The effects of brand ambassador and trust on purchase decisions through social media
Bibliographic record
Abstract
Social Media is one of the digital marketing media that is widely used today and the presence of the brand ambassador phenomenon is one of the determinants of consumer purchasing decisions for a product advertised on social media. In today's advances in digital technology, whether consumers prefer to be a model for the product and also whether trust in consumers is also a factor in the consumer's decision to buy. The purpose of this study is to examine the effect of brand ambassador and trust variables on consumer decisions to shop online and also purchase decisions on repurchase. In this study the researchers used four variables, twelve dimensions where each dimension was represented by two indicators so that in this study there were twenty-four indicators which would later be changed in the form of questions to respondents. The population used is consumers who have shopped for fashion products using brand ambassadors. Given that the population is very large, the researchers used quota sampling to determine the number of samples. The study used 5 times the number of indicators so that there were 120 samples of respondents who were processed through Structural Equation Modeling analysis techniques with AMOS software. The results of data processing indicate that brand ambassadors and trust significantly influence consumer purchasing decisions. It was found that trust had the greatest influence on trust. Moreover, purchasing decisions have a significant effect on online consumer repurchase.
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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 | 0.002 | 0.017 |
| 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) | 0.003 | 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".