Tourists’ Experiences with Smart Tourism Technology at Smart Destinations and Their Behavior Intentions

Figure 1.
Impact of Smart Tourism Technology on Tourists’ Experience and Their Psychological Behavior.

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Journal of Travel Research 00(0)
SPSS AMOS 25. First, based on J. C. Anderson and Gerbing 
(1988), the measurement model in application of confirma-
tory factor analysis (CFA) assessed the quality of measure-
ment. Second, SEM by the maximum likelihood estimator 
tested the hypothesized relationships between the latent vari-
ables. Provided the chi-square was sensitive to sample size 
(Jöreskog and Sörbom 1993), several fit indices helped eval-
uate the goodness of the model and they included compara-
tive fit index (CFI), normed fit index (NFI), incremental fit 
index (IFI), and root mean square error of approximation 
(RMSEA). CFI, NFI, and IFI values greater than 0.90 and an 
RMSEA value smaller than 0.08 are indicative of a good 
model fit (Hair et al. 1995). Tests of hypotheses 2a to 2d, 
which addressed whether the level of security/privacy would 
vary according to the interaction effect of STT attributes and 
memorable experience (high vs. low groups), employed a 
multigroup approach. The multigroup analysis method could 
test whether the same path model was tenable across the dif-
ferent groups of respondents. That is, if there was any signifi-
cantly different causal path between two groups (i.e., high 
vs. low groups), there is a stronger (or weaker) causal rela-
tionship between STT attributes and memorable experience 
in one group than the other.