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. 2022 Dec 28;2022:4241907. doi: 10.1155/2022/4241907

Table 4.

Correlations between demographic variables, experience with 3D printing, UTAUT constructs, and general attitude.

Variables n b M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Demographic variables
(1) Age 119 35.64 12.69
(2) Gender (female vs. male) 117 n/a n/a -.04
(3) Country classification (developed vs. developing economy) 119 n/a n/a -.21 -.37∗∗
(4) Technology generation (3D virtual vs. software) 113 n/a n/a .83∗∗ -.06 -.13
(5) Early adopter status (1-5) 103 2.80 0.85 -.21 -.26∗∗ .37∗∗ .01

Experience with 3D printing
(6) Years of experience with 3D printing 101 13.33 11.36 .94∗∗ .08 -.33∗∗ .79 .04
(7) Number of objects printed 31 2.29 0.94 -.41 -.05 .02 -.09 -.14 -.45
(8) Self-reported level of experience with 3D printing (1-5) 37 2.54 1.17 -.14 -.03 .14 .05 -.05 -.02 .47∗∗
(9) Self-reported skill level regarding 3D printing (1-5) 37 2.54 1.07 -.12 -.27 .32 .06 .00 -.06 .28 .69∗∗

UTAUT constructs and general attitude
(10) Behavioural intention to use 3D printing in one's job (BIU) (1-5) 105 3.55 0.91 .09 -.20 .42∗∗ .07 .50∗∗ .01 .23 -.06 -.08
(11) Performance expectancy (PE) (1-5) 117 3.40 0.83 -.06 -.14 .47∗∗ -.19 .30∗∗ -.10 .08 -.01 -.06 .62∗∗
(12) Effort expectancy (EE) (1-5) 110 3.49 0.54 -.08 .00 .09 -.10 .03 -.14 .10 .00 .11 .23 .19
(13) Social influence (SI) (1-5) 110 2.76 0.73 .21 -.14 .28 .18 .48∗∗ .18 .00 -.04 -.09 .62∗∗ .60∗∗ .09
(14) Facilitating conditions (FC) (1-5) 107 3.21 0.63 .10 -.13 .23 -.06 .47∗∗ .07 .00 -.05 -.05 .62∗∗ .56∗∗ .15 .57∗∗
(15) General attitude towards using 3D printing in one's job (1-7) 100 5.63 1.11 .17 -.27∗∗ .26 .09 .34∗∗ .11 .21 .27 .26 .57∗∗ .61∗∗ .15 .54∗∗ .52∗∗

aGrey cells indicate significant correlations (p < .05; ∗∗p < .01). bThis column shows the number of participants who answered all scale items for the constructs.