INVESTIGATING DRIVERS OF DIGITAL BANKING ADOPTION OF GEN Z IN INDONESIA

Despite the fact that digital banks are the most recent financial service that provides online banking services without direct contact with customers, they have yet to transform the whole financial sector. This study was done in a quantitative manner utilizing primary data to investigate drivers of digital banking adoption by Gen Z using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) approach. There was a total of 232 respondents participating in this study. The data was analyzed using Structural Equation Modeling - Partial Least Square (SEM-PLS). The results show that social influence, facilitating conditions, hedonic motivations, perceived value, and habit positively influence the Gen Z’s behavioral intention to use the digital banks; while performance expectancy and effort expectancy show the opposite influence. Further, the behavioral intention also positively influences the Gen Z’s use behavior of digital banks. To promote digital bank customer commitment and satisfaction, digital banking service providers are suggested to increase the value of benefits outweighing the costs borne by users, as well as to improve service quality in terms of user friendliness.


INTRODUCTION
Innovation, technology, and developments in information and communication technologies have an impact on all aspects of human existence since they can transform the economy (Setiawan, Nugraha, Irawan, Nathan, & Zoltan, 2021;Suzianti, Haqqi, & Fathia, 2021).The financial sector has been unable to avoid massive technological revolutions since there are more advances in digital technologies emerged, including artificial intelligence (AI), blockchain, and the Internet of Things (IoT) (UNCTAD, 2021).These technological advancements have been used as innovations in credit decisioning, risk management, fraud prevention, trading, and banking automation (Bachinskiy, 2019) to increase efficiency, profits, and market share (Leong & Sung, 2018).
Financial technology (fintech) is one of the emerging forms of financial technological advancement.It is defined as a type of banking and financial services through the use of modern technological breakthroughs, particularly those powered by software and algorithms that enable individuals to make financial transactions easily and quickly (Ozili, 2018;Setiawan et al., 2021).Considering that Indonesian society is open to the development of fintech, this industry has the potential to grow very quickly (Bank Indonesia, 2019;Setiawan et al., 2021).Additionally, the pandemic has encouraged internet users to use new online services more frequently (e-Conomy SEA, 2021).There were approximately 81.87 million smartphone users in Indonesia in 2020, making it the world's fourth-largest smartphone market, after China, India, and the United States (MEDICI, 2021).
The banking industry introduces digital banks in order to compete with the fintech in this technological era.The digital banksknown as an internet-only bank -have few or no branches and perform the majority of their operations online utilizing personal computers or mobile devices (Saif, Hussin, Husin, Alwadain, & Chakraborty, 2022;Yoon & Lim, 2020).Thus, users can access financial services provided by the digital banks anytime and anywhere (Windasari, Kusumawati, Larasati, & Amelia, 2022).However, the digital banking adoption in Indonesia is still underutilized.A survey found that the ownership of digital banking applications in Indonesia was only 25% in 2021 and it was expected to reach 31% in 2022 (Laycook, 2021).Similarly, Populix (2022) predicted that the digital bank users might only reach 33% by 2022.Compared to the level of smartphone ownership in Indonesia which reached 76.26% in 2021(Statista, 2022) and the financial inclusion rate reaching 85.10% by 2022 (OJK, 2022), the level of digital banking adoption is still relatively low.This demonstrates that many Indonesians have not used the digital banking services to make financial transactions.
Generation Z (Gen Z)a group of individuals born in 1996-2010is obviously feeling the ongoing technology development, making them the most adept generation in operating the technology (Djafarova & Foots, 2022;Mahapatra, Bhullar, & Gupta, 2022;Oktavendi & Mu'ammal, 2022).Compared to previous generations, Gen Z has different financial needs and expectations, as evidenced by the banking demands to deliver a better banking experience and service quality in addition to fees and interest rate considerations (Kaabachi, Mrad, & Barreto, 2022).Understanding consumer behavior is one of the factors that must be considered in a business.For these reasons, examining the intentions and behavior of Gen Z is intriguing (Chayomchai, 2021).
Numerous previously published studies, one of which is through the Unified Theory of Acceptance and Use of Technology (UTAUT) approach and its extensions, provide insights into the context of consumer behavior when utilizing the digital financial services.The UTAUT is a theory that combines several theoretical models to produce seven independent constructs, namely performance expectancy, effort expectancy, social influence, facilitating conditions, computer selfefficacy, anxiety, and attitudes towards technology use (Venkatesh, Morris, Davis, & Davis, 2003).The UTAUT demonstrates a variety of approaches in which each independent construct influences the dependent construct, especially on the consumer behavior and intention to use the digital financial services (Abrahão, Moriguchi, & Andrade, 2016;Abu-Taieh, AlHadid, Abu-Tayeh, Masa'deh, Alkhawaldeh, Khwaldeh, & Alrowwad, 2022;Boonsiritomachai & Pitchayadejanant, 2019;Kwateng, Atiemo, & Appiah, 2019;Tan & Lau, 2016).Age limits within the Gen Z group are an appropriate subject for analysis using the UTAUT approach which emphasizes the role of age on individual intentions to adopt new technologies (Venkatesh et al., 2003).This present study aims to identify drivers of digital banking adoption of Gen Z in Indonesia using the UTAUT 2 approach focusing on seven independent variables.The results of this study are expected to serve as a guide, particularly for the banks as financial service providers and other interested parties, including the government, in determining the establishment of financial service policies in order to increase the degree of financial inclusion in Indonesia.

THEORETICAL FRAMEWORK AND EMPIRICAL STUDIES
The fintech has been attracting the public's attention.It can be defined as any innovative idea that can improve 5h3 financial services using technological alternatives based on various types of business situations, while the idea can also be directed at reforming business models to the business itself (Leong & Sung, 2018).The fintech aims to promote the financial inclusion of those who are currently not included in the conventional financial system and to provide financial services to financially excluded individuals according to their needs (Ezzahid & Elouaourti, 2021).As part of financial digital technology, the fintech had allowed its users to avoid going to bank branches physically, using ATMs, or making cash payments during the COVID-19 pandemic (Kakinuma, 2022).On the other hand, fintech services combined with technology have also improved efficiency, convenience, and security for its users (Moreira-Santos, Au-Yong-Oliveira, & Palma-Moreira, 2022).
One of the latest banking breakthroughs is the digital banks or internet-only banks, which uses electronic means to provide services to the public while boosting the financial inclusion.In the digital banks, all transactions are completed online, either through a certain banking application or website.The customers can use an online self-service system to search, select, and use financial products / services (Lee & Kim, 2020).The renewed interest of fintech has also resulted in changes in the customer demand and behavior, supporting the development of digital banks as well as the technological advances and the digital economy (Saif et al., 2022).Additionally, affordable prices, quick account opening procedures, and easy access to account management draw a lot of customers to this form of digital banking service (Kaabachi et al., 2022).
The Unified Theory of Acceptance and Use of Technology (UTAUT) has its foundation on eight prior theories, including the theory of reasoned action (TRA), technology acceptance model (TAM or TAM 2), motivation model, theory of planned behavior (TPB), the combination of TAM and TPB, model of personal computer utilization (MPU), innovation diffusion theory (IDT), and social cognitive theory (ICS) (Venkatesh et al., 2003).Based on the combination of these theories, there are four main constructs in the form of performance expectancy, effort expectancy, social influence, and facilitating conditions that affect the behavioral intention (as the dependent variable), with moderators consisting of gender, age, experience, and willingness to use.Although the UTAUT is used as an essential framework to examine diverse technologies, both inside and outside of the organizational context, it is necessary to consider crucial elements that motivate the customers to accept and use the new technology to understand their technology adoption (Kwateng et al., 2019).Venkatesh, Thong, & Xu (2012) developed the UTAUT 2an upgrade of the previous theory.The four core elements of the UTAUT model influencing the customers' behavioral intention to use the technology have been adopted by the UTAUT 2 with a customer perspective (Kwateng et al., 2019).Venkatesh et al. (2012) added three more constructs to the UTAUT 2 model for accuracy purposes in predicting the customers' technology adoption.These constructs include price value, habit and hedonic motivation.They only use three moderating variables of age, experience, and gender.On the other hand, the behavioral intention serves as the dependent variable in this theory.It is defined as a measure of the strength of intention that arises in an individual to perform a certain behavior (Davis, Bagozzi, & Warshaw, 1992).Another dependent variable employed is use behavior.It is the result of the customers' practical application of technology.
Performance expectancy is a measure of the extent to which individuals believe that using a system will help them achieve better job performance (Venkatesh et al., 2003).It has been proven to have a variety of effects on the individuals' intentions to use new technology services.Researches by Rahim, Bakri, Fianto, Zainal, & Shami (2022), Abrahão et al. (2016), Abu-Taieh et al. (2022), Alkhwaldi, Alharasis, Shehadeh, Abu-AlSondos, Oudat, & Atta (2022), Almaiah, Al-Rahmi, Alturise, Alrawad, Alkhalaf, Lutfi, & Awad (2022), Chan, Troshani, Hill, & Hoffmann (2022), Farah, Hasni, & Abbas (2018), Purohit, Kaur, & Chaturvedi (2022), and Tan & Lau (2016) found that the behavioral intentions of the users of the latest technology were significantly influenced by the performance expectancy.However, Boonsiritomachai & Pitchayadejanant (2019) and Sebastián, Antonovica, & Guede (2023) demonstrated the opposite, where the users' behavioral intention to adopt new financial technology was not significantly influenced by the performance expectancy.While the digital banks provide virtual financial services with no space or time constraints, their performance advantages are expected to be a crucial factor in their adoption.Therefore, the first hypothesis that can be proposed is as follows: H1: Performance expectancy positively influences the behavioral intention of Gen Z in adopting the digital banks.
Effort expectancy measures the extent to which the ease of a system function can be performed (Venkatesh et al., 2003).The customers' intentions to adopt a new fintech are significantly influenced by the effort expectancy (Abrahão et al., 2016;Abu-Taieh et al., 2022;Chan et al., 2022;Farah et al., 2018;Purohit et al., 2022;Tan & Lau, 2016).Meanwhile, there were also several studies which found the opposite, where the users' behavior intention to use the new technology was not significantly influenced by the effort expectancy (Alkhwaldi et al., 2022;Boonsiritomachai & Pitchayadejanant, 2019;Rahim et al., 2022;Thaker, Thaker, Khaliq, Pitchay, & Hussain, 2022).When the customers believe that the digital bank technology is simple, clear and easy to understand and use, they are more likely to use it.Therefore, the second hypothesis that can be proposed is as follows: H2: Effort expectancy positively influences the behavioral intention of Gen Z in adopting the digital banks.
Social influence measures the extent of an individual's believe that he / she has to use a new system because the people in their lives also believe it (Venkatesh et al., 2003).Abrahão et al. (2016), Tan & Lau (2016), Farah et al. (2018), Al-Sabaawi, Alshaher, & Alsalem (2021), Sebastián et al. (2023), andPurohit et al. (2022) found that the social influence significantly affected the customers' behavioral intention to adopt a new technology.In contrast, Thaker et al. (2022), Senyo &Osabutey (2020), andMoorthy, T'ing, Yee, Huey, In, Feng, &Yi (2020) found the opposite finding.When the customers realize that the digital bank is important for the people around them, along with their positive views, then the customers are also encouraged to adopt the digital bank.Therefore, the third hypothesis that can be proposed is as follows: H3: Social influence positively influences the behavioral intention of Gen Z in adopting the digital banks.
Facilitating conditions measure the extent of an individual's belief that the conditions (infrastructure and technical) within an organization are available to support a system adoption (Venkatesh et al., 2003).Nel, Boshoff, & Raleting (2012) defined the facilitating condition as an individual's level of belief that external and internal environmental factors around him / her support the adoption of new technology.Boonsiritomachai & Pitchayadejanant (2019), Alkhwaldi et al. (2022), Rahim et al. (2022), Thaker et al. (2022), and Almaiah et al. (2022) found that the influence of facilitating conditions on behavioral intention was significant; while Farah et al. (2018), Abu-Taieh et al. (2022), andPurohit et al. (2022) found the opposite.Infrastructure (such as stable internet access) and personality characteristics (such as feeling comfortable using a smartphone for making online payments) are necessary for the use behavior of digital banks (Migliore, Wagner, Schneider, Francisco, & Cabanillas, 2022).As a result, supporting operational infrastructure will encourage the intention to use digital banks.Therefore, the fourth hypothesis that can be proposed is as follows: H4: Facilitating conditions positively influence the behavioral intention of Gen Z in adopting the digital banks.
Hedonic motivation is defined as an individual's feelings of satisfaction and pleasure as a result of using a technology (Venkatesh et al., 2012).Boonsiritomachai & Pitchayadejanant (2019), Moorthy et al. (2020), andFarah et al. (2018) demonstrated that the hedonic motivation had a significant influence on the users' behavioral intention of using a new technology.However, Thaker et al. (2022), Al-Sabaawi et al. (2021), and Sebastián et al. (2023) did not find it significant.If a technology offers the users with pleasure and excitement when they use it, this will affect their behavioral intention in using the technology (Moorthy et al., 2020).Therefore, the fifth hypothesis that can be proposed is as follows: H5: Hedonic motivation positively influences the behavioral intention of Gen Z in adopting the digital banks.Venkatesh et al. (2012) essentially initiated the price value constructdefined as a cognitive tradeoff made between the benefits of using a system and the costs that must be incurred by the users.However, in its development, the digital bank applications provided by banks do not charge fees from their users.Therefore, perceived value is emerged as a new construct.It refers to an individual's evaluation of the overall value of goods or services by comparing the benefits with expected costs (Zhu, Sunanda, & Ting-Jie, 2010).Regardless of the amount of money and time invested in the activity, the perceived value drives attitudes, intentions, and adoption (Joshi & Chawla, 2023;Zhang, Ying, Khan, Ali, Barykin, & Jahanzeb, 2023).According to Farah et al. (2018) and Yan, Siddik, Akter, & Dong (2021), the perceived value significantly affected the customers' intentions to use the digital bank.Individuals who believe they are receiving more benefits from the technology than predicted costs find it simpler to accept the technology.Therefore, the sixth hypothesis that can be proposed is as follows: H6: Perceived value positively influences the behavioral intention of Gen Z in adopting the digital banks.
Habit is a stage in which a person performs a behavior automatically as a result of prior experience (Venkatesh et al., 2012).According to researches conducted by Farah et al. (2018), Sebastián et al. (2023), andThaker et al. (2022), the consumer behavior, including the decision to use new technologies, was significantly influenced by habit.Conversely, Pramusinto, Nurkhin, Saputro, Kholid, & Septiarini (2021) and Senyo & Osabutey (2020) did not confirm the findings.Individuals may develop a habit after using the digital banking services frequently, which could eventually change their behavior while using the technology (Senyo & Osabutey, 2020).Therefore, the seventh hypothesis that can be proposed is as follows: H7: Habit positively influences the behavioral intention of Gen Z in adopting the digital banks.
Behavioral intention describes the likelihood that a user will be interested in a specific behavior (Ajzen, 2002).In relation to the digital banks, it is assumed that someone who has excellent intentions will utilize them more frequently, and vice versa (Senyo & Osabutey, 2020).Pramusinto et al. (2021), Rahim et al. (2022), Almaiah et al. (2022), and Senyo & Osabutey (2020) have demonstrated a positive and significant relationship between the customers' behavioral intentions and the use behavior of new technologies.Therefore, the last hypothesis that can be proposed is as follows: H8: Behavioral intention positively influences the use behavior of Gen Z in adopting the digital banks.
Figure 1 depicts the conceptual framework of this study, developed based on the theoretical aspects and findings of previous researches as follows:

RESEARCH METHODS
This study was conducted using a quantitative manner.The data was collected from a questionnaire distributed online using Google Forms.The population of this study was Gen Z who were users of digital banks in Indonesia.Considering that the population was very huge and possibly unknown, this study employed a purposive sampling technique based on several criteria as follow: (1) the respondent must be Indonesian citizen; (2) the respondent must be between 17 -27 years old; and (3) the respondent must be users of digital banking services.The questionnaire was distributed online for 40 days from March to April.There was a total of 232.The questionnaire distributed consisted of two sections, including the demographic profile and questionnaire items measuring each variable of this study.The questionnaire items were indicators adopted from Venkatesh et al., (2012), except for the perceived value variable which was adopted from Farah et al. (2018).The questionnaire items were measured using a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).Many previous studies using the same measurement had shown that the five-point Likert scale was an excellent indicator of the extent to which certain behaviors were implicit (Abu-Taieh et al., 2022;Farah et al., 2018;Kwateng et al., 2019;Thaker et al., 2022).
The data was tested for its reliability and validity before it was processed for further analysis.A pilot study was conducted to 40 respondents and the results show that all Cronbach's Alpha values of each variable are higher than 0.70, indicating that they are reliable.Likewise, the results also show that each indicator has an r-statistic of greater than the r-table of 0.304 (α: 5%), indicating that they are valid.Therefore, the data could be used for further analysis.Furthermore, the data was analyzed using Structural Equation Modeling -Partial Least Square (SEM-PLS) approach using SmartPLS 3.2.9.According to Hair, Joe, Sarstedt, Hopkins, & Kuppelwieser (2014), in cases when the population was unknown, the SEM-PLS minimum sample size was determined by multiplying the minimum sample size of the indicator by five.The SEM-PLS analysis conducted outer model and structural model / inner model evaluations.The two models were tested for its discriminant and convergent validity and reliability.Hair, Sarstedt, Ringle, & Gudergan (2017) stated that the analysis consisted of several stages, including the coefficient of determination test, predictive relevance test, and path coefficient test.

DATA ANALYSIS AND DISCUSSIONS
There were 232 respondents participating in this study.All of them had completed the questionnaire and were valid.This study finds that most of the respondents are male (50%), between 18 -22 years old (62%), had a bachelor degree (44%), and were mostly students (39%) with an average monthly of IDR 0 -IDR 2,000,000indicating that they had no job.
In relation to the use behavior of Gen Z in using the digital banks, the Gen Z were allowed to choose more than one digital bank listed in the questionnaire.The results show that most of them have used the digital banks for less than two years (39%), and have used them for five to ten times in a month (40%).Blu by BCA (44%) and Sea Bank (35%) are the favorites.The results of this study show that there were several indicators that must be removed since the loading factor value was not higher than 0.7 and the Average Variance Extracted (AVE) value was also not higher than 0.5 (Hair, Hult, Ringle, Sarstedt, & Thiele, 2017).The results of convergent validity test after removing several indicators are shown in Table 2 below: Furthermore, the results of discriminant validity are presented in the following Table 3.The items had met the requirements for the cross-loading value and Fornell-Larcker criterion if their cross-loading value is higher than 0.7.In addition, the results show that the value for each latent variable is greater than the correlation value between other latent variables (Hair, Black, Babin, & Anderson, 2014).However, a number of studies suggested that the cross-loading value and the Fornell-Larcker criterion were insufficient to assess the discriminant validity.The Heterotrait-Monotrait (HTMT) correlation ratio was more preferred.Henseler, Ringle, & Sarstedt (2015) suggested the rule of thumb for the HTMT criterion was not to be higher than 0.90.The following Table 4 presents the results of discriminant validity test using the HTMT criterion, showing that all variables have values of less than 0.90.Therefore, the data had met the requirements for discriminant validity testing.Furthermore, the data was tested for its reliability and the results were determined based on the composite reliability (CR) and Cronbach's Alpha value.The following Table 5 demonstrates that all constructs have met the requirements, since their CR value is higher than 0.7 (Hair et al, 2014).Likewise, the Cronbach's Alpha value is also higher than 0.7 (Chin, 1998).Thus, the data could be considered reliable and can be used for further analysis.In addition, the data was tested for its multicollinearity based on the Variance Inflation Factor (VIF) value.The results shown in Table 6 show that there is no indication of multicollinearity problems in the model, because the VIF value is lower than 5 (Hair et al., 2014).After evaluating the outer model, the inner model was evaluated to determine the amount of influence between the constructs of the research variables.The results of determination coefficient are presented in Table 7 below.Based on the table, this study could explain 59.3% of the behavioral intention variable and the remaining 40.7% could be explained by other variables not included in the study.Then, the behavioral intention could explain 47.3% on the use behavior and the remaining 52.7% could be explained by other variables not included in the study.Further, the predictive relevance represented by Q 2 value shows that the behavioral intention and use behavior variables have a Q 2 value of 0.435 and 0.399, respectively.This indicated that the research model had good predictive capability (Ghozali & Latan, 2015).Lastly, the hypotheses of this study were tested.Based on the bootstrapping analysis, it was found that seven out of nine structural relationships are significant (p ≤ 0.05).The results can be seen in the following Table 8: The results show that the perceived value has the greatest positive and significant influence on the behavioral intention (β = 0.276 | t = 4.351 | p ≤ 0. The first hypothesis of this study proposes that the performance expectancy positively influences the behavioral intention of Gen Z in adopting the digital banks.Meanwhile, the results show that the performance expectancy has no positive and significant positive influence on the behavioral intention, indicating that the first hypothesis cannot be supported empirically.This finding supports Boonsiritomachai & Pitchayadejanant (2019) and Sebastián et al. (2023) who found that the performance expectancy did not significantly affect individual behavioral intentions to use a new fintech.However, this finding is not in line with the UTAUT 2 and studies by Abrahão et al. (2016) 2020), Tan &Lau (2016), andThaker et al. (2022).The Gen Z, who had been familiar with new technological developments and digital services, had no performance expectation which affected their decision to use new digital services (Miah, Singh, & Rahman, 2023).In addition, there were many other fintech services that also offered similar performance benefits, reducing the influence of performance expectations on the behavioral intention to adopt the digital banks.
Further, the second hypothesis of this study proposes that the effort expectancy positively influences the behavioral intention of Gen Z in adopting the digital banks.However, similar to the first hypothesis, the effort expectancy has no positive and significant influence on the behavioral intention, indicating that the second hypothesis cannot be supported empirically.Individual belief in the convenience offered by the new technology failed to encourage the individual behavioral intention to use the new financial services, including the digital banking services.This finding is consistent with earlier researches conducted by Boonsiritomachai & Pitchayadejanant (2019), Moorthy et al. (2020), Rahim et al. (2022), andSebastián et al. (2023).The Gen Z, who were accustomed to dealing with similar technologies, no longer saw the digital-based services as an obstacle.Therefore, difficulties in using the digital banking services were never a concern for the Gen Z users, especially because the technology offered today had been more user-friendly (Alkhwaldi et al., 2022;Mensah, 2019;Tarhini, El-Masri, Ali, & Serrano, 2016).
In addition, the third hypothesis of this study proposes that the social influence positively influences the behavioral intention of Gen Z in adopting the digital banks.This study finds that the social influence has a positive and significant influence on the behavioral intention, indicating that the third hypothesis can be supported empirically.This finding strengthens previous researches by Al-Sabaawi et al. (2021), Farah et al. (2018), Purohit et al. (2022), Sebastián et al. (2023), andTan &Lau (2016) which claimed that the social influence had a significant impact on the individual behavioral intention to use the new financial services.The Gen Z spent a lot of time socializing with peers.This made their financial behavior in the group of friends was something that influenced individual financial behavior, including the use of digital banks.This form of socialization was also supported by social media which allowed them to receive recommendations from friends and even social media influencers to take certain actions, including using the digital banks (Djafarova & Foots, 2022).
Next, the fourth hypothesis of this study proposes that the facilitating conditions positively influence the behavioral intention of Gen Z in adopting the digital banks.This study confirms that the facilitating condition is a variable with a positive and significant influence on the behavioral intention, indicating that the fourth hypothesis can be supported empirically.Individual interest in new fintech, including the digital banks, was driven by the effect of external and internal factors that could facilitate the use of technology.This finding was supported by Al-Sabaawi et al. (2021), Almaiah et al. (2022), Boonsiritomachai & Pitchayadejanant (2019), Moorthy et al. (2020), Rahim et al. (2022), Tan &Lau (2016), andThaker et al. (2022).Compared to earlier generations, the Gen Z was more familiar to using various digital technologies.As a result, knowledge of new technologies was easy to obtain (Philippas & Avdoulas, 2020).Similarly, the resources needed to use the new technologies, such as smartphones, internet networks, and numerous supporting facilities, were readily available, making the adoption of new financial technology simple.Individuals' access to the technology and digital tools was a significant resource for using the fintech services (Odei-Appiah, Wiredu, & Adjei, 2022; Sahay, Allmen, Lahreche, Khera, Ogawa, Bazarbash, & Beaton, 2020).Furthermore, the fifth hypothesis of this study proposes that the hedonic motivation positively influences the behavioral intention of Gen Z in adopting the digital banks.The results of this study confirm that the hedonic motivation has a positive and significant influence on the behavioral intention, indicating that the fifth hypothesis can be supported empirically.Boonsiritomachai & Pitchayadejanant (2019), Farah et al. (2018), and Moorthy et al. (2020) also found similar results.Feelings of satisfaction, pleasure, and excitement when using a technological service drove individuals to use the new fintech.The Gen Z would be more likely to use the digital banking services if they felt satisfied, happy, and excited.
Additionally, the sixth hypothesis of this study proposes that the perceived value positively influences the behavioral intention of Gen Z in adopting the digital banks.This study finds that the perceived value has the biggest positive and significant influence on the behavioral intention, indicating that the sixth hypothesis can be supported empirically.This finding is in line with studies by Almaiah et al. (2022), Farah et al. (2018), and Thaker et al. (2022).This finding also suggested that the Gen Z perceived the digital banks as a service with more benefits than the cost and effort required.As a result of the various advantages that the digital banking services offered, the Gen Z's behavioral intention to use them continued to increase.
Besides, the seventh hypothesis of this study proposes that the habit positively influences the behavioral intention of Gen Z in adopting the digital banks.This study finds that the habit is the second factor which has the biggest impact towards the behavioral intention.Repeated use of digital banking services by the users developed into a habit, which influenced the users' future behavioral intention.This finding is supported by Farah et al. (2018), Sebastián et al. (2023), andThaker et al. (2022).This finding implied that the users who had become used to using the digital banking services via apps were more likely to use the services again.Furthermore, the Gen Z's experience with the digital banking services could also influence their decision and commitment to continue using the services (Windasari et al., 2022).
Finally, the eighth hypothesis of this study proposes that the behavioral intention positively influences the use behavior of Gen Z in adopting the digital banks…

CONCLUSION, SUGGESTION, AND LIMITATION
The growth of digital banking adoption in Indonesia is a novel phenomenon that merits more investigation.This study concludes that the perceived value, habit, facilitating conditions, hedonic motivation, and social influence variables have a positive and significant influence on the Gen Z's behavioral intention in adopting the digital banks; while the performance expectancy and effort expectancy do not.Further, this study also finds that the behavioral intention has a positive and significant influence on the use behavior of Gen Z in adopting the digital banks.Considering that the Gen Z was accustomed to dealing with multiple fintech advancements, they had the knowledge and skills to use a wide range of digital services.As a result, they were no longer driven to use the digital banking services because of the expectations, benefits and convenience they provided.
To attract the Gen Z to use the digital banking services, the service providers are suggested to employ several strategies.One of them is by providing profitable benefits compared to the expenses anticipated by them.The ease with which the Gen Z has the access to a wide range of digital information enables them to select the most profitable financial services.Furthermore, a user-friendly application design can also improve the experience of using digital banks and increasing customer commitment to continue using the service.This will also improve the users' satisfaction with the digital banking services.The government, as a regulator, can then establish an environment that fosters the digital bank development, both in terms of policies and supporting facilities for the digital ecosystem.