Bass diffusion and grey models to forecast new tourism product: a case study of Tanah Aina Resort in Malaysia

• Auteur principal: Sarah Alyaa, Mohd Khaidi
• Format: Thèse
• Langue: anglais
• Publié: 2021
• Sujets: Q Science (General); QA Mathematics
• Accès en ligne: http://umpir.ump.edu.my/id/eprint/35262/1/Bass%20diffusion%20and%20grey%20models%20to%20forecast%20new%20tourism%20product%20a%20case%20study%20of%20Tanah%20Aina%20Resort.wm.pdf

Tourism demand forecasting has been acknowledged by researchers as tourism industry involves a large investment and gives a high return to the organisations and to the countries. Among various tourism demand researches that have been published, yet little attention that focus on the new tourism product forecasting. This study focuses on the application of Bass diffusion model (BDM) and grey Bass forecasting model to the new tourism product demand forecasting. Bass diffusion model is an influential model among researchers in forecasting the new product and grey forecasting model is popular because of its ability to handle as low as four data. The combination of these two models, called grey Bass forecasting model is used for the first time in the application of forecasting the new tourism product demand in Malaysia. The new tourism products studied are ecotourism resorts; Tanah Aina Fahad and Tanah Aina Farrah Soraya. Monthly data from Tanah Aina Fahad and Tanah Aina Farrah Soraya are collected from 2014 until 2018 and are converted to yearly data for the estimation of potential market. There are three parameters involved in both models namely; potential market, m, coefficient of innovation, pand coefficient of imitation,q. Parameters estimation method and different value of potential market are employed. The study finds that the grey Bass forecasting model has a better performance compared to the basic BDM for Tanah Aina Fahad dataset based on the evaluation of forecast using mean absolute percentage error. Besides, for Tanah Aina Farrah Soraya, BDM shows a better performance than grey Bass forecasting model but the value ofm gives a significant effect in the forecasting performance. Future research can be improved by using other methods in the estimation of parameters and applying the best values ofp andqto achieve the best forecast.