SAJIM South African Journal of Information Management 2078-1865 1560-683X AOSIS SAJIM-28-2176 10.4102/sajim.v28i1.2176 Original Research Internet of Everything in the South African small, medium and micro-enterprises adoption model: A case of Limpopo municipalities https://orcid.org/0000-0001-8790-0420 Mafunzwaini Thanzi 1 https://orcid.org/0000-0002-6138-4285 Adeyelure Tope S. 1 https://orcid.org/0000-0002-2126-3673 Kgoetiane Cecil H. 1 https://orcid.org/0009-0002-4959-8704 Sibiya Sihle 1 Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology, Tshwane, South Africa Corresponding author: Thanzi Mafunzwaini, thanzi.mafunzwaini@gmail.com 23072026 2026 28 1 2176 05022026 05062026 © 2026. The Authors 2026 Licensee: AOSIS. This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. 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.

 South African small, medium and micro-enterprises design science research Internet of Everything Internet of Things information systems technology organisations environment statistical analysis Funding information The authors received no financial support for the research, authorship and/or publication of this article. 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.

Additional related frameworks.

Framework Author (year) Elements Objectives Gaps
Proposed model of information systems (IS) continuance (2023) Mishra et al. (2023) Re-examining the post-acceptance model of information systems continuance to explain users’ satisfaction and IS continuance intentions. To respond to the research inquiry aimed at exploring the significance of trust in the adoption of technology. The framework did not examine how employees utilise information systems within the organisational context.
Leveraging information systems for a strategic management theoretical framework (2024) Hera et al. (2024) The aim is to motivate other business sectors to embrace the technology of ‘leveraging information systems for strategic management’ as a method to boost competitive advantage. The objective is to explore the effect of utilising information systems for strategic management, focusing on improving decision-making and organisational performance. The framework did not examine how technological advancements like AI and machine learning influence the effectiveness of information systems in enhancing SMEs.
Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework (2024) Mhlongo et al. (2025) To recognise, suggest new and revise the current frameworks by adding fewer common constructs for integration into technology adoption theories to enhance the understanding of user acceptance. To improve academic knowledge and practical applications of technology adoption by tackling theoretical gaps and offering comprehensive strategies. This framework addressed only trust, security risk and attitude constructs to further develop technology adoption theories.
Research Model for Adoption of Cloud Computing in SMEs (2023) Aligarh et al. (2023) This article presents a model outlining the main elements that affect the integration of supply chain management utilising cloud computing technology. The objective of this research is to identify these elements and assess how significantly they impact the technology adoption among small- and medium-sized enterprises (SMEs). The research outlines a framework grounded only in the diffusion of innovation (DOI) theory and the technology, organisations and environment (TOE) framework.
A theoretical framework for the determinants of organisational information systems (IS) issues (2024) Palvia et al. (2024) This article developed a multi-tiered theoretical framework consisting of three tiers: an outer tier with three national-level factors, a middle tier featuring two macro-IT factors and an inner tier comprising three organisational-level factors. The objective of the article is to gain deeper insights into the nature of the challenges and their underlying drivers in how IT serves the needs of the business. This article mainly focused on the management and organisational aspects of IT, more so than on any technological challenges.

Note: Please see the full reference list of the article, IMafunzwaini, T., Adeyelure, T.S., Kgoetiane, C.H. & Sibiya, S., 2026, ‘Internet of Everything in the South African small, medium and micro-enterprises adoption model: A case of Limpopo municipalities’, South African Journal of Information Management 28(1), a2176. https://doi.org/10.4102/sajim.v28i1.2176, for more information.

IS, information system; AI, artificial intelligence; SMEs, small- and medium-sized enterprises; IT, information technology; COM, Communication; TMS, Top Management Support; PBF, Perceived Benefits; PEU, Perceived Ease of Use; PTT, Perceived Trust; RWO, Worth of Use; FOU, Frequency of Use; BGH, Business Growth; RAD, Relative Advantage.

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.

A model to adopt the Internet of Everything in South African small, medium and micro-enterprises.

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.

Pearson’s correlation coefficient statistics.

Variables COM TMS PBF PEU PTT RWO FOU BGH RAD IoE
COM 1.000 - - - - - - - - -
TMS 0.721** 1.000 - - - - - - - -
PBF 0.651** 0.777** 1.000 - - - - - - -
PEU 0.627** 0.704** 0.716** 1.000 - - - - - -
PTT 0.681** 0.708** 0.693** 0.756** 1.000 - - - - -
RWO 0.635** 0.744** 0.734** 0.640** 0.701** 1.000 - - - -
FOU 0.690** 0.747** 0.722** 0.704** 0.729** 0.771** 1.000 - - -
BGH 0.689** 0.668** 0.682** 0.718** 0.704** 0.732** 0.742** 1.000 - -
RAD 0.642** 0.642** 0.720** 0.704** 0.633** 0.689** 0.767** 0.753** 1.000 -
IoE 0.717** 0.650** 0.650** 0.659** 0.632** 0.701** 0.760** 0.744** 0.797** 1.000

PEU, perceived ease of use; COM, communication; TMS, top management support; PBF, perceived benefits; PTT, perceived trust; RWO, worth of use; FOU, frequency of use; BGH, business growth; RAD, relative advantage; IoE, Internet of Everything.

, Correlation is significant at the 0.01 level (2-tailed).

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.

Model summary: Hypothesis 1 – Hypothesis 9.

Hypothesis Model or Construct Independent variable(s) R R2 Adj. R2 Std. β t-value p-value
H1 IoE adoption model COM → IoE 0.775 0.600 0.593 0.541 5.945 0.000
H2 IoE adoption model TMS → IoE 0.775 0.600 0.593 0.276 3.034 0.003
H3 Integrated adoption and intention model PBF → IoE 0.791 0.625 0.616 0.455 4.653 0.000
H4 Integrated adoption and intention model PEU → IoE 0.791 0.625 0.616 0.106 0.934 0.352
H5 Integrated adoption and intention model PTT → IoE 0.791 0.625 0.616 0.292 2.985 0.003
H6 Adoption and continuance model RWO → IoE 0.812 0.660 0.654 0.288 2.659 0.009
H7 Adoption and continuance model FOU → IoE 0.812 0.660 0.654 0.549 5.064 0.000
H8 IoE adoption over time model BGH → IoE 0.877 0.769 0.765 0.402 4.670 0.000
H9 IoE adoption over time model RAD → IoE 0.877 0.769 0.765 0.507 5.897 0.000

Std., standard; IoE, Internet of Everything; PEU, perceived ease of use; COM, communication; TMS, top management support; PBF, perceived benefits; PTT, perceived trust; RWO, worth of use; FOU, frequency of use; BGH, business growth; RAD, relative advantage; IoE, internet of things; Adj., adjusted.

 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.

Hypothesis testing.

Hypothesis Results (p-value) Action
H1: Communication will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H2: Top management support will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H3: Perceived benefits will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H4: Perceived ease of use will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.35 > 0.05 Not supported
H5: Perceived trust will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H6: The worth of use will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H7: The frequency of use will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H8: Relative advantage will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 Supported
H9: Business growth will influence the adoption of the Internet of Everything in small, medium and micro-enterprises. 0.00 < 0.05 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.

Validated model to adopt the Internet of Everything in South African small, medium and micro-enterprises in Limpopo municipalities.

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]).

 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.

 References AbuAkel, S.A. & Ibrahim, M., 2023, ‘The effect of relative advantage, top management support and IT infrastructure on e-filing adoption’, Journal of Risk and Financial Management 16(6), 295. https://doi.org/10.3390/jrfm16060295 Adams, W.K. & Wieman, C.E., 2011, ‘Development and validation of instruments to measure learning of expert-like thinking’, International Journal of Science Education 33(9), 1289–1312. https://doi.org/10.1080/09500693.2010.512369 Ahmad, S., Jha, S., Abdeljaber, H.A.M., Imam Rahmani, M.K., Waris, M.M., Singh, A. et al., 2022, ‘An integration of IoT, IoC, and IoE towards building a green society’, Scientific Programming 2022, 2673753. https://doi.org/10.1155/2022/2673753 Ahmetoglu, S., Che Cob, Z. & Ali, N., 2023, ‘Internet of things adoption in the manufacturing sector: A conceptual model from a multi-theoretical perspective’, Applied Sciences (Switzerland) 13(6), 3856. https://doi.org/10.3390/app13063856 Akan, O.B., Dinc, E., Kuscu, M., Cetinkaya, O. & Bilgin, B.A., 2023, ‘Internet of everything (IoE) – From molecules to the universe’, IEEE Communications Magazine 61(10), 122–128. https://doi.org/10.1109/MCOM.001.2200594 Aligarh, F., Sutopo, B. & Widarjo, W., 2023, ‘The antecedents of cloud computing adoption and its consequences for MSMEs’ performance: A model based on the Technology-Organization-Environment (TOE) framework’, Cogent Business & Management 10(2), 2220190. https://doi.org/10.1080/23311975.2023.2220190 Antonios, P., Konstantinos, K. & Christos, G., 2023, ‘A systematic review on semantic interoperability in the IoE-enabled smart cities’, Internet of Things (Netherlands) 22, 100754. https://doi.org/10.1016/j.iot.2023.100754 Baadjie, T.P. & Manyaga, B., 2023, ‘Exploring post-COVID-19 lockdown survival strategies for SMMEs in Johannesburg, Gauteng’, Journal of Contemporary Management 20(1), 560–587. https://doi.org/10.35683/jcman1007.208 Behera, J., Kumar Sahu, S. & Bishi, B., 2025, Unlocking data with SPSS: From curiosity to competence, viewed 31 August 2025, from https://www.researchgate.net/publication/392185231. Bello, K.A., Kanakana-Katumba, M.G. & Maladzhi, R.W., 2024, ‘Adopting smart manufacturing: Specific needs and challenges of small, medium, and micro enterprises (SMMES)’, South African Journal of Industrial Engineering 35(3), 245–256. https://doi.org/10.7166/35-3-3101 Berhanu, A., Semela, T. & Moges, B., 2025, ‘Development and validation of a secondary school classroom engagement instrument in math and science in the Ethiopian context’, Frontiers in Psychology 16, 1491615. https://doi.org/10.3389/fpsyg.2025.1491615 Cao, A., Guo, L. & Li, H., 2025, ‘Understanding farmer cooperatives’ intention to adopt digital technology: Mediating effect of perceived ease of use and moderating effects of internet usage and training’, International Journal of Agricultural Sustainability 23(1), 2464523. https://doi.org/10.1080/14735903.2025.2464523 Chiew, K.-Z. & Cheah, J.S.S., 2025, ‘Re’assessing ICT adoption among Malaysian SMEs: A post-COVID-19 thematic literature review using TOE framework’, Journal of the Community Development in Asia 8(2), 249–265. https://doi.org/10.32535/jcda.v8i2.3893 Demirkol, K., İmamoğlu, S.E., Kayman, Ş.S., İmamoğlu, S.Z. & Erat, S., 2025, ‘Internet use for creative purposes and its correlation with perceived usefulness, computer anxiety, and emotional intelligence: The intermediary effect of the perceived ease of use’, Behavioral Sciences 15(2), 221. https://doi.org/10.3390/bs15020221 Duan, T., Zhang, L., Wang, G. & Liang, F., 2025, ‘Effects of water application frequency and water use efficiency under deficit irrigation on maize yield in Xinjiang’, Agronomy 15(5), 1110. https://doi.org/10.3390/agronomy15051110 Dufera, A.G., Liu, T. & Xu, J., 2023, ‘Regression models of Pearson correlation coefficient’, Statistical Theory and Related Fields 7(2), 97–106. https://doi.org/10.1080/24754269.2023.2164970 Gamage, A.N.K.K., 2025, ‘Research design, philosophy, and quantitative approaches in scientific research methodology’, Scholars Journal of Engineering and Technology 13(2), 91–103. https://doi.org/10.36347/sjet.2025.v13i02.004 Ghobakhloo, M., Hong, T.S., Sabouri, M.S. & Zulkifli, N., 2012, ‘Strategies for successful information technology adoption in small and medium-sized enterprises’, Information 3(1), 36–67. https://doi.org/10.3390/info3010036 Gumbi, L. & Twinomurinzi, H., 2025, ‘SMME readiness framework for smart manufacturing adoption using critical realism: Knowledge and construction phase’, Journal of Innovation and Knowledge 10(2), 100665. https://doi.org/10.1016/j.jik.2025.100665 Gupta, V., 2024, ‘An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs’ perspectives’, Systems 12(3), 103. https://doi.org/10.3390/systems12030103 Hadi, H.J., Harris, S., Cao, Y. & Nisa, E.K.U., 2023, ‘Cybersecurity as a service for Internet of Everything (IoE)’, ResearchGate preprint, pp. 1–22, viewed 31 October 2023, from https://doi.org/10.13140/RG.2.2.28225.94563. Hera, A., Rian, A.A., Faruque, M.O., Sizan, M.M.H., Khan, N.A., Rahaman, M.A. et al., 2024, ‘Leveraging information systems for strategic management: Enhancing decision-making and organizational performance’, American Journal of Industrial and Business Management 14(8), 1045–1061. https://doi.org/10.4236/ajibm.2024.148054 Hossain, R., Ghose, P., Chowdhury, T.M., Hossen, M.D., Hasan, M.N. & Mani, L., 2024, ‘Ownership structures and firm performance: A correlation and regression analysis of financial institutions in Bangladesh’, Pakistan Journal of Life and Social Sciences 22(2), 6278–6295. https://doi.org/10.57239/PJLSS-2024-22.2.00473 Ibrahim, M.I., Shaari, H. & Perumal, S., 2025, ‘Examining the adoption of the IoE among manufacturing SMEs in Malaysia’, PaperASIA 41(1b), 218–231. https://doi.org/10.59953/paperasia.v41i1b.357 Jais, R., Ngah, A.H., Rahi, S., Rashid, A., Ahmad, S.Z. & Mokhlis, S., 2024, ‘Chatbots adoption intention in public sector in Malaysia from the perspective of TOE framework. The moderated and mediation model’, Journal of Science and Technology Policy Management 17(2), 303–324. https://doi.org/10.1108/JSTPM-04-2024-0151 Kartiwi, M., Rfieda, A. & Gunawan, T.S., 2013, ‘A conceptual framework for assessing electronic banking continued use’, in Proceedings of the 2013 8th International Conference on Information Technology in Asia (CITA 2013), IEEE, Kota Samarahan, Sarawak, Malaysia, 1–4 July 2013, pp. 1–7. https://doi.org/10.1109/CITA.2013.6637550 Kellerhuis, B.E., Jenniskens, K., Kusters, M.P.T., Schuit, E., Hooft, L., Moons, K.G.M. et al., 2025, ‘Expert panel as reference standard procedure in diagnostic accuracy studies: A systematic scoping review and methodological guidance’, Diagnostic and Prognostic Research 9(1), 12. https://doi.org/10.1186/s41512-025-00195-7 Kirana Ulum, O., Syerah, H., Ramadhanis, T. & Supratikta, H., 2024, Dampak Internet of Things (IOT) Terhadap Manajemen Sumber Daya Manusia [The impact of the Internet of Things (IoT) on human resource management], viewed from https://openjournal.unpam.ac.id/index.php/SNH/article/view/43960. Kotronoulas, G., 2023, ‘Building on health care professionals’ research literacy for sustained quality care’, Seminars in Oncology Nursing 39(2), 151400. https://doi.org/10.1016/j.soncn.2023.151400 Lapadat, L., Wilson, S., Zhu, L.Y. & Racine, S.E., 2025, ‘Acceptability of a personalised single-session feedback intervention for eating disorders: A qualitative content analysis’, Journal of Eating Disorders 13(1), 133. https://doi.org/10.1186/s40337-025-01333-8 Maja, C.N. & Fatoki, O., 2024, ‘Entrepreneurial orientation and performance of SMMEs in selected municipalities of Limpopo province: The role market orientation and environmental dynamism’, International Journal of Research in Business and Social Science (2147–4478) 13(3), 17–25. https://doi.org/10.20525/ijrbs.v13i3.2899 Mandavilli, S.R., 2025, ‘Minimizing the time taken between hypothesis generation, hypothesis testing and refinement: A necessary adjunct in the epoch of fast-paced science’, SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5390419 Marar, S., Hamza, M.A., Ayyash, M. & Abu-Shaheen, A., 2023, ‘Development and validation of an instrument to assess the knowledge and perceptions of predatory journals’, Heliyon 9(11), e22270. https://doi.org/10.1016/j.heliyon.2023.e22270 Marsa, R., Bahmani, B., Ebadi, A., Azkhosh, M. & Delbari, A., 2025, ‘Grief in the elderly: A qualitative content analysis’, BMC Geriatrics 25(1), 540. https://doi.org/10.1186/s12877-025-06027-z Mdemu, S., Matondo, A.B., Christensen, J.P., Amasha, A.E., Ngowi, H.A., Westwood, E. et al., 2025, ‘Factors influencing the frequency, knowledge, attitudes and practices of antibiotic use in commercial layer chicken farms, Tanzania’, Frontiers in Antibiotics 4, 1571096. https://doi.org/10.3389/frabi.2025.1571096 Mhlongo, S.E., Akintola, G.O., Amponsah-Dacosta, F., Kairiukstis, L. & Ngozi-Chika, C.S., 2025, ‘A mini-review of the development and use of expert systems in mining’, Mining of Mineral Deposits 19(2), 141–147. https://doi.org/10.33271/mining19.02.141 Mishra, A., Shukla, A., Rana, N.P., Currie, W.L. & Dwivedi, Y.K., 2023, ‘Re-examining post-acceptance model of information systems continuance: A revised theoretical model using MASEM approach’, International Journal of Information Management 68(2), 102571. https://doi.org/10.1016/j.ijinfomgt.2022.102571 Mtambo, X.G., Lubbe, S. & Ohei, K.N., 2023, ‘The challenges of small, medium and micro enterprises in engineering: South Africa’, HOLISTICA – Journal of Business and Public Administration 14(2), 15–32. https://doi.org/10.2478/hjbpa-2023-0013 Munasser, H. & Golshan, B., 2024, ‘Degree project understanding the adoption challenges of IoT among small to medium-size enterprises (SMEs) study in Sweden’, Master’s thesis, Linnaeus University, supervised by B. Golshan, viewed 03 March 2025, from https://www.diva-portal.org/smash/get/diva2:1918587/FULLTEXT01.pdf Musirmonov, J.J., Urinova, A.A., Kh, D.D., Tripathi, G., Farooqui, A. & Khujamshukurov, N., 2024, ‘Comprehensive analysis of domestic wastewater for the content of various salts’, Universum: Chemistry & Biology 122(8), 18048. https://doi.org/10.32743/unichem.2024.122.8.18048 Mutezo, A.T., 2024, ‘The determinants of bank credit to SMEs in Gauteng, South Africa’, Journal of Contemporary Management 21(1), 187–206. https://doi.org/10.35683/jcman1044.250 Pallegama, P.M.S.I. & Herath, H.M.A.K., 2024, ‘Factors influencing the customer adoption of internet banking: Case of a leading public sector commercial bank, Kurunegala District, in Sri Lanka’, IOSR Journal of Business and Management 26(12), 16–30. https://doi.org/10.9790/487X-2612131630 Pan, S., Liu, Z., Han, Y., Zhang, D., Zhao, X., Li, J. et al., 2024, ‘Using the Pearson’s correlation coefficient as the sole metric to measure the accuracy of quantitative trait prediction: Is it sufficient?’, Frontiers in Plant Science 15, 1480463. https://doi.org/10.3389/fpls.2024.1480463 Pandey, P. & Rai, A.K., 2023, ‘Post-adoption consequences in internet banking’, Unnayan: International Bulletin of Management and Economics, vol. 11, pp. 304–319. Available at: https://www.researchgate.net/publication/371491197 Pandey, S.C., Virmani, N., Choudhary, D. & Jagtap, S., 2025, ‘Assessing industry 4.0 readiness: A TOE-P framework for the sugar industry in developing economies’, Discover Sustainability 6(1), 384. https://doi.org/10.1007/s43621-025-01212-x Papageorgiou, S.N., 2022, ‘On correlation coefficients and their interpretation’, Journal of Orthodontics 49(3), 359–361. https://doi.org/10.1177/14653125221076142 Putri, E., Bandi, B., Widarjo, W. & Arifin, T., 2025, ‘The value of cloud accounting for MSMEs: A Technology-Organization-Environment (TOE) framework perspective’, Cogent Business and Management 12(1), 2494712. https://doi.org/10.1080/23311975.2025.2494712 Qiu, C. & Pan, X., 2023, ‘Research on strategic transformation path adjustment of small and medium-sized food enterprises’, OALib 10(6), 1–11. https://doi.org/10.4236/oalib.1110255 Ramdas, A. & Wang, R., 2025, Hypothesis testing with e-values, viewed 13 August 2025, from http://arxiv.org/abs/2410.23614. Sabrin, S., Amir, M., Asriani, A., Rahim, A. & Aso, L., 2023, ‘Small micro and medium business development strategy (SMMBDS) in East Kolaka, Southeast Sulawesi Province, Indonesia’, International Journal of Social Science and Human Research 6(1), 621–627. https://doi.org/10.47191/ijsshr/v6-i1-80 Shibiti, R., Masabo, T.T. & Ladzani, W., 2023, ‘Readiness of SMMEs for the adoption of the fourth industrial revolution in Mamelodi Township’, International Journal of Research in Business and Social Science (2147–4478) 12(8), 33–47. https://doi.org/10.20525/ijrbs.v12i8.2986 Sibiya, A., Van der Westhuizen, J. & Sibiya, B., 2023, ‘Challenges experienced by SMMEs and interventions by the South African National and Provincial Government: A literature review’, African Journal of Inter/Multidisciplinary Studies 5(1), 1–11. https://doi.org/10.51415/ajims.v5i1.1224 Situmeang, S. & Sugiyanto, S., 2024, ‘The effect of service quality, customer orientation and customer satisfaction on customer loyalty’, International Journal of Economics (IJEC) 3(2), 1333–1344. https://doi.org/10.55299/ijec.v3i2.1134 Sungwa, J., 2025, ‘Strategic human resources management practices are key to small, medium and micro enterprises effectiveness in South Africa’, SA Journal of Human Resource Management 23, a2763. https://doi.org/10.4102/sajhrm.v23i0.2763 Wandira, R., Fauzi, A. & Nurahim, F., 2024, ‘Analysis of factors influencing behavioral intention to use cloud-based academic information system using extended technology acceptance model (TAM) and expectation-confirmation model (ECM)’, Journal of Information Systems Engineering and Business Intelligence 10(2), 179–190. https://doi.org/10.20473/jisebi.10.2.179-190 Williams, K.M., Nathanson, C. & Paulhus, D.L., 2010, ‘Identifying and profiling scholastic cheaters: Their personality, cognitive ability, and motivation’, Journal of Experimental Psychology: Applied 16(3), 293–307. https://doi.org/10.1037/a0020773 Yasmin, N. & Grundmann, P., 2019, ‘Pre- and post-adoption beliefs about the diffusion and continuation of biogas-based cooking fuel technology in Pakistan’, Energies 12(16), 3184. https://doi.org/10.3390/en12163184 Yoopetch, C. & Chareanporn, T., 2024, ‘Measuring hotel financial and non-financial performance in Thailand: The application of technology-organization-environment (TOE) framework’, Journal of Ecohumanism 3(6), 1703–1718. https://doi.org/10.62754/joe.v3i6.4130 Zhang, J., 2023, ‘Research on the evolution of technological innovation network in China’s logistics industry’, International Journal of Education and Humanities 8(2), 189–198. https://doi.org/10.54097/ijeh.v8i2.7814 Appendix 1

Summary of the factors and frequency values.

Item Factors Frequency Metric Frequency
1. Communication of IoE 38 Digital communication channels 5
Communication of IoE applications 2
Communication satisfaction 4
Social networks 7
Strengthening stakeholder and/or team spirit 6
Online interaction 7
Real-time communication 7
2. Top management support 35 Decision-making strategies 9
Facilitate the adoption 9
Technological leadership 6
Employee engagement 4
Organisational culture 4
Ethical considerations 3
3. Perceived benefits 33 Positive effect 5
Expected advantages 5
Strategic value 6
Compatibility and complexity 5
Technology variable 6
Organisational variable 6
4. Perceived ease of use 25 Emotional intelligence 5
Creativity 4
Technology anxiety 3
System quality 5
Interaction convenience 5
Training and support 3
5. Perceived trust 29 Security 8
Reliability 4
Belief 5
Credibility 7
Integrity 5
6. Reasonability or worth of use 30 Perceived value 9
Money’s worth 9
Expected advantages 6
Perceived enjoyment 6
7. Frequency of use 31 Relevance 4
Change in percentage/statistics 4
Cooperation and goal orientation 5
Use efficiency 6
Adjustments 4
Perceived effectiveness 8
8. Business growth 38 Customer satisfaction 6
Competitiveness 5
Customer relationship management 5
Perceived enjoyment 6
Perceived value and profit 5
Employment growth 6
Business sustainability 5
9. Relative advantage 36 Users’ perception 3
Customer’s intention to adopt 7
More benefits to businesses 6
Improves marketing 6
Strengthens business 7
New business opportunities 7

IoE, Internet of Everything.

How to cite this article: Mafunzwaini, T., Adeyelure, T.S., Kgoetiane, C.H. & Sibiya, S., 2026, ‘Internet of Everything in the South African small, medium and micro-enterprises adoption model: A case of Limpopo municipalities’, South African Journal of Information Management 28(1), a2176. https://doi.org/10.4102/sajim.v28i1.2176