Search results for: information-seeking behaviors

Authors: G. Senoga, D. Nakimuli, B. Ndagire, B. Lukwago, D. Kyamagwa

Abstract:

Background: The COVID-19 epidemic affected the education sector, with some private schools closing while other children missed schooling for fear contracting COVID-19. Post COVID-19, PSIU in collaborated with Kampala City Council Authority Directorate of Education and Social Science, Water and Sanitation department, and Directorate of Public Health and Environment to improve sanitation and hygiene among pupils and staff in 50 public and private school system in Kampala city. The “Be Clean, Stay Healthy Campaign” used a behavioral change approach in educating, reinforcing and engaging learners on proper hand washing behaviors, proper toilet usage and garbage disposal. In April 2022, 40 Washa lots were constructed, to reduce the pupil - hand wash station ratio; distributed KCCA approved printed materials; oriented 50 teachers, WASH committees to execute and implement hygiene promotion. To ensure sustainability, WASH messages were memorized and practiced through hand washing songs, Pledge, prayer, Poems, Skits, Music, dance and drama, coupled with participatory, practical demonstrations using peer to peer approach, guest speakers at assemblies and in classes. This improved hygiene and sanitation practices. Premised on this, PSI conducted an end line assessment to explore the impact of a hand washing campaign in regards to improvements in hand washing practices and hand hygiene among pupils, accessibility, functionality and usage of the constructed hygiene and sanitation facilities. Method: A cross-sectional post intervention assessment using a mixed methods approach, targeting headteachers, wash committee members and pupils less <17 years was used. Quantitative approaches with a mix of open-ended questions were used in purposively selected respondents in 50 schools. Primary three to primary seven pupils were randomly selected, data was analyzed using the Statistical Package for Social Scientists (SPSS) Outcomes and Findings: 46,989 pupils (51% female), 1,127 and 524 teaching and non-teaching staff were reached by the intervention, respectively. 96% of schools trained on sanitation, sustainable water usage and hygiene constituted 17-man school WASH committees with teacher, parents and pupils representatives. (31%) of the WASH committees developed workplans, (78%) held WASH meetings monthly. This resulted into improved sanitation, water usage, waste management, proper use of toilets, and improved pupils’ health with reduced occurrences of stomach upsets, diarrhoea initially attributed to improper use of latrines and general waste management. Teachers reported reduced number of school absenteeism due to improved hygiene and general waste management at school, especially proper management of sanitary pads. School administrations response rate in purchase of hygiene equipment’s and detergents like soap improved. Regular WASH meetings in classes, teachers and community supervision ensured WASH facilities are used appropriately. Conclusion and Recommendations: Practical behaviour change innovations improves pupil’s knowledge and understanding of hygiene messages and usage. Over 70% of pupils had clear recall of key WASH Messages. There is need for continuous water flow in the Washa lots, harvesting rain water would reduce water bills while complementing National water supply coupled with increasing on Washa lots in densely populated schools.

Keywords: handwashing, hygyiene, sanitation, behaviour change

Procedia PDF Downloads 89

Authors: Shari S. C. Shang, Lynn S. L. Chiu

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In the digital payment market, some consumers use only one payment wallet while many others play multi-homing with a variety of payment services. With the diffusion of new payment systems, we examined the determinants of the adoption of multi-homing behavior. This study aims to understand how a digital payment provider dynamically expands business touch points with cross-business strategies to enrich the digital ecosystem and avoid the trap of multi-homing in customers. By synthesizing platform ecosystem literature, we constructed a two-dimensional research framework with one determinant of user digital behavior from offline to online intentions and the other determinant of digital payment touch points from convenient accessibility to cross-business platforms. To explore on a broader scale, we selected 12 digital payments from 5 countries of UK, US, Japan, Korea, and Taiwan. With the interplays of user digital behaviors and payment touch points, we group the study cases into four types: (1) Channel Initiated: users originated from retailers with high access to in-store shopping with face-to-face guidance for payment adoption. Providers offer rewards for customer loyalty and secure the retailer’s efficient cash flow management. (2) Social Media Dependent: users usually are digital natives with high access to social media or the internet who shop and pay digitally. Providers might not own physical or online shops but are licensed to aggregate money flows through virtual ecosystems. (3) Early Life Engagement: digital banks race to capture the next generation from popularity to profitability. This type of payment aimed to give children a taste of financial freedom while letting parents track their spending. Providers are to capitalize on the digital payment and e-commerce boom and hold on to new customers into adulthood. (4) Traditional Banking: plastic credit cards are purposely designed as a control group to track the evolvement of business strategies in digital payments. Traditional credit card users may follow the bank’s digital strategy to land on different types of digital wallets or mostly keep using plastic credit cards. This research analyzed business growth models and inter-firms’ coopetition strategies of the selected cases. Results of the multiple case analysis reveal that channel initiated payments bundled rewards with retailer’s business discount for recurring purchases. They also extended other financial services, such as insurance, to fulfill customers’ new demands. Contrastively, social media dependent payments developed new usages and new value creation, such as P2P money transfer through network effects among the virtual social ties, while early life engagements offer virtual banking products to children who are digital natives but overlooked by incumbents. It has disrupted the banking business domains in preparation for the metaverse economy. Lastly, the control group of traditional plastic credit cards has gradually converted to a BaaS (banking as a service) model depending on customers’ preferences. The multi-homing behavior is not avoidable in digital payment competitions. Payment providers may encounter multiple waves of a multi-homing threat after a short period of success. A dynamic cross-business collaboration strategy should be explored to continuously evolve the digital ecosystems and allow users for a broader shopping experience and continual usage.

Keywords: digital payment, digital ecosystems, multihoming users, cross business strategy, user digital behavior intentions

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Authors: Yu-Lin Chen, Kimberly Koester, Marissa Raymond-Flesh, Anika Thapar, Jay Thapar

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Purpose: Effectively communicating adolescent health topics to teens and their parents is crucial. This study emphasizes critically evaluating the optimal use of artificial intelligence tools (AI), which are increasingly prevalent in disseminating health information. By fostering a deeper understanding of AI voice preference in the context of health, the research aspires to have a ripple effect, enhancing the collective health literacy and decision-making capabilities of both teenagers and their parents. This study explores AI voices' potential within health learning modules for annual well-child visits. We aim to identify preferred voice characteristics and understand factors influencing perceived trustworthiness, ultimately aiming to improve health literacy and decision-making in both demographics. Methods: A cross-sectional study assessed preferences and trust perceptions of AI voices in learning modules among teens (11-18) and their parents/guardians in Northern California. The study involved the development of four distinct learning modules covering various adolescent health-related topics, including general communication, sexual and reproductive health communication, parental monitoring, and well-child check-ups. Participants were asked to evaluate eight AI voices across the modules, considering a set of six factors such as intelligibility, naturalness, prosody, social impression, trustworthiness, and overall appeal, using Likert scales ranging from 1 to 10 (the higher, the better). They were also asked to select their preferred choice of voice for each module. Descriptive statistics summarized participant demographics. Chi-square/t-tests explored differences in voice preferences between groups. Regression models identified factors impacting the perceived trustworthiness of the top-selected voice per module. Results: Data from 104 participants (teen=63; adult guardian = 41) were included in the analysis. The mean age is 14.9 for teens (54% male) and 41.9 for the parent/guardian (12% male). At the same time, similar voice quality ratings were observed across groups, and preferences varied by topic. For instance, in general communication, teens leaned towards young female voices, while parents preferred mature female tones. Interestingly, this trend reversed for parental monitoring, with teens favoring mature male voices and parents opting for mature female ones. Both groups, however, converged on mature female voices for sexual and reproductive health topics. Beyond preferences, the study delved into factors influencing perceived trustworthiness. Interestingly, social impression and sound appeal emerged as the most significant contributors across all modules, jointly explaining 71-75% of the variance in trustworthiness ratings. Conclusion: The study emphasizes the importance of catering AI voices to specific audiences and topics. Social impression and sound appeal emerged as critical factors influencing perceived trustworthiness across all modules. These findings highlight the need to tailor AI voices by age and the specific health information being delivered. Ensuring AI voices resonate with both teens and their parents can foster their engagement and trust, ultimately leading to improved health literacy and decision-making for both groups. Limitations and future research: This study lays the groundwork for understanding AI voice preferences for teenagers and their parents in healthcare settings. However, limitations exist. The sample represents a specific geographic location, and cultural variations might influence preferences. Additionally, the modules focused on topics related to well-child visits, and preferences might differ for more sensitive health topics. Future research should explore these limitations and investigate the long-term impact of AI voice on user engagement, health outcomes, and health behaviors.

Keywords: artificial intelligence, trustworthiness, voice, adolescent

Procedia PDF Downloads 54

Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

Abstract:

Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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