The adoption of self-driving vehicles in Africa: insight from Ghana

Figures & data

Figure 1. Original TAM model (Davis et al., 1989).

Figure 2. Theoretical model adapted from TAM

Table 1. Constructs and measured items.

Construct	Questionnaire Items	Adapted from (Source)
Perceived ease of use (PEOU)	I would find it easy to learn to operate AVs	Davis (1989) and Davis et al. (1989)
	I would not require mental effort to use AVs
	I would find AVs easy to understand
	AVs would be easy to use
Perceived usefulness (PU)	The use of AVs would make me more productive (e.g. eating, working and making calls whilst riding in an AV)	Davis (1989) and Davis et al. (1989)
	Using AVs would make my driving more comfortable
	Using AVs would make my driving easier
	AVs would be a useful means of transport
Construct: Attitude	Using AVs is a wise idea	Davis (1989) and Davis et al. (1989)
	Using AVs is a good idea
	Using AVs is meaningful
	Using AVs is advantageous
Construct: Intention	Should AVs be available in the future, I plan to use them	Davis (1989) and Davis et al. (1989)
	Should AVs be available in the future, I plan to buy one
	I would recommend the use of AVs to family and friends should they be available in the future
Perceived risk	I would be worried about system and technological failures in AVs	Self-developed
	I would be worried about cyber-attacks on AVs (e.g. system hacks)
	I would be worried about the high initial price of AVs
	I would be worried about whether AVs are morally correct and ethical
	I would be worried about my private information being disclosed when using AVs
	I would be worried about the legal liability of users or owners of AVs
	AVs would reduce employment in the automotive industry
Perceived benefit	AVs would reduce vehicle emissions	Self-developed
	AVs would increase traffic safety
	AVs would increase the mobility of those unfortunate not to be able to drive (e.g. the disabled and the elderly)
	AVs would reduce transportation costs
	AVs would increase fuel efficiency
	AVs would require less land use
	AVs would reduce traffic congestion
Subjective norm	People important to me would expect me to use AVs in the future	Ajzen (1991)
	I would also use AVs if people around me use them
	People important to me would want me to use AVs in the future
Trust	AVs are reliable	Choi and Ji (2015)
	AVs are dependable
	AVs can be trusted
Awareness	I am familiar with AVs	Self-developed
	I am familiar with how AVs operate
	I know the difference between AVs and traditional vehicles

Table 2. Demographics of the respondents.

Demographic Group	Frequency	Percent (%)
Gender
Male	208	49.9
Female	209	50.1
Total	417	100.0
Age
18–24	166	39.8
25–29	93	22.3
30–39	56	13.4
40–49	56	13.4
50–59	34	8.2
60+	12	2.9
Total	417	100.0
Education Level
Basic Education (Primary/JHS)	31	7.4
Senior High School	62	14.9
Bachelor degree	261	62.6
Post Graduate	63	15.1
Total	417	100.0
Monthly Income
Below GH₵ 500	116	27.8
GH₵ 500–1000	126	30.2
GH₵ 1000–2000	74	17.7
GH₵ 2000–3000	41	9.8
GH₵ 3000–4000	16	3.8
GH₵ 4000–5000	14	3.4
Greater than GH₵ 5000	30	7.2
Total	417	100.0
Vehicle Ownership
Yes	128	30.7
No	289	69.3
Total	417	100.0
Driving Experience
None	154	36.9
Less than a year	77	18.5
1 to 5 years	83	19.9
5 to 10 years	59	14.1
Greater than 10 years	44	10.6
Total	417	100.0

Table 3. Reliability of constructs.

Construct	Cronbach’s alpha	No. of items
Awareness	0.753	3
Attitude	0.847	4
Intention	0.839	3
PU	0.788	4
PEOU	0.771	4
Trust	0.883	3
Subjective norm	0.880	3
Perceived benefit	0.718	7
Perceived risk	0.793	7

Table 4. Correlations of constructs.

Construct	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
1. Awareness
2.Attitude	.333**
3.PU	.340**	.724**
4.PEOU	.383**	.513**	.583**
5.Trust	.209**	.361**	.390**	.399**
6.Subjective norm	.244**	.593**	.609**	.540**	.426**
7. Perceived Benefit	.346**	.506**	.552**	.499**	.426**	.529**
8.Perceived risk	.189**	.260**	.266**	.161**	−.108*	.099*	.268**
9.Intention	.378**	.770**	.719**	.544**	.346**	.675**	.491**	.217**

ap < .05; **p < .01 (two-tailed)

Table 5. Descriptive statistics of constructs.

Construct	M	SD	SK	KU
Awareness	2.786	1.00	.493	−.006
Attitude	2.362	.777	.568	.854
Intention	2.333	.934	.439	−.144
PU	2.172	.741	.651	1.09
PEOU	2.360	.773	.295	.680
Trust	2.711	.836	.042	.451
Subjective norm	2.707	.950	−.018	−.313
Perceived benefit	2.567	.616	−.153	.992
Perceived risk	2.201	.735	.430	.034

Note. M – Mean; SD – Standard Deviation; SK – Skewness and KU – Kurtosis

Table 6. Model coefficient of determination.

R	R^2	Adjusted R^2	Std. Error of the estimate
.846	.715	.707	.50543

Table 7. Model fit summary.

Model	Df	F-statistic	Significance
Regression	11	92.39	***p < 0.001
Residual	405

Table 8. Summary of direct effects on intention to use.

Variable	Standardized coefficient (β)	t-statistic	Significance
Gender	.008	t < ± 1.96	ns
Age	.116	t > ± 1.96	***
Education level	−.009	t <± 1.96	ns
Monthly income	−.095	t > ± 1.96	**
Awareness	.077	t > ± 1.96	*
Attitude	.415	t > ± 1.96	***
PU	.212	t > ± 1.96	***
Trust	−.022	t < ± 1.96	ns
Subjective norm	.295	t > ± 1.96	***
Perceived benefit (PB)	−.023	t < ± 1.96	ns
Perceived risk	−.006	t < ± 1.96	ns

ap < .05; **p < .01; ***p < .001 (two-tailed); not significant (ns) – p > .05

Table 9. Summary of mediating effects of attitude and PEOU and the standardized indirect effect of variables.

Variable	Effect	Dependent variable	Effect	LLCI	ULCI	Mediating role
PEOU	Direct effect	PEOU→intention	Nil
	Indirect effect	PEOU→attitude→intention	.428**	.3355	.5205	Partial
		PEOU→PU→intention	.435**	.3246	.5495	Partial
		PEOU→PU→attitude→intention	.169*	.0852	.1924	Partial
	Total effect		1.032**	.9511	1.1142
PU	Direct effect	PU→intention	.212***	.1751	.2432
	Indirect effect	PU→attitude→intention	.4979**	.3930	.6086	Partial
	Total effect		.710***	.5892	.8021
Perceived benefit (PB)	Direct effect	PB →intention	−.023	−.0314	−.0169
	Indirect effect	PB→attitude→intention	.537***	.4227	.6542	Partial
		PB→PU→intention	.535**	.4319	.6357	Partial
		PB→PU→attitude →intention	.157**	.0782	.2132	Partial
	Total effect		1.206***	1.075	1.412
Perceived risk	Direct effect	Perceived risk→intention	−.006	−.0122	−.0041
	Indirect effect	Perceived risk→attitude→intention	−.236**	−.3713	−.1065	Full
	Total effect		−.242*	−.3842	−.1981
Trust	Direct effect	Trust→intention	−.022	−.0293	−0.0172
	Indirect effect	Trust→PU→intention	.309***	.2204	.4023	Partial
		Trust→PU→attitude →intention	.116**	.0882	.1725	Partial
	Total effect		.403**	.3791	.5212

ap < .05; **p < .01; ***p < .001

Table 10. Summary of hypotheses.

Hypothesis	Status
H1	Supported
H2	Supported
H3	Supported
H4	Supported
H5	Supported
H6	Supported
H7	Supported
H8	Not supported
H9	Supported
H10	Not supported
H11	Not supported
H12	Supported
H13	Supported
H14	Supported

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Manage. Sci, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982

 Web of Science ®Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart, 13(3), 319–340. https://doi.org/10.2307/249008

 Web of Science ®Google Scholar

Ajzen, I. (1991). The theory of planned behavior. Organ. Behav. Hum. Decis. Process, 50(2), 179–211. https://doi.org/10.1037/t15668-000

 Web of Science ®Google Scholar

Choi, J. K., & Ji, Y. G. (2015). Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Hum.-Comp. Interact, 31(10), 692–702. https://doi.org/10.1080/10447318.2015.1070549

 Web of Science ®Google Scholar