Determining factors affecting tourism demand for Malaysia using ARDL modeling: A case of Europe countries
Bibliographic record
Abstract
Tourism industry is the second largest foreign exchange earner after manufacturing in Malaysia. With regards to the importance of tourism industry in Malaysia, any factors that influence tourism demand should be considered cautiously by the government and tourism authorities in order to attract more international tourists in the near future. The purpose of this study is to investigate the dynamic long-run and short-run relationship between the number of international tourist arrivals from six European countries and four selected economic variables. The economic variables used in this study are exchange rate, gross domestic product, relative price and substitute relative price. This study also examines the impact of the European Sovereign crisis on the number of arrivals from the selected European countries to Malaysia. The data covers the period from quarter 1 (Q1) of 1999 to quarter 3 (Q3) of 2014 and employs the autoregressive distributed lag (ARDL) bounds testing approach proposed by Pesaran et al. (2001). The results of unit root test show a mixture of integrated at level and order one, I(0) and I(1). The results show that there exist long-run cointegration between the number of international tourist arrivals and exchange rate, level of income, tourism price and substitute tourism price for all countries. Generally, the results show that level of income is in line with the economic theory and Thailand is a competing destination for the tourism industry in Malaysia. Surprisingly, relative price is found to have positive impact on the number of arrivals to Malaysia and this suggests that an increase in the price level in Malaysia is unexpectedly increase the number of international tourist arrivals to Malaysia. Therefore the Malaysian government and tourism authorities should continue the efforts to withstand the growth of the tourism industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".