Determining Drivers of Destination Attractiveness: The Case of Nature-Based Tourism of Bangladesh
Bibliographic record
Abstract
The purpose of this study is to identify the most important drivers for developing destination competitiveness of Bangladesh nature-based tourism by evaluating tourists’ perception. A nationwide structured questionnaire survey of total 432 Bangladeshi tourists is carried out by dividing the whole country into two parts for equal representation. Based on this data, a profile of the tourists is constructed before ranking of attributes from most important to least important on a five-point Likert scale. An Exploratory Factor Analysis (EFA) has been conducted finally to identify the most important factors from 24 selected attributes related to nature-based tourism of Bangladesh. The key findings indicate that seven attributes are more important to respondents than others as all these has average importance value more than 4 out of 5 while only two is least important. From the EFA of these attributes, supported by a parallel analysis, four major factors are extracted namely, tourism infrastructure; historical and cultural attractors; natural attractors; and communication facilities and lifestyle similarities. Thus, this study will help both policy makers to develop long term destination policy focusing on natural attractors and service providers to customize their services according to tourists’ expectation. Consequently, this paper conceptualizes the importance of focusing on specific sectors of tourism and the way of developing competitiveness of nature-based tourism of Bangladesh. However further studies can be conducted to match tourists’ evaluation of attributes on importance and performance and/or evaluating same perception from service providers rather than tourists.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".