Consumer Conformity, Social Ties and EWOM in Digital Marketing
Bibliographic record
Abstract
Digital marketing is a marketing technique that is widely discussed in the latest literature. The complexity of digital marketing creates new variables in an effort to increase purchase intention, such as consumer conformity, social ties and EWOM (Electronic Word of Mouth). This study aims to analyze the relationship between consumer conformity, social ties, and EWOM in digital marketing on the Shopee, Lazada, and Bukalapak platforms. Through quantitative methods using Structural Equation Modeling (SEM) analysis using AMOS software on 200 respondents consisting of users of the Shopee, Lazada, and Bukalapak platforms. This study analyzes 5 hypotheses, which is then statistically proven to have a significance value of < 0.05, which means that there is a significant relationship between the variables, thus all hypotheses are supported. The results indicate that social ties and EWOM can have a significant effect on consumer conformity and are also able to directly affect purchase intention. This research also highlights that consumer conformity has a significant effect on purchase intention in digital marketing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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".