The impact of digital advertising channels on the customer buying behavior: The moderating task of advertising strategies
Bibliographic record
Abstract
Regulators and recent academics are now paying attention to digital marketing because it has become a complete marketing resource that might propel a firm toward success. As a result, the current study explores the impact of several digital advertising channels, including mobile, e-mail, and digital retargeting, on customer purchasing behavior in the Information Technology (IT) business in the UAE. The current research also examines the moderating effects of marketing tactics at the intersection of mobile advertising, digital retargeting, e-mail marketing, and customer purchasing choices in the IT sector of the UAE. To collect data for this study quantitatively, questionnaires were employed to solicit information from respondents, which was then analyzed using smart-PLS. The findings showed that digital retargeting, mobile, and e-mail marketing positively affect customer purchasing behavior in the UAE's IT sector. The results also demonstrated that marketing tactics drastically reduced the associations between mobile, e-mail, and consumer purchasing decisions in the UAE's IT sector. This study gave policymakers recommendations on better focusing on digital advertising, which could boost the organization's success.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".