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Predicting residents’ perceptions of cultural tourism attractiveness Table 3. City Service Dimensions Variables Varimax Rotated Loading Communality F1 F2 F3 F4 Factor 1 – Overall Urban Landscape No air pollution .773 .639 No noise pollution/quiet .765 .626 Slow pace/no rush/relaxed .718 .592 Not too much traffic/easy to get around .659 .576 Safety e.g. city reasonably trouble free .613 .564 Nice green city/beautiful parks and gardens .608 .473 Good size/right population size .588 .541 Good/well maintained roads .543 .487 Friendly people .513 .406 Well laid out and spacious .502 .538 Factor 2 – Leisure Services Attractive cultural venues .814 .751 Attractive historic venues .792 .708 Attractive sporting facilities .737 .628 Many attractions/activities/events/things to do .636 .532 Great place to shop/nice shopping malls .585 .568 Attractive buildings .535 .562 Good public transport .491 .408 Factor 3 – Hospitality Services Variety of restaurants .787 .726 Quality hotels .770 .765 Variety of hotels .767 .763 Quality restaurants .753 .730 Factor 4 – Transportation Related Quality of parking .776 .784 Affordable parking .767 .761 Reliable police presence .414 .437 Eigenvalue 10.05 2.37 1.23 .91 Variance (percent) 41.86 9.89 5.14 3.78 Cumulative variance (percent) 41.86 51.75 56.88 60.66 Cronbach’s alpha .886 .859 .887 .767 Summated Mean 2.55 2.02 2.20 2.60 Number of items (total=24) 10 7 4 3 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Tourism Today - Fall 2007 - Full Paper 133
134 Yao-Yi Fu, Amanda K. Cecil, Suosheng Wang, Sotiris H. Avgoustis For the 24 items, the test statistic for sphericity is large (12775.330) which is statistically significant at 0.001. The KMO measure of sampling adequacy of these variables was 0.943. The communality ranges from .406 to .784. Four factors were extracted from the factor analysis, explaining a total variance of 60.66%. These four factors were labeled as “Overall Urban Landscape” (F1); “Leisure Services” (F2); “Hospitality Services” (F3); and “Transportation Related” (F4). In terms of these factors’ summated mean scales, “Leisure Services” (F2) obtains the lowest mean scale of 2.02. The second lowest rated factor is “Hospitality Services” (F3) at 2.11, followed by F1 “Overall Urban Landscape” (F1) at 2.55, and finally “Transportation Related” (F4) at 2.60. In other words, “Leisure Services” (F1)was perceived most favorably by the Indianapolis residents, and “Transportation Related” (F4) as least favorable. PREDICTING OVERALL ATTRACTIVENESS WITH CITY SERVICE FACTORS In conducting the regression analysis, a composite variable labeled as “Indianapolis’ attractiveness as a cultural tourism destination” was created representing a cluster of five items measuring Indianapolis’ overall attractiveness as a cultural tourism destination, i.e. “I understand the concept of ‘cultural tourism’,” “Indianapolis has the potential to succeed as a cultural tourism destination,” “Indianapolis is an attractive destination for all visitors,” “I enjoy the cultural attractions the city has to offer,” and “visitors will enjoy the cultural attractions the city has to offer.” This composite variable was treated as a dependent variable in the regression model. Reliability test was conducted to measure the Cronbach’s Alpha which is .836, indicating a high internal consistency among these items. Independent variables used for the regression analysis are the four factors extracted from the city service items. As illustrated in Table 4, the regression equation model in predicting Indianapolis’ attractiveness as a cultural tourism destination indicates an adjusted R 2 of 0.317, which means that 31.8% of the total variance in the dependent variable can be explained by the independent variables in the model. The F-ratio of 110.27 is significant (Prob.< 0.01), indicating that there is a small possibility that the result of the equation model occurred by chance. The degree of variable collinearity is considered acceptable with the variance inflation (VIF) less than 10. All the t values for the partial correlations are statistically significant at the level of 0.001. As a result, all the four factors are found to be significant in predicting Indianapolis’s overall attractiveness as a cultural tourism destination. Table 4. Predicting Indianapolis’s Potential to Succeed as a Cultural Tourism Destination Sum of Squares df Mean Square F Sig. Regression 217.76 4 54.44 110.27 .001 Residual 463.59 939 .49 Total 681.35 943 R 2 =.320, Adjusted R 2 = .317 Tourism Today - Fall 2007 - Full Paper
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62 Jaume Garau-Taberner competitive
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Page 169 and 170: 168 The european journal of tourism
Page 171 and 172: 170 Contributors Amanda K. Cecil De
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