The influence of financial literacy, digital literacy, digital marketing, brand image and word of mouth on the z generation's interest in Islamic banks
Bibliographic record
Abstract
This study aims to examine the effect of digital literacy, digital marketing, and word of mouth on the interest of the z generation in Islamic banks. Researchers used primary data obtained from distributing questionnaires to students and students with a total sample of 460 respondents. In this study the sample acquisition technique used a purposive sample with the criteria for respondents being in the age range of 17 years to 25 years. The research method uses a quantitative approach and PLS analysis techniques assisted by SmartPLS version 3.0. The variables in this study include exogenous variables in the form of digital literacy, financial literacy, digital marketing, brand image and word of mouth as well as exogenous variables namely interest in Islamic banks. The results of this study indicate that financial literacy, digital marketing and word of mouth have an influence significant to the interest of the z generation in Islamic banks. Meanwhile, digital literacy and brand image have no significant effect on the z generation's interest in Islamic banks. The results of this study can be used as reference material in conducting further research, especially to determine Islamic banking marketing techniques for the z generation. For further research, it is expected to develop this research by adding other variables such as religiosity, location, level of service and other factors as well as other methods of research such as further and in-depth interview techniques with respondents so that more varied information results are obtained.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".