The influence of perceived usefulness and perceived risk on loyalty through satisfaction as an intervening variable for users of Flip fintech application in Semarang City

 Corresponding author: Meyda Santriana Amalia Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. The influence of perceived usefulness and perceived risk on loyalty through satisfaction as an intervening variable for users of Flip fintech application in Semarang City Meyda Santriana Amalia * and Widayanto Department of Business Administration, Faculty of Social and Political Science, Diponegoro University, Semarang, Indonesia. World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 Publication history: Received on 22 May 2025; revised on 05 July 2025; accepted on 08 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2584 Abstract The growth of fintech services in Indonesia has encouraged the public to shift to more practical digital transactions. Flip offers free interbank transfers with efficient service, but recent user complaints about delayed transactions, limited features, and unresponsive service reveal a gap between expectations and reality. This may reduce user satisfaction and weaken loyalty. This study examines the influence of perceived usefulness and perceived risk on user loyalty, with user satisfaction as a mediating variable. This explanatory research used a quantitative approach with 97 Flip users in Semarang City aged 18 to 35 years as respondents. The sample was obtained through purposive and accidental sampling. Data were collected via questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The results show that perceived usefulness has a significant positive effect on satisfaction and loyalty, while perceived risk negatively affects both. Satisfaction partially mediates the relationship between perceived usefulness and loyalty but does not mediate the effect of perceived risk. Based on these findings, it is recommended that Flip enhance its service by adding features that meet user needs, improving transaction speed and reliability, and providing clearer and more responsive customer support. These efforts are expected to enhance user satisfaction and foster stronger loyalty. Keywords: Perceived Usefulness; Perceived Risk; User Loyalty; User Satisfaction; Consumer Behavior; FinTech App 1. Introduction Financial technology (fintech) services in Indonesia have rapidly transformed the way users conduct financial transactions. Digital applications are now widely adopted due to their ability to offer speed, efficiency, and costeffectiveness. Flip is one of the most prominent fintech platforms that provides interbank transfer services without administrative fees. This feature directly addresses the needs of users who seek convenience and practicality, contributing to the platform’s widespread appeal and growth. Flip has attracted a large user base, but challenges remain in retaining consistent user loyalty over time. The fintech industry is highly competitive, and users can easily switch platforms when their expectations are not met. Complaints from Flip users have highlighted various issues such as delays in transaction processing, limited service features, and poor responsiveness from customer service. These recurring issues suggest a gap between user expectations and the actual service delivered, potentially leading to decreased satisfaction and weakened loyalty. Negative user feedback further supports these concerns. Based on data from the Google Play Store, Flip received a total of 433 negative reviews from April 2024 to March 2025. These consisted of 353 one-star and 80 two-star ratings. The highest number of complaints occurred in July 2024 with 55 reviews, followed by September 2024 with 43. Such trends World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 728 indicate that user dissatisfaction is not an isolated event, but a growing pattern that may influence user retention and the platform’s public perception. Figure 1 Negative Feedback Trend for Flip on Google Play over the Past Year Several psychological factors contribute to loyalty in fintech usage, particularly perceived usefulness and perceived risk. Perceived usefulness refers to the extent to which users believe that Flip helps them complete financial tasks effectively. Perceived risk reflects concerns about transaction failures, security, or potential losses. Satisfaction plays a mediating role between these perceptions and loyalty, representing the user’s overall evaluation of their experience. This study aims to examine how perceived usefulness and perceived risk affect user loyalty through satisfaction, providing actionable insights for improving Flip’s service performance and user retention. Based on the problems related to Flip user loyalty, the problem formulation that can be formulated and raised in this research is (1) Is there an influence between Perceived Usefulness and User Satisfaction? (2) Is there an influence between Perceived Risk and User Satisfaction? (3) Is there an influence between Perceived Usefulness and User Loyalty? (4) Is there an influence between Perceived Risk and User Loyalty? (5) Is there an influence between User Satisfaction and User Loyalty? (6) Is there an influence between Perceived Usefulness and User Loyalty through User Satisfaction? (7) Is there an influence between Perceived Risk and User Loyalty through User Satisfaction? 2. Material and Methods 2.1. Consumer Behavior Consumer buying behavior refers to the actions of individuals or households when purchasing goods or services for personal use [1]. This behavior involves observable physical actions rather than internal thoughts or feelings [2]. Four factors influence consumer responses to services, particularly fintech platforms, including cultural, social, personal, and psychological aspects [3]. 2.2. Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) was developed to explain how users adopt and use information systems [4]. It focuses on two primary constructs, perceived usefulness and perceived ease of use. Perceived usefulness refers to the extent to which a system improves user performance, while perceived ease of use relates to the level of effort required. TAM was later refined and applied across various domains such as mobile banking and fintech services [5]. This study uses TAM to assess the influence of perceived usefulness and perceived risk on satisfaction and loyalty. 2.3. User Loyalty User loyalty is defined as a strong commitment to repurchase or continue using a preferred product or service despite the presence of alternatives [3]. Loyalty is measured using the following indicators [3]: • Repeat purchases • Retention • Referrals World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 729 2.4. Perceived Usefulness Perceived usefulness refers to the degree to which using a system is believed to enhance job performance [5]. The following dimensions are used to measure this construct [5]: • Using the system improves my performance in my job • Using the system in my job increases my productivity • Using the system enhances my effectiveness in my job • I find the system to be useful in my job. 2.5. Perceived Risk Perceived risk is defined as the uncertainty faced by consumers when outcomes of their decisions are unpredictable [6]. The dimensions used to assess perceived risk include [6]: • Functional risk • Financial risk • Psychological risk • Time risk 2.6. User Satisfaction User satisfaction refers to a condition in which users feel positively toward a product or service, leading to continued use and favorable word-of-mouth, while dissatisfaction may result in switching behavior [7]. The indicators used to assess satisfaction are based on the following aspects [3]: • Experience • Expectation • Needs 2.7. Research Method This study applies an explanatory quantitative approach, utilizing non-probability sampling through a combination of purposive and accidental techniques to select respondents who meet the research criteria [8]. The population consists of Flip application users in Semarang City aged between 18 and 35 years, with a total of 97 valid responses collected. Data were gathered using an offline questionnaire distributed in shopping malls, employing a five-point Likert scale to measure levels of agreement [8]. The research variables, including perceived usefulness, perceived risk, user satisfaction, and user loyalty, were measured using indicators adapted from previous validated studies [3, 5–7]. The data analysis was conducted using the Structural Equation Modeling (SEM) method with the Partial Least Squares (PLS) approach, employing the embedded two-stage approach [9]. This involved the evaluation of both the measurement model and the structural model [9], and the data were processed using SmartPLS version 4.0 for Windows. Figure 2 Hypothesis Model World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 730 2.8. Hypothesis 2.8.1. The influence of Perceived Usefulness on User Satisfaction Perceived usefulness has been demonstrated to positively affect user satisfaction in the context of digital financial applications. This relationship reflects that users who perceive higher utility from the application tend to report greater levels of satisfaction [10,11]. H1: Perceived usefulness is presumed to have a positive and significant influence on user satisfaction. 2.8.2. The influence of Perceived Risk on User Satisfaction Perceived risk is found to negatively influence user satisfaction, particularly when users feel uncertain about transaction outcomes. The greater the perceived risk, the lower the satisfaction users experience when using the platform [12,13]. H2: Perceived risk is presumed to have a negative and significant influence on user satisfaction. 2.8.3. The influence of Perceived Usefulness on User Loyalty Previous studies have identified that perceived usefulness contributes positively to user loyalty. When users consider an application to be helpful and effective, they are more likely to continue using it over time [11,14]. H3: Perceived usefulness is presumed to have a positive and significant influence on user loyalty. 2.8.4. The influence of Perceived Risk on User Loyalty Research shows that higher perceived risk leads to a decrease in user loyalty. Users who feel insecure or doubtful about a platform are less likely to remain committed to it [13,15]. H4: Perceived risk is presumed to have a negative and significant influence on user loyalty. 2.8.5. The influence of User Satisfaction on User Loyalty User satisfaction has a strong influence on loyalty across various service platforms. When users’ expectations and experiences are met, they tend to recommend and continue using the application [16,17]. H5: User satisfaction is presumed to have a positive and significant influence on user loyalty. 2.8.6. The influence of Perceived Usefulness on Loyalty through Satisfaction Several studies indicate that user satisfaction mediates the effect of perceived usefulness on loyalty. This suggests that usefulness enhances satisfaction, which in turn drives loyalty [10,14]. H6: Perceived usefulness is presumed to positively and significantly influence user loyalty through user satisfaction as a mediating variable. 2.8.7. The influence of Perceived Risk on Loyalty through Satisfaction Satisfaction also mediates the relationship between perceived risk and loyalty. Although risk may reduce satisfaction, its influence on loyalty can be partially offset if users still feel satisfied with the service overall [12,15]. H7: Perceived risk is presumed to negatively and significantly influence user loyalty through user satisfaction as a mediating variable. 3. Results 3.1. Evaluation of Measurement Model (Outer Model) The measurement model, also known as the outer model, describes the relationship between latent variables and the set of indicators used to measure each construct [9]. World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 731 3.1.1. First Stage of Embedded Two-Stage Approach The first stage of SEM-PLS analysis using this approach focuses on examining the main effects of the PLS model by analyzing the dimensional level and generating latent variable scores [9]. Figure 3 Path Diagram of the Measurement Model (Stage 1) Convergent Validity Table 1 shows that all indicators within each dimension obtain loading factors greater than 0.70. This indicates that the latent constructs at the dimensional level are acceptable, as they are well represented by their respective indicators [9]. Table 1 Outer Loadings Results (Stage 1) Indicator Dimension Variable Loading Factor Type (as defined) Description First Order ST1 Experience User Satisfaction 0.900 Reflective Valid ST2 Expectation 0.857 Reflective Valid ST3 Needs 0.886 Reflective Valid LY1 Repeat Purchase User Loyalty 0.897 Reflective Valid LY2 Retention 0.882 Reflective Valid LY3 Referrals 0.860 Reflective Valid World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 732 Indicator Dimension Variable Loading Factor Type (as defined) Description Second Order JPR1 Improves Job Perfomance Perceived Usefulness 0.907 Reflective Valid JPR2 0.880 Reflective Valid PRD1 Increases Productivity 0.833 Reflective Valid PRD2 0.847 Reflective Valid PRD3 0.880 Reflective Valid JEF1 Enhances Job Effectiveness 0.910 Reflective Valid JEF2 0.901 Reflective Valid USF1 Useful 0.905 Reflective Valid USF2 0.911 Reflective Valid FUR1 Functional Risk Perceived Risk 0.917 Reflective Valid FUR2 0.907 Reflective Valid FIR1 Financial Risk 0.900 Reflective Valid FIR2 0.880 Reflective Valid PSR1 Psychological Risk 0.886 Reflective Valid PSR2 0.900 Reflective Valid TIR1 Time Risk 0.903 Reflective Valid TIR2 0.865 Reflective Valid Source: Processed primary data (2025) Table 2 reports that the AVE values for each dimension are above 0.50. This confirms that all indicators have satisfied the convergent validity criteria [9]. Therefore, it can be concluded that convergent validity has been achieved. Table 2 Average Variance Extracted (AVE) Value (Stage 1) Average Variance Extracted (AVE) Improves Job Perfomance (JPR) 0.798 Increases Productivity (PRD) 0.729 Enhances Job Effectiveness (JEF) 0.820 Useful (USF) 0.825 Functional Risk (FUR) 0.831 Financial Risk (FIR) 0.793 Psychological Risk (PSR) 0.797 Time Risk (TIR) 0.782 User Satisfaction (ST) 0.777 User Loyalty (LY) 0.774 Source: Processed primary data (2025) World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 733 Discriminant Validity Table 3 demonstrates that discriminant validity has been established, as indicated by the square root of the AVE for each construct (diagonal values) being greater than the correlations between constructs [9]. Table 3 Fornell-Larcker Criterion Results (Stage 1) FIR FUR JEF JPR LY PRD PSR ST TIR USF FIR 0.890 FUR 0.712 0.912 JEF -0.667 -0.616 0.906 JPR -0.664 -0.654 0.776 0.894 LY -0.725 -0.595 0.711 0.718 0.879 PRD -0.646 -0.601 0.820 0.817 0.768 0.854 PSR 0.708 0.715 -0.720 -0.710 -0.679 -0.692 0.893 ST -0.652 -0.651 0.745 0.836 0.779 0.804 0.725 0.881 TIR 0.697 0.718 -0.679 -0.723 -0.634 -0.687 0.736 -0.713 0.884 USF -0.602 -0.599 0.705 0.751 0.686 0.763 -0.593 0.813 -0.639 0.908 Source: Processed primary data (2025) Composite Reliability Table 4 indicates that all composite reliability values exceed the threshold of 0.70, and the Cronbach's alpha values for each indicator also surpass 0.70. These results confirm that the research instruments employed in this study are reliable [9]. Table 4 Composite Reliability and Cronbach’s Alpha (Stage 1) Cronbach's Alpha Composite Reliability Perceived Usefulness (PU) 0.931 0.951 Perceived Risk (PR) 0.909 0.936 User Satisfaction (ST) 0.856 0.913 User Loyalty (LY) 0.854 0.911 Source: Processed primary data (2025) 3.1.2. Second Stage of Embedded Two-Stage Approach Following the completion of testing at the dimensional level, the subsequent step involves conducting testing at the variable level by utilizing the latent variable scores obtained from the first stage [9]. World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 734 Figure 4 Path Diagram of the Measurement Model (Stage 2) Convergent Validity Table 5 shows that each indicator within its respective dimension has a loading factor exceeding 0.70. This suggests that the latent constructs at the dimensional level are valid, as they are effectively reflected by their corresponding indicators [9]. Table 5 Outer Loadings Results Testing (Stage 2) Indicator Dimension Variable Loading Factor Type (as defined) Description First Order ST1 Experience User Satisfaction 0.900 Reflective Valid ST2 Expectation 0.857 Reflective Valid ST3 Needs 0.886 Reflective Valid LY1 Repeat Purchase User Loyalty 0.897 Reflective Valid LY2 Retention 0.882 Reflective Valid LY3 Referrals 0.859 Reflective Valid Second Order JPR Improves Job Perfomance Perceived Usefulness 0.920 Reflective Valid PRD Increases Productivity 0.935 Reflective Valid JEF Enhances Job Effectiveness 0.904 Reflective Valid USF Useful 0.883 Reflective Valid FUR Functional Risk Perceived Risk 0.881 Reflective Valid FIR Financial Risk 0.881 Reflective Valid PSR Psychological Risk 0.895 Reflective Valid TIR Time Risk 0.889 Reflective Valid Source: Processed primary data (2025) World Journal of Advanced Research and Reviews, 2025, 27(01), 727-739 735 Table 6 reports that the AVE values for all dimensions exceed 0.50, indicating that each set of indicators has fulfilled the requirements for convergent validity. Accordingly, it can be concluded that convergent validity has been successfully established [9]. Table 6 Average Variance Extracted (AVE) Value (Stage 2) Average variance extracted (AVE) Perceived Usefulness (PU) 0.829 Perceived Risk (PR) 0.786 User Satisfaction (ST) 0.777 User Loyalty (LY) 0.773 Source: Processed primary data (2025) Discriminant Validity Table 7 shows that discriminant validity is confirmed, as the square root of the AVE for each construct (represented by the diagonal values) is greater than the correlations among the constructs [9]. Table 7 Fornell-Larcker Criterion Results (Stage 2) ST LY PR PU ST 0.881 LY 0.779 0.879 PR -0.774 -0.744 0.886 PU 0.878 0.792 -0.813 0.911 Source: Processed primary data (2025) Composite Reliability Table 8 shows that all composite reliability values are above the 0.70 threshold, and the Cronbach’s alpha values for each construct also exceed 0.70. These findings verify that the measurement instruments used in this study demonstrate strong reliability [9]. Table 8 Composite Reliability and Cronbach’s Alpha (Stage 2) Cronbach's Alpha Composite Reliability Perceived Usefulness (PU) 0.931 0.951 Perceived Risk (PR) 0.909 0.936 User Satisfaction (ST) 0.856 0.913 User Loyalty (LY) 0.854 0.911 Source: Processed primary data (2025) 3.2. Evaluation of Structural Model (Inner Model) Structural model testing, also known as inner model testing, is conducted to evaluate the predictive relationships among variables within the research model. This analysis involves assessing the coefficient of determination (R-Square) as a key indicator [9].