INFLUENCE
OF MOTIVATION ON MILLENNIAL TOURIST SATISFACTION AND RE-VISIT INTENTION AT
GLAMPING IN KINTAMANI DISTRICT, BANGLI REGENCY, BALI
I Made Swariga, Ni
Made Sofia Wijaya, Yayu Indrawati
Universitas Udayana, Indonesia
Email: [email protected], [email protected], [email protected]
|
KEYWORDS motivation;
satisfaction; intention to re-visit |
ABSTRACT The COVID-19 pandemic has an impact on the tourism industry, Bali is
one of the places affected because most of the people work in the tourism
industry, during the COVID-19 pandemic. Kintamani is one of the tourist
destinations that is still visited by many tourists, especially domestic
tourists, the development of tourism in the pandemic era has triggered the
emergence of new trends in the world of tourism such as glamping business
(glamour camping). This study aims to determine the effect of motivation can
increase the intention of tourists to stay again in addition to affecting
tourist satisfaction. The method used is a quantitative approach and uses a
qualitative approach to describe the characteristics of respondents and the
scale assessment given by respondents. Data collection techniques in this
study use questionnaires, then sampling techniques using accidental sampling.
The data analysis technique used in this study is quantitative descriptive,
then hypothesis testing using smart PLS software. The results of this study
concluded that based on the t-test, t-statistical value of each variable in
this study, there was a positive direct influence between motivation (X) on
satisfaction (Y1), motivation (X) on the re-visit intention (Y2),
satisfaction (Y1) on the re-visit intention (Y2), and the indirect influence
that satisfaction (Y2) was able to mediate motivation (X) and re-visit
intention (Y2) from all hypotheses both direct and indirect influences
Exogenous variables on endogenous variables have a significant positive
influence, namely having a p-value less than (0.05) and having a t-statistic
value greater than (1.96). |
INTRODUCTION
Based
on data from the Central Bureau of Statistics for the Province of Bali, it
shows that the number of tourist visits to Bali in the last five years has
increased rapidly where visits in 2019 reached 16,820,249 visits from foreign
and domestic tourists and this number rose as high as 5.9 percent compared to
the previous year , but there was also a very drastic decrease in 2020, the
existence of the covid 19 pandemic resulted in a very drastic decrease in
visits from 2019 to 2021 reaching a 66.5 percent decrease, behind all that
there were companies that grew and developed in the pandemic era, especially in
the Kintamani area such as glamping or glamor camping which many domestic
tourists visit (Purwahita et al., 2021).
Based on data from the Bangli district tourism office, it shows that the
highest tourist visits in 2019 reached 951,327 over the past five years and
this number has increased as high as 55.9 percent from the previous year but in
2020 there has been a very drastic decrease in the number of visits until 2021
due to the covid 19 pandemic, behind that kintamani is one of the destinations
that still has a lot of visitors from domestic tourists compared to other
tourist objects in Bali.
The high interest
of tourists, especially millennial tourists, to carry out glamping activities
has resulted in an increase in the number of glamping accommodations in the
Kitanamani area, and enthusiasts of glamping activities are more dominant than
millennial tourists where millennial tourists are one of the most numerous
generations after generation Z or the younger generation (Indrayani et al., n.d.), based on data
from the central statistics agency from 2020 to 2022 the number of millennials
will reach 27 percent of the total population of Indonesia. Based on data from
the Bangli Tourism Office until 2022 due to the high interest of tourists in
glamping activities, there are more than 35 available glamping in Kintamani.
Millennials
prefer to visit places that have interesting places to be photographed or that
are often considered Instagrammable (Haddouche & Solomone, 2018).
Millennials love to seek out experiences, especially food and drink and
festivals. In addition, millennial tourists like to travel to destinations that
can offer meeting places or co-working spaces with adequate Wi-Fi connections (Salsabila, 2019).
Millennials are a generation that prefers to spend their money on travel (Febrianto, 2021).
To increase the
intention to return, motivation is the most important thing that determines the
intention of tourists to make a return trip (Anugrah et al., 2022). The most
appropriate approach to understanding a person's motivation to travel is a
psychological and sociological approach, where the definition of motivation is
understood as emotional and cognitive (internal) and social (external) as
reasons for traveling both individually and in groups (Hallab et al., 2003; Yoon & Uysal, 2005). In
addition to motivation, tourist satisfaction is an important factor that
increases tourist intentions to stay again (Marpaung, 2019). Total
satisfaction is an affective expression of an emotional response to a product
or service experience that is influenced by consumers (Mastarida, 2023). Satisfaction
with the product and information used in product selection.
Previous research
by Hasbi (2022) showed service
quality had a positive and insignificant effect on the intention to buy back
from guests of hotels in Tanjung Benoa. The higher the quality of service
provided by the hotel, it will affect the intention of the guest to make a
repurchase or stay again at the hotel. Furthermore, Ariffin (2016) stated loyal
consumers who then decide to return to buy the same product/company, tend to
have felt satisfaction with the experience felt before while consuming the
company's products. Moreover, the study
aims to determine the effect of motivation can increase the intention of
tourists to stay again in addition to affecting tourist satisfaction
RESEARCH
METHOD
This research was conducted to determine the effect of
motivation on millennial tourists' satisfaction and intentions to stay again at
the glamping site in Kintaman, Bangli Regency, Bali, in Kintamani District,
Bangli Regency. This research uses a quantitative descriptive approach (Creswell, 2021) with a sample of
millennial tourists living in Kintamani. This measures the level of re-visit
intention which is influenced by several factors, namely the motivation and
satisfaction of millennial tourists staying at Kintaman. The research results were
analyzed by SEM analysis using SmartPLS software. The research linked the
opinions of tourists staying at glamping in Kintaman by measuring the results
of a survey of 100 samples. According to Sugiyono (2019), quantitative
data is positivist (concrete data), inquiry-based research methods. Data is in
the form of numbers, statistics function as calculation and testing tools and
are measurable numbers related to the problem under study to draw a conclusion. The
data analysis technique used in this study is quantitative descriptive, then hypothesis
testing using smart PLS software.
RESULTS AND
DISCUSSION
In this study the
data analysis technique used was Structural Equation Modeling (SEM) using the
Smart PLS application. This technique is used to explain the relationship
between variables and variables with their constructs, which exist in the
research as a whole. The main purpose of using Structural Equation Modeling
(SEM) analysis techniques is to check and justify a model with existing theory.
Structural Equation Modeling (SEM) using the Smart PLS application is a set of
statistical techniques that allows testing a series of relationships
simultaneously, the relationship is built between one to several independent
variables.
Outer
Model Measurement Evaluation Results
The outer
model is a model that specifies the relationship between the latent variables
and the indicators or it can be said that the outer model defines how each
indicator relates to its latent variables (Ghozali, 2016). To measure the
validity of an indicator it is measured by convergent validity or outer loading
value, while to test the reliability of an indicator it is measured by
composite reliability and average variance extracted or AVE.
Convergent Validity
From the
measurement model with reflexive indicators, it can be seen the correlation
between the item score/component score and the construct score. The individual
reflexive measure is said to be high or valid if it is analyzed to have a
loading factor value of more than 0.70 with the construct you want to measure.
However, for reflexive early stages of developing a measurement scale, loading
factor values above 0.50 to 0.60 are considered to be sufficient (Ghozali, 2016). The
value of convergent validity can be seen in table 1 below.
Table 1. Description of Research
Variables
|
Variable |
Indicator |
Loading
Factor |
|
Motivation |
X1 |
0.754 |
|
X2 |
0.757 |
|
|
X3 |
0.715 |
|
|
X4 |
0.740 |
|
|
X5 |
0.720 |
|
|
X6 |
0.786 |
|
|
X7 |
0.752 |
|
|
X8 |
0.716 |
|
|
X9 |
0.754 |
|
|
X10 |
0.735 |
|
|
Satisfaction |
Y1.1 |
0.818 |
|
Y1.2 |
0.845 |
|
|
Y1.3 |
0.838 |
|
|
Y1.4 |
0.823 |
|
|
Y1.5 |
0.750 |
|
|
Y1.6 |
0.797 |
|
|
Y1.7 |
0.821 |
|
|
Re-visit intention |
Y2.1 |
0.823 |
|
Y2.2 |
0.814 |
|
|
Y2.3 |
0.838 |
Source:
Results of Primary Data Processing, 2023
In table 1 it can
be seen that each indicator in this study has an outer loading value of more
than 0.7 which can be interpreted that all indicators have a high value or can
be said to be valid. Another model that can also be used as a reference for
assessing whether the data in this study is valid or not is through
discriminant validity assessment. Discriminant validity is carried out by
comparing the square root of the average variance extracted (AVE) for each
variable with a correlation between one variable and another in a model. A
model is declared to have good discriminant validity if the square of the
average variance extracted (AVE) on each variable is greater than the
correlation of the variable with other variables in one model or it is
recommended that the average variance extracted (AVE) measurement value is
greater than 0.5. The measurement results can be seen in the table below.
Table 2. Average Variance Extracted
(AVE)
|
Variable |
Avarage
Variance Extracted |
|
Motivation |
0.552 |
|
Satisfaction |
0.662 |
|
Re-visit intention |
0.681 |
Source:
Results of Primary Data Processing, 2023
In table 2 above
it can be seen that the correlation value of the average variance extracted
(AVE) owned by each variable is greater than the other variables and is above
0.5 which is a measurement value to declare the validity of a data, each
variable has an average variance extracted (AVE) value more than 0.5, it can be
said that each variable in this study is valid.
Discriminant Validity
Discriminate
validity is a reflexive measurement model of an indicator that is assessed
based on cross loading measurements with constructs. If the construct's
correlation with the measurement item is greater than the value of the other
constructs, this indicates that the latent construct can predict the size of
the block better than the size of the other blocks. A latent variable can be
used as a comparison for a model if it has a discriminant validity value
greater than 0.5. The results of the discriminant validity measurement of this
study are as follows.
Table
3. Discriminant Validity
|
Variable |
Satisfaction |
Motivation |
Re-visit
Intention |
|
Satisfaction |
0.814 |
|
|
|
Motivation |
0.795 |
0.743 |
|
|
Re-visit Intention |
0.709 |
0.745 |
0.825 |
Source: Results of Primary Data Processing, 2023
Based on table 3, the cross loading value of each
indicator for each other variable is greater than 0.5. This shows that the data
in this study can be declared valid.
Composite Reliability
Composite
reliability is a group of indicators that measure a
variable having a good composite reliability. A latent variable can be said to
have good reliability if the composite reliability value is more than 0.6 and
the Cronbach's alpha value is more than 0.7 (Ghozali, 2016). The measurement
results of composite reliability and Cronbach's alpha in this study can be seen
in the table below.
Table
4. Coefficient of Composite Reliability and Cronbach's Alpha
|
Variable |
Cronbach's Alpha |
Composite
Reliability |
|
Motivation |
0.910 |
0.925 |
|
Satisfaction |
0.915 |
0.932 |
|
Re-visit intention |
0.766 |
0.865 |
Source:
Results of Primary Data Processing, 2023
In table 4 it can
be seen the composite reliability and cronbach's alpha values of each
variable. The data shows that each variable has a composite reliability value
of greater than 0.6 and a Cronbach's alpha value of more than 0.7. So it can be
concluded that the variable values in this study are reliable.
Results
of Inner Model Measurement Evaluation
Evaluation of
inner model measurements in a study using PLS can be seen from the R-Square
value to see the effect of exogenous variables on endogenous variables(Ghozali, 2016), besides that you
can also see the Q-Square value if the value of Q2> 0 indicates a model has
predictive relevance (Ghozali & Latan, 2012).
R-Square
The R-square
function is to see the magnitude of the influence between exogenous variables
on endogenous variables. It can be seen that the weak or strong influence
between other variables is clarified with three criteria, namely it is said to
be weak if the R-square value is between 0.19 to 0.32, then if the R-square
value is R-square is in the range of 0.33 to 0.66 is said to be moderate, and
lastly if the R-square value is in the range > 0.67 then it is said to be
strong. The following is the R-square value for this research variable.
Table 5. R-Square (R) Value of
Endogenous Variables
|
|
R Square |
R Square Adjusted |
|
Satisfaction |
0.632 |
0.628 |
|
Re-visit
intention |
0.592 |
0.584 |
Source:
Results of Primary Data Processing, 2023
Table 5 shows the
R-square value where the satisfaction variable has a value of 0.632 and the re-visit
intention is 0.592, where each variable is between 0.33 and 0.66. Therefore, it
can be concluded that the variable values in this study fall into the moderate
criteria.
Q-Square
Testing the inner
model using PLS by looking at the Q-square value, if the Q-square value is more
than 0 then the model is categorized as having predictive relevance, whereas
vice versa if the Q-square value has a value less than 0 then the model can be
categorized as having less predictive relevance (Ghozali & Latan, 2012). The following is
the Q-square value in this study.
Table 6. Q-Square Value (Q2)
|
Variable |
SSO |
SSE |
Q2 |
|
Motivation |
1000,000 |
1000,000 |
|
|
Satisfaction |
700,000 |
410,608 |
0.413 |
|
Re-visit
intention |
300,000 |
184,474 |
0.385 |
Source:
Results of Primary Data Processing, 2023
In table 6 it can
be seen that the Q-square value where the satisfaction variable has a value of
0.413 and the re-visit intention variable has a value of 0.385, therefore, it
can be concluded that the variable values in this study are included in the
criteria of having predictive relevance.
Goodness
of Fit
Apart from looking
at the R-square value and Q-square value, you can also see the fit model value
in PLS by looking at several values or categories, namely the Chi-square
value of more than 0.5 which indicates that the empirical data is identical to
theory and model, then the value is standardized root residual (SRMR) should be
less than 0.08, and normal fit index (NFI) value is expected to be less than
0.90. The following is the value of the goodness of fit model in this study.
Table 7. Goodness of Fit Model
|
|
Saturated Model |
Estimated Model |
|
SRMR |
0.080 |
0.080 |
|
d_ULS |
1,442 |
1,442 |
|
d_G |
0.798 |
0.798 |
|
Chi_Square |
407,209 |
407,209 |
|
NFIs |
0.726 |
0.726 |
Source:
Results of Primary Data Processing, 2023
Based on table 7
it can be seen that the chi-square value in this study is 407,209 more than 0.5
which indicates the empirical data is identical to the theory and model, then
the standardized root riseual (SRMR) value in this study, namely 0.083, is in
accordance with the expected measurement standards less than 0.08, and finally
the normal fit index (NFI) value in this study, which is 0.726, is in
accordance with the standard expected value of less than 0.90. So it can be
concluded that this study was declared fit because all categories could be
fulfilled, so it was feasible to test the research hypothesis.
Hypothesis
Testing
Hypothesis testing
is a way to see whether there is a direct or indirect effect of each variable
in this research. Testing the hypothesis in this study using the PLS with the
bosstrapping method then looking at the value of the direct effect and indirect
effect testing then comparing it with the t-statistic/t test value in this
study which is 1.98, if the results of the direct effect and indirect effect
testing (> 1, 98) then these variables have an influence in this study.
The direct effect of motivation on
satisfaction and intention to return to glamping in Kintamani
The direct effect
is the effect that occurs between exogenous variables and endogenous variables.
The direct influence in this study, among others.
Table 8. Output Indirect Effect
Boostrapping Results
|
hypothesis |
Variable |
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDE|) |
P Value |
|
H1 |
Motivation – Satisfaction |
0.795 |
0.798 |
0.034 |
23,635 |
0.000 |
|
H2 |
Motivation - Re-visit intention |
0.495 |
0.509 |
0.097 |
5.124 |
0.000 |
|
H3 |
Satisfaction – Intention to Re-Stay |
0.315 |
0.304 |
0.097 |
3,243 |
0.001 |
Source: Results of
Primary Data Processing, 2023
The Effect of Motivation on
Millennial Tourist Satisfaction in Glamping in Kintamani
Based on the
results of the hypothesis test in table 8, the p-value of the motivation
variable on millennial tourist satisfaction is 0.000 with a smaller value than
the significant value of 0.05. Apart from having a significant p-value (0.000
<0.05), the motivation variable on millennial tourist satisfaction also has
a t-statistic value of 23,635 which means it is greater than (1.98), so it can
be concluded that motivation has a positive influence on tourist satisfaction
which means (H1) is accepted and (H0) is rejected.
The Influence of Motivation on Millenial
Tourists' Return Stay Intention to Glamping in Kintamani
In the results of
the hypothesis test in table 8, the p-value of the motivation variable for the
intention to return to millennial tourists is 0.000 with a value less than
significant, namely 0.05. In addition to having a significant p-value (0.000
<0.05), the variable motivation on the intention to return to millennial tourists
also has a t-statistic value of 5.124 which means it is greater than (1.98), so
it can be concluded that motivation has a positive influence on intention
millennial tourists stay again, which means (H1) is accepted and (H0) is
rejected.
The Effect of Satisfaction on Millennial
Tourists' Re-Stay Intention at Glamping in Kintamani
In the results of
the hypothesis test in table 8, the p-value of the variable satisfaction with
the re-visit intention for millennial tourists is 0.001 with a smaller than significant
value of 0.05. Apart from having a significant p-value (0.000 <0.05), the
variable of satisfaction with the re-visit intention for millennial tourists
also has a t-statistic value of 3.243 which means it is greater than (1.98), so
it can be concluded that satisfaction has a positive influence on intention
millennial tourists stay again, which means (H1) is accepted and (H0) is
rejected.
The Indirect Effect of Motivation on
Satisfaction and Intentions to Re-Stay Millennial Travelers at Glamping in Kintamani
Indirect effect is
the effect that occurs between exogenous variables and endogenous variables
mediated or mediated by other variables. The indirect effect in this study,
among others.
Table 9. Output Indirect Effect
Boostrapping Results
|
hypothesis |
Variable |
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDE|) |
P Value |
|
H4 |
Motivation – Satisfaction – Re-visit
intention |
0.251 |
0.243 |
0.079 |
3,160 |
0.002 |
Source:
Results of Primary Data Processing, 2023
Based on table 9,
the p-value and t-statistic variables obtained can be explained as follows.
The Influence of Motivation on Millennial
Travelers' Re-stay Intentions Through Satisfaction
In the results of
the hypothesis table 9, the p-value of the motivation variable on the intention
to return to millennial tourists through satisfaction is 0.002 with a value
less than significant, namely 0.05. In addition to having a significant p-value
(0.000 <0.05), the motivation variable for the intention to return to
millennial tourists through satisfaction also has a t-statistic value of 3.160
which means greater than (1.98), it can be concluded that tourist satisfaction
is able positively mediates the effect of motivation on tourists' re-visit
intention, which means (H1) is accepted and (H0) is rejected. This means that
if motivation increases, satisfaction also increases, this increase will also
affect the re-visit intention.
Consumer
satisfaction can establish a harmonious relationship between producers and
consumers which creates a good basis for repeat purchases, as well as word of
mouth recommendations(Kivetz & Samson, 2002).
CONCLUSION
The conclusion can be drawn
based on results of study are; (1) Motivation has a significant influence on the satisfaction of
millennial tourists staying at glamping in Kintamani, (2) motivation has a significant effect on
millennial tourists' re-visit intention at glamping in Kintamani District, (3) satisfaction has a
significant effect on millennial tourists' re-visit intention at glamping in
the District, and (4) watisfaction
has an indirect effect or is able to mediate between motivation and the intention
to stay back for millennial tourists significantly.
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Copyright holders:
I Made Swariga, Ni Made Sofia
Wijaya, Yayu Indrawati (2023)
First publication right:
Devotion - Journal of Research and Community
Service
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