The Impact of Relationship Quality Between Social Media Influencers and Their Followers on Brand Purchase Intention
Bibliographic record
Abstract
Social media influencers (SMI) have been shown to influence the purchase decision-making of their followers. In this respect, it is necessary to explore the interactive relationship between them, i.e., relationship quality. Researchers will not know the extent to which social media influencers affect their followers and their persistence of persuasion until they understand the SMI-follower relationship quality. This study focused on whether the SMI-follower relationship quality enables followers to transfer their trust in SMIs and affects their attitude toward the advertised or recommended product and brand. Furthermore, it explored whether followers’ skeptical attitude toward advertising moderates the relationship between the SMI-follower relationship quality and trust transference, and whether followers’ self-monitoring moderates the relationship between their advertising skepticism and trust transference. This study distributed survey questionnaires online, and obtained a total of 235 valid copies. The data were used to conduct path analysis through the software PROCESS. The empirical data showed that the higher the SMI-follower relationship quality, the more likely are the followers to transfer their trust in SMIs to the advertised or recommended product or brand. In addition, it was found that trust transference positively mediates the impact of relationship quality on brand attitude. Further, the results indicated that the followers’ high level of advertising skepticism negatively moderates the impact of SMI-follower relationship quality on trust transference, and that a high level of self-monitoring inhibits the negative moderating effect of advertising skepticism on the relationship between SMI-follower relationship quality and trust transference to a larger extent.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".