The Effect of E-Service Quality and E-Wom on Purchase Decisions Through the Tiktok Shop Application among College Students in Surabaya
Bibliographic record
Abstract
The purpose of this script is to explore how E-Service Quality and Electronic Word of Mouth (E-WOM) influence purchasing decisions through the TikTok Shop app. TikTok Shop experienced a decline of 12.4% in the first quarter of 2022, demonstrating that this does not guarantee that TikTok Shop will always be at the top. Kotler & Armstrong (2016:177) found that buying decisions focus on how individuals and groups choose, acquire, and use experiences, services, ideas, and products to meet desires and needs that are part of consumer behavior. Quantitative analysis is the method used in this study. Non-probability sampling and Purposive Sampling techniques are used for sample collection. The survey used a Likert scale questionnaire with a sample of 100 respondents and the population of students in the city of Surabaya. Partial Least squares (PLS) are used to check research findings. It has been found that E-Service Quality and E-WOM have been shown to influence purchasing decisions through TikTok Shop positively.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".