Table 2. Factor loadings of measurement items

Construct Item Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Information analysis quality qua2 .861 .167 .082 .133 .153 .027
qua3 .764 .197 .125 .230 .170 .007
qua1 .749 .052 .216 .149 .099 .102
qua5 .715 .230 .210 .147 .109 –.021
qua4 .703 .101 .164 .088 .317 .006
AI preference pre2 .186 .828 .208 .207 .086 .013
pre3 .184 .786 .286 .254 .087 .002
pre1 .231 .743 .239 .246 .113 .021
pre4 .110 .723 .226 .279 .167 .045
Satisfaction sat5 .170 .220 .804 .209 .139 .077
sat4 .093 .248 .764 .259 .268 .103
sat3 .258 .210 .689 .234 .092 .058
sat2 .295 .295 .683 .198 .196 .017
sat1 .264 .378 .507 .331 .149 .079
Continuous usage intention cui2 .159 .292 .264 .820 .138 .045
cui4 .276 .318 .214 .776 .100 .006
cui3 .206 .260 .274 .752 .111 –.008
cui1 .257 .319 .377 .643 .123 –.004
Information processing quality qua8 .095 –.011 .054 .112 .889 .056
qua10 .184 .221 .173 .001 .777 .023
qua9 .159 .100 .187 .068 .760 .042
qua7 .292 .112 .139 .196 .716 .051
Innovativeness inn2 –.001 –.004 .072 .056 –.005 .788
inn4 –.010 –.012 –.037 .003 .001 .776
inn1 –.013 –.013 .016 .100 .126 .758
inn3 .134 .107 .144 –.143 .021 .752
Note. AI, artificial intelligence.