User Experience Research Articles

Leveraging event portfolios for strategic business development: a case study of Singapore Changi Airport

PurposeThis research article examines event marketing in the airport business, specifically how an airport leverages event portfolios to develop its businesses by marketing itself as a destination, improving the user experience and generating revenue outside of aviation-related activities. Focusing on Singapore Changi Airport as a case study, the research underscores the airport’s business development in a competitive environment.Design/methodology/approachThe study employs thematic data analysis to examine how Changi Airport leverages an event portfolio as a strategic business development tool. It conducts documentary research by gathering a diverse range of grey literature sources from the online public domain. These sources include documents related to airports, online news and media portals and user-generated content on social media platforms. The analysis is conducted in an inductive manner.FindingsSingapore Changi Airport has recently leveraged events as a strategic marketing strategy for developing nonaeronautical business operations. As an attraction to appeal to tourists, the airport organised multiple types of events in its portfolio. Furthermore, the airport offers a wide range of event-related activities within its premises, including both sporting and cultural events. The purpose of these activities is to actively engage visitors and air travellers by offering a range of interactive experiences, including games and challenges. Such event-related activities reflect the airport’s transformation into a lifestyle platform.Originality/valueCritics commonly highlight the lack of empirical research and the restricted relevance of the findings in event and festival research. This article consolidates the existing knowledge on airport management and event business to enhance the event marketing and event portfolio literature, specifically in the setting of airport business.

Exploration of UCD Practice Limitations

Abstract The integration of user-centred design (UCD) practices in software development is fundamental for enhancing the overall experiences of software users. In this paper, we summarize the results of various research studies on what the stated limitations of UCD practices are. A process called the UCD Sprint was introduced recently with the goal of addressing some of the existing UCD practice limitations for integrating UCD practices in software development. The guided usage of the step-by-step process has been studied with students and UCD practitioners in courses showing positive results, where participants describe benefits while using the process, such as the speed of the process, its clear structure, guidance. In this paper, we describe results from a synthesis study summarizing results on how guided usage of the UCD Sprint can address UCD practice limitations. Furthermore, we describe results from a series of interviews with UCD practitioners on how they currently practice UCD, the challenges and limitations they experience and their views on how unguided usage of the UCD Sprint would potentially fit their UCD practices. While the interviewees recognize the importance of user involvement, they highlight challenges in aligning user interests with other stakeholders' interests. All participants had used processes like design sprints for structuring their UCD practices. Interviewees have generally tailored the traditional approach of design sprints, showcasing a nuanced understanding of UCD practices. The results show that the UCD Sprint process is considered by UCD practitioners as a promising approach for practicing UCD. The process is viewed with enthusiasm, but the need for clear objectives and adaptability to the work contexts of the UCD practitioners remains essential. UCD practitioners’ adaptive strategies of their UCD practices emphasize the need for flexibility and adaptation of UCD practices and processes.

Investigating the determinants of user adoption behavior in the context of conflicting health information: a DEMATEL-ISM model approach

PurposeThe purpose of this paper is to investigate the factors influencing user adoption behavior in the context of conflicting health information, thereby contributing to the understanding of users' information decision-making and adoption processes within intricate information environments. Furthermore, this research endeavors to offer guidance for mitigating the adverse effects of contradictory health information on users' cognitive perceptions.Design/methodology/approachFirst, this study used literature coding to identify factors that influence user adoption behavior in the context of conflicting health information. Secondly, we utilized the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to unveil the key influencing factors based on centrality and causality. Lastly, utilizing the Interpretive Structural Model (ISM), we constructed the multilevel hierarchical structure model of influencing factors to delineate the relationships among factors across different levels.FindingsThe key causal factors influencing user adoption behavior in the context of conflicting health information include user basic characteristics, experience accumulation, and information type. The key result factors consist of psychological emotion, perceived trade-offs, and the source credibility. The significant elements include users' psychological emotions, perceived trade-offs, emotional support, source credibility, and informational support. Furthermore, user adoption behavior in the context of conflicting health information is a manifestation of the interplay among surface-level, mid-level, and deep-level factors.Research limitations/implicationsTheoretically, this paper enriches the existing relevant theoretical framework and offers a novel perspective for further investigation into user adoption behavior concerning conflicting health information. Practically, this study extracts factors that can influence user adoption behavior in the context of conflicting health information which is an essential reference value for guiding and optimizing user adoption behavior.Originality/valueThis paper expands the system of factors influencing user adoption behavior in the context of conflicting health information from individual, information and social perspectives. Utilizing DEMATEL quantitative analysis and ISM multilevel hierarchical models, this research examines and illustrates the significance of the influencing factors and their interrelationships.

Testing the Feasibility, User Experiences, and Preliminary Effect of Conversation Cards for Adolescents © For Behavior Change and Collaborative Goal Setting in Primary Care: A Pilot Randomized Controlled Trial

Our team developed Conversation Cards for Adolescents© (CCAs), a tool to help adolescents and health care providers (HCPs) communicate and collaboratively set behavior change goals. The objectives of this pilot randomized controlled trial were to test the feasibility, user experience, and preliminary effect of using CCAs with adolescents in primary care, focusing on behavior change and collaborative goal setting among adolescents. From March 2019 to September 2022, adolescents were recruited from the Northeast Community Health Centre (Edmonton, AB). Adolescents were randomized (1:1) to one of two groups: CCA + goal setting (experimental group) or goal setting alone (control group). Data were collected from both groups at baseline and at 3-weeks post-baseline. In total, 31 adolescents completed the trial (terminated early due to COVID-19). The study demonstrated high practicality. Adolescents reported positive user experiences with CCAs, citing attractiveness, clarity, efficiency, and dependability. No group differences were detected for behavior changes at 3-weeks post-baseline (p > .05), although adolescents in the experimental group qualitatively described improved rapport and communication with HCPs. CCAs can be used practically in a primary health care setting to facilitate communication and rapport with adolescents; however, their effect in enabling behavior change should be investigated in a multi-site, larger-scale trial.

How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information

A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.

Application of augmented reality navigation in oral surgery. A clinical trial.

Background. Augmented reality (AR) is a potential alternative for surgical navigation, as it can provide visualisation of various anatomical structures of maxilla-facial region.We have developed a specific platform for 3D model management and intraoperative AR-navigation during surgical procedures. Objective. The aim of this study was to evaluate the surgeons’ perceptions and usability of AR- navigation for most common surgical procedures in oral surgery. Materials and Methods. We performed a phantom study to determine the accuracy of the AR system in the maxillofacial region. Registration errors are typical in image-guided surgery were measured: Fiducial Registration Error (FRE), Target Registration Error (TRE), and Fiducial Localization Error (FLE). Thereafter we conducted a clinical trial with several surgeons performing surgical procedures on jaws using AR-technology. AR- navigation usability was evaluated with the User Experience Questionnaire (UEQ) (scores range from -3 to +3 for each scale). Results. Mean value of the Fiducial Registration Error was 0.9 mm with SD = 0.7 mm (95% CI 0.4–1.3 mm). Target Registration Error was 1.3 mm (SD= 0.5 mm, 95% CI 1.1–1.5 mm). Fiducial Localization Error was the most significant – 2.2mm (SD= 0.9, 95% CI 1.9–2.5 mm). The higher rankings in the UEQ were related to the novelty and excitement of using an AR-navigation in maxillofacial surgery. The pragmatic quality aspect explains the technical focus of perception to achieve goals in a product, system, or service design. Efficiency was expected to be slightly higher, but in our opinion this is due to the technical difficulties of the system for novel AR technology. Conclusion. Based on our results, accuracy of the AR system in the maxillofacial region and the user experience of the AR-navigation system for oral surgery is good enough.

The role of recommendation algorithms in driving the development of e-commerce platforms

Given the rapid development of e-commerce, recommendation algorithms play a crucial role in enhancing user experience and promoting platform growth. This paper delves into the application of recommendation algorithms within e-commerce platforms, scrutinizes the advantages and disadvantages of collaborative filtering, content-based recommendation, and hybrid recommendation models, and elucidates how these algorithms can enhance user stickiness, improve conversion rates, and promote the overall growth of platforms through personalized recommendations. Through empirical case studies, the author demonstrates how recommendation algorithms can optimize the user shopping experience and enable e-commerce platforms to differentiate themselves in a fiercely competitive market. This research not only aids e-commerce platforms in improving operational efficiency and market competitiveness but also provides valuable practical insights and theoretical support for the fields of data science and artificial intelligence.

From knowledge to action: Assessing the effectiveness of immersive virtual reality training on safety behaviors in confined spaces using the Kirkpatrick model

Safety in the industrial sector is paramount, with national legislations establishing specific regulations to reduce accidents. These regulations emphasize risk removal and proper operator training as preventive measures. However, traditional training can be time-consuming and costly, leading companies to meet only the minimum requirements. In particular, confined spaces pose significant dangers, often resulting in fatal, cascading accidents due to a lack of proper safety procedures. Therefore, effective training is crucial to mitigate these risks.Advanced technologies like Immersive Virtual Reality (IVR) present new opportunities for cost-effective and extensive training campaigns by eliminating the risks associated with real environment training, offering a safe yet realistic experience.Despite the widespread adoption of IVR training applications in industrial research, there is a notable lack of systematic approaches to evaluate their effectiveness. Conducting such evaluations is crucial to ascertain their impact on essential training outcomes, including participant engagement, knowledge transfer, and enhanced safety behavior during work activities.To address this gap, we developed an IVR-based training platform for confined space safety procedures and established a systematic validation procedure using the Kirkpatrick model to assess its effectiveness compared to conventional training methods. Results demonstrate that IVR training provides an excellent user experience, better knowledge transfer than the traditional approach, and improved performance in simulated procedures, reducing execution errors and completion times. This dual focus on creating and validating the IVR system underscores its potential as an effective training tool in hazardous environments such as confined spaces, where proper training can prevent tragic fatalities.

Evaluasi Pengalaman Pengguna Pada Learning Management System Menggunakan Metode User Experience Questionnaire

The purpose of this study was to conduct a survey to assess the performance of the Learning Management System (LMS). This is done related to the need to evaluate its use by users. This study used the User Experience Questionnaire (UEQ) method regarding (1) attractiveness, (2) clarity, (3) efficiency, (4) reliability, (5) stimulation, and (6) novelty. The survey was conducted online via a Google form and had 164 participants. The benchmark results show that all criteria are above the average, but the UEQ scale for novelty is the lowest at 0.87, while attractiveness is 1.44, pragmatic quality is 1.37, and hedonic quality is 1.05. Therefore, it is suggested that the learning management system be improved through interactive learning styles, such as live discussions in LMS, integration with other technologies, updating artificial intelligence and big data-based technologies, and adapting to the metaverse, while maintaining the attractiveness and identity of the platform through design. rework color schemes, content, including engaging videos and gamification, and implement live chat with lecturers to overcome time constraints during video conferencing.

Electricity user behavior analysis and marketing strategy based on internet of things and big data

This paper examines power user behavior and the design of marketing strategies, using a case study of Smart Community A. We explore how advanced analytical models are used to enhance energy efficiency and user services. First, we apply spectral clustering to refine user segmentation and identify distinct electricity consumption patterns among different groups. Then, the Hidden Markov Model (HMM) analyzes user behavior, uncovering shifts in consumption habits and enabling personalized service offerings. Next, the ARIMA model predicts electricity consumption trends, guiding grid scheduling and resource allocation. Based on these analyses, we develop targeted marketing strategies, such as dynamic pricing and energy-saving incentives, which boost user engagement and reduce energy usage. Through an IoT and big data-driven interactive marketing platform, we enhance user experience and foster a culture of energy conservation. Finally, a feedback mechanism ensures continuous improvement and maximizes the effectiveness of the marketing strategies.

User Experience and the Role of Personalization in Critiquing-Based Conversational Recommendation

Critiquing—where users propose directional preferences to attribute values—has historically been a highly popular method for conversational recommendation. However, with the growing size of catalogs and item attributes, it becomes increasingly difficult and time-consuming to express all of one’s constraints and preferences in the form of critiquing. It is found to be even more confusing in case of critiquing failures: when the system returns no matching items in response to user critiques. To this end, it would seem important to combine a critiquing-based conversational system with a personalized recommendation component to capture implicit user preferences and thus reduce the user’s burden of providing explicit critiques. To examine the impact of such personalization on critiquing, this article reports on a user study with 228 participants to understand user critiquing behavior for two different recommendation algorithms: (i) non-personalized , that recommends any item consistent with the user critiques; and (ii) personalized , which leverages a user’s past preferences on top of user critiques. In the study, we ask users to find a restaurant that they think is the most suitable to a given scenario by critiquing the recommended restaurants at each round of the conversation on the dimensions of price, cuisine, category, and distance. We observe that the non-personalized recommender leads to more critiquing interactions, more severe critiquing failures, overall more time for users to express their preferences, and longer dialogs to find their item of interest. We also observe that non-personalized users were less satisfied with the system’s performance. They find its recommendations less relevant, more unexpected, and somewhat equally diverse and surprising than those of personalized ones. The results of our user study highlight an imperative for further research on the integration of the two complementary components of personalization and critiquing to achieve the best overall user experience in future critiquing-based conversational recommender systems.

Understanding critical service factors in neobanks: crafting strategies through text mining

Purpose This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques. Design/methodology/approach This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data. Findings The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation. Research limitations/implications This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences. Practical implications This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services. Originality/value This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

Development and Evaluation of the Telehealth in Motor Neuron Disease System: The TIME Study Protocol.

For more responsive care provision for motor neuron disease and caregivers, a digital system called Telehealth in MND-Care (TiM-C) was created. TiM-C sends regular symptom questionnaires to users; their responses are sent to health care professionals (HCPs). To enable people with motor neuron disease to participate in research studies more easily, a parallel platform was developed from TiM-C, called Telehealth in MND-Research (TiM-R). TiM-R can advertise studies, collect data, and make them available to MND researchers. This study has 4 work packages (WPs) to facilitate service approval, codevelop the TiM systems, and evaluate the service. Each WP aims to understand (1) what helps and hinders the approval of the TiM-C system as a National Health Service; (2) what aspects of MND care and research are currently unmet and can be addressed through the TiM-C and TiM-R systems; (3) how TiM-C influences MND care, from the perspective of people with motor neuron disease, their caregivers, and HCPs; and (4) the costs and benefits associated with TiM-C. WP1 will use semistructured interviews with 10-15 people involved in the approval of TiM-C to understand the barriers and facilitators to governance processes. WP2 will use individual and group interviews with 25-35 users (people with motor neuron disease, caregivers, HCPs, MND researchers, and industry) of TiM-C and TiM-R to understand the current unmet needs of these user groups and how TiM services can be developed to meet these needs. WP3 will use a process evaluation involving 5 elements; local context, engagement, user experiences, service impact, and mechanisms of action. A range of methods, including audits, analysis of routine data, questionnaires, interviews, and observations will be used with people with motor neuron disease, caregivers, and HCPs, both those using the system and those who declined the service when invited. WP4 will use data collected through the process evaluation and known costs to conduct a cost-consequence and budget impact analysis to explore the cost-benefit of the TiM-C service. Most data collected will be qualitative, with thematic and framework analysis used to develop themes from transcripts and observations. Descriptive statistics or t tests and chi-square tests will be used to describe and analyze quantitative data. This study has received ethical approval and has begun recruitment in 1 site. Further, 13 specialist MND centers will adopt TiM-C and the TIME study, beginning in July 2024. The study will conclude in November 2026 and a final report will be produced 3 months after the completion date. This study will facilitate the implementation and development of TiM-C and TiM-R and fully evaluate the TiM-C service, enabling informed decision-making among health care providers regarding continued involvement and contribute to the wider literature relating to how technology-enabled care services can affect clinical care. DERR1-10.2196/57685.

Functional performance comparison of long cane and secondary electronic travel aids for mobility enhancement

This study investigated the impact of secondary electronic travel aids (ETAs) on the mobility performance of individuals with profound visual impairment, specifically focusing on object detection and obstacle avoidance tasks. Seven experienced long-cane users were recruited for the study. The ETAs tested as secondary aids to the long cane included the MiniGuide, utilizing ultrasonic echo location, and the vOICe (Vision-to-Auditory Sensory Substitution Device), which translates visual information into auditory signals. Two software modes of the vOICe were examined: the standard contrast mode and a novel algorithm utilizing relative depth information. Notably, 30 min of familiarization was provided before the trials. Results indicated that while the use of the secondary ETAs led to increased awareness of the surrounding environment, it also resulted in longer processing times and more hesitations, compared with using the long cane alone. Participants demonstrated improved object detection and larger detection and safety ranges during obstacle avoidance. The study also introduced the concept of margin of stability (MoS) as a novel metric for evaluating gait stability while using ETAs. Both MiniGuide and vOICe users exhibited consistent and stable movements, suggesting that these additional aids did not negatively impact overall gait stability. The findings highlight the trade-offs between efficiency and effectiveness when using secondary mobility aids, with longer training periods potentially mitigating these issues. In addition, the study emphasizes the need for ongoing research to optimize the design and training programmes for new ETAs. Future studies may explore the impact of extended training periods on mobility performance. Overall, this research contributes to a deeper understanding of how ETAs can enhance the mobility of individuals with visual impairments while also highlighting some of the challenges associated with their use. Optimizing ETA design and training programmes is crucial for maximizing benefits for individuals with impaired vision.

A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughness Using Smartphone and Bicycle Data

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment.

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.

Comparing the Effectiveness and Efficiency of Machine Learning Models for Spam Detection on Twitter

A comprehensive study focused on the efficiency and effectiveness of machine learning models for Twitter spam detection was presented in this research. Spam detection on social media platforms is not only vital for user experience but also poses computational challenges due to the vast and dynamic nature of Twitter data. This investigation encompassed a range of machine learning models, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbours (KNN), and Decision Trees (DT). Their performances were scrutinized across two critical dimensions: classification accuracy and computational efficiency, as measured by the time taken for model execution. The results of the analysis revealed valuable insights into model performance. The NB and LR models emerged as the most computationally efficient models, with execution times ranging from 1.016 to 1.949 seconds. These models offered an attractive balance between speed and accuracy, making them suitable for real-time or resource-constrained applications. SVM, LR, KNN and DT were effective in classification with a performance of 98%. However, SVM models demanded longer execution times, ranging from 7.670 to 37.657 seconds. KNN and DT stroked a balance between accuracy and efficiency, with execution times ranging from 2.852 to 10.941 seconds and 1.080 to 2.442 seconds, respectively. Our research underscores the importance of considering both model effectiveness and computational efficiency when selecting a Twitter spam detection model. By offering a comparative assessment of these models, this study equipped researchers with valuable insights for making informed decisions in Twitter spam detection. It highlighted the trade-offs between model performance and efficiency, paving the way for more effective and resource-conscious approaches to combating spam on social media platforms.

The Effect of Emotional Intelligence on Higher Education: A Pilot Study on the interplay Between Artificial Intelligence, Emotional Intelligence, and E-Learning

Integrating Artificial Intelligence (AI) and E-learning platforms has become increasingly prevalent in the rapidly evolving landscape of higher Education. However, amidst this technological advancement, the role of Emotional Intelligence (EI) and its impact on the efficacy of AI-driven educational tools still needs to be explored. This pilot study seeks to elucidate the intricate relationship between Emotional Intelligence, Artificial Intelligence, and E-Learning in Higher Education. Drawing upon a multidisciplinary approach, this study investigates the correlation between students' Emotional Intelligence competencies and their engagement with AI-driven E-Learning platforms. The findings of this pilot study are expected to shed light on several critical aspects. Firstly, it aims to uncover how Emotional Intelligence influences students' receptivity to AI-infused E-Learning environments, potentially elucidating strategies for optimizing user experience and learning outcomes. Moreover, by exploring the reciprocal influence between Emotional Intelligence and AI algorithms, this research endeavors to contribute to the refinement of AI technologies, fostering greater personalization and adaptability in educational settings. Furthermore, this study endeavors to address the ethical implications inherent in the intersection of Emotional Intelligence, Artificial Intelligence, and E-Learning. By elucidating the potential risks and benefits associated with integrating these technologies, it seeks to inform policymakers, educators, and AI developers alike, facilitating the responsible deployment of AI-driven educational tools. Therefore, its innovative methodology and comprehensive approach aspire to pave the way for future research endeavors, ultimately enriching the educational landscape with insights prioritizing technological advancement and human well-being.

The Yearning to Be Everything to Everyone: Examining the Homogenization of Social Media Platforms in the Context of Convergence

Social media platforms are continually adding new features and enhancing existing ones to attract users and extend their screen time. These platforms constantly innovate to capture users' attention and gain an edge in the competitive landscape of social media. This study focuses on identifying the similarities between features of popular social media platforms and examines whether these similarities lead to homogenization. Specifically, presenting the chronological development of these features over time is crucial for understanding the historical development and evolution of social media. The framework of this research is based on the concept of convergence. The convergence concept is used to assess whether there are commonalities in the tools, interfaces, and interaction modes offered by the platforms to their users. In this context, the study investigates how the features provided by social media platforms become similar over time and how user experiences become homogenized. The 'walkthrough method' was employed to analyze the interfaces of the selected platforms. This method involves a step-by-step examination of user experiences on the platforms to understand how features are presented and how they are utilized by users. Data were collected through notes and recordings made by researchers while using the platforms. The results indicate a significant similarity and convergence in the features offered by popular social media platforms. For instance, features such as story sharing, live streaming, filter usage, and the explore tab have become widespread across different platforms. Platforms that consider users' habits and preferences offer familiar and easily accessible experiences through these similar features. Consequently, it can be said that social media platforms create a form of homogenization in user experiences by providing similar features over time. This phenomenon offers important insights into the evolution and future development of social media. Additionally, the study suggests that the concept of convergence not only encompasses the merging of features from different technologies but also indicates a tendency for similar platforms to converge internally.

Usability evaluation and reporting for mobile health apps targeting patients with skin diseases: a systematic review.

Usability is an important method for evaluating mobile health apps from a user perspective. Yet, many publicly available apps lack adequate attention to their design, development, and evaluation. To assess usability evaluation and reporting for mobile health apps targeting patients with skin diseases. The protocol was registered with PROSPERO (CRD42022347184). A search strategy combined terms for usability evaluation, user experience, skin disease and mobile health apps (search date from 2012 to 2023). Six databases (Embase, Medline, CINAHL, Cochrane CENTRAL, Cochrane Database of Systematic Reviews, Scopus) were searched, identifying 18052 results. Nine studies (comprising 9 apps) were included in the final analysis. Skin conditions/issues targeted included skin cancer (n=3), sun protection (n=3), and one study identified for each of the following: chronic pruritus/acne/psoriasis/rosacea/laser treatments/actinic damage/monitoring benign moles/alopecia/inflammatory rash/cutaneous leishmaniasis/spina bifida. All studies assessed app usability and feasibility, with the majority concluding that the apps were deemed useful and easy to use. Qualitative methods, such as usability questionnaires and semi-structured interviews, were predominantly employed. Common emerging themes included ease of use and navigation, comprehensibility, security/privacy concerns, data sharing issues, customizability, costs, and the ability to track progress or self-monitor. While smartphone applications for skin disorders show promising usability across diverse diseases, the limited literature compared to the rapid app development highlights the need for meticulous user-centered design and rigorous evaluation. The study emphasises the importance of evaluating and reporting usability findings to optimize the long-term adoption of mobile health apps, particularly those targeting skin diseases.