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

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Table 16: Significant Results 14Equation Type of Attraction Type of SecondAttractionAttraction and Non-Attraction VariablesAdj. R 2(1) PopularEntertainment (Q1)ShoppingAreas(Q1)Room Count 0.69(2) PopularEntertainment (Q2)- Room Count 0.70(3) Amusement Parks(Q3)Specific Structures(Q3)Casinos (TC),Shopping Areas (Q3)& Properties Count0.76(3B)Amusement Parks(Q3)Specific Structures(Q3)Casinos (TC),Shopping Areas (Q3),Properties Count &Pop. Density0.84(4) Amusement Parks(Q3)Specific Structures(Q3)Casinos (TC), PublicTransportation Score& Pop. Density0.78(5) Amusement Parks(Q3)Specific Structures(Q3)Shopping Areas (Q3)& Room Count0.88(5B)Amusement Parks(Q3)Specific Structures(Q3)Shopping Areas (Q3),Room Count & Pop.Density0.90(5C)Amusement Parks(Q3)Specific Structures(Q3)Shopping Areas (Q3),Room Count &Marketing Budgets0.90Source: Global Insight, Inc.I. Interpretation of Regression ResultsResults from our analysis support many of the common themes found in the literaturereview. Empirically, Global Insight was able to confirm the following propositions fromthe literature review.• Tourists are looking for a “quality” experience, not merely to visit a site.• Combinations of quality-rated attractions explain more variation in visits thantotal count of these types of attractions.• Clustering plays an important role. Interaction of multiple sites at the destinationis crucial.• Combination of quality-rated attractions produces higher adjusted R 2 than thesetypes of attractions taken individually.• Infrastructure is important. Sufficient tourist infrastructure is necessary tomaximize tourists’ participation in and enjoyment of the destination.• Hotel room and property count were statistically significant.14 Q3 = three-star rating, Q2 = two-star rating, Q1 = one-star rating, and TC = total count.21
• Public transportation score obtained from “Places Rated Almanac” publicationwas marginally significant.• Urban Tourism Marketing plays an important role in attracting more tourists.Tourism marketing contributes to the increase in visits across 33 cities included inthe sample.• Tourists want to be entertained. For example, the combination of casinos withamusement parks and specific structures (i.e. Statue of Liberty or CN Tower)supports the argument that tourist wants to be entertained.• Quality shopping is an important part of the tourism experience. Quality-ratedshopping areas explain more variation in visits than total count of this type ofattractions.Regression-Specific ConclusionsIn this section, Global Insight addresses the regression-specific results for fiveeconometric models.Model 1: In Model 1, the number of visits was regressed on the number of popularentertainment types of attractions with the one-star rating, the number of shopping areaswith the one-star rating, and the number of hotel rooms. The adjusted R 2 for this model is69%. For more details about this model see Technical Appendix Section B. The mainconclusions of this model are as follows:• On average across all cities, by building a one-star attraction from a popularentertainment category, the number of visitors will increase by 600,000.• On average across all cities, by building a one-star shopping area, the number ofvisitors will increase by 1.15 million.• On average across all cities, by increasing the number of hotel rooms by 100, thenumber of visitors will increase by 10,000.• Combination of one-star attractions from the popular entertainment category withone-star shopping areas supports the argument that tourists want to beentertained.• Quality-rated shopping areas and attractions from the popular entertainmentcategory explain more variation in visits than total count of these types ofattractions.• Clustering of attractions is important. Combination of quality-rated shoppingareas and attractions from the popular entertainment category produces higheradjusted R 2 than these types of attractions taken individually.• Infrastructure is important in explaining tourist visitations since hotel roomcount was statistically significant.Model 2: In Model 2, the number of visits was regressed on the number of popularentertainment types of attractions with the two-star rating and the number of hotel rooms.22
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 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 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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