The role of brand awareness, digital marketing and electronic word of mouth (E-WOM) toward purchase intention on social media: An empirical study on Indonesian SMEs
Bibliographic record
Abstract
This research aims to analyze the relationship between brand awareness and purchase intention. Digital marketing purchase intention and word of mouth on purchase intention. This research is quantitative research with an explanatory method that aims to explain the relationship between symptoms. This study intends to conduct hypothesis testing to explain the relationship and influence between variables. The data used in this study are primary data through an online questionnaire method. The questionnaire answers are in the form of a Likert scale which is an interval scale with a scale of 1 to 7. Data processing in this study uses the Partial Least Square (PLS) method with the help of SmartPLS software. This method is Structural Equation Modeling (SEM) which can accommodate the relationship between very complex variables but the data sample size is small. The respondents who were sampled were 436 MSME owners, while the questionnaires that were returned completely and filled out properly amounted to 344 questionnaires. The questionnaires that were valid and reliable from the next stage were distributed to the research respondents whose results would be processed using SmartPLS software. The stages of data processing carried out include evaluation of the measurement model (outer model) and evaluation of the structural model (inner model). Evaluation of the measurement model consists of validity tests and reliability tests. Validity test can be seen from the standardized loading factor value. An indicator is said to be valid when the loading factor value is greater than or equal to 0.7. While the reliability test is seen from the Cronbach's Alpha and Average Variance Extracted (AVE) values. A construct is declared reliable when the Cronbach's Alpha value is greater than or equal to 0.7 and the minimum AVE value is 0.5. Furthermore, the hypothesis test is to see the significance of the relationship between constructs which can be seen from the path coefficient. This calculation looks at the t-statistic and p-value values generated from calculations using SmartPLS. Path coefficients that have a t-statistic value ≥ 1.96 or have a p-value ≤ 0.05 are declared significant. The results of this research are that Brand awareness has a positive and significant effect on purchase intention. Digital Marketing has a positive and significant effect on purchasing intention. Word of Mouth has a positive and significant effect on purchasing intention.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".