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Citation: Zhou, Y.; Dai, W.; Xiao, M.
Beyond the Battery: The Impact of
Cultural Factor on Electric Vehicle
Consumers’ Service Quality
Expectations in Dealerships. World
Electr. Veh. J. 2025,16, 229. https://
doi.org/10.3390/wevj16040229
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Article
Beyond the Battery: The Impact of Cultural Factor on Electric
Vehicle Consumers’ Service Quality Expectations in Dealerships
Yang Zhou 1,*, Wanwen Dai 2and Miao Xiao 3
1School of Management, Suzhou Vocational University, Suzhou 215104, China
2Business School, Nanjing University, Nanjing 210093, China
3Faculty of Economics, Yamaguchi University, Yamaguchi 753-0841, Japan
*Correspondence: 92108@jssvc.edu.cn
Abstract: This research investigates the impact of cultural dimensions on service quality
expectations in electric vehicle (EV) dealerships. Grounded in Hofstede’s cultural theory
and employing a data-driven approach, the study utilizes a series of robust analytical
techniques, including the SVM algorithm, factor analysis, and ANOVA. Through a compre-
hensive analysis of EV customers’ expectations for expertise, empathy, and responsiveness,
the findings reveal that cultural dimensions significantly shape service quality expectations,
regardless of the service provider’s gender. Notably, consumers with a stronger masculine
orientation have lower expectations for expertise but higher expectations for empathy
than those with a stronger feminine orientation. These findings challenge the traditional
emphasis on gender as a key factor in service quality expectations and underscore the need
to incorporate cultural values in service strategy design and quality improvement in the
EV industry.
Keywords: electric vehicle; cultural dimensions; service quality expectations; service
marketing; data-driven perspective
1. Introduction
Facing the severe challenge of global climate change, the 2015 Paris Agreement set a
2
◦
C warming limit, driving worldwide efforts towards carbon neutrality by mid-century.
Despite over 150 nations pledging to achieve carbon neutrality goals, the World Meteoro-
logical Organization (WMO) reports a 1.55
◦
C rise in global temperatures, underscoring the
urgency for systemic changes in energy, transportation, and industrial sectors [
1
,
2
]. During
this shift towards sustainable development, electric vehicles (EVs), as a pivotal technology
for reducing carbon emissions and enhancing energy efficiency, have been thrust to the
forefront of this green revolution, emerging as a crucial driver in the realization of global
climate objectives [
3
]. Supported by their environmental and technological advantages, the
EV market has experienced remarkable growth, with nearly 14 million new registrations in
2023, bringing the total number of EVs on the road to 40 million. EVs comprise 18% of all
car sales now, up from 14% in 2022 and 2% in 2018, indicating rapid global adoption [
4
].
This rapid adoption has intensified competition in the automotive industry, leading to a
significant rise in product commoditization and a growing emphasis on service quality
as a key differentiation [
5
–
9
]. Modern consumers, particularly the younger generation,
place a greater emphasis on environmental sustainability, intelligence, and personalized
experiences [
10
]. Their focus extends beyond the inherent quality of the vehicle to en-
compass aftermarket services and the overall purchasing experience [
11
,
12
]. This shift
necessitates more attention towards service quality and experiential customer journeys
World Electr. Veh. J. 2025,16, 229 https://doi.org/10.3390/wevj16040229
World Electr. Veh. J. 2025,16, 229 2 of 22
in the marketing strategies of EV enterprises [
13
]. In this context, understanding what
customers seek in terms of service quality has become critical for EV enterprises aiming to
gain a competitive edge.
While research on service quality has flourished since the 1980s, the standardization
of service delivery remains a significant challenge. A fundamental question underpinning
this challenge is the following: what do customers expect from services? Identifying these
expectations is crucial for developing effective service quality measurement scales [14,15].
Although prior research has explored service quality expectations from both company and
customer perspectives, the role of cultural factors in shaping these expectations remains
underexplored [
16
]. In particular, the impact of one dimension of the “cultural perspec-
tive”, namely “masculinity/femininity”, on consumer behavior has not been sufficiently
clarified [
17
]. This is a field that requires further research accumulation. The purpose of this
study is to examine what service customers seek from a cultural perspective, specifically
focusing on expectations of service quality in the EV sector. Particular attention will be
given to the relationship between the cultural dimension of “masculinity/femininity”,
which is controversial in prior research.
The study is structured as follows. In Section 2, through a review of prior research,
the problem setting of this study is clarified. In Section 3, six hypotheses are formulated
to explore how cultural orientation and service provider gender influence expectations
for expertise, empathy, and responsiveness in the context of EV dealerships. Section 4
provides a detailed description of the research methodology, including data collection,
measurement instruments, and data analysis methods. Section 5concludes the research
findings and reveals the impact of cultural factors on electric vehicle consumers’ service
quality expectations. Section 6presents the insights identified in this study and offers
practical guidance for service strategy design and quality improvement in the electric
vehicle industry.
2. Literature Review
The rapid growth of the electric vehicle (EV) market has heightened the importance of
understanding service quality and customer expectations in this emerging sector. Due to
differences in product characteristics, services for electric vehicles (EVs) and traditional
internal combustion engine (ICE) vehicles differ significantly in areas such as customer
education, test-drive experiences, and after-sales services [
18
–
20
]. However, current re-
search on service marketing in the automotive industry has predominantly focused on
after-sales services and charging-related services, with limited attention given to in-store
service experiences [10,21–24]. Given these unique characteristics, a deeper assessment of
service quality and the study of customer expectations are essential, especially as the EV
market continues to rapidly expand [11,12,25,26].
Research on service quality commenced in the United States in the mid-1980s. The
focus of this research has been on the determinants of service quality and how customers
evaluate service quality based on their perceptions of service quality [
27
–
29
]. Early studies
by Zeithaml, Berry, and Parasuraman (1993) initially summarized the determinants of ser-
vice quality into 10 dimensions [
30
]. A pivotal contribution to this field is the SERVQUAL
model refined by Parasuraman et al. into five core dimensions of service quality: tangi-
bles, reliability, responsiveness, assurance, and empathy [
31
,
32
]. While SERVQUAL has
become a cornerstone for assessing service quality, recent studies highlight its limitations in
cross-cultural contexts, thereby emphasizing the adaptations to account for cultural differ-
ences [
33
]. Cultural differences have emerged as a critical factor in shaping service quality
expectations [
16
,
34
–
38
]. For instance, in the banking sector, empathy has been identified as
a dominant factor in customer satisfaction across diverse cultural settings [
17
]. Similarly, in
World Electr. Veh. J. 2025,16, 229 3 of 22
public transportation, researchers have proposed linking “culture” as an additional dimen-
sion with the SERVQUAL model to better capture commuters’ unique expectations [
39
].
These adaptations underscore the importance of integrating cultural nuances into service
quality frameworks.
In the field of service marketing, a seminal study that attempted to address this
issue was conducted by Donthu and Yoo in 1998, which examined the cultural influences
on expectations of service quality [
16
]. In their paper, Donthu et al. sought to clarify
whether customers’ expectations of service quality differ across cultures by analyzing
culture through four dimensions: power distance, uncertainty avoidance, individualism-
collectivism, and long-term orientation. They linked customers’ expectations of service
quality to culture and distributed questionnaires in four countries, conducting an analysis
of variance based on the survey results [
16
]. Their findings suggested that customers from
cultures characterized by low power distance, individualism, uncertainty avoidance, and
short-term orientation have higher overall expectations of service quality. However, we
identify two issues with Donthu et al.’s research. The first is their failure to incorporate the
dimension of “masculinity/femininity” into their cultural analysis. They considered this
dimension unrelated to “expectations of service quality”, but insufficient explanation and
justification were provided for this standpoint, which leaves a gap in understanding how
gender-related cultural values shape service expectations. The second issue is that while
they examine overall expectations of service quality, from a practical business perspective,
their findings may be difficult to apply.
Cultural Factor (Masculinity/Femininity) and Service Quality Dimensions
Prior studies have investigated the relationships between cultural dimensions (mas-
culinity/femininity) and specific SERVQUAL dimensions, revealing mixed findings. In
masculine cultures, consumers often prioritize tangibles as symbolic indicators of status,
while empathy becomes critical in feminine cultures where interpersonal harmony is val-
ued [
17
,
33
]. However, the role of the “masculinity/femininity” dimension in shaping
service expectations remains unresolved, particularly in contexts where the gender of the
service provider is a variable.
The link between cultural dimensions, particularly the masculinity/femininity di-
mension, and the SERVQUAL model has increasingly become a focal point in subsequent
academic research. For example, Furrer et al. (2000) found that masculine customers
tend to value tangibles and responsiveness more when served by female providers, rather
than expertise [
17
]. Similarly, Tsoukatos et al. (2007) observed a negative relationship
between masculinity and the dimensions of reliability and assurance in Greece’s insurance
industry [
40
]. These findings imply that cultural dimension (masculinity/femininity) may
influence the salience of certain service quality dimensions in a specific field. However,
existing studies have been limited to single-gender service provider contexts, focusing
exclusively on either male or female providers. This narrow approach limits our under-
standing of how cultural dimensions (masculinity/femininity) operate across different
gender contexts. To address this gap, future studies should incorporate gender diversity
among service providers to better understand the relationship between cultural dimensions
(masculinity/femininity) and service quality expectations.
Moreover, the role of cultural dimensions (masculinity/femininity) appears context
dependent. Kueh et al. (2007) found no significant relationship between masculinity and
service expectations in Malaysia’s fast-food industry, where standardized processes and job
competence overshadow gender role effects [
41
]. This contrasts with Tsoukatos et al. (2007)
findings in Greece’s insurance sector, which highlights a critical context factor: industry
type [
40
]. Industry characteristics (e.g., technology intensity in EV services vs. labor-centric
World Electr. Veh. J. 2025,16, 229 4 of 22
processes in fast food) may reconfigure how cultural dimensions (masculinity/femininity)
interact with the provider’s gender. For instance, in technology-driven sectors where
expertise is paramount (e.g., EV services), masculine customers may prioritize the service
provider’s competence (e.g., expertise) over gender—a dynamic potentially inverted in
traditional service settings.
This contradiction highlights a critical gap in the literature regarding the impact of
the “masculinity/femininity” dimension on service expectations. The exclusion of this
dimension in Donthu et al.’s study has indeed sparked significant debate in subsequent
research, underscoring the need for further investigation into how cultural dimensions
(masculinity/femininity) shape service quality perceptions [42].
In summary, while the SERVQUAL model provides a foundational framework for
assessing service quality, its application in cross-cultural contexts requires a deeper integra-
tion of cultural dimensions, particularly “masculinity/femininity”. Existing research on
this dimension is both limited and contradictory, with some studies suggesting it influences
the importance of specific service quality dimensions (e.g., tangibles, responsiveness) and
others dismissing its relevance altogether. This controversy highlights three critical gaps:
(1) the unresolved role of cultural dimension (masculinity/femininity) in service expec-
tations (e.g., expertise, empathy), (2) contextual boundedness: the influence of industry
type, and (3) the potential role played by the gender of service providers in the context of
cultural influences on expectations of service delivery.
To address this gap, this study focuses on the “masculinity/femininity” dimension
and its impact on service quality expectations in the context of electric vehicle (EV) ser-
vices. Specifically, this research addresses the following question: how does the cultural
dimension of masculinity/femininity influence expectations for expertise, empathy, and
responsiveness in electric vehicle (EV) services, and do these expectations vary based on
the gender of the service provider? This study extends prior research in two ways. First,
we investigate this cultural dimension within the emerging context of electric vehicle (EV)
services—a technology-driven industry where cultural dynamics may differ from tradi-
tional sectors (e.g., banking, insurance, fast-food industry). Second, we examine whether
the gender of service providers affects the relationship between cultural values and ser-
vice expectations, addressing the gap identified in studies that focused on single-gender
contexts [
17
,
40
]. By integrating cultural theory with industry-specific context, this research
advances both theoretical and practical understanding of service quality in the EV market.
3. Research Hypotheses
Research on gender-role stereotypes indicates that the gender of the service provider
is a factor influencing perceived service quality [
43
–
46
]. Building on this foundation,
this study intends to examine the relationship between consumers’ cultural dimension
(masculinity/femininity) and their service quality expectations in the EV sector, specif-
ically distinguishing male and female service providers as the subjects of investigation.
This research is particularly relevant because vehicle purchases are not purely rational
decisions—they are deeply embedded in cultural and symbolic meanings, making the in-
teraction between cultural values and service expectations a critical area of exploration [
47
].
Hofstede’s cultural dimensions theory identifies masculinity/femininity as a key dimen-
sion measuring cultural tendencies in a society. While gender/sex refers to biological
classification, masculinity/femininity represents culturally determined values and traits
that transcend biological boundaries, highlighting their nature as cultural constructs rather
than biological classifications. These cultural traits can be exhibited or valued by indi-
viduals regardless of their biological sex or gender identity, and they play a significant
role in shaping perceptions and behaviors in service contexts [
48
]. Individuals with “femi-
World Electr. Veh. J. 2025,16, 229 5 of 22
nine” values tend to align with the perspective that social roles of gender overlap in social
life [
49
–
51
]. In other words, it is believed that both men and women possess characteristics
such as being modest, kind, and attentive to the quality of life [
16
,
50
,
51
]. In contrast, those
with masculine cultural values emphasize distinct gender roles, associating men with
assertiveness, ambition, and material success, while expecting women to exhibit greater
warmth and be more modest, kind, and concerned with the quality of life compared to
men [16,50–52].
Expertise, a core dimension of service quality, reflects employees’ depth of knowledge
and skills in addressing customer inquiries, resolving issues, and providing accurate
information throughout the customer journey [
17
,
30
,
32
]. Within the EV industry, expertise
additionally requires the ability to instill trust and confidence through professionalism,
competence, and consistent behavior [
24
]. Given the innovative nature of this sector,
expertise is further demonstrated by the capacity to explain technical concepts clearly while
addressing range anxiety and safety concerns. This includes ensuring reliability, fostering a
sense of security in transactions, and consistently delivering knowledgeable, professional
interactions across the entire customer journey [
14
]. When evaluating service providers,
it is conceivable that variations in expected standards may arise based on the values of
the customers and the gender of the service providers [
49
,
53
–
55
]. For instance, when the
service provider is female, the “feminine” group may expect more specialized answers and
explanations compared to the “masculine” group [
17
]. On the other hand, if the service
provider is male, the “masculine” group may anticipate more specialized answers and
explanations compared to the “feminine” group. Therefore, we propose the following:
H1a. When the service provider of EV is female, the “feminine” group will have higher expectations
for expertise compared to the “masculine” group.
H2a. When the service provider of EV is male, the “masculine” group will have higher expectations
for expertise compared to the “feminine” group.
Empathy, a critical dimension of the SERVQUAL model, refers to the provision of car-
ing, individualized attention to customers. It reflects the extent to which service providers
understand and cater to their unique needs, concerns, and preferences [
17
,
31
,
40
]. In the
context of EV services, empathy involves understanding and alleviating range anxiety,
delivering personalized care during test drives, and tailoring services to create a sup-
portive experience that addresses customers’ specific concerns [
24
]. By offering attentive,
customized interactions throughout the ownership journey, empathy fosters emotional
connections and enhances customer satisfaction [
33
]. Building on Hofstede’s cultural di-
mensions framework, we examine how masculinity/femininity values shape divergent
empathy expectations. Based on this theory, we consider that consumers with masculine
cultural orientations tend to maintain traditional gender role expectations, anticipating
stronger empathy behaviors from female service providers [
56
]. When service providers
are male, the pattern reverses. This theoretical perspective yields two hypotheses regarding
empathy expectations in EV service encounters:
H1b. When the service provider of EV is female, the “masculine” group will have higher expectations
for empathy compared to the “feminine” group.
H2b. When the service provider of EV is male, the “feminine” group will have higher expectations
for empathy compared to the “masculine” group.
World Electr. Veh. J. 2025,16, 229 6 of 22
Responsiveness, a fundamental service quality dimension, refers to the attitude and
ability of service providers to assist customers, actively address their needs, and strive to
fulfill their requests [
17
,
31
,
40
]. It encompasses not only the prompt reaction to customer
inquiries, the timely service delivery but also the willingness to help customers resolve
problems and the clear communication of service timelines and progress. In the EV context,
responsiveness particularly emphasizes timeliness, proactiveness, and transparency in
communication, making it a critical dimension of service quality [
24
]. Building on Hof-
stede’s cultural theory and supported by Furrer et al. (2000) and Tsoukatos et al. (2007),
we propose that responsiveness expectations vary based on the consumers’ cultural val-
ues [
17
,
40
]. If the employee is female, the “feminine” group may have higher expectations
for customer service compared to the “masculine” group. Conversely, if the employee is
male, the “masculine” group may have higher expectations for customer service compared
to the “feminine” group. Therefore, the following hypotheses are constructed:
H1c. When the service provider of EV is female, the “feminine” group will have higher expectations
for customer service responsiveness compared to the “masculine” group.
H2c. When the service provider of EV is male, the “masculine” group will have higher expectations
for customer service responsiveness compared to the “feminine” group.
4. Methodology
4.1. Hypothesis Framework
This study proposes a comprehensive hypothesis framework that examines the impact
of cultural dimensions in shaping service quality expectations within the EV industry.
The framework is organized around two key dimensions: (1) service provider gender
(female/male) and (2) customer’s cultural dimensions: (Masculinity/Femininity) and pre-
dicts differential effects across three service quality dimensions—expertise, empathy, and
responsiveness. Specifically, the framework posits that when the service provider is female,
feminine customers will have higher expectations for expertise (H1a) and responsiveness
(H1c), while masculine customers will expect higher empathy (H1b). Conversely, when the
service provider is male, masculine customers will exhibit higher expectations for exper-
tise (H2a) and responsiveness (H2c), whereas feminine customers will prioritize empathy
(H2b). These hypotheses are grounded in Hofstede’s cultural theory and role congruity
theory, which predicts distinct patterns of expectations based on the alignment (or misalign-
ment) between service provider gender and traditional gender roles. The framework is
summarized in Table 1, which provides a clear mapping of the hypothesized relationships.
Table 1. Hypothesis framework.
Service Provider
Cultural Dimension
Service Quality Dimensions Hypothesis
H1a Female Feminine Expertise ↑
H1b Female Masculine Empathy ↑
H1c Female Feminine Responsiveness ↑
H2a Male Masculine Expertise ↑
H2b Male Feminine Empathy ↑
H2c Male Masculine Responsiveness ↑
4.2. Participants and Procedure
To ensure questionnaire validity, a pilot study was conducted. First, a pre-check with
5 domain experts evaluated question design professionalism. Subsequently, a pilot test was
World Electr. Veh. J. 2025,16, 229 7 of 22
administered to 20 participants recruited from 5 dealerships in China. Participants met the
same criteria as the main study: having visited an EV dealership and either test-driven
or purchased an EV within the past year. They were invited to assess question clarity,
relevance, and survey flow. Based on their feedback, we revised 3 ambiguous questions
and replaced 5 phrases with more accessible language. The pilot results indicated high
clarity (90% of questions understood by >85% of participants), ensuring the final survey’s
suitability for the target population.
The main survey was carried out in 2024, wherein questionnaires were directly dis-
tributed to 396 participants recruited through multiple automobile sales centers and dealer-
ships in China, covering various electric vehicle (EV) brands to enhance the diversity of
the sample. Participants were screened based on specific criteria, including having at least
one test-drive experience or having purchased an EV within the past year, ensuring that all
respondents had direct experience with EV-related services.
The survey was administered in person at the sales centers and dealerships, partici-
pants were provided clear instructions on how to complete the questionnaire. Question-
naires were distributed in paper format and collected on-site to ensure immediate response
and minimize data loss. The survey took approximately 15 min to complete, and partici-
pants were informed about the purpose of the study and provided informed consent before
proceeding. To ensure data quality, incomplete responses and those with contradictory
information were excluded from the analysis. Out of the 396 questionnaires retrieved,
377 were deemed valid, yielding a response rate of 95.2%. Among the valid responses,
204 were from males and 169 from females, with 4 missing values for gender information.
This methodological approach ensured the quality, accuracy, and validity of the data, as
well as the representativeness and effectiveness of the research.
4.3. Survey Design
The survey was designed to assess cultural dimensions (masculinity/femininity) and
customer expectations for service providers in electric vehicle (EV) stores. It consisted of
four main sections:
1. Cultural Dimension: Five items measured respondents’ views on cultural dimen-
sions (masculinity/femininity) using a 7-point Likert scale.
2. Demographic Data: This section collected information on respondents’ gender,
experience with EV stores, and participation in test drives.
3. Expectations for Female Service Providers: Seventeen items evaluated customer
expectations for female service providers across various service aspects, using a 7-point
Likert scale.
4. Expectations for Male Service Providers: Seventeen items evaluated customer
expectations for male service providers across the same service aspects, using a 7-point
Likert scale.
The survey contained a total of 44 items, with independent variables focusing on
perceptions of cultural dimensions regarding gender roles (Part 1) and dependent variables
measuring expectations for service providers’ performance (Parts 3 and 4). The items were
selected based on a comprehensive literature review of cultural dimensions (masculin-
ity/femininity) and customer service expectations in automotive retail, supplemented by
input from professionals in the EV sector to ensure relevance and comprehensiveness. A
pilot test was conducted with a small group of respondents to refine the wording and
structure of the survey. Demographic data, including experience with EV stores and partic-
ipation in test drives, were collected to confirm that respondents met the selection criteria.
World Electr. Veh. J. 2025,16, 229 8 of 22
4.4. Measures
Constructs in this study were measured using well-established scales adapted from
prior literature or with revisions for the EV sector. The questionnaire included multiple
sections, each focusing on a specific dimension. A 7-point Likert scale (1 = strongly disagree,
7 = strongly agree) or (1 = Not at All Important, 7 = Most Important) was used for all items
unless otherwise noted. The reliability and validity of each scale were assessed, and the
internal consistency was calculated to ensure the reliability of the measures.
4.4.1. Service Quality Dimension
The dependent variables (e.g., expertise, responsiveness, and empathy) in this study
were derived from the SERVQUAL model and a service quality scale originally developed
for the retail industry by Dabholkar et al. (1996) [
57
], Park Sun-mi (2006) [
58
]. The focus was
narrowed to aspects of service quality related to employees. Additionally, considering that
the electric vehicle (EV) industry is a typical technology-driven sector where expertise is
paramount, the questionnaire was revised to include specific questions addressing the tech-
nical knowledge and professionalism of employees. The questionnaire items were adapted
to fit the EV store service context, resulting in a final questionnaire comprising 17 items.
Each item was measured using a 7-point Likert scale, ranging from “Most Important” to
“Not at All Important”. Exploratory factor analysis (EFA) was subsequently applied to these
17 items to assess the underlying structure of the data. The reliability, validity, and internal
consistency of each scale were evaluated based on factor loadings, cumulative variance,
and Cronbach’s
α
. The specific values of these metrics (factor loadings, cumulative vari-
ance, and Cronbach’s
α
) will be presented in the next section. The factor scores extracted
from the analysis, which represent expectations regarding the service quality provided by
employees, were utilized as the dependent variables in the subsequent analyses.
4.4.2. Cultural Dimension
This study draws upon Hofstede’s five-dimensional model as a theoretical foundation,
with a specific focus on the cultural dimension of “masculinity”, which has not been thor-
oughly examined in the context of individual customer values within EV dealerships [
59
].
While the original scale, developed by Hofstede in 1980, was designed to measure national
culture within the workplace, its direct application to assess individual customer percep-
tions poses challenges, as noted by Furrer et al. (2000) [
17
,
60
]. Thus, Furrer et al. tended to
avoid workplace-oriented questions in their research to mitigate the risk of committing an
ecological fallacy [17,61].
To address these limitations and gain a deeper understanding of how cultural di-
mensions influence customer expectations of service quality, this study employs a novel
approach using the Support Vector Machine (SVM) algorithm. The SVM algorithm, rooted
in statistical learning theory, represents a sophisticated supervised learning methodology.
The algorithm extracts a set of characteristic subsets from the training samples, ensur-
ing that the classification of these subsets corresponds to the segmentation of the entire
dataset. Extensive empirical evidence across diverse application domains substantiates
the SVM’s robust efficacy in resolving a wide array of complex classification tasks [
62
,
63
].
This classification is based on the extraction of cultural features from the data, providing a
direct assessment of cultural values as expressed by individuals in a natural setting. The
flowchart for cultural feature extraction and classification based on the SVM algorithm is
shown in Figure 1.
World Electr. Veh. J. 2025,16, 229 9 of 22
World Electr. Veh. J. 2025, 16, x FOR PEER REVIEW 10 of 21
Figure 1. Flowchart for cultural feature extraction and classification based on the SVM algorithm.
4.5. Analysis Strategy
The study employed a data-driven methodology, integrating Hofstede’s cultural
framework with advanced statistical techniques to uncover the nuanced influence of cul-
tural values on service quality expectations. Additionally, the study incorporated the SVM
(Support Vector Machine) algorithm to make classification. Following preprocessing and
feature extraction, the model was trained and evaluated to categorize respondents into
“masculine” and “feminine” groups, facilitating the classification of cultural features
within the data and enabling a deeper understanding of gender-specific cultural dynam-
ics.
Data preparation began with cleaning procedures to ensure data integrity. Incom-
plete responses and questionnaires with contradictory information were excluded from
the analysis. Factor scores extracted from exploratory factor analysis (EFA) were used for
subsequent analyses. Exploratory factor analysis (EFA) using principal component anal-
ysis with varimax rotation was conducted to validate the measurement structure. Factors
were retained based on eigenvalues greater than 1.0 and scree plot inspection. Items with
factor loadings below 0.40 were discarded, and a cumulative variance of at least 50% was
considered acceptable. The internal consistency of each scale was evaluated using
Cronbach’s α, with a threshold of 0.70 for acceptable reliability [33]. Pearson’s correlation
coefficients were calculated to examine bivariate relationships between variables (e.g., ex-
pertise, empathy, responsiveness) across gender groups, with significance levels set at p <
0.05. Hypothesis testing involved a combination of one-way ANOVA and cross-gender
Figure 1. Flowchart for cultural feature extraction and classification based on the SVM algorithm.
To illustrate the application of the SVM algorithm in cultural feature extraction and
classification, the SVM implementation will be elaborated in the following part. The
purpose of the elaboration is to enhance understanding of how SVM efficiently classi-
fies samples, thereby offering a theoretical underpinning for the direct assessment of
cultural dimensions.
Linearly Separable Case: Assume there exists a hyperplane
wx +b=
0 that can
separate two classes of samples. For positive class samples, it satisfies
wxi+b≥
1; for
negative class samples, it satisfies
wxi+b≤ −
1. Then, the classification function can be
defined as f(x)=sign(wx +b).
Optimal Hyperplane: An optimal hyperplane should have no misclassification and
the distance from the hyperplane to the nearest samples of both classes is not less than 1.
The optimal hyperplane can be found by minimizing the following objective function:
min
w,b
1
2∥w∥2(1)
subject to yi(wxi+b)≥1, where yiis the label (+1 or −1) of the sample xi.
Lagrange Dual Problem: Introducing Lagrange multipliers α, the original problem is
transformed into a dual problem:
max
α
n
∑
i=1
αi−1
2
n
∑
i=1
n
∑
j=1
αiαjyiyjxiTxj(2)
World Electr. Veh. J. 2025,16, 229 10 of 22
subject to ∑n
i=1αiyi=0 and αi≥0.
Kernel Function: When data are nonlinearly separable, kernel functions
Kxi,xj
can be used to map the data to a higher-dimensional space, making the data linearly
separable in the high-dimensional space. Common kernel functions include linear kernels,
polynomial kernels, Radial Basis Function (RBF) kernels, etc.
Decision Function: After solving for α, the decision function can be obtained:
f(x)=sign(
n
∑
i=1
αiyiK(xi,x)+b)(3)
Soft Margin: When data contains noise or cannot be perfectly linearly separated, slack
variables γiand penalty parameter Ccan be introduced to obtain soft margin SVM:
min
w,b,γ
1
2∥w∥2+C
n
∑
i=1
γi(4)
subject to yi(wxi+b)≥1−γiandγi≥0.
Building on the theoretical groundwork developed by Hofstede and the method-
ological advancements proposed by Donthu and Yoo (2011) [
60
], this study employs the
SVM-based classification to represent the cultural dimension of masculinity/femininity.
Through the process depicted in Figure 1, the classification results based on the SVM
algorithm indicate that there are 177 samples in the feminist group and 199 samples in the
masculinist group. The groups are used in subsequent analyses to explore the impact of
these cultural dimensions on expectations of service quality in EV dealerships, offering a
contemporary and data-driven perspective on cultural influences. The analysis strategy
was designed to systematically examine the relationships between cultural dimensions,
service quality expectations, and gender-specific dynamics in the context of electric vehicle
(EV) dealerships.
4.5. Analysis Strategy
The study employed a data-driven methodology, integrating Hofstede’s cultural
framework with advanced statistical techniques to uncover the nuanced influence of
cultural values on service quality expectations. Additionally, the study incorporated the
SVM (Support Vector Machine) algorithm to make classification. Following preprocessing
and feature extraction, the model was trained and evaluated to categorize respondents into
“masculine” and “feminine” groups, facilitating the classification of cultural features within
the data and enabling a deeper understanding of gender-specific cultural dynamics.
Data preparation began with cleaning procedures to ensure data integrity. Incomplete
responses and questionnaires with contradictory information were excluded from the
analysis. Factor scores extracted from exploratory factor analysis (EFA) were used for
subsequent analyses. Exploratory factor analysis (EFA) using principal component analysis
with varimax rotation was conducted to validate the measurement structure. Factors
were retained based on eigenvalues greater than 1.0 and scree plot inspection. Items with
factor loadings below 0.40 were discarded, and a cumulative variance of at least 50%
was considered acceptable. The internal consistency of each scale was evaluated using
Cronbach’s
α
, with a threshold of 0.70 for acceptable reliability [
33
]. Pearson’s correlation
coefficients were calculated to examine bivariate relationships between variables (e.g.,
expertise, empathy, responsiveness) across gender groups, with significance levels set at
p< 0.05
. Hypothesis testing involved a combination of one-way ANOVA and cross-gender
analysis [
16
,
64
,
65
]. One-way ANOVA was used to assess group differences in service
World Electr. Veh. J. 2025,16, 229 11 of 22
expectations based on “masculinity/femininity” groups, with significance levels set at
p< 0.05.
4.6. Setting and Sample
The study was conducted in the context of electric vehicle (EV) dealerships in China, a
rapidly growing market with increasing consumer demand for EV-related services. Data
collection took place at dealerships, where customers interact directly with sales and service
employees. This setting was chosen to capture real-world service quality expectations
in a technology-driven industry. To ensure a representative sample, the “mall intercept”
technique was employed [
40
]. This approach was selected because it allows for direct
interaction with potential respondents in a real-world setting, ensuring that participants had
recent and relevant experience with EV dealership services. Trained collectors approached
individuals at dealerships and invited them to participate in the survey [
12
]. Participants
were selected based on the following criteria:
1. They had visited an EV dealership;
2. They had either test-driven or purchased an EV within the past year.
The in-person survey approach not only ensured high response rates but also mini-
mized the risk of self-selection bias, which is often associated with online surveys. The
sample consisted of 377 valid responses from EV customers who met the selection criteria.
Participants were recruited through multiple automobile sales centers and dealerships
across various regions in China, ensuring geographic and brand diversity. The sample
included 204 males (54.1%) and 169 females (44.8%), with four missing values for gender
information. The sample’s characteristics indicate a balanced gender distribution and
urban consumer base, which is representative of the typical EV customer profile in China.
However, the study’s focus on EV customers with prior test-drive or purchase experience
may introduce selection bias, as their expectations might differ from those of potential
buyers. Additionally, the sample may not fully represent rural populations, limiting the
generalizability of the findings to these groups. Despite these limitations, the use of the
“mall intercept” technique and the inclusion of diverse brand contexts enhance the validity
of the findings within the EV dealership setting.
5. Hypothesis Testing and Result
5.1. Expectations of Service Quality from Employees: A Factor Analysis Approach
As pointed out in Section 2, prior studies exhibit a bias by concentrating exclusively
on female employees. To revise this bias, this study adopts a more inclusive approach by
measuring the expectation levels for service quality without regard to the gender of the
service providers, thereby encompassing both female and male employees in the analysis.
Factor Analysis and Scale Reliability
For verifying the hypotheses, an exploratory factor analysis (EFA) was conducted. Ex-
ploratory factor analysis using principal components with varimax rotation was conducted
to validate the measurement structure. The number of factors was determined based on
four criteria: (I) eigenvalues greater than 1, (II) cumulative contribution rate of 50% or
more, (III) a significant drop in eigenvalues, and (IV) interpretable from hypotheses. The
results of the factor analysis are shown in Tables 2–7. For female service providers, three
factors were extracted: expertise (5 items), empathy (4 items), and responsiveness (4 items).
Factor loadings exceeded the threshold of 0.40 [
66
,
67
], with 11 out of 13 items meeting
the preferred threshold of 0.50. The cumulative variance explained after rotation reached
53.29%, satisfying the criterion of
≥
50%. Cronbach’s alpha coefficients demonstrated
strong internal consistency: expertise (
α
= 0.86), empathy (
α
= 0.76), and responsiveness
World Electr. Veh. J. 2025,16, 229 12 of 22
(
α= 0.73
), all exceeding the 0.70 benchmark. Similar results were observed for male service
providers, with three factors extracted (cumulative variance = 53.02%). Cronbach’s alpha
coefficients revealed robust internal consistency: expertise (
α
= 0.87), empathy (
α
= 0.77),
and responsiveness (α= 0.70), exceeding the 0.70 benchmark.
Table 2. Eigenvalues (female).
Communality in Factor Analysis
Questionnaire Items Initial After Factor Extraction
s3-1 0.27 0.21
s3-2 0.37 0.41
s3-3 0.35 0.38
s3-4 0.54 0.63
s3-5 0.55 0.64
s3-8 0.33 0.42
s3-9 0.45 0.60
s3-10 0.38 0.44
s3-12 0.52 0.53
s3-13 0.66 0.73
s3-14 0.73 0.79
s3-15 0.70 0.75
s3-16 0.39 0.39
NOTE: Factor Extraction Method (Principal Components Analysis).
Table 3. Total variance explained (female).
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%Total % of
Variance
Cumulative
%Total % of
Variance
Cumulative
%
1 4.61 35.42 35.42 4.23 32.52 32.52 3.06 23.55 23.55
2 2.52 19.36 54.78 2.00 15.40 47.92 2.03 15.65 39.20
3 1.18 9.07 63.84 0.70 5.37 53.29 1.83 14.09 53.29
4 0.92 7.08 70.92
5 0.61 4.70 75.62
6 0.60 4.60 80.22
7 0.55 4.21 84.43
8 0.48 3.67 88.10
9 0.43 3.30 91.40
10 0.37 2.86 94.26
11 0.31 2.40 96.65
12 0.25 1.91 98.56
13 0.19 1.44 100.00
NOTE: Factor Extraction Method (Principal Components Analysis).
World Electr. Veh. J. 2025,16, 229 13 of 22
Table 4. Extracted factors and scale reliability (female).
Post-Rotation Factor Matrix
Questionnaire Items Factors
Cronbach’s α
EXP (F) EMP (F) RES (F)
S3-14 0.84 −0.03 0.3
0.86
S3-13 0.83 −0.07 0.18
S3-15 0.82 0.01 0.29
S3-12 0.65 −0.01 0.31
S3-16 0.5 0.34 0.15
S3-9 0.1 0.76 0.09
0.76
S3-10 0.08 0.65 0.09
S3-8 −0.03 0.65 0.06
S3-3 −0.12 0.6 0.07
S3-5 0.32 0.11 0.72
0.73
S3-4 0.34 −0.06 0.72
S3-2 0.24 0.2 0.56
S3-1 0.1 0.27 0.36
NOTE: Factor analysis (Principal Factor Method and varimax rotation).
Table 5. Eigenvalues (male).
Communality in Factor Analysis
Questionnaire Items Initial After Factor Extraction
s4-1 0.23 0.20
s4-2 0.33 0.37
s4-3 0.32 0.34
s4-4 0.53 0.64
s4-5 0.51 0.51
s4-8 0.33 0.41
s4-9 0.48 0.63
s4-10 0.46 0.54
s4-12 0.56 0.57
s4-13 0.68 0.72
s4-14 0.76 0.84
s4-15 0.68 0.71
s4-16 0.41 0.40
NOTE: Factor Extraction Method (Principal Components Analysis).
World Electr. Veh. J. 2025,16, 229 14 of 22
Table 6. Total variance explained (male).
Component
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%Total % of
Variance
Cumulative
%Total % of
Variance
Cumulative
%
1 4.72 36.33 36.33 4.34 33.38 33.38 3.23 24.81 24.81
2 2.50 19.26 55.59 2.01 15.50 48.87 2.04 15.70 40.51
3 1.11 8.51 64.10 0.54 4.15 53.02 1.63 12.51 53.02
4 0.86 6.61 70.70
5 0.66 5.07 75.77
6 0.60 4.60 80.37
7 0.57 4.35 84.72
8 0.50 3.82 88.55
9 0.39 3.01 91.56
10 0.36 2.79 94.34
11 0.31 2.42 96.76
12 0.25 1.93 98.69
13 0.17 1.31 100.00
NOTE: Factor Extraction Method (Principal Components Analysis).
Table 7. Extracted factors and scale reliability (male).
Post-Rotation Factor Matrix
Questionnaire Items Factors
Cronbach’s α
EXP (F) EMP (F) RES (F)
S4-14 0.89 −0.02 0.24
0.87
S4-13 0.82 −0.06 0.22
S4-15 0.8 −0.02 0.25
S4-12 0.64 −0.04 0.39
S4-16 0.54 0.29 0.18
S4-9 0.14 0.78 0.11
0.77
S4-10 0.05 0.72 0.14
S4-8 −0.05 0.64 0.05
S4-3 −0.07 0.57 0.09
S4-4 0.39 −0.04 0.7
0.7
S4-5 0.41 0.12 0.57
S4-2 0.25 0.22 0.51
S4-1 0.09 0.2 0.39
NOTE: Factor analysis (Principal Factor Method and varimax rotation).
5.2. Correlation Analysis Between Variables
To investigate the relationship between “masculinity” and employees’ expectations of
service, the Pearson’s correlation coefficient was calculated. The correlation is as shown
in Table 8below. Statistically significant correlation coefficients are indicated with (*).
Pearson’s correlation analysis revealed significant relationships between variables (Table 8).
Expertise (EXP(F)) exhibited a strong positive correlation with responsiveness (RES(F))
World Electr. Veh. J. 2025,16, 229 15 of 22
(r = 0.54, p< 0.05). Similarly, empathy (EMP(F)) correlated significantly with responsive-
ness (RES(F)) (r = 0.24, p< 0.05). For male service providers, expertise (EXP(M)) and
responsiveness (RES(M)) also showed a strong positive association (r = 0.58, p< 0.05).
Notably, cross-gender correlations were observed: expertise for female service providers
(EXP(F)) strongly correlated with expertise for male employees (EXP(M)) (r = 0.81, p< 0.05),
while empathy for female employees (EMP(F)) correlated with empathy for male service
providers (EMP(M)) (r = 0.90, p< 0.05).
Table 8. Correlation analysis between variables.
Pearson Correlation Coefficient MAS EXP (F) EMP (F) RES (F) EXP (M) EMP (M) RES (M)
MAS 1.00 −0.10 0.11 * −0.08 −0.05 −0.12 * −0.07
EXP (F) −0.10 1.00 0.10 * 0.54 ** 0.81 ** 0.09 0.54 **
EMP (F) 0.11 * 0.10 * 1.00 0.24 ** 0.12 * 0.90 ** 0.92 **
RES (F) −0.08 0.54 ** 0.24 ** 1.00 0.58 ** 0.26 ** 0.92 **
EXP (M) −0.05 0.81 ** 0.12 ** 0.58 ** 1.00 0.11 * 0.58 **
EMP (M) 0.12 * 0.09 0.90 ** 0.26 ** 0.11 * 1.00 0.26 **
RES (M) −0.07 0.54 ** 0.25 ** 0.92 ** 0.58 ** 0.26 ** 1.00
NOTE: EXP (F) = expertise (female), EMP (F) = empathy (female), RES (F) = responsiveness (female),
EXP (M) = expertise
(male), EMP (M) = empathy (male), RES (M) = responsiveness (male), MAS = masculinity,
N = 302. * p< 0.10. ** p< 0.05.
ANOVA Analysis
In this study, in order to investigate whether the cultural dimension of “masculin-
ity/femininity” influences the level of EV consumers’ service expectations, a one-way anal-
ysis of variance (ANOVA) approach was employed by a between-subjects design [
68
,
69
].
ANOVA was conducted, with the categorized groups serving as the independent variable
and the service expectation scales of “expertise”, “empathy”, and “responsiveness” for
service providers as the dependent variables. The results of this analysis are presented as
shown in Tables 9and 10. For female employees, significant group differences emerged: the
“Feminine” group reported higher expectations for expertise (F = 9.32, p< 0.01), while the
“Masculine” group scored higher on empathy (F = 5.48, p< 0.05). However, no significant
differences were observed for responsiveness (F = 2.30, p= 0.13). These results support
H1a and H1b, as the findings align with the hypotheses that the “Feminine” group would
prioritize expertise, and the “Masculine” group would emphasize empathy. For male
employees, similar trends were observed: significant group differences were found in both
expertise (F = 5.08, p< 0.05) and empathy (F = 8.81, p< 0.01). However, responsiveness
again showed no significant difference (F = 2.91, p= 0.09). The results contrary to H2a and
H2b require insightful discussion. Nevertheless, the relationship between culture and its
influence on expectations of service quality has been clearly demonstrated.
In summary, the findings from this study are as follows: When the employees are
female, (1) groups characterized by higher levels of masculinity exhibit lower expectations
of expertise compared to groups characterized by higher levels of femininity. (2) Groups
characterized by higher levels of masculinity have higher expectations of empathy com-
pared to groups characterized by higher levels of femininity. When the employees are
male, (3) groups characterized by higher levels of masculinity exhibit lower expectations of
expertise compared to groups characterized by higher levels of femininity. (4) Groups char-
acterized by higher levels of masculinity have higher expectations of empathy compared to
groups characterized by higher levels of femininity. Furthermore, previous research has
shown a strong tendency to focus on the gender of the service provider when examining
World Electr. Veh. J. 2025,16, 229 16 of 22
the impact of culture (specifically, the “masculinity” dimension) on service expectations. In
contrast, this study reveals that regardless of the employee’s gender, expectations of service
quality (expertise and empathy) exhibit the same directional trends. In other words, it has
been confirmed that culture (masculinity/femininity) directly influences service quality
expectations, independent of the employee’s gender in the EV sector.
Table 9. Descriptive statistics based on the typology of expectations.
Mean SD 95 CI Min Max
EXP (F)
FEM 5.85 1.02 5.70 6.01 1 7
MAS 5.54 0.97 5.40 5.68 2.6 7
SUM 5.69 1.01 5.59 5.79 1 7
EMP (F)
FEM 3.51 1.24 3.33 3.70 1 6.5
MAS 3.81 1.24 3.64 3.98 1 7
SUM 3.67 1.24 3.54 3.80 1 7
RES (F)
FEM 5.43 1.06 5.28 5.59 1 7
MAS 5.27 0.97 5.14 5.41 1 7
SUM 5.35 1.02 5.25 6.45 1 7
EXP (M)
FEM 5.91 1.00 5.76 6.06 1 7
MAS 5.67 1.01 5.53 5.82 2.6 7
SUM 5.79 1.01 5.68 5.89 1 7
EMP (M)
FEM 3.57 1.25 3.34 3.71 1 6.75
MAS 3.92 1.28 3.74 4.10 1 7
SUM 3.73 1.28 3.60 3.86 1 7
RES (M)
FEM 5.39 1.05 5.24 5.55 1 7
MAS 5.22 0.97 5.08 5.35 1 7
SUM 5.30 1.01 5.20 5.40 1 7
Table 10. ANOVA analysis for hypothesis testing.
SS df MS F Sig.
EXP (F)
Between-group 9.27 1.00 9.27
9.32 0.002 **
Within-group 372.04 374.00 1.00
Total 381.30 375.00
EMP (F)
Between-group 8.38 1.00 8.38
5.48 0.02 *
Within-group 571.38 374.00 1.53
Total 579.75 375.00
RES (F)
Between-group 2.38 1.00 2.38
2.30 0.13
Within-group 385.21 373.00 1.03
Total 387.59 374.00
EXP (M)
Between-group 5.12 1.00 5.12
5.08 0.03 *
Within-group 374.96 372.00 1.01
Total 380.08 373.00
World Electr. Veh. J. 2025,16, 229 17 of 22
Table 10. Cont.
SS df MS F Sig.
EMP (M)
Between-group 14.14 1.00 14.14
8.81 0.003 **
Within-group 597.18 1.61
Total 611.32 373.00
RES (M)
Between-group 2.96 1.00 2.96
2.91 0.09
Within-group 378.96 372.00 1.02
Total 381.92 373.00
NOTE: * p< 0.10. ** p< 0.05.
6. Discussion
This study contributes to the growing body of literature on the role of cultural di-
mensions, particularly masculinity/femininity, in shaping service quality expectations. By
focusing on the electric vehicle (EV) sector, a rapidly evolving technology-driven industry,
our research offers new insights into how cultural values influence consumer behavior
and service expectations. These findings contribute to the literature by extending cultural
dimension theory to electric vehicle markets, and proposing solutions to critical cross-
cultural adoption barriers for EV manufacturers [
70
–
72
]. The analysis reveals significant
evidence of a link between cultural value orientations and customers’ service expectations,
advancing theoretical understanding in this emerging domain.
First, our research addresses a critical gap in the literature by examining the mas-
culinity/femininity dimension, which has been largely overlooked or excluded in prior
studies. While Donthu et al. (1998) explicitly excluded this dimension from their cultural
analysis, subsequent research has produced contradictory conclusions [
16
]. For instance,
Furrer et al. (2000) highlighted its significance in tangible service contexts, whereas Kueh
et al. (2007) found no substantial effects in standardized service environments [
17
,
41
]. This
study reveals that the cultural dimension of masculinity/femininity acts as a foundational
value system, shaping service expectations in technology-intensive sectors. Importantly,
this influence operates independently of the service provider’s gender, directly addressing
our research question and offering new insights into the role of culture in technology-
driven industries.
Moreover, our dual-provider experimental design extends prior research by simulta-
neously analyzing the impact of both cultural dimensions and service provider gender. The
validation of H1a and H2a (lower expertise expectations in high-masculinity groups) con-
firms the findings of Furrer et al. (2000) and Tsoukatos et al. (2007) [
17
,
40
]. However, unlike
single-gender designs (e.g., Furrer et al., 2000 [
17
]; Tsoukatos et al., 2007 [
40
]), our study
reveals that cultural dimensions directly influence service quality expectations, regardless
of the service provider’s gender. Our findings also extend Sarhan and Shishany (2020) by
showing that cultural effects on expertise and empathy expectations are consistent across
both male and female employees, highlighting the universal impact of culture on service
quality expectations [
38
]. This aligns with Kueh et al. (2007) observation that standardized
processes can neutralize gender effects, but with a critical distinction: in the EV industry,
the diminished role of gender could stem from the tech-intensity effect, where the complex-
ity of smart technologies amplifies the need for interpersonal assurance, particularly in
masculine cultures [
41
]. This finding suggests that industry-specific characteristics, such as
technological complexity, can override traditional gender-based expectations, repositioning
empathy as a critical compensatory mechanism in tech-driven services.
Finally, our findings also shed light on the limitations of traditional service qual-
ity models, such as SERVQUAL, in capturing the interplay between cultural values and
World Electr. Veh. J. 2025,16, 229 18 of 22
technology-driven service expectations. While SERVQUAL has been foundational in ser-
vice quality research [
14
,
30
], its traditional dimensions (e.g., tangibles, reliability) may not
fully capture the interplay between cultural values and technology-driven service expecta-
tions. In the EV industry, where expertise and empathy are paramount, cultural dimensions
(masculinity/femininity) redefine the salience of SERVQUAL dimensions. For example,
our finding that masculine cultures value empathy over expertise challenges SERVQUAL’s
traditional hierarchy, suggesting context reshapes the importance of service quality dimen-
sions. This aligns with critiques of SERVQUAL’s cross-cultural relevance and highlights
the need for adaptable frameworks that consider technological and cultural differences [
73
].
Traditional tools like SERVQUAL may need adjustments to suit diverse contexts.
These theoretical insights directly inform practical strategies for the EV sectors. Based
on these findings, we propose that when developing service strategies, it is crucial to
thoroughly consider the influence of cultural dimensions, rather than focusing superficially
on gender alone. By aligning service delivery with these cultural dimensions, EV brands
can ensure a culturally sensitive customer experience and boost their global competitive-
ness. For example, the discovery that masculine cultures value empathy over expertise
suggests that EV service marketing should prioritize emotional engagement over technical
prowess. Transforming test drives into immersive experiences that highlight the emotional
benefits of EV ownership can more effectively resonate with consumers in masculine cul-
tural settings [
12
,
74
]. Moreover, listening to consumer feedback during these interactions
and addressing concerns in real-time—such as demonstrating fast-charging capabilities
or showcasing a reliable charging network—can alleviate anxieties and strengthen the
emotional connection between the brand and the consumer [
75
]. This approach not only
meets the empathy expectations identified in the research but also sets EV brands apart in
a competitive market. Additionally, personalized services are a direct response to empathy
expectations. Offering one-on-one tutorials on EV operation, providing tailored charging
solutions based on individual driving patterns, or designing customized maintenance
plans can address the specific needs and concerns of EV consumers. These personalized
services not only align with the heightened empathy expectations in masculine cultures
but also build trust and reassurance, which are essential for fostering long-term customer
relationships. By demonstrating a deep understanding of consumer challenges, such as
range anxiety or charging accessibility, service providers can create a sense of emotional
support that enhances customer satisfaction and loyalty [
10
]. This strategic focus on cultural
nuances and personalized care can significantly elevate the overall customer experience
and solidify the brand’s position in the global market.
This research focuses on the direct influence of cultural dimension (masculin-
ity/femininity) on service quality expectations in technology-driven industries. However,
given the complexity of consumer behavior, other factors such as emotional experience [
76
],
brand trust/identification [
77
,
78
], and brand loyalty may also shape service quality percep-
tions [
42
,
79
]. Therefore, further research on this topic requires in-depth analysis, particu-
larly on the roles of emotional engagement and brand-related factors (e.g., brand trust and
loyalty) in mediating or moderating the relationship between cultural dimensions, service
quality expectations, and customer satisfaction. Second, limited by the scope of our study,
we only examined the direct impact of cultural dimension (masculinity/femininity) with-
out exploring how contextual factors, such as industry-specific dynamics or technological
advancements, might influence these relationships. Future research should expand the
boundaries of this field by investigating how individual characteristics (e.g., demographic
variables, personal preferences) [
6
,
80
] and situational factors (e.g., service environment,
technological advancements) interact with cultural dimensions to affect consumer behav-
ior [
81
,
82
]. Such explorations would not only deepen theoretical understanding but also
World Electr. Veh. J. 2025,16, 229 19 of 22
provide actionable insights for stakeholders in the EV sector and other technology-driven
industries to design personalized marketing and service strategies.
Author Contributions: Conceptualization, Y.Z.; methodology, Y.Z.; formal analysis, M.X.;
writing—original
draft preparation, Y.Z.; writing—review and editing, M.X. and W.D.; supervi-
sion, W.D. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by [Jiangsu Provincial Universities Philosophy and Social Sciences
Research Project] grant number [2022SJYB1631] And [National Natural Science Foundation of China
General Program] grant number [No. 71972098] And [Jiangsu Provincial Federation of Social Sciences
Applied Research Excellence Project] grant number [2022SYC-142].
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki, and approved by the Institutional Review Board of Suzhou Vocational University
(protocol code SVU20231023), with the approval granted on 23 October 2023.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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