Usable Responses Research Articles

The main aim of this research is to reveal innovative clusters of tourists based on their use of smart technologies when they are planning and/or visiting a destination. A latent class analysis was conducted that considered the information sources used by tourists and their use of smart technologies at a destination. The study included 526 usable responses from tourists who visited or had visited urban destinations in Europe and 525 valid responses from tourists who visited or had visited urban destinations in the United States, resulting in a total sample size of 1,051 respondents. The findings from segmenting tourists in both European and American contexts reveal four primary groups: “Receivers”, “Technophobes”, “Co-creators of experiences” and “Smart tourists”. The body of literature on smart tourism is expanding, reflecting its increasing relevance in academic and practical discussions. Within this broader field, this paper aims to contribute by shedding light on a specific aspect: the segmentation of tourists based on their use of smart technologies. In this context, this study proposes a pioneering model of the tourism literature that aims to assist comprehension of the diverse impacts of smart technologies on tourists, ultimately facilitating the creation of customized services by the business sector that cater to real consumer needs. By exploring this dimension, the study seeks to enhance understanding of how different types of tourists engage with smart technologies and the implications for tourism management and marketing.

Extreme climate change around the world has disrupted the seasonal timeline and made it difficult to predict natural disasters, including in Indonesia. Data from BNPB (Badan Nasional Penanggulangan Bencana or National Board for Disaster Management) recorded that from January 1 to May 26, 2025, there were a total of 1,162 natural disaster events. The need for disaster information has become a hot topic in public discourse, especially on X, because formal government information sources are not disseminated quickly, thereby giving rise to digital volunteers.The evolution of public opinion concerning disaster-related information can be effectively examined through social media platforms. Indonesia ranks among the top five countries globally with the highest number of users on X, making the dissemination of disaster information on this platform a matter of strategic importance. Given the platform’s extensive reach, it is imperative for government agencies to actively engage in shaping and guiding disaster-related narratives on X. Such involvement would help mitigate misinformation and influence public perception regarding disaster management. This study utilizes data from X to track the circulation of disaster-related content across Indonesia, focusing specifically on the period from May 1st to May 31st, 2025, during which a notable surge in disaster events occurred. The primary aim of this research is to explore how digital volunteers disseminate information about natural disasters on X, thereby addressing the informational void left by the absence of timely official communication. Key analytical dimensions include user volume, leading influencers, top-performing tweets, user interaction networks, and sentiment dynamics. The study employs social network analysis as its methodological framework, with X serving as the principal data source.The findings of this study reveal that the dissemination of natural disaster information on the X platform is predominantly driven by digital volunteers, largely due to the limited presence and communication from government agencies. A total of 1,476 users were actively involved, forming 2,157 distinct communication networks. Sentiment analysis indicates that user responses comprised 16% positive, 39% negative, and 45% neutral expressions. Within these networks, the most influential account identified was @tanyakanrl, operated by an individual unaffiliated with formal institutions. Further analysis confirms that the leading source of disaster-related information originates from a member of the digital volunteer community, effectively filling the informational void left by the government’s absence on the platform.

ABSTRACT While the meal kit service industry has grown substantially in recent years, the industry has entered a maturation phase. As competition intensifies, it is increasingly important for meal kit marketers to understand different segments of consumers. This study attempted to profile meal kit consumer segments based on their food lifestyle. A cluster analysis using a total of 300 meal kit user responses identified three distinct segments of meal kit users: Lifestyle Optimizers, Culinary Pragmatists, and Healthy Novelty-seekers. This study provides practical insights into distinct consumer segments, which meal kit brands can use to develop products, services, and communication strategies that are aligned with the food lifestyles of their target segments.

The role of notification modality in smartwatches: A context of sedentary behavior interventions.

This paper aims to examine the variables involved in improving Teacher effectiveness through self-efficacy (SE) while focusing on higher education. The authors collected data from 600 usable responses from Northern India. The data was analyzed by structural equation modelling using analysis of moment structures (AMOS). The results indicate that drivers such as emotional intelligence (EI) and proactive personality (PP) contribute to the raised levels of SE, working as a mediating variable, which in turn triggers teacher effectiveness (TE) positively in the field of higher education. This study provides valuable insights for educational institutions to improve TE. The drivers propose that achieving widespread recognition of these factors necessitates implementing many training programs and workshops to boost TE. The contribution lies in providing a holistic study of TE drivers and the mediating role of SE in enhancing overall TE. This study is a new endeavor since it combines EI, PP, SE, and TE into a unified research model and tests it through empirical data. This study explores topics in research that have not been studied before by examining the interconnections between these variables with the context to higher education.

Abstract - In today’s competitive job market, effective interview preparation is essential for career success. This paper presents CareerPilot, an AI-powered interview preparation system platform designed to assist candidates in enhancing their technical, communication, and analytical skills through intelligent automation and personalized feedback. The system integrates multiple modules, including AI Interview Coach, Resume Analyzer, Domain Knowledge Assessment, and an Interactive Code Editor, to provide a comprehensive preparation experience. Leveraging advanced Machine Learning (ML) techniques, the platform evaluates user responses, measures communication fluency, and offers targeted recommendations for improvement. Additionally, CareerPilot provides real-time analytics such as quiz performance, problem-solving progress, skill rating, and communication score tracking, enabling users to monitor their overall readiness. The user-friendly dashboard and cloud-based architecture ensure accessibility across devices, while continuous AI-driven adaptation personalizes the learning path for each user. This approach significantly enhances self-assessment, confidence, and employability by simulating realistic interview environments and providing data-driven insights into performance. Keywords : Career Preparation Platform, AI-Based Resume Analyzer, Coding Practice Environment, Communication Skill Evaluation, Skill Assessment and Tracking, Automated Feedback System, Interactive Code Editor .

Abstract - In today’s competitive job market, effective interview preparation is essential for career success. This paper presents CareerPilot, an AI-powered interview preparation system platform designed to assist candidates in enhancing their technical, communication, and analytical skills through intelligent automation and personalized feedback. The system integrates multiple modules, including AI Interview Coach, Resume Analyzer, Domain Knowledge Assessment, and an Interactive Code Editor, to provide a comprehensive preparation experience. Leveraging advanced Machine Learning (ML) techniques, the platform evaluates user responses, measures communication fluency, and offers targeted recommendations for improvement. Additionally, CareerPilot provides real-time analytics such as quiz performance, problem-solving progress, skill rating, and communication score tracking, enabling users to monitor their overall readiness. The user-friendly dashboard and cloud-based architecture ensure accessibility across devices, while continuous AI-driven adaptation personalizes the learning path for each user. This approach significantly enhances self-assessment, confidence, and employability by simulating realistic interview environments and providing data-driven insights into performance. Keywords : Career Preparation Platform, AI-Based Resume Analyzer, Coding Practice Environment, Communication Skill Evaluation, Skill Assessment and Tracking, Automated Feedback System, Interactive Code Editor .

Identifying white blood cells (WBCs) is a critical skill for first-year medical students, but it is often challenging in the early years of medical education. Subtle morphological differences in size, granules, and nuclear shape can be confusing, especially when staining quality is inconsistent. Based on our teaching experience, we observed that during undergraduate practical sessions, students frequently encounter difficulty in achieving optimal staining quality, which often hampers accurate visualization and interpretation of cellular morphology. Reliance on color further increases the risk of misidentification. HIMO 1.0 is an educational game developed to address this challenge. Using black-and-white images, the game trains students to recognize WBCs based on reliable structural features rather than variable staining quality. Developed in Python, it presents randomly oriented images of WBCs and requires players to identify cell size, granules, nucleus shape, and overall type. A built-in database stores defining features of each cell type, enabling instant validation of user responses. Immediate feedback reinforces correct answers, supporting an iterative learning process. HIMO 1.0 provides a safe, engaging, and realistic environment for practicing WBC identification. While not a substitute for real smear examination, it strengthens foundational skills and complements traditional hematology teaching.

Social media increasingly shapes how visitors engage with aquatic locations in national parks, where risky behaviours contribute to drowning and injury risk. Selfies and photography with mobile phones have been implicated in fatal and non-fatal injury, due to distraction leading to a loss of awareness of a person’s surroundings. We partnered with Queensland Parks and Wildlife Service (QPWS) to co-produce and evaluate a targeted Instagram water safety campaign at an injury hotspot, with specific focus on campaign design, audience engagement, message recall, and user perceptions. Campaign development was informed by social media user and influencer interviews, surveys with social media users, frontline ranger observations, and patterns of visitor behaviour on social media. Between January and February 2024, Instagram posts highlighting site-specific risks were disseminated. The campaign generated strong engagement: over 4000 link clicks, more than 100 shares, and 254 post saves. Content analysis showed polarized user responses: 20% found the messaging informative and useful, while another 20% found it patronizing. In-person surveys at the site (n = 50) found that 32% of visitors reported social media influenced their decision to visit. Separately, a third (32%) of participants recalled at least one of the campaign’s safety messages, with Post B being the most recalled. The findings suggest that social media can be a low-cost and impactful tool for aquatic risk communication at high-visitation sites. Future work should focus on refining tone, testing co-design approaches with target audiences, and exploring the potential role of influencer partnerships to enhance reach and resonance.

This study examines the effect of financial literacy on the growth of medium enterprises (MEs) in Kisumu County, Kenya. Specifically, it assesses four dimensions of financial literacy—investment literacy, working capital management literacy, accounting knowledge, and financial analysis skills—and their association with enterprise growth. A descriptive research design was applied, and structured questionnaires were distributed to a stratified sample of 400 registered MEs, yielding 112 usable responses. Data were analyzed employing descriptive statistics, Pearson’s correlation coefficient, and multiple regression analysis. Results indicate that overall financial literacy explains 60.3% of the variation in enterprise growth (R² = 0.603). Working capital management literacy, accounting knowledge, and financial analysis skills had statistically significant positive effects on growth, whereas investment literacy did not have a significant effect. The study recommends targeted capacity building in practical financial management skills for ME owners and calls for further research to investigate additional factors explaining the remaining variance of 39.7% in growth.

In the digital learning era, user engagement is shaped not only by functionality and aesthetics but also by how interfaces adapt to users’ emotional experiences. While gamified learning environments have become common, few designs systematically integrate affective computing principles to support real-time emotional adaptation. This study proposes an Affective UX Model that combines affective computing with user experience (UX) design to enhance emotional engagement in gamified educational interfaces. The research aims to explore how emotionally adaptive features can be designed, implemented, and evaluated within a learning context. Using a user-centered design (UCD) approach, the study employed mixed methods, including visual storytelling, interactive prototyping, and affective mapping via self-report measures and biometric tracking (facial expressions and heart rate variability). A gamified language-learning prototype was tested with 30 adolescent participants (aged 15–18) through usability and emotional response evaluations. Results show that empathetic avatars, responsive feedback, and adaptive difficulty adjustments significantly increased motivation, engagement, and emotional comfort. Participants reported a stronger sense of connection when system feedback matched their affective State and performance. The study contributes a validated conceptual framework for designing emotionally intelligent, human-centered educational interfaces, emphasizing how affective adaptivity enhances the quality of user experience and learning outcomes. The findings demonstrate that dynamic emotional adaptation can redefine engagement and inform future directions in emotionally responsive design research.

This quantitative cross-sectional study explores how ultra-Orthodox Jewish social work students in Israel navigate the cultural silence surrounding sexual abuse, examining both awareness and self-reported experience. In a community where open discussion of sexuality is constrained by religious norms, a culturally adapted anonymous survey was administered to 190 undergraduate students at a religiously affiliated academic institution. Usable responses were obtained from 174 students (52% men, 48% women). Participants were asked whether they had personally experienced sexual abuse or knew someone close to them who had. Among women, 29% reported personal experience and 18% were uncertain whether their experience qualified as abuse; among men, 21% reported abuse and 21% expressed uncertainty. Awareness of others' abuse was reported by 82% of women and 60% of men. Logistic regression indicated that women were more likely than men to report personal experience with sexual abuse, and students aged 30 and older were more likely than those aged 17 to 24 to do so. Women were also more likely than men, and students aged 25 to 29 were more likely than younger students, to report knowing someone who had been abused. Findings offer novel insights into the ways sexual abuse is understood and disclosed in a closed religious society and highlight the need for culturally sensitive frameworks in social work education and assessment.

This study investigated technology's role in advancing Strategic Human Resource Alignment in Tanzanian Ministries, Independent Departments, and Agencies (MDAs), focusing particularly on emergent Information Systems, Leadership Commitment, and Institutional Readiness. Guided by the Resource-Based View (RBV) and Contingency Theory, the study theorizes these constructs as critical organizational resources and contextual factors influencing the alignment of HR practices and institutional goals. The study adopted a cross-sectional survey design, which was quantitative in nature. HR managers, line managers, and IT managers in selected MDAs constituted the study population. Through the application of purposive sampling, 111 respondents were targeted, and 104 usable responses were obtained, representing a response rate of 93.7%. Data was collected using structured questionnaires, and hypotheses were tested through the application of Structural Equation Modeling (SEM). The findings show that strategic human resource alignment is significantly facilitated by the use of emerging information systems, with leadership commitment and institutional readiness also exerting strong direct effects. The explanatory model demonstrated high variance in strategic human resource alignment and confirmed its predictive validity. These findings confirm RBV's contention that human capital and technology are strategic resources, while contingency theory emphasizes organizational fit as a condition for alignment effectiveness. Theoretically, the study adds to digital HR transformation knowledge by illustrating how new information systems, leadership, and institutional readiness together enhance alignment in the public sector. Practically, it offers lessons for MDAs: technological investments must be followed up with leadership commitment and organizational preparedness so that HRIS implementation can contribute to strategic results and national development agendas. The results emphasize the need to adopt a synergistic strategy: technological implementation should be followed by aggressive leadership backing and good organizational preparedness. Managers and policymakers need to accord topmost priority to creating capacity, training leaders, and infrastructure so that HRIS could make a stronger strategic impact. On theoretical contributions, this study adds to theoretical understanding by resolving the conflict between the Resource-Based View (RBV) and Contingency Theory, demonstrating that new information systems as strategic assets appreciate only when supported by leadership and institutional readiness. In showing how alignment comes out of the interaction between resources within the organization and conditions within the context.

This study aims to develop an Electronic Student Worksheet (E-LKPD) based on the RADEC (Read, Answer, Discuss, Explain, Create) learning model using the Liveworksheets platform to enhance learning quality and student engagement. The study employed the 4-D development model (Define, Design, Develop, Disseminate), limited to the development stage. The research instruments consisted of expert validation sheets and user response questionnaires. Data were analyzed using descriptive quantitative and qualitative techniques. Quantitative data were obtained from expert validation questionnaires and teacher and student response questionnaires, while qualitative data were derived from observations and open-ended responses, analyzed through data reduction, data display, and conclusion drawing. The validation results from two material experts indicated that the E-LKPD achieved validation scores ranging from 91.6% to 95.8%, categorized as “Valid.” Meanwhile, media validation by one media expert resulted in a score of 100%, also categorized as “Valid.” Trials involving teachers yielded a response score of 90.63%, and trials involving students obtained 92.32%, both classified as “Very Good.” Therefore, the RADEC-based E-LKPD was declared valid and received highly positive responses, indicating its potential for use in teaching thermochemistry to Grade XI SMA/MA students.

This paper explores the effects between ESQ and CL of online shopping in Saudi Arabia. As the model structures, the PV and CS become a mediator between ESQ and CL. The diagrammatic picture of the structure is as below: It used quantitative research method, and the study was deductive. The questionnaire data were in a structured online questionnaire circulated via Google Forms. The survey was conducted among 209 valid responses of adult users of the Western Region of Saudi Arabia who had made online purchases of products. The data was analyzed by means of SPSS version 23 and SmartPLS version 4 PLS-SEM. In the study, E-SQ displays a robust positive correlation with both PV and CS that significantly influence CL. Both PV and CS interrupted the entire bond between E-SQ and CL. These results highlight the importance of creating high-quality service experiences that enhance value perceptions and satisfaction to enhance long-term loyalty. In this study, researchers considered only individuals engaged in online shopping in Saudi Arabia, might reduce the extent to which the results are able to be generalized to other locations or countries. The research contributes to the knowledge of digital service quality by integrating e-SERVQUAL dimensions within a loyalty framework. Practical implications include guiding e-commerce platforms and digital marketers in enhancing customer retention by focusing on ESQ, PV, and CS. The insights are particularly relevant to the evolving digital economy in Saudi Arabia.

ObjectiveThe objective of this work is to develop a standard-based taxonomy of features that might affect user response to alerts using evidence from literature and public alert logic repositories.MethodsWe developed a taxonomy of features using multiple sources: (1) the Agency for Healthcare Research and Quality (AHRQ) CDS Connect Repository, (2) alert logic from commercial electronic health record (EHR) customers, and (3) published literature. Three categories (patient, provider, environment/context) were used a priori to develop the taxonomy. The final taxonomy was mapped to the Fast Healthcare Interoperability Resources (FHIR) standard for development of standardized CDS services.ResultsAggregating potential features extracted from three data sources, we identified 95 unique features, which we then mapped to the FHIR standard, encompassing 24 FHIR resources. The common features differed depending on the knowledge source. In the AHRQ public alert repository, frequently occurring features were observations in flowsheets, procedures, diagnoses, medications, and patient age. On the other hand, the commercial EHR customers primarily presented features such as diagnosis type, patient age, diagnosis grouper, diagnosis, medication value set. Literature-based insights revealed that provider type, medication, patient age, alert severity, and medication dose were the most common features.ConclusionThis study demonstrated a standard-based taxonomy of features that could impact user responses to CDS alerts, bridging insights from academic studies and industry practices. The taxonomy stands as a foundational tool, guiding the CDS development, implementation, and evaluation, with the overarching goal of improving user acceptance and healthcare quality.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03231-0.

Abstract. We report on an exploratory study focusing on how people interpret uncertainties in noise models related to road traffic, as assessed using the Common Noise Assessment Methods in Europe (CNOSSOS-EU). Specifically, via an online eye movement study with 35 participants, we investigate how viewers’ visual attention and behaviour can reveal uncertainties in studied uncertainty models. As a case study, we generated a preliminary noise model for Munich using the library and model builder by the NoiseModelling project. For simplicity, the examined model only accounts for road traffic noise and does not represent dynamic variations in noise levels throughout the day. Participants (n=35) engage in tasks using different noise maps and colour schemes, including those from the NoiseModelling documentation and ColorBrewer. The eye tracking data reveals significant patterns in user responses, including awareness of noise in major intersections, train stations, and residential areas. The study also assesses the performance of the participants while using RealEye.io’s webcam-based eye tracking across devices: desktops, tablets, and smartphones. The participants using desktops exhibit the highest performance, while participants using smartphones show the lowest. Our exploratory study reveals the importance of device-specific considerations in eye tracking-based cartographic user studies and suggests future work to tailor stimuli for each device type.

Youth unemployment and underemployment remain persistent in developing contexts, calling for scalable, engaging entrepreneurship education that aligns with Gen-Z’s digital habits. This study aimed to develop and validate an Instagram-based training module to strengthen entrepreneurial attitudes and behaviors among young MSME actors in Tondano, Indonesia. Using a Research & Development approach with the 4-D model (Define–Design–Develop; Disseminate planned), we conducted needs analysis, iterative prototyping, expert validation (content and media), and pilot testing with purposively sampled participants (n = 30; ages 18–30). Data were gathered via Likert-type questionnaires and Focus Group Discussions and analyzed descriptively with percentage criteria for feasibility. Results show high feasibility and acceptance: material validation = 90.34% (very good), media validation = 82% (good), platform/Instagram usability = 92.5% (very good); user responses indicated 92.5% strongly agree and 7.5% agree that the module improved understanding of entrepreneurial attitudes/behaviors. We conclude that an Instagram-based, micro-modular design is practicable, context-fit, and engaging for youth entrepreneurship learning. Practically, the module offers a low-cost, mobile, and scalable alternative for educators, community organizations, and local governments to complement formal programs and expand outreach. Future research should (a) conduct larger multi-site trials, (b) embed formal assessment (e.g., quizzes, performance tasks) and gamified elements, (c) evaluate longitudinal outcomes (self-efficacy, venture activity), and (d) compare cross-platform delivery (e.g., TikTok/YouTube/LMS) to mitigate algorithmic visibility bias.

This study qualitatively investigates user responses to privacy invasion by social media algorithms and identifies the fundamental needs that drive the feelings of privacy violation. In-depth interviews with 17 users in the United States revealed that the dimensions of physical, social, informational, and psychological privacy, originally defined in interpersonal communication, can be extended to human–algorithm interaction and are still deemed to be significant by users. Popular episodes of privacy invasions showed three main themes that privacy violation perceptions are grounded upon: lack of choice, non-interpretability of algorithms, and lack of understanding and respect. The three basic human needs for growth and motivation proposed by the self-determination theory (SDT) – autonomy, competence, and relatedness – are extended to understand why these invasions matter to users.

ABSTRACT This study develops an integrated machine-learning framework to analyse user responses to travel content on YouTube. Using 10,893 comments from Expedia’s YouTube channel, we combined Latent Dirichlet Allocation with topic modelling, sentiment analysis, and K-means clustering. Six distinct topics emerged: travel vlog enjoyment, tourist attractions, recommended destination appreciation, travel wishes list, content production quality, and the city-living experience. Sentiment analysis revealed complex emotional patterns with a predominance of joy, trust, and anticipation. User clustering identified distinct viewer segments, whereas Shapley Additive Explanations analysis showed that travel vlogs generated the highest engagement. As a limitation, this study relied on text-mining analysis of user comments from a single channel without incorporating the visual and auditory elements of the video content. Nevertheless, the findings can help tourism marketers create targeted content for specific viewer segments, prioritise travel vlog formats, and develop sentiment-aware recommendation systems that enhance user experience and social media engagement.