Evidence map›Paper›PMID 34876758›Full record

ArticleTechnological forecasting and social change2021

COVID-19 information overload and generation Z's social media discontinuance intention during the pandemic lockdown.

Hongfei Liu, Wentong Liu, Vignesh Yoganathan, Victoria-Sophie Osburg

Abstract read
In one paragraph

Article in Technological forecasting and social change, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

77 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hongfei LiuSouthampton Business School, University of Southampton, Southampton SO17 1BJ, United Kingdom.
Wentong LiuSchool of Business Administration, Zhongnan University of Economics and Law, No.182 Nanhu Avenue, Wuhan, Hubei 430076, China.
Vignesh YoganathanSheffield University Management School, University of Sheffield, Sheffield, S10 2TN, United Kingdom.
Victoria-Sophie OsburgMontpellier Business School, University of Montpellier, Montpellier Research in Management, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While previous research highlights the benefits of social media in times of a pandemic, this research focuses on the potential dark side of social media use among Generation Z (Gen Z) in the UK during the COVID-19 pandemic lockdown between March and May 2020. The study reveals that COVID-19 information overload through social media had a negative impact on Gen Z social media users' psychological well-being. Moreover, perceived information overload heightened both social media fatigue and fear of COVID-19, which, in turn, increased users' social media discontinuance intention. In addition, considering that social media is the predominant method of maintaining connectivity with others for Gen Z users during the lockdown, the fear of missing out (FoMO) buffered the impact of social media fatigue and fear of COVID-19 on Gen Z users' social media discontinuance intention. Our research adds a hitherto underexplored perspective to the impact of the COVID-19 pandemic on young people's mental health. We offer a series of practical suggestions for social media users, social media platform providers, and health officials, institutions, and organizations in the effective and sustainable use of social media during the global COVID-19 pandemic and in the post-pandemic time.

Indexed as

Fear of COVID-19Generation ZInformation overloadMental healthSocial media fatigue

Identifiers

PMID34876758
PMCPMC8640972

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.