Can social marketing undo the COVID-19 infodemic? Predicting consumer preventive health behavior in the marginalized communities in Zimbabwe

Abstract

: This study focuses on predicting preventive health behavior in the marginalized communities in Zimbabwe. The aim of the study was to investigate the determinants of consumer preventive health behavior based on the Coronavirus Disease 2019 (COVID-19) vaccination program in Zimbabwe. Using a convenience sampling procedure and a structured questionnaire, a cross-sectional survey was conducted in the rural districts of Zimbabwe enrolling model assessment through the Confirmatory Factor Analysis and Structural Equation Modeling. The examination of the Health Belief Model (HBM) revealed that perceived benefits, perceived susceptibility, perceived severity, cues to action and self-efficacy positively influenced preventive health behavior (COVID-19 vaccination). However, the influence of perceived barriers was statistically insignificant. The findings of this study are key for governments, healthcare policy makers, health professionals and community educators as they attempt to understand COVID-19 vaccination acceptance from a consumer perspective. This research also enlightens health consumers that the objective of government health programs and social marketing initiatives remains promotion of positive social behaviors that enhance population health and longevity.

Keywords:

• COVID-19 vaccination
• Health Belief Model
• preventive health behavior
• marginalized communities
• Zimbabwe

PUBLIC INTEREST STATEMENT

The COVID-19 pandemic culminated into a global health emergency that resulted in a significant loss of human lives. Extensive social marketing campaigns were launched to tackle COVID-19 vaccine hesitancy caused by public misinformation. This research evaluates the determinants of COVID-19 preventive behavior based on the vaccination rollout program in rural Zimbabwean communities. The results confirm that consumers are more apt to adopt COVID-19 preventive behavior if they believe that it brings adequate health benefits, they feel that they are highly exposed to a serious infection and if they understand that contacting the illness may be life threatening. The belief that one can successfully manage the consequences of the health action (vaccination) and availability of motivating or stimulating conditions also encourage uptake of COVID-19 preventive behavior. The research enlightens governments, health practitioners and community healthcare providers as they attempt to understand consumer preventive health behavior, particularly COVID-19 vaccination acceptance in developing economies.

1. Introduction

Despite longstanding efforts to provide better health services and eradicate health inequalities, rural residents endure relatively more health challenges. Incidences of relatively lower health consciousness in rural communities have been suggested in the literature (Shekhar, 2022; Weinhold & Gurtner, 2014; Yuan et al., 2015). The dominant obstacle is not access to healthcare, but poor cognitive abilities to expedite adoption of pro-health behaviors (Carpenter, 2010; Moradhaseli et al., 2019). Whilst the cost of healthcare is rising, poor health decisions stress the limited financial resources in low-income countries (Bardus et al., 2023). Proponents of social marketing maintain that irresponsible consumer behavior is mainly influenced by lack of knowledge, information and awareness about the long-term effects of such behaviors (Lee & Kotler, 2016; Weinreich, 2010). Over the past decades, social marketing has emerged as a powerful tool for mainstreaming positive social behavior change and pro-health behaviors.

Social marketing promotes education and raises awareness with the goal of enhancing pro-life behaviors and human longevity (French & Gordon, 2015; Hong, 2023). It is premised on influencing social behaviors to improve public health, prevent injuries, protect the environment, engender community involvement, and, most recently, enhance financial wellbeing (Lee & Kotler, 2016). Evident among dimensions of social behavior change has been the promotion of consumer health consciousness and adoption of preventive health behaviors (Hong, 2023). Educating and raising awareness towards adoption of preventive behaviors has been one of the key social marketing strategies to enhance human longevity.

The outbreak of the Severe Acute Respiratory Syndrome (SARS-COV-2) virus resulted in the Coronavirus Disease 2019 (COVID-19) pandemic that culminated in 615 million infections and 6.54 million deaths globally (by 7 September 2022) (European Centre for Disease Prevention and Control, 2022). As COVID-19 vaccination became a global imperative (Mahmud et al., 2021; Puri et al., 2020; Tanzir Mehedi et al., 2022), Sinopharm BIBP and Sinovac-CoronaVac vaccines were introduced in Zimbabwe. According to the Ministry of Health and Childcare (MOHCC) (2022), the targeted herd immunity was 60% of the country’s population (Appendix 1). However, research evidence confirms rife vaccine hesitancy amid public misinformation and religious myths surrounding COVID-19 (Jaravaza et al., 2023; Puri et al., 2020). To connote the impact of public misinformation on potentially triggering vaccine hesitancy, the World Health Organisation (WHO) Director-General, Tedros Adhanom Ghebreyesus was quoted, “We are not just fighting an epidemic; we’re fighting an infodemic” (Pennycook et al., 2020, p. 1). Despite extensive social marketing efforts to stimulate consumer interest and undo consumer skepticism (Bardus et al., 2023; Shekhar, 2022), vaccination remains a personal choice.

While it is evident that the COVID-19 pandemic overwhelmed the global economy, governments, national health services, strategic industries, regional trade, critical supply chains, business and community ecosystems (Islam et al., 2020; Niculaescu, 2021; Taz et al., 2021), the effects have been far outsized in subsistence markets (Bardus et al., 2023). Contrastingly, despite relatively lower health funding and inequitable health access (Moradhaseli et al., 2019), the empirical efforts directed towards COVID-19 preventive behaviors are still sparse in pre-emerging economies (Jaravaza et al., 2023). Further, the virus has been described as “novel”, confirmatory of its unique biological characteristics (Islam et al., 2022; Mahmud et al., 2021), hence its potentially novel behavioral implications should merit an empirical enquiry. This research examines the determinants of COVID-19 preventive behavior in the rural communities in Zimbabwe based on the Health Belief Model (HBM).

Although significant preventive health research has been evident in the relatively more affluent economies, these studies were conducted under stable conditions and in contexts endowed with advanced healthcare, better population literacy, lower population mortality and more health-conscious consumers (Bardus et al., 2023; Shekhar, 2022). More so, significant prior research focused on conventional infections, such as cancer, hypertension and HIV/AIDS (Moradhaseli et al., 2019). The current study focuses on COVID-19, a virus with little of it that had been known prior (Islam et al., 2022). Whilst, extensive social marketing campaigns were launched through the national television, radio, social media and personal selling to tackle vaccine hesitancy (Jaravaza et al., 2023; Shekhar, 2022), no studies have been conducted to examine the effectiveness of these campaigns in influencing COVID-19 preventive behaviors in Zimbabwe. Further, rural communities endure significant health inequalities relative to urban populations (Moradhaseli et al., 2019) and have often been sidelined from mainstream research despite constituting more than 65% and 50% of Zimbabwean and African population, respectively (Jaravaza et al., 2023). More so, the vulnerability of rural communities to misinformation, religious myths and their relatively lower cognitive abilities to evaluate information and make right health choices (Shekhar, 2022; Weinhold & Gurtner, 2014) makes them the ideal unit of analysis for this research.

The findings of this study are key for governments, health authorities, health professionals, researchers and community healthcare providers as they attempt to understand COVID-19 vaccination acceptance from a consumer perspective. They aid healthcare policy decisions, governmental social marketing planning, resource allocation, implementation, monitoring and evaluation from an informed viewpoint. Further, the study makes key advancements to the literature in an under-researched area in low-income economies. Although social marketing has been earmarked to achieve significant gains in changing social behaviors for good, the domain has received underwhelming research attention in subsistence markets. This study provides a key impetus for future research in the domain of health promotion and social marketing. The subsequent sections of this paper cover literature review, materials and methods, results, implications, conclusions and future study prospects.

1.1 Research questions

The study intended to answer the following research questions;

1. What determines COVID-19 preventive health behavior in the marginalized communities in Zimbabwe?

2. What are the relationships between constructs of the Health Belief Model and COVID-19 preventive behavior in the context of rural consumers?

2. Literature review

Health is the state or condition of physical, mental and social wellbeing (Sreelakshmi & Prathap, 2020; Zareipour et al., 2020). Preventive health behavior refers to the actions, measures or strategies devised to curb or protect consumers or generally people from a health hazard, infection or illness (Karimy et al., 2021; Shitu et al., 2022). Extant literature identifies several theories that explain preventive health adoption behavior (Sreelakshmi & Prathap, 2020; Weng et al., 2020).

2.1 Theoretical Background

The Health Belief Model (HBM) was developed in the US Public Health Service around the 1950s as an effort to understand the failure of people to adopt disease prevention strategies or screening tests for early detection of diseases (Glanz et al., 2008; Rosenstock, 2000). The model proposed that an individual’s belief in a threat of an illness together with an individual’s belief in the effectiveness of the recommended preventive behavior or action would predict the likelihood that the individual will adopt the behavior (Heydari et al., 2023; Hidayati et al., 2022). Further, the framework was derived from psychological and behavioral theory with the foundation that two aspects of health behavior are: the desire to avoid illness or conversely recover if already ill and the belief that a certain specific health action will prevent or cure the illness (Shitu et al., 2022; Vasli et al., 2022). As such, a person’s course of action would most likely depend on the individual’s perceptions of barriers and benefits of adopting a health behavior (Shahnazi et al., 2020).

The original model proposed four constructs that predicted adoption of recommended health behavior in the USA (Rosenstock, 2000). These were perceived severity, perceived susceptibility, perceived barriers and perceived benefits (Glanz et al., 2008; Mahindarathne, 2021). Empirical health research emerged with an additional two, which are cues to action and self-efficacy (Vasli et al., 2022). With the popularity of social marketing amid sustainability and consumer welfare concerns, the Health Belief Model has been established as a useful tool for predicting consumer health behaviors (Bardus et al., 2023; Heydari et al., 2023; Zareipour et al., 2020).

2.2 Development of Hypotheses

2.2.1 Perceived susceptibility

It was defined as an individual’s subjective perception of the risk of contacting the disease or illness (Rosenstock, 2000). High-perceived susceptibility is most likely to motivate adoption of preventive health behavior than low-risk perception (Zareipour et al., 2020). Thus, a person who felt that they were highly exposed to contact the COVID-19 virus would seek to adopt preventive mechanisms to safeguard themselves from illness (Mahindarathne, 2021). If a person perceives that he or she has a high risk of getting infected with a new disease, they are likely to adopt the recommended health behavior. In contrast, if a person perceives their risk of getting a disease to be low, they are unlikely to adopt a recommended health action or behavior (Vasli et al., 2022). Amid vaccine hesitancy, the role of social marketing remains raising community awareness on the levels of risk exposure to COVID-19 so that consumers fully understand the importance of taking preventive behaviors (Bardus et al., 2023; Lee & Kotler, 2016).

According to Shitu et al. (2022) a person’s perception of risk exposure determines their health action. Several studies also report the positive impact of perceived susceptibility on preventive behavior, e.g., Dehghani et al. (2022), Freivogel and Visschers (2021), Moradhaseli et al. (2019) and Zhang et al. (2020). Interestingly, Sreelakshmi and Prathap (2020) and Zareipour et al. (2020) also confirm the positive influence of perceived susceptibility on COVID-19 preventive behavior in India and Urmia, respectively. However, Yap et al. (2021) and Karimy et al. (2021) reported contrasting findings. Thus, given the foregoing discussion, this study predicted that;

H₁:

Perceived susceptibility positively influences COVID-19 preventive behavior in the marginalized communities in Zimbabwe.

2.2.2 Perceived severity

Refers to one’s feelings of the extremity or seriousness of the condition be that he or she gets exposed to the disease (Shahnazi et al., 2020). If the person perceives the exposure to be highly extremely severe, thus endangering his or her health, that individual is likely to take the recommended preventive health behavior (Carpenter, 2010). In contrast, if the person perceives the severity to be low or insignificant, the person is unlikely to adopt the preventive behavior (Heydari et al., 2023). Social marketing messages target fully communicating the severity of the hazard should one contact an illness, such as COVID-19, hence encouraging preventive action (Hong, 2023; Shekhar, 2022).

The relationship between perceived severity and preventive health behavior has been investigated in several studies. The positive effect of perceived severity on preventive behavior was confirmed in the studies of Moradhaseli et al. (2019), Smail et al. (2021) and Zhang et al. (2020). Further, Shahnazi et al. (2020), Sreelakshmi and Prathap (2020) and Zareipour et al. (2020) also reported similar results in the context of COVID-19 preventive behavior. Given this empirical standpoint, the paper also proposed that;

H₂:

Perceived severity positively influences COVID-19 preventive behavior in the marginalised communities in Zimbabwe.

2.2.3 Perceived benefits

Perceived benefits refers to an individual’s subjective perception of the attractiveness of engaging in a recommended health action (Rosenstock, 2000). If the person perceives the preventive behavior to be worth of health value, he or she is likely to adopt the preventive behavior (Vasli et al., 2022). In contrast, if the individual thinks that there are no benefits associated with the recommended health behavior, they are unlikely to endorse it (Moradhaseli et al., 2019). The task of social marketing is to develop and convey a compelling health value proposition that preempts the health benefits, appeals to targeted health consumers and make COVID-19 preventive behavior an attractive choice (Bardus et al., 2023; Shekhar, 2022).

Literature confirms the positive relationship between perceived benefits and preventive health behavior (Moradhaseli et al., 2019; Yap et al., 2021). In the context of COVID-19 research, Maleka and Matli (2022), Heydari et al. (2023), Hidayati et al. (2022), Karimy et al. (2021) and Mahindarathne (2021) also reported the positive effect of perceived health benefits on preventive action. Leaning on this empirical support, this study also hypothesized that;

H₃:

Perceived benefits positively influence COVID-19 preventive behavior in the marginalized communities in Zimbabwe.

2.2.4 Perceived barriers

Refers to the person’s subjective perception of obstacles in the way of adopting the recommended health action (Hidayati et al., 2022; Shahnazi et al., 2020). If the individual perceives the health behavior to be associated with severe difficulties, that person is less likely to adopt it (Rosenstock, 2000). On the contrary, if the person views a recommended health action as free of challenges or without obstacles, that person is more likely to adopt the preventive behavior (Glanz et al., 2008). The key role of social marketing is to mitigate or clearly communicate mitigation of the purported barriers so that consumers are evoked to explore adoption of the recommended preventive action (Bardus et al., 2023; French & Gordon, 2015; Lee & Kotler, 2016).

In a meta-analytic study on the assessment of the Health Belief Model constructs, Carpenter (2010) notes the negative impact of perceived barriers on preventive behavior in many studies. Further, most rural communities in pre-emerging economies endure a constrained healthcare infrastructure, depleted medical supplies, poor public transport service, disenfranchised road networks and distant healthcare centres (Moradhaseli et al., 2019; Weinhold & Gurtner, 2014; Yuan et al., 2015). Thus, rural health consumers are unlikely to adopt preventive behaviors if they perceive significant barriers in accessing COVID-19 preventive measures. Extant literature supports the negative effect of perceived barriers on preventive behavior (Moradhaseli et al., 2019; Weng et al., 2020; Yap et al., 2021). More so, Hidayati et al. (2022), Karimy et al. (2021), Mahindarathne (2021), Shahnazi et al. (2020), Shitu et al. (2022) and Vasli et al. (2022) also confirm the negative impact of perceived barriers on COVID-19 preventive behaviors. Given that, this study also proposed that;

H₄:

Perceived barriers negatively influence COVID-19 preventive behavior in the marginalized communities in Zimbabwe.

2.2.5 Cues to action

These are defined as the motivating, facilitating or stimulating variables which expedite adoption of preventive health behaviors (Karimy et al., 2021; Vasli et al., 2022). Availability of driving factors that motivate a person to adopt preventive behavior was cited as key in supporting preventive behavior uptake (Glanz et al., 2008). Thus, the absence of these cues to action may propagate complacency towards a preventive behavior (Heydari et al., 2023; Shahnazi et al., 2020). Social marketing advocacy remains the key communicators, stimulants as well as health motivators and their task is to provide knowledge, support, and evidence that taking COVID-19 preventive behavior is actually of huge health benefit (Bardus et al., 2023; Hong, 2023; Shekhar, 2022).

Cues to action have been observed to be important especially in rural communities where the population literacy and cognitive abilities are relatively limited (Weinhold & Gurtner, 2014; Yuan et al., 2015). Moradhaseli et al. (2019) reported the positive impact of cues to action on farmers’ preventive behavior in Iran. Similarly, Hidayati et al. (2022), Karimy et al. (2021), Mahindarathne (2021) and Vasli et al. (2022) also confirm the positive impact of health motivation on COVID-19 preventive behavior. Based on this empirical support, this study also predicted that;

H₅:

Cues to action positively influence COVID-19 preventive health behavior in the rural communities in Zimbabwe.

2.2.6 Self-efficacy

Self-efficacy is the conviction that an individual has about their ability to manage a prescribed course of health action (Glanz et al., 2008; Rosenstock, 2000). The belief that one is able to manage taking the recommended behavior was cited as key to its adoption (Mahindarathne, 2021). Self-efficacy evokes adoption because the consumer has full conviction that they are able to cope up with the prescribed health action during the post-adoption phase (Shahnazi et al., 2020). COVID-19 vaccination erected significant acceptance challenges because health consumers were misinformed and confused and this lowered the perceived self-efficacy (Bardus et al., 2023; Vasli et al., 2022). Thus, social marketing aimed at educating and inducing confidence that consumers are going to normally manage their daily routines with ease upon taking COVID-19 preventive behavior (Bardus et al., 2023; Hong, 2023; Shekhar, 2022).

The positive influence of self-efficacy on preventive health action has been observed in a number of studies (Freivogel & Visschers, 2021; Moradhaseli et al., 2019; Smail et al., 2021; Yap et al., 2021). Further, the positive impact of self-efficacy on COVID-19 preventive health behavior was confirmed in the studies of Heydari et al. (2023), Mahindarathne (2021), Shahnazi et al. (2020) and Shitu et al. (2022). Given the evidence in related contexts, this study also hypothesized that;

H₆:

Self-efficacy positively influences COVID-19 preventive health behavior in the marginalized communities in Zimbabwe.

Figure 1 illustrates the hypothesized research model.

Figure 1. Hypothesized research model.

Source: Modified from Glanz et al. (2008).

3. Material and methods

3.1 Design

The purpose of the research was to examine the determinants of preventive health behavior in the marginalized communities in Zimbabwe. Thus, guided by the positivism research philosophy, the study employed an explanatory research design. This design is employed where the researcher seeks to examine causal relationships between variables or explain causal relationships in a proposed model (Malhotra et al., 2017; Saunders et al., 2018).

3.2 Population and sampling

The cross-sectional survey was conducted in 12 selected rural districts in provinces of Manicaland, Mashonaland Central, Mashonaland West, Matebeleland North, Matebeleland South and Midlands. These were conveniently selected based on their accessibility to the researchers by their university location. A sample of 388 conveniently selected participants in the Generation Y (born 1981–1995; 17–43 years old) and Z (1996–2012; 16–27 years old) was employed. Convenience sampling method was employed to due to non-availability of a sampling frame and to enable data collection from participants who were available (Saunders et al., 2018). Researcher’s expert evaluation and filter questions were used to ensure that the intended demographic properties were captured. The Generation Y and Z segments were a rich target as they have been subject to intense social marketing, environmental activism and health programs designed to influence their behavior towards a better planet, healthier and longer lives (Lee & Kotler, 2016; Muposhi et al., 2015). Further, most Zimbabwean consumers in this segment have basic education and this alleviated potential fieldwork constraints.

The sample size was determined by data analysis methods, sizes employed in similar studies, resource constraints and completion rates (Malhotra et al., 2017). The item-to-response ratio should range from 1: 4 to 1: 10 for each set of variables (Hair et al., 2019; Kline, 2023). As 28 items were adopted during the design stage, at least 120–280 samples were considered sufficient. Further, related studies, for example, Heydari et al. (2023), Mahindarathne (2021) and Vasli et al. (2022) employed similar sample size guidelines.

3.3 Measures of the variables

The measurement scales were adopted from extant literature. All constructs were measured on a 7-point Likert scale from strongly disagree to strongly agree. Table 1 illustrates the measures of constructs adopted in this study.

Table 1. Measures of the variables

Variable	Definition	Studies
Perceived susceptibility	An individual’s perception of his or her risk of contacting the disease or infection.	Sreelakshmi and Prathap (2020); Vasli et al. (2022); Zareipour et al. (2020).
Perceived severity	An individual’s perception of the seriousness of the health condition he or she may receive on exposure to the infection or illness.	Shahnazi et al. (2020); Sreelakshmi and Prathap (2020); Zareipour et al. (2020).
Perceived benefits	An individual’s perception of the attractiveness or value of engaging in a recommended health action.	Heydari et al. (2023); Hidayati et al. (2022); Karimy et al. (2021).
Perceived barriers	An individual’s perception of obstacles standing in the way of obtaining or adopting the preventive health behavior.	Hidayati et al. (2022), Karimy et al. (2021) Shitu et al. (2022).
Cues to action	The motivating or stimulating factors which facilitate adoption of a recommended preventive health behavior.	Hidayati et al. (2022); Karimy et al. (2021); Vasli et al. (2022).
Self-efficacy	The conviction that an individual has about their ability to manage a prescribed course of health action during the post adoption phase.	Heydari et al. (2023); Mahindarathne (2021); Shahnazi et al. (2020).
Preventive health behavior	Course of health action or recommended behavior necessary to avoid, control or mitigate an unwanted health condition.	Heydari et al. (2023); Mahindarathne (2021); Shahnazi et al. (2020).

3.4 Data collection procedures and ethical compliance

Cognisant of COVID-19 major risk factors, the research was conducted off the heightened state of the pandemic when new infections had sharply declined with social distancing regulations and lockdowns long eased in Zimbabwe (December 2022). Further, effective precautionary countermeasures used in similar studies, for example, Shitu et al. (2022) such as applying hand sanitisers, wearing facemasks and gloves, practicing good hygiene and social distancing were employed. In addition to the ethical clearance that the research received, ethics inherent in consumer surveys were also adhered to (Saunders et al., 2018). The objective of the study was communicated to the participants and participation was voluntary. Further, respondents did not include any personally identifiable information with their responses. Participant confidentiality and privacy were also strictly observed during and after fieldwork.

3.5 Data analysis methods

Confirmatory Factor Analysis (CFA) was used to assess the measurement model and Structural Equation Modeling (SEM) to estimate model parameters in AMOS (Kline, 2023). SEM consists of a set of multivariate techniques that are confirmatory in evaluating whether hypothesized models fit data (Kline, 2023). Structural Equation Modeling is recommended in examination of causal models based on its explicit assessment of measurement error, estimation of latent variables and model testing where a structure can be imposed and assessed as to fit of the data (Hair et al., 2019; Kline, 2023).

The model fit indices used in this study belong to absolute and relative fit indices. The absolute fit indices were the Chi Square (x²), degrees of freedom (df), normed Chi Square or Chi square normalised by degrees of freedom (x²/df), Goodness-of-Fit Index (GFI), Root Mean Square Error of Approximation (RMSEA) and the Standardized Root Mean Residual (SRMR). The relative or incremental fit indices used were the Comparative Fit Index (CFI), normed Fit Index (NFI), Incremental Fit Index (IFI) and the Tucker-Lewis Index (TLI) (Kline, 2023). Convergent validity was examined using Average Variance Extracted (AVE >0.5), standardized factor loadings (>0.7) and significant t-values (Hair et al., 2019). Discriminant validity was examined using the Fornell-Larcker criterion, where square root of the AVE should be greater than any correlation between the construct and any other construct in the model (Fornell & Larcker, 1981). Construct reliability was verified using a composite reliability (>0.7) (Kline, 2023). The statistical significance of the path estimates (p < 0.05) and t-values (±1.96) were used to examine a priori hypothesized relationships in the structural model (Hair et al., 2019).

4. Results

4.1 Sample characterization

The socio-demographic properties of the sample were examined. Table 2 shows the socio-demographic profile of the study participants.

Table 2. Sample characterization

Variable	Category	Frequency	Percentage
Gender	Male	147	48.68
	Female	155	51.32
Age	16–20 years	155	51.32
	21–25 years	100	33.11
	26–30 years	47	15.56
Education	Ordinary level	103	34.11
	Advanced level	97	32.12
	Certificate	53	17.54
	Bachelor’s degree	49	16.23
Employment status	Unemployed	77	25.49
	Formally employed	22	7.28
	Self-employed	203	67.23
History of Corona	Yes	27	8.94
	No	275	91.06

4.2 Assessment of the measurement model

A two-step procedure of Confirmatory Factor Analysis and by Structural Equation Modeling was followed (Hair et al., 2019). Using Maximum Likelihood estimation, 28 items were loaded on their respective latent constructs. They produced favorable standardized loadings, except CTA5 (0.56), SEF4 (0.5) and PSE5 (0.6). Following the recommendations of Kline (2023), the items with loadings less than 0.7 were subsequently deleted. The fit indices for the re-specified model were as follows; CMIN/x² = 1033.17, df = 254, x²/df = 4.06, GFI = 0.898, RMSEA = 0.07, SRMR = 0.08, CFI = 0.938, IFI = 0.936, NFI = 0.945 and TLI = 0.950. These results provide evidence of a good fitting model (Hair et al., 2019; Kline, 2023).

Convergent validity was examined using the average variance extracted (AVE) for each latent construct. The lowest AVE was 0.604 on perceived Susceptibility and the highest was 0.783 on Perceived Benefits. All factor loadings were greater than 0.7 and their t-statistics were significant, p < 0.001. The squared multiple corrections were all above 0.5. According to Hair et al. (2019) convergent validity is present when AVE > 0.5, unidimensionality is satisfied (factor loadings > 0.7) and squared multiple correlations > 0.5, thus convergent validity was satisfactory as shown in Table 3. To assess discriminant validity, the method suggested by Fornell and Larcker (1981) was used. The square root of the AVE should be greater than any correlation between the construct and any other construct in the model. The results in Table 4 confirm that there were no discriminant validity problems in this study.

Table 3. Psychometric properties of the measurement model

Construct/Observed Variable	SFL	SMC	AVE	CA	CR
Perceived Susceptibility			0.604	0.850	0.859
PSU1	0.721***	0.520
PSU2	0.787***	0.620
PSU3	0.742***	0.551
PSU4	0.852***	0.727
Perceived Severity			0.646	0.833	0.845
PSE1	0.848***	0.720
PSE2	0.841***	0.708
PSE3	0.716***	0.513
Perceived Benefits			0.783	0.928	0.935
PBE1	0.887***	0.787
PBE2	0.875***	0.765
PBE3	0.848***	0.720
PBE4	0.928***	0.861
Perceived Barriers			0.769	0.928	0.930
PBA1	0.810***	0.656
PBA2	0.906***	0.820
PBA3	0.885***	0.783
PBA4	0.903***	0.816
Cues To Action			0.766	0.923	0.929
CTA1	0.838***	0.702
CTA2	0.871***	0.758
CTA3	0.846***	0.715
CTA4	0.943***	0.890
Self-Efficacy			0.749	0.898	0.899
SEF1	0.873***	0.762
SEF2	0.882***	0.778
SEF3	0.840***	0.706
Preventive Health Behavior			0.750	0.898	0.900
PHB1	0.869***	0.755
PHB2	0.844***	0.712
PHB3	0.885***	0.783

SFL= Standardized Factor Loading; AVE= Average Variance Extracted; SMC = Squared Multiple Correlations, CA= Cronbach Alpha; CR = Composite Reliability; *** denotes p<0.001.

Table 4. Assessment of discriminant validity

Construct	R^2	1	2	3	4	5	6	7
Perceived Susceptibility (1)		0.777
Perceived Severity (2)		0.671	0.804
Perceived Benefits (3)		0.626	0.749	0.885
Perceived Barriers (4)		0.568	0.663	0.640	0.877
Cues to Action (5)		0.542	0.701	0.692	0.799	0.875
Self-Efficacy (6)		0.644	0.733	0.723	0.534	0.537	0.865
Preventive Health Behavior (7)	0.654	0.699	0.791	0.820	0.545	0.584	0.823	0.866

Notes: Bolded values on the diagonal are square roots of Average Variance Extracted (AVE); construct correlations are below the diagonal; R² = Model R Square.

Further, composite reliability (CR) ranged between 0.845 (perceived severity) and 0.935 (perceived benefits). According to Kline (2023) values should be at least 0.7 to confirm construct reliability and this requirement was met. Table 3 shows the psychometric properties of the measurement model.

Before estimating the structural model, the Harman’s single-factor test was used to examine any incidences of common methods variance (Podsakoff et al., 2012). The model fit between a single-factor model and the 7-factor model were compared. The one-factor model produced a very bad fit (CMIN/x² = 3223.37, df = 275, x²/df = 11.721, GFI = 0.446, RMSEA = 0.189, SRMR = 0.151, CFI = 0.623, NFI = 0.600, IFI = 0.624 and TLI = 0.588). The 7-factor model had a very good fit (CMIN/x² = 1033.17, df = 254, x²/df = 4.06, GFI = 0.898, RMSEA = 0.07, SRMR = 0.08, CFI = 0.938, NFI = 0.945, IFI = 0.930 and TLI = 0.946); hence, this study had no issues with common methods variance.

4.3 Assessment of the structural model

Prior to estimating model parameters, assumptions of SEM were examined. Data normality was tested using skewness and kurtosis and the results confirmed that the recommended thresholds of −2 to + 2 for skewness and −7 to + 7 for kurtosis were not surpassed (Hair et al., 2019). Multicollinearity was examined using Tolerance (T) and Variance Inflation Factor (VIF). According to Hair et al. (2019), values below 4 and above 0.25 confirm absence of multicollinearity, respectively. In this model, Variance Inflation Factor (VIF) was 1.486 and Tolerance (T) was 0.672; hence, any incidences of multicollinearity were ruled out.

The structural model was evaluated on three important criterion; the model fit, the significance of path estimates and the explanatory power of the model (Kline, 2023). The following model fit results were obtained; CMIN/x² = 1012, df = 268, x²/df = 3.77, GFI = 0.896, RMR = 0.07, RMSEA = 0.07, CFI = 0.922, NFI = 0.946, IFI = 0.938 and TLI = 0.950. According to Kline (2023) and Hair et al. (2019), the results provide evidence of a good fitting model.

Second, the path estimates and their statistical significance were evaluated and the results were used to examine priori-hypothesized relationships (Hair et al., 2019). The standardized estimate (β) on the causal path between perceived susceptibility and preventive health behavior was 0.259, supported by a t-statistic of 5.189 (p < 0.001). The Pearson’s correlation coefficient (r) between perceived susceptibility and preventive health behavior was 0.699 and p < 0.001. Given these results, hypothesis H1 gained empirical support to confirm the positive effect of an individual’s perception about his or her susceptibility to contracting COVID-19 on his or her preventive health behavior (vaccination).

The standardized estimate (β) on the causal path between perceived severity and preventive health behavior was 0.654, supported a t-statistic of 8.451, which was also statistically significant (p < 0.001). The Pearson’s correlation coefficient (r) between perceived severity and preventive health behavior was 0.791 and p < 0.001. Based on these results, hypothesis H2 was supported to confirm the positive effect of an individual’s perception of the seriousness of a COVID-19 infection on his or her preventive behavior (vaccination).

More so, the standardized path coefficient (β) between perceived benefits and preventive health behavior was 0.129, with a t-statistic of 3.809 and p < 0.001. The Pearson’s correlation coefficient (r) between perceived benefits and preventive health behavior was 0.820 and p < 0.001. Based on these results, hypothesis H3 gained empirical support and the positive effect of an individual’s perception about the benefits of taking action towards mitigation of the illness (vaccination) was confirmed. Figure 2 illustrates the path diagram.

Figure 2. The Structural Model.

Notes: Perc = Perceived; Suscepti = Susceptibility, Prev_H_Behavior = Preventive Health Behavior (COVID-19 vaccination; *** denotesp < 0.001.

The standardized path estimate (β) on the causal path between perceived barriers and preventive health behavior was −0.074, a t-statistic of −0.860 which was not significantly significant (p = 0.385). Although the results were suggestive of a weak negative influence of perceived barriers on preventive health behavior, the relationship was not statistically significant. Further, although the correlation (r) between perceived barriers and preventive health behavior was 0.545, p < 0.001, no causal effects were substantiated. Based on these results, hypothesis H4 was not supported. Thus, perceived barriers to COVID-19 vaccination were not a deterrent to COVID-19 vaccine uptake in Zimbabwean rural communities.

The standardized estimate (β) on the causal path between cues to action and preventive health behavior was 0.797, a t-statistic of 11.266 and p < 0.001. The Pearson’s correlation coefficient (r) between cues to action and preventive health behavior was 0.584 and p < 0.001. In the light of these results, H5 gained empirical support to confirm the positive influence of available supporting, motivating and facilitating conditions on preventive health action (COVID-19 vaccination).

Finally, the standardized path estimate (β) on the causal link between self-efficacy and preventive health behavior was 0.500, a t-statistic of 9.283 and p < 0.001. The Pearson’s correlation coefficient (r) between cues to action and preventive health behavior was 0.823 and p < 0.001. Informed by these results, hypothesis H6 was supported and the positive influence of an individual’s perception of his or her ability to cope with the effects of a recommended health action on his or her preventive behavior (COVID-19 vaccination). Table 5 illustrates the results of hypothesis testing.

Table 5. Results of hypothesis testing

Hypothesized Relationship	Estimate	Standardized	t/C.R	p	Result
H1:Perceived Susceptibility➔PHB	0.284	0.259	+5.189	***	Supported
H2: Perceived Severity➔PHB	0.656	0.654	+8.451	***	Supported
H3: Perceived Benefits➔PHB	0.124	0.129	+3.809	***	Supported
H4: Perceived Barriers➔PHB	−0.075	−0.074	−0.86	0.385	Not supported
H5: Cues to Action➔PHB	0.682	0.797	+11.266	***	Supported
H6: Self Efficacy➔PHB	0.362	0.500	+9.283	***	Supported

PHB = Preventive Health Behavior; Standardized are standardized estimates (β); *** denotes p < 0.001.

4.5 Discussion of findings

This research examined the predictors of COVID-19 preventive behavior (vaccination) in rural communities in Zimbabwe using the Health Belief Model. Model evaluation through Structural Equation Modeling resulted in confirmation of 5/6 hypothesized relationships. These findings inform health policy, government health marketing planning, implementation and evaluation as well as community healthcare providers and health professionals in understanding behavioral aspects of rural health consumers. The support of H1 encapsulates that consumers’ perception of their risk exposure to COVID-19 positively influenced COVID-19 preventive behavior (β = 0.259, t = 5.189, p < 0.001). Consistent with previous research (Dehghani et al., 2022; Freivogel & Visschers, 2021; Smail et al., 2021; Zhang et al., 2020), the results support that if an individual understands the high risk they are subjected to of contacting a life threatening infection (COVID-19), their likelihood of adopting the recommended health action (COVID-19 vaccination) is high. Sreelakshmi and Prathap (2020) confirmed similar results in COVID-19 preventive behavior in India. However, current findings contest Karimy et al. (2021), Yap et al. (2021) and Vasli et al. (2022) who found perceived susceptibility to be an insignificant predictor of COVID-19 preventive behavior. Extensive social marketing educating rural consumers about risk exposure and emphasizing the vulnerability of citizens to COVID-19 in the marketing strategy could explain these findings.

The positive effect of perceived severity on preventive health behavior was evident, resulting in support of hypothesis H2. The path had the second strongest coefficient in the model, denoting the significance of perceived threat on COVID-19 preventive behavior (β = 0.654, t = 8.451, p < 0.001). The results concur with broader preventive health findings (Moradhaseli et al., 2019; Smail et al., 2021; Zhang et al., 2020) and specific COVID-19 preventive behavior (Shahnazi et al., 2020; Sreelakshmi & Prathap, 2020; Zareipour et al., 2020). The results suggest that rural consumers were more privy to adopting COVID-19 vaccination if they believed that the threat of contacting the infection poses important health consequences. This confirms that social marketing awareness has been pivotal in educating consumers on COVID-19 severity by communicating global new inflection and mortality statistics, rigorous radio advertising, social media intensity and television graphic displays (Bardus et al., 2023; Jaravaza et al., 2023). However, Hidayati et al. (2022), Karimy et al. (2021) and Shitu et al. (2022) reported that the perceived severity was insignificant from their studies in Iran, Ethiopia and Jakarta, respectively.

The positive effect of perceived benefits on preventive health behavior was also confirmed. Given these findings, H3 was also supported (β = 0.129, t = 3.809, p < 0.001). The results confirm earlier claims in the extant health behavior literature (Maleka & Matli, 2022; Moradhaseli et al., 2019; Yap et al., 2021). Consumers are likely to adopt recommended health action if they believe that the associated benefits outweigh the costs (Mahindarathne, 2021; Rosenstock, 2000). Confirmatory findings were also reported in COVID-19 preventive behavior research, for example, Heydari et al. (2023), Hidayati et al. (2022) and Karimy et al. (2021). The findings suggest that preventive health programs like COVID-19 vaccination require extensive social marketing promotion so that consumers understand their value proposition. Services like vaccination are a category of unsought products in marketing, thus without aggressive selling effort, consumers are less likely to consume them (Bardus et al., 2023; Hong, 2023; Lee & Kotler, 2016). Based on current findings, social marketing intervention in communicating the health benefits from COVID-19 vaccination cannot be undermined. However, Shitu et al. (2022) and Vasli et al. (2022) found perceived benefits to be an insignificant antecedent of COVID-19 preventive behavior.

The study also predicted that perceived barriers negatively influence consumer preventive health behavior. Although a weak negative effect was observed on the path between perceived barriers and preventive health behavior (β = −0.074, t = −0.86, p = 0.385), it was not statistically significant, hence H4 was not accepted. In this context, perceived barriers were not effective impediments on adoption on COVID-19 vaccination. The findings could be explained by improved consumer awareness, availability of mobile COVID-19 healthcare providers, toll-free COVID-19 communication lines, peer educators and enhanced accessibility to vaccines in rural health centers in Zimbabwe. Further, Hong (2023) supports that effective social marketing campaigns can effectively reduce barriers to adoption of recommended preventive behavior. The findings did not substantiate support for Yap et al. (2021), Weng et al. (2020) and Moradhaseli et al. (2019) who reported significant negative impact of perceived barriers on preventive behavior. Further, Hidayati et al. (2022), Karimy et al. (2021) and Mahindarathne (2021) contest the current findings from their studies in the domain of COVID-19 preventive behavior.

Cues to action were confirmatory of their significant positive effect on preventive health behavior. Interestingly, the construct had the strongest causal effect among all the predictors in the model (β = 0.797, t = 11.266, p < 0.001). These results influenced support for H5. There was compelling evidence that facilitating conditions had a key influence on COVID-19 vaccination decisions. This connotes that consumers are more apt to adopt recommended health action if there are stimulating conditions that undo complacency, negative attitudes and unsubstantiated traditional beliefs (Jaravaza et al., 2023). These results are in sync with extant health behavior literature (Glanz et al., 2008; Moradhaseli et al., 2019). Further, Karimy et al. (2021), Mahindarathne (2021) and Vasli et al. (2022) also confirmed the positive impact of health motivation on COVID-19 preventive behavior. These findings have strong implications on social marketing implementation through its integrated marketing mix strategy in rural communities. Availability of free education, awareness, counselling, mobilization, consultation and peer support offers better cues to taking COVID-19 preventive behavior (Bardus et al., 2023; Hong, 2023). However, Shitu et al. (2022) argue that cues to action were insignificant on COVID-19 preventive behavior.

The impact of self-efficacy on preventive health behavior was also positive and statistically significant (β = 0.500, t = 9.283, p < 0.001), hence H6 was accepted. The findings suggest that if an individual had a strong conviction in their ability to manage with the perceived outcomes of COVID-19 vaccination, they were more privy to adopt the health action. Previous studies also confirm the positive impact of self-efficacy on preventive behavior, or example, Freivogel and Visschers (2021), Smail et al. (2021) and Yap et al. (2021). Self-efficacy confirms the belief that a consumer has in his or her ability to navigate through the post-adoption stage (Shahnazi et al., 2020; Vasli et al., 2022). With COVID-19 vaccination, most consumers were misinformed that they would die, get seriously ill, develop long COVID or get sterile during post-adoption (Puri et al., 2020). The social marketing goal remained undoing the status-quo, induce confidence, and trust that COVID-19 vaccination brings health benefits such as better coping and stronger immune resistance if one contracts COVID-19 (Bardus et al., 2023; Vasli et al., 2022). Shahnazi et al. (2020), Shitu et al. (2022) and Vasli et al. (2022) also confirmed the positive impact of self-efficacy on COVID-19 preventive behavior from their studies in Ethiopia and Iran.

5. Conclusions, implications and recommendations

5.1 Conclusions

The purpose of the study was to examine the determinants of consumer preventive health behavior in the rural communities in Zimbabwe. Based on the Health Belief Model, the findings support the positive effects of perceived susceptibility, perceived severity, perceived benefits, cues to action and self-efficacy on preventive health behavior. Thus, this research concludes that these predictors strongly influence consumer adoption of preventive behavior in rural communities in Zimbabwe. This research makes key contributions to the understanding of COVID-19 vaccination acceptance in subsistence economies, thus this paper also provides theoretical, practical and social implications of the study, as well as the limitations and lines of future research.

5.2 Theoretical implications

This research advances health-marketing theory in social marketing. The application of the Health Belief Model has been vast in the most affluent economies, this study adopted the framework in the context of a low-income economy, providing a springboard for future consumer health research in sub-Saharan regions. The confirmation of perceived susceptibility, perceived severity, perceived benefits, cues to action and self-efficacy empirically substantiates the relevance of the Health Belief Model in the context of developing countries. The structural model explained 65.4% of the variability in COVID-19 preventive behavior (Figure 2), a confirmation of the validity of the priori hypothesized relationships. Among the key findings, noteworthy is the disconfirmation of perceived barriers on preventive health behavior, an indication that despite perennial health inequalities inherent in developing countries, health access and infrastructural demerits were significantly mitigated in an effective COVID-19 vaccination campaign. More so, although social marketing has been earmarked to mainstream significant behavior change in communities, fewer research efforts have been directed to it in developing countries hence this study provides a key impetus into future social marketing research in low-income African economies.

5.3 Practical implications

With sustainability concerns proliferating, governments, national- and community-level healthcare providers need a deeper understanding of consumer health behavior and its underlying motivations, beliefs and perceptions. Knowledge of underlying consumer beliefs towards pro-health behaviors provides valuable insights into healthcare management, social marketing planning and positioning aimed at stimulating informed health decisions. Consumer education and awareness remain key health marketing strategies in the pursuit of sustainable consumption and consumer longevity. This research hopes to provide a key impetus for the application of the Health Belief Model as a focal framework for determining the antecedents of preventive health behavior from the perspective of the rural consumer. This study also lends a baseline for health practitioners to understand COVID-19 vaccination acceptance from a consumer perspective in subsistence economies and hence better forecast health behavior changes over time. Finally, this study provides feedback on the effectiveness of the social marketing by the Ministry of Health and Childcare (MOHCC), directed toward COVID-19 vaccination acceptance in Zimbabwe during the heightened state of the pandemic.

5.4 Social implications

With the world grappling from impactful negative externalities, such as the outbreak of the COVID-19 pandemic, adverse climate effects and perennial food deficits, more responsible human choices are imperative to support human sustainability and longevity. The goal of social marketing remains promotion of positive social behaviors that enhance social welfare, consumer health, longer and better lives. The cost of healthcare is rising and this exerts financial pressure on governments especially in low-income economies. This research provides a proxy for determining the impact of social marketing efforts on community health behavior in subsistence markets. The onus is on health consumers to cooperate and support greener consumption and scientifically recommended health actions like COVID-19 vaccination. Our findings suggest that with better consumer understanding of the negative impact of poor health decisions, longer and even better livelihoods are achievable in pre-emerging economies.

5.5 Originality of the study

Although the Health Belief Model is a vastly applied framework in the extant preventive health behavior literature, its application in subsistence economies, such as Zimbabwe has been scarce, especially in rural communities. This research examined the model in the context of a developing economy. Further, most studies used the Health Belief Model to examine chronic illness early diagnosis and treatment (secondary preventive behavior). This study applies the framework to evaluate preventive behavior (primary preventive action). The COVID-19 context also presented a novel phenomenon globally, and hence that had potential connotations on consumers’ behavioral responses, and the inclusion of social marketing paradigm also created a unique research context.

5.6 Recommendations

Based on our findings, the recommendations to health authorities (MOHCC) are also provided. The MOHCC should adequately resource and deploy trained, self-managing and local health staff in rural communities prior to rolling out a critical health program to promote its adoption. Availability of cues to action (facilitating conditions or triggers) enhances preventive health behavior. Cues to action (β = 0.797) had the strongest influence on preventive health behavior (COVID-19 vaccination). Secondly, the MOHCC should promote rural community health education campaigns that promote awareness on hazards to which they are exposed during pandemics like COVID-19. Knowledge on disease severity (β = 0.654) enhances the uptake of preventive health action, hence perceived severity influences preventive health behavior.

Further, the MOHCC should expedite public health consultation services in rural areas to support community health. Consultation improves a person’s self-esteem in coping up with the health action. Self-efficacy had a positive effect on preventive health action (β = 0.500). Health consultation provides a pathway for educating communities that the recommended action will positively impact their self-efficacy and they are not being pushed into danger. More so, the MOHCC should extensively educate rural communities effectively on their susceptibility to contacting a disease. Risk exposure is a key dimension on which health products consumers make a decision whether to adopt a health or not. With more authentic information on the magnitude of risk of exposure, more rural consumers would be most likely to adopt COVID-19 vacination (β = 0.259).

The benefits of adopting recommended health action should be extensively communicated by the MOHCC prior and whilst rolling out a health program like COVID-19 vaccination. The health authorities also need to be educated into adopting a “customer oriented” Social Marketing approach so that they can sufficiently “position” their health promotion campaigns. A “unique value proposition” should be created so that consumers understand the key benefits of taking the health action. Perceived benefits significantly influenced preventive health behavior in the rural communities in Zimbabwe (β = 0.129).

Lastly, although the relationship between perceived barriers and preventive health behavior failed to reach statistical significance (β = −0.074, p = 0.385), a weak negative relationship still reflects the negative, albeit weak, impact of perceived barriers on health action. Both statistical and practical significance takes consideration in research. MOHCC still needs to iron out the barriers that inhibit preventive health action in rural communities. Healthcare access should be convenient in the marginalized areas to enhance better uptake of preventive behaviors in Zimbabwe.

To the health consumers resident in rural communities, the paper recommends that they need to embrace the value that consumer health education brings and acknowledge role of Social Marketing in mainstreaming positive behavior change that benefits them. Education and awareness are designed to safeguard them from hazards which obstruct their long-term longevity. The community is urged to continue cooperating with the government, health authorities and health practitioners on programs meant to support national health. This paper implores health consumers to effectively expedite national health programs that are deployed to mitigate population mortality and promote community well-being.

5.7 Limitations and future research directions

The use of a non-student sample, robust statistical analyses and a geographically representative sample were some of the valuable products of this research. However, the study was not without limitations, which avail prospects for future research. From social marketing viewpoint, the study achieved its objective. However, cognizant of Lee and Kotler’s (2016) social marketing process, more research still needs to operationalize the building blocks of a comprehensive social marketing process for sustainable behavior change. Thus, future studies may explore key aspects such as the social environmental analysis, Segmentation-Targeting-Positioning (STP) strategies, formulating behavioral goals and objectives, examining barriers, benefits, competitors, and the influential others, integrated social marketing mix strategy, monitoring and evaluation strategies, budgeting, financing and social marketing implementation plan and feedback mechanisms.

Secondly, the study was based on the young adult population of the Generations Y and Z. The results may possibly reflect deviations due to variations within the demographic characteristics that may explain some behavioral dispositions in response to an individual’s health. Future research may dwell on the unsampled population segments for cross validation of research findings. The study was also conducted in the rural communities, often marginalized from mainstream research and equitable health access. This presents a limitation on generalizability of the findings. Future studies may explore both the rural and urban population so that results are representative of the all health consumers.

Finally, we present a limitation on the mono quantitative approach. From a social marketing perspective, in-depth interactions with targeted segments have been envisaged for enhanced social dialogue (Lee & Kotler, 2016; Shekhar, 2022). This elevates qualitative research to inform development and communication of better social value propositions. Although the study used advanced statistical analyses, the approach does not allow respondents to give detailed accounts of their underlying health perceptions, motivations and lived experiences. To gain deeper insights on the subject matter, future social marketing-oriented research may adopt triangulation to explain the research phenomenon from different perspectives.

Acknowledgments

The authors would like to thank all the participants who provided their responses in this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used in this research is available at https://data.mendeley.com/datasets/94jg7jkt6n/1

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Phillip Dangaiso

Phillip Dangaiso is a lecturer at the Zimbabwe Ezekiel Guti University (ZEGU). He is a multi-award winner and this research presents one of his co-authored works. Extensive Social Marketing application amid COVID-19 vaccine hesitancy motivated this research. His research interests are in Social Marketing, Sustainable and Inclusive Development, Services Marketing, Higher Educational Technology, Customer Experience Management, Digital Servicescapes and Relationship Marketing.

Forbes Makudza

Forbes Makudza is a senior lecturer and PhD finalist candidate at the University of Zimbabwe (UZ). He is a seasoned researcher with publications in high impact journals advancing Strategic Marketing, E-Commerce, Entrepreneurial Marketing and Services Marketing. Sinothando Tshuma, Hope Hogo, Nyasha Mpondwe, Courage Masona, Upenyu Sakarombe, Tendai Nedure, Regis Muchowe, Gift Nyathi, Knowledge Jonasi, Tendai Towo, Tendai Manhando and Daniel Tagwirei are lecturers and researchers at universities located in different provinces in Zimbabwe.

Appendix 1.

Table

COVID-19 vaccination statistics for Zimbabwe as at 07 September 2022.
Total Doses Given	At least One dose	Percentage of population	Fully Vaccinated	Percentage of fully vaccinated population	Herd Immunity Target	Deviation from Herd Immunity
12.2 Million	6.4 Million	43.2%	4.73 Million	31.8%	60%	−28.2%

Source: Ministry of Health and Childcare (MOHCC) (2022).