Travel behavioral intention of choosing Malaysia as destination for medical tourism

Aziz, Azdel Abdul; Md-Yusof, R.; Abu-Bakar, Najmin; Taib, S. N.H.; Ayob, Mastura

doi:10.1201/b17390-37

Cited by 1 publication

(1 citation statement)

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“…For example, historical places like A'Famosa, skyscrapers like Petronas Twin Tower, islands, beautiful natures and Malaysian different cultural [5,6]. Instead of travelling for holidays, international tourists come to Malaysia for medical treatment, business trip, sports trip and others [7,2,8]. Since tourism industry is crucial to boost Malaysia's economy, accurate forecasting of the future tourism demand may help people who work in tourism-related-business to make a correct judgment, plan strategy and minimize the side effect if the prediction is decreasing for the next year.…”

Section: Introductionmentioning

confidence: 99%

Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach

Hamzah¹,

Nor²,

Saharan³

et al. 2018

IJET

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Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.

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