The role of social media marketing, entertainment, customization, trendiness, interaction and word-of-mouth on purchase intention: An empirical study from Indonesian smartphone consumers
Bibliographic record
Abstract
The purpose of this study is to analyze the effect of customization purchase intention of Smartphones, entertainment, interaction, social media marketing, trendiness, and Word-of-Mouth on purchase intention of Smartphones. The study uses a quantitative method by distributing online questionnaires to 217 consumers in Banten Indonesia and the method of distributing questionnaires is a snowball sampling system. Data processing and testing of hypotheses and models in this study are based on Structural Equation Modeling (SEM). The research has benefits in increasing knowledge and information for companies about the importance of brand awareness through increasing influencing factors such as social media marketing and word of mouth. Based on the results of SmartPLS analysis, Interaction, Word-of-Mouth, Social media marketing, Entertainment and Trendiness have insignificant effects on purchase intentions of Smartphones while Customization has significant effects on purchase intentions of Smartphones during.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".