What’s done in the dark will be brought to the light: effects of influencer transparency on product efficacy and purchase intentions
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of the type of disclosure used by social media influencers on consumer evaluations of influencer transparency, product performance expectations and how those factors ultimately influence purchase intentions. Design/methodology/approach An experiment was conducted with 321 participants recruited from MTurk to test a moderated serial-mediation model. Findings The results indicate that when consumers become cognizant that an influencer’s branded promotional post may have been motivated by an underlying financial relationship, they evaluate the influencer as significantly less transparent if a more ambiguous disclosure is used relative to a clearer disclosure. Transparency perceptions of the influencer impact consumers’ perceptions of product efficacy as well as purchase intentions. Originality/value Social media influencers are rapidly emerging as a popular marketing tool for brand managers, but consumer response to this form of promotion is not well understood. To the best of the authors’ knowledge, this is the first study to investigate how the type of endorsement disclosure used by a social media influencer impacts consumer perception of influencer transparency, product efficacy and purchase intentions. Further, this research demonstrates the applicability of the persuasion knowledge model in the domain of influencer marketing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".