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Citation: Li, Z.; Tulcanaza-Prieto,
A.B.; Lee, C.W. Effect of
E-Servicescape on Emotional
Response and Revisit Intention in an
Internet Shopping Mall. J. Theor. Appl.
Electron. Commer. Res. 2024,19,
2030–2050. https://doi.org/10.3390/
jtaer19030099
Academic Editor: Carla Ruiz Mafe
Received: 9 April 2024
Revised: 4 July 2024
Accepted: 30 July 2024
Published: 5 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Effect of E-Servicescape on Emotional Response and Revisit
Intention in an Internet Shopping Mall
Zeyu Li 1, Ana Belén Tulcanaza-Prieto 2and Chang Won Lee 3,*
1KDB Beijing Branch, Beijing 100025, China; lovermo777@163.com
2Grupo de Investigación Lugar Medio Sociedad (LMS), Escuela de Negocios, Universidad de Las
Américas (UDLA), Vía a Nayón, Quito 170124, Ecuador; ana.tulcanaza@udla.edu.ec
3School of Business, Hanyang University, Seoul 04763, Republic of Korea
*Correspondence: leecw@hanyang.ac.kr
Abstract: This study aims to explore the effect of the e-servicescape on the emotional response and
revisit intention of customers in an internet shopping mall (ISM) environment. The literature was
reviewed on the e-servicescape, emotional response, and revisit intention in an internet shopping
mall. A relevant model and hypothesis were established. For the empirical study, a survey form was
developed and conducted on 150 customers with experience using a certain ISM. Reliability analysis
and confirmatory factor analysis were performed using SPSS 27.0 and Amos 26.0 software, and the
causal relationships were identified through structural equation modeling (SEM). Study results and
implications were discussed and suggested. Among the factors of the e-servicescape in an ISM,
aesthetics and surrounding elements did not have a significant effect on emotional responses, and
spatial functionality showed a positive effect on emotional responses. Aesthetics had a weak negative
effect on revisit intention. Surrounding elements and spatial functionality had no significant effect
on revisit intention. The emotional response had a positive effect on revisit intention. This study
identified the importance of the e-servicescape in the ISM environment and especially emphasized
the importance of spatial functionality on the emotional response and aesthetics on revisit intention.
This study presented several suggestions and implications to corporate managers regarding the
development and management of the future ISM environment and other similar business settings.
Keywords: e-servicescape; emotional response; revisit intention; internet shopping mall
1. Introduction
Recent internet shopping malls (ISMs) in general have intense pressures to sustain
in a digital environment of a rapidly changing customer’s emotional response and revisit
intention, along with increasing financial pressures and utilizing efficient operational
resources. Such internet shopping malls seeking a proper e-servicescape address the
internet for the growing requirements for effective strategic planning for e-servicescape
development. When customers purchase products and services in an ISM environment
rather than an onsite shopping mall environment, the proper design of an e-servicescape in
an ISM has become very important because customers obtain product information from
ISMs only through limited text messages and videos [1,2].
While the potential for ISM companies to develop in the market increases, competition
between companies may also become fiercer. Therefore, there is a point where ISM compa-
nies should pay attention to how they can secure their competitiveness and satisfy their
customers, resulting in more revisits to their ISMs. In addition, the source of profit for all
ISM companies is the customers, and if an ISM company wants to grow well, the first thing
to consider is the customer’s emotional response and revisit intention. Also, managers
have faced how to better satisfy customer demands and maintain long-term customer
relationships through identifying the causal relationship between the emotional response
J. Theor. Appl. Electron. Commer. Res. 2024,19, 2030–2050. https://doi.org/10.3390/jtaer19030099 https://www.mdpi.com/journal/jtaer
J. Theor. Appl. Electron. Commer. Res. 2024,19 2031
and revisit intention [
3
–
6
]. However, most ISM companies tend to focus only on price and
technological innovation to survive in a highly competitive market [
7
,
8
]. If ISM companies
want to secure and retain customers, establishing a strategy based on price alone may not
work in this marketplace. They can use the e-servicescapes that can influence the emotional
response of customers and establish ways to differentiate themselves from other companies.
Thus, it is necessary to recognize the relationship among the e-servicescape, the emotional
response, and revisit intention in ISMs. Recent studies on the e-servicescape have explored
booking intention [
9
], stimulus and response [
10
], shopping intention [
11
], shopper-based
salient attributes [
12
], trust and purchase intention [
13
], and satisfaction [
14
]. These studies
have explored the relationships between the e-servicescape and revisit intention, rather
than between the e-servicescape and emotional responses, and between the emotional
responses and revisit intention in an ISM context.
The purpose of this paper is to explore the effect of the e-servicescape on emotional
responses and the revisit intention of customers with shopping experience in a certain
ISM. Specifically, this study focuses on two aspects as follows: first, the effect of the e-
servicescape of ISMs on the emotional response and revisit intention; second, the effect of
the emotional response on revisit intention. The questions of this study to achieve the above
study purpose were set as follows. Research question 1 is what effect can the e-servicescape
have on emotional responses in a certain ISM? Research question 2 is what effect can the
e-servicescape have on revisit intention in a certain ISM? Research question 3 is how can
emotional response affect revisit intention in a certain ISM?
When consumers purchase products and services in an ISM environment, they obtain
product information only through text messages, videos, and other relevant ways provided
by the ISM. Therefore, the design of the site’s e-servicescape is very important. It is very
important to study the effect of the e-servicescape on the emotional response and revisit
intention. While ISM companies’ development opportunities in the market are increasing,
competition among these companies may also become fiercer. Therefore, there is a point
where ISM companies should pay attention to how they can secure their competitiveness.
In addition, the source of profit for all ISM companies is their customers, and if these com-
panies want to grow well, the first thing to consider is the customer response, perspective
and intention, and what to do to better satisfy customer needs and maintain long-term
customer relationships. Through this study’s findings, this study will provide business
decision-makers and corporate managers with important implications and insights for
establishing e-servicescape planning strategies in the ISM environment and other similar
business settings.
This paper is organized as follows. Section 1introduces the current issues and study
motivation in ISMs and presents the purpose of this study. Section 2reviews the literature
on the e-servicescape, emotional response, revisit intention, ISM, and pertinent topics.
Section 3provides research methodologies including hypotheses, the study model, and the
operational definition of measures. Section 4describes the study results with demographical
analysis, reliability and validity analysis, and hypotheses tests. Section 5concludes with a
study summary, implications, limitations, and future directions.
2. Literature Review
2.1. E-Servicescape
In the digital era, the concept of an electronic servicescape, or e-servicescape, has risen
due to the online environment. An e-servicescape refers to the digital atmosphere and
virtual elements that influence (1) the customer’s perception, (2) the customer’s experience,
and (3) the customer’s purchase behavior. All these factors are influenced by theories of
environmental psychology, consumption, and management. Bitner [
15
] and Ardiansyahmi-
raja et al. [
16
] mentioned that environmental psychology is affected by physical aspects,
human behavior, knowledge, and experiences. This concept was improved using the actual
context and virtuality, extending to the online environment, tendency of consumption, and
customer preferences.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2032
The e-servicescape involves several dimensions [
17
] such as (1) visual elements that
influence user perceptions and engage their consumption and loyalty, which include
website layout, aesthetic designs, color schemes, and imagery [
18
]; (2) functional elements
that include a user-friendly interface and intuitive navigation, which also promote user
satisfaction and seamless interaction through the easy website navigation, functionality,
responsiveness, and usability [
19
]; (3) ambient elements to evoke emotions and past feelings
in a virtual atmosphere, which enhance the overall user experience using background music,
sounds, animations, and other sensory stimuli [
20
]; and (4) social elements linked to user
reviewers, ratings, testimonials, feedbacks, and social media interaction to develop trust,
credibility, loyalty, and social presence in the e-servicescape [21,22].
Basu and Mandal [
23
] mentioned that an e-servicescape influences positively on
customer behavior given that it increases perceived service quality, degree of satisfaction,
purchase intention (repeat usage and loyalty), and a brand’s and firm’s trust and credibility.
All these factors include security features and social proof mechanisms, which act as
user-friendly determinants and are perceived by customers as signals of professionalism
and competence, enhancing a long-term relationship between firms and customers [
24
].
Therefore, an e-servicescape offers significant opportunities for enhancing customers’
recognition and experiences by the correct identification of their perceptions, attitudes,
and behavior in the digital marketplace. However, there are several challenges, including
technological limitations, privacy concerns, legal statements, and diverse consistency in
multiple digital channels (including features of virtual reality and augmented reality).
2.2. Emotional Response
Emotions refer to a customer’s internal reactions to things, and these feelings are
usually expressed as positive or negative emotions that occur inside. An emotional re-
sponse refers to the emotions that customers subjectively feel that occur at specific times
and situations, and these internal responses can affect customer’s decisions and actions.
Emotions can be viewed as internal complex emotional experiences that occur when cus-
tomers receive external stimulation, and this emotional response can be viewed as a broader
concept than feelings. An emotional response also refers to a type of emotional and psycho-
logical state that occurs naturally in a customer. The characteristics of emotional responses
are emotions that can arise from a customer’s internal causes or emotions that can arise
from external stimulation. Expression can mainly be seen in the following two aspects:
emotional responses and negative emotions. Emotions were said to include pride, accom-
plishment, gratitude, guilt, shame, and anger. Emotional responses are comprised of the
following three primary statements: a set of specific emotions, the experience of such states
of emotion, and the conceptual (re)organization [25–27].
As the importance of emotional responses is increasingly recognized, many studies
are focused on the emotions felt by customers as an important factor in the shopping
environment. Mehrabian and Russell [
28
] presented a representative model for emotional
responses that can be measured by the following three dimensions: pleasure, arousal, and
dominance. It has been said that environmental stimuli can affect customer’s emotional
responses and can further influence customer’s behavior (approach and avoidance). Huang
et al. [
29
] believed that emotional responses have a significant relationship with customers’
behavioral intentions, and that the more emotional responses customers feel when shopping
on the internet, the more enjoyably they can shop in the shopping environment. It is said
that it can greatly increase the positive behavior of customers, and the likelihood of repur-
chase and return visits is higher. Yadav and Mahara [
30
] studied the role of e-servicescape
dimensions on customer online shopping and showed that e-servicescape dimensions are a
strong predictor of trust that strongly impacts customer purchase intention.
2.3. Revisit Intention
Revisit intention is associated with retained customers, repurchase intention, and
repeat visit intention, which involves the probability or likelihood of a customer returning
J. Theor. Appl. Electron. Commer. Res. 2024,19 2033
to a particular business or service provider [
31
]. This intention of repetition is grounded on
customer loyalty and the long-term success of firms given the customer’s recognition of a
firm’s products and services. The theoretical literature of revisit intention includes concepts
of marketing, customer behavior, psychology, and behavioral economics. Specifically,
revisit intention involves the theory of planned behavior [
32
,
33
], which introduces the
following three key factors of an individual’s intention to revisit a business: the attitude
of the customer, subjective norms regarding to social environment, and the degree of
accessibility of revisiting (behavioral control). Moreover, the theory of planned behavior is
aligned with expectation confirmation theory [
34
], which links the degree of satisfaction
of a previous experience with the fulfillment of current expectations and the increase in
revisit intentions.
The revisit intention is motivated by [
35
] (1) high service quality, (2) customer satisfac-
tion associated with previous experiences and the short gap between an expected and re-
ceived good or service [
36
], (3) customer recommendations or word-of-mouth, (4) perceived
value (benefits over costs), (5) trust and credibility, (6) a brand’s and firm’s loyalty over
competitive alternatives, (7) social influence and emotional attachment/connection [
37
],
(8) convenience and easy accessibility of the business location or online platform, and
(9) personalization and customization.
Practical implications of revisit intention are linked with fostering customer loyalty
and retention, which positively impacts on the financial and nonfinancial variables in a
firm in the short- and long term [
38
]. Moreover, the revisit intention reduces marketing
and switching costs given that word-of-mouth and social influence replace both costs and
drive the sustainable growth of the business, providing comfort, convenience, and person-
alization in the customer experience, which also increases their competitive advantage in
the market or online environment.
2.4. Internet Shopping Mall
Internet shopping malls, also known as online marketplaces or e-commerce platforms,
have been increasing in number and revolutionizing the way that customers buy and/or sell
their products and services. Internet shopping malls refer to digital and virtual platforms
that aggregate multiple sellers and offer a wide range of products and services to customers.
This online environment provides easy access to customers given their functionality (user-
friendly interfaces), secure transactions with diverse payment options, efficient logistics,
and appropriate feedback to enhance site quality and the shopping experience with con-
venient, trusted, and satisfied buyers and sellers [
39
,
40
]. Internet shopping malls include
personalized recommendations using users’ profiles and preferences, given the deep study
of customers using several algorithms of artificial intelligence, augmented reality for virtual
try-on experiences, and developed blockchain for secure transactions and supply chain
management [
41
]. Therefore, the current customers are adapted to internet shopping malls
and this tendency has been steadily increasing, driven by factors such as convenience,
variety, flexibility of time (24 h, 365 days), competitive pricing, and accessibility.
Internet shopping malls host a diverse range of sellers, including individual en-
trepreneurs and large corporations, which also compete in the online marketplace using
strategies such as pricing, quality, promotion, branding, and customer service to increase
their competitive advantage, retain current customers, and attract new clients. Moreover,
internet shopping malls promote a transparent ecosystem and facilitate cross-border trade
by connecting buyers and sellers across different regions and countries, which is the main
benefit of globalization. They are also regulated by e-commerce laws and norms, including
ethical guidelines for customer privacy, data security, counterfeit products, unfair competi-
tion, and taxation [
42
]. Therefore, the proliferation of internet shopping malls is guided by
the reshaping of the retail landscape with unparalleled convenience, choice, and value to
customers and sellers.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2034
2.5. Effect of the E-Servicescape on Revisit Intention in an Internet Shopping Mall
The e-servicescape and its digital environment play a crucial role in shaping customers’
perceptions and their purchase behavior in internet shopping malls, provoking the increase
in their revisit intention given an integrated user experience, which involves both aesthetic
and functional characteristics of their website, such as design, layout, practicality, utility,
interactivity, and security features. All these aspects have been studied using the stimulus–
organisms–response model and the expectation–confirmation model [
34
]. Mehrabian and
Russell [
43
] argued that environmental stimuli (e-servicescape) affect internal customers’
responses (perceptions, emotions, and preferences) leading to the recurrence behavior
of revisit or repurchase intention while the second theory established that the degree of
customer satisfaction is determined by the gap between the expectations and reality of a
product or service, which is translated to future revisit intention according to customers’
previous perception.
The e-servicescape involves characteristics such as visual design, functionality, usabil-
ity, interactivity, security, and privacy. Specifically, visual design integrates the aesthetic
appeal of website layout, color schemes, imagery, and typography. Peng et al. [
44
] found
that visually appealing websites positively influence customers’ revisit intention through
perceived honesty, credibility, and professionalism. On the other hand, the dimensions of
functionality and usability established the characteristics of easy access and navigation on
the website, utility, simple and clear checkout process, and serviceability. The findings of
Albshaier et al. [
45
] revealed that websites with intuitive navigation and seamless check-
out processes are more likely to produce higher customer revisit intentions given their
satisfaction with products, services, and their e-commerce environment.
Moreover, interactivity with users allows customers to believe in a brand, product,
or service given that it comprises live chat support, personalized recommendations, and
interactive product or service demonstrations that enhance the degree of user satisfaction
and engagement, showing a positive effect on revisit intention [
46
,
47
]. Finally, security
and privacy are considered signals of trust, credibility, safety, and transparency, which are
aligned with national and international laws and norms and mitigate risks and concerns
about online payments and regulations, generating a positive impact and fostering revisit
intention in the online shopping context [48].
3. Research Methodology
3.1. Hypotheses Development and Research Model
For this study, the e-servicescape is classified into the following three factors: aesthetics,
surrounding elements, and spatial functionality as independent variables; set emotional
response and revisit intention as the dependent variable on e-servicescape; and emotional
responses as a related variable to revisit intention.
3.1.1. Relationship between Components of the E-Servicescape and Emotional Responses
The greater the aesthetic appeal of the design of the e-servicescape for an internet
shopping mall, the more likely it is to induce customer’s feelings of enjoyment. Aesthetics
have a significant effect on emotional responses such as enjoyment. Among the components
of the e-servicescape, aesthetics and spatial functionality have a significant impact on
emotional responses, if the overall attractiveness and convenience of the shopping mall
site’s design has a positive effect on emotional responses. The e-servicescape, which
includes three factors (aesthetics, surrounding elements, and spatial functionality), has
a positive (+) effect on emotional responses and the importance of the e-servicescape is
explained [49–51].
Therefore, in this study, based on the theoretical results of previous studies, the
following hypothesis was established to examine the influence of the relationship between
the aesthetics, surrounding elements, and spatial functionality of the e-servicescape and
the emotional responses of customers in an internet shopping mall.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2035
Aesthetics will have an effect on emotional responses. It was analyzed that the aesthet-
ics have a positive emotion, and is explained as revealing the importance and role of the
e-servicescape [52,53]. Therefore, based on the previous studies, the following hypothesis
was established. Recent studies elucidate the positive effect of aesthetics on emotional
responses using different perspectives [
54
], such as neuroscientific, cross-sectional, virtual
reality, and social media investigations. The results revealed a direct link between aes-
thetic appreciation and qualities (art, architectural design, and natural scenery) [
55
] and
positive emotional responses and well-being, which influences mood and user engage-
ment, highlighting the importance of digital aesthetics in online communication and the
e-servicescape [56]. Therefore, our hypothesis is the following:
H1-1. Aesthetics will have a positive (+) effect on emotional responses.
Surrounding elements are linked positively to emotional responses given that urban
landscapes, urban green spaces, serene natural settings, and environmental factors influence
emotions, health, mood, and well-being [
57
]. Zhang et al. [
58
] found that spatial complexity
and aesthetic richness produced more positive emotional responses. Similarly, Zhang
et al. [
59
] suggested that the exposure to natural scenes elicited greater activation in brain
regions associated with positive emotions, lower heart rates, cortisol levels, and stress,
showing the restorative effects of nature, positive emotions, and overall quality of life.
Therefore, the hypothesis is presented as follows:
H1-2. Surrounding elements will have a positive (+) effect on emotional responses.
The findings of Stefanucci [
60
] revealed that environments with open layouts and clear
circulation pathways influence positively emotional responses compared to crowded or
cluttered spaces. Moreover, Makhbul [
61
] suggested that well-designed workspaces with
optimal lighting, temperature, and acoustics were associated with higher positive emotions
and lower levels of stress, showing the importance of creating environments that prioritize
user comfort to enhance emotional well-being. Therefore, our hypothesis is the following:
H1-3. Spatial functionality will have a positive (+) effect on emotional responses.
3.1.2. Relationship between the Components of the E-Servicescape and Revisit Intention
The components of the e-servicescape of an ISM can have a direct effect on customers’
behavioral intention, and aesthetics and surrounding elements are important in a revisit
intention. The components of the e-servicescape of online sites have a positive effect
on customer’s behavioral intentions. The more customers are satisfied with the online
site structure environment, the more positive behavioral intentions can be induced by
customers [
62
–
64
]. Therefore, based on the results of previous studies, the following
hypothesis was established to examine the influence of the relationship between each factor
of the e-servicescape and customers’ revisit intention in an ISM.
Aesthetics influence shaping perceptions and the decision-making process, which is
associated with the revisit intention in digital environments. Websites with attractive, well-
designed, and pleasing aesthetic features (clean layouts, high-quality images, consistent
branding, adequate color schemes, minimalist design, and typography) are more likely
to elicit positive emotions and enhance user satisfaction, showing higher rates of revisit
intention given that aesthetic consistency is perceived as more professional, user-friendly,
and trustworthy [65,66]. Therefore, our hypothesis is presented as follows:
H2-1. Aesthetics will have a positive (+) effect on revisit intention.
Physical, virtual, cultural, and natural elements positively impact revisit intention [
67
]
given that cultural immersion, pleasant environment, comfortable lighting, attractive
J. Theor. Appl. Electron. Commer. Res. 2024,19 2036
displays, greenery, natural textures, aesthetic pleasing design elements, and interactive
features in websites [
68
] were more likely to influence customers’ perceptions, enhance
users’ sense of connection with the environment, and attract repeat visits from clients [
69
].
Therefore, the hypothesis proposed is detailed as follows:
H2-2. Surrounding elements will have a positive (+) effect on revisit intention.
Spatial functionality involves the design and organization of physical or virtual en-
vironments to optimize usability and user experience, which also has a positive effect
on customer engagement and retention [
70
,
71
]. This positive effect on revisit intention is
grounded on features, such as ease of navigation, a user-friendly interface, clear wayfind-
ing signage, interactive maps, efficient search functionalities, straightforward checkout
processes, and overall shopping experience, which facilitate visitors’ navigation and explo-
ration and contribute to users’ satisfaction and loyalty [
72
]. Therefore, the hypothesis is
presented as follows:
H2-3. Spatial functionality will have a positive (+) effect on revisit intention.
3.1.3. Relationship between Emotional Responses and Revisit Intention
The e-servicescape can induce customer’s pleasure, and that pleasure has a signifi-
cant effect on revisit intention. Emotional responses have a significant relationship with
customers’ behavioral intentions. The more emotional responses customers feel when
shopping on the internet, the more enjoyable they can shop in the shopping environment.
It is said that it can greatly increase the positive behavior of customers, and the likelihood
of repurchase and return visits is higher. Customers’ emotional responses during the online
shopping process have a significant effect on revisit intention [
63
,
73
,
74
]. Based on previous
studies, this study proposes that the emotional responses felt by customers while shopping
at an internet shopping mall can have a significant effect on revisit intention. Therefore, the
following hypothesis was established:
H3. Emotional responses will have a positive (+) effect on revisit intention.
In addition, this study model was designed to investigate the effect of each factor of
the e-servicescape on the emotional response and revisit intention, and the effect of the
emotional response on revisit intention. The research model is shown in Figure 1.
图 1[그림 3-1] 연구 모형
AES
SUR
SPA
REV
EMO
H3
H1-1
H2-2
H2-1
H2-3
H1-2
H1-3
Figure 1. Research model. AES: aesthetics, SUR: surrounding elements, SPA: spatial functionality;
EMO: emotional response, REV: revisit intention.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2037
3.2. Operational Definitions of Measurements
Operational definitions of measurements were made for the e-servicescape (aesthetics,
surrounding elements, spatial functionality), emotional responses, and revisit intention.
3.2.1. Aesthetics
Aesthetics is defined as whether the design of an internet shopping mall has its unique
charm and the degree of attractiveness of the site’s exterior. A total of four questions were
measured on a 5-point Likert scale with ‘sophistication’, ‘beauty’, ‘excellence’, and ‘design
colors’ of the site’s design.
3.2.2. Surrounding Elements
Surrounding elements have characteristics that can stimulate customers’ feelings
through the presentation of videos and background music in an internet shopping en-
vironment. The surrounding elements are the shopping mall site’s ‘background music
is excellent’, ‘sound effects are excellent’, ‘video presentation is excellent’, and ‘videos
presented are vivid’. Four questions were measured on a 5-point Likert scale.
3.2.3. Spatial Functionality
Spatial functionality is defined as the ability to quickly find information for customers
in an internet shopping mall and the level of convenience. Spatial functionality is ‘ease of
use’, ‘navigation speed’, ‘easy information search’, and ‘easy to browse’ of the shopping
mall site. A total of four questions were measured on a 5-point Likert scale.
3.2.4. Emotional Responses
Emotional responses are the good feelings that customers can feel while shopping at
an online shopping mall. Emotional responses are a total of four questions asked about
how customers felt with ‘pleasure’, ‘satisfaction’, ‘comfort’, and ‘trust’ while shopping at
the shopping mall site. It was measured on a 5-point Likert scale.
3.2.5. Revisit Intention
Revisit intention is defined as customers’ desire to visit and use an online shopping
mall again. For the revisit intention measure, customers answered a total of three questions
about the shopping mall site as follows: ‘will use it again’, ‘will visit again’, and ‘will visit
often’. It was measured on a 5-point Likert scale.
Table 1shows the operational measurements and the related sources. The ques-
tionnaire for this study consisted of a total of 26 questions. First, general information
(demographic characteristics and internet shopping experience) consisted of seven ques-
tions, and the measurements of the e-servicescape consisted of four questions each about
aesthetics, surrounding elements, and spatial functionality. Additionally, it consisted of
four questions to understand the emotional response felt by consumers. Lastly, revisit
intention consisted of three questions.
Table 1. Operational measurements and related sources.
Variables Items Sources
AES
The design of this shopping mall site is sophisticated.
The design of this shopping mall site is beautiful.
The design of this shopping mall site is excellent.
The design colors of this shopping mall site are attractive.
[2,18,51,62]
SUR
The background music on this shopping mall site is excellent.
The sound effects of this shopping mall site are excellent.
The video presentation on this shopping mall site is excellent.
The videos presented on this shopping mall site are vivid.
[15,17,20,75]
J. Theor. Appl. Electron. Commer. Res. 2024,19 2038
Table 1. Cont.
Variables Items Sources
SPA
This shopping mall site is convenient to use.
This shopping mall site has fast navigation.
This shopping mall site is easy to search for information.
This shopping mall site is easy to browse.
[2,17,19,76]
EMO
Happy while shopping at this mall site.
Fulfilled while shopping at this shopping mall site.
Comfortable while shopping at this mall site.
Trustworthy while shopping on this shopping mall site.
[25,27,32,77]
REV Like to use this shopping mall site again.
Visit this shopping mall site again.
Visit this shopping mall site often. [1,34,37,72]
3.3. Sampling and Analysis
Previous research and theoretical basis were used to draw the relationship between
the e-servicescape on emotional responses and revisit intention as well as the relationship
of emotional responses on revisit intention in an internet shopping mall. A questionnaire
was developed targeting customers with an experience of using an internet shopping
mall who were expected to be able to understand and respond to the survey questions.
The convenience sampling method and self-entry measurement method were utilized to
collect data for this study. An issue at the research design stage is a common method bias
(CMB) where an error occurs when the independent and dependent variables are measured
using the same measurement tool and respondent. This can seriously affect the validity
of the measurement. CMB can cause distortion in the correlation between independent
variables and dependent variables, affect the results of hypothesis testing, cause errors in
study results, and cause problems with the internal validity of the empirical study. In this
empirical study, the independent and dependent variables measure cognition, attitude, and
intention; thus, the self-entry method is a practical option to use. Therefore, to minimize the
CMB, this study utilized the method of providing as much explanation as possible about
concepts such as the e-servicescape, emotional response, and revisit intention and clarifying
the questions in the survey form. Another bias in an empirical restudy is nonresponse bias
(NRB) that refers to the convenience of respondents refusing to accept a given scenario,
survey content, or the response unit itself. The convenience of refusing to respond to the
survey item itself is called ‘item nonresponse’, and the convenience of not responding
by not accepting the unit itself is called ‘unit nonresponse’. To resolve this bias, a data
substitution method can be used for item nonresponse, and a weight adjustment method
can be used for unit nonresponse. Since it is difficult to completely solve the NRB problem,
nonresponse data were excluded from this analysis.
The survey was conducted for a month by distributing an electronic questionnaire to
Chinese shopping mall consumers. JingDong (JD) internet shopping mall in China was
selected. JD is a representative company among Chinese internet shopping mall companies
and has a good reputation from many consumers. Initially, a total of 558 questionnaires
were collected in a given survey period. Among them, a total of 185 samples were filtered,
excluding questionnaires from subjects who had no shopping experience at the JD internet
shopping mall and those who gave insincere answers. Results from this sample set showed
that most customers with shopping experience at JD internet shopping mall were in their
twenties and thirties. There were 130 customers (64.9%) in their twenties and 30 customers
(16.2%) in their thirties. All other age categories consisted of less than 15 customers as
follows: 10 customers (5.4%) under 20 years old, 10 customers (5.4%) in their forties, and
15 customers (8.1%) aged 50 plus. To increase the homogeneity of the sample, customers in
their twenties and thirties were selected for this study purpose. Thus, a final sample set for
this study was 150. These age groups can be called MZ generations (consumers aged in
J. Theor. Appl. Electron. Commer. Res. 2024,19 2039
their twenties and thirties) that have a higher digital literacy and artistic motivation than
other generations.
A sampling adequacy analysis was conducted in this study. The sampling adequacy
was judged through the Kaiser–Meyer–Olkin (KMO) test and the Bartlett test. If the KMO
sampling adequacy value is 0.8 or higher, the sampling is considered appropriate. The
Bartlett test was used to check equal variance of samples. To justify a sample size adequacy,
385 is a minimum sample size for a 95% confidence interval with a margin of error of 5%,
assuming a population proportion of 50%. Ninety-seven is a minimum sample size for a
95% confidence interval with a margin of error of 10%, assuming a population proportion
of 50%. If a sample size falls between these two numbers, the sample size should be
acceptable for a study test. As a result of the analysis in this study, the KMO value was
0.903, confirming that the sampling adequacy was justified. Its Chi-square value was
2722.175 (df = 171) and the pvalue was 0.000, confirming homogeneity of variances in
samples for this study. The final sample size is 150. Thus, an initial sampling adequacy
is secured.
In this study, the collected data were analyzed using SPSS 27.0 and Amos 26.0 statisti-
cal package programs. First, frequency analysis was conducted to determine the general
demographic characteristics of the survey subjects. Second, reliability analysis was con-
ducted to consider the consistency and reliability between measurement items and was
measured using Cronbach’s alpha coefficients. Third, confirmatory factor analysis was con-
ducted to ensure the validity of the measurement questions. Fourth, Pearson’s correlation
analysis was performed to determine the correlation between variables. Fifth, structural
equation modeling (SEM) analysis was conducted to verify the influence of the relationship
between the variables presented in this research model. To examine the fit of the model, the
absolute fit index was determined using CMIN (Chi-square value), CMIN/DF (minimum
discrepancy), GFI (goodness-of-fit index), RMR (root-mean-square residual), and RMSEA
(root-mean-square error of approximation). The relative fit index was determined using
the NFI (normed fit index), TLI (Tucker–Lewis index), IFI (incremental fit index), and CFI
(comparative fit index).
4. Results
4.1. Demographic Characteristics of the Sample
This study conducted a frequency analysis on 150 valid samples, and the demographic
characteristics of the survey subjects are shown in Table 2. The details of the analysis results
are as follows. First, regarding the gender of customers who had shopping experience at
the internet shopping mall, there were 53 males (35.3%) and 97 females (64.7%), showing
that the proportion of females was higher than that of males. In the age groups, there were
120 customers (80.0%) between the ages of 21 and 30 and 30 customers (20.0%) between the
ages of 31 and 40. Customers aged 21 to 30 accounted for the highest percentage at 80.0%,
showing that most customers using the internet shopping malls were in their twenties
this study.
Regarding their educational background, 18 (12.0%) had a junior college degree or
lower, 94 (62.7%) had a college graduate, and 38 (25.3%) had a graduate school degree or
higher. Regarding customers’ monthly living expenses, 45 customers (30.0%) earned USD
415 or less, 41 (27.3%) earned between USD 416 and USD 690, 27 (18.0%) earned between
USD 691 and USD 970, and 21 (14.0%) earned between USD 971 and USD 1380. There were
16 people (10.7%) earning USD 1381 or higher.
Of the products that customers frequently purchased from the internet shopping mall,
electronic products were the most purchased with 96 customers (64.0%). Next came daily
necessities with 37 customers (24.7%), other products with 8 customers (5.3%), cosmetics
with 6 customers (4.0%), and clothing with 3 customers (2.0%). Regarding the average
amount spent each time at the internet shopping mall, 51 customers (34.0%) spent less than
USD 27, 66 (44.0%) spent between USD 28 and USD 83, 12 (8%) spent between USD 84 and
USD 138, and 21 (14.0%) spent more than USD 139.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2040
Table 2. Results of demographic characteristics.
Variables Categories Frequencies Percents
Gender Male 53 35.3
Female 97 64.7
Age 21–30 120 80.0
31–40 3020.0
Education
Junior college degree or lower 18 12.0
College degree 94 62.7
Graduate school degree or higher 38 25.3
Monthly living
expenes
USD 415 or less 45 30.0
USD 416–690 41 27.3
USD 691–970 27 18.0
USD 971–1380 21 14.0
USD 1381 or higher 16 10.7
Frequently purchased
products
Electronic products 96 64.0
Cosmetics 6 4.0
Clothing 3 2.0
Necessities 37 24.7
Others 8 5.3
Average spending
USD 27 or less 51 34.0
USD 28–83 66 44.0
USD 84–138 12 8.0
USD 139 or higher 21 14.0
4.2. Reliability and Validity
4.2.1. Reliability Analysis Results
Reliability means that the results of measuring the same concept should be similar and
is said to be the degree of safety, consistency, and accuracy of the measurement values. In
addition, there are methods for verifying reliability analysis, such as test–retest reliability.
Composite reliability (or construct reliability) is a measure of internal consistency in scale
items. Its reasonable threshold is 0.6 or higher. The most common method is Cronbach’s
alpha test, which can be used to evaluate internal consistency between measurement
items. In this study, to ensure consistency and accuracy between measurement items
in the questionnaire, reliability analysis was performed for internal consistency through
Cronbach’s alpha coefficients. In general, if Cronbach’s alpha is lower than 0.6, reliability is
insufficient; if it is between 0.6–0.8, it can be considered reliable; and if it is higher than 0.9,
it can be judged to have high reliability.
As shown in Table 3, the results of the reliability test of the measurement items in this
study are as follows. Looking at the structural factors of the e-servicescape of the internet
shopping mall, the composite reliability (CR) values were 0.938 for aesthetics, 0.921 for
surrounding elements, 0.926 for spatial functionality, 0.91 for positive emotions, and 0.91
for revisit intention, all of which were above 0.9. Cronbach’s alpha coefficient of aesthetics
is 0.936, the surrounding elements factor was 0.919, and the spatial functionality was 0.925.
Emotional responses were 0.918 and revisit intention was 0.900. All Cronbach’s alpha
coefficients were above 0.8. The overall Cronbach’s alpha coefficient of 0.949 is higher than
all other individual variable’s values. Therefore, it indicates high reliability and internal
consistency between measurement items.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2041
Table 3. Results of the reliability analyses.
Variables Items Composite
Reliability
Cronbach’s
Alpha
Overall
Cronbach’s Alpha
AES
AES1
0.938 0.936
0.949
AES2
AES3
AES4
SUR
SUR1
0.921 0.919
SUR2
SUR3
SUR4
SPA
SPA1
0.926 0.925
SPA2
SPA3
SPA4
EMO
EMO1
0.918 0.917
EMO2
EMO3
EMO4
REV
REV1
0.900 0.884
REV2
REV3
4.2.2. Validity Analysis Results
The validity analysis using factory analysis is shown in Table 4. The validity analysis
of the measurement items of the questionnaire can verify the convergent validity and
discriminant validity. It is said that the better the results of convergent validity and discrim-
inant validity, the better the validity of the questionnaire’s construct concept can be secured.
Therefore, this study was mainly examined to verify the validity of the measurement items
to determine the accuracy of the collected survey data.
Table 4. Results of the validity analyses.
Variables Items Unstandardized
λSE CR Standardized
λAVE
AES
AES1 1 0.859
0.792
AES2 0.992 0.061 16.361 *** 0.934
AES3 1.035 0.065 15.821 *** 0.918
AES4 0.915 0.068 13.544 *** 0.846
SUR
SUR1 1 0.853
0.744
SUR2 1.011 0.070 14.440 *** 0.895
SUR3 1.119 0.084 13.356 *** 0.855
SUR4 1.098 0.084 13.117 *** 0.846
SPA
SPA1 1 0.855
0.759
SPA2 1.030 0.074 13.848 *** 0.865
SPA3 1.072 0.070 15.424 *** 0.918
SPA4 1.055 0.079 13.280 *** 0.845
EMO
EMO1 1 0.800
0.736
EMO2 1.074 0.090 11.915 *** 0.845
EMO3 1.207 0.095 12.690 *** 0.883
EMO4 1.266 0.097 13.030 *** 0.900
REV
REV1 1.000 0.918
0.749
REV2 1.021 0.072 12.278 *** 0.880
REV3 1.160 0.090 12.903 *** 0.795
*** p< 0.01, SE: standard error; CR: critical ratio; AVE: average variance extracted value.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2042
Factor analysis is a technique used to examine the covariance or correlation coeffi-
cient structure between measured variables and to analyze the interrelationship between
variables. Factor analysis can be divided into two methods depending on the different
purposes. Exploratory factor analysis is generally conducted in SPSS when the theoretical
background is somewhat lacking. Confirmatory factor analysis is to conduct using Amos
when the theoretical background is sufficient. In this study, measurement questions for
variables were set based on the theoretical basis of previous research, and confirmatory
factor analysis was conducted using Amos 26.0 to measure survey questions corresponding
to each variable.
Confirmatory factor analysis was conducted to verify the validity of the survey
questions corresponding to each following variable of the constructed concept of the
e-servicescape: aesthetics, surrounding elements, spatial functionality factors, emotional
responses, and revisit intention. Additionally, to verify convergent validity, this study
examined the following three measurement indices: standardized lambda (factor loading),
critical ratio, and the value of the square root of the average variance extracted value
(AVE). As shown in Table 4, the results of the confirmatory factor analysis of this study
showed that the standardized lambda (factor loading) for aesthetics was 0.859, 0.934, 0.918,
and 0.846. The surrounding elements factors were 0.853, 0.895, 0.855, and 0.846. Spatial
functionality was 0.855, 0.865, 0.918, and 0.845. Emotional responses were 0.800, 0.845,
0.883, and 0.900, and revisit intention was 0.918, 0.880, and 0.795. The standardized lambda
(factor loading) values of all measured variables were higher than 0.6 and met the criteria.
Also, all critical ratio (CR) values were presented according to the corresponding items and
significant at a pvalue of 0.01 in aesthetics of 16.361, 15.821, and 13.544; the surrounding
elements of 14.440, 13.356, and 13.117; spatial functionality of 13.848, 15.424, and 13.280;
emotional responses of 11.915, 12.690, and 13.030; and revisit intention of 12.278 and 12.903.
The average variance extracted value (AVE) was 0.792 for aesthetics, 0.744 for surrounding
elements, 0.759 for spatial functionality, 0.736 for positive emotions, and 0.749 for revisit
intention, all of which were above 0.5.
Therefore, through Tables 3and 4, internal consistency and convergent validity were
secured because the measurement questions corresponding to each variable in this study
all satisfied the threshold values.
4.2.3. Correlation Analysis Results
Correlation analysis mainly examines the degree and direction of correlation between
two factors. In this study, Pearson’s correlation analysis method was used to measure the
correlation between constructs. Before proceeding with structural equation model (SEM)
analysis, Pearson’s correlation was used to identify the correlation among aesthetics, sur-
rounding elements, spatial functionality (which are the components of the e-servicescape),
along with emotional responses, and revisit intention of the internet shopping mall used in
this study.
The results of the correlation are shown in Table 5. First, the correlation analysis
results between each factor, the correlation coefficients between aesthetics, surrounding
elements, spatial functionality, emotional responses, and revisit intention, were 0.559, 0.429,
0.387, and 0.233, respectively. The correlation coefficients between the surrounding factors,
spatial functionality, emotional responses, and revisit intention were 0.546, 0.520, and
0.407, respectively. The correlation coefficients between spatial functionality and emotional
responses and revisit intention were 0.751 and 0.619, respectively. The correlation coefficient
between emotional responses and revisit intention was found to be 0.765. Therefore, the
correlation coefficient between each factor was significant (p< 0.01) and there was a positive
(+) correlation.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2043
Table 5. Results of the correlation analysis.
Variables AES SUR SPA EMO REV
AES 0.890
SUR 0.634 *** 0.863
SPA 0.486 *** 0.569 *** 0.871
EMO 0.425 *** 0.526 *** 0.744 *** 0.858
REV 0.298 *** 0.430 *** 0.632 *** 0.770 *** 0.865
*** p< 0.01. The bolded values presented on the diagonal are the square roots of the average variance extracted
value (AVE).
Discriminant validity exists if the square root of the average variance extracted value
(AVE) of each factor is greater than the correlation coefficient between the two factors. The
AVE value must be higher than 0.5 to ensure validity. The results also showed that the
square root of the average variance extracted value (AVE) of aesthetics was 0.890, which was
greater than 0.634, 0.486, 0.425, and 0.298, which were the correlation coefficients between
each variable. The square root of AVE of the surrounding elements was 0.863, which was
greater than 0.569, 0.526, and 0.430. The square root of AVE of spatial functionality was 0.871,
which was greater than 0.744 and 0.632. The square root of AVE of emotional responses
was 0.858, which was greater than 0.770. Therefore, in this study, discriminant validity also
was ensured because the square roots of the average variance extracted values (AVE) of the
constructs were all larger than the correlation coefficients between variables. Table 6shows
the results of the heterotrait–monotrait ratio of correlations (HTMT) values, which are all
below the 0.9 threshold. Thus, constructs are sufficiently distinct and discriminant validity
is secured.
Table 6. Results of the heterotrait–monotrait ratio of correlations (HMTH) analysis.
Variables AES SUR SPA EMO
SUR 0.682
SPA 0.521 0.620
EMO 0.457 0.575 0.807
REV 0.322 0.477 0.699 0.850
4.3. Verification of Research Hypothesis
The structural equation model (SEM) is an important analysis technique that has
many advantages in that measurement errors can be controlled, and theoretical models
constructed through goodness-of-fit indices can be evaluated. In this study, the influ-
ence relationship of aesthetics, surrounding elements, and spatial functionality, which are
components of the e-servicescape on emotional responses and revisit intention, and the
influence relationship of emotional responses on revisit intention in the internet shopping
mall were used in this study. To verify the effect, SEM analysis was conducted using the
Amos 26.0 statistical package program.
Before verifying the established hypothesis, the suitability of the SEM was examined.
When evaluating the SEM, absolute fit indices include x
2
, GFI, AGFI, RMSEA, and RMR,
and incremental fit indices (relative fit indices) include CFI, TLI, NFI, and IFI. It is explained
that it is good to comprehensively evaluate the model using several goodness-of-fit indices.
Therefore, to examine the suitability of the model, the absolute fit index was mainly
determined using x
2
(CMIN), CMIN/DF, GFI, RMR, and RMSEA, and the relative fit index
was mainly determined using NFI, TLI, IFI, and CFI.
As shown in Table 7, the results of examining the suitability of the SEM of this study
are x
2
= 305.814 (df = 142, p= 0.000), CMIN/DF = 2.154, GFI = 0.830, RMR = 0.027,
RMSEA = 0.088
, NFI = 0.893, TLI = 0.927, IFI = 0.940, and CFI = 0.939. When the results
were compared with the recommended thresholds, the pvalue for x
2
= 0.000 and GFI was
found to fall short of the recommended thresholds. Since all the results were found to
J. Theor. Appl. Electron. Commer. Res. 2024,19 2044
meet the recommended thresholds, the structural model of this study can be judged to
be appropriate.
Table 7. Results of goodness of fit.
Goodness-of-Fit Index Recommended Threshold Model Fit Results
CMIN/DF ≤3 2.154
GFI ≥0.9 0.830
RMR ≤0.05 0.027
RMSEA ≤0.08 0.088
NFI ≥0.9 0.893
TLI ≥0.9 0.927
IFI ≥0.9 0.940
CFI ≥0.9 0.939
CMIN: relative chi-square index; DF: degree of freedom; GFI: goodness of fit; RMR: root mean square resid-
ual; RMSEA: root mean square error of approximation; NFI: normed fit index; TLI: Tucker–Lewis index; IFI:
incremental fit index; CFI: comparative fit index.
4.3.1. Verification of Hypotheses H1-1, H1-2, and H1-3
To explore the impact relationships among the independent variables and dependent
variables presented in the research model, the research hypotheses were examined. In this
study, the internal consistency and validity of the measurement questions for each variable
were secured. Additionally, the structural model of this study was judged to be appropriate.
Therefore, the results of verifying the hypothesis for the research model were confirmed.
The results of the hypothesis verification are shown in Table 8.
Table 8. Results of the hypothesis tests.
Hypotheses Path
Standardized
Coefficent SE t Decision
H1-1 AES →EMO −0.016 0.070 −0.228 Not supported
H1-2 SUR →EMO 0.127 0.089 1.437 Not supported
H1-3 SPA →EMO 0.681 0.091 7.480 *** Supported
H2-1 AES →REV −0.113 0.066 −1.703 * Supported
H2-2 SUR →REV 0.042 0.084 0.497 Not supported
H2-3 SPA →REV 0.073 0.105 0.695 Not supported
H3 EMO →REV 0.825 0.119 6.958 *** Supported
*p< 0.1, *** p< 0.01. SE: standard error
Regarding the impact relationship of the ISM’s e-servicescape on emotional responses,
since the path coefficient from aesthetics to emotional responses among the components of
the e-servicescape is
−
0.016 (t =
−
0.228, p= 0.820), H-1 was statistically insignificant. Thus,
hypothesis H1-1 was not supported. This result implied that it was difficult for customers
to feel an emotional response about ISM through aesthetic factors such as ‘sophistication’
and ‘beauty’ of the site design.
Since the path coefficient from surrounding elements to emotional responses was 0.127
(t = 1.437, p= 0.151), H1-2 was statistically insignificant. Thus, hypothesis H1-2 was not
supported. This result implied that surrounding elements were found to have no effect
on emotional responses. This can be seen from the fact that customers did not have many
feelings about the background music and video presentation of the internet shopping mall.
Since the path coefficient from spatial functionality to emotional responses was 0.681
(t = 7.480, p< 0.01), H1-3 was statistically significant. Thus, hypothesis H1-3 was supported.
Spatial functionality was found to have a positive (+) effect on emotional responses. It
implied that the higher the convenience of using the internet shopping mall and the ease of
information search and browsing, the higher the emotional responses felt by customers.
Therefore, it was verified that among the components of the e-servicescape, aesthetics
and surrounding elements did not have effects on emotional responses, but spatial func-
J. Theor. Appl. Electron. Commer. Res. 2024,19 2045
tionality had a positive effect on emotional responses. Hypotheses H1-1 and H1-2 were not
supported, and H1-3 was supported.
4.3.2. Verification of Hypotheses H2-1, H2-2, and H2-3
In the impact relationship between the e-servicescape and revisit intention in the ISM,
the path coefficient from aesthetics to revisit intention was
−
0.113 (t =
−
1.703, p< 0.1). It
was found that hypothesis H2-1 was supported at the significant level of 0.1. This means
that aesthetics had an effect on revisit intention, but it showed a negative effect. The reason
for this negative effect could imply that excessive aesthetics in the e-servicescape can have
a negative, rather than positive, impact on customers’ intention to revisit.
The path coefficient from surrounding elements to revisit intention was 0.042 (t = 0.497,
p= 0.619), which was insignificant, and hypothesis H2-2 was not supported. It means that
surrounding factors did not appear to have a positive influence on revisit intention.
The path coefficient from spatial functionality to revisit intention was 0.073 (t = 0.695,
p= 0.487), which was insignificant, and hypothesis H2-3 was not supported. It means that
spatial functionality did not appear to influence revisit intention.
Therefore, it may imply that the aesthetics of the e-servicescape in the ISM did have
a negative effect on customers’ intention to revisit, even though the effect is weak. Sur-
rounding elements and spatial functionality did not influence customers’ intention to
revisit.
4.3.3. Verification of Hypothesis H3
In the impact relationship between emotional responses and revisit intention, the path
coefficient from emotional responses to revisit intention was significant at 0.825 (
t = 6.958
,
p< 0.01), and hypothesis H3 was supported. Emotional responses were found to have a
positive (+) effect on revisit intention. This result implied that the more emotional responses
that customers feel, the higher their intention to revisit the ISM.
5. Conclusions
5.1. Study Summary
ISMs can promote the formation of business trade by providing a bridge between
ISM stores and customers and are one of the representative models in B2C (business-
to-consumer) e-commerce. It was recognized that designing the components of the e-
servicescape of ISMs is very important if ISM companies want to maintain long-term
customer relationships and increase their competitiveness. This study classified the compo-
nents of the e-servicescape into three factors as follows: aesthetics, surrounding elements,
and spatial functionality as independent variables; and revisit intention and emotional
responses as the dependent variables. Also, this study examined the causal relationship
between the emotional response and revisit intention. This study analyzed how the compo-
nents of the e-servicescape affect customers’ emotional responses and revisit intention, and
how emotional responses affect revisit intention focusing on MZ generation consumers
with purchasing experience in a certain ISM setting.
The research hypothesis was verified through empirical analysis, and the results
are summarized as follows. First, as a result of examining how the components of the e-
servicescape of an ISM affects customers’ emotional responses, this study found that among
the components of the e-servicescape, aesthetics and surrounding elements did not have a
significant effect on customers’ emotional responses. It was found that spatial functionality
had a positive effect on customers’ emotional responses. This is consistent with the results
of previous studies showing that spatial functionality has a significant effect on customers’
emotional responses [
77
,
78
]. These results mean that spatial functions such as convenience
of use and ease of information search and browsing of ISM are very important. If customers
can quickly and conveniently obtain information about the products that they want, they
can feel more positive emotional responses.
J. Theor. Appl. Electron. Commer. Res. 2024,19 2046
Second, as a result of examining how the components of the e-servicescape of an ISM
affects the intention to revisit, it was found that aesthetics did have a significant effect
on the intention to revisit, while surrounding elements and spatial functionality did not
have a significant effect on the intention to revisit. It was found that the components of
the e-servicescape partially have a negative effect on customers’ intention to revisit. This
finding is controversial. Similar findings were proposed by previous studies showing
that aesthetic features reflect positive emotions and improve users’ satisfaction, which
also reflects higher rates of revisit intention [
65
,
66
], while ref. [
79
] showed that users
paid relatively less attention to spatial functionality and prefer the combination of new
technologies and the analysis of users’ perceptions on a large scale.
Third, as a result of examining how customers’ emotional responses affect revisit
intention, emotional responses were found to have a positive (+) effect on revisit intention.
This is consistent with the argument by previous studies that addressed that enjoyment
among customers’ emotional responses has a significant effect on revisit intention, and that
the more joy that customers feel, the more likely they are to revisit an ISM and increase
their return visits [63,73,74].
5.2. Theoretical Implications
Based on the empirical analysis results, this study is to present the following theo-
retical implications. First, this study demonstrates the effects of the components of the
e-servicescape on customers’ emotional response and revisit intention, and the effect of
customers’ emotional reactions on revisit intentions. Through this empirical study, the
theoretical foundation was established for the e-servicescape of an ISM industry. Thus,
this study differentiates from previous research on emotional responses to the similar
business environment by identifying that spatial functionality of the e-servicescape can
affect consumers’ emotional responses in an ISM environment. Among the components of
the e-servicescape in an ISM, aesthetics and surrounding elements did not have a signifi-
cant effect on emotional responses, and spatial functionality had a positive (+) effect on
emotional responses.
Second, this study confirmed that the e-servicescape factors that affect consumers’
emotional response and intention to revisit are different. By examining whether the emo-
tional response affects revisit intention in an ISM environment, this study was able to
identify the causal relationship between the two variables.
Third, in previous studies, there was little literature on the e-servicescape in an ISM
environment. Therefore, this study is meaningful in that the study model provided an inte-
grated research model on the relationship among the e-servicescape, emotional response,
and purchase intention.
5.3. Managirial/Practical Implications
With the rapid development of the e-commerce industry and in combination with the
ISM-based business environments, this study findings suggested the following manage-
rial/practical implications. First, from a managerial and practical perspective, customer
satisfaction can be increased by utilizing the e-servicescape factors that evoke emotional
responses on the ISM. In particular, if the importance of spatial functionality is recognized
and utilized more effectively, the customer’s revisit to the ISM can be much more improved.
Second, by providing appropriate e-servicescape factors suited to consumer charac-
teristics, such as age and region, it will be possible to maximize satisfaction and increase
customer share by adjusting the ISM environment to suit the needs of the customized
services. For example, since positive emotional responses to ISMs may differ depending on
age and region, managers and decision-makers can create an ISM environment tailored to
their needs. Therefore, it is important to build an e-servicescape environment that matches
these characteristics.
Third, the analysis results of this study showed that designing the components of
the e-servicescape, responding customers’ emotion, and identifying revisit intention have
J. Theor. Appl. Electron. Commer. Res. 2024,19 2047
become very important and can improve user satisfaction as well as managerial competency.
Therefore, it is necessary to recognize the importance of spatial functionality and establish
effective measures to improve spatial functionality. Accordingly, to increase the emotional
response of customers, a convenient ISM environment must be provided to customers, and
efforts must be made to increase the ease of product information search and the speed of
navigation. Business managers and decision-makers need to pay attention to the design
and management of spatial functionality in ISMs.
5.4. Study Limiations and Future Study Directions
This study has several limitations. First, the questionnaire consisted of a total of
26 questions, and a total of 558 copies were collected. Statistical analysis was conducted on
the final 150 valid samples of customers in their twenties and thirties, and there were many
invalid samples. Additionally, this study only focused on a specific age of customers from
a specific country. Also, there are limitations because the demographic characteristics of
the sample are not appropriately balanced by gender and age group.
Second, in this study, the e-servicescape of a specific ISM was classified and studied
into the following three dimensions: aesthetics, peripheral elements, and spatial func-
tionality. However, considering previous studies, the components of the e-servicescape
may be more diverse and subdivided. So, this study only on these three elements has its
own limitation.
Third, in this study, only emotional responses were studied among customers’ emo-
tional reactions. In an ISM environment, in addition to emotional responses, customers
may also feel other emotions.
Future research directions are as follows. First, future research can broaden the scope
when selecting research subjects and conducting research targeting customers. Also, when
conducting a survey, the universality of the research can be pursued by collecting more
data. Second, future research needs to further increase and subdivide the components of
the e-servicescape and consider the e-servicescape as a second-order construct, examining
the relationship between the e-servicescape and emotional responses and between the
e-servicescape and revisit intention. Third, future research needs to conduct additional
research on the elements of emotional reactions that customers may experience in an
internet shopping environment. In addition, there is a need for research into whether these
emotional reactions affect customer behavior.
Author Contributions: Conceptualization, Z.L. and C.W.L.; literature reviews, A.B.T.-P. and C.W.L.;
methodology, Z.L., A.B.T.-P. and C.W.L.; software, Z.L. and C.W.L.; validation, Z.L., A.B.T.-P. and
C.W.L.; formal analysis, Z.L. and C.W.L.; investigation, Z.L., A.B.T.-P. and C.W.L.; resources, Z.L.,
A.B.T.-P. and C.W.L.; data curation, Z.L. and C.W.L.; writing—original draft preparation, Z.L., A.B.T.-P.
and C.W.L.; writing—review and editing, A.B.T.-P. and C.W.L.; visualization, A.B.T.-P. and C.W.L.;
supervision, A.B.T.-P. and C.W.L.; project administration, A.B.T.-P. and C.W.L. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available upon request to the
corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest.
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