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

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series, simple autoregressive and “no change” models, to more complicated errorcorrectionmodels.Key FindingsThe most common, recurring themes evident in the literature review are identified in thefollowing summary points 4 . The key findings highlight the importance of:Promotion: Information available to the traveller prior to his departure, and thepresentation of this information, is important in determining the destination for manytravellers.Quality: The tourist is searching for a high-quality travel experience. Quality is shapedby all elements affecting the tourist at the site, and it is primarily by offering a highqualityexperience that destinations compete.Entertainment: The “entertainment value” of a destination is important to manytourists—even at museums and other “cultural” sites.Shopping Atmosphere: Shopping is an important element to a tourist, but it must help todeliver the “atmosphere” of the destination to the visitor.Tourism Infrastructure: The destination’s tourism infrastructure (generally speaking,elements that ease the tourists access to the destination—hotels, transportation, attitudesof locals towards tourists, etc.) is a very important contributor to the tourist’s overallexperience.Multiple Attractions: Complementary or interaction of multiple sites at the destinationis important.B. Selection of North American CitiesFor the purpose of this study, Global Insight selected the sample of 50 majormetropolitan areas 5 (MSA), including 40 U.S. and 10 Canadian cities. The selected citieseach offer a broad range of attractions by themselves, and do not rely on attractions nearthat are not a part of the metropolitan area. All 50 cities have a population of 500,000 ormore. Global Insight did not consider a number of cities that develop a significantamount of leisure visitation by virtue of one main type of attraction.4 Please refer to the Technical Appendix for a full coverage of Literature Review.5 In Canada, these areas are called census metropolitan areas (CMA).7
Canada (10)TorontoMontrealVancouverOttawa-HullCalgaryEdmontonQuebecWinnipegVictoriaHalifaxTable 2: List of Selected North American CitiesUnited States (40)OrlandoLos AngelesMilwaukeeLas VegasSeattleTampaAustinNew York CityPortlandBostonSan DiegoIndianapolisColumbusChicagoHoustonDetroitSan AntonioDallasSt. LouisWashingtonPhiladelphiaAtlantaNew OrleansMiamiKansas CitySalt Lake CityBaltimoreSan FranciscoNashvilleCincinnatiFort LauderaleOklahoma CityMinneapolisClevelandSacramentoDenverPittsburghCharlottePhoenixMemphisC. Developing an Attractions MatrixThe purpose of this study was to quantify the relative importance of a range of factorsthat influence tourists' decisions to visit a particular destination within North America. Inorder to achieve this goal, Global Insight developed an empirical scheme describing theattractions offered by each city. Global Insight's scheme also incorporated a combinationof the quantity of attraction (i.e. number of amusement parks) and the quality of theattraction (i.e. rating of an amusement park). Global Insight collected the city attractiondata in concordance with the structure of the attractions matrix.The attractions base can be defined as broadly as possible. The purpose of developing anattractions matrix is to include a wide range of attractions that are generally believed tostimulate tourist visitations. Therefore, a tourist attractions matrix was constructed for thefour most obvious categories that are known to draw tourists and are consistent acrosscities: Arts and Culture; Environment and Built Form; Entertainment; andAccommodation and Food. Each attraction category included several sub-categories,which were subsequently broken down to specific types of attractions. Once again, thesesub-categories and specific types of attractions are consistent across cities.8
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 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 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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