Brand balance: the effect of influencer brand encroachment on interactivity
Bibliographic record
Abstract
Purpose As social media platforms continue to have an increased presence in influencer marketing, researchers and practitioners are seeking ways to optimize the use of these platforms. The purpose of this study is to investigate the effect of brand encroachment, a level of brand's control over the promotion executed by social media influencers (SMIs), on the importance of interactivity of SMIs. Design/methodology/approach This study examined the effect of brand encroachment on interactivity whilst examining the mediating effects of both passion and personal power. The study was an online, one-factor between-subjects design comparing high level of brand encroachment vs low level of brand encroachment. Findings The results of the experimental study suggest that as brand encroachment decreases, there is an increasing importance of being interactive. In addition, with lower brand encroachment, SMIs portray more personal power and passion toward the product or service being promoted. Originality/value As influencers create communities via increased levels of engagement, authenticity and relatability, it is of paramount importance that SMIs build relationships through interactivity in low-brand encroachment settings. Brands should offer more opportunities for SMIs to be interactive with their audience, while intrinsically building their personal power and passion as sources for these interactions.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".