Available online at www.HighTechJournal.org HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 216 ISSN: 2723-9535 Examining User Satisfaction and Continuous Usage Intention of Digital Financial Advisory Platforms Athapol Ruangkanjanases 1 , Taqwa Hariguna 2* 1 Department of Commerce, Chulalongkorn Business School, Chulalongkorn University, Bangkok, 10330, Thailand. 2 Department of Information System and Magister Computer Sciences, Universitas Amikom Purwokerto, Purwokerto Utara, 53127, Indonesia. Received 02 November 2024; Revised 07 January 2025; Accepted 19 January 2025; Published 01 March 2025 Abstract This study investigates the factors influencing user satisfaction (US) and continuous intention (CI) to use digital financial advisory platforms in Indonesia, focusing on perceived ease of use (PEU), perceived enjoyment (PE), and service quality (SQ). Data from 413 respondents were collected through an online survey and analyzed using structural equation modeling (SEM) with SmartPLS. Results revealed that PEU significantly influences PE (β = 0.923, t-value = 88.677, p < 0.001) and CI (β = 0.471, t-value = 13.950, p < 0.001), demonstrating the critical role of usability in enhancing user engagement. PE positively affects US (β = 0.211, t-value = 7.248, p < 0.001), while SQ strongly predicts US (β = 0.773, t-value = 29.423, p < 0.001). The strong impact of the US on CI (β = 0.518, t-value = 15.117, p < 0.001) highlights satisfaction’s importance for user retention. R-squared values of 0.851 for PE, 0.876 for US, and 0.878 for CI indicate substantial explanatory power. This study extends the Technology Acceptance Model (TAM) by integrating enjoyment and SQ, offering a comprehensive framework for understanding user behavior in digital finance. Findings underscore the need for user-friendly design, engaging features, and high service standards to enhance satisfaction and retention. Keywords: Continuous Usage Intention; Digital Financial Advisory; Perceived Ease of Use; Perceived Enjoyment; Service Quality. 1. Introduction In recent years, the financial industry has witnessed a significant transformation driven by technological advancements. Among the various innovations, digital financial advisory platforms have emerged as a pivotal development, revolutionizing access to financial advice. These platforms leverage advanced algorithms and user-friendly interfaces to provide personalized financial advice, making it more accessible to a broader audience. While numerous studies have explored general trends in fintech and digital financial services, fewer studies have focused on understanding integrated user experiences and satisfaction within digital advisory platforms, particularly in emerging markets like Indonesia. The rapid growth of these platforms is evident as they cater to the evolving needs of users who seek convenience, efficiency, and tailored financial solutions. The proliferation of smartphones and internet penetration has further accelerated the adoption of digital financial advisory services, positioning them as indispensable tools in modern financial management [1, 2]. A digital financial advisory platform is a technology-driven service that utilizes algorithms, artificial intelligence, and automation to provide users with personalized financial advice and management [3, 4]. These platforms function as digital tools offering tailored financial guidance, investment recommendations, and * Corresponding author: taqwa@amikompurwokerto.ac.id http://dx.doi.org/10.28991/HIJ-2025-06-01-015  This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). © Authors retain all copyrights. https://creativecommons.org/licenses/by/4.0/ https://orcid.org/0000-0001-6766-5785 https://orcid.org/0000-0003-1801-6791 HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 217 portfolio management without extensive human intervention [5]. Robo-advisory services, powered by advanced technologies like artificial intelligence, can create customized investment strategies based on individual preferences and risk profiles [6]. However, there remains a gap in understanding how factors like perceived ease of use (PEU), perceived enjoyment (PE), and service quality (SQ) collectively impact user satisfaction (US) and continuous usage intention (CI) within these platforms. Existing models, while informative, often fall short of addressing the unique challenges and user expectations in the context of digital financial advisory platforms. Digital financial platforms, including robo-advisory services, are instrumental in democratizing access to financial services and enhancing financial literacy [7, 8]. They empower individuals, including lay investors, to make well-informed investment decisions, practice sustainable investing, and navigate personal finance complexities conveniently and user-friendly [9, 10]. Despite these benefits, barriers such as financial literacy, usability challenges, and user trust must be addressed to fully realize their potential [Reference]. Additionally, integrating technologies like Digital Twin (DT) in robo-advisory services enriches the user experience by providing dynamic and comprehensive financial advice [11]. The adoption of digital financial platforms, such as robo-advisory services, is influenced by factors like usability, trust, and perceived helpfulness. These platforms are designed to deliver efficient and user-centric financial solutions, aiming to boost financial inclusion, economic growth, and overall financial well-being [12, 13]. Nonetheless, challenges persist in ensuring regulatory compliance, user data security, and user engagement, particularly when users prefer human advisors for complex or personalized financial decisions. Addressing these issues requires a nuanced understanding of user expectations and satisfaction [14]. Ensuring the security and integrity of transactions and user data is paramount in the digital financial space [15, 16]. This study seeks to fill a critical gap by examining integrated user experiences through the combined influence of PEU, PE, and SQ on US and CI in digital financial advisory platforms. Moreover, the rapid digitalization of financial services brings challenges related to financial literacy, usability, and governance [5]. While digital platforms offer numerous benefits to customers, such as convenience and accessibility, they also pose challenges in terms of ensuring that users have the necessary knowledge and skills to navigate these platforms effectively. Another critical challenge is the need for personalized and tailored financial advice. While robo-advisory services offer automated investment recommendations, some users may still prefer human financial advisors for more customized guidance, especially when investment decisions are complex or require high involvement [6]. Recent studies have further explored the complexities of consumer behavior and the evolving role of robo-advisory services in the post-COVID-19 era. The limited focus on holistic models examining PEU, PE, and SQ collectively highlights a significant gap in the existing literature. Addressing this gap can provide a deeper understanding of user satisfaction and continuous engagement in the digital financial advisory sector. Research shows that perceived interactivity and quality play a significant role in shaping behavioral intention, particularly in virtual environments, such as online financial conferences and platforms [17]. The rise of artificial intelligence (AI)-empowered financial advisory services has also transformed how financial advice is delivered, challenging traditional models of human interaction in financial management [18]. This study builds upon these insights by developing and validating an integrated model tailored to the unique characteristics and user needs of digital financial platforms. Robo-advisors are expected to continue reshaping the financial landscape, as their ease of use and low fees make them accessible to a wider segment of the population, forcing traditional advisors to adapt to the changing market [19]. Moreover, factors such as perceived value, risk, and financial knowledge have been identified as key drivers of consumer adoption of robo-advisory services, highlighting the importance of addressing user concerns and improving perceived reliability and trust [20]. These insights suggest that while robo-advisory services offer significant potential for financial inclusion, their success depends on effectively managing user expectations, perceived value, and the evolving technological landscape. Additionally, integrating digital banking technology and its impact on various industries, such as the hotel sector, presents challenges in educating the population about the functionalities and benefits of these platforms [21, 22]. Overcoming the barriers to adopting and using digital financial platforms in different sectors is essential for maximizing their potential benefits [23, 24]. Furthermore, the potential for intelligent investment advisory platforms to engage in illicit activities, such as deceptive marketing and unauthorized fundraising, poses a significant challenge that requires robust regulatory frameworks and oversight [14]. Ensuring compliance with financial regulations and ethical standards is crucial for maintaining the integrity of digital financial advisory services. Another challenge is the need for continuous innovation and updates to keep the platform relevant and valuable for users. The financial industry is dynamic, with frequent changes in market conditions, regulations, and user preferences. Digital financial advisory platforms must continuously adapt to these changes by updating their algorithms, enhancing user interfaces, and integrating new features. This requires significant investment in technology and human resources, which can be a strain on the operational capabilities of the platforms [25]. Additionally, ensuring data security and maintaining user trust is critical, as any breach or misuse of financial data can lead to a loss of users and potential legal repercussions. Understanding the factors influencing user satisfaction (US) and continuous intention (CI) is paramount for addressing these challenges. The US is influenced by various factors, including perceived ease of use (PEU), enjoyment, and service quality (SQ) provided by the platform. If users find the platform easy to navigate and enjoyable to use and perceive the SQ to be high, they are more likely to be satisfied. This satisfaction, in turn, influences their intention to HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 218 continue using the platform. CI is crucial for the long-term success of digital financial advisory platforms, as it ensures a stable user base and contributes to positive word-of-mouth and brand loyalty. Moreover, gaining insights into these factors can help platform providers design and implement strategies that enhance user experience and engagement. For example, by identifying the key determinants of the US, providers can prioritize improvements in areas with the most significant impact on user retention. Understanding the relationship between these factors and CI enables providers to predict user behavior and take proactive measures to prevent churn. In the context of the Indonesian market, where digital literacy and financial behaviors are rapidly evolving, such insights are invaluable for creating tailored solutions that resonate with the local user base and drive sustained growth. Despite the progress made in understanding these individual factors, few recent studies have examined integrated models that combine PEU, PE, SQ, and their collective influence on US and CI specifically in digital financial platforms over the past two to three years that need to be developed. An integrated approach is essential to fully comprehend the multifaceted nature of user experiences and decision-making processes on digital platforms. Considering how these variables interact and influence each other, existing models may provide an incomplete picture, limiting their practical applicability for platform developers and marketers aiming to enhance user retention and engagement. Furthermore, the empirical evidence on how these factors interplay to influence the US and CI remains limited. Most studies have been conducted in contexts different from digital financial advisory platforms, such as e-commerce or general technology adoption, which may only partially translate to the specific nuances of financial advisory services. The unique characteristics of these platforms, such as the complexity of financial products and the need for personalized advice, necessitate a tailored investigation into the combined effects of PEU, PE, and SQ [26]. Addressing this gap is crucial for developing more effective strategies to improve the US and foster long-term usage intentions in the digital financial advisory sector. To bridge this gap, the present study aims to develop and validate an integrated model that combines these key variables and examines their collective impact on the US and CI. By doing so, it seeks to provide a more comprehensive understanding of the user experience in digital financial advisory platforms and offer actionable insights for enhancing user engagement and retention. This research fills this gap by providing a validated model that will contribute to the existing literature by filling the void of integrated models and providing empirical evidence specific to the digital financial advisory context platforms, particularly within the Indonesian market. The primary objective of this study is to develop and validate an integrated model that elucidates the factors influencing the US and CI of digital financial advisory platforms. By synthesizing insights from existing literature and addressing the identified research gaps, this study aims to provide a comprehensive understanding of the complex interplay between various determinants of the US and their subsequent impact on users' intentions to continue using these platforms. The goal is to create a robust framework that can guide platform developers and marketers in designing more user-centric and effective digital financial advisory services. The study will specifically focus on the relationships between PEU, PE, SQ, US, and CI to achieve this objective. PEU refers to the degree to which users believe that using the platform will be free from effort, which is crucial for user adoption and continued use. PE captures how users find the platform enjoyable, enhancing their overall experience. SQ encompasses various dimensions of the service provided by the platform, such as reliability, responsiveness, and competence, all of which significantly affect user perceptions and satisfaction. US is a critical mediator in this model, linking the antecedent factors (PEU, PE, and SQ) to the outcome variable, CI. CI denotes the user's intention to keep using the platform in the future, which is vital for the platform's long- term success and sustainability. By examining these relationships in an integrated manner, this study aims to uncover these variables' direct, indirect, and mediated effects, providing a nuanced understanding of how each factor contributes to US and CI. This research offers theoretical contributions by extending the Technology Acceptance Model (TAM) through the integration of enjoyment and service quality, providing a more holistic understanding of user satisfaction in the context of digital finance. Through this comprehensive investigation, the study will contribute to the theoretical advancement in digital financial advisory services by offering a validated model that integrates critical determinants of US and CI. Furthermore, the empirical findings will provide actionable insights for practitioners, enabling them to implement targeted strategies that enhance the US and foster long-term engagement with their platforms. In a post-pandemic digital economy, these insights are particularly valuable as financial advisory platforms must adapt to changing user behaviors and preferences, ensuring user-centered design and engagement. This research is particularly relevant in the Indonesian context, where the digital financial advisory market is burgeoning, and understanding local user behavior is crucial for success. In summary, this study contributes to the existing literature by developing and validating an integrated model that explores the interplay between PEU, PE, SQ, US, and CI in digital financial advisory platforms. By focusing on the Indonesian market, where digital literacy is rapidly evolving, this research offers actionable insights to enhance user engagement, satisfaction, and retention, bridging critical gaps in existing models and providing a comprehensive framework for platform development and user experience optimization. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 219 2. Literature Review 2.1. Perceived Ease of Use (PEU) PEU is a fundamental concept in technology acceptance and user experience. It refers to the degree to which a person believes that using a particular system or technology will be free of effort. PEU is a critical determinant in the Technology Acceptance Model (TAM), introduced by Davis [27], which posits that ease of use directly influences the acceptance and usage of technology. In digital financial advisory platforms, PEU pertains to how intuitively users can navigate the platform, understand its features, and accomplish their financial management tasks with minimal difficulty. The easier a platform is to use, the more likely users are to adopt and continue using it. PEU is a crucial factor influencing the adoption and acceptance of digital financial platforms. Research has shown that users are more likely to adopt digital financial services, such as robo-advisory platforms, when they perceive them as helpful, easy to use, and free of challenges [28]. Factors such as financial literacy, trust, service quality (SQ), and perceived usefulness contribute to users' perception of the ease of use of fintech services [29]. In the context of digital trade adoption, perceived usefulness and PEU are critical determinants of individuals' attitudes and intentions to use digital trade platforms [30]. Similarly, for MSMEs in Indonesia, PEU and perceived usefulness significantly affect the behavioral intention to use fintech services, as demonstrated in the analysis of MSMEs' adoption of fintech technologies [31]. Additionally, factors like perceived usefulness, credibility, intention to use, and actual use play a role in determining consumer acceptance of digital financial inclusion, with PEU being a significant component of the TAM [32]. Moreover, integrating e-trust with the TAM model has shown that PEU positively affects perceived usefulness and attitude toward digital financial services [33]. In digital financial advisory platforms, PEU can reduce user resistance to complex financial tools, fostering a more seamless and sustained engagement. Understanding the role of PEU in shaping user perceptions and behaviors is crucial for designing effective digital financial advisory platforms that meet user needs and expectations. 2.2. Perceived Enjoyment (PE) PE is a crucial factor in understanding user interactions with technology, particularly in the context of digital financial advisory platforms. PE refers to the extent to which the activity of using a specific technology or platform is perceived to be enjoyable and intrinsically satisfying, independent of any performance consequences. In simpler terms, it is the pleasure derived from the experience of using the technology itself. This concept is especially relevant in the adoption of new technologies, where users' emotional responses can significantly influence their overall satisfaction and continued engagement. To comprehend the PE of users towards digital financial advisory platforms, it is crucial to consider the subjective psychological experience users have when interacting with these platforms. PE significantly influences users' intention to use digital platforms, as it contributes to making the experience enjoyable, attractive, and interesting [34]. Users are more likely to engage with and continue using digital financial platforms when they find the experience enjoyable and satisfying [35]. Additionally, PE is linked to satisfaction and can strongly predict users' continuous intention (CI) to utilize digital services [36]. Research indicates that PE positively influences users' attitudes and intentions towards digital financial services [37]. Intrinsic motivation drives users to adopt new technologies and platforms [38]. Moreover, PE is associated with satisfaction and can enhance users' overall experience with digital platforms, leading to increased engagement and continued usage [39]. This finding aligns with recent research in the mobile payment space, which highlights that perceived enjoyment and usefulness are crucial factors shaping continuance intention (CI), as users tend to continue using platforms that provide both utility and satisfaction [40]. In the context of digital financial advisory platforms, ensuring that users perceive the experience as enjoyable, interactive, and satisfying is crucial for fostering engagement and promoting adoption. Factors such as visual appeal, interactivity, and convenience can contribute to enhancing users' PE of these platforms [41, 42]. By focusing on creating a user-friendly, visually appealing, and engaging interface, digital financial advisory platforms can enhance users' PE, leading to increased satisfaction and continued usage. This interconnected relationship underscores the importance of designing digital financial advisory platforms that prioritize usability and enjoyment to achieve higher user satisfaction (US) and foster long-term engagement. 2.3. Service Quality (SQ) SQ is a critical concept in digital financial advisory platforms, as it directly influences user perceptions and satisfaction. SQ refers to the overall assessment of the performance of a service, encompassing various dimensions such as reliability, responsiveness, assurance, empathy, and tangibles. In digital financial advisory platforms, SQ signifies the degree to which the service meets or exceeds user expectations, ensuring a seamless, efficient, and trustworthy experience. High SQ is essential for establishing user trust and satisfaction, which are paramount for the sustained success of these platforms. Various studies have highlighted the importance of SQ in enhancing customer satisfaction on different digital platforms. Research by Ibrahim et al. [43] explores the determinants of SQ in robo-advisor platforms, emphasizing factors such as the accuracy of financial advice, user interface design, response time, transparency, and communication efficiency. Research by Egala et al. [44] discusses the impact of digital banking SQ dimensions like ease of use, efficiency, privacy/security, and reliability on customer satisfaction and retention intentions. Research by HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 220 Mainardes & Freitas [45] underscores the significance of providing maximum SQ to users of investment platforms, including factors like smooth connection, responsiveness of the application, good user interface, user consultation services, and minimal interference or problems, to enhance user satisfaction (US) and positive impressions. Research by Piotrowski & Orzeszko [46] identifies a positive correlation between SQ measures, experience, satisfaction, and loyalty with trust in the provider, highlighting the importance of SQ in building trust and enhancing customer satisfaction. Additionally, Bai [47] reveals a positive association between robo-advisory usage and perceived financial satisfaction, indicating the role of robo-advisory services in improving customer satisfaction in the financial domain. Moreover, research integrating SERVQUAL and the Technology Acceptance Model (TAM) has shown that SQ plays a significant role in the utilization of FinTech services, influencing both perceived usefulness and user behavior in financial platforms [48]. In digital financial advisory platforms, maintaining high SQ is crucial for attracting and retaining users, ultimately driving the platform's success in a competitive market. 2.4. User Satisfaction (US) The US is critical in studying digital services, particularly within digital financial advisory platforms. US refers to the degree to which users are content with their service experience, encompassing their overall happiness and fulfillment derived from the interaction. It is a comprehensive measure of the effectiveness, efficiency, and pleasure associated with the service. High levels of US are essential for the success of digital platforms as they lead to increased user retention, positive word-of-mouth, and higher user engagement. These references provide insights into the US with digital financial advisory platforms, discussing usability, SQ, customer satisfaction, and the impact of digital technology on user experiences. Research by Abrantes et al. [49] conducted a usability study on a multipurpose platform for ambient assisted living (ActiveAdvice). The study found high task completion rates and reasonable participant satisfaction rates with the platform. Research by Mrkývka & Šiková [4] explored the impact of implementing digital technology innovation on banking performance in Indonesia, highlighting factors such as ease of use of digital services, fast response to risks, and protection of customer personal data as crucial for maintaining customer satisfaction in digital banking. Research by Koskelainen et al. [5] investigated the association between robo-advisory and perceived financial satisfaction. The study utilized data from the National Financial Capability Study 2015 and conducted a logistic analysis to examine this association. Research by Mainardes & Freitas [45] analyzed determinant factors of e-satisfaction and repurchase intention of investment platform users in Indonesia, emphasizing the importance of providing high-quality services to increase user numbers and positive impressions for investment platforms. Additionally, a study by Mainardes et al. [50] analyzing the satisfaction of users of the OVO fintech application in Denpasar found that SQ and user satisfaction were strongly linked, highlighting the importance of maintaining high service standards in digital payment platforms. The critical determinants of US in digital services include PEU, PE, and SQ. PEU refers to the user’s perception of how effortlessly they can navigate and utilize the platform. When users find the platform easy to use, it reduces their cognitive load and enhances their overall experience, leading to higher satisfaction. PE pertains to the pleasure and intrinsic satisfaction users derive from using the platform. If users enjoy the interaction with the platform, their satisfaction levels are likely to be higher. SQ encompasses various dimensions, such as reliability, responsiveness, assurance, empathy, and tangibles. High SQ ensures users’ expectations are met or exceeded, significantly contributing to their satisfaction. US is measured through various quantitative and qualitative methods. Quantitatively, it is often assessed using surveys that include Likert scale questions to gauge users' overall satisfaction levels and specific aspects of their experience. Key performance indicators (KPIs) such as Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES) are commonly used metrics. Qualitatively, US can be measured through in- depth interviews, focus groups, and user feedback analysis, providing deeper insights into user experiences and perceptions. 2.5. Continuous Intention (CI) CI refers to a user's intention to persist in using a digital service over an extended period. In the context of digital financial advisory platforms, CI signifies a user's commitment to continually engage with the platform for their financial planning and advisory needs. This construct is crucial as it determines the long-term viability and success of the platform. High CI indicates that users find consistent value and satisfaction in the service, leading to sustained engagement and reduced churn rates. CI is not merely about frequent use but about an ongoing decision to utilize the platform as a trusted financial advisor. The US is a primary determinant of CI. When satisfied with their experience on a digital financial advisory platform, users are more likely to continue using the service. Satisfaction encompasses various dimensions, including PEU, PE, and SQ. Each of these factors contributes to a positive user experience, reinforcing the user's intention to remain engaged with the platform. Research by Bhattacherjee [51] and subsequent studies in information systems have consistently shown that higher levels of US lead to stronger intentions to continue using a service. Besides the US, other factors also significantly influence CI. Trust in the platform is critical; users need to feel confident that the platform will consistently provide accurate, secure, and reliable financial advice. The perceived value of the service, which includes the benefits users derive from using the platform relative to the costs, also plays a crucial HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 221 role. When users perceive high value, they are more likely to develop a commitment to continuous use. Additionally, personalization and customization of services to meet individual user needs can enhance CI by making users feel understood and catered to on a personal level. Research by Kang et al. [52], considering network externalities and herding, investigated the factors influencing users' CI to use internet wealth management services. This study identified vital determinants affecting users' CI of internet wealth management services, shedding light on the factors that drive users to continue using such services. Research by Otuma & Andako [14] explored the challenges in digital platforms for economic empowerment, aiming to enhance economic inclusion and financial independence. Understanding these challenges and opportunities in digital platforms can provide insights into factors influencing users' CI to engage with financial services. Research by Kanapathipillai et al. [53] delved into how acceptance factors shape Malaysia's banking evolution in the digital era, providing valuable insights into the factors that influence users' acceptance and continuous engagement with digital banking services. 3. Material and Methods The study employed a quantitative research approach using Partial Least Squares Structural Equation Modeling (PLS- SEM) with SmartPLS to analyze the relationships between the variables. SmartPLS software was chosen for its flexibility in handling complex models and its suitability for estimating relationships with smaller sample sizes. Additionally, PLS-SEM is a non-parametric method, making it ideal for datasets that may not meet normality assumptions, as was the case with the slightly non-normal distribution of our data. While classical multivariate linear regression is a valid and widely used method for analyzing relationships between observed variables, it does not fully meet the requirements of this study due to the complexity of the model being tested. Classical regression is limited in its ability to model latent constructs, which are unobserved variables that play a critical role in this research. US, PEU, and CI, for example, are latent constructs that cannot be directly measured but are instead represented by multiple indicators. PLS-SEM, on the other hand, allows for the simultaneous analysis of multiple relationships between both observed and latent variables. This flexibility was necessary for understanding the complex interactions and mediation effects present in the relationships between PEU, PE, SQ, US, and CI. Furthermore, PLS-SEM provides the added benefit of handling measurement errors and is more appropriate for data with slight deviations from normality, as was the case in this study. Thus, while classical regression would have offered valuable insights, it would not have allowed for the comprehensive modeling of latent variables and complex relationships, which were essential to the goals of this research. PLS-SEM provided the necessary robustness and flexibility to address these methodological needs, ensuring a more nuanced understanding of the factors influencing US and CI in digital financial advisory platforms. A convenience sampling technique was used to collect data from users of digital financial advisory platforms in Indonesia. Respondents were selected based on their active use of digital financial platforms, with a minimum of three months of platform usage. Exclusion criteria included users who had only signed up for platforms but had not actively used them. This method was chosen due to its practicality and ease of access to respondents who were already familiar with and actively using these platforms. The target sample size was 413 respondents. This range was selected to ensure adequate statistical power for PLS-SEM analysis, which requires a sufficient number of observations to produce reliable and valid results. A sample size within this range allowed for robust analysis of the hypothesized relationships among the constructs. The target population included users of digital financial advisory platforms in Indonesia. These users were individuals who utilized online platforms for financial advice, investment management, and other related services. The population was diverse, encompassing a wide range of demographic characteristics, including age, gender, education level, and frequency of platform use. Data were collected through an online survey using JotForm. Before launching the survey, a pilot test was conducted with 30 respondents to ensure clarity and reliability of the items, leading to minor adjustments in question wording. The survey was distributed to potential respondents via various online channels, including social media (e.g., Facebook), email, and relevant online communities focused on finance. The data collection period took place between February and March 2024. The survey included questions designed to measure the key constructs of the study—PEU, PE, SQ, US, and CI—along with demographic information to provide context for the analysis. This structured and systematic approach to research design and data collection ensured that the study yielded comprehensive and reliable data for analysis, providing valuable insights into the factors that drive US and CI in digital financial advisory platforms. The research model for this study was developed to examine the relationships between critical constructs influencing US and CI in digital financial advisory platforms. The hypothesized relationships explored in this study are grounded in established theories such as the TAM, user satisfaction frameworks, and service quality models. TAM, introduced by Davis [27], emphasizes the influence of PEU and PE on user acceptance and usage behavior. In this study, TAM is extended to incorporate SQ as a critical determinant of US and CI to use the platform. This theoretical approach was chosen because it provides a comprehensive framework for understanding how usability, enjoyment, and service quality interact to shape user satisfaction and subsequent behaviors. By integrating TAM with service quality considerations, the model reflects the unique characteristics of digital financial advisory platforms, where user interactions, trust, and service delivery play pivotal roles in influencing HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 222 user experiences. As illustrated in Figure 1, these hypotheses explore the interactions between ease of use, enjoyment, service quality, user satisfaction, and continuous intention to use the platform. H1: Perceived Ease of Use → Perceived Enjoyment This hypothesis posited that the ease with which users can navigate and interact with the digital financial advisory platform would positively influence their enjoyment of using the platform. Easier-to-use interfaces are expected to enhance the user's overall experience, making the interaction more enjoyable. H2: Perceived Ease of Use → Continuous Intention This hypothesis suggested that when users perceive the platform as easy to use, their intention to continue using it would increase. Ease of use reduces the effort required to interact with the platform, making users more inclined to keep using it. H3: Perceived Enjoyment → User Satisfaction This hypothesis suggested that when users find the platform enjoyable (PE), their overall US with it would increase. Enjoyment derived from using the platform is a critical factor contributing to higher US levels. H4: Service Quality → User Satisfaction This hypothesis proposed that the SQ provided by the platform, including reliability, responsiveness, and assurance, would positively impact the US. High SQ is essential for meeting user expectations and ensuring a satisfactory user experience. H5: User Satisfaction → Continuous Intention This hypothesis posited that higher levels of US would lead to a stronger CI using the platform. Satisfied users are more likely to remain loyal to the platform and continue using it in the future. To illustrate the proposed research model, a visual representation of the hypothesized relationships was created. This diagram helped to conceptualize how the constructs are interconnected and the direction of the hypothesized effects. Figure 1. Research Framework Figure 1 depicted the research framework, showing the paths between PEU, PE, SQ, US, and CI. The diagram provided a clear and concise visualization of the theoretical model being tested in this study, highlighting the direct and indirect relationships among the variables. By establishing these hypothesized relationships and developing a comprehensive research model, the study aimed to provide a deeper understanding of the factors that drive US and CI in digital financial advisory platforms. The framework served as a guide for the empirical analysis, ensuring that the research objectives were systematically addressed. To measure the constructs in this study—PEU, PE, SQ, US, and CI— reliable and valid scales will be developed or adapted from existing literature. Each variable will be measured using three carefully selected indicators to ensure comprehensive coverage of the construct. Table 1 provides a detailed overview of the questionnaire items for each construct, ensuring that all relevant aspects of each variable are captured accurately. This comprehensive approach ensures the reliability and validity of the measurement instruments, providing a robust foundation for subsequent data analysis (Appendix I). HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 223 Table 1. Item Questionnaire Item Questionnaire PEU, source: Adapted from Davis (1989) [27] PEU1 I find the app easy to use. PEU2 Learning to operate the app is easy for me. PEU3 My interaction with the app is clear and understandable. PE, source: Adapted from van der Heijden (2004) [54] PE1 I enjoy using the app. PE2 Using the app is fun. PE3 The app provides an enjoyable user experience. SQ, source: Adapted from Parasuraman et al. (1985) [55] SQ1 The app provides high-quality services. SQ2 The app’s customer service is responsive and helpful. SQ3 I am satisfied with the reliability of the app's services. US, source: Adapted from Oliver (1980) [56] US1 I am satisfied with my experience using the app. US2 The app meets my expectations. US3 I am happy with the decision to use this app. CI, source: Adapted from Bhattacherjee (2001) [51] CI1 I intend to continue using the app in the future. CI2 I will frequently use the app. CI3 I will recommend the app to others. This structured and methodical approach to developing and adapting measurement instruments ensures that the constructs are accurately and reliably measured, providing a solid basis for analyzing US and CI in the context of digital financial advisory apps in Indonesia. The data analysis for this study was conducted using SmartPLS. The analysis proceeded in several vital steps to ensure a comprehensive evaluation of both the measurement and structural models. First, the data preparation step involved importing the collected data into SmartPLS, ensuring all data were clean and appropriately formatted. This step was crucial for maintaining the integrity and accuracy of the subsequent analysis. Next, the model specification step defined the measurement and structural models in SmartPLS, specifying the relationships between the latent variables and their indicators. This step set the foundation for the SEM analysis by clearly outlining how the variables were expected to interact. Following model specification, the model estimation step ran the PLS algorithm to estimate the parameters of the model, including path coefficients and loadings. This step provided the initial results, showing the strength and direction of the relationships between the constructs. Finally, the model evaluation step assessed the quality of both the measurement model and the structural model using a series of diagnostic tests and criteria. This included evaluating the reliability and validity of the constructs, as well as the overall fit of the model to the data. Each of these steps was critical in ensuring that the analysis was thorough and the results were robust and reliable. To evaluate the measurement model, we assessed the reliability and validity of the constructs using several metrics. Reliability was measured using Cronbach's Alpha and Composite Reliability (CR), with values above 0.7 indicating acceptable internal consistency. Cronbach's Alpha measures the consistency of the items within a construct, while CR provides a more accurate measure of internal consistency. Validity was assessed through Convergent Validity and Discriminant Validity. Convergent Validity was evaluated using the Average Variance Extracted (AVE), with values above 0.5 indicating that the construct explains more than half of the variance in its indicators. Discriminant Validity was evaluated using the Fornell-Larcker criterion, where the square root of the AVE for each construct should be greater than the correlations with other constructs, ensuring that each construct is distinct and uniquely measured. To evaluate the structural model, we examined the path coefficients, hypothesis testing results, and model fit indices. Path coefficients were assessed to determine the strength and direction of the relationships between constructs. Hypothesis testing was conducted using the bootstrapping method with 5,000 resamples in SmartPLS, which generated standard errors and t-statistics for each path coefficient. Bootstrapping is a non-parametric statistical method that HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 224 resamples the data multiple times to estimate the standard errors, providing robust p-values and confidence intervals for hypothesis testing. This approach does not assume normality in the data distribution, making it suitable for small sample sizes and complex models. The p-values obtained from this method were used to assess the significance of the hypothesized relationships. Model fit indices included measures such as R-squared (R²) values for endogenous constructs, which indicated the proportion of variance explained by the model. These evaluations provided a comprehensive understanding of the model's explanatory power and the robustness of the relationships between the constructs. By following these steps and using these evaluation criteria, the data analysis will provide a robust and comprehensive assessment of the measurement and structural models, ensuring the validity and reliability of the findings in evaluating financial stability in digital financial advisory apps in Indonesia. 4. Results and discussion 4.1. Descriptive Statistics This study focuses on the research object of digital financial advisory apps in general. The respondents are users of various applications that provide financial advisory services through digital platforms. These apps are designed to offer users insights and guidance on managing their finances, making investment decisions, and planning for their financial future. The apps falling into this category include popular platforms used in Indonesia, such as Ajaib, Bibit, Tanamduit, Bareksa, and Investree. These applications leverage technology to deliver personalized financial advice, portfolio management, budgeting tools, and investment tracking, making financial advisory services more accessible to a broader audience. The demographic data provide context for the sample's composition, while the descriptive statistics offer insights into the central tendencies and variability of the variables measured. The sample consists of 413 respondents who use digital financial advisory apps in Indonesia. The demographic profile of the respondents, illustrated through charts in Figure 2 and summarized in Table 2, offers key insights into the sample composition for this study. The gender distribution is relatively balanced, with 53% of respondents identifying as male and 47% as female, suggesting a nearly even representation that could impact perspectives on user satisfaction and continuous usage intention. In terms of education, 65% of respondents hold a college degree, while the remaining 35% do not, indicating that the majority possess a higher level of education, which may influence their interaction and engagement with digital financial advisory platforms. Figure 2. Demographic Charts HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 225 The age distribution reveals a predominant presence of younger users, with 24% aged 18-24 and 38% aged 25-34, accounting for a combined 62% of the sample. Respondents aged 35-44 comprise 20%, while older age groups, including those aged 45-54 (12%), 55-64 (4%), and 65+ (2%), are less represented. This demographic skew toward younger age brackets highlights a potentially tech-savvy user base. Additionally, the frequency of app usage shows that 15% of respondents have been using the app for less than one year, 30% for 1-2 years, 40% for 3-5 years, and another 15% for more than five years, indicating a solid mix of both newer and more experienced users. Together, these demographic insights provide a comprehensive understanding of the sample’s characteristics and their potential influence on the study's findings related to user satisfaction and continuous usage intention. Table 2. Demographic Data Demographic Variable Category Frequency Percentage Gender Male 219 53% Female 194 47% Education College 269 65% Non-College 144 35% Age 18-24 99 24% 25-34 157 38% 35-44 83 20% 45-54 50 12% 55-64 17 4% 65+ 7 2% Frequency of Use Less than 1 year 62 15% 1-2 years 124 30% 3-5 years 165 40% More than 5 years 62 15% The descriptive statistics for the main variables—PEU, PE, SQ, US, and CI—provide an overview of the data distribution, with each variable measured using a 7-point Likert scale. The mean scores for PEU1, PEU2, and PEU3 are 5.8, 5.7, and 5.9, respectively, with standard deviations around 1.1, indicating that respondents generally find the app easy to use, with moderate variability. For PE, the mean scores for PE1, PE2, and PE3 are 5.6, 5.5, and 5.7, respectively, with standard deviations around 1.2, suggesting that users typically enjoy using the app, though some variability exists. The mean scores for SQ are 5.4, 5.3, and 5.5 for SQ1, SQ2, and SQ3, respectively, with standard deviations around 1.3, indicating that respondents perceive the SQ as good, but with noticeable variability. The US has mean scores of 5.5, 5.4, and 5.6 for US1, US2, and US3, respectively, with standard deviations around 1.2, showing that users are generally satisfied with the app, though responses vary. Finally, the mean scores for CI are 5.7, 5.6, and 5.8 for CI1, CI2, and CI3, respectively, with standard deviations around 1.1, indicating that users intend to continue using the app, with moderate variability. These descriptive statistics suggest that overall, users have positive perceptions of the digital financial advisory app's ease of use, enjoyment, SQ, and satisfaction, which are likely to contribute to their intention to continue using the app. However, the variability in responses indicates that while many users have favorable experiences, there are differences in how these aspects are perceived, highlighting areas for potential improvement. The inner VIF (Variance Inflation Factor) results provide insight into the multicollinearity among the predictor variables in the structural model, shown in Table 3. A VIF value above 5 indicates a potential multicollinearity problem, while values between 1 and 5 are considered acceptable. In this study, the VIF value for PEU influencing CI is 2.726, indicating that while there is some degree of multicollinearity, it is within acceptable limits. This suggests that PEU can reliably predict CI without severe multicollinearity issues. Similarly, the VIF value for PEU influencing PE is 1.000, which shows no multicollinearity concerns, affirming that PEU is an independent and strong predictor of PE. The VIF value for PE affecting US is 2.072, indicating that PE can reliably predict US with acceptable multicollinearity. Likewise, the VIF value for SQ influencing US is also 2.072, suggesting that SQ is a reliable predictor of US within acceptable multicollinearity limits. Lastly, the VIF value for US influencing CI is 2.726, indicating that the US can reliably predict CI without significant multicollinearity issues. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 226 Table 3. Inner Variance Inflation Factor (VIF) Results Path VIF Value PEU → CI 2.726 PEU → PE 1.000 PE → US 2.072 SQ → US 2.072 US → CI 2.726 4.2. Measurement Model Evaluation The evaluation of the measurement model is critical to ensure the reliability and validity of the constructs used in this study. This section assesses the reliability through Cronbach's alpha and composite reliability and evaluates the convergent and discriminant validity of the constructs. Reliability refers to the consistency of a set of indicators in measuring a construct. Two primary metrics are used to assess reliability: Cronbach's alpha and composite reliability. Cronbach's alpha values above 0.7 indicate acceptable internal consistency, while composite reliability values above 0.7 suggest good reliability (Table 4). Table 4. Reliability Analysis and Convergent Validity Construct Item Factor Loading Cronbach's Alpha Composite Reliability AVE CI CI1 0.859 0.836 0.902 0.753 CI2 0.86 CI3 0.885 PE PE1 0.858 0.79 0.877 0.704 PE2 0.812 PE3 0.847 PEU PEU1 0.877 0.855 0.912 0.775 PEU2 0.874 PEU3 0.89 SQ SQ1 0.701 0.587 0.784 0.548 SQ2 0.739 SQ3 0.779 US US1 0.793 0.678 0.823 0.608 US2 0.767 US3 0.779 The results in Table 4 show that Cronbach's alpha for all constructs, except for SQ and US, exceeds the threshold of 0.7, indicating good internal consistency. The CR values for all constructs are above 0.7, further confirming the reliability of the measurement model. All constructs' AVE values are above 0.5, suggesting adequate convergent validity. Validity assesses the extent to which the indicators measure the intended construct. Convergent validity and discriminant validity are the two main types of validity evaluated. Convergent validity is assessed using AVE, with values above 0.5 indicating that the construct explains more than half of the variance of its indicators, confirming that the items represent the intended construct. Discriminant validity ensures that each construct is distinct from the others. This is evaluated using the Fornell- Larcker criterion, which compares the square root of the AVE of each construct with the correlations between constructs. The diagonal elements in Table 5 represent the square root of the AVE for each construct, and these values should be greater than the off-diagonal elements in the same row and column to confirm discriminant validity. The results show that while most constructs meet this criterion, there are potential issues with discriminant validity between some constructs, such as CI and US, as well as PEU and PE. The reliability analysis, supported by Cronbach's alpha and HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 227 composite reliability, demonstrates that the constructs used in this study are measured consistently and accurately. The convergent validity is confirmed by AVE values above 0.5 for all constructs, indicating that the indicators appropriately represent their intended constructs. However, the discriminant validity assessment reveals potential overlaps between some constructs, suggesting that further refinement may be necessary to ensure that each construct is distinct. These findings underscore the robustness of the measurement model while highlighting areas for potential improvement in future research. Table 5. Discriminant Validity Construct CI PE PEU SQ US CI 0.868 PE 0.855 0.839 PEU 0.883 0.923 0.880 SQ 0.841 0.719 0.761 0.740 US 0.893 0.767 0.796 0.925 0.780 4.3. Hypothesis Testing Results This section presents the detailed results for each hypothesis (H1 to H5), summarizing the path coefficients, t-values, and significance levels obtained from the structural model analysis. The hypothesis testing used bootstrapping with 5,000 resamples to calculate the standard errors, t-statistics, and p-values, ensuring the robustness of the results. The hypothesis testing results provide insights into the relationships between the constructs and validate the proposed model. H1: PEU → PE The β for H1 is 0.923, with a t-value of 88.677 and a p-value of 0.000. However, it is important to note that the p- value, as reported by SmartPLS, is rounded to 0.000, and the actual value is less than 0.001. This solid and significant relationship indicates that PEU is a major determinant of PE. When the app is easy to navigate and use, users are more likely to find it enjoyable, emphasizing the need for user-friendly interface design. H2: PEU → CI For H2, the β is 0.471, the t-value is 13.950, and the p-value is 0.000. Again, while SmartPLS reports a p-value of 0.000, the true value is less than 0.001. This significant positive relationship suggests that when users perceive the digital financial advisory app as easy to use, they are more likely to continue using it. This finding highlights the critical role of usability in retaining users and ensuring their continuous engagement with the app. H3: PE → US The β for H3 is 0.211, with a t-value of 7.248 and a p-value of 0.000. Here, too, the p-value is less than 0.001, though rounded to 0.000 by the software. This indicates a significant positive relationship between PE and US. Users who find the digital financial advisory app enjoyable are more likely to be satisfied with their overall experience. The vital significance of this relationship underscores the importance of enhancing the enjoyment aspect of the app to improve US. H4: SQ → US The β for H4 is 0.773, the t-value is 29.423, and the p-value is 0.000. As before, the actual p-value is less than 0.001, despite being rounded by the software. This indicates a significant and robust positive relationship between SQ and US. High-quality services, including reliable performance and responsive customer support, significantly enhance US. This finding underscores the necessity of maintaining high service standards to keep users satisfied. H5: US → CI For H5, the β is 0.518, with a t-value of 15.117 and a p-value of 0.000. As in other cases, the true p-value is less than 0.001, though rounded by SmartPLS. This significant positive relationship indicates that higher US leads to a stronger CI using the digital financial advisory app. Satisfied users are likelier to remain loyal to the app, highlighting the importance of ensuring US for long-term retention. The inner model results, including path coefficients, t-values, and significance levels, are summarized in the following Table 6 and Figure 3. It is important to note that the p-values displayed as 0.000 in the table are actually less than 0.001, a result of rounding by the SmartPLS software. These p- values are highly significant, confirming the robustness of the proposed model. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 228 Table 6. Hypothesis Testing Results Hypothesis Path Path Coefficient T Statistics P Values Supported H1 PEU → PE 0.923 91.873 0.000 Yes H2 PEU → CI 0.471 13.225 0.000 Yes H3 PE → US 0.211 7.628 0.000 Yes H4 SQ → US 0.773 30.797 0.000 Yes H5 US → CI 0.518 14.378 0.000 Yes Figure 3. Inner Model Results Framework The hypothesis testing results confirm that all hypothesized relationships in the model are supported, with significant positive path coefficients. These results validate the proposed integrated model, demonstrating the critical roles of PEU, PE, SQ, and US in influencing CI of digital financial advisory apps in Indonesia. These findings provide actionable insights for developers and marketers aiming to enhance user experience and retention in the competitive landscape of digital financial advisory platforms. 4.4. Testing for Mediating Effects To assess the role of mediating variables, we conducted the Sobel test to determine the significance of the mediation effects. Specifically, we examined the mediation effect of PE between PEU and US. The Z-value for the Sobel test is approximately 7.261. This value is greater than the critical value of 1.96, indicating that the mediation effect of PE between PEU and US is statistically significant at the 0.05 level. Next, we examined the mediation effect of user satisfaction (US) between PE and CI. The Z-value for the Sobel test is approximately 6.57. This value is greater than the critical value of 1.96, indicating that the mediation effect of the US between PE and CI is statistically significant at the 0.05 level. Lastly, we examined the mediation effect of the US between SQ and CI. The Z-value for the Sobel test is approximately 13.55. This value is greater than the critical value of 1.96, indicating that the mediation effect of the US between SQ and CI is statistically significant at the 0.05 level. The results are summarized in the following Table 7. Table 7. Mediation Testing Results Construct Construct Relationship t-value of Path Coefficient Sobel test PEU → PE → US PEU→PE 91.873 7.261 PE→US 7.628 PE→US→CI PE→US 7.628 6.57 US→CI 14.378 SQ→US→CI SQ→US 30.797 13.55 US→CI 14.378 HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 229 5. Discussion The findings of this study align with existing literature on User Satisfaction (US) and Continuous Intention (CI) in digital financial advisory platforms, particularly in the context of digital financial advisory apps. The sample for this study was drawn from users of digital financial advisory platforms in Indonesia, which may exhibit unique cultural, economic, and regulatory influences. Potential variations in financial literacy, trust in technology, and attitudes towards digital finance in other regions or countries may impact user behavior differently. While the findings offer valuable insights into user satisfaction and continuous usage intentions within the Indonesian context, further research is necessary to determine if these relationships hold true across diverse markets. When applying this model in other settings, it may require adaptation, such as the inclusion of additional variables to reflect localized user behavior or regulatory conditions. The demographic distribution of respondents, including gender, age, education, and frequency of app use, offers crucial insights into user behavior. Younger users, for example, may prioritize ease of use and interactivity, while older users may place greater emphasis on trust and perceived security. Higher education levels can correlate with a better understanding and more frequent use of complex app features. These trends suggest that user satisfaction and continuous usage intention may be influenced by demographic factors, reflecting broader trends in digital financial service adoption. The strong positive relationship between Perceived Ease of Use (PEU) and Perceived Enjoyment (PE) underscores the importance of usability in enhancing user enjoyment, consistent with Davis [27] and subsequent studies by Venkatesh & Davis [57]. Research confirms that ease of use directly influences the acceptance and continued usage of digital services, as users are more likely to adopt platforms that are intuitive and user-friendly [6]. Similarly, PEU’s role in encouraging user adoption and satisfaction is supported by findings in fintech and digital trade platforms [29, 30]. In this study, users were more likely to enjoy the app if it was easy to navigate, confirming the critical role of intuitive design in digital platforms. The findings demonstrate that PEU plays a pivotal role in enhancing PE, as evidenced by the high path coefficient (0.923) observed in this study. This suggests that digital financial advisory platforms must prioritize intuitive and seamless navigation to maximize user satisfaction and engagement. When users experience minimal friction while interacting with these platforms, their enjoyment and likelihood of continued usage increase substantially. This is consistent with the objectives of enhancing user engagement and long-term retention, as articulated by the TAM. The strong influence of SQ on the US underscores the need for reliability, responsiveness, and trustworthiness in digital services, aligning with [55] the emphasis on the importance of high SQ standards. This indicates that even minor service disruptions can negatively impact user satisfaction and trust, highlighting the need for robust support mechanisms and continual monitoring of user needs. The significant impact of Service Quality (SQ) on the US aligns with the findings of Parasuraman et al. [55], who emphasized the importance of reliable and responsive service in customer satisfaction. In digital financial advisory platforms, SQ plays a key role in shaping user perceptions and satisfaction, with high SQ leading to enhanced trust and loyalty [43, 46]. Research in related fields highlights the necessity of ensuring high service standards, as poor quality can reduce user satisfaction and retention [44, 45]. This study reinforces the notion that high SQ is essential for maintaining US in digital platforms, particularly in the highly competitive and sensitive area of financial services. Perceived security and trust are also critical components of user satisfaction in digital financial services due to concerns over data privacy, fraud, and compliance with regulations. These factors can significantly influence user satisfaction and continuous intention to use digital platforms by fostering trust and confidence in the platform's reliability. Including perceived security and trust as variables could enhance the existing model, offering a more holistic understanding of how service quality interacts with user perceptions of safety and trustworthiness, which are pivotal for user engagement. The findings have several implications for both theory and practice. Theoretically, the results support the integrated model combining PEU, PE, SQ, US, and CI, offering a comprehensive framework for understanding user behavior in digital financial advisory platforms. This model aligns with other studies that have identified PEU, PE, and SQ as key determinants of user satisfaction and long-term engagement in digital platforms [39, 40]. This framework can serve as a basis for future research exploring similar constructs in different contexts or with additional variables. It is important to note that the cross-sectional nature of this study limits the ability to draw causal inferences or observe changes in user behavior over time. Future research should consider employing longitudinal designs to capture the evolution of user satisfaction and continuous usage intentions as users become more familiar with platform features or as market conditions change. Longitudinal data could offer deeper insights into engagement strategies and the impact of external factors on user behavior. The findings also highlight the importance of focusing on usability and SQ to enhance US and retention. Developers and marketers of digital financial advisory apps should prioritize user-friendly designs and high service standards to ensure a positive user experience. Ensuring users perceive the platform as enjoyable and easy to use can increase satisfaction, which is crucial for fostering long-term engagement [41, 42]. Moreover, the significant relationship between US and CI suggests that maintaining high levels of US is crucial for user retention, consistent with findings that link user satisfaction to continued use in digital services [51, 52]. Companies should implement continuous feedback mechanisms to monitor and improve US, ensuring long-term engagement with their platforms. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 230 The findings of this study support and extend the existing body of literature on digital platform adoption. The critical role of PEU in driving US and CI is in line with previous studies on digital financial platforms, such as fintech adoption among SMEs and digital trade platforms [29, 30]. Moreover, the positive correlation between the US and CI (path coefficient = 0.518) highlights similarities with prior studies in e-commerce and general technology use [51, 52]. This finding emphasizes that the US plays a pivotal role in user retention and suggests that users are particularly sensitive to their satisfaction levels when deciding whether to continue using digital financial advisory apps. This sensitivity may be due to the inherent risks and complexities associated with financial services, where user trust and satisfaction are paramount [14, 53]. However, our results reveal a particularly strong influence of PEU on PE (path coefficient = 0.923), suggesting that usability is an even more critical determinant of enjoyment in digital financial advisory apps than in other digital services. This underscores the importance of intuitive user experiences, as ease of use significantly reduces user anxiety and enhances their overall experience. The complexity and novelty of digital financial advisory technologies may amplify the impact of PEU, making it essential to provide seamless and user-friendly interactions. By comparing these results with previous studies, we highlight the nuanced role of PEU, PE, and SQ in shaping user experiences and suggest that, in contexts where financial complexity is a barrier, further investigation is warranted to explore how these factors interact to drive user engagement and satisfaction. 6. Conclusion This study underscores the pivotal role of PEU, PE, SQ, US, and CI in shaping user experiences on digital financial advisory platforms. The findings reveal that usability significantly enhances user enjoyment and satisfaction, emphasizing the importance of intuitive, user-friendly design for platform engagement and retention. The positive correlation between US and CI highlights that user satisfaction is a key determinant of continuous platform use. Practical implications suggest that developers and marketers should focus on enhancing platform usability, service reliability, and trustworthiness to maximize user satisfaction and retention. By extending existing models with an integrated approach, this study provides a comprehensive framework for understanding user behavior in digital financial services, particularly within the Indonesian context. Nevertheless, this study has limitations due to its cross-sectional design, which restricts the ability to infer causality or track changes in user behavior over time. Future research should employ longitudinal approaches to capture the evolving nature of user satisfaction and continuous intention, as market conditions and user familiarity with platform features change. Furthermore, exploring the role of perceived security and trust could offer a more holistic view of user engagement by addressing data privacy and trust-related concerns, which are crucial in digital financial advisory services. This study serves as a foundation for future work that aims to adapt and validate the model across different cultural and market settings, enhancing our understanding of user behavior in this dynamic field. 6.1. Limitations and Implications Despite its contributions, this study has several limitations. One limitation relates to the sample size, which, although adequate for statistical analysis, may not fully represent the broader population of digital financial advisory app users in Indonesia. Future research should consider expanding the sample size and ensuring greater demographic diversity to enhance the generalizability of the findings. Additionally, the cross-sectional design of the study limits the ability to infer causality between the variables. Longitudinal studies are recommended to explore how user satisfaction and continuous intention evolve over time and how changes in platform design or user experience might affect these relationships. Furthermore, the study only examined a limited set of variables—Perceived Ease of Use, Perceived Enjoyment, Service Quality, User Satisfaction, and Continuous Intention. Exploring additional factors such as perceived security, trust, or social influence in future research could provide a more nuanced understanding of user behavior in digital financial advisory platforms. The findings of this study offer valuable practical implications for providers of digital financial advisory platforms. To enhance user satisfaction and foster continuous use, providers should prioritize improving the platform’s ease of use, as this significantly impacts both enjoyment and users' intentions to continue using the platform. Simplifying the app's interface and ensuring quick, intuitive navigation can help reduce user effort and improve retention. Additionally, incorporating engaging features such as gamification or interactive tutorials can enhance perceived enjoyment, leading to greater satisfaction. Ensuring high service quality through reliable performance and responsive customer support is also essential, as this study found a strong link between service quality and user satisfaction. By focusing on these areas, platform providers can strengthen user loyalty and position themselves competitively in the market. Given the rapid digital transformation in the financial sector, understanding and addressing the key factors driving user satisfaction and continuous intention is crucial for long-term success. HighTech and Innovation Journal Vol. 6, No. 1, March, 2025 231 7. Declarations 7.1. Author Contributions Conceptualization, A.R. and T.H.; methodology, A.R.; software, A.R.; validation, A.R. and T.H.; formal analysis, A.R.; investigation, A.R.; resources, A.R.; data curation, A.R.; writing—original draft preparation, A.R.; writing— review and editing, A.R.; visualization, A.R.; supervision, A.R.; project administration, A.R.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript. 7.2. Data Availability Statement The data presented in this study are available on request from the corresponding author. 7.3. Funding The authors received no financial support for the research, authorship, and/or publication of this article. 7.4. Institutional Review Board Statement Not applicable. 7.5. Informed Consent Statement Not applicable. 7.6. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 8. References [1] Hariguna, T., & Chen, S. C. (2024). Customer Segmentation and Targeted Retail Pricing in Digital Advertising using Gaussian Mixture Models for Maximizing Gross Income. Journal of Digital Market and Digital Currency, 1(2), 183–203. doi:10.47738/jdmdc.v1i2.11. [2] Pratama, S. F., & Sugianto, D. (2024). 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Your valuable insights as users of digital financial advisory platforms will greatly contribute to this study. We kindly request a few minutes of your time to answer the questions provided in this study tool. Please respond with complete accuracy, transparency, and objectivity by marking the option (√) that you consider most appropriate for each statement. The information you provide will be treated with the utmost confidentiality and will be used solely for academic research purposes. Thank you for your participation and valuable contribution. 1. Gender [ ] Male [ ] Female 2. Education [ ] College [ ] Non-College 3. Age [ ] 18-24 [ ] 25-34 [ ] 35-44 [ ] 45-54 [ ] 55-64 [ ] 65+ 4. Frequency of Use [ ] Less than 1 year [ ] 1-2 years [ ] 3-5 years [ ] More than 5 years Please indicate your level of agreement with each statement by ticking the box that best represents your opinion. Strongly Disagree Disagree Somewhat Disagree Neutral Somewhat Agree Agree Strongly Agree 1 2 3 4 5 6 7 No Questionnaire Perceived Ease of Use 1 I find the app easy to use. 1 2 3 4 5 6 7 2 Learning to operate the app is easy for me. 1 2 3 4 5 6 7 3 My interaction with the app is clear and understandable. 1 2 3 4 5 6 7 Perceived Enjoyment 4 I enjoy using the app. 1 2 3 4 5 6 7 5 Using the app is fun. 1 2 3 4 5 6 7 6 The app provides an enjoyable user experience. 1 2 3 4 5 6 7 Service Quality 7 The app provides high-quality services. 1 2 3 4 5 6 7 8 The app’s customer service is responsive and helpful. 1 2 3 4 5 6 7 9 I am satisfied with the reliability of the app's services. 1 2 3 4 5 6 7 User Satisfaction 10 I am satisfied with my experience using the app. 1 2 3 4 5 6 7 11 The app meets my expectations. 1 2 3 4 5 6 7 12 I am happy with the decision to use this app. 1 2 3 4 5 6 7 Continuous Intention 13 I intend to continue using the app in the future. 1 2 3 4 5 6 7 14 I will frequently use the app. 1 2 3 4 5 6 7 15 I will recommend the app to others. 1 2 3 4 5 6 7