HOW
TO MAKE DECISIONS OF GEN Z TRAVELERS? EXPLORING THE INFLUENCE OF PERCEIVED
RISK, ONLINE REVIEW, AND PRICE ON TRAVEL IN BALI
Putu Ayu Aryasih*,
Ida Bagus Putu Puja, Made Krishna Wira Gunawan, Brahmantya Agung Priharjuna, I
Made Mahendra Putra
Tour and Travel Business Study Program, Politeknik Pariwisata Bali, Indonesia
Email: [email protected]*, [email protected], [email protected], [email protected], [email protected]
|
KEYWORDS perceived risk;
online review; price; travel decision; generation z Received: February 13, 2023 Revision: March 15, 2023 Accepted: May 8, 2023 Published Online: June 26, 2023 |
ABSTRACT This study aims to examine how to make decisions of Gen Z influence
of the current COVID-19 public health crisis on consumer risk perceptions,
and what is more significant is how risk perceptions have the potential to
influence tourist post-crisis recovery travel behavior. The research sample had 250 respondents. Baby
Boomers, Gen Xers, and Millennials value input from their Gen Z kids. Gen Z is
an individual from the most recent generation born between 1995 and early
2012. Multiple linear regression analysis was employed in this quantitative
study. The result was: (1) Perceived
Risk has no effect on Gen Z Travel Decision which a coefficient value is
0.061 (2) e-WOM Argument Quality (Online Review) has significantly influenced
Gen Z Travel Decision which a coefficient value of 0.394 (3) Price has no
effect on Gen Z Travel decision which the coefficient value is 0.375. The
goal of the growth strategy is to increase sales, assets, earnings, or a
combination of the three. This can be done through reducing costs, creating new
items, improving the quality of existing products or services, or gaining
access to a larger market. That approach may be used in other places that
could become popular tourist destinations. |
INTRODUCTION
In the
implementation of tourism, the tourism industry is a collection of
interconnected tourism enterprises that provide goods and/or services to meet
the demands of visitors. The tourism sector contributes significantly to
economic development and progress in many nations by providing employment,
raising revenue, and upgrading local infrastructure
Risk is a
complicated notion, and identifying it necessitates a sophisticated technique
that is frequently misinterpreted
This
research looks at how perceived risk, online review, and price might impact
travel decisions. The primary goal of this research is to determine the impact
of perceived risk, online review, and price on Generation Z travel decisions in
Bali. Bali is an Indonesian island with an area of about 5,634.40 km2.
Administratively, this province is split into nine districts and cities, namely
the regencies of Buleleng, Jembrana, Tabanan, Bangli, Badung, Gianyar,
Klungkung, Karangasem, and Denpasar, as well as one municipality, Denpasar, as
the capital. Our research focuses on Bali since it is one of Indonesia's
tourist markets that attract both local and foreign visitors. The number of
international visitors reached 181,625 visits in June 2022, while the number of
local tourists visited was 246,504 visits. Because the majority of visitors and
locals on the island of Bali are Gen Z, we picked Bali as our study location.
This study provides several contributions. First, we enhance the variable
notion in light of prior study constraints. As a result, we included research
on the varied aspects of perceived risk, online review, and price. Second, our
study demonstrates that perceived risk, online review, and price impact Gen Z
travel decisions. Finally, this study includes age and gender factors. This has
the potential to significantly affect Gen Z's travel selections. Fourth, this study
adds to the body of knowledge on Gen Z individuals, both directly and
indirectly impacting their travel decisions. Finally, the findings of risk,
review, and pricing research may be followed up on and inferred by businesses
to enhance travel selections.
The next portion
of the article describes and explores the conceptual impacts of perceived risk,
online review, and price on the travel decisions of Generation Z. The
conceptual framework discussion can be expanded into an article hypothesis.
This article discusses the study methods featured in the research results in
parts three and four. Part five is the last section, which provides the
research's results as well as its implications and limits.
Perceived Risk
Risk is a
multidimensional notion, and its detection needs a complicated method that is
frequently misinterpreted. As a result, judgments are made based on narrow
views rather than the whole worth and significance of what risk is; hence, its
management is flawed
The Relationship
between Risk and Travel Decisions
Risk is a
multidimensional notion, and its detection needs a complicated method that is
frequently misinterpreted. The majority of study has mostly concentrated on how
tourists' perceptions of risk affect their travel intentions, paying little
attention to how it affects travelers' choices for travel alternatives within a
set of possibilities
Online Review
Online reviews are
very diagnostic and can be called a "high-coverage cue," which has a
decisive effect on consumers' ordering decisions, whereas brand familiarity and
pricing are low-coverage and low-scope cues, respectively
The Relationship
between Online Reviews and Travel Decisions
The world's
tourist locations have evolved rapidly, and the managers of these sites are
keenly aware of technological advancements; thus, they are focusing on
smartphone innovations
Price
Price is the sum
of money consumers must spend to obtain a good or service. It stands for a cost
or financial sacrifice. Higher costs imply larger sacrifices, which may make
consumers less willing to pay them
The Relationship
between Price and Travel Decision
Each
potential traveler assesses the potential benefits against the potential costs
while making travel selections
Figure
1. Conceptual Framework
Hypothesis 1: Perceived risk positively
affects travel decision.
Hypothesis 2: Online reviews positively
affects travel decision.
Hypothesis 3: Price positively affects the
travel decision.
RESEARCH
METHOD
The
research sample had 250 respondents. Baby Boomers, Gen Xers, and Millennials
value input from their Gen Z kids. Gen Z is an individual from the most recent
generation born between 1995 and early 2012. Multiple linear regression
analysis was employed in this quantitative study. From Table 1, the
characteristics of Generation Z tourists who make travel decision-making to
Bali are presented in the tabulation.
Table 1. Characteristics of
Generation Z Travelers who make travel decision-making to Bali
|
No |
Characteristics |
Classifications |
Total of Respondents (Person) |
Percentage of Respondents (%) |
|
1 |
Gender |
Man |
135 |
54% |
|
Women |
115 |
46% |
||
|
Total |
250 |
100% |
||
|
2 |
Age |
10-19 Years
Old |
60 |
24% |
|
20-27 Years
Old |
190 |
76% |
||
|
Total |
250 |
100% |
||
|
3 |
Nationality |
Indonesian |
244 |
97,6% |
|
American |
3 |
1,2% |
||
|
Belgian |
1 |
0,4% |
||
|
Australian |
1 |
0,4% |
||
|
Malaysian |
1 |
0,4% |
||
|
Total |
250 |
100% |
||
Source: Survey
Data (2023)
RESULTS AND
DISCUSSION
Based on
gender, men dominate as the majority of Generation Z tourists who make travel
decision-making to Bali with a percentage of 54%. Based on age, Generation Z
tourists aged 20-27 years are more dominant in travel decision-making to Bali
with a percentage of 76%. Based on the origin of tourists, Generation Z
tourists who make travel decision-making to Bali are dominated by tourists from
Indonesia with a percentage of 97.6% (with a record of tourists from outside
Bali).
Figure 2. Outer Model
The outer
model demonstrates how the latent variable to be measured is represented by the
manifest variable, also known as the observed variable. The relationship
between latent variables and their indicators is defined by the analysis of
this model.
Validity Test
Table
2. Validity Test
|
E-WOM Argument Quality (X2) |
Gender |
Price (X3) |
Perceived Risk (X1) |
Travel Decision (Y) |
Age |
|
|
EWAQ.1 |
0.777 |
|
|
|
|
|
|
EWAQ.2 |
0.797 |
|
|
|
|
|
|
EWAQ.3 |
0.753 |
|
|
|
|
|
|
EWAQ.4 |
0.759 |
|
|
|
|
|
|
Gender |
|
1.000 |
|
|
|
|
|
P.1 |
|
|
0.770 |
|
|
|
|
P.2 |
|
|
0.745 |
|
|
|
|
P.3 |
|
|
0.769 |
|
|
|
|
P.4 |
|
|
0.734 |
|
|
|
|
PR.2 |
|
|
|
0.729 |
|
|
|
PR.4 |
|
|
|
0.829 |
|
|
|
PR.5 |
|
|
|
0.826 |
|
|
|
TD.1 |
|
|
|
|
0.858 |
|
|
TD.4 |
|
|
|
|
0.787 |
|
|
TD.5 |
|
|
|
|
0.761 |
|
|
Age |
|
|
|
|
|
1.000 |
Source:
Survey Data (2023)
A validity
test was performed during the course of this research to determine whether each
item offered in the form of a questionnaire was capable of representing the
variables evaluated in this case, namely the effect of perceived risk, online
review, and price on travel decisions. Based on the presentation of the table
data above, it is known that each of the research variables has many outer
loadings of more than 0.7. The data above has no indicators that show less than
0.7 so all items are valid. Thus, the three variables evaluated in accordance
with the questionnaire provided in this study are valid and have a direct
effect on the intention of returning tourists.
Descriminant
Validity, Composite Reliability, Cronbach’s Alpha
Table 3. Descriminant validity,
composite reliability, cronbach’s alpha
|
|
Cronbach's Alpha |
rho_A |
Composite Reliability |
Average Variance Extracted (AVE) |
|
EWAQ |
0.773 |
0.774 |
0.855 |
0.595 |
|
Gender |
1.000 |
1.000 |
1.000 |
1.000 |
|
P |
0.748 |
0.748 |
0.841 |
0.569 |
|
PR |
0.708 |
0.711 |
0.838 |
0.633 |
|
TD |
0.724 |
0.737 |
0.845 |
0.645 |
|
Age |
1.000 |
1.000 |
1.000 |
1.000 |
Source:
Survey Data (2023)
Based on
the data above, the AVE value for all variables above 0.5 shows that the
Perceived Risk variable is > 0.5 or equal to 0.633, for the Online Review
variable value > 0.5 or 0.595, for the Price variable value > 0, 5 or
0.569 and for the Travel Decision variable > 0.5 or 0.645 which means that
all variables have a good Discriminant Validity value. Composite reliability is
the part that is used to test the reliability value of the variable indicator,
the composite reliability value for all variables above 0.7, it can be seen
that the variable Perceived Risk > 0.7 or equal to 0.838, for that value for
the Online Review variable > 0.7 or equal to 0.855, for the value of the
variable Price > 0.7 or 0.841 and for the variable Travel Decision > 0.7
or 0.845 which means that all variables are reliable. Furthermore, utilizing
Cronbach's alpha will help the reliability test with composite reliability.
Cronbach's alpha value for all variables above is 0.7, it can be seen that the
Perceived Risk variable is > 0.7 or equal to 0.708, for the Online Review
Variable value is > 0.7 or equal to 0.733, for the value of the variable
Price > 0.7 or 0.748 and for the variable Travel Decision > 0.7 or 0.724
which means that all variables are reliable.
Discriminant Validity
Table
4. Discriminant Validity
|
E-WOM Argument Quality (X2) |
Gender |
Price (X3) |
Perceived Risk (X1) |
Travel Decision (Y) |
Age |
|
|
EWAQ |
|
|
|
|
|
|
|
Gender |
0.124 |
|
|
|
|
|
|
P |
0.604 |
0.117 |
|
|
|
|
|
PR |
0.381 |
0.217 |
0.521 |
|
|
|
|
TD |
0.773 |
0.074 |
0.772 |
0.429 |
|
|
|
Age |
0.057 |
0.045 |
0.065 |
0.058 |
0.081 |
|
Source:
Survey Data (2023)
The discriminant value of validity is less than
0.9 which means that all items are valid. Based on the data above, it shows
that the discriminant validity value is less than 0.9. This can be seen from
the Travel Decision to Perceived Risk variable of 0.429, to Online Review of
0.773 and Price of 0.772. Then the value of the Perceived Risk to Online Review
variable is 0.381 and for the Price variable is 0.521. The value of the Price
to Online Review variable is 0.604 which means that all items are valid.
Multicollinearity Test
Table 5. Multicollinearity Test
|
E-WOM Argument Quality (X2) |
Gender |
Price (X3) |
Perceived Risk (X1) |
Travel Decision (Y) |
Age |
|
|
EWAQ |
|
|
|
|
1.289 |
|
|
Gender |
|
|
|
|
1.039 |
|
|
P |
|
|
|
|
1.390 |
|
|
PR |
|
|
|
|
1.221 |
|
|
TD |
|
|
|
|
|
|
|
Age |
|
|
|
|
1.007 |
|
Source:
Survey Data (2023)
Based on
the data above, it can be seen that the VIF value of each variable is less than
3 so that it can be said that the model is free from multicollinearity
symptoms. Testing for multicollinearity symptoms is done by looking at the
value of VIF (Variance Inflation Factor).
Figure 3. Inner model
The power
of estimate between latent variables or constructs is demonstrated by the inner
model. The findings of the path coefficient test, goodness of fit test, and
hypothesis testing are explained in this paper. Using PLS, evaluate the
structural model.
Direct Influence Path Analysis (Path Coefficient)
Table 6. Direct Influence Path Analysis
|
Original Sample (O) |
Sample Mean (M) |
Standard Deviation (STDEV) |
T Statistics (|O/STDEV|) |
P Values |
|
|
EWAQ
-> TD |
0.394 |
0.390 |
0.081 |
4.843 |
0.000 |
|
Gender
-> TD |
-0.019 |
-0.017 |
0.042 |
0.440 |
0.660 |
|
P
-> TD |
0.375 |
0.373 |
0.085 |
4.418 |
0.000 |
|
PR
-> TD |
0.061 |
0.067 |
0.062 |
0.987 |
0.324 |
|
Age
-> TD |
-0.092 |
-0.090 |
0.045 |
2.027 |
0.043 |
Source:
Survey Data (2023)
With a
value of 4,843, the Online Review variable's impact on travel decisions can be
seen to have the most impact. The effect of the Price variable on travel
decisions comes in at number two, with a value of 4,418. The salary variable's
impact on the motivation of 4724 people has the third-largest impact. The
fourth biggest influence is the Age variable on Travel Decisions of 2.027. Then
the smallest is the influence of the Gender variable on the Travel Decision of
0.440. The effectiveness of estimating between latent variables or constructs
is demonstrated by the inner model. The findings of the path coefficient test,
goodness of fit test, and hypothesis testing are explained in this study. PLS
analysis of the structural model.
1)
The sig value on E-WOM Argument Quality,
Price, and Age on Travel Decision is less than 0.05 which means it influences
Travel Decision.
2)
The sig value for Gender and Perceived
Risk for Travel Decision is more than 0.05 which means it has no effect on
Travel Decision.
R Square
Table 7. R
Square
|
|
R Square |
R Square Adjusted |
|
Travel Decision |
0.468 |
0.457 |
Source:
Survey Data (2023)
R square
is used to see the effect of the Travel Decision variable, it is known that the
influence of the Travel Decision variable is 0.468 and shows that the Adjusted
Value for RI is 0.457 which means that 45.7% of the independent variables
explain the Travel Decision.
CONCLUSION
This study explores and
investigates the impact of perceived risk, online reviews, and price on future
travel decisions. The primary goal of this study is to look into the
relationship between perceived risk, online reviews, and price in relation to
Generation Z's travel decisions to Bali. The findings back up one of the three
theories proposed to study the link. The findings emphasize the significance of
including all essential independent criteria, such as e-WOM Argument Quality or
Online Review, into a single framework for studying Travel Decision-Making in
Generation Z. This research also demonstrates that perceived risk and price
have no substantial influence on travel decisions of Generation Z. In the
meantime, ratings on websites or review applications have a substantial impact
on the travel decisions of Generation Z. This indicates that the higher the
rating of a tourist destination in a review application or review website, the
more indicates that a location with a positive review rating will be more
appealing to Gen Z travelers. Because Generation Z visitors do not evaluate the
perceived risk of a tourist destination but rather the price they pay, they
compare what they receive to the amount they pay.
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Copyright holders:
Putu Ayu Aryasih, Ida Bagus
Putu Puja, Made Krishna Wira Gunawan, Brahmantya Agung Priharjuna, I Made
Mahendra Putra (2023)
First publication right:
Devotion - Journal of Research and Community
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