Evaluating the predictors of mobile health acceptance among Zimbabwean university students during the COVID-19 era: an integrated framework

Figures & data

Figure 1. Hypothesized research model.

Source: Developed from Ajzen (1991) and Venkatesh and Davis (2003).

Table 1. Demographic profile.

Variable	Category	Frequency	Percentage
Gender	Female	134	49.4
	Male	137	50.6
Age	18–25 Years	96	35.4
	26–30 Years	76	28.0
	31–35 Years	48	17.7
	36–40 Years	42	15.5
	41 Years & above	9	3.3
Marital status	Single	183	67.5
	Married	81	29.9
	Divorced	2	0.7
	Not interested	5	1.8
Use of a mobile communication device	Yes	271	100
	No	0	0
Received COVID-19 text messages	Yes	268	98.9
	No	1	0.4
	Not sure	2	0.7
Mobile COVID-19 messages	Useful	260	95.9
	Not useful	11	4.1
COVID-19 mobile application(s) usage	Yes	266	98.2
	No	5	1.8
COVID-19 information via mobile application(s)	Important	265	97.8
	Unimportant	4	1.5
	I don’t know	2	0.7

Source: Primary data.

Table 2. Psychometric properties of the measurement model.

Construct/Observed	Loading	AVE	CA	CR
Performance expectancy		0.811	0.927	0.928
PE1: I find mobile health useful	0.901
PE2: Mobile health helps me get COVID-19 information more quickly	0.900
PE3: Mobile health improves my safety from COVID-19	0.900
Effort expectancy		0.797	0.912	0.922
EE1: Learning to use my mobile health is easy for me	0.865
EE2: Mobile health service use fits my knowledge	0.873
EE3: My interaction with mobile health is clear and understandable	0.938
Facilitating conditions		0.584	0.802	0.808
FC1: I have the resources necessary to use mobile health	0.790
FC2: I have the knowledge necessary to use mobile health	0.765
FC3: Mobile health is compatible with other technologies I use	0.736
Attitude		0.584	0.778	0.804
ATT1: I like using mobile health applications/messages	0.845
ATT2: Using mobile health services is good for my health	0.843
ATT3: I prefer using mobile health to other applications	0.572
Subjective norms		0.556	0.756	0.782
SN1: My friends approve use of mobile health services	0.871
SN2: My family advised me to consider mobile health during COVID-19	0.805
SN3: Fellow students encouraged me to use mobile health messages and applications during COVID-19	0.511
Perceived behavioral control		0.804	0.919	0.925
PBC1: I do not suffer from anxiety associated with reading COVID-19 related messages	0.879
PBC2: I have no mental health challenges related to the COVID-19 information I receive	0.871
PBC3: I use COVID-19 mobile health service free of challenges	0.939
Behavioral intention		0.709	0.877	0.880
BI1: I intend to continue using my mobile health service in the future	0.803
BI2: I will always try to use my mobile health services	0.874
BI3: I plan to continue to use my mobile health frequently	0.848
M-health usage behavior		0.747	0.896	0.899
MUB1: I have used mobile health services during COVID-19	0.863
MUB2: I am using mobile health to access important COVID-19 information	0.880
MUB3: My choice to use mobile health services helped me to learn more about COVID-19	0.850

Notes: Loading: standardized loading; AVE: average variance extracted; CA: Cronbach Alpha; CR: composite reliability.

Figure 2. The structural model.

Notes: Perf: performance; Perc: perceived; Facilita: facilitating; Beh: behavioral; Usa_Beh: usage behavior.

Table 3. Outcomes of hypothesis testing.

Hypothesis	Estimate	Standardised estimate (β)	Standard error	t-Value	p-Value	Result
H1: PE->BI	0.775	0.773	0.043	18.023	<0.001	Accepted
H2: EE->BI	0.816	0.815	0.050	16.320	<0.001	Accepted
H3: FC->BI	0.925	0.927	0.055	16.818	<0.001	Accepted
H4: ATT->BI	0.544	0.542	0.051	10.666	<0.001	Accepted
H5: SN->BI	0.570	0.566	0.044	12.954	<0.001	Accepted
H6: PBC->BI	0.345	0.347	0.053	6.509	0.01	Accepted
H7: BI->MUB	0.648	0.644	0.041	15.804	<0.001	Accepted

Notes: PE: performance expectancy; EE: effort expectancy; FC: facilitating conditions; ATT: attitude; SN: subjective norms; PBC: perceived behavioral control; BI: behavioral intention; MUB: M-health usage behavior.

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 1–17. https://doi.org/10.1016/0749-5978(91)90020-T

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Venkatesh, M., & Davis, D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

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Data availability statement

The materials and data used in this research will be availed by the authors upon a reasonable request.