User Interest Research Articles

User Interface Optimization Using Human Centered Design Method to Improve Website Usability (Case Study: Entrance Cilacap)

A good user interface design on a website will make users feel at home using the website. On the contrary, a bad user interface will make users quickly leave the site or bounce rate. User involvement in the system development process will have a positive and significant impact on satisfaction with a system. The objective of this study is to obtain a new user interface that can overcome the usability problems in the web design of the Cilacap Entrance. To meet user needs, this study uses the human centered design (HCD) method for web redesign. User identification and interviews were conducted to obtain user context data. While user personas are used to determine user needs. In addition, A/B testing was used by applying the results of heatmap analysis and data from Google Analytics and Google Web Console to compare the results before and after the interface redesign. The results showed an increase in the number of users, longer visits, more content exploration, and high activity on the site. This means that the redesigned interface was successful in improving user experience, increasing user activity, and maintaining user interest in the Cilacap Entrance website.

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer

With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.

Data statistical analysis on Amazon e-commerce platform for recommender system

Recommendation systems are a crucial element in engaging users and maintaining their engagement with e-commerce platforms. By recommending products or services that are likely to be relevant to each user's interests and preferences, the system can help maintain user interest and encourage them to spend more time on the platform. This work analyzes the principles of several off-the-shelf recommendation models, including the collaborative filtering model, the singular value decomposition model, and the rating-based collaborative filtering model. These models play a crucial role in the field of recommendation systems for e-commerce. To gain further insights from the comments of various merchandise items, sentiment analysis techniques and word cloud analysis are applied. Evaluation of these results demonstrates the critical role of recommender systems in shaping the future landscape of e-commerce. Sentiment analysis allows us to identify patterns in user feedback and understand how different factors influence user satisfaction with products or services. Word cloud analysis provides a visual representation of the most frequently mentioned features or keywords in the comments, allowing us to identify trends and patterns in user behavior. By combining these techniques with traditional recommendation models, more accurate and personalized recommendations could be made that better meet user needs and enhance their shopping experience on e-commerce platforms.

Machine learning algorithms in the development of recommender systems

With the continuous development and widespread use of the Internet, individuals often find themselves overwhelmed by the vast amount of information available. To address this issue and better cater to users' personalized needs, recommender systems have emerged. A recommender system is a technology that provides users with customized content or services based on their preferences and interests. Its primary goal is to predict user behavior as accurately as possible in order to recommend relevant items. Machine learning algorithms play a crucial role in the functioning of recommender systems. These algorithms not only automatically analyze and process large volumes of data but also extract valuable features from historical data, enabling accurate predictions for unknown data. The rapid progress of deep learning technology has significantly enhanced the accuracy and efficiency of recommender systems. Deep learning enables recommender systems to better grasp user behavior, interests, and preferences, leading to more precise predictions of user needs and behaviors. Furthermore, deep learning can handle unstructured data such as images, audio, and text, extracting valuable features to improve recommendations. In the future, as machine learning and artificial intelligence technologies continue to advance and gain popularity, recommender systems will find broader applications. Apart from the traditional e-commerce sector, recommender systems can also be applied to social networks, news media, online education, and other fields. Simultaneously, in the research and implementation of recommender systems, due attention must be given to user privacy protection and data security. Only by ensuring user privacy and data security can recommender systems effectively meet user needs and gain wider acceptance.

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

Plug-In Diffusion Model for Sequential Recommendation

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

LGMRec: Local and Global Graph Learning for Multimodal Recommendation

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals; (2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests.

ВНЕДРЕНИЕ ТЕХНОЛОГИИ БЛОКЧЕЙН В СИСТЕМУ БУХГАЛТЕРСКОГО УЧЕТА ПРЕДПРИЯТИЯ

With the development of the digital economy, all areas of activity are undergoing transformational changes, and accounting is no exception. The integration of digital technologies has revolutionized traditional accounting practices, opening up unprecedented opportunities to improve efficiency, accuracy and strategic decision-making. Currently, there are a sufficient number of online services that allow you to maintain accounting records. One of the most promising and most adaptive is blockchain technology. An unprecedented level of transparency, a high level of security for financial transactions, automatic updating and the ability to integrate into a control system characterize blockchain technology as one of the most reliable and recommended for implementation in the accounting services of economic entities. The blockchain technology adoption process includes training and understanding, conducting a needs assessment, selecting the right blockchain platform, developing a proof of concept, establishing clear governance and standards, integrating with existing systems, pilot implementation, full-scale implementation and training, and continuous monitoring. and optimization. Each stage includes a set of interrelated activities that allow the implementation of blockchain technology. As a result of the stage-by-stage implementation of all the proposed measures, an effective accounting system is formed at the enterprise, accounting practices are optimized, operational information is provided to interested users in a timely manner for making strategic decisions, the integrity and accuracy of financial reporting is ensured, access to real-time data is facilitated, a more flexible and informed business environment, enterprise capabilities are expanded by increasing transparency, security and efficiency of financial transactions.

Perceptions and needs for a community nursing virtual simulation system for Chinese nursing students during the COVID-19 pandemic: A qualitative study

BackgroundVirtual simulation systems are being increasingly used in the field of nursing education. However, these systems are mostly designed based on the perspective of developers, and the needs of the end users are often neglected. The purpose of this study was to explore the perceptions and needs of Chinese undergraduate nursing students for the development of a community nursing virtual simulation system. MethodsThis was a descriptive qualitative study enrolling 12 undergraduate nursing students at a University in China. Data were collected through semi-structured interviews. The content analysis method was used for data analysis. ResultThree themes and 15 sub-themes were extracted from this study: (1) Positive perceptions regarding virtual systems: a) Provides space for trials and errors, b) Not limited by time and space, c) Provides auxiliary tools; (2) Design and use requirements: a) Performance needs, b) Contents design needs, c) Appearance design needs, d) Support Needs; (3) Competency enhancement needs: a) Community nursing practice ability, b) Critical thinking ability, c) Independent thinking ability, d) Ability to deal with emergencies, e)Teamwork skills, f) Self-efficacy, g) Resilience, h)Interpersonal communication skills. ConclusionDesigners and engineers should consider students' needs, aim to improve students' abilities, improve the diversity, the scientific and rigor of content, and enhance user immersion and interest. The system should be programmed to provide real-time feedback, timely technical and professional support, in order to optimize use experience of nursing students.

Wallet Digital Cross-Generation in the Acceleration of Digital Economic Transformation

The phenomenon of digital wallets as electronic payment transaction tools is primarily due to changes in people's lifestyles, leveraging technological advancements, and various advantages, which consequently influence the decision to use digital wallets compared to other options. These digital wallet products are regulated through the regulations and rules established in Bank Indonesia Regulation No. 18/40/PBI/2016 and Bank Indonesia Circular Letter No. 18/41/DKSP regarding Payment Transaction Processing Implementation. Indonesia is ranked as the fourth most populous country with a population of 277.7 million people, and it is also one of the world's largest populations of internet users, totaling 204.7 million internet users in the country as of January 2022, or 73.7% of the entire Indonesian population. The aim of this research is to identify the differences in digital wallet usage frequency based on demographic factors, users' perceptions of digital wallets, and respondents' views on their interest in digital wallets in the future. The research method employs a direct field survey with a quantitative descriptive approach. The results indicate that the frequency of digital wallet usage is still low and uneven, while users' perceptions and interest in digital wallets are high. The findings of this research corroborate the findings of previous studies.

Designing an Educational Metaverse: A Case Study of NTUniverse

An up-and-coming concept that seeks to transform how students learn about and study complex systems, as well as how industrial workers are trained, metaverse technology is characterized in this context by its use in virtual simulation and analysis. In this work, a virtual environment is created that duplicates real-world situations and enables immersive and interactive learning in the educational metaverse. For this purpose, we built a digital twin of the Nanyang Technological University (NTU) campus as a foundation, called NTUniverse. It is designed as an educational metaverse in which various academic and analytical applications are digitized as 3D content embedded within this virtual campus. The approach to digitally twinning educational systems and embedding them within virtual campuses enables remote and collaborative learning as well as professional technical skills training. It also makes feasible the analysis of abstract concepts, complicated structures, dynamic processes, and sensitive industrial procedures virtually, which is otherwise challenging if not impossible to perform in the real world. The work offers important insights into the behaviors and interactions of systems in the metaverse by evaluating design choices and user interests. NTUniverse is an attempt to explore a novel approach that addresses remote education and training challenges. Three efforts with NTUniverse will be discussed in this work, including (1) digitalization of the NTU campus; (2) campus train modelling and simulation; and (3) science, technology, engineering and mathematics education.

Sentiment-Time Heterogeneous Residual Graph Attention Transformer for Session-Based Recommendation

Session-Based Recommendation (SBR) systems are facing considerable challenges, with their primary objective being to implement precise recommendations based on users’ historical behavior sequences. Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data in recommendation systems. Although recent research has advanced in this area, a significant gap remains in the in-depth exploration of transitional relationships between user interests. Additionally, real-world recommendation scenarios typically involve various heterogeneous relationships, which contain a wealth of information that can significantly enhance the learning of user preferences. To address this research gap, in this paper, we introduce a model termed the Sentiment-Time Heterogeneous Residual Graph Attention Transformer (STH-ResGAT), which is designed to capture the dynamic nature of user interests and the complexity inherent in heterogeneous graphs. In STH-ResGAT, we develop a Sentiment-Time-Heterogeneous Graph (STH-Graph) that integrates sentiment and time factors into the edges of the graph structure. Furthermore, the Residual Graph Attention Transformer for Heterogeneous Networks (ResGAT-Het) is designed to manage diverse node and edge types based on the STH-Graph. Extensive experiments are conducted on four widely-used benchmark datasets: Ciao, Yelp, Epinions and LibraryThing. The results demonstrate that our proposed STH-ResGAT method significantly outperforms previous state-of-the-art baseline approaches.The implementation of ResGAT-Het is available in https://github.com/zhangyu2234/ResGAT-Het.git .

Cross‐network service recommendation in smart cities

SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of smart services by users and the full servicelization of several cities around the world is faced with two major issues: the lack of knowledge regarding smart services' capacities (e.g., features, contextual requirements, pricing models, privacy policies, provisioning terms, etc.), and the lack of unified rating and quantification of smart services' QoS behavior. Indeed, interested users often exploit traditional search tools (e.g., Web search engines, social networks) to find and rate the needed services. This behavior has scattered the smart services' usage data (e.g., users contexts, ratings) across multiple providers platforms, which makes the search task beyond the capacity of users and, even, other service providers. Although recommender systems are a natural solution to exempt users from exploring the huge space of the offered smart services, current recommendation approaches for smart city environments are unable to deliver correct recommendations. In fact, they have been initially designed to single‐network settings (a single service repository), while smart services' consumers often are involved in multiple provider platforms. To the best of our knowledge, there exists no approach that treated smart service recommendation across multiple information networks. Therefore, the goal of this paper is to propose a cross‐network recommender system for smart cities. We first model the multiplex network of smart services' providers as a multirelational fuzzy lattice family thanks to fuzzy relational concept analysis (fuzzy RCA), which is a powerful mathematical method for data analysis and clustering. We also use the concept of anchor users to connect providers networks via the users involved in more than one provider platform. Guided by anchors' cross‐network relations, we compute the similarity between users and we define algorithms for exploring the smart services' information network, i.e. lattice family. Extensive experiments have proved the effectiveness of cross‐network recommendation and the quality of produced recommendations, compared to state‐of‐the‐art single‐network recommendation.

The Logic and Mechanism of VR Marketing: Market Shaping in the Perspective of Virtual Reality

With the rapid development of technology, VR technology has become an emerging trend in the field of marketing. This paper explores the application of VR technology in marketing and analyzes its impact on consumer behavior and marketing Ddelves into two successful cases of VR in marketing: the Volvo XC90 VR test drive and Audi's virtual showroom, revealing the practical application of VR in improving user engagement and creating a unique shopping experience. VR technology provides an immersive experience that enhances the user's interaction with the product and brand and improves the user's behavior and marketing strategy. VR technology provides an immersive experience that enhances user interaction with products and brands, increasing user retention and advertising effectiveness. VR marketing is more effective than traditional marketing in increasing user engagement, emotional connection, memory, and satisfaction, thus increasing user conversion and loyalty. The article gives the advantages and disadvantages of VR marketing. The advantages of VR marketing are more realistic product demonstrations or trial experiences, the ability to create in-depth, emotional brand storytelling narratives, and expanding global markets and audiences. Its disadvantages are high technology costs, user access thresholds, technical limitations consumer adaptation issues, and limitations of the experience. In addition, this paper discusses the challenges faced by VR technology in marketing: including user acceptance, quality of experience, clarity of user needs, protection of user rights and interests, and future development potential: improving user education and guidance, optimizing user experience, conducting in-depth research on user needs, and protecting user rights and privacy.

Responsible living labs: what can go wrong?

PurposeThis study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field.Design/methodology/approachThe research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI.FindingsThis emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection.Originality/valueThe proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.

Where you go is who you are: a study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

A trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof

AbstractTo meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.

Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.

Open Science Recommendation Systems for Academic Libraries

An interdisciplinary academic team offers a comprehensive case study describing the development of a predictive model as the cornerstone for an open science recommendation system tailored to the Carnegie Mellon University community. This initiative will empower users in choosing open science services that align with their academic requirements, introduce academics to resources they find valuable, and bridge gaps within academic library service offerings.As an institution with a longstanding commitment to a science-informed approach and a focus on computer science, engineering, and artificial intelligence, Carnegie Mellon University has enthusiastically embraced open science practices. The Carnegie Mellon University’s Libraries has been instrumental in bringing these practices into our academic landscape.The authors strive to develop a predictive model which will evolve into a recommendation system. The pursuit of this endeavor has led the authors through several ethical considerations, such as data privacy, the involvement of student contributors, and the design of a persuasive recommendation system. We are committed to exploring ethical approaches for delivering user-centered recommendations and to preserving individual autonomy.The authors have actively engaged with diverse academic departments, students, and faculty, embarking on data exploration, and applying open science principles throughout the process. The resulting system will raise awareness of library services and deliver tailored recommendations for the adoption of proven research tools and practices.This case study serves as an exemplar of how universities can enact open science principles and develop systems that prioritize the user's interests, navigate institutional complexities to forge interdisciplinary collaboration, and muster resources to support innovative, multi-disciplinary efforts. 

Exploring the Stimulus Organism Response (SOR) Framework that Shapes Impulse Buying in the Epoch of E-Wallets in the Philippines

In the progressing digital financial landscape of the Philippines, the surge in e-wallet applications has vastly captured the interest of Filipino users. As e-wallets restructure how consumers manage their finances, a crucial question emerges: Can they influence impulsive buying tendencies? Using the (S-O-R) framework, the study reconnoitered the factors that incite impulse buying, expanding prior models by integrating both intrinsic and extrinsic stimuli. A total of 828 active e-wallet users were gathered using homogenous purposive sampling. After employing a Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis, the results elucidated five significant stimuli (i.e., perceived risk, visual appeal, subjective norms, convenience, and monetary savings) in shaping user satisfaction and perceived enjoyment. Conversely, information quality demonstrated statistical significance only in association with satisfaction. Moreover, the study highlighted those three factors (i.e., perceived enjoyment, subjective norms, and monetary savings) that directly impact impulse buying. Ultimately, it accentuates the full mediating role of perceived enjoyment in linking visual appeal with impulsive buying whilst showcasing a partial mediation effect in the relationship between cost savings and impulsive buying. Edifice on these findings, e-wallet providers have a strategic outlook to further invest in the following: (a) user-centric design strategies, (b) process enhancement for seamless user experience, and (c) optimization of user education.