The adoption of TikTok application using TAM model
Bibliographic record
Abstract
One of the most used social media platforms is TikTok, which is widely and increasingly used due to the short-video interactive music. Very few studies about why people prefer to use TikTok applications were carried out. Therefore, the objective of the current research is to examine the effect of perceived usefulness, perceived ease of use, perceived enjoyment, sense of belonging, and user-generated content on the adoption of TikTok application, using the TAM model. Quantitative research has been applied as a methodological approach and was successfully carried out through an online survey, gathering a total of 255 filled surveys to test the applicability of the developed research model. The results show that the user-generated content has the highest significant positive influence on the intention to use TikTok. Followed by the perceived enjoyment, then the sense of belonging, the perceived ease of use, and the perceived usefulness, consequently. Also, results show that the independent variables explain 47.8% of the variance in the intention to use TikTok. Finally, to assure the generalizability of the study results the study recommends conducting further research in different countries and communities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".