To unpack this failure, the researchers turned to the Strategic Triangle, a framework developed by Harvard public management scholar Mark Moore in the 1990s. The triangle holds that any public institution creates value only when three conditions align: the initiative must be publicly valuable, meaning it reflects what citizens actually want; it must be operationally and administratively feasible, meaning institutions can actually deliver it; and it must be politically and legally sustainable, meaning it retains support from the governing regime. When one corner of the triangle lags behind the others, the whole enterprise wobbles. Applied to vocational higher education, the framework offers a diagnostic lens that goes beyond the usual complaints about outdated equipment or weak industry links.

The team conducted a qualitative document analysis of twenty-one sources, spanning national policy documents, regulations, statistical reports, and institutional materials. Document analysis of this kind treats policy texts as evidence of how a state frames its own ambitions and priorities, and it is a recognised method in educational leadership research. By reading these documents against the three corners of the Strategic Triangle, the researchers could trace where Indonesian vocational higher education is winning support and where it is quietly losing it.

On the first corner, societal preferences, the picture is stark. Indonesian families and students remain concentrated on universities and government-affiliated academies because both are perceived to offer stronger credentials, better career prospects, and higher social prestige. National admission data reflect this tilt: the distribution of applicants in the 2025 state university entrance selection shows far more candidates competing for academic programmes than for vocational ones. Prestige, in other words, is not a cosmetic problem. It shapes who enrols, which graduates employers trust, and ultimately whether the sector can attract the investment it needs to modernise.

The second corner, the political regime, reveals a different kind of stagnation. The study finds that successive regime changes in Indonesia have produced only relatively slow progress in building public value for vocational higher education. Policies have been written and reorganisations announced, but the underlying political commitment has not translated into the sustained legitimacy the sector requires. This matters because vocational education is unusually dependent on state signalling: when governments treat polytechnics as second-tier institutions, employers and families read that signal and act accordingly, reinforcing the very hierarchy the policies claim to want to change.

The third corner, operational capacity, may be the most technically alarming. The researchers conclude that Indonesian vocational higher education institutions remain dominated by an Industry 2.0-based skills orientation, a model rooted in mechanised mass production and standardised manual competencies. Meanwhile, the global economy has moved through Industry 3.0 and 4.0, defined by digitalisation, automation, and data-driven manufacturing, and is now entering Industry 5.0, which emphasises human-centric production, collaborative robotics, and sustainable, resilient systems. A curriculum calibrated to the second industrial revolution cannot credibly prepare graduates for factories that are already deploying digital twins, machine learning, and human-machine collaboration on the shop floor.

The consequences ripple outward. Indonesia’s own statistical agency reports persistent open unemployment among graduates, and comparative studies of vocational versus general school graduates show that labour market advantages fluctuate sharply between stable and crisis conditions. International comparisons sharpen the point. Germany’s dual vocational training system, in which apprentices alternate between classroom and paid workplace learning under strong employer co-governance, and Singapore’s SkillsFuture programme, which has steered adult learners toward career-relevant training, both demonstrate that vocational pathways can command genuine prestige when industry involvement is structural rather than ceremonial. Indonesia’s sector, by contrast, still struggles with the so-called pilot trap, in which promising technology adoption projects in manufacturing fail to scale beyond demonstration sites.

Perhaps the study’s most original contribution is conceptual. The authors identify what they call differential velocity: a condition in which public value, operational capacity, and political legitimacy develop at different speeds within the same system. In Indonesia, societal preferences and institutional capacity have drifted far apart, with legitimacy moving slowly while the industrial world races ahead. This insight extends Moore’s Strategic Triangle into the context of vocational higher education in developing countries, where the triangle’s corners are not merely unbalanced but moving on different clocks. It suggests that policy fixes aimed at any single corner, such as new funding streams or rebranding campaigns, are likely to fail unless the velocity gap itself is addressed.

What would closing that gap require? The study points to structured policy interventions, broad industry involvement, and institutional capacity building that actively supports the formation of legitimacy. In practical terms, that means aligning curricula with Industry 5.0 competencies such as digital employability skills, human-machine collaboration, and adaptive problem solving; embedding employers in programme design and certification rather than consulting them after the fact; and building credential systems that signal genuine labour market value to skeptical families. Research on vocational graduates in Indonesia has already shown that recognised competency certification measurably improves graduate acceptance in sectors such as machining, hinting that credibility can be engineered rather than merely hoped for.

The stakes extend well beyond campus gates. With Indonesia’s undergraduate population projected to reach roughly 8.2 million by 2025 and tertiary enrolment expanding across Southeast Asia, the composition of the future workforce depends on whether applied institutions can compete for talent. The OECD’s skills strategy for the region warns that post-pandemic recovery depends on reskilling at scale, and Indonesia’s push to move factories past the pilot trap into full industrial digitalisation will fail without technicians trained for that environment. The Diponegoro team’s analysis offers policymakers a sobering but useful map: the triangle of public value can be rebalanced, but only if society’s aspirations, the state’s commitment, and the institutions’ capabilities are pulled forward together, and pulled forward fast.

Subject of Research: Public value and legitimacy of vocational higher education in Indonesia analysed through the Strategic Triangle framework

Article Title: Examining public value toward vocational education in Indonesia: a strategic triangle analysis

Article References: Dwimawanti, I. H., Hasibuan, A., & Permanasari, Y. W. (2026). Examining public value toward vocational education in Indonesia: a strategic triangle analysis. SN Social Sciences, 6(9), Article 400. https://doi.org/10.1007/s43545-026-01692-2

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01692-2

Keywords: vocational education, Indonesia, public value, Strategic Triangle, higher education, Industry 5.0, policy analysis, labour market, social legitimacy, Diponegoro University, industrial transformation, developing countries

The study, led by Asma Dammak of the LEO-Laboratoire d’Économie d’Orléans in France and the University of Sfax in Tunisia, together with Diana Alhajjeah of Applied Science Private University in Jordan, Foued Badr Gabsi of the University of Sfax, and Amine Lahiani of the University of Orléans, examined 41 developing countries over the period 2007 to 2020. Their central question was deceptively simple: does the relationship between global value chain participation and carbon dioxide emissions follow a straight line, or does it bend?

Most previous research has assumed a linear relationship, treating every increment of integration as environmentally equivalent to the last. The team rejected that assumption outright. Instead, they deployed a Panel Smooth Transition Regression model, a statistical framework designed to detect regime-dependent behavior, in which the effect of one variable changes smoothly as another crosses a critical level. They complemented this with linear and dynamic System GMM estimations to check whether the nonlinear pattern survived more conventional econometric treatment.

The results were striking. At low to moderate levels of global value chain participation, integration significantly reduces carbon dioxide emissions. This finding offers conditional support for what economists call the pollution halo hypothesis, the idea that participation in sophisticated international production networks transfers cleaner technologies, stricter management practices, and higher environmental standards to developing economies. Foreign firms and lead companies in global chains often bring efficiency gains that developing countries would struggle to achieve alone.

But the halo has a boundary. Once participation crosses the estimated threshold, the emission-reducing effect weakens and loses statistical significance entirely. The explanation lies in the collision between two competing forces. In the early stages of integration, efficiency effects dominate: countries adopt better processes, reduce waste, and lower the energy needed per unit of output. As integration deepens, however, expansion-driven scale effects take over. The sheer growth of production activity, driven by ever-larger volumes of intermediate goods flowing through the country, begins to offset the initial efficiency gains. The country produces more efficiently, but it also produces vastly more.

This pattern implies something counterintuitive for policymakers: the environmental benefits of global value chain participation are concentrated in the earlier stages of integration, not in the later ones. The study provides only conditional support for the pollution halo hypothesis, confined to the earlier phases. Once a country becomes substantially integrated, the environmental gains from participation appear to plateau or vanish, suggesting that merely increasing participation is not a sustainable environmental strategy.

The analysis also identified other structural factors shaping the emissions landscape. Energy intensity, the amount of energy consumed per unit of economic output, was found to exacerbate emissions, as was economic globalization, the growing cross-border integration of markets and financial flows. Digitalization, by contrast, played a modest mitigating role, suggesting that the spread of digital technologies may help developing countries manage their environmental footprint, though the effect is smaller than the damage done by energy intensity and globalization.

The technical sophistication of the approach matters for how the findings should be interpreted. The Panel Smooth Transition Regression framework does not impose an abrupt break between regimes; instead, it allows the effect of participation to change smoothly as the transition variable crosses the threshold, capturing the gradual nature of the shift. The dynamic System GMM estimations, which account for the persistence of emissions over time and address potential endogeneity, confirmed that the nonlinear pattern was not an artifact of the model choice. The consistency across methods strengthens the case that the threshold effect is a genuine feature of the data rather than a statistical curiosity.

The policy implications are pointed. The study suggests that upgrading within global value chains, moving toward higher-value, cleaner, and more technologically sophisticated activities, is more likely to support sustainable environmental outcomes than simply increasing participation. Developing countries that seek environmental benefits from integration should focus on the quality of their position in global production networks, not just the quantity. The findings underscore the importance of accounting for threshold effects in future research on the environmental consequences of economic integration.

For a world grappling with how developing economies can grow without locking in high-carbon trajectories, the study offers a sobering message. The environmental dividend from joining global production networks is front-loaded, available to countries in the early and middle stages of integration, and evaporates once integration becomes substantial. The GVC-environment nexus, the authors conclude, is inherently nonlinear and shaped by structural factors. Countries that treat participation as an end in itself may find that the green gains they counted on were never permanent, while those that invest in upgrading their role within global chains may yet convert integration into lasting environmental improvement.

Subject of Research: Nonlinear effects of global value chain participation on environmental quality in developing countries

Article Title: Nonlinear effects of global value chain participation on environmental quality in developing countries

Article References: Dammak, A., Alhajjeah, D., Gabsi, F. B., & Lahiani, A. (2026). Nonlinear effects of global value chain participation on environmental quality in developing countries. Discover Sustainability. https://doi.org/10.1007/s43621-026-04773-7

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04773-7

Keywords: Nonlinear, effects, global, value, chain, participation, environmental, quality, developing, countries, scientific research

The research, conducted by Ammar Ali Al-Raimi of Al-Razi University in Sana’a, adopted a descriptive analytical design grounded in established technology adoption theory. The study drew on the DeLone and McLean Information Systems Success Model and the Technology Acceptance Model to frame its hypothesis that AI functions as enabling infrastructure for pedagogical quality. Data were collected between January and June 2025 using a structured questionnaire administered both electronically and on paper, with a response rate of 87.5 percent. The sample was drawn from a population of roughly 1,001 academic staff across colleges of medicine, pharmacy, nursing, and allied health sciences at both public and private universities, using stratified random sampling to ensure proportional representation.

The analytical approach was rigorous. The researcher employed partial least squares structural equation modeling, or PLS-SEM, a technique well suited to exploratory, prediction-oriented research with complex multidimensional constructs and moderate sample sizes. Bootstrapping with 5,000 resamples generated path coefficients, t values, and p values, while a battery of validity checks — indicator loadings, Cronbach’s alpha, composite reliability, average variance extracted, the Fornell-Larcker criterion, and heterotrait-monotrait ratios — confirmed that the measurement model was sound. Common method bias, a persistent risk in single-survey studies, was assessed twice: Harman’s single-factor test extracted only 35 percent of variance, below the 40 percent threshold, and Kock’s full collinearity assessment returned variance inflation factors well under the conservative limit of 3.3.

The headline result was striking. The structural model showed a path coefficient of 0.79 between AI technology use and educational process quality, with a t value of 24.10 and a p value below 0.001 — an exceptionally robust association. The model explained 62 percent of the variance in perceived educational quality, with a Q² of 0.41 confirming strong predictive relevance and fit indices, including an SRMR of 0.067 and an NFI of 0.912, indicating satisfactory model fit. In plain terms: where faculty reported heavier use of AI tools, they also reported markedly better teaching, assessment, and learning outcomes.

The study broke AI adoption into four dimensions, and the differences between them proved revealing. Profiling and prediction — the use of learning analytics to identify at-risk students and support academic decision-making — emerged as the strongest predictor, with a path coefficient of 0.34. Personalized learning followed at 0.29, and intelligent assessment at 0.26. Intelligent tutoring and support systems, such as virtual conversational assistants and AI-driven academic advising, showed the weakest but still significant effect at 0.18. The reason for that gap is deeply contextual: interactive tutoring systems demand continuous real-time connectivity, high server capacity, and sophisticated instructional design, all of which are scarce in an environment marked by recurrent power outages and constrained internet bandwidth, and by the absence of Arabic-language, medically tailored conversational platforms.

Baseline levels told their own story. Overall AI utilization registered a mean score of 3.12 on a five-point scale — moderate, at 62.4 percent — indicating that adoption has begun but remains far from fully integrated. Educational process quality scored similarly at 3.25, or 65 percent, suggesting institutions perceived as acceptable but with substantial room for improvement. Within the AI dimensions, profiling and prediction led at a mean of 3.28, while intelligent tutoring trailed at 2.94. Among quality dimensions, good teaching scored highest at 3.34, followed by learning outcomes at 3.27. The pattern suggests a system leaning on low-cost, asynchronous analytics rather than bandwidth-hungry interactive tools — a rational adaptation to infrastructure constraints.

The barriers data may be the study’s most consequential contribution. Weak technological infrastructure topped the list at a high level, with a mean of 3.78, followed closely by the shortage of specialized AI training programs at 3.65. Thematic categorization of the barriers placed infrastructure limitations at 75.6 percent intensity and faculty training deficits at 73.0 percent, with governance, financial, and ethical concerns representing moderate institutional challenges. The absence of clear institutional policies on AI use compounded these problems. In other words, the constraint is not skepticism or lack of interest among educators — it is plumbing, training, and policy.

Equally notable was what did and did not predict positive perceptions. Faculty members’ digital experience significantly shaped their views of AI’s value, with a one-way ANOVA returning F = 4.39 and p = 0.013, and participation in AI-related training programs produced a significant difference as well, with t = 2.32 and p = 0.021. Academic qualifications, by contrast, made no significant difference, with F = 1.26 and p = 0.284. Practical exposure and targeted professional development, not formal degrees, determine who embraces educational AI — a finding with direct implications for how ministries and universities should spend scarce capacity-building funds.

From these findings, the study proposes a phased, pragmatic implementation strategy tailored to resource-constrained environments. Phase one prioritizes low-bandwidth, open-access solutions: automated grading assistance, offline learning management systems, and open-source analytics for performance tracking. Phase two establishes modular faculty training workshops covering AI literacy, prompt engineering, and ethical usage — the intervention the data show moves perceptions most. Phase three calls for policy and infrastructure investment, including transparent guidelines on student data privacy and academic integrity, and secure digital architectures such as cloud-hosted platforms. The study also outlines a five-component strategic framework spanning technological infrastructure, human resource development, pedagogical AI integration, institutional governance, and continuous quality evaluation, positioned as an operational blueprint for academic leaders rather than a statistically tested model.

The study’s limitations are candidly acknowledged: the data are self-reported, the sample is confined to faculty and leaders in Sana city, and the cross-sectional design cannot establish causation over time. The author calls for longitudinal and mixed-method research across diverse regions, incorporating student perspectives and classroom observation. Still, the contribution is substantial. By quantifying the relationship between AI adoption and educational quality in one of the world’s most challenging educational environments, the study demonstrates that the AI-in-education revolution is not a luxury of wealthy institutions. Even under severe constraint, the evidence suggests, the right low-cost tools — analytics first, tutoring later — can measurably strengthen the training of the healthcare professionals a health system most depends on.

Subject of Research: The role of artificial intelligence technologies in enhancing educational process quality in university health education institutions in Yemen

Article Title: Employing artificial intelligence technologies to enhance educational process quality in health education institutions in Yemen

Article References: Al-Raimi, A. A. (2026). Employing artificial intelligence technologies to enhance educational process quality in health education institutions in Yemen. Discover Education, 5(1), Article 861. https://doi.org/10.1007/s44217-026-02133-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02133-1

Keywords: artificial intelligence, higher education, health education, Yemen, educational quality, PLS-SEM, learning analytics, personalized learning, intelligent tutoring, faculty training, developing countries, educational technology

The research, conducted by Samia Islam Sara, Mohammad Lutfur Rahman and Jannatul Ferdous Momo of the Department of Urban and Regional Planning at Jahangirnagar University in Savar, Dhaka, set out to evaluate the housing quality of middle-income households in an urban area of Bangladesh. The team adopted a quantitative research approach, collecting data through a structured questionnaire administered to households in the study area. After screening, a total of 300 valid responses were included in the analysis, a sample size the authors justify with reference to established recommendations for survey research in the social sciences.

What makes the study methodologically interesting is the way it treats housing quality. Rather than measuring quality solely through objective indicators such as floor area or building age, the researchers captured residents’ own perceptions of their dwellings on an ordered scale. Because the outcome variable was ordinal, meaning households rated their housing quality in ranked categories rather than on a continuous scale, the team turned to ordinal logistic regression, a statistical technique designed for exactly this kind of ordered categorical outcome. The method, grounded in the framework developed by statistician Alan Agresti, models the probability that a household falls into a higher rather than a lower quality category as a function of multiple explanatory variables simultaneously.

Those explanatory variables were deliberately broad. The researchers drew on decades of housing literature to assemble nine domains of potential influence: dwelling unit characteristics, user comfort, neighborhood features, building services, structural quality, economic factors, maintenance, sustainability, and social support. This multidimensional framing reflects a shift in housing scholarship since the 1990s, when researchers such as Roderick Lawrence argued that housing quality cannot be reduced to physical structure alone but must encompass the environment, services, and social context in which a dwelling is embedded. Earlier work on residential environment quality and neighborhood attachment, as well as studies linking housing conditions to health outcomes, reinforced the case for treating housing as a system rather than a single object.

The headline finding is one of cautious adequacy. Most middle-income households in the study perceived their housing as fair to good, a result that suggests the group is neither thriving in high-quality formal housing nor trapped in the worst conditions of informal settlements. That middle position, however, is precisely why the authors argue the group deserves policy attention. International bodies, including the OECD in its analysis of the squeezed middle class, have documented how middle-income households in rapidly urbanizing economies face mounting pressure on housing affordability, often spending large shares of income on accommodation that may still fall short of comfort and service standards.

Within the regression results, a consistent set of predictors emerged as statistically significant. Crowding, meaning the number of occupants relative to the space available, was a key determinant, echoing a long line of environmental psychology research showing that household and community crowding degrade satisfaction and well-being. Ventilation also mattered, a finding with particular resonance in Dhaka’s hot, humid climate, where airflow shapes both thermal comfort and health. Access to essential services, the use of quality construction materials, affordability, and the presence of a supportive neighborhood each added explanatory power. In other words, the physical fabric of the dwelling, the economics of occupying it, and the social fabric around it all contributed independently to how residents judged their housing.

The significance of neighborhood effects deserves emphasis. The finding that supportive neighborhoods predict perceived housing quality aligns with a substantial body of evidence, including studies of socially contrasting neighborhoods in the United Kingdom and research on low-cost housing in Malaysia, showing that satisfaction with place depends heavily on social and environmental conditions beyond the front door. For urban planners in developing countries, this suggests that investments in street-level infrastructure, safety, and community facilities may yield returns in perceived housing quality that are comparable to improvements in the dwellings themselves.

The study’s policy implications are framed explicitly for decision-makers. The authors state that the research provides evidence for policymakers and urban planners seeking to enhance housing standards, especially for middle-income groups in rapidly growing cities, particularly in developing countries. Because the significant predictors are actionable, reducing crowding through floor-area and occupancy standards, enforcing ventilation requirements in building codes, expanding access to essential services such as water and sanitation, promoting quality materials, and addressing affordability, the findings translate relatively directly into regulatory and programmatic levers. The work also situates itself within the United Nations Sustainable Development Goals framework, which calls for adequate, safe and affordable housing for all, and within UN-Habitat’s recent emphasis on placing housing at the centre of urban futures.

The research team is careful to acknowledge the limits of a single-city, single-income-group survey. Perceptions of housing quality are shaped by cultural expectations, as theorized in the work of Amos Rapoport on culture, architecture and design, and by the specific housing market conditions of Dhaka, one of the world’s most densely populated cities. The authors therefore propose that future research could conduct a comparative analysis across different income groups in rapidly urbanizing cities, which would reveal whether the drivers of housing quality identified here hold for lower-income households facing more acute deprivation, or for higher-income households with greater choice, and whether they generalize across other developing-country contexts.

For a field often dominated by case studies of slums on one side and gated developments on the other, the study fills a genuine gap by quantifying the experience of the urban middle. Its message is ultimately an optimistic one: the factors that most strongly shape how middle-income residents judge their homes are measurable, comparable and, in principle, fixable. As cities across South Asia, Africa and Latin America absorb hundreds of millions of new residents in the coming decades, evidence of this kind, grounded in the stated perceptions of the households themselves, will be essential for steering urban growth toward housing that is not merely sheltered but genuinely adequate.

Subject of Research: Statistical assessment of perceived housing quality among middle-income urban households in Bangladesh

Article Title: Housing quality assessment in an urban area: perspectives from the context of a developing country

Article References: Sara, S. I., Rahman, M. L., & Momo, J. F. (2026). Housing quality assessment in an urban area: perspectives from the context of a developing country. SN Social Sciences, 6(11), Article 555. https://doi.org/10.1007/s43545-026-01870-2

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01870-2

Keywords: housing quality, middle-income households, Bangladesh, Dhaka, ordinal logistic regression, urban planning, housing affordability, crowding, ventilation, neighborhood effects, developing countries, sustainable development

The research team, led by Ibrahim Magboul of Ahfad University for Women in Omdurman and Diaeldin Osman of Clark Atlanta University, surveyed 201 employees across Sudan’s three major telecommunication operators — Zain, MTN, and Sudani — all headquartered in the capital, Khartoum. Using a purposive sampling technique, the researchers distributed 291 questionnaires between July and August 2023 and achieved a 69 percent valid response rate. Respondents ranged from junior staff to top-level executives, with 60 percent identifying as middle-level employees. Most had substantial hands-on experience with analytics tools: 60 percent had used big data analytics for one to three years, and 90 percent reported generating reports with these tools twice a day.

What makes the study methodologically distinctive is its integration of four established theoretical frameworks into a single validated model. The Technology Acceptance Model (TAM) captures individual user perceptions at the micro level; the Technology-Organization-Environment (TOE) framework captures organizational context at the meso level; the IS Success Model focuses on information and system characteristics; and the Resource-Based View (RBV) explains how analytics capabilities become strategic resources. The authors argue that big data adoption is a multi-level phenomenon that no single theory can adequately explain, and that the causal mechanisms identified in developed economies may not universally apply in resource-constrained environments.

To test their model, the researchers deployed partial least squares structural equation modeling (PLS-SEM), a statistical technique that estimates relationships between latent constructs while accounting for measurement error. The analysis used 5,000 bootstrap subsamples with bias-corrected 95 percent confidence intervals. Before testing the structural paths, the team rigorously validated the measurement model: all factor loadings exceeded the 0.7 threshold recommended by Hair and colleagues, all Cronbach’s alpha values surpassed 0.7, composite reliability exceeded 0.7 for every construct, and average variance extracted exceeded 0.50, confirming convergent validity. Discriminant validity was confirmed using both the Fornell-Larcker criterion and the Heterotrait-Monotrait (HTMT) ratio, with all values below the 0.9 threshold. Variance inflation factors stayed well below 3.3, ruling out pathological collinearity and common method bias.

The structural results were striking. Six of the eight hypothesized antecedents significantly predicted big data analytics adoption. Information quality emerged as the strongest driver, with a path coefficient of 0.397 (t = 7.048, p < 0.001), confirming that accurate, complete, consistent, and timely data is the single most powerful enabler of analytics success. Information center support followed at 0.239 (t = 3.511), vendor support at 0.201 (t = 3.173), business-IT alignment at 0.195 (t = 3.703), big data culture at 0.154 (t = 2.891), and training at 0.149 (t = 2.955). All six effects were statistically significant at the 5 percent level.

But two findings defied expectations. Big data competency — the organization’s collective ability to manage, analyze, and derive insights from big data — showed essentially no effect on adoption (β = 0.001, t = 0.020, p = 0.984). Perceived usefulness, the core TAM construct capturing an employee’s belief that analytics tools enhance job performance, was similarly non-significant (β = 0.011, t = 0.267, p = 0.790). The authors interpret these null results not as measurement failures but as theoretically generative anomalies that reveal something fundamental about technology adoption in extreme resource-constrained environments.

For big data competency, the researchers propose a “capability-use gap”: organizations may possess competent personnel but lack the complementary infrastructure, managerial prioritization, or resource alignment necessary to translate that competency into actual adoption. Sudan’s telecom sector faces persistent challenges including electricity outages, limited bandwidth, and outdated computing equipment — and since April 2023, armed conflict has disrupted telecommunications infrastructure across the country. Three mechanisms may explain the finding: infrastructural dependency, where skills alone cannot compensate for inadequate hardware and networks; strategic misalignment, where competent employees work in departments where analytics is not prioritized by top management; and a measurement-reality gap, where survey-based self-reports overstate actual capabilities or distribute them unevenly across departments.

The non-significant perceived usefulness result challenges the universal applicability of TAM itself. The authors suggest a “feasibility-before-perception” pathway: in Sudan’s telecom sector, adoption decisions may be constrained by external feasibility barriers — electricity outages, internet instability, hardware unavailability, and conflict-related infrastructure destruction — that render perceptions of usefulness irrelevant if the technology cannot physically be used. Mandatory adoption policies and “symbolic adoption,” where organizations claim analytics usage for legitimacy without substantive implementation, may further disconnect employee perceptions from actual behavior. The authors propose that future research test a “reverse TAM” model in which objective infrastructural conditions predict perceived usefulness and ease of use, rather than the traditional path where perceptions predict adoption intentions.

Crucially, the study confirms that big data analytics adoption has a strong positive effect on perceived performance (β = 0.647, t = 15.871, p < 0.001). The model explained 66.1 percent of the variance in analytics adoption and 41.9 percent in perceived performance — both classified as substantial predictive ability under Cohen’s criteria. This finding aligns with prior research showing that analytics-enabled sensing capabilities and analytics-driven cultures improve organizational outcomes, and it assures managers that the technology can deliver measurable business value even in difficult operating environments.

The practical implications are clear. Managers should carefully select analytics vendors with deep telecommunications experience, invest in continuous training in data literacy and visualization, institute robust data governance policies to safeguard information quality, and strengthen business-IT alignment so that analytics initiatives connect to strategic objectives. The authors recommend that governments in developing countries initiate technology interventions specifically aimed at helping companies adopt analytics tools that promote better performance. The study’s limitations — a sample drawn from only three operators in one country, a cross-sectional design, and the extraordinary circumstances of data collection during an active armed conflict — suggest future research should expand to larger samples, other sectors, and longitudinal designs. But the core message stands: in resource-constrained environments, feasibility comes before perception, and infrastructure, data quality, and external support are the true engines of analytics success.

Subject of Research: Big data analytics adoption and perceived performance in Sudanese telecommunication companies

Article Title: An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies

Article References: Magboul, I., Osman, D., Herzallah, F., Osman, A. N., & Gittens, D. (2026). An empirical test of antecedents and perceived performance of big data analytics in telecommunication companies. Frontiers in Big Data, 9, Article 1763520. https://doi.org/10.3389/fdata.2026.1763520

Image Credits: AI Generated

DOI: 10.3389/fdata.2026.1763520

Keywords: big data analytics, telecommunications, Sudan, structural equation modeling, technology acceptance model, developing countries, empirical, test, antecedents, perceived, performance, data

The team tackled a persistent blind spot in climate adaptation research. Scientists have long measured household resilience by tallying assets, income diversification, and coping strategies, and separately studied government programs and community institutions. What remained poorly understood was the connective tissue: the mechanisms through which governments, communities, and households actually exchange support, coordinate action, and feed information back to one another. Without that integration, the researchers contend, policy designed to strengthen one level can quietly undermine another.

Methodologically, the study is a scoping review combined with framework synthesis, a technique that systematically maps existing literature and then reassembles its findings into a coherent conceptual structure. The authors began with 80 carefully retained seed studies focused on China, then traced citations forward and backward to capture 41 additional supporting studies. This snowball approach allowed them to reconstruct the full web of evidence on collaborative climate governance and rural livelihood resilience without being captive to any single database or disciplinary tradition.

From this synthesis, five interrelated dimensions emerged as the channels through which collaborative governance shapes livelihood resilience. The first is risk perception: whether households, with prompting from officials and neighbors, accurately recognize the hazards they face, from shifting rainfall regimes to intensifying heat waves. The second is buffer capacity, the stock of financial, physical, social, and natural assets that absorbs shocks before they become catastrophes. The third is livelihood strategy adjustment, the ability to reconfigure farming practices, migrate temporarily, or shift into off-farm income when conditions demand it.

The fourth and fifth dimensions are more social in character. Self-organization capacity describes a community’s ability to mobilize collectively, arranging labor exchanges, irrigation rotations, or mutual aid when external help is slow to arrive. Learning capacity captures how households and communities digest experience, updating their practices after each flood, drought, or policy experiment. Crucially, the framework treats these five dimensions not as a checklist but as a dynamic system, in which government support, community mediation, and household response continuously feed back into one another across scales.

Perhaps the most striking contribution is the study’s refusal to portray collaboration as an unalloyed good. The evidence shows that public support can coexist with dependence, leaving households less motivated to invest in their own adaptive capacity when subsidies flow reliably. Coordination between levels of government can coexist with mismatch, where well-intentioned programs arrive misaligned with local ecological or cultural realities. And collective organization, often celebrated as the backbone of rural resilience, can coexist with exclusion, silencing women, migrants, or poorer households who lack standing in village decision-making. These paradoxes, the authors argue, are not anomalies but predictable properties of governance systems that must be designed for explicitly.

To capture both the enabling and the constraining sides of cooperation, the researchers built a government–community–household framework that integrates feedback processes and boundary conditions within a single analytical structure. Boundary conditions matter enormously: a governance arrangement that empowers households in a well-connected peri-urban village may fail in a remote plateau community with thin administrative reach. The framework therefore provides not a universal prescription but a structured lens for asking, in any given setting, which mechanisms are active, which are distorted, and which are absent altogether.

For China specifically, the findings arrive at a consequential moment. The country has poured resources into rural revitalization, poverty alleviation consolidation, and climate adaptation pilots, yet the review suggests that outcomes hinge less on the volume of investment than on the quality of interaction between state programs, village institutions, and family decision-making. A drought-relief subsidy that arrives without community deliberation may reinforce dependence; a community irrigation cooperative that excludes tenant farmers may deepen vulnerability precisely among those most exposed. The framework gives policymakers a diagnostic vocabulary for spotting these failures before they harden into chronic fragility.

The implications extend well beyond China’s borders. Much of the Global South faces the same triad of actors, often with even weaker links between them, and the review’s governance-mediated understanding of resilience offers analytical insights for rural climate adaptation across developing contexts. By demonstrating that resilience is not simply a property of households but an emergent outcome of multi-level collaboration, the study reframes adaptation as an institutional design problem as much as an agronomic or economic one. As climate pressures intensify, the research suggests, the families best positioned to survive will be those embedded in governance systems where support flows downward, information flows upward, and communities mediate without excluding, a balance that is difficult to strike but, the evidence now shows, entirely possible to specify.

Subject of Research: Collaborative governance mechanisms shaping household livelihood resilience to climate change in rural China

Article Title: Collaborative governance for household livelihood resilience under climate change: evidence from China and a government–community–household framework

Article References: He, X., Fan, L., Wu, Y., Dong, H., Sun, Y., & Yan, J. (2026). Collaborative governance for household livelihood resilience under climate change: evidence from China and a government–community–household framework. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09464-2

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09464-2

Keywords: climate change adaptation, livelihood resilience, collaborative governance, rural China, scoping review, framework synthesis, community institutions, household coping strategies, risk perception, self-organization, developing countries, social sciences

The stakes are considerable. Agricultural systems in developing countries sit at the intersection of climate change, population growth, food insecurity, and rural migration, and policy-makers increasingly rely on models to test decisions before implementing them in the real world. Agent-based modelling, or ABM, has emerged as the tool of choice for this task because it can capture what statistical and optimization models often cannot: the heterogeneity of individual farmers. Two farmers in the same village may differ in farm size, labour availability, cropping choices, and responses to a new subsidy, and ABM simulates each of them as a distinct decision-making agent interacting with neighbours, markets, organizations, and the environment itself.

As the authors note, agent-based models are particularly well suited to developing-country contexts, where quantitative data are scarce and statistically reliable surveys are difficult to mount. Yet the field’s greatest strength has also been its weakness. A review of the literature revealed that although the number of farm-level agent-based models has grown substantially over time, most were built for specific purposes and locations and are not easily adaptable or reusable for policy evaluation. Each modelling effort starts nearly from scratch, duplicating work and making it nearly impossible to compare findings across regions or to build cumulatively on prior results.

To address this, the research team first conducted a systematic review of existing methodologies, dividing them into two families. The first comprises software-oriented approaches developed by computer scientists, such as Tropos, ADELFE, MESSAGE, INGENIAS, and extensions of the Unified Modelling Language like AUML, AML, and MAS-ML. These are powerful but ill-suited to agricultural simulation, because agent-based models of farming are usually built by agronomists, economists, and geographers rather than software engineers. The second family includes social-simulation frameworks such as ARDI, ORIGAMI, and the widely used ODD protocol. These provide valuable foundations for identifying stakeholders and documenting models, but they are too general to be applied directly to agricultural systems without substantial adaptation.

MAASSD synthesizes the strengths of these approaches into five sequential steps. Step one establishes system requirements: identifying the stakeholders, the processes to be simulated, and the scales of analysis. Drawing on the ARDI method, modellers map out the scientists, decision-makers, and local actors whose perspectives shape the model. The processes are organized around five components of the agricultural system identified in earlier work: cropping systems, livestock production, socio-economic factors, environmental factors, and policy factors. Step two is domain analysis, in which the team extracts the core concepts of the target system from existing studies, agricultural ontologies, and participatory workshops, producing a shared vocabulary and a UML class diagram that all participants can understand.

The methodology’s commitment to multi-stakeholder engagement is one of its distinguishing features. Farmers, researchers, and policy-makers pursue different objectives and hold different kinds of knowledge, and MAASSD treats their integration as a formal requirement rather than an afterthought. Ontologies, explicit specifications of the concepts and relationships within a domain, serve as bridges between these knowledge communities. The authors also propose a set of meta-models, essentially templates covering household structure, cropping strategies, household occupations, and policy interventions, that can be extended to build specific models for any given region, dramatically lowering the barrier to entry for new modelling projects.

Step three addresses the selection of tools, reviewing platforms such as Cormas, NetLogo, GAMA, and Repast, as well as integration frameworks like RECORD, Crop2ML, and OpenMI that allow existing models of soil dynamics, plant growth, or hydrology to be coupled into a larger simulation. Step four covers model design, anchored by a meta-model in which interacting households, composed of individuals differentiated by sex and age, apply cropping strategies across plots of land, and by a simulation framework with initialization, simulation, and output phases. Step five turns to implementation, recommending rapid prototyping so that simplified versions of the model can be built, discussed with stakeholders, and refined before the final version is completed.

To demonstrate the framework’s robustness, the researchers applied MAASSD to a pressing question in Burkina Faso: the feedback loop between agricultural dynamics and migration. The test case focused on the Province of Boulkiemdé in the country’s Sudano-Sahelian climatic zone, simulating a population of 400 individuals distributed across 40 households in four categories, from small farmers with lower potential to large farmers with higher potential. The model integrated a climate module, a crop production module, a biophysical component, and an explicit social network representation, and it ran three climate scenarios over 45 simulated years each, with drought probabilities ranging from 30 percent in the wet scenario to 70 percent in the drought scenario.

The results were striking. Migration increased sharply under the driest scenario, confirming that households turn to migration as an adaptation strategy when climate shocks prevent them from meeting their food and income needs. More unexpectedly, the largest and most resource-endowed household types, types 3 and 4, sent the most migrants under every scenario, revealing that they were the most vulnerable to climate shocks, while the small farmers of type 2 proved the most resilient. This kind of counterintuitive finding, which depends on representing the heterogeneity of farm households rather than treating them as an average, illustrates exactly why agent-based modelling matters for policy design in the developing world.

The authors acknowledge limitations: the methodology does not yet cover scenario development, validation, or a dedicated software tool. But they position MAASSD as the first methodology specifically designed for simulating agricultural systems in developing countries, one that captures farmer heterogeneity, supports multi-scale representation from field to community, and integrates knowledge from stakeholders of different origins. The next step is to test the framework across a large number of contexts, and if it holds up, the era of one-off, unrepeatable farm models may finally be drawing to a close.

Subject of Research: A standardized agent-based modelling methodology for simulating agricultural systems in developing countries

Article Title: MAASSD: Methodology for Agent-based modelling for Agricultural System Simulation in Developing Countries

Article References: Belem, M. (2025). MAASSD: Methodology for Agent-based modelling for Agricultural System Simulation in Developing Countries. Food and Ecological Systems Modelling Journal, 6, Article e167755. https://doi.org/10.3897/fmj.6.167755

Image Credits: AI Generated

DOI: 10.3897/fmj.6.167755

Keywords: agent-based modelling, agricultural systems, developing countries, Burkina Faso, climate change, migration, food security, multi-scale modelling, UML, stakeholder engagement, simulation methodology, household heterogeneity

The study, conducted by Rasha Talib Ahmed Alnaqeeb and Nasuha Lee Abdullah of Universiti Sains Malaysia, tackles a critical gap in the technology adoption literature. Most previous research has focused on why people adopt a technology in the first place, treating continued use as an afterthought. Yet adoption and continuance are fundamentally different phenomena. A student may log into a learning management system once because a professor requires it, but whether that student returns week after week depends on a complex interplay of expectations, experiences, and capabilities. In Iraq, where only about 25 percent eLearning penetration persists and fewer than 35 percent of universities had solid eLearning frameworks after the pandemic, understanding this distinction is not academic hair-splitting but a matter of educational policy.

To model continuance intention, the researchers built an integrated framework combining two of the most influential theories in information systems research. The first is the Expectation-Confirmation Model, which explains post-adoption behavior through a simple psychological mechanism: users compare their actual experience with prior expectations, and the degree of confirmation shapes both satisfaction and the perceived usefulness of the system, which together drive the decision to continue. The second is UTAUT2, the Unified Theory of Acceptance and Use of Technology, which contributes behavioral and motivational factors such as facilitating conditions, social influence, and hedonic motivation. Crucially, the team added a third ingredient rarely tested in this context: digital literacy, the student’s own technological competence, positioned as a capability factor that could be decisive in resource-constrained environments.

The methodology was rigorous and quantitative. Using a structured questionnaire administered to students in their second year of study or above across public universities in northern, central, and southern Iraq, selected through multi-stage sampling to ensure geographical representation, the researchers collected 332 valid responses, well above the minimum of 146 calculated with statistical power analysis. The data were analyzed using Partial Least Squares Structural Equation Modeling in SmartPLS 4, a technique well suited to prediction-oriented models with multiple latent constructs and mediation pathways. Measurement quality was confirmed through convergent validity, discriminant validity via the Fornell-Larcker criterion and the Heterotrait-Monotrait Ratio, and composite reliability scores all exceeding accepted thresholds.

The results paint a striking picture of what actually matters to students in this setting. Four factors significantly shaped satisfaction: facilitating conditions, hedonic motivation, digital literacy, and confirmation of expectations. Hedonic motivation, the sheer enjoyment of using the platform, emerged as the strongest driver, with a path coefficient of 0.316, suggesting that engaging and pleasurable design is not a luxury but a necessity. Confirmation, the sense that the system met expectations, strongly boosted perceived usefulness as well, with a coefficient of 0.469. Satisfaction itself explained a remarkable 61 percent of the variance in the model and, in turn, exerted the strongest direct influence on continuance intention, with a coefficient of 0.403.

Just as revealing were the factors that failed to register. Social influence, the encouragement of family, friends, and peers that features prominently in Western technology acceptance studies, had no significant effect on satisfaction. Perceived usefulness, the workhorse construct of the Technology Acceptance Model, also did not significantly influence satisfaction, contradicting the original Expectation-Confirmation Model. Yet perceived usefulness did have a significant direct effect on continuance intention, with a coefficient of 0.228. This dissociation suggests that Iraqi students may keep using eLearning because it helps them complete academic tasks, even when usefulness alone does not make them happy with the experience. Satisfaction, by contrast, appears to depend on broader experiential factors such as technical support, accessibility, enjoyment, and digital capability.

The mediation analysis sharpened these insights further. Satisfaction significantly mediated the pathways from facilitating conditions, hedonic motivation, digital literacy, and confirmation to continuance intention, meaning that infrastructure, enjoyment, competence, and met expectations all work through the emotional evaluation of the experience. But satisfaction did not mediate the effects of social influence or perceived usefulness. In other words, the social pressure of others neither improves the student’s experience nor translates into lasting engagement, while the instrumental value of the system operates on a separate, direct track. This pattern implies that established continuance theories may require contextual adaptation when applied in developing-country educational settings, where infrastructural instability reshapes the psychology of technology use.

The practical implications for universities and policymakers are concrete. Because facilitating conditions carry such weight, investment in reliable internet connectivity, hardware availability, and responsive technical support should be the first priority, particularly given that fragile infrastructure and system overload during exam periods frequently limit access in Iraq. Because hedonic motivation proved so powerful, institutions should develop interactive, gamified, and genuinely enjoyable course content rather than treating online platforms as mere repositories of lecture slides. Because digital literacy significantly predicted satisfaction, structured training programs for both students and lecturers are essential, especially in a country where uneven exposure to technology has left many users unfamiliar with effective platform use.

The study also carries a broader message for the global education technology community. Over 90 percent of higher education institutions in industrialized nations have adopted digital platforms such as Moodle, Google Classroom, and Microsoft Teams, yet the factors sustaining their use in wealthy, well-connected societies may not transfer cleanly to contexts where electricity stability and internet reliability cannot be taken for granted. The Iraqi case demonstrates that satisfaction-based retention models remain internationally applicable, but the specific levers that produce satisfaction differ. Where peers and perceived utility dominate in some settings, experiential and support-related factors dominate in others.

The authors acknowledge limitations, including the exclusion of first-year students and private universities, reliance on self-reported data, and a cross-sectional design that captures perceptions at a single point in time. Future research, they suggest, should explore the contextual reasons behind the non-significant effects of social influence and perceived usefulness, compare public and private institutions, and test whether digital literacy moderates the relationship between enjoyment and satisfaction. But the central finding stands firm: in resource-constrained higher education, the path to sustainable eLearning runs not through persuasion or promises of usefulness, but through building systems that are accessible, enjoyable, and matched to the real digital capabilities of the students who use them.

Subject of Research: Determinants of student continuance intention toward eLearning usage in Iraqi higher education institutions

Article Title: Determinants of continuance intention toward eLearning usage in higher education institutions

Article References: Alnaqeeb, R. T. A., & Abdullah, N. L. (2026). Determinants of continuance intention toward eLearning usage in higher education institutions. Discover Education, 5(1), Article 1165. https://doi.org/10.1007/s44217-026-02259-2

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02259-2

Keywords: eLearning, continuance intention, higher education, digital literacy, student satisfaction, Expectation-Confirmation Model, UTAUT2, PLS-SEM, Iraq, educational technology, developing countries, technology adoption

The numbers behind that shift are sobering. According to the brief, developing countries may need between 310 and 365 billion US dollars a year for adaptation by 2035. Against that projected requirement, international public adaptation finance stood at just 26 billion US dollars in 2023. Even if flows grow substantially in the coming decade, the gap between what is needed and what is available will remain enormous, which makes the efficiency of every dollar spent a matter of first-order importance. It is precisely this arithmetic that pushes the question of implementation to the centre of the climate agenda in the run-up to COP31 in Türkiye, where implementation, including on adaptation, is expected to be a central theme.

Marta Ellena, lead author of the brief, distils the problem into a single sentence: developing countries know what they need to do to adapt to climate change, but the hard part is turning that knowledge into projects that will actually be financed. The brief describes this as the missing middle, the space between identifying a national adaptation priority and developing a project or programme that is sufficiently well prepared to secure finance and be implemented on the ground. A ministry may know that a coastline needs protection or that an agricultural region needs drought-resilient water systems, but converting that knowledge into a costed, technically specified, investment-ready proposal requires a chain of capabilities that many countries are still building.

Crucially, the brief insists that money is only one part of the problem. Coordination failures across national and sub-national governance structures can produce weak project proposals in the first place. Weak proposals, in turn, struggle to attract finance. And without finance, countries cannot improve the institutions, data systems and monitoring frameworks needed to prepare stronger proposals in the future. This circular trap, in which each weakness reinforces the others, helps explain why the proliferation of adaptation plans has not translated into a proportional surge in implemented projects. Breaking the cycle requires intervening at several points of the delivery chain simultaneously rather than treating finance, capacity and governance as separate problems with separate solutions.

Anna Pirani, co-author of the brief, describes the alternative as a country-driven process that strengthens an integrated delivery chain linking national priorities, technical preparation and investment pipelines. Such a chain, she notes, is expected to mobilise financing and implementation and then deliver measurable results. The emphasis on country ownership is significant. Previous generations of adaptation support have sometimes been organised around donor priorities or stand-alone pilot projects that never scaled. An integrated delivery chain, by contrast, starts from a government’s own adaptation priorities and works backwards to the technical preparation and investment structuring needed to make them real, with results that can be measured and reported.

Two recent multilateral initiatives are positioned as the machinery for doing exactly this, and the brief examines both in detail. The first is the Adaptation Accelerator Hub, launched under Italy’s 2024 G7 Presidency. The Hub is country-facing and plays a coordination role among G7 partners, helping governments translate their own adaptation priorities into coordinated, investment-ready programmes. Its logic is that when major donor economies align their support around a single country-led pipeline, rather than each running parallel channels, the transaction costs for recipient governments fall and the quality of project preparation rises. The Hub effectively operates at the level where national priorities meet international finance.

The second initiative, the NAP Implementation Alliance, was launched under the COP30 Action Agenda by Brazil and works at system level. Where the Hub focuses on individual countries, the Alliance aims to improve coordination among the full cast of actors that determines whether adaptation projects move from concept to construction: governments, technical partners, climate funds, development banks and investors. The distinction matters because adaptation finance flows through a fragmented landscape of multilateral funds, bilateral donors and private lenders, each with its own requirements, timelines and reporting formats. A system-level alliance that harmonises these actors addresses one of the quiet but persistent frictions that has slowed adaptation delivery for years.

Perhaps the brief’s most important analytical claim is that these two platforms should be read together as complementary pieces of the same delivery chain, rather than as separate, competing initiatives. Ellena argues that the Hub and the Alliance work best in combination: the Hub converting country priorities into investment-ready programmes, the Alliance aligning the wider ecosystem of financiers and technical partners that those programmes must ultimately pass through. In the crowded field of climate initiatives, where overlapping mandates and duplicated structures are a chronic complaint, the framing of two platforms as sequential links in one chain is itself a design choice worth watching. Whether that complementarity is communicated clearly to governments and funders will, the brief suggests, shape how much value either initiative delivers.

The brief also issues a caution about how success is measured. Bankability, the capacity of a project to attract commercial or near-commercial investment, cannot be the only yardstick of adaptation progress. Many of the activities that matter most for protecting vulnerable populations, including institutional capacity building, climate data and information services, social protection systems and direct support for particularly exposed communities, may not generate commercial returns at all. These functions remain essential to effective adaptation, and the brief is explicit that they will continue to require grants, concessional finance and public investment. In other words, a delivery chain optimised purely for bankable projects would systematically under-provide for the people most at risk, and the architecture now being built must leave room for public and concessional money alongside private capital.

As negotiators and observers look toward COP31 in Türkiye, the brief identifies several markers that will indicate whether the delivery agenda is gaining traction. The first is how far a country-level pilot advances, providing an early test of whether the Hub model can genuinely move a national priority through preparation toward finance. The second is how clearly the respective roles of the Adaptation Accelerator Hub and the NAP Implementation Alliance are communicated to governments, funds and investors, since ambiguity about mandates could dissipate the very coordination both were created to provide. The third is the growth of the project portfolio linked to finance, the most concrete measure of whether the missing middle is actually being filled. CMCC contributes to this agenda as a science-policy partner to the Italian Ministry of Environment and Energy Security, translating climate knowledge into evidence-based strategy for international climate processes, and the brief benefited from contributions by the UNDP team supporting the G7 Adaptation Accelerator Hub. What is at stake is larger than any single initiative: with adaptation needs measured in the hundreds of billions and current flows an order of magnitude smaller, the world cannot afford for well-made plans to remain on shelves. The task now, the brief concludes, is to build the delivery systems that turn them into results.

Subject of Research: Implementation and financing of National Adaptation Plans in developing countries

Article Title: Climate adaptation’s delivery gap: Turning national plans into action

Article References: Climate adaptation’s delivery gap: Turning national plans into action. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: climate adaptation, National Adaptation Plans, adaptation finance, developing countries, CMCC, Adaptation Accelerator Hub, NAP Implementation Alliance, COP31, G7, project bankability, climate policy, concessional finance

The research pursues two complementary objectives. The first is a systematic literature review conducted according to the PRISMA 2020 guidelines, the international standard for transparent evidence synthesis. The authors searched five major academic databases, namely Scopus, Web of Science, ScienceDirect, IEEE Xplore, and SpringerLink, covering the period from 2016 to 2026. The initial sweep returned 850 records, which the team filtered against explicit eligibility criteria until 72 studies remained for qualitative synthesis. That funnel matters: it means the findings rest on a curated body of peer-reviewed evidence rather than a loose assembly of pilot projects and vendor claims. The second objective is more unusual for a technology review. The researchers confronted their literature findings with empirical data gathered on the ground, through a household survey in the Dakar metropolitan area, to see whether the digital tools celebrated in academic papers could plausibly take root in a resource-constrained West African urban context.

At the heart of the review is a taxonomy of emerging technologies organized across the entire waste management life cycle, from generation and source separation through collection, transport, sorting, treatment, and final disposal. The Internet of Things features prominently at the front end of the chain. Ultrasonic fill-level sensors mounted inside bins and containers can report how full they are in real time, allowing municipalities to replace fixed collection schedules with demand-driven dispatch. The authors’ comparative analysis shows that such sensing, combined with route optimization algorithms, can substantially reduce the distance traveled by collection fleets, with corresponding savings in fuel, vehicle wear, and greenhouse gas emissions. In cities where collection can account for the majority of a waste budget, those algorithmic gains translate directly into freed-up resources for other services.

Artificial intelligence occupies the middle of the life cycle. Machine learning and deep learning models, particularly convolutional neural networks, have demonstrated strong performance in classifying waste streams from camera imagery, enabling automated sorting at material recovery facilities and even citizen-facing applications that identify recyclable items through a smartphone camera. The review’s comparative assessment of algorithmic performance is one of its technical contributions, cataloguing accuracy figures, training data requirements, and computational demands across published models. Robotics appears at the sorting stage, where robotic arms guided by computer vision can pick targeted materials from moving conveyor belts at speeds and consistency beyond human sorters, while reducing occupational exposure to hazardous waste. Next-generation communication networks, including low-power wide-area protocols and cellular technologies, form the connective tissue that lets sensors, vehicles, and processing facilities exchange data reliably and affordably.

Yet the review is deliberately unsentimental about the barriers. The authors offer a critical assessment of interoperability problems, since sensors, platforms, and analytics software from different vendors frequently cannot communicate with one another, locking cities into fragmented systems. Cost remains a decisive constraint: the capital expenditure for sensor networks, connectivity, and data infrastructure is difficult to justify in municipalities that struggle to fund basic collection. Energy consumption is a further concern, because battery-powered devices deployed across a hot, dusty urban environment require maintenance cycles and power strategies that are rarely discussed in technology-focused papers. The study’s insistence on a cost-aware, energy-aware reading of the literature is what separates it from promotional accounts of smart city technology, and it is precisely this framing that makes the findings relevant beyond Senegal to developing countries generally.

The empirical half of the study provides the socio-technical baseline that most reviews lack. The field survey collected 435 valid household responses across ten neighborhoods of Dakar, capturing waste generation rates, source-separation practices, access to collection services, and citizens’ stated willingness to adopt digital tools. This baseline is essential for calibrating any smart system: route optimization algorithms are only as good as the generation and composition data feeding them, and public acceptance of pay-as-you-throw schemes or reporting apps depends on trust in existing services. The survey also grounds the analysis in a specific geography, including the Mbeubeuss landfill, Dakar’s principal disposal site, which exemplifies the environmental and social pressures that conventional management has failed to resolve. The authors are careful to state that the survey informs their framework but does not constitute a field validation of it, an honest distinction that strengthens rather than weakens the work’s credibility.

From the synthesis of literature and field data, the researchers propose a seven-layer conceptual framework tailored to Senegalese realities. While the full architecture is detailed in the paper, its logic is that of a layered socio-technical system: physical sensing and actuation at the bottom, communication and data management above, analytics and decision support in the middle layers, and governance, financing, and citizen engagement at the top. The layering is not decorative. It reflects a central finding of the review, namely that technology failures in developing-country waste systems are rarely purely technical. A sensor network without a maintenance budget, a routing algorithm without reliable vehicle data, or a citizen app without digital literacy support will each fail for reasons that no amount of algorithmic sophistication can fix. The framework therefore treats institutional capacity and community participation as structural components, not afterthoughts.

The practical implication is a phased deployment roadmap for decision-makers. Rather than advocating a big-bang smart city rollout, the authors’ cost-aware approach suggests starting where digital interventions deliver the clearest returns, such as fill-level sensing and route optimization for collection fleets, before progressing to automated sorting, treatment monitoring, and landfill management systems. Each phase generates data and operational experience that de-risks the next, an incremental logic well suited to municipalities with limited capital and competing priorities. For Dakar, where the gap between waste generated and waste collected continues to widen, such sequencing could mean that the first digital investments pay measurable dividends within existing budgets instead of waiting for infrastructure that may never arrive.

The authors are transparent about the study’s main limitation: the proposed framework has not yet undergone pilot-scale field validation. The household survey provides a baseline, but no sensor has been deployed, no route has been optimized, and no sorting robot has been tested under Dakar’s conditions. That gap defines the research agenda that follows. Pilot deployments at Mbeubeuss or in selected collection districts would test whether the performance figures reported in the international literature survive contact with heat, dust, intermittent power, and informal waste-sector dynamics. The study was funded by Assane Seck University of Ziguinchor and its LI3 Laboratory, and the authors declare no competing interests, which situates the work as independent academic analysis rather than technology advocacy.

What makes this research resonate beyond its national case is its reframing of the smart waste debate. Much of the global conversation about Waste Management 4.0 has been written by and for wealthy cities, where the marginal gains of optimization are measured against systems that already function. Thiam, Diallo, and Ndoye invert that perspective, asking which digital technologies can deliver the greatest leverage where the baseline system is broken, budgets are thin, and the consequences of failure are borne by the poorest households. Their answer, grounded in 72 rigorously selected studies and 435 household voices from Dakar, is that the technology exists and the economics can work, but only if deployment is phased, contextually adapted, and honest about interoperability, cost, and energy constraints. For the rapidly urbanizing cities of the developing world, that may be the most important roadmap yet offered for turning the fourth industrial revolution into cleaner streets.

Subject of Research: Enabling technologies for smart municipal solid waste management in developing countries, with a contextual framework for Senegal

Article Title: Enabling technologies for waste management 4.0 in developing countries a systematic review and contextual framework for Senegal

Article References: Thiam, A., Diallo, O., & Ndoye, E. H. M. (2026). Enabling technologies for waste management 4.0 in developing countries a systematic review and contextual framework for Senegal. Discover Sustainability. https://doi.org/10.1007/s43621-026-04901-3

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04901-3

Keywords: Waste Management 4.0, municipal solid waste, Internet of Things, artificial intelligence, robotics, smart waste collection, route optimization, Senegal, Dakar, developing countries, systematic review, sustainable development