Tavares, Jorge Manuel Santos FreireOliveira, Tiago André Gonçalves Félix dePereira, Filipe José Viana2022-04-012024-01-282022-01-28http://hdl.handle.net/10362/135682Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceVideo consultations have the potential to play a significant role for the future of healthcare. The objective of this dissertation is to explore and understand individual video consultation acceptance drivers. An extended technology acceptance model was created based on the diffusion of innovation theory (DOI), unified theory of acceptance and use of technology (UTAUT), health belief model (HBM), and concerns for information privacy framework (CFIP). The predictors of intention to use are performance expectancy, attitude, and COVID-19. Attitude is statistically influenced by performance expectancy, effort expectancy, and COVID-19. The statistically significant drivers on performance expectancy are results demonstrability, compatibility, effort expectancy, and perceived health risk. The statistically significant drivers on effort expectancy are results demonstrability and compatibility. The model explained 77.6% of the variance on intention to use, and 71.4% of the variance in attitude, evidencing the need to combine different theories to achieve high explanatory power. This study shows that COVID-19 pandemic, perceived health risk, compatibility, and performance expectancy have an important impact on the intention to use video consultations.engVideo consultationsTelemedicineAcceptanceStructural equation modelingmaster thesis202981355