ABSTRACT
The outbreak of COVID-19 in 2020 has posed several challenges to the sporting industry, caused by the change in behavior of sporting fans from purchasing event tickets to watching live broadcasts of events on the Internet. This study aims to gain a deeper understanding of fan behavior in this “new normal”. It adopts a technology acceptance model (TAM) to explore the effects of social presence (SP) in the context of online viewing of professional sports. For this purpose, the authors conducted an online survey of viewers who watched NBA sports events on the Amazon Mechanical Turk website using the Together Mode feature of Microsoft Teams. We collected 209 valid questionnaires and performed a partial least squares structural equation modeling analysis. The results showed that the SP-TAM structural model has adequate predictive relevance, and SP has a statistically significant positive relationship with both perceived ease of use and perceived usefulness. The model was thus validated, contributing to the existing body of knowledge on emerging technologies such as the creation of a virtual audience in sports. The study’s findings suggest that technology developers should focus on the effects of SP and emphasize practical functions to increase the use intention of sporting fans. Furthermore, professional sporting leagues should prioritize the use of virtual fan technology to optimize the viewing experience of their fans.
ABSTRACT
‘Federated Learning’ (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the “EXAM” (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.