HOW TO MAKE DECISIONS OF GEN Z TRAVELERS? EXPLORING THE INFLUENCE OF PERCEIVED RISK, ONLINE REVIEW, AND PRICE ON TRAVEL IN BALI

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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 (J. Kim et al., 2021) The tourism business has evolved as a tool for generating significant economic advantages. Many of the judgments made by travelers are intrinsically complicated (Dellaert et al., 2014). Making judgments about the many leisure and travel services to be purchased while on vacation is a difficult process. Consumers' risk perceptions in light of the present COVID-19 public health crisis, and more crucially, how such views could affect travelers' post-crisis recovery travel patterns (Matiza, 2022). The recent worldwide COVID-19 epidemic has threatened people's health and lives, interrupted daily life, impacted the economy, and halted tourism (Terzić et al., 2022). The progression of the COVID-19 pandemic in Bali has continued to improve, as seen by cumulative data up to April 30, 2022. The rational human paradigm and microeconomic theory may be linked back to behavioral assumptions on tourist decisions used in mainstream travel behavior models (Avineri & Ben-Elia, 2015). According to (Baykal, 2020) study, Gen Z is the most developed demographic and has been demonstrated to be the most influential influencer in purchase decisions made by older generation family members. Baby Boomers, Generation Xers, and Millennials value input from their Generation Z children. Individuals born between 1995 and early 2012 are considered Gen Z. (Barhate & Dirani, 2022).
We analyze Gen Z in this study because they have distinct beliefs and ideals from Gen X or Gen Y. And Generation Z is a prospective client category for premium businesses. As a result, it is stated that understanding Gen Z consumer behavior in terms of luxury consumption is critical for both the academic and tourist industries.
Risk is a complicated notion, and identifying it necessitates a sophisticated technique that is frequently misinterpreted (Girlando et al., 2021). According to (Terzić et al., 2022) prospective travelers consider multiple factors before embarking on a trip, including risk, because an earlier study did not include tourist decision behavior among various travel possibilities. A number of studies disclose the underlying pathways between risk perception and tourist decision-making(J. Kim et al., 2021). A traveler's behavior is strongly influenced by risk, as risk encompasses several factors, such as health and finances. During post-Covid 19, health is a major worry (Terzić et al., 2022). (Gentilviso & Aikat, 2019) argue that every post-millennial or so-called Gen Z generation is strongly connected to social media; therefore, they examine a social media assessment of a place before visiting it. Numerous research studies have recommended standardized, optimal threshold values for the appropriate categorization of review aids (Park et al., 2022). Travelers read peer-written advice on review websites (Wang & Li, 2019). Consistent reviews are deemed more beneficial since they set standards. The manipulation of informational influence takes the form of reviewer expertise, where evaluations written by professionals are deemed more valuable (Book & Tanford, 2020). Online reviews / e-WOM Argument Quality provide the possibility to assess consumer happiness and the causes of discontent(X. Xu, 2018). Prices play an essential part in any business, including the tourist industry(Dominique-Ferreira & Antunes, 2020). According to research, Price and quality of an item or service are strongly correlated, with a high price indicating superior quality (D. Xu et al., 2022). Pricing options show a high priority on spending money on holidays (Giroux et al., 2021).
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 (Girlando et al., 2021). In tourism research, the study of risk in travel decisions is dominated by two approaches. One type of study investigates actual destination selections using secondary data from visitor arrivals (Karl, 2016). Nevertheless, (Khan et al., 2018) suggest that A situation where the only possible result is a loss of a specific amount is not a risk. Travel motivation, susceptibility to perceived risks, and travel limitations all have a significant impact on individual travel behavior. Risk is an important consideration in the choice to travel because of its ability to influence decisions. Risk is the anticipated likelihood that something negative may happen during a certain purchase transaction, demonstrating customers' uncertainty about the results of their selections (Eun Lee & Stoel, 2014). The risk was initially articulated in consumer purchases as physical, financial, psychological, social, and temporal concerns, and the same terminology was subsequently used for travel purchases. These risks in travel research are the risks posed by visitors' enjoyment before and after a journey. Previous studies have included health, terrorism, and political instability as three additional elements of perceived risk. Then, using factor analysis to identify nine risk dimensions due to human, service quality, financial, socio-psychological, natural catastrophes, automotive accidents, food safety, and weather issues, thorough research was undertaken to assess perceived travel hazards (Khan et al., 2018).

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 (J. Kim et al., 2021). Tourists will alter their trip plans based on their travel decisions if they believe a situation to be unacceptable or undesirable (Karl, 2016). Risk perception may influence independent travel decisions (Hyde, 2008). Physical, economic, and socio-psychological risks are considered by potential tourists (Terzić et al., 2022). The physical risk in question is the physical condition of the prospective traveler who will visit the place or tourist destination he is going to (Khan et al., 2018); additionally, the socio-psychological condition and the financial condition of the prospective traveler have an effect on the attractiveness of the location to be visited. visited.

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 (Wen et al., 2020). In addition, prospective travelers believe that they require information to make the best travel decisions (Yadav et al., 2023), and according to (Akdim, 2021), Due to the perception of other travelers as more trustworthy than other sources of information and the creation of more realistic expectations, visitors prefer to depend on internet reviews for unbiased information. This is also tied to the development of tourists utilizing social media so that they can write reviews based on their personal experiences (Vukolic et al., 2022), which other travelers can use as a reference.

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 (Moon et al., 2022). Extrinsic cues that consumers can utilize in conjunction with intrinsic signals such as price, brand name, and descriptions of service facilities, and relevant visual information are customer reviews (Wen et al., 2020). As we all know, the Z generation is the generation that was born and raised with technology; hence, the Z generation has a close relationship with technology, particularly smartphones. Digital media such as site reviews and social media are more appealing to Generation Z than traditional media such as television, radio, and newspapers (Gentilviso & Aikat, 2019). According to (Moon et al., 2022), the experiences shared by one person can be felt by others without being bound by geography or time, allowing them to reflect the experiences of others. (Park et al., 2022) implying that customers assess both the review's text and accompanying star rating when they acquire information about internet reviews.

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 (Wen et al., 2020). Price may convey information about a product's quality and worth. If people view the pricing as fair, they are more inclined to purchase the product (Wen et al., 2020). Price has long been seen as a significant indicator of consumer purchases are made. Prices may tell customers about the value and quality of products and services. Pricing, in the eyes of the consumer, is the sum of money necessary to purchase a good or service. Many consumers view price as an indicator of quality, reflecting the adage "you get what you pay for." Consumers do not always recall a product's exact price. Instead, they encode pricing in a manner relevant to them (Lien et al., 2015). Consumers typically compare reference prices (the price offered by other vendors) to objective pricing (the price provided by current vendors) while buying online in order to create an opinion on price (H.-W. Kim et al., 2012) As a heuristic indicator, price is easier to perceive than quality (Yoon et al., 2014). The association between distance/travel experience and review qualities reveals the price impact that a high price suggests excellent quality(D. Xu et al., 2022). Price is a complex concept that strongly influences consumer purchase decisions. Customers' choices are influenced by price both as a restriction and as a product characteristic. Price serves as a constraint, representing the compromise customers must make in order to obtain a good or service. The cost as an attribute represents the amount of quality that people expect (Dominique-Ferreira & Antunes, 2020).

The Relationship between Price and Travel Decision
Each potential traveler assesses the potential benefits against the potential costs while making travel selections (Terzić et al., 2022). When evaluating pricing, prospective travelers frequently utilize reference prices, which are established based on prior experience or price ranges across distribution channels (Piga & Melis, 2021). Because they are aware of pricing variations across distribution channels, prospective passengers are more likely to seek out discounts(J. Kim & Lee, 2020). Pricing in tourism is a difficult phenomenon due to its characteristics and aspects, including perishable items, intense investment, high fixed costs, tourist traits and varying price sensitivity, product uniqueness, market competitiveness intensity, and uncertain demand(Dominique-Ferreira & Antunes, 2020). Price is typically viewed as an incentive that drives consumers to make online travel decisions (Wen et al., 2020). Price discounts have the potential to enhance the usefulness of purchasing transactions (Kuo & Nakhata, 2016). It has been demonstrated that pricing has a favorable and significant effect on consumer perceptions of product quality (Shin et al., 2018).

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.

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. 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   (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. 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.  (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. 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.  (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.