The study has formulated the objective of synthesizing the extent to which technological barriers intervene in the transparency and effectiveness of public management (PM). Methodologically, the study was of a fundamental or basic nature, with a systematic review design, the databases of Scopus (369), SciELO (2), Web of Science (184) were explored, after the review process a set of 22 articles was available. The registration was made in an Excel table where the main data of the articles were included. 32% of the articles selected for the analysis of the evidence are from the period 2020, 27% were from 2022 and 18% from the year 2023; as far as origin is concerned, 14% of the articles come from Peru and 9% from Australia, Brazil, South Korea, Spain and Indonesia. In summary, the study points out that government institutions are making progress in digitizing and improving the citizen experience through electronic services, but they face challenges in areas such as resource management, the low adoption of advanced technologies such as blockchain and artificial intelligence, as well as the lack of transparency in PM. Despite this, it is highlighted that e-government improves citizen satisfaction, and the need to invest in digital innovation, training and overcoming technological barriers to achieve an effective transformation in state administration and promote a more inclusive and advanced society is emphasized.

Smallholder paprika farmers in Zimbabwe contribute to local economies and food security but face supply chain challenges like limited market access and poor infrastructure which lead to post harvest losses and unpredictable prices. To survive, these farmers must adopt sustainable value networks to reduce operational costs and improve performance. This study sought to establish the effect of sustainable value networks on the operational performance of smallholder paprika farming in Zimbabwe. This study, using a positivist research philosophy and a quantitative approach, surveyed 288 smallholder paprika farmers in Zimbabwe. Exploratory factor analysis and partial least squares structural equation modelling were used to validate the constructs and test the hypothesised relationships. Results demonstrate a moderate level of implementation of value networks in smallholder paprika farming characterised by successes and challenges. The findings illustrated resource sharing among smallholder farmers, facilitated by initiatives, such as recycled seed exchanges and financial support through village savings and loan associations. However, results show that challenges persist, particularly with market access and financial support. Results indicate that there is a significant awareness and implementation of green supply chain management practices among smallholder paprika farmers even though they do not have access to resources and live in rural areas. The findings demonstrate that value networks significantly influence the adoption of green supply chain management practices, which in turn positively impact operational performance, environmental performance, and social performance. Green supply chain management practices were found to mediate the relationship between value networks and environmental performance, social performance, and operational performance, underlining the critical role of sustainable practices in enhancing performance outcomes. While environmental performance showed a positive effect on operational performance, the direct influence of social performance on operational performance was found to be statistically insignificant, suggesting the need for further exploration of the factors linking social benefits to operational efficiency. The research contributes to both theory and practice by presenting a sustainable value network model for smallholder paprika farmers, integrating value network, green supply chain management practices and environmental performance to enhance operational performance. Practical implications include policy recommendations to strengthen collaboration between smallholder farmers and other stakeholdersand address power imbalances with intermediaries. Future research should extend the study to other agricultural sectors and incorporate more diverse stakeholder perspectives to validate and generalise the proposed sustainable value network model.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

This study focuses on the use of the Soil and Water Assessment Tool (SWAT) model for water budgeting and resource planning in Oued Cherraa basin. The combination of hydrological models such as SWAT with reliable meteorological data makes it possible to simulate water availability and manage water resources. In this study, the SWAT model was employed to estimate hydrological parameters in the Oued Cherra basin, utilizing meteorological data (2012–2020) sourced from the Moulouya Hydraulic Basin Agency (ABHM). The hydrology of the basin is therefore represented by point data from the Tazarhine hydrological station for the 2009–2020 period. In order to optimize the accuracy of a specific model, namely SWAT-CUP, a calibration and validation process was carried out on the aforementioned model using observed flow data. The SUFI-2 algorithm was utilized in this process, with the aim of enhancing its precision. The performance of the model was then evaluated using statistical parameters, with particular attention being given to Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R²). The NSE values for the study were 0.58 for calibration and 0.60 for validation, while the corresponding R² values were 0.66 and 0.63. The study examined 16 hydrological parameters for Oued Cherra, determining that evapotranspiration accounted for 89% of the annual rainfall, while surface runoff constituted only 6%. It also showed that groundwater recharge was pretty much negligible. This emphasized how important it is to manage water resources effectively. The calibrated SWAT model replicated flow patterns pretty well, which gave us some valuable insights into the water balance and availability. The study’s primary conclusions were that surface water is limited and that shallow aquifers are a really important source of water storage, especially for irrigation during droughts.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.