The Future of Influencer Marketing: Trust, Authenticity, and Consumer Behavior in the Social Media Age
Bibliographic record
Abstract
The rapid rise of influencer marketing has reshaped the landscape of consumer engagement, particularly in the social media age where trust and authenticity play critical roles in shaping purchasing behavior. This study explores how consumers perceive and respond to trust and authenticity in influencer content and how these perceptions influence decision-making processes. Utilizing a qualitative research design, in-depth semi-structured interviews were conducted with twenty active social media users to gain insights into their emotional, cognitive, and behavioral responses to influencer marketing. Thematic analysis revealed that trust is primarily built through perceived expertise, consistency, and ethical transparency, while authenticity is constructed through personal storytelling, value alignment, and selective brand endorsement. The findings also highlight the significant influence of emotional engagement and parasocial interaction in fostering consumer loyalty. Despite these positive dynamics, challenges such as commercial pressures, algorithmic content distortion, and audience skepticism threaten the sustainability of influencer credibility. The study contributes to the growing body of literature on digital consumer behavior and offers practical implications for brands and influencers striving to maintain authentic connections in an increasingly competitive digital environment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".