Building Consumer Trust: Key Factors Shaping Responses to Influencer Marketing Campaign
Bibliographic record
Abstract
Trustworthiness has emerged as a fundamental determinant in shaping consumer responses to influencer marketing campaigns, influencing how audiences perceive and engage with promotional content. Current data underscores that 63% of consumers in developed nations feel deceived by influencers who do not disclose paid partnerships, contributing to heightened distrust and scepticism. This figure was derived from a consumer survey which surveyed 1,500 social media users across five developed nations (United States, United Kingdom, Germany, Canada, and Australia). Conversely, 67% of millennials and Gen Z in developing countries, such as Malaysia, express a degree of trust in influencer endorsements, but this trust is contingent upon the influencer's perceived authenticity and transparency. This study aims to examine the influence of influencer expertise, authenticity, transparency, relatability, and emotional appeal on consumer trust in influencer marketing. By focusing on these trustworthiness factors, the research explores how they shape consumer attitudes, engagement, and purchase intentions, particularly across culturally diverse Southeast Asian settings. The findings indicate that influencers who exhibit transparency and demonstrate substantive expertise cultivate a stronger sense of credibility, which in turn positively influences consumer behaviour. Conversely, perceived dishonesty or opacity in such engagements markedly diminishes the efficacy of marketing efforts. For practitioners, the implications are clear: successful marketing campaigns necessitate partnerships with influencers who embody transparency, authenticity and demonstrated expertise, as these qualities are instrumental in fostering enhanced consumer engagement and brand loyalty.
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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 | 0.028 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".