The Dutch electric car charging network is currently being used very inefficiently. 90% of the charging transactions last up to three times as long as required. Therefore, Social Charging introduced a mobile app which enables electric car drivers to communicate with each other, in order to share charging stations efficiently. The pre-eminent goals of this research project were (a) to determine the charging behaviour of electric car users in the Netherlands, as well as their issues regarding the Dutch charging infrastructure and (b) to identify their perception towards the Social Charging application and their intention to use it. This consultancy project followed both an exploratory and a descriptive research design and both qualitative and quantitative research has been performed. A review of the existing literature on technology acceptance, diffusion of innovation and EV charging behaviour revealed the factors which would theoretically influence electric car drivers’ level of use of charging facilities, which are EV range, charging point availability, observability, planning and range anxiety, and the factors influencing their intention to use the Social Charging app, which are trialability, performance expectancy and social influence. Three focus groups and six interviews were held to qualitatively explore electric car drivers’ charging behaviour, their issues towards the current charging infrastructure and their acceptance of the app. Subsequently, a quantitative questionnaire among 181 Dutch electric car drivers empirically tested the hypotheses. The results show that charging point availability, observability and planning are positively associated with the level of use of charging facilities. EV range and range anxiety do not seem to influence usage level of charging facilities. Additionally, trialability, performance expectancy and social influence are positively associated with the intention to use the Social Charging app. Therefore, managerial implications are that the app should be easy and free of charge to download and use, it should facilitate the process of charging electric cars and a large number of electric drivers should use the app in for the app to be successful. When Social Charging complies with these recommendations, the company goals should be reached.

1. Leeds University
Business School
A study into the preferences and behaviour of electric car users
regarding the electric car charging network in the Netherlands and the
Social Charging mobile app
Faniëlle de Wit
Dissertation supervisor: Charalampos Saridakis
Month and year of submission: September 2015
Word count: 11,951
This dissertation is submitted in part fulfilment of the requirements for the degree of
MA Advertising & Marketing

2. Acknowledgements
I wish to thank Dr. Charalampos Saridakis for his constant support and guidance throughout this
project. I would also like to thank Gertjan Geurts for the opportunity to perform this research for
Social Charging and the participants of the focus groups, interviews and surveys for their valuable
information.

3. Executive summary
The Dutch electric car charging network is currently being used very inefficiently. 90% of the
charging transactions last up to three times as long as required (Spoelstra, 2014). Therefore, Social
Charging introduced a mobile app which enables electric car drivers to communicate with each
other, in order to share charging stations efficiently.
The pre-eminent goals of this research project were (a) to determine the charging behaviour of
electric car users in the Netherlands, as well as their issues regarding the Dutch charging
infrastructure and (b) to identify their perception towards the Social Charging application and their
intention to use it.
This consultancy project followed both an exploratory and a descriptive research design and both
qualitative and quantitative research has been performed. A review of the existing literature on
technology acceptance, diffusion of innovation and EV charging behaviour revealed the factors
which would theoretically influence electric car drivers’ level of use of charging facilities, which are
EV range, charging point availability, observability, planning and range anxiety, and the factors
influencing their intention to use the Social Charging app, which are trialability, performance
expectancy and social influence.
Three focus groups and six interviews were held to qualitatively explore electric car drivers’ charging
behaviour, their issues towards the current charging infrastructure and their acceptance of the app.
Subsequently, a quantitative questionnaire among 181 Dutch electric car drivers empirically tested
the hypotheses. The results show that charging point availability, observability and planning are
positively associated with the level of use of charging facilities. EV range and range anxiety do not
seem to influence usage level of charging facilities. Additionally, trialability, performance expectancy
and social influence are positively associated with the intention to use the Social Charging app.
Therefore, managerial implications are that the app should be easy and free of charge to download
and use, it should facilitate the process of charging electric cars and a large number of electric drivers
should use the app in for the app to be successful. When Social Charging complies with these
recommendations, the company goals should be reached.

4. Table of contents
Abbreviations ...................................................................................................................................... 1
1. Introduction ................................................................................................................................ 2
1.1 Background.......................................................................................................................... 2
1.2 Research aim and objectives............................................................................................... 3
1.3 Importance of the study...................................................................................................... 3
1.4 Organisation of the study.................................................................................................... 4
2. Literature review......................................................................................................................... 5
2.1 Introduction ........................................................................................................................ 5
2.2 Theories............................................................................................................................... 5
2.2.1 Technology acceptance models .................................................................................. 5
2.2.2 Diffusion of innovation theory .................................................................................... 6
2.2.3 EV charging behaviour................................................................................................. 6
2.2.4 Electric vehicle adoption............................................................................................. 7
2.2.5 Concept testing ........................................................................................................... 7
2.2.6 Electric vehicle types................................................................................................... 7
2.3 Vehicle related constructs................................................................................................... 7
2.3.1 Electric vehicle range .................................................................................................. 7
2.4 Environment related constructs.......................................................................................... 8
2.4.1 Charging point availability........................................................................................... 8
2.4.2 Observability ............................................................................................................... 8
2.5 Driver related constructs..................................................................................................... 9
2.5.1 Planning....................................................................................................................... 9
2.5.2 Range anxiety.............................................................................................................. 9
2.5.3 EV experience............................................................................................................ 10
2.5.4 Trialability.................................................................................................................. 11
2.5.5 Performance expectancy........................................................................................... 11
2.5.6 Social influence.......................................................................................................... 12
2.5.7 Effort expectancy ...................................................................................................... 12
2.5.8 Facilitating conditions ............................................................................................... 12
2.6 Summary ........................................................................................................................... 13
3. Conceptual model and research hypotheses............................................................................ 14
3.1 Introduction ...................................................................................................................... 14
3.2 Conceptual model ............................................................................................................. 14

5. 3.3 Research hypotheses ........................................................................................................ 16
3.4 Summary ........................................................................................................................... 20
4. Research design and methodology........................................................................................... 21
4.1 Introduction ...................................................................................................................... 21
4.2 Research design................................................................................................................. 21
4.3 Secondary research........................................................................................................... 21
4.4 Primary research ............................................................................................................... 22
4.4.1 Qualitative research.................................................................................................. 22
4.4.2 Quantitative research................................................................................................ 25
4.5 Summary ........................................................................................................................... 28
5. Results and analysis................................................................................................................... 29
5.1 Introduction ...................................................................................................................... 29
5.2 Respondent profile............................................................................................................ 29
5.3 Construct reliability........................................................................................................... 30
5.4 Descriptive analysis........................................................................................................... 31
5.5 Hypotheses testing............................................................................................................ 34
5.6 Summary ........................................................................................................................... 36
6. Conclusions and implications.................................................................................................... 37
6.1 Introduction ...................................................................................................................... 37
6.2 Conclusions ....................................................................................................................... 37
6.3 Recommendations ............................................................................................................ 39
6.3.1 Managerial implications............................................................................................ 39
6.3.2 Theoretical implications............................................................................................ 40
6.4 Limitations......................................................................................................................... 40
6.5 Future research directions................................................................................................ 41
References......................................................................................................................................... 43
Appendices........................................................................................................................................ 51
Appendix A. Discussion guide ....................................................................................................... 51
Appendix B. Coding scheme qualitative research......................................................................... 53
Appendix C. Transcript focus group.............................................................................................. 55
Appendix D. Translated transcript focus group ............................................................................ 79
Appendix E. Qualitative research results .................................................................................... 103
Appendix F. Operationalisation of the variables......................................................................... 112
Appendix G. Questionnaire (Dutch)............................................................................................ 118
Appendix H. Questionnaire (English) .......................................................................................... 128

6. Appendix I. Invitation letter surveys........................................................................................... 138
Appendix J. Consumer insights: functionalities of the app......................................................... 140
Appendix K. Tables, figures and graphs ...................................................................................... 141
Appendix L. SPSS output tables................................................................................................... 151

7. 1
Abbreviations
BEV Battery electric vehicle (full electric vehicle)
DIT Diffusion of Innovation Theory
EREV Extended range electric vehicle
EV Electric vehicle
HEV Hybrid electric vehicle
PHEV Plug-in hybrid electric vehicle
TAM Technology Acceptance Model
TPB Theory of Planned Behaviour
TRA Theory of Reasoned Action
UTAUT Unified Theory of Acceptance and Use of Technology

8. 2
1. Introduction
The Dutch government encourages electric transport by letting residents use public charging
stations (Van Raaij, 2014) and by relieving electric cars from additional tax liability, vehicle tax and
road tax (Rijksoverheid, 2011). There are currently 49,000 electric cars in Netherlands, an increase
of 50% compared to 2013 (RVO, 2015). The Dutch government aims for 200,000 electric cars by
2020 and one million electric cars by 2025 (Rijksoverheid, 2011).
Nevertheless, Groot (2014) argues that the lack of charging stations in the Netherlands impedes the
increase in electric cars. Additionally, previous studies advocate that the Dutch charging facilities
can be used more efficiently. Most of the Dutch electric car drivers employ a routine pattern when
charging their cars. They charge their car when they arrive at work or home, irrespective of the
battery level, and do not disconnect or move their car when the battery is full. Most charging
transactions last up to three times as long as required (RVO, 2014; Spoelstra, 2014). If this current
charging infrastructure will not be improved, the governmental goals will not be reached (De Wit,
2015; Groot, 2014). Social Charging developed a smartphone app based on these research insights,
with the aim to enable electric car drivers to share charging stations in order to encourage a more
efficient charging network. The next paragraph explains this app. Subsequently, this chapter presents
the background, research aim and objectives of the study as well as the importance of the research.
1.1 Background
The Social Charging mobile application is the missing social infrastructure to the existing technical
infrastructure. It supports drivers to share charging stations efficiently. When drivers wish to charge
their car, but all charging points are in use, the Social Charging application enables them to
communicate with other drivers which are currently using these stations, to request them to remove
their car. Herewith, Social Charging provides assurance about charging and facilitates CO2
reduction, as electric car users drive less miles. Moreover, the app enables a larger charging network
by encouraging drivers to make their private charging stations publicly available (Social Charging,
2015).

9. 3
Social Charging aims to have 2,500 app users within 1 year, which is 5% of the current target
population. In order to achieve this, the company wishes to figure out their attitudes towards the
app and their intention to use the app.
1.2 Research aim and objectives
In this consultancy project, the management problem is to examine whether the new app should be
further developed and introduced. The research problem is to identify consumer preferences and
use intentions for the new app.
The research aims (a) to provide insights into the attitudes and charging behaviour of electric car
drivers and (b) to identify their attitudes and usage intention towards the app.
Objectives:
• To build an understanding of the current research on electric vehicle (EV) charging, EV
adoption and the acceptance of new technologies;
• To gain insight into the charging behaviour of electric car users in the Netherlands,
based on the predictor variables EV range, charging point availability, observability,
planning and range anxiety;
• To identify the issues electric car users have concerning the Dutch charging
infrastructure and to assess the importance of each issue;
• To determine the gap between existing charging behaviour and the preferences of
electric car users concerning the charging infrastructure;
• To identify the perception of Dutch electric car users of the Social Charging application
and their behavioural intention to use it, based on the predictor variables trialability,
performance expectancy and social influence;
• To provide recommendations to Social Charging to reach their company goals.
1.3 Importance of the study
This research adds value by identifying electric car users’ charging behaviour and whether their
behaviour is related to their perception of the Social Charging app. This study also assesses the

10. 4
behavioural intention of electric car drivers to use the app. This information is important for the
company to decide whether they should further develop and introduce the app, in order to reach
their company objectives.
1.4 Organisation of the study
Chapter Contents
1: Introduction This chapter presented the background, research aim and
objectives of the study as well as the importance of the project.
2: Literature review Chapter two presents a review of the existing literature, discusses
the key constructs and moderating variables that may influence the
use of charging facilities and the intention to use the Social
Charging app.
3: Conceptual model and
research hypotheses
Chapter three shows the conceptual model, the research
hypotheses and the relationship between the hypotheses.
4: Research design and
methodology
Chapter four explains the research design of this study, followed by
the research methods, research approach and data analysis
procedures that will be used.
5: Results and analysis Chapter five presents the results and analysis of the primary data,
which consists of the respondent profile, construct reliability
procedures and a descriptive analysis of the study constructs.
Moreover, hypotheses testing indicates whether the results
support the hypotheses.
6: Conclusions and
implications
Chapter six offers the conclusions, recommendations and
limitations of the study, followed by future research directions.
Table 1.1. Organisation of the study.

11. 5
2. Literature review
2.1 Introduction
The previous chapter presented an overview of the study, including the background, research aim,
objectives and importance of the study. The current chapter first presents an overview of the
overarching academic theories and models related to the acceptance of new technologies, diffusion
of innovation, EV charging behaviour, EV adoption, concept testing and EV types. It then discusses
and critically evaluates the key constructs and moderating variables, obtained from the theories,
that may influence the usage level of charging facilities and the intention to use the Social Charging
app.
2.2 Theories
This study is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) by
Venkatesh et al. (2003); the Diffusion of Innovation Theory (DIT) by Rogers (1983) and the EV
Charging Behaviour Model by Spoelstra (2014).
2.2.1 Technology acceptance models
The Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003) is a
useful instrument to estimate if the introduction of a new technology will be successfully adopted
by the target consumer.
UTAUT shows that the four factors performance expectancy, effort expectancy, social influence and
facilitating conditions affect consumers’ behavioural intention and subsequent actual use.
Experience is a key moderator in this model (figure 2.1 in appendix K). Performance expectancy is
conceptualised as the extent to which an individual believes that a technology will enhance his or
her performance. Effort expectancy is defined as the degree to which an individual believes that
using the technology will be effortless. Social influence occurs when an individual’s thoughts,
feelings and behaviour are influenced by people who are important to him or her. Facilitating
conditions reflects the beliefs that an individual possesses necessary resources and opportunities to
perform a particular behaviour (Venkatesh et al., 2003).

12. 6
Dudenhöffer (2013) argues that UTAUT can be applied to electric cars. However, due to limitations
of her research, she failed to prove this. Nevertheless, Meschtscherjakov et al. (2009) successfully
applied this model within an automotive context. In addition, various researchers have examined
how the factors of this technology acceptance model affected smartphone app acceptance (e.g.
Chao, 2013; Kang, 2014; Lee et al., 2012).
Osswald et al. (2012) argue that anxiety is another factor affecting behavioural intention and
subsequent use behaviour (figure 2.2 in appendix K). Anxiety is defined as the extent to which an
individual responds to a situation with feelings of arousal or fear (Osswald et al., 2012).
2.2.2 Diffusion of innovation theory
The Diffusion of Innovation Theory (DIT) by Rogers (1983) explains the diffusion of innovations
among members of a social group. Rogers (1983) states that several factors affect an individual’s
attitude towards an innovation and eventually whether or not he or she will adopt this innovation.
These determinants are relative advantage, compatibility, complexity, trialability and observability
(figure 2.3 in appendix K). The factors relative advantage, compatibility and complexity are already
incorporated into Venkatesh et al.’s (2003) UTAUT, where they are named facilitating conditions,
performance expectancy and effort expectancy respectively.
Trialability concerns the degree to which an innovation can be tested on a limited basis before
committing to its usage and observability is defined as the extent to which the results of innovations
are visible by other people (Rogers, 1983).
2.2.3 EV charging behaviour
Spoelstra (2014) states that an electric car driver’s charging behaviour dimensions consist of
charging point location and type, charging frequency, charging time of day, charging duration and
energy transfer. These charging behaviour dimensions are influenced by the driver-related factors
range anxiety, planning, mobility planning and EV experience; the vehicle related factors battery
size, battery range and vehicle type; and the environment related factor charging point availability
(figure 2.4 in appendix K). Only the most relevant constructs, which will be included in the current
research, will be defined in paragraph 3, 4 and 5.

13. 7
2.2.4 Electric vehicle adoption
Sierzchula et al. (2014) state that charging infrastructure forms the best predictor of EV adoption,
whereas socio-demographic variables such as income and education level are not good predictors
of adoption levels. On the other hand, Mansor et al. (2014) state that the main factors persuading
consumers to buy EVs are environmental benefit, benefit to self, comparative cost and attainable
cost.
2.2.5 Concept testing
The Social Charging app concerns a new-product concept. Therefore, the study will also use
academic theories regarding concept testing. Kotler and Armstrong (2011, p.G2) define this as
“testing new-product concepts with a group of target consumers to find out if the concepts have
strong consumer appeal”. The opinions and potential issues of a group of electric car drivers
regarding the Social Charging app will be revealed by showing them the Social Charging app
physically, and by asking them questions about the app.
2.2.6 Electric vehicle types
It is important to note that there are different EV types, depending on how the car is powered and
how it can be charged (Spoelstra, 2014). Battery electric vehicles (BEVs) are full electric vehicles
using a battery, which are charged by plugging the vehicle into an electric power source. Plug-in
hybrid electric vehicles (PHEV) and extended range electric vehicles (EREVs) are powered by both
an electric motor and an internal combustion engine. They are charged both by plugging them into
an electric power source and by fuel. Hybrid electric vehicles (HEVs) also have both an engine and
an electric motor. This type of EV can also be charged by fuel. However, this battery cannot be
charged by plugging it into an electric power source, but by regenerative braking (Thomas, 2013).
Therefore, only BEVs, PHEVs and PHEVs will be considered in this research.
2.3 Vehicle related constructs
2.3.1 Electric vehicle range
In the literature about EV charging behaviour, EV range has been conceptualised as the range an
electric vehicle can drive on a battery which is fully charged (Spoelstra, 2014). Pearre et al. (2011)
state that EV range influences driving behaviour. When the range is not sufficient to reach a

14. 8
destination, the driver might opt to stop to charge during the day or along the way (Pearre et al.,
2011).
Pearre et al. (2011) state that electric cars with a small range of 100 miles per charge satisfy the
needs of a substantial share of car drivers. However, Dimitropoulos et al. (2011) found that electric
car buyers prefer vehicles with a considerably higher range. Pearre et al. (2011) demonstrate that
the larger the EV range is, the less drivers change their driving and charging behaviour.
2.4 Environment related constructs
2.4.1 Charging point availability
In the EV charging literature, charging point availability is conceptualised as the quantity and
coverage of charging points around the electric vehicle (Spoelstra, 2014). Schoeder and Traber
(2012) and Skippon and Garwood (2011) state that the amount of EV charging stations depends on
population density. Visscher (2013) argues that in the Netherlands, large regional differences in
charging point availability exist because each municipality decides if and how many charging points
are placed. In many Dutch cities, the number of charging points is lagging behind the expanding
electric car fleet, resulting in a shortage of charging points. Many Dutch electric car drivers
increasingly encounter cars occupying charging stations, either non-EVs or EVs that are not currently
being charged (Visscher, 2013).
Spoelstra (2014) states that charging point availability concerns an important issue for electric car
drivers. A higher amount and a larger coverage of charging points in the vicinity of their electric car
leads to a lower need for planning and lower range anxiety.
2.4.2 Observability
In the literature about innovations, observability has been conceptualised as the extent to which
the results of innovations are visible by other people (Rogers, 1983). Observability influences an
individual’s attitude towards an innovation and ultimately whether or not the innovation will be
accepted.

15. 9
The construct observability has been thoroughly studied by researchers (e.g. Gärling and Thøgersen,
2001; Janssen and Jager, 2002; Lane and Potter, 2007). However, observability has not yet been
examined in the electric car charging context and very little research has been conducted in the
electric car context. Welzel and Schramm-Klein (2013) found that observability does not have a
significant influence on attitude towards BEVs. However, this could be due to the fact that only a
small number of electric cars were visible on the streets at the time of research.
2.5 Driver related constructs
2.5.1 Planning
In the literature on electric vehicles, planning has been conceptualised as the degree to which an EV
driver needs to actively match his or her driving plans with the charging opportunities, before he or
she starts driving (Spoelstra, 2014). Spoelstra (2014) states that effective planning reduces range
anxiety and range safety buffers and improves the efficiency of the use of the EV charging network.
However, Graham-Rowe et al. (2012) found that EV drivers consider this aspect as a major
disadvantage of electric cars over conventional combustion engines. Spoelstra (2014) states that
BEV drivers mostly plan their journeys, whereas PHEV drivers mainly do not.
Doherty and Miller (2000) argue that people generally plan their activities on the day itself or one
day ahead. With regard to EV driving behaviour, work trips show the smallest difference between
the planned and actual number of trips. This difference is higher for leisure and shopping trips, as
these trips reflect impulsive behaviour whereas work trips mostly reflect habitual behaviour (Hahnel
et al., 2013; Jakobsson, 2004). Spoelstra (2014) confirms that EV drivers do not plan day-to-day
journeys, but they plan incidental longer trips to make sure that they know where to find a charging
stations along the way.
2.5.2 Range anxiety
Range anxiety has been conceptualised in the EV literature as the fear for not reaching the
destination before the battery of the electric vehicle is empty (Spoelstra, 2014). Ford and Alverson-
Eiland (1991) demonstrate anxiety as an influential factor in predicting performance. Osswald et al.
(2012) argue that range anxiety is an important construct in the car context, due to the strong
relation between anxiety and driving. This might be the cause that range anxiety has been heavily

16. 10
discussed in the existing electric car literature (e.g. Franke et al., 2012; Osswald et al., 2012; Tate et
al., 2009).
Osswald et al. (2012) state that range anxiety may occur when drivers cannot rely on the information
system presenting the information about the energy range of the electric vehicle. Spoelstra (2014)
argues that the limited range of the electric car may be the main cause for range anxiety. When an
EV driver experiences range anxiety, this leads to an overestimation of the range of the car. Range
anxiety increases the need for a range safety buffer and therefore affects charging behaviour in such
a way that drivers charge their cars longer and more often than needed. Range anxiety could be
reduced when EV drivers develop routines in driving and charging; when their understanding of the
technology of the car improves; when the car has a larger range; and when more charging points
are available (Spoelstra, 2014).
2.5.3 EV experience
EV experience has been conceptualised as the amount of experience electric car drivers have in
dealing with limitations and possibilities of the electric vehicle (Spoelstra, 2014). Kurani et al. (1994)
demonstrate that a lack of experience causes a preference for high range electric vehicles. Franke
and Krems (2013c) state that electric car drivers with a high level of EV experience encounter low
range anxiety and have a low need for range safety buffers. However, Spoelstra (2014) states that
this applies mainly to low range BEV drivers, as range anxiety is low for PHEV drivers and high range
BEV drivers both with and without EV experience.
Moreover, when drivers have a larger amount of EV experience, their charging behaviour becomes
more routinized (Hahnel et al., 2013). However, Spoelstra (2014) found that this routine is already
set after two weeks of driving in an electric vehicle.
Franke and Krems (2013a) demonstrate that electric car drivers with a good prior knowledge of their
car range use their EV range more efficiently, i.e. show more sustainable behaviour in charging their
electric car. On the other hand, Franke and Krems (2013b) argue that most electric car drivers
usually recharge their car albeit it has a lot of range left.

17. 11
2.5.4 Trialability
Trialability has been conceptualised in the innovations literature as the degree to which an
innovation can be tested on a limited basis before committing to its usage (Rogers, 1983). As stated
before, trialability is one of the factors influencing an individual’s attitude towards an innovation
and whether or not he or she will accept the innovation.
As well as observability, the phenomenon of trialability has been heavily discussed in the literature
about innovations (e.g. Bunce et al., 2014; Gärling and Thøgersen, 2001; Welzel and Schramm-Klein,
2013). Various researchers examined the influence of trialability on consumers’ attitude towards
smartphone use. They found that trialability does not affect consumer adoption of smartphones
(Ling and Yuan, 2012; Persaud, 2012). A small amount of research has been performed on the
influence of trialability on consumer adoption of smartphone apps. Most of this research is
performed within the past two years. Liu (2014) argues that trialability speeds up the adoption
procedure of apps. Moreover, Zhang (2014) states that trialability encourages early adopters of
smartphone apps in the healthcare sector to recommend these apps to others.
2.5.5 Performance expectancy
From the existing literature about electric vehicles, performance expectancy can be conceptualised
as the degree to which electric car drivers believe that using the app will support them to reach their
goals in driving performance (Osswald et al., 2012). Osswald et al. (2012) state that these goals could
consist of global goals like spending less energy or task-related goals like utilising apps in such a way
that they support efficient and effective task completion. The construct performance expectancy is
derived from the concepts perceived usefulness from the Technology Acceptance Model (TAM) of
Davis et al. (1989) and relative advantage from the DIT of Rogers (1983) (Venkatesh et al., 2003).
Various researchers examined the relation between performance expectancy and smartphone apps.
Chao (2013) and Lee et al. (2012) argue that performance expectancy positively affects usage
intention and subsequent use behaviour. However, Kang (2014) states the contrary, namely that
performance expectancy does not influence intention of app use. The author states that other
devices may better satisfy their performance expectancy. It can therefore be concluded that
performance expectancy of smartphone apps could depend on the goals in driving performance.

18. 12
2.5.6 Social influence
Social influence is conceptualised in the electric vehicle literature as the extent to which electric car
drivers believe that other people, whose opinions are important to these electric car drivers, think
the same way about using a new app (Osswald et al., 2012). The construct social influence stems
from the construct subjective norm from the Theory of Reasoned Action (TRA), developed by Ajzen
and Fishbein (1980) (Osswald et al., 2012). Ajzen and Fishbein, (1980) Venkatesh et al. (2003) and
Zhou (2011) state that subjective norm generally has a positive effect on behavioural intention,
wherewith actual behaviour can be predicted.
Cars are often considered as a status symbol, which suggests a connection between acceptance and
social environment (Osswald et al., 2012). This finding is affirmed by Chao (2013), who states that
social influence positively affects behaviour intention. On the contrary, Lee et al. (2012) argue that
mobile app users are not influenced by important other people’s opinions in determining usage
intention of the app.
2.5.7 Effort expectancy
Effort expectancy in the mobile app context can be conceptualised as the degree of ease in the use
of mobile apps (Venkatesh et al., 2003). The construct effort expectancy comprises two constructs
of existing technology acceptance and innovation models, namely perceived ease of use from the
TAM and ease of use from the DIT (Venkatesh et al., 2003).
Chao (2013), Kang (2014) and Lee et al. (2012) demonstrate the positive relation between effort
expectancy and usage intention and subsequent use behaviour. Kang (2014) adds that smartphone
users consider easiness as the most important factor in the use of smartphone apps. Therefore, apps
should be easy in order to facilitate use intention of apps (Kang, 2014).
2.5.8 Facilitating conditions
Facilitating conditions can be conceptualised from the existing literature as the degree to which an
electric car driver believes that a technical infrastructure could support in the use of the mobile app
(Venkatesh et al., 2003). The construct facilitating conditions is derived from perceived behavioural
control from the Theory of Planned Behaviour (TPB) of Ajzen (1991) and compatibility from the DIT
(Venkatesh et al., 2003).

19. 13
Lee et al. (2012) demonstrate that facilitating conditions do not positively affect the use mobile
apps. On the contrary, Chao (2013) states that the construct does have a positive effect on
behaviour intention and use behaviour. However, the reliability of this outcome is questionable,
because the test statistic value of this construct in this research was below .70.
2.6 Summary
Various academic theories have revealed the key factors that may influence electric car charging
behaviour and intention to use the Social Charging app. The relevant factors from the academic
literature form the basis of the conceptual model, which will be presented in the next chapter.
The factors experience and anxiety from the Venkatesh et al.’s (2003) and Osswald et al.’s (2012)
technology acceptance models will be used to measure their influence on the usage level of EV
charging facilities. The constructs performance expectancy, effort expectancy, facilitating conditions
and social influence from Venkatesh et al.’s (2003) UTAUT will be used to measure their effect on
the intention to use the Social Charging app.
From Rogers’ (1983) DIT, trialability is relevant to measure electric car driver’s attitudes towards the
Social Charging app and observability will be used to measure their attitudes towards the current
EV charging network.
The constructs range anxiety, planning, EV experience, EV range, vehicle type and charging point
availability from Spoelstra’s (2014) research will be used to measure their influence on electric car
driver’s level of use of the EV charging network.

20. 14
3. Conceptual model and research hypotheses
3.1 Introduction
The preceding chapter showed a variety of research models on technology acceptance and EV
charging behaviour which described the key constructs influencing behavioural intention and actual
behaviour of apps and EV charging facilities. This current chapter first presents the conceptual
model of this study, which is based on the key constructs mentioned in the previous chapter. This
conceptual model forms the basis of the hypotheses presented in paragraph 3.3. Subsequently, this
chapter shows the relationship between these hypotheses.
3.2 Conceptual model
The literature review presented a variety of research models describing the way various factors drive
behavioural intention and actual behaviour. Some of these factors have received more attention by
researchers in the electric car charging context than others. For instance, observability has not yet
been examined in the electric car charging context, whereas range anxiety has been heavily
discussed in the existing literature about EV charging. In addition, only a small amount of research
has been performed on the influence of the key factors trialability, performance expectancy and
social influence on the intention to use mobile apps. Most of this research is performed within the
past two years. This could be due to the fact that apps concern an innovation which is even newer
than electric cars (Anderson and Anderson, 2005; Strain, 2015).
In order to be able to provide recommendations to Social Charging to enable a more efficient
charging network, the attitude towards and use of current EV charging facilities and the intention
to use the Social Charging app will be measured. These are the outcome constructs of the study.
The five central constructs leading to the usage level of charging facilities are EV range, charging
point availability, observability, planning and range anxiety. A potential moderator of this outcome
construct is EV experience. The three central constructs influencing the intention to use mobile apps
are trialability, performance expectancy and social influence. Potential moderators influencing this
relationship are effort expectancy and facilitating conditions. Figure 3.1 shows the relationships
between those variables graphically.

21. 15
Figure 3.1. Conceptual model.

22. 16
3.3 Research hypotheses
3.3.1 H1a,b,c,d,e
EV range has been defined as the distance an electric vehicle can drive on a battery which is fully
charged (Spoelstra, 2014). Pearre et al. (2011) argue that EV range influences charging behaviour.
When the range is not sufficient to reach a destination, the driver might charge during the day or
stop to charge along the way. Pearre et al. (2011) also demonstrate that the larger the EV range is,
the less drivers change their charging behaviour. Based on these academic findings, I predict the
following:
H1a: EV range is negatively associated with the level of use of charging facilities.
Charging point availability is defined as the amount and coverage of charging points around the
electric vehicle (Spoelstra, 2014). Spoelstra (2014) states that charging point availability concerns
an important issue for electric car drivers, as a lower availability leads to a higher need for planning
and higher range anxiety. However, many Dutch cities have a shortage of charging points (Visscher,
2013). This leads to the following hypothesis:
H1b: Charging point availability is negatively associated with the level of use of charging facilities.
Observability can be defined as the extent to which the results of innovations are visible by others
(Rogers, 1983). Rogers (1983) states that observability influences an individual’s attitude towards
an innovation and ultimately whether or not he or she will accept the innovation. Based on these
findings, the usage level of charging facilities should improve when they are clearly visible by other
people. However, observability has not yet been examined in the electric car charging context. It is
therefore interesting to fill this hole in the literature by positing the following:
H1c: Observability of charging stations is positively associated with the level of use of charging
facilities.
Planning is defined as the degree to which an EV driver needs to actively match his or her driving
plans with the charging opportunities, before he or she starts driving (Spoelstra, 2014). Spoelstra

23. 17
(2014) states that effective planning improves the efficiency of the use of the EV charging network
and that EV drivers plan longer trips to ensure that they know where to find charging stations along
the way. Hence:
H1d. Planning is positively associated with the level of use of charging facilities.
Range anxiety has been defined as the fear for not reaching the destination before the battery of
the electric vehicle is empty (Spoelstra, 2014). Ford and Alverson-Eiland (1991) and Osswald et al.
(2012) demonstrate anxiety as an influential factor in predicting charging performance. Range
anxiety increases the need for a range safety buffer and therefore affects charging behaviour in such
a way that drivers charge their cars longer and more often than needed (Spoelstra, 2014). Therefore,
I predict the following:
H1e. Range anxiety is positively associated with the level of use of charging facilities.
3.3.2 H2a,b,c
Trialability has been defined as the degree to which an innovation can be tested on a limited basis
before committing to its usage. Trialability influences an individual’s attitude towards an innovation
and whether or not he or she will accept the innovation (Rogers, 1983). Trialability speeds up the
adoption procedure of mobile apps (Liu, 2014) and encourages early adopters of smartphone apps
to recommend these apps to others (Zhang, 2014). These findings lead to the following hypothesis:
H2a: Trialability of the Social Charging app is positively associated with the intention to use the app.
Performance expectancy can be defined as the degree to which electric car drivers believe that using
the app will support them to reach their goals in driving performance (Osswald et al., 2012). Chao
(2013) and Lee et al. (2012) argue that performance expectancy positively affects mobile app usage
intention. Kang (2014) states the contrary, due to the fact that other devices may better satisfy their
performance expectancy. However, as most people nowadays carry their smartphone with them all
day long and the average smartphone user uses over 7 social apps (Stadd, 2013), I predict the
following:

24. 18
H2b: Performance expectancy is positively associated with the intention to use the Social Charging
app.
Social influence is defined as the extent to which electric car drivers believe that other people,
whose opinions are important to them, think the same way about using a new app (Osswald et al.,
2012). Ajzen and Fishbein, (1980) Venkatesh et al. (2003) and Zhou (2011) state that social influence
generally has a positive effect on behavioural intention. In the mobile app context, this finding is
affirmed by Chao (2013). Thus:
H2c: Social influence is positively associated with the intention to use the Social Charging app.
3.3.3 H3
The use of charging facilities can be defined as the frequency with which electric car drivers charge
their electric cars and which charging point type they generally use (Spoelstra, 2014). Research
shows that most Dutch electric car drivers charge their cars upon arriving at work or home and do
not disconnect their car when the battery is fully charged (RVO, 2014). This could encourage the
intention to use the Social Charging app. Therefore:
H3: Use of charging facilities is positively related to the intention to use the Social Charging app.
3.3.4 H4a,b,c,d,e
EV experience concerns the amount of experience electric car drivers have in dealing with
limitations and possibilities of the electric vehicle (Spoelstra, 2014). In the existing literature, EV
experience has not previously been used as a moderator on the relation between the antecedent
variables and the level of use of charging facilities. However, Franke and Krems (2013a) demonstrate
that electric car drivers with a good prior knowledge of their car range show more sustainable
behaviour in charging their electric car. This leads to the following hypothesis:
H4a: As the value of the moderator EV experience increases, the negative relationship between EV
range and the usage level of charging facilities decreases.

25. 19
Franke and Krems (2013c) state that electric car drivers with a high level of EV experience have a
low need for range safety buffers. Thus:
H4b: As the value of the moderator EV experience increases, the negative relationship between
charging point availability and the usage level of charging facilities decreases.
H4c: As the value of the moderator EV experience increases, the positive relationship between
observability and the usage level of charging facilities decreases.
Hahnel et al. (2013) state that drivers with a larger amount of EV experience routinize their charging
behaviour, which means they require less planning. Hence:
H4d: As the value of the moderator EV experience increases, the positive relationship between
planning and the usage level of charging facilities decreases.
Franke and Krems (2013c) state that electric car drivers with a high level of EV experience encounter
low range anxiety. This leads to the following hypothesis:
H4e: As the value of the moderator EV experience increases, the positive relationship between range
anxiety and the usage level of charging facilities decreases.
3.3.5 H5a,b,c
Effort expectancy concerns the degree of ease in the use of mobile apps (Venkatesh et al., 2003). In
the existing literature, effort expectancy has not previously been used as a moderator. However,
Chao (2013), Kang (2014) and Lee et al. (2012) demonstrate the positive relation between effort
expectancy and usage intention of mobile apps. Kang (2014) adds that smartphone users consider
easiness the most important factor in the use of smartphone apps. Thus:
H5a: As the value of the moderator effort expectancy increases, the positive relationship between
trialability and the intention to use the Social Charging app decreases.

26. 20
H5b: As the value of the moderator effort expectancy increases, the positive relationship between
performance expectancy and the intention to use the Social Charging app decreases.
H5c: As the value of the moderator effort expectancy increases, the positive relationship between
social influence and the intention to use the Social Charging app decreases.
3.3.6 H6a,b,c
Facilitating conditions relates to the degree to which an electric car driver believes that a technical
infrastructure could support in the use of the mobile app (Venkatesh et al., 2003). If an individual
experiences a lack of resources or opportunities, his or her behavioural intention will be low, even
though other circumstances may be positive (Ajzen, 1991). Therefore:
H6a: As the value of the moderator facilitating conditions increases, the positive relationship
between trialability and the intention to use the Social Charging app also increases.
H6b: As the value of the moderator facilitating conditions increases, the positive relationship
between performance expectancy and the intention to use the Social Charging app also increases.
H6c: As the value of the moderator facilitating conditions increases, the positive relationship
between social influence and the intention to use the Social Charging app also increases.
3.4 Summary
This chapter proposed the conceptual model of this study, as well as a set of hypotheses and their
relations. These hypotheses will be tested upon collection of quantitative primary data results.

27. 21
4. Research design and methodology
4.1 Introduction
The previous chapter proposed the conceptual model of this study and the research hypotheses.
This current chapter presents the research design type this study will adopt, followed by the
research methods, research approach and data analysis procedures.
4.2 Research design
This consultancy project follows both an exploratory and a descriptive research design. Exploratory
research aided to gain insights into the management problem and took place in the initial phase of
the entire research design (Malhotra, 2009). Secondary research is performed in order to gain an
understanding of the key constructs and to gain insights in the current EV charging situation.
Qualitative research methods are used to gain an understanding of Dutch electric car drivers’
attitudes and behaviour towards current EV charging facilities. These unique insights are not
statistically measurable. Therefore, descriptive research is performed by means of quantitative
research methods to better define Dutch electric car drivers’ attitudes and behaviour and to
statistically infer this to the whole Dutch electric car driving population (Malhotra, 2009). Descriptive
research concerns the key research design of this study.
4.3 Secondary research
Secondary research is performed during the exploratory research stage (Saunders et al., 2009).
Secondary data provided the necessary insights to establish the marketing research problem and
revealed the key factors influencing behavioural intention and use behaviour, which formed the
basis of the conceptual model, hypotheses and research design formulation (Malhotra, 2009). These
data have been obtained from academic journal articles about technology acceptance and EV
charging, as well as books on marketing research.

28. 22
4.4 Primary research
4.4.1 Qualitative research
Additionally, the exploratory research comprised qualitative research (Malhotra, 2009). Focus
groups were held to gain insight into electric car drivers’ charging behaviour and issues regarding
the Dutch EV charging facilities. Additionally, the Social Charging app and its role as a solution to the
current charging infrastructure problems was demonstrated in order to obtain insight into drivers’
preferences and intention to use the app.
4.4.1.1 Research methods
Saunders et al. (2009) define research methods as the techniques and procedures with which data
can be obtained and analysed. By means of qualitative research, unique insights into the behaviour
and attitudes of participants can be discovered (Malhotra and Birks, 2007).
The most popular qualitative research techniques are focus groups and in-depth interviews
(Malhotra, 2009). Saunders et al. (2009) point out that focus groups and interviews are useful in
determining consumer insights which can later be included in surveys to obtain more valid and
replicable findings. Therefore, focus groups and interviews form the most suitable research
techniques for this study.
A focus group concerns an unstructured discussion among a small group of participants, conducted
by a moderator (Malhotra and Birks, 2007). The advantages of focus groups are that the participants
can try the app (Saunders et al., 2009), rich findings are generated because participants might probe
topic areas that the researcher has not considered (Malhotra, 2009) and large amounts of data are
provided within a short timeframe (Rabiee, 2004).
Additional interviews took place with six individual participants that could not attend the focus
groups. During interviews, deeper insights about underlying motives can be discovered, due to the
lack of social pressure (Malhotra, 2009).

29. 23
4.4.1.2 Discussion guide design and testing
The focus groups and in-depth interviews are semi-structured. The questions follow a discussion
guide, and the same questions are asked in each focus group and interview. However, the sequence
of the questions may be altered per interview and the interviewer may probe for further
information. Appendix A presents the focus group and interview discussion guide (McDaniel and
Gates, 1999).
The discussion guide has been divided into four sections: an introduction to make the group feel at
ease and to describe the process of the focus group, motivations to drive an electric car, opinions
about current charging facilities and opinions about the Social Charging app.
Davis and Venkatesh (2004) argue that potential users should get acquainted with a working
prototype. Hence, an invitation to download the app as well as a request to try out the app before
the focus group started has been sent to the participants on forehand. As all participants are Dutch,
and the physical focus groups and interviews took place in the Netherlands, the focus groups and
interviews were held in Dutch. However, the results are presented in English. One transcript is
provided for illustration purposes, both the original Dutch version (appendix C) and a translation
into English (appendix D).
Malhotra (2009) emphasises the importance of open-ended questions in exploratory research. The
focus groups and interviews used unstructured, open-ended questions, because this question
format enabled participants to express their attitudes and opinions so that their underlying
motivations, beliefs and attitudes could be identified (Fern, 2001).
The focus groups had a duration of approximately 50 minutes, whereas the interviews lasted about
30 minutes each. The audio recorder has been tested and a Skype group call has been established
on forehand, in order to pretest the technical equipment for the qualitative research.
4.4.1.3 Sampling design
In order to set up appropriate groups, the sampling design process proposed by Malhotra (2009) is
used. The first step is to define the people to whom the study is addressed, which are electric car

30. 24
drivers in the Netherlands. These people do not have to possess an electric car, as long as they have
EV charging experience.
Secondly, the sampling frame, which is a list of the elements of the target population, is selected. In
this study, the sampling frame consists of the database of Social Charging and internet communities
and discussion groups about electric cars on Facebook, Twitter and LinkedIn.
The third step concerns the selection of a sampling technique. Nonprobability sampling is used in
this qualitative research. As the target audience concerns a small, specific part of the Dutch
population, these people cannot be selected randomly from the whole population. The
nonprobability techniques used are self-selection sampling, through which participants of internet
communities and discussion groups are asked to take part in the focus groups and snowball
sampling, by which these participants were asked to ask other electric car drivers to participate in
the other focus groups (Malhotra, 2009; Saunders et al., 2009). Although nonprobability sampling
aids in estimating the population characteristics, the precision of the sample results cannot be
objectively estimated (Malhotra, 2009).
Next, the sample size is determined. Saunders et al. (2009) posit that focus groups typically comprise
four to eight participants. Krueger (1994) states that a number of three to four focus groups is
usually sufficient to reach to the point that only repetitious information is given. This study will use
three focus groups of 4, 6 and 6 participants each, as well as six one-to-one interviews.
4.4.1.4 Data collection procedure
The data collection is undertaken by the researcher of this study. The focus groups took place from
3 to 12 June 2015 and the interviews were organised from 8 to 19 June 2015. In both physical focus
groups, an external person assisted by taking notes.
The physical focus groups took place in pre-booked conference rooms in Utrecht and at the
University of Amsterdam. These places were geographically most convenient for the participants.
The rooms were free of background noise, in order to audiorecord the conversations. The online
focus group and the six interviews took place on Skype.

31. 25
In order to make the participants feel at ease, an informal setting was arranged where hot and cold
drinks and snacks were provided. Moreover, the pre-screened participants were placed in
homogenous demographic and socio-economic groups (Malhotra, 2009; Venkatesh et al., 2003).
The demographic profile of the sample is presented in table 4.1 in appendix K.
Ethical guidelines were met by explaining the participants what the focus groups and interviews
would include, how long they would take and that they would not have to provide answers that
made them feel uncomfortable.
The problem faced in the qualitative research process was to find a suitable time and location to
bring all participants together. Therefore, the third focus group was held online and the last focus
group changed to online one-to-one interviews.
4.4.1.5 Data analysis procedures
The data obtained from the focus groups and in-depth interviews are coded into 8 pre-established
categories, 12 codes and 73 themes as presented in appendix B to provide measurement scales in
quantitative procedures (Rabiee, 2004). In interpreting the coded data of the focus groups and
interviews, the criteria words, context, internal consistency, trends throughout the groups and
frequency, extensiveness, specificity and intensity of comments were of key importance (Krueger,
1994). These formed the basis of the different major themes (Rabiee, 2004). A complete analysis of
the categories and related themes derived from the participants’ narratives are presented in
appendix E. Additionally, appendix C and D present the transcript and translation of one focus group.
4.4.2 Quantitative research
The descriptive research is based on surveys (Malhotra, 2009) to test the issues regarding the EV
charging infrastructure which emerged from the focus groups and interviews. Besides quantitative
data about electric car users’ attitude and behaviour towards the existing charging infrastructure in
the Netherlands, their intention to use the Social Charging application is measured. With these data,
the hypotheses will be tested and the conceptual model validated.

32. 26
4.4.2.1 Research methods
The results of quantitative research are used to validate the results of the qualitative research and
to provide recommendations to Social Charging (Malhotra, 2009). As this study aims to ask a large
group of respondents the same list of questions in a pre-determined order (Saunders et al., 2009),
the survey method is most relevant. Surveys are reliable and the results are easy to code, analyse
and interpret (Malhotra and Birks, 2003). Moreover, surveys are useful to collect electric drivers’
opinions and behaviours towards the charging network and opinions of the app (Saunders et al.,
2009).
Surveys can be conducted by telephone, in person, by mail and online (Malhotra, 2009). Due to cost
and time constraints, an online survey is carried out in Qualtrics. Data collected through online
surveys can be analysed quickly and it is very time and cost-effective. The questionnaire concerns a
self-administered survey, as the respondent takes the survey without intervention of the
researcher. Online surveys have a low response rate (Malhotra, 2009), which I aim to tackle by
entering the respondents into a prize draw to win a month of free EV charging.
4.4.2.2 Questionnaire design and testing
The questionnaire is structured, which means that every respondent answers the same list of
questions in a pre-determined order. Before the respondent can start the survey, he has to answer
two selection questions to determine whether or not he belongs to the target group, in order to
prevent that everyone can fill in this survey and to prevent subsequent inaccurate data (Easterby-
Smith et al., 2008). The questionnaire itself is divided into four sections: electric car use, use of EV
charging facilities, opinion towards the Social charging app and demographic information. The
survey consists of various dichotomous, nominal, fixed allocation and 7-points Likert scale
questions. The latter are used to test the constructs in the conceptual model. These scale questions
are mainly based on items found in the literature (appendix F). Solely two unstructured, open-ended
questions are used due to their complexity in analysing (Malhotra, 2009). Appendix G presents the
questionnaire as it is sent out to the respondents, which is in Dutch. Appendix H presents a
translation of the survey into English.
The questionnaire used a pilot test among 25 respondents to reduce problems in answering the
questions (Saunders et al., 2009). The pilot test revealed that the video did not work on smartphones

33. 27
and tablets and it took most respondents more than 20 minutes to complete the survey. Bryman
and Bell (2007) argue that respondents are not likely to complete long questionnaires. Therefore, I
fixed the video problem and I removed various constructs and security questions in order to shorten
the survey. This resulted in a survey which took between 12 and 15 minutes to complete.
4.4.2.3 Construct operationalisation and measures
Appendix F shows the operationalisation of the key constructs and items as used in the
questionnaire. These constructs have been found in the existing literature about technology
acceptance, innovation and diffusion and electric car charging behaviour, as previously shown in
chapter 2. The measures stem mainly from previous quantitative articles (e.g. Franke et al., 2012;
Osswald et al., 2012; Venkatesh et al.; 2003; Welzel and Schramm-Klein, 2013).
4.4.2.4 Sampling design
The target population is the same as in the qualitative study. The sampling frame is also the same
as in the qualitative study, extended by the database of the Dutch municipality The Hague and the
viewers of the website of Nederland Elektrisch and Facebook page of Amsterdam Elektrisch, who
posted the survey.
Self-selection and snowball nonprobability sampling are also used for the quantitative study.
However, for the questionnaire, The Hague provided a database of 427 email addresses of electric
car drivers. By means probability sampling, all these respondents have been contacted to participate
in the questionnaire.
Although 228 people started the survey, only 181 of them filled in all questions. Due to time
constraints, the data analysis proceeded with the sample size of 181 respondents.
4.4.2.5 Data collection procedure
The quantitative research data collection is undertaken by the researcher of this study. The
quantitative data are collected within two months, from 16 June to 18 August 2015. The survey took
place online1
, by means of the program Qualtrics, because it transfers the answers automatically
into an SPSS file.
1
https://leedsubs.eu.qualtrics.com/SE/?SID=SV_8cVYNZKtkn9vwUd

34. 28
The elements selected themselves by answering the first questions “Do you currently own and drive
any kind of electric car?” and “Have you ever driven and charged any kind of electric car?” positively.
The respondent profile of the quantitative research is discussed in the next chapter.
Ethical guidelines were met by explaining the respondents at the start of the survey what the
purpose of the survey was, who the client is, how long the survey would take and that all questions
would be treated anonymously.
4.4.2.6 Data analysis procedures
To analyse the quantitative data, the analytical methods multiple regression analysis, interaction
effects, correlation analysis, reliability tests, categorical variables and one sample t-tests (Pallant,
2010) are used. Table 4.2 in appendix K shows which analytical methods tested which hypotheses
and explains the purpose of each analytical method.
4.5 Summary
This chapter presented an explanation of the steps followed in conducting qualitative and
quantitative research. A combination of exploratory and descriptive design is used to provide the
most extensive and reliable results within the three-month time frame. Moreover, a justification of
the chosen research methods focus groups, in-depth interviews and surveys was given, as well as
an overview of the data analysis procedures.

35. 29
5. Results and analysis
5.1 Introduction
The previous chapter presented the research design and methods. The current chapter presents the
results and analysis of the primary data. Exploratory qualitative research was used to gain insight
into Dutch electric car drivers’ attitudes and behaviour, which formed the input of the quantitative
research. Descriptive research concerns the key research design, as the hypotheses will be tested
and the conceptual model validated by means of the quantitative research. Therefore, this chapter
will focus on the results of the quantitative research. The preliminary qualitative research results
are presented in appendix H.
This chapter first presents the respondent profile. The next section presents a discussion of the
mean scores and standard deviations of the studied constructs, which is followed by a construct
reliability analysis. The chapter concludes with a descriptive analysis of the results and hypotheses
testing.
5.2 Respondent profile
Of all 228 people who started the survey, the total valid response is 181 respondents. Table 5.1 in
appendix K presents the respondent profile in detail. In short, 87.8% of the respondents is male, and
the majority (64.1%) is between 36-55 years old. The largest proportion of the respondents (44.8%)
is from The Hague, due to the large database of the municipality of The Hague used to gain
respondents. The respondents have generally studied at a high level, primarily Higher Vocational
Education (34.8%) or University (50.8%).
93.9% of the respondents own an electric car themselves. The majority of the respondents drive a
PHEV or a EREV (60.2%), 39.8% drive a BEV. Most respondents state that their car has a range of 50
kilometres or less (44.8%). The majority of the respondents already drives an electric car more than
1 year (74%), mainly on a daily basis (79.6%). The main purposes for EV use are commuting (44.2%)
and business travel (42.5%). The main reason respondents chose to drive an electric car concerns
financial advantages (36.7%), followed by environmental benefits (26.3%)

36. 30
Most respondents indicate that they usually charge their car when they are on the road (90.1%),
80.1% charge their car near their homes and 64.1% at work. They charge mostly in the evening
(61.3%) and at night (67.9%). Nearly a quarter of the respondents charges almost every day (71.8%).
68.6% declare that they charge their car at all times when they don’t drive in it and 61.9% affirm
that they usually do not unplug their car when the battery is fully charged.
5.3 Construct reliability
With regard to reliability procedures, Cronbach’s Alpha is used to measure the internal consistency
of the scales and to purify the research data. Constructs offer reliability when their alpha level is 0.6
or higher (Malhotra, 2009). Table 5.2 in appendix K presents an overview of the initial results.
Most constructs have an alpha value between 0.60 and 0.93. However, the alpha levels of the
constructs planning, EV range and observability are 0.59, 0.55 and 0.40 respectively, which means
that the scales are internally not consistent and therefore not reliable. This could be due to the fact
that these constructs have solely 2 items, because pretests indicated that the questionnaire was too
long. In order to increase the reliability of these constructs, the theoretically irrelevant items are
removed, and only the items which are theoretically relevant to these constructs are retained.
One of the items that forms the construct charging point availability presented a negative inter-item
correlation. In order to purify this construct, the item has been removed, which led to an increase
in the alpha level from .71 to .72. Moreover, one item for the construct trialability has been
removed, which led to an increase in the alpha level for trialability from .80 to .83. Table 5.3 shows
the results of the reliability test after removal of these items.

37. 31
Constructs Cronbach’s Alpha Number of items
EV range N/A 1
Charging point availability .72 7
Observability N/A 1
Planning N/A 1
Range anxiety .70 2
Level of use of charging facilities .61 10
Trialability .83 2
Performance expectancy .93 4
Social influence .87 3
Intention to use app N/A 1
EV experience .74 3
Effort expectancy .92 2
Facilitating conditions .80 2
Table 5.3. Results of the reliability test after purification of the research data.
5.4 Descriptive analysis
All constructs are based on 7-point Likert scales, ranging from 1=strongly disagree to 7=strongly
agree and from 1=never to 7=always. Tables 5.4-5.8 in appendix K present the mean scores and
standard deviations of these constructs per item. One sample T-tests indicate that all items have a
significance of .000, which indicates that all items are highly significant. Therefore, all items are kept
in the data analysis. The higher the mean rate, the more important the respondents perceived the
items. The standard deviations range from 1.05 to 2.68, which means that the responses are
scattered wide around the mean.
Per construct, the items are averaged to form composites. Table 5.9 below summarises these
statistics per construct. The mean is highest for the moderators facilitating conditions (6.13) and EV
experience (6.04), and lowest for the constructs level of use of charging facilities (3.05) and range
anxiety (3.28).

38. 32
Constructs and moderators Mean Standard
deviation
Significance
EV range 5.09 1.97 .000
Charging point availability 3.79 1.05 .000
Observability 4.13 1.86 .000
Planning 4.09 2.19 .000
Range anxiety 3.28 1.71 .000
Level of use of charging facilities 3.05 0.96 .000
Trialability 5.12 1.45 .000
Performance expectancy 4.77 1.58 .000
Social influence 4.80 1.56 .000
Intention to use app 5.07 1.96 .000
EV experience 6.04 1.00 .000
Effort expectancy 5.52 1.26 .000
Facilitating conditions 6.13 1.29 .000
Table 5.9. Mean scores and standard deviations for all constructs and moderators.
Pearson’s product moment correlation analysis is used to show the relationships between all
continuous independent variables and continuous dependent variables (Malhotra, 2009). Table 5.10
presents this matrix. Of all 78 relations, 36 correlations (46.2%) are significant as their p-levels are
≤0.05. These variables are therefore linked to the hypotheses. For instance, all three independent
variable trialability, performance expectancy and social influence have a positive correlation with
and therefore a positive effect on the intention to use the Social Charging app. Of the predicted
independent variables EV range, charging point availability, observability, planning and range
anxiety, only planning seems to influence the level of use of charging facilities.
The highest correlation involving an independent variable is 0.72. As this highest correlation is less
than 0.90, no variables have to be removed from the analysis.

39. 33
Variable EV
range
Charging
point
availa-
bility
Observa-
bility
Planning Range
anxiety
Level of
use of
charging
facilities
Triala-
bility
Perfor-
mance
expec-
tancy
Social
influence
Inten-
tion to
use
app
EV
expe-
rience
Effort
expec-
tancy
Facili-
tating
condi-
tions
EV range 1
Charging
point
availability
.14 1
Observability .08 .54** 1
Planning .21** -.04 .02 1
Range
anxiety
-.04 -.20** -.12 .06 1
Level of use
of charging
facilities
.08 -.06 .13 .18* -.01 1
Trialability .16* -.15* -.01 .15* -.01 .07 1
Performance
expectancy
.13 -.19* .03 .18* .11 .13 .37** 1
Social
influence
.21** -.10 .11 .23** -.01 .14 .43** .71** 1
Intention to
use app
.17* -.05 .09 .26** .07 .11 .40** .72** .72** 1
EV
experience
.26** .02 .13 .22** -.18* .16* .06 -.01 .14 .01 1
Effort
expectancy
.17* .08 .12 .20** -.08 .03 .43** .47** .53** .41** .11 1
Facilitating
conditions
.11 -.05 .02 .16* -.10 .07 .18* .32** .34** .28** .21** .35** 1
Table 5.10. Correlation matrix (*p≤0.05; **p≤0.01).

40. 34
5.5 Hypotheses testing
Two multiple regression analyses using the Enter-method have been performed to test the
hypotheses. These multiple regression analyses included interaction terms to test whether the
moderators affect the relations between the independent variables and the dependent variables.
The interaction terms were calculated by multiplying the mean-centred independent variables with
the mean-centred moderators. In order to avoid multi-collinearity, merely the mean-centred
independent and moderator variables are used in both multiple regression analyses. The first
analysis included the dependent variable level of use of charging facilities, its antecedent
independent variables, moderator and interaction terms. The second analysis included the
dependent variable intention to use the Social Charging app, its antecedent independent variables
(including the level of use of charging facilities), moderators and interaction terms. The results are
presented in table 5.11 and 5.12 in appendix K.
5.5.1 H1a,b,c,d,e
Whereas Pallant (2010) argues results to be statistically significant for p<0.05, Palihawadana (2013)
states that p-values smaller than 0.1 are statistically significant for dissertation purposes. The results
in table 5.11 indicate that charging point availability (p<0.05), observability (p<0.05) and planning
(p<0.1) significantly affect the level of use of charging facilities. Observability makes the strongest
unique contribution towards explaining the dependent variable due to its largest beta-coefficient
(0.24). On the other hand, charging point availability negatively affects the level of use of charging
facilities due to its negative beta-coefficient (-0.21). However, the hypothesis already predicted a
negative relation. Hence, H1b, H1c and H1d are supported.
The significance scores for EV range and range anxiety are greater than 0.1. Thus, H1a and H1e are
not supported by these results.
5.5.2 H2a,b,c
The results in table 5.12 indicate that performance expectancy and social influence have highly
significant positive correlations (P<0.001) and trialability has marginally significant positive
correlations (p<0.01) with the intention to use the Social Charging app. Therefore, H2a, H2b and H2c

41. 35
are all supported. The largest beta-coefficient (0.43) for performance expectancy shows that this
relationship is strongest, followed by social influence (0.38) and trialability (0.10).
5.5.3 H3
The results in table 5.12 show that, as p>0.10, the relationship between the independent variable
usage level of charging facilities and the dependent variable intention to use the Social Charging app
is statistically not significant. Thus, H3 is not supported by these results.
5.5.4 H4a,b,c,d,e, H5a,b,c and H6a,b,c
Table 5.11 shows that EV experience is not a significant moderator of any of the relationships
between the independent variables EV range, charging point availability, observability, planning and
range anxiety and the dependent variable usage level of the charging facilities, as p>0.1 for all these
interaction terms. Thus, H4a,b,c,d,e are all not supported by the research results.
The results in table 5.12 indicate that effort expectancy is a significant moderator of the relationship
between performance expectancy and the intention to use the app at p<0.1 level, hence supporting
H5b. The negative regression (beta=-0.19) indicates that a higher effort expectancy shows a lower
association between performance expectancy and intention, which corresponds with H5b. Based
on table 5.12, effort expectancy is not a significant moderator of the relationships between
trialability and social influence and the intention to use the app, as p>0.1, which means that H5a
and H5c are not supported.
Moreover, the results in table 5.12 show that facilitating conditions significantly moderates the
relationship between trialability and facilitating conditions and the intention to use the app at
p<0.05. The positive regression (0.297) for H6c shows that higher favourable facilitating conditions
correspond with a stronger association between social influence and the intention to use the app.
Therefore, H6c is supported.
The negative regression (-0.219) for H6a shows that less favourable facilitating conditions
correspond with a stronger association between trialability and app usage intention. Therefore, H6a
is not supported. Facilitating conditions does not seem to be a significant moderator of the

42. 36
relationship between performance expectancy and the intention to use the app, as p>0.1. Hence,
H6b is also not supported.
5.6 Summary
This chapter presented the results and analysis of the primary data. Analysis of the data shows that
H1b, H1c, H1d, H2a, H2b, H2c, 5b, and H6c are supported by the research. The other hypotheses
are rejected. These results are clearly presented in table 5.13 in appendix K.

43. 37
6. Conclusions and implications
6.1 Introduction
The preceding chapter presented the results and analysis of the primary data and hypothesis testing.
The current chapter comprises the conclusions, recommendations, limitations and future research
directions of the study.
6.2 Conclusions
The research aimed to provide insights into the attitudes and charging behaviour of electric car
drivers and to identify their attitudes and usage intention towards the app. Therefore, the influence
of EV range, charging point availability, planning, range anxiety (Spoelstra, 2014) and observability
(Rogers, 1983) on the usage level of charging facilities (Davis, 1989) has been investigated, as well
as the influence of trialability (Rogers, 1983), performance expectancy, social influence (Venkatesh
et al., 2003) and the level of use of charging facilities on the intention to use the Social Charging app
(Venkatesh et al., 2003). These interrelationships and their moderators, as presented in the
conceptual model, were revealed after reviewing the existing literature on technology acceptance,
diffusion of innovation and EV charging behaviour. Three focus groups and six interviews provided
insight in electric car drivers’ attitudes and behaviour towards the current EV charging infrastructure
in the Netherlands. Subsequently, a survey among 181 electric car drivers was carried out to
measure the interrelationships in the conceptual model.
Hypothesis tests showed that six hypotheses were supported for the relation between various
antecedent variables and the level of use of charging facilities and the intention to use the Social
Charging app. For the predicted moderators, only two hypotheses were supported.
6.2.1 Influence of the antecedent variables on usage level of charging facilities
The results of the first multiple regression analysis show that H1b, H1c and H1d are supported with
significance levels ranging from 5% to 10%. The negative beta-coefficient for charging point
availability matches the predicted negative relation. The finding that planning is positively
associated with the use of charging facilities is consistent with the empirical findings of Spoelstra

44. 38
(2014). Observability on the other hand has not yet been examined in the car charging context.
Therefore, this finding could fill the hole in the existing literature. The same applies to the findings
for charging point availability, as the direct relation between charging point availability and the level
of use of charging facilities has not been examined in the previous literature.
On the contrary, H1a and H1e are not supported as EV range and range anxiety have been found to
have a significance level of >10% with the level of use of charging facilities. These findings contradict
the empirical findings of Pearre et al. (2011) and Spoelstra (2014), which might be explained because
items for usage of charging facilities were not provided in the literature and the research results are
mainly based on quantitative research, in contrast to Spoelstra’s (2014) research.
6.2.2 Influence of the antecedent variables on app usage intention
The results of the second multiple regression analysis indicate that H2a, H2b and H2c are supported
with significance levels ranging from 1% to 10%. These findings are consistent with the empirical
findings of Chao (2013), Lee et al. (2012) and Liu (2014).
On the contrary, H3 is not supported as the level of use of charging facilities has been found to have
a significance level of >10% with the intention to use the Social Charging app. This might be due to
the fact that merely the opposite relation has been previously studied (Venkatesh et al., 2003) and
because usage intention and actual use did not measure the same object.
6.2.3 Influence of the moderator EV experience
The results of the first multiple regression analysis show that H4a, 4b, 4c, 4d and 4e are not
supported due to their significance levels of >10%. EV experience has not previously been used as a
moderator on the relation between the antecedent variables and the level of use of charging
facilities. The hypotheses are predominantly based on findings from the existing literature
explaining the relations between EV experience and the antecedent factors (Franke and Krems,
2013c; Hahnel et al., 2013; Kurani et al., 1994; Spoelstra, 2014) rather than its moderating effects.
This might explain why none of H4a,b,c,d,e are supported in this research.

45. 39
6.2.4 Influence of the moderators effort expectancy and facilitating conditions
The results of the second multiple regression analysis indicate that H5b and H6c are supported at
p<0.1 level and p<.05 level respectively. Neither effort expectancy nor facilitating conditions have
been used as moderators of these relations in previous research. Therefore, these positive results
propose interesting future research directions.
On the other hand, H5a, H5c and H6b are not supported as the results show significance levels of
>10% for these predicted moderating effects. The lack of support of H5a and 5c could be explained
by the fact that effort expectancy has not been used as moderator of these relations in previous
research.
H6a is not supported due to its negative regression (-0.219), despite its positive significance level of
P<0.05. The contradicting research results with Ajzen’s (1991) findings may be caused by the small
number of items used for trialability, facilitating conditions and usage intention, or by the relatively
small sample size of 181 respondents, as the construct performance expectancy consists of 4 items
with a high alpha value of .93.
6.2.5 Qualitative findings
The qualitative findings indicate that participants consider financial advantages as most important
and environmental benefits as least important reasons to drive an electric car. In contrast, the
quantitative research shows that environmental benefits are most important after financial
advantages.
Whereas 21 out of 22 participants in the qualitative research intend to use the app, only 64.6% of
the respondents of the survey show app usage intention. This could be due to the fact that they
have not tried the app yet, in contrast to the participants of the qualitative research.
6.3 Recommendations
6.3.1 Managerial implications
For the manager of Social charging, the quantitative results are of importance. The research results
show that trialability of the app, performance expectancy and social are positively associated with

46. 40
consumers’ intention to use the Social Charging app. Therefore, it should be easy, accessible and
free of charge to download and use the app, so that potential users can experiment with its use
before deciding whether to keep using it. Secondly, the app should prove to facilitate the process of
charging electric cars. The qualitative results show that if the app does not work properly, people
will quickly make the decision to use other apps instead. Finally, both quantitative and qualitative
research show that the success of the app depends on the number of electric drivers using the app.
The management problem leading to this research was to examine whether the new app should be
further developed and introduced. The research results that the management should indeed further
develop and introduce the app. Detailed recommendations about the contents of the app, based on
the primary research results, are presented in appendices E and J.
6.3.2 Theoretical implications
This research tested a number of relationships which were not previously examined. As stated in
the previous section, this concerns the significant positive relations between the independent
variables observability and charging point availability and the dependent variable usage level of
charging facilities; the positive association between performance expectancy and the intention to
use the Social Charging app, which is stronger when effort expectancy is low; and the positive
association between social influence and the intention to use the Social Charging app, which is
stronger when facilitating conditions are favourable. Therefore, this study is a valuable addition to
the existing literature in the electric car charging context.
6.4 Limitations
This study has various limitations. A first limitation concerns time constraints, as a three-month
period is relatively short to perform both secondary research and qualitative and quantitative
primary research effectively. Additionally, the results of the focus groups could therefore not all be
transcribed. Moreover, personal surveys were not held at charging stations as this procedure will
be too costly and time-consuming.
Another limitation concerns the sample size. Due to time and cost constrains, this sample size
comprised 181 respondents, representing the current 49,000 electric car users (RVO, 2015) in the

47. 41
Netherlands. However, for this population size, the sample size should have contained at least 382
respondents (Stangor, 2006). Moreover, the largest proportion of the respondents (44.8%) is from
The Hague, which does not accurately match the actual population. Another limitation concerns the
convenience sampling method.
An additional limitation is the model size. Initially, every construct in the conceptual model
comprised at least four items. However, questionnaire pre-tests showed the urgent need to reduce
the number of questions, because the survey took on average more than 20 minutes to complete.
When the model size would be smaller, and each construct comprised of at least 4 items, the
research results might have been more significant and accurate.
Finally, existing literature does not clearly show the items used to compose the construct level of
use. Only Rellinger (2014) proposes to use the frequencies with which respondents use a certain
product, in this case charging stations, on a scale ranging from 1=never to 7=always. As the
significances are low for hypotheses related to this outcome construct, other measures may have
been used in order to find more significant results.
6.5 Future research directions
EV charging facilities and smartphone apps concern relatively new technologies, and acceptance
varies according to the familiarity with technologies (Osswald et al., 2012). Therefore, future
research is needed to assess how the constructs perform when these technologies have become
more familiar.
Moreover, future research should include a sample size of at least 382 respondents, using non-
convenience sampling methods. The questionnaire should include at least four items per construct
in order to generate more accurate results.
In addition, the constructs EV range, range anxiety and the moderator EV experience should be
examined further in future research, as this research failed to find significant effects for these
factors.

48. 42
Furthermore, current charging behaviour indicated by respondents should be compared with actual
charging behaviour data obtained from charging stations. These data can be used to determine the
level of use of charging facilities more precisely.

49. 43
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