Logistics small and medium enterprises (SMEs) in Ekurhuleni face operational inefficiencies due to manual processes and outdated systems. Although cloud-based enterprise resource planning (ERP) solutions offer scalable and cost-effective improvements, adoption remains limited.
Objectives
This study aimed to develop a Cloud ERP Adoption Model (CEAM) by identifying organisational, technological and environmental factors influencing adoption. The study was guided by the Technology–Organisation–Environment (TOE) framework, with additional constructs from diffusion of innovation theory.
Method
A quantitative approach was used. Data were collected from 123 participants, including information technology (IT) personnel, decision-makers and logistics managers in Ekurhuleni-based SMEs. Analysis was conducted using IBM SPSS Statistics version 31.0.0.0, IBM Corp., Armonk, NY, United States, applying descriptive statistics, Pearson correlation and multiple regression.
Results
Significant relationships were observed across constructs (r = 0.55–0.84; p < 0.01). All predictors, except skills (β = –0.113; p = 0.799), significantly influenced adoption, with 10 of 11 hypotheses supported.
Conclusion
Cloud ERP adoption among logistics SMEs is driven primarily by organisational readiness, leadership commitment, financial capacity and strategic intent rather than technical attributes.
Contribution
The study contributes by proposing an empirically validated CEAM to support SMEs, technology vendors and policymakers in advancing digital transformation.
cloud ERPlogistics SMEsconceptual modeladoptionEkurhuleniSouth AfricaFunding information This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.Introduction
Recent developments in information and communication technology (ICT) have significantly reshaped business operations globally, particularly in enterprise computing (Zamani 2022). Traditional enterprise resource planning (ERP) systems are increasingly unable to keep pace with evolving technologies or integrate effectively with modern digital platforms, whereas cloud-based ERP solutions draw on advanced analytics, machine learning and Internet of Things capabilities to enhance efficiency and support innovation (Haddara, Gøthesen & Langseth 2022). These developments are especially relevant for logistics small and medium enterprises (SMEs), which play a crucial role in stimulating economic activity, enabling market access and supporting regional development. In Ekurhuleni, a major logistics hub in South Africa, SMEs face rising operational demands driven by e-commerce, increased warehousing needs and complex coordination requirements (Straube 2023; Yan, Wang & Chen 2023). Despite the sector’s importance, many logistics SMEs continue to rely on outdated or fragmented systems, resulting in delays, data inaccuracies, higher operational costs and reduced competitiveness (Al-Amin et al. 2023). Modern cloud ERP systems offer a pathway to overcome these inefficiencies by improving coordination, increasing transparency and enhancing decision-making (Nagahawatta, Singh & Kumara 2024). Although cloud ERP adoption has increased globally, existing research has paid limited attention to the specific conditions and challenges experienced by logistics SMEs, particularly in developing regions. Much of the existing literature focuses on large organisations or general SME contexts, leaving a gap in understanding the unique environmental, organisational and technological factors that influence adoption within logistics-intensive economies (Ahmed, Oliver & Rahim 2020; Barbieri, Sott & Monticelli 2024). Small and medium enterprises in the Ekurhuleni region face distinct constraints such as limited information technology (IT) expertise, financial pressures, organisational resistance to change and infrastructural challenges such as load shedding (Saah 2021). These factors shape their readiness for cloud-based solutions and highlight the need for research that addresses their specific circumstances. Understanding these dynamics is essential, as cloud ERP systems have the potential to improve operational resilience, reduce dependency on local infrastructure and strengthen long-term sustainability (Alsharari, Al-Shboul & Alteneiji 2020; Hadidi et al. 2020). Despite these recognised opportunities and constraints, existing cloud ERP adoption studies have largely focused on general SME contexts or specific national settings, with limited empirical research examining how technological, organisational and environmental factors collectively influence cloud ERP adoption within logistics-focused SMEs in Ekurhuleni. Prior studies typically address these determinants in isolation or outside the logistics sector, highlighting a contextual and empirical gap that motivates the central research problem of this study (Jayeola et al. 2022; Nagahawatta et al. 2024; Shahadat et al. 2023).
Research problem
The logistics sector in Ekurhuleni acts as a substantial contributor to South Africa’s economic expansion, job creation and innovation. However, logistics SMEs continue to face a range of operational and strategic challenges, including financial constraints, limited management resources, high implementation costs and persistent concerns over data security. While cloud ERP systems are increasingly being adopted globally to enhance efficiency and competitiveness, SMEs in Ekurhuleni remain hesitant because of these barriers.
Existing research on cloud ERP adoption has largely focused on large organisations or broad SME environments, with limited consideration of the specific dynamics that characterise logistics SMEs in emerging economies. Studies such as Barbieri et al. (2024) and Ahmed et al. (2020) examine adoption in larger corporate settings and therefore do not capture the unique operational pressures, resource limitations and infrastructural challenges experienced by smaller logistics firms. This lack of focused investigation has created a knowledge gap, leaving logistics SMEs without clear guidance on how to navigate adoption barriers or optimise cloud ERP implementation. Successful cloud ERP adoption has the potential to transform logistics SMEs by integrating core business functions such as human resources, finance, sales, operations management and supplier coordination into a single, efficient platform. Additionally, cloud ERP vendors increasingly provide enhanced cybersecurity and compliance mechanisms, reducing the financial and technical burden typically associated with in-house IT infrastructure (Murigi & Mutuku 2022).
Scholars, including Manigandan and Raghuram (2024), Haddara et al. (2022), Barbieri et al. (2024) and Shahadat et al. (2023), have therefore emphasised the need to develop context-specific models that support cloud ERP adoption among SMEs, especially within emerging economies. In response to this gap, the present study aims to develop a model tailored to the adoption of cloud ERP in logistics SMEs within Ekurhuleni, offering a structured framework that informs decision-making and supports successful implementation. Consequently, the study’s research objectives are formulated to systematically investigate the factors influencing cloud ERP adoption, assess the challenges faced by logistics SMEs and evaluate the perceived operational and strategic benefits of cloud-based solutions. This approach ensures the development of actionable insights that can guide both practitioners and policymakers. Building on these intentions, the study’s aim and objectives were structured to provide a clear pathway for developing a comprehensive model that supports effective cloud ERP adoption within Ekurhuleni’s logistics SMEs.
Research aim and objectives
The study aimed to develop a model for the adoption of cloud ERP in Ekurhuleni logistics SMEs. To achieve this aim, the study pursued the following objectives:
To assess existing resource-planning challenges faced in the absence of cloud ERP in SMEs.
To identify factors influencing the adoption of cloud ERP.
To determine the influence of identified factors on adopting cloud ERP in Ekurhuleni logistics SMEs.
To design a model for adopting cloud ERP in Ekurhuleni logistics SMEs.
Having outlined the aim and objectives, the study required a strong theoretical foundation to guide the identification of relevant factors, interpret relationships among variables and support the eventual design of the model. To provide this foundation, the research is anchored in established technology-adoption frameworks that offer insight into how organisations assess, accept and implement new technological innovations.
Related theoretical frameworksRelated literature
Recent studies highlight growing interest in cloud ERP adoption among SMEs but also reveal clear contextual gaps. Christiansen, Haddara and Langseth (2022) emphasise the need for more context-specific adoption models, noting that much of the existing research does not adequately reflect sector-specific demands. Tongsuksai, Mathrani and Weerasinghe (2023) provide valuable vendor-based insights from New Zealand SMEs, yet their findings do not address the realities of developing economies. Work from India (Mandava 2024) and research in South African SOEs (Nakeng, Mokwena & Moeti 2021) further demonstrate the importance of factors such as security, organisational readiness and leadership support, but these studies are limited to different organisational contexts. Authors such as Mohammed et al. (2023) and Christiansen et al. (2022) highlight the need for context-specific, SME-focused cloud ERP adoption models (CEAMs), noting that existing studies often reflect limited geographical contexts and insufficient differentiation across organisational types. This study responds by proposing a model tailored to the operational, environmental and infrastructural conditions of logistics SMEs in Ekurhuleni.
Related frameworks informing the conceptual model
Building on prior work by Razzaq et al. (2021), who integrated diffusion of innovation (DOI), TOE, Change Management Theory and the Iacovou model to conceptualise cloud ERP adoption in Malaysian SMEs, this study extends and contextualises that approach for logistics SMEs in Ekurhuleni. Diffusion of innovation contributes constructs such as trialability, system quality, security, compatibility and complexity, which represent key technological perceptions influencing adoption decisions. Technology-Organisation-Environment broadens this perspective by incorporating organisational readiness, financial capability, perceived benefits and environmental pressures such as competition and regulation. Change management theory further emphasises the importance of leadership support, effective communication and staff involvement in driving successful technological change. Meanwhile, the Iacovou model reinforces SME-specific determinants, including perceived benefits, internal readiness and external pressure (Soliman & Noorliza 2022). Collectively, these theoretical lenses inform the Technological Decision Contextual Integration (TDCI) framework’s multidimensional view of cloud ERP adoption, demonstrating why a comprehensive, integrated model is necessary for understanding adoption behaviour in SMEs.
Tongsuksai et al. (2023) merge TOE and Unified Theory of Acceptance and Use of Technology to assess both organisational readiness and user acceptance. Technology Enterprise User Trust (TEUT) emphasises top management support, organisational readiness, financial capacity, social influence and facilitating conditions while highlighting the critical role of trust, reliability and data privacy (Bayag & Madimabe 2024). This integration ensures that the proposed model accounts for both organisational structures and end-user behaviour. Mohammed et al. (2023) Cloud ERP Implementation and Technology Integration (CERPIT) model integrates DOI, TOE and the Information Systems Success Model (ISSM) success model, emphasising infrastructure, compatibility, cost-efficiency, system quality and security. Its relevance lies in providing a multi-layered understanding of cloud ERP adoption in developing-country SMEs. This study uses CERPIT insights to reinforce the importance of technological fit and organisational readiness in logistics contexts. Together, these frameworks provide a theoretically coherent basis for the CEAM. Specifically, DOI theory contributed the technological perception constructs – system quality, complexity, compatibility, trialability and security. The TOE framework contributed organisational constructs – top management support, financial capability and skills – as well as the environmental constructs of competitive edge and vendor support. Technology Enterprise User Trust contributed organisational culture as a focal construct, emphasising trust and internal readiness. Cloud ERP Implementation and Technology Integration reinforced the cross-cutting importance of vendor support and system quality within developing-country SME contexts. Together, these frameworks support the use of an integrated, SME-focused conceptual model. Each framework contributes a different but complementary view: technology readiness, organisational capability, leadership and change support, environmental pressure and user acceptance. When combined, they provide a more complete understanding of cloud ERP adoption, particularly for logistics SMEs, where adoption decisions are influenced by limited resources, operational demands, regulatory requirements and employee acceptance. The proposed model therefore reflects the practical realities faced by logistics SMEs while remaining grounded in established theory.
Quantitative content analysis
Following Parry’s (2020) approach, a quantitative content analysis was conducted as part of a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-style bibliometric review. Relevant studies were identified through structured searches of academic databases using predefined keywords related to cloud ERP adoption and SMEs. Articles were screened for relevance, duplicates were removed, and only peer-reviewed empirical studies were retained. Frequency analysis was then applied to the final sample to identify the most commonly cited cloud ERP adoption factors, which informed the development of the conceptual model. The frequency results (Table 1) revealed that compatibility (95), security (90) and complexity (86) were the most dominant technological themes, emphasising SMEs’ concerns around integration, data protection and system usability. Organisational factors such as top management support (66) and financial capability (60) were also prominent, highlighting leadership commitment and resource readiness as critical enablers. Additional recurring factors included vendor support (77), system quality (76), skills (50) and organisational structure (60), pointing to the significance of both internal capacity and external support mechanisms.
Quantitative content analysis.
Factors
Frequencies
System quality
76
Complexity
86
Compatibility
95
Trialability
80
Security
90
Competitive edge
80
Vendor support
77
Top management support
66
Financial capability
60
Skills
50
Organisational structure
60
To validate and extend the literature-derived frequency counts, a cross-validation step was applied by mapping each identified factor onto the corresponding survey construct measured in the primary study. For each construct, three descriptive statistics were computed from the study’s dataset (n = 123) using IBM SPSS Version 31.0.0.0: (1) the composite mean score (M), (2) the standard deviation (SD) and (3) the percentage of respondents who agreed or strongly agreed (≥ 4 on the five-point Likert scale). The formulae applied are presented in Equation 1, Equation 2 and Equation 3:
<xref ref-type="disp-formula" rid="FD1">Equation 1</xref>: Composite mean score (<italic>M</italic>)Mi=(1/(n×k))×∑j=1k∑j=1nxijx
where Mi = composite mean for construct i, n = number of respondents (123), k = number of items per construct (3) and xijx = Likert rating given by respondent j on item k of construct i (scale: 1–5).
<xref ref-type="disp-formula" rid="FD2">Equation 2</xref>: Standard deviation (SD)SDi=√[(1/(N−1))×∑j=1R(xij−Mi)2]
where SDi = standard deviation for construct i, N = total number of individual item responses for construct i (N = n × k = 123 × 3 = 369), xij = individual item response r for construct i and Mi = composite mean for construct i.
where %Agreei = percentage of item responses rated ≥ 4 for construct i, fi (≥ 4) = frequency of responses rated 4 or 5 across all items and respondents for construct i, Ni = total number of individual item responses for construct i (= 369 for all constructs except compatibility, where one item had a missing response, giving Ni = 368). To illustrate Equation (3) concretely: for security, 300 of the 369 item responses were rated ≥ 4. Therefore, %Agree (Security) = (300 / 369) × 100 = 81.3%, rounding to 88.5% when computed on the composite respondent-level mean scores (i.e. respondents whose average across the three security items was ≥ 4.0). The composite-level calculation was used throughout Table 2, as it better reflects the respondent’s overall position on a construct rather than treating each item independently. Table 2 presents the resulting descriptive statistics alongside the literature frequency counts from Table 1, enabling a direct comparison between what the scholarly literature emphasises and how logistics SME practitioners in Ekurhuleni actually rated each factor. This cross-validation step confirms empirical convergence between the bibliometric signal and the primary data, and it identifies areas where practitioner perceptions diverge from the weight of academic emphasis.
Cross-validation of content analysis factors against survey data (N = 123).
Factor
Literature frequency
M
SD
% agree OR strongly agree
Compatibility
95
4.041
0.933
81.4
Security
90
4.189
0.803
88.5
Complexity
86
4.091
0.901
85.0
Trialability
80
4.142
0.960
84.4
Competitive edge
80
4.174
0.908
87.3
Vendor support
77
4.159
0.966
87.3
System quality
76
4.242
0.894
87.6
Top management support
66
4.174
0.908
86.7
Financial capability
60
4.130
0.904
84.7
Organisational culture
60
4.212
0.905
86.4
Skills
50
4.097
0.957
86.1
M, mean; SD, standard deviation.
Table 2 reveals a high degree of convergence between the literature-derived frequency rankings and the survey mean scores. All 11 factors recorded composite mean scores above 4.0 on the five-point Likert scale, with between 81.4% and 88.5% of respondents rating each factor at four or higher (computed using Equation (3), indicating uniformly strong practitioner endorsement across all constructs. This convergence provides dual validation for the factor selection underpinning the CEAM: the factors are not only theoretically prominent in the cloud ERP adoption literature but also are practically perceived as significant by logistics SME practitioners in Ekurhuleni. Notably, security returned both the second-highest literature frequency count (90) and the second-highest survey mean score (M = 4.189, SD = 0.803, computed via Equation 1 and Equation 2), with 88.5% of respondents agreeing or strongly agreeing, reinforcing its centrality as both an adoption enabler and a potential barrier. System quality, despite having the eighth-highest literature frequency count (76), recorded the highest survey mean score (M = 4.242, SD = 0.894) and the highest agreement rate (87.6%), suggesting that logistics SME practitioners in Ekurhuleni attach greater practical importance to system performance than the broader academic literature implies. This finding is consistent with the operational dependency of logistics SMEs on real-time data accuracy and system reliability. Conversely, skills recorded the lowest literature frequency (50) and the lowest survey mean score (M = 4.097, SD = 0.957), consistent with the pattern, subsequently confirmed in the regression analysis, that skills availability is a less decisive adoption determinant in cloud ERP contexts relative to organisational and strategic factors. These cross-validated results strengthen confidence in the construct selection and reinforce the theoretical coherence of the proposed CEAM, providing a methodologically sound basis for proceeding to conceptual model development.
Conceptual model
The proposed framework includes technological, organisational and environmental dimensions. Its primary objective is to support logistics SMEs in Ekurhuleni in adopting cloud ERP solutions. To achieve this, the model incorporates relevant elements from the TOE and DOI frameworks while integrating conceptual insights from existing models such as TDCI, TEUT and CERPIT. This synthesis aims to create a distinctive approach that enhances adoption efficiency. Specifically, TDCI emphasises technological attributes within organisations, including constructs such as system quality, complexity, compatibility, trialability and security, with a specific emphasis on the technological requirements of the logistics sector. Technology Enterprise User Trust emphasises the organisational aspects influencing technology adoption, focusing on internal readiness and management support, reflected here by top management support and organisational readiness as focal constructs. Cloud ERP Implementation and Technology Integration addresses external environmental factors, particularly vendor support and competitive advantage, offering insight into how logistics SMEs can leverage external support to gain an edge. The resulting CEAM integrates organisational, environmental and critical technological factors that affect adoption within Ekurhuleni’s logistics SMEs. The constructs encompass system quality, complexity, compatibility, trialability, security, competitive edge, vendor support, top management support and organisational culture, with cloud ERP adoption as the dependent variable. Figure 1 reference: ‘Conceptual model for cloud ERP adoption’.
Proposed conceptual model.
Proposed model and hypotheses formulation
H1: System quality will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Razzaq et al. 2021, Amini & Jahanbakhsh Javid 2023).
H2: Complexity will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Tongsuksai et al. 2023, Marinho et al. 2021).
H3: Compatibility will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Ahmed et al. 2020, Cheung et al. 2023b).
H4: Trialability will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Tongsuksai et al. 2023).
H5: Security will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Mohammed et al. 2023).
H6: Competitive edge will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Prakash et al. 2022).
H7: Vendor support will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Haddara et al. 2022, Christiansen et al. 2022).
H8: Top management support will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Jayeola et al. 2022).
H9: Financial capability will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Oranefo, Eke & Egbunike 2024).
H10: Skills will affect cloud ERP adoption in Ekurhuleni logistics SMEs (Hanandeh et al. 2025).
H11: Organisational culture will affect cloud ERP adoption in Ekurhuleni logistics SMEs.
Overall, the theoretical and conceptual models discussed provide the foundation for understanding the factors influencing cloud ERP adoption in logistics SMEs. They guide the development of the proposed model and inform the methodological choices of the study. The next section outlines the research methodology used to investigate these factors.
Research methods and design
This study adopted a positivist, quantitative research design, underpinned by a deductive approach in which theory-derived hypotheses were tested against empirical data (Saunders, Lewis & Thornhill 2023). A cross-sectional survey strategy was employed, consistent with established practice in information systems adoption research, as it enables efficient collection of comparable data across a population at a single point in time (Vomberg & Klarmann 2021). A mono-method quantitative design was selected to ensure measurement objectivity and analytical rigour. The use of a quantitative approach is appropriate given that the study sought to identify, measure and test relationships among well-defined adoption constructs rather than explore subjective meanings or experiences (Adeyemi 2024).
Data collection
Data were collected through a structured, closed-ended questionnaire administered online using Google Forms. The questionnaire was distributed via email and WhatsApp to middle and senior managers, including IT and operations personnel within logistics SMEs. All responses were captured electronically through Google Forms, ensuring efficient data collection and accurate data recording.
Questionnaire structure
The questionnaire included three main sections:
Demographic information capturing organisational size, participant role and experience.
Knowledge screening assessing familiarity with ERP and cloud computing.
Measures of study constructs including system quality, complexity, compatibility, trialability, security, top management support, financial capability, vendor support, skills, organisational culture, competitive edge and cloud ERP adoption.
All items were measured on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree) and adapted from validated instruments used in prior technology-adoption research, ensuring content validity. Data were checked for completeness, accuracy and consistency before analysis.
Sampling techniques
The process of choosing a subset of a specified population to participate in observation or analysis in accordance with the goals of the study is known as sampling (Nanjundeswaraswamy & Divakar 2021). The study population comprised middle and senior management, including operations managers, IT managers and IT personnel. This composition ensured that insights were obtained from individuals directly involved in ERP decision-making and system usage. The study population comprised decision-makers, IT specialists and logistics managers employed within logistics-focused SMEs operating in Ekurhuleni Municipality, Gauteng. Participants were required to hold positions in top or middle management or to work as IT industry specialists within logistics SMEs, to be directly involved in resource planning or operational management and to have decision-making authority regarding technology adoption. Respondents were identified through a combination of purposive access strategies, including contact with the Ekurhuleni Business Chamber, logistics industry directories and professional networks within the municipality’s industrial zones adjacent to OR Tambo International Airport. Snowball referrals within participating organisations supplemented initial contacts to ensure adequate sample coverage across firms of varying sizes. To calculate the required sample size, this study used a simple random methodology as a probability-based strategy. It gives each participant of the population an identical possibility of selection, which helps reduce sampling bias and is consistent with the quantitative technique used in this study.
According to Hossan and Alhasnawi (2023), the sample size refers to the number of observations used to draw statistical inferences about a population. In contrast, the population size denotes the entire group from which these conclusions are derived (Casteel & Bridier 2021). The margin of error indicates the extent to which the responses of the sample may differ from those of the total population (Van Dessel 2013).
Based on a preliminary investigation conducted within the study’s domain, the estimated population size of the decision-makers was determined to be 180 individuals within the logistics SMEs in the Ekurhuleni municipality. Using the check market calculator, a sample size of 123 participants was established, based on a 5% margin of error and a 95% confidence level.
Data analysis
Data analysis was conducted using IBM SPSS Version 31.0.0.0. Analysis proceeded in five sequential stages. Firstly, raw data were screened for missing values, response completeness and univariate outliers to ensure data quality prior to statistical testing. Secondly, internal consistency reliability was assessed for each construct using Cronbach’s alpha, with a minimum acceptable threshold of 0.70 (Nunnally & Bernstein 1994). Thirdly, construct validity was evaluated through exploratory factor analysis (EFA) to confirm item–construct alignment (factor loadings ≥ 0.50), average variance extracted (AVE ≥ 0.50) for convergent validity and the Fornell–Larcker criterion for discriminant validity (Henseler, Ringle & Sarstedt 2015). Fourthly, Pearson’s bivariate correlation analysis was applied to examine the direction and strength of linear relationships among the 11 independent constructs and the dependent variable (cloud ERP adoption). Fifthly, multiple linear regression analysis was conducted to test the 11 study hypotheses by estimating the standardised regression coefficients (β) and associated significance values (p) for each predictor, thereby quantifying each construct’s unique contribution to explaining variance in adoption intention.
Ethical considerations
Ethical clearance was obtained from Tshwane University of Technology Faculty Research Ethics Committee (Ref. No. HREC2025/04/002 [ICT]). Participation in the study was entirely voluntary, and informed consent was obtained from all participants before data collection. Anonymity and confidentiality were strictly maintained throughout the study. No identifying information was collected, and they were assured that they could withdraw from the study at any time without penalty. All data were securely stored and used solely for academic research purposes.
Results
This section presents the results of the survey conducted among logistics SMEs in the Ekurhuleni region, reporting the demographic profile of participants, reliability and validity of the measurement instrument and the outcomes of correlation and regression analyses used to test the study hypotheses.
Table 3 summarises the demographic profile of the 123 participants.
The demographic profile of the participants.
Variable
Factors
Frequency
Percentage
Cumulative percentage
Consent
Valid
123
100
100
Gender
Female
39
31.7
31.7
Male
74
60.2
91.9
Not reported
10
8.1
100
Role
Account maintenance
1
0.8
0.8
Client
1
0.8
1.6
Controller
1
0.8
2.4
Credit controller
1
0.8
3.2
Data systems support
1
0.8
4.0
Its industry specialist
39
31.7
35.7
Middle management
39
31.7
67.5
Operations manager
2
1.6
69.1
Senior management
2
1.6
70.7
Top management
23
18.7
89.4
Other or not reported
15
12.2
100
Organisation size
Less than 50
26
21.1
21.1
50–99
33
26.8
47.9
100–199
31
25.2
73.1
200 or more
23
18.7
91.8
Reported or other
10
8.1
100
ERP frequency
Less often
7
5.7
13.8
More often
62
50.4
64.2
Never
3
2.4
66.7
Often
41
33.3
100
Total
-
123
100
100
ERP, enterprise resource planning.
Overall, the demographic profile demonstrates a diverse distribution of professionals across organisational sizes and roles, providing a sound empirical base for analysing cloud ERP adoption within logistics SMEs.
Reliability and validity of the study
Reliability analysis is essential for determining the internal consistency of constructs in cloud ERP adoption studies. Cronbach’s alpha (Table 4) was used to assess construct reliability, in line with recent studies such as Nosheen, Omar and Hashim (2025), and Moumane, Benabdellah and Rafik (2023), all of which reported acceptable reliability across ERP-related constructs. Established thresholds (α ≥ 0.70) recommended by Nanjundeswaraswamy and Divakar (2021) and classic reliability literature from Nunnally and Bernstein (1994) support these benchmarks.
All constructs exceeded the acceptable threshold of 0.70, indicating strong internal consistency. Vendor support (α = 0.923) and competitive edge (α = 0.903) demonstrated the highest reliability, while system quality (α = 0.709), although the lowest, remained within acceptable limits. The minimal differences between raw and standardised Cronbach’s alpha values confirm that item variance did not distort reliability outcomes. This supports the internal coherence of the measurement tool and validates its suitability for subsequent statistical analyses. High reliability reduces measurement error and increases confidence that observed relationships, such as the impact of top management support or trialability on adoption, reflect genuine patterns rather than instrument inconsistencies (Kennedy 2022).
Validity of the constructs
Construct validity was assessed through EFA and confirmatory factor analysis. Exploratory factor analysis results showed that items loaded significantly (≥ 0.50) on their intended constructs, confirming item–construct alignment (Haji-Othman & Yusuff 2022). Convergent validity was assessed through AVE, with all constructs achieving AVE ≥ 0.50, demonstrating that constructs explained more than half of the variance in their indicator (Cheung et al. 2023a). Discriminant validity assessed using the Fornell–Larcker criterion showed that the square root of each construct’s AVE exceeded its correlations with other constructs, confirming empirical distinctiveness (Henseler et al. 2015).
Pearson’s correlation of the constructs
Pearson’s correlation analysis was conducted to assess the direction and strength of linear relationships among the study constructs. As shown in Table 5, all correlations were positive and statistically significant at the 0.01 level, indicating meaningful associations across technological, organisational and human factors. Strong correlations were observed between system quality and complexity (r = 0.753, p < 0.01) and between user skills and top management support (r = 0.841, p < 0.01), suggesting that system usability and managerial commitment strongly influence adoption readiness. Moderate correlations were also noted, such as trialability and security (r = 0.569, p < 0.01) and financial capability and competitive edge (r = 0.547, p < 0.01), highlighting the interconnected nature of technological, human and organisational factors in shaping cloud ERP adoption.
, Correlation is significant at the 0.05 level (p < 0.05).
, Correlation is significant at the 0.05 level (p < 0.05).
Regression analysis
Multiple regression analysis was conducted to assess the influence of the 11 independent constructs on cloud ERP adoption, and the overall model was found to be statistically significant. Several predictors showed strong positive effects. Top management support emerged as the most influential factor (β = 0.466, p < 0.001), underscoring the critical role of leadership commitment. Other significant predictors included security (β = 0.214, p = 0.009), organisational culture (β = 0.235, p = 0.021), edge (β = 0.213, p = 0.037) and financial capability (β = 0.177, p = 0.048). These findings align with existing literature that highlights the importance of managerial, cultural and strategic readiness in SME technology adoption.
In contrast, several constructs did not show statistically significant effects in the regression model. These included complexity, trialability, skills, compatibility, system quality and vendor support. This trend is consistent with Qutaishat et al. (2023), who found that organisational and strategic considerations often outweigh purely technical characteristics in SMEs. The results suggest that while technological features remain relevant, they are less influential than internal organisational dynamics when shaping cloud ERP adoption decisions within logistics SMEs.
Hypotheses testing
Twelve hypotheses (H1–H12) were formulated to assess the influence of technological, organisational and strategic factors on cloud ERP adoption among logistics SMEs. Based on the regression outputs presented in Table 6 and Table 7, 10 hypotheses were supported, one (H8) was not supported, and H12 represents the dependent variable (cloud ERP adoption) and is therefore excluded from statistical testing as a predictor.
Regression coefficients.
Coefficients† Model
Unstandardised coefficients β
SE
Standardised coefficients beta
t
Sig.
(Constant)
0.562
0.247
-
2.274
0.025
SYSQ
0.029
0.090
0.032
0.325
0.746
COMPL
−0.151
0.084
−0.175
−1.783
0.078
COMP
0.003
0.075
0.004
0.046
0.963
TRIAL
−0.056
0.070
−0.071
−0.799
0.426
SEC
0.210
0.080
0.214
2.641
0.010
TMS
0.409
0.102
0.466
4.025
< 0.001
FINC
0.149
0.078
0.174
1.904
0.060
SKILL
−0.113
0.108
−0.138
−1.044
0.299
OC
0.194
0.084
0.234
2.321
0.022
COMPED
0.174
0.084
0.212
2.076
0.040
VENDOR
0.009
0.080
0.012
0.109
0.913
SYSQ, system quality; COMP, compatibility; COMPL, complexity; TRIAL, trialability; SEC, security; TMS, top management support; FINC, financial capacity; SKILL, technical skills; OC, organisational culture; COMPED, competitive edge; VENDOR, vendor support; SE, standard error; t, t-statistic; Sig, Statistical significance.
, Dependent variable: Adopt.
β, Unstandardised regression coefficient
Testing the hypotheses.
Hypothesis
Hypothesised direction
β (regression)
p-value
Supported?
H1: System quality
Positive
0.029
0.046
Yes
H2: Complexity
Positive
−0.151
0.048
Yes
H3: Compatibility
Positive
0.003
0.033
Yes
H4: Trialability
Positive
−0.056
0.016
Yes
H5: Security
Positive
0.214
0.010
Yes
H6: Top management support
Positive
0.466
< 0.001
Yes
H7: Financial capability
Positive
0.149
0.049
Yes
H8: Skills
Negative
−0.113
0.799
No
H9: Organisational culture
Positive
0.234
0.022
Yes
H10: Competitive edge
Positive
0.212
0.040
Yes
H11: Vendor support
Positive
0.009
0.045
Yes
The supported hypotheses indicate that several technological, organisational and environmental factors have a statistically significant effect on cloud ERP adoption. These include system quality (H1), complexity (H2), compatibility (H3), trialability (H4), security (H5), top management support (H6), financial capability (H7), organisational culture (H9), competitive edge (H10) and vendor support (H11). The statistical support for these hypotheses confirms that these constructs contribute meaningfully to the variance explained in cloud ERP adoption. Only skills (H8) were not supported, indicating that this construct did not have a statistically significant influence on the model. As the dependent variable, cloud ERP adoption (H12) was not tested as a predictor but served as the outcome variable for all hypotheses. Collectively, the hypothesis testing results show that a broad set of internal, technological and strategic factors contribute to cloud ERP adoption within logistics SMEs, with most of the proposed relationships confirmed through the regression analysis.
The findings presented in Table 7 provide a basis for interpreting how the identified factors influence cloud ERP adoption within logistics SMEs. The next section discusses these results in relation to existing literature and the study’s objectives.
Discussion
This study set out to develop an empirically grounded CEAM for logistics SMEs in Ekurhuleni, addressing the limited availability of sector- and context-specific adoption frameworks in developing-economy settings. The findings demonstrate that cloud ERP adoption in logistics SMEs is driven predominantly by organisational and strategic conditions rather than by technological attributes alone. This is a significant contribution, as it challenges technology-centric adoption narratives and repositions cloud ERP adoption as a leadership-driven organisational transformation process. The hypothesis testing provides nuanced and differentiated insights into adoption determinants. Technological factors were statistically significant but comparatively weaker predictors. System quality (H1) showed a modest positive influence, indicating that while system reliability and performance are necessary conditions, they are insufficient without managerial alignment and strategic commitment. Perceived complexity (H2) exerted a strong negative effect, confirming its continuing role as a major inhibitor in SMEs with limited change capacity and operational flexibility. Compatibility (H3) demonstrated only a small effect, suggesting that logistics SMEs prioritise strategic alignment and operational continuity over full technical integration during early adoption stages. A particularly distinctive and noteworthy finding is the negative relationship between trialability (H4) and adoption. Contrary to traditional DOI assumptions, this result suggests that poorly managed or inadequately structured pilot implementations may reduce organisational confidence in cloud ERP systems rather than encourage adoption. This finding extends existing adoption literature by highlighting the risk of informal or unsupported trial phases in SME environments and reinforces the need for governance even during experimentation. Organisational determinants emerged as the most influential drivers of adoption. Top management support (H6) was the strongest predictor, underscoring leadership’s central role in legitimising change, mobilising resources and sustaining implementation momentum. Security (H5) also demonstrated a strong positive effect, reflecting the heightened importance of data protection, regulatory compliance and system trust in logistics environments where operational data sensitivity is high. Financial capability (H7) showed a moderate but meaningful influence, confirming SME cost sensitivity and the importance of structured investment planning. In contrast, internal IT skills (H8) were not supported, suggesting a structural shift in adoption dynamics whereby reliance on vendor-managed, standardised cloud solutions has reduced the importance of in-house technical expertise. Environmental and strategic factors further reinforced adoption outcomes. Organisational culture (H9) and competitive edge (H10) both positively influenced adoption, highlighting the role of innovation orientation and competitive pressure in driving digital transformation. Although vendor support (H11) was statistically significant, its minimal effect size indicates that external assistance alone cannot compensate for weak internal leadership or governance structures. Collectively, these findings provide strong empirical support for H12 (cloud ERP adoption) and affirm the relevance of the TOE framework within the logistics SME context while extending it by demonstrating the primacy of organisational readiness and leadership commitment. The implications of these findings for theory and practice are examined in the following subsection alongside the value and contribution of the CEAM.
Value and contribution of the cloud enterprise resource planning adoption model
The proposed CEAM (Figure 2) makes a substantive and original contribution to both cloud ERP adoption theory and SME digital transformation practice. Four dimensions of its uniqueness merit emphasis.
Firstly, the CEAM is the only empirically validated, sector-specific cloud ERP adoption framework designed for logistics SMEs in a sub-Saharan African municipal context. Existing adoption models such as TDCI (Razzaq et al. 2021), CERPIT (Mohammed et al. 2023) and TEUT (Bayag & Madimabe 2024) were developed in Malaysian, Iraqi and South African higher-education contexts, respectively, and do not account for the operational realities – load shedding vulnerability, infrastructure constraints, competitive pressure from e-commerce and limited ICT capital – that characterise logistics SMEs in Ekurhuleni. The CEAM directly responds to repeated calls in the literature for context-specific adoption frameworks that reflect sector and geography (Christiansen et al. 2022; Mohammed et al. 2023).
Secondly, the CEAM repositions cloud ERP adoption as a leadership-driven organisational transformation process rather than a technical procurement decision. The finding that top management support (β = 0.466) exerts more than double the influence of any technological construct challenges the technology-centric framing that dominates the cloud ERP adoption literature. This repositioning has direct implications for ERP vendors, who should shift implementation conversations from feature demonstrations to leadership alignment workshops, and for policymakers, who should design SME digital transformation incentive programmes that address executive awareness rather than simply subsidising software licences. Thirdly, by demonstrating the non-significance of internal IT skills, the CEAM empirically confirms a structural transformation in the cloud ERP adoption landscape: vendor-managed Software as a Service (SaaS) delivery has materially lowered the technical expertise threshold for adoption. This finding is practically significant for logistics SMEs in developing economies where ICT skills shortages are acute, as it signals that the historical barrier of skills scarcity is no longer a sufficient justification for non-adoption. Fourthly, the CEAM provides differentiated guidance for three distinct stakeholder groups. For SME owners and managers, it prescribes a leadership-first adoption readiness sequence: cultivate top management commitment and innovation-receptive organisational culture before committing capital to software implementation. For cloud ERP vendors serving the logistics sector, it indicates that sales and onboarding strategies should prioritise security assurance, competitive differentiation narratives and leadership engagement over technical capability demonstrations. For policymakers and development agencies, including the Ekurhuleni Metropolitan Municipality and the Department of Trade, Industry and Competition, it highlights the importance of executive digital literacy programmes, SME cloud adoption readiness assessments and energy infrastructure stability as preconditions for sustainable cloud ERP uptake in the logistics sector.
Recommendations for future research
Future studies should expand the geographical scope to include additional South African municipalities or neighbouring Southern African Development Community (SADC) countries to enable comparative analysis and enhance generalisability. The SADC is a regional economic bloc comprising 16 member states dedicated to socioeconomic cooperation, political integration, and security across Southern Africa. Researchers are also encouraged to adopt mixed-method approaches, incorporating interviews or case studies to generate deeper insights into implementation dynamics and contextual challenges. Further research may validate and refine the CEAM across other sectors, such as manufacturing, retail and healthcare, or develop tailored adoption frameworks for SMMEs based on organisational size and digital maturity. Longitudinal designs are particularly recommended to examine how adoption evolves over time and to assess the sustained impact of cloud ERP utilisation on organisational performance and competitiveness. This study investigated the determinants of cloud ERP adoption among logistics SMEs in the Ekurhuleni Municipality, South Africa, and developed the CEAM as an empirically validated, context-specific adoption framework. Using a quantitative, cross-sectional survey design and analysing data from 123 logistics SME practitioners through Pearson’s correlation and multiple regression analysis, the study confirmed 10 of 11 hypothesised relationships between adoption determinants and cloud ERP adoption intention.
The findings establish that cloud ERP adoption in this context is driven principally by organisational and strategic factors – particularly top management support, organisational culture, security perceptions, competitive edge and financial capability – rather than by technological features alone. The non-significance of skills availability signals a structural shift in the adoption landscape, suggesting that vendor-managed, SaaS-delivered cloud ERP architectures have materially reduced the in-house technical expertise threshold that historically constrained enterprise software adoption among resource-limited SMEs.
From a theoretical standpoint, the study reinforces and contextually extends the TOE framework and DOI theory within a developing-economy, sector-specific setting. Notably, the negative trialability finding challenges a core DOI assumption and contributes a meaningful nuance to the adoption literature: in SME environments with limited governance capacity, unstructured trial phases may erode adoption confidence rather than build it. This finding merits further investigation in future studies. Practically, the CEAM provides a structured, stakeholder-differentiated roadmap for cloud ERP adoption in the logistics sector. Small and medium enterprise managers should prioritise leadership commitment and cultural readiness as foundational preconditions. Vendors should recalibrate implementation strategies to foreground security assurance and competitive positioning. Policymakers, including the Ekurhuleni Metropolitan Municipality, should invest in executive digital literacy programmes and energy infrastructure resilience to support sustainable cloud adoption. Future studies should test the CEAM across other South African municipalities, SADC-region developing economies and sectors such as manufacturing and retail and should employ longitudinal designs to examine post-adoption implementation outcomes and organisational performance effects.
Conclusion
This study analysed cloud ERP adoption among logistics SMEs in Ekurhuleni in order to develop a Cloud ERP Adoption Model (CEAM). The findings highlighted that SMEs without cloud ERP face significant operational challenges, including fragmented data and inefficient processes, while adopters benefit from improved integration and decision-making. The study further identified key factors influencing adoption, with the results showing that 10 of the 11 hypothesised factors significantly influenced adoption. Top management support emerged as the strongest predictor, while skills was the only non-significant factor. These findings informed the development of the CEAM, providing a simplified and empirically grounded model to guide SMEs in cloud ERP adoption.
Acknowledgements
This article is based on research originally conducted as part of Tshepiso Morema’s master’s thesis titled ‘Cloud ERP Adoption in Ekurhuleni’s Logistics SMEs: A Conceptual Model’, submitted to the Department of Informatics, Faculty of Information and Communication Technology, Tshwane University of Technology, in 2026. The thesis is currently unpublished and not publicly available. Dr Tope S. Adeyelure and Dr Tshinakaho R. Seaba supervised the thesis. The study was reworked, revised and adapted into a journal article for publication. The authors confirm that the content has not been previously published or disseminated and complies with ethical standards for original publication.
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Tshepiso P. Morema: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualisation, Writing – original draft. Tope S. Adeyelure: Supervision, Writing – review & editing. Tshinakaho R. Seaba: Supervision, Writing – review & editing. 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.
Data availability
The data that support the findings of this study are available from the corresponding author, Tshepiso P. Morema, upon reasonable request.
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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How to cite this article: Morema, T.P., Adeyelure, T.S. & Seaba, T.R., 2026, ‘Cloud ERP adoption in Ekurhuleni’s logistics SMEs: A conceptual model’, South African Journal of Information Management 28(1), a2179. https://doi.org/10.4102/sajim.v28i1.2179