The adoption of TikTok application using TAM model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it