Organic food and Instagram health and wellbeing influencers: an emerging country's perspective with gender as a moderator
Bibliographic record
Abstract
Purpose Instagram health and wellbeing influencers (HWIs) have been increasingly considered as important sources of information and advice for their followers. This study aims to investigate the key antecedents of followers' attitude towards HWIs as well as their influence on their followers' intent to purchase organic products. The moderating effect of gender is also taken into account. Design/methodology/approach Based on data collected from 251 Instagram HWIs followers, the authors empirically tested the conceptual model using structural equation modeling. Findings First, the authors demonstrate that attitude towards HWIs positively impacts followers' attitude towards the promoted brands as well as their intention to purchase organic food brands. Second, followers' attitude towards HWIs is mainly influenced by perceived congruence, influencer credibility, and physical attractiveness. Finally, gender acts as a moderator, e.g. attitude towards HWIs is more likely to be influenced by perceived congruence and physical attractiveness among female followers. Practical implications The findings allow organic brands' managers to understand the key antecedents of followers' attitudes toward HWIs, and therefore, better select talented influencers who are able to create purchase intentions among both existing and potential customers. Originality/value This original research bridges a gap pertaining to the potential use of HWIs to shape consumer intention to purchase organic products. To the authors' knowledge, this study is the first of its kind to investigate the impact of attitudes toward influencers on both brand attitude and purchase intention in the organic food industry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".