p–ISSN: 2723 - 6609 e-ISSN: 2745-5254
Vol. 5, No. 12, Desember 2024 http://jist.publikasiindonesia.id/

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6022

Revitalizing Loyalty: Unveiling the Dynamics of E-Service
Quality, Customer Satisfaction, and Trust in Amplifying

User Engagement with Electricity Mobile Application

Rahayu Hafiza, Yeshika Alversia
Universitas Indonesia, Indonesia
Email: [email protected]

*Correspondence
ABSTRACT

Keywords: e-service
quality; customer
satisfaction; customer
trust; repurchase intention;
word of mouth.

The development of internet-based technology and the
increasing use of smartphones have become essential tools
for companies to enhance their online service quality. This
research aims to analyze the influence of e-service quality,
customer satisfaction, and customer trust on loyalty intention
in optimizing the utilization of the PLN Mobile application.
This study employs a quantitative approach using an online
questionnaire to collect data from 386 respondents using the
PLN Mobile application. The data are then analyzed using
structural equation modeling. The results of this study reveal
that e-service quality has a significant positive impact on
customer satisfaction and customer trust. Furthermore,
customer satisfaction and customer trust are found to have a
positive and significant influence on repurchase intention
and word of mouth in optimizing application usage. These
findings have significant implications in the energy and
information technology industries, particularly in
developing strategies to enhance the utilization of electricity
mobile applications.

Introduction

Almost all activities can be done and accessed using a smartphone (Ahmadi, 2019).
Smartphones can be used by people to communicate with each other and are capable of
various activities, such as internet access, social media applications, and document
processing with good screen resolution (Almaiah et al., 2022). In recent decades,
consumers worldwide have witnessed a dramatic and rapid increase in smartphone usage.
Currently, Indonesia is the fourth-largest smartphone market in the world after China,
India, and the United States (Dhingra, Gupta, & Bhatt, 2020). Indonesia is one of the
largest mobile markets in Asia, characterized by its high degree of dynamism: by 2025,
the number of smartphone users is expected to increase more than threefold compared to
2015. According to a survey report by Indonesiabaik.id that implies implications for the
socio-cultural aspects of Indonesian society, nearly two-thirds of the Indonesian
population already owns a smartphone. Furthermore, according to data from Indonesia.id,

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Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6023

Indonesia ranks as the fourth-largest smartphone user in the world with a total of 192.15
million users as of 2022.

Moreover, throughout 2022, as many as 80% of Indonesia's population owned
smartphones, and over 212 million users accessed the internet from their mobile phones.
Indonesia is emerging as a mobile-first country and one of the fastest-growing app
markets in the world, with revenues reaching $1.7 billion in 2022 (Han & Hyun, 2018).
In 2021, mobile users in Indonesia downloaded 7.31 billion apps, which means that every
minute, more than 13,000 apps were downloaded by mobile users in Indonesia, and on
average, mobile users in Indonesia spent 5.4 hours per day using their mobile devices
(Wu, Hwang, Sharkhuu, & Tsogt-Ochir, 2018). This significant increase in smartphone
usage and mobile app usage in Indonesia can be leveraged by companies to engage more
closely with their customers, enhance services, and increase profits by creating mobile
apps that are fast and easy to access. PT Perusahaan Listrik Negara (Persero) is one of the
companies that has harnessed this development by creating an app called PLN Mobile
(Wang, Lin, & Liu, 2021).

PLN Mobile has become the primary provider for meeting the electricity needs of
the public (PLN, 2021). This program is a flagship of PLN's digital platform to meet all
customer needs, providing convenience and differentiated electricity services. The main
goal of launching this app is to enhance customer focus and loyalty, which can be seen in
active app usage, repeat purchases, and positive recommendations to other customers,
ultimately expected to increase the company's profits. Since its launch in 2016, this app
has been downloaded by more than 35 million users in Indonesia. The emergence of this
app is also a form of PLN's transformation in providing the best services to the public
(PLN, 2022).

However, technology-based application innovations alone are not enough to ensure
that service quality is well met. Sustainable business growth must maintain high service
quality (Jalilvand, Nasrolahi Vosta, Kazemi Mahyari, & Khazaei Pool, 2017). Online
customers expect consistent service quality from the first click to the final product
delivery, and service quality is directly related to a company's profitability (Zia, Rafique,
Rehman, & Chudhery, 2023). According to Miranda et al. (2018), customer satisfaction
is based on the experience with the service provider and the quality of service provided.
This indicates that service quality is often associated with customer satisfaction. Jones
and Suh (2000) concluded that customer satisfaction is based on the cumulative
evaluation of service experience, while (Shaikh, Banerjee, & Singh, 2023) concluded that
customer satisfaction is the evaluation of the customer's purchasing experience. On the
other hand, customer satisfaction has been considered a primary goal of companies. It is
closely related to customer loyalty, which in turn is related to profitability. Over the years,
companies have invested measurable resources to measure and improve customer
satisfaction. Satisfaction does not necessarily lead to customer retention, as existing
findings have shown that satisfied customers also make purchases elsewhere. However,
delivering value to customers is fundamental to marketing activities and effective
competitive advantage.

Revitalizing Loyalty: Unveiling the Dynamics of E-Service Quality, Customer
Satisfaction, and Trust in Amplifying User Engagement with Electricity Mobile
Application

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6024

In their research, (Shafiee & Bazargan, 2018) found that e-service quality has a
positive impact on three customer behavior intentions: purchase intention, site revisit, and
Word of Mouth (WOM). Then, Blut (2016) stated that e-service quality has a positive
effect on customer satisfaction, repurchase intention, and WOM for online customers in
the United States. Several studies have shown that customer satisfaction has a positive
influence on repurchase intention. According to (Mahadin, Akroush, & Bata, 2020),
repeat purchase behavior occurs when customers form a positive attitude toward the
products or services they purchase. Customers with high levels of satisfaction also tend
to be loyal customers. These customers are more likely to spend more and are unlikely to
switch suppliers in their future purchases. In a study by Law et al. (2022), customer
satisfaction was also found to have a positive and significant impact on repeat purchases.
Another study by Miao et al. (2021) on e-customer satisfaction also found its significant
impact on repurchase intention. According to Chang et al. (2013), trust is the most
important factor in attracting e-commerce buyers. However, there has been little research
on the impact of service quality on trust, especially in the online business environment
(Rita et al., 2019). Saleem et al. (2017) tested this in the aviation industry in Pakistan and
found that trust plays a crucial role in driving repurchase intention for all business
services. Blut et al. (2015) developed a hierarchical model of e-service quality to
understand how this e-service quality model can better predict customer behavior than
other instruments. The model was then empirically tested by Rita et al. (2019) to
understand how the impact of e-service quality extends not only to customer satisfaction
and repurchase intention but also to customer trust, Word of Mouth (WOM), and site
revisit.

Thus, the goal of this research is also to test the e-service quality model on customer
satisfaction, customer trust, repurchase intention, and WOM in the specific context of
PLN Mobile. It is hoped that PLN Mobile can be used as the primary application that can
facilitate customers in their lives, not only related to electricity but also other activities,
and have a positive impact on the company.

Method

This research employed a quantitative method and used an online questionnaire for
data collection. The study utilizes nonprobability sampling, specifically the purposive
sampling technique, which is carried out by establishing specific criteria for research
respondents according to the required data (Daniel, 2011). The criteria for respondents in
this study are all active PLN (State Electricity Company) customers aged 17 and above
at the time of questionnaire completion, who have used the PLN Mobile application
within the last 6 months and have reported complaints, issues, or problems related to
products and deliveries through the PLN Mobile application. The questionnaire is created
using Google Forms and consists of 47 questions. The questionnaire link will be
distributed through customers' mobile phone numbers, email, and social media. The

Rahayu Hafiza, Yeshika Alversia

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6025

research data were collected from 386 respondents who are active users of the application
and meet the pre-established criteria.

The questionnaire consists of several sections: (1) containing screening questions
to ensure respondents meet the research criteria, (2) main questions about the tested
variables using a 6-point Likert scale ranging from "strongly disagree" to "strongly
agree," and (3) respondent profiles.

This study tests hypotheses using Partial Least Squares-Structural Equation
Modelling (PLS-SEM). The software used is Smart PLS 3.0. The purpose of using SEM
is to analyze the correlation between multiple variables so that hypotheses between these
variables can be tested, both among indicators and constructs and vice versa.
Additionally, PLS also supports various constructions, can explain complex model
relationships by eliminating unacceptable solutions, and can handle small sample
quantities. PLS can eliminate uncertainty factors and is capable of handling non-normally
distributed data.

Table 1 illustrates the operationalization of variables, showing indicators
representing the variables to be measured in the study. In this research, the independent
variables include e-service quality with four dimensions: efficiency, fulfillment, system
availability, and privacy/security. Additionally, e-recovery service quality with three
dimensions: responsiveness, compensation, and contact, along with customer satisfaction
and customer trust. Meanwhile, the dependent variables in this study are repurchase
intention and word of mouth.

Table 1

Variable Operational Definitions

Variable Dimension Indicator Item Source

E-
Service
Quality

Efficiency
(EFF)

EFF1 1. The PLN Mobile application makes
it easy for me to find what I need.

Parasuraman
et al. (2005)

EFF2 2. It is easy for me to access any menu
and services within the PLN Mobile
application.

EFF3 3. The PLN Mobile application allows
me to complete any transactions,
including electricity services, quickly
and efficiently.

EFF4 4. The information in the PLN Mobile
application is well-managed.

EFF5 5. The PLN Mobile application is very
fast in loading its interface.

EFF6 6. The PLN Mobile application is user-
friendly.

EFF7 7. The PLN Mobile application allows
me to access it quickly.

EFF8 8. The PLN Mobile application is well-
maintained.

Revitalizing Loyalty: Unveiling the Dynamics of E-Service Quality, Customer
Satisfaction, and Trust in Amplifying User Engagement with Electricity Mobile
Application

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6026

Fulfillment
(FUL)

FULL 1 1. The PLN Mobile application
responds to complaints and issues as
promised.

Parasuraman
et al. (2005)

FULL 2 2. The PLN Mobile application
provides easy access to submit
complaints and issues within the
appropriate timeframe.

FULL 3 3. The PLN Mobile application delivers
the products/services I ordered
promptly and effectively.

FULL 4 4. The PLN Mobile application
responds to electricity services
promptly and effectively.

FULL 5 5. The PLN Mobile application follows
up on the complaints and issues I report
regarding electricity products and
services.

FULL 6 6. The PLN Mobile application has
service support as promised by the
company.

FULL 7 7. All information about the products
and services presented in the PLN
Mobile application is accurate.

FULL 8 8. The PLN Mobile application
provides accurate estimates for product
and electricity service delivery.

System
Availability
(SYS)

SYS1 1. The PLN Mobile application is
always available for business and
service needs.

Parasuraman
et al. (2005)

SYS2 2. The PLN Mobile application can be

opened and input the required
information quickly.

SYS3 3. The PLN Mobile application
operates without issues.

SYS4 4. The PLN Mobile application does
not freeze or hang when I input
information related to electricity
products or service needs.

Results and Discussion
Respondent Characteristics

The criteria for respondents in this study are all active PLN customers who use
the PLN Mobile application, aged 17 and above at the time of filling out the questionnaire,
have used the PLN Mobile application in the last 6 months, and have reported complaints,
feedback, or issues related to products and services through the PLN Mobile application.
These two criteria serve as screening questions that respondents must pass. If respondents
meet all of these criteria, they can proceed to answer the core questionnaire. However, if

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Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6027

any of these criteria are not met, respondents will be directed to the closing page and do
not need to continue the questionnaire process.

Based on the data collection conducted, 386 respondents who meet the criteria
were obtained and can be used as data for testing in the main test of the research. This
also exceeds the minimum sample size according to the criteria set by Hair et al. (2019)
for 47 questionnaire indicators. Overall, the profile of the 386 respondents who meet the
research criteria can be summarized in Table 2.

Table 3 summarizes the outer model analysis, wherein the analysis of the outer
model, values is generated for the statistical validity analysis of the measurement models.
The results of the validity test can be observed in the recommended outer loading values
(> 0.708), indicating that the constructs explain more than 50 percent of the indicator
variations, thus providing acceptable item reliability.

The next step is to assess internal consistency reliability, most commonly using
Jöreskog’s (1971) composite reliability, where higher values generally indicate higher
levels of reliability. For instance, reliability values between 0.60 and 0.70 are considered
"acceptable in exploratory research," while values between 0.70 and 0.90 range from
"satisfactory to good." Cronbach’s alpha is another measure of internal consistency
reliability, assuming similar thresholds, but producing lower values than composite
reliability.

Subsequently, convergent validity is examined, measurable with Average Variance
Extracted (AVE). The AVE value should be ≥ 0.5 to be acceptable, indicating that the
construct explains at least 50 percent of the variations in its indicators (Hair et al., 2019).
AVE is obtained by summing the reliabilities of the indicators for a construct and then
taking the average. This metric measures the average variance shared between the
construct and its indicators.

Table 2
Validity and Reliability of Variables

Variable Dimension
Cronbach's
Alpha

Composite
Reliability

AVE

E-Service
Quality

Efficiency
0.906 0.924 0.604

Fulfillment 0.922 0.937 0.650
System Availability 0.839 0.892 0.675
Privacy/Security 0.852 0.910 0.771
Responsiveness 0.900 0.926 0.715
Compensation 0.901 0.938 0.835
Contact 0.857 0.913 0.777

Customer
Satisfaction

0.851 0.899 0.691

Customer
Trust

0.875 0.923 0.800

0.882 0.927 0.809

Revitalizing Loyalty: Unveiling the Dynamics of E-Service Quality, Customer
Satisfaction, and Trust in Amplifying User Engagement with Electricity Mobile
Application

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6028

Repurchase
Intention
Word of
Mouth
(WOM)

0.930 0.955 0.877

Source: Processed Data (2023)

The study indicates that the indicators measuring dimensions and variables can be

considered effective in measuring both, as evidenced by the Average Variance Extracted
(AVE) values exceeding ≥ 0.5 for all variables, signifying good convergent validity.
Furthermore, it can be concluded that all variables in this model meet the reliability
criteria, with Cronbach's Alpha and Composite Reliability (CR) values exceeding 0.70.

Furthermore, Table 4 presents the attainment of discriminant validity values
measurable through the Heterotrait-Monotrait Ratio (HTMT). Discriminant validity aims
to determine the extent to which a construct empirically differs from other constructs in
the structural model. The heterotrait-monotrait Ratio (HTMT) is defined as the average
correlation between items across constructs compared to the average (geometric mean)
correlation between items measuring the same construct. An HTMT correlation ratio
value below 0.9 is considered acceptable.

Based on Table 4, it can be concluded from the discriminant validity testing that all
variables in the model meet the criteria according to Fornell-Larcker, with the square root
of AVE being greater (>) than the inter-construct correlations. Similarly, the results of
the HTMT Ratio Approach indicate that most constructs have good discriminant validity,
as their HTMT ratios are below the accepted threshold of 0.9. Although some values are
somewhat high (for example, between CS and CT), these results can be considered
acceptable. However, further examination and deeper interpretation may be conducted to
ensure that discriminant validity can be deemed good.

Table 3

Heterotrait-Monotrait (HTMT) Ratio
HTMT Ratio Approach

CO
M CON CS CT EFF

FUL
L PRI RI RES

SY
S

WO
M

COM
CON 0.574

CS
0.58
2 0.632

CT
0.49
8

0.61
2 0.898

EFF
0.54
8

0.58
2

0.78
7 0.731

FULL
0.55
9

0.63
6

0.80
8

0.76
4 0.840

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Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6029

PRI
0.53
5

0.62
1

0.60
8

0.69
5

0.58
6 0.592

RI
0.48
9

0.56
7

0.77
0

0.85
5

0.71
0 0.674 0.570

RES
0.66
7

0.66
6

0.84
1

0.82
9

0.74
6 0.855

0.73
6 0.751

SYS
0.62
7

0.61
5

0.76
4

0.70
5

0.84
8 0.828

0.68
9

0.65
2 0.792

WO
M

0.42
4

0.50
8

0.74
9

0.82
4

0.68
1 0.709

0.60
4

0.79
9

0.77
0 0.641

Source: Processed Data (2023)

Table 3 presents hypothesis testing involving the evaluation of the extent to which
empirical data supports or rejects hypotheses proposed in a structural model. Hypothesis
testing is conducted by analyzing the P-value in the structural model, and a hypothesis is
considered accepted if the P-value is less than (<) 0.05. The magnitude of the influence
and how significant it is can also be observed from the t-statistic, where to determine
positive significance, we can check if the t-statistic is greater than the positive threshold,
typically taken as 1.96 at a significance level of 0.05. Conversely, if the t-statistic is less
than 1.96, it indicates that the coefficient is not statistically significant at a 95%
confidence level. If the P-value is > 0.05, the hypothesis is rejected.

Table 4

Structural Relationship Test Result

Hyp Path
Original
Sample
(O)

Sampl
e Mean
(M)

Std.
Deviatio
n
(STDEV)

T Statistics
(|O/STDEV|
)

P
Values

Result

H1
E-S-QUAL ->
CS

0.499 0.502 0.063 7.902 0.000
Accept

H2
E-RECS-
QUAL -> CS

0.326 0.326 0.062 5.254 0.000
Accept

H3
E-S-QUAL ->
CT

0.486 0.494 0.066 7.357 0.000
Accept

H4
E-RECS-
QUAL -> CT

0.311 0.306 0.067 4.658 0.000
Accept

H5
CS -> RI

0.225 0.230 0.080 2.805 0.003
Accept

H6
CS -> WOM

0.246 0.254 0.085 2.874 0.002
Accept

H7
CT -> RI

0.578 0.576 0.078 7.389 0.000
Accept

H8
CT -> WOM

0.553 0.546 0.086 6.435 0.000
Accept

ed
Source: Processed Data (2023)

Based on the research findings, it is known that the dimensions forming e-service

quality, consisting of E-S-Qual and E-Recs-Qual, have a significant positive impact on

Revitalizing Loyalty: Unveiling the Dynamics of E-Service Quality, Customer
Satisfaction, and Trust in Amplifying User Engagement with Electricity Mobile
Application

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6030

customer satisfaction and customer trust. As shown in Table 5, E-S-Qual correlates
positively with customer satisfaction (B = 0.499, t = 7.902, p<0.05), and E-Recs-Qual
correlates positively with customer satisfaction (B = 0.326, t = 5.254, p<0.05).
Furthermore, E-S-Qual correlates positively with customer trust (B = 0.486, t = 7.357,
p<0.05), and E-Recs-Qual correlates positively with customer trust (B = 0.311, t = 4.658,
p<0.05).

Next, customer satisfaction and customer trust have a significant positive impact
on repurchase intention and word of mouth. As seen in Table 5, customer satisfaction
correlates positively with repurchase intention (B = 0.225, t = 2.805, p<0.05), and
customer satisfaction correlates positively with word of mouth (B = 0.246, t = 2.874,
p<0.05). Similarly, customer trust correlates positively with repurchase intention (B =
0.578, t = 7.389, p<0.05), and customer trust correlates positively with word of mouth (B
= 0.553, t = 6.435, p<0.05). Therefore, based on the obtained results, it can be concluded
that E-S-Qual and E-Recs-Qual play a significant role in increasing customer satisfaction
and customer trust, while customer satisfaction and customer trust also have a substantial
impact on increasing repurchase intention and word of mouth.
E-Service Quality on Customer Satisfaction

The research findings indicate that the e-service quality variable has a positive
and significant impact on customer satisfaction. From these results, it can be concluded
that changes in the e-service quality variable can significantly affect positive changes in
the level of customer satisfaction. In other words, it can be interpreted that when a
company or electronic service provider enhances or improves their electronic service
quality, it is likely to increase customer satisfaction. Conversely, a decrease in electronic
service quality can hurt customer satisfaction. This is also consistent with the study by
Zia et al. (2022), which states that e-service quality has a positive and significant impact
on customer satisfaction, where overall service quality and customer satisfaction are
positive and significant when consumers perceive high-quality service and believe that
service delivery meets or exceeds customer expectations. The research data indicates that
the e-recovery service quality variable has a positive and significant impact on customer
satisfaction. From these results, it can be said that improvements in the company's ability
to recover electronic service quality will have a positive effect on increasing customer
satisfaction. This is mentioned by Shafiee and Bazargan (2018) in their research, where
e-recovery service quality influenced by responsiveness, compensation, and contact can
enhance customer satisfaction.
E-Service Quality on Customer Trust

The research results indicate that the e-service quality variable has a positive and
significant impact on customer trust. Based on these findings, it can be interpreted that
when a company improves or enhances its electronic service quality, it not only increases
customer satisfaction, as obtained in the previous results but also builds a level of trust
among customers towards the company or brand. In their study, Wu et al (2018) also state

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Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6031

that e-service quality influences trust, and retailers should consider service quality as a
priority in attracting customers because providing good service quality can enhance
customer trust. Meanwhile, the research results indicate that the e-recovery service quality
variable also has a positive and significant impact on customer trust. The presence of this
positive and significant influence suggests that improvements in e-recovery service
quality can be associated with an increase in customer trust, where a company's efforts to
enhance the quality of electronic service recovery after a failure can contribute positively
to the level of customer trust.
Customer Satisfaction on Repurchase Intention

The research results indicate that the variable of customer satisfaction has a
positive and significant impact on repurchase intention. This suggests that customer
satisfaction can contribute positively to the level of customer trust in the company.
Consumers are likely to make repeat purchases when they have a positive experience with
the brand/company.
Customer Satisfaction on Word of Mouth

In the same study, it was also found that customer satisfaction has a positive and
significant impact on word of mouth (WOM). This indicates that the higher the level of
customer satisfaction, the greater the likelihood that they will provide positive
recommendations about the product or service to others. In other words, the company's
success in improving customer satisfaction can contribute to an increase in word of mouth
(WOM), which can help enhance the company's reputation in the eyes of customers.
Satisfied customers positively influence their WOM intentions (Kitapci et al., 2014).
Additionally, Sanchez-Garcia et al. (2012) stated that customer satisfaction is crucial in
repurchase intention behavior, where satisfied consumers are more likely to make future
purchases compared to dissatisfied consumers.
Customer Trust on Repurchase Intention

The research results indicate that the variable of customer trust has a positive and
significant impact on repurchase intention. This can be interpreted as the higher the level
of customer trust in the company, the greater the likelihood that they will make a
repurchase. In their study, Javed and Wu (2020) also found that trust has a positive and
significant relationship with repurchase intention. Additionally, consumers' positive
interest in purchasing or making repeat purchases of products or services is related to
trust.
Customer Trust on Word of Mouth

On the other hand, the research results show that the variable of customer trust
has a positive and significant impact on word of mouth (WOM). This can be interpreted
as the higher the level of customer trust in the company, the greater the likelihood that
they will provide positive recommendations to others. These findings align with the
research conducted by Rita et al. (2019), who found that customer trust has a positive
impact on WOM.

Revitalizing Loyalty: Unveiling the Dynamics of E-Service Quality, Customer
Satisfaction, and Trust in Amplifying User Engagement with Electricity Mobile
Application

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6032

Figure 2

Estimated Model

Conclusion

Regarding e-service quality itself, the findings in this research confirm the
importance of efficiency, fulfillment, system availability, and security/privacy, as well as
responsiveness, compensation, and contact as part of service recovery to address
customer issues that can drive customer satisfaction and customer trust. Ultimately, the
improvement of electronic service quality will have a positive impact, either directly or
indirectly, on satisfaction and trust, which then also influences repurchase intention and
word of mouth by providing positive recommendations to others or fellow customers.

These findings provide insights for managers to better understand how electronic
service quality is shaped and the importance of each attribute and dimension in ensuring
customer satisfaction and trust. In the end, this can help retain customers, enhance the
company's image through positive recommendations to others, and increase profits
through online services. Companies should ensure that all attributes influencing online
service quality and recovery systems are met so that the provided online services will be
optimal. This includes ease of application use, product availability, data security, and
quick response in the event of service failures.

Rahayu Hafiza, Yeshika Alversia

Indonesian Journal of Social Technology, Vol. 5, No. 12, Desember 2024 6033

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