Exploring Motivations for TikTok Usage and Impact Factors of TikTokers’ Continuance Intention
Bibliographic record
Abstract
The objectives of this study are to examine the motivations for people's TikTok usage and the determinants of their continued use of the app.As a relatively new social media gaining unprecedented popularity so rapidly, it begs the question of why TikTokers keep using the app.This study is essential since social media marketers who seek to engage with Chinese consumers on TikTok and platform designers who wish to optimize the app might gain valuable insights from this study.The research model was developed by integrating uses and gratifications theory (UGT) with technology acceptance model (TAM).Based on descriptive analysis, TikTok is primarily used for entertainment (ENT) and information seeking (IS) motivations, followed by sociability seeking (SS) and escape (ESC) motivations, rather than for fashion (FAS) or money making (MM) reasons.Based on an analysis of 421 TikTokers utilizing structural equation modelling (SEM), perceived usefulness (PU) and attitude (ATT) have significant positive impacts on continuance intention (CI) of TikTok usage, whereas perceived ease of use (PEOU) exerts a significant negative effect, quite the opposite of previous research findings.PEOU and PU have both been shown to positively impact ATT toward TikTok.Meanwhile, PEOU has a significant positive effect on TikTok's PU.There is significant positive impact of ENT and IS motivations on PEOU of TikTok, while FAS motivation exerts significant negative impact, whereas ESC, SS, and MM motivations are insignificant.Moreover, it has been revealed that ENT, SS, and IS motivations significantly contribute to TikTok's PU, whereas FAS, ESC, and MM motivations are negligible.A total of twelve hypotheses were supported out of eighteen.Several theoretical and managerial implications have been drawn from the current research.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".