The Effect of Tourism and Market Institutional Policies on Tourist Visits from ASEAN Countries
Bibliographic record
Abstract
Tourism industry has experienced a significant development. This can be seen from several indicators, such as the number of foreign and domestic tourist visits, tourist spending, employment opportunities and the tourism sector's contribution to national income (GDP). Indonesian Tourist Policy has been combined with Law No.10 of 2009 concerning Tourism, and operationalized by Government Regulation No. 50 of 2011 concerning the National Tourism Development Master Plan (RIPPARNAS). It coincides with the ASEAN liberalization policies. Previous economic tourism research has not accommodated the role of economic institutions as a determinant of foreign tourism demand in Indonesia. This study focuses on tourism demand from ASEAN countries. The model is expected to explain the optimization of tourism resources for development. This data panel study revealed that Fixed Effect Model (FEM) is the most appropriate econometrical model used to estimate tourism in Indonesia in ten years (2006-2015). This study revealed some similarities with previous studies, especially in strengthening the theory of demand. The relative cost of tourism which tends to be more efficient will increase the number of tourist visits. Access to communication infrastructure and the proportion of city residents in the country of origin will increase tourism demand to Indonesia. Liberalization of policies need not be feared by many people, because the institutional aspects of the market which include rules, regulations, fiscal strength and market openness will encourage the acceleration of tourism. However, this study found that tourism services in Indonesia are still inferior compare to those of ASEAN countries. This requires international tourism marketing that changes perceptions and develops adequate destinations to facilitate ASEAN community members. In addition, this study considers that modernization in rural areas and the strengthening of related policies are related to the implementation of tourism master plans to improve tourism optimization.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".