تأثیر ارزش ویژه شناسه مقصد گردشگری بر قصد بازدید مجدد بر اساس الگوی ارزش ویژه برند مبتنی بر مشتری (مطالعه موردی: شهر اصفهان)
Bibliographic record
Abstract
صنعت گردشگری در طی دهه اخیر با نرخ رشدی شتابان گسترش یافته و منبع درآمد قابل ملاحظه ای برای بسیاری از کشورهای درحال توسعه بوده است. اما کشور ایران با وجود قابلیتهای بالا، سهم ناچیزی از این کسب وکار جهانی را به خود اختصاص داده است. یکی از راهکارهایی که کشورها برای جذب گردشگر به کار می گیرند، ایجاد شناسه برای مقاصد گردشگری است. در این مقاله شهر اصفهان به عنوان مقصد گردشگری انتخاب شده و گردشگرانی که در سه ماهه اول سال 1391 به این شهر سفر کرده بودند به عنوان جامعه آماری انتخاب شدند که از این میان با توجه به فرمول کوکران و روش نمونه گیری سهل و آسان 384 پرسشنامه میان آن ها توزیع شد. اطلاعات مورد نیاز توسط نرمافزار آماری AMOS مورد تجزیهوتحلیل قرار گرفت. همچنین لازم به ذکر است که برای آزمون فرضیه ها نیز از الگوسازی معادلات ساختاری استفاده شد. نتایج نشان میدهد مدیران صنعت گردشگری باید بر سه متغیر آگاهی از شناسه، تصویر شناسه و کیفیت شناسه مقصد تأکید بیشتری داشته باشند، همچنین نتایج حاکی از آن است که آگاهی از شناسه مقصد بیشترین تأثیر را بر ارزش شناسه مقصد داشته است. Abstract Tourism industry has had an increasing growth rate in the past decade and has been a significant income source for many developing countries. But Iran, in spite of its rich and extensive tourism related assets, has had a small share of this global market. One of the strategies in attracting visitors for countries is Destination Branding. In this article, Isfahan city was selected as tourism destination and the tourists who have traveled to Isfahan in the first quarter of the year 1391 were chosen as the research community and then by the use of Cochran formula and convenience sampling, 384 questionnaires was distributed among them. At this article, the Amos software has been used. For testing research hypotheses, the structural equation modeling was applied. The results of data analysis represents that In order to creating a better brand in Isfahan city, managers of tourism industry should emphasize more on 3 independent variables i.e. destination brand awareness, destination brand image and destination brand quality. Among all the variables, destination brand awareness has had the most effect on destination brand value. Keywords: Tourism Industry; Tourist, Tourism Destination; Destination Brand Equity; Revisit Intention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.121 | 0.077 |
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".