“Determinants of Indonesian Gen Z’s purchase behavior on online travel platforms: Extending UTAUT model”

Understanding Gen Z’s purchase behavior in online travel agents is essential to effectively engage and meet the unique preferences of this generation, fostering long-term loyalty and satisfaction. Utilizing the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation, this study aims to analyze the impact of performance expectancy, effort expectancy, social influence, facilitating condition, and trust on purchase decision of flight tickets through online travel agent platforms. The data were collected through an online survey of 253 Gen Z users of online travel agent applications in Indonesia, such as Traveloka, Tiket.com, Pegipegi.com, Agoda, and Booking.com. The study employed PLS-SEM to test the hypotheses. The results indicate that performance expectancy, effort expectancy, social influence, and facilitating conditions influence trust (t-value 1.645, p-value < 0.05). Further, performance expectancy, effort expectancy, and facilitating conditions influence purchase decisions (t-value 1.645, p-value < 0.05). However, social influence does not significantly affect purchase decisions (t-value 1.041, p-value > 0.05). The analysis also shows that trust fully mediates the relationship between social influence and purchase decisions, while no mediating effect is identified in the relationship between effort expectancy and purchase decisions. By investigating the key factors contributing to Gen Z’s buying behavior in online travel agent platforms, this paper provides valuable insights for online travel businesses to effectively engage and cater to Gen Z’s unique needs and preferences.


INTRODUCTION
The use of digital travel platforms is prevalent worldwide.In Indonesia, online travel sales amounted to USD 10.2 billion in 2019 and were projected to reach USD 25 billion by 2025, signaling a substantial rise in online travel expenditure (Statista, 2023).The proliferation of online travel agents has contributed significantly to the country's tourism industry by allowing easier access for travelers to manage trips, including buying flight tickets.The ease, convenience, and flexibility these online platforms offer have attracted consumers (Mohd Suki & Mohd Suki, 2017).
With the increasing ease of internet access and the higher penetration of smartphone usage, a growing number of individuals utilizes online travel agents to facilitate booking travel tickets (Damanik et al., 2023), including Gen Z consumers.Gen Z population in Indonesia constitutes the largest consumer group, with approximately 68 million individuals (Utomo & Heriyanto, 2022).Gen Z exhibits a high level of engagement in travel-related activities, making them a significant seg-ment of travelers (Robinson & Schänzel, 2019).More than half of Gen Z individuals are eager to resume traveling, signaling their readiness to overcome the health restrictions imposed in recent years, highlighting the importance of understanding and catering to the preferences and needs of Gen Z travelers in the tourism industry (Meta Indonesia, 2022).
Compared to other consumer groups, Gen Z stands out with its pronounced tech-savviness and digital nativism (Damanik et al., 2023), relying predominantly on online sources like online travel websites and social media for comprehensive travel information (Kim et al., 2015).As a result, success in the tourism industry is related to technology and the ability to recognize and respond effectively to generational changes (Robinson & Schänzel, 2019).
Trust is the foundation of online transactions (Nugroho & Hati, 2020).Trust also determines the quality of service and reliability of online travel agents (Almunawar et al., 2022).Therefore, understanding the role of trust in Gen Z purchasing decision in online travel agents help capture the loyalty of this techsavvy and value-driven generation.For managers looking to optimize their strategies and meet Gen Z's specific preferences and needs, understanding how they make buying decisions is essential.This can help online travel agents improve their services, set themselves apart from the competition, and maintain an edge over them, allowing them to successfully target and engage Gen Z consumers and take advantage of Indonesia's developing digital environment.

LITERATURE REVIEW AND HYPOTHESES
The unified theory of acceptance and use of technology (UTAUT) is a conceptual framework designed to elucidate and forecast how individuals accept and utilize technology (Venkatesh et al., 2003).Its purpose is to offer a thorough comprehension of the various factors that impact the adoption and usage of technology.It is a model used to predict the user acceptance of technology by considering four key constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2016).The UTAUT theory has gained widespread recognition and reference due to its ability to accurately explain 70% of the variance in adoption behavior (Venkatesh et al., 2012) Effort expectancy refers to an individual's perception of their feelings and the extent of their connection when encountering a technology and its use within a system (Venkatesh et al., 2012).Effort expectancy plays a crucial role in the intentional adoption of new technology, as studies have consistently demonstrated a significant positive relationship between technology adoption and effort expectancy (Shaikh & Amin, 2023).Previous research has validated effort expectancy's effect on increasing customer trust (Cai et al., 2023;Namahoot & Jantasri, 2023) and their use behaviors (Hanaysha, 2022).
Social influence encompasses an individual's perception of the extent to which significant others believe they should utilize the new system (Venkatesh et al., 2012).Consumers' perceptions regarding the influential recommendations and support from important individuals, which can significantly affect their decision to adopt new technology, are critical in their decision-making behavior (  (Talwar et al., 2020).
Although previous studies have attempted to identify the factors that influence consumer behavior when purchasing flight tickets online, a limited amount of research in Indonesia attempts to identify the factors driving consumer purchase decisions through online travel agents, mainly focusing on Gen Z consumers.
This study aims to investigate how performance expectancy, effort expectancy, social influence, and facilitating conditions impact purchase decisions of flight tickets among Gen Z consumers in Indonesia.
Additionally, this paper seeks to determine the role of trust in directly and indirectly influencing their purchase decisions.Hence, the following hypotheses are proposed: H1: Performance expectancy positively influences trust.
H7: Social influence positively influences purchase decisions.
H8: Facilitating conditions positively influence purchase decisions.

METHODS
For this study, all the items utilized to evaluate each construct were derived from validated measures in previous research, employing a 5-point Likert scale.UTAUT framework consists of performance expectancy, effort expectancy, social influence, and facilitating condition constructs (Venkatesh et al., 2003).This study adapted three items each to measure performance expectancy, effort expectancy, social

RESULTS
Prior to hypotheses testing, an analysis of the measurement model was conducted to assess the reliability and validity of the constructs.First, reliability is examined by analyzing the composite reliability of the constructs by observing if the values of Rho_A and Rho_C are no less than the required thresholds of 0.7 (Benitez et al., 2020).Cronbach's alpha was also assessed to indicate internal consistency with the threshold of 0.7.Table 3 indicates that all composite reliability parameters satisfy the threshold of 0.7, indicating the construct reliability.Next, the study reviewed the convergent validity by observing if the factor loadings of each item to their corresponding construct are above the threshold of 0.708 (Hair et al., 2022).The value of the average variance extracted from each construct is checked to determine if they are no less than 0.5 (Hair et al., 2020).(Fornell & Larcker, 1981).In PLS-SEM, the suggested method to assess discriminant validity is using hetero-monotrait ratio of correlations method (HTMT) rather than the Fornell-Larcker criterion with recommended values of HTMT lower than 0.9 (Henseler et al., 2015).Table 4 presents all HTMT values below 0.9, indicating that discriminant validity has been established.
Multicollinearity is also not an issue in this study, allowing the analysis to proceed to hypotheses testing, as indicated by the VIF values of the independent variables, all of which are below 3 (Hair et al., 2021).
The study assessed the determinant coefficient (R 2 ) to measure the amount of variance in dependent variables predicted by the independent variables.As seen in Table 5, the R 2 of the dependent variables of trust and purchase decision are 0.447 and 0.486, respectively, indicating a moderate predictive power (Chin, 1998).
Effect size (f 2 ) indicates the changes in R 2 when an independent variable is removed from the model with the criteria of f 2 ≥ 0.02 indicating small effect size, f 2 ≥ 0.15 indicating medium effect size, and f 2 ≥ 0.35 indicating large effect size (Chin, 1998).Table 5 presents the small effect size for the majority paths, except facilitating condition → trust (medium effect), while no significant effect sizes for effort expectancy → trust and social influence → purchase decision.
This study also assesses predictive relevance or Q 2 , which indicates the level to which the model can predict the dependent variable based on the inde-pendent variables.Q 2 values above 0 show the model has predictive relevance (Shmueli et al., 2016) Mediation analysis was conducted to assess the mediating role of trust on the linkage between performance expectancy, effort expectancy, social influence, facilitating conditions, and purchase decisions.As presented in Table 6, the total effects of performance expectancy (β = 0.278, t-value = 4.520, p-value < 0.001), effort expectancy (β = 0.258, t-value = 4.292, p-value < 0.001), facilitating conditions (β = 0.218, t-value = 3.373, p-value < 0.001), and social influence (β = 0.119, t-value = 1.685, p-value < 0.05) on purchase decisions were significant.
However, with the inclusion of trust as the mediating variable, the impact of social influence on purchase decisions became insignificant (β = 0.069, t-value = 1.041, p-value > 0.05).The indirect effect of social influence on purchase decision through trust was found significant (β = 0.050, t-value = 1.931, p-value < 0.05), indicating that the relationship between social influence and purchase decision is fully mediated by trust.Note: p-value < 0.05, PE -performance expectancy, EE -effort expectancy, SI -social influence, FC -facilitating conditions, TR -trust, PD -purchase decision, n.s -not significant.The analysis shows no mediation effect on the relationship between effort expectancy and purchase decision since the indirect effect is insignificant (β = 0.018, t-value = 1.445, p-value > 0.05), while trust partially mediated the relationships between perceived expectancy (β = 0.055, t-value = 2.509, p-value < 0.05) and facilitating conditions (β = 0.030, t-value = 2.618, p-value < 0.05) with purchase decisions.Overall, Tables 7 and 8 and Figure 1 shows the results of hypotheses testing.

DISCUSSION
This study has revealed that when Gen Z perceives high levels of performance expectancy, effort expectancy, social influence, and facilitating conditions, they are more likely to trust the online purchasing system.The results support previous findings that performance expectancy positively influences trust (Cai et  The following reasons are suggested why performance expectancy, effort expectancy, social influence, and facilitating conditions have a positive influence on trust.First, customers who are not familiar with online technology may perceive buying flight tickets online as posing some risks because they may not be familiar with the security measures that are in place to protect their personal and financial information when making online purchases.Second, they may also be concerned about the possibility of technical errors that could result in the loss of their money or the inability to purchase the desired ticket. Performance expectancy is associated with how much customers believe that online flight ticket purchasing applications benefit them.This study shows that performance expectancy positively influences purchase behavior, which supports Hanaysha (2022) and Slade et al. (2015).Purchasing through online applications has been shown to provide customers advantages over traditional methods.Such advantages include competitive prices, effective and efficient search for choice, and time-cost savings.When these advantages are perceived as greater than the risks, customers perceive these values to promote trust in the system, eventually leading to the purchase.
Similarly, effort expectancy is related to the level of ease and simplicity that customer perceives in using the system.The analysis has revealed that effort expectancy affects purchase decisions, which aligns with Hanaysha (2022).Online purchase today is different from that of ten or twenty years ago.The proliferation of smartphones has shifted the focus to a customer-centric online purchase system.Mobile commerce through mobile marketplaces or appbased online travel agents is now prominent.It delivers timely and seamless technology, allowing users to purchase without prior knowledge of specific internet know-how.As a result, customers perceived they require minimal effort to engage with the technology, which increases trust and reduces risk.Therefore, individuals who perceive purchasing flight tickets through online applications as not demanding are more inclined to utilize them (Chaouali et al., 2016).This leads to customer trust and supports them to purchase because of their simplicity and practicality.
Further, social influence relates to social factors, such as recommendations from friends and family, on the customer's purchasing behavior.However, the results have shown no effect of social purchase decisions, which contrasts with Slade et al. (2015).Such  (Hajli, 2020).Using an online travel agent platform to buy flight tickets requires Gen Z to be assured that the application will bring value to them.

CONCLUSION
The present study analyzed the impact of performance expectancy, effort expectancy, social influence, and facilitating conditions on increasing Gen Z consumer group's trust, thus motivating their purchase behavior of flight tickets using online travel platforms.The results of this study show that performance expectancy, effort expectancy, social influence, and facilitating conditions positively affect trust leading to purchase decisions.The results also highlight the direct effect of performance expectancy, effort expectancy, and facilitating conditions in influencing purchase decisions.In contrast, no effect is found of the role of social influence on purchase decisions.Facilitating condition is revealed to be the strong predictor of trust, while performance expectancy is the weakest, as shown by their path coefficients and effect sizes.Furthermore, Gen Z's trust is critical in determining their purchase decisions.
The findings of this study hold important implications for managers in the online travel agent and travel retail industry.The most significant factor in determining whether Gen Z will buy flight tickets using online travel agent applications through increased trust in the system is their perceived availability of support and resources, followed by expectation of its performance, social influence, and their belief in their own abilities to use it.These results could assist decision-makers in online travel agent applications in constructing a reliable and secure online purchasing infrastructure and ensuring that it can provide the utmost value for the users through a seamless and secure online buying experience.
There are shortcomings in the present study that may affect the generalizability of the findings.First, the study was conducted in an emerging country setting.Information technology infrastructure is excellent only in large cities.Further studies should consider these differences as moderating variables to determine if the differences are prominent to explore deeper understanding.Second, the sampling technique can create a biased view.Future studies should differentiate only those less exposed to the investigated system to distinguish from repeating users.

H1
Performance expectancy positively influences trust Accepted H2 Effort expectancy positively influences trust Accepted H3 Social influence positively influences trust Accepted H4 Facilitating conditions positively influence trust Accepted H5 Performance expectancy positively influences purchase decision Accepted H6 Effort expectancy positively influences purchase decision Accepted H7 Social influence positively influences purchase decision Rejected H8 Facilitating conditions positively influence purchase decision Accepted H9 Trust positively influences purchase decision AcceptedNote: p-value < 0.05, n.s -not significant.

Figure 1 .
Figure 1.Conceptual model with path analysis results

Table 1 .
Ng (2013)al.(2020)litatingconditionsfromJeonet al. (2019).The study employed ten items to measure trust fromSvare et al. (2020)and four items fromNg (2013)to measure purchase decisions.The study utilized a purposive sampling technique.To collect the required data, an online survey was conducted.Screening questions were used to ensure appropriate samples.The study collected 253 valid responses.As shown in Table1, the respondents involved in this study were 36.4% male (n = 92) and 63.6% female (n = 161) from five different regions of Jakarta.Demographic profile of respondents

Table 3
presents the confirmatory composite analysis (CCA) results showing that all factor loadings and AVEs are above the threshold.As a result, the items in this study are reliable and valid, with t-values above 1.645 and p-values below 0.05.Discriminant validity is defined as to what extent a variable is distinct from one another

Table 8 .
(Alotaibi et al., 2019;Le et al., 2022)).Customers with higher trust in brands tend to buy more(Comegys et al., 2009).Trust creates confidence, interpersonal usefulness, the attractiveness of the brand, and the desire for convenience, which positively affect buying decision-making(Alotaibi et al., 2019;Le et al., 2022).The consumer purchase decision process is impacted by the level of trust felt by consumers