Keywords

Pandemic Era, Destination Attributes, Local Tourist, Health and Safety Facilities

Introduction

Up to this time, mid-2021, 19 Covid pandemic is not over, either in the world of course, including in Indonesia. Some countries are already showing significant progress, but in some other countries, there is still a spread of Corona virus (SARS-CoV-2), and even the occurrence of new variants such as variants of the delta, which is said to be faster and more dangerous. In one of the weekly report, the WHO stated that on July 20, 2021, the pervasiveness of the delta between the cases were sorted during the last four weeks exceeds 75%. This happens in various countries around the world including Australia, Bangladesh, Botswana, China, Denmark, India, Indonesia, Israel, Portugal, Russia, Singapore, South Africa, and the United Kingdom (cnbcindonesia.com).

In addition to the health sector, this pandemic will of course also have an impact on various fields, including the economic sector. One of the most affected areas of the economy is the tourism sector. This is easy to understand, because restricting human movement is one of the efforts to prevent and reduce the risk of transmitting this virus. Bali, as a part of Indonesia which is very synonymous with tourism, has certainly experienced quite a heavy impact. As reported on regional.kompas.com on August 2, 2021, it was recorded that Bali was only visited by 43 foreign tourists during January to June 2021. It is very clear that the atmosphere of the locations that were once crowded with tourists, now looks deserted.

Of course, this is worrying news, but indeed the current world situation still requires efforts to prevent the spread of the virus, through restrictions on human movement. It is understandable that as long as the pandemic is not fully under control, the tourism sector in Bali cannot expect significant contributions from foreign tourists. Thus, the remaining hope will rest on local tourists. For that, of course, many efforts have been made by many parties so that tourism activities can still exist in Bali, especially various efforts that rely on local tourists.

One of the attractions in Bali is Alas Kedaton, in Kukuh Village, Marga District, Tabanan Regency. Alas Kedaton is a protected jungle area of roughly 12 hectares. In addition to many trees, this area is inhabited by monkeys that are tame, and enjoy interacting with visitors. In the forest area there is also a temple called the Alas Kedaton temple.

As reported by Antaranews.com, December 30, 2012 edition, Alas Kedaton is an interesting tourist attraction. In the 2012 Christmas holiday period, this Alas Kedaton tourist attraction was crowded with around 750 visitors, even on Christmas day there were more than 1000 visitors. On normal days, this tourist attraction is visited by at least 300 to 500 visitors, every day. Of the number of tourists, about 60 percent are local tourists. From the data above, it can be expected that Alas Kedaton can become one of the potential tourism objects that continue to be in demand by local tourists. Unfortunately, according to data obtained from the 2018 Bali Tourism Office, local tourists visiting Alas Kedaton tourism objects from 2014 to 2018 continued to decline, as can be ascertained in the table below.

Table 1 Number of Domestic Tourists to Alas Kedaton 2014 – 2018
Year	Visitors
2014	31.469
2015	26.121
2016	23.818
2017	17.881
2018	12.821

Source: Bali Tourism Office 2018

As one of the efforts to anticipate the conditions mentioned above, this research is intended to be able to contribute, to develop Alas Kedaton tourism objects back into favorite destinations for local tourists, through the approach of 6 main attribute components of tourism destinations, proposed by Buhalis (Buhalis, 2000). Through this approach, it will be known which attributes are attracted by local tourists, which can then be prioritized for the development of Alas Kedaton tourism objects, which of course is also match with efforts to rearrange various components of the attributes of tourism destinations in the post-pandemic era.

Literature Review

Tourism is one of the important driving forces for economic development for a country. This is because of its ability to contribute to job creation and enhancement of many interrelated industries (San Martin & del Bosque, in Boit, 2014). Therefore, it can be said that every country will seek to develop its tourism potential. As a result, competition between countries will be even tighter. For this reason, the efforts of tourism destinations to improve, retain, protect, or intensify its competitive position in competitive and global market is a challenge in the tourism industry (World Economy Forums [WEF] 2007, in Crouch 2011).

The competitiveness of a tourism destination depends primarily on its capability to deliver services and add or generate the value of the tourist experience by public and private sector management activities (Dwyer and Kim, in Hossain and Islam, 2019). Attempts to identify various destination attributes will lead to the existence of many attributes, and their variations, and it is still debated that a universal attribute measurement scale does not yet exist, by reason of the diverse nature and composition of each destination. (Jumanazarov, et al, 2020).

Efforts to identify the attributes of a destination can also be seen as pull and push factors, where these pull and push factors explain the motivation of tourists to visit a certain destination (Dann, in Guzel, 2017). This pull and push theory was developed in the mid-eighteenth century by E.G Ravenstein, a Fellow of the Royal Geographic Society (Bello, in Busayo and Ojo, 2019). Initially, Ravenstein used this theory to explicate the migration configurations of people, both within countries and between countries.

This theory states that travelers embarked on a journey since they are driven by their internal forces and their external forces (Jacqueline, in Busayo and Ojo, 2019). This theory emphasizes that tourists are attracted to migrate to certain destinations on account of the attractiveness of the destination as perceived by them (Bashar and Ahamad in Busayo and Ojo, 2019).

A tourist destination can be illustrated as a location that has various destination selection attributes that represent a favorable image for visitors (Pattiyagedara and Fernando, 2020). Basically each destination will be attached with various attributes (Pena, Jamilena and Molina in Phu, 2017). One of the efforts to recognize these attributes is based on the five senses of human being. For instance, Australia symbolized by visual attributes, sourced from animals kangaroos and the Sydney Opera House, olfactory attributes represented by the aroma of seafood, auditory attributes are taken from the singing of birds and waves, and tactile attributes taken from the diversity of animals and sand, (Son and Pearce, in Phu, 2017).

In the example above, Australia is also seeking to benefit from the cognitive attributes of nature, in the form of attractions and water sports. These cognitive elements can be subdivided into three subsections, namely functional or visible attributes (e.g., natural attractions, nightlife and entertainment), psychological or invisible attributes (e.g., hospitality, customs), and the mixture functional and psychological attributes (e.g., crowds, cleanliness) (Echtner and Ritchie, 2003).

In addition, destination attributes are also used for research related to tourist satisfaction. For instance, the four dimensions of British tourists' satisfaction when visiting Mallorca, Spain are ‘availability of English language,’ ‘destination attractiveness,’ ‘facilities and services at the destination airport,’ and ‘tourist attractions and facilities’ (Kozak and Rimmington in Phu 2017). Besides, research related to tourist satisfaction to the central region of Portugal resulted in three important components, namely 'accessibility', 'attractions' and 'basic services' (Eusébio and Vieira, 2011).

Furthermore, one of the destination attribute frameworks that are often used in tourist satisfaction research, including in this research is the 6A framework proposed by Buhalis (Buhalis, 2000, Pattiyagedana and Fernando, 2020) , as follows: Attractions, for instance: natural, man-made, artificial, heritage, special events, Accessibility, for instance: entire transportation system covering routes, terminals and vehicles, Amenities, for instance: accommodation and catering facilities, retailing, other tourist services, Available packages, for instance: pre-arranged packages by intermediaries and principals, Activities, for instance: all activities available at the destination and what consumers will do for the period of their visit, and Ancillary services, for instance: services used by tourists such as banks, telecommunications, post, newsagents, hospitals, etc.

Thus, the theoretical framework of this research can be seen as follows:

Source: Buhalis, 2000

The research hypotheses used in this study, in accordance with the research objectives to be achieved, are as follows:

H1: Accessibility has a significant contribution toward Tourist Satisfaction.

H2: Attraction has a significant contribution toward Tourist Satisfaction

H3: Amenities has a significant contribution toward Tourist Satisfaction

H4: Activity has a significant contribution influence toward Tourist Satisfaction.

H5: Ancillary Service has a significant contribution toward Tourist Satisfaction.

H6: Available Package has a significant contribution toward Tourist Satisfaction.

Meanwhile, several previous studies that were taken into consideration in this research can be checked in the table below.

Table 2 Previous Research
No.	Title, (Author and Year)	Variables	Result	Methodology
1	The Influence of the Quality of Tourism Attractiveness of Tourism Satisfaction in Jatiluhur Reservoir, Purwakarta Regency. Rahmat Darsono, (2013)	Accessibility, Attractions, Amenities, Activities, Available Package, Ancillary Services, Satisfaction	All mentioned variables have a significant value towards satisfaction	Regression Analysis
2	The Influences of Destination Quality on Tourists’ Destination Loyalty: An Investigation of an Island Destination. Aswin Sangpikul, (2017)	Destination Quality, Tourist Satisfaction. And Tourist Loyalty.	Iit was discovered that destination quality dimension related to beach attraction was found to have the significant influence on destination loyalty in a positive direction.	SEM
3	Tourist Rating of Tourism Attributes in Batu City, Abdullah, (2017)	Attractions, Facilities, Transportation and Hospitality	Batu City has good tourism attributes. Average value the highest is in the hospitality dimension, followed with facilities, attractions andthe last is transportation	Descriptive and Qualitative
4	Assessment of the Quality of Tourist Destinations in The Nilgiris District based on Tourist Perception. Arpit Gupta, Aman Gupta, & Banibrata Choudhury, (2018)	Attractiveness, Accessibility, Amenities, Attendance, Accord and Alertness	Assessment of tourist destination according to tourist perception will directly depict	Graphical Analysis
5	Tanggapan Wisatawan terhadap Kualitas Atribut Tujuan Wisata (Attractions, Accessibility, Amenities, Available Packages, Activities, AncillaryServices) yang Ditawarkan Desa Wisata Pentingsari Kabupaten Sleman, Aimah U. Harma (2016)	Attractions, Accessibility, Amenities, Available packages, Activities and Ancillary services	Tourist attribute form positive perceptions on tourists so that it does not directly potentially affect the increase in visits tourists, fostering satisfaction and loyalty of tourists towards Pentingsari Tourism Village	Descriptive and Qualitative
6	Customer Satisfaction in Tourist Destination: The Case of Tourism Offer in the City of Naples. Valentina Della Corte, Mauro Sciarelli, Clelia Cascella, Giovanna Del Gaudio (2015)	Access, Attractions, Accommodation, Amenities, Assemblage, and Ancillary services	It is known that tourist satisfaction depends on a complex process involving the roles of each actor. This role is felt to be very basic and must be in harmony with other actors. The findings show that tourists visiting Naples are not completely satisfied.	Systematic Review
7	Correlation Between Tourists’ Perceptions/Evaluations of Destination Attributes and Their Overall Satisfactions: Observations of a Meta-Analysis. Bình Nghiêm-Phú (2017)	Destination Image, Destination Quality, Destination Attribute Satisfaction	It is known that not all attribute-based components (destination image, destination quality, destination attribute satisfaction) can have a significant effect on overall tourist satisfaction. As a result, if there are attributes of a destination that are considered unfavorable, it will interfere with tourist satisfaction.	Comprehensive Meta-Analysis (CMA)
8	Destination Attributes in The Eye of The Local People. Berrin GÜZEL (2017)	Destination attributes, local people, pull factors, destination	This study states that the most important attributes in this case are historical places, religious places, villages, climate and entertainment facilities.	Factor Analysis
9	Destination Attributes and Domestic Tourists’ Choice of Obudu Mountain Resort Calabar. Yekinni Ojo Bello, PhD. (2019)	Attractions, Amenities, Activities, Accessibility, Available Package, Affordability, Attitude of Host, Accomodation	This study shows that the eight destination attributes have a positive and significant relationship with tourist choices	Pearson Moment Correlation analysis
10	Destination Competitiveness: A Structural Model For Measuring Attributes Competitiveness of Bagan, Myanmar. Ei Ei Khin, Dr Jaruwan Daengbuppha & Dr Petchsri Nonsiri (2014)	Destination Attraction, Tourism Superstructures, General Infrastructure, Destination Management, Demand Condition, Destination Image	The conclusion that can be drawn is that Bagan is superior in terms of beautiful scenery, natural landscapes, artistic and cultural heritage, and local hospitality, but is weak in several matters relating to destination management including fare management.	Structural Model, Importance-Performance Analysis (IPA)
11	(SA) A New Framework for The Analysis of Smart Tourism Destinations. Hà My Trần, Assumpció Huertas & Antonio Moreno (2017)	Smart attractions, Smart accessibility, Smart amenities, Smart ancillary services, Smart activities and Smart packages	There are new insights about conceptualizing smart tourist destinations, and proposing a new framework for the analysis process. This will be useful for destination managers to evaluate the various dimensions and indicators that their city should focus on to become a smart city as a tourist destination.	A comparative case study
12	Understanding the Tourists’ Perspective of Sustainability in Cultural Tourist Destinations. Begüm Aydın & Maria D. Alvarez (2020)	Economic Attributes, Socio-cultural Attributes, Environmental Attributes	It is concluded that tourists view sustainability from a more varied perspective than that adopted by the classical concept definition, which consists of economic, environmental and socio-cultural dimensions. Travelers like the sustainability attributes that contribute to enhancing their own travel experience.	Exploratory and confirmatory factor analyses
13	Rural Tourism Niche-Market as a Development Strategy on Rural Community: Reference to Hiriwadunna Village Track, Meemure and Heeloya Knuckless Valley Tourism Village, Sri Langka. S.S. Pattiyagedara & P.I.N. Fernando (2020)	Attractions, Accessibility, Amenities, Ancillary services, Available ppackage, Activities	Findings exhibited a significant influence on the visitor satisfaction of all destination attributes (6A’s), and ancillary services have highlighted as the most influential attribute on rural tourism destinations	Mixed-method approach
14	Destination Attributes and Destination Image Relationship in Volatile Tourist Destination: Role of Perceived Risk. Hardeep Chahal & Asha Devi (2015)	Destination Attributes, Destination Image, Perceived Risk	This study concludes that risk perception significantly moderates the relationship between tourism destination attributes and destination image	EFA, CFA and SEM
15	The Role of Destination Attributes and Visitor Satisfaction on Tourist Repeat Visit Intentions: The Case of Lake Nakuru National Park, Kenya. Joanne Boit & Minsun Doh (2014)	Destination attributes, tourist satisfaction	This study found a positive effect between tourist satisfaction and intention to revisit Lake Nakuru National Park.	Descriptive
16	A model of Perceived Image, Memorable Tourism Experiences and RevisitIntention. Hongmei Zhang, Yan Wub & Dimitrios Buhalis (2017)	Memorable tourism experiences, Country image, Destination image, Revisit intention	The results show that the image of the country and the image of the destination affect the intention to revisit through the mediating effect of MTE.	PLS- SEM

Previous research shows that there are several studies that also use the 6A attributes, but have different tourist destinations, respondents, and also the methodology. In particular, there has not been much research related to Alas Kedaton tourist destinations. Considering all of this, the methodology used in this study is as follows.

Research Methodology

As previously discussed, this research is intended to identify destination attributes that can support local visitor satisfaction in tourist destinations, in this case the Alas Kedaton tourist area. Thus, the research population is Indonesian tourists who have visited this tourist destination in 2019. Determining the period of time to visit this tourist area is important, to avoid research bias. This is certainly related to the time of data collection carried out at the end of 2019 to early 2020, when the pandemic began to hit Indonesia. Thus, the sampling technique used is purposive sampling.

Questionnaires were distributed to obtain data. The questionnaire in this study was adapted from previous research conducted in the tourist destination of the Jatiluhur reservoir in West Java, Indonesia, which was conducted by Rahmat Darsono in 2013. The adaptation of research conducted in tourist destination in Indonesia was intended to increase the level of similarity of the research population. The answer choices in this questionnaire are arranged based on a Likert scale. As it is known that there is still much debate whether the Likert scale can be categorized as ordinal data or interval data. In this study, the Likert scale will be treated as Ordinal data, then transformed into Interval data, through the Successive Interval Method (SIM), so that multivariate statistics can be applied (Asdar and Badrullah, 2016).

As expected, distributing questionnaires in tourist destination area at that time was not easy, considering the relatively small number of visitors. Thus, the distribution of questionnaires was also carried out through electronic questionnaires, and finally 362 questionnaires were collected, all of which were fully answered by respondents. By considering the relatively not so big the amount of data, the analytical tool used is PLS-SEM (Anggorowati, 2014, Zhang, et.al., 2017), with the SmartPLS version 3.0 software.

Thus the research framework can be seen as the diagram below:

Source: Researchers

Legend:

ACC: Accessibility

ATT: Attraction

ACT: Activities

AME: Amenities

ACS: Ancillary Service

AVP: Available Package

TS: Tourist Satisfaction

SIM: Successive Interval Method

PPM: Pearson Product Moment

AC: Alpha Cronbach

SEM: Structural Equation Model

V: Validity Test

R: Reliability test

From the diagram above, it can be seen that after the questionnaire is structured, a pre-test for the questionnaire is carried out, namely the validity test using the Pearson Product Moment (PPM) correlation. In this pre-test, 20 respondent data were used, which resulted in the r-table score of 0.444. Thus, statement in the questionnaire that has an r-count score lower than the r-table score will be eliminated. Furthermore, a reliability test is carried out using the Alpha Cronbach (AC) formula, with a reference value if a > 0.6, then it can be considered reliable (Hair, et.,al., 2003). This step is taken to reduce the potential for calculation bias, although at the time of application of the analytical tool (in this case PLS-SEM), validity and reliability calculations will also be carried out at the outer model stage.

Structural Equation Modeling (hereinafter referred to as SEM), is one of the multivariate analysis technique, which is often used by marketing and social science researcher (Wong, 2013). PLS-SEM is another approach to SEM. In many kinds of literature, it is mentioned that PLS-SEM is equivalent to covariance-based SEM (CB-SEM). However, PLS-SEM has several differences with CB-SEM, among others, as mentioned by Hair (in Anggorowati, 2014), PLS-SEM is a causal model to maximize the explained variance of the dependent latent construct. In essence, there are two sub models in the structural equation model, in this case PLS-SEM, namely:

1. The outer model settles the relationship between the latent variable and the observed indicators. At this stage, the validity and reliability of the model were tested. For validity testing, the calculation of t-value and loading factor is carried out, while for reliability testing, the calculations are Construct Reliability (CR) and Average Variance Extracted (AVE), with the following guidelines:

Tabel 3 Rule of Thumb in Outer Model
Test	Parameter	Rule of thumb
Validity	t-Value	≥ 1.96
	Standardized loading factor	≥ 0.5
Reliability	CR	≥ 0.7
	AVE	≥ 0.5
	Cronbach’s Alpha	≥ 0.6

Source: Ramayah, et.el., in Rehman and Hashim ( 2019)

1. The inner model, this sub model determines the relationship between the independent and dependent latent variables. The calculations executed in this section are t-value and p-value, with the following guidelines:

Tabel 4 Rule of Thumb in Inner Model
Criteria	Rule of thumb	Information
t-Value	≥ 1.96	Accepted
p-Value	≤ 0.05	Accepted

Source: Fan, et al., (2016)

Data Analysis

As stated in the earlier section, the number of respondents in this study amounted to 362 people. Of these respondents, 138 respondents are male and 224 are female. Of this number, it turns out that the majority of respondents came from respondents with an age of less than 20 years, which were 163 people. Furthermore, there are 53 people in the 21-30 year age group, 68 people in the 31-40 age group, 55 people in the 41-50 age group, and 23 respondents with more than 50 years of age. Subsequently, in terms of the respondent's occupation, 138 people are students, 88 are entrepreneurs, 63 are civil servants, and 73 respondents answered 'other'. From this illustration of the respondent's profile, it can be estimated that the potential visitors to the Alas Kedaton tourist area are students.

Validity Test of Questionnaire

The validity test in this study involved 20 respondents, thus, the r-table score for the 5% significance level is 0.444. The calculation results for each variable can be seen in the table below.

a. Accessibility

Table 5 Validity Test for Accessibility
Statement	R Table	R Count Score	Results
Accessibility 1	0.444	0.480	Valid
Accessibility 2	0.444	0.546	Valid
Accessibility 3	0.444	0.241	Invalid
Accessibility 4	0.444	0.723	Valid
Accessibility 5	0.444	0.168	Invalid
Accessibility 6	0.444	0.598	Valid
Accessibility 7	0.444	0.528	Valid
Accessibility 8	0.444	0.754	Valid
Accessibility 9	0.444	0.706	Valid
Accessibility 10	0.444	0.543	Valid

Source: calculation result.

From the table above, it can be seen that the indicators of accessibility 3 and accessibility 5 have an r count score of less than 0.444, so they are categorized as invalid. Therefore these indicators will be omitted and not included in further calculations. Similarly, the same thing will be applied to the other variables.

b. Attraction

Table 6 Validity Test for Accessibility
Statement	R Table	R Count Score	Results
Attraction 1	0.444	0.790	Valid
Attraction 2	0.444	0.428	Invalid
Attraction 3	0.444	0.428	Invalid
Attraction 4	0.444	0.815	Valid

Source: calculation result

Hence, indicator Attraction 2 and Attraction 3 will be eilminated.

c. Amenities

Table 7 Validity Test for Amenities
Statement	R table	R Count Score	Results
Amenities 1	0.444	0.685	Valid
Amenities 2	0.444	0.813	Valid
Amenities 3	0.444	0.736	Valid
Amenities 4	0.444	0.569	Valid
Amenities 5	0.444	0.428	Invalid
Amenities 6	0.444	0.713	Valid

Source: calculation result

Hence, indicator Amenities 5 will be eliminated.

d. Acitivity

Table 8 Validity Test for Activity
Statement	R Table	R Count Score	Results
Activity 1	0.444	0.614	Valid
Activity 2	0.444	0.600	Valid
Activity 3	0.444	0.469	Valid
Activity 4	0.444	0.315	Invalid
Activity 5	0.444	0.765	Valid

Source: calculation result

Hence, indicator Activity 4 will be eliminated

e. Available Package

Table 9 Validity Test for Available Package
Statement	R Table	R Count Score	Results
Available Package 1	0.444	0.725	Valid
Available Package 2	0.444	0.471	Valid
Available Package 3	0.444	0.662	Valid
Available Package 4	0.444	0.519	Valid

Source: calculation result

There is no indicator that should be eliminated.

f. Ancillary Service

Table 10 Validity Test for Ancillary Service
Statement	R Table	R Count Score	Results
Ancillary Service 1	0.444	0.787	Valid
Ancillary Service 2	0.444	0.561	Valid
Ancillary Service 3	0.444	0.481	Valid
Ancillary Service 4	0.444	0.773	Valid
Ancillary Service 5	0.444	0.732	Valid

Source: calculation result

There is no indicator that should be eliminated.

g. Tourist Satisfaction

Table 11 Validity Test for Ancillary Service
Statement	R Table	R Count Score	Results
Tourist Satisfaction 1	0.444	0.627	Valid
Tourist Satisfaction 2	0.444	0.708	Valid
Tourist Satisfaction 3	0.444	0.578	Valid
Tourist Satisfaction 4	0.444	0.619	Valid

Source: calculation result

There is no indicator that should be eliminated.

Reliability Test of Questionnaire

This reliability test is carried out after invalid statements, as previously stated, are removed. The questionnaire will be considered reliable if the Cronbach's alpha value is above 0.60. The calculation results can be seen in the table below.

Table 12 Reliability Test of QuestionnaireReliability Statistic
Cronbach’s	Cronbach’s Alpha	N of Items
Alpha	Based on
	Standardized Items
.951	.959	38

Source: calculation result

From the calculations above, it can be concluded that the questionnaire can be considered reliable, as well as valid, and can be used for the next stage of research.

Source: Calculation Result

Measurement Model (Outer Model)

In essence, the measurement model or outer model is an effort to test validity and reliability. At this stage, three measurements will be carried out, namely the convergent validity test, discriminant validity test and composite reliability test, and generate the outer model as follows.

Convergent Validity

In this validity test, what is done is the calculation of construct validity, which consists of two items, namely Convergent Validity and Discriminant Validity. Convergent Validity will be sourced from the loading factor value, which shows the correlation between latent variables and indicators. The results of the calculation of these loading factors can be seen in the following table.

Table 13 Convergent Validity (Loading Factor)
Variable	Indicator	Loading Factor	Information
Accessibility	ACC1	0.774	Valid
	ACC2	0.755	Valid
	ACC3	0.768	Valid
	ACC4	0.751	Valid
	ACC5	0.813	Valid
	ACC6	0.778	Valid
	ACC7	0.785	Valid
	ACC8	0.781	Valid
Attraction	ATT1	0.897	Valid
	ATT2	0.922	Valid
Amenities	AME1	0.564	Valid
	AME2	0.751	Valid
	AME3	0.748	Valid
	AME4	0.797	Valid
	AME5	0.72	Valid
Activities	ACT1	0.7	Valid
	ACT2	0.795	Valid
	ACT3	0.798	Valid
	ACT4	0.775	Valid
Available package	AVP1	0.667	Valid
	AVP2	0.766	Valid
	AVP3	0.772	Valid
	AVP4	0.757	Valid
Ancillary Service	ACS1	0.633	Valid
	ACS2	0.811	Valid
	ACS3	0.727	Valid
	ACS4	0.746	Valid
	ACS5	0.709	Valid
Tourist satisfaction	TS1	0.799	Valid
	TS2	0.84	Valid
	TS3	0.818	Valid
	TS4	0.77	Valid

Source: Calculation Result

The rule of thumb used to assess convergent validity is that the loading factor value must be higher than 0.5 (Chin, in Rahman et.al., 2013). Based on table 13 it can be seen that all loading factor values are > 0.5, hence it can be concluded that all indicators in this research are valid.

Discriminant Validity

Discriminant validity is measured by observing the Cross Loading Factor value, then comparing the Cross Loading value for the original construct that must be bigger than the Cross Loading value to other constructs. The Smart-PLS output results will be explained in the table 14 as follows:

Table 14 Cross Loading in Discriminant Validity
	Accessibility	Attraction	Amenities	Activities	Available package	Ancillary Service	Tourist Satisfaction
ACC1	0.774	0.256	0.441	0.337	0.117	0.307	0.322
ACC2	0.755	0.178	0.41	0.291	0.094	0.272	0.295
ACC3	0.768	0.247	0.347	0.258	0.071	0.298	0.301
ACC4	0.751	0.293	0.318	0.255	0.101	0.24	0.28
ACC5	0.813	0.255	0.359	0.322	0.162	0.266	0.302
ACC6	0.778	0.213	0.449	0.306	0.161	0.281	0.364
ACC7	0.785	0.228	0.38	0.312	0.159	0.283	0.291
ACC8	0.781	0.29	0.316	0.225	0.101	0.234	0.299
ATT1	0.286	0.897	0.174	0.189	0.069	0.361	0.279
ATT2	0.287	0.922	0.236	0.254	0.119	0.404	0.318
AME1	0.541	0.38	0.564	0.402	0.15	0.331	0.245
AME2	0.336	0.179	0.751	0.456	0.161	0.326	0.383
AME3	0.321	0.049	0.748	0.45	0.16	0.339	0.35
AME4	0.309	0.097	0.797	0.432	0.234	0.282	0.398
AME5	0.341	0.198	0.72	0.429	0.212	0.298	0.337
ACT1	0.236	0.153	0.474	0.7	0.154	0.3	0.29
ACT2	0.313	0.164	0.539	0.795	0.207	0.35	0.358
ACT3	0.307	0.157	0.458	0.798	0.135	0.337	0.362
ACT4	0.284	0.263	0.39	0.775	0.282	0.438	0.419
AVP1	0.114	0.026	0.217	0.21	0.667	0.157	0.201
AVP2	0.127	0.189	0.154	0.14	0.766	0.237	0.189
AVP3	0.094	-0.005	0.165	0.189	0.772	0.163	0.225
AVP4	0.129	0.107	0.216	0.219	0.757	0.286	0.277
ACS1	0.217	0.279	0.276	0.27	0.187	0.633	0.361
ACS2	0.265	0.313	0.327	0.379	0.257	0.811	0.516
ACS3	0.208	0.368	0.286	0.337	0.145	0.727	0.368
ACS4	0.298	0.289	0.311	0.387	0.161	0.746	0.485
ACS5	0.273	0.3	0.351	0.326	0.277	0.709	0.55
TS1	0.313	0.24	0.417	0.399	0.254	0.528	0.799
TS2	0.352	0.273	0.405	0.385	0.228	0.552	0.84
TS3	0.32	0.317	0.379	0.368	0.258	0.493	0.818
TS4	0.297	0.234	0.351	0.369	0.254	0.498	0.77

Source: Calculation Result

From table 14 discriminant validity test displays that the cross loading value of each item to its construct is bigger than the value of loading with the other construct. Therefore, it can be determined that there is no problem in discriminant validity.

Reliability Test (Composite Reliability and Cronbach Alpha)

The reliability of this research was tested using Composite Reliability and Cronbach's Alpha coefficient. A construct can be articulated to be reliable if the Composite Reliability value is 0.7 or more and the Cronbach Alpha value is 0.6 or more (Ramayah, et.el., in Rehman and Hashim 2019). The calculation shows the results, as can be seen in the table below.

Table 15 Composite Reliability
Variable	Composite Reliability	Value
Accessibility	0.924	≥ 0.7
Attraction	0.906	≥ 0.7
Amenities	0.842	≥ 0.7
Activities	0.852	≥ 0.7
Available package	0.830	≥ 0.7
Ancillary Service	0.848	≥ 0.7
Tourist Satisfaction	0.882	≥ 0.7

Source: Calculation Result

Table 16 Cronbach Alpha
Variable	Cronbach’s Alpha	Value
Accessibility	0.906	≥ 0.6
Attraction	0.793	≥ 0.6
Amenities	0.766	≥ 0.6
Activities	0.769	≥ 0.6
Available package	0.730	≥ 0.6
Ancillary Service	0.778	≥ 0.6
Tourist Satisfaction	0.821	≥ 0.6

Source: Calculation Result

The table above shows the high consistency and stability of the instruments used. In other words, all research constructs or variables can be said to be fit to be a measuring tool, and all statements used to measure each construct have high reliability.

Structural Model (Inner Model)

After evaluating the model and it is found that each construct has fulfilled the Convergent Validity, Discriminant Validity, and Composite Reliability requirements, then the next is the evaluation of the structural model which comprises testing the path coefficient, and R². The calculation results display that the value of R² is 0.49. This means that 49% of the variation or change in Tourist satisfaction is determined by the variables of Accessibility, Attractiveness, Activities, Facilities, Available Packages, and Ancillary Service, while the remaining 51% is determined by other reasons. Based on this, the results of the calculation of R² for tourist satisfaction can be categorized as moderate R².

Path Coefficient Measurement

In PLS-SEM, each relationship is tested using a simulation of the sample bootstrap method. This test aims to minimize the problem of abnormalities in research. The test results using the bootstrap method from PLS are as follows:

Source: Calculation Result

Structural Model Test

In order to determine the significance of the contribution of Accessibility to Tourist Satisfaction, the contribution of Attractiveness to Tourist Satisfaction, the contribution of Accessibility to Tourist Satisfaction, the contribution of Attractiveness to Tourist Satisfaction and the contribution of Amenity to Tourist Satisfaction, by looking at the parameter coefficient values (P-value) and the statistical significance value of t (t-statistics). The output of Smart-PLS using count-PLS Bootstrapping is as follows:

Table 17 Structural Model Test Result
Hypothesis	Information	T Statistics (|O/STDEV|)	P Values
Accessibility -> Tourist Satisfaction	Accepted	2.272	0.024
Attraction -> Tourist Satisfaction	Rejected	0.905	0.366
Amenities -> Tourist Satisfaction	Accepted	2.577	0.01
Activities -> Tourist Satisfaction	Accepted	1.995	0.047
Ancillary Service -> Tourist Satisfaction	Accepted	9.185	0
Available package -> Tourist Satisfaction	Accepted	2.44	0.015

Source: Calculation Result

By using the rule of thumb that the hypothesis will be accepted if the t-statistic is more than 1.96, and/or the P value is less than 0.05, then as can be seen in the table above, that only hypothesis 2 is rejected. Thus, it can be concluded that the variables of Accessibility, Amenities, Activities, Ancillary Service and Available Packages will contribute to Tourist Satisfaction.

Conclusion

As can be seen above, the calculation results show that only the Attraction variable does not contribute to Tourist Satisfaction. The Accessibility variable contributes to Tourist Satisfaction, and this is match with the results of research conducted by Chiu et.al (2016). Based on the biggest t-statistic value in the path diagram, it can be seen in the ACC5 indicator, which is about achieving to tourist destination without traffic jams. Thus, efforts to improve the effortlessness to this tourist destination deserve to be a priority.

For the Amenities Variable, it also contributes to tourist satisfaction, and this is match with the research findings of Nurcahyo et.al (2017), and based on the biggest t-statistic number seen in the AME4 indicator, which is about the ease of getting a place to stay around this tourist location, which need attention.

For the Acitivities variable, which also contributes to tourist satisfaction, it is match with the conclusion of the research conducted by Chiu et.al. (2016). The indicator with the biggest t-statistic is ACT2, which is about the ease with which tourists interact with tame monkeys that inhabit this tourist destination.

The Ancillary Service variable, which also contributes to tourist satisfaction, is match with the results of research conducted by Aimah H.U (2016). The indicator with the biggest t-statistic is ACS2, which is about the availability of health facilities at tourist destination.

As for the Available Package variable, which also contributes to tourist satisfaction, it is match with the results of research conducted by Abdullah (2017). The indicator with the biggest t-statistic is AVP3, which is about the affordability of entrance fees for visitors.

However, if we look at the overall t-statistics figures, then a number of things that deserve to be prioritized are:

a. Medical and Safety facilities

b. Smooth transportation to tourist destination, and

c. The road to the tourist destination is always maintained.

Thus, it appears that potential local visitors have been very concerned about the importance of matters relating to health and safety, which of course also deserves to be a priority during the pandemic or can be also for post-pandemic era. Alas, Kedaton as an open nature destination is a distinct advantage when it comes to the safety factor in this pandemic era. Open nature and sufficient sunlight, of course, is a very potential attraction and deserves to be promoted. Furthermore, the availability of protective masks, face shields, gloves, hand sanitizers, cleanliness in toilets, cleanliness at snack places, and anti-virus spraying at various locations within tourist destinations seems to be mandatory. The preparation of these facilities will likely determine the condition of Alas Kedaton tourist destination in the future.

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