Determinants of BSI mobile banking adoption intentions: DeLone & McLean and UTAUT Model integration with religiosity

Purpose − This research aims to identify influencing factors on Bank Syariah Indonesia (BSI) mobile banking adoption intention by integrating the UTAUT and DeLone & McLean models and the role of religiosity variables. Methodology − This research used primary data from 150 Bank Syariah Indonesia customers who used mobile banking. The analysis method applied SEM PLS to assess the relation among exogenous and endogenous variables using SmartPLS software. Findings − The findings show that from the factors identified, service quality, information quality, performance expectancy, effort expectancy, social influence, and religiosity are critical variables in BSI mobile banking adoption intention. Because these six factors significantly impact the intention of BSI Mobile Banking adoption


Introduction
Information technology becoming universal recently can pump the economy soaring. The Covid-19 pandemic in recent years has shifted people's tendency to use their devices to transact online (Riza, 2021). The increase is proven by the rise in online transactions, which reached 45%, and vice versa, and offline transactions decreased by 50%. As the usage of gadgets increases in routine situations, people are increasingly adept at using various applications on their devices. In addition, the application's diversity of product design can also facilitate daily activities (Suhartanto et al., 2020).
Along with the increase in online transactions, banks see technology as an opportunity to support the business potential of the banking industry (Riptiono et al., 2021). Therefore, they offered diverse service innovations for their customers. People are facilitated with digitized bank services such as checking customer balances, paying bills, transferring funds to multiple banks, scheduling payments, topping up Gadget credit, and even transactions abroad (Rawashdeh, 2015). The popularity of online transactions using mobile banking is in line with Indonesia, a developing country with a high adoption rate of mobile banking compared to internet banking.
A prior study confirmed that the unified theory of acceptance and use of technology (UTAUT) theory could explain up to 70% of adoption intentions (Farah et al., 2018). The UTAUT concept is a model to predict a person's behavior and attitudes regarding various types of technology. In addition, as previously described by the researchers, UTAUT represents the most universally applied model for multiple investigations related to technology adoption intentions (C. M. Chao, 2019). UTAUT clarifies differences in individual willingness to adopt a technology based on problem analysis (Gupta & Arora, 2020;Tarhini et al., 2019).
Another model, the DeLone & McLean model, is considered a breakthrough for assessing the success of information systems (Thar et al., 2017;Prasetyo et al., 2021). The DeLone & McLean model is a modern theory for evaluating the success of information systems in terms of technology use (Abdelkareem, 2021;Sabeh et al., 2021). Some observers have applied this DeLone & McLean concept to various applications to prove its relevance, including the intention of adopting mobile banking and internet banking (Mansour, 2020;Purwati et al., 2021), online shopping apps (Ali et al., 2018;Tarhini et al., 2019), telecommunications providers (Adroni & Sitorus, 2017), e-learning (Sabeh et al., 2021), and academic app (Hudin et al., 2018).
On the other hand, Indonesia is a country with identic Islamic religiosity, in which Islamic norms and principles have a global reach at all levels of life, including the use of technology (Alkhowaiter, 2022). Recent studies revealed that Islamic banking in Indonesia had gained a strategic market position among the public since Indonesia has the world's largest Muslim population (Abror et al., 2022;Wandira, 2022). That is in line with the merger of 3 Sharia commercial banks on February 1, 2020, namely Bank Mandiri Syariah, BNI Syariah, and BRI Syariah, into one of the most prominent Islamic banks in Indonesia, namely Bank Syariah Indonesia (BSI). At this young age, BSI has made many satisfactory achievements; the average financial performance and cooperation with domestic and abroad have been increasing. At the beginning of its journey, BSI was recognized and inaugurated to have a representative office in Dubai, the center of World Finance (Annual Report BSI, 2021). BSI is listed as the number seven Bank in Indonesia according to its assets. Operationally, BSI implements Sharia principles of management and adopts Banking Technology.
This satisfactory achievement has driven researchers to investigate further the underlying mechanism of customers' behavior toward mobile banking adoption intention. Previous studies have tried to explain several factors and theories about the adoption rate, such as the cultural background of customers (Hoehle et al., 2015), individual behavior in using information technology (Rehman et al., 2020), different models (i.e., Theory of Planned Behavior and Technology Acceptance Model) (Aboelmaged & Gebba, 2013;Jouda et al., 2020;Suhartanto et al., 2020). Furthermore, current literature on mobile banking usage focuses on the average of developed countries rather than developing countries (Chaouali et al., 2016;Malaquias & Hwang, 2016;Tarhini et al., 2019). However, only a few have researched how much religiosity influences mobile banking adoption (Riptiono et al., 2021;Sudarsono et al., 2022), mainly Islamic mobile banking in developing countries with the largest Muslim population. Moreover, researchers have yet to find any literature related to adopting one of the mobile banking with the DeLone & McLean model that is integrated with other models, such as the UTAUT model. So, applying these two models in this study is considered necessary.
Thus, this study examined religiosity, DeLone & McLean model, UTAUT model, and mobile banking adoption intentions. Specifically, this study aims to provide an understanding of the mechanism of mobile banking adoption intention in the context of BSI customers through religiosity, DeLone & McLean model with the mediating role of the UTAUT Model. Research focuses on service quality, system quality, information quality, performance expectancy, effort expectancy, social influence, facilitating condition, and religiosity.

Literature Review Unified Theory of Acceptance and Use of Technology (UTAUT)
This famous unified theory of acceptance and use of technology (UTAUT) model originated from Venkatesh et al. (2003) and is applied to predict a person's behavior and attitudes regarding various types of technology. This model is based on numerous theories of technology adoption. The UTAUT concept includes four main components: performance expectancy, effort expectancy, social influence, and facilitating conditions that encourage the intention of behavior using technology (Venkatesh et al., 2012). As previously described by the researchers, UTAUT is the most universally used model in various studies related to technology adoption intentions. That is because UTAUT shows differences in individual willingness to adopt a technology based on problem analysis (Gupta & Arora, 2020;Tarhini et al., 2019). Among them are the adoption of mobile payments (Alkhowaiter, 2022;Gupta & Arora, 2020), mobile banking (Kholid, 2019;Mansour, 2020;Samsudeen et al., 2022), internet banking (Mohd Thas Thaker et al., 2022), online shopping applications (Tarhini et al., 2019), food delivery applications (Zhao & Bacao, 2020), and e-learning (Alghazi et al., 2021;Almaiah et al., 2019).

DeLone & McLean Model
In addition to the UTAUT model, our research is based on information system success models  (Abdelkareem, 2021;Sabeh et al., 2021). This model has gained appreciation from information system researchers and is considered a breakthrough in evaluating the success of information systems Thar et al., 2017). In the latest version, DeLone and McLean (2003) confirmed three crucial factors: service quality, system quality, and information quality, where each factor is critical and interrelated in evaluating the information technology benefits.

Service quality
Good service quality can increase individual intentions to accept technology. Following the DeLone & McLean model, service quality refers to the supporting facilities that service providers supply, including information technology. The quality of support users get from the technology includes reliability, responsiveness, security assurance, and empathy (DeLone & McLean, 2003;Salim et al., 2021). Among the many service quality indicators, Ali et al. (2018) and Tarhini et al. (2019) found that reliability is the most crucial aspect that drives adoption intentions for technology users, while non-users are more interested in security aspects. The findings of Salim et al. (2021) and Hidayah et al. (2020) stated that service quality in mobile apps significantly affects a user's performance expectancy and effort expectancy. Thar et al. (2017) have successfully proven that service quality is positively associated with the people of Myanmar's intention to use mobile banking. Mansour (2020) also mentioned that service quality significantly affects Palestine's adoption intention of Islamic mobile banking. Furthermore, service quality has also proven essential in encouraging user behavior intentions to adopt online shopping apps (Tarhini et al., 2019). Thus, it can be estimated by a hypothesis: H1: Service quality significantly influenced performance expectancy. H2: Service quality significantly influenced effort expectancy. H3: Service quality significantly influenced the adoption intention of BSI mobile banking.

System quality
According to DeLone and McLean (2003), system quality is the expected quality of the system and the desired quality of information required on technological characteristics. System quality is measured by what is provided and can solve consumer problems, including ease of use, functionality, reliability, flexibility, data quality, portability, integration, and urgency. If the vendor strives for a higher quality system, it is also expected to result in more significant technology adoption intentions. Because it is often found that the physical limitations of the device, such as small and poorly structured display sizes, can hinder the intention of individual adoption of a technology (Salim et al., 2021;Tam & Oliveira, 2017). Koo et al. (2013) state that system quality is vital in increasing mobile and internet banking performance expectancy. Furthermore, Prasetyo et al. (2021) also mentioned that system quality affects the effort expectancy of e-learning users. For example, Hidayah et al. (2020) have proven that system quality significantly affects the performance expectancy and effort expectancy of mobile app users. That confirms that system quality significantly impacts the intention of adopting technology, such as mobile banking, online shopping application, and telecom company (Adroni & Sitorus, 2017;Mansour, 2020;Tam & Oliveira, 2016;Tarhini et al., 2019). Thus, it can be stated that: H4: System quality significantly influenced performance expectancy. H5: System quality significantly influenced effort expectancy. H6: System quality significantly influenced the adoption intention of BSI mobile banking.

Information quality
Information Quality refers to the desired characteristic of using information technology, as it can be measurable in accuracy, timeliness, completeness, relevance, and consistency. Thus, information quality influences positive attitudes toward technology's benefits. The greater the availability of information quality in technology, the higher the individual's intention to adopt the technology (Ali et al., 2018;DeLone & McLean, 2003). Koo et al. (2013) have produced findings stating that information quality can determine the performance expectancy of mobile or internet banking service users. In addition, Salim et al. (2021) and Prasetyo et al. (2021) also underline the significant influence between information quality and performance expectancy on e-learning users. As well as Hidayah et al. (2020) also test that information quality plays an essential role in increasing performance expectancy and effort expectancy from mobile app users. It is known that the critical role of information quality is widely studied and recognized as one of the crucial factors in predicting the intention of information technology adoption, specifically the intention of mobile banking adoption (Mansour, 2020;Tam & Oliveira, 2017;Thar et al., 2017) and online shopping applications (Tarhini et al., 2019). Thus, it can be estimated by a hypothesis: H7: Information quality significantly influenced performance expectancy. H8: Information quality significantly influenced effort expectancy. H9: Information quality significantly influenced the adoption intention of BSI mobile banking.

Performance expectancy
Under the model of UTAUT, performance expectancy significantly affects behavior intent to adopt the technology. Venkatesh et al. (2003) understand performance expectancy as a person's extent of believing any technology helps him achieve profit in his activity. The performance expectancy in question represents functional and instrumental uses that can arouse individual intentions to adopt new technology. Such usefulness refers to several indicators, such as productivity, functionality, saving time and effort, and increased usability relative to traditional activity methods (Venkatesh et al., 2012). Some literature has previously proven that performance expectancy is recognized as one factor that can increase a person's adoption of technology. For example, Farah et al. (2018), Kholid (2019), Mansour (2020), and Samsudeen et al. (2022) stated that performance expectancy has proven successful in encouraging consumer intentions in various countries to accept mobile banking services. Then, according to the results of Alkhowaiter (2022) and Gupta and Arora (2020), performance expectancy positively influences the adoption intention of mobile payment. In addition, performance expectancy also plays a significant role in the intention of online shopping app adoption (Tarhini et al., 2019). Moreover, performance expectancy mediates the relationship between service quality (Amjad-ur- Rehman et al., 2019), system quality (Izuagbe, 2021), and information quality (Prasanna & Huggins, 2016) on adoption intention. Thus, it can be stated that: H10: Performance expectancy significantly influenced the adoption intention of BSI mobile banking. H11: Performance expectancy mediates the effect of service quality and the adoption intention of BSI mobile banking. H12: Performance expectancy mediates the effect of system quality and the adoption intention of BSI mobile banking. H13: Performance expectancy mediates the effect of information quality and the adoption intention of BSI mobile banking.

Effort expectancy
Effort expectancy is the convenience consumers perceive when adopting technology precisely (Venkatesh et al., 2003). When a new technology is easy and practical, one will be more enthusiastic about utilizing it (Davis, 1989). A higher level of effort expectancy increases one's intention to use it because they do not need a lot of effort to learn to operate it (Farah et al., 2018;Alkhowaiter, 2022). Venkatesh et al. (2012) stated that some of these effort expectancy indicators are easy to understand, learn, use, and master. Referring to Almaiah et al. (2019) and C.-M. Chao (2019) mentioned that students' increasing use of mobile learning is due to their belief that mobile learning is customerfriendly, simplified, and effortless to operate. Hassan et al. (2022) and Raza et al. (2019) consistently found that effort expectancy significantly influences FinTech and mobile banking adoption. In a way, effort expectancy can also mediate the role between service quality, system quality, and information quality towards adoption intention (Amjad-ur-Rehman et al., 2019; S. W. Lee et al., 2019;Rahi & Abd.Ghani, 2019). Therefore, the hypotheses adopted are: H14: Effort expectancy significantly influenced the adoption intention of BSI mobile banking. H15: Effort expectancy mediates the effect of service quality and the adoption intention of BSI mobile banking. H16: Effort expectancy mediates the effect of system quality and the adoption intention of BSI mobile banking. H17: Effort expectancy mediates the effect of information quality and the adoption intention of BSI mobile banking. Venkatesh et al. (2003) conceptualize social influence as a person's level assumption that the primary persons agree for him to adopt a technology. Social influence has been identified as a powerful influencer on personal intentions for technological adoption. Individuals are more accepting of being influenced by the ideas and views of their group of reference, for example, family, colleagues, and even relatives, especially those related to new technologies. Among the causes is the need for more experience and information it has on the technology (Tarhini et al., 2019). Thus, the opinions of those around him regarding the technology are believed to encourage individuals to use it (Kholid, 2019). Some of its measurement indicators include recommendations from people who influence user behavior, the role of essential people around users, and efforts to promote social status (Venkatesh et al., 2012). Michael et al. (2021) confirm that people feel more valued in their relationships when using a widespread technology, digital payments. In the financial technology industry context, social influence is also well recognized as an influential factor impacting the intention to adopt (Chan et al., 2022;Hassan et al., 2022). Thus, it can be stated that: H18: Social influence significantly influenced the adoption intention of BSI mobile banking.

Facilitating Condition
Another factor in adopting technology in the UTAUT concept is facilitating conditions. Referring to the opinion of Venkatesh et al. (2003), facilitating condition means a person's acceptance of the accessibility of both the resources and supporting infrastructure when using a selected technology. This aspect pays attention to the availability of technical support when using technology. The better-facilitating conditions provided to users, the more the intention to adopt technology will increase (Samsudeen et al., 2022;Mohd Thas Thaker et al., 2022). Thus, the scope of facilitating condition indicators includes basic knowledge, resource services' openness, and compatibility using other technologies (Venkatesh et al., 2012). Sudarsono et al. (2022) highlighted that facilitating conditions impact the intention of Muslim students to use mobile banking services, both in Islamic and conventional banks. As the findings of Alalwan et al. (2018) state that facilitating conditions are proven to be a significant potential predictor of internet banking adoption intention in Jordanian society. Thus, it is proven that facilitating conditions significantly impact technology adoption intentions (Mansour, 2020;Raza et al., 2019;Samsudeen et al., 2022). Accordingly, the adopted hypothesis is: H19: Facilitating conditions significantly influenced the adoption intention of BSI mobile banking.

Religiosity
Religion guides both personal and social life. For this reason, commercial and religious affairs are integral to permitted and prohibited products and services. Muslims have also applied these fundamentals when adopting banking products and services, especially when using the technology of mobile banking or internet banking (Amin, 2020;Junaidi et al., 2022). Religiosity refers to devotion, faith, and reverence for God. So, religiosity is an individual belief and commitment to obeying the commands of Allah Almighty (Abror et al., 2022;Alhazmi, 2019;Yussaivi et al., 2021). Islamic religiosity is identical in Indonesia, where Islamic norms and principles have a global reach at all levels of life, as is the use of technology (Alkhowaiter, 2022;Sun et al., 2012). Suhartanto et al. (2020) state that religiosity, which indicates one's observance of religion, significantly impacts the preference to adopt Islamic mobile banking services. Religiosity's effect on the individual's behavior is caused by religion's role in their attitudes and faith, shaping their mindset and participation with the world around them. The findings correspond with the work put forward by Riptiono et al. (2021) that religiosity's role positively encourages consumers' perception and usage intention of Islamic mobile banking services. Similarly, religiosity can substantially moderate the connection between behavioral intention and mobile payment usage (Alkhowaiter, 2022). Thus, it is stated: H20: Religiosity significantly influenced the adoption intention of BSI mobile banking.
The conceptual framework describes the research paradigm to answer the problems in this study, as illustrated in Figure 1 below:

Research Methods
This research is a quantitative study with an explanatory or causal design that aims to describe how a variable affects or causes changes in various variables (Cooper et al., 2006). The data measured in this analysis are primary data sourced from a survey of BSI customers who use mobile banking. BSI mobile banking was selected for observation because BSI is an Islamic bank inaugurated on February 1, 2021. Being so young, researchers want to know the quality of its mobile banking services according to users. It is intended to provide recommendations to BSI regarding mobile banking services. The survey was conducted on 150 respondents using online questionnaires via Google Forms. In multivariate analysis research, such as regression, the number of samples taken is at least ten times the number of variables studied (Sugiyono, 2016). Because this study used nine variables, the minimum number of samples used was 90 samples. Therefore, using 150 samples is considered sufficient to represent the population. The sampling technique used in this study is simple random sampling. The number of questions in the survey was 45 questions using a four-point Likert scale. This research's latent variables were performance expectancy (5 items), effort expectancy (5 items), social influence (5 items), facilitating condition (5 items), and also adoption intention (4 items), as measured by indicators developed according to Gupta and Arora (2020), Mansour (2020), and Venkatesh et al. (2012). The latent variables of service quality (4 items), system quality (6 items), and information quality (6 items) use indicators developed by Mansour (2020) also Tam and Oliveira (2016). The religiosity (5 items) variable is based on indicators developed by Alhazmi (2019) and Junaidi et al. (2022). The analysis method used in this study is SEM-PLS to test the influence between exogenous variables on endogenous variables. SEM is a multivariate analysis method that can describe the simultaneous linkage of linear relationships between observation variables (indicators) and latent variables (Wong, 2013). The structural equation system consists of structural and measurement models. So, this method is ideal to be applied to this study. Furthermore, data analysis was completed by SmartPLS software version 3.

Results
The questionnaire was distributed to 150 respondents from BSI mobile banking customers and users spread throughout Indonesia. The criteria for respondents were determined based on gender, age, recent education, occupation, and frequency of use. Respondents' characteristics are displayed below in Table 1 Based on Table 1 above, there were fewer male respondents than females in this survey, which amounted to 69.3%. At the same time, the dominating age is the productive age, namely respondents aged 27-40 years. The respondents who dominate are bachelor (S1) and postgraduate (S2 and S3), reaching 109 respondents. The occupations that dominated the respondents in this study were private employees, with the majority of the frequency of using BSI mobile banking being more than three times a month and once a month.
Before conducting hypothesis testing for prediction relations of latent variables in the structural models, evaluate the measurement model to verify indicators and latent variables that can be tested next. As below:  Table 2 above shows that the loading factor value is more than 0.7, meaning all indicators used can represent research variables. The AVE value also shows more than 0.5, meaning the indicator has met its validity. The results from Cronbach's Alpha, rho A, and Composite Reliability show values greater than 0.7, representing a reliable statement of all items.
Next, having assessed the measurement model, a structural or inner model must be tested. A structural model is a model that describes relationships between latent variables considered using path coefficients. As stated in Table 3 below, the test results of the hypothesis in this research:  Table 3 presents the hypothesis test results using a 95 percent confidence interval. With tstatistics above 1.96 and p-values below 0.05, service, system, and information quality significantly affect performance expectancy (H1, H4, and H7 acceptable). Service quality and information quality significantly affect effort expectancy (H2 and H8 acceptable), but system quality did not significantly affect effort expectancy (H5 declined). Service quality and information quality significantly affect adoption intention (H3 and H9 acceptable), but system quality did not significantly affect adoption intention (H6 declined). Performance expectancy mediates the path of system quality and adoption intention (H12 acceptable) but not for service quality and information quality (H11 and H13 declined). Effort expectancy mediates the relationship between service quality and information quality to adoption intention (H15 and H17 acceptable) but not for system quality (H16 declined). Meanwhile, performance expectancy, effort expectancy, social influence, and religiosity significantly affect adoption intention (H10, H14, H18, and H20 acceptable), and facilitating condition did not significantly affect adoption intention (H19 declined).
Furthermore, the feasibility testing of the model was carried out using the R-Square value. Table 4 below shows that the R-Square score for the endogenous variable, adoption intention, is 0.780, so the structural model obtained is reasonably feasible. The figure explains that the variability of endogenous variables that the variability of exogenous variables can explain is 78%, as follows:

Discussion
The results of this survey reveal that service quality significantly affects performance expectancy, which means that the better the service provided by BSI mobile banking, consumers will also have high expectations regarding the performance of BSI mobile banking. As with the increasing service quality, there is also an increase in effort expectancy by BSI mobile banking users. Moreover, service quality significantly influences BSI mobile banking adoption intention, with effort expectancy as a mediation variable. Thus, good service quality can increase individual intentions in using technology, especially BSI mobile banking. The survey results show that indicators of the availability and timeliness of assistance from service personnel are the dimensions that most influence the intention of BSI mobile banking adoption. These results are in line with findings from Loureiro et al. (2018), Abu-Taieh et al. (2022), , and Amjad-ur- Rehman et al. (2019). System quality is significantly influential on performance expectancy. It means that the better the BSI mobile banking quality system, the higher the performance expectancy. These results are consistent with E. Y. Lee et al. (2017) and Zhou (2014)'s studies. However, system quality is not significantly influential on effort expectancy. This finding differs from what Prasetyo et al. (2021) said: system quality affects the effort expectancy of e-learning users. Effort expectancy cannot mediate the relationship between system quality and BSI mobile banking adoption intention. Therefore, system quality not having significantly influenced the adoption intention of BSI mobile banking. Where Indonesians feel that they are not used to operating BSI mobile banking, they consider that the system from BSI mobile banking is still tricky to access. Sometimes even users need help to solve their problems. This result is significant to previous findings from Thar et al. (2017), indicating that system quality does not influence adoption intention. But contrary to the conclusions from Adroni & Sitorus (2017), Mansour (2020), also Tam and Oliveira (2016).
Information quality can impact performance expectancy significantly. Similarly, information quality can impact effort expectancy significantly. Information quality was also found to significantly affect BSI mobile banking adoption intention, with effort expectancy as a mediation variable. As Hidayah et al. (2020) mentioned, information quality can be considered a determining factor for performance expectancy and effort expectancy from mobile app users. Based on a BSI mobile banking users survey, this information quality can be measured in accuracy, completeness, convenience, and usefulness. Thus, the higher the value of these aspects of information quality, the more confident someone will be to use BSI mobile banking in their daily lives. It shows that most users have felt satisfied with the information provided by BSI through mobile banking, hence significantly making Indonesians intent on adopting BSI mobile banking. These validate the existing results from Cidral et al. (2018), Sewandono et al. (2022), Shahzad et al. (2021), and Tarhini et al. (2019. Performance expectancy significantly influences the intention of BSI mobile banking adoption. In this inquiry context, users believe using BSI mobile banking can increase their banking activities. In addition, they also tend to use BSI mobile banking due to the perception that the technology can boost performance opportunities in financial activities. That means a higher personal performance expectancy and daily usage of BSI mobile banking. The observations confirm that the most dominating indicators of Indonesian people's interest in adopting BSI mobile banking are usability and improved performance, followed by increased productivity, functionality, and usability relative to traditional activity methods, such as transaction speed compared to cash transactions. Thus, these results are therefore consistent with some of the previous results, confirming a solid link between performance expectancy and adoption intention of mobile banking among Pakistanis (Farah et al., 2018), Palestina (Mansour, 2020), and also in Sri Lanka (Samsudeen et al., 2022). Alkhowaiter (2022) also underlines the significance of performance expectations in increasing the intention of mobile payment adoption.
Effort expectancy has proven to affect BSI mobile banking adoption intention. Based on the survey results, evidence was obtained that users do not consider effort expectancy a critical element to increase their intention to adopt BSI mobile banking in their financial activities. So this finding is consistent with the conceptual model that has been proposed (Alalwan et al., 2018;Alghazi et al., 2021;Raza et al., 2019). As users, the Indonesian people always attach importance and care about the simplicity and ease of using BSI mobile banking. These findings confirm that the role of indicators of ease of understanding use, the comfort of learning operations, and clarity and ease of use of BSI Mobile Banking are essential aspects that users always consider.
Social influence significantly influences adoption intentions. Social influence explains the importance of the role of people around users in influencing the adoption intentions of BSI mobile banking. Survey results confirm social influence can represent users' perceptions of the part of their closest people, whether family, colleagues, or friends, on the intention to accept BSI mobile banking, specifically during the early adoption period. In addition, users have shown high levels of intent when many people have used it, especially the role of the most valuable people. These findings approve the earlier works of Samsudeen et al. (2022) that people prefer mobile banking instead of going to the bank when the people closest to them do the same. In addition, they also appreciate other people's opinions and preferences over their own. Similarly, Chan et al. (2022) and Hassan et al. (2022) also stated social influence significantly encourages the intention to accept FinTech.
Facilitating conditions cannot significantly affect BSI mobile banking adoption intention. That implies the amount of facilitating conditions available may not necessarily increase the Indonesian people's intention to accept BSI mobile banking. Developing digital technology among the public today makes the availability of technical support when using technology less concerning and even less necessary. These findings align with what Thar et al. (2017) suggest: Myanmar's people are not expecting powerful support facilities or the resources and knowledge needed to assist them in using mobile banking services, so they are not overly concerned about it. As Kholid (2019) validated his findings, facilitating conditions showed no meaningful impact on the millennial generation's intention to accept Islamic mobile banking in Indonesia. Thus, this finding contradicts the conclusions established by Raza et al. (2019), Mansour (2020), and Samsudeen et al. (2022).
Islamic religiosity is identical in Indonesia, where Islamic norms and principles have global implications at all levels of human life, including the use of technology (Alkhowaiter, 2022;Sun et al., 2012). These findings prove that religiosity significantly affects BSI Mobile Banking adoption intention. These findings align with previous results that confirm that religiosity significantly affects adoption intention (Riptiono et al., 2021). In addition, Suhartanto et al. (2020) also mentioned that religiosity factors significantly affect a person's trust in financial services. Religious people always put forward attitudes and beliefs that form awareness and interaction with the surrounding environment. The vital role of religious thought in daily activities and the existence of religious beliefs always affect all matters in an individual's life, including in the adoption of BSI Mobile Banking to support economic activities.

Conclusion
This research contributes theoretically to integrating the UTAUT and DeLone & McLean models and the role of religiosity variables in testing the factors of BSI mobile banking adoption intentions in Indonesian society. The findings show that the factors influencing BSI mobile banking adoption intention are service quality, information quality, performance expectancy, effort expectancy, social influence, and religiosity. These six factors significantly impact the intention of BSI Mobile Banking adoption. Along with service quality, system quality, and information quality as antecedent variables of the path of performance expectancy, effort expectancy, and BSI Mobile Banking adoption intentions. But other than that, system quality and facilitating condition did not significantly impact BSI Mobile Banking adoption intention.
With this young age, BSI has offered its product innovations, including mobile banking. Mobile banking, whose purpose is to make it easier for customers to transact banking, is considered to have still to improve their performance. This study can be a reference for banks in offering more innovative and flexible product technology, especially with customers' increasingly complex needs. Therefore, banks must pay more attention to things that support improving mobile banking performance, including the sophistication or quality of technology systems.
The following study can consider increasing the scope of the research sample to obtain more optimal results. In addition, this study used a questionnaire survey approach that only took perceptions at one particular moment. So, the generalization of the findings still needs to be made optimally. Therefore, subsequent researchers need to consider the use of other methods.