User Experience Research Articles

Moderating Effects of Visual Order in Graphical Symbol Complexity: The Practical Implications for Design

In the field of visual graphic design, complexity plays a crucial role in visual information processing, and it is assumed to be an absolute quantity based on the number of the presenting features and components. However, it remains unclear whether the visual order of the constituent elements in graphical symbol complexity affects cognitive processing, especially memory processing. Our research innovatively generated four groups of novel, meaningless graphical symbols (complex and ordered, complex and disordered, simple and ordered, and simple and disordered) and experimentally manipulated the level of complexity and order in these stimuli. Before the formal experiment, a five-point scale was used to further rule out differences between objective and subjective definitions of these graphical symbols on ratings of complexity, order, concreteness, and familiarity. Then, we used a cue-recall task to compare subjects’ memory performance of those four graphical symbol groups. The analytical results showed a significant interaction between visual order and graphical symbol complexity, with the complexity effect appearing only when the stimuli were in disordered condition and disappearing once the stimuli were ordered. In addition, this study conducted a practical application validation to confirm that increasing the level of visual order is an effective way to improve user experience while maintaining the same level of complexity. The findings can serve as a reference for graphical symbol design, graphic design, and visual communication design.

Drug providers’ perspectives on antibiotic misuse practices in eastern Ethiopia: a qualitative study

ObjectiveAntibiotic misuse includes using them to treat colds and influenza, obtaining them without a prescription, not finishing the prescribed course and sharing them with others. Although drug providers are well...

Why is KOL (Key Opinion Leader) Advertising on Social Media more Effective at Attracting Consumers than Official Brand Accounts?-Exploring Lancmes Promotion on Chinese Platform RED

In the context of the flourishing digital market, effectively utilizing social media for advertising has become one of the primary concerns for brands. This article aims to investigate the reasons why advertising campaigns by KOLs (Key Opinion Leaders) on Chinese social media platforms garner more attention from consumers compared to official brand accounts. This research employs a case study and interview methodology. The first set of data presents the advertising content and responses of both Lancme's official account and KOL accounts on the Chinese social media platform, Xiaohongshu (RED). The second set of data showcases the interview results from five recruited participants on RED. Each participant was asked to assess their attitudes towards different forms of product promotion. The results indicate that KOLs' posts on RED offer a more relatable and curiosity-inducing perspective by sharing product experiences in everyday life scenarios. This approach resonates with users and brings the promotion closer to consumers. In contrast, the content strategy of official brand accounts tends to focus more on product features, which increases the perceived distance from consumers. Furthermore, interaction data and user feedback from KOL accounts reveal that users are more inclined to trust and reference the authentic usage experiences of fellow consumers rather than official product descriptions. These findings elucidate the practical impact of official brand accounts versus KOL accounts in social media advertising, highlighting the unique value of KOL advertising. Our research is poised to inspire brands to reconsider their social media advertising strategies, potentially bringing forth greater appeal and competitive advantage for the brands.

Research on the Subjective and Objective Evaluation Strategy of the Intelligent Vehicle Cockpit Based on the User Experience

The trend of intelligent networking in automobile industry is developing in full swing, with more and more functions. But feature richness does not necessarily reflect user needs. In order to perfect the evaluation system in the field of automobile intelligent cockpit, this paper constructs the subjective and objective evaluation strategy and tests the function of automobile intelligent cockpit. By building evaluation strategy, standardizing the tester, testing process pole test dimension, combining with subjective evaluation and eye movement, EEG device objective data, clear model and algorithm. Finally, the feasibility of the modified strategy is verified by a practical case.

The Growth of Mobile Learning Applications in Language Education: A Bibliometric Overview

The rapid advancement of mobile technology has transformed education, with mobile learning applications (m-learning) becoming integral to language education. This study provides a bibliometric analysis of research on mobile learning applications in language education, examining trends, types of research, and key themes from 2010 to 2023. Data were collected from major academic databases, including Scopus, Web of Science, SINTA, and Google Scholar, using keywords related to mobile learning and language education. The findings reveal a significant increase in publications over the past five years, reflecting the growing academic interest in m-learning. Qualitative research methods have been predominantly used, focusing on user experiences and the contextual nature of mobile learning. Critical thinking skills have emerged as a key area of focus, indicating a shift towards using mobile learning for deeper cognitive processes. The study also highlights the central role of students as research subjects, while noting a gap in research on other stakeholders, such as teachers and administrators. Data collection has primarily relied on tests to measure cognitive outcomes, but the study suggests incorporating a variety of methods to capture a more holistic view of the learning experience. The conclusion emphasizes the need for ongoing research to optimize the use of mobile learning applications in language education, advocating for a broader inclusion of stakeholders and diverse research methodologies. This bibliometric overview contributes to the understanding of mobile learning's impact on language education and offers insights for future research directions.

A Review of the Impact of AIGC on User Behavior in Digital Marketing

As an important means for modern enterprises to promote their products and services, the effect of digital marketing is affected by a variety of factors, and the emergence of AIGC technology brings new possibilities for digital marketing, which not only improves the efficiency of marketing but also brings a more personalized and intelligent experience for users. This study will focus on the impact of AIGC on user behavior in digital marketing to explore how AIGC technology affects users' purchase decisions, information search behavior, interactive participation, etc. and analyze the mechanism behind it. This study adopts the method of literature review to collect and analyze academic papers, case studies and industry reports in related fields at home and abroad. By combing and summarizing these literatures, this paper will distil the main ideas and conclusions of AIGC's influence on user behavior in digital marketing, and construct the corresponding theoretical framework.

Librarian Expertise, Responsiveness, and Virtual Reference Service Quality: Do Communication Channels Matter?

This study examines the impact of librarian expertise and responsiveness on virtual reference service (VRS) quality, focusing on the mediating role of communication channels. Data were gathered via Google Forms survey involving 161 university librarians in Indonesia and analyzed through structural equation modeling using Lisrel 8.8 to assess validity, reliability, and relationships between variables. Results indicate that librarian expertise and responsiveness significantly contribute to the overall quality of VRS. Furthermore, communication channels were found to play a crucial role in enhancing the influence of librarian expertise and responsiveness on service quality. These findings suggest the importance of effective communication strategies in shaping user experiences within VRS platforms. The study offers valuable insights into the dynamics of VRS and underlines the significance of communication channels as mediators between librarian actions and service qualiyt.

The experiences of minority language users in health and social care research: A systematic review.

The planning and management of health policy is directly linked to evidence-based research. To obtain the most rigorous results in research it is important to have a representative sample. However, ethnic minorities are often not accounted for in research. Migration, equality, and diversity issues are important priorities which need to be considered by researchers. The aim of this systematic review (SR) is to explore the literature examining the experiences of minority language users in Health and Social Care Research (HSCR). A SR of the literature was conducted. SPIDER framework and Cochrane principles were utilised to conduct the review. Five databases were searched, yielding 5311 papers initially. A SR protocol was developed and published in PROSPERO: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020225114analysis. Following the title and abstract review by two reviewers, 74 papers were included, and a narrative account was provided. Six themes were identified: 1. Disparities in healthcare; 2. Maternal health; 3. Mental health; 4. Methodology in health research; 5. Migrant and minority healthcare; 6. Racial and ethnic gaps in healthcare. Results showed that language barriers (including language proficiency) and cultural barriers still exist in terms of recruitment, possibly effecting the validity of the results. Several papers acknowledged language barriers but did not act to reduce them. Despite research highlighting cultures over the past 40years, there is a need for this to be acknowledged and embedded in the research process. We propose that future research should include details of languages spoken so readers can understand the sample composition to be able to interpret the results in the best way, recognising the significance of culture and language. If language is not considered as a significant aspect of research, the findings of the research cannot be rigorous and therefore the validity is compromised.

Medical information management system based on multi-source heterogeneous big data

ABSTRACT This study aims to provide a medical information management system using multi-source heterogeneous big data to improve medical service quality and efficiency, with a motivation on its potential in medical insurance DRG payment. The system framework uses Back Propagation Neural Network (BPNN) technology to efficiently process and analyze multi-source medical data. Comparative experiments and parameter adjustments evaluated the system’s performance. Results show that the BPNN model achieved excellent accuracy (92.5%), recall (93%), and F1 value (92.8%) on the test data set, outperforming other models such as PSO(88%), CNN(89%), and RNN(90%). The system’s response speed was also significantly improved, with an average response time of 0.38 seconds, compared to 0.89 seconds for traditional systems. A 72-hour stability test confirmed the system’s reliability and ability to meet user needs. The proposed system demonstrates improved performance and user experience, making it a promising solution for medical information management and DRG payment applications.

A dual-channel multimodal sentiment analysis framework based on three-way decision

In the exploration of human–computer interaction, accurately identifying and understanding the user’s sentimental state helps enhance the user experience. Multimodal sentiment analysis aims to identify sentiments through various signals. However, inconsistencies in sentiments between text and visual–audio signals pose a challenge, as models often struggle to capture these differences adequately. To alleviate this issue, we propose a dual-channel sentiment analysis framework based on three-way decision. This framework includes a multimodal model, a visual–audio model, and a three-way decision module. Specifically, the multimodal model and visual–audio model generate predictions, and the three-way decision module selects an appropriate prediction. We improve the binary cross-entropy loss function for the visual–audio model to integrate the three-way decision module better and improve sentiment prediction accuracy. Additionally, we introduce an adversarial training task to ensure differences in predictions between the two models, thereby enhancing the influence of the visual–audio model. Experimental results on four public datasets demonstrate that our framework significantly improves the accuracy of sentiment recognition and reduces the average decision time.

Development of Interactive Learning Media Crossword Puzzle Integrated Virtual Laboratory in Science Learning Vibrations and Waves Material

Learning media is an essential component in supporting learning objectives. Teachers are expected to make optimal use of these media. This research aims to develop interactive learning media through crossword puzzles integrated with virtual laboratories that are valid, practical, and effective. This research is included in the type of development research that refers to the ADDIE model Research and Development Model: Analysis, Design, Development, Implementation, and Evaluation. The tools used in this research are the validation sheet, learning process implementation observation sheet, student response questionnaire, student activity observation sheet, and learning outcomes test. The research results show that the interactive crossword puzzle integrated virtual laboratory learning media is categorized as valid, practical, and effective because the combination of learning and games provides a deep and enjoyable learning experience for users. Meanwhile, the virtual laboratory allows experiments to be conducted virtually so that they can be accessed anytime and anywhere.

Quasi-experimental quality evaluation of educational-purposed user-generated contents under a stochastic multi-criteria environment

ABSTRACT As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.

Behavioral insights on individual and social adaptation to automated mobility using a serious game

IntroductionSerious games generate a virtual environment where players are immersed in simulated conflicts, guided by distinct rules. They replicate intricate systems, like transportation, encouraging social engagement via reinforced learning. Unlike preference-based studies, these games offer enhanced real-time feedback on players' actions. Thus, they reveal how user experience and social interaction influence decision-making over time. We use a serious game to study the willingness of travelers to adopt automated mobility, specifically shared modes of transport, an important step toward alleviating congestion, enhancing the quality of urban living, and improving people's health and well-being. MethodsFor each scenario, 100 participants were randomly divided into ten groups of ten interacting players. They chose independently out of three automated transportation modes - shared ride, shared car, and automated transit-over 50 simulated days how to commute to work. They aimed to maximize their overall score by arriving punctually, which was influenced by their mode and departure time and the choices of fellow players. Cross-nested logit kernel choice models were estimated based on the game data. ResultsIn the recurring congestion scenario players learned to adopt the shared ride at the expense of transit; in the nonrecurring congestion scenario, random incidents increased the use of transit and shared car (ride alone). ConclusionsCongested traffic motivated a shift to ridesharing at the expense of private rides and public transport; however, the latter was highly demanded when traffic became unsmooth and travel times more uncertain. The implications can be translated to health promoting polices to encourage sustainable travel behaviors while also improving transportation efficiency.

Functional outcomes and participants’ perspectives during short-term application of spinal stimulation in individuals with spinal cord injury

Objective The primary objective of this study was to quantify changes in performance of task-specific motor activities over 12 motor rehabilitation sessions with lumbosacral spinal cord stimulation (SCS) via either transcutaneous stimulation or epidural stimulation. Both stimulation modalities have been used in recent years to restore functions lost to spinal cord injury (SCI). Secondary outcomes examine participants’ perspectives captured via the User Experience Questionnaire (UEQ) upon study completion to further understand their perception of SCS. Methods Six individuals with SCI completed 12 sessions with one modality of SCS during supine and/or side-lying, seated forward reaching, and standing activities. Changes in volitional lower extremity movement, the number of points of contact needed at hips and/or knees to facilitate standing, and changes in seated forward reaching distance were used to quantify performance. The UEQ was administered to gauge participants’ perspectives following use of SCS to enable functions impaired due to SCI. Results For all participants, performance of motor activities improved with SCS compared to without stimulation. Responses for the UEQ showed an overall positive perception of trialing SCS with rehabilitation to enhance motor functions impaired by SCI. Conclusions Regardless of injury severity, location of injury, time since SCI, or SCS modality, all participants experienced gains in motor function in the presence of SCS combined with a condensed rehabilitation program. However, no evidence of sustained motor functions was found in the absence of SCS. UEQ results highlight the positive perception of SCS with rehabilitation as well as the importance of consulting persons with lived experience of SCS during clinical trial design and protocol development. Trial registration: ClinicalTrials.gov identifier: NCT05095454.

Identifying and resolving conflict in mobile application features through contradictory feedback analysis

As mobile applications proliferate and user feedback becomes abundant, the task of identifying and resolving conflicts among application features is crucial for delivering satisfactory user experiences. This research, motivated to align application development with user preferences, introduces a novel methodology that leverages advanced Natural Language Processing techniques. The paper showcases the use of sentiment analysis using RoBERTa, topic modeling with Non-negative matrix factorization (NMF), and semantic similarity measures from Sentence-BERT. These techniques enable the identification of contradictory sentiments, the discovery of latent topics representing application features, and the clustering of related feedback instances. The approach detects conflicts by analyzing sentiment distributions within semantically similar clusters, further enhanced by incorporating antonym detection and negation handling. It employs majority voting, weighted ranking based on rating scores, and frequency analysis of feature mentions to resolve conflicts, providing actionable insights for prioritizing requirements. Comprehensive evaluations on large-scale iOS App Store and Google Play Store datasets demonstrate the approach's effectiveness, outperforming baseline methods and existing techniques. The research improves mobile application development and user experiences by aligning features with user preferences and providing interpretable conflict resolution strategies, thereby introducing a novel approach to the field of mobile application development.

Physicians' Views of a Digital Intervention to Support Respiratory Patients: Take-a-Breath Pre-Pilot Usability Study.

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end's usability. This study aims to evaluate the Healthcare Professional's (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

Evaluation of Different Visual Feedback Methods for Brain-Computer Interfaces (BCI) Based on Code-Modulated Visual Evoked Potentials (cVEP).

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices using electroencephalography (EEG) signals. BCIs based on code-modulated visual evoked potentials (cVEPs) are based on visual stimuli, thus appropriate visual feedback on the interface is crucial for an effective BCI system. Many previous studies have demonstrated that implementing visual feedback can improve information transfer rate (ITR) and reduce fatigue. This research compares a dynamic interface, where target boxes change their sizes based on detection certainty, with a threshold bar interface in a three-step cVEP speller. In this study, we found that both interfaces perform well, with slight variations in accuracy, ITR, and output characters per minute (OCM). Notably, some participants showed significant performance improvements with the dynamic interface and found it less distracting compared to the threshold bars. These results suggest that while average performance metrics are similar, the dynamic interface can provide significant benefits for certain users. This study underscores the potential for personalized interface choices to enhance BCI user experience and performance. By improving user friendliness, performance, and reducing distraction, dynamic visual feedback could optimize BCI technology for a broader range of users.

Frame-level nonverbal feature enhancement based sentiment analysis

Multimodal Sentiment Analysis (MSA) comprehensively utilizing data from multiple modalities to obtain more accurate sentiment attribute, has important applications in other fields, such as social media analysis, user experience evaluation and medical health, etc. It is worth noting that previous studies have paid little attention to the inconsistency of the initial representation granularity between verbal (textual) and nonverbal (acoustic and visual) modalities. As a result, the imbalanced emotional information between them complicates the interaction process, and ultimately affects the model’s performance. To solve this problem, this paper proposes a Frame-level Nonverbal feature Enhancement Network (FNENet) to improve performance on MSA by reducing the gap and integrating asynchronous affective information between modalities. Specifically, Vector Quantization (VQ) is applied to nonverbal modalities to reduce the granularity differences and improve the performance of the model. Additionally, nonverbal information is integrated through the Sequence Fusion mechanism (SF) into a pre-trained language model to enhance the textual representation, which benefits the word-level semantic expression according to the asynchronous affective cues preserved in unaligned frame-level nonverbal features. Extensive experiments on three benchmark datasets demonstrate that FNENet significantly outperforms baseline methods. It indicates that our model has potential application on MSA.

Understanding the perception of design students towards ChatGPT

The benefits of artificial intelligence (AI)-enabled language models, such as ChatGPT, have contributed to their growing popularity in education. However, there is currently a lack of evidence regarding the perception of ChatGPT, specifically among design students. This study aimed to understand the product design (PD) and user experience design (UXD) students' views on ChatGPT and focused on an Indian university. The study employed a survey research design, utilizing questionnaires as the primary data collection method. The collected data (n = 149) was analyzed using descriptive statistics (i.e., frequency, percentage, average, and standard deviation (SD). Inferential statistics (i.e., one-way ANOVA) was used to understand the significant differences between the programs of study, gender, and academic level. The findings indicate that the students expressed admiration for the capabilities of ChatGPT and found it to be an interesting and helpful tool for their studies. In addition, the students' motivation towards using ChatGPT was moderate. Furthermore, the study observed significant differences between PD and UXD students and differences based on gender and academic level on certain variables. Notably, UXD students reported that ChatGPT does not understand their questions well, and formulating effective prompts for the tool was more challenging than for PD students. Based on the findings, the study recommends how educators should consider integrating ChatGPT into design education curricula and pedagogical practices. The insights aim to contribute to refining the use of ChatGPT in educational settings and exploring avenues for improving its effectiveness, ultimately advancing the field of AI in design education.

VAEnvGen: A Real-Time Virtual Agent Environment Generation System Based on Large Language Models

Environment plays an important role in non-verbal communication for human-virtual agent interaction. Existing research explores the influence of an agent’s appearance and attributes to enhance human-virtual agent communication. However, there is no common practice for dynamically adjusting the surrounding environments of the virtual agent. In this paper, we introduce a real-time virtual agent environment generation system (VAEnvGen), which contributes to the field by enhancing users’ content perception and improving task performance through dynamic environment adjustment. The system dynamically analyzes both the appropriate communication environment and filters the key information according to the current context. Leveraging Large Language Models, it generates a pseudo-3D background space to create an engaging atmosphere and a dynamic foreground content space for vivid key information display, thereby significantly enhancing content perception. For widespread adoption and flexibility, VAEnvGen is developed as a web application. We further evaluate the impact of VAEnvGen on content perception, user attention, and subjective satisfaction through a mixed-design user study with 50 participants. Quantitative and qualitative results reveal significant improvements in content perception, task completion time, and user satisfaction when using VAEnvGen. The system effectively redistributes user attention from subtitles and the virtual agent itself to the dynamically generated background and key foreground information, leading to a more immersive and less fatiguing user experience.