Quenching Gen Z’s thirst: teen-targeted beverage marketing on TikTok
Bibliographic record
Abstract
Purpose Despite increased marketing attention on Gen Z, little is known about the beverage ads that teens actually see on social media. Given the growing popularity of TikTok, this study aims to examine teen-targeted beverage marketing on this platform to shed light on the drink types, brands, products and persuasive techniques (i.e. power) directed at them in beverage ads. Design/methodology/approach This exploratory, participatory study used a smartphone app to allow teen participants (ages 13–17, the youngest members of Gen Z) to capture examples of teen-targeted beverage ads on TikTok for a seven day period, and tag the content for brand, product and persuasive appeals. A content analysis was performed, also coding for drink type/subtype, and celebrity endorsers present. Study data were summarised using frequencies and percentages. Findings In total, 108 teenagers participated, capturing 223 beverages ads from TikTok. The majority of participants were female and older (93.5%, ages 15–17). Most beverage ads captured belonged to five main categories: soda (38%), blended drink (19%), coffee (14%), fruit drink and iced tea (12%) and energy drink (9%). The top indicator identified was visual style (54% of ads), followed by music/audio and teen themes. The types of celebrity endorsers most commonly identified were singer/rapper/musician and influencer. Originality/value This study provides new insight into the vast array of beverage products and brands targeting the youngest members of Gen Z on TikTok, and identifies the persuasive power of those ads for teens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".