Table 2.
Structural equation modeling results predicting the intention to use the feedback tool (n=206).
| Effect | Feedback tool | |||
|
|
B (SE) | β (95% CI) | P value | |
| Direct effects (DVa=IUb) | ||||
|
|
PUc | 0.63 (0.11) | .51 (.30 to .72) | <.001 |
|
|
PEd | 0.06 (0.06) | .03 (−.09 to .15) | .59 |
|
|
SIe | 0.37 (0.07) | .32 (.19 to .46) | <.001 |
|
|
TRf | 0.06 (0.12) | .04 (−.19 to .27) | .72 |
|
|
CRg | 0.12 (0.05) | .12 (.02 to .22) | .02 |
|
|
TUh | −0.07 (0.05) | −.07 (−.18 to .03) | .16 |
|
|
PCi | −0.03 (0.05) | −.04 (−.13 to .06) | .42 |
|
|
ANXj | −0.18 (0.06) | −.18 (−.29 to −.07) | .001 |
|
|
Age | 0.00 (0.04) | −.01 (−.10 to .07) | .74 |
|
|
Genderk | −0.08 (0.04) | −.03 (−.12 to .05) | .48 |
|
|
Countryl | 0.04 (0.04) | .02 (−.07 to .11) | .66 |
| Direct effects (DVs=PU, PE, and TR) | ||||
|
|
TU→PU | 0.09 (0.08) | .12 (−.03 to .27) | .13 |
|
|
CR→PU | 0.04 (0.08) | .04 (−.12 to .20) | .60 |
|
|
TU→PE | 0.24 (0.06) | .45 (.32 to .57) | <.001 |
|
|
CR→PE | 0.02 (0.07) | .04 (−.11 to .18) | .62 |
|
|
TU→TR | 0.09 (0.08) | .13 (−.02 to .28) | .09 |
|
|
CR→TR | 0.07 (0.08) | .10 (−.06 to .26) | .22 |
| Indirect effects | ||||
|
|
TU→PU→IU | 0.06 (0.04) | .06 (−.02 to .14) | .16 |
|
|
TU→PE→IU | 0.01 (0.03) | .01 (−.04 to .07) | .59 |
|
|
TU→TR→IU | 0.01 (0.02) | .01 (−.02 to .04) | .73 |
|
|
CR→PU→IU | 0.02 (0.04) | .02 (−.06 to .10) | .60 |
|
|
CR→PE→IU | 0.00 (0.00) | .00 (−.01 to .01) | .71 |
|
|
CR→TR→IU | 0.00 (0.01) | .00 (−.02 to .03) | .73 |
aDV: dependent variable.
bIU: intention to use the tool.
cPU: perceived usefulness.
dPE: perceived ease of use.
eSI: social influence.
fTR: trust in the tool.
gCR: cognitive technology readiness.
hTU: tool understanding.
iPC: privacy concerns.
jANX: artificial intelligence anxiety.
kCode: 0=man and 1=woman and nonbinary.
lCode: 1=Germany and 0=English-speaking country.