The Impact of Word of Mouth, Product Quality, and Price on Trust and Repurchase Intentions: A SEM-Based Case Study of Indonesian Live Streaming E-Commerce
Bibliographic record
Abstract
This study investigates how Word of Mouth (WoM), Product Quality (PQ), and Price (PR) influence Trust (TR) and Repurchase Intentions (RI) in the context of live streaming e-commerce in Indonesia. Using Structural Equation Modeling (SEM) and data from 300 respondents, the results show that trust plays a key role in influencing Repurchase Intentions. WoM and PR have a significant impact on trust, with WoM having a dual effect—it positively affects trust but negatively influences repurchase intentions directly. PQ does not significantly affect either trust or repurchase intentions, suggesting that in live streaming e-commerce, consumer interactions and experiences may be more influential than product attributes. The model fit indices support the reliability of the results, with strong explanatory power for both trust (R² = 0.765) and repurchase intentions (R² = 0.890). This research enhances our understanding of consumer behavior in live streaming e-commerce, highlighting the importance of trust-building strategies, managing WoM effectively, and transparent pricing to encourage consumer loyalty. Future studies should consider other factors like platform usability and influencer credibility for a broader view.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".