Ski – resort and regional development: profile of visitors ... - E-Journal

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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