Factors Influencing Visitor's Choices of Urban Destinations in North ...

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Results: Lim and McAleer found that the Holt-Winters additive and multiplicativeseasonal models outperform the single, double, and the Holt-Winters non-seasonalexponential smoothing models. This finding suggests that forecasters should beconcerned with seasonality of tourism demand data in Australia.Another finding of this paper is that forecasting the first difference of tourist arrivalsperforms worse than forecasting its levels. This result means that forecasters should notadjust for the presence of unit root in the tourist arrival data.“SFTIS 30 : A Decision Support System for Tourism Demand Analysis andForecasting”Petropoulos et al. (2003) believe that it is important to separate the decision byconsumers to travel in two separate stages. In the first stage, consumers decide whetherthey are going to travel or stay at home. In the second stage, those who have decided totravel choose a destination of interest. At each stage, different factors influence theconsumers’ decision to travel. This approach allowed Petropoulos et al. to include avariety of explanatory variables in the total system, a desirable attribute given thenumerous factors that affect the travel decision, but limit the number that appear in anyone equation in order to control statistical problems such as multicollinearity andheteroscedasticity.Relevance to Our ResearchUnlike the theoretical work quoted earlier, much of the empirical work noted in thissection focuses on the determination of tourism demand for a specific destination, ratherthan an examination of why tourists select one destination over others, the goal of thisproject. Consequently, the empirical research Global Insight reviewed seems of verylimited interest to this project. Lulendran and Witt’s look at the usefulness of errorcorrection models does emphasize the need to consider newly developed econometrictechniques in modelling tourist behaviour. Papatheodorou criticises many of the demandstudies for failing to take into account the degree to which the tourist travel market differsfrom the simple competitive situation; but his article is theoretic, and he does not offer hisown version of a model approach. A number of authors suggest that various time-seriesapproaches can be superior to a structural (econometric) demand model.30 Innovative decision support system used to forecast tourism demand for Greece.57
Bibliography:Baker, J., Grewal, D. and Parasuraman, A. (1994). “The Influence of Store Environmenton Quality Inferences and Store Image”. Journal of the Academy of Marketing Science,22(4), pp. 328-339.Buhalis, Dimitrios. (2000). “Marketing the Competitive Destination of the Future”.Tourism Management (21), pp. 97-116.Crouch, G. I., and Ritchie, J. R. Brent. (2000). “The Competitive Destination: ASustainability Perspective”. Tourism Management (21), pp. 1-7.Crouch, G. I., and Ritchie, J. R. Brent. (1999). “Tourism, Competitiveness, and SocietalProsperity”. Journal of Business Research (44), pp. 137-152.Echtner, C.M. and Ritchie, J. R. Brent. (1993). “The Measurement of Destination Image:An Empirical Assessment”. Journal of Travel Research (31), pp. 3-13.Ferrario, Francesco. (1976). “The Tourist Landscape: A Method of Evaluating TouristPotential and its Application to South Africa”. Ph.D. dissertation, Department ofGeography, University of California, Berkley.Jansen-Verbeke, Myriam and Johan Van Rekom. (1996). “Scanning Museum Visitors:Urban Tourism Marketing”. Annals of Tourism Research (23), pp. 364-375.Kim, C.W. (2000). “A Study on an Evaluation Model for Competitiveness of the TourismIndustry”. http://www.ktri.re.kr/eng/rep/a2000_02.htmKotler, P., Haider, D. H. and Rein, I. (1993). “Marketing Places”. New York: Free Press.Kulendran, Nada and Stephen F. Witt. (2001). “Cointegration Versus Least SquaresRegression”. Annals of Tourism Research (28), pp. 291-311.Lieper, Neil. (1990). “Tourist Attraction Systems”. Annals of Tourism Research (17), pp.367-384.Lim, Christine. (1997). “Review of International Tourism Demand Models”. Annals ofTourism Research (24), pp. 835-849.Lim, Christine and Michael, McAleer. (2001). “Forecasting Tourist Arrivals”. Annals ofTourism Research (28), pp. 965-977.Lew, Alan A. (1987). “A Framework of Tourist Attraction Research”. Annals ofTourism Research (14), pp. 553-575.Murphy, Peter et all (2000). “The Destination Product and Its Impact on TravellerPerceptions”. Tourism Management (21), pp. 43-52.Papatheodorou, Andreas. (2001).“Why People Travel to Different Places”. Annals ofTourism Research (28), pp. 164-179.Petropoulos, C. et all (2003).“SFTIS: A Decision Support System for Tourism DemandAnalysis and Forecasting”. Journal of Computer Information Systems.Piperoglou, John. (1966).“Identification and Definition of Regions in Greek TouristPlanning”. Regional Science Association 18: 169-76.58
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Table of ContentsI. EXECUTIVE SUMMA
Page 4 and 5:
I. Executive SummaryHighlightsThe r
Page 6 and 7:
Next StepsThis project has surfaced
Page 8 and 9:
IV.coefficients from these models p
Page 10 and 11: series, simple autoregressive and
Page 12 and 13: Arts & Culture Museums Historymuseu
Page 14 and 15: The tourist visitations data was al
Page 16 and 17: Hotel Room Count and Hotel Property
Page 18 and 19: Table 9: Michelin’s RatingsRating
Page 20 and 21: value below one suggests that the s
Page 22 and 23: Table 13: Individual Attractions Th
Page 24 and 25: Table 16: Significant Results 14Equ
Page 26 and 27: The adjusted R 2 for this model is
Page 28 and 29: • On average across all cities, b
Page 30 and 31: Model 5C 17 : In Model 5C, the numb
Page 32 and 33: The model overpredicts the number o
Page 34 and 35: Attraction Elasticities for Ottawa
Page 36 and 37: Table 22: Marketing Budget Elastici
Page 38 and 39: VII.Technical AppendixA. Attraction
Page 40 and 41: • Metropolitan Opera House in New
Page 42 and 43: Model 3: Three-Star Quality-Rated A
Page 44 and 45: Model 5B: Three-Star Quality-Rated
Page 46 and 47: 3. The cognitive perspective involv
Page 48 and 49: also found that almost half of the
Page 50 and 51: Also, the model seems to completely
Page 52 and 53: strengths and its weaknesses, devel
Page 54 and 55: Items from QuestionnaireTable 23: M
Page 56 and 57: Tourism Demand/Econometric Modellin
Page 58 and 59: Table 25: Types of Explanatory Vari
Page 62 and 63: Richards, Greg. (2002). “Tourism
Page 64 and 65: Figure 4: Overseas Visitors to U.S.
Page 66 and 67: Figure 6: Cities Person-Trips Volum
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