Improve User Experience Research Articles

Empirical Research on the Impact of Technological Innovation on New Energy Vehicle Sales

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China’s NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government’s public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation.

Generation Z Satisfaction with Smart Homestays: ASCI and Web Crawler Insights from China

In the digital context, smart homestays have developed rapidly in the post-epidemic era and have become a new form of accommodation. Homestays are favored by many young people in China, such as those belonging to Generation Z. According to data concerning China’s national tourism and related reports, the demand for homestays has increased dramatically in recent years. Thus, we need to consider how to improve the smart homestay user experience. Based on the American Customer Satisfaction Index (ACSI) model, this study explores the factors that affect the user experience of smart homestays. An online survey of 370 respondents of Generation Z in China was conducted, followed by descriptive statistical analysis and hypothesis model validation using SPSS 26.0. The data show that among the five service variables (reservation, check-in, living, check-out, and information sharing), perceived value has a positive and positive impact on service variables in all aspects. Finally, machine learning is used for emotion text analysis, and the results show that users are biased towards smart homestays in the sentiment analysis of the comments. Although smart homestays have a certain amount of attention, there is still a lot of room for progress in technology and services. The purpose of this study is to improve and perfect the rules for making smart homestay service standards based on understanding the satisfaction of Generation Z when using smart homestays while also providing a theoretical basis and practical manuals for the industry to promote the development of the industry and improve user experience. Based on the research results of the above literature, it is imperative to carry out research on Generation Z, the main force of future consumption, especially in the field of artificial intelligence.

THE ADOPTION OF AI-DRIVEN CHATBOTS INTO A RECOMMENDATION FOR E-COMMERCE SYSTEMS TO TARGETED CUSTOMER IN THE SELECTION OF PRODUCT

The research looks into the Adoption of AI-Driven chatbots into a recommendation for E-Commerce systems to targeted customer in the selection of product, particularly their function in product selection and overall customer experience. The study is based on five assumptions: improved product selection accuracy, increased user happiness, influence on customer engagement and purchase decisions, enhanced user experience and retention rates, and effect on consumer trust. The findings show that AI-powered chatbots greatly improve product selection accuracy, with a regression weight of 0.693 and a beta coefficient of 0.480, representing a 48% improvement per unit of implementation. User satisfaction has also significantly improved, as evidenced by a regression weight of 0.897 and a beta coefficient of 0.840, both with high statistical significance. Chatbots have a favourable impact on consumer engagement and purchasing decisions, as indicated by substantial regression weights and beta coefficients for each variable. AI chatbots improve user experience and retention, as evidenced by high regression weights and R² values. Finally, chatbots improve customer trust, with a regression weight of 0.447 and a beta coefficient of 0.200. Overall, the study shows that AI-powered chatbots significantly increase several aspects of e-commerce, including product selection accuracy, user pleasure, engagement, retention, and trust.

Redesain User Interface Pada Website Arlang Tour and Travel Menggunakan Metode User Centered Design Sebagai Media Promosi

The development of information technology in Indonesia, particularly in the tourism sector, has experienced rapid progress. Tourism in Indonesia has become one of the main pillars in supporting national economic growth. CV. Arlang Tour and Travel, a travel agency, has also contributed to this sector. However, their website is still not optimal in terms of attractiveness and completeness of information. The initial survey results, conducted using the System Usability Scale, showed an average score of 52 from 30 respondents, with a grade scale of D, indicating that the website's usability is still below average. This research aims to evaluate and redesign the website, focusing on enhancing content completeness using the User Centered Design (UCD) method for design development, while the System Usability Scale (SUS) questionnaire is applied for evaluation. After the redesign using the UCD method, there was an improvement, with the average score reaching 80 with a grade scale of B. These changes successfully addressed the main issues related to content completeness and improved promotions to expand reach, which is expected to provide a better user experience. This is anticipated to contribute to an overall improvement in user experience and strengthen the image of Arlang Tour and Travel in the tourism industry.

Interactive Interface Design for Adaptive and User Behavioural Habits

This study aims to explore adaptive interaction design in relation to user behavior habits, optimizing the user interface (UI) of mobile applications through statistical analysis of user habits. Specifically, the research analyzes user behavior regarding landscape and portrait browsing modes when using mobile applications. The results indicate that adjusting the UI according to different browsing habits can significantly enhance user satisfaction. Therefore, adaptive interaction design strategies play a crucial role in accommodating user behavior habits, contributing to improved user experience and satisfaction.

Cooperative Advisory Residual Policies for Congestion Mitigation

Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute time and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and also fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers’ reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model, and evaluated in simulation and through a user study (N=16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalize to drivers.

Designing User Experience Improvement and User Behavior Pattern Recognition Algorithms in Design Operation

Enhancing user experience (UX) is a key component in customer retention and sales promotion in e-commerce platforms. To build an effective UX model it is necessary to predict the user behavior more accurately and develop UX model that is tailored based on those behavior patterns. Existing models lack the ability to integrate advanced Machine Learning (ML) models to address the challenges. This study is an attempt to tackle these limitations that employs advanced AI tools to predict user behavior so that to construct an more effective UX model. The study involved 80 users from China who were aged 26 to 52, with diverse backgrounds in education, occupation, and tech proficiency. The work have employed Google Analytics, Hotjar, and FullStory to collect the user interactions and by using Generalized Sequential Pattern (GSP) algorithm, Decision Trees (DT), and Logistic Regression (LR) the work attempts to accurately predict the user behavior patterns. The results show that the model achieved better accuracy of 0.8795 and an F1 Score of 0.8610 on the test dataset. It also excelled in conversion rate (12.34%) and bounce rate (28.65%) which show effectiveness in retaining users and converting visits into actions.

Enhancing autumn greenway walking experience: Exploring the combined effects of noise and thermal environment.

The evaluation of outdoor green spaces is influenced by diverse sensory perceptions. Traffic noise and thermal conditions significantly impact greenway-walking satisfaction; their optimization is vital for improving user experience and encouraging outdoor engagement. The study examines a typical Beijing greenway during autumn, focusing on strategies to enhance the walking experience under the combined effects of noise and thermal environments through mobile measurements and surveys. The results show that: 1) The interplay between noise and thermal factors varies depending on the walking state. Upon arrival, an increase in noise significantly worsens thermal comfort; higher sound levels intensify warm thermal sensations, though this effect is not consciously perceived. Upon departure, the effect of noise on thermal perception is not obvious. In both walking states, thermal sensation significantly affects subjective noise perception, yet the trends of influence differ. Subjective noise loudness increases as thermal comfort worsens, showing significant correlation only upon departure. 2) During autumn greenway walks, acoustic factors exert a greater impact on Overall Environmental Satisfaction (OES), with subjective noise loudness being more influential than noise level, followed by air temperature (Ta). Greater noise decreases OES, while OES increases initially with Ta and then decreases. The integrated effects of noise-thermal factors on OES show significant changes. 3) To enhance the autumn greenway-walking experience, the advised parameters are A-weighted Sound Level (ASL) ≤ 59.12 dBA and 15.17°C ≤ Ta ≤ 18.75°C. Finally, three design strategies are proposed: reducing subjective noise loudness, differentiating design based on walking status and balancing acoustic-thermal perceptual preferences.

Educating Dora: Teaching a conversational agent to talk

Many organisations are witnessing the introduction of automated systems to mediate between them and their clients, often designed as dialogic user interfaces which are conversational in nature. Despite calls for the design of AI-powered conversational agents to draw on Ethnomethodological Conversation Analysis (EMCA), how an EMCA-informed conversation design can improve interactions between humans and conversational agents is yet to be empirically examined. This paper reports on a collaboration between EMCA researchers and AI conversation designers at digital health company Ufonia to develop Dora, a pre-existing ‘automated clinical assistant’ that provides telephone consultations for patients of the UK’s National Health Service. Our analysis identified differences between the conversation design of the product and conversational practices found in equivalent activities involving human clinicians, relevant to implementing changes to improve user experience. We demonstrate that users are prompted to recalibrate their practices (such as for turn-taking) in situ as they engage with a system which does not match their prior experiences of this activity type. Such insights can be leveraged to adapt system design so that it more closely approximates users’ prior experiences of such engagements.

Investigating the Behavioral Intention of Smart Home Systems among Older People in Linyi City

Background: With an aging population and the continuous advancement of smart technology, the Chinese government is exploring smart elderly care models to address the challenges posed by aging. Although smart home systems are viewed as a promising solution, their adoption rate among older people remains low. Objectives: This study aimed to investigate the factors influencing the behavioral intention to use smart home systems among older people in Linyi City, Shandong Province, China. Methods: A literature review revealed a lack of quantitative research on older people’s behavioral intention toward smart home systems based on the Innovation Diffusion Theory. This study developed an extended model based on the Innovation Diffusion Theory, Technology Acceptance Model, and external variables, incorporating eight variables: intergenerational technical support, perceived cost, self-reported health conditions, compatibility, observability, trialability, perceived usefulness, perceived ease of use, and behavioral intention. Results: Analysis of 387 valid questionnaires showed that compatibility and trialability significantly and positively affect perceived ease of use, while self-reported health conditions, perceived ease of use, and perceived usefulness have significant effects on behavioral intention. In addition, perceived cost had a negative influence on behavioral intention. Contributions/Significance: These findings highlight the importance of considering these factors in the design of smart home systems to improve user experience and provide valuable practical guidance to smart home system developers, R&D institutions, and policymakers.

Sum rate optimization in STAR-RIS assisted multiuser massive MIMO-OFDM VLC systems

Visible Light Communication (VLC) offers a promising solution for future networks, leveraging existing lighting infrastructure in indoor environments. However, VLC requires a direct line of sight to function which can be limiting. Reconfigurable Intelligent Surface (RIS) is a new technology that can bend light and radio waves, addressing this limitation in VLC. RIS come in three types: passive which reflects signals, active that boosts and reflects signals, and Simultaneous Transmission and Reflection (STAR)-RIS which can both reflect and transmit signals simultaneously. STAR-RIS offers the most control over the signal. In this paper, we propose a new multi-user VLC system with a massive Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) architecture, leveraging STAR-RIS to optimize data rates and improve coverage, particularly in non-line-of-sight scenarios. We formulate a system model and solve a convex optimization problem to determine the optimal transmission and reflection coefficients for STAR-RIS elements, aiming to maximize the total sum rate for all users. By employing maximum ratio transmission precoding, we minimize interference among users and demonstrate significant performance gains. Simulation results show that our proposed energy splitting-based STAR-RIS configuration outperforms traditional mode selection and time switching approaches with fixed or random coefficients, yielding substantial improvements in data rates and user experience. This study offers the first detailed exploration of STAR-RIS in VLC systems, highlighting its potential for future high-speed, multi-user communication networks. Our findings set the stage for further research into optimizing VLC systems using STAR-RIS, particularly in complex environments with non-line-of-sight users and massive MIMO-OFDM configurations.

Developing a Motion Sensor-Based Game to Support Frozen Shoulder Rehabilitation in Older Adults through a Participatory Design Approach.

Objective: Although some serious games have been developed for physical therapy, little work has been conducted through a participatory design approach. Therefore, a game prototype was developed, which involved related stakeholders in the design process. Materials and Methods: The iterative participatory design process was adopted with the input of 18 patients with frozen shoulder symptoms, 4 health professionals, 2 game designers, and 5 researchers in an iterative process to design, test, and evaluate the game prototype. In total, 17 patients participated in the interviews to explore their needs and desires for a serious game. The health professionals participated in the interviews to understand the medical requirement and experience pertaining to frozen shoulder and were included in the workshop to give feedback on the game prototype. At the conclusion of the iterative design process, a Kinect-based prototype game with three levels was used for a case study with one patient who was diagnosed with frozen shoulder and has been receiving medical treatment in the hospital. Results: Based on the outcomes derived from data collected among diverse stakeholders, the prototype game underwent iterative development by the team and was assessed by a participant with frozen shoulder symptoms. Findings revealed that the participant demonstrated enhanced shoulder mobility and a reduction in pain intensity, despite the lack of significant improvement for health-related quality of life. Nevertheless, the participant reported a positive experience with the prototype game. Conclusion: This study underscores the importance of involving diverse stakeholders in the development process to create more effective and user-centric serious games for rehabilitation. The participatory approach, exemplified by the prototype game, demonstrates potential improvements in both user experience and overall effectiveness during the rehabilitation process.

Comparative Analysis of the Distribution of Natural Lighting with Artificial Lighting in Auditorium at Indonesia University of Education, at Faculty of Mathematics and Natural Sciences Building

Abstract This article provides an indepth analysis of the lighting conditions in the auditorium at the Indonesia University of Education, specifically within the Faculty of Mathematics and Natural Sciences. The study aims to assess the effectiveness of both natural light from windows and artificial light from lamps in accommodating large gatherings of up to 300 people. Using AutoCAD and DIALux, we conduct a simulation experiment to visualize the lighting system, considering factors such as the distribution of natural light and the impact of artificial lighting. Our methodology involves specific parameters for data analysis to ensure a comprehensive evaluation. While observing the auditorium, we note potential challenges, such as the limited impact of natural light. This finding raises questions about the need for adjustments to the architectural design or the inclusion of additional natural light sources. Conversely, the artificial lighting appears to be abundant and sufficient for illuminating the entire space. Considering the broader implications, the study addresses the significance of optimizing lighting conditions in auditoriums for improved user experience and potential energy efficiency. The findings contribute to the ongoing discourse on effective lighting design in educational spaces. This research is not without limitations, and future work could explore more nuanced aspects of lighting design or investigate the feasibility of implementing sustainable lighting solutions. In conclusion, this study offers valuable insights into auditorium lighting, guiding future research and practical interventions for enhanced lighting conditions in similar settings.

EVALUASI USABILITY WEBSITE DISDUKCAPIL KABUPATEN MAJALENGKA MENGGUNAKAN METODE HEURISTIC EVALUATION

This study evaluates the usability of the DISDUKCAPIL website of Majalengka Regency using the heuristic evaluation method. By collecting questionnaire data referring to ten heuristic principles, this study identified key issues such as aesthetics, flexibility, and system feedback. The results indicate that a more minimalist and consistent design is needed to improve user experience. Recommendations for improvement are focused on increasing visibility, user control, and help features. Implementation of recommendations is expected to improve the effectiveness and efficiency of services provided through this website, so that it better meets the needs of the Majalengka community.

Algorithm Identification and Integrated with Push Service for Telemedicine System

Telemedicine systems, while overcoming physical space constraints, often lack personalized interactions. By incorporating a push service and leveraging prediction-oriented algorithms, these systems can offer an improved user experience. Such enhancements enable timely treatment options and reduce unnecessary resource usage in on-site outpatient clinics. This research work starts by creating a robust algorithm using data mining techniques. Next, it establishes the foundation for a telemedicine push service. The service includes essential modules for disease differentiation, doctor recommendations, and diagnosis predictions. To optimize these modules, a merged algorithm combining k-nearest neighbor classification, nearest neighbor recommendation, and FP-growth is needed. This work aims to enhance treatment options for patients and streamline resource usage in on-site outpatient clinics. Moreover, this work has carried out empirical research for identification of algorithm by using available data at a public Chinese telemedicine system. The results of data analysis show the follows: 1. For disease diagnosis, the KNN model (k=1) is more accurate but less efficient, SVM and LibSVM are more efficient but less accurate than the KNN model; 2. In terms of doctor recommendation, nearest neighbor recommendation performs better but is not as efficient as matrix factorization; 3. in diagnostic prediction, the combination of introducing association mining and data segmentation can play a better role. The developed algorithm and its conclusions from this study could make easier and more efficient to provide treatment options for undecided-condition patients.

Enhancing User Experience in VR Environments through AI-Driven Adaptive UI Design

This paper presents a new approach to improving user experience in virtual reality (VR) environments using AI-driven user interface (UI) design. The proposed system uses advanced machine learning techniques to update UI content based on user interaction and real-time context. A comprehensive literature review examines the current state of VR interfaces, AI applications in UI/UX design, and evolving UI technologies. Data is a multi-layered process combining data collection, processing, and editing over time. The design was developed and evaluated through a rigorous study involving 50 participants, comparing a modified UI against a static UI control. The results showed a significant improvement in performance, experience reduction, and overall user satisfaction. The modified UI group showed an 18.6% reduction in completion time, a 47.8% reduction in errors, and a 34.9% increase in user satisfaction scores compared to the static UI group. Physiological data analysis supports these findings, showing reduced stress and increased engagement. This research contributes to the field of VR interface design by providing empirical evidence for the effectiveness of AI-driven adaptive UIs in improving user experience and field performance. Virtual.

Research on intelligent building decoration design and user experience based on VR technology

The continuous development of virtual reality (VR) technology has been widely used in all walks of life, bringing unprecedented design possibilities and user experience improvement to the field of architectural decoration design. This paper aims to explore how to use VR technology for intelligent building decoration design and evaluate its impact on user experience through empirical research. Firstly, through the review of relevant literature at home and abroad, the application status and development trend of VR technology in architectural decoration design is sorted out. Then, the design process and implementation scheme based on VR technology are introduced in detail, and the design framework suitable for different application scenarios is constructed. Then, through the design of the user experience evaluation index system, using the combination of qualitative and quantitative methods, the intelligent decoration design scheme based on VR technology is systematically evaluated. Finally, select a typical case for in-depth analysis, show the application effect of VR technology in actual projects, and summarize the feedback of users. The research results show that the intelligent building decoration design based on VR technology can not only improve the design efficiency and quality but also significantly enhance the user's sense of immersion and satisfaction, providing a new perspective and development direction for future architectural design. In addition, this paper also points out the challenges in this field and the possible research directions in the future, which provides a valuable reference for subsequent scholars.

Artificial Intelligence-Based Electric Vehicle Smart Charging System in Malaysia

The worldwide transition to electric vehicles (EVs) is gaining momentum, propelled by the imperative to reduce carbon emissions and foster sustainable transportation. In Malaysia, the government is facilitating this transformation through targeted initiatives aimed at promoting the use of electric vehicles (EVs) and developing the required infrastructure. This paper investigates the crucial role of artificial intelligence (AI) in developing intelligent electric vehicle (EV) charging infrastructure, specifically focusing on the context of Malaysia. The paper examines the current electric vehicle (EV) charging infrastructure in Malaysia, highlights advancements led by artificial intelligence (AI), and references both local and international case studies. Fluctuations in the Total Industry Volume (TIV) and Total Industry Production (TIP) reflect changes in market demand and production capabilities, with notable peaks in March 2023 and March 2024. The research reveals that AI technologies, such as machine learning and predictive analytics, can enhance charging efficiency, improve user experience, and support grid stability. A mathematical model for an AI-based smart charging system was developed, and the implemented system achieved 30% energy savings and a 20.38% reduction in costs compared to traditional methods. These findings underscore the system’s energy and cost efficiency. In addition, we outline the potential advantages and challenges associated with incorporating artificial intelligence (AI) into Malaysia’s electric vehicle (EV) charging infrastructure. Furthermore, we offer recommendations for researchers, industry stakeholders, and regulators. Malaysia can enhance the uptake of electric vehicles and make a positive impact on the environment by leveraging artificial intelligence (AI) to enhance its electric vehicle charging system (EVCS).

Using Artificial Intelligence in Digital Video Production: A Systematic Review Study

Artificial Intelligence (AI) advancements have significantly enhanced computer systems to better cater to user needs, consequently improving user experiences. One area witnessing widespread adoption of AI technology is in the production of digital videos, which find application across various fields, including education. This study aims to delineate the emerging trends in research concerning the integration of AI technology in digital video production. To achieve this objective, we conducted a systematic literature review across several databases, including Web of Science, ERIC, Taylor & Francis, Education Full Text EBSCO, and Sciencedirect. Following the PRISMA flowchart, studies were meticulously screened and selected based on predefined inclusion criteria aligned with the study's purpose. Our analysis encompassed 14 international studies, revealing diverse digital content creations facilitated by AI support. These technologies, offering various interaction dimensions, serve multiple purposes such as providing general guidance, reinforcing knowledge, facilitating design or experimentation, and delivering personalized experiences through tailored technologies. Despite these advancements, the full potential of AI technology remains underutilized. Hence, future research should prioritize the development of digital content that accommodates individual differences, fosters social interaction, incorporates enriched features, and is adaptable to diverse environments. In conclusion, while AI-driven advancements in digital video production hold promise, there exists ample room for further exploration and optimization to fully leverage the capabilities of this technology.

Rfam 15: RNA families database in 2025.

The Rfam database, a widely-used repository of non-coding RNA (ncRNA) families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26,106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronisation with miRBase, resulting in 1,603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation, and the development of machine learning models. Rfam is freely available at https://rfam.org.