Investigating university students’ intention to use mobile learning management systems in Sweden

Figures & data

Figure 1. Research model.

Table 1. Factor loadings, composite reliability, and AVE for each construct.

Construct	Item	Factor Loading	CR	Cronbach’s Alpha	AVE
PEOU	PEOU1	0.886	0.90	0.84	0.76
	PEOU2	0.909
	PEOU3	0.813
PU	PU1	0.848	0.89	0.82	0.73
	PU2	0.800
	PU3	0.904
ATT	ATT1	0.883	0.87	0.70	0.77
	ATT2	0.869
BI	BI1	0.927	0.92	0.83	0.86
	BI2	0.925
AR	AR1	0.878	0.89	0.74	0.80
	AR2	0.904
UMS	UMS1	0.947	0.94	0.88	0.90
	UMS2	0.944
PMV	PMV1	0.898	0.91	0.81	0.84
	PMV2	0.933

Table 2. Discriminant validity.

	AR	ATT	BI	PMV	PU	PEOU	UMS
AR	0.891
ATT	0.620	0.876
BI	0.719	0.706	0.926
PMV	0.479	0.731	0.637	0.916
PU	0.641	0.746	0.650	0.626	0.851
PEOU	0.467	0.606	0.550	0.592	0.544	0.870
UMS	0.592	0.678	0.691	0.695	0.586	0.475	0.946

Note: Diagonals represent the average variance extracted, while the other matrix entries represent the squared correlations.

Table 3. Test of hypotheses based on path coefficient.

Hypothesis	Path coefficients	Result
H1: PEOU->PU	0.150	Not Supported
H2: PEOU->ATT	0.285**	Supported
H3: PU-> BI	0.276**	Supported
H4: PU->ATT	0.588**	Supported
H5: ATT-> BI	0.501**	Supported
H6: PMV->PU	0.294**	Supported
H7: AR-> PU	0.382**	Supported
H8: UMS->PU	0.082	Not supported
H9: UMS->PEOU	0.475**	Supported

*P < 0.05 **P < 0.01.

Figure 2. Structural measurement model.