4.4 Assessing research questions and hypothesizes
4.4.10 Research questions 14 to 21: path analysis to predict support for tourism
4.4.10 Research questions 14 to 21: path analysis to predict support for tourism
Examining the full model can be noted (Figure 4.2):
Community concern influences negatively and significantly the attitudes towards both PSEI (p=-.95) and PEI (p=-.170) and affects positively and significantly both NSEI (p=.737) and NEI (p=.471) also support for tourism development (p=.123).
Attachment to the community has a positive and significance influence on PSEI (p=.146), NESI (p=.085), PEI (p=.110) and NEI (p=.137) but no significant direct effect on support for tourism was found.
Utilization of tourism facilities has an direct influence on support for tourism development (p=.316) as well as PSEI (p= .301) and PEI (p=.385).
General understanding of economic benefits has no direct effect on support for tourism development but influence positively and significantly PSEI (p=.216) and PEI (p=.190) and negatively and significantly (NEI=-.090).
The perceived positive socioeconomic impacts influence positively and significantly support for tourism development (p=.513).
The perceived negative environmental impacts has negatively and significantly relationship with support for tourism development.
No significant relationship were found between both PEI and NSEI and support for tourism.
Figure 4.2 Fit model of support for tourism development
Support for tourism development
.316 e=.88
e=.85
.123
.515
-.097 -.095
.737
-.170 .147
.468
.081 .110 .140
-.085
.385 .301
.216 .190
.75
PSEI
NSEI
NEI CC
CA
UT ECRC
PEI
4.4.11 Decomposition of the correlation between exogenous variables and tourism development
Table3 4.30 to 4.34 illustrates the results of decomposition of the correlation between independent variables and support for tourism development.
The total influence of community concern on support for tourism development (.0597) is less than direct influence of this variable on the support for tourism (.123). The negative relationship of this variable with PSEI and the strongly positive influence on NEI which has a negative effect on support for tourism development reduced the total influence of community concern on support for tourism development (.0597).
Table 4.30 Decomposition of the correlation between community concern and support for tourism development
Community concern
Variable A
Effect of CC on perceived impacts
B
Effect on support for tourism development
(A×B) Indirect effect
Percentage of total effect on support for tourism
Community concern .123* 205.88
PSEI -.095 .515* -.0489 -81.89
NSEI .737* .029 .0213 35.77
PEI -.170* -.057 .0099 16.21
NEI .468* -.097* -.0453 -75.98
Total indirect effects -.0632
Total effects .0597 100
The strength and direction of the influence of community attachment on support for tourism development has been changed due to the indirect effect of this variable on PSEI (table 4.31). While the direct effect of community attachment on support for tourism is -.031 the total effect is .0272.
Table 4.31 Decomposition of the correlation between community attachment and support for tourism development
Community attachment
Variable A
Effect of CA on perceived impacts
B
Effect on support for tourism development
(A×B) Indirect effect
Percentage of total effect on support for tourism
Community attachment -.031 -113.95
PSEI .147* .515* .0757 278.28
NSEI .081* .029 .0023 8.63
PEI .110* -.057 -.0062 -23.04
NEI .140* -.097* -.0135 -49.91
Total indirect effects .0582
Total effects .0272 100
The direct relationship between utilization of tourism facilities by residences (.316) and support for tourism development accounts for 70.63% of total effect (.447) of this variable.
The indirect effects of this variable on PSEI explain the remaining effect (table 4.32.
Table 4.32 Decomposition of the correlation between utilization of tourism facilities and tourism development
Utilization of tourism facility and services by residents
Variable A
Effect of UT on perceived impacts
B
Effect on support for tourism development
(A×B) Indirect effect
Percentage of total effect on support for tourism
UT .316* 70.63
PSEI .301* .515* .1550 34.64
NSEI .046 .029 .0013 0.29
PEI .385* -.057 -.0219 -4.90
NEI -.031 -.097* -.0030 -0.672
Total indirect effects .1313
Total effects .4473 100
Table 4.33 Decomposition of the correlation between general understanding of economic benefits of tourism and support for tourism development
General understanding of economic benefits of tourism
Variable A
Effect of ECRC on perceived impacts
B
Effect on support for tourism development
(A×B) Indirect
effect
Percentage of total effect on support for tourism
ECRC - .008 - 6.92
PSEI .216* .515* .1112 96.26
NSEI -.038 .029 -.0011 -.95
PEI .190* -.057 -.0108 -9.37
NEI -.085* -.097* .0082 7.13
Total indirect effects .1075
Total effects 0.1155 100
The total effect of general understanding of tourism benefits of tourism on support for tourism (.115) is increased compare to its direct influence (.008) because of the indirect positive relationships with PSEI (table 4.33).
4.4.12 Path analysis to predict support for tourism development in reduced model
A multiple regression was run to predict support for tourism development from positive socioeconomic impacts, negative environmental impacts, community concern, community attachment, utilization of tourism facilities by residents and general understanding of economic benefits of tourism.
Some of the path coefficients for model were derived from multiple regression analyses (part 4.3.9) and multiple regression analyses for reduced model (table 4.34). For the reduced model three layers of multiple regressions were used:
Figure 4.3 shows the reduced model in which all the above-mentioned effects were regarded. For the reduced model three layers of multiple regressions were used:
1) With PSEI as criterion and community concern, community attachment, utilization of tourism facilities by residents and general understanding of economic benefits of tourism as predictors.
2) With NEI as criterion and community concern, community attachment, utilization of tourism facilities by residents and general understanding of economic benefits of tourism as predictors.
3) With support for more tourism development as criterion and CC, CA, UT, EB, PSEI and NEI as predictors.
There was independence of residuals, as assessed by a Durbin-Watson statistic of 1.919.
The assumptions of linearity, independence of errors, homoscedasticity, unusual points and normality of residuals were met. All variables statistically significantly predicted support for tourism development, F (4, 576) = 75.26, p < .05, adj. R2 = .44. Regression coefficients and standard errors can be found in table 4.34.
Table 4.34 multiple regression results of predictors for support for tourism in reduced model Sig.
Beta SE
B Variable
.725 .287
.086 (Constant)
.000 .494
.051 .684
Positive socioeconomic impacts
.008 -.095
.047 -.110
Negative environmental impacts
.000 .147
.048 .151
Community concern (CC)
.000 .299
.038 .314
Utilization of tourism services (UT)
a. Dependent Variable: support for tourism development
Note: Significant level at p<.05, R2 = .44, B=unstandardized regression coefficient, SE= standard error of the coefficient, Beta= standard coefficient
Figure 4.3 illustrates an input path diagram representing proposed reduced model.
Figure 4.3 Reduced model of support for tourism development
-.095 .468
.147
.140
.301 -.085 .216
e=.88
e=.85
.299 .494
-.095 .147
Support for tourism development CA
UT
e=.74 CC
ECRC
PSEI
NEI
Testing the reduced model involves comparing how well it fits the data compared to how well the full model fits the data.
Fit of the full model = 1- 0.89²×0.85²×0.75² = 0.678 Fit of the reduced model = 1- 0.882×0.852×0.752 = 0.676
The summary statistic showing the relative fit of the reduced model to the full model is
= = 0.9907
The redurced model like full model fit the data.
1 - Fit of full model 1 - Fit of reduced model
1 - 0.678 1 – 0.676