Forecasting the demand for health tourism in Asian countries using a GM(1,1)-alpha model

Abstract

The purpose – Accurately forecasting the demand for international health tourism is important to newly-emerging markets in the world. The aim of this study was presents a more suitable and accurate model for forecasting the demand for health tourism that should be more theoretically useful. Design – Applying GM(1,1) with adaptive levels of α (hereafter GM(1,1)-α model) to provide a concise prediction model that will improve the ability to forecast the demand for health tourism in Asian countries. Methodology – In order to verify the feasibility of the proposed approach, using available secondary and primary data covering the period from 2002 through 2009 obtained from the RNCOS “Opportunities in Asian Health tourism” report. Based on a unique and characteristics database for the health tourism industry, this study applies the adaptive α in a Grey forecasting model (GM(1,1)-α) to predict the demand for health tourism in Asian countries. Approach – Implementation of demand forecasting in health tourism is examined on the short-term and limited dataset, due to importance of a minimum the predicated error on underlying basis for the econometric model for health tourism markets. Findings – Key findings present that the optimal value of α in GM(1,1) can minimize the predicted error. Finally, in the case of the demand for health tourism in Asian countries, using GM(1,1)-α to predict error is clearly better than the use of the original GM(1,1) and time series models. The originality of this research – The originality comes from the analysis of the demand forecasting in health tourism of Asian countries, which provides an easy and accurate method to predict the demand for health medical tourism and ideas for further improvements in the sector of health tourism.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.