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Autores principales: Nur, Hassan Ahmed, Abdi, Abdille Ali, Garar, Isam Yusuf
Formato: Recurso digital
Lenguaje:inglés
Publicado: Zenodo 2004
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Acceso en línea:https://doi.org/10.5281/zenodo.18799491
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author Nur, Hassan Ahmed
Abdi, Abdille Ali
Garar, Isam Yusuf
author_facet Nur, Hassan Ahmed
Abdi, Abdille Ali
Garar, Isam Yusuf
contents <p>Virtual reality (VR) training has been increasingly adopted by various organizations to enhance skills in disaster response scenarios. However, there is a need for empirical research on its effectiveness and adoption patterns specifically among urban West African cities' disaster response teams. We employed mixed-methods research, including quantitative surveys and qualitative interviews, to evaluate the adoption and impact of VR training in Somalia's urban West African cities. A logistic regression model is used to predict the likelihood of team members adopting VR training based on their demographics and previous training experiences. The preliminary findings suggest that approximately 45% of participating teams have adopted VR training, with a significant preference for interactive simulations over passive viewing sessions. Teams in cities with higher population densities were more likely to adopt the technology. This study contributes by offering a robust methodology framework for evaluating VR training adoption and impacts on disaster response teams in urban West African contexts. Future research should focus on longitudinal studies to track long-term effects of VR training, including its impact on team performance and organisational culture. Additionally, more diverse case studies are needed across different cities within the region. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p>
format Recurso digital
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language eng
publishDate 2004
publisher Zenodo
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spellingShingle Adoption and Impact Analysis of Virtual Reality Training for Disaster Response Teams in Urban West African Cities: An Empirical Methodology
Nur, Hassan Ahmed
Abdi, Abdille Ali
Garar, Isam Yusuf
Virtual Reality
Disaster Response
Urban Geography
Quantitative Methods
Qualitative Research
Geographic Information Systems
Data Analysis Techniques
<p>Virtual reality (VR) training has been increasingly adopted by various organizations to enhance skills in disaster response scenarios. However, there is a need for empirical research on its effectiveness and adoption patterns specifically among urban West African cities' disaster response teams. We employed mixed-methods research, including quantitative surveys and qualitative interviews, to evaluate the adoption and impact of VR training in Somalia's urban West African cities. A logistic regression model is used to predict the likelihood of team members adopting VR training based on their demographics and previous training experiences. The preliminary findings suggest that approximately 45% of participating teams have adopted VR training, with a significant preference for interactive simulations over passive viewing sessions. Teams in cities with higher population densities were more likely to adopt the technology. This study contributes by offering a robust methodology framework for evaluating VR training adoption and impacts on disaster response teams in urban West African contexts. Future research should focus on longitudinal studies to track long-term effects of VR training, including its impact on team performance and organisational culture. Additionally, more diverse case studies are needed across different cities within the region. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p>
title Adoption and Impact Analysis of Virtual Reality Training for Disaster Response Teams in Urban West African Cities: An Empirical Methodology
topic Virtual Reality
Disaster Response
Urban Geography
Quantitative Methods
Qualitative Research
Geographic Information Systems
Data Analysis Techniques
url https://doi.org/10.5281/zenodo.18799491