Enhancing satisfaction and word of mouth of young mobile banking users through system quality and individual performance

Figures & data

Figure 1. Research model.

Source: Developed from HEM Model and Signaling theory.

Table 1. Variables and items of the research model.

Variables	Code	Items	Factor loadings	Source
1. System quality (SYSQ)	SYSQ1	Mobile banking (MB) app is easy to navigate	0.850	Tam and Oliveira (2017)
	SYSQ2	MB app allows me to easily find the information I am looking for	0.873
	SYSQ3	MB app is well structured	0.908
	SYSQ4	MB app is easy to use	0.885
	SYSQ5	MB app offers appropriate functionality	0.864
2. Individual performance (IP)	IP1	The MB enables me to accomplish tasks more quickly	0.936	Tam and Oliveira (2017)
	IP2	The MB makes it easier to accomplish tasks	0.952
	IP3	The MB is useful for my job	0.937
3. Customer Satisfaction (SA)	SA1	I am satisfied that MB meets my needs	0.882	Sharma and Sharma (2019); Tam and Oliveira (2017)
	SA2	I am satisfied with the effectiveness of MB	0.935
	SA3	I am satisfied with the performance of MB	0.880
	SA4	In general, I’m satisfied with the mobile banking service.	0.902
4. Word of Mouth (WoM)	WoM1	I will talk about the power of MB with people I know	0.875	Mehrad and Mohammadi (2017)
	WoM2	I will talk about MB in a rather positive way	0.900
	WoM3	If you ask me about mobile baking, I’d certainly recommend it	0.893

Table 2. Survey sample characteristics.

Demographic	Categories	Frequency	Percentage
Gender	Male	106	35.7%
	Female	191	64.3%
Total		297	100
Age	18–30	261	87.9%
	31–40	36	12.1%
Total		297	100
Income per month (million of VND)	<10	210	71.4%
	11–20	53	17.8%
	21–30	13	4.4%
	31–40	11	3.7%
	>41	8	2.7%
Total		297	100
Mobile banking Experience	1–5 years	256	86.2%
	>5 years	41	13.8%
Total		297	100
Education Level	High school or lower	32	10.8%
	Bachelor	242	81.5%
	Master	18	6.1%
	Doctor/PhD	5	1.7%
Total		297	100
Intensity of using mobile banking	Everyday	81	27.3%
	Every week	135	45.5%
	Every month	78	26.3%
	Others	3	1.0%
Total		297	100

Table 3. Measurement model assessing results.

Constructs and associated Items	Outer loading	Cronbach’s alpha	CR	AVE
Customer Satisfaction		0.922	0.923	0.810
SA1	0.882
SA2	0.935
SA3	0.880
SA4	0.901
System quality		0.924	0.925	0.768
SYSQ1	0.850
SYSQ2	0.873
SYSQ3	0.908
SYSQ4	0.885
SYSQ5	0.864
Individual performance		0.936	0.937	0.887
IP1	0.936
IP2	0.952
IP3	0.937
Worth of Mouth		0.868	0.872	0.791
WoM1	0.875
WoM2	0.900
WoM3	0.893

Figure 2. Measurement model.

Table 4. Discriminant validity (Fornell and Larcker).

	Customer’s Satisfaction	Individual Performance	System quality	Word-of-Mouth
Customer’s Satisfaction	0.900
Individual Performance	0.753	0.942
System quality	0.657	0.556	0.876
Word-of-Mouth	0.705	0.59	0.616	0.889

Table 5. Discriminant validity (HTMT).

	Customer’s Satisfaction	Individual Performance	System quality	Word-of-Mouth
Customer’s Satisfaction
Individual Performance	0.809
System quality	0.711	0.599
Word-of-Mouth	0.786	0.654	0.689

Table 6. Structural Model evaluation and hypothesis testing results.

Hypothesis	P	T Values	P values	Confidence Interval Bias Corrected		Supported
				LL	UL
H1. Customer Satisfaction → Word of Mouth	0.705	21.735	<0.01	0.645	0.753	YES
H2. System Quality → Customer Satisfaction	0.345	6.407	<0.01	0.254	0.432	YES
H3. Individual Performance → Customer Satisfaction	0.561	10.724	<0.01	0.469	0.642	YES
H4a. System Quality → Customer Satisfaction → Word of Mouth	0.243	5.762	<0.01	0.163	0.328	YES
H4b. Individual Performance → Customer Satisfaction → Word of Mouth	0.395	9.837	<0.01	0.316	0.473	YES

Table 7. Effect size (f²) and R².

	f^2	VIF	Adjusted R Square
Customer Satisfaction – Word of Mouth (SA-WOM)	0.988	1.000
Individual Performance – Customer Satisfaction (IP-SA)	0.617	1.448
System quality – Customer Satisfaction (SYSQ-SA)	0.234	1.448
Customer Satisfaction (SA)			0.646
Word of Mouth (WOM)			0.495

Figure 3. Structural model.

Table

Factor	Initial eigenvalues			Extraction sums of squared loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	16.861	45.571	45.571	16.355	44.202	44.202
2	2.903	7.847	53.418
3	2.244	6.064	59.482
4	1.582	4.274	63.757
5	1.392	3.761	67.518
6	1.086	2.934	70.452
7	1.052	2.842	73.294
8	.868	2.346	75.640
9	.816	2.206	77.846
10	.780	2.108	79.954
11	.635	1.716	81.670
12	.577	1.559	83.230
13	.528	1.428	84.657
14	.479	1.295	85.952
15	.430	1.162	87.114
16	.372	1.005	88.119
17	.364	.983	89.101
18	.335	.906	90.008
19	.321	.867	90.875
20	.291	.786	91.661
21	.282	.761	92.422
22	.277	.749	93.171
23	.248	.671	93.841
24	.233	.631	94.472
25	.218	.590	95.061
26	.210	.568	95.629
27	.199	.537	96.167
28	.192	.520	96.687
29	.172	.466	97.153
30	.164	.443	97.596
31	.158	.427	98.022
32	.153	.413	98.435
33	.140	.379	98.814
34	.128	.347	99.161
35	.115	.310	99.470
36	.103	.278	99.749
37	.093	.251	100.000

Extraction Method: Principal Axis Factoring.

Tam, C., & Oliveira, T. (2017). Understanding mobile banking individual performance: The DeLone & McLean model and the moderating effects of individual culture. Internet Research, 27(3), 538–562. https://doi.org/10.1108/IntR-05-2016-0117

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Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44(September 2018), 65–75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013

 Google Scholar

Mehrad, D., & Mohammadi, S. (2017). Word of Mouth impact on the adoption of mobile banking in Iran. Telematics and Informatics, 34(7), 1351–1363. https://doi.org/10.1016/j.tele.2016.08.009

 Web of Science ®Google Scholar