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RUSSIAN LAW JOURNAL Volume XI (2023) Issue 5s
UNDERSTANDING TOURIST INERTIA BASED ON DESTINATION SERVICE QUALITY: ROLE OF PERCEIVED VALUE AND PERCEIVED RISK
ARI ARISMAN1*, RAISA HILLIA AINI SYIFA2, ADHITYA RAHMAT TAUFIQ3
1 Management Department, Universitas Perjuangan
2 Management Department, Universitas Cipasung
3 Management Department, Universitas Siliwangi e-mail: 1ariarisman@unper.ac.id, 2raisa.hillia@uncip.ac.id, 3adhityarahmattaufiq@unsil.ac.id
Abstract: The growth of tourism has occurred all over the world, including in big cities in Indonesia. It is interesting to discuss that destination service providers are currently competing to retain their tourists. This study examines the relationship between destination service quality and tourist inertia through perceived value and perceived risk. A study on tourist destinations in the cities of Bandung, Yogyakarta and Bali using a survey method with the Structural Equation Modeling analysis technique revealed that destination service quality has a positive effect on perceived value and a negative effect on perceived risk. Perceived value has a positive effect on tourist inertia, but perceived risk is not proven to have an effect on tourist inertia. Thus, these findings expand the role of intervention from the theory of planned behaviour, especially from the aspect of perceived behavioural control. Implications, limitations, and future study directions are included at the end of the article.
Keywords: destination service quality, perceived value, perceived risk, tourist inertia
Table of Contents
1. INTRODUCTION
2. LITERATURE REVIEW
3. METHOD
4. RESULTS
5. VALIDITY TEST
6. DISCUSSION
7. CONCLUSION
8. LIMITATION
1. INTRODUCTION
Return visits by tourists to the same destination are part of post-visit behaviour that has a significant impact on destination marketing efforts and loyalty (Anderson & Srinivasan, 2003). The importance of return visits in tourism research has been recognized by many researchers because it is believed to provide a stable, predictable source of income (Fakeye & Crompton, 1992) and is a significant criterion for measuring the maturity of a destination (Oppermann, 1998). However, tourist retention based on satisfaction and loyalty is uncertain (Ranaweera & Neely, 2003), while satisfied tourists cannot always be relied upon to revisit (Rust & Zahorik, 1993). Loyalty is very difficult to achieve (Oliver, 1999). Therefore, this study argues that tourist return visits to the same destination are driven by inertia, or what is known as tourist inertia.
According to Kuo et al. (2013), tourist inertia plays a more influential role in determining return visits than satisfaction. The relationship between inertia and loyalty also needs to be clarified because it is similar to the concept of false loyalty (Dick & Basu, 1994). Interpretation of the results of inertia research also varies. Some studies define inertia as repeated purchases by dissatisfied customers (White & Yanamandram, 2004), while other studies incorporate the concepts of satisfaction and commitment (Howcroft et al., 2007). In addition, various interpretations of inertia involve differing opinions about their relationship to other constructs (Rahantoknam et al., 2017). This encourages researchers to apply a more critical assessment by making a deeper conceptualization of inertia.
Referring to tourist inertia, by facing rapid growth and intense competition in tourism destinations, destination service providers are required to identify the factors that contribute to competitiveness. One of the factors that contribute to destination competitiveness is destination service quality (DSQ). Tourist destinations can gain an advantage over their competitors by increasing destination service quality (Ghobadian et al., 2015). Therefore, destination service quality is increasingly seen as a vital aspect of achieving competitive advantage. Efforts to build this competitive advantage require continuous improvement and diversification to increase the overall competitiveness of a tourism destination.
Besides that, several empirical studies have been conducted to examine the effect of service quality on inertia. Baksi & Parida (2012) revealed a positive relationship between service quality and inertia. However, Anderson & Srinivasan (2003) revealed the opposite, that service quality has a negative impact on inertia. This reflects that there is still a bias in the relationship between inertia and the level of service quality. The bias that arises can be explained when this interaction is accompanied by a process that reflects the consumer's response in the form of an evaluation of the behaviour that appears. The evaluation phase of consumers can become a perception of what they have felt before, namely perceived behavioural control which is the main aspect of the theory of planned behaviour (Ajzen, 1991). Perceived behavioural control can influence behaviour in the evaluation process of one's intentions both in the form of perceived value and perceived risk offered in this study to explain the concept flow of the linkage between tourist inertia and destination service quality. Research on the theme of inertia including tourist inertia has been widely carried out and published (Baksi & Parida, 2012; Cui et al., 2019; Li et al., 2018; G. Wang et al., 2022). However, research with the theme of tourist inertia which has the main antecedent of destination service quality through the evaluation stage of perceived value and perceived risk has not been found. Researchers believe that these factors are vital aspects that need to be studied comprehensively in order to seize market share in the midst of a tourism boom. Therefore, this study aims to better examine tourist inertia by offering the concept of tourist evaluation represented by perceived quality and perceived risk as an effort to clarify its relationship with destination service quality.
2. LITERATURE REVIEW
There are two possible tourist behaviours, namely loyal or inertial tendencies. On this basis, tourist inertia is defined as the persistent tendency of a tourist to choose a destination, even when they know that moving to another destination will result in a better tourist experience, which demonstrates a substantial and continuing attachment to destination validation. In determining tourist inertia, it is necessary to deepen the relationship of research variables so that they can represent good and relevant research.
Perceived behaviour control, which is an important part of the theory of planned behaviour, is defined as the extent to which consumers can have control over internal and external factors that help or hinder consumers in carrying out behaviour or action (Al-Nahdi, et al., 2015). Perceived behaviour control shows the consumer's ability to overcome obstacles in carrying out behaviour or someone's action. This leads to an overall positive or negative evaluation process of individuals in carrying out certain behaviours (Kim et al., 2013). Each positively evaluated attribute usually occurs when consumers associate positive experiences with specific objects, leading to better behavioural intentions (Wansink et al., 2013). That is, an evaluation of tourists' perceptions of a destination will also lead to every experience they get when visiting. The form of perception of visitor experience takes into account the value aspects (Choi et al., 2018), and the risks (Yang & Nair, 2014) they will get.
Destination service quality is part of the concept of parental service quality in marketing studies, in the form of tourists' assessment of the performance of services consumed at certain destinations (Kayat & Hai, 2014). Several studies have been devoted to investigating the consequences of destination service quality including perceived value (Gallarza & Saura, 2006; Moutinho et al., 2011; Prayogo et al., 2017). Perceived value can be measured through destination service quality because it is considered to have a real contribution to destination competitiveness, has a positive effect on
tourist satisfaction, and indirectly encourages tourists to make return visits. Based on the results of Khalifa et al. (2020), there is a positive correlation between the dimensions of destination service quality, including accommodation, local transport, cleanliness, hospitality, leisure activities, language and airport, on perceived value which will affect satisfaction. Perceived value also has a different level for each destination. This is described by Kozak (2001) regarding destination properties, such as accommodation, services, local transport services, hygiene and cleanliness, hospitality and customer care, availability of facilities and activities, level of prices, language communication, and airport services, the results of which shows that although hospitality and customer care factors are considered important for British tourists, it is not the same for German tourists, who view accommodation services as the most important thing for Turkish tourists. In addition, according to Laarman & Gregersen (1996), the benefits of destinations can be determined by accommodations, food, attractions, and infrastructure factors such as the quality and presence of transportation, guide services, and cooperative governments, this is in line with the dimensions of destination service quality initiated by Tosun et al. al. in 2015. Thus, the first hypothesis is H1: Destination Service Quality affects Perceived Value
Current risk research concerning tourism is composed of different layers, each of which contributes to service quality risk in some way. Saribas & Oter (2014) consider risk as an important element of travel motivation and destination selection. However, based on research by Karamustafa & Erbas (2011), it was found that functional risk factors at the time of purchasing tour packages were more dominant than other risk factors, and risk factors differed according to the demographic and cultural characteristics of tourists during visits. In the destination selection process, tourists understand and try to minimize various risks associated with alternative destinations, one of which is by identifying destination service quality. Many researchers believe that destination service providers have control over destination service quality to the level of consumer expectations either positively or negatively through general antecedents (Hossain et al., 2015). In addition, a study on tourist selection criteria concluded that low perceived risk is the main motivator in choosing accommodations, airports, and others, related to tourism trips (Standard & Poors, 2002). All of these findings are following the dimensions of perceived risk, namely physical, financial, social, personal satisfaction, technical and time (Ahmadinejad et al., 2014). This is in accordance with the results of research by Lee & King (2006) which states that destination service quality can minimize the risks that occur. Thus, the second hypothesis is
H2: Destination Service Quality affects Perceived Risk
Most of the current tourism studies still focus on investigating the effect of tourist satisfaction and behaviour intention, and little examines the novelty of tourist inertia. Tourist inertia is predominantly driven by comfort, so there is a tendency to maintain the perceived value received. Even if the buying situation and marketing methods can lead to changes in behaviour, tourists will repeat visits to the same destination, which will lead to tourist inertia. Understanding of perceived value in tourists comes from two perspectives, namely cost-benefit analysis and evaluation of utilities that influence destination decisions (Li, 2021). Thus, perceived value is an important factor of future buying behaviour that reflects tourist inertia. Thus, the third hypothesis is H3: Perceived Value affects Tourist Inertia
Transportation and walking guides are the categories with the highest inertia in the tourism context. This suggests that travellers using transportation categories and destination guides rely on past travel experiences or recommendations, and arrange good itineraries for travelling to tourist destination wines, by minimizing perceived risk (Wang et al., 2021). Tourists tend to avoid risks because they can spend more time and effort, thereby developing an emotional attachment to the destination (tourist inertia). Tourists often stick with the status quo (inertia) choice rather than making a new choice when assessing a tourism destination. Perceived risk is a pre-purchase variable that has an impact on reducing tourist expectations. Murray & Haubl (2007), established that tourist inertia produces a significant cognitive locking effect by making dissatisfied tourists passively stick with the same destination. This is driven by the experience that tourists have at a destination, the occurrence of errors during visits and the destinations that are activated when making choices. Therefore, tourist
inertia is closely related to the risk assessment of tourists about the tourism destinations they visit.
Thus, the fourth hypothesis is
H4: Perceived Risk affects Tourist Inertia
3. METHOD
This study used a survey method with a quantitative approach, where the questionnaire was chosen as the main instrument in data collection. The quantitative approach measures numerically a predetermined set of attributes. This quantitative research involves inferential (hypothesis testing) using scale levels (Uysal & Altin, 2017). The following is the operationalization of the variables used in the research.
Table 1. Operationalization of Research Variables
Variable
Dimensions
Indicator
Information
Destination Service Quality (Tosun et al., 2015)
Accommodation
Local transportation
Cleanliness
Friendliness
Activity
Language Airport
Food quality is satisfactory Guaranteed room safety Extensive network of local transport services
Convenience of transportation services Cleanliness of destination facilities is guaranteed
Attitude of local people
Attitude of staff in tourism as a whole
Availability of outdoor activities
Availability of daily tour services to each
existing attraction destination
Entertainment suitability
Indonesian/English language level in the
destination as a whole
Incoming and outgoing speed at the
destination airport
DSQ1 DSQ2
DSQ3
DSQ4
DSQ5
DSQ6 DSQ7 DSQ8
DSQ9
DSQ10
DSQ11
DSQ12
The destination provides good service PV1
Functional value Destination quality standards are PV2
acceptable
Get recognition from the social PV3 PV4
Social values environment Can give a good impression for the social
environment
Perceived Value Emotional value (Shen, 2016) Destinations make you want to visit again PV5
Destination services provide satisfaction PV6
Requires a commensurate financial PV7 PV8
Sacrifice felt sacrifice Comparable time sacrifice
Comparable power sacrifice PV9
Experience value Spend leisure time comfortably PV10
epistemic value The destination creates a high sense of interest PV11
Perceived Risk (Ahmadinejad et al., 2014)
Show
Finance
Social Physique
It is possible that the facility cannot be used
Additional costs when visiting the destination destination Purchase decisions made lower self-esteem
High crime rate
PR1
PR2
PR3 PR4
Psychological Time
Excessive tourist density at the destination
Worried about safety while at the destination
Slow destination service
PR5
PR6 PR7
Tourist Inertia (Polites & Karahanna, 2012)
Cognitive Inertia
Affective Inertia
Behavioral Inertia
Destination services are ranked first
among other destinations _
Will use the destination service for a long
time
Destinations can create a pleasant impression
The decision to visit this destination is the right decision
Will try the new service provided by the destination
Recommend destination destinations compared to other destinations
TI1 TI2 TI3 TI4 TI5 TI6
Source: Developed for Research, 2023
The population in this study are tourists at destinations in big cities in Indonesia, especially Bandung, Yogyakarta and Bali. This city was chosen because it has easy access to transportation for domestic tourists. According to Hair et al. (2010), the representative sample size is 100 to 200 respondents with a minimum good sample of five times and a maximum of ten times the number of indicators. The number of estimated parameters is 79 so the minimum sample size is 5 times the estimated parameter or 5 x 79 = 395 respondents. This study uses a non-probability sampling technique with purposive sampling (determining the sample with certain criteria). The criteria for determining the research sample are 1. Tourists to the cities of Bandung, Yogyakarta and Bali, who have visited at least the last 6 months; 2. At least once make a tourist visit; 3. Over 18 years of age; and 4. Willing to be a respondent.
Bipolar adjective scale is a development of the differential semantic scale used in this study. Research data collection was carried out by distributing closed questionnaires to respondents. Closed questionnaires are made in the form of statements that have alternative answer choices. Each respondent was asked to respond to each statement on a scale of 1-10. The even scale range aims to ensure that respondents will not tend to choose neutral or middle numbers. Rating 1 -5 means disagree, and conversely rating 6-10 means agree.
Structural Equation Modeling (SEM) analysis technique is the development of a multiple equation model derived from econometrics, psychology, and sociology principles (Hair et al., 1984). SEM is a general statistical modelling technique that is widely used in the behavioural sciences (Hox & Bechger, 1999). SEM was chosen because it is in accordance with the research objective, namely to examine the relationship between variables in the model, both between manifest variables and latent variables, as well as the relationship between latent variables. The latent variables used in this study are destination service quality, perceived value, perceived risk, and tourist inertia. The analysis of research data is assisted by AMOS 22 software.
To explain the flow of the concept of interrelationships between inertia with destination service quality which requires an evaluation phase both in terms of perceived value and perceived risk, the following is the model used in this research:
Figure 1. Research Model Source: Developed for Research, 2023
4. RESULTS
Respondents obtained in this study amounted to 430 tourists, with characteristics divided into several grouping criteria, namely based on gender, age, favourite destinations frequently visited, and intensity of visiting the same destinations. The following are details of the characteristics of the respondents selected in the study.
Table 2. Tourist Characteristics
Characteristics Attribute Frequency %
Gender Man Woman 187 243 43.5 56.5
18-25 93 21,6
Age 26-35 36-45 208 121 48,4 28,1
>46 8 1,9
West Java 313 72,8
domicile Central Java East Java 58 12 13.5 2,8
Outside Java 47 10,9
Favorite destination Bandung Yogyakarta Bali 209 144 77 48,6 33.5 17,9
visiting intensity 1 time in 1 year 2 times in 1 year >2 times in 1 year 166 235 29 38.6 54.7 6,7
Source: Processed data, 2023
Based on Table 2 it can be seen that tourists who are respondents are dominated by women with a proportion of 56.5%. This is in line with the current actual condition that women are more interested in travelling than men (McNamara, 2010). Tourists in the 3 main destinations are dominated by the age range of 26-35 years, namely 48.4%, which indicates that tourists are still of productive age and need tourist trips to balance their busy work. Most tourists who are willing to be research respondents come from West Java with a proportion of 72.8 %. Tourists prefer Bandung as their favourite destination, namely 48.6 %. This happened because the tourists who were the respondents came from West Java. More tourists have the intensity of visiting the same destination, namely 2 times in 1 year with a proportion of 54.7 %. This can be caused by tourist activities that follow the school holiday period in Indonesia.
Descriptive statistical analysis was carried out to know the respondents' responses regarding destination service quality, perceived value, perceived risk and tourist inertia for tourist destinations in the cities of Bandung, Yogyakarta and Bali. The sample size of this study was 430 respondents who were eligible to be processed and per the requirements needed in the study. The scale used in this study is a bipolar adjective scale with a range of 1 -10. Destination service quality is defined as an overall evaluation of service performance (Santos, 2003) or an overall evaluation of the good or bad of service (Athiyaman, 1997). Respondents' responses regarding the indicators of destination service quality were considered very good. This can be seen from the total scores obtained in the table above so that based on the assessment classification of each indicator measurement, it is included in the very good criteria. Except for the question "cleanliness of guaranteed destination facilities" (DSQ5) it has a score with a good classification. Perceived value is the difference between the benefits received and the sacrifices made by tourists (Carvache-Franco et al., 2022). Respondents' responses regarding perceived value indicators were considered very good. This can be seen from the total scores obtained in the table above so that based on the assessment classification of each indicator measurement, it is included in the very good criteria. Except for the question "comparable labour sacrifice" (PV9) has a value with a good classification. Perceived risk is a pre-purchase variable that has an impact on reducing tourist expectations (Murray & Haubl, 2007). Respondents' responses regarding perceived risk indicators were considered poor. This can be seen from the total scores obtained in the table above so that based on the assessment classification of each indicator measurement, it is included in the bad criteria. Tourist inertia was adopted to explain the relationship between tourist satisfaction and visit intention (Cui et al ., 2019). Respondents' responses regarding indicators of tourist inertia were considered very good. This can be seen from the total scores obtained in the table above so that based on the assessment classification of each indicator measurement, it is included in the very good criteria. Except for the question "recommend destination destinations compared to other destinations" (TI6) has a value with good classification. CFA is used to emphasize that all indicators group themselves into factors related to how the researcher has related the indicators to latent variables (unidimensionality). CFA models in SEM are used to assess the role of measurement error in the model, validate multifactorial models, and determine group effects on factors. The CFA test was carried out on exogenous and endogenous variables. Based on the results of the analysis, it can be seen that each variable forming measurement shows good results, namely, the CR value is greater than 2x the standard error with P less than 0.05. In other words, the measurements forming the variables have shown unidimensionality. Based on this confirmatory factor analysis, the research model can be used for further analysis without modification or adjustment. The results of the analysis can be seen in the following table: Table 3. Confirmatory Factor Analysis of Exogenous and Endogenous Variables
Estimates SE CR P Label
DSQ1 <--- DSQ 1,000
DSQ2 <--- DSQ ,912 .044 20,914 *** par_1
DSQ3 <--- DSQ ,946 .044 21,392 *** par_2
DSQ4 <--- DSQ ,889 .047 19,042 *** par_3
DSQ5 <--- DSQ ,976 .044 22,023 *** par_4
DSQ6 <--- DSQ ,941 .048 19,557 *** par_5
DSQ7 <--- DSQ ,927 .043 21,448 *** par_6
DSQ8 <--- DSQ ,973 .042 23,252 *** par_7
DSQ9 <--- DSQ ,924 .042 21,801 *** par_8
DSQ10 <--- DSQ ,997 .045 22,373 *** par_9
DSQ11 <--- DSQ ,985 .041 23,820 *** par_10
DSQ12 <--- DSQ ,954 .041 23,301 *** par_11
PV1 <--- PV 1,000
PV2 <--- PV 1,062 .042 25.106 *** par_1
Estimates SE CR P Label
PV3 <--- PV ,954 .049 19,337 ■kick par_2
PV4 <--- PV ,969 .059 16.504 kkk par_3
PV5 <--- PV ,974 ,067 14,611 kkk par_4
PV6 <--- PV ,971 ,071 13,664 kkk par_5
PV7 <--- PV 1.012 ,071 14,230 kkk par_6
PV8 <--- PV ,972 ,057 17,072 kkk par_7
PV9 <--- PV 1.016 .065 15,668 kkk par_8
PV10 <--- PV 1.035 .075 13,717 kkk par_9
PV11 <--- PV ,971 ,061 15,927 kkk par_10
PR1 <--- homework 1,000
PR2 <--- homework ,956 .047 20,514 kkk par_11
PR3 <--- homework ,968 .053 18,240 kkk par_12
PR4 <--- homework ,968 .062 15,678 kkk par_13
PR5 <--- homework 1,031 ,063 16,299 kkk par_14
PR6 <--- homework 1.008 ,057 17,568 kkk par_15
PR7 <--- homework 1,066 ,057 18,814 kkk par_16
TI1 <--- IT 1,000
TI2 <--- IT ,952 .042 22,747 kkk par_17
TI3 <--- IT 1.033 .056 18,591 kkk par_18
TI4 <--- IT ,946 .060 15,804 kkk par_19
TI5 <--- IT ,989 .066 15,039 kkk par_20
TI6 <--- IT ,978 ,063 15,458 kkk par_21
Description: *** P = 0.001
Source: Amos Calculation Results, 2023
Goodness of fit performance evaluation analysis is used to determine the structural relationship between the variables studied. Structural relationships that occur between variables can be tested for suitability with the goodness of fit index . The results of the analysis can be seen in the following figure:
Figure 2. SEM Full Model Test Results Source: AMOS Calculation Results, 2023 From the picture above the Goodness of Fit value of the full SEM model can be seen in the table below:
Table 4. SEM Full Model Goodness of Fit Test
goodness of Index Cut-Off Value Results Model Information
Chi Square Expected Small 1766,679
RMSEA <0.08 0.0 68 fit
GFI >0.90 0.771 _ Marginal Fit
AGFI >0.90 0.742 _ marginal fit
CMIN/DF <2.0 2,994 Marginal Fit
TLI >0.95 0.214 _ marginal fit
CFI >0.95 0.264 _ Marginal Fit
Source: AMOS Calculation Results, 2023
The tested model is considered good or satisfactory if the chi-square value is low. The smaller the x2 value, the better the model (because in the difference test chi-square, x2 = 0, means that there really is no difference, Ho is accepted) and is accepted based on probability with a cut off value of p > 0.05. Based on the calculation results, a chi-square value of 1766.679 is obtained so that the model being tested is said to be good. The AGFI value was 0.742, the CMIN/DF value was 2.994, the TLI value was 0.214, and the CFI was 0.264, so it could be said that the AGFI, CMIN/DF, TLI and CFI values were included in the marginal fit category. The RMSEA value indicates the goodness-of-fit that can be expected if the model is estimated in the population. An RMSEA value that is smaller or equal to 0.08 is an index for the acceptability of a model that shows a close fit, meaning that the model is based on degrees of freedom . Based on the calculation results, the RMSEA value is 0.068 so that the model is acceptable. The fit RMSEA value is less than 0.08. From the various suitability indices it can be concluded that the measurement model on the proposed endogenous construct is fit or has a good fit. So that in this study the entire research model involves the interaction of the variable destination service quality, perceived value, perceived risk, and tourist inertia acceptable and can be analyzed further.
5. VALIDITY TEST
Validity test is used to measure whether a questionnaire is valid or not. A questionnaire is said to be valid if the questions on the questionnaire are able to reveal something that will be measured by the questionnaire. To measure construct validity, it can be seen from the loading factor value in the standardized direct effects table.
Table 5. Validity, Construct Reliability and Variance Extracted
Variable Indicators lf error r ve
DSQ1 0.847 0.153 0.980 0.802
DSQ2 0.803 0.197
DSQ3 0.824 0.176
DSQ4 0.788 0.212
Destination DSQ5 0.839 0.161
Service Quality DSQ6 0.798 0.202
(Tosun et al., DSQ7 0.823 0.177
2015) DSQ8 0.851 0.149
DSQ9 0.826 0.174
DSQ10 0.843 0.157
DSQ11 0.860 0.140
DSQ12 0.857 0.143
PV1 0.842 0.158 0.983 0.841
Perceived PV2 0.867 _ 0.133
Value PV3 0.856 _ 0.144
(Shen, 2016) PV4 0.871 _ 0.129
PV5 0.858 _ 0.142
PV6
PV7
PV8
PV9
PV10
PV11
0.858 _ 0.142
0.870 _ 0.130
0.846 _ 0.154
0.867 _ 0.133
0.863 _ 0.137
0.862 _ 0.138
Perceived Risk (Ahmadinejad et al., 2014)
PR1 PR2 PR3 PR4 PR5 PR6 PR7
0.845 0.155 0.973 0.839
0.840 0.160
0.855 0.145
0.876 0.124
0.882 0.118
0.861 0.139
0.853 0.147
Tourist Inertia (Polites & Karahanna, 2012)
TI1 TI2 TI3 TI4 TI5 TI6
0.853 0.147 0.969 0.837
0.854 0.146
0.866 0.134
0.858 0.142
0.857 0.143
0.854 0.146
Source: AMOS Calculation Results, 2023
Convergent validity can be used to determine each measurement that is estimated to validly measure the dimensions of the concept being tested and has a direct relationship or direct effect. The minimum figure for the loading factor (lf) is > 0.4 or ideally > 0.7 (Hair et al., 2009). Table 5 shows that all measurements produce the appropriate loading factor values so that all measurements are declared valid. Reliability test with construct reliability test, namely testing the reliability and consistency of data. The reliability coefficient ranges from 0 - 1 so the higher the coefficient (closer to 1), the more reliable the measuring instrument is. Construct reliability is good if the construct reliability value (r) is > 0.7 and the variance extracted value (ve) is > 0.5. Based on the results of the data processing it can be seen that reliability construct on variables destination service quality has result of r = 0.980, construct reliability on the perceived value variable has a result of r = 0.983, construct reliability on the perceived risk variable has a result r = 0.973, construct reliability on variable tourist inertia has a result of r = 0.969. Can it be concluded that the construct reliability in this study was good because it has been greater than 0,7. So have qualified which means that the indicators used are reliable and relatively able to explain the latent variables it forms. Based on the results of the data processing, it can be seen that the variance extract (ve) on the variable destination service quality has a result of 0.802, variable perceived value has a result of 0.841, the variable perceived risk has a result of 0.839, and tourist inertia variable has results as much as 0.837. For variance extract, the variables declared reliable must meet the minimum requirements of more than 0.50. It can be concluded that the questionnaire used in this research was declared reliable.
Hypothesis testing is carried out to determine whether or not the independent variable influences the dependent variable. The hypothesis is declared accepted if the probability value (P) < 0.05. The results of hypothesis testing can be seen in the table below:
Table 6. Coefficient Significance Test
Estimates CR P Information
PV <--- DSQ 1.058 13,949 *** Accepted
homework <--- DSQ -1.015 -11,909 *** Accepted
IT <--- PV ,994 15,481 *** Accepted
IT <--- homework ,015 ,866 ,386 Rejected
Source: AMOS Calculation Results, 2023
From the results, it is known that three hypotheses are accepted and one hypothesis is rejected. Furthermore, it can be seen the influence between variables exogenous to endogenous variables from the following table:
Table 7. Effect of Variables
Estimates
PV <--- DSQ 1.058
homework <--- DSQ -1.015
IT <--- PV ,994
IT <--- homework ,015
Source: AMOS Calculation Results, 2023
Based on the table it can be concluded destination service quality own effect on perceived value that is equal to 1.058, destination service quality influence perceived risk that is equal to -1.015, perceived value has influence over tourist inertia of 0.994 and perceived risk has an influence on tourist inertia of 0.015.
6. DISCUSSION
Destination service quality for tourists visiting the cities of Bandung, Yogyakarta and Bali is stated to be very good. This shows that the quality of destination service in tourist destinations in the cities of Bandung, Yogyakarta and Bali provides satisfying food quality, guaranteed room safety, extensive network of local transport services, the convenience of transportation services, guaranteed cleanliness of destination facilities, local community attitudes, staff attitudes in tourism overall, the availability of outdoor activities, the availability of daily tour services to each existing destination attraction, the suitability of entertainment, the level of proficiency in Indonesian/English at the destination as a whole and the entry and exit speed at the destination airport. Perceived value and tourist inertia for destination tourists are also stated to be very good. This can be interpreted that the perceived value felt by tourists towards tourist destinations creates a desire to visit again, destination services provide satisfaction, provide comparable financial sacrifices, provide comparable sacrifices of time and energy, spend free time comfortably, and destinations generate a strong sense of attraction. As for the perceived risk to destination tourists, it is stated as bad. This shows that the perceived risk felt by tourists towards tourist destinations regarding the possibility of unusable facilities, additional costs when visiting destinations, purchasing decisions made lowers self-esteem, high crime rates, excessive tourist density at destinations, worry about safety while at the destination and service destinations that are considered slow are low. In addition, tourist inertia on tourist destinations is stated to be very good. This shows that tourists consider destination services to be the most important compared to other attributes at a destination, tourists will use the destination's services for a long time, destinations are able to create a pleasant impression and tourists consider this to be the right decision, tourists will try new services provided by the destination, and recommend tourist destinations compared to other destinations. Based on the parameter estimation of the relationship between destination service quality and perceived value, it can be seen that destination service quality in tourist destinations in the cities of Bandung, Yogyakarta and Bali has a positive effect on perceived value. So that if the destination can perform the quality of destination service well, the level of perceived value of tourists will also increase. This is in line with the research of Khalifa et al. (2020), there is a positive correlation between the dimensions of destination service quality, including accommodation, local transportation, cleanliness, hospitality, recreational activities, language, and airports, on perceived value which will affect satisfaction. Furthermore, the relationship between destination service quality and perceived risk, it can be seen that destination service quality for tourists visiting the cities of Bandung, Yogyakarta and Bali has a negative effect on perceived risk. So that if the destination can perform destination service quality well, it will reduce the level of perceived risk felt
by tourists. This is in line with research by Lee & King (2006) which states that the quality of destination services can minimize the risks that occur.
The relationship between perceived value and tourist inertia, it can be seen that the perceived value of tourist destinations in the cities of Bandung, Yogyakarta and Bali has a positive effect on tourist inertia. So if the perceived value obtained by tourists from Bandung, Yogyakarta and Bali destinations is good, it will increase tourist inertia. This is in line with Li's research (2021) which states that understanding perceived value in tourists comes from two perspectives, namely cost-benefit analysis and utility evaluation that influence destination decision decisions, thus perceived value is an important factor of future buying behaviour that reflects inertia traveller.
In the relationship between perceived risk and tourist inertia, it can be seen that perceived risk for tourists visiting the cities of Bandung, Yogyakarta and Bali has not proven to affect tourist inertia. So even though the perceived risk that tourists get is low from the destinations, it is not enough to increase tourist inertia and vice versa when the perceived risk that tourists feel is high it will reduce the level of tourist inertia. This is in line with research by Wang et al. (2021), tourists tend to avoid risks because they can spend more time and effort, thereby developing an emotional attachment to destinations (tourist inertia). Murray & Haubl (2007) determines that Tourist inertia produces a significant cognitive locking effect by making dissatisfied tourists passively stick with the same destination. This is driven by the experience that tourists have at a destination, the occurrence of errors during visits and the destinations that are activated when making choices.
7. CONCLUSION
This study broadens the boundaries of knowledge regarding the application of the theory of planned behaviour to the formation of tourist inertia. The results of the study show that destination service quality has a positive effect on perceived value, in which the higher the destination service quality of a tourism destination, the higher the perceived value of tourists. However, destination service quality does not have a positive effect on perceived risk, this can be interpreted that the higher the destination service quality of a tourist destination, the lower the perceived risk of tourists. As for perceived value, it has a positive effect on tourist inertia, so in this case, it can be interpreted that when the perceived value of tourists towards a tourism destination is high, the better the tourist inertia. Besides that, perceived risk has a positive but not significant effect on tourist inertia, so in this case it can be interpreted that when the perceived risk from tourists to a tourism destination is high, the tourist inertia will be worse. Based on this, perceived value and perceived risk prove to be a full mediation for the effect of destination service quality on tourist inertia. The results of this study help destination service providers to understand how destination service quality plays an important role in the formation of tourist inertia, through perceived value and perceived risk. Service providers need to pay attention to the destination service quality they offer by maintaining the consistency and image of the destination, trying to improve service, ensuring the safety of tourists, and increasing involvement that can affect tourist inertia. In addition, improving accessibility, maintaining competitive prices, offering attractive tour packages, establishing good relations with tourists, and implementing the latest technology are also recommended, to optimize the potential of destinations that have implications for tourist return visits.
8. LIMITATION
This study has several limitations, where this study is cross-sectional in nature so further researchers need to conduct a more in-depth investigation. This research can also be influenced by external factors beyond the control of the researcher which can affect the perceptions and behaviour of tourists, so the research results may not fully describe the inertia of tourists. In addition, research on destination service quality on tourist inertia can also experience subjectivity problems that depend on factors such as individual preferences, experiences, and expectations. Therefore, further research can conduct a more in-depth investigation of tourist inertia and also examine external factors and other factors that can influence tourist subjectivity.
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