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Digital Socialligators? Social Media-Induced Perceived Support During the Transition to the COVID-19 Lockdown
Social Science Computer Review ; 41(3):748-767, 2023.
Article in English | Academic Search Complete | ID: covidwho-20243040
ABSTRACT
The sudden COVID-19-induced transition from a physical university life to a virtual one was a painful one for many students. Social distancing measures mean more than a simple change from face-to-face to online education. This study investigates how different social aspects, such as the students' psychological sense of community, social capital, and use of social media, facilitated the perceived social support during the transition to the COVID-19 lockdown. Our results not only underline social media's role, but also indicate that the perceived social support, as well as the bonding and bridging social capital, were particularly relevant during the transition process. Our findings are aimed at organizational management by recommending actionable ways in which they could improve social support by organizing computer-supported social networks, social support predictors, and specialized interventions for students with less perceived social support. As such, the study provides unique insights into the COVID-19-induced lockdown situation among students, while offering a transition model that also generalizes to other settings. [ FROM AUTHOR] Copyright of Social Science Computer Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Prognostic study Language: English Journal: Social Science Computer Review Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Academic Search Complete Type of study: Prognostic study Language: English Journal: Social Science Computer Review Year: 2023 Document Type: Article