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In a sample of 1202 Belgians, the determining factors of the usage intention of an electric car and the differences between early and late usage intention segments are investigated. The Theory of Planned Behaviour (TPB) framework is extended with emotional reactions towards the electric car and car driving in general. Emotions and the attitude towards the electric car are the strongest determinants of usage intention, followed by the subjective norm. Reflective emotions towards car driving and perceived behavioural control factors also play a significant role. Differences in the relative importance of the determinants of usage intention between subgroups based on environmental concern and behaviour and social values are also studied. In general, people in segments that are more inclined to use the electric car are less driven by emotions towards the electric car and more by reflective emotions towards car driving, and take more perceived behavioural concerns into account.
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Journal of Marketing Management
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Emotions as determinants of electric
car usage intention
Ingrid Moons
a
& Patrick De Pelsmacker
b
a
Artesis Hogeschool Antwerpen, Belgium
b
University of Antwerp, Belgium
Available online: 21 Mar 2012
To cite this article: Ingrid Moons & Patrick De Pelsmacker (2012): Emotions as determinants of
electric car usage intention, Journal of Marketing Management, 28:3-4, 195-237
To link to this article: http://dx.doi.org/10.1080/0267257X.2012.659007
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Journal of Marketing Management
Vol. 28, Nos. 3–4, March 2012, 195–237
Emotions as determinants of electric car usage
intention
Ingrid Moons, Artesis Hogeschool Antwerpen, Belgium
Patrick De Pelsmacker, University of Antwerp, Belgium
Abstract In a sample of 1202 Belgians, the determining factors of the usage
intention of an electric car and the differences between early and late usage
intention segments are investigated. The Theory of Planned Behaviour (TPB)
framework is extended with emotional reactions towards the electric car and
car driving in general. Emotions and the attitude towards the electric car
are the strongest determinants of usage intention, followed by the subjective
norm. Reflective emotions towards car driving and perceived behavioural control
factors also play a significant role. Differences in the relative importance of the
determinants of usage intention between subgroups based on environmental
concern and behaviour and social values are also studied. In general, people
in segments that are more inclined to use the electric car are less driven by
emotions towards the electric car and more by reflective emotions towards car
driving, and take more perceived behavioural concerns into account.
Keywords emotions; Theory of Planned Behaviour; electric car; adoption
intention; early and late usage intention
Sustainable product usage intention and the role of emotions
The development and the successful introduction of new products are important
for companies, as they are aware that this is a key factor for their existence and
future success (Bayus, Erickson, & Jacobson, 2003). Nowadays, product innovations
are often triggered by growing concerns about the changing resources needed to
produce and use products (sustainable product development). Ehrenfeld (2008)
and Manzini (2009) argue that a sustainable future is not reached by diminishing
‘unsustainability’. In recent years, there is consensus about the large potential for
achieving environmental benefits, from altering users’ behaviour and the way they
interact with products (Sitarz, 1994). The present study focuses on the factors
affecting the usage intention of electric vehicles, a newly developed product that
can lead to a fundamental change in sustainable mobility behaviour (Smith, 2008).
What will make or break the successful introduction of electric mobility in the
ISSN 0267-257X print/ISSN 1472-1376 online
© 2012 Westburn Publishers Ltd.
http://dx.doi.org/10.1080/0267257X.2012.659007
http://www.tandfonline.com
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196 Journal of Marketing Management, Volume 28
real world is consumer acceptance (Feitelson & Salomon, 2000; Verhoef, Bliemer,
Steg, & Van Wee, 2008). Therefore, insights into the motivations and barriers of
this acceptance, especially by early adopter market segments, are important for a
successful introduction of the electric car.
What are the determinants of the usage intention of an electric car? Some argue
that the emotional side of a new product may be more critical to a product’s success
than its rational or practical elements (McLean, 1990; Norman, 2004). Emotions are
an important component of consumer response (Richins, 1997). However, the role
of affect in the adoption decision process of this type of product has rarely been
investigated. As Loewenstein (1996) states: ‘With all its cleverness, however, decision
theory is somewhat crippled emotionally, and thus detached from the emotional
and visceral richness of life’ (p. 289). The single utilitarian (cognitive) viewpoint
of most consumer decision-making models may be traced to the traditional economic
perspective of products as objects. One contribution of this study is that it explicitly
integrates emotions towards car driving and electric cars in the Theory of Planned
Behaviour (TPB) framework and that it investigates the relative importance of these
emotional factors compared to the traditional cognitive dimensions in the TPB.
Typically, market penetration starts with a small segment consisting of customers
with particular characteristics, needs, or wants (A. Gärling & Thøgersen, 2001). It is
therefore important to study the particular decision-making process of these early
adopter groups that may be different from that of late usage intention segments.
In their overview article of the Technology Acceptance Model (TAM), Sun and
Zhang (2005) state that the inclusion of moderators leads to enhancing a model’s
explanatory power. Also Leonidou, Leonidou, and Kvasova (2010) suggest more
research into psychographic factors as moderators of the decision-making process.
A second contribution of this study is that it develops insights into the specific
motivators for those segments that might become the early adopters of the electric car
compared to those who would adopt this innovation at a later stage. More specifically,
and following suggestions by Jansson (2011), Kilbourne and Beckmann (1998), and
Kilbourne and Pickett (2008), the difference in the decision-making process between
individuals with high and low environmental concern and behaviour, and individuals
with different personal value orientations is studied.
Identifying the sociodemographic characteristics of early adoption or usage
segments is very important for marketing new products. However, several authors
concluded that the explanatory effect of sociodemographic variables on adoption is
generally found to be low (Jansson, 2011; Kilbourne & Beckmann, 1998; Leonidou
et al., 2010). Results on the usage intention of new products may be mixed
because their effect may be different for different types of products or issues. More
specifically, their relevance in a high-involvement ecological consumer decision such
as the usage of an electric car has seldom been studied. Therefore, sociodemographic
factors are added as control variables in our model of the usage intention of the
electric car.
The study was carried out on a sample of Belgians and contributes to both
theory and practice. The main theoretical contribution is the study of the role of
emotions in the usage intention decision process of a new and more sustainable
high-involvement and environmentally friendly durable consumer product, and
differences in motivations to use these new products depending on environmental
concern and behaviour and on personal values perceived as important. The
contribution to practice is that it provides marketers of sustainable products such
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 197
as the electric car with both a deeper insight into the main determinants of
early and late usage intention groups. This should enable them to better fine-tune
segmenting, targeting, and positioning their persuasive efforts in marketing electric
vehicles.
First, the role of emotions in the consumer decision process and in
traditional models such as the TPB is discussed. Next, potentially relevant
consumer characteristics that could moderate this process are highlighted, and
sociodemographic variables that have been shown to drive environmentally friendly
behaviour in other contexts are described. Subsequently, the method and results are
discussed, and implications for theory and practice are highlighted.
Models of new product and behaviour adoption
In the context of the adoption (intention) of innovations and (new) behaviour, two
conceptual frameworks have been extensively studied. In the TAM, the attitude
towards and the adoption or continuous use (intention) of an innovation is
determined by three antecedents: perceived ease of use (PEOU), perceived usefulness
(PU), and compatibility (Taylor & Todd, 1995). PEOU is the degree to which a person
believes that using the system will be effortless (Davis, 1989). PU is the degree to
which a person believes that using a particular technology will enhance his/her job
performance (Davis, 1989). Compatibility is the degree to which the innovation fits
with the potential adopter’s existing values, previous experiences, and current needs
(Rogers, 1995).
The TPB is a second conceptual framework that has often been used for analysing
and predicting a variety of intentions and behaviours. The TPB predicts behavioural
intention which, in turn, is assumed to be a predictor of actual behaviour (Ajzen,
1991; Ajzen & Fishbein, 1980). Behavioural intention is explained by three general
dimensions: the attitude towards the behaviour, the social influence (subjective norm)
on the behaviour, and the perceived behavioural control in conducting the behaviour.
Attitudes are evaluative responses to the behaviour. The subjective norm stands
for perceived social pressure by significant others or different reference groups to
perform or not to perform a certain behaviour. A reference group is a group that
serves as a comparison point and the opinion of which is perceived as important
for the individual (Kim, Chan, & Chan, 2007). Perceived behavioural control over
performing the behaviour is a person’s perception about whether different aspects
of the behaviour are in his or her control or are easy or difficult. It is related to the
perceived ability and the external source constraints and facilitators of the behaviour
(Ajzen, 1991; Ajzen & Fishbein, 1980; Bandura, 1986; Taylor & Todd, 1995). The
TPB has been applied to a variety of contexts, and has often been used to describe
environmentally related behaviours (Bamberg & Schmidt, 2003; Cheung, Chang, &
Wong, 1999; Goldenhar & Connell, 1992; Kaiser, Woefling, & Fuhrer, 1999; Lam,
1999). The TPB, as a general model of consumer behaviour, has been shown to be
equally or even more relevant for predicting intentions than more ad hoc models
(e.g. De Cannière, De Pelsmacker, & Geuens, 2009), and has therefore required
the status of a general consumer behaviour model that is applicable in a variety of
situations. We therefore concentrate on this model as the conceptual framework for
the present study.
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198 Journal of Marketing Management, Volume 28
The role of emotions in consumer decision making
Notwithstanding the overwhelming evidence of the role of affective reactions
in consumer decision making, conceptual models and empirical research on the
adoption (intention) of innovations or (new) behaviour have largely ignored
the role of emotions (Bagozzi, Gopinath & Nyer, 1999; Kim et al., 2007;
Perlusz, 2011; Richins, 1997). Visceral states, emotions, and cravings can have a
disproportionate effect on behaviour (Loewenstein, 1996). However, the connection
between emotions and behaviour may be stronger and more direct than between
attitude and behaviour (Bagozzi, Gurhan-Canli, & Priester, 2002), and in certain
situations, spontaneously evoked affective reactions rather than cognitions tend
to have a greater impact on choice (Shiv & Fedorikhin, 1999). In fact, two
conceptual systems tend to operate in parallel: affective and rational (Shiv & and
Fedorikhin, 1999). This is explicitly recognised in dual processing models, such
as the Elaboration Likelihood Model (ELM; Petty & Cacioppo, 1986). Standard
expectations of the ELM imply that in high-involvement situations, people would
centrally and predominantly cognitively process stimuli or messages, while in low-
involvement situations, peripheral processing would take place, and peripheral cues
such as affective reactions to the stimulus would determine responses.
However, emotions can also be a strong determinant of consumer behaviour
in high-involvement situations. For instance, Pham (1998) found that emotions
strongly determine behaviour, provided they are relevant and diagnostic. Forgas’s
(1995) Affect Infusion Model (AIM) posits that there are two underlying mechanisms
of affect infusion: affect as information and affect priming. The affect-as-information
theory suggests that rather than forming a judgement based on features of a target,
individuals may ask themselves, ‘How do I feel about it?’ and in doing so, may be
guided by their feelings to judge a message or a stimulus. In the affect-priming theory,
affect can indirectly inform judgements by facilitating access to related cognitive
categories (Bower, 1981; Isen, 1987). Within the AIM model, it is implied that
it is in the course of substantive, constructive processing that affect is most likely
to play a significant role in what is perceived and how a stimulus is interpreted
(Forgas, 1995). The AIM also implies that judgements about more complex stimuli
(substantive processing context), requiring more elaborate processing and made
without the benefit of objective evidence, should show greater affective effects.
Previous research (e.g. Petty, Schumann, Richman, & Strathman, 1993) reported that
positive affect produces more positive judgements in both high- and low-elaboration
conditions. Geuens, De Pelsmacker, and Faseur (2011) also concluded that affective
reactions to advertising stimuli were important determinants of brand attitudes
for different product categories, varying both in product category involvement
and buying motivation (hedonic or utilitarian). For instance, in the context of car
purchase and use, a relatively high-involvement decision about a complex product for
most people, it is well recognised by both academics and practitioners that emotions
play an important role in consumers’ decision making (Carsalesprofessional.com,
2011; Sheller, 2004; Steg, Vlek, & Slotegraaf, 2001). Also in the context of car
advertising, Morris, Woo, Geason, and Kim (2002) concluded that the affective
reaction had more explanatory power for purchase intention than cognitive attitude.
People also have feelings about (new) technology and innovations (Perlusz,
2011). Venkatesh (2000), studied the effect of computer anxiety on the adoption
intention of new information technology, and found it a very relevant factor. Also
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 199
Chaudhuri, Aboulnasr, and Ligas (2010) argue that the innovation literature has often
overlooked emotions as a cause of successful diffusion. They argue that the role of
emotions is particularly eminent in the context of new products that represent radical
innovations. One of the examples they give is the adoption of hybrid cars. They
develop a model of information processing for initial exposure to radical innovations
that includes consumers’ emotional responses. The results illustrate the importance
of emotions in the process of innovation diffusion and evaluation.
In conclusion, affect plays an important role as a determinant of intentions to
use (new) products or to adopt (new) behaviour in both high-involvement and low-
involvement contexts.
Emotions in t he Theory of Planned Behaviour
Although the TPB has been widely used to predict behaviour(al intentions), various
authors question its completeness. For instance, Perugini and Bagozzi (2001) argue
that ‘although there is little question that the TPB offers a parsimonious account
of purposive behaviour, its sufficiency can be questioned’ (p. 81). Bagozzi (2007)
states that the TPB has seemingly seduced researchers into overlooking the fallacy of
simplicity. Therefore, various authors suggest extending the TPB with other factors
(e.g. Pavlou & Fygenson, 2006). One of the most often suggested improvements
is extending the TPB with measures of emotional responses to the product or the
issue for which the intention to use is predicted. In the TPB, emotions are only a
background factor without direct effect on intention and behaviour (Mazzon, 2011).
However, the anticipation of emotions or stimulus-induced affect is important in the
elicitation of behavioural intentions (Bagozzi, Baumgartner, & Pieters, 1999; Shiv &
Fedorikhin, 1999). According to Wood and Moreau (2006), the affective influence is
often stronger and more far-reaching than previously considered, and the addition
of emotional responses benefits traditional models of diffusion. Therefore, many
authors argued for integrating feelings into extant decision models (Hirschman &
Holbrook, 1982; Holbrook & Hirschman, 1982; Parker, Manstead, & Stradling,
1995; Peterson, Hoyer, & Wilson, 1986; Pham, 2004; Richard, van der Pligt, &
de Vries, 1995). One could argue that integrating emotional factors into the TPB
conceptually alters the nature of the model in that emotions are not compatible
with ‘planned behaviour’. On the other hand, emotions can a determinant factor
of planning behaviour too.
How should emotions be integrated in the TPB? In this respect, there are two
distinct conceptual questions: are emotions different from attitudes, and how should
emotions be modelled in the classical TPB framework? First, emotions are a distinct
construct from general attitudes or affective attitude (Bagozzi, 2007; Wang, 2011).
An attitude is an evaluative judgement of a stimulus that can contain both cognitive
and affective elements (Kim et al., 2007; Schepers & Wetzels, 2007). Affect (feelings)
are valenced affective reactions to emotion-eliciting objects/states being processed
by the individual, a valenced affective reaction to perceptions of situations (Dolan,
2002; Kim et al., 2007; Richins, 1997). Bagozzi et al. (1999) state that research
based on the reaction to a single stimulus frequently finds that emotions often
cluster in only a limited number of dimensions. Bagozzi (2007) also concludes that
when emotions are modelled in the TAM, they measure affect towards use (e.g.
joy–sadness) and/or affect as liking for a particular behaviour. Since in the present
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study we measure emotions towards a new product that is in its very early stage of
introduction, in line with this, it is assumed that consumers can only express their
affective reactions in general valenced feeling terms, and we measure the emotional
response to using the electric car accordingly. However, we also measure emotions
towards car driving. Since all consumers in our sample drive a car, they can be
expected to have more specific emotional reactions to the car-driving experience.
Therefore, for the emotions towards car driving in general, we measure a number of
specific emotions.
How should emotions be integrated in the TPB? Some authors argue that emotions
are antecedents of TPB components (e.g. Doll, Mentz, & Orth, 1991; Perugini &
Bagozzi, 2001). However, most authors argue that emotions are an extra factor in
the TPB and thus a direct driver of intentions (Bagozzi, 2007). Allen, Machleit, and
Kleine (1992) and Morris et al. (2002) claim that emotions can have a direct influence
on behaviour that is not captured or summed up by attitude judgement. Affect is not
necessarily dependent on or related to cognitive variables (Machleit & Wilson, 1983).
Emotional appraisal and more cognitive attribute analysis independently determine
intentions, preference, and choice (Kim et al., 2007; Kwortnik & Ross, 2007).
Perugini and Bagozzi (2001) and Wang (2011) model emotion as a determinant of
intentions at the same level and as a parallel predictor as the other TPB constructs.
Therefore, we model e motional reactions to the electric car and car driving in general
as an independent determinant of usage intentions alongside the traditional cognitive
variables of the TPB model.
Emotions as a relevant addition to the TPB in different decision
contexts
Are emotional responses to a (new) product or behaviour equally relevant in all
situations? Emotions have been added to the TPB for various products and issues,
and have consistently been found to be highly relevant for predicting behavioural
intentions and continuance behaviour. For instance, Hsu, Yen, Chiu, and Chang
(2006) found that continuance intention of online shopping could be predicted by
TPB components enriched with feelings of disconfirmation and satisfaction. Kim et al.
(2007) integrated pleasure and arousal as main dimensions of feelings in their model.
They found that affective dimensions as well as cognitive (rational) components
had a positive effect on the intention to continue using mobile Internet services.
Wang (2011) predicted physical activity intentions by means of TPB components and
anticipated negative emotions. Negative emotions explained behavioural intentions
over and above TPB variables, especially for people with low physical activity.
In spite of the many examples of the added value of emotional factors to the
more cognitive TPB model, the evidence for their relevance in case of a relatively
high-involvement environmentally friendly new durable consumer product in the
early stage of its introduction (such as an electric car) is circumstantial. However,
this circumstantial evidence suggests that emotions may also be an important factor
in these situations. First, various studies on the role of emotions in the TPB relate
to issues or behaviours that are relatively highly involving. Bae (2008) found that
emotions enhance the explanatory power of the TPB in predicting intentions to
cornea donations. Hynie, MacDonald, and Marques (2006) found that self-conscious
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 201
emotions like shame and guilt had a significant effect on condom use, above a nd
beyond the TPB variables. Perugini and Bagozzi (2001) established that anticipated
emotions appeared to be highly significant in explaining body-weight regulation and
studying effort.
Electric car usage intention reflects environmentally friendly behavioural intention.
In the area of mobility decisions, for instance, Duran, Alzate, Lopez, and Sabucedo
(2011) extended the TPB with emotional aspects to predict low vehicle use behaviour.
The anger emotion appeared to be more important to predict behaviour than,
for instance, perceived behavioural control. In the domain of car use, previous
studies have explicitly established the role of affect in explaining environmental
behaviour (Gatersleben & Appleton, 2007). Emotional factors have also been shown
to be very important in the context of sustainable mobility (www.trendy-travel.
eu/emotions/start.phtml?link=project, 2011). There appears to be s upport for the
idea of including emotional factors in predicting the behaviour with regard to
relatively highly involving and environmentally friendly products or issues.
Is it particularly relevant to include emotions in a model of early stage usage
intention for a technological innovation such as the electric car? Wood and Moreau
(2006) claim that emotional influences are the greatest during the early learning
stages of product usage. Moreover, they argue that the emotional (anticipation
of) experience is more influential for technological or functional innovations (e.g.
computer programs, GPS) than for simple experiential or aesthetic products. Also
Kwortnik and Ross (2007) state that emotions are particularly important early
in the decision process. The pleasure of consumption can begin before the act
of consuming. Emotions are motivators that influence goal-directed behaviour.
Consequently, there also seems to be a case for including emotional reactions
to the anticipated use of a new technological product in the early stages of its
introduction.
Besides electric-car-specific emotions, more product-category-related emotions
can also be relevant for decision making. Richins (1997) states that emotions that
result from consumption of the product category itself are an important research
focus. A. Gärling and Thøgersen (2001) argue that for a new product, benefits are
compared to the benefits of other products within the individual’s evoked set. For
instance, they argue that car drivers who prefer prestigious, sporty, or off-road cars
are less likely to perceive an electric vehicle as a satisfactory substitute and that many
car lovers do not like the electric vehicle idea. On the other hand, people who feel bad
about the negative environmental impact of their car use are more likely to perceive
that there exists a social norm about changing to the electric vehicle. J oireman, Van
Lange, and Van Vugt (2004) argue that the consideration of future consequences
of using a product can have a significant effect on the choice between travel modes.
Therefore, besides specific emotional responses to the idea of using an electric car, we
also include various dimensions of emotional responses towards car driving into the
TPB. In that respect, three specific emotional processing levels can be distinguished:
visceral, behavioural, and reflective (McLean, 1990; Norman, 2004). The first level,
visceral affect, is perception-based and relates to visceral aspects that are related to
product appearance. The second level, behavioural emotion, is expectation-based
and corresponds with behavioural aspects that have to do with the pleasure and
effectiveness of use. The third level, reflective emotion is intellectually based and
corresponds with reflective dimensions that are concerned with self-image, personal
satisfaction, and memories.
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202 Journal of Marketing Management, Volume 28
Finally, in his study on the use of alternative fuel vehicles in Sweden, Jansson
(2011) argues that the study of consumer behaviour of purchasing high-involvement
products that are marketed as being environmentally responsible received far less
attention than the study of reducing negative environmental impact. This is an
additional argument to study the usage intention process of electric cars. Overall,
there is a strong case for studying the role of emotions in the early adoption stage of a
technological innovation for a high-involvement environmentally friendly consumer
durable such as the electric car.
It can be expected that positive attitudes, the subjective norm, and perceived
behavioural control, as well as positive affective reactions towards the electric car
will all have a positive effect on the usage intention of the electric vehicle. For
instance, Oliver and Rosen (2010) in their study in the United States showed the
impact of social factors (neighbours) on purchasing hybrid electric vehicles. They
also concluded that e nvironmental self-efficacy is an important factor for predicting
environmental behaviour. However, the effects of emotional reactions associated
with current car driving are less clear. Reflective emotions towards car driving may
make people more critical towards the (environmental) consequences of car driving,
and may therefore have a positive effect on the intention to use the electric car
that could alleviate these consequences. However, depending upon the perception
of electric cars, strong positive visceral and behavioural emotions towards current
car driving may lead people to dislike the electric car as a less satisfying alternative
to their current vehicle. Since there is no formal conceptual framework to formulate
directional hypotheses with respect to the effect of car-driving emotions, and no clear
guidance as to the relative importance of the determinants of adoption intention, we
formulate the following research question:
RQ1: To what extent are the attitude towards the electric car, the subjective norm,
perceived behavioural control factors, emotions towards the electric car,
and the visceral, behavioural, and reflective emotions towards car driving
significant determinants of the usage intention of the electric car?
Sociodemographic characteristics and electric car usage intention
Many studies conclude that sociodemographic factors have no or an inconclusive
effect on consumer behaviour in general and on environmentally friendly behaviour
in particular (Wang, 2011). Jansson (2011) concludes that attitudinal factors explain
consumer behaviour to a much higher degree than sociodemographics. Leonidou
et al. (2010) state that the use of traditional sociodemographic factors, even though
important, was characterised as inappropriate for identifying green consumers
because of contradictory and inconclusive findings. Also Kilbourne and Beckmann
(1998) state that the literature on ‘who is the green consumer is frequently
inconclusive and sometimes contradictory.
Nevertheless, links between social and demographic characteristics and
environmental concern and behaviour have been explored by several researchers.
A. Gärling and Thøgersen (2001) conclude that early adopters of electric vehicles
are generally high in social status and better educated. Age has shown positive
(Clark, Kotchen, & Moore, 2003; Rice, 2006; Roberts, 1996), negative (Moon,
Florkowski, Braäuckner, & Schonhof, 2002), or no impact (Loureiro, McCluskey, &
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 203
Mittelhammer, 2001). Also gender could play a role. Most studies report that women
express greater concern about environment than men (Hunter, Hatch, & Johnson,
2004; Mohai, 1992; Schahn & Holzer, 1990) and behave in a more ecologically
conscious way than men (Johnston, Wessels, Donath, & Asche, 2001; Loureiro et al.,
2001; Zelezny, Chua, & Aldrich, 2000). The level of education has been found to
have positive (Blend & van Ravenwaay, 1999), negative (Johnston et al., 2001), or
no effect (Moon et al., 2002).
Additionally, for the electric car adoption, the place where one lives may be
important. Living in a city can be an opportunity for electric car driving, as driving
in the city often implies short trips, which reduces the problem of the small distance
autonomy of an electric car. But on the other hand, citizens may have restricted
charging facilities at home. Another reason for taking this variable into account is the
fact that urban residents are more likely to be environmentally concerned than rural
residents (Arcury & Christianson, 1990; Buttel & Flinn, 1974; Howell & Laska,
1992; Tremblay & Dunlap, 1978). The car one drives could be a differentiating
characteristic, as electric cars nowadays are most typically smaller family cars. Finally,
the longer one has had a driving licence, the more car driving may become a habit or
the more one has positive and negative experiences with car driving. So the period of
time one has a driving license might be a relevant determinant of usage intention.
The conclusions discussed in the previous findings were obtained for various
types of environmentally friendly behaviour in different contexts. It may well be
that in a specific context and with respect to a specific usage situation or products,
some sociodemographics are relevant. Therefore, although several studies have found
that these factors are poor predictors of green consumer behaviour, they are often
used as control variables. For that reason, we add a number of potentially relevant
sociodemographic variables as control variables to our model.
Different determinants of usage intention for different consumer
segments?
In their study of marketing of electric vehicles, A. Gärling and Thøgersen (2001)
argue that it is very important to identify early adopters correctly. Equally important
is to find out to what extent their decision-making process is different from late
adoption intention groups. The processes that drive the usage intention of a new
environmentally friendly or sustainable product may be different for different
consumer segments (Urban & Hauser, 1993). In their overview study of 55 TAM
studies on the acceptance of technology systems, Sun and Zhang (2005) conclude
that the inclusion of moderators leads to enhancing a model’s explanatory power.
Also Kilbourne and Beckmann (1998) and Leonidou et al. (2010) argue that there
is more need for the study of psychographic factors as moderators of the decision-
making process. Many potential moderators can be envisaged. In the context of the
present study, the most important criterion to select a moderator is the extent to
which it is related to differences in usage intention of the electric car. Subsequently,
differences in the decision-making process between consumer segments scoring
differently on this dimension can be explored. In this study, environmental concern,
environmental behaviour, opinion leadership with respect to cars, and personal values
are considered.
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204 Journal of Marketing Management, Volume 28
Environmental concern
Environmental concern is the evaluative response towards environmental issues
(Eagly & Chaiken, 1998; Fransson & Gärling, 1999; Schultz, 2001). Bamberg (2003)
and Bamberg and Möser (2007) showed that the degree of environmental concern
can have a direct and strong impact on people’s behaviour in specific environmentally
related domains like recycling, energy savings, buying environmentally friendly
products, and travel-mode choices. A. Gärling and Thøgersen (2001) found that
environment-friendliness is highly important for people in favour of electric cars.
Oliver and Rosen (2010) in their study on hybrid vehicles state that environmental
values have been in previous research very predictive of environmental behaviour.
Environmental behaviour
Thøgersen (1999) demonstrates the relevance of general environmental behaviour.
He concluded that environmentally friendly behaviours are not independent. When
people start to act in an environmentally friendly way in one area, this behaviour
tends to spill over into other areas. On the other hand, Leonidou et al. (2010)
make the distinction between green purchasing behaviour and general environmental
behaviour. They found that inward eco-attitudes are predictive of green purchasing
behaviour, while outward eco-attitudes are predictive of green behaviour in general.
This demonstrates the potential moderating role of (different types of) environmental
behaviour.
Opinion leadership with respect to cars
Opinion leadership refers to the inclination of individuals to be ‘lead users’, core
consumers at the forefront who drive an innovation forward. They are often the first
ones to adopt an innovation and, having information and having used the product,
they articulate the link between their need and the technology. Opinion leaders are
often innovators and adopt the innovation first (Chaudhuri et al., 2010; A. Gärling &
Thøgerson, 2001; Gatignon & Robertson, 1991; Jansson, 2011; Rogers, 1995).
In the context of the adoption of hybrids, Oliver and Rosen (2010) also identified
opinion leadership as a relevant variable to describe segments.
Personal values
Personal values are guiding principles that are important in a person’s life ( Rohan,
2000; Rokeach, 1973; Schwartz, 1992). Similar personal values are referred to
as value types. Value orientations are defined as clusters of compatible values or
value types. Personal value orientations have often been referred to as determinants
of pro-environmental behaviour or moderators of the decision-making process
(Jansson, 2011; Jansson, Marell, & Nordlund, 2011; Kilbourne & Beckmann, 1998).
For instance, Schwartz’s (1992) self-transcendence value types universalism and
benevolence have been found to be positively related to pro-environmental attitudes
and behaviour (Thøgersen, 1996). The self-enhancing achievement and power value
types have been shown to be negatively related to pro-environmental attitudes and
behaviour (T. Gärling, Fujii, A. Gärling, & Jacobsson, 2003; Kilbourne & Pickett,
2008; Leonidou et al., 2010).
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 205
The second research question of the present study explores the potentially
different relative importance of the determining factors of the usage intention process
of electric cars for different consumer segments:
RQ2: To what extent does the relative importance of the determinants of
the usage intention of the electric car differ between low and high
environmentally concerned individuals, between individuals with weak and
strong environmental behaviour, between weak and strong opinion leaders and
between individuals in different social value type groups?
Method
Procedure and respondents
After an exploratory qualitative study (Moons, De Pelsmacker, De Bont, & Standaert,
2010), between 20 November and 20 December 2009 an online survey was sent
out to investigate the usage intention decision process of the electric car. Data
were gathered via the snowball method. Sixty students were mailed and asked to
complete the questionnaire and also to forward the link to their friends, parents,
and neighbours. The latter were invited to mail the survey again to their friends
and colleagues. The survey was also placed on an Internet forum about car driving.
In total, the responses of 1202 participants are used in this study. The respondents all
have a driving licence: 37.1% has held one for three years or less, 35.5% for more
than three and up to 20 years, and 27.4% more than 20 years. A total of 57.3%
are male. The sample over-represents younger and higher-educated people: 53.9%
are aged between 18 and 25 years; 21.5% between 26 and 45 years, and 24.6% are
46 or older. A total of 32.7% have a high school diploma or less, 67.3% have a higher
education diploma. The place of living of the sample more or less reflects the Belgian
situation: 39.3% lives on the countryside, 38.6% in the suburbs, and 22.1% in the
city centre. In terms of car use, 33.7% drives a small car, 36.6% a large one, and
29.7% a special one.
Measures
The intention to use an electric car was the dependent variable in the regression
models in which a number of variables were included as independents. An overview
of the definition of these variables, alphas, and references is given in Appendix 1. The
independent variables in the model are: the attitude towards the electric car, emotions
towards the electric car, subjective norm peers (interpersonal), and subjective norm
media (external; following Bhattacherjee, 2000, and Hsu et al., 2006). Ten perceived
behavioural control (PBC) items were generated in the exploratory qualitative study.
Cheung et al. ( 1999) and Terry and O’Leary (1995) identified two conceptually
different PCB constructs: difficulty (personal ability) and control (perceived external
constraints). The 10 items generated in the qualitative study reflect both dimensions.
A factor analysis on the 10 items did not generate a meaningful reduction into a
more limited number of PBC dimensions. Therefore, it was decided to enter all
10 PBC items separately into the regression analyses. The PBC factor analysis results
are shown in Appendix 2. The three levels related to car-driving emotions were
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206 Journal of Marketing Management, Volume 28
measured by means of 17 items based on the qualitative study. We asked respondents
to what extent 17 different car features contributed to positive emotions towards
car driving. Principal component analysis reveals that these 17 affective reactions
represent the three process levels proposed by Norman (2004): visceral, behavioural,
and reflective. The details of this factor analysis are given in Appendix 3. The item
scores of each construct were averaged, and these mean scores were used in further
analysis.
For the moderator analysis, environmental concern, environmental behaviour,
and opinion leadership were measured as shown in Appendix 1. With respect to
environmental behaviour, initially we measured the number of behaviours people
indicated they conducted for environmental reasons. We then summed the number
of behaviours one conducts. However, these behaviours relate to various domains:
some of them are purchase-related, others are general, and some of them are
reduction-oriented, others promotion-oriented. We defined four different sets of
behaviours consistent with these four types of behaviours, and calculated the
correlation between the overall behavioural variable and the four variables related to
subtypes of behaviour. These correlations were very strong: overall environmentally
friendly behaviour versus purchase-oriented (r = .933, p < .001), general (r = .904,
p < .001), reduction-oriented (r = .930, p < .001), and promotion-oriented (r = .858,
p < .001). Therefore, we decided to proceed with the overall environmentally
friendly behaviour variable.
Schwartz’s (1992) 15-item value scale was used to compose value orientation
groups. A factor analysis revealed four relevant factors: idealism, altruism,
achievement,andpower. The scores on the items loading on each factor were
averaged and the mean scores were used in a k-means cluster analysis, leading to
three meaningful clusters: the self- transcendent universalist (CL1), the benevolent
(CL2), and the self-enhancing power-seeking achiever (CL3). Details on this factor
and cluster analysis are given in Appendix 4. Finally, a number of sociodemographic
variables were also measured: age, gender, level of education, place of living, number
of years the respondent had a driving licence, and type of car.
Results
Predictors of usage intention
By means of regression analysis, the effect of the attitude towards electric cars, the
subjective norm peers, the subjective norm media, the 10 perceived behavioural
control items, emotions towards the electric car, visceral, behavioural, and reflective
emotions towards car driving, and a number of sociodemographic variables on the
intention to use an electric car is estimated. The descriptives of the variables used are
shown in Table 1a and b. Correlations between the variables in the model are given
in Table 2. The results of the regression analysis are shown in Table 3.
The beta-coefficients show that emotions and the attitude towards the electric
car are the strongest predictors of usage intention. The subjective norm of both
reference groups (peers and media) is significant as well, but of secondary importance.
Reflective emotions evoked by current car driving also determine the usage intention
of the electric car. All these variables have a significantly positive effect on usage
intention. Having sufficient budget (personal ability), not being able to load the car
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 207
Table 1(a) Descriptives of metric variables used in regression analyses.
Variable Mean
Standard
deviation N
Intention to use an electric car 2.865 .982 1200
Attitude towards the electric car 4.860 .278 1200
Subjective norm peers 2.992 .500 1200
Subjective norm media 2.728 .794 1200
Emotion towards the electric car 3.090 .801 1200
Visceral emotion towards car driving 2.988 .957 1200
Reflective emotion towards car driving 2.870 1.076 1200
Behavioural emotion towards car driving 3.465 .940 1199
My budget is sufficient to buy an electric car 2.531 1.103 1199
Charging an electric car is possible with an
ordinary electric socket
3.372 1.004 1199
Cars with a combustion engine will soon not
be allowed to enter the city
3.201 1.015 1199
You can drive a long distance with an
electric car
2.764 .959 1199
I will not be able to charge an electric car at
home
3.092 .980 1199
Our society offers the necessary means and
instruments to use an electric car
2.643 .938 1199
The costs of using an electric car are
acceptable
3.023 .742 1199
The maintenance of an electric car is well
organised
2.872 .625 1199
I will not be allowed to charge my electric
car with energy I have produced myself
3.225 .874 1199
The battery of an electric car cannot be
charged underway
2.675 .900 1199
(b) Descriptives of sociodemographic variables used in the full-sample regression
analysis.
Dummy Definition
Gender 0 = male, 1 = female
Education level 1 = higher education, 0 = until high
school
City centre 1 = city centre, 0 = countryside or
suburbs
Suburbs 1 = suburbs, 0 = countryside or city
centre
Age middle 1 = 26–45, 0 = other
Age old 1 > 45, 0 = other
Driving licence 1 1 = 3–20 years, 0 = other
Driving licence 2 1 > 20 years, 0 = other
Car type 1 1 = special car (SUV, two-seater,
etc.), 0 = other
Car type 2 1 = small car, 0 = other
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208 Journal of Marketing Management, Volume 28
Table 2 Correlation between metric variables in the full sample regression analysis (correlation coefficient (p-value)).
PBC1 PBC2 PBC3 PBC4 PBC5 PBC6 PBC7 PBC8 PBC9 PBC10
Intention to use .012 .158 .295 .136 .022 .029 .160 .126 .064 .121
(.341) (<.001) (<.001) (<.001) (.220) (.104) (<.001) (<.001) (.013) (<.001)
Attitude .089 .075 .248 .223 .007 .107 .159 .149 .110 .092
(.002) (.005) (<.001) (<.001) (.199) (<.001) (<.001) (<.001) (.001) (<.001)
SN peers .031 .103 .281 .193 .004 .100 .196 .160 .108 .100
(.145) (<.001) (<.001) (<.001) (.440) (<.001) (<.001) (<.001) (<.001) (<.001)
SN media .012 .086 .261 .139 .071 .094 .177 .120 .098 .022
(.341) (.001) (<.001) (<.001) <.001) (<.001) (<.001) (<.001) (<.001) (.223)
Emotion selec. car .053 .108 .293 .200 .012 .084 .167 .124 .120 .173
(.034) (<.001) (<.001) (<.001) (.334) (.002) (<.001) (<.001) (<.001) (<.001)
Visceral em. car
use
.026 .123 .019 .092 .049
.038 .025 .049 .045 .051
(.185) (<.001) (.255) (<.001) (.045) (.094) .193) (.044) (.059) (.034)
Reflective em. car
use
.085 .038 .177 .103 .033 .042 .063 .071 .008 .012
(.001) (.092) (<.001) (<.001) (.126) (.073) (.014) (.007) (.396) (.340)
Behavioural em.
car use
.001
(.481)
.035
(.111)
.021
.237)
.040
(.82)
.042
(.071)
.032
(.136)
.007
(.404)
.034
(.121)
.060
(.018)
.003
(.458)
PBC1 1 .104 .041 .026 .023 .053 .154 .012 .016 .014
(<.001) (.072) (.182) (.208) (.034) (<.001) (.333) (.294) (.318)
PBC2 1 .165 .010 .360 .048 .143 .062 .076 .057
(<.001) (.364) (<.001) (.047) (<.001) (.004) (.024)
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 209
PBC3 1 .171 .011 .041 .126 .101 .012 .047
(<.001) (.351) (.080) (<.001) (<.001) (.343) (.052)
PBC4 1 .054 .248 .181 .202 .092 .066
(.031) (<.001) (<.001) (<.001) (<.001) (.011)
PBC5 1 .051 .080 .056 .107 .074
(.037) (.003) (.025) (<.001) (.005)
PBC6 1 .287 .364 .056 .003
(<.001) (<.001) (<.001) (.412)
PBC7 1 .389 .103 .006
(<.001) (<.001) .411)
PBC8 1 .025 .002
(.190) (.476)
PBC9 1 .144
(<.001)
PBC10 1
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210 Journal of Marketing Management, Volume 28
Table 2 Correlation between metric variables in the full sample regression analysis (continued).
Intention
to use Attitude
SN
peers
SN
media
Emotions
elec.
car
Visceral
emotion
car use
Reflective
emotion
car use
Behavioural
emotion car
use
Intention to use 1 .560 .510 .404 .597 .041 .283 .003
(<.001) (<.001) (<.001) (<.001) (.077) (<.001) (.454)
Attitude 1 .487 .384 .644 .193 .235 .014
(<.001) (<.001) (<.001) (<.001) (<.001) .313)
SN peers 1 .402 .596 .145 .258 .005
(<.001) (<.001) (<.001) (<.001) (<.001)
SN media 1 .390 .022 .169 .009
(<.001) (.224) (<.001) (.371)
Emotions elec. car 1 .205 .309 .034
(<.001) (<.001) (.121)
Visceral em. car
use
1 .074 .336
(.005) (<.001)
Reflective em. car
use
1 .216
(<.001)
Behavioural em.
car use
1
PBC1-10 are the Perceived Behaviour Control variables listed in Table 1a. Cells are correlation coefficients; one-tailed significance levels in brackets. N = 1199.
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 211
Table 3 Regression results: Full sample. Dependent: Intention to use an electric car
Variable B St. error Beta t-value Sig.
Intercept 1.572 .341 4.605 <.001
Attitude towards the electric car .932 .103 .263 9.071 <.001
Subjective norm peers .299 .054 .152 5.512 <.001
Subjective norm media .137 .030 .111 4.564 <.001
Emotion towards the electric car .337 .039 .275 8.562 <.001
Visceral emotion towards car driving .050 .026 .049 1.941 .052
Reflective emotion towards car
driving
.081 .021 .089 3.774 <.001
Behavioural emotion towards car
driving
.035 .024 .034 1.461 .144
My budget is sufficient to buy an
electric car
.058 .021 .066 2.757 .006
Charging an electric car is possible
with an ordinary electric socket
.048 .023 .049 2.060 .040
Cars with a combustion engine will
soon not be allowed to enter the
city
.055 .022 .057 2.452 .014
You can drive a long distance with an
electric car
.022 .024 .021 .917 .359
I will not be able to charge an
electric car at home
.014 .023 .014 .592 .554
Our society offers the necessary
means and instruments to use an
electric car
.057 .025 .054 2.296 .022
The costs of using an electric car are
acceptable
.000 .032 .000 .0140 .989
The maintenance of an electric car is
well organised
.043 .038 .027 1.140 .255
I will not be allowed to charge my
electric car with energy I have
produced myself
.028 .024 .025 .137 .256
The battery of an electric car cannot
be charged underway
.029 .024 .026 1.186 .236
Gender .135 .048 .068 2.829 .005
Education level .052 .046 .025 1.155 .248
City centre .007 .056 .003 .122 .903
Suburbs .044 .047 .022 .937 .349
Age middle .212 .110 .089 1.932 .054
Age old .080 .069 .035 1.161 .246
Driver’s licence 1 .051 .107 .023 .474 .635
Driver’s licence 2 .018 .061 .009 .292 .770
Car type 1 .003 .052 .002 .063 .950
Car type 2 .024 .052 .012 .463 .644
R
2
= .484; R
2
adj
= .472; F(27, 1171) = 40.617, p < .001. Multicolinearity diagnostics: all VIF < 2.
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212 Journal of Marketing Management, Volume 28
in a normal socket (functional source constraint), and not being allowed to enter the
city centre with a car with a combustion engine (regulatory source constraint) are
also significantly predictors of usage intention, but of lesser importance. A counter-
intuitive but small effect is the negative effect of society offering the necessary
means and instruments to use an electric car on usage intentions. Behavioural and
visceral emotions towards car driving do not have a significant effect on the adoption
intention. Of all the sociodemographic variables, only gender has a small significant
effect on usage intention: women are more inclined to use the electric car than men.
Determinants of usage intention for subgroups
Before exploring the differences in usage intention decision processes between
subgroups of respondents differing in environmental concern, environmental
behaviour, and opinion leadership, first a correlation analysis was carried out to
test whether there is a significant correlation between these variables and electric
car usage intention. The correlation between intention and environmental concern
is r = .307 (p < .001) and between intention and environmental behaviour r = .300
(p < .001). The correlation between intention and opinion leadership is r =−.028
(p = .334). In order to test the difference in usage intention between the three
personal value clusters, a one-way analysis of variance (ANOVA) was carried out.
The three value clusters are significantly different in terms of usage intention (F(2,
1194) = 16.527, p < .001). Cluster 1 (M = 2.976) is not significantly different
from cluster 2 (M = 2.966), but both show a significantly stronger intention to
use the electric car than cluster 3 (M = 2.622; p < .001). Based on this analysis,
it does not make sense to study the decision-making process for individuals who
are low and high in opinion leadership separately, because this variable does not
correlate with usage intention. For the other moderating variables, the regression
model was estimated for subsamples of respondents. For environmental concern and
environmental behaviour, the 33% lowest-scoring respondents and the 33% highest-
scoring ones were compared. For value orientation, the three clusters were compared.
In these regression models, for parsimony reasons the sociodemographic were not
included, since in the general model most of them did not contribute meaningfully
to the prediction of adoption intention. The results of these regression analyses are
given in Tables 4–6.
In general, attitudes and emotions towards the electric car remain the most
important determinants of usage intention in all subgroups. However, there are
remarkable differences between the groups. In the low environmental concern
groups and the self-enhancing power-seeking achiever segment, emotions towards
the electric car are relatively more important determinants of usage intention than
in the high environmental concern and the universalist and benevolent groups. This
pattern does not emerge in the environmental behaviour groups. With respect to
the subjective norm, the results are mixed. Peer pressure is a significant determinant
of usage intention in most groups, especially in the achiever social segment, and,
generally speaking, media pressure is somewhat less important. A very outspoken
pattern is that reflective emotions towards car driving in general are a significant
determinant in the highly environmentally concerned, the high environmental
behaviour, and the universalist and benevolent groups, but are not significant in
the lowly concerned, low environmental behaviour, and achiever segments. Visceral
emotions towards car driving seem to affect the achievers, but not the other personal
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 213
Table 4 Regression results: Low and high environmental concern groups. Dependent: Intention to use an electric car.
Low environmental concern High environmental concern
Variable Beta t-value Sig. Beta t-value Sig.
Attitude towards the electric car .337 6.158 <.001 .296 5.721 <.001
Subjective norm peers .129 2.622 .009 .132 2.566 .011
Subjective norm media .078 1.691 .092 .140 3.107 .002
Emotion towards the electric car .278 4.682 <.001 .193 3.517 <.001
Visceral emotion towards car driving .053 1.184 .237 .071 1.569 .118
Reflective emotion towards car driving .038 .887 .376 .088 2.000 .046
Behavioural emotion towards car driving .004 .099 .921 .047 1.013 .312
My budget is sufficient to buy an electric car .008 .203 .839 .107 2.423 .016
Charging an electric car is possible with an ordinary
electric socket
.092 2.143 .033 .110 2.371 .018
Cars with a combustion engine will soon not be allowed
to enter the city
.018 .428 .669 .031 .712 .477
You can drive a long distance with an electric car .098 2.279 .023 .054 1.251 .212
I will not be able to charge an electric car at home .060 1.441 .150 .030 .657 .511
Our society offers the necessary means and instruments
to use an electric car
.052 1.123 .262 .020 .459 .646
The costs of using an electric car are acceptable .022 .480 .631 .037 .789 .431
The maintenance of an electric car is well orgasized .044 .931 .351 .049 1.078 .282
I will not be allowed to charge my electric car with
energy I have produced myself
.035 .858 .392 .073 1.743 .082
The battery of an electric car cannot be charged
underway
.051 1.231 .219 .097 2.268 .024
R
2
= .447, R
2
adj
= .421
F(17, 361) = 17.173, p < .001)
R
2
= .415, R
2
adj
= .387
F(17, 354) = 14.750, p < .001
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214 Journal of Marketing Management, Volume 28
Table 5 Regression results: Low and high environmental behaviour groups. Dependent: Intention to use an electric car.
Low environmental behaviour High environmental behaviour
Variable Beta t-value Sig. Beta t-value Sig.
Attitude towards the electric car .272 6.447 <.001 .248 6.596 <.001
Subjective norm peers .167 4.046 <.001 .141 3.864 <.001
Subjective norm media .110 3.007 .003 .118 3.637 <.001
Emotion towards the electric car .237 5.167 <.001 .275 6.863 <.001
Visceral emotion towards car driving .099 2.762 .006 .109 3.452 .001
Reflective emotion towards car driving .031 .870 .385 .061 1.964 .050
Behavioural emotion towards car driving .026 .718 .473 .039 1.235 .217
My budget is sufficient to buy an electric car .052 1.535 .126 .068 2.263 .024
Charging an electric car is possible with an ordinary
electric socket
.089 2.431 .015 .063 1.929 .054
Cars with a combustion engine will soon not be
allowed to enter the city
.095 2.665 .008 .084 2.706 .007
You can drive a long distance with an electric car .002 .057 .955 .018 .596 .552
I will not be able to charge an electric car at home .012 .329 .742 .006 .184 .854
Our society offers the necessary means and
instruments to use an electric car
.054 1.463 .144 .078 2.482 .013
The costs of using an electric car are acceptable .058 1.524 .128 .010 .298 .766
The maintenance of an electric car is well organised .034 .911 .363 .032 1.004 .316
I will not be allowed to charge my electric car with
energy I have produced myself
.038 1.123 .262 .064 2.153 .032
The battery of an electric car cannot be charged
underway
.079 .2304 .022 .070 2.364 .018
R
2
= .473, R
2
adj
= .455
F(17, 367) = 26.537, p < .001
R
2
= .469, R
2
adj
= .469
F(17, 365) = 33.286, p < .001
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 215
Table 6 Regression results: Three personal value type groups. Dependent: Intention to use an electric car.
Self-transcendent
universalist Benevolent
Self-enhancing and Power
seeking achiever
Variable Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.
Attitude towards the electric car .249 5.092 <.001 .301 6.235 <.001 .190 3.586 <.001
Subjective norm peers .081 1.658 .098 .142 2.985 .003 .234 4.819 <.001
Subjective norm media .159 3.723 <.001 .079 1.912 .056 .091 1.994 .047
Emotion towards the electric car .267 4.911 <.001 .221 4.127 <.001 .305 5.315 <.001
Visceral emotion towards car driving .067 1.710 .088 .034 .847 .397 .118 2.865 .004
Reflective emotion towards car
driving
.086 2.006 .046 .102 2.546 .011 .054 1.211 .197
Behavioural emotion towards car
driving
.084 1.976 .049 .040 1.000 .318 .030 .740 .460
My budget is sufficient to buy an
electric car
.028 .695 .488 .076 1.987 .048 .011 .273 .785
Charging an electric car is possible
with an ordinary electric socket
.014 .354 .723 .035 .849 .396 .053 1.287 .199
Cars with a combustion engine will
soon not be allowed to enter the
city
.084 2.066 .040 .017 .438 .662 .075 1.831 .068
You can drive a long distance with an
electric car
.029 .660 .509 .027 .673 .501 .054 1.333 .183
I will not be able to charge an
electric car at home
.014 .327 .744 .011 .269 .788 .054 1.337 .182
Our society offers the necessary
means and instruments to use an
electric car
.048 1.241 .215 .094 2.293 .022 .001 .012 .990
(Continued)
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216 Journal of Marketing Management, Volume 28
Table 6 (Continued).
Self-transcendent
universalist Benevolent
Self-enhancing and Power
seeking achiever
Variable Beta t-value Sig. Beta t-value Sig. Beta t-value Sig.
The costs of using an electric car
are acceptable
.111 2.802 .005 .013 .310 .757 .040 .424 .356
The maintenance of an electric car
is well organised
.089 2.096 .037 .067 1.625 .105 .005 .111 .912
I will not be allowed to charge my
electric car with energy I have
produced myself
.079 1.897 .059 .033 .886 .376 .017 .429 .668
The battery of an electric car
cannot be charged underway
.022 .540 .590 <.001 .003 .998 .037 .935 .350
R
2
= .498, R
2
adj
= .474
F(17, 365) = 21.274, p < .001
R
2
= .436, R
2
adj
= .413
F(17, 429) = 19.490, p < .001
R
2
= .511, R
2
adj
= .487
F(17, 348) = 21.372, p < .001
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 217
value types. Finally, perceived behavioural control factors appear to play a much more
prominent role in highly environmentally concerned, high environmental behaviour,
and universalist and benevolent segments. Various personal ability and technical and
regulatory constraints appear to determine usage intention significantly, w hile this
is far less the case for lowly concerned, low environmental behaviour, and achiever
groups.
The fact that these different segments show meaningful differences in their
usage intention formation makes it meaningful to explore the sociodemographic
characteristics of the groups (detailed figures available from the authors). Women are
more highly environmentally concerned, as well as people with a higher educational
level. The longer people have their driving licence, the more environmentally friendly
behaviours they report. The older people are, the more environmentally friendly
they seem to behave. Women behave in a more environmentally friendly way than
men and so do more highly educated individuals compared to lower educated
ones. Individuals who only recently got their driving licence are more often in
the self-enhancing and power-seeking value segment; people who have held their
driving licence for 3–20 years in the benevolent value group, and the majority of
people who have held their driving licence for more than 20 years are in the self-
transcendent universalists group. A similar pattern emerges for young versus older
people. Individuals with special vehicles are most often in the benevolent group.
Large car owners are less often in the achiever group, while the opposite is true
for small car owners. Males are most often in the achiever group, while only a
minority of females is part of this value cluster. Highly educated people are most
often in the benevolent group, while lowly educated people are most often found in
the self-transcendent universalist segment.
Although the evidence is mixed and sometimes inconsistent, individuals who
are in groups that are most inclined to use the electric car (high environmental
concern, strong environmental behaviour, universalists, and benevolents) appear to
be more often female, higher educated, and older, and consequently have held
their driving licence for a longer time. The type of car people currently drive
does not seem to be of importance. Also the place people live does not directly or
indirectly relate to their electric car usage intention. Belgium is so densely populated
that the distinction between city centre, suburbs, and countryside is probably not
relevant.
Discussion and c onclusions
Adding affective components to the TPB appears to be highly relevant for predicting
the usage intention of electric cars. Both in the general model and in all models
for subgroups, emotions towards the electric car are the most or the second most
important factor that determine electric car usage intention. This is in line with an
overwhelming amount of earlier behavioural and TPB research in different contexts.
The unique contribution of the present study is to show that in a high-involvement
environmentally friendly durable consumer product context, emotional factors are
important determinants of usage intention. Also all traditional cognitive TPB are
significant factors. A positive attitude towards the electric car is very important, and
is the first or second most important determinant of usage intention in all models.
One or both subjective norm factors are mostly in third place as determinants of
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218 Journal of Marketing Management, Volume 28
intention, but substantially less important than attitude and emotion towards the
electric car. This is consistent with the findings of Munnukka and Järvi (2011) that
consumers are more influenced by personal considerations while social factors have
a more of a background effect on perceptions. In general, peer pressure is somewhat
more important than media pressure. Behavioural control factors are generally less
important than the previous factors, but both personal ability and control variables
are still relevant. Emotions towards current car driving are generally of minor
importance. If they are a significant determinant of intentions at all, mostly reflective
(and thus cognitively oriented) emotions seem to be important. The results confirm
the claims of, amongst others, Bagozzi et al. (1999), Bagozzi et al. (2002), Chaudhuri
et al. (2010), Kim et al. (2007), and Richins (1977) that emotional reactions to
(innovative) products and (new) behaviour, also in the context of environmentally
friendly consumer durables, are at least as important as cognitive considerations in
the usage intention formation process.
Highly environmentally concerned people, people already showing strong
environmental behaviour, universalists, and benevolents are segments of the
population that are more strongly inclined to use the electric car once it becomes
widely available. This is in line with Bamberg (2003), Jansson (2011), Kilbourne
and Beckmann (1998), and Thogerson (1999) who also i dentified these personal
characteristics as important factors in pro-environmental behaviour. Somewhat
surprisingly, opinion leadership with respect to cars is not correlated with usage
intention of the electric car. Apparently, opinion leaders for cars are not the ones
that are at this point of time inclined to adopt the electric car. The reason for
this could be that these opinion leaders may well be at the forefront of adopting
new car models and technology, but at present they do not see the electric car as a
particularly appealing way to express this opinion leadership. They may perceive the
electric-car technology to date as not innovative and appealing enough with respect to
the car characteristics they perceive as important. Earlier research (Grewal, Metha,
& Kardes, 2000) demonstrated the importance of the social identity function of a
product for opinion leaders. Their desired social identity is contingent upon owning
the product. Public access also pictures the owner as well informed. In the case of the
electric car, it is too early to get public access to the product. This may explain why
opinion leadership and electric-car adoption are not related in this study.
In terms of their decision-making process, these groups are different from less
interested groups in a number of ways. First, although still highly significant,
emotions towards the electric car seem to be a somewhat less important determinant
of usage intention than for less interested groups. This is consistent with both
the ELM and the AIM. Environmentally concerned people and universalist and
benevolent individuals can be considered to be more involved in environmental
socially responsible issues. According to the ELM (Petty & Cacioppo, 1986),
stronger involvement leads to more cognitive, central processing of information,
and peripheral cues such as affective reactions could be less important. Conversely,
lower environmentally concerned people and achievers are less involved with
environmental issues. In their case, peripheral cues such as affective reactions towards
the electric car could be relatively more important. However, the results also support
the principles of the AIM (Forgas, 1995). Even for the highly environmentally
involved individuals, emotions towards using the electric car are still important and
relevant, and are therefore significant determinants of usage intentions.
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 219
Another remarkable difference between subgroups is that reflective emotions
towards car driving play a significant role in usage intention formation i n
environmentally concerned, strong environmental behaviour, and universalist and
benevolent segments, but not in those groups that exhibit a lower intention to use
the electric car. This type of emotions originates in reflecting upon the consequences
of car driving on society in general and the environment in particular. It is not
surprising that these emotions play a significant role in more environment- and
society-oriented groups. The role of visceral and behavioural emotions towards car
driving is ambiguous and not very important. If these emotions are significant at all,
their effect is positive (e.g. in the achiever and the universalist groups). Probably, in
these groups, the electric car is seen as a product that, because of its new technology
and special features, will enhance their visceral and behavioural driving experience.
Maybe they are thrilled by the electric car as the latest ‘toy’. Jansson et al. (2011) also
remark that there is a segment of the population that exhibits both green norms and
interest for innovative products simultaneously.
Finally, there is a remarkable difference between the groups with respect to
the importance of perceived behavioural control factors in their decision process.
Environmentally concerned, strong environmentally behaviour, and universalist and
benevolent groups appear to take more behaviour control elements into account
than segments of the population that are less inclined to use the electric car. These
concerns relate to both personal ability (e.g. being able to afford the car, the cost of
the car) and control factors (e.g. being able to charge the car, use it in city centres,
maintaining it). This is again in line with the ELM. The high-intention-to-use groups
are generally more involved in environmental and societal factors in general, and in
the electric car in particular. They are therefore expected to take more and more
rational and cognitive factors into account when considering their usage intention.
As in previous studies, the role of sociodemographic factors is somewhat
inconsistent, although certain patterns e merge. Women have a significantly higher
intention to use the electric car, and in those subgroups that have a higher intention
of using it, women, highly educated, and older individuals are over-represented. This
confirms the findings of, amongst others, Clark et al. (2003), Hunter et al. (2004),
Moon et al. (2002), Rice (2006), and Shen and Saijo (2008).
The insights of the present study can be used by designers, marketers, public
policymakers, and advocates of the electric car. First, in terms of target-group
definition, there are indications that a s omewhat older, female, and higher educated
public is most susceptible to using the electric car. Psychographically, the electric car
will not so much have to appeal to opinion leaders in cars, but to people who have
a high environmental concern and adhere to values such as idealism and altruism
(the universalists and the benevolents). In terms of behavioural segmentation criteria,
people who are already acting in an environmentally friendly way are also most
inclined to adopt the additional environmentally friendly behaviour of electric car
driving. One of the most important insights of this study is that designers and
marketers will have to develop and advertise the electric car in such a way that it
is emotionally appealing. Apart from all the rational considerations that people take
into consideration, a positive emotional response to the idea of electric car driving is
vital f or its success. This is not just a matter of designing the car in such a way that
it evokes pleasurable emotions, but also of positioning the electric car as a product
to love and that friends and neighbours will envy you for. Also stressing the fact
that electric cars will make you feel good about your impact on society and the
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220 Journal of Marketing Management, Volume 28
environment (reflective emotions) will support these groups in their electric car usage
intentions. At the same time, high-intention groups have a lot of control concerns.
Not only marketers but also public policy actors and advocacy groups should take
the necessary measures to remove the constraints that potential users of electric
cars are concerned about by developing clear legislation and regulation concerning
(electric) car use, providing the right infrastructure to charge the car regularly and
conveniently, and by disseminating the right information to counter misperceptions
and take away concerns, for instance with respect to driving and charging regulations.
The present study has a number of limitations and also offers opportunities for
further research. The study was carried out using an online snowball sample that is
sociodemographically not entirely representative of the Belgian population. Snowball
sampling is uncontrolled and non-probabilistic and has its limitations in terms of
representativeness. Although about 70% of the Belgian population regularly uses the
Internet, the representativeness of online surveys may suffer from the fact that certain
segments of the population, such as older people and digitally not very experienced
groups, may be under-represented. However, this does not necessarily jeopardise
the validity of our findings. Many studies that aim at testing theory or conceptual
models use non-probability or non-representative samples of student or Internet
populations and are nevertheless considered as useful (Basil, Brown, & Bocarnea,
2002). Moreover, since hardly any sociodemographic factor has a direct impact
on electric car usage intention and the model i s controlled for sociodemographic
variables, this non-representativeness will probably not have affected our conclusions.
One obvious limitation of almost all empirical studies using the TPB as a
conceptual framework is that it predicts intentions and not actual behaviour.
Bagozzi (2007) states that the intention–behaviour linkage is probably the most
uncritically accepted assumption in social science research. Behavioural intentions
do not evidently translate into objectively measured buying behaviour. Therefore,
the usefulness of the TPB to predict real buying behaviour has been questioned (for
an overview, see De Cannière et al., 2009; Foxall, 2005). Nevertheless, for instance,
Sheppard, Hartwick, and Warshaw (1988) in their meta-analysis of 87 studies found
a correlation of .53 between intention and behaviour. Consequently, intentions can
be expected to be relevant for predicting actual behaviour. Nevertheless, testing the
model to predict actual adoption of electric cars at a later stage would enhance the
validity of our findings.
Future research could explore more in depth to what extent opinion leadership
with respect to environmentally positioned products in general and eco-friendly cars
in particular is related to usage intention of electric cars. Building upon the results
of the present study, in further research, the determinants of the attitude towards
the electric car, such as complexity, availability, compatibility, and relative advantage
(Taylor & Todd, 1995), will be studied and the role of emotions in the adoption
process will be explored more extensively. The determinants of the emotions towards
the electric car will be investigated in more depth by means of setting up experiments
to explore product and design features that evoke positive and negative emotional
responses to different types of electric cars and how persuasive different advertising
strategies for electric cars are for different market segments. Also the relationship
with existing brands is important. Car models, as any other brand on the market,
have a certain personality and are associated with certain typical experiences.
An interesting question is how the addition of an electric vehicle to the product line
will influence this personality and experience, and which type of brand personality
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 221
will most benefit from which type of emotional positioning focus (visceral,
behavioural, reflective) that is used to design and promote the electric model.
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Appendix 1. Variables used in the analyses Items, scale
definition, source of scale, factor structure, and alphas
Variables and items Type of scale and labels Alpha
Intention to use electric car Five-category Likert scale
I have the intention to drive an
electric car in the near future
Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
α = .875
I will recommend the use of the
electric car to other people
I expect that I will be driving an
electric car in the near future
Cauberghe and De
Pelsmacker (2011)
Attitude towards electric car Sum of positive choices
made on six
dichotomous items (0–6)
Not
applicable
good–bad
like–don’t like
clever–stupid
nice–not nice
useful–useless Cauberghe and De
Pelsmacker (2011)
suitable–not suitable
Subjective norm peers Five-category Likert scale
People driving an electric car are
making a fool of themselves (r)
Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
α = .616
Driving an electric car is cool
My friends will find it weird that I’m
driving an electric car (r)
My family will raise objections
against driving an electric car (r)
Haustein, Klöckner, and
Blöbaum (2009) and
exploratory qualitative
study
(Continued)
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 227
Appendix 1. (Continued).
Variables and items Type of scale and labels Alpha
People who are important to me
will support me when I should
drive an electric car
People who are important to me
tell me that I should consider
driving an electric car
People who are important to me try
to convince me to drive an electric
car
Subjective norm media Five-category Likert scale
The media gave me a good feeling
about using an electric car
Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
α = .731
Articles in the media influenced me
to use an electric car
Cauberghe and De
Pelsmacker (2011)
Emotion towards electric car Five-category Likert scale
I will like driving an electric car Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
α = .795
I look forward to drive an electric
car
Driving an electric car could
frustrateme(r)
Cauberghe and De
Pelsmacker (2011); Kim
et al. (2007)
Perceived behavioural control Five-category Likert scale No useable
factor
structure.
My budget is sufficient to buy an
electric car
Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
Charging an electric car is possible
with an ordinary electric socket
Single
items
used
Cars with a combustion engine will
soon not be allowed to enter the
city
Exploratory qualitative
study
You can drive a long distance with
an electric car
I will not be able to charge an
electric car at home
Our society offers the necessary
means and instruments to use an
electric car
The costs of using an electric car
are acceptable
The maintenance of an electric car
is well organised
I will not be allowed to charge my
electric car with energy I have
produced myself
The battery of an electric car
cannot be charged underway
(Continued)
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228 Journal of Marketing Management, Volume 28
Appendix 1. (Continued).
Variables and items Type of scale and labels Alpha
Visceral emotions towards car
driving
‘To what extent do the
following aspects
contribute to you
experiencing positive
emotions when driving a
car:’ 1 = ‘not at
all’–5 = ‘a lot’
α = .880
Throb of the engine
Rapid acceleration
Information on the dashboard
Beauty of the interior
Looks of the car
High speed possibility
Norman (2004) and
exploratory qualitative
study
Technological sophistication
Behavioural emotions towards car
driving
Enjoying the environment while
driving
Getting relaxed while driving
‘To what extent do the
following aspects
contribute to you
experiencing positive
emotions when driving a
car:’ 1 = ‘not at
all’–5 = ‘a lot’
α = .738
Norman (2004) and
exploratory qualitative
study
Reflective emotions towards car
driving
Low cost of the car
Environmentally friendly car
Economic fuel consumption of the
car
‘To what extent do the
following aspects
contribute to you
experiencing positive
emotions when driving a
car:’ 1 = ‘not at
all’–5 = ‘a lot’
α = .856
Norman (2004) and
exploratory qualitative
study
Environmental concern Five-category Likert scale
The major part of the population
does not act in an
environmentally conscious way
Labels: 1 = ‘fully disagree’;
5 = ‘fully agree’
α = .778
Limits of economic growth have
been crossed or will be reached
very soon
Environmental protection
measures should be carried out
even if this costs jobs
Preisendorfer (1998)
I am concerned about the
environmental conditions our
children will have to live in
Newspaper articles or TV reports
about environmental problems
make me angry
(Continued)
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 229
Appendix 1. (Continued).
Variables and items Type of scale and labels Alpha
If we continue as before, we are
heading towards an
environmentally catastrophe
Politicians do far too little for
environmental protection
For the benefit of the environment,
we should be ready to restrict our
style of living
Environmental behaviour Respondents could choose
between: no, because I
had to, because it saves
money, because
everyone else does it,
because it is better for
the environment
Not
applicable
I use energy-saving bulbs
I use biological soap
I selectively collect garbage
Installed a renewable energy
system
Member of an environmentalist
organisation
Use rainwater or well water
Switched to green energy Scale is sum of behaviours
(0–15) that respondent
indicate to conduct for
environmental reasons
(last category)
Avoid needless packaging
Always take a quick shower in
order not to waste too much
water
Installed a low flush toilet
Often talk with others about a more
environmentally friendly way of
living
Whitmarsh (2009) and
exploratory qualitative
study
Clothes are made in an
environmentally friendly way
Installed insulation in house
Installed a heat pump
Whenever possible, don’t use the
car
Opinion leadership with respect to
cars
Five-category Likert scale
Compared to your circle of friends,
how likely are you to be asked
about cars?
Items 1–3: Labels: 1 = ‘very
seldom’; 5 = ‘very often’
α = .721
Overall, in all of your discussions
with friends and neighbours, are
youusedasasourceof
information?
Item 4: labels: 1 = ‘fully
disagree’; 5 = ‘fully
agree’
(Continued)
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230 Journal of Marketing Management, Volume 28
Appendix 1. (Continued).
Variables and items Type of scale and labels Alpha
Are you the first in your circle of
friends to buy a new model of car
when it appears on the market?
Grewal et al. (2000)
In general, can you tell a lot about a
person by seeing which car he
drives?
Value dimension idealism Five-category Likert scale
Equality ‘To what extent are the
following values a
guideline in your life?’
α = 0.810
Social justice
World peace Labels: 1 = ‘very weak
guideline’; 5 = ‘very
strong guideline’
Hansla, Gamble, Juliusson,
& Gärling (2008);
Schwartz (1992)
Value dimension altruism Five-category Likert scale
Helpfulness ‘To what extent are the
following values a
guideline in your life?’
α = .758
Forgivingness
Loyalty
Responsibility Labels: 1 = ‘very weak
guideline’; 5 = ‘very
strong guideline’
Hansla et al. (2008);
Schwartz (1992)
Value dimension achievement Five-category Likert scale
Ambition ‘To what extent are the
following values a
guideline in your life?’
α = .797
Efficiency Labels: 1 = ‘very weak
guideline’; 5 = ‘very
strong guideline’
Success
Hansla et al. (2008);
Schwartz (1992)
Value dimension power Five-category Likert scale
Social power ‘To what extent are the
following values a
guideline in your life?’
α = .814
Authority Labels: 1 = ‘very weak
guideline’; 5 = ‘very
strong guideline’
Influence
Hansla et al. (2008);
Schwartz (1992)
All items of each scale load on one factor, unless mentioned otherwise.
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 231
Appendix 2. Exploratory factor analysis output for the 10 perceived behavioural control items
Total variance explained.
Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings
Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %
Dimension 0
1 2.028 20.278 20.278 2.028 20.278 20.278 1.822 18.224 18.224
2 1.370 13.698 33.977 1.370 13.698 33.977 1.369 13.686 31.911
3 1.120 11.197 45.174 1.120 11.197 45.174 1.170 11.701 43.612
4 1.021 10.211 55.385 1.021 10.211 55.385 1.117 11.174 54.787
5 1.005 10.054 65.439 1.005 10.054 65.439 1.065 10.653 65.439
6 .850 8.499 73.938
7 .795 7.946 81.884
8 .665 6.653 88.537
9 .593 5.933 94.470
10 .553 5.530 100.000
Extraction method: Principal component analysis.
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232 Journal of Marketing Management, Volume 28
Rotated component matrix
a
.
Component
12345
My budget is sufficient to buy an
electric car
.065 .013 .050 .025 .922
Charging an electric car is possible
with an ordinary electric socket
.028 .802 .000 .198 .153
Cars with a combustion engine will
soon not be allowed to enter the
city
.030 .088 .026 .911 .071
You can drive a long distance with an
electric car
.452 .072 .234 .423 .286
I will not be able to charge an
electric car at home
.068 .827 .113 .107 .108
Our society offers the necessary
means and instruments to use an
electric car
.741 .002 .034 .044 .035
The costs of using an electric car are
acceptable
.676 .119 .022 .088 .299
The maintenance of an electric car is
well organised
.761 .041 .062 .054 .031
I will not be allowed to charge my
electric car with energy I have
produced myself
.115 .106 .733 .138 .025
The battery of an electric car cannot
be charged underway
.096 .003 .746 .159 .014
Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser
normalisation.
a
Rotation converged in five iterations.
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 233
Appendix 3. Exploratory factor analysis output for the 17 car-driving emotions items
Total variance explained.
Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings
Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %
Dimension 0
1 5.413 31.840 31.840 5.413 31.840 31.840 4.615 27.147 27.147
2 2.699 15.877 47.717 2.699 15.877 47.717 2.538 14.928 42.075
3 1.338 7.871 55.588 1.338 7.871 55.588 2.297 13.513 55.588
4 1.156 6.802 62.390
5 .898 5.283 67.673
6 .774 4.555 72.228
7 .727 4.277 76.505
8 .618 3.633 80.138
9 .599 3.523 83.661
10 .530 3.116 86.777
11 .426 2.505 89.282
12 .415 2.441 91.723
13 .391 2.301 94.024
14 .364 2.139 96.163
15 .237 1.396 97.560
16 .230 1.355 98.915
17 .184 1.085 100.000
Extraction method: Principal component analysis.
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234 Journal of Marketing Management, Volume 28
Rotated component matrix
a
.
Component
123
Throb of the engine .674 .180 .159
Rapid acceleration .766 .178 .102
Information on the dashboard .654 .169 .143
The beauty of the interior .761 .176 .111
High speed possibility .814 .065 .119
High speed possibility .779 .148 .107
Technological sophistication .768 .142 .090
The other things I can do while
driving
.411 .102 .250
The personalisation of my car .496 .081 .183
On the road with friends .239 .129 .356
Realising my car is environmentally
friendly
.077 .833 .116
The low cost of my car .023 .833 .153
The economic fuel efficiency of my
car
.055 .882 .173
The flexibility of my journey .012 .310 .564
Car handling .409 .228 .522
Enjoying the environment while
driving
.108 .121 .823
Getting relaxed while driving .216 .075 .798
Extraction method: Principal component analysis.
Rotation method: Varimax with Kaiser normalisation.
a
Rotation converged in five iterations.
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 235
Appendix 4. Factor and cluster analysis results for the personal value types
Total variance explained.
Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings
Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %
Dimension 0
1 4.060 27.068 27.068 4.060 27.068 27.068 2.589 17.263 17.263
2 3.581 23.871 50.939 3.581 23.871 50.939 2.562 17.081 34.344
3 1.452 9.679 60.619 1.452 9.679 60.619 2.498 16.653 50.997
4 .923 6.157 66.775 .923 6.157 66.775 2.367 15.778 66.775
5 .755 5.031 71.806
6 .649 4.328 76.134
7 .620 4.132 80.266
8 .495 3.303 83.569
9 .449 2.995 86.564
10 .425 2.835 89.400
11 .420 2.802 92.201
12 .359 2.391 94.592
13 .291 1.943 96.535
14 .283 1.887 98.422
15 .237 1.578 100.000
Extraction method: Principal component analysis.
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236 Journal of Marketing Management, Volume 28
Rotated component matrix
a
Component
1234
Social control .105 .042 .083 .870
Authority .092 .003 .250 .830
Prosperity .242 .021 .525 .402
Ambition .061 .043 .783 .277
Competence .082 .270 .770 .088
Success .013 .137 .825 .180
Influence .071 .051 .271 .747
Helpfulness .329 .631 .075 .184
Forgivingness .316 .676 .103 .077
Loyalty .035 .824 .172 .076
Responsibility .121 .742 .344 .104
Tolerance .526 .508 .141 .115
Equality .815 .238 .017 .118
Social justice .850 .243 .068 .051
World peace .781 .078 .065 .003
Extraction method: Principal component analysis.
Rotation method: Varimax with Kaiser normalisation.
a
Rotation converged in six iterations.
Factor 1: Idealism: equality. social justice. world peace.
Factor 2: Altruism: Helpfulness, forgivingness, loyalty, responsibility.
Factor 3: Achievement: Ambition, competence, success.
Factor 4: Power: Social control, authority, influence.
Mean scores of the four value factors per cluster (value type).
Cluster
1
Self-transcendent
universalist 2 Benevolent
3Self-
enhancingpower
seeking achiever
(32.0%) (37.3%) (30.7%)
Idealism 3.74 4.12 2.57
Altruism 3.89 4.30 3.68
Achievement 3.10 4.19 3.97
Power 1.86 2.92 3.23
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Moons and De Pelsmacker Emotions as determinants of electric car usage intention 237
About the authors
Ingrid Moons holds a master’s degree in psychology. She teaches consumer behaviour,
marketing and marketing management at the Department of Design Sciences at the Artesis
University College Antwerp and is working on a PhD on the role of emotions in the adoption
process of electric cars. Her research interests are consumer behaviour, adoption processes of
new products, and the role of emotions in product design.
E ingrid.moons@artesis.be
Patrick De Pelsmacker holds a PhD in Economics and is professor of marketing at
the Faculty of Applied Economics of the University of Antwerp. He teaches marketing
communications, marketing research, and consumer behaviour. His research interests
are advertising effectiveness, marketing communications in interactive media (interactive
television, consumer-generated content), new communication formats such as product
placement, branding strategy, health communication, cross-cultural advertising, and ethical
consumer behaviour and social marketing.
Corresponding author: Patrick De Pelsmacker, University of Antwerp, Faculty of Applied
Economics, Prinsstraat 13, B-2000 Antwerpen, Belgium.
T 32 3 265 4022
E Patrick.depelsmacker@ua.ac.be
Downloaded by [ ] at 04:29 03 April 2012
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