The effect of influencers on consumers' purchasing intention of Jordanian entrepreneurs' start-ups
Bibliographic record
Abstract
Nowadays, influencers play a crucial role in product and/or service promotion as well as in purchase intention and decision-making. Therefore, the present study aims to explore the effect of influencers on consumers' purchasing intention of Jordanian entrepreneurs' start-ups. The research paper uses a quantitative method, as well as the descriptive, cause-effect, and cross-sectional approaches has been used to actualize this study. The data was collected from 85 participants who were available and willing to answer the questionnaire by using a Google Form-based survey. The findings demonstrate that there is a significant influence of influencer marketing on purchase intentions, where Frequency / Consistency is rated the highest influence on purchase intentions, then content authenticity on purchase intentions, while the expertise of influencers shows no significance on purchase intentions. The contribution of this research was to clarify the influencer marketing effect on purchasing intentions and to identify whether content authenticity, expertise, and presence frequency could impact consumer-purchasing decisions. Finally, the study recommends investigating the effect of influencers' expertise to confirm or not the research results.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".