile 4,000 3,995

Federal state

(x2 = 4.125, d.f. = 8,

p = 0.843)

(x2 = 0.817, d.f. = 8,

p = 0.999)

Burgenland 7.9 2.8 3.3

Carinthia 5.8 5.8 6.4

Lower Austria 19.9 22.1 18.9

Upper Austria 20.8 18.8 16.7

Salzburg 4.8 5.4 6.3

Styria 14.3 14.3 14.1

Tyrol 6.9 8.4 8.5

Vorarlberg 5.0 4.5 4.4

Vienna 14.5 17.9 21.4

a The results of chi-squared tests are included in parentheses, showing whether significant differences between the total/final

study sample and the Austrian population could be identified. b Source: STATISTIK AUSTRIA, 2018b).

Paper 3 146

Scale/Dimension Items Source(s)

Cultural worldviews:

individualism-

communitarianism

The government interferes far too much in our everyday

lives. (Recoded)

Cherry et al., 2014;

Kahan et al., 2007

The government should do more to advance society’s

goals, even if that means limiting the freedom and

choices of individuals.

The government should stop telling people how to live

their lives. (Recoded)

The government should limit individual freedom of

choice to promote the well-being of society.

Cultural worldviews:

hierarchy-egalitarianism

We have gone too far in pushing equal rights in this

country. (Recoded)

Cherry et al., 2014;

Kahan et al., 2007

Our society would be better off if the distribution of

wealth were more equal.

Society as a whole is too soft nowadays. (Recoded)

Discrimination against minorities is still a very serious

problem in our society.

Pro-environmental

attitude

I think of myself as an environmentally-friendly

consumer.

I think of myself as someone who is very concerned

about environmental issues.

Whitmarsh and

O'Neill, 2010

I would be embarrassed to be seen as having an

environmentally friendly lifestyle. (Recoded)

I would not want my family or friends to think of me as

someone who is concerned about environmental issues.

(Recoded)

Technology readiness New technologies contribute to a better quality of life. Parasuraman and

Colby, 2015

Technology gives people more control over their daily

lives.

Technology gives more freedom of mobility.

Technology makes me more productive in my personal

life.

Other people come to me for advice on new

technologies.

In general, I am among the first in my circle of friends to

acquire new technology when it appears.

I can usually figure out new high-tech products and

services without help from others.

I keep up with the latest technological developments in

my areas of interest.

TABLE A.3. PSYCHOLOGICAL MEASUREMENT SCALES USED IN THE SURVEY

147

Attributes and attribute

levelsb Intercept Age Education Income

Gender

(male)

Housing

(apartment)

EV

experience

Communi-

tarian

worldview

Pro-

environmen

tal attitude

Technology

readiness


PV owner (no monthly

payment) 4,186 -22 241 -8 -2,139 -2,349 -279 360 563 -1,005


payment) 5,874 -77 561 -31 19 -4,318 827 -1,151 -780 -242

PV + BS leaser with

ownership option (monthly

payment) -5,428 51 -506 68 512 3,696 -69 334 1,271 89

PV leaser with ownership

option (monthly payment) -4,633 47 -296 -29 1,608 2,971 -479 456 -1,055 1,158


Up to max. 25% -5,196 95 -20 -65 -1,053 716 -546 -1,251 1,639 85

Up to max. 50% -312 88 659 -50 -1,365 -695 -1,642 510 4 199

Up to max. 75% 1,147 -60 -33 69 1,203 540 780 625 -251 -382

Up to max. 100% 4,361 -123 -605 45 1,215 -560 1,409 1,051 -1,392 98

Amortization period

8 years 3,301 10 101 24 1,395 780 -66 -264 -1,130 -145

12 years 1,491 15 -149 95 452 517 231 -485 -958 495

16 years -763 -34 608 -1 -340 -869 -197 939 545 -271

20 years -4,029 9 -560 -117 -1,507 -428 31 609 1,543 -79

TABLE A.4. EFFECT OF COVARIATES ON THE WTP (IN EUR) FOR ATTRIBUTE LEVELS

148

Provider

All-in-one car dealer/OEM 902 -61 -310 21 -1,572 172 -48 111 -134 -245

All-in-one utility 264 101 132 -52 1,664 -1,603 205 911 445 519

All-in-one specialist dealer 1,129 35 336 62 -461 454 -223 -680 -420 -164

Diverse specialist dealers -2,295 -75 -159 -30 369 977 66 341 110 -110

Policy incentive

0% -3,264 44 -234 -67 -120 -419 -189 -1,066 2,124 -51

Up to max. 10% 253 48 149 -40 -1,256 240 647 444 74 356

Up to max. 20% 338 -40 -8 59 445 1,098 -245 -28 40 -715

Up to max. 30% 2,673 -52 93 48 932 -919 -212 834 -2,237 410

None option -3,785 484 1,623 65 -6,706 7,611 1,637 1,800 -225 -84

149

CURRICULUM VITAE

ALFONS PRIESSNER MSc. Birthday: March 5th, 1988 Nationality: Austria Relationship: Married

Goldschlagstraße 73/8/24 | 1150 Vienna [email protected]

+ 43 676 900 67 20 https://www.linkedin.com/in/alfons-priessner-4132b723/

PROFESSIONAL EXPERIENCE

Since 01/14 McKinsey & Company, Inc., Consultant (job offer after internship) Vienna

▪ Designed modules for steering committee presentations to clients’ senior management

▪ Advised clients in agriculture, automotive, telecommunication and oil & gas on lean service operation,

marketing and go-to-market strategy, restructuring, sustainability strategy and org. design topics

▪ Developed and conducted trainings and workshops with clients in 10 countries on 4 continents

▪ Part of the interviewer- and recruiting core-team in German-speaking-region

Since 10/16 Carinthia University of Applied Sciences – FH Kärnten, Guest Lecturer Villach

▪ Holding guest lectures in Business Project Seminar, Consulting Readiness Toolkit, Cross-Border

Negotiation, Business Development for master students’ programs

Since 05/16 Fernwärme Pischeldorf, Co-Operator & Co-Owner Magdalensberg

▪ Co-owner of a biomass district heating plant heating over 50 apartments with clean and sustainable heat

▪ Responsible for investment decisions, budgeting and accounting, procurement, contract negotiations

01/13-03/13 The Boston Consulting Group, GmbH., Visiting Associate – Mining & Metals London/Vienna

▪ Researched and documented specific topics (e.g. business organization)

▪ Performed value chain analysis for commodity platinum (qualitative and quantitative)

09/11-07/12 Institute for Strategic Capital Market Research - Portfolio Management Program Vienna

▪ Analyzed diverse asset categories, prepared research-reports and presentations

▪ Calculated portfolio-risk and -return and conducted portfolio-monitoring

06 – 08/11 Citigroup, Inc., Summer Investment-Banking-Trainee –Export Agency Finance Hong Kong

▪ Analyzed telecom, shipping, metals/mining, oil/gas industry and developed deep dive talk-books

▪ Managed sales ledger dilution due diligence for Trade Finance

09/10 – 03/11 KPMG Advisory, GmbH., Associate Intern - Financial Services Advisory Vienna

▪ Advised and supported financial institution on restructuring and Basel III topics and mobile payments

▪ Interviewed clients for feasibility-study: innovative distribution channels for mass affluent clients

03/ – 06/11 Hirsch Verwaltung, GmbH., Assistant to CEO (part time) Klagenfurt

▪ Responsible for the management of a seven-digit real-estate- and capital assets

▪ Tax advisory for income-, corporate-, sales- and real estate transfer-tax

05/08-06/11 Uni Management Club Vienna, Managing Director and Board Member Vienna

▪ Managed a team of 15-18 students, performed budget planning and financial accounting

▪ Co-organized Austrian’s biggest student conference win² 2011 (www.winquadrat.at)

▪ Developed team coaching and team buildings, conducted recruiting process for new team members

06 – 09/10 Dell, Inc., Summer Intern - General Procurement Finance Austin/USA

▪ Planned and executed project: optimization of an internal management and accounting tool

▪ Coordinated and chaired a global project team (tool was used in Asia, Europe, America)

10/06 – 09/07 Military Service: Lehrabteilung 3 (Infantry School) - Officer Education Wiener Neustadt

▪ Participated one-year voluntary program to become group commander and lieutenant of reserve

▪ Educated and trained recruits on military techniques and leaderships

http://www.winquadrat.at/

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EDUCATION

Since 04/16 Alpen-Adria University Klagenfurt Klagenfurt

PhD in Applied Business Administration (Focus Electric-Vehicles) (GPA 1.0/1.0)

▪ Conduct research project with KELAG in field of electric vehicles acceptance and product bundling

▪ Speaker on conferences in Vienna, St. Gallen and Stuttgart about e-cars lead-users & product bundling

▪ Project Manager of the 1. Clean Energy Design Thinking Challenge 2017 in Alpen-Adria region

▪ Supervised several master thesis’ in the area of electric vehicles adoption

10/11-01/14 Vienna University of Economics and Business Vienna

Master of Science in Strategic Management, Innovation & Management Control (GPA: 1.2/1.0)

▪ Thesis: "Sustainable business models for energy efficiency (EE) services: identifying motivations and

barriers for introducing EE services in European electric utilities" (Prof. Speckbacher & Massa PhD)

▪ Business Project with OMV AG: "Integration/disintegration decisions in the gas value chain": Project

leader of 20 student-consultants in a 4-month project (Prof. Hoffmann)

▪ Founded KUNSTICO: Start-up which sells high-quality art prints to affordable prices (Prof. Lettl)

▪ Scholarships for outstanding achievements (11/12, 12/13)

08/13-12/13 Landwirtschaftliche Fachschule Steiermark Graz

Education for Landwirtschaftlichen Facharbeiter (GPA: 1.0/1.0)

▪ Seminars in crop production, animal breeding, forestry, agricultural policy, agricultural machinery

▪ Development of a business plan for the family-farm (focus: biomass district heating system)

11/11-06/12 Competence Center for Central Eastern Europe (Prof. Schuh) – Vienna/CEE

▪ Project Paper: "Current & Future Role of Regional Headquarters in Austria"

▪ Participated in complementary intercultural trainings, soft skill & hard skill courses for CEE

09/12-12/12 Indian Institute of Management Bangalore (GPA: 1.4/1.0) Bangalore

▪ MBA Exchange program with courses in corporate finance, M&A, sustainability management

10/07 - 04/11 Vienna University of Economics and Business Vienna/Miami/Beijing

Bachelor of Science in International Business Administration and Corporate Finance (GPA: 1.6/1.0)

▪ Top 5% of class, scholarships for outstanding achievements (07/08, 08/09, 09/10)

▪ Member of WU-Top League class 07/08 (high potential program)

▪ Exchange programs in Miami University and Tsinghua University with focus on marketing

09/98 - 06/06 BG/BRG Mössingerstraße Klagenfurt

A-Levels with focus on economics and history (GPA: 1.3/1.0)

COMPETENCE & SKILLS

▪ Languages: German (native), English (fluent), Italian (intermediate), Mandarin (low level of basics), Latin

▪ Profound knowledge of MS Excel, MS Power Point, MS Word, MS Outlook, SPSS, Sawtooth-Conjoint

▪ Relevant experiences with Bloomberg, Reuters, R, STATA

PERSONAL INTERESTS

Initiative für Kärnten (http:/www.fuer-kaernten.at/): As proud Carinthian participating in panel discussion e.g., studying

and working in Carinthia, supporting events e.g., Foundership Factory (event aiming at triggering start-up initiatives)

Sports: Skiing, wakeboarding, running (half- and full-marathons), hiking (climbed e.g., Kilimanjaro 2015, crossed the Alps

2017), soccer and tennis (youth-teams between the age of 7 to 18), diving (PADI open water diver since 2011)

Nature: Passionate farmer (trained agriculture-worker 2014) and hunter (certified since 2005)

Dancing Debutant Vienna Opera Ball 2011 and diverse other Viennese balls (as student)

Real-estate investor: invested in a couple of small apartments in Graz and Vienna

Wine-lover: participated several wine-seminars at Austrian Wein-Akademie

Music: Guitarist (6 years education at high school) and member of the school band

Fire fighter: Trained emergency driver and smoke diver at voluntary fire brigade Pischeldorf (since 2004)

Traveling: Traveled to over 35 countries on 5 continents

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PUBLICATIONS

Priessner A., Sposato R., Hampl N. (2018) Predictors of electric vehicle adoption: an analysis of potential electric vehicle

drivers in Austria. Energy Policy, Volume 122, p.701-714

Priessner A., Hampl N. (2018) Can product bundling increase the joint adoption of electric vehicles, solar panels and battery

storages? Explorative evidence from a choice-based conjoint study in Austria. Reviewed with outcome “Revise and Resubmit”

from Ecological Economics. (Status Dec. 2018)

Priessner A., Hampl N. (2018) Exploring consumer heterogeneity in willingness to pay for electric vehicles product bundles.

First Round of revision in Transportation Research Part A. (Status Nov. 2018)

CONFERENCES & POSTERS

International Association of Energy Economics 2017 European Conference in Vienna 4.-6. September 2017 – Presentation

of paper: Priessner A., Sposato R, Hampl N (2017): How to trigger mass market adoption for electric vehicles? – An analysis

of potential electric vehicle drivers in Austria

5. Energie-Konzept Kongress in St. Gallen Switzerland, 11.05-12.05. 2017. Presentation results from paper: Priessner A.,

Sposato R, Hampl N (2017): The impact of cultural worldviews and policy incentives on the market adoption for electric

vehicles? – An analysis of potential electric vehicle drivers in Austria

10. International Energiewirtschaftstage (IEWT) in Vienna 10.-12. February 2017 – Presentation of paper: Priessner A.,

Sposato R, Hampl N (2017): How to trigger mass market adoption for electric vehicles? – An analysis of potential electric

vehicle drivers in Austria

11. International Energiewirtschaftstage (IEWT) in Vienna 13-15. February 2019 – Presentation of paper: Can product

bundling increase the joint adoption of electric vehicles, solar panels and battery storages? Explorative evidence from a

choice-based conjoint study in Austria (paper accepted)

30. Electric Vehicles Symposium in Stuttgart 7-10.10. 2017 – Poster Session of paper: Priessner A., Sposato R., Hampl N.

(2017) The impact of worldviews and policy incentives on the adoption of electric vehicles: profiling potential adopters

32. Electric Vehicles Symposium in Lyon 7-10.05. 2019 – Poster Session of paper: Priessner A., Hampl N. (2018) Exploring

consumer heterogeneity in willingness to pay for electric vehicles product bundles (paper in application process)

TEACHING

Alpen-Adria-University Klagenfurt:

• 1. Clean Technology Design Thinking Challenge – lead by Prof. Hampl (winter term 2017)/nominated for the "Ars

Docendi-Staatspreis für exzellente Lehre an den öffentlichen Universitäten Österreichs"

FH Kärnten /Carinthian University of Applied Sciences

• Business Project Seminar (winter term 2016, 2017,2018)

• Cross-Border Negotiations (summer term 2018, 2019)

• Business Development (winter term 2017/18)

• Project Management (summer term 2019)

Vienna, January 2019

exploring predictors of electric vehicle adoption and

Documents