Improving the design of tourism demand forecasting support system

Pao Yue-kong Library Electronic Theses Database

Improving the design of tourism demand forecasting support system

 

Author: Gao, Zixuan
Title: Improving the design of tourism demand forecasting support system
Degree: Ph.D.
Year: 2015
Subject: Tourism -- Forecasting
Tourism -- Data processing.
Hong Kong Polytechnic University -- Dissertations
Department: School of Hotel and Tourism Management
Pages: xii, 295 pages : color illustrations
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2828157
URI: http://theses.lib.polyu.edu.hk/handle/200/8259
Abstract: Accurate tourism demand forecast is the foundation of all tourism-related businesses. As a particular type of decision support system, forecasting support systems (FSS) have been widely applied in tourism demand forecasting in recent years. One of the typical characteristics of existing tourism demand forecasting support systems (TDFSS) is the combination of statistical and judgmental forecasting techniques. A review of recent studies in this area shows that most studies on the development of TDFSS focus on the improvement of statistical forecasting methods. The effectiveness of human participation in the forecasting process is largely neglected,especially the influence of forecasters' cognitive bias on forecast accuracy during the judgmental forecasting process when using TDFSS.Focusing on three typical cognitive biases (desire bias,anchoring bias, and overconfidence bias) in the literature of judgmental forecasting, this study represents the first attempt to identify the influence of these three cognitive biases on the judgmental forecasting of tourism demand and how they affect forecast accuracy. The second purpose of this study is to propose a systematic debiasing model that is able to effectively reduce the forecast error associated with the identified cognitive biases and can be easily implemented in the design of TDFSS. The proposed debiasing model comprises two parts:cognitive bias detection and debiasing. In the first part, potential cognitive biases involved in forecasters' judgmental forecasts can be detected with a series of post-hoc tests. Based on the typical design features of FSS, both informative guidance and suggestive guidance are used as the debiasing strategies in the second part of the model. To test its effectiveness and related hypotheses,the proposed debiasing model has been implemented in the design of the Hong Kong tourism demand forecasting support system (HKTDFS).A two-stage laboratory experiment using HKTDFS and the empirical data of international tourist arrivals to Hong Kong from 10 destination-origin (D-O) pair markets was conducted.The experiment proceeded in three sessions and 75 qualified forecasters agreed to participate.Ultimately, 68 participants provided qualified data for further analysis.The results show that 14 of 21 hypotheses are supported,one is partially supported,and the remaining six are rejected.Generally,the three cognitive biases examined are common in judgmental forecasting of tourism demand and contribute significantly to forecast error.Both performance feedback (PF) and system-suggested forecasts are effective in eliminating the influence of cognitive bias on forecast accuracy.In the design of TDFSS, these two debiasing strategies should be used in dealing with different cognitive biases.To be specific,PF should be provided to forecasters when desired outcome-related cognitive biases are detected;system-suggested forecasts should be recommended to replace forecasters' judgmental forecasts when forecasters anchor their judgmental forecast on the statistical forecast or the latest observation of the forecasting series.In extreme cases,when system-suggested forecasts are not available,keeping statistical forecasts unchanged is the backup strategy when forecasters anchor their judgmental forecasts on statistical forecast;Naive I forecast is the backup strategy when forecasters anchor on the latest observation of the forecasting series. These results provide evidence to further revise the debiasing model in order to improve the design of TDFSS.

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