Investigating the role of viral marketing, and brand awareness on purchase decisions: An empirical study in Indonesian online shops
Bibliographic record
Abstract
Previous research states that viral marketing has an important role in increasing consumer purchasing intentions, viral marketing can also encourage increased brand awareness. This research aims to analyze the relationship between Viral marketing and purchasing decisions, Brand awareness and purchasing decisions, and Viral marketing and purchasing decisions. This research method is quantitative through surveys, research data was obtained by distributing online questionnaires to 720 selected online shop customers in Indonesia using a simple random sampling method, and the online questionnaire was designed using statement items with a Likert scale of 1 to 7. Data analysis used a structural equation model based on covariance (CB-SEM) with SmartPLS 4.0 software to analyze research data. The independent variables are viral marketing and brand awareness, and the dependent variable is purchasing decisions. The stages of data analysis are validity testing, reliability testing, model fit testing, and significance testing of hypothesis testing. The results of this research are that Viral marketing has a positive and significant relationship with purchasing decisions, Brand awareness has a positive and significant relationship with purchasing decisions, and Viral marketing has a positive and significant relationship with purchasing decisions. Viral marketing is one of the factors that can influence purchase intentions. Viral marketing is expected to have multiple effects because many people who receive the message can convey it to tens or even hundreds of other internet users. Brand awareness can influence consumers toward purchasing decisions, which states that brand awareness has a significant influence on purchasing decisions.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".