User Satisfaction Research Articles

Improving Human Connections and Audience Satisfaction in Online Concerts and Music Conferences

Abstract Online concerts and music conferences are becoming increasingly necessary. Although online events bring numerous benefits, many event attendees find the online format unsatisfying, mainly because of the lack of human connections and social interactions in comparison with in-person music events. Guided by literature on networked music performance, online social presence, and social computing design principles, the authors designed and developed Deck 10 Intermedia, an online music event platform that aims to replicate human connections from in-person music events. The effectiveness of the system design and overall user satisfaction are evaluated using data collected from two global music events: the International Computer Music Conference 2021, and the Summer Institute for Contemporary Performance Practice 2021. The results indicate that the system was effective at restoring human connections and that users found the participation experience socially connected and satisfying. The results also led to design recommendations for future online music event systems.

Infinite Potential of AI Chatbots: Enhancing User Experiences and Driving Business Transformation in E-commerce: Case of Palestinian E-Commerce

In the much-evolving digital age landscape, it is difficult to overlook artificial intelligence's enormous role in people. This study entails an empirical examination of the impact of AI-powered chatbots on improving customer experience, customer satisfaction, and potential benefits in the digital era. This study was handled by the use of quantitative data collection methods through an online survey from 221 online users of AI-powered chatbots in Palestine. The collected data was then analyzed using descriptive statistics and structural equation modeling techniques. According to the results, it was shown that service quality significantly influenced the intention to use chatbots by customers or users thus indirectly influencing the net benefits enjoyed by users in the digital era. Similarly, the service quality provided by AI-powered chatbots was found to significantly influence the intention to use them, thus indirectly influencing the net benefits of AI-powered chatbots. Additionally, service quality was found to influence user satisfaction with chatbots, which indirectly influenced the net benefits enjoyed by users/customers in the digital era. On the other hand, user satisfaction significantly and directly influenced the net benefits of AI-powered chatbots in the digital era. In this regard, this study significantly contributed to the AI-powered chatbot's body of knowledge in the Palestinian digital context by identifying the factors that influence the use of chatbots in the digital era from the users’ point of view with the capacity to help transform the Palestinian business landscape

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the heuristic–systematic model, we examine how algorithm transparency, accountability, and legitimacy influence users’ perceptions of fairness and, subsequently, their satisfaction with and likelihood to recommend AI-driven financial inclusion services. Through a survey-based quantitative analysis of 675 users in China, our results reveal that perceived algorithmic fairness acts as a significant mediating factor between the ethical attributes of AI systems and the user responses. Specifically, higher levels of transparency, accountability, and legitimacy enhance users’ perceptions of fairness, which, in turn, significantly increases both their satisfaction with AI-facilitated financial inclusion services and their likelihood to recommend them. This research contributes to the literature on AI ethics by empirically demonstrating the critical role of transparent, accountable, and legitimate AI practices in fostering positive user outcomes. Moreover, it addresses a significant gap in the understanding of the ethical implications of AI in financial inclusion contexts, offering valuable insights for both researchers and practitioners in this rapidly evolving field.

Validation of a Bluetooth Self-Fitting Device for People With Mild-to-Moderate Hearing Loss in Quiet or Noisy Environments.

The purpose of this study was to evaluate the benefits of a Bluetooth self-fitting device using an in situ hearing screening test and self-fitting processes. Thirty Mandarin-speaking individuals (16 men and 14 women) with mild-to-moderate hearing loss participated in this study. The hearing screening test was conducted using neckband earphones of a Bluetooth self-fitting device controlled by a smartphone app. The hearing screening tests were compared with the pure-tone audiometry in an audiometric booth. The amplification benefits of the self-fitting program-aided condition were evaluated and compared with those of unaided and preset program-aided conditions using the Mandarin Hearing in Noise Test (MHINT) and questionnaires. The pure-tone audiometry and hearing screening test results exhibited strong positive correlations at all test frequencies. In the environment with 90° noise, the sentence reception thresholds (SRTs) obtained for the self-fitting program-aided condition were significantly lower (p = .032) than those obtained for the unaided condition, whereas no significant difference was observed between the preset program-aided and the unaided conditions. Moreover, the participants' satisfaction ratings for the devices were consistent with their MHINT results, with a strong positive correlation observed between satisfaction with self-fitting and user satisfaction in smartphone app's control, functionality, and learning. Younger participants were significantly more satisfied in learning to use the app. Bluetooth self-fitting devices can be used for simplified in situ hearing screenings with a sensitivity of 90.4%. The satisfaction ratings and improvements in SRTs indicate significant clinical benefits of the self-fitting program compared with the preset program, particularly in the 90°-noise environments.

Designing AR Interfaces for Enhanced User Experience

Augmented Reality (AR) interfaces are transforming biological education and research by providing immersive and interactive learning environments that increase user engagement and learning results. This study analyzes the use of augmented reality (AR) technology in biological sciences, highlighting the importance of User Experience (UX) design concepts in improving AR interfaces for educational effectiveness. The research provides a comprehensive overview of designing AR interfaces tailored to biological education, highlighting intuitive navigation, interactive feedback mechanisms, and personalized learning experiences. By examining current advancements and future trends in AR technology, the paper identifies key opportunities for innovation in educational settings. A poll of sixty students was conducted to collect data on demographics, AR usage experience, educational efficacy, user satisfaction, and perceived advantageous features. The data indicate that AR is well-received, with high scores for simulation realism, ease of use, and engagement. This lecture finishes by bringing together information about augmented reality's transformative potential in biology education and research. It proposes avenues for further research and development to harness the full capabilities of AR technology, shaping the future of learning in the biological sciences.

The art of prompts' formulation: limitations, potential, and practical examples in large language models

Introduction: "prompt engineering" is crucial in the use of AI models like GPT-3 and GPT-4, as it helps obtain effective responses in areas such as text generation and programming. A well-crafted prompt improves the quality of the responses. The study analyzed how LLMs function and gathered advice for prompt engineering, also examining technological limitations and the impact of user language. Methods: the evolution of large language models, from recurrent neural networks (RNN) to the introduction of the Transformer architecture in 2017, is explained. Responses from ChatGPT 3.5 and 4.0 were evaluated in two case studies to analyze the complexity and personalization of the prompts. Results: in the case studies, it was found that adding context and specificity improved the models' responses. Detailed and personalized responses resulted in greater accuracy and relevance. Conclusion: the quality of LLM responses depends on the precision and specificity of the prompts. Personalization and appropriate technical language enhance interaction with Artificial Intelligence (AI), increasing user satisfaction. Future studies should analyze semantic fields and metrics to evaluate the quality of AI-generated responses.

Efficacy of a cognitive-behavioral digital therapeutic on psychosocial outcomes in rheumatoid arthritis: randomized controlled trial

Cognitive behavioral therapy improves psychosocial outcomes in rheumatoid arthritis (RA), but access is limited. We conducted a randomized controlled trial to evaluate the efficacy of a cognitive-behavioral digital therapeutic, reclarit, on psychosocial outcomes in adult RA patients with impaired health-related quality of life. Participants were randomized to reclarit plus treatment as usual (TAU) or TAU plus educational and informational material (active control). The primary outcome was SF-36 mental (MCS) and physical (PCS) component summary scores at 3 months, with additional assessments at 6 months. reclarit significantly improved SF-36 MCS scores compared to control (mean difference 3.3 [95% CI 0.7, 5.9]; p = 0.014), with high user satisfaction and sustained improvements at 6 months. Depression, anxiety, fatigue, and social/work functioning also improved significantly, while SF-36 PCS, pain, and disability scores did not differ. In conclusion, reclarit offers immediate, effective, evidence-based and personalized psychological support for RA patients.

Penilaian Kepuasaan User dalam Menggunakan APlikasi SIM ASN Menggunakan Metode Website Quality

Implementation of the State Civil Apparatus Management Information System (SIM ASN) is a must. The ASN SIM is not only an administrative tool, but is a digital ecosystem that integrates all personnel application services. The problem that occurs in the ASN SIM application is that users find it difficult to complete the complete history requested by the ASN SIM in the personnel database to update employee data history. The aim of this study is to assess the impact of three factors on user satisfaction: application quality, user-friendliness, and interaction quality. The evaluation process is based on the Website Quality technique, which takes into account multiple aspects of website quality, such as as information content, security, usability, speed, and interface design. Respondents in this study were frequent users of the ASN SIM Application. Questionnaires were used to gather information about user opinions and experiences with the quality of the program. To characterize the degree of user satisfaction and the variables influencing it, data analysis was carried out using both statistical and qualitative methodologies. In order to enhance the user experience and efficacy of using the ASN SIM Application in the everyday work environment, it is intended that the research findings will offer comprehensive insight into the areas that require further development. This study adds to the body of knowledge about user satisfaction evaluation in the context of government apps and may serve as a model for future research on the subject.

Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces

This paper presents a comprehensive study on developing and evaluating an adaptive UI/UX framework to enhance user engagement in mobile applications through personalized interfaces. The research investigates key factors influencing user engagement, including demographics, cognitive abilities, and contextual variables. A context-aware adaptation engine was designed to adjust interface elements based on real-time user data dynamically. The proposed framework was implemented in a mobile learning application and subjected to rigorous usability testing and user engagement analysis. Results demonstrated significant improvements in task completion rates, user satisfaction, and overall engagement metrics compared to non-adaptive interfaces. This study contributes valuable insights into the design and optimization of adaptive mobile interfaces, emphasizing the importance of personalization in creating compelling user experiences.

An Analysis the Adaptive Routing Algorithm for Mobile Cloud Computing

Cloud management provides facilities to access and manage a shared pool of networking resources including servers’ infrastructure, wide variety of web applications and services. The cloud service classification categorized in to three different ways, such as IaaS, PaaS and SaaS. Cloud computing provides flexible and virtualized services based on the user requirement. The scalability can be increased due to non-maintenance of servers and other required infrastructure. Mobile or smart phone usage had a significant growth from past years, and it is one of the daily needs for internet users. Recent advances in mobile development made a huge interest with cognitive capabilities, voice recognition, machine learning, virtual and augmented reality. In mobile cloud computing, it is very necessary to provide the user with the best possible seamless and cost-effective services. User satisfaction is the major goal of mobile cloud computing, the communication through mobile devices to the cloud can lead to long latencies over the wide area network (WAN) and it cannot be ignored. User satisfaction majorly depends on it. In this chapter the problem is identified as networking in MCC, so a new routing algorithm is proposed to solve the problem under high data traffic.

On Development of a SIM-Based Prototype for Enhancing the Usability of Mobile Value-Added Services

The advent of Mobile Value-Added Services (VAS) has witnessed widespread adoption in developing regions, notably within East Africa. This surge is attributed to advancements in information and communication technology, 3G/4G proliferation, growth of mobile internet accessibility, and rising smartphone penetration. Despite this progress, a significant segment of the population continues to rely on basic phones. Studies have shown that the text-based applications used on these types of mobile phones still pose significant obstacles in delivering a user-friendly VAS experience. This paper explores the potential for improving the usability of mobile value-added services on basic phones. We employed an action research approach, utilizing quantitative and qualitative data collection methods. Questionnaires gathered feedback on end-user testing, while in-depth interviews with five industry experts provided insights into potential solutions. This multi-faceted approach ensures a comprehensive understanding of user feedback and expert insights, culminating in the design of an intuitive SIM-based user interface prototype. This highly customised prototype prioritises services and functionalities simplify the user experience through improved navigability and accessibility. The effectiveness of this prototype was quantitatively analysed using descriptive statistics, focusing on ease of use, efficiency, effectiveness, and user satisfaction. With an average ease of use score of 85.8%, a geometric mean time on task of 29.43 seconds, and a user satisfaction score of 79.5%, the prototype demonstrates impressive functionality and garners acclaim for improved navigation and information discovery from users and experts. Further, comparative analysis from expert interviews reveald a more nuanced understanding of technology preferences for basic phone usability, contrary to previous studies. Building upon these demonstrably positive results, the paper concludes by advocating for user interface standardisation and recommending user-generated content for a more inclusive VAS ecosystem.

User satisfaction with the service quality of ChatGPT

User satisfaction with the service quality of ChatGPT

UTILIZING DEEP LEARNING AND PERSUASIVE TECHNOLOGY TO INCREASE USER TRUST IN DIGITAL PAYMENTS

The paper examines the foundations of user trust in digital payments, using quantitative analysis of data from 277 participants. The study focuses on the associations between perceived security, convenience, privacy protection, and user trust in digital payments. Our findings suggest that all three attributes, safety, security, and privacy, are powerful positive correlates of trust. Furthermore, past experience with digital payments (PEDP) modifies these relationships, suggesting that the impact of these attributes on trust can have significant implications for engaged user digital payments are greatly enhanced We can use persuasive design principles and advanced deep learning to enhance user trust. The study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for enhanced feature extraction and temporal sequence analysis of user interactions and preferences within digital payment environments. This model is complemented by a hybrid optimization algorithm merging the Zebra Optimization Algorithm (ZOA) and Seagull Optimization Algorithm (SOA), aimed at refining system performance and user engagement through iterative enhancement of persuasive elements. This paper lays the groundwork for further research on other factors affecting the use of trust and cultural diversity in digital payment channels. Combining mind-blowing technologies with sophisticated deep learning techniques, it seeks to develop digital payment systems that not only secure transactions but also provide lasting user trust, satisfaction planning and implementation improvements.

The Relationship of EMR (Electronic Medical Record) Documentation Performance with Patient Satisfaction

Technological developments have an impact on developments in the world of health, especially information technology in hospitals. The use of EMR makes medical records more orderly and improves health services that are faster, more precise, valuable and accountable. User usage and satisfaction will influence individual impact. Methods: An analysis of the relationship between EMR documentation and nurse satisfaction is needed. This study used a cross-sectional, The sampling technique in this research used purposive sampling correlational design with a sample of 80 patient at RSUD Dr. Soetomo. Research data was collected through questionnaires from the EMR documentation performance variables and nurse satisfaction. The results of the validity test of the EMR Documentation Performance instrument obtained a Parson correlation value in the range 0.590-0.926 > r table 0.514 and the results of the reliability test in this research showed a Cronbach's Alpha value of 0.966 so it is reliable. results of the validity and reliability of the patient satisfaction instrument Cronbach α reliability coefficient was high, ranging from 0.900 to 0.940. Research data was processed using the Spearmen rho test with a p value <0.05. Results: The results of the study showed that EMR documentation performance was related to patient satisfaction (0.009). Discussion: Good electronic medical record documentation performance can increase patient satisfaction.

Knowledge-reinforced explainable next basket recommendation

The next basket recommendation task aims to predict the items in the user’s next basket by modeling the user’s basket sequence. Existing next basket recommendations focus on improving recommendation performance, and most of these methods are black-box models, ignoring the importance of providing explanations to improve user satisfaction. Furthermore, most next basket recommendation methods are designed for consumer users, and few methods are proposed for business user characteristics. To address the above problems, we propose a Knowledge Reinforced Explainable Next Basket Recommendation (KRE-NBR). Specifically, we construct a basket-based knowledge graph and obtain pretrained embeddings of entities that contain rich information of the knowledge graph. To obtain high-quality user predictive vectors, we fuse user pretrained embeddings, user basket sequence level embeddings, and user repurchase embeddings. One highlight of the user repurchase embeddings is that they are able to model business user repurchase behavior. To make the results of next basket recommendations explainable, we use reinforcement learning for path reasoning to find the items recommended in the next basket and generate recommendation explanations at the same time. To the best of our knowledge, this is the first work to provide recommendation explanations for next basket recommendations. Extensive experiments on real datasets show that the recommendation performance of our proposed approach outperforms several state-of-the-art baselines.

Sustainable Healthcare in China: Analysis of User Satisfaction, Reuse Intention, and Electronic Word-of-Mouth for Online Health Service Platforms

Online health service (OHS) platforms can provide sustainable healthcare services in China where healthcare demand continues to grow along with the scarcity of healthcare resources. This study investigated the levels of satisfaction of Chinese consumers’ experiences with OHS platforms and their reuse and electronic word-of-mouth (e-WOM) recommendation intentions. This study provides data for educating consumers on OHS platforms and for guiding strategic business planning for the OHS market. The theoretical model applied was the theory of planned behavior, augmented by integrating factors such as perceived service quality and subjective knowledge into its framework. Data were collected in April 2024 through an online survey of Chinese consumers who had used OHS platforms in the past year. The data were assessed using structural equation modeling and multiple group analysis. The findings indicate that various factors, including attitudes, perceived interaction quality, behavioral control, perceived system quality, perceived information quality, and subjective knowledge concerning OHS platforms, are significant enhancers of user satisfaction and reuse and e-WOM intentions. Health consciousness is a moderating variable in the dynamics between consumer satisfaction and their propensity to engage in e-WOM. Strategies targeting factors that influence satisfaction require development.

The Analysis and Improvement of User Interface Design on Climate Information Service Mobile Application Using the Lean UX Method

Info BMKG is an iOS and Android app providing weather, temperature, air quality, and earthquake data across Indonesia. Issues related to the usability of the BMKG Info application were identified through observations and feedback found on the Google Play Store, which has a rating of 4.4. The responses identified included an unattractive appearance, limited features, increasing complexity, and a need for more innovation, necessitating usability evaluation and UI design improvements to enhance user experience. The analysis and redesign follow the Lean UX approach and Heuristic Evaluation method. Usability testing, specifically the Post-Study System Usability Questionnaire, is used to assess user satisfaction. The study finds that implementing Lean UX significantly improves the system's quality and user experience. UI design enhancements, based on usability testing, improve navigation, information clarity, and ease of use. Recommendations result in better outcomes, with Heuristic Evaluation yielding scores of H1 = 1 (System status visibility), H2 = 1 (Match between the system and the real world), H4 = 1 (Consistency and standards), H5 = 1 (Error prevention), H7 = 1 (Flexibility and efficiency of use), H8 = 1 (Aesthetic and minimalist design), and H9 = 1 (Help users recognize, diagnose, and recover from errors). The Post-Study System Usability Questionnaire method shows an improvement from high to a better category.

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.

The Role of TikTok in Mental Health Support: Analyzing the Efficacy and Risks of Mental Health Content

This study investigated the effectiveness of mental health content on TikTok, focusing on engagement, algorithm-driven recommendations, and gender-based differences in content effectiveness. Utilizing a quantitative research design, the study analyzed data from 350 university students in Punjab, Pakistan. The research aimed to evaluate how engagement with TikTok's mental health content correlates with user satisfaction and perceived mental health improvements. Statistical analyses, including t-tests, correlation, and regression, revealed significant findings: higher engagement with mental health content was positively associated with self-reported mental health benefits (Pearson r = 0.65, p < 0.001), and TikTok’s algorithm-driven recommendations significantly enhanced user satisfaction (B = 0.55, β = 0.62, p < 0.001). Gender differences were notably observed, with females finding emotional support content more effective (M = 4.35, SD = 0.85) compared to males, who preferred practical solutions (M = 3.90, SD = 0.92), supported by a t-value of 4.45 (p < 0.001). These results underscore the importance of tailoring mental health content to gender-specific preferences and optimizing algorithm-driven recommendations to enhance the effectiveness of digital mental health resources on platforms like TikTok.

Innovative Applications of Intelligent Technology in Infant Care Products: A Study on the Design of Bottle Warmers and Milk Warmers

This paper delves into the innovative application of intelligent technology in the design of infant care products, with a particular focus on the design of smart bottle warmers and milk warmers, and their impact on user experience. The study begins by analyzing the current market demands and limitations of existing products in the field of infant care products, followed by a detailed discussion of the design features, technological advantages, and user experience of HAUTURE brand products. Through an in-depth analysis of the temperature control technology of the smart bottle warmer, the user interface design, and the dual-sealing system and vibration reduction and noise reduction technology of the milk warmer, this paper reveals how intelligent technology enhances product performance and user satisfaction. In addition, this paper examines the role of user feedback in product design improvement and proposes solutions to technical challenges. Finally, it provides an outlook on the future development trends of intelligent infant care products, predicting how intelligent technology will further shape innovation in this field.