Cyberloafing activities and social media addiction among netizens: A predictive approach
Bibliographic record
Abstract
Social media usage has increased tremendously in recent years. However, when users cannot control their social media usage, it might have some negative impacts on personal and social life, which lead to the cyberloafing phenomenon. This study aims to examine the influence of cyberloafing activities (sharing, shopping, gaming, accessing online content, real-time updating) and social media addiction among netizens. This study utilized Uses and Gratification Theory (U&G) as a theoretical basis to explain the framework. The quantitative method was implemented in this study. An online survey questionnaire was used to collect data and 318 valid respondents were generated. Partial Least Square Structural Equation Modelling via Smart-PLS was used to analyze the data. The study showed that two cyberloafing activities, namely real-time updating and sharing significantly impact social media addiction. However, the other cyberloafing activities (accessing online content, gaming, shopping) do not contribute to social media addiction. This study may help students and employees to be cognizant of the symptoms of cyberloafing and social media addiction. In addition, it also helps government agencies such as Malaysian Communication and Multimedia Commission (MCMC) to produce strategies that can address addiction among netizens and youths. Conclusion, implications, and future research directions were discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".