Digital Innovation and Pop-Up Ad Dilemma: Unraveling How Social Media Drives Gen Z's Decision Shopping
Bibliographic record
Abstract
This study aims to determine the effect of pop-up ads and digital innovation on online purchase intention among Generation Z in Indonesia, and the mediating role of social media. This study collected 260 samples using purposive sampling technique. The data collection method used a questionnaire with a Likert scale of 1-5. The data test in this study used SEM PLS on 260 collected responses to examine the proposed hypotheses on the relationships among pop-up ads and digital innovation on online purchase intention Generation Z in Indonesia. The results showed that pop-up ads significantly influenced online purchase intention, but the effect is negative. This suggests that the effect is negative, as people perceive them as intrusive. In contrast, digital development significantly influences online purchase intention by improving user experience and transaction efficiency. Furthermore, social media was identified as a key mediator, which is able to drive from pop-up ads and digital developments to online purchase intentions. Marketers and entrepreneurs should be aware of the importance of implementing suitable methods to utilise pop-ups and leverage digital technologies in order to increase user engagement and purchase intentions. Marketers should be careful with pop-up ads to prevent user annoyance and prioritise digital innovations that improve user experience. A wide variety of studies have contributed to evaluating the direct impact of social media on purchase intent. However, other researchers have ignored the impact of mediating variables. This study investigates how social media marketing is able to mediate the effect of pop-up advertising on consumer purchase intentions, especially among Gen Z consumers. Furthermore, this study incorporates digital innovation as a determinant of influence.
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 | 0.001 | 0.004 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".