User Experience (UX) refers to users’ overall perceptions, evaluations, and responses resulting from interaction with a product, system, or service. The concept was introduced by Don Norman in the early 1990s to emphasize a holistic perspective on user interaction that extended beyond interface usability to include emotional, cognitive, and experiential dimensions (Norman, 2013). UX has become a central focus in digital product and service development, where sustained user engagement, efficiency, and satisfaction are critical due to the evolution of digital technologies and interactive systems (Delgado, 2022); (Narayana, 2021).

ISO 9241-210 defines UX as “a person’s perceptions and responses obtained from the anticipated use of a product, system, or service.” This definition shows the subjective and dynamic nature of UX, which is shaped by previous expectations as well as actual interaction experiences (Lecossier et al., 2018). Therefore, UX includes both functional aspects, such as usability and efficiency, and affective dimensions, namely emotions, preferences, and overall satisfaction throughout the interaction process.

This research adopts the User Experience Questionnaire (UEQ) developed by (Schrepp et al., 2017) to operationalize UX measurement in digital service research. UEQ is widely used in empirical UX research because the concept provides a comprehensive and efficient measurement of pragmatic and hedonic dimensions of user experience. The instrument consists of Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty scales, which are grouped into two higher-order constructs, namely Pragmatic and Hedonic Quality (Schrepp, 2019). UEQ is particularly suitable for evaluating mobile banking applications since the variable captures task-oriented performance and usability as well as users’ emotional responses and perceived innovativeness. The standardized structure and proven reliability make UEQ an appropriate methodological choice for assessing UX and the influence on customer satisfaction and loyalty in digital banking contexts.

Customer satisfaction and loyalty are closely interconnected constructs that play a central role in explaining user behavior in digital services. Customer satisfaction reflects users’ cognitive and emotional evaluations formed after interacting with a product or service, resulting from a comparison between prior expectations and perceived performance (Oliver 1980; (Kotler & Armstrong, 2008)). Positive confirmation occurs when perceived performance meets or exceeds expectations, leading to satisfaction. Conversely, unmet expectations generate dissatisfaction. This evaluative mechanism, formalized in ECT, has been widely applied in service, e-commerce, and information systems research.

In digital contexts, satisfaction functions as a key antecedent of customer loyalty, which is commonly manifested through continuance usage intention and recommendation intention. Satisfied users repeatedly use a digital service, maintain long-term relationships with the provider, and recommend the service to others through positive word-of-mouth (Miller, 2015);(Miryanda, 2023). In mobile and digital banking, where switching costs are relatively low, loyalty is primarily behavioral and attitudinal. This is reflected in sustained usage and advocacy rather than contractual commitment. Therefore, understanding customer satisfaction as a mediator between perceived performance and loyalty is essential for explaining continued adoption and competitive advantage in digital banking services.

Customer loyalty is a complex construct that reflects commitment to a brand or service, often manifested through repeated purchase behavior (Azhakarraja, 2020). Even though the critical role in marketing strategy is widely recognized, a universally accepted definition remains in dispute. Empirical research shows that loyalty is closely connected to customer satisfaction with a product or service, which drives repeat purchases (Miryanda, 2023). Loyalty is a key determinant of stable, long-term revenue, making the variable a central focus of contemporary marketing practices (Kirillova & Zyk, 2023).

Several factors determine customer loyalty, including service quality. High service quality significantly impacts loyalty since satisfied customers can repurchase the same product (Susilawati et al., 2024). Price and location also play important roles. Competitive pricing and strategic locations increase customer convenience and promote brand loyalty (Arizal et al., 2018). Beyond service and price, customer trust and satisfaction also contribute to loyalty. Customers who trust a brand and are satisfied with the experiences are more inclined to remain loyal and engage in repeat purchases (Lubis & Parhusip, 2023). According to (Kotler & Armstrong, 2008), customer loyalty can be gauged using three primary indicators, namely (i) repurchase intention, (ii) willingness to recommend (positive word-of-mouth/advocacy), and (iii) resistance to switching (attitudinal commitment).

This describes the customers’ propensity to repurchase the same product or service. The indicator reflects the degree of customer satisfaction and perceived value derived from a product or service (Yanti et al., 2023). Firms commonly monitor purchase frequency to assess loyalty levels. The higher the rate of repeat purchases, the stronger the customer’s loyalty to the brand.

Customer retention is an important measure of loyalty. Retention refers to the percentage of customers who continue to use a service or engage with a brand over a given period of time. High retention rates signal the success of loyalty strategies and customer satisfaction, contributing to long-term business profitability (Kolbushkin & Cherpak, 2021). Research shows that retaining existing customers is more cost-effective than acquiring new ones, underscoring the need for effective retention strategies (Carmen-Maria & Alexandrina, 2013).

This occurs when a brand is referred to prospective new customers. Referrals reflect customer satisfaction and also serve as a powerful marketing strategy. Recommendations from existing customers are often trusted more than traditional advertising and can support new acquisitions at a lower cost (Saleky et al., 2018). Many brands provide incentives to customers who make referrals, which increases engagement and loyalty to the brand.

Mobile banking (M-Banking) is a banking service that enables customers to conduct a range of financial transactions through mobile devices, such as smartphones (Aslam et al., 2025). This service provides access to banking features, enhancing convenience and efficiency in financial management. Due to the ease of access, the concept has become a practical solution for highly mobile customers (Wulandari & Aisah, 2022). Core functionalities typically include fund transfers, bill payments, and account management, all of which are accessible through mobile applications. These services are designed to meet the needs of busy customers who prioritize speed and convenience (Assensoh-Kodua, 2025). The adoption of mobile banking depends on the perceived benefits and external factors such as technology anxiety, user preferences, and cultural contexts. Security, ease of use, and customer trust are key determinants of individuals’ willingness to use the services. Users’ experiences with mobile banking are positive. However, concerns persist regarding data security and fraud risks, including smishing (Wulandari & Aisah, 2022). Continuous enhancements to mobile banking features (interface design), system quality and security systems are required to strengthen perceived safety and transparency, encourage broader adoption and continued usage, and ultimately sustain long-term customer loyalty (Zhou et al., 2021).

According to Bank Jago (2024), the company was originally established as PT Bank Artos Indonesia on December 14, 1992, and became publicly listed in 2016 under the ticker symbol ARTO. A major transformation occurred in December 2019, when PT Metamorfosis Ekosistem Indonesia (MEI) and PT Wealth Track Technology Limited (WTTL) acquired a combined 51% stake in Bank Artos. Subsequently, the bank changed its name to Bank Jago on June 11, 2020. This strategic step signaled a shift in the corporate focus toward technology-driven digital banking services. As part of the vertical expansion and commitment to value-based financial inclusion, Jago Syariah was launched in 2022 as a personal finance application grounded in Islamic principles (Jago, 2022).

Generation Y, commonly referred to as Millennials, comprises individuals born between 1981 and 1996. This cohort is highly adaptable to technological changes and tends to prioritize flexibility in the workplace, having come of age amid rapid advances in information and communication technologies. Millennials are also known for pronounced entrepreneurial orientation and strong commitment to inclusivity (Tschantz, 2016). Generation Z, born between 1997 and 2012, is the first cohort to have grown up entirely in the digital era. Members of this generation are adept at using a wide range of technologies and have extensive access to information from an early age. These individuals are more independent, creative, and critical in thinking. In addition, increased social and environmental awareness is exhibited, and brand engagement is carried out through digital platforms (Prasanna & Priyanka, 2024).

Convergent validity analysis assesses the extent to which indicators adequately represent the underlying constructs. The test examines the validity of the relationships between the indicators and the latent variables. Convergent validity is evaluated using the AVE for all items associated with a given construct. AVE is computed as the mean of the squared standardized loadings of the construct’s indicators. In addition, convergent validity can be assessed using the outer loadings of the indicators.

All indicators in the model have factor loadings above 0.50, showing that the criterion for convergent validity is met and is appropriate for retention in the model(Hair et al., 2021). The User Experience (UX) construct comprises six dimensions with a total of 26 indicators (UX11 to UX64). The outer loadings of the UX indicators ranged from 0.631 to 0.842. Most indicators exceeded 0.70, showing good validity. Meanwhile, several indicators fell within the 0.60–0.70 band, which remained acceptable for exploratory research. For Customer Satisfaction and Loyalty constructs, with eight and six indicators, all outer loadings ranged from 0.705 to 0.860 and 0.760 to 0.885, respectively, showing strong and very strong indicator validities.

The indicators in the model were valid and did not need to be eliminated. The model is suitable for proceeding to reliability testing and discriminant validity assessment. The structural relationships show that User Experience exerts a strong effect on Customer Satisfaction (coefficient = 0.817) and Loyalty (0.498). Furthermore, Customer Satisfaction also exerts a strong effect on Customer Loyalty (0.443). These results support the mediating role of customer satisfaction in shaping loyalty through the user experience. Subsequent analysis examined the AVE values, as reported in Table 2. Based on Table 2, all AVE values exceeded 0.50, showing that each construct adequately represents the research model.

In PLS-SEM, discriminant validity is assessed based on each construct’s ability to be empirically distinct from other constructs. In this study, discriminant validity was evaluated using the Fornell–Larcker criterion and the HTMT (Heterotrait–Monotrait Ratio).

The Fornell–Larcker criterion was applied to assess discriminant validity. Under this criterion, discriminant validity was established by comparing the square root of each construct’s AVE with the correlations of other constructs. A measurement model showed adequate discriminant validity when the square root of a construct’s AVE exceeded the inter-construct correlations (Hair et al., 2021).

As shown in Table 3, the square roots of the AVE (diagonal entries) exceed the inter-construct correlations (off-diagonal entries). The constructs satisfied discriminant validity and were valid for subsequent analysis. In addition to the Fornell–Larcker test, discriminant validity can be assessed using cross-loadings. An indicator is deemed to satisfy discriminant validity when the loading on the construct is the highest compared to others (Hair et al., 2021).

(Henseler et al., 2015) proposed the heterotrait–monotrait ratio (HTMT) of correlations to assess discriminant validity. The HTMT is the ratio of between-trait to within-trait correlations. Formally, this ratio is the mean of all correlations among indicators across constructs with different measurements relative to the mean of the average. HTMT approximates the true correlation between two constructs when both are measured without error. True or disattenuated correlation, approaching 1, shows a lack of discriminant validity. As shown in Table 4, HTMT values were below 0.90. The constructs satisfied discriminant validity and were empirically distinct.

Reliability reflects the accuracy, consistency, and precision of a measurement tool (Hair et al., 2021). Research showing reliability has dependable data and consistent results. In PLS, reliability can be assessed using two metrics, namely Cronbach’s alpha and CR. Table 5 shows the results of the reliability assessment as follows. The constructs are declared reliable because the Cronbach’s alpha and CR values are above 0.60 and 0.70, respectively (Hair et al., 2021).

The Structural Model (Inner Model) was tested after the estimated model met the criteria of the Outer Model. The R-squared (R²) values for the constructs in this research are as follows.

Based on Table 6, the adjusted R-squared value for Customer Loyalty is 0.803, indicating a strong model fit. This result suggests that User Experience and Customer Satisfaction explain 80.3% of the variance in Customer Loyalty. In addition, Customer Satisfaction has an adjusted R-squared value of 0.665, which also indicates a strong model fit, meaning that 66.5% of the variance in Customer Satisfaction is explained by the independent variables.