Enhance User Experience Research Articles

Penerapan Metode Design Thinking untuk Perancangan UI/UX Website MARQETIQ

In the rapidly evolving digital era, the digital marketing service industry in Indonesia faces the challenge of providing more effective and relevant solutions amid intense competition. This research utilizes the Design Thinking methodology to develop the UI/UX of the MARQETIQ website, aiming to enhance user experience by offering digital marketing services tailored to clients' specific needs. Through the five stages of Design Thinking: Empathize, Define, Ideate, Prototype, and Test. The researchers gained deep insights into user needs, formulated key problems, and created and tested innovative design solutions. The research findings indicate that this approach successfully resulted in a more intuitive and responsive website, facilitated the ordering process, and improved client satisfaction and loyalty. These findings demonstrate that applying the Design Thinking approach can significantly enhance user experience and the relevance of services in a digital marketing platform..

Development of a Mental Health Apps Recommender Platform.

The aim of the paper is to establish the requirements and methodology for the development and implementation of a recommender system for mental health apps to support patients in self-managing their mental health while awaiting formal treatment. The system was developed using an algorithm-based approach, including: (1) user needs assessment through literature review and interviews with various stakeholders, (2) software modelling and prototype creation, and (3) bench testing of the prototype with health experts and users. Based on initial exploration of users' requirements, relevant standards and regulations, a library of trusted mental health apps was compiled and a recommendation engine was built to generate accurate user profiles and deliver personalised health recommendations, which will be further tested to ensure quality. Developing a constructive mental health recommendation system requires the establishment of clear and comprehensive requirements, as well as a robust methodology adressing concerns related to data security, confidentiality, safety, and reliability. Subsequent research may compare various indicators of mental health outcomes at the start and end of patients' waiting period to gain more insights into how the recommender system could be further improved to enhance user experience and their overall well-being.

Jupyter widgets and extensions for education and research in computational physics and chemistry

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.

Dynamic elicitation and forecasting innovation requirement of smart product-service system via user-manufacturer value co-creation perspective using multi-source data

Achieving greater efficiency and lower resource consumption is a constant pursuit for successfully developing smart product-service system (PSS). Conducting dynamic requirements analysis is increasingly important in the process while the value co-creation of smart PSS stakeholders has not been sufficiently considered. This study adopts a user-manufacturer value co-creation perspective to explore smart PSS dynamic requirement elicitation and forecasting. Using multi-source data of user online reviews, historical descriptive texts and patent abstracts, this study dynamically elicits the smart PSS innovation requirements using dynamic topic model (DTM) and bidirectional encoder representation from transformers (BERT). Furthermore, the key innovation requirements of smart PSS are identified by integrating their importance and satisfaction values into a dynamic importance-performance analysis (DIPA). Then, the trend of smart PSS key innovation requirements is predicted by the grey forecasting model to determine the smart PSS improvement direction. An empirical study of smartwatch service system is conducted to validate the proposed approach. By blending both user and manufacturer requirements, this study provides a novel framework for dynamic requirement elicitation and forecasting of smart PSS based on multi-source data. It contributes to facilitating the value co-creation during smart PSS development to optimize manufacturer’s R&D resource allocation and enhance user experience.

Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse.

Website Design and Network Marketing in the Printing Service Industry

Websites become a necessity in helping users to first see the information and content of a product/service offered. Therefore, the development of a website to help small businesses/industries becomes a necessity to help customers get information related to the products/services offered. Marketing product websites has become a necessity today, given that technological advancements have led users to focus more on online purchases. Website design plays a crucial role in enhancing user experience and product marketability more effectively. The aim of this study is to find the optimal combination of design elements for website design according to different user perceptions, so that a reasonable optimization reference based on user perceptions can be provided. Through Kansei Engineering methods and neural network modeling, this study measures and predicts users' perceptions of various website design forms for product marketing. Overall, this study provides conceptual and methodological support for research on the emotional design of human-computer interaction interfaces, as well as supplements existing theories of website design for product marketing. The findings of this study can serve as feedback to website developers for product marketing and related design practitioners to help them improve website design.

From Boomers to Millennials: unraveling the complexities of online shopping behavior in Indonesia

This study examines the intricate dynamics of utilitarian browsing, hedonic browsing, electronic word of mouth (e-WOM), e-satisfaction, and e-loyalty across three generational cohorts –Millennials, Gen X, and Baby Boomers – in Indonesian online marketplaces. The research addresses the need to understand how different age groups engage with online platforms amidst technological advancements and emerging generational cohorts. A quantitative approach is employed to systematically investigate these phenomena. Data from a structured online survey of 962 respondents are analyzed using descriptive statistics, ANOVA, and correlation analysis. The majority of respondents (50.5%) belong to the Millennial generation, followed by Generation X (31.2%) and Baby Boomers (16.2%).The findings reveal significant differences in utilitarian browsing (F = 11.222, p < 0.001), hedonic browsing (F = 6.338, p = 0.002), e-WOM (F = 3.488, p = 0.031), e-satisfaction (F = 6.239, p = 0.002), and e-loyalty (F = 9.298, p < 0.001) across different generational groups. Notably, Millennials and Gen X exhibit higher levels of utilitarian and hedonic browsing, e-satisfaction, and e-loyalty compared to Baby Boomers, while e-WOM engagement remains consistent across all cohorts. These findings provide valuable insights for businesses operating in the Indonesian online marketplace, guiding targeted marketing strategies, enhancing user experiences, and fostering customer loyalty. The research emphasizes the importance of aligning marketing approaches with the distinct preferences and behaviors of different generational segments. Furthermore, it suggests avenues for future research, including exploring additional demographic variables and conducting longitudinal studies to track evolving consumer dynamics.

Evaluation and optimization of intelligent recommendation system performance with cloud resource automation compatibility

This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.

Research and Analysis of the Development of Live E-commerce on Xiaohongshu

This article analyzes the competitiveness and development prospects of the buyer e-commerce model on Xiaohongshu in the social e-commerce field. Firstly, by examining Xiaohongshu's community ecosystem, buyer resources, social shopping experience, and innovative brand partnerships, the unique advantages and business value of Xiaohongshu's buyer e-commerce model are revealed. Secondly, combining market demands and trends analysis with buyer resource and operational cost analysis, the feasibility and development prospects of Xiaohongshu's buyer e-commerce model are discussed. Lastly, from the perspective of competitor analysis and summarizing prospects, recommendations are provided to further optimize buyer resources, strengthen brand partnerships, control operational costs, enhance user experience, and improve community management. These suggestions aim to consolidate Xiaohongshu's leading position in the social e-commerce field, create better shopping experiences and marketing platforms for users and brand merchants, and achieve sustainable business growth.

Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.

Integrating advanced human-computer interaction and machine learning models for optimizing VR systems in educational and business applications

This paper presents the development of an advanced Human-Computer Interaction (HCI) model and algorithm integrating Natural Language Processing (NLP) to optimize Virtual Reality (VR) systems for educational and business applications. The proposed model enhances user experience and operational efficiency by addressing interface design, user engagement, real-time data processing, and accessibility. Continuous learning and contextual data integration ensure adaptive and personalized interactions, improving the functionality and applicability of VR environments. Fig.: 2. Refs.: 11 titles.

Research on Interior Design of New Energy Vehicles under the Concept of Information Materialization

This research focuses on the interior designing of electric vehicles (EVs), considered as new energy vehicles (NEVs), within the information materialization framework. The idea of information materialization refers to the quantifiable demonstrations of given data within the vehicle interior space, enhancing user interactions and experiences. Considering the State of Charge (SoC) as the crucial designing parameter, the research investigates the several implications of SoC on NEV interior designing. SoC, a critical parameter that reflects the battery's charging level, profoundly influence the merging of SoC data into the vehicle's dashboard or infotainment systems, ensuring seamless accessibility and intuitive interpretations for the driver. Instant alerts and notifications are designed based on SoC levels to provide real-time feedback and guidance to end users, optimizing driving ranges and strategies for charging. Furthermore, the study emphasizes the significance of customization options in SoC displaying formats and accommodates varying users preferences and requirements of visibility. Analysis of empirical and feedback from users, the research confirms the effectiveness of SoC-driven designing intervention in enhancing user experiences, safety, and overall functionalities interiors of NEV. The findings underscore the pivotal role of SoC as a fundamental designing parameter in shaping the next generations of NEV interiors and highlight the need for complete consideration of energy management aspects in future vehicle design endeavors.

Implementing social and affective touch to enhance user experience in human-robot interaction.

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces.

Penerapan Bentuk Dinamis Dalam Membangun Suasana Pada Ruang Tunggu RSUD Pandega Pangandaran

The interior design of hospital waiting rooms plays a crucial role in providing comfort and reducing stress for patients and visitors. In a medical environment, the psychological and physical factors of waiting room design can significantly impact the overall user experience. This study aims to explore and implement dynamic design forms in the waiting room of RSUD Pandega Pangandaran to enhance user experience quality. The methodology employed in this research includes field observations to understand the existing conditions, in-depth interviews with users to identify their needs and preferences, and a comprehensive literature review on dynamic interior design, ergonomic principles, and environmental psychology theories. The findings of the study indicate that the use of dynamic forms, combined with soothing color elements, optimal natural and artificial lighting, and ergonomic and flexible furniture arrangements, can significantly improve the comfort and satisfaction of waiting room users. This study emphasizes the importance of a holistic approach in designing hospital waiting rooms that not only focuses on aesthetics but also on functional and psychological aspects to create a more humane environment that supports the healing process

A Multilayer Architecture towards the Development and Distribution of Multimodal Interface Applications on the Edge.

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly.

A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

In the domain of engineering interactive product design, the optimization of Human-Computer Interaction (HCI) is central to enhancing user experience and system efficiency. Notably, the application of cognitive load theory holds significant prominence in this field. Efficient evaluation of cognitive load not only facilitates the optimization of resource management and task allocation, but is also the key to creating a fluent interactive experience. With the advancement of Artificial Intelligence (AI) and physiological measurement technologies, new methodologies have emerged to measure and understand cognitive load. This research introduces a system for HCI training and assessment. The system, through the development of digital memory paradigms, induces and verifies varying levels of cognitive load with precision, thereby providing data-driven insights for HCI design. Specifically, this study constructs an AI-assisted multimodal cognitive load assessment framework, integrating physiological data collected from Functional Near-Infrared Spectroscopy (fNIRS) and eye-tracking technologies. By extracting and analyzing 29 physiological features, including channel features, graph-theoretic features, and features pertaining to eyelid and iris movements, we propose an innovative multimodal recognition approach to classify cognitive load across individuals. The experimental results not only verified the validity of the method, but also revealed changes in physiological patterns at different levels of cognitive load. In particular, the significant potential of the fNIRS feature in band I. This discovery suggests that we can monitor and predict cognitive load with greater precision through physiological signals, hence optimizing the design of HCI systems to alleviate user strain, prevent information overload, and enhance the naturalness and intuitiveness of interaction.

Task offloading scheme in Mobile Augmented Reality using hybrid Monte Carlo tree search (HMCTS)

Mobile Augmented Reality (MAR) applications enhance user experiences by providing realistic information about the current location through mobile devices. However, MAR applications are computationally intensive, leading to high energy consumption and latency issues. To address these challenges, this research presents a Hybrid Monte Carlo Tree Search (HMCTS) based task offloading scheme, combining a genetic algorithm with Monte Carlo tree search for efficient task management. The proposed method uses YoloV7 for object recognition and aims to reduce energy consumption, response time, and migration time. Experimental results demonstrate that the HMCTS approach significantly reduces energy consumption to 1290 kJ, response time to 24 ms, and migration time to 0.52 ms, outperforming existing techniques. These improvements highlight the potential of the HMCTS method for enhancing the performance of MAR applications. Proposed hybrid approach aims to improve the efficiency and effectiveness of task offloading in MAR applications. The HMCTS model dynamically offloads tasks to edge servers, optimizing scheduling time, response time, and energy consumption.

Paths to digital mobile payment platforms acceptance and usage: A topology for digital enthusiast consumers

The rapid development of digital mobile payment platforms (DMPPs) has significantlytransformed the financial services landscape, offering consumers unparalleled convenience and efficiency in payment options. This study delves into the acceptance and usability factors among digital enthusiast consumers, employing fuzzy set Qualitative Comparative Analysis (fsQCA) to uncover key determinants. Analyzing data from 325 digital enthusiasts, the research identified four distinct configural paths that significantly influence DMPP usage, revealing two primary user typologies: “systems readiness and ease of use” and “affection and secured platforms.” Performance expectancy emerged as a core condition, highlighting its critical role in driving DMPP adoption among digital enthusiasts. The study also underscored the importance of grievance redress and perceived trust as essential conditions influencing user adoption and continuous usage, emphasizing their pivotal role in enhancing user experience and fostering trust. These insights suggest that addressing users' concerns and building trust are fundamental for the sustained success of DMPPs. By tailoring DMPP features to align with the distinct preferences of the identified user typologies, service providers can significantly boost adoption rates. The fsQCA approach offers novel insights into the nuances of emerging technology acceptance and usage, underscoring the necessity of categorizing users to better understand DMPP adoption dynamics. This research provides valuable contributions to both businesses and policymakers,offering strategies to optimize DMPP design, ensure superior user experiences, and promote broader adoption. The findings highlight the dynamic interplay of user expectations and technological capabilities, essential for navigating the evolving digital financial landscape.

RealtimeGen: An Intervenable AI Image Generation System for Commercial Digital Art Asset Creators

Recent advances in artificial intelligence-generated content (AIGC) have led to the rapid generation of high-quality images. AIGC has attracted the attention of commercial digital art asset creators. Traditional artist-led processes contrast with current AI tools that often reduce creators to passive roles. This study examines the integration of AI image generation into commercial digital art, emphasizing the importance of preserving creators’ creative autonomy. Our formative study (S1) involved interviews with commercial digital art creators, highlighting a need for greater control and transparency in AI-assisted painting. In response, we developed RealtimeGen, an integrated tool that merges human creativity with AI’s capabilities, allowing creators to intervene in the generative process. A user study (S2) comparing RealtimeGen with the popular AIGC tool Stable Diffusion was also carried out. The results showed its enhanced user experience and workflow compatibility. Our work contributes to understanding and improving AI-assisted painting workflows for commercial creators, offering them greater creative agency.

A qualitative analysis of Chinese higher education students’ intentions and influencing factors in using ChatGPT: a grounded theory approach

The emergence of ChatGPT has significantly impacted the field of education. While much of the existing research has predominantly examined the theoretical implications of ChatGPT, there is a notable absence of empirical studies substantiating these claims. As pivotal stakeholders in education and primary users of ChatGPT, exploring the willingness and influencing factors of higher education students to use ChatGPT can offer valuable insights into the real-world needs of student users. This, in turn, can serve as a foundation for empowering education with intelligent technologies in the future. This study focuses specifically on the demographic of students in Chinese higher education who have utilized ChatGPT. Using semi-structured interviews and grounded theory methodology, we aim to comprehensively understand the extent to which students embrace new technologies. Our objective is to elucidate the behavioral inclinations and influencing factors of student users. The findings of this study will contribute practical insights for refining policy frameworks, expanding the dissemination of quality resources, optimizing and upgrading products for an enhanced user experience, and fostering higher-order thinking skills to adeptly navigate evolving technological landscapes. In conclusion, this research endeavors to bridge the gap between theoretical discussions and practical applications.