The Impact of Marketing Mix on Perceived Value, Destination Image and Loyalty of Tourists (Case Study: Khalkhal City, Iran)
Bibliographic record
Abstract
This study examines the relationship between marketing mix with loyalty, perceived value, perceived quality and destination image. This is an applied descriptive study the aim of which is to determine the impact of marketing mix of services (Product, Pricing, Place, Promotion, People, Process and Physical Evidence) on perceived quality, loyalty, perceived value and destination image of the tourists in Khalkhal city, Iran. Population of the study included all the tourists visiting Khalkhal city from among whom 385 respondents participated in this study. A questionnaire was used for data collection. The conceptual model was analyzed based on linear regression analysis in SPSS software and model fitness was analyzed using LISERL software. Cronbach’s alpha of the questionnaire was equal to 0.791 that is higher than 0.7 and so reliability of the questionnaire was acceptable. Findings of the study showed that promotion, people and physical evidences had the highest effect on perceived value, destination image and perceived quality. Finally, some suggestions were provided for the managers of the tourism destinations to improve the perceived value and quality and also develop a proper destination image.
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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 | 0.001 |
| 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 | 0.000 |
| 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".