Breaking boundaries: unveiling hurdles in embracing internet banking services in Sub-Saharan Africa

Figures & data

Table 1. Summary of selected e-banking studies.

References	Model used	Context	Constructs
Thaker et al. (2021)	UTAUT 2	The contextual focus of the research was Malaysia, based on 319 respondents. The structural equation modelling (SEM) method was used for data analysis.	Performance expectancy Effort expectancy Social influence Facilitating conditions Price value Hedonic motivation
Safari et al. (2020)	TAM	The contextual focus of the research was the Democratic Republic of Congo, based on 215 respondents. The structural equation modelling (SEM) method was used for data analysis.	Perceived usefulness Perceived ease of use Perceived web security Internet trust
Sharma and Govindaluri (2014)	Extended TAM	The research was conducted in India based on 314 respondents. The structural equation modelling (SEM) method was used for data analysis.	Perceived usefulness Perceived ease of use Social influence Awareness Quality of Internet connection Computer self-efficacy
Rahi and Ghani (2019)	DeLone and McLean IS success model and self-determination theory (SDT)	The research was conducted in Pakistan based on 750 respondents. The structural equation modelling (SEM) method was used for data analysis.	Information quality Service quality Satisfaction Introjected regulation External regulation Intrinsic regulation Identified regulation
Rawashdeh (2015)	Extended TAM	The research was conducted in Jordon based on 298 respondents. The structural equation modelling (SEM) and SPSS method were used for data analysis.	Perceived usefulness Perceived ease of use Perceived web privacy (PWP) Attitude Intention
Arora and Sandhu (2018)	UTAUT and SERQUAL	The research was conducted in India and involved 524 respondents. The multiple regression analysis (MRA) method was used for data analysis.	Effort expectancy Performance expectancy Information quality Service quality Self-interest Experience
Sánchez-Torres et al. (2018)	UTAUT with modification	The research was conducted in Colombia and involved 600 respondents. The structural equation modelling (SEM) method was used for data analysis.	Performance expectancy Effort expectancy Government support Trust
Yaseen and El Qirem (2018)	UTAUT 2 with modification	The research was conducted in Jordon and involved 348 respondents. The data analysis method used was multiple regression.	Performance expectancy Effort expectancy Social influence Hedonic motivation Service quality Perceived ease of use
Wang et al. (2017)	UTAUT with modification	The research was conducted in China based on 181 respondents. The data analysis method used was multiple regression.	Performance expectancy Effort expectancy Personalisation Compatibility
Lee and Kim (2020)	Benefit-risk and network externality framework	The research was conducted in South Korea and involved 672 respondents. The data analysis method used was multiple regression.	Convenience Economic efficiency Functional risk Security risk Critical mass Number of services Trust

Figure 1. Research framework.

Source: Authors’ construct.

Table 2. Summary of respondents’ profile.

Variable category		Frequency	Percent (%)
Mode of administration	Offline (hard copy)	433	64.43
	Online (soft copy)	239	35.56
Gender	Male	423	67.7
	Female	241	31.4
	Prefer not to say	6	0.9
Age	18–25yrs	162	24.1
	26–30yrs	165	24.6
	31–35yrs	273	40.6
	36–40yrs	63	9.4
	41yrs and Above	9	1.3
Educational level	Basic & SHS level	60	8.9
	Undergraduate	339	50.4
	Postgraduate	252	37.0
	Others	21	3.1
Citizenship status	Ghanaian	663	98.7
	A foreign resident in Ghana	9	1.3
Respondent’s geographical location	Northern/North East/Savanna Region	36	5.4
	Upper East Region	18	2.7
	Upper West Region	3	.4
	Bono/Bono East/Ahafo Region	111	16.5
	Ashanti Region	144	21.4
	Eastern Region	15	2.2
	Greater Accra Region	201	29.9
	Western/ Western North Region	42	6.3
	Volta/Oti Region	18	2.7
	Central Region	84	12.5
Employment status/sector	Formal sector	387	57.6
	Informal sector	84	12.5
	Student	114	17.0
	Others	87	12.9
Respondent’s income level	Less than 500 GHS	63	9.4
	Between 500 to 1,000 GHS	228	33.9
	Between 1,100 to 2,000 GHS	201	29.9
	Between 2,100 to 3,000 GHS	78	11.6
	Between 3,100 to 4,000 GHS	45	6.7
	Above 4,000 GHS	57	8.5
Online visitation (to buy/enquire a product/service before)	Yes	534	79.5
	No	138	20.5
Use of online banking service	Yes	534	79.5
	No	138	20.5
The medium of Internet access	Own Laptop	90	13.4
	Home/Personal PC	9	1.3
	School/Office PC	9	1.3
	Smartphone/Mobile Phone	558	83.0
	Cybercafé PC	6	0.9
The frequency of Internet use	Daily	552	82.1
	Weekly	36	5.4
	Once or twice a month	33	4.9
	Once or twice in 3 months	39	5.8
	Few times in a year	9	1.3
	Never	3	0.4
Ever use the Internet to buy a product/service before	Yes	532	79.2
	No	140	20.8
Ever use the Internet to transfer/deposit before	Yes	459	68.3
	No	213	31.7
Frequent bank transacted with	Ecobank Ghana Ltd.	123	18.4
	Agricultural Development Bank (ADB)	24	3.6
	Zenith Bank Ghana	21	3.1
	Ghana Commercial Bank (GCB) Ltd.	177	26.3
	Fidelity Bank Ghana Ltd	57	8.5
	Barclays Bank Ghana Ltd.	39	5.8
	Access Bank Ltd.	27	4.0
	Bank of Africa Ghana Ltd.	15	2.2
	CAL Bank Ghana	36	5.4
	Prudential Bank Ltd.	27	4.0
	National Investment Bank Limited (NIB)	84	12.5
	Other	42	6.3
Years transacted with current bank	Less than 1 year	96	14.3
	1–3 yrs	177	26.3
	4–10 years	282	42.0
	11–15 years	81	12.1
	more than 15 years	36	5.4
Debit/Credit card used	Master Card	81	12.1
	Visa Card	255	37.9
	ATM Card	198	29.5
	e-Zwich Card	51	7.6
	Other	87	12.9
	Total (n)	672	100.0

Source: Author’s field data from Ghana (January to September 2020).

Table 3. Construct reliability and convergent validity.

Construct	Dijkstra-Henseler’s rho (ρA)	Jöreskog’s rho (ρc)	Cronbach’s alpha(α)	Average variance extracted
Fear of finance loss	0.9697	0.9263	0.8973	0.7589
Fear of reputation damage	0.9702	0.9411	0.9329	0.8002
Avoidance motivation	0.8841	0.8758	0.7815	0.7815
Price of digital device	0.8302	0.8982	0.8299	0.7463
Perc-Knowledge gap	0.9631	0.9723	0.9573	0.9212
INTENT to E-Banking	0.9303	0.9389	0.9192	0.7549
Infrastructure gap	0.8920	0.9222	0.8877	0.7478
Perc. financial charge	0.9027	0.8989	0.8390	0.7483

Source: Authors’ computation from ADANCO 2.2.1.

Table 4. Item loading and Variance inflation factor (VIF).

Construct	ITEMS	Loadings	VIF
Fear of financial loss	FOFL1: I am afraid that somebody could steal my money while I am transferring my personal data online	0.8057	1.8171
	FOFL2: I am scared that a criminal could use my credit card account number to do online shopping in my name	0.8952	3.3081
	FOFL3: I am frightened that somebody could do online shopping at my expense	0.8753	3.5141
	FOFL4: I am worried about an unauthorised person making online purchases using my personal data	0.9050	2.7201
Fear of reputational damage	FORD1: I am frightened of somebody using my personal data on the Internet in order to run me down	0.9394	2.4585
	FORD2: I am very worried that the unauthorised use of my personal data online could damage my reputation	0.9116	4.0676
	FORD3: I am worried about my reputation being damaged due to the illegal use of my personal data online	0.8867	5.3863
	FORD4: The thought that a stranger could damage my reputation by using my personal data online scares me.	0.8374	4.2139
Avoidance motivation	AVDMOT2: I am scared that when I have to give my bank account number to shop online that it could be misused	0.7732	1.6987
	ADVMOT3: I am scared that my bank account could be hacked by an unknown person	0.9824	1.6987
Price of digital device	PDD1: I would easily buy a digital device if I am offered a price discount	0.8845	2.2304
	PDD2: I am generally concerned about the price of digital devices	0.8599	1.8994
	PDD3: The lower prices I get a digital device, the more attracted I will engage/transact in online banking	0.8469	1.7694
Perceived knowledge gap	PKG/DG1: I will use Internet banking when I have adequate knowledge about its usefulness	0.9466	4.9884
	PKG/DG2: I will engage in Internet banking transactions when I have adequate knowledge about its convenience.	0.9713	8.0307
	PKG/DG3: I will use internet banking if I have much adequate knowledge about how to use it	0.9613	6.0018
Intent to e-banking	INTENT1: I would use Internet banking for purchasing a product/service	0.8755	3.1462
	INTENT2: I would always consider using Internet banking for my e-shopping	0.8045	2.9459
	INTENT3: Given the chance, I would try to buy items online via Internet banking	0.8976	4.1103
	INTENT4: In the future, I will most likely be using the Internet banking for payments.	0.9050	4.5280
	INTENT5: Using Internet banking for transfer/deposit is something I would do	0.8578	3.1732
Infrastructure	INFRAST1: In my opinion, I think lack of government support infrastructure in Information Communication Technology (ICT) sector negatively affects easy internet accessibility	0.8742	2.6860
	INFRAST2: I think the inability of the government to create more awareness of the benefits of ICT (INTERNET) usage for all citizens negatively affects online-related transactions.	0.8926	3.1426
	INFRAST3: I think lack/inadequate internet access to all areas in the country as well as FREE WIFI in schools and recreation centres affect my willingness to engage in online-related transactions	0.8461	2.2810
	INFRAST4: I think the Internet and other devices such as computers and smartphones prices should be moderate to be afforded by giving subsidies to dealers/operators in the Telcom industry	0.8453	2.0949
Perceived financial charge	PFC1: I won’t use an online transaction when the charging fee is high	0.8927	1.8454
	PFC2: Engaging in Internet banking may attract some deduction from my bank account	0.9068	2.4857
	PFC3: Using Internet banking would affect the amount of my deposit/savings with unusual fee	0.7911	1.9663

Source: Authors’ computation from ADANCO 2.2.1.

Table 5. Discriminant validity using HTMT(Heterotrait–Monotrait ratio).

Construct	1	2	3	4	5	6	7	8
1. Fear of finance loss
2. Fear of reputation damage	0.7787
3. Avoidance motivation	0.5308	0.5265
4. Price of digital device	0.1534	0.1664	0.2021
5. Perc-Knowledge gap	0.2253	0.2859	0.4139	0.4070
6. Intent to E-Banking	0.1242	0.0577	0.0254	0.4892	0.4060
7. Infrastructure gap	0.2153	0.2431	0.4137	0.3565	0.4715	0.4039
8. Perc. financial charge	0.3067	0.2124	0.3666	0.3153	0.3957	0.2871	0.5631

Source: Authors’ computation from ADANCO 2.2.1.

Table 6. PLS-SEM-Hypothetical path.

Effect	Beta (β)	M	SD	T-value	P-value	Decision
H1: Fear of finance loss -> INTENT to E-Banking	−0.1756	−0.1908	0.0432	−4.0599	0.0000	Supported
H2: Fear of reputation damage -> INTENT to E-Banking	−0.0726	−0.0710	0.0622	−1.1678	0.2430	Not supported
H3: Avoidance motivation -> INTENT to E-Banking	−0.0965	−0.0815	0.0480	−2.0104	0.0444	Supported
H4: Price of digital device -> INTENT to E-Banking	0.2973	0.2994	0.0451	6.5917	0.0000	Supported
H5: Perc-Knowledge gap -> INTENT to E-Banking	0.2476	0.2387	0.0407	6.0890	0.0000	Supported
H6: Infrastructure gap -> INTENT to E-Banking	0.2162	0.2121	0.0446	4.8511	0.0000	Supported
H7: Perc. financial charge -> INTENT to E-Banking	0.0843	0.0823	0.0419	2.0107	0.0444	Supported
Coefficient of determination (R^2) Intent to E-banking = 0.352

Source: Authors’ computation from ADANCO 2.2.1.

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Data availability statement

Data is available upon reasonable request.