Behavioral Stimulus for Using Bank Mestika Mobile Banking Services: UTAUT2 Model Perspective

: Adopting such a large number of users makes mobile banking services one of the primary needs to meet financial transactions. Therefore, there is a need for additional information for mobile banking service developers on the dominant factors influencing user behavioral intentions. This research is present as a forum to provide an overview of the user's perspective on Mestika bank's mobile banking service. This research aims to determine what factors play an important role in behavioral intention to use Mestika bank's mobile banking services based on the UTAUT2 concept. The research instrument used a questionnaire distributed online to 240 respondents. Next, the researcher tested the hypothesis using Structural Equation Model (SEM) based on a variant called Partial Least Square (PLS) and the SmartPLS as a tool to analyze it. This research concludes that effort expectancy has the most significant influence and plays an essential role in shaping the behavioral intention of using Mestika bank's mobile banking. Then performance expectancy and social influence hedonic motivation also significantly influence behavioral purposes to use Mestika bank's mobile banking. Furthermore, the results of this study also concluded that facilitating conditions and habits have no significant effect on behavioral intentions to use Mestika bank's mobile banking.


INTRODUCTION
The progress and acceleration of the digital technology wave have almost appeared in all perspectives of public life in Indonesia, such as transportation, health, learning, retail, hotels, and finance (Fitria & Winata, 2020). The growth of technology has a direct impact on the development of the internet in Indonesia. The use of smartphones increases the high intensity of internet-based banking services (Mansur et al., 2019). This is undoubtedly influenced by the convenience available in smartphone services, so the higher the use of smartphones, the easier it is for people to access the internet (Welly et al., 2020). The digital era provides relief from various existing technologies (Nana Triapnita Nainggolan, Munandar, et al., 2020). Still, the public cannot easily access all the latest technologies because this latest technology recognition method requires terms and adjustments (Juhri & Dewi, 2017). Openness in accessing digital technology to obtain information pathways that are currently happening is becoming increasingly complex, including making consumer purchasing decisions through extracting digital information as a trusted reference source. It can even be a source of inspiration (Indahingwati et al., 2019). One of the technological breakthroughs in the field of Page 62 of 72 financial services in banking and currently one of the most trusted references is the emergence of mobile banking applications (Ginantra et al., 2020).
Mobile banking is a service that allows bank customers to conduct banking transactions via mobile phones or smartphones (Oliveira et al., 2014). Mobile banking, also referred to as mobile phone banking, uses mobile terminals such as mobile phones and personal digital personal assistants to access banking networks via wireless application protocols (Zhou et al., 2010). Mobile banking services can be used using the menu already available through an application that customers can download and install (Kim et al., 2009). Mobile banking offers convenience compared to SMS banking because customers do not need to remember the format of the SMS message to be sent to the bank and the SMS banking destination number (Barati & Mohammadi, 2009). Customers can take advantage of mobile banking services by accessing the available menu on the application installed on the cellphone. If the customer uses mobile banking through an application installed on the mobile phone, the customer must first download and install the application on the mobile phone. The system's representatives of mobile banking services can be interpreted through various channels, with the most popular being downloadable applications, SMS, and interactive voice responses (Shankar & Kumari, 2016). As one of the most innovative and new technologies, mobile banking is an excellent example of breakthrough mobile technology in banking, enabling customers to independently generate financial transactions (Alalwan et al., 2017). Information technology systems can be said to be successful if the user receives them well. Mobile banking also offers customers various benefits, including real-time mobility, usability, adaptability, security, interactivity, and overall universality (Laukkanen et al., 2007). Changing a user's behavior cannot be done directly to his behavior but must first identify the determinants or causes of the behavior. Identifying the determinants of acceptance of information technology is essential for the development of information systems. This needs to be done so that high investment in information technology facilities becomes useful and provides value for consumers and companies (Bharata & Widyaningrum, 2020). Several previous research results found facts that lead to constituents that influence the behavioral intention of mobile banking, which is determined by the performance expectancy factor (Farah et al., 2018); effort expectancy (Zhou et al., 2010); social influence (Albashrawi et al., 2019); facilitating conditions (Chawla & Joshi, 2019); hedonic motivation (Alalwan et al., 2017); and habits (Gupta & Arora, 2019).
Mobile banking technology represents an exciting area to research and study especially considering the significant challenges of introducing the technology. Considering the level of mobile banking services from bank customers, it is crucial to investigate further, given the number of similar mobile banking services from competitors. The urgency of the study is to examine the main factors that can shape user intentions in adopting Mestika bank mobile banking services in Medan City and Pematangsiantar.

Literature Review
The technology acceptance model with the UTAUT concept was first introduced and developed comprehensively for knowledge and understanding related to the perception of technology acceptance (Venkatesh et al., 2003). Based on empirical studies by (Venkatesh et al., 2003), then proposes an integrated model, namely the UTAUT model, which can explain 70% of the variance in user intentions. The UTAUT model contains four determinants and four moderators. Based on the model, the four components that determine BI and behavioral usage are PE, EE, SI, and FC. The results of the empirical study show that the UTAUT model is the most effective model for analyzing technology acceptance. Furthermore, based on developing theories related to technology acceptance by several experts, a synthesis was then carried out to create a more comprehensive UTAUT model (Chao, 2019). The essence of developing the UTAUT model is none other than to enrich knowledge related to the adoption of new consumer-oriented technologies. The addition of three new constructions to the UTAUT model consisting of Hedonic Motivation, price values , and habits, after this known as the UTAUT2 model (Putri et al., 2019). The urgency of the development of UTAUT2 aims to provide a deeper picture based on a better construction to explain the level of acceptance of new technology for users. Several factors encourage customers' intention to use mbanking applications and use them sustainably; performance expectancy is included in one of these factors (Purwanto & Loisa, 2020). Interpretation of performance expectancy is defined as the degree to which an individual believes that using the system will help him gain gains in performance (Venkatesh et al., 2003). Customers tend to develop positive intentions to use methods considered beneficial for their satisfaction and job performance (Baptista & Oliveira, 2015). Representatives of a person's experience using a financial service tend to be moderate and affect performance expectations on behavioral intention (Maduku, 2017). Several research results reveal a significant effect of performance expectancy on the behavioral purpose of mobile banking. Research (Iskandar et al., 2020), with the substance of mobile banking behavior in Indonesia using the unified theory of acceptance and use of the technology 2 model approach, states that performance expectancy significantly contributes to behavioral intention. More (Farah et al., 2018), who discuss the adoption of mobile banking in Pakistan, concludes that performance expectancy contributes significantly to behavioral sense. Following the results of several previous studies, the formulation of the developed hypothesis is:

H1: Performance expectancy affects behavioral intention
Effort expectancy can be defined by the perceived ease of how a person feels about receiving a technology, whether he efficiently uses the technology and how much power there is in using the technology (Sair et al., 2018). An attitude to easily accept mobile banking services will tend to influence users to show positive behavioral intentions (Baptista & Oliveira, 2015). Usage experience has been shown to moderate the relationship between expected expectations and behavioral intention (Venkatesh et al., 2003). In the context of using mobile banking, performance expectations reflect user perceptions of payment convenience, quick response, and service effectiveness (Zhou et al., 2010). If it is associated with research that examines the behavioral intention of mobile banking, it shows that effort expectancy has a significant influence on behavioral intention. Study (Bhatiasevi, 2015), with the context of the UTAUT model research on the adoption of mobile banking, concluded that performance expectations and behavioral intentions to use mobile banking suggest a positive relationship. The study conducted by (Alkhaldi 2014) stated that effort expectancy significantly affects behavioral intention mobile banking in Saudi Arabia. Following the results of several previous studies, the formulation of the developed hypothesis is:

H2: Effort expectancy affects behavioral intention
The thing that might trigger someone to use mobile banking services is the influence of social networks (Alam, 2014). Social influence or social influence in mobile banking services can be defined as the encouragement or influence of external factors to become a user (Ghalandari, 2012). The dynamics of changes that occur so quickly make the position of the social environment affect the adoption of new technologies (Halim et al., 2020). Social influence consists of various types, such as the influence of relatives or family, recommendations of friends, the environment, advertisements, user testimonials, and the like (Rembulan & Firmansyah, 2020). Research results by (Mashagba & Nassar, 2014), with the substance of the research modification of the UTAUT model to study the factors of mobile banking adoption in Jordan, concluded that social influence is one of the significant factors contributing to behavioral intention. In line with the results of this study, the research submitted by (Saparudin et al., 2020), who tried to research the level of customer trust using mobile banking services in Indonesia, concluded that social influence has an effect and has a significant relationship with behavioral intention. Following the results of several previous studies, the formulation of the developed hypothesis is:

H3: Social influence affects behavioral intention
Facilitating conditions also significantly influence behavioral intentions to adopt mobile banking (Islam et al., 2017). In the context of web-based question and answer services, usage behavior has a close relationship and is strongly influenced by the conditions of the facilities provided (Gupta & Arora, 2019). Facility Condition (FC) or facility condition can be expressed as the user's feeling of trust in the existence of a service unit provided by a service provider for supporting facilities such as technical infrastructure, usage knowledge, and customer assistance services (Madan & Yadav, 2016).In the mobile wallet research conducted by (Chawla & Joshi, 2019), facility conditions (FC) such as knowledge of individual user usage and the availability of payment services with electronic money affect mobile wallet adoption in India. Then the research conducted by (Tamilselvi & Balaji, 2019)with the main discussion of determinants of behavioral intention in the adoption of mobile banking in India concluded that facilitating conditions have a significant relationship and impact on behavioral intention. Then the research conducted by (Al-Tarawneh, 2016)with the theme Factors influencing the adoption of mobile banking in Jordan stated that facilitating conditions have a significant relationship and impact on behavioral intention. Following the results of several previous studies, the formulation of the developed hypothesis is:

H4: Facilitating conditions affect behavioral intention
Hedonic motivation is interpreted as the level of preference or pleasure that comes from the use of innovations such as mobile banking applications (Venkatesh et al., 2012). For several mobile banking users, the desire to use an application is very dependent on the level of acceptance that can reduce their workload in their daily life using banking services (Thusi & Maduku, 2020)-for the context of consumer use of UTAUT2-based mobile Internet technology, finding the hedonic motivation to be an essential factor in predicting behavioral intentions (Venkatesh et al., 2012). In other words, the adoption and sustainable use of technology will arise depending on the level of enjoyment felt by its users (Merhi et al., 2020). Several previous research results found facts that lead to a significant relationship between hedonic motivation and behavioral intention of mobile banking. A study to examine the adoption of consumer banking in Jordan from research (Alalwan et al., 2017) found a significant positive relationship between hedonic motivation and behavioral intention. More research (Mufingatun & Prijanto, 2020) stated that hedonic motivation substantially affects the behavioral sense of mobile banking in Indonesia. Following the results of several previous studies, the formulation of the developed hypothesis is:

H5: Hedonic motivation affects behavioral intention
The level of consumers who tend to perform behavior automatically with learning and acceptance of new technology can also be interpreted as a habit (Hariyanti et al., 2020). Practices that reflect the various outcomes of previous experiences and their implications will encourage spontaneous behavior (Venkatesh et al., 2012). The frequency of past behavior is considered one of the main determinants of current behavior for using similar applications (Ajzen, 1991). Habits affect the user's intention to continue using mobile banking to fulfill the need for financial transactions (Farah et al., 2018). When the behavior has been performed many times, future behavior becomes automatic (Gupta & Arora, 2019). Therefore, after the user uses the application, these actions become routines and habits that influence the individual to use the application in the future (Rahardjo et al., 2020). The results of previous studies reveal that addiction has a significant influence on the behavioral intention of mobile banking in the context of use by students in Indonesia (Septiani et al., 2017). Then research (Win et al., 2021), with the theme of behavioral intention research from mobile banking in Myanmar using a model based on unified acceptance theory, obtaining discussion results that lead to a significant contribution of habit to behavioral intention. Following the results of several previous studies, the formulation of the developed hypothesis is:

H6: Habit affects behavioral intention
Reasoned Action (TRA) theory is one of the theories that explain a person's behavioral intentions. TRA was proposed by (Lavorata 2014). Their research built an attitude model that comprehensively integrates the components of attitude into a structure designed to provide better explanatory and predictive power of behavior. This model is called the Reasoned Action Model. TRA indicates that a person's behavioral intentions depend on a person's attitude about behavior and subjective norms. If a person intends to perform a behavior, the person will do it (Lavorata, 2014). Furthermore, behavioral intention to adopt new digital-based technology is a central concept in TAM (Davis et al., 1989) and UTAUT (Venkatesh et al., 2003). The UTAUT model shows that a person's performance can be interpreted through certain behaviors and is further determined by his behavioral intentions to use new technology. Several literature studies explain that the behavior that encourages someone to use new technology is primarily due to its decisive role in shaping the experience of use (Putri et al., 2019). The intention is closely related to motivation, which is an impulse that arises in a person consciously or unconsciously to take action with a specific purpose; good intentions will encourage motivation to do good (Gupta & Arora, 2019).

Research Method and Materials
A quantitative research design with a causality approach is used in this study. The data used in this study uses primary data by collecting online questionnaires distributed online. The research population is Mestika bank customers in Medan City and Pematangsiantar City. Due to the unknown number of the population, the sample was taken using a convenience sampling technique. According to (Hair 2014), if the number of the population is not known ideally, the size of the representative respondents depends on the sum of all indicators in the variable multiplied by 5-10. This study has 24 indicators, so the minimum number of respondents for this research is 24 x 10 = 240. Therefore, the number of respondents taken for this study involved 240 respondents. This number is considered representative to be observed as a representative of the population because it has met the minimum sample threshold. Next, the researcher tested the hypothesis using Structural Equation Modeling (SEM) based on a variant called Partial Least Square (PLS) and the SmartPLS version 3.0 application as a tool to analyze it.

Demographic Summary of the Sample
The number of samples used in this study was 240 respondents who came from Medan and Pematangsiantar city. The demographics of the respondents in this study consisted of 105 men (43.75%) and 135 women (56.25) (See Table 1). This indicates that women dominate the behavioral intention of using mobile; given the lifestyle and rapid changes in behavior to adopt new products, it is not uncommon for female users to often purchase beauty and fashion products through ecommerce. Furthermore, the most dominant mobile banking users aged 40-49 (35.42%)year with a frequency of 85 people, and almost all of them have a bachelor's degree. Customers dominate the income level with incomes between 1-5 million (60.42%) per month, and the most widely used period of use is more than three years (30.83).  The validity test results that are oriented to each variable's loading factor and AVE values show results following the criteria. For all exogenous and endogenous variables from this research, the loading factor value is above 0.70, and the AVE value is above 0.5. After that, composite reliability values were also obtained above 0.70, and Cronbach's alpha values for each variable were above 0.60, indicating that all research variables have good reliability values. Judging from the R-square value of the endogenous variables, obtained a deal of 0.650 for behavioral intention shows that the overall ability of exogenous variables to explain behavioral intention is vital. The R-square value indicates that the overall effect of all exogenous constructs explaining employee performance is also moderate (Ghozali, 2014).

Hypotheses Test
Furthermore, to prove the hypothesis testing, a significance test was carried out to determine the relationship between the exogenous variables and the endogenous variable. The significance criterion was seen from the p-value. With a significance level of 5%, if the p-value between the exogenous variables and the endogenous variable is less than 0.05, the exogenous variables significantly affect the endogenous variable. In contrast, if the value is higher than 0.05, it means that the exogenous variables do not have a significant effect in building the endogenous variable. The results of the hypothesis test are presented in Based on the results of data analysis to prove hypothesis testing, it can be said that overall exogenous variables have a significant effect on endogenous variables. However, two exogenous variables do not affect endogenous variables. Of the six hypotheses put forward, there are two rejected hypotheses, namely the fourth hypothesis, which states the effect of facilitating conditions on behavioral intention; the results lead to an insignificant effect. The following hypothesis that was rejected was the sixth hypothesis, which concluded that habit had no significant effect on behavioral intention based on the results of data analysis. Then the other four hypotheses were declared accepted based on the results of the significance test, namely the effect of performance expectancy, effort expectancy.

Discussion
Based on testing the first hypothesis (H1), it is stated that performance expectancy has a significant effect on behavioral intention. This condition reflects that Bank Mestika's mobile banking features have met the expectations of customers. On the other hand, the presence of mobile banking services increases the opportunity for customers to complete essential tasks, especially those related to financial transactions. Testing the second hypothesis (H2) concluded that effort expectancy has a significant effect on behavioral intention. Data analysis also concluded that effort expectancy is the most dominant factor influencing behavioral intention compared to other factors. These results reflect that the use of Mestika bank's mobile banking service is straightforward to learn to use. The clarity of the features provided also makes it easy for users to use them. Most importantly, no special skills are needed to use mobile banking, so it is effortless for users to be experts in using them. The following hypothesis, namely the third hypothesis (H3), obtained results that stated that social influence had a significant effect on behavioral intention. These results illustrate that social influence in the form of recommendations for other users is a crucial part of encouraging behavioral intention of mobile banking services. The presence of people in the surrounding environment contributes to the adoption of mobile banking in the future. More and more people are recommending the use of mobile banking services, Furthermore, in the results of the fourth hypothesis (H4), it is stated that Facilitating Conditions have no significant effect on behavioral intention, so the statement of the fourth hypothesis is rejected. The limited availability of resources in the mobile banking application causes some customers to reduce their mobile banking activities. In addition, the frequency of errors and system disturbances causes mobile banking services not to run optimally, so it takes maintenance time for the bank to fix it. Furthermore, the fifth hypothesis (H5) stated that hedonic motivation significantly affects behavioral intention. The use of mobile banking is fun and very convenient for customers, which will have implications for high stimulation to use the product. Even more, If the service is repeatedly entertained, especially regarding the features offered on the Mestika bank mobile banking, it will have strong recognition for customers' minds, and the impact will encourage behavioral intentions in the future. The results of testing the last hypothesis, namely the sixth hypothesis (H6), states that habit has no significant effect on behavioral intention. Failures that sometimes occur in the system cause customers to reduce their use of mobile banking services. Instead, customers are more interested in coming directly to the bank to make money transfer transactions, especially for sending large amounts of money. Then the habit of conducting transactions conventionally resulted in a decrease in the intensity of using the mobile banking service.

Conclusion
For the development of mobile banking services in the future, the bank needs to understand the characteristics of its customers to predict their behavioral intentions towards the mobile banking service. For this reason, this study uses a UTAUT2-based model to assess technology acceptance in the field of Mestika bank mobile banking services because this model is more effective than other models in explaining user behavioral intentions to use the technology. This study indicates that the constructs of effort expectancy, performance expectancy, social influence, and hedonic motivation are the most significant predictors of the behavioral intention of mobile banking services. These results confirm that customers well receive the mobile banking services offered by Mestika bank. However, the other two constructs, such as facilitating conditions and habit, cannot influence customers' behavioral intentions. In the end, this research produces helpful information and a new conceptual model of m-banking for bankers and banking managers to take policies related to the development and improvement of the mobile banking service system. This study has limitations that need to be considered for further research. The first thing is that this study uses the UTAUT2 construct, which does not include the price value construct. so for further investigation it is necessary to add these variables and additional constructs such as age and gender to moderate the relationship between the exogenous constructs of the UTAUT2 model and the endogenous constructsbehavioral intentions. Second, the research focuses on examining the adoption of special mobile banking services for Mestika bank customers, so a general study is needed on certain types of mobile banking for further research. The essence will help study various mobile banking service systems offered by several banks. Finally, future research must apply the conceptual model developed in this study in different settings, countries, and technologies.