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Bhutto, M.H., Shaikh, A.A. & Sharma, R. (2021). Factors
Affecting the Consumers’ Purchase Intention and
Willingness-to-Pay More for Electric-Vehicle Technology. In
Proceedings of the 21st International Conference on
Electronic Business. ICEB’21, Hohai University, in Nanjing,
China, DEC 3-7, 2021.
Bhutto, Shaikh & Sharma
The 21st International Conference on Electronic Business (ICEB 2021), Hohai University, in Nanjing, China, DEC 3-7, 2021
1
Factors Affecting the Consumers’ Purchase Intention and Willingness-to-Pay More
for Electric-Vehicle Technology
(Full Paper)
Maqsood H. Bhutto*, University of Jyväskylä, Finland, maqsood.h.bhutto@student.jyu.fi
Aijaz A. Shaikh, University of Jyväskylä, Finland, aijaz.a.shaikh@jyu.fi
Ravishankar Sharma, Zayed University, United Arab Emirates, ravishankar.sharma@zu.ac.ae
ABSTRACT
This study conducted an in-depth analysis of the factors affecting consumers’ intention to purchase and willingness to pay
more for an electric vehicle (EV) in the developing-country context, extending the theory of planned behavior with two new
variables: environmental concern and willingness to pay (WTP) a premium. Survey data were collected from 358 responses
and were analyzed using partial least squares structural equation modeling. Multi-group analysis was conducted, and the
moderating role of gender was examined. The findings showed the significant effects of the theory-of-planned-behavior
variables and environmental concern on EV technology purchase intention. The present study provides theoretical
contributions and policy guidelines concerning high (vs. low)-sensitivity consumer attitudes toward EV technology that
marketers and automobile manufacturers can make use of when designing and strategizing their pricing strategies.
Keywords: Electric vehicle technology, purchase intention, theory of planned behavior, willingness to pay more, automobile
industry.
*Corresponding author
INTRODUCTION
The issue of greenhouse gas (GHG) emissions has become one of the most debated issues globally. The emergence of
ecological problems has given rise to global warming, energy crises, climate change, ozone layer depletion, air pollution, and
depletion of natural resources, all of which have a substantial impact not only on the ecosystem but also on consumer
wellbeing (Shah, 2015). Global communities have been recognizing the impacts of these ecological problems on
environmentally socially responsible activities, and this has led to the organization of international climate forums such as the
Bonn Climatic Conference (2017), the Paris Agreement (2015), and the Copenhagen Conference (2009).
Carbon dioxide (CO2) has been reported to be the most highly emitted GHG in the atmosphere. These emissions mainly come
from the transport sector (World Health Organization [WHO], 2019). An increase in gross domestic product improves the per
capita income in a country, which increases the rate of vehicle ownership (Jain, 2006). This ultimately generates more energy
consumption and results in higher CO2 emissions globally.
Hybrid and electric vehicles (EVs) can be considered technological solutions to the problem of GHG emission as they can
reduce GHG emission (Bhutto et al., 2020) by replacing gasoline vehicles (Asamer et al., 2016). EVs, the context of this study,
have electric batteries consisting of hundreds of lithium ion cells plugged in parallel series, and are charged with cables
connecting the batteries to the optimal electric current and voltage. According to the International Energy Association (2018),
adopting such innovative technology can be an effective strategy to minimize the GHGs emitted into the atmosphere by fossil
fuel vehicles, which account for 21% of the total emissions from the transport sector worldwide.
In emerging and developing countries, the transport sector plays a key role in socioeconomic development but contributes to
severe air pollution with motorization and urbanization. Despite these challenges and the proliferation of EVs and associated
technologies, research considering and examining the consumer perspectives on the purchase and use of EVs is scarce.
Moreover, much of the research that has been conducted on this topic has considered the Western regions, overlooking the
non-Western or emerging/developing countries with high population densities and demands for transport means, including
EVs, for the people’s regular commute.
To fill the aforementioned research gap, this study was conducted in a non-Western country and investigated people’s EV
purchase intention. We provided a theoretical framework for our research and empirically tested the hypothesized relationships
of certain factors with consumers’ EV purchase intention and willingness to pay more for an EV. We also gathered insights
from male and female consumers on the factors that most strongly affect their EV purchase intention and willingness to pay
more for an EV. Venkatesh and Morris (2000) consider investigating gender difference in this regard important for two
reasons. First, men’s and women’s decision-making processes are different. Second, as Zhou et al. (2014) reported,
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information can easily be obtained from both men and women, and marketing managers can monitor different gender segments
using different marketing strategies.
The following research questions were thus proposed: What factors influence EV purchase intention and willingness to pay
more for an EV? Are there gender differences in terms of EV purchase intention and willingness to pay more for an EV? Do
the explained variances in the theory constructs differ between males and females?
Pakistan, a non-Western country, was selected for this study for two reasons. First, it is fast becoming more urbanized and is
undergoing rapid motorization. In the last decade, the automobile industry showed rapid growth (63% and 69%, respectively)
from 2010 to 2018 in terms of production and sales (PAMA, 2018). Second, the WHO (2019) cited the cities of Lahore,
Peshawar, and Rawalpindi in Pakistan as the most polluted cities globally, with their high air pollution levels giving rise to
airborne disorders and untimely deaths.
EV technology covers a wide range of transport means, including cars, trucks, buses, and motorcycles, but only electric cars
were included in this study.
For the remaining sections of this paper, section 2 presents and discusses the theoretical background of the research; section 3,
the research model and hypotheses; section 4, the research method that was used; section 5, the study results; and section 6, the
discussion and implications of the study findings, the study limitations, and the future research directions. Section 7 concludes
the paper.
THEORETICAL BACKGROUND
Theory of Planned Behavior
Ajzens’ theory of planned behavior was used to measure consumer behavior. According to this theory, the first predictor of
purchase intention is attitude. Attitude refers to “a learned predisposition to respond in a consistently favorable or unfavorable
manner with respect to a given object” (Fishbein & Ajzen, 1975, p. 211). However, attitude also shows consumers’ likes and
dislikes, which may indicate consumers’ intention to purchase green products. Therefore, attitudes can be general or specific
(Chen & Chai, 2010). A specific attitude reveals the stronger antecedent of a single behavior in a particular industry or
object/product/service while a general attitude shows a common predisposition involving a significant behavior (Tan, 2011).
The second predictor of purchase intention according to Ajzens’ theory of planned behavior is the subjective norm. The term
refers to “the perceived social pressure to perform or not to perform the behavior” (Ajzen, 1985, 2002). Social norms are
influenced by one’s peers, family members, friends, or prominent members of the community, and exert pressure on people
(Fishbein & Ajzen, 1975).
The third predictor of purchase intention according to Ajzens’ theory of planned behavior is perceived behavioral control,
which consists of two constructs: self-efficacy and controllability (Ajzen, 2002). Self-efficacy pertains to a person’s ease or
difficulty of carrying out a certain intention or behavior that he/she wants to carry out, also known as internal control according
to Armitage and Conner (1999). Another construct of perceived behavioral control is controllability, which pertains to a
person’s belief that individuals have control over their own actions. The addition of perceived behavioral control extends the
theory of reasoned action into the theory of planned behavior, whose predictive power has been significantly increased.
Perceived behavioral control belongs to the “rational-choice model,” which assumes that “people behave rationally and
logically during the process of decision making” (Ajzen, 1991, p. 182). Several management scholars (e.g. Bhutto et al., 2020;
Channa et al., 2020; Kumar et al., 2017) have frequently used the theory of planned behavior and have found that perceived
behavioral control is its fundamental factor.
Environmental Concern
Environmental concern shows consumers’ emotional responses to environmental issues, including compassion, dislike, and
worry (Ramayah et al., 2012), and considerations to ensure environmental quality (Yeung, 2004). For instance, several studies
have validated the environmental-concern impacts on the green product choice, including organic foods (Hoffmann & Schlicht,
2013) and renewable energy (Bang et al., 2000). People with more environmental concern are likely to have a positive attitude
toward green products (Karatu & Mat, 2014).
In particular, reports show that consumers’ interest in EVs has been stimulated by their environmental concern, and that
consumers with higher environmental concern tend to be less price-sensitive toward EVs (Tanner & Wölfing Kast, 2003),
showing a higher willingness to pay more for the environmental benefits of the product (Hansla et al., 2008). For example,
consumers have shown a willingness to pay more for organic products (Loureiro & Hine, 2002).
Gender Differences
Gender differences have been widely considered in several studies in the marketing field (Mostafa, 2007), but few studies have
been conducted on the effects of gender differences on EV purchase intention and willingness to pay more for an EV in a
developing-country context.
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RESEARCH MODEL AND HYPOTHESES
Figure 1 shows the research model in this study. The theory of planned behavior was used in the study, with a focus on a
particular behavior of people: EV purchase. However, all the constructs of this theory individually or collectively lead to a
consumer intention, which precedes an action. The following general rule was developed: the stronger the intention, the more
likely the action corresponding to it will occur. Thus, the theory of planned behavior aims to show how people’s acquired
information and motivation affect their intention and behavior.
Impact of Attitude on Electric-Vehicle Purchase Intention
Attitudes consist of all the beliefs that influence an individual’s behavioral intentions. They are a result of an internal
assessment and association process and have a direct role in the development of positive or negative intentions (Ajzen, 2002).
In researches on green consumer psychology, attitudes have always been stressed as important antecedents of behavioral
intention and real behavior. Various researchers have validated the effect of attitude toward green products on the intention to
purchase green products in developed countries (Qi & Ploeger, 2019; Tan et al., 2019; Jaiswal & Kant, 2018). However, the
existing literature clearly does not address the impact of consumer intention on consumer purchase behavior in the developing-
country context. Thus, the hypothesis below was proposed.
H1: Attitude toward EVs is positively related to EV purchase intention.
Impact of Subjective Norms on Electric-Vehicle Purchase Intention
From the social-impact perspective, the individuals in a segment seem to be more closely connected to the other segment
members than to non-segment members, and to be generally influenced by the opinions of the segment and by the normative
pressure exerted by it (Ajzen, 1991, 2002). Thus, the segment’s influence is viewed as the influence of subjective norms.
Various studies have confirmed that subjective norms in the context of social pressure influence consumers to buy green
products more than attitude does (Jayaraman et al., 2015; Lai & Cheng, 2016; Lee, 2009). However, the existing literature does
not describe the effects of subjective norms on EV purchase intention. Thus, the hypothesis below was proposed.
H2: Subjective norms are positively related to EV purchase intention.
Figure 1: Research Model
Impact of Perceived Behavioral Control on Electric-Vehicle Purchase Intention
The study reported herein was among the few that had determined the impact of perceived behavioral control on purchase
intention. Joergens (2006) argued that many consumers prefer to buy non-green products due to the high prices and
unaffordability of eco-friendly products. Thus, the purchasing power control factor appeared to be the major consideration for
deciding to purchase healthy food products or not to (Mai & Hoffmann, 2012). The government interventions in terms of
policy and regulations support consumers’ EV purchase behaviors and their willingness to pay more for an EV (Helveston et
al., 2015; Oreg & Katz-Gerro, 2006; Sang & Bhet, 2015). They are thus important predictors of green consumption behavior
and have been confirmed to have a positive relationship with EV purchase intention (Egbue et al., 2017).
Environmental
Concern
Attitude
Subjective
Norm
Perceived
Behavioral
Control
Purchase
Intention
Willingness-to-Pay
more
Moderator:
Gender
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Thus, the market for EVs is still an emerging market, and consumers’ self-efficacy and control leading to willingness to buy at
a premium is the most important factor that determines EV purchase intention. The pertinent studies clearly lack an
explanation of the direct and indirect connections between perceived behavioral control and EV purchase intention to be able
to accept or reject the hypotheses below.
H3: Perceived behavioral control is positively related to EV purchase intention.
Impact of Environmental Concern on Attitude toward Electric Vehicles, Electric-Vehicle Purchase Intention, and
Willingness to Pay More for an Electric Vehicle
A significant driver of EV purchase intention is environmental concern. This is understandable because EVs have less
detrimental effects on the environment than petrol or diesel engines do. Several leading carmakers have resolved to stop their
production of non-electric cars in the coming decade. Moreover, Sinnappan and Rahman (2011) reported that consumers with
stronger environmental concern are most inclined toward EV purchase. Generally, the literature (e.g., Bang et al., 2000) also
indicates that consumers with a high level of environmental concern are less price-sensitive and are more willing to pay a
premium for green products (Moser, 2015). Consumers may be concerned about the environment because they are aware that
fossil fuel cars have significant negative effects on the environment. Accordingly, Junquera et al. (2016) looked into whether
consumers could easily distinguish between the ecological factors of EVs and of gasoline cars and are thus willing to pay a
premium for an EV. The hypotheses below were thus proposed.
H4: Environmental concern is positively related to attitude toward EVs.
H5: Environmental concern is positively related to EV purchase intention.
H6: Environmental concern is positively related to willingness to pay more for an EV.
Moderating Role of Gender
The role of gender differences seems important to understand because men and women behave differently from each other
because of their different structural positions in the labor market and because of their different socialization processes in terms
of how they think, behave, and act with regard to the caregiver role (Blocker & Eckberg, 1997).
The theory of gender socialization (Gilligan & Attanucci, 1988) refers to the socialization process where males and females
learn different social values and perceive different expectations of them since their early childhood. For example, Gu and Feng
(2020) reported the positive effects of heterogeneous groupings (including individuals and latent groups) on mobility tool
purchase, particularly on the choice of EVs, which affects households’ future technology adoption in relation to energy
equipment preferences. However, the multi-country comparison of Belgium, Denmark, and Italy showed significant
differences in the consumers’ attitudes towards EVs and EV purchase intention (Barbarossa et al., 2015).
In the context of developing countries, the men from South Asian countries are nurtured to take care of their respective
families as the sole breadwinners. They thus become competitive and more insensitive than the women who had grown up
playing the role of a caregiver and thus had become more cooperative and compassionate. Past studies (e.g., Lee, 2009;
Zelezny et al., 2000) have shown that females show greater concern than males regarding environmental issues, and thus have
a more positive attitude toward products that aim to save nature and the environment.
Conversely, Huang and Ge (2019) performed multi-group analysis (MGA) of the gender demographics in China and found that
males have more positive attitudes toward EVs and stronger EV purchase intentions than females. Very few studies have
examined gender differences using MGA to assess consumer attitudes toward EVs and EV purchase intention. Hence, we came
up with the hypotheses below.
H7: There is a more significantly positive relationship between attitude toward EVs and EV purchase intention among males
than among females.
H8: There is a more significantly positive relationship between environmental concern and EV purchase intention among
females than among males.
Environmental concern is a multi-dimensional variable demonstrating susceptibility to showing concern for environmental
issues and thus to purchasing an environment-friendly product and to having the willingness to pay more for it (consumer price
sensitivity) in terms of time and money (Dunlap & Jones, 2002). We thus formulated the hypothesis below.
H9: There is a more significant relationship between environmental concern and willingness to pay more for an EV among
females than among males.
EMPIRICAL METHODOLOGY
Participants and Sampling Design
The target study population was estimated to be around 1.26 million automobile users in Pakistan from 2010 to 2018 (PAMA,
2018), who may have car awareness, including of the electric battery technology and EVs. The survey method was utilized,
and online survey questionnaires were sent to about 1,000 automobile consumers for data collection, using a link shared
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through Google Form (via e-mail), WhatsApp, and Facebook. The back-translated questionnaires were administered to the
automobile users in their local languages (e.g., Sindhi and Urdu) so that responses would be received from them.
A hybrid convenience–snowballing sampling technique was employed to collect data from EV users, who were aware of
battery and plug-in hybrid EVs. A total of 390 online questionnaires were retrieved, 32 of which were removed due to
incomplete data. Thus, a final sample of 358 accomplished questionnaires was retained for analysis. Of the final usable sample,
51.1% were obtained from females, and 38.8% of the respondents had a monthly income of above Rs100,000. As regards age,
67.6% of the respondents were within the 25–35 age bracket, and 22.4% were within the middle age bracket (36 and above).
As regards the respondents’ education level and marriage status, 55.6% had a college degree and 61.5% were married. The
respondents’ detailed characteristics are shown in Table 1.
Measurement Instrument
The online survey form was divided into two parts: the respondents’ demographic information and the questionnaire proper. A
5-point Likert scale was adopted for the respondents’ response options, ranging from 1 (strongly disagree) to 5 (strongly agree)
(Lin & Huang, 2012; Wang et al., 2014). The questionnaire items were obtained from previous studies but were modified to
make them fit the research context. To confirm that all the questionnaire items could be clearly understood, a pilot study was
conducted with a sample of 50 automobile owners who were students and staff of Sukkur IBA University belonging to
different countries and regions of Pakistan and who were obtained via a hybrid convenience–snowballing sampling technique.
One professor and two Ph.D. students who were well versed in research were involved in the pilot study.
Table 1: Demographic Statistics
Demographic
Variables
Categories
Sample
Percent
(%)
Gender
Male
175
48.9
Female
183
51.1
Age (years)
≤ 25
58
16.2
26–35
184
51.4
36–45
107
29.9
≥ 46
9
2.5
Education
Undergraduates
70
19.5
Graduates
199
55.6
Post-graduates
89
24.9
Marital
status
Unmarried
138
38.5
Married
220
61.5
Income
(rupees)
30,000–60,000
125
34.9
61,000–99,999
94
26.3
≥ 100000
139
38.8
After the pilot study, the questionnaire with a total of 21 items was found fit for measuring all the constructs therein (refer to
the Appendix). However, the constructs of the theory of planned behavior were measured by adopting 14 items from Ajzen
(1985), and the Cronbach’s alpha values for the theory-of-planned-behavior variables were 0.764 for attitude toward EVs,
0.802 for subjective norms, 0.895 for perceived behavioral control, and 0.860 for EV purchase intention. Environmental
concern was measured by four items from Ramayah et al. (2012) and Kumar et al. (2017), and the Cronbach’s alpha of
environmental concern was 0.734. Finally, a scale of three items for measuring the willingness to pay more for an EV was
adopted and modified as per the requirements of the study from Moser (2015), and the Cronbach’s alpha for willingness to pay
more for an EV was found to be 0.832.
Analytical Procedures
Previous studies (Byrne & Vijver, 2010; Channa et al., 2020; Henseler et al., 2009) have suggested that structural models can
be analyzed by applying either a variance- or covariance-based approach. We employed the partial least squares structural
equation modeling (PLS-SEM) technique for the following reasons: (a) as our study (please refer to the research model, Fig. 1)
focused on prediction, we regarded PLS-SEM as appropriate for the study; (b) the preference for PLS-SEM over the traditional
multivariate analysis approaches has been highly accepted, according to Haenlein and Kaplan (2004); (c) PLS-SEM’s strength
is that it can estimate the causal relationship between the latent constructs and their indicators as reflected in the measurement
model (Henseler et al., 2009), and can simultaneously predict hypothesized relationships as reflected in structural models (Hair
et al., 2016); (d) PLS-SEM has less strict multivariate analysis assumptions and is useful for prediction (Urbach & Ahlemann,
2010); and (e) for exogenous constructs, PLS-SEM helps maximize the explained variance (Hair et al., 2016).
RESULTS AND FINDINGS
To examine the hypothetical model in this study, we used PLS-SEM version 3.2.8 (Ringle et al., 2015). We conducted the two-
step SEM process (measurement and structural model assessment) and then proceeded to conduct PLS Predict and MGA.
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Measurement Model Assessment
Following the instructions of Hair et al. (2016) for the analysis of the measurement model, the individual item reliability,
internal consistency reliability, content validity, convergent validity, and discriminant validity were determined.
Individual Item Reliability
The reliability of the individual items was analyzed by evaluating the factor loadings of all the individual items for each latent
variable (Hair et al., 2016; Hulland, 1999). Accordingly, a factor loading below 0.5 is unacceptable. Following the
recommendation of Hulland (1999), the items with minimum loadings of 0.5 were retained. The items’ loadings are presented
in Table 2.
Internal Consistency Reliability
To ensure internal reliability, the composite reliability (CR) value was used. CR estimates are much less biased than the
Cronbach’s alpha coefficient, and the reliability of a scale may be underestimated or overestimated by Cronbach’s alpha (Hair
et al., 2011). The CR values should be 0.70 or above (Hair et al., 2011). Table 2 presents the CR values for each latent
variable, ranging from 0.817 to 0.918. As the CR values met the criteria recommended by Bagozzi and Yi (1988) and Hair et
al. (2011), the measures were proven to have adequate internal consistency.
Convergent Validity
Convergent validity means that the test that evaluates certain constructs with average variance extracted (AVE) values actually
tests such constructs (Fornell & Larcker, 1981). The AVE values should be 0.50 or above (Chin, 1998). Table 2 shows the
AVE scores obtained in this study, ranging from 0.543 to 0.834, indicating that there was adequate convergent validity.
Table 2: Measurement Model
Constructs
Items
Loadings
Alpha
CR
AVE
Attitude
ATT1
0.903
0.764
0.895
0.809
ATT2
0.896
Purchase Intention
PI1
0.877
0.860
0.915
0.782
PI2
0.907
PI3
0.869
Environmental Concern
EC1
0.739
0.734
0.831
0.555
EC2
0.626
EC3
0.762
EC4
0.837
Perceived Behavioral Control
PBC1
0.779
0.895
0.918
0.615
PBC2
0.764
PBC3
0.724
PBC4
0.804
PBC5
0.757
PBC6
0.805
PBC7
0.851
Willingness-To-Pay more
WTP1
0.864
0.832
0.9
0.75
WTP2
0.928
WTP3
0.803
Subjective Norm
SN1
0.906
0.802
0.91
0.834
SN2
0.921
CR = composite reliability; AVE = average variance extracted
Discriminant Validity
Considering the recent criticism of the Fornell and Larcker (1981) criterion, we analyzed the discriminant validity through the
heterotrait-monotrait (HTMT) method. HTMT follows the multi-trait multi-method matrix developed by Henseler et al. (2015)
to ascertain discriminant validity. According to Kline (2011) and as also recommended by Henseler et al. (2015), if the HTMT
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value is greater than 0.85, then there is a discriminant validity issue. Table 3 shows that the HTMT values for all the constructs
in this study were lower than 0.85. Thus, there was no discriminant validity issue in this study.
Collinearity Statistics
The variance inflation factor values were obtained in this study. As they were all less than 5, the exogenous variables in this
study had no multicollinearity problem.
Table 3: Discriminant Validity (HTMT Ratio)
Latent Constructs
Attitude
Purchase
Intention
Environmental
Concern
Perceived
Behavioral
Control
Willingness-
To-Pay more
Subjective
Norm
Attitude
Purchase Intention
0.507
Environmental Concern
0.274
0.601
Perceived Behavioral
Control
0.458
0.791
0.492
Willingness-To-Pay more
0.305
0.673
0.515
0.568
Subjective Norm
0.493
0.567
0.257
0.598
0.358
Structural Model
After determining the significant results of the measurement model, we proceeded to analyze the structural model. The
standard bootstrapping method was used to test the hypotheses, and the results are presented in Table 4.
Table 4: Assessment of Path Coefficients
Hypothesis
Relationships
Beta
T – Values
P – value
Hypothesis
supported
(Y/N)
H1
Attitude -> Purchase Intention
0.131
2.450
0.014
Yes
H2
Subjective Norm -> Purchase Intention
0.129
3.161
0.002
Yes
H3
Perceived Behavioral Control -> Purchase Intention
0.481
9.590
0.000
Yes
H4
Environmental Concern -> Attitude
0.189
3.860
0.000
Yes
H5
Environmental Concern -> Purchase Intention
0.249
5.958
0.000
Yes
H6
Purchase Intention -> Willingness-To-Pay more
0.364
8.445
0.002
Yes
R2 (Purchase Intention) = 0.569
R2 (Willingness-To-Pay more) = 0.386
The results of H1 (β = 0.132; t = 2.429; p = 0.015), which states that attitude toward EVs is a stronger predictor of EV
purchase intention, were found to be statistically significant. Thus, H1 was accepted. The results of H2 (β = 0.129; t = 3.126; p
= 0.002), which states that consumers’ subjective norms are positively related to their EV purchase intention, were also found
to be statistically significant. Thus, H2 was also accepted. The results of H3 (β = 0.480; t = 9.427; p = 0.000), which states that
perceived behavioral control is positively related to EV purchase intention, were also found to be statistically significant. Thus,
H3 was also accepted. The results of H4 (β = 0.188; t = 3.795; p = 0.000), which states that environmental concern is positively
related to attitude toward EVs, were also found to be statistically significant. Thus, H4 was also accepted. The results of H5 (β
= 0.250; t = 5.929; p = 0.000), which states that environmental concern is positively related to EV purchase intention, were
also found to be statistically significant. Thus, H5 was also accepted. Finally, the results of H6 (β = 0.364; t = 5.218; p =
0.000), which states that EV purchase intention is positively related to willingness to pay more for an EV, were also found to
be statistically significant. Thus, H6 was also accepted.
R2 Assessment
R2 reveals the proportional variance in the predictive dependent variable(s), which can be interpreted through their independent
variables (Elliott & Woodward, 2007). Thus, the acceptable value of R2 is 0.10, as suggested by Falk and Miller (1992),
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whereas Hair et al. (2011) and Henseler et al. (2009) suggested that an R2 value of 0.75 could explain accuracy substantially,
0.50 could explain it moderately, and 0.25 could explain it weakly. The R2 values obtained for both EV purchase intention
(0.569) and willingness to pay more for an EV (0.386) show that the research model had goodness of fit or good predictive
accuracy (Hair et al., 2016). This further suggests that all the predictable variables combined explain 57% of the variance in
EV purchase intention and 39% of the variance in willingness to pay more for an EV.
Partial Least Squares Predict Assessment
PLS Predict assessment aims to analyze the predictive relevance in terms of the quality of the structural model used and the
ability to create accurate predictions (Shmueli & Kopplus, 2011; Shmueli et al., 2019). Predictive validity expresses the set of
constructs’ measures that can foresee the dependent variable (Straub et al., 2004). The present study utilized cross-validation
with holdout samples to measure the study model’s predictive validity. For the PLS Predict algorithm, we followed the method
suggested by Shmueli et al. (2016), using SmartPLS software version 3.2.8 (Ringle et al., 2015). This procedure helped us find
the prediction error summaries’ statistics and the k-fold cross-validated prediction error. For example, these include the root
mean square and the mean absolute error for the purpose of analyzing the PLS path model’s predictive relevance for the
constructs. The current study applied two new benchmarks based on the guidelines developed by the SmartPLS team to gauge
the study model’s predictive relevance.
First, we employed the Q2 blindfolding procedure to analyze the predictive relevance of the study model. A cross-validated
redundancy value Q2 greater than 0 suggests that the model has predictive relevance (Chin, 1998). As Table 5 shows, the Q2
value obtained for the model was 0.416 for EV purchase intention and 0.271 for willingness to pay more for an EV, which
confirmed that the model had predictive relevance. All the obtained root mean square error (RMSE) and mean absolute error
(MAE) values suggested that the values that were found were smaller than the RMSE value in the PLS model and the MAE
values in the LM model. Similarly, the Q2 values in the LM model were lesser than the Q2 values in the PLS model. The results
of the PLS Predict assessment thus strongly establish the study model’s predictive relevance.
Table 5: Partial Least Squares Predict Assessment
Endogenous Latent Variable Prediction Summary
Q2
Purchase Intention
0.416
Willingness-To-Pay more
0.271
Constructs Prediction Summary
PLS
LM
PLS - LM
RMSE
MAE
Q2
RMSE
MAE
Q2
RMSE
MAE
Q2
PI1
0.667
0.489
0.398
0.676
0.487
0.383
-0.009
0.002
0.015
PI2
0.604
0.443
0.392
0.622
0.454
0.354
-0.018
-0.011
0.038
PI3
0.682
0.51
0.472
0.692
0.519
0.457
-0.01
-0.009
0.015
WTP1
0.803
0.584
0.151
0.808
0.586
0.141
-0.005
-0.002
0.01
WTP2
0.773
0.572
0.245
0.796
0.59
0.199
-0.023
-0.018
0.046
WTP3
0.812
0.62
0.291
0.823
0.62
0.272
-0.011
0
0.019
Multi-Group Structural Equation Modeling Results
Using multi-group PLS-SEM analysis, we analyzed the gender differences (male vs. female) between the theory-of-planned-
behavior and extended variables (i.e., attitude toward EVs, environmental concern) and the EV purchase intention and
willingness to pay more for an EV. Table 6 shows the three hypotheses for the MGA SEM results.
For H7 (female β 0.004 < male β 0.261; p < 0.006), the positive relationship between attitude toward EVs and EV purchase
intention was significantly stronger for the males than for the females. Unexpectedly, H8 (female β 0.003 < male β 0.142; p <
0.002) was supported, showing that the positive relationship between environmental concern and EV purchase intention was
significantly stronger for the females than for the males. As for H9 (female β 0.350 > male β 0.175; p < 0.028), it was slightly
supported, meaning that the positive relationship between environmental concern and willingness to pay more for an EV was
significantly stronger for the females than for the males.
Table 6: Multi-Group Analysis Structural Equation Modeling Results
Paths
Hypothesized Relationships
β Path Coefficients
p-Value new (Male vs Female)
Male
Female
H7
Attitude -> Purchase Intention
0.261
0.004
0.006
H8
Attitude -> Willingness-To-Pay more
0.142
0.003
0.002
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H9
Environmental Concern -> Purchase Intention
0.175
0.350
0.028
DISCUSSION AND FUTURE DIRECTIONS
This study examined how the transport sector influences the environment in terms of air quality and pollution (Oberhofer &
Dieplinger, 2014), and whether that significantly affects consumer behavior. We tried to understand the antecedents of the
theory of planned behavior and the impact of environmental concern on the consumers’ EV purchase intention and willingness
to pay more for an EV and their relative significance.
The relevant literature shows some gaps regarding the robustness of the theoretical framework and the results’ generalizability,
as discussed in the literature section. For instance, many of the previous studies had a theoretical framework that included only
behavioral intentions or that did not include all the variables of the theory of planned behavior. Few studies have extended the
theory of planned behavior by adding variables, and many studies involved only student participants. The present study offset
some of these inadequacies by offering an extension of the theory of planned behavior with a sample of different respondents
in the developing-country context (i.e., Pakistan). This pioneering empirical study was conducted using an extended theory-of-
planned-behavior model (Ajzen, 1991) considering the original constructs’ effects on EV purchase intention.
Theoretical Implications
This paper offers an alternative theoretical lens for understanding EV purchase intention and how it is influenced by all the
theory-of-planned-behavior variables, environmental concern, and willingness to pay more for an EV in a developing-country
context.
The results for H1 suggest that consumer attitude toward EVs is the strongest predictor of EV purchase intention. The findings
regarding attitude toward EVs and how it affects EV purchase intention are in line with the theory of planned behavior,
showing that consumer attitudes are significant predictors of behavioral intention. Furthermore, the results of the direct effect
of attitude toward EVs on EV purchase intention are consistent with those of the previous studies in other contexts on
consumer pro-environmental behavior, such as that by Ramayah et al. (2012), who stated that consumer attitude is an
important predictor of the intention to purchase environment-friendly products. H2 was also supported by the study results,
meaning that subjective norms, perceived as having a social influence on people’s acts, were also found to have a significant
positive effect on EV purchase intention.
In the context of the Pakistani consumers, H3 was supported by the study results, meaning that perceived behavioral control
was found to have a significant positive effect on EV purchase intention. Perceived behavioral control concerns people’s belief
that they have the resources and opportunities needed to be able to carry out a particular action. It can be further divided into
two different aspects: the degree to which one believes he or she possesses the “control factors” needed to carry out a certain
behavior and the amount of confidence one has in performing a specific behavior (Kim & Han, 2010). This study showed that
the more resources and confidence a consumer has in purchasing environment-friendly products, the higher his or her EV
purchase intention will be.
The results for H4 and H5 clearly show that consumers’ environmental concern has a significant positive effect on attitude
toward EVs and EV purchase intention. This study thus revealed that consumers who care for the environment are likely to
have a positive attitude toward EVs and to be willing to pay more for an EV.
In the opposite direction, H6 was supported by the study results. That is, environmental concern was shown to be significantly
positively related to willingness to pay more for an EV, meaning that consumers are more willing to pay a premium for an EV
if they are concerned about environmental sustainability and thus want to purchase environment-friendly products.
As for the effect of gender on EV purchase intention in the developing-country context, H7 and H8 were supported by the
study results, meaning that attitude toward EVs more strongly affects EV purchase intention in males than in females, and that
environmental concern more strongly affects EV purchase intention in females than in males. The results for H9 add to the
existing knowledge that environmental concern has a stronger relationship with willingness to pay more for an EV in females
than in males.
This study thus extended the role of environmental concern in EV purchase intention besides assessing the link between the
theory-of-planned-behavior variables and EV purchase intention, and provided consumers’ insights on the relationship
between willingness to pay more for an EV and EV purchase intention in Pakistan.
Managerial Implications
First, EV producers are concerned with the price that can influence EV purchase intention. The results of this study suggest
that price is a major consideration in buying EVs; that is, consumers are willing to pay more for green products (Bhutto &
Bhutto, Shaikh & Sharma
The 21st International Conference on Electronic Business (ICEB 2021), Hohai University, in Nanjing, China, DEC 3-7, 2021
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Hussain, 2019). However, this study enables EV producers to adjust their pricing strategy by focusing on the marketing aspects
that promote sustainability. When setting pricing strategies for EVs, EV producers should make sure that such vehicles are
better than fossil fuel vehicles in terms of product design, quality, and functionality. The performance will make consumers
willing to pay more for an EV.
Second, the findings of this study provide automobile manufacturers with consumers’ insights regarding the sustainable
consumption patterns of green automobiles. This will help automakers devise and implement a proactive strategy in the
competitive market that promotes consumer-conscious subjective norms and the pursuit of environmental sustainability
through green-product development (Bhutto & Hussain, 2019).
Third, the results of this study contribute to the achievement of the United Nations 2030 Sustainable Development Goals
(SDGs), especially climatic industrial change, wellbeing and good health, innovative infrastructure, and affordable and clean
energy. The manufacture of green automobile products may lead to a sophisticated manufacturing environment that will reduce
GHG emissions and will thus have a less negative impact on ecological landscapes. The minimal CO2 in the atmosphere will
lead to healthier populations. Therefore, the development and adoption of green technology contribute to the attainment of a
number of SDGs.
Fourth, this study offers scientific results on consumer EV acceptance to EV manufacturers. EVs help improve transportation
sustainability, protect the environment, and reduce petroleum dependence.
Finally, the Pakistani government encourages the national and international automobile manufacturers to make major
investments in electric automobile technology. The production of EVs will open up employment opportunities for domestic
and international workers, especially those with automobile production experience.
Limitations and Directions for Future Research
The present study had various limitations, which could motivate other researchers to further expand their relevant studies.
Among the major limitations were that the present study focused only on EV purchase intention and had a small sample size.
Further studies on the relationships of new constructs with EV purchase intention with a large sample size may shed more light
on the important factors affecting consumer EV purchase intention. This study also focused on automobile consumers; the
future studies can consider the consumers of other products that contribute to environmental degradation and can investigate
the roles played by attitudes, subjective norms, perceived behavioral control, environmental concern, and willingness to pay
more for the product in shaping consumers’ intention to purchase such products.
This study’s sample consisted of various cities in Pakistan, both rural and urban centers. It would be interesting to determine if
there is a difference between rural and urban consumers in terms of how the theory-of-planned-behavior variables affect EV
purchase intention. An extended version of this study can be undertaken across Pakistan not only to verify this study’s results
but also to explore the variations across population groups (e.g., different age groups).
Lastly, the impacts of government incentives and reliability (in terms of EV range, facilities, and charging time) should be
analyzed, with the aim of determining whether the effects of reliability (in terms of EV range, facilities, and charging time) on
attitude toward EVs would be similar or different. This would further reveal the consumers’ acceptance of EVs in terms of
reliability in the developing-country context.
CONCLUDING REMARKS
The world is currently facing environmental issues such as air pollution from the transport sector especially in developing
countries like Pakistan. The solutions direly needed for EV technology adoption were thus examined in this study, and policy
recommendations were made accordingly. We argue that adopting EVs will solve the problem of air pollution caused by the
transport sector, and will promote a clean environment, a more robust economy, and power self-sufficiency. Having reviewed
the past relevant studies, we gathered that this study was among the few that had attempted to better understand EV purchase
intention or EV technology acceptance by investigating its relationship with extended theory-of-planned-behavior variables,
environmental concern, and consumers’ willingness to pay more for an EV. Among the study’s interesting results are that
attitude toward EVs has the most significant influence on EV purchase intention and that environmental concern more strongly
influences EV purchase intention and willingness to pay more for an EV in females than in males. How the variable of
environmental concern affects consumers’ willingness to pay more for an EV in the developing-country context is a new
addition to the body of literature on EV purchase intention.
To conclude, as EVs emit a low level of carbon compounds, we may conjecture that the consumer reaction to their adoption in
developing countries may allow such countries to leapfrog into environment-friendly and sustainable societies (Sharma et al.,
2021). The wide use of EVs can abate health hazards and climate change due to their low GHG emissions, paving the way for
a safe environment and healthy humans.
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Appendix: Scale items
Attitude: Ajzen (1991)
1 It is environment-friendly to buy EV.
2 It is fuel-efficient to purchase EV.
Subjective Norm: Ajzen (1991)
3. Most people who are important to me think I should use EV.
4. Because I care about the people whom I value influence me to use EV.
Perceived Behavioral Control: Ajzen (1991)
5. I can buy EV if I want.
6. It would be easier for me to buy EV.
7. I am confident to buy EV if it were entirely up to me.
8. I am confident that I will be able to buy EV.
9. It is mostly up to me to buy or not to buy EV.
10. I have personal control to feel over buying EV.
11. I have full control over buying EV.
Purchase Intention: Ajzen (1991)
12. I intend to purchase EV in the future.
13. I will try to consider buying EV.
14. I plan to switch my FFV with EV.
Willingness-to-Pay more: Moser (2015)
15. I accept to pay 10% more for EV.
16. I am willing to pay 10% more for EV.
17. I show my willingness to spend extra amount of Rs.300,000 for EV.
Environmental Concern: Ramayah et al. (2012) and Kumar et al. (2017)
18. I think environmental problems are becoming more and more serious in recent years.
19. Pakistan’s environment is my major concern.
20. I am emotionally involved in environmental protection issues in Pakistan.
21. I often think about how the environmental quality in Pakistan can be improved.
