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

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