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

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• Metropolitan Opera House in New York• Montreal Opera2.2 Theatre• Princes of Wales• Royal Alexandra Theatre• Medieval Times2.3 Ballet• Toronto Dance Theatre• National Ballet of Canada• The American Ballet Theatre in New York2.4 Orchestra• Carnegie Hall• Toronto Symphony Orchestra• Montreal Symphony OrchestraAccommodation and Food1. Accommodation1.1 Luxury hotel rooms• Sutton Place• Windsor Arms• The Sheraton Centre2. Food2.1 High-end restaurants• 360 Revolving Restaurant• Far Niente• Canoe2.2 Food-related events and festivals• Taste of Little Italy• Wine Festival• Festival of Beer2.3 Range of restaurants• Little Italy• West Indian food• Chinatown37
B. Regression ResultsModel 1: One-Star Quality-Rated Popular Entertainment and Shopping AreasDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant1.110.711.56Popular Entertainment(Q1)0.600.730.83Shopping Areas (Q1)1.150.581.97Room Count0.00010.000027.04Adj. R-sq0.69S.E. of Reg.2.82Source: Global Insight, Inc.Model 2: Two-Star Quality-Rated Popular EntertainmentDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant1.400.682.07PopularEntertainment (Q2)Room Count0.520.00010.210.000022.498.42Adj. R-sq0.70S.E. of Reg.2.74Source: Global Insight, Inc.38
Page 2 and 3: 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 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 60 and 61: Results: Lim and McAleer found that
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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