Little is known about ‘uninformative live-streaming’, a new genre of online streaming media that has become a new trend in China. In these real-time streams, streamers would not interact and communicate with viewers and not perform in front of cameras. The content is about uninterrupted trivial everyday activities, such as sleeping and studying for several hours. This thesis aims to obtain a comprehensive understanding of this media and to explore what motivates users to continuously watch it. This research employed qualitative methods of online observation and semi-structured in-depth interviews to collect empirical data, through the cases of study-stream and sleep-stream. The concept of media life by Mark Deuze provides a general theoretical context of mediatized lifeworld. Based on Uses and Gratifications Theory and Compensatory Internet Use Theory, I described and explained the prominent features of uninformative live-streaming and examined the user motivation for it. The research results show that this authentic, less-interactive, and non-narrative live-streaming creates an undisturbed media environment, in which users can escape media distraction and media overload. The prominent user motivations for uninformative live streams are self-discipline and self-management, and compensation for the real-life deficiency of ‘non-social companion’. These findings offer new insight into user motivation and help to expand and improve related theories.

The current study aims to validate and apply an instrument to assess the relationship between communication overload, social overload, technostress, exhaustion and academic performance. We performed a cross-sectional, analytical study of 2286 university medical students to assess the influence of technostress as a mediator of social media overload, communication overload and mental exhaustion and its detrimental effect on the academic performance of university students in Peru during the COVID-19 pandemic. The research model was validated using partial least square structural equation modeling (PLS-SEM) to establish the influence of variables on the model. Communication and social overload were found to positively influence technostress by correlations of 0.284 and 0.557, respectively. Technostress positively influenced exhaustion by 0.898, while exhaustion negatively influenced academic performance by-0.439. Bootstrapping demonstrated that the path coefficients of the research model were statistically significant. The research outcomes may help university managers understand students’ technostress and develop strategies to improve the balanced use of technology for their daily academic activities. / Revisión por pares