Making it real on social media: exploring authenticity strategies for sport and fitness influencers
Bibliographic record
Abstract
Authenticity is a multi-faceted construct, encompassing various dimensions such as originality, truthfulness, and genuineness. Its importance is particularly significant in social media influencer (SMI) marketing, although it is also paradoxically threatened by it. This article aims to emphasize the relevance of implementing strategies that promote influencer authenticity when collaborating with brands on social media platforms. The study focuses specifically on how sport and fitness influencers maintain their authenticity while partnering with brands. The research design includes semi-structured interviews, content analysis and an experiment. The study reveals that influencers in this industry adopt authenticity management strategies, which are shaped by two motives: (internal vs. external) and three drivers (i.e., attractiveness, reliability, and expertise). Six different strategies that combine these two elements are identified. Results also indicate that SMI authenticity has a significant main effect on SMI credibility, and that SMI-brand fit mediates the relationship between SMI authenticity and SMI credibility. Managerial implications and avenues for future research are identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".