The role of buzz and viral marketing strategic on purchase intention and supply chain performance
Bibliographic record
Abstract
The purpose of this study is to analyze the effect of viral marketing and purchase intention, the influence between viral marketing and supply chain performance, the influence buzz marketing and purchase intention, the positive buzz marketing and supply chain performance, and the influence between purchase intention and supply chain performance. This study uses quantitative methods and data analysis techniques Structural Equation Modeling Equation Modeling using SmartPLS 3.0 software. The sample selection method used the snowball sampling method. Online questionnaires were sent to respondents as many as 120 Freight Forwarders in DKI Jakarta. Based on data analysis, it was found that there is a positive influence between viral marketing and purchase intention, there is a positive influence between viral marketing and supply chain performance, there is a positive influence between buzz marketing and purchase intention. There is a positive influence between marketing buzz and supply chain performance. There is a positive influence between purchase intention and supply chain performance. The novelty of this research is a model of the relationship between viral and buzz marketing on purchase intention and supply chain performance and the results of this study can be applied in other organizations and in other countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".