A Multi-Industry Analysis of the Future Use of AI Chatbots
Table 5
Regression analysis of predictors of behavioural intentions to use AI chatbots for mental health care, online shopping, and online banking in Australia.
Steps
Mental health care
Online shopping
Online banking
Adj.
B
SE B
95% CI
ß
Adj. R2
SE B
95% CI
ß
Adj.
SE B
95% CI
ß
1.
.075
.080
.051
Age
-.014
.343
-.021, -.006
-.198
-.014
.004
-.022. -.007
-.211
-.015
.004
-.022, -.007
-.220
Gender
.260
.130
.004, .516
.110
.252
.126
.003, .500
.109
.087
.127
-.162, .336
.039
PEK
.459
.190
.086, .832
.128
.424
.183
.064, .783
.122
.196
.183
-.164, .556
.058
2.
.639
.745
.658
Age
.000
.002
-.005, .004
-.006
-.002
.002
-.006, .002
-.023
-.001
.002
-.006, .003
-.019
Gender
.114
.082
.048, .275
.048
.057
.067
-.074, .189
.025
-.077
.076
-.227, .169
-.034
PEK
.075
.121
-.162, .312
.021
.296
.096
.106, .486
.085
-.048
.110
-.265, .169
-.014
PU
.810
.040
.731, .889
.777
.836
.031
.774, .898
.811
.762
.034
.695, .829
.786
PEOU
.020
.047
-.071, .112
.016
.106
.041
.026, .186
.079
.099
.044
.013, .185
.078
3.
.697
.774
.751
Age
.001
.002
-.004, .005
.011
.001
.002
-.003, .004
.010
-.002
.002
-.006, .002
-.030
Gender
.134
.075
-.013, .281
.057
.056
.063
-.067, .180
.024
.035
.066
-.095, .164
.015
PEK
.069
.110
-.147, .286
.019
.290
.091
.111, .469
.084
.012
.094
-.173, .198
.004
PU
.582
.045
.492, .671
.558
.643
.041
.562, .724
.624
.546
.036
.476, .617
.564
PEOU
.022
.043
-.062, .106
.018
.055
.040
-.022, .133
.041
.065
.037
-.009, .139
.051
PC
.038
.041
-.043, .118
.030
.071
.033
.006, .135
.064
.003
.046
-.087, .093
.002
Trust
.397
.048
.201, .492
.348
.332
.050
.234, .430
.298
.407
.045
.318, .497
.382
Note. B = unstandardised coefficients, SE = standard error, β = standardised coefficients, CI = confidence intervals. PEK = pre-existing knowledge, PU = perceived usefulness, PEOU = perceived ease of use, PC = privacy concerns. . .
