The research, led by Tsegaye Alemu Tola of Ethiopia’s Federal TVET Institute together with Yanqiu Che and Jing Mi of Tianjin University of Technology and Education in China, surveyed 428 TVET teachers drawn from 47 public institutions spanning ten Ethiopian regions and city administrations. The respondents taught across 16 different occupational majors, from engineering trades to service-sector disciplines, and ranged from diploma holders to master’s graduates, with teaching experience stretching from a single year to more than two decades. Of the 450 questionnaires distributed, 428 valid responses were retained, a response rate exceeding 95 percent, with roughly 81 percent of participants male.

At the heart of the study lies the European Union’s Digital Competence of Educators framework, known as DigCompEdu, which organizes teacher digital competence into 22 competency items across six domains: professional engagement, digital resources, teaching and learning, assessment, empowering learners, and facilitating learners’ digital competence. Before drawing any conclusions about skill levels, the team first had to prove that this European-born instrument could withstand statistical scrutiny in an Ethiopian vocational context — a validation step the authors say had never been performed before for the country’s TVET sector.

That validation hinged on confirmatory factor analysis, a structural equation modeling technique that tests whether observed survey responses genuinely map onto the theoretical constructs they are meant to measure. The results were emphatic. The six-factor model produced a chi-square of 425.89 with 194 degrees of freedom, a chi-square-to-degrees-of-freedom ratio of 2.2, and fit indices that comfortably exceed accepted benchmarks: a Comparative Fit Index of 0.966, a Tucker-Lewis Index of 0.959, a Root Mean Square Error of Approximation of 0.053, and a Standardized Root Mean Square Residual of just 0.036. Standardized factor loadings for all 22 items ranged from 0.709 to 0.898, well above the 0.50 threshold, while the overall instrument achieved a Cronbach’s alpha of 0.95, signaling excellent internal consistency.

The team also ran a battery of discriminant validity checks to ensure the six domains were statistically distinct rather than blurred reflections of a single underlying trait. Cross-loading analysis, the Heterotrait-Monotrait ratio — which stayed below the 0.90 cutoff — and the Fornell-Larcker criterion all confirmed that each construct captured unique variance. Average variance extracted values ranged from 0.59 to 0.79, above the recommended 0.50, and variance inflation factors between 1.7 and 3.51 ruled out problematic multicollinearity among items. In short, the DigCompEdu framework, originally designed for European classrooms, proved psychometrically robust for measuring the digital competence of vocational teachers in a developing-country context.

With the instrument validated, the descriptive picture that emerged was sobering. Ethiopian TVET teachers rated their digital competence as low to moderate across all six domains, with mean scores ranging from 2.72 to 3.32 on a five-point Likert scale where 3 marks the neutral midpoint. The weakest areas were professional engagement, assessment, and empowering learners’ digital competence, all of which fell below the midpoint, suggesting teachers feel least confident using digital technologies for their own professional development, for evaluating student work, and for fostering learner-centered digital strategies. Relative strengths appeared in teaching and learning, digital resources, and facilitating learners’ digital competence, though even these hovered near neutral rather than signaling genuine proficiency.

The inferential analysis then produced the study’s most striking result. Mann-Whitney U tests comparing male and female teachers, and Kruskal-Wallis H tests comparing teachers across age bands, experience levels, and educational qualifications, found no statistically significant differences in any of the six competence domains. This contradicts a substantial body of international literature in which younger teachers and, in many studies, male teachers outperform their peers on digital competence measures. The authors interpret the finding as evidence that in the Ethiopian TVET context, personal demographics simply do not drive digital capability — a conclusion with significant policy implications, because it means interventions need not be tailored to demographic subgroups.

What did move the needle were two institutional variables. Teachers with prior digital technology training scored significantly higher across the overall spectrum of digital competence, confirming that structured ICT training programs translate directly into measurable capability. Meanwhile, the availability of internet access at vocational schools produced significant differences in professional engagement, assessment, empowering learners, and facilitating learners’ domains, and the presence of digital teaching tools significantly lifted competence across all dimensions overall. Notably, the digital resources and teaching and learning domains did not differ significantly by internet availability, hinting that basic resource-handling skills may develop even in connectivity-poor environments while higher-order competencies do not.

The findings land at a pivotal moment for Ethiopian vocational education. The country’s 2023 Education and Training Policy and its 2025 TVET Strategy explicitly prioritize creating a digitally competent workforce, yet the study notes that graduates have consistently struggled to meet labor market expectations, and that the system is constrained by outdated equipment, shortages of qualified educators, and insufficient funding. As Industry 4.0 technologies — artificial intelligence, digital twins, simulation software, and the Internet of Things — reshape the workplaces TVET graduates are meant to enter, the gap between policy ambition and classroom reality threatens to widen unless the teaching workforce itself is upskilled.

The authors close with a layered set of recommendations. At the policy level, they call for a national digital competency framework for TVET with modular certification, alongside regional digital innovation centers that could pool simulation technologies as scalable substitutes for costly physical equipment. Institutions are urged to invest in equitable infrastructure — reliable laptops, high-speed internet, and industry-relevant tools — while adopting low-bandwidth and offline-capable technologies suited to resource-constrained settings. Teachers themselves are encouraged to pursue continuous, collaborative professional learning through communities of practice and peer mentoring. The study’s limitations, including its cross-sectional design, self-assessment instrument, and non-probabilistic sampling, mean the findings capture a snapshot rather than a trajectory. But the core message is unambiguous: closing Ethiopia’s vocational digital divide is not about who the teachers are, but about what their institutions give them.

Subject of Research: Digital competence of technical and vocational education and training teachers in Ethiopia and its sociodemographic and institutional determinants

Article Title: Evaluation of TVET teachers’ digital competence in Ethiopia and Its sociodemographic determinants

Article References: Tola, T. A., Che, Y., & Mi, J. (2026). Evaluation of TVET teachers’ digital competence in Ethiopia and Its sociodemographic determinants. Discover Education, 5(1), Article 1016. https://doi.org/10.1007/s44217-026-02107-3

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02107-3

Keywords: digital competence, TVET, Ethiopia, DigCompEdu, vocational education, teacher training, ICT infrastructure, confirmatory factor analysis, professional development, digital divide, educational technology, sociodemographic factors

The starting point of the study is an input–output model, a framework borrowed from economics in which a system is described as a set of components that feed into and draw from one another. Using this approach, the team identified fourteen key components of smart cities, ranging from data assets, information and communication technology infrastructure, and smart mobility to human capital, governance arrangements, and the natural environment. The choice of an input–output structure is deliberate: rather than listing smart city features in isolation, the model forces every component to be positioned in relation to the others, capturing the idea that a city becomes smart not because of any single technology but because of the way technologies, people, and institutions interact.

From those fourteen components, the researchers extended the model using a social network framework. In network terms, each component becomes a node, and every possible relationship between two components becomes a potential edge. With fourteen nodes, the mathematics of complete graphs yields ninety-one possible pairwise connections, and the authors mapped all of them as potential interconnections within the smart city system. This is where the study departs from most earlier conceptual work. Traditional smart city frameworks, such as the widely cited integrative framework of technology, people, and institutions, tend to group components into categories and describe their relationships in qualitative terms. A network representation goes further: it makes the relationships themselves objects of measurement, so that researchers can ask not only which components matter but which connections matter, and how both change over time.

To move from a theoretical network to an evidence-based one, the team turned to bibliometrics, the quantitative analysis of scientific publications. They searched the literature from 1998 to 2023, a twenty-five-year window that covers essentially the entire history of the modern smart city debate, and retrieved 214,211 articles. For each of the ninety-one potential edges in the network, they counted how often the corresponding pair of components appeared together in the smart city literature, using carefully structured search queries that combined the terms smart city or smart cities with the keywords of each component pair. The volume of co-occurrence served as a proxy for the significance of each connection: an edge that appears in tens of thousands of publications is clearly more central to how scholars and practitioners think about smart cities than an edge that appears in only a handful.

The results of this quantitative mapping are striking. The analysis highlights the central role of data assets, which emerge as the most significant node in the network, connected to nearly everything else. This finding reflects a broader transformation in how cities are imagined and managed. In the early years of the smart city debate, around the late 1990s and 2000s, discussions centered on information and communication technology infrastructure and e-government, echoing the maturity models of digital government research. As sensor networks, mobile devices, and urban data platforms proliferated, data itself became the connective tissue of the smart city, the resource that links mobility systems to energy grids, public services to citizen engagement, and environmental monitoring to policy decisions. The bibliometric evidence shows this shift happening in the publication record itself, with data-related connections growing in prominence as the decades progress.

The study does not stop at counting. The authors complemented the bibliometric analysis with a qualitative review of the twenty-five most relevant articles in the corpus, focusing on the interplay among three main components of the network. This mixed-methods design matters because raw co-occurrence counts can be misleading: two terms may appear together frequently simply because both are fashionable, not because the underlying relationship is well understood. By reading the most influential papers closely, the researchers were able to interpret what the strongest connections in the network actually mean in practice, how they are theorized, and where the literature reveals genuine integration versus superficial association. The qualitative layer also helps explain the dynamics behind the numbers, showing how the meaning of individual components has evolved as the smart city concept has matured from a technology-driven vision into a more multidimensional model of urban governance.

One of the study’s central conclusions is that smart city development is dynamic and multidimensional in a way that static frameworks fail to capture. The network is not fixed: nodes gain and lose importance, and the edges between them strengthen or weaken as technologies mature, policies change, and societal priorities shift. For example, the relationship between data assets and human capital or intellectual capital has become increasingly visible in recent years, reflecting debates about the skills, privacy protections, and institutional capacities cities need to turn raw data into public value. Similarly, connections involving governance and sustainability have grown as critics have pushed back against purely technological visions, asking whether a city can be smart without being sustainable, inclusive, or democratic. The network model makes these evolutions measurable, allowing researchers to track them across successive time windows rather than relying on impressionistic assessments.

For practitioners, the implications are concrete. City officials and urban planners often face difficult decisions about where to invest limited resources: in sensors and platforms, in skills and organizational change, or in partnerships with private providers. A network-based view of the smart city offers a way to ground those decisions in evidence about which components and connections the accumulated research literature considers most consequential. It also provides a diagnostic tool: a city that has invested heavily in infrastructure but neglected the human capital and governance nodes may find that its data flows never translate into better services, because the edges connecting technology to people and institutions are weak. The authors suggest that the model can help researchers and practitioners alike pay attention to changes in the smart city concept over time and to the connections between the various components involved in the development process, rather than treating each element in isolation.

The methodological contribution may prove as important as the substantive findings. The authors present their approach as a new modeling tool for analyzing the relative importance of nodes and edges in the scientific literature, and they have made the data collection structure and results available in a supplementary document to ensure reproducibility, including the exact search queries used for each component pair. This transparency means other researchers can update the analysis as new publications appear, extend the network with additional nodes, or apply the same technique to neighboring concepts such as sustainable cities, resilient cities, or low-carbon cities, concepts that the same research group has previously compared and untangled. In a field often criticized for conceptual vagueness and vendor-driven hype, a reproducible, quantitative map of what the smart city actually consists of, and how that composition has changed over a quarter century, is a genuinely useful piece of infrastructure for the research community itself.

Ultimately, the study is a reminder that the smart city is less a product than a process, an evolving web of relationships among data, technology, people, and institutions rather than a fixed blueprint to be purchased and installed. By modeling that web explicitly, the researchers have given urban scholars a common language for debating which connections deserve attention and which assumptions deserve skepticism. As cities around the world continue to digitize, the question is no longer whether data assets sit at the center of urban governance, the evidence now shows they do, but whether the rest of the network, the human and institutional nodes that give data its meaning, will receive the investment they need to keep the whole system connected.

Subject of Research: Network-based modeling of smart city governance components and their evolution in the scientific literature

Article Title: Smart city modeling: a social network analysis approach

Article References: Noori, N., de Jong, M., Joss, S., & Ranjbar-Sahraei, B. (2024). Smart city modeling: a social network analysis approach. Global Public Policy and Governance, 4(4), 420-446. https://doi.org/10.1007/s43508-024-00101-9

Image Credits: AI Generated

DOI: 10.1007/s43508-024-00101-9

Keywords: smart cities, social network analysis, input–output modeling, bibliometrics, data assets, urban governance, smart mobility, human capital, ICT infrastructure, sustainability, network models, public policy