Website Quality Analysis, Trust and Loyalty of Indonesian Railway
Service
MIKAEL JULIAN IRSA, MAHIR PRADANA
Department of Business Administration,
Telkom University,
Jalan Terusan Buah Batu, Bandung 40257,
INDONESIA
Abstract: - This study intends to discover the analysis of quality websites to consumer trust and loyalty since
online transaction activities are quickly increasing. The number of online clients in Indonesia grows year after
year, as does the number of online shops selling different internet-based media. Opportunities for online stores
are expanding, but competition is also rising. For customers to be trusted and loyal, online business owners
must understand and meet their customers' desires. The study employed quantitative research methodologies,
and the sample size was 100 respondents, who were analyzed using path analysis and the third edition of the
SmartPLS program. Data acquired will be picked first, and unnecessary data will be removed, before being
filtered and processed through data gathering; this stage is required. The analysis findings are extremely
maximum in deciding the final result, and the study results indicated that all factors have a positive and
substantial influence on the result, which is very nice and acceptable.
Key-Words: - Website Quality Analysis, Customer Trust, Customer Loyalty, Digital Business
Received: June 11, 2022. Revised: September 15, 2023. Accepted: October 19, 2023. Available online: November 22, 2023.
1 Introduction
Online business development in Indonesia is
increasingly accelerating, [1]. Online shopping e-
commerce is one method of shopping that uses
electronic communication tools or social networks
in buying and selling transactions, where buyers do
not need to go to the store to see and buy what they
are looking for, but instead simply look at the
desired item on the internet and order the item
accordingly, [2]. option and transmit the money
through the internet, the products will be delivered
to the house by the online retailer, [3].
According to the Indonesian Information
Portal, the e-commerce sector in Indonesia is
becoming more promising, [4]. Despite the
epidemic, this digital-based trade company is
expected to increase 33.2 percent, from Rp253
trillion to Rp337 trillion by 2021. E-commerce acts
as a third party, allowing both sides to purchase and
sell online, [5]. Online purchasing has now become
the preferred method for people due to its
convenience. Along with the advancement of
browsing, additional methods of purchasing online
or employing internet facilities that have the
advantage of service reach, efficiency, and security
assurances have emerged. Online shopping is a
modern retail invention that makes it easier for
people to purchase the things they want, [6].
Online transaction activities are rapidly
expanding. The rise in online store clients from year
to year has been extremely large, and it is aided by
the growing number of online retailers. It may be
found in several internet-based media. Opportunities
for online stores are expanding, but competition is
also rising. For customers to be trusted and loyal,
online business owners must understand and meet
their customers' desires. The issues are rather
complex since stores compete for clients from those
that come. Similarly, prospective buyers and online
media users will utilize the online store wherever
they can. Online business operators must give the
greatest service possible by paying attention to
website quality to persuade visitors, build trust, and
increase client loyalty, [7].
Trust is one of the numerous aspects that
influence the incidence of buying and selling
transactions in an online store. Only clients who
trust will be willing to do transactions via the
internet. It is difficult to conduct internet
transactions without the trust of customers, [8].
Several studies on the link between service quality
and customer trust, including a study entitled
"Model of the Relationship among Consumer Trust,
Web Design, and User Attributes" claimed that
completing buying and selling activities or
transactions online is a matter of trust. It's simple,
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DOI: 10.37394/232018.2024.12.14
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E-ISSN: 2415-1521
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yet consumer trust in the organization is extremely
susceptible and tough to obtain. According to this
study, the quality of a website can boost the trust of
customers who conduct online transactions, because
consumers who transact online rely only on
information obtained from websites that provide
these items or services, [9].
According to the research, vendors with
excellent website quality, even if they do not have a
strong reputation, are more trusted than sellers with
good reputations but poor website quality, [10]. The
quality of service has an impact on customer
loyalty, however, the quality of service did not
affect customer loyalty. This survey was performed
on internet banking, and some of the respondents
were accustomed to dealing with humans.
Transactions made using an Automatic Teller
Machine (ATM) or other electronic equipment may
result in disappointment due to interference. Some
aspects of service quality, such as tangibles and
timeliness, do not have a major influence on
customer happiness and loyalty, [11]. The research
was conducted on the subject of the computer
software industry, where respondents were satisfied
with the quality of the computer software used, but
most of them were hesitant to buy back due to
several factors, including the high cost of the
software and the use of the software, [12]. It
necessitates sophisticated computer requirements,
[13].
As a result, the consistency of the findings of
the influence of service quality in online shops is
still weak, as is the quality of the website to loyalty.
Based on the foregoing data, further study into the
impact of website quality on trust and trust loyalty
among online store consumers is required.
2 Literature Review
Consumer Behavior
From the end of the 1960s, consumer behavior was
a relatively new subject of research, [14]. Because
the field of consumer behaviour studies has no
history of research, marketing management theory
draws on ideas from other disciplines, such as
psychology, which studies individuals, sociology,
which studies groups of people/society, social
psychology, which studies how an individual acts in
a group, anthropology, which studies the impact of
society on an individual, and economics to form the
basis of a new theory, [15]. Consumer behavior is
concerned with how an individual decides to spend
their resources (time, money, and effort) in
purchasing connected goods and services.
Website Quality
The quality of a website is one of the notions that
measures the end user's perception of a web page.
This notion is the result of Servqual's research into
the dissemination of high-quality services that were
previously extensively used, [16]. Website Quality,
or Website Quality, has been established since 1988
and has seen significant development . Website
quality is based on studies in three domains,
namely: information quality from information
systems research, interaction and service quality
from information systems research, e-commerce and
marketing, and usability (usability) of human-
computer interaction, [17].
Trust
A party's belief in the intentions and behavior of
others is defined as trust. As a result, consumer
confidence is defined as a belief in a service
provider's commitment to satisfy a party's
expectations. Trust is referred to as credibility.
Credibility in the research is regarded as the degree
to which customers think providers have the
knowledge to fulfill operations effectively and
dependably, [18]. Believing is a virtue since it is
founded on the amount to which corporations feel
their partners have positive goals and purposes,
[19].
Customer Loyalty
One of the most essential outcomes of an internet
business is customer loyalty. Loyalty is perhaps a
crucial predictor of an organization's success in a
competitive market setting. Consumer loyalty is "a
firmly held commitment to consistently buy back a
selected product or service in the future, thus
leading to repeated purchases of products or
services with the same brand, despite situational
influences and marketing efforts that have the
potential to cause behavior to switch to another
brand." Efforts that can affect behavior "products or
services sold under other brands" Based on value
propositions, loyalty, branding, trust and security,
websites and technology, and customer service,
[20]. In the natural e-commerce setting, trust and
satisfaction influence consumer loyalty. As a result,
people who trust an online business and opt to buy
other goods and commit to the product supplied are
referred to as loyal customers. Furthermore,
committed consumers may be able to further
introduce subscription items to the surrounding
ecosystem.
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3 Methodology
Making transactions online is simple, but consumer
trust in the organization is extremely susceptible and
difficult to establish. The quality of a website can
boost the confidence of consumers who do online
transactions, [21]. At the level of customer trust in
transacting online, there is a beneficial relationship
between the company's reputation and the quality of
the website, either separately or combined. Sellers
with good website quality, even if they don't have a
good reputation, are more trustworthy than sellers
with a high reputation but bad website quality. The
given explanation leads to the hypothesis:
H1: Website quality influences customer trust in
online stores.
Trust determines buying interests, which is one type
of client loyalty. consumer trust in online stores
influences purchasing interest, which is one type of
consumer loyalty. Many consumers do not make
purchases due to a lack of trust, thus online store
owners must work hard to earn the trust of their
clients. Loyalty and trust are inextricably linked.
The hypothesis in this study is based on this
description:
H2: Trust influences client loyalty in online stores.
H3: A high-quality website influences online store
consumer loyalty.
H4: Website quality influences client loyalty via
consumer trust.
The research model is presented in Figure 1.
Fig. 1: Research Model
4 Results
Research techniques are fundamentally scientific
methods of obtaining data with the goal of
discovery, proof, and progress, and may be used to
analyze, solve, and foresee issues, [22]. This study
employs quantitative descriptive approaches.
Quantitative research techniques may be defined as
a research approach based on the concept of
positivism that uses random sampling to observe
samples or populations. it is collected using research
tools, and it is analyzed quantitatively / statistically
to evaluate the hypothesis that has been formulated,
[23].
1. Results
After the dissemination of questionnaires,
researchers grouped respondents into several of
these criteria:
Table 1. Sociodemographic Data
Profile
Sum
Percentage
Woman
63
63%
Man
37
37%
Entire
100
100%
<17 years
3
3%
18-25 years
94
94%
26> years
Entire
3
100
3%
100%
<1,000,000
36
36%
1.000.000-
3.000.000
45
45%
3.000.000-
5.000.000
>5.000.000
Entire
15
3
100
15%
3%
100%
1-5 times
38
38%
Above 5 times
62
62%
Entire
100
100%
Source: Author Results 2021
To gather data in accordance with the chosen
model, relevant data-collecting techniques and
methodologies must be used throughout the study
implementation. The author's data-collecting
approach of choice at this moment is the
questionnaire method. Instagram, Whatsapp, and
Line are examples of online surveys or Google
forms that are delivered through online media. The
study included 100 participants, including 63
women and 37 males ranging in age from 17 to 26
years old.
The data acquired from the distribution of
surveys is further analyzed quantitatively using the
SmartPLS program. This application's data
processing mechanism is bootstrapping, which may
also be recognized by random doubling. SmartPLS
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is a method for describing the existence or lack of
links between latent variables. The Partial Least
Squares approach may be used to do analysis using
reflexive and formative indicators.
Data is grouped into sociodemographic criteria
to categorize respondent data into numerous
categories such as extremely disagree criteria,
disagree, simply agree, agree, and strongly agree to
be examined for data quality and validity. The data
in Table 1 above is divided into four categories
depending on gender, age, income, and frequency of
spending in the previous year. The standard loading
factor is the link between each indication in the
questionnaire and the indicator of the outer loading
value in this test. For any indicator to be legitimate
at this stage of the investigation, the outer loading
value must be between 0.6 and 0.7.
Table 2. Loading Factor /Outler Loading
CODE
STATEMENT
XI
(WQ)
X2
(CT)
AND
(CL)
CL1
I will promote this
shopping website to
my close friend and
many people others
0,638
CL2
I am sure that in the
years to come, I will
continue to shop at
this shopping site
0,800
CL3
I can hardly consider
changing to other
shopping websites.
0,513
CL4
I will recommend
this online shopping
to someone who asks
my advice on online
shopping.
0,767
CL5
I will say positive
things about this
online shopping to
other people.
0,811
CT1
I believe that the
online website has
the transaction
expertise I expected
0,738
CT2
I believe that online
websites can meet
the needs and
expectations of their
customers
0,814
CT3
I believe that online
websites have skills
in providing services
to their customers
0,713
CT4
I believe that the
online shop will be
honest in conducting
transactions with its
0,634
CODE
STATEMENT
XI
(WQ)
X2
(CT)
AND
(CL)
customers
WQ1
The online website
provides a customer
testimonial column
that makes it easy to
evaluate a product
0,715
WQ2
The online website
provides a
transaction guide
0,584
WQ3
WQ4
WQ5
WQ6
WQ7
The online website
displays a visual
design that is
comfortable to see
The online website
has a fast response
The product
information listed on
the website is
complete and easy to
understand
I find it easy to use
online websites
Product information
listed on the website
online details
Product information
listed on the website
online details
0,657
0,611
0,794
0,789
0,818
Source: Author Results
Based on the results of loading factor/outlier
loading in Table 2 three variables are removed,
namely the statement I believe that the online shop
will send the product according to the shipping term
described on the website (I believe that the online
shop will send you product products under the
delivery provisions described in the website), I
believe that the online shop will send the product
according to the description written on the website
(I believe that the online shop will send the product
in accordance with the description written on the
website), I believe that online shop will listen and
consider suggestions from customers. The
elimination is carried out because the loading
factor/outler loading result value is less than 0.5,
indicating that low outer loading values on the
construct suggest that associated indicators have a
lot of similarities. the construct has been captured.
Table 3. Average extracted variance (AVE)
Variable
AVE
X1 WQ
0,511
X2 CT
0,529
AND CL
0,511
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Source: Author Results
The Average Variance Extracted (AVE) test is
designed to put to the test theories that characterize
the size of indicator variations supported by
construction. If the AVE value threshold for each
indicator is more than 0.5, it indicates that the
indicator has excellent convergent validity. As
shown in Table 3, he average variance value for the
website quality variable is 0.511. The customer trust
variable has a value of 0.529, where a value greater
than 0.5 indicates. Variael customer loyalty has a
value of 0.511, where a number greater than 0.5
indicates. All variables have a value greater than
0.5, indicating that they both fulfill the requirements
of the AVE test and the validity test, indicating that
the concept explains more than half of its indicator
variations on average.
Table 4. Fornell Larcker
X1 (WQ)
X2 (CT)
AND (CL)
X1 (WQ)
0,715
0,496
0,403
X2 (CT)
0,727
0,430
AND (CL)
0,715
Source: Author Results 2021
The Fornell Larcker criteria is a method for
determining discriminant validity. This compares
the square root value of the AVE value to the latent
variable's correlation. Each AVE construct's square
root value must be bigger than the highest
connection with the other construct. It is used as part
of Fornell Larcker's criterion to examine whether
AVE is larger than the quadratic correlation with
other constructs. The reasoning of the Fornell-
Larcker approach concept is based on the premise
that related indicators share greater variance with
constructs than other constructs.
Indicator outer loadings should be greater than
all cross-loadings with other constructs, according
to classic discriminant validity evaluation
techniques (cross-loadings and Fornell-Larcker
criteria, Table 4). According to Table 5, the value of
the cross-loading indicator is bigger than the
association with the correlation of the values of each
other constructs.
Table 5. Cross Loading
CODE
XI (WQ)
X2 (CT)
AND (CL)
CL1
0,416
0,347
0,638
CL2
0,335
0,319
0,800
CL3
-0,001
0,125
0,513
CL4
0,246
0,242
0,767
CL5
0,210
0,376
0,811
CT1
0,417
0,738
0,239
CT2
0,546
0,814
0,323
CT3
CT4
0,175
0,185
0,713
0,634
0,346
0,375
WQ1
wQ2
WQ3
WQ4
WQ5
WQ6
WQ7
0,715
0,584
0,657
0,611
0,794
0,789
0,818
0,379
0,148
0,292
0,220
0,241
0,469
0,522
0,386
0,283
0,185
0,224
0,210
0,375
0,274
Source: Author Results 2021
Table 6. Reliability Test Results
Composite Reliability
Alpha Cronbach
X1 (WQ)
0,878
0,840
X2 (CT)
0,817
0,710
AND (CL)
0,836
0,773
Source: Author Results
When the reliability test result is more than 0.6,
it indicates that the criteria are recommended and
have high indication reliability. Tables 6 shows that
all variables, which are website quality, customer
trust, and customer loyalty all have values greater
than 0.6, indicating that the indicator is reliable.
Essentially, this study employs one dependent
variable, customer loyalty, which is impacted by
two independent factors, website quality and
consumer trust.
Table 7. R-Square Value
Variable
R-Square
X2 (CT)
AND (CL)
0,233
0,246
Source: Author Results
Table 7 shows that the R-square value of data
processing results using Smart PLS amounted to
24.6% of website quality variables and customer
trust can influence customer loyalty variables. The
R-value is also found in the customer trust variable
of 23.3%.
Based on the results of the analysis in Figure 2,
it can be concluded that the effect of website quality
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on online shop customer trust shows a path
coefficient value of 0.496 which means there is a
positive influence on customer trust. online shop.
Then having a statistical value of t 7.503, the value
is greater than 1.96 and the value p 0.000 means it
shows a significant effect. This means that website
quality has a positive effect on the trust variables of
online shop customers. Therefore, the hypothesis
regarding website quality affects the rust of online
shop customers is acceptable.
Fig. 2: Path Model
Table 8. Hypothesis Test
Variable
Orig
inal
Sam
ple
Exa
mple
s of
avera
ges
Stand
ard
Devia
tion
(STD
EV)
Statis
tics T
(O/S
TDE
V)
P
Va
lue
s
Website Quality -
> Customer Trust
0.49
6
0.51
9
0.066
7.503
0,0
00
Website Quality -
> Customer
loyalty
0.25
2
0.25
9
0.119
2.114
0,0
00
Customer Trust ->
Customer Loyalty
0.30
5
0.32
7
0.129
2.367
0,0
00
Website Quality -
> Customer
Loyalty mediated
by Customer Trust
0.15
1
0.16
1
0.077
1.976
0,0
00
Source: Author's Results (2021)
As elaborated in Table 8, the study findings
suggest that the effect of website quality on online
shop customer loyalty has a path coefficient value of
0.252, indicating that there is a positive influence on
online shop customer loyalty. The statistical value
of t 2.114, which is larger than 1.96, and the value
of p 0.000 indicate that there is a substantial impact.
This suggests that the quality of a website has a
beneficial influence on the loyalty variables of
online shoppers. As a result, the idea that great
websites impact consumer loyalty in online stores
may be accepted.
According to the findings of the investigation,
the effect of customer trust on online shop customer
loyalty has a path coefficient value of 0.305,
indicating that there is a positive influence on online
shop customer loyalty. The statistical value of t
2,367, which is more than 1.96, and the value of p
0.000 indicate that there is a substantial impact. This
suggests that customer trust has a positive influence
on the customer loyalty variable in an online store.
As a result, the theory that consumer trust influences
online store customer loyalty is acceptable.
According to the findings of the study, the
impact of website quality on online consumer
loyalty is significant with a path coefficient value of
0.151 which means there is a positive influence on
online shop customer loyalty. Thus, having a
statistical value of t 1.976, the value is greater than
1.96 and the value p 0.000 means it shows a
significant effect. This means that customer trust has
a positive effect on the online shop customer loyalty
variable. Therefore, the hypothesis regarding quality
websites affecting the loyalty of online shop
customers can be accepted.
5 Conclusion
Three factors may influence the chance of growing
sales activity in online retailers in the community's
mushrooming online purchasing activities. This is
based on a study of 100 people who had bought
from online businesses. The three aspects are
website quality, trust, and client loyalty. The
presence of the influence was demonstrated by the
authors' research in this study, which was conducted
by examining these three factors using the
SmartPLS program. Based on the facts, we can infer
that our three original assumptions were valid after
utilizing the SmartPLS tool to analyze them.
Initially, researchers had three hypotheses in this
study, the first of which was that the initial quality
website would impact the trust of online store
consumers. Second, trust influences client loyalty in
online stores. Third, the quality of a website
influences the loyalty of online shoppers. All
independent variables have a substantial effect, and
their influence has a favorable effect on consumer
trust and loyalty, allowing all of the researcher's
hypotheses in this work to be accepted.
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References:
[1] Dewi, W.S., Pradana, M., Sari, D., Nugraha,
D.W., Adiputri, L.C. (2021). The influence of
knowledge, social media utilization and
motivation on entrepreneurial intention (Case
study: Telkom university students).
Proceedings of the International Conference
on Industrial Engineering and Operations
Management, pp. 6840–6847.
[2] Eugene W. Anderson and Mary W. Sullivan,
(1993). The Antecedents and Consequences of
Customer Satisfaction for Firms. Marketing
Science, 12, (2) 125-143
[3] Gregg, D.G. & Walczak, S. (2010, March).
The relationship between website quality,
trust and price premiums at online auctions.
Journal Electronic Commerce Research.
10(1), pp. 1-25.
[4] Gutavsson, M. & Johansson, A.
(2006).Consumer Trust in E-Commerce,
[Online], http://www.diva-portal.
org/smash/get/diva2:230780/FULLTEXT01.p
df (Accessed Date: 19 October 2021).
[5] Ganesan, S. (1994). Determinants of
Longterm Orientation in Buyer-Seller
Relationship, Journal Marketing. 58, pp. 1-19.
[6] Kartawinata, B.R., Pradana, M., Akbar, A.,
Trenggana, A.F.M., Cahyaningrum, S.D.
(2021). The effect of easy perception and risk
of users of financial technology services in
smes of bandung, Indonesia. Proceedings of
the International Conference on Industrial
Engineering and Operations Management, 59,
pp. 997–1007.
[7] Kartawinata, B. R., Pradana, M., Akbar, A.,
Trenggana, A. F. M., & Cahyaningrum, S. D.
(2020). The effect of easy perception and risk
of users of financial technology services in
smes of bandung, indonesia. In Proceedings of
the International Conference on Industrial
Engineering and Operations Management
(Vol. 59, pp. 997-1007).
[8] Susanty, A. I., Yuningsih, Y., & Anggadwita,
G. (2018). Knowledge management practices
and innovation performance: A study at
Indonesian Government apparatus research
and training center. Journal of Science and
Technology Policy Management.
[9] Syarifuddin, S., Pradana, M., Fakhri, M.,
Putra, A. D. P., & Arwiyah, M. Y. (2021).
The Effect of Knowledge Management, Skill
and Attitude on Employee Performance at
Telkom Education Foundation. Webology,
18(2).
[10] Wijayangka, C., Kartawinata, B. R., &
Novrianto, B. (2018). Pengaruh Motivasi
Terhadap Minat Berwirausaha Mahasiswa
Program Studi Administrasi Bisnis
Universitas Telkom. ECo-Buss, 1(2), 8-14.
[11] Willayat, F., Saud, N., Ijaz, M., Silvianita, A.,
& El-Morshedy, M. (2022). Marshall–Olkin
Extended Gumbel Type-II Distribution:
Properties and Applications. Complexity,
2022.
[12] Zhao, Y., Wang, L., Tang, H., & Zhang, Y.
(2020). Electronic word-of-mouth and
consumer purchase intentions in social e-
commerce. Electronic Commerce Research
and Applications, 41, 100980.
https://doi.org/10.1016/J.ELERAP.2020.1009
80
[13] Chin. (1998). Handbook of Partial Least
Squares: Concepts, Methods and
Applications. Springer Berlin Heidelberg. In
The Journal of biological chemistry (Vol. 206,
Issue 1).
[14] Asghar, F., Mahmood, S., Khan, K. I.,
Qureshi, M. G., & Fakhri, M. (2021).
Eminence of leader humility for follower
creativity during COVID-19: the role of self-
efficacy and proactive personality. Frontiers
in Psychology, 12.
[15] Ghozali, I., & Hengky, L. (2015). Konsep,
Teknik, Aplikasi Menggunakan Smart PLS
3.0 Untuk Penelitian Empiris. Badan Penerbit
Universitas Diponegoro.
[16] Hair, J. F., Risher, J. J., Sarstedt, M., &
Ringle, C. M. (2019). When to use and how to
report the results of PLS-SEM. European
business review, 31(1), 2-24.
[17] Henderson, R., & Divett, M. J. (2003).
Perceived usefulness, ease of use and
electronic supermarket use. International
Journal of Human-Computer Studies, 59(3),
383–395. https://doi.org/10.1016/S1071-
5819(03)00079-X
[18] Abdillah, W., & Hartono, J. (2015). Partial
Least Square (PLS) - Alternatif Structural
Equation Modeling (SEM) dalam Penelitian
Bisnis. ANDI.
[19] Pratomo, T. P., & Suhartati, W. S. (2021,
July). Identification of AKHLAK Values-
Based Management’s Determinant in
Indonesia State-Owned Enterprises
Ecosystem. In The 2021 12th International
Conference on E-business, Management and
Economics (pp. 610-615).
[20] Kartawinata, B. R., Wardhana, A., Akbar, A.,
& Dewi, A. R. C. (2021). The Effect of
WSEAS TRANSACTIONS on COMPUTER RESEARCH
DOI: 10.37394/232018.2024.12.14
Mikael Julian Irsa, Mahir Pradana
E-ISSN: 2415-1521
149
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Confidence, Motivation, and Innovation on
the Interest in Entrepreneurship of Indonesian
Students (Case Study on Students at Telkom
University). In Proceedings of the
International Conference on Industrial
Engineering and Operations Management (pp.
2055-2063).
[21] Riache, H., & Pradana, M. (2023).
Continuance Intention of Social Networking
Services in Indonesia. WSEAS Transactions
on Environment and Development, 19, 489-
493.
[22] Asghar, F., Mahmood, S., Iqbal Khan, K.,
Gohar Qureshi, M., & Fakhri, M. (2022).
Eminence of leader humility for follower
creativity during COVID-19: the role of self-
efficacy and proactive personality. Frontiers
in Psychology, 12, 790517.
[23] Hasbi, I., Fakhri, M., Saragih, R., Kurnia, B.,
& Aini, A. G. (2020). Determinant factors of
consumer preferences on electronic wallet
users in bandung. In Proceedings of the
International Conference on Industrial
Engineering and Operations Management,
Vol. 59, pp. 914-919.
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Mikael Julian Irsa carried out the field survey,
data analysis, and optimization.
- Mahir Pradana was responsible for the
conceptualization and review.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The authors report no source of funding.
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en
_US
WSEAS TRANSACTIONS on COMPUTER RESEARCH
DOI: 10.37394/232018.2024.12.14
Mikael Julian Irsa, Mahir Pradana
E-ISSN: 2415-1521
150
Volume 12, 2024