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Record W3000588179 · doi:10.46743/2160-3715/2020.4099

Impact of Social Media Addiction on Employees’ Wellbeing and Work Productivity

2020· article· en· W3000588179 on OpenAlexfundno aff
Chetna Priyadarshini, Ritesh Kumar Dubey, YLN Kumar, Rajneesh Ranjan Jha

Bibliographic record

VenueThe Qualitative Report · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCyberloafing and Workplace Behavior
Canadian institutionsnot available
FundersConcordia University
KeywordsPsychologyQualitative researchSocial mediaCompromiseFeelingDistractionApplied psychologyWork (physics)Interpretative phenomenological analysisPublic relationsSocial psychologySociologyComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

The objective of this study is to gain insights into the experiences of employees regarding their social media usage and consequences of social media overuse at the workplace. Fourteen semi-structured interviews were conducted, audio-recorded, transcribed, and analyzed using the Interpretative Phenomenological Analysis (IPA) procedures. The qualitative data was collected from the employees working in renowned IT/ITES companies in India. The themes that emerged are lack of sleep; backache and eye strain; feeling of envy; lack of depth in the relationships; tendency to seek approvals; not meeting deadlines; compromise with the work quality; distraction from work. The present study intends to assist human resource managers in designing appropriate policies and guidelines pertaining to employees’ social media usage at the workplace.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.114
GPT teacher head0.466
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2020
Admission routes1
Has abstractyes

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