Factors influencing acceptance of Robo-Advisors for wealth management in Malaysia

Figures & data

Figure 1. Theoretical Framework of Study.

Table 1. Convergent Validity, Internal Consistency Reliability, and Discriminant Validity

Latent Variable	Indicators	Convergent Validity			Internal Consistency Reliability		Discriminant Validity
		Loadings	Indicator Reliability	AVE	Composite Reliability	Cronbach’s Alpha
		>0.7	>0.5	>0.5	0.6–0.9	0.6–0.9	HTMT confidence interval does not include 1
BIA	AC1	0.937	0.878	0.845	0.916	0.819	Yes
	AC2	0.902	0.814
EE	EE2	0.935	0.874	0.848	0.918	0.822	Yes
	EE4	0.906	0.821
PR	PR1	0.904	0.817	0.797	0.94	0.919	Yes
	PR2	0.888	0.789
	PR3	0.850	0.723
	PR5	0.927	0.859
RA	RA1	0.872	0.760	0.697	0.901	0.853	Yes
	RA2	0.890	0.792
	RA3	0.771	0.594
	RA4	0.800	0.640
SI	SI1	0.717	0.514	0.695	0.901	0.852	Yes
	SI2	0.868	0.753
	SI3	0.864	0.746
	SI4	0.876	0.767

Table 2. Significance Testing Results of the Structural Model Path Coefficients

	Original Sample (O)	T Statistics (|O/STDEV|)	P-values	95% Confidence Interval	Significant (p < 0.05)?
EE -> BIA	0.217	2.700	0.007	[0.072, 0.393]	Yes
PR -> BIA	−0.091	1.010	0.312	[−0.236, 0.119]	No
RA -> BIA	0.405	3.843	0.000	[0.174, 0.587]	Yes
SI -> BIA	0.340	3.776	0.000	[0.165, 0.523]	Yes

Table 3. Predictive Power and Predictive Relevance Assessment of The Proposed Model

	Predictive Power		Predictive Relevance
	R Square	Status	Q Square	Status
BIA	0.636	Moderate	0.496	Large

Table 4. Predictive Relevance and Size

	q^2 Effect Sizes	Size of Predictive Relevance
EE—> BIA	−0.0018	Small
PR—> BIA	0.0000	None
RA—> BIA	0.0018	Small
SI—> BIA	−0.0037	Small