Table 3.

Structural model results

Structural relationsHypothesisEffect
size (f²)
Path
coefficient
Standard
error
t-valuep-valueQ²
predict
R²
ajust.
PAC-ind → RAC-indH1(+)0.5580.6310.0639.890.0000.0780.436
Educa → RAC-indH4a(+)0.0040.0460.0710.640.520
Work Exp → RAC-indH4b(+)0.0000.0140.0710.190.845
Life Exp → RAC-indH4c(+)0.001−0.0250.0720.340.730
IM → RAC-indH7(+)0.0090.0800.0711.120.260
Educa → PAC-indH3a(+)0.0240.1390.0701.970.0470.1770.214
Work Exp → PAC-indH3b(+)0.0220.1430.0702.030.041
Life Exp → PAC-indH3c(+)0.0410.1910.0692.740.006
IM →PAC-indH6(+)0.0720.2590.0683.770.000
Educa → IBWH5a(+)0.002−0.0360.0720.490.6200.1810.503
Work Exp → IBWH5b(+)0.0080.0670.0710.940.346
Life Exp → IBWH5c(+)0.0230.1140.0701.610.106
RAC-ind → IBWH2(+)0.7510.6380.06310.00.000
AW*CAR-ind → IBWH9(+)0.0750.1930.0692.770.005
AW → IMH8(+)0.1370.3470.0675.140.000–0.116

Notes: VIF < 1.3, therefore, multicollinearity is not a problem for interpreting the relative importance of predictors. t-values and p-values were estimated from Kock’s (2018) standard error. Subtitles: AW = Autonomy at Work; PAC-ind = Potential individual absorptive capacity; RAC-ind = Absorptive capacity for individual achievement; Educa = Education; IBW = Innovative behavior at work; IM = Intrinsic motivation; Life Exp = Life experience; Work Exp = Work experience; Q2_predict = Predictive validity; R2 adj. = Adjusted R square

Source: Prepared by the authors

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