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International Journal of Business and Economy (IJBEC) eISSN: 2682-8359 | Vol. 4 No. 3 [September 2022]

Journal website: http://myjms.mohe.gov.my/index.php/ijbec

THE INFLUENCE OF DESTINATION ATTRIBUTES AND TOURIST’S LIFESTYLE TOWARDS LOCAL TOURIST

PREFERENCES IN CHOOSING DOMESTIC TOURIST DESTINATIONS IN INDONESIA

Davelynn Danielle1*, Mustika Sufiati Purwanegara2*

1 2 School of Business and Management, Bandung Institute of Technology, Bandung, INDONESIA

*Corresponding author: davelynn_danielle@sbm-itb.ac.id; mustika@sbm-itb.ac.id

Article Information:

Article history:

Received date : 9 August 2022 Revised date : 25 August 2022 Accepted date : 1 September 2022 Published date : 10 September 2022 To cite this document:

Danielle, D., & Purwanegara, M. S.

(2022).THE INFLUENCE OF DESTINATION ATTRIBUTES AND TOURIST’S LIFESTYLE TOWARDS LOCAL TOURIST PREFERENCES IN CHOOSING DOMESTIC TOURIST DESTINATIONS IN INDONESIA. International Journal of Business and Economy, 4(3), 183-199.

Abstract: Tourism is one of the fastest growing economic industries in the world. In Indonesia, the tourism industry plays a very large role in economic growth. Realizing the importance of the tourism industry, the government is aggressively developing the tourism industry. This study examines the relationship between destination attributes and tourist lifestyles on tourist preferences in choosing domestic tourist destinations in Indonesia. This study is expected to help the Ministry of Tourism and Creative Economy in developing the tourism industry, especially in developing competitiveness among local tourist destinations and can help increase tourist arrivals. The research objectives of this study are to find out the destination attributes that local tourists consider when choosing a domestic tourism destination, To find out the local tourist domestic destination preferences and To find out the relationship between lifestyle and destination attributes on local tourist preferences in choosing domestic tourist destinations. This study uses a mixed method. Qualitative approach using netnography and analysed using coding. For the quantitative approach, questionnaires are distributed online using google form and analysed using multiple linear regression. The results of this study indicate that there is a significant influence between lifestyle and destination attributes on tourist preferences. However, not all lifestyles or destination attributes influence each other, depending on the preferences of tourists who want to go to which destinations. The “Culture Seeker” lifestyle,

“Complete” Destination Attributes and “Attractions”

affect tourist preferences, especially recreational

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1. Introduction

Tourism is one of the fastest growing economic industries in the world. According to International Tourism Highlight (2020) Since 2010 until 2019, the ten year in a row international tourism was experiencing sustainable growth. International tourism rose faster than the world economy, with 3.6 percent growth in 2018 and 2.8 percent growth in 2019 for global gross domestic product (GDP) (International Tourism Highlights, 2020 Edition, 2021).

In recent years the tourism industry has become the main source of foreign exchange income for the economy in Indonesia. Tourism contributed as much as 5.9 percent to Indonesia's GDP or Rp941,107 billion in 2019 and tourism provides 13,180.4 million jobs or 10.1 percent of total employment (Travel & Tourism Economic Impact | World Travel & Tourism Council (WTTC), 2021). According to Pratomo, D (2017) the growth of tourism in Indonesia is not only due to foreign tourists but local tourists who travel domestically also play an important role. Domestic travel continues to increase, it can be seen in the graph from 2002 to 2020. The peak of the increase in the number of domestic trips in 2019 was 722.16 million, from 2018 there were only 303.4 million trips. Although there was a decrease in 2020 to 518.59 million trips.

The complexity of tourism has recently been acknowledged as a result of changing customer tastes and preferences, making products more competitive within the sector. In terms of keeping tourist demand, tourism destinations face competition and obstacles (Dieke, 2005).

The attractiveness of a destination as a tourist product is determined by its unique attributes or qualities, which influence tourist satisfaction and preferences. The tourism literature also stated that tourists' travel decisions are influenced by their preferred destination. Destination preferences are a crucial key of destination selection and are developed as a result of the selection process because destination preference is a passenger's comparative attitude towards a destination. Tourists' preferences have been examined based on their perceptions, which have been linked to numerous attributes they take at the destination (Saikia, Buragohain and Choudhury, 2019; Mao, R.Y.; Zhang, H.Q., 2014; Woodside, A.G.; Lysonski, S.,1989). In addition to destination attributes, tourist lifestyle is also an important factor that can determine the behavior of domestic tourists (Duman, Erkaya and Topaloglu, 2020).

destinations. Furthermore, the "Wasteful" and "Nature Lover" lifestyles, "Comfort" and "Attractions"

Destination Attributes have an influence on tourist preferences, especially urban destinations. The lifestyle of “Adventurers”, “Nature Lovers”, “Family”,

“Complete” destination attributes affect tourist preferences, especially natural destinations. In addition, this study also finds out what destination attributes are often considered by local tourists in choosing tourist destinations and lifestyles owned by local tourists. The destination attributes found can be used as selling points for marketing strategies.

Keywords: Destination attributes, Tourist Preferences, Tourist Lifestyle.

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2. Literature Review Tourist’s Lifestyle

Lifestyle is defined as a multidimensional psychological notion that includes aspects such as activities, interests, and opinions among other factors. Lifestyle characteristics influence people's everyday purchase, consumption, and disposal patterns (Anderson and Golden,1984).

Lifestyle describes how individuals live and determines how they consume products and services, including holiday destinations and activities (Füller and Matzler, 2008). Tourists' travel objectives, values, and inclinations for travel behavior are mirrored in such lifestyle categories (Chen, Huang and Cheng, 2009; Sirgy et al., 2010). Lifestyle factors affect how they participate in various categories of tourism experiences (Gonzàles and Bello, 2002; Pizam &

Sussman, 1995). According to Gonzalez and Bello (2002) to identify the three AIOs is very difficult, there will be validity problems and the survey that is made will also be very long.

This will make the respondent feel overwhelmed. Research from Dann (1981) said that the motivation of tourists on vacation can be considered as "Interest", tourists will consider their lifestyle on vacation based on their interests while on vacation. Interest can also predict the activities a person does (Sparks, 2018). This research focuses on measuring the lifestyle of tourists based on their interests.

Tourist’s Preference

Customer preferences reveal consumer choices among a large range of current service items (Kotler, 2000). An affection for something is called preference. Preference may also be described as a person's decision to favor or detest a product or services. Meanwhile, customer preferences are defined as personal tastes as assessed by satisfaction from diverse commodities.

In brand choices, preference has been constructed as a measure of loyalty. Tourist preference refers to the choices made by visitors when it comes to the tourist locations they visit. The standard of visitor preferences will define the types of commodities people desire in tourist sites. According to Pearce (1988) motivation is less specific to preferences because preferences can reveal what tourists will do when they go and where tourists will go. Goodall (1991) says similarly that one's preference is the best way to eliminate existing choices even though motivation is one that guides a person in choosing. Tourist preference shows a destination's capacity to attract tourists and capture market share by efficiently employing tourism resources.

Furthermore, visitor choice is an essential aspect in determining destination competitiveness (Sun, Ma and Chan, 2017).

Destination Attributes

A set of features that characterize a location as a travel destination is known as destination attributes (Heung and Quf, 2000). Destination attributes are very influential on tourists who are choosing destinations. Tourists will consider the attributes that exist in one destination with another (Papatheodorou, 2001). Crouch (2011) defines a destination attribute as a collection of diverse features of a tourism destination that travelers may use to assess the destination. These attributes can be used to define a set of destination characteristics that describe a location as a tourist destination and have an impact on the destination's image (Kim, Ritchie and McCormick, 2010). Destination attributes can influence tourists in choosing tourist destinations (Guzel, 2017; Waas, & Chandrarathne, 2020). However, not all destination attributes have an impact on the selection of tourist destinations equally, there are some attributes that are more important than other attributes (Swarbrooke, 1999). According to Reisinger, Mavondo and Crotts (2009) destination competitiveness is highly dependent on

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destination attributes and has an impact on visitor satisfaction and experience, therefore it is important to identify destination attributes that provide better offers and services for tourists in order to attract tourists to come.

2.1 Problem Statement

According to "Rencana Strategis 2020 - 2024" published by the Indonesian Ministry of Tourism and Creative Economy, they stated their goal in 2020-2024 is to increase the contribution of tourism and the creative economy to national economic resilience. To meet these objectives, strategic objectives are made. Two of them are to increase the number of tourists and increase competitiveness between destinations in the national tourism industry.

According to Reisinger, Mavondo and Crotts (2009), the competitiveness of a destination is highly dependent on destination attributes which will have an impact on visitor satisfaction and experience, therefore it is important to identify destination attributes that provide better offers and services for tourists in order to attract tourists to visit. Developing tourist destinations can be through the development of both public and tourist facilities, accessibility to facilities and infrastructure based on tourist attractions; these are also known as destination attributes. The Strategic Plan Report lists any problems that cause tourism development to be less than optimal. Such as the lack of development of Indonesian tourism destination attributes and marketing strategies. There are still many tourist destinations that do marketing about the features or attributes of the destination but are not in accordance with the original so that many tourists are disappointed. For this reason, it is necessary to find out what destination attributes have been considered by tourists when choosing a tourist destination. It is a crucial subject to assess current tourist preferences for various places (Sun, Ma and Chan, 2017).

In the Strategic Plan Report In the 2020-2024 made by the ministry of tourism and creative economy, stated that tourism development is still not optimal. and there is still no research in Indonesia that discusses about the influences of destination attributes, lifestyle towards tourist preferences in Indonesia. Therefore, this study aims to find out the destination attributes that local tourists consider when choosing a domestic tourism destination. The second is to find out the local tourist domestic destination preferences and the last is to find out the relationship between lifestyle and destination attributes on tourist’s preferences in choosing domestic tourist destination. With the hope the findings from research can help overcome tourism destination development to help fulfil the Indonesian government's planned program for tourism in Indonesia and can revive the tourism industry in Indonesia. This study also has 2 hypotheses:

H1. Local Tourist’s Lifestyle significantly influences Local Tourist’s Preferences in choosing Domestic Destination

H2. Destination attributes significantly influences Tourist’s Preferences in choosing a domestic destination

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3. Method

This study uses a mixed method, namely using a qualitative approach and a quantitative approach.

3.1 Materials

For the qualitative approach, this study used netnography and the data is taken from the conversations of the Indonesian online traveling group in the Line Open Chat and then the data will be analysed using coding. For the quantitative approach, the data was taken using a questionnaire distributed online using Google Forms and the data was analysed using Confirmatory factor analysis and multiple linear regression.

3.1.1 Samples

The Criteria for Online Group for this conversation is an Active Group that discusses traveling or tourist destinations in Indonesia, consisting of Indonesian citizens. The reason for choosing this criterion is because the topic of this research is about tourism in Indonesia. As for the respondents to the questionnaire, they must be people who have travelled domestically.

According to Isaac and William Burton Michael (1995) with an infinite population (>

1,000,000) and with an error rate of 5% So the researcher aims to get the minimum number of samples assigned for this research to be 349 respondents.

3.1.2 Site

from the netnography results it was found that most of the Group members from the Line Open Chat were people from the island of Java and the destinations discussed were also tourist destinations on the island of Java. Therefore, the requirements for respondents from the questionnaire are tourists who live on the island of Java.

3.1.3 Procedures

This research is an exploratory research in order to generate hypotheses. This study seeks to find out whether the local lifestyle of tourists and destination attributes can influence tourist preferences in choosing tourist destinations. Lifestyle and Destination attributes act as independent variables and tourist preferences as dependent variables. the three variables will be measured using a 10-likert scale.

3.2 Data Analysis

For qualitative and quantitative data, the results of the analysis will be displayed using descriptive analysis in the form of a table

3.3.1 Netnography Results

The researcher downloaded the conversations from the groups above and coded the existing conversations using Nvivo software. This netnography aims to find out which destination attributes are the most frequently asked or considered by tourists before choosing a destination and to find out what domestic tourist destinations local tourists like to visit. Researchers read every chat that exists and code each keyword according to what is needed and categorize it.

The result of the open coding of the Group Open Chat Line conversation formed 10 codes

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Group, namely: Accessibility, Accommodation, activities, attractions, local hospitality, packages, prices, services, Special Events and Stay Duration.

Table 1: Destination Attributes Analysis from Netnography

Destination Attributes Frequencies Percentage

Attractions 650 31.54%

accessibility 335 16.25%

Cost 282 13.68%

Stay duration 189 9.17%

Activities 180 8.73%

Services 159 7.71%

Accommodation 128 6.21%

Tour Package 69 3.35%

Special Event 58 2.81%

local hospitality 11 0.53%

Table 1 shows the result of the first coding process which is destination attributes considered by domestic tourists in choosing tourist destinations. The table shows which destination attributes are most frequently discussed and those that are rarely discussed in the chat by tourists. As can be seen, Attractions are the most talked about destination attributes. It can be seen in the table 4 from the total destination attribute found, 31.54% contains attractions.

Meanwhile, local hospitality is in the lowest position because only about 0.53% of the total destination attributes found talk about local hospitality. The results from this netnography will then be used for a quantitative approach, namely questionnaires.

3.3.1 Validity and Reliability

For the validity and reliability test, this study uses Confirmatory factor analysis. For lifestyle variables, researchers have divided them into 7 sub variables, while for destination attributes there are 13 indicators and tourist preferences consist of 12 indicators.

Table 2: Lifestyle CFA Validity and Reliability Results

Indicator KMO Extracted

Variances Factor Loading Cronbach Alpha Adventurous

0.792 51.960% 0.792

L_Adv1 0.670

L_Adv2 0.722

L_Adv3 0.758

L_Adv4 0.537

L_Adv5 0.822

L_Adv6 0.781

Extravagant

0.839 63.778% 0.886

L_Ext1 0.763

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L_Ext2 0.809

L_Ext3 0.841

L_Ext4 0.805

L_Ext5 0.744

L_Ext6 0.825

Relaxation

0.645 47.459% 0.505

L_Rlx1 0.651

L_Rlx2 0.360

L_Rlx3 0.815

L_Rlx4 0.826

Nature Lovers

0.500 76.569% 0.671

L_Nat1 0.875

L_Nat2 0.875

MODEST

0.538 44.372% 0.359

L_Mod1 0.505

L_Mod2 0.706

L_Mod3 0.760

Family Concern

0.500 71.293% 0.597

L_Fam1 0.844

L_Fam2 0.844

Culture Seekers

0.808 55.38% 0.789

L_CS1 0.731

L_CS2 0.735

L_CS3 0.838

L_CS4 0.805

L_CS5 0.586

Table 2 shows the CFA for lifestyle variables. Researchers have divided lifestyle into 7 dimensions, namely Adventurous, Extravagant, Relaxation, Nature Lovers, Modest, Family Concern, and Culture Seekers. However, As shown on table 2 Relaxation and Modest did not pass the CFA validity and reliability test, therefore these two lifestyle dimensions will not be used further.

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Table 3: Destination Attributes CFA Validity and Reliability Results

Destination Attributes KMO Extracted Variances Factor Loading Cronbach Alpha Factor 1 (All In)

0.881

22.96%

0.863

DA_NatureAttraction 0.624

DA_Cost 0.547

DA_Activity 0.500

DA_StayDuration 0.735

DA_TourPackage 0.600

DA_hospitality 0.666

Factor 2 (Convenience)

21.95%

DA_Accesible 0.514

DA_ServicesFacility 0.854

DA_ServiceQuality 0.847

DA_accomodation 0.603

Factor 3 (Attractions)

15.87%

DA_CultureAttraction 0.702

DA_ShoppingPlace 0.654

DA_SpecialEvent 0.850

Total Variance Explained 60.78%

Table 3 shows the result from the CFA destination attribute. Of the 13 destination attribute indicators after going through the CFA process, they are divided into 3 factors. Namely All in, Convenience and attractions. All of these 3 factors have passed the validity and reliability test, so there was no indicator has been eliminated.

Table 4: Destination Attributes CFA Validity and Reliability Results Tourist Preferences KMO Extracted

Variances Factor Loading Cronbach Alpha Factor 1 (Recreational

Destination)

0.848 23.69% 0.838

TP_Marine 0.860

TP_CulturalPlace 0.780

TP_ReligiousPlace 0.637

TP_RuralDestination 0.542

TP_HistoricalPlace 0.755

Factor2 (Urban

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Destination)

TP_AmusementParks

23.38%

0.685

TP_CulinaryPlace 0.711

TP_EntertainmentArea 0.789

TP_TouristCity 0.686

TP_ShoppingCenter 0.739

Factor 3 (Nature Destination)

14.31%

TP_Mount 0.818

TP_NaturePreserve 0.791

Total Variance Explained 61.37%

Table 4 shows the result from the CFA of Tourist preferences. Of the 13 destination attribute indicators after going through the CFA process, they are divided into 3 factors. Namely Recreational Destination, Urban Destination and Nature Destination. All of these 3 factors have passed the validity and reliability test, so there was no indicator has been eliminated. All variables that have passed the validity and reliability tests will proceed to the next analysis process, namely multiple linear regression

4. Results and Discussion

Table 5: Classical Assumption Test Results

Independent Variables

Dependent Variables

Kolmogorov -Smirnov (Normality

Test)

Multicollinearity

Test Sig. Spearman rho (heteroscedastic

ity test)

Durbin- Watson (Autocorrelati

on test) Collinearity

tolerances VIF Lifestyle_Adventuro

us

Tourist Preferences (Recreational

Destination)

0.9

0.433 2.308 0.283

1.822 Lifestyle_Extravagan

t 0.651 1.536 0.962

Lifestyle_Nature

Lovers 0.640 1.562 0.992

Lifestyle_Family

Concern 0.674 1.484 0.878

Lifestyle_Culture

Seekers 0.564 1.774 0.941

DA_AllIn 0.370 2.701 0.928

DA_Convenience 0.459 2.177 0.976

DA_Attraction 0.509 1.965 0.729

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Lifestyle_Adventuro us

Tourist Preferences

(Urban Destination)

<0.001

0.433 2.308 0.486

2.028 Lifestyle_Extravagan

t 0.651 1.536 0.758

Lifestyle_Nature

Lovers 0.640 1.562 0.802

Lifestyle_Family

Concern 0.674 1.484 0.499

Lifestyle_Culture

Seekers 0.564 1.774 0.752

DA_AllIn 0.370 2.701 0.793

DA_Convenience 0.459 2.177 0.855

DA_Attraction 0.509 1.965 0.435

Lifestyle_Adventuro us

Tourist Preferences

(Nature Destination)

<0.001

0.433 2.308 0.968

2.049 Lifestyle_Extravagan

t 0.651 1.536 0.955

Lifestyle_Nature

Lovers 0.640 1.562 0.632

Lifestyle_Family

Concern 0.674 1.484 0.667

Lifestyle_Culture

Seekers 0.564 1.774 0.472

DA_AllIn 0.370 2.701 0.219

DA_Convenience 0.459 2.177 0.283

DA_Attraction 0.509 1.965 0.265

Table 5 shows the results of the classical assumption test. For testing of tourist's preferences 2 and 3 For normality test results from Kolmogorov-Smirnov are <0.001 which is below Sig.

0.05 which means the data is not normally distributed. However, normality tests are generally carried out for small samples, if the amount of data is more than 100 then normality is not a major concern (Altman and Bland, 1995; Spss-tutorials.com, 2019). This study has 400 where

> 100, then this data is considered normally distributed. Multicollinearity test is used to determine whether there is an exact linear relationship between variables. The test can be seen through the value of Variance Inflation Factor (VIF) and collinearity tolerance. If the VIF value is > 1.00 and the collinearity tolerance is > 0.10, then there is no multicollinearity (Gujarati, 2010). If seen in the table 4.25 the VIF value of each variable is greater than 1.00 and the collinearity tolerance value is > 0.10 then this research is free from multicollinearity problems.

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According to Gujarat (2012), if the value of sig. >0.05, it can be said that there is no heteroscedasticity problem. It can be seen in the table that each sub variable has a sig value >

0.05, so it can be said that this data is free from heteroscedasticity. Finally, there is the autocorrelation test, this test is used to determine the correlation between the observed subjects which are governed by space and time. This study uses the Durbin-Watson method. According to Ghozali (2011) if the value of Durbin Watson lies between the value of du and (4-Du) then there is no symptom of autocorrelation. If seen in the table, the value of Du which is in accordance with this study is 1.87158 and the value of 4-Du is 2.12842. The results of the D- W for the first, second and third tests are 1,822, 2,028, 2,049. The results of the three D-Ws are located between the two, so it can be said that this study passed the autocorrelation test. It can be concluded that this research model passed the classical assumption test

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Table 6: Multiple Linear Regression Results Independent Variables Dependent

Variables β t-score P-Value Sig. F R-square Lifestyle_Adventurous

Tourist Preferences (Recreational)

0.990 1.925 0.055

<0.001 0.552 Lifestyle_Extravagant -0.003 -0.074 0.941

Lifestyle_Nature Lovers 0.830 1.967 0.500

Lifestyle_Family Concern -0.042 -1.029 0.304 Lifestyle_Culture Seekers 0.346 7.675 <0.001

DA_AllIn 0.218 3.925 <0.001

DA_Convenience -0.093 -1.857 0.064

DA_Attraction 0.310 6.531 <0.001

Lifestyle_Adventurous

Tourist Preferences

(Urban)

0.460 0.767 0.444

<0.001 0.401 Lifestyle_Extravagant 0.232 4.777 <0.001

Lifestyle_Nature Lovers -0.144 -2.939 0.003

Lifestyle_Family Concern 0.039 0.815 0.416

Lifestyle_Culture Seekers 0.006 0.108 0.914

DA_AllIn 0.112 1.738 0.083

DA_Convenience 0.184 3.188 0.002

DA_Attraction 0.286 5.218 <0.001

Lifestyle_Adventurous

Tourist Preferences

(Nature)

0.260 3.769 <0.001

<0.001 0.193

Lifestyle_Extravagant 0.001 0.016 0.987

Lifestyle_Nature Lovers 0.218 3.837 <0.001 Lifestyle_Family Concern -0.120 -2.166 0.031

Lifestyle_Culture Seekers -0.011 -0.185 0.854

DA_AllIn 0.177 2.376 0.018

DA_Convenience -0.123 -1.834 0.067

DA_Attraction 0.007 0.105 0.917

Table 6 shows the results of the multiple linear regression test. To test whether the independent variable (X) has an effect on the dependent variable (Y), this research partially uses p-value and t-score.

According to Ghozali (2011) the value of Sig. <0.05 and according to Sujawerni (2014), if the value of the t-score > T table means that the independent variable (X) has an effect on the dependent variable (Y) partially. The t table value for this research is 1.9666.

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To test the first dependent variable, namely "Recreational" Tourist preferences, only lifestyle culture seekers (t-score = 7,675, p = <0.001) and for the Destination attributes variable All in (t-score = 3.925, p = <0.001) and Attractions (t-score = 6.531, p = <0.001) which has a p-value of less than 0.05 and a t-score that is greater than the t table. So, Culture seekers, all in and attractions Significantly influence tourist's preferences in choosing a destination, especially in the "Recreational" destination.

For the second test to the dependent variable "Urban Destination", for the lifestyle variable only Extravagant (t-score = 4.777, p = <0.001), Nature lovers (t-score = -2.939, p = <0.001) which has a p-value and the value of t according to the criteria. As for the destination attribute variable which has a p-value and a t-value according to the criteria, namely convenience (t- score = 3.188, p = 0.002) and attraction (t-score = 5.218, p = <0.001). So, it can be concluded that these two tests were Extravagant lifestyle, Nature lovers lifestyle, convenience and attraction. Significantly influences tourist's preferences in choosing a destination, especially in

"Urban" destinations. Nature Lovers lifestyle has a minus value in its beta coefficient, so the value of nature lovers has a negative influence on the tourist preferences of "Nature Lovers".

In contrast to the Extravagant lifestyle, convenience Destination attributes and Attraction destination attributes which have a positive beta coefficient value which has a positive effect on the tourist preferences of "Nature Lovers".

For the third test to the dependent variable Tourist preferences "Nature", for the lifestyle variable only Adventurous (t-score = 3.769, p = <0.001), Nature lovers (t-score = 3.837, p =

<0.001), Family Concern (t- score = -2.166, p = 0.031) which has a p-value and a t-value according to the criteria. Meanwhile, for the destination attribute variable that has a p-value and a t-value according to the criteria, namely only All in (t-score = 2.376, p = 0.018). So, it can be concluded that these three tests are Adventurous lifestyle, Nature lovers lifestyle, Family Concern lifestyles, All In Destination attributes Significantly influences tourist's preferences in choosing a destination, especially in "Nature" destinations. Family concern lifestyle has a minus coefficient value, so this variable has a negative effect on "Nature" tourist's preferences.

While the Adventurous lifestyle, Nature lovers lifestyle, All In Destination attributes have a positive coefficient value, which means that these three variables have a positive effect on the

"Nature" tourist's preferences.

Overall, the lifestyle variable itself significantly influences tourist's preferences, although there are two lifestyle dimensions that are not like family concern lifestyle and nature lovers. This study supports the study of Duman, Erkaya and Topaloglu (2020) where lifestyle significantly influences tourist's preferences in choosing a destination. Then H1 is supported. It is the same with lifestyle, although there are several destination attributes that have no significant influence on Tourist preferences, it can be concluded that the Destination attributes variable has an influence on the tourist's preferences variable. This study supports studies from Saikia, Buragohain and Choudhury (2019) and (Camacho-Murillo, Gounder and Richardson (2021) that destination attributes significantly influence tourist's preferences in choosing a destination.

Then H2 is supported

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Next, we look at the value of the F-score. According to Ghozali (2011) the value of Sig. <0.05, it means that all independent variables (X) have an effect on the dependent variable (Y) simultaneously. Can be seen in table 4.26 the results of Sig. F in the three tests has a value of

<0.001 so it can be said that all independent variables have an influence on the dependent variable, namely tourist's preferences simultaneously.

According to Gujarati and Porter (2017) to see how the influence of the independent variable on the dependent variable can be seen through the value of R square. If seen in the table 4.26 the value of R square for the first test is 0.552. This means that the dependent variable, namely Tourist preferences (Recreational Destination) can be explained by the independent variable by 55.2% and the remaining 44.8% is explained by other variables not discussed in this study.

The second test has an r square value of 0.401. This means that 40.1% of the tourist preferences (Urban Destination) variables can be explained by the independent variables studied, while the remaining 60.9% are explained by other variables. The third test has an R-square value of 0.193, so the independent variable can only explain 19.3% of the tourist preferences variable

"Nature". The remaining 81.7% is explained by variables outside this study.

5. Conclusion

The conclusion for this research is the first is destination attributes that Local tourists consider when choosing a domestic tourist destination are Attractions, accessibility, Price, Stay duration, Activities, Services, Accommodation, Tour Packages, Special Events, local hospitality. Second, Local tourist's domestic destination preferences are mountain destinations, marine tourism destinations, nature reserves destinations, cultural destinations, religious destinations, Amusement Parks, culinary places, villages, Entertainment Areas, tourist cities, historical places, and shopping places. The last is There is a relationship between lifestyle, destination attributes and tourist preferences, namely tourist lifestyle and destination attributes can significantly influence tourist preferences in choosing tourist destinations.

For recommendation The attribute destinations found in this study can be used by destination developers to improve attribute destinations for the better in order to invite more tourists to visit. The attribute destinations found can also be used as selling points for marketing when doing promotions. These attribute destinations can also be used as to increase competitiveness by managers or other people in developing tourist destinations. From this research, it can also be seen some of their lifestyle and destination preferences. In the future, psychographic segmentation and destination preferences may be possible to determine the market according to the destination.

This research is still very limited, the respondents only come from the island of Java and the lifestyle categories used are still few. There may be respondents who feel they are not suitable to enter the existing lifestyle category. For future research, the researcher hopes to get respondents from other islands. In addition, it may be possible to add other new lifestyle groups. Future researchers can also conduct further research on attribute destinations because in Indonesia alone, journals that discuss attribute destinations are still limited.

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6. Acknowledgement

Thanks to my supervisor Dr. Ir. Mustika Sufiati Purwanegara, M. Sc who has taught and guided me patiently so that I can complete this study.

References

Anderson Jr. (1984). Lifestyle and Psychographics: A critical Review and Recommendation.

Advances in consumer research. [Online] 11 (1), 405–411.

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