Online banking adoption in Spanish cities and towns. Finding differences through TAM application

Figures & data

Table 1. Online banking use by population size.

Habitat by population size	Total inhabitants	16 to 74-year-old inhabitants	75-year-old + inhabitants
Total population	34,910,798	54.90%	6.60%
More than 100,000 inhabitants and provincial capitals	14,537,063	60.10%	10.10%
From 50,000 to 100,000 inhabitants	4,349,791	56.20%	7.50%
From 20,000 to 50,000 inhabitants	5,366,596	51.20%	3.70%
From 10,000 to 20,000 inhabitants	3,968,647	51.20%	4.50%
Less than 10,000 inhabitants	6,688,701	47.90%	2.10%

Source: Own elaboration. Based on data obtained from the Survey of equipment and use of ICT in households (National Institute of Statistics, 2019).

Figure 1. Hypothesised research model.

Source: Author.

Table 2. Survey features.

Category	Description
Study Number	3213
Area	National
Date of achievement	From 1 to 10 May 2018
Type of survey	Personal interview at home
Sample size	2,481
Sampling Points	256 municipalities and 49 provinces

Source: Sociological Research Centre (2019).

Table 3. Individual item reliability, construct reliability, and convergent validity.

Construct/Indicators	Outer Loadings	Cronbach’s Alpha	rho_A	Composite Reliability	AVE
Perceived Usefulness (PU)		1.000	1.000	1.000	1.000
PU.01	1.000
Perceived Ease of Use (PEOU)		0.774	0.802	0.868	0.688
PEOU.01	0.807
PEOU.02	0.787
PEOU.03	0.890
Perceived Security (PS)		0.799	0.833	0.868	0.624
PS.01	0.717
PS.02	0.825
PS.04	0.877
PS.05	0.728
Use Behaviour (UB)		1.000	1.000	1.000	1.000
UB.01	1.000
Control Variables
Sex	1.000	1.000	1.000	1.000	1.000
Age	1.000	1.000	1.000	1.000	1.000
Education	1.000	1.000	1.000	1.000	1.000
Economic Situation	1.000	1.000	1.000	1.000	1.000
Income Level	1.000	1.000	1.000	1.000	1.000

Note: rho_A: Dijkstra-Henseler’s indicator; AVE: average variance extracted.

Source: Author.

Table 4. Discriminant validity.

	PEOU	PS	PU	UB
PEOU
PS	0.215
PU	0.373	0.559
UB	0.291	0.516	0.484

Source: Author.

Table 5. Structural model results for towns.

R^2UB=0.349, R^2PU= 0.188, R^2PS=0.270
				95% BCCI
	Path coefficient	t-value	p-value	2.50%	97.50%	Statistical support
Direct effects
H1: PEOU → PU	0.434	12.305	0.000	0.365	0.502	Yes
H2: PU → UB	0.294	6.514	0.000	0.205	0.379	Yes
H3: PEOU → UB	0.167	4.576	0.000	0.099	0.241	Yes
H4: PU → PS	0.525	14.95	0.000	0.457	0.593	Yes
H5: PEOU → PS	−0.013	0.280	0.780	−0.098	0.076	No
H6: PS → UB	0.237	5.320	0.000	0.149	0.327	Yes
Indirect effects
H7: PEOU → PU → PS	0.228	9.632	0.000	0.184	0.276	Yes
H8: PU → PS → UB	0.125	4.884	0.000	0.077	0.177	Yes
H9: PEOU → PS → UB	0.178	7.417	0.000	0.132	0.229	Yes
Control variables
Gender → UB	−0.068	1.819	0.069	−0.141	0.006	No
Age → UB	−0.011	0.209	0.834	−0.117	0.091	No
Economic situation → UB	0.018	0.478	0.633	−0.056	0.094	No
Education → UB	−0.039	2.841	0.005	−0.066	−0.012	Yes
Income Level → UB	0.118	2.863	0.004	0.035	0.196	Yes

Source: Author.

Table 6. Structural model results for cities.

R^2UB=0.345, R^2PU= 0.082, R^2PS=0.252
				95% BCCI
	Path coefficient	t-value	p-value	2.50%	97.50%	Statistical support
Direct effects
H1: PEOU → PU	0.286	13.077	0.000	0.243	0.329	Yes
H2: PU → UB	0.264	11.587	0.000	0.219	0.308	Yes
H3: PEOU → UB	0.070	3.742	0.000	0.034	0.108	Yes
H4: PU → PS	0.495	28.719	0.000	0.462	0.529	Yes
H5: PEOU → PS	0.022	1.046	0.296	−0.019	0.064	No
H6: PS → UB	0.307	14.252	0.000	0.265	0.349	Yes
Indirect effects
H7: PEOU → PU → PS	0.142	11.812	0.000	0.119	0.166	Yes
H8: PU → PS → UB	0.152	12.601	0.000	0.129	0.176	Yes
H9: PEOU → PS → UB	0.126	10.076	0.000	0.102	0.151	Yes
Control variables
Gender → UB	0.037	1.881	0.060	−0.003	0.074	No
Age → UB	−0.031	1.169	0.243	−0.084	0.021	No
Economic situation → UB	0.020	1.015	0.310	−0.020	0.058	No
Education → UB	−0.010	1.281	0.200	−0.025	0.007	No
Income Level → UB	0.182	8.555	0.000	0.142	0.225	Yes

Source: Author.

Table 7. Nonparametric confidence set approach multi-group analysis (MGA).

	Path coefficients		95% BCCI (Towns)
	Towns	Cities	2.50%	97.50%	Statistical support
Direct effects
H1: PEOU → PU	0.434	0.286	0.365	0.502	Yes
H2: PU → UB	0.294	0.264	0.205	0.379	No
H3: PEOU → UB	0.167	0.070	0.099	0.241	Yes
H4: PU → PS	0.525	0.495	0.457	0.593	No
H5: PEOU →PS	−0.013	0.022	−0.098	0.076	No
H6: PS → UB	0.237	0.307	0.149	0.327	No
Indirect effects
H7: PEOU → PU → PS	0.228	0.142	0.184	0.276	Yes
H8: PU → PS → UB	0.125	0.152	0.077	0.177	No
H9: PEOU → PS → UB	0.178	0.126	0.132	0.229	Yes

Source: Author.

Figure 2. IPMA: Constructs’ standardised effects.

Source: Author.

National Institute of Statistics. (2019). Survey on equipment and use of information and communications technology in households. Year 2019. https://www.ine.es/prensa/tich_2019.pdf

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Sociological Research Centre. (2019). Report of activities 2018. http://www.cis.es/cis/export/sites/default/-Archivos/Memorias/MEMORIA_CIS_2018.pdf

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