formula F
table
= (k; n-k) , FF
table
=2,42 were ob-
tained. where k is the number of variables and n is
the number of respondent, so it can be concluded as
below:
a 0,000 < 0,05, then hypothesis 2 is accepted
b 84,846 > 2,42, then hypothesis 2 is accepted
The Figure 12 shows that the percentage of the
effect of the Quality of Information, Service Inter-
action Quality and Usability variables has a simul-
taneous effect of R Square, which is 57.8%, the re-
maining 42.2% is influenced by other variables out-
side the research. then the regression equation is
Y = a + b1X1 + b2X2 = b3X3 + b4X4, so Interest =
2, 058 + 0, 133 + 0, 103 + 0,224.
Figure 13: Multiple Linear Regression to Y3.Desire.
F
count
= 69,553 0,000
R Square = 0,529
According Figure 13, it is known that F
count
is
69,553 with a significance value of 0.000, from the
formula F
table
= (k; n-k) , FF
table
=2,42 were ob-
tained. where k is the number of variables and n is
the number of respondent, so it can be concluded as
below
a 0,000 < 0,05, then hypothesis 3 is accepted
b 69,553 > 2,42, then hypothesis 3 is accepted
The Figure 13 shows that the percentage of the
effect of the Quality of Information, Service Interac-
tion Quality and Usability variables has a simultane-
ous effect of R Square, which is 52,9%, the remaining
47,1% is influenced by other variables outside the re-
search.
Then the regression equation is Y = a + b1X1 +
b2X2 = b3X3 + b4X4, so Desire = 0, 030 + 0, 195 +
0, 343 + 0, 097
F
count
= 5,335 0.002
R Square = 0,079
According Figure 14, it is known that F
count
is
5,335 with a significance value of 0.000, from the for-
mula F
table
= (k; n-k) , FF
table
=2,42 were obtained.
where k is the number of variables and n is the number
of respondent, so it can be concluded as below :
a 0,002 < 0,05, hypothesis 4 is accepted
b 5,335 > 2,42, then hypothesis 3 is accepted
Figure 14: Multiple Linear Regression to Y4 Action.
The Figure 14 shows that the percentage of the
effect of the Quality of Information, Service Inter-
action Quality and Usability variables has a simul-
taneous effect of R Square, which is 7,9%, the re-
maining 92,1% is influenced by other variables out-
side the research. Then the regression equation is
Y = a + b1X 1 + b2X2 = b3X 3 + b4X 4, so Action =
2, 649 + (−0, 058) + 0, 119 +0, 045
7 CONCLUSION AND
RECOMMENDATION
Based on the results of the analysis and discussion
that have been explained, there are some conclusions
from this study. First, the results of this study show a
high valuation response to the stages of Attention, In-
terest, Desire. So that the effectiveness of the website
visitingjogja.com effectively only reaches the third
stage, which is Desire from the four stages of AIDA.
Second, there is the effect/influence of each area
on WebQual 4.0 to AIDA, this is evidenced by the
value of the Significancy of each step of less than
0.05. In addition, it is also known the percentage
of the influence of area variables on WebQual 4.0
on AIDA, which is Attention stage is 57.3%, Inter-
est is 57.8%, and Desire is 52.9%, this indicates the
influence of quality of information, service interac-
tion quality and usability affect more than half to the
stages of attention, interest, desire, and the rest is in-
fluenced by other factors outside this research. While
Action, the percentage of the influence of the We-
bqual 4.0 area is only 7.9%, this proves that the deci-
sion to take action is influenced by many factors out-
side of this research.
In the future, an increase in each of the WebQual
4.0 areas is needed, because this area has an influence
on the effectiveness of website e-tourism for promo-
tion side and besides that there needs to be research to
find out other factors that influence the effectiveness
of tourism promotion on website etourism.
Effectiveness of E-tourismWebsite as a Tourism Promotion Media using AIDA Model: A Study in Context of visitingjogja.com
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