The Impact of Perceived Brand Control on Influencer Authenticity and Gen Z Consumer Engagement
Bibliographic record
Abstract
This study investigates how perceptions of brand control affect influencer authenticity and how perceptions of brand control shape consumers in Generation Z (Gen Z). Since influencer marketing has become increasingly important to brands, there has been concern about the naturalness of influencers amid growing brand interference. The research examines the impact of having different levels of brand control, based on content, messaging, and sponsorship disclosures, on activation of perceived authenticity of influencers. It also examines the mediating role of negative emotions in the relationship between perceptions of brand control and influencers' avoidance among Gen Z consumers. Quantitative research was employed, with a structured online survey administered to 400 Gen Z social media users. Findings from regression analysis indicated that high brand control, as a factor, significantly decreased perceptions of influencer authenticity and elicited negative emotions, which, in turn, served as a mediator of avoidance behavior. The results have valuable implications for brands, influencers, and policymakers, who should strike a balance between branding and independence to shape credibility and trust. This study adds to the existing body of research on influence marketing by showing a deeper psychological process through which brand control influences Gen Z consumers' behavior.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".