Abstract
Background: Small, medium and micro-enterprises (SMMEs) are crucial contributors to economies worldwide; they significantly contribute to job creation, poverty reduction and innovation. In the context of the South African landscape, SMMEs often face a high likelihood of failure.
Objectives: The primary objective of this study was to develop a model to adopt the Internet of Everything (IoE) in South African SMMEs within Limpopo municipalities.
Method: This is a quantitative study, and a quantitative questionnaire was administered to 122 participants through Google Forms. A multi-method analytical strategy integrated qualitative and quantitative findings, supported by expert validation and literature synthesis to bridge theory and practice. Statistical analysis was conducted using the Statistical Package for the Social Sciences, and random sampling was employed to minimise sampling error. Design Science Research served as the primary research strategy, guided by Saunders’ research onion.
Results: The results demonstrate that communication, top management support, perceived benefits, perceived ease of use, perceived trust, worth of use, frequency of use, relative advantage and business growth were all shown to exert significant influence on IoE adoption.
Conclusion: The IoE adoption in SMMEs is shaped by a combination of organisational, technological, behavioural and strategic factors.
Contribution: This study delivers a validated, theoretically sound and practically relevant IoE adoption model that advances both scholarly understanding and real-world implementation of IoE in SMMEs.
Keywords: South African; small, medium and micro-enterprises; design science research; Internet of Everything; Internet of Things; information systems; technology organisations environment; statistical analysis.
Introduction
Small, medium and micro-enterprises (SMMEs) are crucial to global economies, especially in developing regions of the Global South, as they drive job creation, innovation and poverty reduction (Maja et al. (2024); Sibiya, Van der Westhuizen & Sibiya 2023). However, they often encounter challenges like limited access to capital and a high failure rate, particularly in South Africa, where 70% – 80% of Limpopo SMMEs may not survive beyond 5 years (Baadjie et al. (2023; Gumbi et al. (2025; Maja & Fatoki 2024; Mutezo 2024). The implementation of technological advancements, particularly the Internet of Everything (IoE), offers SMMEs the opportunity to enhance economic growth, streamline business strategies and improve customer engagement (Sabrin et al. 2023). The Internet of Things (IoT) refers to the use of communication gadgets such as laptops, tablets, mobile phones and smartphones (Kirana Ulum et al. 2024).
The IoT describes the global network of electronic devices, home appliances and automobiles that are embedded with various sensors that can send and receive data through a network. The IoE refers to the intelligent connection of things, data, people and processes, extending beyond IoT, which focuses on device connectivity (Ibrahim et al. 2025). By integrating data, processes and individuals, IoE transcends the IoT by fostering advanced interactions that improve decision-making and customer satisfaction (Antonios, Konstantinos & Christos 2023; Hadi et al. 2023; Ibrahim, Shaari & Perumal 2025). This digital transformation can assist SMMEs in reducing operational costs, driving productivity and achieving sustainability, thereby contributing significantly to overall economic development and employment objectives set by the South African government to generate 90% of jobs through SMMEs by 2030 (Mtambo, Lubbe & Ohei 2023).
Problem awareness
The IoE holds promise for improving business efficiency, yet numerous SMMEs in Limpopo struggle with its integration, resulting in inefficiencies and heightened failure rates. A study by Munasser et al. (2024) reveals a lack of IoE adoption among SMMEs, which could otherwise spur economic growth. Sungwa (2025) notes that this absence leads to poor management and diminished satisfaction among employees and customers, hindering sustainability. The problems are compounded by outdated technology, insufficient innovation and inadequate strategic support. Bello, Kanakana-Katumba and Maladzhi (2024) emphasise that financial and operational disparities contribute to these challenges, while Qiu et al. (2023) highlight concerns regarding privacy and connectivity. With the rapid pace of technological change necessitating new strategies, further research is required to address the unique barriers faced by SMMEs in Limpopo as they contemplate adopting IoE solutions.
Problem statement
Small, medium and micro-enterprises are a critical pillar of the global and South African economy, accounting for approximately 95% of registered businesses and providing employment to nearly 68% of South Africa’s workforce (Shibiti, Masabo & Ladzani 2023). Despite their economic significance, SMMEs experience alarmingly high failure rates, with an estimated 70% – 80% collapsing within their first year of operation (Mtambo et al. 2023; Shibiti et al. 2023). These failures are largely attributed to persistent financial constraints, weak strategic capabilities, limited access to digital infrastructure and inadequate adoption of enabling technologies (Mtambo et al. 2023).
The IoE presents a transformative opportunity for SMMEs by enabling operational efficiency, real-time decision-making, enhancing customer engagement and long-term sustainability (Bello et al. 2024; Hadi et al. 2023; Mtambo et al. 2023). However, IoE adoption among South African SMMEs remains critically low (Ibrahim et al. 2025). This is primarily due to the absence of coherent, context-specific adoption frameworks, compounded by barriers such as limited financial resources, low technological awareness and insufficient managerial readiness (Gumbi & Twinomurinzi 2025; Mtambo et al. 2023). This study notes that existing research on IoE adoption models in South African SMMEs is limited, particularly in Limpopo province, where no empirically validated adoption model currently exists.
This lack of a contextualised, structured and validated IoE adoption model represents a significant gap in both academic literature and practical guidance for SMME decision-makers. Consequently, this study seeks to address this gap by developing a model for the adoption of the IoE in South African SMMEs, with specific reference to Limpopo municipalities.
Research questions
The primary research question was: What model will inform the adoption of the IoE in South African SMMEs in Limpopo municipalities?
The secondary (sub)questions were: (1): Which factors (generic) will influence the adoption of the IoE in South African SMMEs? (2) What is the influence of the identified factors (generic) on the adoption of the IoE in South African SMMEs in Limpopo municipalities? (3) Which model will inform the adoption of the IoE in South African SMMEs in Limpopo municipalities? and (4) How can the developed model for the adoption of the IoE in South African SMMEs in Limpopo municipalities be validated?
Related theoretical frameworks
The issues identified during the preliminary investigation led the researcher to explore the related literature on the adoption of the IOE in South African SMMEs in Limpopo municipalities.
Table 1 presents a list of academic frameworks, elements, objectives and gaps identified that influence a model to adopt the IoE in South African SMMEs in Limpopo municipalities. The table was compiled from the literature review, put together from several academic journals and peer-reviewed research articles, to assess the different frameworks that can influence the adoption of the IoE in SMMEs. A systematic literature search was conducted using databases such as IEEE Xplore, Google Scholar, ScienceDirect, ResearchGate and several other educational websites. Keywords including ‘Internet of Everything’, ‘SMMEs/SMEs’, ‘South Africa’, ‘technology adoption’ and ‘theoretical frameworks’ were used, covering publications from 2023 to 2026.
A quantitative content analysis was used to examine factors influencing the adoption of information systems (IS).
The study identified nine critical factors through systematic coding, with communication and business growth emerging as the most significant, each cited 38 times. Other noteworthy factors included top management support (35), relative advantage (36) and perceived benefits (35), highlighting the importance of leadership communication and user confidence. Additionally, perceived trust (29), worth of use (30) and frequency of use (31) were acknowledged, while perceived ease of use (PEU) (25) was deemed less critical. These findings underline the complex nature of IoE adoption and advocate for a multifaceted approach to strategy development. Overall, the research suggests a model where communication, business growth, top managerial support, benefits and relative advantage are primary predictors of IoE adoption, with ease of use, trust and perceived value also playing influential roles. A detailed summary of the factors and their frequency values is presented in Appendix 1 (Table 1-A1). The analysis illustrates that the adoption process in SMMEs is shaped by a blend of strategic, organisational, technological and human factors, providing a robust empirical foundation for the proposed IoE adoption model.
Conceptual model
In their 2023 study, Ahmetoglu, Che Cob and Ali (2023) advocate for the utilisation of multiple theoretical frameworks to enhance the understanding of emerging technology adoption, addressing the limitations of individual theories in organisational decision-making, as supported by Gupta (2024), and further explored by Yoopetch et al. (2024) and Pandey et al. (2025). This research introduces a conceptual model, as presented in Figure 1.
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FIGURE 1: A model to adopt the Internet of Everything in South African small, medium and micro-enterprises. |
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The conceptual model is specifically for IoE adoption in South African SMMEs in Limpopo municipalities, integrating the Technology Organisations Environment framework with various IoE adoption models.
This study identifies communication as a vital component that fosters information exchange and collaboration, thereby facilitating knowledge sharing and swift decision-making, leading to the hypothesis that enhanced communication positively influences IoE adoption (H1). Furthermore, it underscores the importance of top management support in digital innovation adoption, positing that leadership engagement and resource allocation significantly improve the chances of successful IoE technology integration, resulting in the hypothesis that top management support positively affects IoE adoption in SMMEs (H2). Perceived benefits emerge as critical in the IoE adoption process, often outweighing associated risks and costs, encompassing advantages such as digital marketing, cost efficiency, enhanced security and streamlined operations, as noted by Akan et al. (2023). This aspect is operationalised by analysing customer experiences and growth trajectories, leading to hypothesis H3, which posits that perceived benefits significantly impact IoE adoption.
Additionally, PEU plays a crucial role in technology acceptance, influencing user attitudes towards IoE applications, especially in contexts with limited technical expertise. This study examines PEU through dimensions such as accessibility and user-friendliness, proposing in hypothesis H4 that such ease of use positively contributes to IoE adoption among SMMEs.
Perceived trust significantly influences users’ intentions to adopt new technologies, as it encompasses reliability, integrity and security. When users have positive experiences and trust in a system’s quality and reliability, they are more inclined to adopt it (Putri et al. 2025). The hypothesis H5 posited that perceived trust would positively affect the adoption of IoE in SMMEs. Additionally, the concept of ‘worth of use’, the perceived value that relates to costs, also affects adoption decisions, particularly emphasising the importance of cost-effectiveness and improved competitiveness provided by IoE technologies. The hypothesis H6 posited that the worth of use would positively affect the adoption of IoE in SMMEs. Engagement metrics, such as frequency of use, are critical for understanding user interaction, which is correlated with satisfaction and system performance. Thus, it hypothesises that the worth of use will facilitate IoE adoption (H6).
Frequency of use is a critical indicator of sustained engagement with technology, reflecting both its adoption and future utilisation. When technology meets or surpasses user expectations, the likelihood of continued use increases (Cao et al. 2025; Wandira, Fauzi & Nurahim 2024). This study operationalises frequency of use through survey items addressing behavioural and attitudinal aspects of technology interaction, focusing on expectations, skills adequacy, performance efficacy, satisfaction and loyalty. It proposes that these elements collectively influence initial adoption and ongoing engagement with IoE systems, particularly within SMMEs, leading to the hypothesis: H7 that the frequency of use positively impacts IoE adoption.
Relative advantages of new technologies significantly influence the adoption by enhancing efficiency, competitiveness and sustainability AbuAkel et al. (2023); Chiew et al. (2025). The study posits that relative advantage significantly influences IoE adoption in SMMEs (H8), indicating a complex interplay of growth, collaboration, performance, reputation and adaptability. Additionally, by fostering networking, brand marketing and market expansion, IoE secures competitive advantages while driving profitability and sustainability (Zhang 2023). The study measures business growth by examining IoE’s impact on profitability, market expansion and customer relations, ultimately affirming that business growth significantly influences the adoption of IoE within SMMEs (H9).
Research methods and design
Research methodology
This study adopts a rigorous, mixed-method research methodology to investigate the adoption of IoE models among South African SMMEs in Limpopo, integrating qualitative and quantitative approaches within a robust Design Science Research (DSR) framework. Guided by Saunders’ research onion, the study systematically aligns its philosophical, methodological and empirical layers to enhance rigour, coherence and credibility. A pragmatic research philosophy underpins the methodology, emphasising contextual relevance, practical applicability and user engagement in the development of the IoE adoption model. The research follows a primarily deductive approach, drawing on established IoE theories, while incorporating qualitative interviews for model validation and survey-based quantitative methods for empirical testing. A cross-sectional research decision and survey strategy were employed to collect primary data within a defined timeframe, enabling triangulation and reducing the methodological biases associated with single-method studies.
Within a positivist paradigm, the study applies quantitative content analysis and statistical techniques to support objective data interpretation (Gamage 2025; Kotronoulas 2023). Quantitative analysis focuses on identifying relationships, patterns and trends using primary survey data (Pan et al. 2024), employing both deductive categorical coding and structured analytical matrices (Musirmonov et al. 2024), with category validity anchored in conceptual accuracy (Lapadat et al. 2025). Data interpretation and reporting follow systematic category-based synthesis procedures (Marsa et al. 2025). Empirically, a quantitative questionnaire was administered to 122 participants (final dataset = 120 valid responses), and qualitative insights were obtained from five IoE experts through Google Forms. A multi-method analytical strategy integrated qualitative and quantitative findings, supported by expert validation and literature synthesis, to bridge theory and practice (Kellerhuis et al. 2025). Statistical analysis was conducted using Statistical Package for the Social Sciences (SPSS), enabling the transformation of large datasets into structured analytical outputs and testing the influence of adoption factors within Limpopo SMMEs (Behera, Kumar Sahu & Bishi 2025).
This study employed probability-based simple random sampling to ensure representativeness and minimise sampling error among SMMEs in Limpopo municipalities. Using online sample size calculators, a population of approximately 174 decision-makers was estimated, with a final sample size of 122 respondents, calculated at a 95% confidence level and 5% margin of error, ensuring statistical validity, generalisability and robustness in hypothesis testing.
Design Science Research served as the primary research strategy, enabling the development of an innovative, theory-grounded and context-sensitive IoE adoption model. The study operationalised Hevner’s three-cycle DSR framework, comprising the relevance cycle (identifying business needs and contextual constraints), the design cycle (iterative development and refinement of the IoE model based on empirical evidence) and the rigour cycle (grounding the model in established theories and scholarly literature). Through structured DSR processes, including problem awareness, suggestion, development, evaluation and conclusion phases, the study produced a validation IoE adoption model that is both theoretically robust and practically applicable. This integrated methodological architecture ensures high levels of scientific rigour, empirical reliability, theoretical contribution and practical relevance, demonstrating the strength of DSR in bridging IS theory and real-world SMME adoption challenges.
Pearson’s correlation of the constructs
The Pearson correlation analysis confirms that the selected constructs exhibit strong, positive and statistically significant relationships with IoE adoption among South African SMMEs in Limpopo. Consistent with prior studies, the Pearson correlation coefficient is appropriate for assessing the strength, direction and significance of relationships among constructs, while also supporting internal validity and consistency (Berhanu, Semela & Moges 2025; Dufera, Liu & Xu 2023; Mdemu et al. 2025; Pan et al. 2024). Using established significance thresholds, correlations marked with double asterisks are significant at the 0.01 level (99% confidence), indicating robust empirical support for the hypothesised relationships (Duan et al. 2025; Pallegama et al. (2024); Ramdas et al. (2025).
Overall, the results presented in Table 2 demonstrate that all independent variables (COM, TMS, PBF, PEU, PTT, RWO, FOU, BGH and RAD) are strongly and positively correlated with the dependent variable IOE, with coefficients ranging from 0.632 to 0.797, all significant at the 0.01 level (2-tailed). According to the adopted classification, these values fall within the strong to very strong range (Papageorgiou 2022), confirming meaningful associations rather than weak or spurious relationships. The strongest relationship with IoE adoption is observed for RAD (r = 0.797), followed closely by FOU (r = 0.760) and BGH (r = 0.744), indicating that these factors are particularly influential predictors of IoE adoption in the SMME context.
| TABLE 2: Pearson’s correlation coefficient statistics. |
Inter-construct correlations are also consistently strong and significant, suggesting theoretical coherence and complementary effects among organisational, technological and environmental factors. For example, strong associations are evident between TMS and PBF (r = 0.777), FOU and RWO (r = 0.771) and RAD with both FOU (r = 0.767) and BGH (r = 0.753). These patterns indicate that IoE adoption is not driven by isolated factors but rather by interconnected enablers that jointly reinforce adoption readiness and capability.
Reliability analysis further strengthens these findings. Cronbach’s alpha coefficients exceeding 0.8 for all constructs confirm high internal consistency and measurement reliability (Demirkol et al. 2025), supporting the robustness of the measurement model. Taken together, the strong correlations, high significance levels and reliable constructs provide compelling empirical evidence that the proposed factors are valid predictors of IoE adoption.
In conclusion, the correlation results conclusively demonstrate that IoE adoption among South African SMMEs in Limpopo is influenced by a network of strongly interrelated factors, all exhibiting significant positive relationships with adoption outcomes. These findings validate the integration of the identified constructs into a comprehensive IoE adoption model and justify its applicability within SME operational contexts. The evidence supports proceeding to subsequent multivariate analyses and model testing, with confidence that the underlying relationships are statistically sound, theoretically aligned and empirically reliable.
Regression analysis
The regression process leverages existing data to formulate a mathematical model that predicts the value of a dependent variable based on independent variables, thereby facilitating extrapolation within a dataset (Ahmad et al. 2022). This model can also estimate outcomes for unencountered observations (Ramdas & Wang 2025).
A significant predictability was achieved through the P-statistic, which combines the strength of two associations (Williams, Nathanson & Paulhus 2010). Table 3 presents the analysis of the dependent variable (IoE) against various independent variables (COM, TMS, PBF, PEU, PTT, RWO, FOU, BGH and RAD). The model demonstrated an overall predictive accuracy of 89.8% (R2 = 0.792). This indicates that the framework and constructs associated with IoE contribute substantially to the development of a model to adopt IoE in South African SMMEs in Limpopo municipalities.
| TABLE 3: Model summary: Hypothesis 1 – Hypothesis 9. |
Hypothesis testing
A hypothesis represents an unverified assumption aimed at explaining particular factors or phenomena (Mandavilli 2025). Its evaluation involves regression analysis, incorporating independent variables and a moderating variable (Situmeang & Sugiyanto 2024). The process of hypothesis testing is crucial for assessing the statistical significance and persuasiveness of participants’ evidence (Hossain et al. 2024). A hypothesis is deemed supported when the beta value is positive, t-values exceed 1.645, and the p-value remains below 0.05 (Jais et al. 2024). The hypothesis testing presented in Table 4 shows that H4 “PEU” = 0.352 is non-significant; the p-value is above 0.05; therefore, H4 is not supported.
Table 4 shows that data analysis confirmed the support for only eight hypotheses; one hypothesis was not supported; consequently, the conceptual model was not revised before being presented to the experts for validation.
Validation of the Internet of Everything adoption model
The validation of the developed IoE adoption model was conducted to ensure its relevance, suitability, reliability and practical applicability within the context of South African SMMEs, with specific focus on Limpopo municipalities. Consistent with established validation principles, the study adopted an expert review approach, recognising that validity is context-dependent and must be assessed against the intended population and purpose rather than treated as an absolute property (Adams & Wieman 2011; Marar et al. 2023). This approach aligns with best practises for validating artefacts in applied IS and DSR.
A purposive sample of five domain experts, comprising SMME stakeholders and information technology decision-makers in Limpopo, evaluated the artefact using a structured online questionnaire. The validation instrument was designed to assess the model’s relevance, appropriateness, usefulness and alignment with SMME business needs, as well as its potential to provide actionable guidance for IoE adoption. The expert review method was selected to ensure that the model addressed real-world business challenges while remaining theoretically grounded (Berhanu et al. 2025).
Figure 2 presents the validation findings that demonstrate strong consensus among experts regarding the value of the proposed model. Reviewers agreed that the model can meaningfully support IoE adoption by enhancing technological infrastructure, improving business networking, simplifying operational processes, facilitating access to information and enabling innovation and growth, particularly in under-resourced and rural SMME environments. The model was consistently rated as suitable and appropriate, with experts highlighting its potential to strengthen SMME livelihoods, modernise business practises and improve connectivity across municipalities. Furthermore, the reviewers confirmed that the model addresses core SMME business needs, including access to markets, integration of business ideas and timely availability of information for decision-making.
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FIGURE 2: Validated model to adopt the Internet of Everything in South African small, medium and micro-enterprises in Limpopo municipalities. |
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In terms of guidance, the experts largely affirmed that the model can serve as a practical reference framework for SMMEs and policymakers. While one reviewer noted infrastructural challenges related to internet connectivity in certain rural areas, this limitation was reviewed as a contextual constraint rather than a weakness of the model itself. Importantly, the experts’ recommendations for future enhancements, such as extending connectivity to rural villages, incorporating trusted information sources and increasing youth participation, were incremental improvements rather than fundamental design concerns.
In conclusion, the expert validation confirms that the proposed IoE adoption model is relevant, reliable, contextually appropriate and fit for purpose. The consistent support across all validation criteria indicates that the artefact meets its intended objectives and can effectively guide IoE adoption in South African SMMEs, particularly within Limpopo municipalities. As all constructs were validated and supported, no modifications were required, reinforcing the robustness and readiness of the model for practical application and academic contribution.
Conclusion
The goal is to develop a contextually grounded model for the adoption of the IoE within South African SMMEs, with specific reference to Limpopo municipalities. Guided by a DSR approach and supported by robust quantitative analysis and expert validation, the study successfully achieved its primary objective of producing a reliable, relevant and practically applicable IoE adoption artefact.
Empirically, nine hypotheses were formulated and tested, of which eight were statistically supported, confirming that IoE adoption in SMMEs is shaped by a combination of organisational, technological, behavioural and strategic factors. Communication, top management support, perceived benefits, perceived trust, worth of use, frequency of use, relative advantage and business growth were all shown to exert significant influence on IoE adoption. These findings reinforce the view that IoE adoption is not a purely technical decision, but a multidimensional organisational process that requires leadership commitment, value realisation, sustained use and strategic advantage. The literature review presented in Table 1-A1 in Appendix 1 indicates that PEU is relatively less critical compared to other determinants. This finding is further corroborated by the regression analysis presented in Table 3, where PEU yields a coefficient of 0.352 but is statistically non-significant, as its associated p-value exceeds the conventional threshold of 0.05. Overall, the single unsupported construct, PEU, aligns with prior evidence suggesting that in complex, interconnected technologies, ease of use may be secondary to perceived value, trust and strategic relevance, particularly in resource-constrained SMME environments.
The expert-based validation further confirms the robustness of the developed model. Feedback from domain experts demonstrated strong consensus regarding the model’s relevance, suitability and usefulness in addressing real SMME challenges, including infrastructure limitations, business integration, networking and access to information. While infrastructural constraints such as uneven internet connectivity were acknowledged, these were identified as contextual limitations rather than weaknesses of the model itself.
Importantly, the absence of required structural modifications following validation underscores the maturity and readiness of the artefact for practical deployment.
From a theoretical perspective, this study contributes to the IS literature by extending IoE adoption research into an under-explored developing country SMME context. It addresses a notable gap by providing a contextualised, empirically validated IoE adoption model tailored to South African SMMEs, particularly within rural and semi-urban municipalities. Methodologically, the integration of DSR, statistical modelling using SPSS, Pearson correlation analysis, regression analysis and expert judgment strengthens the rigour of the study and demonstrates a replicable pathway for artefact-driven research in emerging digital ecosystems.
Practically, the proposed model offers actionable guidance for SMME owners, managers, policymakers and technology practitioners. It highlights critical preparatory factors required for successful IoE integration and provides a structural framework for managing, monitoring and sustaining IoE-enabled operations. Although developed for Limpopo municipalities, the model is sufficiently adaptable to inform IoE adoption strategies in similar SMME-dominated regions and sectors.
Notwithstanding its contributions, the study is limited by its geographical focus and cross-sectional design.
Future research is encouraged to test the model across other provinces and sectors, employ longitudinal designs to assess long-term IoE impacts and explore synergies between IoE and emerging technologies such as artificial intelligence, blockchain and 5G. Developing a practical IoE readiness assessment tool could further support SMMEs in evaluating their preparedness for digital transformation.
In conclusion, this study delivers a validated, theoretically sound and practically relevant IoE adoption model that advances both scholarly understanding and real-world implementation of IoE in SMMEs. By bridging theory and practice through a rigorous DSR approach, the study provides a solid foundation for future research and a strategic roadmap for SMMEs seeking to leverage IoE for competitiveness, growth and sustainability.
Acknowledgements
This article is based on research originally conducted as part of Thanzi Mafunzwaini’s master’s thesis titled ‘Internet of Everything in South African Small, Medium, and Micro Enterprises adoption model: a case of Limpopo Municipalities’, submitted to the Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology in 2025. The thesis is currently unpublished and not publicly available. The thesis was supervised by Tope S. Adeyelure, Cecil H. Kgoetiane and Sihle Sibiya. The thesis was reworked, revised and adapted into a journal article for publication. The author confirms that the content has not been previously published or disseminated and complies with ethical standards for original publication.
The authors would like to express special thanks to all the participants who took the time out of their busy schedules to contribute to this study.
Competing interests
The authors, Thanzi Mafunzwaini, Tope S. Adeyelure, Cecil H. Kgoetiane and Sihle Sibiya, declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Thanzi Mafunzwaini: Writing – original draft. Tope S. Adeyelure: Supervision. Cecil H. Kgoetiane: Supervision. Sihle Sibiya: Supervision. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication and take responsibility for the integrity of its findings.
Ethical considerations
Ethical clearance to conduct this study was obtained from the Tshwane University of Technology and the TUT Research Ethics Committee (No. HREC2024=07=005 [ICT]).
Funding information
The authors received no financial support for the research, authorship and/or publication of this article.
Data availability
The data that support the findings of this study are not openly available due to their sensitive nature (including human participant data) and are available from the corresponding author, Thanzi Mafunzwaini, upon reasonable request, subject to relevant ethical approvals and data access protocols.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
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Appendix 1
| TABLE 1-A1: Summary of the factors and frequency values. |
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