Relationship between Advertising Disclosure, Influencer Credibility and Purchase Intention
Bibliographic record
Abstract
Understanding influencer credibility and online advertising and explaining its implications is the basis for analyzing customer purchase behavior. Novelties in digital marketing are visible in the growth of advertising through digital platforms using micro-influencers, compared to the former trend of using celebrities in creating brand awareness with the purpose to reach many customers and influence their buying decisions. The aim of this study is to examine how advertising disclosure (displayed/not displayed) affects influencer credibility, while analyzing influencer type (celebrity/micro-influencer) as a moderator variable underlying this relationship. Further, this paper investigates whether brand awareness mediates the relationship between influencer credibility and purchase intention. The questionnaire was designed and data were collected from 364 respondents using the convenience sampling method on the student population from one Croatian university. Regression analysis was performed to test the set hypothesis in SPSS using the PROCESS approach and independent sample t-test. The findings show: (1) displayed advertising status increases influencers’ credibility, and (2) this relationship is not moderated by influencer type. Moreover, (3) influencer credibility has a positive and significant relationship with purchase intention, and (4) this relationship is mediated through brand awareness. Research results indicate the importance of advertising disclosure and influencer credibility in influencer marketing, since brand awareness created through influencers’ credibility increased by displayed advertising disclosure significantly affects purchase intention of participants.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".