Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9, 2007-2021 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 14 July 2025; Revised: 25 August 2025; Accepted: 28 August 2025; Published: 30 September 2025 * Correspondence: erinasovania9@gmail.com Optimizing user loyalty through user interface quality in MSME mobile application: A study on product customization's mediating role Erina Sovania1*, Armanu2, Fatchur Rohman3, Mugiono4 1,2,3,4Faculty of Economic and Business, Marketing and Management, Brawijaya University. Indonesia; erinasovania9@gmail.com (E.S.). Abstract: This study investigates the complex relationships among user interface quality, product customization, and gamification, with the aim of determining their combined impact on user loyalty in mobile marketplace applications targeted at Indonesian MSMEs. Utilizing SmartPLS 4 and structural equation modeling (SEM), the research analyzes data from 400 users of the Umkm Bangkit mobile application in Central Java, based on the Stimulus-Organism-Response (S-O-R) paradigm. The findings highlight the critical role of a high-quality user interface in enhancing user loyalty by encouraging increased user engagement and facilitating effective customization. Interestingly, the anticipated moderating effect of gamification on the relationship between customization and user loyalty was not supported. Platform stakeholders should prioritize improving user interface design and customization tools to foster user loyalty. Despite gamification's potential, cautious implementation is recommended until more empirical evidence is available. The study's focus on Central Java limits the generalizability of its findings. Potential sample bias and reliance on self-reported data may also influence the applicability of the results to a broader population. Additionally, the impact of external factors on user loyalty remains unexplored. This research offers new insights into the interaction between user interface quality, product customization, and gamification in mobile marketplace applications, contributing valuable knowledge to the field. It provides practical recommendations for stakeholders seeking to enhance user engagement and loyalty strategies, emphasizing the importance of user interface design and customization while advocating a cautious approach to integrating gamification elements. Keywords: Gamification, Loyalty, MSME, Product customization, User interface quality. 1. Introduction Indonesia has seen significant growth in mobile marketplace apps [1] presenting challenges for platform owners to maintain competitiveness and user loyalty in a saturated market [2]. With numerous options available, sustained engagement and loyalty are crucial for platform sustainability and growth. As Indonesia's digital landscape evolves, understanding user loyalty in mobile apps has become critical [3]. Marketplace platforms face unique challenges in securing and retaining users [4]. A key factor is User Interface design, which serves as the gateway for user interaction and significantly impacts long-term retention and loyalty [5]. Additionally, customization features offer personalized experiences that meet individual user preferences and needs [6, 7]. Many studies highlight the significant role of User Interface (UI) in shaping user loyalty toward mobile applications [8-12]. These findings affirm that a well-designed UI can greatly enhance long- term user retention and engagement. UI is crucial in mobile applications, serving as both the visual representation and the initial interaction point for users [13, 14]. In a competitive market, an appealing and intuitive UI is essential https://orcid.org/0009-0009-0015-9445 mailto:erinasovania9@gmail.com https://orcid.org/0009-0007-3846-7174 https://orcid.org/0000-0003-0761-3240 2008 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate for an app's success [15, 16]. Effective UI design includes aesthetically pleasing visuals, well-structured information, easy navigation, and swift responsiveness [17, 18]. Moreover, UI should incorporate features that fulfill users' functional needs, enhancing satisfaction and loyalty [19, 20]. User-friendliness is key, aiming to provide an intuitive and enjoyable experience [21]. Users comfortable and satisfied with the UI are more likely to continue using the app. Thus, optimal UI design balances aesthetics, functionality, and user experience [22] creating an emotional bond and a foundation for sustained loyalty [23, 24]. However, unlike prior research [25-28] suggesting that UI is less significant than functionality for customer loyalty, our study reexamines this perspective in the context of marketplace app users. While some argue that a well-functioning app overshadows UI importance, we explore the nuanced relationship between UI and user loyalty in mobile applications. Product customization is crucial for enhancing user loyalty in mobile apps [29]. By offering personalized profiles, customizable interfaces, and tailored content, apps empower users to shape their experiences [30, 31]. This customization fosters a sense of ownership and personal connection, boosting user satisfaction and engagement. Users are more likely to develop a strong affinity for apps that resonate with their preferences and values [32-34]. Product customization contributes to positive word-of-mouth referrals and user advocacy [35, 36]. Satisfied users who customize their experiences are more likely to recommend the app to others [37], facilitating new user acquisition and reinforcing existing user loyalty. This creates a self-sustaining cycle of engagement and growth. Customization, by providing personalized experiences, fosters loyalty and commitment, making the app indispensable in users' digital lives [38, 39]. Additionally, incorporating gamification in mobile apps enhances user engagement and loyalty [38, 39]. Game-like mechanics such as rewards, challenges, and social interactions increase interactivity and enjoyment, encouraging continued use and fostering a sense of community [40-42]. This study examines the interplay between UI design, customization, gamification, and user loyalty in Indonesian mobile marketplace apps. It aims to reveal how customization mediates and gamification moderates the relationship between UI design and user loyalty. The research offers insights and practical implications for platform owners and stakeholders in the Indonesian mobile app market, aiding in the cultivation of lasting user loyalty. 2. Theoretical Background and Hypotheses Development 2.1. A perspective S-O-R Framework In today's rapidly evolving digital landscape, comprehending the factors influencing user loyalty towards mobile applications is imperative for ensuring platform sustainability and growth. To tackle this challenge, scholars have turned to theoretical frameworks such as the Stimulus-Organism-Response (S-O-R) model pioneered by Mehrabian and Russell [43]. This model offers a comprehensive framework for analyzing the intricate interplay between external stimuli, individual perceptions, and resultant behaviors [44-46]. • Stimulus: External factors that elicit responses from individuals. In this study, the stimuli are the elements of the UI in mobile marketplace applications, including visual design, layout, features, and user interactions [46, 47]. The aim of these stimuli is to elicit reactions or responses from users towards the application. • Organism: Refers to the recipients of stimuli and their reactions. In this study, the organism is product customization, which allows users to tailor their application experience to their preferences, needs, and individual characteristics [48]. This customization fosters heightened engagement and a sense of control over their interaction with the application [49]. • Response: This represents the outcomes resulting from the interaction between stimuli and the organism, reflected in user actions or behaviors. In this study, the response is user loyalty towards mobile marketplace applications. Loyalty is measured by usage frequency, retention 2009 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate rates, recommendations to others, and emotional attachment to the app [50]. These responses reflect the degree to which users are positively influenced and engaged with the application due to the stimuli and personalized experiences provided. 2.2. Enhancing Marketplace Loyalty: Insights from UI Strategies In mobile marketplace applications, the UI is the primary means through which users interact with the platform [24, 51]. The UI's design, layout, features, and interactivity greatly influence user experiences and perceptions [52]. Research highlights the critical role of the UI in fostering user loyalty. The UI is not only a point of interaction but also a key factor in determining user loyalty levels towards the platform [53]. Studies consistently show that UI elements—such as design aesthetics, intuitive navigation, feature richness, and overall usability—significantly impact user experiences. These elements help cultivate a sense of attachment and commitment to the platform. Therefore, the design and functionality of the UI are essential in shaping user perceptions, attitudes, and loyalty towards the marketplace [54, 55]. Therefore, in this study, we hypothesize that: H1: proposes that the quality of the UI significantly impacts user loyalty within the marketplace, emphasizing the critical role of design in fostering user engagement. 2.3. The Role of Product Customization in Driving User interface and Loyalty A study titled "Object-Oriented User Interface Customization: Reduce Complexity and Improve Usability and Adaptation" examines the link between UI and customization. Adaptation, which involves personalization and customization, uses AI and machine learning to predict user preferences and tailor interfaces to enhance relevance [48]. Researchers and practitioners see customization as a powerful tool for management, enhancing user control, reducing errors, and increasing acceptance in human-machine interactions [56]. Effective customization also helps users quickly find desired products, saving time [57]. This perspective advocates for improving user engagement by facilitating product discovery. Human-computer interaction research emphasizes a shift towards individualized design, enhancing user performance [58]. Empirical evidence supports a strong positive correlation between customization and loyalty [57, 59, 60]. Superior website customization leads to higher loyalty, while inadequate customization diminishes it, reinforcing the significant influence of customization on loyalty [61-63]. Building upon empirical observations and theoretical foundations, the authors propose the following hypotheses: H2: suggests that UI effectiveness extends to influencing product customization, thereby highlighting user interactions and preferences within the marketplace environment. This hypothesis posits that UI quality significantly influences customization levels, impacting customer perceptions and loyalty. Research underscores UI's pivotal role in shaping user experiences and loyalty. H3: states that product customization significantly affects user loyalty by providing tailored experiences that drive user engagement and retention. This hypothesis suggests that customization significantly impacts customer loyalty by enhancing satisfaction, engagement, and commitment. Existing literature indicates that customization fosters ownership, relevance, and connection, key drivers of loyalty. H4: posits that customization mediates the relationship between UI and loyalty, suggesting that personalized experiences serve as a pathway through which design influences user engagement and retention. This hypothesis explores how customization mediates the relationship between UI quality and customer loyalty, suggesting that customization channels the effects of UI design into loyalty outcomes. It aims to deepen scholarly understanding of these dynamics and provide empirical insights into enhancing customer loyalty through personalized user experiences. 2010 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate 2.4. Synergizing Dynamics: Gamification's Role as Moderator Between Customization and UI The integration of customization into gamification frameworks has become a key topic in academic discussions, aimed at identifying game elements that resonate with diverse user profiles [64]. Researchers have focused on finding game elements that match specific user characteristics through customization, emphasizing the importance of understanding different player types. This highlights the crucial role of customization in tailoring gamified experiences to individual user preferences and behaviors [65]. Additionally, the development of adaptable and customizable gamification engines has become a critical area of focus. The goal is to create platforms that allow for the parameterization of various game mechanics, enabling the creation of highly configurable gaming experiences [61]. In customizable gamification platforms, two user roles have emerged: system administrators and end-users. System administrators are responsible for creating and managing gamified tasks, specifically integrating customization elements into the game design process. Scholarly literature highlights the importance of gamification in boosting loyalty and potentially increasing company profitability [66]. Gamification effectively influences loyalty by tapping into basic human desires such as goal pursuit and recognition [67]. By incorporating game design elements, gamification enhances non-game products and services, increasing customer value and promoting behaviors such as higher consumption, stronger loyalty, greater engagement, and product advocacy. Based on this discussion, we propose the following hypothesis: H5: indicates that gamification significantly influences user loyalty, underscoring the role of game elements in enhancing user engagement. H6: proposes that gamification moderates the impact of customization on loyalty, suggesting that gamified experiences can amplify the effects of customization in driving user engagement and retention within the marketplace. This hypothesis proposes that gamification enhances the impact of customization on loyalty, emphasizing their synergistic effects on user engagement and loyalty. It underscores the intricate relationships between customization, gamification, and loyalty in contemporary research. This study employs the S-O-R framework to investigate how UI influences user loyalty in mobile marketplace apps via product customization. By integrating this framework with mobile apps, it aims to unveil the impact of UI stimuli and customization on loyalty, offering strategies for enhancing user engagement. Figure 1. Summarizes our research framework. 3. Methodology 3.1. Construct Measurement To evaluate this study's outcomes, operational definitions and measurement items were developed for each construct, based on prior research insights. A total of 47 items were created to assess five constructs using a five-point Likert scale (1 = 'strongly disagree' to 5 = 'strongly agree'). These operational definitions and measurement items are detailed in Table 1. 2011 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate Table 1. Operational and measurement. Construct Indicator Source User interface (X1) 1. User-Friendliness Roy, et al. [68] 2. Visual Quality 3. Consistency 4. Ease of Learning 5. Task Suitability Product Customization (X2) 1. Advanced Filters and Search Srinivasan, et al. [27] 2. User Profiles 3. Personal Product Recommendations 4. Display Customization Gamification (X3) 1. Motivation Piligrimienė [69] 2. Engagement Loyalty (Y) 1.Word of mouth (WOM) Kassim and Abdullah [70] 2.Intention to repurchase 3.2. Data Collection and Analysis This study uses an explanatory research design with a quantitative survey method. The target population includes all 4833 users of the Umkm Bangkit mobile application in Central Java. Due to the dynamic nature of user data, this population is considered infinite, as defined by Daniel and Terrell [71]. The sample size of 400 respondents was determined using the Cochran formula. Non-probability sampling, specifically purposive sampling, was employed to meet criteria such as location and age [72]. The survey utilized a five-point Likert scale (1 = 'strongly disagree' to 5 = 'strongly agree'). Data analysis was conducted using Structural Equation Modeling (SEM) with SmartPLS 4. The demographic characteristics of the sample are detailed below. Table 2. Demographic characteristic of sample. Item Characteristic Frequency Ratio Gender Male 180 45 Female 220 55 Age 17-25 yo 71 17,8 26-36 yo 153 38,3 37-47 yo 94 23,4 > 48 yo 82 20,5 Occupation Student 51 2,8 Worker 151 37,8 Government Inst. 104 26 Others 94 23,5 Last use Within 1 week 130 32,5 Within 2 week 153 38,3 > 1 month 117 29,3 Transaction 2 times 180 45 3 to 5 times 123 30,8 more than 5 times 97 24,2 4. Result and Discussion 4.1. Measurement Model Evaluation The outer model serves as a critical tool for assessing the validity and reliability of the model, particularly due to the reflective nature of the indicators utilized. This measurement encompasses several key components, including convergent validity, discriminant validity, composite validity, and Cronbach's alpha. 2012 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate 4.2. Validy and Reliability Test 4.2.1. Convergent Validity Convergent validity is assessed by examining the outer loading coefficients of each indicator relative to its latent variable. Indicators are considered valid when their outer loadings are between 0.60 and 0.70, with a significance level of 0.05 [73]. In this study, 47 instruments were initially considered. For the UI variable, 4 out of 20 instruments (UI7, UI1, UI6, UI7) were invalid (loading factor < 0.7), while the rest were valid (loading factor > 0.7). For Customization, 3 out of 14 instruments (CUS6, CUS13, CUS14) were invalid, and for Gamification, 1 out of 6 instruments (GAM4) was invalid. Similarly, in the Loyalty variable, 1 out of 7 instruments (UI3) was invalid. Invalid instruments were eliminated in a subsequent round of data processing to meet the convergent validity threshold (> 0.7). The UI construct, with 16 items, demonstrated high validity (outer loadings: 0.708 to 0.902), excellent internal consistency (Cronbach's Alpha and Composite Reliability > 0.70), and convergent validity (AVE: 0.647 > 0.50), explaining 64.7% cumulative variance. Customization, with 11 items (outer loadings: 0.727 to 0.911), showed high reliability (Cronbach's Alpha: 0.950, Composite Reliability: 0.957) and convergent validity (AVE: 0.670 > 0.50), explaining 67% variance. Gamification, with 5 items (outer loadings: 0.775 to 0.841), demonstrated commendable reliability (Cronbach's Alpha: 0.874, Composite Reliability: 0.908) and convergent validity (AVE: 0.665 > 0.50), explaining 66.5% variance. Loyalty, measured with 5 items (outer loadings: 0.759 to 0.872), exhibited high reliability (Cronbach's Alpha: 0.874, Composite Reliability: 0.909) and convergent validity (AVE: 0.666 > 0.50), explaining 66.6% variance. 4.2.2. Discriminant Validity Assessing discriminant validity is vital and follows Fornell and Larcker's criteria. It ensures that variables are theoretically distinct and empirically supported. According to these criteria, the square root of the AVE for each variable should be greater than the correlations between variables. The evaluation table for this analysis is shown below: Table 3. Fornell-Larcker Criterion Test. Gamification Loyalty Product Customization User Interface Gamification 0.815 Loyalty 0.758 0.816 Product Customization 0.759 0.768 0.818 User Interface 0.752 0.757 0.764 0.790 The Gamification construct shows stronger correlations with Loyalty (0.758) and Customization (0.752), meeting discriminant validity. Loyalty (0.816) has larger correlation values compared to Customization (0.768) and UI (0.757), also fulfilling discriminant validity. Customization's correlation with UI (0.764) is notably higher (0.818), indicating discriminant validity. 4.3. Structural Model Evaluation The structural model evaluation pertains to testing hypotheses regarding the influence among the research variables. 4.4. Inner VIF Multicollinearity Test The Inner VIF Values are tested to assess the model's fitness, with the model considered suitable if the VIF coefficients are < 5.0. The results of the VIF test can be seen in Table 4 below: 2013 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate Table 4. Collinearity Statistic (VIF) - Inner Model. Inner VIF Criteria Result Gamification -> Loyalty 4.745 < 5.0 Fit Product Customization -> Loyalty 1.922 < 5.0 Fit User Interface -> Loyalty 3.649 < 5.0 Fit User Interface -> Customization 1.000 < 5.0 Fit Gamification x Customization -> Loyalty 2.808 < 5.0 Fit The inner VIF values, all below 5, show no multicollinearity among variables, following Hair, et al. [74] guidelines. As shown in the table, the estimation results consistently confirm inner VIF values below 5, ensuring reliable parameter estimation in PLS SEM and reducing potential biases. 4.5. Hypothesis Test Hypothesis testing utilizes the bootstrapping technique within the Structural Model framework, drawing on data from the Measurement stage. This approach simulates relationships to determine their direction and significance for each latent variable. The bootstrapping results from SmartPLS 4 provide a detailed analysis of the structural model. Figure 2. Bootstrapping Output. Figure 2 illustrates the relationships between variables, highlighting the significant influence of the UI on user loyalty. The UI also indirectly affects loyalty through customization. However, gamification does not effectively moderate the impact of customization on user loyalty, as its influence is negative and not statistically significant. According to Solimun [75] moderating variables can be classified into four types: 2014 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate Table 5. Moderation Variable Type. No Moderation Type Coefficient 1 Pure Moderation β1 Not significant β2 Significant 2 Quasi Moderation β1 Significant β2 Significant 3 Homologizer Moderation β1 Not significant β2 Not significant 4 Predictor Moderation β1 Significant β2 Not significant Source: Solimun [75]. Furthermore, the table below presents the path coefficients for each construct: Table 6. Path Coefficient Value. Original sample (O) T statistics (|O/STDEV|) P values Result Gamification -> Loyalty 0.296 6.162 0.000 Significant Customization -> Loyalty 0.478 9.265 0.000 Significant UI -> Loyalty 0.219 4.179 0.000 Significant UI -> Product Customization 0.964 260.877 0.000 Significant Gamification x Product Customization -> Loyalty 0.006 0.342 0.732 Not Significant The outcomes depicted in the preceding table elucidate the path coefficients, signifying the results of the direct effect analysis. The deductions drawn from these findings are outlined as follows: • UI and Loyalty: The UI significantly positively impacts loyalty, with a sample value of 0.296 and a t-statistic of 6.162, meeting statistical significance criteria (t-statistic > 1.977, p-value < 0.05). This supports the hypothesis that a high-quality UI enhances user loyalty. • UI and Customization: The UI has a highly significant positive effect on customization, with a sample value of 0.964 and a t-statistic of 260.877, confirming the hypothesis that a better UI leads to increased customization. • Customization and Loyalty: Customization significantly positively influences loyalty, with a sample value of 0.478 and a t-statistic of 9.265, supporting the hypothesis that customized products enhance user loyalty. • Gamification and Loyalty: Gamification significantly impacts loyalty, with a sample value of 0.296 and a t-statistic of 6.162, meeting the criteria for statistical significance. This supports the hypothesis that gamification enhances user loyalty. • Gamification as a Moderator: The moderating effect of gamification on the relationship between customization and loyalty is not significant, with a sample value of 0.006 and a t-statistic of 0.342 (t-statistic < 1.977, p-value > 0.05). Thus, the hypothesis that gamification moderates the impact of customization on loyalty is not supported. The subsequent step entails conducting examinations on the associations between exogenous latent variables and endogenous latent variables via intervening variables. The ensuing results are observable in Table 7 as follows: 2015 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate Table 7. Mediation Test Specific Indirect effect. Original sample (O) T statistics (|O/STDEV|) P values Result User Interface -> Product Customization -> Loyalty 0.461 9.246 0.000 Significant The analysis of the Indirect Effect table confirms a significant indirect effect of the UI variable on loyalty through Customization. With a coefficient of 0.461 and a t-statistic of 9.246, surpassing the threshold of 1.977 (t-table) and a p-value < 0.05, the fourth hypothesis is validated. This underscores Customization's role as a mediator between UI quality and loyalty, enhancing our comprehension of user dynamics within the context. 4.6. f-Square (Effect Size) The effect size (f-square) analysis was conducted to assess the goodness of the model, revealing the relative influence of latent independent variables on the latent dependent variable. Following Ghozali and Latan [76] criteria: a. An f² value of 0.35 indicates a high substantial impact of latent independent variables on the latent dependent variable. b. An f² value of 0.15 suggests a moderate or moderate-sized influence between latent independent variables and the latent dependent variable. c. An f² value of 0.02 signifies a small/low impact of latent independent variables on the latent dependent variable. In conclusion, the f-Square values, as presented in Table 8, indicate the following: Table 8. f-square Value. f-square Result Product Customization -> Loyalty 0.244 Moderate Gamification -> Loyalty 0.114 Low User Interface -> Product Customization 13.272 High User Interface -> Loyalty 0.060 Low Customization's effect on Loyalty has a moderate effect size (f² = 0.244), while Gamification's effect on Loyalty is small (f² = 0.114). UI's influence on Customization is notably substantial (f² = 13.272), whereas its impact on Loyalty is small (f² = 0.060). 4.7. Statistical Measurement of Upsilon (V) The statistical measurement of Upsilon (V) assesses the effect size of the mediating variable, indicating the magnitude of its impact at the structural level. Following guidelines by Ogbeibu, et al. [77] effect sizes of 0.175 indicate a high mediating effect, 0.075 signify a medium mediating effect, and 0.01 suggest a low mediating effect. The formula for Upsilon (V) calculation is = 𝜷𝑴𝑿 𝟐 𝜷𝒀𝑴.𝑿. 𝟐 . Below presents the statistical measurement of the Upsilon (V) statistic to evaluate the effect on the mediating variables of Customization. Table 9. The Upsilon (V) Statistical Measurement. Construct Upsilon (V) Statistic Result User Interface -> Product Customization -> Loyalty (0,964)² x (0,478)² = 0,212 High mediating effect The computed Upsilon (V) value of 0.212 indicates a substantial mediating effect of Customization between UI and Loyalty, surpassing the threshold of 0.01 and categorized as high mediation. This 2016 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate statistic offers vital insights into the significance of the mediating variable within the study's structural framework, enhancing our understanding of the underlying dynamics. 4.8. Model Fit Evaluation (Goodness of Fit) The questionnaire's validity and reliability support the credibility of the utilized indicators. Hypothesis testing was then performed to examine the impact of UI Customization, and Gamification on Loyalty. Additionally, Structural Equation Model (SEM) analysis assessed model fit, meeting satisfactory criteria including R-square, Q-square, Residual Square Mean Root Standard (SRMR), and Normed Fit Index [78]. 4.9. R-Square Analysis R-square quantifies the variance in the endogenous variable explained by the exogenous variables. Chin [79] suggests qualitative interpretations: 0.19 (low influence), 0.33 (moderate influence), and 0.66 (high influence). The analysis presents R-square values for this study, indicating the model's explanatory power. Table 10. R-Square. R-square adjusted Criteria Loyalty 0.951 High influence Product Customization 0.930 High influence The table displays R-square values for Customization and Loyalty, indicating their influence from model factors. Customization's R-square of 0.930 signifies 93.0% variability explained by the UI variable, with 7% attributed elsewhere. Loyalty's R-square is 0.951, with 95.1% variability explained by UI and Customization, and 4.9% influenced by external factors. All R-square values surpass the threshold for high influence, emphasizing model variables' significant impact compared to unaccounted external factors. 4.10. Q-Square Analysis The Predictive Relevance Analysis, using Q-square values, evaluates the model's ability to predict changes in variables affecting the endogenous variable. As per Hair, et al. [74] Q-square values are interpreted as follows: 0 for low impact, 0.25 for moderate impact, and 0.50 for high impact. Table 11 presents these values for the model, indicating its predictive relevance. Table 11. Q-Square. Q² Predict Criteria Product Customization 0.618 High impact Loyalty 0.628 High impact The Q-square values suggest high predictive accuracy for both the Customization and Loyalty variables. Customization has a Q-square of 0.618, and Loyalty has a Q-square of 0.628. Hence, both variables effectively predict the model's outcomes. 4.11. Fit Model Measurement The model fit test results in Table 12 show that the Standardized Root Mean Square Residual (SRMR) is below 0.08, indicating a good fit. Additionally, the Normed Fit Index (NFI) yields values between 0 and 1, suggesting a satisfactory fit for all indicators. 2017 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate Table 12. Fit Model Test. Saturated model Estimated model SRMR 0.081 0.081 d_ULS 4.613 4.646 d_G 11.330 11.412 Chi-square 14.036 14.073 NFI 0.477 0.475 Table 12 indicates an SRMR value of 0.081, suggesting an acceptable fit for the model. The values of d_ULS (4.646) and d_G (11.412) align with this interpretation. Additionally, the Chi-Square value of 14.073 and the Normed Fit Index (NFI) of 0.475 fall within the acceptable range, indicating satisfactory model fit across all indicators [80]. Based on the explanations provided above, the following table summarizes the results of the research hypotheses: Table 13. The summary of Hypothesis Test Result. Hypothesis Description Result H1 Quality of the UI significantly impacts user loyalty within the marketplace, emphasizing the critical role of design in fostering user engagement. Accepted H2 UI effectiveness extends to influencing product customization, thereby highlighting user interactions and preferences within the marketplace environment. Accepted H3 Product customization significantly affects user loyalty by providing tailored experiences that drive user engagement and retention. Accepted H4 Customization mediates the relationship between UI and loyalty, suggesting that personalized experiences serve as a pathway through which design influences user engagement and retention. Accepted H5 Gamification significantly influences user loyalty, underscoring the role of game elements in enhancing user engagement. Accepted H6 Gamification moderates the impact of customization on loyalty, suggesting that gamified experiences can amplify the effects of customization in driving user engagement and retention within the marketplace. Rejected 5. Conclusions 5.1. Findings and Implications This study examines how the UI influences loyalty, mediated by customization and moderated by gamification. Results support the first hypothesis, indicating a significant positive impact of the UI on user loyalty. Similarly, the second hypothesis is validated, showing the UI's influence on product customization. The third hypothesis is also supported, highlighting the positive impact of customized products on loyalty. Effect size analysis reveals varying degrees of influence: the UI has a low effect, customization has a moderate effect, and the UI on customization has a high effect. Mediation analysis indicates a strong mediating effect of customization between the UI and loyalty. However, the moderation analysis of gamification shows a non-significant negative effect. Theoretical contributions expand the Stimulus-Organism-Response (S-O-R) model to include customization, offering deeper insights into mobile commerce dynamics. Practically, the study suggests strategies for improving service quality and boosting user loyalty in the Umkm Bangkit mobile application, such as regular UI updates, enhanced customization features, and potential gamification integration. In summary, integrating findings from each variable can help Umkm Bangkit develop effective strategies for enhancing service quality and retaining customer loyalty. 5.2. Limitations and Future Research The study has several limitations requiring careful consideration. Firstly, its scope is confined to Central Java, Indonesia, cautioning against broad application to regions with different user 2018 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate demographics. Secondly, with only 400 respondents from Umkm Bangkit's extensive user base, generalization risks are present. Additionally, sample imbalance, skewed towards more females and private sector employees, limits applicability to diverse groups. Thirdly, reliance on self-reported questionnaire data may introduce bias, impacting result validity. Fourthly, external factors like economic influences, not accounted for, could affect user loyalty. Lastly, the insignificance of gamification in impacting the UI-loyalty link suggests potential overlooked factors. Addressing these limitations is essential for contextualizing the results and avoiding overgeneralization. For future research, broadening the sample's diversity and geographic representation can enhance understanding of user preferences. Additionally, exploring additional loyalty-influencing factors and employing diverse research methods can yield richer insights into user motivations. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] G. D. P. Dewi and A. E. Lusikooy, "E-commerce transformation in Indonesia: Innovation and creative destruction," Nation State: Journal of International Studies, vol. 6, no. 2, pp. 117-138, 2023. https://doi.org/10.24076/nsjis.v6i2.1304 [2] M. I. N. Susiang, D. A. Suryaningrum, A. Masliardi, E. Setiawan, and F. Abdillah, "Enhancing customer experience through effective marketing strategies: The context of online shopping," SEIKO: Journal of Management & Business, vol. 6, no. 2.1, pp. 437-447, 2023. [3] L. Stocchi, N. Pourazad, N. Michaelidou, A. Tanusondjaja, and P. Harrigan, "Marketing research on Mobile apps: Past, present and future," Journal of the Academy of Marketing Science, vol. 50, no. 2, pp. 195-225, 2022. https://doi.org/10.1007/s11747-021-00815-w [4] N. Rane, S. Choudhary, and J. Rane, "Metaverse marketing strategies: Enhancing customer experience and analysing consumer behaviour through leading-edge Metaverse technologies, platforms, and models," SSRN Electronic Journal, 2023. https://doi.org/10.2139/ssrn.4624199 [5] C. Bogdan et al., "Generating an abstract user interface from a discourse model inspired by human communication," in Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008) (pp. 36-36). IEEE, 2008. [6] J. S. Chouhan and G. Mariya, "Customized Experiences: Unleashing the Power of AI-Driven Content Personalization," 2021. [7] A. T. Abtahi, T. Shafique, T. Al Haque, S. A. J. Siam, and A. Rahman, "Exploring consumer preferences: The significance of personalization in e‑commerce," Malaysian E‑Commerce Journal, vol. 8, no. 1, pp. 1–7, 2024. https://doi.org/10.26480/mecj.01.2024.01.07 [8] U. Bhandari, K. Chang, and T. Neben, "Understanding the impact of perceived visual aesthetics on user evaluations: An emotional perspective," Information & Management, vol. 56, no. 1, pp. 85-93, 2019. https://doi.org/10.1016/j.im.2018.07.003 [9] J. Kim, "The effect of design characteristics of mobile applications on user retention: An environmental psychology perspective," presented at the 18th Americas Conference on Information Systems 2012, AMCIS 2012, 6, 4366–4374, 2012. [10] P. O. H. Putra, R. A. W. W. C. Kirana, and I. Budi, "Usability factors that drive continued intention to use and loyalty of mobile travel application," Heliyon, vol. 8, no. 9, 2022. https://doi.org/10.1016/j.heliyon.2022.e10620 [11] U. Rahardja, C. T. Sigalingging, P. O. H. Putra, A. Nizar Hidayanto, and K. Phusavat, "The impact of mobile payment application design and performance attributes on consumer emotions and continuance intention," SAGE Open, vol. 13, no. 1, p. 21582440231151919, 2023. https://doi.org/10.1177/21582440231151919 [12] C. Xu, D. Peak, and V. Prybutok, "A customer value, satisfaction, and loyalty perspective of mobile application recommendations," Decision Support Systems, vol. 79, pp. 171-183, 2015. https://doi.org/10.1016/j.dss.2015.08.008 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.24076/nsjis.v6i2.1304 https://doi.org/10.1007/s11747-021-00815-w https://doi.org/10.2139/ssrn.4624199 https://doi.org/10.26480/mecj.01.2024.01.07 https://doi.org/10.1016/j.im.2018.07.003 https://doi.org/10.1016/j.heliyon.2022.e10620 https://doi.org/10.1177/21582440231151919 https://doi.org/10.1016/j.dss.2015.08.008 2019 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate [13] A. C. d. Barros, R. Leitão, and J. Ribeiro, "Design and evaluation of a mobile user interface for older adults: Navigation, interaction and visual design recommendations," Procedia Computer Science, vol. 27, pp. 369-378, 2014. https://doi.org/10.1016/j.procs.2014.02.041 [14] A. Seffah and H. Javahery, Multiple user interfaces: Cross‑platform applications and context‑aware interfaces. In Multiple User Interfaces: Cross‑Platform Applications and Context‑Aware Interfaces (ed. Seffah & Javahery). Hoboken, NJ, USA: John Wiley & Sons, 2005. [15] P. Cremonesi, M. Elahi, and F. Garzotto, "User interface patterns in recommendation-empowered content intensive multimedia applications," Multimedia Tools and Applications, vol. 76, no. 4, pp. 5275-5309, 2017. https://doi.org/10.1007/s11042-016-3946-5 [16] A. Kot, "Enhancing data analytics for heavy industry through UI/UX design and streamlined reporting: Improving usability, scalability, and visual representation through application of design principles," 2023. [17] T. Gülenman, "Designing Better Mobile Apps: An Experimental Evaluation of Apple’s and Google’s Design Guidelines: How analysing the Human Interface Guidelines for iOS and Material Design for Android better our understanding of the usability challenges app users face and what we can do to overcome key issues," 2022. [18] E. Pihlajamäki, "From desktop to mobile: Ui patterns for user interface adaptation in games," Tampere: University of Tampere, 2016. [19] J. Fang, Z. Zhao, C. Wen, and R. Wang, "Design and performance attributes driving mobile travel application engagement," International Journal of Information Management, vol. 37, no. 4, pp. 269-283, 2017. https://doi.org/10.1016/j.ijinfomgt.2017.03.003 [20] S. Ritonummi and O. Niininen, User experience of an e‑commerce website: A case study. In O. Niininen (Ed.), Contemporary Issues in Digital Marketing. Abingdon, United Kingdom: Routledge, 2022. [21] A. Sutcliffe, "Designing user interfaces in emotionally-sensitive applications," in FIP Conference on Human-Computer Interaction (pp. 404-422). Cham: Springer International Publishing, 2017. [22] J. Silvennoinen, M. Vogel, and S. Kujala, "Experiencing visual usability and aesthetics in two mobile application contexts," Journal of Usability Studies, Vol. 10, no. 1, pp. 46–62, 2014. [23] D. Cyr, M. Head, and A. Ivanov, "Perceived interactivity leading to e-loyalty: Development of a model for cognitive– affective user responses," International Journal of Human-Computer Studies, vol. 67, no. 10, pp. 850-869, 2009. https://doi.org/10.1016/j.ijhcs.2009.07.004 [24] M. Niranjanamurthy, N. Kavyashree, S. Jagannath, and D. Chahar, "Analysis of e-commerce and m-commerce: advantages, limitations and security issues," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 6, pp. 2360-2370, 2013. [25] D. Lee, J. Moon, Y. J. Kim, and M. Y. Yi, "Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty," Information & Management, vol. 52, no. 3, pp. 295-304, 2015. https://doi.org/10.1016/j.im.2014.12.001 [26] S. Levy, "Does usage level of online services matter to customers’ bank loyalty?," Journal of Services Marketing, vol. 28, no. 4, pp. 292-299, 2014. https://doi.org/10.1108/jsm-09-2012-0162 [27] S. S. Srinivasan, R. Anderson, and K. Ponnavolu, "Customer loyalty in e-commerce: an exploration of its antecedents and consequences," Journal of Retailing, vol. 78, no. 1, pp. 41-50, 2002. [28] G. H. Tanuwijaya and Y. Suharto, "The influence of user interface design and user experience to E-loyalty (case study of online transportation: GO-JEK)," in Proceedings of International Conference on Management in Emerging Markets (ICMEM) SBM ITB, 2019. [29] A. A. Alalwan, R. S. Algharabat, A. M. Baabdullah, N. P. Rana, Z. Qasem, and Y. K. Dwivedi, "Examining the impact of mobile interactivity on customer engagement in the context of mobile shopping," Journal of Enterprise Information Management, vol. 33, no. 3, pp. 627-653, 2020. https://doi.org/10.1108/jeim-07-2019-0194 [30] A. Belhadi, S. Kamble, I. Benkhati, S. Gupta, and S. K. Mangla, "Does strategic management of digital technologies influence electronic word-of-mouth (eWOM) and customer loyalty? Empirical insights from B2B platform economy," Journal of Business Research, vol. 156, p. 113548, 2023. https://doi.org/10.1016/j.jbusres.2022.113548 [31] S. Thirumalai and K. K. Sinha, "Customization strategies in electronic retailing: Implications of customer purchase behavior," Decision Sciences, vol. 40, no. 1, pp. 5-36, 2009. https://doi.org/10.1111/j.1540-5915.2008.00222.x [32] N. Chen and Y. Yang, "The role of influencers in live streaming e-commerce: Influencer trust, attachment, and consumer purchase intention," Journal of Theoretical and Applied Electronic Commerce Research, vol. 18, no. 3, pp. 1601- 1618, 2023. https://doi.org/10.3390/jtaer18030081 [33] S. Wu and D. Trottier, "Dating apps: A literature review," Annals of the International Communication Association, vol. 46, no. 2, pp. 91-115, 2022. https://doi.org/10.1080/23808985.2022.2069046 [34] F.-C. Yang and P.-W. Tasi, "Measuring the mediating effect of satisfaction and compatibility on the relationship between podcast features and users’ intention of continuous usage and word of mouth," Multimedia Tools and Applications, vol. 83, no. 15, pp. 44527-44554, 2024. https://doi.org/10.1007/s11042-023-17417-z [35] M. A. S. Goraya, Z. Jing, M. A. Shareef, M. Imran, A. Malik, and M. S. Akram, "An investigation of the drivers of social commerce and e-word-of-mouth intentions: Elucidating the role of social commerce in E-business," Electronic Markets, vol. 31, no. 1, pp. 181-195, 2021. https://doi.org/10.1007/s12525-019-00347-w https://doi.org/10.1016/j.procs.2014.02.041 https://doi.org/10.1007/s11042-016-3946-5 https://doi.org/10.1016/j.ijinfomgt.2017.03.003 https://doi.org/10.1016/j.ijhcs.2009.07.004 https://doi.org/10.1016/j.im.2014.12.001 https://doi.org/10.1108/jsm-09-2012-0162 https://doi.org/10.1108/jeim-07-2019-0194 https://doi.org/10.1016/j.jbusres.2022.113548 https://doi.org/10.1111/j.1540-5915.2008.00222.x https://doi.org/10.3390/jtaer18030081 https://doi.org/10.1080/23808985.2022.2069046 https://doi.org/10.1007/s11042-023-17417-z https://doi.org/10.1007/s12525-019-00347-w 2020 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate [36] R. Lacey and R. M. Morgan, "Customer advocacy and the impact of B2B loyalty programs," Journal of Business & Industrial Marketing, vol. 24, no. 1, pp. 3-13, 2008. https://doi.org/10.1108/08858620910923658 [37] J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, "Recommender system application developments: A survey," Decision Support Systems, vol. 74, pp. 12-32, 2015. https://doi.org/10.1016/j.dss.2015.03.008 [38] D. Mourtzis, M. Doukas, and C. Vandera, "Smart mobile apps for supporting product design and decision-making in the era of mass customisation," International Journal of Computer Integrated Manufacturing, vol. 30, no. 7, pp. 690-707, 2017. https://doi.org/10.1080/0951192X.2016.1187295 [39] A. Shen and A. Dwayne Ball, "Is personalization of services always a good thing? Exploring the role of technology‑mediated personalization (TMP) in service relationships," Journal of Services Marketing, vol. 23, no. 2, pp. 79-91, 2009. https://doi.org/10.1108/08876040910946341 [40] P. Bitrián Arcas, "Analysing the effectiveness of gamification: A strategy to create engaging, motivating and enjoyable user experiences," Master’s Thesis, Universidad de Zaragoza. Zaragoza, Spain: Universidad de Zaragoza, 2023. [41] C. F. Hofacker, K. de Ruyter, N. H. Lurie, P. Manchanda, and J. Donaldson, "Gamification and mobile marketing effectiveness," Journal of Interactive Marketing, vol. 34, no. 1, pp. 25-36, 2016. https://doi.org/10.1016/j.intmar.2016.03.001 [42] H.-P. Lu and H.-C. Ho, "Exploring the impact of gamification on users’ engagement for sustainable development: A case study in brand applications," Sustainability, vol. 12, no. 10, p. 4169, 2020. https://doi.org/10.3390/su12104169 [43] A. Mehrabian and J. A. Russell, An approach to environmental psychology. Cambridge, MA: M.I.T. Press, 1974. [44] P. K. Chopdar, J. Paul, N. Korfiatis, and M. D. Lytras, "Examining the role of consumer impulsiveness in multiple app usage behavior among mobile shoppers," Journal of Business Research, vol. 140, pp. 657-669, 2022. https://doi.org/10.1016/j.jbusres.2021.11.031 [45] S. Kumar, A. Jain, and J.-K. Hsieh, "Impact of apps aesthetics on revisit intentions of food delivery apps: The mediating role of pleasure and arousal," Journal of Retailing and Consumer Services, vol. 63, p. 102686, 2021. https://doi.org/10.1016/j.jretconser.2021.102686 [46] X.-Y. Xu, Q.-D. Jia, and S. M. U. Tayyab, "Exploring the stimulating role of augmented reality features in E- commerce: A three-staged hybrid approach," Journal of Retailing and Consumer Services, vol. 77, p. 103682, 2024. https://doi.org/10.1016/j.jretconser.2023.103682 [47] O. Schneider, K. MacLean, C. Swindells, and K. Booth, "Haptic experience design: What hapticians do and where they need help," International Journal of Human-Computer Studies, vol. 107, pp. 5-21, 2017. https://doi.org/10.1016/j.ijhcs.2017.04.004 [48] B. Zhang and S. S. Sundar, "Proactive vs. reactive personalization: Can customization of privacy enhance user experience?," International Journal of Human-Computer Studies, vol. 128, pp. 86-99, 2019. https://doi.org/10.1016/j.ijhcs.2019.03.002 [49] M. A. Raji, H. B. Olodo, T. T. Oke, W. A. Addy, O. C. Ofodile, and A. T. Oyewole, "E-commerce and consumer behavior: A review of AI-powered personalization and market trends," GSC Advanced Research and Reviews, vol. 18, no. 3, pp. 066-077, 2024. [50] B. Kim, "Understanding key antecedents of user loyalty toward mobile messenger applications: An integrative view of emotions and the dedication-constraint model," International Journal of Human–Computer Interaction, vol. 33, no. 12, pp. 984-1000, 2017. https://doi.org/10.1080/10447318.2017.1304607 [51] D. A. Griffith, R. F. Krampf, and J. W. Palmer, "The role of interface in electronic commerce: Consumer involvement with print versus on-line catalogs," International Journal of Electronic Commerce, vol. 5, no. 4, pp. 135-153, 2001. https://doi.org/10.1080/10864415.2001.11044219 [52] A. Srivastava, S. Kapania, A. Tuli, and P. Singh, "Actionable UI design guidelines for smartphone applications inclusive of low-literate users," Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CSCW1, pp. 1-30, 2021. https://doi.org/10.1145/3449210 [53] M. Kim, Y. Chang, M.-C. Park, and J. Lee, "The effects of service interactivity on the satisfaction and the loyalty of smartphone users," Telematics and Informatics, vol. 32, no. 4, pp. 949-960, 2015. https://doi.org/10.1016/j.tele.2015.05.003 [54] D. Cyr, "Return visits: A review of how web site design can engender visitor loyalty," Journal of Information Technology, vol. 29, no. 1, pp. 1-26, 2014. https://doi.org/10.1057/jit.2013.25 [55] T. Porat and N. Tractinsky, "It's a pleasure buying here: The effects of web-store design on consumers' emotions and attitudes," Human–Computer Interaction, vol. 27, no. 3, pp. 235-276, 2012. https://doi.org/10.1080/07370024.2011.646927 [56] P. S. Coelho and J. Henseler, "Creating customer loyalty through service customization," European Journal of Marketing, vol. 46, no. 3-4, pp. 331-356, 2012. https://doi.org/10.1108/03090561211202503 [57] C. Ki-Han and S. Jae-Ik, "The relationship among e-retailing attributes, e-satisfaction and e-loyalty," Management Review: An International Journal, vol. 3, no. 1, pp. 23-45, 2008. https://doi.org/10.1108/08858620910923658 https://doi.org/10.1016/j.dss.2015.03.008 https://doi.org/10.1080/0951192X.2016.1187295 https://doi.org/10.1108/08876040910946341 https://doi.org/10.1016/j.intmar.2016.03.001 https://doi.org/10.3390/su12104169 https://doi.org/10.1016/j.jbusres.2021.11.031 https://doi.org/10.1016/j.jretconser.2021.102686 https://doi.org/10.1016/j.jretconser.2023.103682 https://doi.org/10.1016/j.ijhcs.2017.04.004 https://doi.org/10.1016/j.ijhcs.2019.03.002 https://doi.org/10.1080/10447318.2017.1304607 https://doi.org/10.1080/10864415.2001.11044219 https://doi.org/10.1145/3449210 https://doi.org/10.1016/j.tele.2015.05.003 https://doi.org/10.1057/jit.2013.25 https://doi.org/10.1080/07370024.2011.646927 https://doi.org/10.1108/03090561211202503 2021 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 2007-2021, 2025 DOI: 10.55214/2576-8484.v9i9.10284 © 2025 by the authors; licensee Learning Gate [58] D. Burkolter, B. Weyers, A. Kluge, and W. Luther, "Customization of user interfaces to reduce errors and enhance user acceptance," Applied Ergonomics, vol. 45, no. 2, Part B, pp. 346-353, 2014. https://doi.org/10.1016/j.apergo.2013.04.017 [59] M. Hu, P. E. Chaudhry, and S. S. Chaudhry, "Linking customized logistics service in online retailing with E- satisfaction and E-loyalty," International Journal of Engineering Business Management, vol. 14, p. 18479790221097528, 2022. https://doi.org/10.1177/18479790221097528 [60] J. Semeijn, A. C. R. van Riel, M. J. H. van Birgelen, and S. Streukens, "E‑services and offline fulfilment: How e‑loyalty is created," Managing Service Quality, vol. 15, no. 2, pp. 182-194, 2005. https://doi.org/10.1108/09604520510585361 [61] M. Böckle, J. Novak, and M. Bick, "Towards adaptive gamification: A synthesis of current developments," in Proceedings of the 25th European Conference on Information Systems, ECIS 2017, 2017. [62] A. Fels, B. Falk, and R. Schmitt, "User-driven customization and customer loyalty: A survey," Procedia CIRP, vol. 60, pp. 410-415, 2017. https://doi.org/10.1016/j.procir.2017.02.013 [63] M. A. Sarwar, A. Amin, R. Manager -Sme, and A. B. Limited, "Service customization leading to customer loyalty," Asia Pacific Journal of Emerging Markets, vol. 3, no. 1, pp. 1–24, 2019. [64] Z. Luo, "Gamification for educational purposes: What are the factors contributing to varied effectiveness?," Education and Information Technologies, vol. 27, no. 1, pp. 891-915, 2022. https://doi.org/10.1007/s10639-021-10642-9 [65] L. Cónego, R. Pinto, J. Pinto, and G. Gonçalves, "Leveraging gamification in industry 5.0: Tailored Solutions for workplace’ employees," Procedia Computer Science, vol. 232, pp. 1769-1778, 2024. https://doi.org/10.1016/j.procs.2024.01.175 [66] M. Putri and R. U. Nugrahani, "The influence of shopee usage and the implementation of “Goyang Shopee” gamification on shopee user engagement levels," eProceedings of Management, vol. 7, no. 2, pp. 4737–4744, 2020. [67] W. Kristian and T. A. Napitupulu, "Analysis of the effect of gamification on customer loyalty of the use of the online transportation application," Journal of Theoretical and Applied Information Technology, vol. 100, no. 7, pp. 1941-1950, 2022. [68] S. Roy, C. L. A. Clarke, and S. H. Ibbotson, "User interface design: Key concepts and applications," Journal of Systems and Software, vol. 59, no. 3, pp. 151-167, 2001. [69] Z. Piligrimienė, "Gamification and its impact on user motivation and engagement in digital platforms," Journal of Digital Media & Policy, vol. 12, no. 1, pp. 45-59, 2021. [70] N. M. Kassim and N. A. Abdullah, "The effect of perceived service quality dimensions on customer satisfaction, trust, and loyalty in e-commerce settings," Journal of Marketing Management, vol. 26, no. 9-10, pp. 914-939, 2010. [71] W. W. Daniel and J. C. Terrell, Business statistics: Basic concepts and methodology. Boston, MA: Houghton Mifflin, 1975. [72] U. Sekaran and R. Bougie, Research methods for business: A skill-building approach, 7th ed. Chichester, UK: Wiley, 2017. [73] A. Sylva, "Assessing convergent validity in structural equation modeling," Unpublished Manuscript or Report, 2020. [74] J. F. Hair, G. T. M. Hult, C. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS- SEM), 3rd ed. Thousand Oaks, CA: Sage Publications, 2021. [75] Solimun, Path analysis: Concepts and applications with SPSS program. Yogyakarta, Indonesia: CAPS (Center for Academic Publishing Service), 2010. [76] I. Ghozali and H. Latan, Partial least squares: Concepts, techniques, and applications using SmartPLS 3.0, 1st ed. Semarang, Indonesia: Badan Penerbit Universitas Diponegoro, 2015. [77] S. E. Ogbeibu, J. Gaskin, and J. Burgess, "A novel measure of effect size for mediation analysis," Journal of Business Research, vol. 124, pp. 1–12, 2021. [78] B. Wah Yap, T. Ramayah, and W. Nushazelin Wan Shahidan, "Satisfaction and trust on customer loyalty: A PLS approach," Business Strategy Series, vol. 13, no. 4, pp. 154-167, 2012. https://doi.org/10.1108/17515631211246221 [79] W. W. Chin, The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates, 1998. [80] S. Soehardi, L. Purnamaasih, and D. Rapitasari, "The impact of the Covid-19 pandemic on foreign and domestic tourist visits and the occupancy rate of star-rated hotels in Indonesia," Jurnal Kajian Ilmiah, vol. 20, no. 3, pp. 291 - 308, 2020. https://doi.org/10.31599/jki.v20i3.287 https://doi.org/10.1016/j.apergo.2013.04.017 https://doi.org/10.1177/18479790221097528 https://doi.org/10.1108/09604520510585361 https://doi.org/10.1016/j.procir.2017.02.013 https://doi.org/10.1007/s10639-021-10642-9 https://doi.org/10.1016/j.procs.2024.01.175 https://doi.org/10.1108/17515631211246221 https://doi.org/10.31599/jki.v20i3.287