Acceptance of Health-Related User Generated Content Among Muslim Consumers: A Case of Senna Makki During COVID-19
Bibliographic record
Abstract
Purpose The paper examines the information acceptance of health-related user-generated content (UGC) about the herbal remedy Senna Makki (a widely touted herbal treatment as per the Islamic traditions) by Muslim consumers during the COVID-19 health crisis. Grounded in the Information Acceptance (IACM), with a specific focus on the role of religiosity, this research explores how cognitive and religious factors shape the acceptance of Senna Makki UGC in the Pakistani context.Design and approach The paper is based on a quantitative research design (n = 390) following an established information-acceptance framework. The analysis used Partial Least Squares-Structural Equation Modeling (PLS-SEM), an established multivariate analysis technique.Findings Employing the Information Acceptance Model (IACM), the study found that the quality and credibility of information, as well as the attitudes toward the information, influenced the perceived usefulness of Senna Makki-related UGC among Muslim consumers. The need for information was not found to be a significant predictor. Religiosity was a significant predictor of acceptance of Senna Makki UGC, although it did not moderate the relationship between perceived usefulness of UGC and UGC acceptance.Originality There is limited research on health-related information acceptance, especially in any religious context. This research provides an impetus to explore the uptake of health information in the religious context.Limitations and areas of future research The study was conducted only in the context of Pakistan. Exploring the diversity of influence of religion in other Muslim consumer markets and other faiths is a potential area of further research.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".