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

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Model 5C 17 : In Model 5C, the number of visits was regressed on the number ofamusement parks with the three-star rating, the number of specific structures with thethree-star rating, the number of shopping areas with the three-star rating, the number ofhotel rooms and the marketing budgets. The adjusted R 2 for this model is 90%. For moredetails about this model see Technical Appendix Section B. The main conclusions of thismodel are as follows:• A smaller sample was used for this model, since the city-by-city marketingbudgets were only available for 33 cities.• On average across all cities, by building one three-star specific structure, thenumber of visitors will increase by 1.02 million.• On average across all cities, by building one three-star amusement park, thenumber of visitors will increase by 4.52 million.• On average across all cities, by building one three-star shopping area, the numberof visitors will increase by 690,000.• On average across all cities, by increasing a marketing budget by $1 million, thenumber of visitors will increase by 100,000.• On average across all cities, by increasing the number of hotel rooms by 100, thenumber of visitors will increase by 7,900.• Combination of amusement parks with specific structures (i.e. Statue of Libertyand CN Tower) and shopping areas supports the argument that tourists want to beentertained.• Quality-rated amusement parks, specific structures and shopping areas explainmore variation in visits than total count of these types of attractions.• Clustering of attractions is important. Combination of amusement parks withspecific structures and shopping areas produces higher adjusted R 2 than thesetypes of attractions taken individually.• Infrastructure is important in explaining tourist visitations since hotel roomcount was statistically significant.• Urban tourism marketing contributes to the increase in visits across 33 citiesincluded in the sample.J. Analysis of Model ResidualsIn the cross-sectional analysis, it is important not only to look at the adjusted R 2 andindividual t-statistics associated with both attraction and non-attraction variables, but alsoto examine the individual residuals (model errors) for all 50 North American citiesincluded in the sample. It is expected that the residuals would follow a random pattern.However, if the residuals are not random, then this may indicate that an importantvariable was omitted from the model.17 This is model 5 with the addition of the marketing budget variable.27
The conclusions presented in this section are based upon an analysis of Models 5 and 5B.(Please note that similar arguments can be made for Models 3 and 3B.)To analyze the residuals associated with 50 cities, the following formula was used:Residuals = Actual – Fitted (4)If the residuals were positive, the model under-estimated the number of visits.Conversely, if residuals were negative, the model over-estimated the number of visits.The residuals for the 50 North American cities are illustrated in terms of a percentage ofthe total visits. While the residuals for most cities fall within a similar range and lookrandomly distributed from one city to the next, the model overestimates visits for a groupof cities plotted at the right of the figure. Interestingly, seven out of ten Canadian citiesare part of this group.Figure 1: Residuals as a Percentage of Total Visits in Model 575%50%% of Total Visits25%0%Toronto0 10 20 30 40 50Ottawa-25%-50%-75%-100%-125%Halifax CalgaryQuebecDetroitEdmontonVictoriaFort LauderaleVancouver-150%City Ranking (in terms of Number of Visits)1-highest and 50-lowest in terms of # of visitsWinnipeg(50, -168%)Source: Global Insight, Inc.Table 17: Residuals as a Percentage of Total Visits in Model 5City # of Visits (In Millions) Residuals, % of # of VisitsVancouver 4.29 -111.72%Quebec City 3.37 -69.26%Detroit 3.26 -72.33%Fort Lauderale 2.51 -86.09%Victoria 1.70 -75.77%Edmonton 1.69 -61.31%Calgary 1.65 -54.59%Halifax 1.32 -57.64%Winnipeg 0.95 -168.64%Source: Global Insight, Inc.28
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 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 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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