Usable Responses Research Articles

Temporal analysis of users’ engagements in CDC public health information on Twitter/X

PurposeThe objective of this paper is to investigate the temporal patterns, trends and valuable insights regarding user engagements in public health information diffusion from the Centers for Disease Control and Prevention (CDC) channel on Twitter/X.Design/methodology/approachThe health information regression models on the 10 CDC channels on Twitter/X were developed by applying regression analysis methods. The user engagement velocities were explored, and the half-life of user engagement and response times was examined.FindingsThe findings show that logarithmic regression models were the most suitable for predicting user engagement types. The most frequent user engagement types on Twitter/X were views, followed by likes and retweets. Regarding average user engagement velocity, views ranked first, followed by likes, retweets, replies, quotes and bookmarks in that order. The half-life values for various user engagement types ranged from 52.68 to 54.60 days. The study also revealed response times for different user engagement types: bookmarks (1.53 days), likes (1.05 days), quotes (2.22 days), replies (1.21 days), retweets (1.07 days) and views (0.71 days).Social implicationsThe findings serve as invaluable tools for public health practitioners and administrators, facilitating scientific assessments and predictions regarding the impact of CDC public information on the public.Originality/valueThis study used scientific research methods to accurately describe user engagement changes over time for an authoritative public health agency channel on social media. The analysis on user engagements included views, likes, retweets, replies, quotes and bookmarks. Other important characteristics like velocity and half-life for user engagement types were addressed.

Smart Posture Correction Wearable for Individuals with Cerebral Palsy

Abstract – The smart Posture Correction Wearable is designed to assist individuals with cerebral palsy (CP) in maintaining proper posture through real-time feedback. The device utilizes posture sensors and a haptic feedback mechanism to detect misalignments and provide gentle vibrations or alerts for correction. The methodology involved designing a wearable system with embedded sensors, processing real-time data using machine learning algorithms, and integrating the device with a mobile application for posture tracking. A pilot study was conducted on individuals with CP over four weeks, where data on posture deviations, user responses, and comfort levels were recorded. The mobile app provided data-driven insights, enabling users to track progress and adjust their posture accordingly. The lightweight and adjustable design ensured comfort, making it suitable for prolonged use. Overall, the wearable technology demonstrated significant potential in enhancing postural alignment, reducing the risk of musculoskeletal issues, and improving the quality of life for individuals with CP. Future enhancements will focus on increasing sensor precision and expanding clinical trials for broader validation. Keywords: Smart Posture Correction, Wearable Device, Cerebral Palsy, Rehabilitation Technology, Quality of Life.

Strategies of promoting sustainable alternative to rice in Bangladesh: investigating factors influencing willingness to pay more for cassava among green hotel visitors

PurposeThe main purpose of this study is to identify factors that can influence green hotel visitors’ willingness to pay more for cassava as an alternative to rice for promoting cassava as a sustainable source of energy food supply.Design/methodology/approachThis study identifies factors that can influence green hotel visitors’ willingness to pay more for cassava by reviewing relevant literature and focus group discussion (FGD) with experts’ opinions. Subsequently, a survey is conducted through structured interview schedules and collects 394 usable responses which are further analyzed by Partial least squares structural equation modeling (PLS-SEM) technique, using Smart PLS3.3.3.FindingsThe findings of this study reveal that perceived food security, perceived nutritional value, environmental norms (EN) and sustainability concerns have significant effects on willingness to pay more for cassava. Additionally, this study has identified that sustainability concerns moderate on the strength of relationships between perceived food security, perceived nutritional value, EN and willingness to pay more for cassava among green hotel visitors in Bangladesh.Research limitations/implicationsThe findings of this study indicate that green hotel visitors who are more conscious about food security, food nutrition, environment and over all sustainability, they are more willing to pay more for cassava; thus, green hotel industry should improve visitors’ consciousness about the sound health, sustainable food supply and environmental sustainability by tailoring programs in their hotels’ yards. This will increase green hotel visitors’ cassava consumption willingness.Originality/valueThis study is a one of the first empirical research studies that uniquely identifies factors influencing willingness to pay more for cassava among green hotel visitors for developing strategies to promote cassava as a sustainable source of energy food supply in Bangladesh.

Measuring Stress and Perceptions for a Virtual Reality-Based Pericardiocentesis Procedure Simulation for Medical Training: Usability Study.

Virtual reality (VR) is increasingly used in medical education, providing immersive environments for training in high-risk procedures such as pericardiocentesis. This lifesaving procedure requires technical precision and induces cognitive and physiological stress. Evaluating both usability and stress responses in a VR-based pericardiocentesis simulation is essential. Heart rate variability (HRV) serves as an objective stress marker, while prior VR experience may influence usability and stress perception. This study aimed to assess the usability of a VR-based pericardiocentesis simulation, examine the relationship between usability perceptions and physiological stress (HRV), and determine the impact of prior VR experience on usability scores and stress responses. A total of 119 final-year medical students participated in a VR pericardiocentesis simulation. Usability was evaluated using the System Usability Scale (SUS), the Post-Study System Usability Questionnaire, the Presence Questionnaire, and the Simulator Sickness Questionnaire. Physiological stress was assessed through HRV parameters, including the root-mean-square of successive differences (rMSSDs), percentage of differences greater than 50 ms (PNN50), low-frequency to high-frequency ratio, and nonlinear HRV indices (SD1/SD2 ratio, Poincaré area). Statistical analyses included descriptive statistics, Spearman correlations, and Mann-Whitney U tests to explore relationships between usability, stress, and prior VR experience. The VR simulation received a mean SUS score of 75.00 (SD 6.41; 95% CI 73.42-76.58), exceeding the general usability threshold of 68 (P=.002). The mean Post-Study System Usability Questionnaire score of 2.92 (SD 1.83; 95% CI 2.55-3.29) indicated moderate satisfaction, while the mean Presence Questionnaire score of 109.46 (SD 9.12; 95% CI 107.88-111.04) reflected strong immersion. Simulator sickness symptoms were mild (mean Simulator Sickness Questionnaire score 12.43, SD 15.41; 95% CI 9.28-15.58), although novice users reported significantly higher nausea levels (P=.02). Physiological stress analysis revealed a mean rMSSD of 281.27 (SD 98.99; 95% CI 259.45-303.09) ms and PNN50 of 56.85% (SD 19.70%; 95% CI 52.23%-61.47%), indicating moderate autonomic balance. A significant negative correlation was observed between HRV parameters (rMSSD and PNN50) and simulator sickness (P=.04; Spearman ρ=-0.23), suggesting that higher physiological stress was associated with increased simulator sickness symptoms. Prior VR experience was linked to higher usability scores (SUS +5.2; 95% CI 3.12-7.28; P=.03) and lower simulator sickness symptoms (P=.02) but did not significantly affect HRV markers. VR-based simulations for high-risk medical procedures are effective training tools with high usability (SUS=75) and strong immersion. Simulator sickness correlated with physiological stress, emphasizing the need for design refinements to improve user comfort. Prior VR experience improved usability and reduced simulator sickness but did not significantly impact HRV markers. Future research should refine VR interfaces to balance immersion with minimized cognitive and physical discomfort.

The operational performance effect of digitalized manufacturing processes and the mediation effect of supply chain resilience

PurposeThe purpose of the paper is to investigate the impact of digitalized manufacturing (DM) on operational performance (OP) and the mediating role of supply chain resilience (SCRES) in this relationship.Design/methodology/approachThe paper is based on 328 complete and useable responses to a questionnaire survey conducted from November 2022 to January 2023 among Danish manufacturers.FindingsThe paper reveals a positive association between companies’ adoption of DM and OP, with SCRES mediating this relationship. Additionally, the paper identifies a moderating positive effect of demand uncertainty on the DM-OP relationship, as well as a negative moderating effect of demand uncertainty on the mediating role of SCRES.Research limitations/implicationsThe paper relies on responses from a single representative per company, exclusively from Danish respondents.Practical implicationsCompanies and governments should invest in digital transitions and SCRES to enhance OP. Managers must ensure DM systems are flexible to adapt to demand fluctuations, leveraging technological capabilities for resilience. Policymakers should support flexible digital adoption while addressing supply-side challenges through resource security initiatives.Originality/valueThe paper is among the first to investigate the mediating role of SCRES on the relationship between DM and OP and how demand and supply uncertainty moderate the relationships.

Detection of anomalous spatio-temporal patterns of app traffic in response to catastrophic events

In this work, we uncover patterns of usage mobile phone applications and information spread in response to perturbations caused by unprecedented events. We focus on categorizing patterns of response in both space and time, tracking their relaxation over time. To this end, we use the NetMob2023 Data Challenge dataset, which provides mobile phone applications traffic volume data for several cities in France at a spatial resolution of 100 m2 and a time resolution of 15 minutes for a time period ranging from March to May 2019. We analyze the spread of information before, during, and after the catastrophic Notre-Dame fire on April 15th and a bombing that took place in the city centre of Lyon on May 24th using volume of data uploaded and downloaded to different mobile applications as a proxy of information transfer dynamics. We identify different clusters of information transfer dynamics in response to the Notre-Dame fire within the city of Paris as well as in other major French cities. We find a clear pattern of significantly above-baseline usage of the application Twitter (currently known as X) in Paris that radially spreads from the area surrounding the Notre-Dame cathedral to the rest of the city. We detect a similar pattern in the city of Lyon in response to the bombing. Further, we present a null model of radial information spread and develop methods of tracking radial patterns over time. Overall, we illustrate novel analytical methods we devise, showing how they enable a new perspective on mobile phone user response to unplanned catastrophic events and giving insight into how information spreads during a catastrophe in both time and space.

Novel insights into rural spatial design: A bio-behavioral study employing eye-tracking and electrocardiography measures.

In order to objectively assess the effectiveness of rural space design on the affective dimension, this study utilized eye-tracking and electrocardiogram (ECG) monitoring techniques to quantify users' visual attention and emotional responses and to assess the impact of rural design on users' affective experience. The results show that incorporating natural elements and cultural features into the design enhances the values of the subjects' pupil diameter change rate, Heart Rate Variability Index (HRV), subjective evaluation and reduces their Saccade Velocity Average. The experimental results not only verify the application value of eye tracking and ECG monitoring techniques in assessing the design effect of rural space, but also provide a scientific assessment method based on the user's physiological and emotional responses, thus providing a strong support for the optimization of rural space design.

Development of Virtual Science Laboratory Oriented Higher Order Thinking Skills Using Articulate Storyline

This study aims to examine the feasibility and practicality of a science virtual laboratory oriented towards Higher Order Thinking Skills (HOTS) using Articulate Storyline. The research employed a Design and Development Research (DDR) approach based on the ADDIE model, comprising the stages of Analysis, Design, Development, Implementation, and Evaluation. Data were collected using validation questionnaires completed by six experts in content and media, along with user responses from 37 pre-service science teachers. The results were analyzed descriptively to determine the feasibility and practicality categories of the developed product. Content expert evaluations yielded average scores of 3.78 for presentation, 3.83 for content quality, 3.70 for language use, and 4.00 for HOTS orientation, all categorized as valid. Media expert evaluations resulted in average scores of 3.73 for software engineering, 3.83 for instructional design, 3.67 for visual communication, and 3.56 for virtual laboratory components, all falling within the valid and usable range. Meanwhile, user responses indicated a practicality percentage of 87.79%, categorized as very practical. The structured practical activities and post-practice evaluation tasks provided within the virtual laboratory effectively contributed to enhancing the Higher Order Thinking Skills of pre-service science teachers.Keywords: articulate storyline, higher order thinking skills, pre-service science teachers, virtual science laboratory,

Project WORMS (working to overcome resistance and make for a sustainable future): Stud owner and manager survey. Part 1: Prevalence of parasite‐associated disease and management practices on farms

SummaryBackgroundYoungstock are susceptible to parasitic disease, and the development of anthelmintic resistance is increasing.ObjectivesTo investigate stud owners'/managers' perceived prevalence of parasite‐associated diseases and current management approaches to control parasites in UK studs.Study DesignA cross‐sectional study by an online survey.MethodsSurveyMonkey questionnaires (multiple choice, Likert scale and free text questions) distributed via practices, press, social media and snowball sampling. Descriptive and inferential statistics were performed in Microsoft Excel and STATA, and statistical differences were assessed with Fisher's exact test, Mann–Whitney U or Kruskal–Wallis tests.ResultsFifty‐six usable responses were collated. The median number of foals born per farm per year was 9, and the median number of resident mares/permanent boarders was 14. The predominant breeds were Thoroughbreds and Warmbloods. Perceived parasite‐associated illness in the past year was reported by 35.7% of respondents (weight loss (65.0%), diarrhoea (40.0%), colic (15.0%)), with young adults (1–3 years) most affected (47.7%), followed by adults (31.8%), weanlings (27.3%) and foals (20.5%). New mares were reportedly only turned out after anthelmintic administration by 53.6% of respondents, whilst 41.1% performed faecal egg counts and treated, if necessary, prior to turnout and 16.1% turned mares out without treating or testing. The median stocking density was 0.3 mares/acre (significantly lower for Thoroughbreds than Warmbloods or ‘others’ (p < 0.01)). Poo‐picking was never performed on 32.1% of farms, while 89.3% harrowed paddocks and 57.1% co‐grazed horses with ruminants.Main LimitationsSmall sample size and the potential for selection and response biases.ConclusionsParasite‐associated diseases on stud farms are perceived to be common. Many farms fail to implement management practices (e.g. poo‐picking, co‐grazing with ruminants) that could reduce the risk of parasite transmission on the premises. Harrowing paddocks could increase the risks of parasite transmission. Results identify areas where management practices should be improved.

ANALISIS PENGGUNAAN BAHASA INDONESIA DALAM IKLAN PRODUK KOSMETIK DI TIKTOK TERHADAP DAYA TARIK KONSUMEN

The purpose of this study is to evaluate how the use of Indonesian language in cosmetic advertisements on the TikTok application affects customer attraction. Indonesian, as the official language used by most Indonesians, is very important for building successful relationships between sellers and buyers, especially in digital marketing. The method of data collection is to thoroughly look at user ratings and comments on cosmetic product promotional videos on TikTok. The number of likes or comments, as well as the language patterns used in the content and user responses, especially those using Indonesian, are recorded to obtain data. To get a better picture, several additional criteria are also examined, such as the number of views and engagement rate. The results of the study indicate that using formal, clear, and innovative Indonesian in product descriptions, reviews, and promotions can increase customer trust and comfort and have a positive impact on their level of interaction. These results strengthen the theory of marketing communication and consumer perception, which states that marketing content with the right language can increase consumer loyalty and strengthen brand image. In addition, this study provides deeper insight into language-based marketing tactics in e-commerce, especially on TikTok, and offers suggestions on how to optimize local communication to support business success.

Breaking Feedback Loops in Recommender Systems with Causal Inference

Recommender systems play a key role in shaping modern web ecosystems. These systems alternate between (1) making recommendations (2) collecting user responses to these recommendations, and (3) retraining the recommendation algorithm based on this feedback. During this process, the recommender influences the user behavioral data that is subsequently used to update the recommender itself, thus creating a feedback loop. Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior, raising ethical and performance concerns around deploying recommender systems. To address these concerns, we propose the causal adjustment for feedback loops (cafl) , an algorithm that uses causal inference to break feedback loops for any loss-minimizing recommendation algorithms. The key observation is that a recommender system does not suffer from feedback loops if it reasons about causal quantities, namely the intervention distributions of recommendations on user ratings. Moreover, we can calculate these intervention distributions from observational data by adjusting for the recommender system’s predictions of user preferences. Using simulated environments, we demonstrate that cafl improves recommendation quality when compared to prior correction methods.

An Artificial Intelligence Powered Interview Preparation System using MERN Stack

The Artificial Intelligence Preparation System is designed to support job seekers by enhancing their aptitude, data structures, and algorithm (DSA) skills while also providing AI-powered resume optimization and mock interviews. In today’s competitive job market, candidates often face challenges in finding structured preparation resources, refining their resumes, and gaining realistic interview practice. This system tackles these issues using Machine Learning (ML) and Natural Language Processing (NLP) technologies. It features three main components: a Prebuilt Question Bank, an AI-Powered Resume Analyzer, and an AI-Driven Interview Simulator. The Prebuilt Question Bank offers a curated set of aptitude and DSA questions categorized by difficulty level, helping users learn progressively while tracking their performance and receiving personalized study recommendations. The Resume Analyzer evaluates resumes by comparing them with job descriptions and suggests improvements in content and structure. It uses NLP for keyword extraction to ensure alignment with job roles, improving the chances of passing applicant tracking systems (ATS). The Interview Simulator replicates real-world technical interviews based on selected topics, analyzing user responses for technical accuracy, communication skills, and overall performance using AI models. Developed with the MERN (MongoDB, Express.js, React.js, Node.js) stack, the system integrates AI to improve resume assessment accuracy and enhance clarity in interview feedback. By offering structured learning, AIdriven insights, and realistic practice, this innovative platform empowers users to be more confident, competitive, and wellprepared for real-world hiring processes.

AI-Driven Burnout Management System: A Novel Approach Using Generative AI, MongoDB Atlas, Python, And Training Large Language Models (LLMs) - Version 3

Burnout has become a significant concern in high-stress industries, impacting mental health and productivity. Traditional burnout management solutions lack real-time adaptability and personalized intervention strategies. This research explores an AI-driven burnout management mechanism leveraging Generative AI, MongoDB Atlas, Python, and Large Language Models (LLMs) to provide real-time detection, personalized coping strategies, and predictive analytics. The system collects and stores user responses, behavioral patterns, and sentiment data in MongoDB Atlas, while LLMs analyze and generate tailored burnout mitigation recommendations. Experimental results demonstrate the effectiveness of the proposed approach in early burnout detection, personalized intervention, and user engagement, showing improvements in stress reduction and work-life balance. The proof of concept for AI-powered burnout detection and intervention demonstrates the technical feasibility of using Large Language Models (LLMs), Generative AI, MongoDB Atlas, and Python-based automation for real-time mental health support. The system successfully classifies burnout severity using LLMs trained on sentiment analysis, achieving an accuracy of 91.2%, which significantly outperforms traditional rule-based sentiment models. The use of MongoDB Atlas for scalable data storage ensures real-time burnout tracking, while FastAPI-based backend automation allows for seamless integration across platforms. Furthermore, Generative AI provides personalized stress interventions, dynamically adapting to user feedback and improving engagement. These technical validations highlight how AI-driven burnout management can be scalable, real-time, and enterprise-ready, paving the way for AI-powered diagnostics in mental health applications across various industries.

PassionPulse: Personalized Hobby Exploration and Suggestion System

Abstract— In today’s fast-paced world, individuals often struggle to explore and pursue their personal interests and hobbies due to busy schedules and a lack of self-awareness regarding their preferences. PassionPulse: Personalized Hobby Exploration and Suggestion System aims to bridge this gap by leveraging a data-driven approach to recommend hobbies based on user responses. The system collects user inputs through a structured questionnaire, applies an internal mapping mechanism to analyze responses, and suggests personalized hobby recommendations. By integrating web technologies such as HTML, CSS, JavaScript, Bootstrap, and Spring Boot, along with MySQL for data management, the system ensures seamless functionality and user experience. Unlike conventional recommendation models, PassionPulse prioritizes a personalized and interactive approach, enabling users to discover hobbies aligned with their interests. The study explores the effectiveness of PassionPulse through user engagement and recommendation accuracy, highlighting its potential in self-discovery, mental well-being, and skill development. Additionally, the system presents future scalability options, including a hobby purchase feature to provide users with structured guidance for pursuing their interests. With a growing need for personalized recommendation systems in various domains, this research contributes to enhancing hobby exploration through intelligent, user-centric methodologies. Keywords— Data-Driven Recommendation, Hobby Exploration, Interest Mapping, Machine Learning, MySQL, Personalized Recommendation, Questionnaire-Based Analysis, Spring Boot, User Engagement, Web Technologies

MockMate: AI-Powered Online Mock Interview Assessment and Evaluation System

Abstract: In the current competitive job market, being well-prepared for interviews is essential to landing a job. Traditional mock interviews, however, are not scalable and can call for a large human resource commitment. By providing a tailored, automated, and interactive online platform driven by artificial intelligence, MockMate tackles this problem. The system mimics actual interview situations, assesses candidate responses in real time, and provides thorough, data-driven feedback by utilizing cutting-edge Natural Language Processing (NLP) and speech-to-text technologies. In addition to customizing their interviews and choosing career roles, candidates can also obtain performance ratings based on their ability to communicate, solve problems, and be technically proficient. The system analyzes the responses using NLP models like BERT, TF-IDF, and Cosine Similarity and records them using OpenAI Whisper. A thorough report that highlights areas for development and compares user responses with ideal answers is produced. Secure, effective, and high-availability service is guaranteed by MockMate, which is built with a scalable architecture utilizing Next.js, Node.js, and PostgreSQL and deployed using Docker and Kubernetes on cloud platforms like AWS/GCP. This platform enables users to increase their interview readiness, boost their confidence, and increase their chances of success by bridging the gap between traditional interview preparation and contemporary AI capabilities. Keywords- BERT, TF-IDF, Cosine Similarity, OpenAI Whisper, Evaluation .

ASSESSING UNIVERSITY STUDENTS’ PURCHASE INTENTION TOWARD THE ECO-FRIENDLY STATIONERY: THE MODERATION ROLE OF ENVIRONMENTAL CONCERN

Sustainable products like eco-friendly stationery have gained much attention recently due to the increasing awareness and consciousness of consumers towards environmental issues. Numerous sustainable products have been studied in the literature but very limited studies have primarily concentrated on eco-friendly stationery from the perspective of university students. Therefore, this study aimed to identify the factors that affect Malaysian university students’ purchase intention (PI) towards eco-friendly stationery. Two environmental factors (perceived environmental responsibility (PER) and environmental knowledge (EK)) have been incorporated into the theory of planned behaviour (TPB) model together with environmental concern (EC) proposed as a moderator to develop this study’s research model. In this study, 261 usable responses were gathered via purposive sampling. This study utilised the partial least square-structural equation modelling to validate the proposed hypotheses. The result first showed that attitude (ATT), subjective norms (SN), and PER played significant roles in affecting students’ PI towards eco-friendly stationery. Additionally, the moderation analysis further revealed that the influence of SN on PI is significantly strengthened by EC. These findings signified that the students’ PI is directly impacted by ATT, SN and PER only, while the greater EC tends to further enhance the effect of SN on PI. This study’s findings firstly enrich the literature by offering new evidence on the students’ sustainable behaviour, especially in eco-friendly stationery. Besides, the moderating effect of EC on sustainable behaviour is also proven in this study. Moreover, numerous practical implications have also been resulting from this study and it’s useful for the stakeholders to cultivate the students’ sustainable behaviour.

Psychometric performance of a new condition-specific preference-weighted measure, VILL-UI, and EQ-5D-5L in patients with age-related macular degeneration: A MACUSTAR Study Report.

Psychometric performance of a new condition-specific preference-weighted measure, VILL-UI, and EQ-5D-5L in patients with age-related macular degeneration: A MACUSTAR Study Report.

E-Module Math O_Pedia: A Digital Learning Innovation for Elementary Students' Understanding of Fractions

Fractions are one of the materials that are quite difficult to understand. Efforts made to overcome this problem through the development of learning resources in the form of E- Module Math O_Pedia. This study aims to describe the design results of the E-Module Math O_Pedia as a digital learning innovation for understanding fractional numbers in elementary school students and explore perceptions related to the design of the E-Module Math O_Pedia as a digital learning innovation for understanding fractional numbers in elementary school students. The research method uses Research and Development (R&D) using the ADDIE model. The product trial design consists of an initial trial stage. The data collection instruments used were interviews, observations, questionnaires, a feasibility scale by material experts, a feasibility scale by media experts, and a feasibility scale by language experts, as well as a student response scale. The research subjects were 20 grade 3 students of Selogiri District Elementary School. The research findings showed that the material aspect was in the very good category with a score of 92; the media aspect was in the very good category with a score of 92, and the language aspect was in the very good category with a score of 96; while the feasibility of the e-module on the user response factor showed that the product was classified as a very feasible category. The benefits of technology and self-confidence by teachers' perceptions in using digital devices influence their teaching decision-making when planning technology integration. The results of the study concluded that the E- Module Math O_Pedia was very good and quite effective in improving students' understanding of fractional number material.

Special Issue on CDS Failures: Redesigning Clinical Decision Support for Retinopathy of Prematurity Screening After Alert Failure.

Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness. Guidelines recommend screening for infants with gestational age at birth < 31 weeks or birthweight ≤ 1500g. However, ensuring timely screening during readmissions after birth is challenging. To analyze the performance of an interruptive alert at a large academic pediatric hospital for identifying premature infants needing ROP screening upon hospital readmission and to describe how data informed the transition to a non-interruptive dashboard. The alert appeared for patients 1-365 days of age hospitalized in acute care or pediatric intensive care and instructed providers to order an ophthalmology consult from within the alert and to call ophthalmology for at-risk patients. For quality improvement, the clinical decision support (CDS) advisory group evaluated the effectiveness and efficiency of the alert. We exacted alert metrics from the hospital's enterprise data warehouse, including the user response and feedback, patient characteristics (age, birth gestational age, and birth weight), and any ophthalmology consultations. We analyzed the percentage of encounters seen by ophthalmology using a statistical process control chart during alert implementation and 6 months before and after. The alert appeared 3309 times during 2194 patient encounters usually. Users chose "Accept and place order" for 43% (943/2194) of encounters, but only 11% (102/943) had an ophthalmology consult; 34% (53/155) of ophthalmology consultations occurred in encounters with a final response other than "Accept and place order". The intervention was redesigned using a non-interruptive surveillance dashboard with greater specificity, and the alert was deimplemented. Analysis of a failed interruptive alert for identifying patients at risk for ROP led to a transition to targeted surveillance using a dashboard. This case emphasizes the importance of aligning the CDS modality to the clinical workflow, information availability, and user decision-making needs and should be supported by governance.

Examining the key factors of implementing digital storytelling to increase travel intentions to cultural heritage destinations

PurposeThis study aims to identify key factors for effectively implementing digital storytelling (DS) to enhance viewer’s travel intentions (TIs) to cultural heritage destinations.Design/methodology/approachA factorial experimental design was used to test whether including DS in promotional material for cultural heritage destinations influences viewer’s TIs. Convenience sampling was used to gather data from Generation Z in Malaysia, resulting in 240 usable responses. Hypotheses were tested using the Mann–Whitney U test, path analysis and mediation analysis.FindingsThis study found that DS significantly enhances narrative transportation (NT), increasing the persuasiveness of promotional messages and TIs. Participants exposed to promotional material containing DS demonstrated higher levels of NT and TIs. Additionally, the persuasiveness of promotional messages partially mediates the relationship between NT and TI, underscoring the importance of engaging storytelling in destination marketing.Originality/valueThis study provides empirical evidence on the effectiveness of DS in cultural heritage tourism marketing. The developed conceptual framework and insights from the analysis offer practical guidelines for destination marketers to implement DS effectively, enhancing cultural heritage destinations’ attractiveness. Moreover, this study highlights the significance of persuasiveness as a mediator between NT and TI, offering a deeper understanding of how NT can influence individuals’ TIs.