User Satisfaction Rate Research Articles

Analisis Perceived Enjoyment Dan Perceived Ease Of Use Untuk Pembayaran Digital Menggunakan Layanan Gopay Pada Masyarakat di Bandar Lampung

This study examines the influence of perceived enjoyment and perceived ease of use on the adoption of GoPay digital payment services in Bandar Lampung, Indonesia. Using a qualitative case study approach, the research involved 20 active GoPay users from diverse demographic backgrounds, with data collected through in-depth interviews, participant observation, and focus group discussions. The findings reveal that perceived enjoyment significantly influences GoPay adoption, with an average score of 4.65 out of 5, indicating users' highly positive experiences with the service. Similarly, perceived ease of use demonstrates a substantial impact, achieving an average score of 4.77, reflecting the platform's user-friendly interface and efficient transaction process. The study identifies a strong positive correlation (r=0.893) between these two factors, contributing to an impressive 89% adoption rate among Bandar Lampung residents. The research also highlights GoPay's success in creating an effective digital payment ecosystem, evidenced by a 92% user satisfaction rate and average transaction times under 10 seconds. Furthermore, the study shows that GoPay's reward system and intuitive interface have successfully fostered user engagement and loyalty. These findings contribute to the understanding of digital payment adoption in medium-sized Indonesian cities and provide valuable insights for financial technology service providers and policymakers in developing inclusive digital payment ecosystems.

Mobile-Based Event Decoration Ordering System Using UAT Method with PIECES Framework

The Mobile Event Decoration Booking System is an innovative solution designed to facilitate users in ordering event decorations. By implementing the User Acceptance Testing (UAT) method and the PIECES framework, this system ensures that the developed application meets the needs and expectations of users. This research aims to identify and analyze key features in the ordering process and evaluate user satisfaction with the application. Respondents provide valuable feedback regarding the interface, functionality, and overall user experience through UAT. The research results indicate that this application can enhance the efficiency of bookings, reduce communication errors between service providers and customers, and offer a better experience. With the application of the UAT method, users feel that this system effectively meets their needs, resulting in an improved experience in event planning. These findings suggest that the factors influencing user satisfaction and interest are adequate and should be maintained. The Mobile Event Decoration Booking System has successfully improved the efficiency and effectiveness of the booking service, with an average user satisfaction rate of 95%.

The application of artificial intelligence-assisted technology in cultural and creative product design

This study proposes a novel artificial intelligence (AI)-assisted design model that combines Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model’s superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.

AI‐Driven Multi‐Modal Information Fusion System for Real Applications in Physical Training

ABSTRACTThis paper introduces an AI‐driven multi‐modal information fusion system tailored for real‐world physical training applications, integrating state‐of‐the‐art computer vision, deep learning, and multi‐sensor technologies. The system combines high‐precision cameras and inertial measurement unit (IMU) sensors to capture real‐time motion trajectories and posture data. These data are processed through convolutional neural networks (CNNs) and long short‐term memory (LSTM) models for accurate movement analysis. Additionally, augmented reality (AR) provides real‐time visual feedback, while deep reinforcement learning algorithms deliver personalized training recommendations. In an 8‐week study involving 100 high school students, the system improved movement accuracy by 23% and received a 4.6/5 user satisfaction rating. Although challenges remain in optimizing computational efficiency and user interface design, the system's modular and scalable nature makes it an adaptable solution for enhancing physical training across educational, home, and professional settings.

A IMPORTÂNCIA DA USABILIDADE, UX E UI DESIGN EM SISTEMAS E SUAS IMPLICAÇÕES

This study aims to analyze the importance of usability, UX (User Experience), and UI (User Interface) in system development, emphasizing their implications for the success or failure of digital applications. The research adopts a literature review method, exploring theoretical concepts and case studies related to best practices and issues caused by the neglect of these factors. The findings indicate that proper application of usability, UX, and UI significantly enhances efficiency, user satisfaction, and system adoption rates, while their absence can lead to frustrating experiences, reduced engagement, and financial losses. It concludes that investing in these aspects is essential to ensuring effective systems aligned with user needs

Research on the Design Element Weights of Women's Shirts Based on Visual Perception

With the continuous change of the consumer market, clothing companies are paying more and more attention to users' experience. Quantifying the users' visual perceptions can help fashion designers to design clothing more scientifically. For the design element weights of women's shirts, this paper takes women aged 18-28 as subjects, and quantifies the subjects' visual perception through eye-tracking experiments. By analyzing the subjects' visual hotspots and key operational index data of ladies' shirts, the weights of each design element are derived. Then the experimental results are compared with the results of the expert hierarchical analysis method, and it is found that they are basically consistent. It shows that by quantifying users' visual perception, it can help fashion designers determine the weights of design elements more conveniently and effectively. Thus, it helps service designers to carry out personalized clothing design, and has important research significance for improving user satisfaction and design hit rate.

Construction of smart tourism system integrating tourist needs and scene characteristics

The demand for travel is increasing as human living conditions rise. The paper presents a smart tourism system architecture that incorporates visitors' demands and scenario characteristics, and performs path planning using path search algorithms and selective tour path recommendation algorithms, in order to improve tourists' travelling experiences and save them time. The experimental data showed that the enhanced heuristic search algorithm visited 122 nodes, which is 62.9% and 52.3% less than the sparrow search algorithm and the improved genetic search strategy, respectively. The number of iterations required to reach convergence for the selective tour path recommendation algorithm, genetic algorithm, discrete particle swarm algorithm, and genetic particle swarm algorithm, respectively, was 39, 90, 85, and 63, indicating that the proposed selective tour path recommendation algorithm has the fastest computational speed. The accuracy, stability, user satisfaction, and overall rating of the smart tourism system that integrates tourists' needs and scenario characteristics are all higher than those of the three types of tourism systems, such as the iBeacon Smart Tourism System, indicating that this smart tourism system is the best to use, helping to enhance tourists' experiences and promote the robust development of the tourism industry.

Development and Empirical Validation of an Information Technology Acceptance Framework

This paper explores the constructs affecting IT acceptance in modern organisations using theoretical frameworks such as the TAM and the UTAUT models. Realising the drawbacks of such dichotomous models for capturing the dynamics of contemporary IT enablers and use processes, the present study plans to offer and test new antecedent and moderating constructs that may provide a more accurate picture of today’s IT deployment and utilisation. The type of research in the study is the quantitative research method, which is adopted in the study where the researcher adopted a structured questionnaire to be administered to 200 participants across the different sectors of the economy, such as health, education, and business. Hypotheses of primary concern are those related to the construct of IT acceptance, including perceived usefulness, ease of use, trust, system transparency, perceived risk, and ethical concerns. The analysis of the data gathered is done using Structural Equation Modeling (SEM) because of the possibility of analysing the associations between several variables. Behavioural construct analysis revealed a significant positive correlation with IT acceptance behaviours, with the value of R for the new construct being 0.935 and the R square value being 0.874, which means that newly derived constructs explain a fair percentage of IT acceptance behaviours. The findings, therefore, point out the lack of conventional frameworks in addressing the complex nature of IT use in various organisational and cultural environments. Full-spectrum system requirements in newly developing domains, such as AI, machine learning, and blockchain, require new fundamental concepts regarding user trust, system openness and ethical considerations. This work extends the IT acceptance literature by providing a better view of the determinants of IT uptake in modern organisations. In totality, the validated constructs have practical implications for organisations desirous of adopting new IT systems, particularly highlighting the role of trust and perceived risk and the need for transparency or the adoption of transparency to guarantee user satisfaction rates and consequent adoption of new IT systems. In conclusion, the study points to the need to develop new models that address today’s diverse IT contexts to inform the next generation of IT acceptance and adoption research.

Integration of renewable energy-powered cold storage solutions for reducing post-harvest food waste in rural agricultural areas

Post-harvest food loss remains a critical challenge in rural agricultural areas, exacerbated by inadequate storage facilities and unreliable energy access. This study develops and optimizes an advanced renewable energy-powered cold storage system tailored for rural settings, integrating solar and wind energy with phase change materials (PCMs) for efficient energy storage. The system incorporates Internet of Things (IoT)-based sensors and artificial intelligence (AI)-driven energy management to maintain optimal storage conditions and enhance energy efficiency. Field trials conducted in Lincolnshire, UK, and Appalachian regions of the US demonstrated significant reductions in post-harvest food loss by an average of 43.5%, extensions in produce shelf-life by up to 300%, and increased income for smallholder farmers by approximately 43%. The system also achieved an 80% reduction in greenhouse gas emissions compared to conventional diesel-powered systems. Economic analyses revealed a shorter payback period and higher return on investment, confirming the system's viability. High user satisfaction and adoption rates indicate the system's practicality and potential for widespread implementation. The findings suggest that integrating renewable energy with smart technologies in cold storage solutions offers a scalable and sustainable approach to enhancing food security, promoting economic growth in rural areas, and supporting environmental objectives globally.

Vision assistant for visually impaired individuals

Visually impaired individuals face significant challenges in accessing environmental information, social interactions, and safety in their daily lives. This article aims to increase the independence of visually impaired individuals and simplify their daily lives using an AI-supported image and sound recognition system. The developed prototype is a pair of glasses consisting of a Raspberry Pi 4 microprocessor, integrated headphones, touch controls, and a powerful battery management system. The electronic diagram includes the integration of a camera and microprocessor. The AI algorithms developed successfully perform tasks such as object recognition, text reading, and facial recognition. The article provides a comprehensive literature review and introduces the prototype to visually impaired users. Based on the feedback from the introduction, the prototype's ease of use and effectiveness were evaluated, and improvements were made based on user feedback. The algorithms were tested for accuracy, with the prototype achieving a 78% accuracy score, close to GPT-4's 82%. Additionally, tests with 20 users resulted in an 85% user satisfaction rate.

Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand

ABSTRACTThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA‐driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research.

Constructing renewable energy sphere for efficient search

In the field of renewable energy (RE), traditional methods for understanding, development, and education have heavily relied on 2D table formats for data retrieval and presentation. However, these formats suffer from inefficiency and an inability to fully capture the relationships between variables. In response to this challenge, Li et al. (2022) demonstrated that their therapeutic 3D sphere system significantly outperformed the therapeutic 2D table in query processing time. Furthermore, Shih et al. (2020) utilized a T-test to underscore the superiority of the TED Talks sphere over the TED Talks 2D table format website in terms of convenience, query processing efficiency, usefulness, and intuitiveness. Building upon this foundation, this study referenced and enhanced the work of Li et al. by introducing a three-dimensional spherical visualization model driven by cluster-based binary integer programming (BIP). This novel approach resulted in a 58% reduction in information retrieval time and achieved an impressive 86.4% user satisfaction rating. It is worth noting that compared to the previous Systematic Review and Synthesis (SRS), user satisfaction experienced a significant 19% improvement. This improvement enhances the search efficiency for RE information, thereby fostering public awareness and development of RE. Taking energy supply-demand balance sheets as an example, this study demonstrates that the spherical retrieval system (SRS) constructed by the visualization 3D spherical model resolves the issue of inefficient retrieval in 2D table formats, and the newly added functionality of highlighting objects on the spherical surface intuitively illustrates the one-to-many relationships between individual RE supply and demand. To optimize hardware and software performance, data mining techniques are employed to cluster supply data, reducing complexity, and improving optimization efficiency. The experiment is that testers search for the same targets and compared the proposed 3D SRS with the Taiwan Power Company (Taipower) 2D website in terms of retrieval speed and accuracy. The statistical results of the T-test indicate that the proposed 3D SRS is more effective and accurate than the Taipower 2D website. The research findings indicate: 1. The new 3D SRS reduces query time by 58%. 2. Adopting the k-means algorithm for clustering and batch optimization can transform from an initially infeasible solution to finding the optimal solution within 12 s. This method also reduces computer software and hardware costs, providing effective solutions for relevant applications. These technologies can be used for the education, promotion, publicity, and dissemination of RE.

WebGIS-Based Land Asset Visualization (Case Study: Pangkep Regency Government)

This study discusses the implementation of e-Government in Pangkep Regency with the aim of improving public services, especially in land asset management. This research responds to requests from the Department of Housing, Settlement Areas and Land (Disperkimtan) by addressing the challenges of complex administrative procedures and increasing public awareness of land asset management. The goal is to design a web-based Land Asset Mapping Information System using WebGIS to simplify administrative processes and increase transparency. This research applies the Rapid Application Development (RAD) method. The results show an effective user interface (UI) in land certificate creation as well as smooth functions in the login and land acquisition process. This system is able to handle user loads well, which is reflected in the user satisfaction rate of 76.4%.

Application of Sentiment Analysis and Data-Driven User Profiling in Product Iteration Design

Our study employs sentiment analysis and data-driven methodologies to construct precise user profiles and apply them to product iteration design, aiming to enhance marketing effectiveness and user satisfaction. The research data comprises 178,563 user comments and 42,786 social media posts. Sentiment features are extracted using advanced sentiment analysis algorithms such as BERT and Transformer. These features are combined with user behavior data and classified into five primary groups using the K-means clustering algorithm, each representing distinct user needs and sentiment tendencies. Based on the user profiles, three product iteration schemes were designed and implemented. The effectiveness of these iterations was validated through A/B testing, resulting in significant improvements in user satisfaction and usage rates. Regression analysis reveals that both sentiment scores and user behavior features have a significant positive impact on user satisfaction. The findings demonstrate that combining sentiment analysis with data-driven user profiling plays a crucial role in product design and marketing, offering new insights for optimizing products and enhancing competitiveness.

Nollywood Movie Sequences Summarization Dataset for Machine Learning Applications

Summarization in recent times has become one of the most exploited areas of natural language processing tasks, due to significant increase in data volume, which is more than terabytes and petabytes. In this article, we present a custom dataset for Nollywood movie sequences, an extensive collection of 13 Nollywood movies representing various genres, including drama, comedy and action. This custom dataset was impendent into pre-train benchmark dataset TVSum following the same file format, frame size and others in order to suit our purpose. Fast Forward Moving Picture Expert Group (FFMPEG) was used for video encoding during pre-processing and final skimming. The proposed framework is effective for creating movie summaries with a high user satisfaction rate and having reasonably low computing requirements.

Using QR Code and a Smartphone to Provide University of Cross River State (UNICROSS) Certificate Authentication

In the modern world, people frequently fabricate their credentials in order to secure a job or to show up where and when it is necessary. Verification is the most effective approach to identify those fraudulent certificates. Despite this, the paper verification method requires a drawn-out and time-consuming process to handle certificate verification because the certificates must be returned to the institutions who awarded them. As a result, the certificates either fail to pass verification or are delayed by the lengthy process. Hence, a certificate verification method using QR codes is developed in this study for quick verification of University of Cross River State (UNICROSS) certificate genuineness. A Smartphone and 3D Printed QR Code were employed for the enhancement of UNICROSS certificate mobile authentication services. ScanTrust anti-counterfeiting services were used to secure and host the encrypted information. Results show that our framework is effective for creating mobile authentication services with high user satisfaction rate and having reasonably low computing requirements.

Exploring User Attitudes and Behavioral Intentions towards Augmented Reality Automotive Assistants: A Mixed-Methods Approach

As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving user acceptance and engagement were identified. The research reveals that trust in AR technology, perceived utility, and ease of interaction are prioritized by users. Clustering analysis identified three distinct user groups: a ‘Safety-Conscious Group’, a ‘Technology Enthusiast Group’, and an ‘Experience-Seeking Group’, each displaying unique preferences towards AR features. Additionally, a support vector machine (SVM) model effectively predicted user behavior with a training set accuracy of 89.96%. These findings provide valuable insights for the design and marketing of AR automotive assistants, acknowledging both essential features and delighters identified through the Kano model. By understanding user preferences and expectations, tailored AR solutions can be developed to enhance user satisfaction and adoption rates in the automotive sector. Moreover, this research contributes to the sustainable development goals related to the automotive industry by fostering innovation in vehicle technology, promoting eco-friendly driving practices, and enhancing overall mobility efficiency.

Behavioral Activation mHealth Application for Smoking Cessation: A Randomized Controlled Pilot Trial.

Behavioral activation (BA) is an effective intervention for both depression and substance use disorders. Combining BA with a standard smoking cessation intervention may improve quit rates by addressing depressive symptoms, a key barrier to quitting. This study preliminarily evaluated the acceptability and efficacy of the BA-based Actify! mobile health application (mHealth app) for smoking cessation. We conducted a pilot randomized controlled trial (n = 242) comparing Actify! with the National Cancer Institute's (NCI) app for smoking cessation (QuitGuide) on acceptability (user satisfaction, app openings), smoking abstinence, and mechanisms of change (BA and depressive symptoms) at 8 weeks and 6 months postrandomization. Participants were US adults recruited online who smoked daily. Treatment satisfaction was uniformly higher in the Actify! arm. Number of app openings was similar across arms (M = 34.3 openings over 8 weeks in both arms). Self-reported 30-day point prevalence abstinence (PPA) at 8 weeks was 12.6% for Actify! versus 7.3% for QuitGuide. Differences in 30-day PPA continued through 6 months (18.5% for Actify! vs. 12.2% for QuitGuide). Changes between baseline and 8 weeks in depressive symptoms and BA favored Actify!. Planned subgroup analyses suggested greater benefit of Actify! among participants with pretreatment mild-to-moderate depression symptom severity compared to those with no depression symptoms. Actify! showed considerable promise as a novel mHealth treatment, as evidenced by its high usage and higher user satisfaction and quit rates than QuitGuide at both short- and long-term follow-up. The next step is to evaluate Actify! in a fully powered efficacy trial. Study findings demonstrate the promise of a BA-based mobile health app (Actify!) for smoking cessation as a population-level intervention that can effectively address depressive symptoms as a risk factor for worse smoking treatment outcomes. The Actify! app is the first standalone BA-based app to demonstrate potential for improved acceptability and efficacy relative to a standard care comparison app, with user satisfaction and smoking quit rates descriptively exceeding those of the NCI's QuitGuide app.

Security protocols in healthcare: A comprehensive study of AI-Enabled IoMT

The Internet of Medical Things IoMT has driven a paradigm shift in the way things have been done traditionally within medical Healthcare practices and this is evidence that Smart Personal systems (Health Care) are shaping up with mighty forces. By merging these technologies, IoT has transformed data communication and provided the means to directly interconnect medical devices and sensor systems. The use of advanced technologies like Artificial Intelligence( AI), Machine Learning, Deep Learning, and Federated Learning has highly equipped and transformed the IoMT’s performance. These integrations also raise concerns on data privacy and security, and addressing these challenges become more important as without proper security and privacy measures people can face serious disruption both online and offline leading to upcoming catastrophes already waiting at its end line. This chapter looks into the intricacies of a highly secure communications system powered by AI, specifically in the age where disasters like COVID-19 [10], emphasizes the need for increased security on data transmission as well as storage. IoMT has wide applications for integrating remote patient monitoring, enhancing user satisfaction and interdimensional hospitalization rates, we delve into how IoMT works in conjunction with machine learning, particularly federated Learning intends to address any possible concerns. The chapter begins with IoMT systems, including IoMT ecosystems, implantable medical devices IMDs, and Internet-worn gadgetry. Special attention is given to the AI-powered Intrusion Detection Systems (IDSs) in IoMT environments, while network security remains the research focus helping practitioners to implement safety measures enterprise-wide; analysis, comparison, and evaluation all encompass machine learning, deep learning mechanisms as precautions against potential attacks. We will also consider another study on the services of an AI-provisioned lightweight protocol communication system that ensures data authenticity and responds to diverse security implications in IMD. The chapter makes a U-turn by alluding to internet wearable devices, pointing out federated learning and blockchain reinforcements that indicate the need for an integrated framework. The amalgamation of future technologies supports the ethical and secure integration of IoMT. Collaboration, as a guiding principle, compels healthcare practitioners and policymakers to partner efficiently to guide the future where the security of health is ever easier. In the end, a summary of modern ethical and regulatory paradigms in IoMT is provided, emphasizing that there could be an urgent need for new laws.

Rancang Bangun Peta Digital Desa di Kaligentong Boyolali Berbasis Web dan Android

To support the management of large-scale area potential such as rural spatial planning, Kaligentong Village uses area mapping as an effort in village development planning. The mapping is displayed in paper media which has limited access so that this is inefficient and hinders the Kaligentong Village government in managing the assets and potential contained in it. Therefore, an information system is needed that can monitor the assets and potential of Kaligentong Village in the form of web-based digital maps and android. This study aims to create a dynamic digital mapping of villages for Kaligentong Village so that it is more efficient in finding and managing information related to geographical conditions and potential of Kaligentong Village. The method used is waterfall which consists of the stages of analysis, planning, modeling, construction, and testing. The system is built using javascript leaflets, native PHP, QGIS, and MySQL databases. Based on data from black box testing, the village digital map application runs according to the functional system. The satisfaction rate of application and web users is 94.10%. The average response time per activity generated by the application was 1.44s at 96 Mbps bandwidth and 0.89s at 144 Mbps bandwidth for websites.