Study of the
Development Potential of Geospatial-Based PSEL (Waste Processing into
Electrical Energy) Areas
Hani Kartika Hariyanto1,
�Rina Kurniati2,
Muhammad Helmi3
Universitas Diponegoro
|
Keywords |
Abstract |
|
Waste to Electrical
Energy Management (PSEL), Geospatial Analysis, Fuzzy Analytic Hierarchy
Process (FAHP) |
Since 1989, most of the waste processing in DKI
Jakarta Province has ended at the Bantar Gebang TPST, causing the available
capacity to accommodate waste to approach the maximum limit. One alternative
processing that has a high level of efficiency in reducing the volume of
waste is the PSEL (Waste to Electrical Energy Management) facility. Planning
PSEL facilities in urban areas requires consideration from all aspects. While
the space utilization in DKI Jakarta is quite dense and there are no
practical guidelines in determining the location of PSEL facilities, it is
necessary to conduct a comprehensive study, namely a study of the potential
development of PSEL areas. The purpose of this study is to obtain the
location and spatial pattern of the potential development of the PSEL area
and obtain the characteristics of the area with the 'Recommended' category as
the location of PSEL facility development. This study uses geospatial
analysis with the quantitative Weighted Overlay method, where geospatial
modeling is carried out by inputting the weight of each criterion (aspects
that have an influence on determining the location of PSEL facilities)
obtained from the results of the Fuzzy Analytic Hierarchy Process (FAHP)
analysis assisted by a questionnaire tool. The results of the study obtained
2 (two) areas that have High Category status with a total area of 1.52 ha
located in Cilincing District and Kelapa Gading District, North Jakarta with
the condition that the first area occupies an improper designation zone
(Industrial and Warehousing Zone) while the second area has a radius distance
of less than 500 m to the nearest settlement and tourist location. |
Corresponding Author: Hani Kartika Hariyanto
E-mail: [email protected]
INTRODUCTION
According
to data from the Central Statistics Agency, Indonesia's population in mid-2022
will reach 275,773.8 thousand people with a population growth rate of 1.17%
(Statistik,
2022). Indonesia's population is projected to
reach 296,405 people in 2030 (Statistik, 2014).
It cannot be denied that as time goes by, Indonesia's population will continue
to increase. The increase in population is accompanied by an increase in urban
waste production (Feyzi,
Khanmohammadi, Abedinzadeh, & Aalipour, 2019). Annual Waste Production Data (Tons)
from 2019 to 2021 shows that the top 5 provinces that have the most waste
generation in Indonesia are East Java, Central Java, West Java, DKI Jakarta and
South Sumatra (Farahdiba
et al., 2023). If seen in 2021, the total waste generation in DKI Jakarta
Province will reach 3 million tons, with the largest source of waste coming
from East Jakarta with 836 thousand tons/year or 27.14%
(Rizaty,
2022).
High waste generation is caused by waste
handling that is not yet fully effective in reducing waste.� Most waste handling in Indonesia,
especially in DKI Jakarta, still uses the open dumping method. This method does not involve comprehensive
handling of waste so the impact on the environment is quite large .
The majority of waste handled in East Jakarta is sent to the Bantargebang
Integrated Waste Disposal Site (TPST), which has been operating since 1985. The
Bantargebang TPST has reached a height of 50 to 60 meters. If converted into a
building, the height of the trash mountain is equivalent to a 20-story building
.
This volume certainly has a negative impact on the surrounding environment in
the form of unhealthy air and murky water .
So there is a
need for waste management that can be the final treatment with the minimum
impact possible. In recent
years, a shift from landfill to
burning/incinerator practices has been observed in many Asian, European, and
American countries (Feyzi et al., 2019).
The ability of the
burning/incineration process to reduce the volume of waste and produce energy
simultaneously makes it an attractive alternative compared to conventional
landfill methods (Nabavi-Pelesaraei,
Bayat, Hosseinzadeh-Bandbafha, Afrasyabi, & Chau, 2017). Most research
shows that thermal method technology (incinerators) as a waste processing
facility has superior value compared to other technologies because in the
technical aspect, namely easy operation, the environmental aspect, namely
reducing the volume of waste by up to 70-80% and the impact on public health is
lower compared to processing landfill method. Apart from being a solution offered in reducing the volume of
waste, the energy produced from thermal can be converted into electrical
energy, where the energy efficiency level reaches 98.6% (Sarasati et al., 2021).
The
DKI Jakarta Common Government through the DKI Jakarta Common Cleanliness
Benefit is attempting to bargain with the squander issue by building
alternative waste handling offices within the city. The preparing office
alluded to is PSEL (Squander Administration into Electrical Vitality) based on
ecologically inviting innovation with its portrayal based on directions.
Presiden Republik Indonesia No. 35 concerning the Increasing speed of
Advancement of Squander to Electrical Vitality Handling Establishments Based on
Ecologically Inviting Innovation, to be specific "machines/equipment that
can handle squander into electrical vitality, and decrease squander volume and
preparing time essentially through naturally inviting and demonstrated
innovation" (Damayanti, Waluyo, & Candrakirana, 2023). Through the Decree of the Head of the DKI Jakarta Environmental
Service No. 732 of 2020 determines the Perumda Sarana Jaya service area to
build waste processing in South Jakarta (zone 4) and East Jakarta (zone 2).
Population
density and waste generation produced by the East Zone have higher values than
the South Zone, giving a higher sense of urgency in conducting the study. So in
this study, the waste source in the Jakarta East Zone service area is used as a
study location, with the hope that the results of this study can be used as a
reference in analyzing location suitability in the Jakarta South Zone service
area and/or other service areas.
Reducing
the volume of waste by establishing PSEL (Waste Management into Electrical
Energy) with burning/incineration technology can reduce costs related to land
requirements when compared to landfill waste management
(Hassaan,
2015), because PSEL facilities require less
land than TPST/TPA (Nabavi-Pelesaraei
et al., 2017). Determining the PSEL area needs to be considered from an
environmental, social and economic perspective so that it can operate
effectively, efficiently and in accordance with applicable regulations (Wu, Wang, Hu, Ke, & Li, 2018). Decision making in the waste management sector involves
several criteria (environmental, social, economic and technical criteria) which
increase the complexity of the entire process. Therefore, in the
decision-making process, the initiator generally arranges a hierarchy according
to the priority of each criterion (Kazimieras
Zavadskas, Bau�ys, & Lazauskas, 2015). Multi Criteria Decision Making (MCDM) techniques
help solve complex problems that are difficult to handle consistently. This
happens because of the large amount of information and the involvement of
various criteria in decision making or the process of preparing the hierarchy
(Qazi
& Abushammala, 2020). MCDM methods are often implemented together with
geographic information system (GIS)-based methods in site selection
considerations because both methods have unique capabilities that complement
each other (Kabak
& Keskin, 2018). The integration of MCDM and GIS offers a powerful
technique that facilitates any regional suitability determination process and
offers an efficient way to manage solid waste systems, so this study uses a GIS-based method and
the Fuzzy Hierarchical Analytical Process method as one part of MCDM in the
PSEL suitability study in the East Jakarta Service Area Zone (Bilgilioglu, Gezgin, Orhan, & Karakus, 2022).
In the modern
era that emphasizes sustainability and environmental protection, it is quite
worrying that there are no standard methods or standard guidelines recognized
by the state for determining effective and efficient locations for establishing
waste processing facilities into electrical energy, especially in the context
of urban areas for the population. In the absence of clear guidelines, decision
makers and parties involved in waste management can find themselves in a
difficult situation. Because the absence of official guidelines creates
uncertainty in the process of determining the location of PSEL facilities.
Decision makers must consider various factors, such as environmental impacts,
public safety, and existing infrastructure, without having a clear framework.
Apart from that, the absence of standard guidelines also creates inequality
between different regions in waste management. Some regions may take different
approaches without having consistent national guidance.
In this
situation, local communities can also feel the impact. They live in uncertainty
regarding the environmental and social impacts of waste processing facilities
that might be built in their area. They have no guarantee that the decisions
taken by decision makers are the result of a structured and science-based
process.
In
the absence of state-recognized methods or guidelines, it is crucial for
governments and experts in the field of waste management to work together to
develop consistent and comprehensive guidelines. These guidelines must consider
various aspects, from environmental impacts to the needs of local communities.
In this way, sustainability and safety in waste management can be guaranteed,
and local communities can live without uncertainty in the process of building
waste processing facilities into electrical energy.
It
is hoped that this research can be a method for determining decisions in
selecting PSEL locations, especially in urban areas, based on the results of
modeling and studying the characteristics of PSEL areas. It is hoped that the
location, distribution and characteristics of PSEL locations as a result of
this study can be taken into consideration in the management and utilization of
space, especially in waste processing using PSEL facilities.
This
research aims to examine the location and spatial pattern of PSEL areas based
on geospatial modeling of PSEL areas in the East Zone of the Jakarta Service
Area in DKI Jakarta Province. Examining the characteristics of the PSEL area based
on geospatial modeling of the PSEL area in the East Zone Jakarta Service Area
in DKI Jakarta Province.
RESEARCH
METHODS
The research method applied is Quantitative
Descriptive Research. This research was carried out through Data Collection
steps which included a weighting process with FAHP, Environmental Component
Modeling; Social Component Modeling; Economic Component Modeling; Geospatial
Modeling of PSEL Area Suitability; and Analysis of the characteristics of High
Category Areas in PSEL. The number of samples taken was approximately 5
respondents. After obtaining the results of the interviews and questionnaires
which had been filled in by the respondents, the questionnaire results were
then tabulated for validation testing with consistency testers. If inconsistent
results are found, it is necessary to distribute the questionnaire again.
RESULTS AND
DISCUSSION
Hypothesis 1:
"Service Quality Has a Positive Influence on
Customer Satisfaction"
Simple Linear Regression Analysis
Table 1 Simple Linear Regression Test Results
Hypothesis 1
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
7,522 |
3,138 |
|
2,397 |
.017 |
|
Quality
of Service |
,733 |
,029 |
,864 |
25,442 |
,000 |
|
|
a.
Dependent Variable: Customer Satisfaction |
||||||
Y = 7.522 +
0.733X +
Based on the regression equation above, it can be explained
as follows:
1. A
consistent esteem of 7.522 shows that in the event that the autonomous
variable, specifically Benefit Quality (X), includes a consistent esteem or
zero, at that point the Client Fulfillment Variable (Y) is 7.522.
2. The
relapse coefficient for the Service Quality variable (X) is positive at 0.733,
which implies there's a unidirectional relationship between the two factors, so
appears that for each 1 unit increment in Benefit Quality (X), expecting other
factors are consistent, the Client Fulfillment variable (Y) increments by
0.733.
Hypothesis test
The following results from the t test
are presented in the table below:
Significant value
H0: no effect
Ha1: influential
�
Prob < 0.05
then H0 is rejected, and H1 is accepted
�
Prob > 0.05, H0 is accepted, H1 is rejected
T value
�
T count > t
table, H0 is rejected
�
T count < t table, H0 is accepted
Table 2 Hypothesis 1 T Test Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
2,397 |
.017 |
|
|
Quality
of Service |
25,442 |
,000 |
|
|
|
a.
Dependent Variable: Customer Satisfaction |
|
|||
Based
on the table above, it can be explained as follows:
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
The Service
Quality variable (X) has a probability value of 0.000 < 0.05 and t count of
25.442 > 1.9707, meaning that H0 is rejected and H1 is accepted, namely the
Service Quality variable (X) has a partially significant effect on Customer
Satisfaction (Y).
b.
Coefficient
of Determination
Table 3 Test Results for the Determination
Coefficient of Hypothesis 1
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
,864 a |
,746 |
,745 |
2.89951 |
2,060 |
|
a.
Predictors: (Constant), Quality of Service |
|||||
|
b.
Dependent Variable: Customer Satisfaction |
|||||
Based on the obtained R-Squared value of 0.746 or 74.6 % . This
indicates that the independent variable in this research, namely Service
Quality (X), contributes to a significant influence on Customer Satisfaction
(Y) of 74.6%, and the remaining 25.4% is explained by other variables.
Hypothesis 2:
Service Quality Has a Positive Influence on Customer
Trust
Simple Linear Regression Analysis
Table 4 Simple Linear Regression Test Results
Hypothesis 2
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
,690 |
2,148 |
|
,321 |
,748 |
|
Quality of Service |
,596 |
,020 |
,898 |
30,224 |
,000 |
|
|
a. Dependent Variable: Customer
Trust |
||||||
Y = 0.690 +
0.596X +
�������������� Based on
the regression equation above, it can be explained as follows:
1.
A constant value of 0.690 indicates that if the independent variable,
namely Service Quality (X), is constant or zero, then the Customer Trust
Variable (Y) is 0.690.
2. The
regression coefficient for the Service Quality (X) variable is positive at
0.596, which means there is a unidirectional relationship between the two
variables, so shows that for every 1 unit
increase in Service Quality (X), assuming other variables are constant, the
Customer Trust variable (Y) increases by 0.596.
Hypothesis test
Table 5 Hypothesis 2 T Test Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
,321 |
,748 |
|
|
Quality of Service |
30,224 |
,000 |
|
|
|
a. Dependent Variable: Customer Trust |
|
|||
Based
on the table above, it can be explained as follows:
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
The Service Quality variable (X) has a
probability value of 0.000 < 0.05 and t count of 30.224 > 1.9707, meaning
that H0 is rejected and H2 is accepted, namely
the Service Quality variable (X) has a partially significant effect on Customer
Trust (Y).
b.
Coefficient
of Determination
Table 6 Test Results for the Determination
Coefficient of Hypothesis 2
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
,898 a |
,806 |
,805 |
1.98437 |
1,908 |
|
a. Predictors: (Constant), Quality
of Service |
|||||
|
b. Dependent Variable: Customer
Trust |
|||||
Based
on the obtained R-Squared value of 0.806 or 80.6 % . This indicates that the
independent variable in this research, namely Service Quality (X), contributes
to a significant influence on Customer Trust (Y) of 80.6%, and the remaining
19.4% is explained by other variables.
Hypothesis 3
"Trust Has a Positive Influence on Customer
Satisfaction"
Simple Linear Regression Analysis
Table 7 Simple Linear
Regression Test Results Hypothesis 3
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
19,513 |
3,325 |
|
5,868 |
,000 |
|
CUstomer trust |
1,034 |
,051 |
,809 |
20,409 |
,000 |
|
|
a. Dependent Variable: Customer
Satisfaction |
||||||
Y
= 19.513 + 1.034X +
�������������� Based on
the regression equation above, it can be explained as follows:
1. A
steady esteem of 19.513 demonstrates that on the off chance that the free
variable, to be specific Client Believe (X), is consistent or zero, at that
point the Client Fulfillment Variable (Y) is 19.513.
2. The
relapse coefficient for the Client Believe (X) variable is positive at 1.034,
which implies there's a unidirectional relationship between the two factors, so
appears that for each 1 unit increment in Client Believe (X), accepting other
factors are steady, the Client Fulfillment variable (Y) increments by 1.034.
Hypothesis test
Table 8 Hypothesis 3 T Test
Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
5,868 |
,000 |
|
|
CUstomer trust |
20,409 |
,000 |
|
|
|
a. Dependent Variable: Customer
Satisfaction |
|
|||
Based
on the table above, it can be explained as follows:
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
The
Client Believe variable (X) features a likelihood esteem of 0.000 < 0>
1.9707, meaning that H0 is rejected and H3 is accepted, specifically the Client
Believe variable (X) incorporates a mostly noteworthy impact on Client
Fulfillment (Y).
b. Coefficient of Determination
Table 9 Results of Hypothesis
3 Determination Coefficient Test
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
,809 a |
,654 |
,653 |
3.38458 |
2,229 |
|
a. Predictors: (Constant), Customer
Trust |
|||||
|
b. Dependent Variable: Customer
Satisfaction |
|||||
R-Squared esteem of 0.654 or 65.4 % . This demonstrates that
the free variable in this investigate, to be specific Client Believe (X),
contributes to a critical impact on Client Fulfillment (Y) of 65.4%, and the
remaining 34.6% is clarified by other factors.
Hypothesis
4
"Service
Quality Has a Positive Influence on Behavioral
Intention"
Simple
Linear Regression Analysis
Table 10 Simple Linear
Regression Test Results Hypothesis 4
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
3,445 |
1,536 |
|
2,243 |
.026 |
|
Quality of Service |
,169 |
.014 |
,628 |
11,981 |
,000 |
|
|
a. Dependent Variable: Behavioral
Intention |
||||||
Y
= 3.445 + 0.169X +
Based on the regression equation above, it can be explained
as follows:
1. A
consistent regard of 3.445 illustrates that on the off chance that the free
variable, to be particular Advantage Quality (X), is unfaltering or zero, at
that point the Behavioral Intentional Variable (Y) is 3.445.
2. The
backslide coefficient for the Advantage Quality variable (X) is positive at
0.169, which proposes there's a unidirectional relationship between the two
variables, so shows up that for each 1 unit increase in Advantage Quality (X),
anticipating other variables are unfaltering, the Behavioral Intentional (Y)
variable increases by 0.169.
Hypothesis test
Table 11 Hypothesis 4 T Test
Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
2,243 |
.026 |
|
|
Quality of Service |
11,981 |
,000 |
|
|
|
a. Dependent Variable: Behavioral
Intention |
|
|||
Based
on the table above, it can be explained as follows:
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
The Service Quality variable (X) has a
probability value of 0.000 < 0.05 and t count of 11.981 > 1.9707, meaning
that H0 is rejected and H4 is accepted, namely
the Service Quality variable (X) has a partially significant effect on
Behavioral Intention (Y).
b.
Coefficient
of Determination
Table 12 Test Results for the
Determination Coefficient of Hypothesis 4
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
.628 a |
,395 |
,392 |
1.41906 |
2,321 |
|
a. Predictors: (Constant), Quality
of Service |
|||||
|
b. Dependent Variable: Behavioral
Intention |
|||||
R-Squared value of 0.395 or 39.5 %. This indicates that the
independent variable in this research, namely Service Quality (X), contributes
to a significant influence on Behavioral Intention (Y) of 39.5%, and the
remaining 60.5% is explained by other variables.
Hypothesis 5
"Customer Satisfaction Has a Positive Influence
on Behavioral Intention"
Simple Linear Regression Analysis
Table 13 Simple Linear
Regression Test Results Hypothesis 5
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
3,138 |
1,376 |
|
2,280 |
.024 |
|
Customer satisfaction |
,214 |
.016 |
,676 |
13,595 |
,000 |
|
|
a. Dependent Variable: Behavioral
Intention |
||||||
Y
= 3.138 + 0.214X +
Based on the regression equation above, it can be explained
as follows:
1.
A constant value of 3.138 indicates that if the independent variable,
namely Customer Satisfaction (X), has a constant value or zero, then the
Behavioral Intention Variable (Y) is 3.138.
2. The
regression coefficient for the Customer Satisfaction variable (X) is positive
at 3.138, which means there is a unidirectional relationship between the two
variables, so shows that every time there is
an increase in Customer Satisfaction (X) 1 unit assuming other variables are
constant the Behavioral Intention variable (Y) increases by 3.138
Hypothesis test
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
Table 14 Hypothesis 5 T Test
Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
2,280 |
.024 |
|
|
Customer satisfaction |
13,595 |
,000 |
|
|
|
a. Dependent Variable: Behavioral
Intention |
|
|||
The Client Fulfillment variable (X)
encompasses a likelihood esteem of 0.000 < 0> 1.9707, meaning that H0 is
rejected and H5 is accepted, specifically the Client Fulfillment variable (X)
features a somewhat critical impact on Behavioral Purposeful (Y).
b.
Coefficient
of Determination
Table 15 Test Results for the
Determination Coefficient of Hypothesis 5
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
.676 a |
,457 |
,454 |
1.34475 |
2,045 |
|
a. Predictors: (Constant), Customer
Satisfaction |
|||||
|
b. Dependent Variable: Behavioral
Intention |
|||||
R-Squared esteem
of 0.457 or 45.7 %. This appears that the free variable in this explore,
particularly Client Fulfillment (X), contributes to a basic affect on
Behavioral Intentional (Y) of 45.7%, and the remaining 54.3% is clarified by
other variables.
Hypothesis 6
"Customer Trust Has a Positive Influence on Behavioral Intention"
Simple Linear Regression Analysis
Table 16 Simple Linear
Regression Test Results Hypothesis 6
|
Coefficients a |
||||||
|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
|
B |
Std. Error |
Beta |
||||
|
1 |
(Constant) |
6,550 |
1,466 |
|
4,469 |
,000 |
|
CUstomer trust |
,233 |
,022 |
,575 |
10,437 |
,000 |
|
|
a. Dependent Variable: Behavioral
Intention |
||||||
Y
= 6.550 + 0.233X +
Based on the regression equation above, it can be explained
as follows:
1. A
consistent regard of 6.550 appears that in case the independent variable, to be
particular Client Accept (X), is relentless or zero, at that point the
Behavioral Intentional Variable (Y) is 6.550.
2. The
backslide coefficient for the Client Accept (X) variable is positive at 0.233,
which proposes there's a unidirectional relationship between the two variables,
so shows up that for each 1 unit increase in Client Accept (X), anticipating
other variables are consistent, the Behavioral Intentional variable (Y)
increases by 0.233.
Hypothesis test
N:
222
K:
1
Df
: nk = 222-1= 221
Ttable
= 1 .9707
Table 17 Hypothesis 6 T Test
Results
|
Coefficients a |
|
|||
|
Model |
t |
Sig. |
||
|
1 |
(Constant) |
4,469 |
,000 |
|
|
CUstomer trust |
10,437 |
,000 |
|
|
|
a. Dependent Variable: Behavioral
Intention |
|
|||
The Client Believe variable (X)
includes a likelihood esteem of 0.000 < 0> 1.9707, meaning that H0 is
rejected and H6 is accepted, to be specific the Client Believe variable (X)
encompasses a mostly critical impact on Behavioral Deliberate (Y).
a.
Coefficient
of Determination
Table 18 Hypothesis
Determination Coefficient Test Results 6
|
Model Summary b |
|||||
|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
|
1 |
,575 a |
,331 |
,328 |
1.49182 |
2,266 |
|
a. Predictors: (Constant), Customer
Trust |
|||||
|
b. Dependent Variable: Behavioral
Intention |
|||||
R-Squared esteem of 0.331 or 33.1 % .
This demonstrates that the autonomous variable in this investigate,
specifically Client Believe (X), contributes to a critical impact on Behavioral
Deliberate (Y) of 33.1%, and the remaining 66.9% is clarified by other factors.
Based on the comes about of this
investigate, it can be concluded that TikTok benefit quality incorporates a
positive and noteworthy impact on buyer devotion. Typically upheld by the comes
about of the speculation test which appears that the p-value for the
relationship between benefit quality and customer devotion is littler than the
importance esteem (α = 0.05). TikTok benefit quality contributes 74.6 % to
customer fulfillment. In line with investigate by analysts, which states that
the administrations given to clients will give client fulfillment (Putri & Albari, 2024). This finding is also in line with
customer satisfaction theory and customer trust theory, which states that
consumers who are satisfied with service quality tend to be more loyal to the
company (Juwaini et al., 2022).
Besides, the benefit quality that has
the foremost impact on buyer devotion is unwavering quality, taken after by
responsiveness, completeness, information and openness. This appears that it is
vital for TikTok to guarantee that its administrations are dependable,
responsive, total, simple to get to, and backed by existing information and
abilities.
Service quality moreover encompasses a
noteworthy impact on client believe, with an impact commitment of 80.6 % . This
appears that clients will have more believe and certainty in a company or stage
in case they feel that the benefit given is of tall quality. This reflects the
significance of astuteness, straightforwardness and consistency in giving
services to clients.
The inquire about comes about too
uncover that client believe incorporates a noteworthy influence on client
fulfillment, with an impact commitment of 65.4%. This affirms that to attain
tall levels of fulfillment, companies ought to not as it were offer quality
items or administrations but too construct and keep up client believe. This
believe can be built through steady benefit quality, responsiveness to issues,
and viable communication.
Isolated from particularly affecting
buyer constancy, TikTok advantage quality as well contributes to the course of
action of client behavioral energetically, especially repurchase deliberate,
area return to and word-of-mouth. Customers who are satisfied with the quality
of TikTok's organizations tend to form go over buys, visit the TikTok location
once more, and endorse TikTok to others. Advantage quality contains a positive
affect on Behavioral Intentional (Y), and this affect is genuinely noteworthy.
The affect commitment of 39.5% shows up that about 40% of the assortments or
changes in behavioral deliberate can be clarified by assortments or changes in
advantage quality. This can be in line with analysts who expressed that there's
an impact of benefit on behavioral purposeful (Othman, Zahari, & Radzi, 2013). That is, when the quality of services
offered by a company or platform improves, this tends to encourage or motivate
customers or users to have stronger intentions to interact or behave further
with the company or platform. This could be a desire to continue using the service,
recommend it to others, or take other positive actions that are profitable for
the company.
Not as it were that, when client
fulfillment levels increment, this will empower or impact clients to have a
more grounded deliberate to act in a way that underpins the company or stage.
In other words, the more fulfilled clients are with a item or benefit, the more
likely they are to have positive eagerly to require certain activities, such as
repurchasing the item, prescribing it to others, or connection assist with the
company (Cho, 2015).
The impact commitment of 45.7 % appears
that nearly half of the variety or alter in behavioral deliberate can be
clarified by the level of client fulfillment. Typically a strong sign that
client fulfillment plays an awfully imperative part in forming their behavioral
intentions. Therefore, to realize higher trade objectives, companies must
center on endeavors to extend customer fulfillment. By guaranteeing that
clients are fulfilled, companies not as it were keep up positive connections
with them but also empower them to act in a positive way, which can eventually
result in trade development and supportability.
Following, there's a positive
relationship between client believe and behavioral deliberate. The impact
commitment of 33.1 % demonstrates that around one third of the variety or alter
in behavioral purposeful can be clarified by the level of client believe. In
spite of the fact that this commitment is somewhat lower compared to client
fulfillment, this still appears the significance of building and keeping up
client believe in making positive behavioral eagerly (Fang et al., 2014).
Thus, the importance of paying
attention to various service indicators that will be provided to customers will
have a positive and good impact on sellers, in this case the quality of service
provided by TikTok Shop is considered good by respondents (consumers), so the
current quality needs to be maintained and highly allows it to be improved.
CONCLUSION
Based on the results of
geospatial modeling using the weighted overlay method, assisted by the weighted
values of the FAHP analysis results, the "Environmental" criterion is
at the top with a value of 0.623, followed by the "Social" criterion
with a value of 0.306, and the "Economic" criterion with a weighted
value of 0.071. Of the total area, 0.01% or 1.52 ha is included in the High
Category, 92.46% or 16,345.74 ha is included in the Medium Category, and 7.53%
or 1,331.77 ha is included in the Low Category. Two areas with High Category
status, located in Cilincing and Kelapa Gading Districts, North Jakarta, are
theoretically recommended as potential locations for developing PSEL
facilities.
Areas with High Category
status require special consideration at further planning stages. High Category
1 areas are in Industrial and Warehouse Zones, requiring special permits and
communication with the government. High Category 2 areas are located close to
residential and tourist sites, which makes obtaining approval to build and
operate PSEL facilities a challenge. Licensing processing can be made easier by
choosing the right technology and management, as well as prioritizing the
environment and surrounding ecosystem. This study is expected to provide useful
information for decision makers in the planning stage of installing PSEL
facilities in the East Jakarta Zone, DKI Jakarta Province.
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