Perceived Ease of Use and Perceived Usefulness as Determinants of
Green IT Attitudes and Engagement Green IT Practice for
Environmental IT Performance
CUT ANNISA MEIDINA NATASHA*, AGUNG NUGROHO LUTHFI IMAM FAHRUDI,
ARI DARMAWAN
Department of Business Administration, Faculty of Administrative Sciences,
University of Brawijaya,
Veteran St., Ketawanggede, Kec. Lowokwaru, Malang City, East Java 65145,
INDONESIA
*Corresponding Author
Abstract: - This research aimed to explore the effect of Perceived Ease of Use, Perceived Usefulness, GIT
Attitudes, and Engagement in Green IT Practice on Environmental IT Performance. This research was carried
out individually using an online questionnaire to the 230 employees who work in a logistics company in East
Java. Data was analyzed using Structural Equation Modelling (SEM). The results show that Perceived Ease-of-
Use has a significant effect on GIT Attitude, Perceived Usefulness has a significant effect on GIT Attitude, GIT
Attitude has a significant effect on Engagement in GIT Practice, Engagement in GIT Practice has a significant
effect on Environmental IT Performance, and Engagement in GIT Practice has a significant effect as a mediator
in influencing the relationship between GIT Attitude and Environmental IT Performance. The novelty of this
research is integrating the Technology Acceptance Model and Belief-Action-Outcome Framework as the
foundation of the conceptual model. Organizations should actively promote and integrate perceived usefulness,
Green IT attitudes, and Green IT practices into their operations to achieve better environmental IT
performance.
Key-Words: - Belief Action Outcome, Engagement Green IT Practice, Environmental IT Performance, Green
IT Attitudes, Sustainable Technology Adoptio, Sustainable Development, Technology
Acceptance Model.
Received: June 29, 2023. Revised: March 23, 2024. Accepted: April 25, 2024. Published: May 27, 2024.
1 Introduction
Climate change, driven by human activities, is a
pressing global challenge with far-reaching impacts
on the environment and society, [1], [2]. The
UNFCCC, recognizing the need for a coordinated
response, emphasizes the principle of “common but
differentiated responsibilities” in addressing climate
change issues, [3]. One of the main factors
triggering climate change is the digital economy,
especially due to the high energy consumption that
occurs in data centers and digital infrastructure.
Data centers have a very important role in storing
and processing large amounts of data generated in
the digital era, so these data centers consume large
amounts of energy. According to a recent report
from the International Energy Agency (IEA), data
centers and network infrastructure consumed
approximately 1% of the world's total electricity
production in 2019, and it is predicted that this
number will continue to increase with the wider
expansion of digitalization, [4].
The rapid growth in the digital economy has
encouraged the development of Green Information
Technology (Green IT) as a necessary response to
reduce its negative impact on the environment.
Green IT, as explained in [5], focuses on the use and
research of information technology to improve
environmental sustainability. The main concern is
IT operational energy consumption, which has a
significant impact on global carbon emissions, [6].
Apart from that, the problem of electronic waste
arising from technological advances also shows the
importance of Green IT, [7].
The connection between IT and climate change
is not only in energy consumption but also involves
the lifecycle stages of IT, from manufacturing to
disposal. The IT industry is estimated to contribute
2% of global CO2 emissions, equivalent to the
aviation industry, [6]. Amid digital economic
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.24
Cut Annisa Meidina Natasha,
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Ari Darmawan
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growth, synchronization with green economic
growth becomes crucial to preventing a climate
crisis and involving comprehensive actions, [7]. The
importance of Green IT brings changes in how we
understand, implement, and measure the
environmental impact of IT use. Green IT includes
aspects such as energy efficiency, electronic waste
management, and the contribution of IT in solving
environmental issues.
Previous studies focused on the organizational
level, accommodating models such as the
Technology Acceptance Model (TAM) and the
Belief-Action-Outcome (BAO) framework.
Nevertheless, there is a research gap at the
individual level, particularly in the relationship
between the behavior of IT users and the
environmental performance of organizations. In this
study, we aim to bridge a gap by merging two
theoretical models (TAM and BAO), to explore how
IT users perceive and approach the implementation
of Green IT practices for environmental
sustainability. This research holds significant
importance in the effort to enhance Environmental
IT Performance by gaining a profound
understanding of how Perceived Ease of Use and
Perceived Usefulness are associated with GIT
Attitudes and Engagement in Green IT Practice.
This research has the potential to provide valuable
guidance for organizations in developing policies
and strategies that support green economic growth.
Furthermore, the study may contribute to
reducing carbon footprints and greenhouse gas
emissions by motivating the adoption of more
sustainable IT practices. By integrating
psychological and practical aspects, this research is
expected to stimulate positive changes towards more
responsible IT use, impacting the environment
positively, and understanding the dynamics between
individuals, organizations, and the sustainability
context.
2 Literature Review
2.1 Belief – Action – Outcome (BAO)
Framework
BAO framework is introduced by Nigel P. Melville.
The underlying issue addressed by this theory
revolves around information systems and
environmental sustainability, encompassing human
behavior within social, organizational, and
environmental contexts.
According to [8], these three phenomena
collectively comprise micro and macro problems.
[9] micro-macro relationship model serves as the
foundation for the conceptual framework known as
Belief-Action-Outcome (BAO). Emphasizing the
mediating role of individuals, this model establishes
a link between macro-level variables, such as social
structure, and the behavior of social systems. The
three types of relationships outlined include (1)
macro-level variables, like social structure,
influencing the psychic states (beliefs, desires,
opportunities, etc.) of individuals; (2) psychological
states influencing individual actions; and (3) the
cumulative actions of individuals impacting macro-
level variables, such as the behavior of social
systems.
In the elucidation of this theory, belief is
delineated as how psychic states (beliefs, desires,
opportunities, etc.) regarding the natural
environment are shaped through the lens of macro-
micro analysis, [8]. Meanwhile, action in the BAO
framework, serves as the manifestation of psychic
expression regarding the natural environment
translated into tangible actions, [8]. The analytical
level encompasses micro-micro, with the primary
constructs being the actions undertaken by
individuals. Examples include the adoption of an
information system to enhance organizational
recycling or facilitate ride-sharing, [8]. The
terminologies of various theories, such as game
theory, social cognitive theory, technology
acceptance model, theory of planned behavior, and
theory of reasoned action, contribute to the
underpinning of this aspect.
Within the BAO framework, the terminology
associated with outcomes delineates how
sustainability actions influence social systems and
organizations and how macro-level states impact
individual and organizational behavior, [8]. The
constructs involve community behavior, reflecting
the interplay between society and the natural
environment (inclusive of performance), and
organizational behavior, encapsulating the
operational dynamics of the organization (inclusive
of performance).
2.2 Technology Acceptance Model (TAM)
The Technology Acceptance Model (TAM) is a
theoretical framework employed to elucidate an
individual's acceptance of technology use,
particularly information technology systems, [10].
According to [10], TAM is derived from the Theory
of Reasoned Action (TRA), an action-oriented
theory positing that an individual's reactions and
perceptions shape their attitudes and behavior. [11],
asserts that TAM predicts user acceptance of
information technology systems based on two
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variables: perceived usefulness and perceived ease
of use.
Throughout its development, TAM underwent
several modifications, including the addition of
behavioral intention variables directly influenced by
perceived usefulness, [12]. [12], propose instances
where users might form an intention to use a system
considered easy without initially exhibiting an
attitude. The model is depicted as follows. The
testing results of the above model reveal a direct
impact of perceived usefulness and perceived ease
of use on the intention to use. The model was
subsequently refined into the ultimate TAM model.
In the final model suggested by [13], the influence
of attitude toward use variables could be eliminated
and consists of five main components: external
variables, perceived usefulness, intention to use,
perceived ease of use, and actual use.
As explained in [12], Perceived Ease of Use
includes the belief that using a particular system will
require minimal effort. On the other hand, Perceived
Usefulness is defined as the belief that the use of a
particular system will improve job performance,
[14], [15].
2.3 Research Conceptual Model and
Hypothesis Development
This research focuses on the significant role of
information technology (IT) in today's society and
its impact on the environment, [16]. This research
integrates two main theories, namely the Belief-
Action-Outcome (BAO) and Technology
Acceptance Model (TAM). Based on TAM, [17],
developed a model for adopting Green IT that
included Green IT awareness and subjective norms
as external variables. The importance of TAM in
examining environmental behavior facilitators is
highlighted by their study. TAM also served as the
fundamental theoretical model that, [18], used to
forecast and elaborate on individual acceptance of
Green IT. The use of TAM to forecast user
acceptance of IT systems is dependent on two
factors, namely perceived utility and perceived ease
of use, as confirmed by Davis.
Hypotheses in research are needed as tentative
assumptions about the research questions or, in
other words, hypotheses are predictions about the
expected research outcomes. In line with the GIT
context, the author argues that the applied TAM
model in this study, namely, perceived ease of use,
directly influences the intention to use IT. Based on
these considerations, the conceptual model of this
study is outlined in Figure 1.
OutcomeActionBelief
TAM Model
Perceived Ease of Use/
PEU (X1)
Perceived Usefulness/
PU (X2)
Green IT Attitude (Y1) Engagement in Green IT
Practices/EGP (Y2)
Environmental IT
Performance (Y3)
Fig. 1: Research Conceptual Model
Source: Processed by Author (2023)
Therefore, the following hypotheses are
proposed:
H1: Perceived Ease-of-Use significantly influences
GIT Attitude
H2: Perceived Usefulness significantly influences
GIT Attitude
H3: GIT Attitude Significantly Influences
Engagement in GIT Practice
H4: Engagement in GIT Practice Significantly
Influences Environmental IT Performance
H5: Engagement in GIT Practice mediates the
relationship between GIT Attitude and
Environmental IT Performance
3 Methodology
The location of this research is IT users who are in a
logistics company in East Java in 2023. This
research was carried out individually using an
online Google form which was distributed to the
permanent employees and contract companies of the
company. The population in this study are all
employees who work in a logistics company in East
Java.
To assist in addressing the established research
questions, this research will utilize a survey method,
with a questionnaire distributed to IT users in
companies. The questionnaire results will undergo
processing and analysis using PLS-SEM data
analysis. The researcher anticipates that the
outcomes of this data analysis will effectively
address the research questions, achieve the research
objectives, and provide academic and governmental
insights for promoting wiser usage of information
technology within society.
The sample is a part of the population to be
investigated and its information is collected to
explain a phenomenon in research, [19]. To achieve
the objectives of this study, the author uses a sample
that is a small part of the population. The research
sampling method used is proportionate stratified
sampling. Proportionate stratified sampling involves
stratifying the population into homogeneous groups
(Strata).
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As a general guideline, [20], the minimum
sample size should be at least five times the number
of questionnaire items to be analyzed, and a more
acceptable sample size maintains a ratio of 10:1.
With 23 questionnaire items used in this study, the
required minimum sample size is 23 x 10 = 230
samples. The population and sample allocation in
this research are shown in Table 1.
Table 1. Outer Loading for Each Variable
Department
Population
Logistics Facilities Division
194
Project and Development
Division
152
Finance and Procurement
Division
31
HR, General, and K3 Divisions
23
Total
400
Source: Processed by Author (2023)
The data collection method employed in this
research comprises both primary and secondary
approaches. Primary data collection involves an
online survey, where primary data refers to
information obtained directly by the researcher
related to the variables of interest for specific
purposes, [20]. This primary data can be processed
and analyzed directly in this study. The primary data
for this research comprises respondents who are IT
users working in logistics companies in East Java by
online survey. The online survey utilized Google
Forms and was distributed to the respondents.
Secondary data collection involves gathering
various supporting research information from
literature or reading materials, such as books,
articles, journals, official websites, or other sources
that can be authenticated. This study utilizes two
exogenous variables: perceived ease of use (X1) and
perceived usefulness (X2). The endogenous
variables in this study are GIT attitude (Y1),
engagement in GIT practices (Y2), and
environmental IT performance (Y3).
4 Results and Discussion
4.1 The Results of the Validity and
Reliability Test
The convergent validity of the measurement model
was carried out with reflexive indicators assessed
based on the correlation between the component
scores and latent variable scores or construct scores.
The convergent validity results are shown in Table
2.
Table 2. Convergent Validity Test Results
Variable
Items
Loading
Factor
Information
Perceived Ease of
Use
X1.1
0.862
Valid
X1.2
0.883
Valid
X1.3
0.914
Valid
X1.4
0.882
Valid
Perceived
Usefulness
X2.1
0.729
Valid
X2.2
0.836
Valid
X2.3
0.766
Valid
X2.4
0.750
Valid
X2.5
0.764
Valid
GIT Attitudes
Y1.1
0.763
Valid
Y1.2
0.722
Valid
Y1.3
0.700
Valid
Y1.4
0.760
Valid
Engagement in
GIT Practice
Y2.1
0.785
Valid
Y2.2
0.910
Valid
Y2.3
0.757
Valid
Y2.4
0.904
Valid
Environmental IT
Performance
Y3.1
0.700
Valid
Y3.2
0.700
Valid
Y3.3
0.785
Valid
Y3.4
0.864
Valid
Y3.5
0.792
Valid
Y3.6
0.768
Valid
Source: Processed by Author (2023)
Based on Table 2, the loading factor value of
each item associated with a variable or construct
with a value of 0.5, the measurement instrument can
be said to be valid, [21]. Table 2 shows that all
loading factor values are greater than 0.5. Based on
this, it can be concluded that all the indicators used
for each variable have the function of measuring
properly and precisely with the measuring
instruments that have been used in the study
Next, the discriminant validity can be seen from
the Average Variant Extracted (AVE) value.
Fulfillment for testing discriminant validity, which
is equal to 0.5. AVE test results in Table 3 are as
follows.
Table 3. Average Variant Extracted (AVE)
Variable
AVE
Information
Perceived Ease of Use
0.784
Valid
Perceived Usefulness
0.593
Valid
GIT Attitudes
0.542
Valid
Engagement in GIT
Practice
0.709
Valid
Environmental IT
Performance
0.593
Valid
Source: Processed by Author (2023)
Based on Table 3, it can be seen that the value
of the Average Variance Extracted (AVE) has been
able to explain that all research variables have an
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AVE value of more than 0.5. The existence of this
has shown that the instruments used in research are
valid. The AVE value can also be used to measure
the variables in the study
Composite reliability is a way to test the level of
reliability of variables provided that the Cronbach
alpha value is more than 0.6 and the composite
reliability value is more than 0.7. In addition to the
Cronbach's Alpha value, the rho_A value is also
seen. A construct is said to be reliable (indicator) if
the value of rho_A > 0.7, [22]. The composite
reliability results are shown in Table 4.
Table 4. Composite Reliability Test Results
Variable
Cronbach'
s Alpha
rho_
A
Composite
Reliability
GIT Attitudes
0.719
0.721
0.826
Perceived Ease of
Use
0.908
0.912
0.936
Perceived
Usefulness
0.828
0.832
0.879
GIT Attitudes
0.719
0.721
0.826
Engagement in
GIT Practice
0.860
0.862
0.906
Environmental IT
Performance
0.862
0.874
0.897
Source: Processed by Author (2023)
The value of Cronbach Alpha and the composite
reliability value of all variables have a cut-off value
over 0.6, as Table 4 above demonstrates.
Additionally, Table 4 demonstrates that the total
value of rho_A is higher than 0.7. Concurrently, the
composite reliability's overall value is more than
0.7. This leads one to the conclusion that every
research variable was trustworthy. These findings
demonstrate that every instrument utilized in the
study was error-free and appropriate for additional
investigation.
4.2 Hypothesis Testing Results (Inner Model)
To ascertain the direct and indirect effects of each
hypothesis put forward in this study, hypothesis
testing was done. Testing the structural model in
route analysis is the same as analyzing the link
between latent/construct variables in the SEM
model. The analysis in this paper made use of
SmartPLS software and bootstrapping. Hypothesis
testing gives the following results (Table 5).
Table 5. Hypothesis Testing Results (Inner Model)
Hypothesis
Path
Coefficient
P-
values
Information
H1: Perceived
Ease of Use
(PEU) (X1)
GIT Attitude
(Y1)
0.136
0.058
Not
significant
H2: Perceived
Usefulness
(PU) (X2)
GIT
Attitudes(Y1)
0.471
0.000
Significant
H3: GIT
Attitudes (Y1)
Engagement in
GIT Practice
(EGP) (Y2)
0.468
0.000
Significant
H4:
Engagement in
GIT Practice
(EGP) (Y2)
Environmental
IT
Performance
(Y3)
0.562
0.000
Significant
H5: GIT
Attitudes (Y1)
Engagement in
GIT Practice
(EGP) (Y2)
Environmental
IT
Performance
(Y3)
0.263
0.000
Significant
Source: Processed by Author (2023)
The test results in Table 5 will be explained in
detail in the following discussion.
4.3 Discussion
4.3.1 The Effect of Perceived Ease-of-Use on GIT
Attitude
The effect of Perceived Ease-of-Use on GIT
Attitude (Attitude towards General Information
Technology) is a crucial aspect frequently examined
in the context of technology adoption. Perceived
Ease-of-Use pertains to an individual's perception of
how easily and effortlessly a system or technology
can be utilized. GIT Attitude delineates an
individual's stance toward information technology in
general, encompassing a positive or negative
inclination toward technology use.
The inner model test results revealed that the
absence of a significant influence relationship
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between the Perceived Ease Of Use variable and
GIT Attitude led to the substantiation of the
hypothesis. The theoretical foundation for this
hypothesis rests upon the Technology Acceptance
Model (TAM) and Belief-Action-Outcome (BAO)
theories. According to these theories, issues
involving information and environmental
sustainability entwine human behavior and the
social, organizational, and environmental contexts.
Theoretically, IT users might provide further
context for respondents' comments by clarifying
how the Perceived Ease of Use variable affects GIT
Attitude. The Perceived Ease Of Use variable
gauges the extent to which an individual believes
that using a specific system will be effortless.
Additionally, Green Information Technology is
defined as information technology with minimal
direct effects on the environment.
The non-significant research findings contradict
[23] but align with the [24] study. [24], suggest that
the non-significant impact of perceived usefulness
on user attitudes may be attributed to users'
familiarity with a long-established information
technology system within a company. This
observation is linked to user age, work tenure,
education level, and the duration of system usage.
Conversely, the non-significant findings in this
study indicate the presence of other potentially more
dominant factors shaping attitudes toward
technology, such as Perceived Usefulness
(perception of benefits derived from technology),
users' prior experiences, social factors, or other
personal characteristics. It's possible that while
creating their general opinions regarding
information technology, people will value these
qualities more than simple usability. IT users may
have a broader understanding of technology and
base their opinions on it on factors including
scalability, security, dependability, and
interoperability with current systems.
4.3.2 The Effect of Perceived Usefulness on GIT
Attitude
The results of this investigation agree, for the most
part, with the theories (Belief-Action-Outcome
(BAO) and Technology Acceptance Model (TAM))
that guided the creation of the hypotheses. The
diffusion theory of the Technology Acceptance
Model (TAM) also acknowledges that the
impression of ease of use impacts technology
acceptance. This indicates that in a technological
setting, the intention to continue using technology is
influenced by the Perceived Usefulness component.
The findings of the final model fit test
demonstrated a significant influencing association
between the GIT Attitude and the Perceived
Usefulness variable. Based on these results, the
desire to continue using the technology is driven by
its benefits as long as the benefits are clear.
Research to date suggests that GIT attitudes are
significantly influenced by perceived benefits.
People are more likely to have positive attitudes
toward information technology in general if they
believe that using the system or technology will
improve their productivity or quality of life. A
person's attitude toward technology can be
positively influenced by their belief that technology
helps them achieve their goals and provide better
work results.
Concerning green technology use, the extent to
which perceived effectiveness influences GITs'
attitudes toward continued use of information
technology is consistent with research findings,
[25], [26]. Respondents' perceptions of positive or
negative attitudes toward environmental change and
their attitudes toward the use of information
technology, as well as their work as IT users in
companies that prioritize technology development,
may have contributed to the observed significant
impact.
4.3.3 The Effect of GIT Attitude on Engagement
in GIT Practice
The study's findings indicate a strong relationship
between engagement in GIT practice and GIT
Attitude. This result suggests that different elements
of commitment in the implementation of technology
usage are aligned with the components of
technology adoption, comprehension, learning,
utilization, and appeal. Environmental performance
may be influenced by actions made to maintain a
business.
The Belief-Action-Outcome (BAO) Theory, as
explained by [27], holds that a company's
sustainability activities may affect environmental
performance across GIS strategy, GIT practices, and
GIS practices. The research findings are
conceptually consistent with this theory. The goal of
the GIS approach is to limit environmental
deterioration through organizational and functional-
level interventions. The results of this study close
the gap in empirical testing on the relationship
between GIT Attitude and Engagement in GIT
Practice.
Energy efficiency, lowering carbon emissions,
recycling hardware, managing electronic waste,
using resources sustainably, and utilizing
technology that promotes sustainable behaviors all
fall under the category of "green IT attitudes". These
quotes demonstrate social responsibility as well as
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knowledge of the environmental effects of
technology use. When making information
technology decisions, people or companies with
good Green IT attitudes typically consider the
environment. They could actively look for green
tech developments and solutions, use energy-saving
gear and software, or embrace sustainable
maintenance and use procedures.
Positive attitudes toward information
technology are associated with higher levels of
motivation for people to use technology in their
daily lives. They view technology as a useful and
efficient instrument for achieving their goals. The
accomplishment of these objectives promotes
acceptance and use of different pertinent features,
applications, or technology systems, as well as
active participation in technology use practices.
Positivity also makes people more curious and open
to learning about new developments in the field of
information technology. People who have a
favorable outlook on information technology are
more likely to adopt best practices in technology
use, learn new functionalities, and keep up with the
newest advancements.
4.3.4 The Effect of Engagement in GIT Practice
on Environmental IT Performance
To meet corporate or personal goals, one must
understand, embrace, and actively use information
technology (GIT Practice). Environmental IT
Performance can benefit from more Engagement in
GIT Practice. People who engage in these activities
usually use information technology as a tool to help
them achieve their goals, improve productivity,
increase efficiency, and improve their quality of life
in general. An important relationship between the
impact of the Engagement in GIT Practice variable
and Environmental Performance has been confirmed
by the analysis of the final model that was provided
in this study. This impact's theoretical foundation
supports the Belief-Action-Outcome (BAO)
hypothesis. These results can be utilized to clarify
that technology advances environmental
performance when it is understood and applied with
strong dedication. This study also validates previous
research conducted by [28] and [29].
4.3.5 Engagement in GIT Practice mediates the
relationship between GIT Attitude and
Environmental IT Performance
Engagement in GIT Practice includes sustainable
use of information technology, that requires
implementing additional environmental action, such
as participating in e-waste management programs,
energy-saving technologies, etc. The findings from
this study indicate that Engagement in GIT Practice
can act as a mediator between GIT attitudes and
environmental IT performance. These findings are
consistent with previous research conducted by [30].
4.4 Recommendations
We would like to highlight several
recommendations from this study that should be
considered to fully understand the study's findings.
These limitations are:
1) Inserting technology that supports the
environment is very important and must be the
main focus, both for individuals and businesses.
By promoting and emphasizing the benefits of
sustainable use of information technology, we
can inspire a more positive view of
environmentally friendly information
technology.
2) Organizations need to take proactive action to
encourage and support the use of
environmentally friendly information technology
practices. This can be achieved through
providing guidance, providing resources, as well
as encouraging sustainable actions such as the
use of energy-efficient software, responsible
management of e-waste, and participating in
carbon emission reduction efforts.
5 Conclusion
From the results of the research and analysis
produced, several findings can be concluded. First,
it was found that perceived ease of use does not
have a significant influence on GIT attitude. This
indicates that people's views on information
technology in general tend to be less influenced by
the extent to which they believe that the technology
is easy to use. On the other hand, GIT attitudes are
strongly influenced by perceived usefulness.
Furthermore, it is noteworthy that GIT Attitude
has a substantial influence on Engagement in GIT
Practice, signifying that a constructive outlook
toward technology encourages proactive
involvement in Green Information Technology
activities. Additionally, the study shows that
Environmental IT Performance is highly influenced
by GIT Practice Engagement, suggesting that active
participation in sustainable IT practices has a
favorable impact on environmental results.
Furthermore, participation in GIT Practice acts as a
mediator and has a major impact on the connection
between Environmental IT Performance and GIT
Attitude.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.24
Cut Annisa Meidina Natasha,
Agung Nugroho Luthfi Imam Fahrudi,
Ari Darmawan
E-ISSN: 2224-3496
239
Volume 20, 2024
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.24
Cut Annisa Meidina Natasha,
Agung Nugroho Luthfi Imam Fahrudi,
Ari Darmawan
E-ISSN: 2224-3496
240
Volume 20, 2024
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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2024.20.24
Cut Annisa Meidina Natasha,
Agung Nugroho Luthfi Imam Fahrudi,
Ari Darmawan
E-ISSN: 2224-3496
241
Volume 20, 2024