The influences of service marketing mix on customer loyalty towards Umrah travel agents: Evidence from Malaysia
Bibliographic record
Abstract
In the Malaysian Umrah industry, there are so many new entrants selling and offering similar products and services. As a result, Umrah providers must compete to survive in the Umrah travel industry, as there are currently so many competitors. They need to focus on customers' special needs and preferences to maintain the long-term relationship. Hence, the purpose of this research is to identify the relationship between service marketing mix and customer's loyalty towards Umrah travel agents in Malaysia. The customers who performed Umrah more than once were on the focus of this study. The necessary data were collected from 384 respondents through a structured questionnaire using a convenience sampling technique. The results of the research confirm that all "service marketing mix" elements ("price, product, place, promotion, process, people, and physical evidence") show significant positive effects on customer loyalty. This study will be of interest to the Umrah travel industry in understanding how marketing mix strategies are essential for maintaining a long term relationship with customers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".