The impact of Islamic attributes and motivations on visitor satisfaction and word of mouth in halal supply chain management
Bibliographic record
Abstract
Halal supply chain management, with its focus on Islamic values in tourism, is increasingly recognized for enhancing destination competitiveness and visitor satisfaction. This study examines how Islamic attributes, motivations, and norms influence visitor satisfaction and word of mouth in Medan City's tourism sector. In the halal tourism industry, supply chain management plays a crucial role in ensuring that every stage from procurement, production, distribution, to service delivery complies with halal standards. The research aims to evaluate the impact of Islamic Physical Attributes, Islamic Non-Physical Attributes, Push Motivation, Pull Motivation, and Islamic Norms on visitor satisfaction and their subsequent effect on word of mouth. By employing a quantitative approach, the study utilizes structured questionnaires based on previous research and applies Structural Equation Modeling (SEM) for analysis. Data were collected from 300 tourists in Medan City, including both local and international visitors. The study finds that Islamic Non-Physical Attributes and Pull Motivation significantly enhance visitor satisfaction. Islamic Physical Attributes also positively affect satisfaction, while Push Motivation does not. Satisfaction strongly influences word of mouth, but Islamic Norms negatively moderate this relationship. Islamic attributes and motivations play a crucial role in shaping visitor satisfaction in the tourism context. Islamic norms, however, may complicate the satisfaction-word of mouth link. The implementation of effective halal supply chain management can ensure that all aspects of supply chain management in halal tourism, from products to services, meet the expected halal standards. Practically, destination managers should emphasize Islamic attributes and motivational factors to improve visitor satisfaction. Theoretically, the study contributes to understanding the complex dynamics between satisfaction, norms, and word of mouth in halal tourism.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".