SMEs’ digital maturity: analyzing influencing factors and the mediating role of environmental factors

Edwin Omol (Department of Software Development and Information Systems (SDIS), KCA University, Nairobi, Kenya)
Paul Abuonji (Department of Networks and Applied Computing (NAC), KCA University, Nairobi, Kenya)
Lucy Mburu (Department of Networks and Applied Computing (NAC), KCA University, Nairobi, Kenya)

Journal of Innovative Digital Transformation

ISSN: 2976-9051

Article publication date: 26 November 2024

384

Abstract

Purpose

This study investigates the relationships among various dimensions influencing the digital maturity of small- and medium-sized enterprises (SMEs). A novel variable, namely SMEs' dependence level on environmental factors, is introduced to broaden the scope beyond traditional linear relationships, providing insights into the multifaceted nature of SME digital maturity.

Design/methodology/approach

The study employs correlation and regression analyses to unravel significant correlations and explore the impact of predictors on the dependent variable. The interconnectedness of Technology, Product, Organization, People, Strategy and Operations is scrutinized, revealing their collective influence on SMEs' digital maturity. Importantly, the absence of multicollinearity issues is confirmed, validating the reliability of the study’s results. The regression models demonstrate robust explanatory power, with the inclusion of a mediator significantly enhancing overall model performance.

Findings

The findings highlight the interconnected nature of key dimensions, emphasizing the collective influence of Technology, Product, Organization, People, Strategy and Operations on SMEs' digital maturity. Analysis of variance results further support the effectiveness of these predictors in capturing variability in the dependent variable. Beta values provide insight into the distinct contributions of each predictor, emphasizing their individual impacts on SMEs' digital maturity.

Originality/value

This study contributes to the field by emphasizing the need for more holistic models and methodological advancements to understand the complex dynamics that shape SMEs' digital maturity. By introducing the novel variable of SMEs' dependence level on environmental factors, the research expands the conceptual framework, offering a fresh perspective on the multifaceted nature of SME digital maturity. The study’s originality is underscored by robust statistical analyses and an exploration of relationships among key dimensions. The comparison and contrast of findings with existing literature further enhance the study’s unique contributions to the field.

Keywords

Citation

Omol, E., Abuonji, P. and Mburu, L. (2024), "SMEs’ digital maturity: analyzing influencing factors and the mediating role of environmental factors", Journal of Innovative Digital Transformation, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JIDT-01-2024-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Edwin Omol, Paul Abuonji and Lucy Mburu

License

Published in Journal of Innovative Digital Transformation. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

Small and medium-sized enterprises (SMEs) play a crucial role in the global economy, contributing significantly to employment, economic growth and innovation (Bai et al., 2021; Matt and Rauch, 2020; Kljajić Borštnar and Pucihar, 2021). However, despite their importance, SMEs often face unique challenges that limit their growth, particularly in accessing resources such as finance, advanced technologies and markets (Rakshit et al., 2022). The flexibility that characterizes many SMEs can also hinder their ability to fully adopt digital tools, which are increasingly vital in today’s business landscape.

The integration of digital technologies, such as cloud computing, artificial intelligence (AI), the internet of Things (IoT) and mobile applications, has become essential for improving operational efficiency, decision-making and competitiveness (Omol et al., 2024a). While the COVID-19 pandemic has accelerated digital transformation, many SMEs struggle to leverage these technologies effectively (Bai et al., 2021; Ifenthaler and Egloffstein, 2020). Assessing digital maturity is a critical step in understanding how SMEs can advance in their digital journeys, but current digital maturity models are often insufficiently tailored to their specific needs (Aguiar et al., 2019; Anderson and Ellerby, 2018; Chonsawat and Sopadang, 2021). Many models lack clear methodologies and practical tools for implementation, limiting their effectiveness.

This research addresses the need for a more tailored approach by focusing on the specific challenges SMEs face in achieving digital maturity. The study’s objectives are to identify the key factors influencing digital maturity in SMEs and to understand how external environmental factors mediate the relationship between these factors and digital maturity.

The study contributes to the literature by reviewing existing digital maturity models, analyzing the factors that influence digital transformation in SMEs and exploring the role of environmental factors in this process. These findings provide both theoretical insights and practical recommendations to help SMEs enhance their digital maturity and improve their competitiveness in an increasingly digital economy.

The key research questions guiding this study are:

  • (1)

    What are the key factors influencing the digital maturity of SMEs?

  • (2)

    How do environmental factors mediate the relationship between these factors and digital maturity?

By addressing these questions, this research aims to bridge gaps in existing models and provide SMEs with actionable frameworks to guide their digital transformation efforts.

2. Literature review

2.1 Related SME maturity models

Digital maturity in SMEs refers to the extent to which SMEs have integrated digital technologies and strategies into their operations (Yu et al., 2023; Omol et al., 2024c). It encompasses the adoption of digital tools, development of digital skills, effective data management, innovation capabilities and fostering a digital mindset within the organization. Furthermore, digital maturity involves a strategic approach that aligns digital initiatives with overarching business objectives. Achieving high digital maturity enables SMEs to enhance efficiency, adapt to market changes and maintain competitiveness in an increasingly digital environment (Omol et al., 2023). In today’s rapidly evolving business landscape, attaining a high level of digital maturity has become essential for the sustainability and advancement of SMEs (Omol, 2024).

The landscape of digital maturity models (DMMs) in SMEs is characterized by a variety of frameworks, each emphasizing distinct dimensions and action fields. A review of existing literature reveals numerous DMMs aimed at assessing and improving SMEs' digital capabilities. For instance, the DMM proposed by Williams et al. (2019) focuses primarily on strategy and technology as key action fields. Kljajić Borštnar and Pucihar (2021) multiattributed assessment digital model (MAADM) extends these dimensions to include strategy, transformation, technology, people, culture and digital support highlighting the multifaceted nature of digital transformation. Similarly, Schallmo et al. (2021) underscore the importance of leadership, alongside strategy, product, technology, people and culture, in driving SMEs' digital maturity.

Other models, such as Yezhebay et al.'s (2021) Digital Maturity Readiness Model (DMRM), stress the significance of operations alongside traditional dimensions like strategy, product, leadership and technology. Aguiar et al.'s (2019) Digital Transformation Capability Maturity Model Framework (DTCMMF) incorporates customer and value proposition, demonstrating a comprehensive approach that blends internal and customer-centric elements. Gimpel et al.'s (2018) Structured Digital Transformation (SDT) and Goumeh and Barforoush (2021) DMM also emphasize operations, customer engagement and legal factors.

Other noteworthy models include Blatz et al.'s (2018) Maturity Model Digital (MMD), which narrows down to strategy, leadership and operations, and Lin et al. (2020) Maturity Model for Readiness (MMR), focusing on technology and organization as foundational aspects of digital preparedness. Pirola et al.'s (2020) Digital Readiness Assessment (DRA) expands on these by including strategy, product, technology, people, culture, organization and operations, offering a broad evaluation framework for SMEs. Finally, Mittal et al.'s (2018) Maturity Model (MM) and Anderson and Ellerby’s (2018) Digital Maturity Model (DMM) recognize the critical role of financial and cultural dimensions in shaping SMEs' digital maturity. These diverse models collectively highlight the complex, multidimensional nature of digital maturity in SMEs, offering valuable frameworks for understanding and assessing their digital transformation journeys.

As delineated in Table 1, an extensive array of models, each characterized by distinct dimensions and spheres of operation, is at the disposal of SMEs embarking on the digitization journey. This diversity poses a challenge in pinpointing the specific dimensions or operational areas that SMEs should prioritize when undertaking digital transformation. To tackle this challenge, the current study introduces an 18 by 20 matrix encapsulating model dimensions. The matrix’s objective is to identify dimensions universally acknowledged across existing research on digital maturity assessment for SMEs.

Drawing from the works of Kljajić Borštnar and Pucihar (2021), Viloria-Núñez et al. (2022) and Aguiar et al. (2019), this research establishes a minimum threshold of 40% for these dimensions. This implies that these dimensions should be present in at least 40% of the entire set of models, effectively meaning they should appear in nearly every second model (Bumann and Peter, 2019). Applying the 40% criterion dictates that a dimension should score a minimum of 8 out of 20. According to the outcomes, only Technology (14/20), Product (9/20), Strategy (14/20), People (11/20), Organization (8/20) and Operations (9/20) meet this criterion. Consequently, these six dimensions as depicted in Figure 1 are adopted in this study to establish the dimensions or operational areas for SMEs digital maturity.

This aligns with the findings of a specific model (Pirola et al., 2020) presented in Table 1, which collectively identifies these six dimensions as focal points for the digital transformation journey of SMEs. Notably, only two models (Chonsawat and Sopadang, 2021; Yezhebay et al., 2021) incorporate five dimensions out of the selected six, while four models (Schallmo et al., 2021; Goumeh and Barforoush, 2021; Blatz et al., 2018; Berger et al., 2020) mention three dimensions. Models such as (Williams et al., 2019; Aguiar et al., 2019; Lin et al., 2020; Zentner et al., 2021) account for only two dimensions.

However, more expansive models like (Kljajić Borštnar and Pucihar, 2021; Santos and Martinho, 2020; Mittal et al., 2018; Anderson and Ellerby, 2018; Trotta and Garengo, 2019) incorporate four dimensions while also addressing additional operational fields or dimensions such as leadership, Customer, Processes or Culture. Other frequently encountered dimensions encompass data maturity, ecosystem, transformation management and digital support. On average, the models cover 4.6 operational fields or dimensions. The model developed by Pirola et al. (2020) stands out, encompassing seven different dimensions. In comparison, a combined total of seven models (Kljajić Borštnar and Pucihar, 2021; Schallmo et al., 2021; Yezhebay et al., 2021; Aguiar et al., 2019; Goumeh and Barforoush, 2021; Chonsawat and Sopadang, 2021) exhibit six dimensions each.

While internal dynamics hold undeniable significance (Chonsawat and Sopadang, 2021; Pirola et al., 2020; Kljajić Borštnar and Pucihar, 2021; Schallmo et al., 2021; Yezhebay et al., 2021; Aguiar et al., 2019; Goumeh and Barforoush, 2021), the trajectory toward digital maturity extends beyond the confines of organizational boundaries. External forces (Zentner et al., 2021), often encapsulated as 'environmental factors,' introduce additional layers of influence, contributing to the nuanced landscape of digital evolution (Omol et al., 2023). Regulatory frameworks, market conditions and socioeconomic trends constitute key external elements shaping the digital terrain in which SMEs operate. This study adopts Resource Dependence Theory (RDT) as a framework, delving into the underexplored terrain of the unique characteristics displayed by SMEs within the broader context of their environment.

The susceptibility of an SME to external factors emanates from its reliance on diverse resources, including materials, workforce, financial capital (Mittal et al., 2018), equipment, knowledge and market opportunities for its products or services (Zentner et al., 2021). Despite this reliance, the SME maintains control within the sphere of its environment, positioning itself strategically (Zhang et al., 2022). RDT posits that entities lacking crucial resources will seek associations or dependencies with others to fulfill their resource needs (Gure and Karugu, 2018).

In emerging economies, SMEs acquire resources primarily through financial institutions, obtaining loans, securing grants from donors and forming partnerships with nongovernmental organizations (NGOs) that offer training opportunities (Omol et al., 2023). Government interventions, such as favorable legislation, training initiatives and grant programs, play a pivotal role in supporting SMEs (Gure and Karugu, 2018). However, in the context of digital maturity, SMEs may encounter challenges in adopting digital technologies due to their dependence on external actors for resources, coupled with the potential control exerted by these actors (Omol et al., 2024b). Analyzing the digital maturity of SMEs through the lens of RDT can offer valuable insights into the dynamics of resource dependencies and their impact on an SME’s ability to embrace digital transformation.

In light of this context, it becomes evident that the digital maturity of SMEs is significantly influenced by external factors (Mittal et al., 2018; Omol et al., 2024b; Zentner et al., 2021). This challenges the prevailing misconception in existing literature, which predominantly focuses on maturity dimensions while overlooking the profound impact of external stakeholders within the SMEs' environment (Kemal and Shah, 2023). Consequently, there is a pressing need to develop a conceptualization (Omol et al., 2024c), as illustrated in Figure 2, for this study that comprehensively considers these external factors to effectively assess the digital maturity of resource-dependent SMEs. The conceptualization in Figure 2 outlines an understanding of the factors shaping SMEs’ digital maturity. The independent variables, including Product, Technology, Organization, People, Strategy, and Operations collectively represent the internal dimensions that directly influence the digital evolution of SMEs. Each of these variables plays a distinct role in structuring and managing different facets of the SME’s digital landscape.

Furthermore, the mediating variable, SMEs environment, serves as a crucial link between the independent variables and the ultimate goal of SME digital maturity. SMEs environment encapsulates external factors, such as government support, partnerships and the acquisition of loans and grants. Government support encompasses policies and regulations facilitating or hindering SMEs in their digital initiatives. Partnerships reflect collaborations with external entities, like NGOs or industry partners, influencing the SME’s digital trajectory. Additionally, the acquisition of loans and grants represents financial aspects, indicating the SME’s ability to invest in digital capabilities.

The interplay between the action fields and SMEs environment underscores the intricate relationship between internal organizational dynamics and external environmental factors. It acknowledges that the effectiveness of SMEs in achieving digital maturity is not solely contingent on their internal strategies and operations but is significantly impacted by the external support, collaborations and financial resources available to them. Ultimately, the dependent variable, SME digital maturity, represents the culmination of these relationships. It reflects the overall proficiency of SMEs in leveraging digital technologies, organizational strategies and human capital to thrive in the contemporary business landscape (Omol et al., 2024a). The conceptual framework emphasizes the need for a holistic approach, considering both internal and external dimensions, to comprehensively assess and enhance SMEs' digital maturity.

2.2 Theoretical underpinning

This study is grounded in several theoretical frameworks that provide valuable insights into the factors influencing SMEs' digital maturity and the role of environmental factors. Resource dependence theory (RDT) underscores the importance of external resources, such as government support, banking institutions and partnerships, in facilitating SMEs' access to digital technologies and expertise (Kanyoma et al., 2018). By diversifying their resources, SMEs can reduce dependence on any single source, which is crucial for enhancing digital capabilities in an ever-evolving technological landscape (Omol et al., 2024c). Additionally, managing relationships with external digital service providers, consultants and other stakeholders plays a pivotal role in acquiring the necessary digital infrastructure and expertise (Sassanelli and Terzi, 2022).

Another foundational framework is the technology acceptance model (TAM), which explains the factors driving the adoption of digital technologies by SMEs. According to TAM, perceived usefulness and ease of use are key determinants of technology acceptance (Omol and Ondiek, 2021). SMEs' perception of the utility and ease of integrating technologies like cloud computing, mobile payment systems or customer relationship management (CRM) tools directly impacts their digital maturity. Studies have shown that higher perceptions of usefulness and ease of use lead to increased technology adoption and, consequently, higher levels of digital maturity (Liang et al., 2021).

The Information Systems Success Model (ISSM) further provides a framework for understanding how the success of information systems influences SMEs' digital maturity. The model emphasizes the importance of system quality, information quality and user satisfaction in determining the effectiveness of digital systems within organizations (Omol and Ondiek, 2021). By ensuring that digital tools and systems meet technical standards, deliver high-quality information and satisfy users, SMEs can optimize their digital strategies and enhance overall digital maturity (Parry and Hansen, 2020).

Finally, Dynamic Capability Theory (DCT) is crucial in assessing SMEs' ability to adapt and innovate in response to technological advancements and market changes. DCT emphasizes the capabilities of sensing, seizing opportunities and reconfiguring resources as critical to achieving digital maturity and sustaining competitive advantage (Teece et al., 1997). SMEs that invest in developing dynamic capabilities; such as organizational learning and innovation, are better positioned to leverage digital technologies for long-term success (Omol et al., 2023). This theory highlights the importance of continuous improvement and the flexibility to navigate the challenges posed by the digital economy.

3. Research procedure

As illustrated in Figure 3, this study synthesized and combined insights from existing digital maturity and transformation literature to identify the core research problem. This process led to the adoption of a positivist philosophy and the Likert scale used in the study. To further explore gaps in the literature and knowledge base, a Literature Review (LR) was conducted, alongside a comparative analysis (Omol et al., 2017), as detailed in Table 1. This helped identify the study’s focus areas, depicted in Figure 2.

Building on these insights, the study developed a research questionnaire tailored to the identified focus areas. The study concentrated on SMEs within Nairobi’s Central Business District (CBD), categorized as outlined in Table 2. The sample size was calculated using the sampling formula by Fisher et al. (Masakala et al., 2017; Sapienza et al., 2023; Wauyo et al., 2017), resulting in a sample of 382 SMEs from a population of 53,600 in the CBD. These SMEs were stratified as shown in Table 2. The questionnaire was configured in Kobo Toolbox and distributed online to the 382 targeted SME owners. The research adhered to protocols and approvals from KCA University, Kenyatta University and the National Commission for Science and Technology (NACOSTI). Data collection occurred between November and December 2023, achieving a remarkable 99% response rate (378/382).

The questionnaire, informed by a thorough literature review and expert consultations, consisted of three sections: general business inquiries, Likert scale questions on various business aspects and digital maturity outcomes. It was validated by three information systems professors, receiving an average validity score of 7.7/10. Reliability was confirmed with Cronbach’s alpha coefficients ranging from 0.756 to 0.987, indicating strong internal consistency as established in Omol et al. (2016). A pilot study conducted in Kisumu’s CBD, with 39 respondents (10% of the total sample), achieved an 87% response rate, further validating the questionnaire as a reliable tool for assessing the digital maturity of licensed SMEs in Nairobi’s CBD.

For data analysis, the study remained focused on its objectives, systematically categorizing and coding raw data for statistical inference. SPSS 29.0 was used for quantitative analysis, employing multiple regression and Pearson correlation to evaluate the model’s fitness and the significance of relationships. Descriptive statistics provided a detailed profile of the SMEs, and the study ensured methodological rigor by addressing issues such as missing data, outliers and normality. The regression equations derived from the analysis summarized key insights and proved the basis and foundation of this study.

4. Findings

4.1 Handling missing data

Following data collection, the study conducted data cleaning using Little’s MCAR test (Van Ginkel et al., 2020), as presented in Table 3. The test indicated a p-value of 1.000, signifying a Missing Completely at Random (MCAR) pattern for the missing data in this study.

Due to the observed randomness in missing data, the study employed regression imputation techniques to address these gaps (Van Ginkel et al., 2020), To assess multivariate outliers, Mahalanobis distance (D2) served as a measure of distance, indicating the deviation in standard deviation units between each observation and the mean of all observations, following Onyango’s (2022) guidance. A substantial D2 value implies that an observation is an extreme value on one or more variables. The study applied a stringent statistical significance test with p < 0.001 to D2. Mahalanobis distance was computed using SPSS version 29.0, and the results were compared with the critical χ2 value of 73.402, considering degrees of freedom (df = 72), equivalent to the number of independent variables, at a probability of p < 0.001 (Omol et al., 2023). While Table 4 exhibited a few outliers, it is recognized that removing outliers may enhance multivariate analysis but could also limit generalizability, as cautioned by McLachlan et al. (2019). Therefore, the decision was made to retain all cases identified as multivariate outliers in this study.

The normality of the data for latent factors was assessed through the Kolmogorov–Smirnov and Shapiro–Wilk tests. The results indicated significant departures from normality, except for two cases under the Shapiro–Wilk test. Skewness and kurtosis statistics further supported the deviation from normality, exceeding ±1 in most instances. To address this, data transformations using logarithmic (log10) and square root functions were applied, but the reaffirmation of non-normality persisted (Table 5). Despite these outcomes, the study invoked Laplace’s Central Limit Theorem (CLT), justifying the assumption that, given the large sample size (378), the data could be treated as approximately normal for statistical inference purposes. The CLT posits that the distribution of averages of a large number of independent, identically distributed random variables tends to converge to a normal distribution, particularly with larger sample sizes (McLachlan et al., 2019). The subsequent normality tests after transformation (Table 4) exhibited statistical significance but were interpreted in light of the CLT, supporting the contention that the data, while not conforming to specific normality tests, could be considered approximately normal for statistical analysis.

4.2 Collinearity and model summary

In addition to multivariate correlation, the investigation utilized tolerance and variance inflation factor (VIF) values to assess the presence of multicollinearity. The findings of the study indicate the absence of any multicollinearity issues concerning technology, product, organization, people, strategy, operations, and the mediator, as these variables demonstrated significance (Table 6).

The tolerance values for the variables surpass the threshold of 0.1, indicating the absence of multicollinearity issues. Conversely, VIF values exceeding 1.0 suggest the presence of collinearity (refer to Table 5). However, in the present research model, all VIF values were below 10, and tolerance values were above 0.1, signifying the absence of collinearity in the study’s data (Onyango, 2022).

The regression models were constructed to examine the relationship between the predictors and the dependent variable. Model 1, with predictors including operations, people, organization, strategy, product, and technology, demonstrates a substantial explanatory power, as indicated by an R2 value of 0.789. The adjusted R2 value, which considers the number of predictors, stands at 0.785, suggesting a robust fit (Table 7).

The standard error of the estimate is 0.36899, indicating the average distance between the observed and predicted values. In Model 2, an additional predictor, Mediator, was introduced. This modification resulted in an increase in both R and R2 values to 0.915 and 0.837, respectively. The adjusted R2 value also improved to 0.834, suggesting that the inclusion of Mediator enhanced the model’s explanatory power. The standard error of the estimate decreased to 0.32474, indicating a more precise fit. The regression analysis highlights the effectiveness of the models in explaining the variance in the dependent variable. The inclusion of Mediator in Model 2 contributed to a notable improvement in the model’s performance, as evidenced by the enhanced R2 and adjusted R2 values and the reduced standard error of the estimate.

4.3 Analysis of variance

The analysis of variance (ANOVA) results for the regression models provides insights into the distribution of variance in the dependent variable (Table 8).

For Model 1 (Table 8), the sum of squares for regression is 188.335, indicating the amount of variance in the dependent variable that is explained by the predictors (Operations, People, Organization, Strategy, Product and Technology). The residual sum of squares is 50.514, representing the unexplained variance. The total sum of squares is 238.849, reflecting the overall variability in the dependent variable. For Model 2, which includes an additional predictor (Mediator), the sum of squares for regression increases to 199.830. The residual sum of squares decreases to 39.019, suggesting that the inclusion of Mediator contributes to explaining more variance in the dependent variable. The total sum of squares remains constant at 238.849. These results indicate that both models contribute to explaining the variance in the dependent variable, with Model 2 showing a slight improvement. The ANOVA results complement the regression metrics, providing an overview of the effectiveness of the predictors in capturing the variability in the dependent variable.

4.4 Beta values

In the first model (Model 1), the predictors collectively accounted for a significant portion of the variance in the dependent variable. Notably, Technology, Organization, Strategy, and Operations demonstrated substantial positive associations, signifying their impactful roles in predicting the dependent variable. However, Product and People did not exhibit statistically significant relationships in this model. Model 1 yielded a set of findings, including unstandardized coefficients, standardized coefficients (Beta), t-values and associated significance levels for each predictor. As shown in Table 9, the constant term in Model 1 was −0.921 (t = −5.964, p < 0.001), highlighting its statistical significance. The predictor technology showed a positive association with the dependent variable, with a Beta of 0.428 and a t-value of 8.851 (p < 0.001). Conversely, Product did not significantly contribute to predicting the dependent variable in this model.

Expanding upon the analysis, Model 2 introduced an additional predictor, Mediator, to further enhance the model’s explanatory power. In this extended model, all predictors, including the newly introduced Mediator, exhibited statistically significant relationships with the dependent variable. The unstandardized coefficients, standardized coefficients (Beta), t-values, and associated significance levels for each predictor in Model 2 were thoroughly examined. Model 2 revealed the impact of the introduced Mediator alongside the existing predictors. The constant term in Model 2 was −0.934 (t = −6.875, p < 0.001), maintaining its statistical significance. Technology, Product, Organization, People, Strategy and Operations all retained their significant associations with the dependent variable. The introduction of Mediator brought an additional layer of complexity and depth to the model, enhancing its overall explanatory capacity. The regression analysis conducted in this study delved into the intricate relationships between various predictors; Technology, Product, Organization, People, Strategy, and Operations; and their impact on the dependent variable, SMEs' digital maturity level. The inclusion of SMEs' Dependence Level on environmental factors in Model 2 significantly enriched the model’s explanatory power, revealing an additional layer of influence on SMEs' digital maturity.

5. Discussion

Comparing these results with the studies on DMMs reveals interesting insights. The studies, such as Williams et al. (2019), Kljajić Borštnar and Pucihar (2021) and Schallmo et al. (2021), focus on diverse dimensions, including strategy, technology, transformation, leadership and culture. The models in these studies recognize the multifaceted nature of digital maturity, aligning with the idea that a combination of dimensions contributes to a comprehensive understanding. Aguiar et al.'s (2019) DTCMMF and Gimpel et al.'s (2018) SDT emphasize transformation, management, organization, operations, customers and value proposition. These dimensions align with the broader focus of Model 2, which includes a mediator, possibly reflecting the influence of external factors or a more comprehensive view of the digital transformation process.

The consideration of external factors in Zentner et al.'s (2021) DBMM and the emphasis on finance in Mittal et al.'s (2018) Maturity Model (MM) resonate with the recognition of a mediator in Model 2. This suggests that external factors or financial considerations may play a role in mediating the relationship between operational dimensions and the dependent variable. Williams et al. (2019) identified people/culture, technology and processes as significant dimensions, aligning with the approach of the current study. Similarly, Yezhebay et al. (2021) proposed a model with six dimensions, emphasizing People, Leadership, Strategy, Technology, Products and Operations. Kljajić Borštnar and Pucihar (2021) and Viloria-Núñez et al. (2022) highlight the importance of organizational capabilities, human resources and strategy, resonating with the emphasis on Technology, Organization, People and Strategy in the current study. The literature collectively underscores the complexity of SME digital maturity, with each study contributing unique insights (Williams et al., 2019; Yezhebay et al., 2021; Kljajić Borštnar and Pucihar, 2021; Viloria-Núñez et al., 2022).

Unique to the current study is the introduction of SMEs' dependence level on environmental factors, recognizing its potential impact on digital maturity. While other studies offer novel elements, such as Goumeh and Barforoush (2021) developing a model specific to the digital revolution in Iranian banks, the specific introduction of this variable distinguishes the current research. Furthermore, the current study advocates for embracing complexity in research designs, echoing the sentiment of other studies like Kljajić Borštnar and Pucihar (2021), Aguiar et al. (2019) and Zentner et al. (2021), which employed design science research approaches for methodological advancements (Aguiar et al., 2019; Goumeh and Barforoush, 2021; Kljajić Borštnar and Pucihar, 2021; Zentner et al., 2021). The acknowledgment of deficiencies in existing studies and the need for the current research is evident. Williams et al. (2019) pointed out limitations such as a lack of validation in digital maturity models. Several studies emphasize challenges faced by SMEs in digitization, including a lack of digital skills and financial constraints (Jeansson and Bredmar, 2019; Kljajić Borštnar and Pucihar, 2021; Rakshit et al., 2022; Schallmo et al., 2021; Viloria-Núñez et al., 2022; Williams et al., 2019; Yezhebay et al., 2021). The specificity of the focus on SMEs, particularly resource-dependent SMEs, is highlighted by the current study and supported by other research (Jeansson and Bredmar, 2019; Trotta and Garengo, 2019).

6. Conclusion

This study provides an exploration of the factors that shape the digital maturity of SMEs, emphasizing the critical role of environmental factors. By analyzing data from 378 SMEs, the study sheds light on the relationships between various organizational components, such as Technology, Product, Organization, People, Strategy, and Operations and their influence on digital maturity.

To ensure the robustness of the findings, rigorous data preparation was undertaken. Little’s MCAR test confirmed that any missing data was random, allowing for its careful imputation. This step was crucial as it preserved the integrity of the dataset, enabling accurate analysis. Additionally, Mahalanobis distance was used to identify potential multivariate outliers, with values as high as 85.435 indicating significant deviations in certain cases. Despite this, all outliers were retained to maintain the generalizability of the results, a decision aligned with the study’s focus on capturing a realistic snapshot of SME digital maturity.

Normality tests revealed significant deviations from a normal distribution, which initially posed a challenge for statistical analysis. However, by applying the Central Limit Theorem (CLT), it was possible to treat the data as approximately normal due to the large sample size. This allowed for the continuation of analysis despite the statistical anomalies, reflecting the study’s commitment to practical applicability over strict adherence to ideal conditions.

The study’s regression models provide compelling insights. In the first model (Table 8), core variables like Technology and Operations emerged as strong predictors of digital maturity, with Technology alone accounting for a significant portion of the variance (Beta = 0.428, p < 0.001). This underscores the pivotal role that technological investment plays in advancing SMEs' digital capabilities. On the other hand, the Product and People variables showed weaker contributions, with Product even displaying a negative association (Beta = −0.039, p = 0.261), suggesting that not all aspects of digital strategy equally impact maturity.

When environmental factors were introduced as a Mediator in the second model, the explanatory power of the model increased notably. The R2 value rose from 0.789 to 0.837, illustrating how the inclusion of external influences can better account for variations in digital maturity (Table 8). The Mediator itself demonstrated a significant positive impact (Beta = 0.343, p < 0.001), revealing that SMEs' success in digital transformation is not just a matter of internal capabilities but also heavily influenced by the external environment (Table 9). These findings have practical implications for SMEs striving to enhance their digital maturity. The analysis reveals that while investments in technology and operations are crucial, attention must also be paid to the broader environment in which an SME operates. This could include factors like regulatory frameworks, market conditions or even cultural attitudes toward technology adoption.

This study enriches our understanding of SMEs' digital maturity by highlighting the multifaceted nature of digital transformation. The results suggest that a one-size-fits-all approach is insufficient; instead, SMEs must consider both their internal resources and external environments to fully realize their digital potential. Future research could expand on these findings by exploring specific environmental factors in greater detail, further aiding SMEs in crafting strategies that are both comprehensive and contextually relevant.

7. Recommendations for future research

Future research should build on the implications of this study by developing and testing practical prototypes based on the proposed digital maturity framework to validate its real-world applicability. This will help bridge the gap between theoretical models and practical implementation. Longitudinal studies are recommended to assess the long-term impacts of digital transformation on SMEs, providing insights into the sustainability of digital initiatives. Expanding the research scope to include SMEs from diverse industries and regions will enhance the generalizability of the findings, offering a broader perspective on digital maturity. Additionally, further investigation into specific environmental factors and the interplay between internal and external influences is essential. This will enable the development of more targeted strategies for enhancing digital maturity, addressing both organizational and contextual challenges.

8. Limitations of the study

A key limitation is the absence of a practical prototype to validate the theoretical framework, which restricts the ability to test the findings in real-world settings. The reliance on self-reported data and selected 20 models introduces potential bias, and the cross-sectional design limits insights into the ongoing nature of digital transformation. Additionally, the findings may not be fully generalizable to SMEs in different contexts. Future research should aim to develop and test a prototype and consider longitudinal studies to better capture the dynamics of digital maturity in SMEs.

Figures

Digital maturity action fields

Figure 1

Digital maturity action fields

SMEs digital maturity factors

Figure 2

SMEs digital maturity factors

The research process

Figure 3

The research process

Models comparative analysis matrix for study’s variables

Digital maturity action fieldsExisting SMEs digital maturity models
DMMMAADMDMMDMRMDTMDTCMMFSDTDMMMMDMMERMMRMMPDRAMMDMMMDMDDBMMDCMDAMDTMTotal
Strategy0111000111011011111114
Product101100000001110011109
Leadership001100001000000000003
Technology0111000101111011111114
Transformation Management110001100000000000004
Data management000001000000000000001
People0111100001011100011111
Culture010000000000101100004
Organization000001100110111001008
Operations000101111100101100009
Customer001001110000000111007
Value Preposition000001100000000000002
Ecosystems000000010000000000001
Laws000000010000000000001
Digital Support010000000100000000002
Finance000000000000010000001
External Factors000000000000000010001

Source(s): Authors

SMEs categories, sample size and target population

SMEs categorySMEs in the CBD%Sample size
Retail sector18,53434.6132
Transport1,8793.513
Hospitality2,3834.417
Catering2,9065.421
Entertainment4,3248.131
Pharmaceuticals and Health Services2,7685.220
Technology16,89131.5120
Real Estate3,9157.328
Total53,600100382

Source(s): Nairobi County Licensing Department (2023)

Little’s MCAR test results

Chi square (χ2)DfSig
629.4528801.000

Source(s): Authors

Mahalanobis distance for multivariate outliers

Observation numberMahalanobis D2
11885.435
11684.486
10982.274
10877.762
9477.281
11076.585
8476.223
8375.072
8175.798
7073.887
13673.322
13574.23341

Source(s): Authors

Normality test after transformation

FunctionVariablesKolmogorov–SmirnovShapiro–Wilk
StatisticDfSigStatisticdfSig
Log 10Technology10.1263780.0000.9403780.000
Product10.0853780.0000.9783780.000
Organization10.0983780.0000.9773780.000
People10.0853780.0000.9563780.000
Strategy10.1143780.0000.9333780.000
Operation10.1063780.0000.9483780.000
Mediator10.0853780.0000.9613780.000
DMAM10.1553780.0000.8863780.000
Square rootTechnology20.1483780.0000.9053780.000
Product20.0973780.0000.9643780.000
People20.0993780.0000.9383780.000
Strategy20.1373780.0000.8893780.000
Operation20.1163780.0000.9163780.000
Mediator20.1103780.0000.9183780.000
DMAM20.1803780.0000.8263780.000

Source(s): Authors

Collinearity statistics

Collinearity statistics
ToleranceVIF
Technology0.2424.140
Product0.3732.678
Organization0.3053.277
People0.8411.189
Strategy0.5621.779
Operations0.4562.195
Mediator0.4082.449

Source(s): Authors

Model summary

ModelRR squareAdjusted R squareStd. error of the estimate
10.888a0.7890.7850.36899
20.915b0.8370.8340.32474

Source(s): Authors

ANOVA

ModelSum of squaresDfMean squareFSig
1Regression188.335631.389230.5380.000
Residual50.5143710.136
Total238.849377
2Regression199.830728.547270.7030.000
Residual39.0193700.105
Total238.849377

Source(s): Authors

Beta values of the variables

ModelUnstandardized coefficientsStandardized coefficientstSig
BStd. errorBeta
1(Constant)−0.9210.154 −5.9640.000
Technology0.5550.0630.4288.8510.000
Product−0.0560.050−0.039−1.1260.261
Organization0.2020.0630.1393.2210.001
People−0.0980.031−0.081−3.1440.002
Strategy0.2280.0380.1846.0000.000
Operations0.4350.0390.36511.0920.000
2(Constant)−0.9340.136 6.8750.000
Technology0.6120.0550.47211.0400.000
Product−0.3080.050−0.2116.1240.000
Organization0.1960.0550.1363.5660.000
People−0.0580.028−0.0482.0890.037
Strategy0.1340.0350.1083.8530.000
Operations0.2930.0370.2457.8800.000
Mediator0.4060.0390.34310.4410.000

Source(s): Authors

References

Aguiar, T., Gomes, S.B., da Cunha, P.R. and da Silva, M.M. (2019), “October. Digital transformation capability maturity model framework”, 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC), IEEE, pp. 51-57.

Anderson and Ellerby (2018), Digital Maturity Model, Deloitte, available at: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Technology-Media-Telecommunications/deloitte-digital-maturity-model.pdf (accessed 18 November 2024).

Bai, C., Quayson, M. and Sarkis, J. (2021), “COVID-19 pandemic digitization lessons for sustainable development of micro-and small-enterprises”, Sustainable Production and Consumption, Vol. 27, pp. 1989-2001, doi: 10.1016/j.spc.2021.04.035.

Berger, S., Bitzer, M., Häckel, B. and Voit, C., (2020), “Approaching digital transformation-development of a multi-dimensional maturity model”.

Blatz, F., Bulander, R. and Dietel, M. (2018), “June. Maturity model of digitization for SMEs”, 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE, pp. 1-9.

Bumann, J. and Peter, M. (2019), “Action fields of digital transformation–a review and comparative analysis of digital transformation maturity models and frameworks”, Digitalisierung und andere Innovationsformen im Management. Innovation und Unternehmertum, Vol. 2, pp. 13-40.

Chonsawat, N. and Sopadang, A. (2021), “Smart SMEs 4.0 maturity model to evaluate the readiness of SMEs implementing industry 4.0. CMUJ”, Natural Sciences, Vol. 20 No. 2, e2021027, doi: 10.12982/cmujns.2021.027.

Gimpel, H., Hosseini, S., Huber, R., Probst, L., Röglinger, M. and Faisst, U. (2018), “Structuring digital transformation: a framework of action fields and its application at ZEISS”, Journal of Information Technology Theory and Application, Vol. 19 No. 1, p. 3.

Goumeh, F. and Barforoush, A.A. (2021), “March. A Digital Maturity Model for digital banking revolution for Iranian banks”, 2021 26th International Computer Conference, Computer Society of Iran (CSICC), IEEE, pp. 1-6.

Gure, A.K. and Karugu, J. (2018), “Strategic management practices and performance of small and micro enterprises in Nairobi City County, Kenya”, International Academic Journal of Human Resource and Business Administration, Vol. 3 No. 1, pp. 1-26.

Ifenthaler, D. and Egloffstein, M. (2020), “Development and implementation of a maturity model of digital transformation”, TechTrends, Vol. 64 No. 2, pp. 302-309, doi: 10.1007/s11528-019-00457-4.

Jeansson, J. and Bredmar, K., (2019), “Digital transformation of SMEs: capturing complexity”, pp. 523-541, doi: 10.18690/978-961-286-280-0.28.

Kanyoma, K.E., Agbola, F.W. and Oloruntoba, R. (2018), “An evaluation of supply chain integration across multi-tier supply chains of manufacturing-based SMEs in Malawi”, The International Journal of Logistics Management, Vol. 29 No. 3, pp. 1001-1024, doi: 10.1108/ijlm-10-2017-0277.

Kemal, A.A. and Shah, M.H. (2023), “Digital innovation in social cash organizations – the effects of the institutional interactions for transforming organizational practices”, Information Technology and People, Vol. 37 No. 5, pp. 2092-2126, doi: 10.1108/itp-02-2023-0176.

Kljajić Borštnar, M. and Pucihar, A. (2021), “Multi-attribute assessment of digital maturity of SMEs”, Electronics, Vol. 10 No. 8, p. 885, doi: 10.3390/electronics10080885.

Liang, X., You, J. and Liu, X. (2021), “Examining the adoption intention of cloud computing by SMEs in China: an empirical investigation based on technology acceptance model”, Journal of Enterprise Information Management, Vol. 34 No. 1, pp. 120-136.

Lin, T.C., Wang, K.J. and Sheng, M.L. (2020), “To assess smart manufacturing readiness by maturity model: a case study on Taiwan enterprises”, International Journal of Computer Integrated Manufacturing, Vol. 33 No. 1, pp. 102-115, doi: 10.1080/0951192x.2019.1699255.

Masakala, C., Omol, E., Wauyo, F. and Okumu, J. (2017), “The role of budgeting process in financial performance: a case study of Bugisu Cooperative Union ltd Mbale, Uganda”, American Journal of Finance, Vol. 1 No. 5, pp. 31-48, doi: 10.47672/ajf.224.

Matt, D.T. and Rauch, E. (2020), “SME 4.0: the role of small-and medium-sized enterprises in the digital transformation”, in Industry 4.0 for SMEs: Challenges, Opportunities and Requirements, pp. 3-36, doi: 10.1007/978-3-030-25425-4_1.

McLachlan, G.J., Lee, S.X. and Rathnayake, S.I. (2019), “Finite mixture models”, Annual review of statistics and its application, Vol. 6 No. 1, pp. 355-378, doi: 10.1146/annurev-statistics-031017-100325.

Mittal, S., Romero, D. and Wuest, T. (2018), “Towards a smart manufacturing maturity model for SMEs (SM 3 E)”, Advances in Production Management Systems. Smart Manufacturing for Industry 4.0: IFIP WG 5.7 International Conference, APMS 2018, Seoul, Korea, August 26-30, 2018, Proceedings, Part II, Springer International Publishing, pp. 155-163.

Omol, E.J. (2024), “Organizational digital transformation: from evolution to future trends”, Digital Transformation and Society, Vol. 3 No. 3, pp. 240-256, doi: 10.1108/DTS-08-2023-0061.

Omol, E. and Ondiek, C. (2021), “Technological innovations utilization framework: the complementary powers of UTAUT, HOT–fit framework and; DeLone and McLean IS model”, International Journal of Scientific and Research Publications (IJSRP), Vol. 11 No. 9, pp. 146-151, doi: 10.29322/ijsrp.11.09.2021.p11720.

Omol, E.J., Ogalo, J.O., Abeka, S.O. and Omieno, K.K. (2016), “Mobile money payment acceptance model in enterprise management: a case study of MSE's in Kisumu City, Kenya”, Mara Research Journal of Information Science and Technology, Vol. 1, pp. 1-12.

Omol, E., Abeka, S. and Wauyo, F. (2017), “Factors influencing acceptance of mobile money applications in enterprise management: a case study of micro and small enterprise owners in Kisumu central business District, Kenya”, IJARCCE, Vol. 6, pp. 208-219, doi: 10.17148/ijarcce.2017.6140.

Omol, E., Mburu, L. and Abuonji, P. (2023), “Digital maturity action fields for SMEs in developing economies”, Journal of Environmental Science, Computer Science, and Engineering and Technology, Vol. 12 No. 3, doi: 10.24214/jecet.B.12.3.10114.

Omol, E., Mburu, L. and Abuonji, P. (2024a), “Unlocking digital transformation: the pivotal role of data analytics and business intelligence strategies”, International Journal of Knowledge Content Development and Technology, Vol. 14 No. 3, pp. 77-91.

Omol, E., Mburu, L. and Abuonji, P. (2024b), “Pioneering digital transformation in Africa: the path to maturity amidst unique challenges and opportunities”, Can. J. Bus. Inf. Stud., Vol. 6 No. 2, pp. 35-48, doi: 10.34104/cjbis.024.035048.

Omol, E.J., Mburu, L.W. and Abuonji, P.A. (2024c), “Digital maturity assessment model (DMAM): assimilation of design science research (DSR) and capability maturity model integration (CMMI)”, Digital Transformation and Society, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/DTS-04-2024-0049.

Onyango, D.A. (2022), “Determinants of profitability on street vending in Kisumu Central Business District, Kenya”, Doctoral dissertation, University of Nairobi.

Parry, G. and Hansen, R. (2020), “Evaluating the impact of information systems on digital maturity in SMEs”, Journal of Small Business Management, Vol. 58 No. 3, pp. 587-602.

Pirola, F., Cimini, C. and Pinto, R. (2020), “Digital readiness assessment of Italian SMEs: a case-study research”, Journal of Manufacturing Technology Management, Vol. 31 No. 5, pp. 1045-1083, doi: 10.1108/jmtm-09-2018-0305.

Rakshit, S., Islam, N., Mondal, S. and Paul, T. (2022), “Influence of blockchain technology in SME internationalization: evidence from high-tech SMEs in India”, Technovation, Vol. 115, 102518, doi: 10.1016/j.technovation.2022.102518.

Santos, R.C. and Martinho, J.L. (2020), “An Industry 4.0 maturity model proposal”, Journal of Manufacturing Technology Management, Vol. 31 No. 5, pp. 1023-1043, doi: 10.1108/jmtm-09-2018-0284.

Sapienza, L.G., Maia, M.A., Gomes, M.J., Mattar, A., Baiocchi, G. and Calsavara, V.F. (2023), “Randomized clinical trial of tissue equivalent bolus prescription in postmastectomy radiotherapy stratified by skin involvement status”, Clinical and Translational Radiation Oncology, Vol. 39, p. 100570, doi: 10.1016/j.ctro.2022.100570.

Sassanelli, C. and Terzi, S. (2022), “The D-BEST reference model: a flexible and sustainable support for the digital transformation of small and medium enterprises”, Global Journal of Flexible Systems Management, Vol. 23 No. 3, pp. 345-370, doi: 10.1007/s40171-022-00307-y.

Schallmo, D.R., Lang, K., Hasler, D., Ehmig-Klassen, K. and Williams, C.A. (2021), “An approach for a digital maturity model for SMEs based on their requirements”, in Digitalization: Approaches, Case Studies, and Tools for Strategy, Transformation and Implementation, Springer International Publishing, Cham, pp. 87-101.

Teece, D.J., Pisano, G. and Shuen, A. (1997), “Dynamic capabilities and strategic management”, Strategic Management Journal, Vol. 18 No. 7, pp. 509-533, doi: 10.1002/(sici)1097-0266(199708)18:7<509::aid-smj882>3.0.co;2-z.

Trotta, D. and Garengo, P. (2019), “Assessing industry 4.0 maturity: an essential scale for SMEs”, 2019 8th International Conference on Industrial Technology and Management (ICITM), IEEE, pp. 69-74.

Van Ginkel, J.R., Linting, M., Rippe, R.C. and van der Voort, A. (2020), “Rebutting existing misconceptions about multiple imputation as a method for handling missing data”, Journal of Personality Assessment, Vol. 102 No. 3, pp. 297-308, doi: 10.1080/00223891.2018.1530680.

Viloria-Núñez, C., Vázquez, F.J. and Fernández-Márquez, C.M. (2022), “A review of the digital transformation maturity models for SMEs in search of a self-assessment”, in 2022 IEEE Andescon, pp. 1-6, doi: 10.1109/andescon56260.2022.9989889.

Wauyo, F., Omol, E. and Okumu, J. (2017), “Effectiveness of business intelligence technology absorptive capacity and innovation competency of university staff, case of Uganda Christian University Mbale Campus”, European Journal of Technology, Vol. 1 No. 2, pp. 55-73, doi: 10.47672/ejt.223.

Williams, C., Schallmo, D., Lang, K. and Boardman, L. (2019), “Digital maturity models for small and medium-sized enterprises: a systematic literature review”, ISPIM Conference Proceedings, The International Society for Professional Innovation Management (ISPIM), pp. 1-15.

Yezhebay, A., Sengirova, V., Igali, D., Abdallah, Y.O. and Shehab, E. (2021), “Digital maturity and readiness model for Kazakhstan SMEs”, 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST), IEEE, pp. 1-6.

Yu, W., Liu, Q., Chavez, R. and Zheng, L. (2023), “Does training provision matter? Unravelling the impact of digital transformation on environmental sustainability”, Information Technology and People, Vol. ahead-of-print No. ahead-of-print, doi: 10.1108/itp-01-2023-0007.

Zentner, H., Spremić, M. and Zentner, R. (2021), “Measuring digital business models maturity for SMEs”, 2021 IEEE Technology and Engineering Management Conference-Europe (TEMSCON-EUR), IEEE, pp. 1-6.

Zhang, X., Xu, Y. and Ma, L. (2022), “Research on successful factors and influencing mechanism of the digital transformation in SMEs”, Sustainability, Vol. 14 No. 5, p. 2549, doi: 10.3390/su14052549.

Acknowledgements

We would like to express our sincere appreciation to the scholars whose work forms the foundation of this research. Their insights and contributions have been instrumental in shaping the theoretical framework and methodology of this study. Their meticulous research and innovative perspectives have enriched the intellectual discourse in the field. Furthermore, I would like to acknowledge the insightful reviews and critiques provided by anonymous peer reviewers during the preparation of this manuscript. Their constructive feedback has significantly strengthened the rigor and clarity of the research, contributing to its overall quality.

This work was supported by Emerald Publishing in partnership with Saudi Electronic University.

Corresponding author

Edwin Omol is the corresponding author and can be contacted at: omoledwin@gmail.com

About the authors

Edwin Omol is an Information Systems PhD student within the Department of Software Development and Information Systems (SDIS) at the School of Technology, KCA University. He is a dedicated researcher with a passion for the field of Digital Transformation technologies. His current research is focused on the development of Digital Maturity Models customized for application in both SMEs and larger organizations. Edwin’s academic interests extend to advanced models rooted in deep learning, with a strong emphasis on areas such as business intelligence, artificial intelligence and data analytics.

Dr Paul Abuonji is the Head of ICT at KCA University, spearheading a strategic digital transformation agenda to enhance service delivery, reduce costs and optimize business processes. His extensive academic background includes a PhD in Information Technology Security and Audit, a Master’s in Information Technology Security and Audit, an MBA in Strategic Management, and a Bachelor’s in Computer Science. Prior roles include being the Associate Director of ICT and Lecturer at Jaramogi Oginga Odinga University of Science and Technology, where he led ICT security initiatives and managed ERP system implementation. Additionally, he held positions at Great Lakes University of Kisumu as a Lecturer and Head of the Department of Information Technology, making significant contributions to curriculum development and department establishment. His latest technological achievement is the creation and implementation of the innovative “Stratified Cyber Security Vigilance (SCSV) Model” for securing ICT infrastructure.

Dr Lucy Mburu is a senior lecturer and also serves as the Chair of the Department of Networks and Applied Computing (NAC) within the School of Technology at KCA University. Her doctoral research was conducted at the Chair of GI Science, Heidelberg University in Germany, where she was awarded a Deutscher Academischer Austausch Dienst (DAAD) scholarship. Her research focused on the analysis of Geographic Information for understanding and predicting risk patterns. Dr Mburu completed her Master’s degree in Computer-based Information Systems at the University of Sunderland in the UK, supported by the Commonwealth scholarship fund. Her research in this program was centered on usability in the context of improving the human–computer interface for medical prescription systems.

Related articles