The Role of Advertising Disclosure and Persuasion Knowledge in Travel Influencer Videos on Instagram
Bibliographic record
Abstract
The study investigates the role of advertising disclosure and persuasion knowledge in travel influencer videos on Instagram. In a 2 (ad disclosure: present vs. absent) x 2 (level of persuasion knowledge: high vs. low) between-subjects factorial experiment, the study explores the role of advertising disclosure on consumers’ awareness of persuasive intent, travel intent and sharing intent. In addition, the moderating role of persuasion knowledge about influencer marketing is investigated. The results indicate that the presence of advertising disclosure in the form of “#sponsored” is effective in informing consumers about the commercial nature of the video, without having a significant effect on behaviour. For marketers and influencers, the study is particularly important as it shows that advertising disclosure is a critical element in informing consumers about the sponsored partnership, without having a detrimental effect travel intent and sharing intent. .Whereas previous studies on advertising disclosure have primarily considered blog posts or images, this study focuses on travel videos on Instagram. As a contribution to literature, the Persuasion Knowledge Model is extended in the context of travel influencer videos on Instagram.
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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 | 0.003 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".