Jelajah: Journal Tourism and Hospitality

e-ISSN 2685-094X

Vol. 3 No. 1, 2021

FACTORS INFLUENCING TOURIST SATISFACTION AND REVISIT INTENTION TO CIBUNTU
TOURIST VILLAGE DURING COVID-19 PANDEMIC
Rosianna Sianipar1, Jimmy M.H. Situmorang2, Vasco A.H. Goeltom3, Kevin Gustian Yulius4

rosianna.sianipar@uph.edu, jimmy.mhs@uph.edu, vasco.goeltom@uph.edu, kevin.yulius@uph.edu
a,b,c,dProgram Studi D4 Pengelolaan Perhotelan, Fakultas Pariwisata, Universitas Pelita Harapan, Indonesia

Abstract
The COVID-19 pandemic has a devastating impact on all world economic activities, including the
tourism industry. Tourist satisfaction and revisit intention are two important factors in the recovery of
economic activity in the tourism sector. These two variables are also influenced by tourists' perceptions
of the COVID-19 protocol implemented by tourist destinations. Cibuntu Tourist Village puts forward the
concept of community-based tourism. Located in Kuningan Regency, West Java, its’ homestay is their
main business. This study aims to analyze the factors that influence tourist satisfaction and tourists’
revisit intention to Cibuntu Tourist Village during the COVID-19 pandemic. This research is an
explanatory study that seeks to explain the causative relationships between endogenous and exogenous
variables. The sampling technique used in this study was non-probability sampling with a convenience
sampling method. The research instrument used was a questionnaire with 25 indicators and the data
were analyzed using SEM with 100 respondents. The data obtained from the research instrument shows
that all indicators meet the requirements of discriminant validity, convergent validity and reliability.
The data were analyzed using the bootstrapping method using 5000 subsamples. The result is the
coefficient of determination for tourist satisfaction is 87.6% and for interest in return visits is 91.6%. In
the t test, 4 hypotheses were rejected, and 5 hypotheses were accepted. COVID-19 protocol has
significant influence over tourist satisfaction and revisit intention of domestic tourists to Cibuntu
Tourist Village.
Keyword: Tourist Village, Tourist Satisfaction, Revisit Intention, COVID-19 Protocol

INTRODUCTION
COVID-19 or Coronavirus Disease 19 is a disease that was first discovered in Wuhan, China and then spread
rapidly throughout the world (Lipistch et al., 2020). WHO raised the status of COVID-19 from an epidemic to a
pandemic in March 2020 because of its continued spread to various countries. Its highly contagious nature and long
incubation period make COVID-19 difficult to control and supervise (Singhal, 2020). This pandemic has had a
negative impact not only on social conditions but also on the world economy. Various types of industries including
tourism have been affected by this pandemic (Crossley, 2020). Following WHO recommendations, almost all
countries have imposed restrictions on their tourism activities, both domestic and international, closed tourist
attractions, and also postponed events or other forms of tourism business (Ioannides & Gyimóthy, 2020). Tourism
as a labor-intensive industry that sells services has been badly affected by this pandemic. The number of tourism
business activities that are limited by the government and the difficulty of traveling have forced many business
people to temporarily close their businesses and lay off their employees (Wen et al., 2020). The United Nations
World Tourism Organization (UNWTO) estimates that 100 to 120 million tourism workers are threatened with
work due to this pandemic and the possible recovery of the tourism industry by 2023 (UNWTO, 2020).
Tourism industry activities and growth are also hampered in Indonesia, a country that relies on tourism as a
contributor to its foreign exchange earnings. Since it was announced in early March 2020, various steps have been
taken by the central and regional governments to prevent the spread of this pandemic. An example is the LargeScale Social Restrictions (PSBB) regulated in the PP of the Republic of Indonesia number 21 of 2020. This PSBB
activity has a direct impact on tourism activities through reducing public transportation capacity, temporarily
closing restaurants and shopping centers, as well as restrictions on entering and exiting an area. The Indonesian
Ministry of Tourism and Creative Economy (Kemenparekraf) made a mitigation plan to deal with the impact of this
pandemic in March 2020 (Kemenparekraf, 2020). The tourism recovery phase begins in June to December 2020
and continues with the normalization phase in January to December 2021. In August 2020, the Ministry of Tourism
and Creative Economy issued CHSE (Cleanliness, Health, Safety, & Environment) guidelines for various forms of
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tourism business, including hotels, restaurants , tourist attractions, and homestays (tourist lodges). This guide will
be used in tackling and preventing the spread of the COVID-19 pandemic in tourist places or destinations.
A tourist village or tourist village is a form of tourist destination developed by a tourism awareness group or
Pokdarwis that is domiciled or lives in the village area (Kemenparekraf, 2012). The tourist village was developed
based on the concept of Sapta Pesona which consists of safe, orderly, clean, cool, beautiful, friendly, and memorable.
CHSE was developed by the Ministry of Tourism and Creative Economy in order to complement Sapta Pesona as a
means of returning visitors to tourist villages during and after the COVID-19 pandemic. Tourism activities in tourist
villages are more personal because they involve the local community as business people and tourists as consumers.
Tourist satisfaction and revisit intention to tourist villages is an important component in the ongoing tourism
activities, and in this case depends on the interactions they do with the surrounding community (Mai et al., 2019;
Nguyen et al., 2020). Several things that affect tourist satisfaction and interest in returning to a tourist destination
are attraction, accommodation, cultural contact, and perceived risk (Nguyen et al., 2020; Tosun et al., 2015;
Chaulagain et al., 2019). One of the tourist villages in Indonesia that is interesting to study is the tourist village of
Cibuntu. The village, which is located at the foot of Mount Ciremai, was declared a tourist village in 2012 and has
since adopted the concept of Sapta Pesona Tourism. The Cibuntu tourist village was also ranked as the 5th tourist
village with the best homestay/tourist lodge in Southeast Asia and the 2nd best tourist village in the CBT
(Community Based Tourism) event held by the Ministry of Tourism (Wamad, 2017).
As a tourist village and a destination, the main income Cibuntu Tourist Village is from the tourism and
accommodation business. Almost all houses in Cibuntu are converted or being utilized as a homestay to
accommodate visitors during their activity there. The residents of Cibuntu tourist village are trying to develop other
type of business to support their main income source, homestay, during the pandemic. They also try to implement
the CHSE standard prepared by the Ministry to help restarting the business when the time come. Cibuntu tourist
village as a tourist destination is also supported by tourism grant funds which are expected to help improve the
implementation and implementation of CHSE in welcoming back domestic tourists (desacibuntu.com, 2020). From
the explanation above, the researcher decided to conduct a study with the title "FACTORS INFLUENCING TOURIST
SATISFACTION AND REVISIT INTENTION TO CIBUNTU TOURSIT VILLAGE DURING COVID-19 PANDEMIC”. This
study was conducted to identify the influence of several factors on tourist satisfaction and interest in returning
visits to Tourism Villages during the COVID-19 pandemic. The selected tourist village is Cibuntu Tourism Village,
located in Kuningan Regency. The COVID-19 Health Protocol is used as the fourth factor to replace perceived risk
because the risk of COVID-19 transmission is considered one of the barriers to tourist satisfaction and return visit
interest (UNWTO, 2020; Kour et al., 2020; Ahmad et al., 2020).
This study aims to identify and determine the effect of tourist attraction, accommodation services, cultural
contacts, and COVID-19 protocols on tourist satisfaction and interest in returning visits to Cibuntu Tourism Village
during the COVID-19 pandemic. The influence of tourist satisfaction on the interest in returning to the Cibuntu
Tourism Village will also be measured to identify the relationship between the two variables. This research also
aims to help stakeholders in tourism activities in tourist villages to normalize after the COVID-19 pandemic. This
study is a replication of Bang Nguyen Viet, Huu Phuc Dang & Ho Hai Nguyen’s research with the title "Revisit
intention and satisfaction: The role of destination image, perceived risk, and cultural contact" in 2020. In this study,
Nguyen et al. sought to identify the relationship between tourist attraction, accommodation and food services,
cultural contact, and perceived risk to tourist satisfaction and return visit interest to Binh Thuan province in
Vietnam. In the results of the study, it was found that all independent variables had a significant effect on tourist
satisfaction and return visit interest. Tourist satisfaction also has a direct effect on the interest in returning to Binh
Thuan province. In 2020, the COVID-19 pandemic spreads throughout the world and the risk of transmission is one
of the considerations for tourists when traveling (Sigala, 2020). Tourists tend to travel domestically such as to rural
areas (Hui & Deng, 2020) and expect good COVID-19 health protocols to have been prepared by tourist destinations
(Matiza, 2020). Tourism research during the COVID-19 pandemic was carried out to restore tourism conditions as
they were before the pandemic. Studies that are also used as references for this study are research on tourist
satisfaction (Hong et al., 2020) and return visit interest (Kour et al., 2020; Ahmad et al., 2020; Hassan & Soliman,
2020; Rahimizhian & Irani, 2020) during the COVID-19 pandemic.

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Rerangka Pemikiran

Attractiveness
H1
Satisfaction
H5
Accommodati
on Service

H2
H6

H9

H3
Cultural
Contact

Revisit
Intention

H7
H4
H8

COVID-19
Protocol

Gambar 1 Theoritical Framework
LITERATURE REVIEW

1. Tourism Attraction
Tourist Attraction is defined as everything which has ability to invoke the interest of people to visit it (Cooper,
2016). This attraction also reflecting the value and expectation of a tourist towards a destination to fulfill their needs
(Smith & Warburton, 2012). It has several components such as accessibility, facility, local community, and view
(Reitsamer et al., 2016), as well as entertainment and historical site (Nguyen et al., 2020). Cooper (2016) also
identified five factors that might influence attraction especially to rural tourism, which are, cultural appraisal,
multiple use, perishability, economic significance, and ownership.

2. Accommodation Service
As a part of tourism component, accommodation also play a role as product and infrastructure (Cong, 2016).
Accommodation business owner not only had to provide basic needs of the guest such as eating and drinking but
also the fulfilment of other secondary needs (Kumar et al., 2018). The provision of local cuisine is also important
for tourist with different cultural background due to their tendency to find unique cuisine experience to themselves
(Cong & Dam, 2017; Mai et al., 2019). Good perception towards the accommodation and its services will bring
positive impact to tourist satisfaction which in turn will invoke their revisit intention in the future (Chin et al., 2018).
In this research, the accommodation service will refer to homestay, or translated to Indonesian as pondok wisata
based on Peraturan Pemerintah nomor 52 tahun 2012. In Peraturan Menteri Pariwisata dan Ekonomi Kreatif no 9
tahun 2014, homestay is defined as an accommodation service in a form of residential building inhabited by the
owner and partly used for rent by providing opportunities for tourists to interact in the owner's daily life.

3. Cultural Contact
Rural tourism is a global phenomenon which is popular not too long ago (Aslam & Awang, 2015; Dimitrovski
et al., 2012). It is considered as part of cultural tourism due to the tendence of difference in cultural background
between the tourist and the people of the village itself (Lane & Kastenholz, 2015). The purpose of rural tourism is
usually to study, find, do, and consume the cultural product in a tangible or intangible form (Richards, 2018). In
rural tourism the cultural contact between tourist and the local community are inseparable (Kour et al., 2020) and
apply not only to international tourist but domestic tourist as well in a multicultural country such as Indonesia (du
Cros & McKercher, 2015). Cultural contact emphasizes the desire of tourists to involve themselves in local cultural
activities and involves tourists' behavior towards that culture (Chen & Rahman, 2018).

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4. COVID-19 Protocol
Tourist perception of tourism risk is one of the key factors in deciding to visit a destination (Çetinsöz & Ege,
2013; Chew & Jahari, 2014). Tourism risk can be defined as misfortune possibility which can afflict a tourist (Sohn
et al., 2016). It commonly associated with human risk, psychosocial, food, weather, finance, service quality, natural
disaster and accident (Cui et al., 2016) but mainly towards the transmission of COVID-19 during this pandemic
(Rahimizhian & Irani, 2020). COVID-19 health protocol must be well developed and implemented by the tourist
destination to reduce the perception towards tourism risk during this pandemic (Sigala, 2020). Good perception on
COVID-19 health protocol will improve the tourist satisfaction during this pandemic time (Bae & Chang, 2020; Hong
et al., 2020).

5. Tourist Satisfaction
There are four characteristics of service which are intangibility, inseparability, variability and perishability.
Those characteristics influence people differently and that is why satisfaction toward service is measured
differently than the satisfaction towards a product (Kotler et al., 2017). Satisfaction is the result of tourists'
assessment of their values and expectations (Ford et al., 2012), an important component in a tourist experience
(Zhang et al., 2018), and one of the most researched topics in relation to revisit intention in tourism industry (Chiu
et al., 2016).

6. Revisit Intention
Revisit intention is a common research topic in tourism and hospitality related research (Li et al., 2018).
Revisit interest is defined as post-consumption behavior in which a tourist has a desire to re-experience a tourism
product or tourist destination (Tosun et al., 2015). Tourists who have a revisit intention and in turns eventually
become repeat visitors tend to stay longer, are more active in consumptive activities, are more satisfied with
tourism activities, spread good WOM, and require lower marketing costs compared to first-time visitors. (Zhang et
al., 2014, 2018).
RESEARCH METHODS
This research is an explanatory research that seeks to identify causality or the relationship between one
variable and another or more variables (Veal, 2018). The types of data used in this study are quantitative and
qualitative. Quantitative data will be used to measure the relationship between variables using a questionnaire
instrument and a Likert scale. Qualitative data obtained from unstructured interviews are used to support the
results of quantitative data analysis.
The population in this study were domestic tourists who had visited Cibuntu Tourism Village and the
sample was domestic tourists who visited Cibuntu Tourism Village during the COVID-19 pandemic from March to
October 2020. The sampling technique used was non-probability sampling with convenience sampling. This was
done due to the limitations of researchers and the absence of exact data on the population of tourist destinations
during the COVID-19 pandemic. The field study was conducted on the first visit on November 13, 2020 to
November 15, 2020 and the distribution of questionnaires was carried out on the second visit on December 11,
2020 until December 13, 2020. Measurement or analysis of quantitative data will be carried out using SMART PLS
software with a large number of samples. taken as many as 100 respondents. Testing the quality of the data
produced by the research instrument was tested for convergent validity, discriminant validity, and reliability.
Convergent validity refers to the similarity or similarity of constructs between variables while discriminant
validity refers to how one construct differs from another (Hair et al., 2019). While reliability is the ability of the
instrument itself to be used in different environment.
Data testing is done by measuring the coefficient of determination, path analysis and t test or partial
hypothesis testing. The coefficient of determination or R2 refers to the ability of exogenous variables to predict
the value of endogenous variables in percentage. Path analysis is carried out to determine the nature of the
relationship between exogenous and endogenous variables, whether it is positive or negative. The last is the
partial hypothesis test or t test. The t-test was conducted to determine the magnitude of the relationship between
one exogenous variable and one endogenous variable and to determine the significance of the relationship. The ttest was carried out after passing the bootstrapping stage. Bootstrapping is a method of resampling or resampling
that is carried out to ensure statistical significance. Bootstrapping in this study used subsamples of 5000 samples.
After bootstrapping is done then the results of the t-test can be analyzed for this study. In table 1, it can be seen
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the relationship between exogenous and endogenous variables along with the number of hypotheses.

Table 1 Relationship of variables in this research
Hypothesis
Exogenous Variable
H1
Tourist Attraction
H2
Accomodation Service
H3
Cultural Contact
H4
COVID-19 Protocol
H5
Tourist Attraction
H6
Accomodation Service
H7
Cultural Contact
H8
COVID-19 Protocol
H9
Tourist Satisfaction
Source: Data Processing Results (2020)

Endogenous Variable
Tourist Satisfaction
Tourist Satisfaction
Tourist Satisfaction
Tourist Satisfaction
Revisit Intention
Revisit Intention
Revisit Intention
Revisit Intention
Revisit Intention

RESULTS AND DISCUSSION

1.

Convergent Validity test

Table 2 Convergent Validity Test
Att1
X1 (ATT)
0,817
Acc1
X2 (ACC)
0,873
Cul1
X3 (CUL)
0,865
Cov1
X4 (COV)
0,856
Sat1
0,927
Y1 (SAT)
Rev1
Y2 (REV)
0,902
Source: Data Processing Results (2020)

Att2
0,866
Acc2
0,872
Cul2
0,891
Cov2
0,905
Sat2
0,834
Rev2
0,926

Att3
0,75
Acc3
0,87
Cul3
0,854
Cov3
0,902
Sat3
0,942
Rev3
0,818

Att4
0,805
Acc4
0,889
Cul4
0,806
Cov4
0,918
Sat4
0,937
Rev4
0,907

From table 2, it can be seen that the loading factor test results obtained from calculations using the PLS
Algorithm. All loading factor indicators used in the research instrument, the value above 0.7 means that it meets the
first requirement of convergent validity. The convergent validity test was then continued by checking the AVE
(Average Variance Extracted) value which was the value generated by the variables used in the research instrument.
The AVE value can be seen in table 3.

Table 3 Average Variance Extracted (Ave) Test Results
X1 (ATT)
X2 (ACC)
X3 (CUL)
X4 (COV)
Y1 (SAT)
Y2 (REV)
Source: Data Processing Results (2020)

Average Variance Extracted (AVE)
0,657
0,768
0,731
0,802
0,830
0,791

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In table 3 it can be seen that each variable used in this study, both the independent (exogenous) and
dependent (endogenous) variables has an AVE value above 0.5. The AVE value above 0.5 proves that all the variables
used in this study show a fairly good convergence. After fulfilling the two conditions of the convergent validity test,
testing the quality of the research instrument continued with the discriminant validity test.

2.

Discriminant Validity Test

Table 4 Test Results Fornell-Larcker Criterion
X1 (ATT)
0,896
0,768
0,825
0,822
0,835
0,894

X1 (ATT)
X2 (ACC)
X3 (CUL)
X4 (COV)
Y1 (SAT)
Y2 (REV)

X2 (ACC)

X3 (CUL)

X4 (COV)

Y1 (SAT)

Y2 (REV)

0,893
0,845
0,807
0,867
0,878

0,887
0,853
0,881
0,861

0,896
0,891
0,895

0,950
0,898

0,927

Source: Data Processing Results (2020)
In table 4, the results of the Fornell-Larcker criterion test can be seen. The Fornell-Larcker criterion value
in the variable must be greater than the value between variables to be accepted as valid in the discriminant validity
test. In this study, there are several indicators with the lowest loading factor values that must be removed so that
each variable meets the Fornell-Larcker criterion validity. The deleted indicators were ATT3, ATT4, ACC3, CUL4,
SAT 2, and REV 3. After these indicators were deleted, the research instrument met the Fornell-Larcker criterion
of discriminant validity with the values listed in table 4.

Table 5 Cross Loading Test Results
Acc1
Acc2
Acc4
Att1
Att2
Cov1
Cov2
Cov3
Cov4
Cul1
Cul2
Cul3
Rev1
Rev2
Rev4
Sat1
Sat3
Sat4

X1 (ATT)
0,698
0,588
0,756
0,885
0,908
0,584
0,723
0,810
0,806
0,723
0,749
0,722
0,825
0,853
0,813
0,777
0,795
0,807

X2 (ACC)
0,907
0,884
0,889
0,626
0,745
0,730
0,725
0,740
0,702
0,798
0,773
0,670
0,766
0,829
0,843
0,823
0,806
0,841

X3 (CUL)
0,767
0,668
0,814
0,742
0,738
0,711
0,739
0,803
0,798
0,896
0,910
0,853
0,802
0,803
0,790
0,836
0,841
0,832

X4 (COV)
0,702
0,619
0,824
0,691
0,779
0,857
0,905
0,902
0,918
0,746
0,758
0,769
0,813
0,876
0,797
0,827
0,864
0,845

Y1 (SAT)
0,770
0,673
0,861
0,687
0,805
0,718
0,768
0,843
0,849
0,799
0,812
0,729
0,786
0,856
0,853
0,940
0,952
0,957

Y2 (REV)
0,782
0,723
0,836
0,778
0,828
0,702
0,780
0,860
0,850
0,790
0,779
0,720
0,924
0,934
0,922
0,817
0,869
0,871

Source: Data Processing Results (2020)
For the last discriminant validity test, the results of the cross loading values in the research instrument can
be checked. The cross loading value is the correlation value between each indicator and all variables in the research
instrument. The cross loading value that can be considered valid is if the correlation value between the indicator
and the variables containing the indicator is greater than the correlation value between the indicator and other
variables. The column marked in yellow in table 5 shows the value of the cross loading indicator with its own
variable that has met the discriminant validity requirements.

3.

Reliability Test

After meeting the requirements of convergent and discriminant validity, the research instrument must go
through a reliability test to check the level of consistency of the research instrument. There are 2 things that can be
measured when conducting a reliability test, namely composite reliability and Cronbach's Alpha coefficient. Both
can be seen in table 6 which is the result of the reliability test conducted using SMART PLS.
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Table 6 Reliability Test Results
Cronbach's Alpha

Composite Reliability

0,756
0,874
0,864
0,918
0,945
0,918

0,891
0,922
0,917
0,942
0,965
0,948

X1 (ATT)
X2 (ACC)
X3 (CUL)
X4 (COV)
Y1 (SAT)
Y2 (REV)

Source: Data Processing Results (2020)
From the results of the reliability test above, it was found that the value of composite reliability and
Cronbach's Alpha coefficient of all variables was above 0.7. Hair et al. (2019) sets 0.6 to 0.7 as the minimum
standard for the value of composite reliability and Cronbach's Alpha coefficient. Thus, the research
instrument can be declared valid and reliable and the data can be tested. Analysis of the Structural Equation
Model Determination Coefficient (R2). After it was found that the research instrument used met the
requirements of convergent validity, discriminant validity, and reliability, the data could be tested further.
First, the coefficient of determination or R2 can be checked to influence the percentage of the effect of
exogenous variables on endogenous variables. The value of R2 can be seen in table 7.

TABLE 7 Coefficient Of Determination Test Results (R2)
R Square

R Square Adjusted

Y1 (SAT)

0,876

0,871

Y2 (REV)

0,916

0,911

Source: Data Processing Results (2020)
Endogenous variable 1 or tourist satisfaction is influenced by four exogenous variables, namely tourist
attraction, accommodation services, cultural contacts, and COVID-19 protocols. The R2 value obtained is 0.876 or
87.6% where the remaining 12.4% of the other tourist satisfaction variables can be influenced by other exogenous
variables not examined in this study. Endogenous variable 2 or return visit interest is influenced by five exogenous
variables, namely tourist attraction, accommodation services, cultural contacts, COVID-19 protocols, and tourist
satisfaction. The R2 value obtained is 0.916 or 91.6%. There is a remaining 8.4% where the return visit interest
variable can be influenced by other exogenous variables not examined in this study.
The high value of the coefficient of determination proves that the research instrument used is able to measure
the research variables well. Tourist satisfaction during the COVID-19 pandemic can be influenced by other variables,
such as travel safety, use of technology, and other conveniences offered during their trip (Hong et al., 2020). While
the interest in returning tourists during this pandemic period is further influenced by technological innovation and
the use of contactless technology both in travel and in accommodation services (Rahimizhian & Irani, 2020; Ahmad
et al., 2020).

4.

Path Analysis Test (Path Analysis)

Table 8 Path Analysis Test Results
X1 (ATT)

X2 (ACC)

X1 (ATT)
X2 (ACC)
X3 (CUL)
X4 (COV)
Y1 (SAT)

X3 (CUL)

X4 (COV)

Y1 (SAT)
0,145

Y2 (REV)
0,379

0,285
0,215
0,358

0,304
-0,030
0,275
0,100

Y2 (REV)
Source: Data Processing Results (2020)
Path analysis is carried out to determine or estimate the direction of the relationship between
variables in the structural equation model. If the value is positive then the relationship between the
variables is positive and vice versa. If the value obtained from the path analysis is negative, then the
influence between the exogenous and endogenous variables is negative. The results of this research path
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analysis can be seen in table 7 above and the discussion will be described along with the results of the
partial hypothesis test or t test.

5.

Partial Hypothesis Test (t Test)

The t-test was conducted to partially test the truth of the hypothesis. The relationship between variables is
explained respectively through the value of the coefficient t or t statistic. Before performing the t-test, the data must
go through a bootstrapping process where there is a resampling process to test the statistical significance of the
data used. The subsamples value that will be used in this bootstrapping is 5000 samples and a significance level of
0.05 or 5%.

TABLE 9 Path Coefficients Test Results With Bootstrapping
Hypothesis

X1 (ATT) ->
Y1 (SAT)
X2 (ACC) > Y1 (SAT)
X3 (CUL) > Y1 (SAT)
X4 (COV) > Y1 (SAT)
X1 (ATT) ->
Y2 (REV)
X2 (ACC) > Y2 (REV)
X3 (CUL) -> Y2
(REV)
X4 (COV) > Y2 (REV)
Y1 (SAT) ->Y2
(REV)

Original
Sample
(O)

Sample
Mean
(M)

Standard
Deviation
(STDEV)

T Statistics
(|O/STDEV|)

P
Values

1

0,145

0,139

0,089

1,631

0,103

2

0,285

0,296

0,094

3,020

0,003

3

0,215

0,223

0,135

1,588

0,112

4

0,358

0,343

0,117

3,057

0,002

5

0,379

0,386

0,098

3,855

0,000

6

0,304

0,298

0,092

3,293

0,001

7

-0,030

-0,021

0,091

0,331

0,741

8

0,275

0,262

0,105

2,608

0,009

9

0,100

0,100

0,117

0,858

0,391

Source: Data Processing Results (2020)
In table 9, the path coefficient values are shown in the original sample column (O), then the calculated t
value or t coefficient in the T Statistics column (|O/STDEV|), and the significance of the relationship in the P Values
column. The t value after going through bootstrapping with a significance level of 5% must be greater than 1.96 so
that the hypothesis between these variables can be accepted.
Based on table 8, the relationship between variables can be described as follows:
a. The path coefficient value between tourist attraction (X1) and tourist satisfaction (Y1) is 0.145. The
relationship between tourist attraction and tourist satisfaction is positive. However, the t-value of this
relationship is 1.631 and is smaller than the standard t-value of 1.96 with a significance value of 0.103 above
0.05. Thus H1 is rejected because it does not meet these requirements.
b. The path coefficient value between accommodation services (X2) and tourist satisfaction (Y1) is 0.285 and
indicates a positive relationship. The t value of 3.020 is greater than the standard of 1.96 with a significance of
0.003 which means that H2 is accepted. Accommodation services affect tourist satisfaction significantly.
c. The path coefficient value between cultural contact (X3) and tourist satisfaction (Y1) is 0.215 and shows a
positive relationship. The t-value is 1.588 with a significance of 0.112, indicating that there is no significant
effect between cultural contact and tourist satisfaction. This means that H3 is rejected.
d. The path coefficient value between the COVID-19 protocol (X4) and tourist satisfaction (Y1) is 0.358 and shows
a positive relationship between the two variables. The t value of 3.057 and a significance of 0.002 indicate that
there is a significant relationship between the COVID-19 protocol and tourist satisfaction and H4 is accepted.
e. The path coefficient value between tourist attraction (X1) and return visit interest (Y2) which is 0.379
indicates a positive relationship between the two variables. The t value is 3.855 with a significance of 0.000 so
it can be stated that H5 is accepted.
f. The path coefficient value between accommodation service (X2) and return visit interest (Y2) is 0.304 and
shows a positive relationship. The t-value obtained is 3.293 with a significance of 0.001 meaning that there is

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a significant relationship between accommodation services and the interest of returning tourists. H6 is
acceptable.

Gambar 2 Structural Equation Model

Source: Data Processing Results (2020)

CONCLUSION
Based on the results of data analysis obtained in chapter 4, there are several conclusions that can be drawn.
The following is a conclusion on the results of this study:
a. In this study, two endogenous variables were used, namely tourist satisfaction and return visit interest. Tourist
satisfaction is influenced by four exogenous variables, tourist attraction, accommodation services, cultural
contacts, and the COVID-19 protocol. Meanwhile, return visit interest is influenced by five exogenous variables,
namely tourist attraction, accommodation services, cultural contacts, COVID-19 protocols, and tourist
satisfaction.
b. In this study there are three relationships between variables that are positive and not significant, and there is
one relationship that is negative and insignificant. This difference is due to the different research environment
and the COVID-19 pandemic conditions that affect the responses of respondents.
c. Tourist attraction does not significantly affect the satisfaction of tourists visiting Cibuntu Tourism Village
during the COVID-19 pandemic. However, in relation to return visit interest, tourist attraction has a positive
relationship and significant influence. Meanwhile, cultural contact has a positive relationship with both
endogenous variables but the effect is not significant. Local tourists tend to be less concerned with culture as
a determinant of satisfaction or interest in returning visits (Nguyen et al., 2020).
d. Tourist satisfaction as an exogenous variable and return visit interest as an endogenous variable also has a
positive relationship but the effect is not significant. During the COVID-19 pandemic, the interest in return
visits cannot only be influenced by the satisfaction of the tourists themselves. However, it must also be
supported by other variables such as the other four variables in this study or other variables such as motivation
and destination image (Ramli et al., 2020).
e. The COVID-19 protocol significantly affects tourist satisfaction and return visit interest and has a positive
relationship with these two endogenous variables. Tourists already have good enough awareness to always
apply the COVID-19 protocol and choose to visit tourist destinations with good COVID-19 protocols. During
the COVID-19 pandemic and beyond, health protocols such as those contained in the CHSE guidelines of the
Ministry of Tourism and Creative Economy will be an important part of tourism recovery in Indonesia
(Lumanauw, 2020).

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