Rural sub-Saharan Africa faces limited medical access, healthcare worker shortages, and inadequate health information systems. Mobile health (mHealth) technologies offer potential solutions but remain underdeveloped in these settings. This review aims to explore the sociocultural context of mHealth adoption in rural sub-Saharan Africa to support sustainable implementation. A comprehensive Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) search was conducted in databases like PubMed, MEDLINE, and African Journals Online, covering peer-reviewed literature from 2010 to 2024. Qualitative studies of mHealth interventions were included, with quality assessed via the Critical Appraisal Skills Program (CASP) checklist and data synthesized using a meta-ethnographic approach. Out of 892 studies, 38 met the inclusion criteria. Key findings include sociocultural factors like community trust influencing technology acceptance, local implementation strategies, user empowerment in health decisions, and innovative solutions for infrastructure issues. Challenges include privacy concerns, increased healthcare worker workload, and intervention sustainability. While mHealth can reduce healthcare barriers, success depends on sociocultural alignment and adaptability. Future interventions should prioritize community co-design, privacy protection, and sustainable, infrastructure-aware models.

State support for agriculture is a crucial tool for adjusting the competitive advantages of agricultural producers to a volatile market environment. In countries with diverse natural conditions for agriculture, however, the allocation of subsidies often focuses on bridging spatial development gaps rather than maximizing the return on inputs. To improve the efficiency of resource use in agriculture, it is essential to tailor subsidy criteria to regional disparities in agricultural potential. Using the example of Russia’s 81 administrative regions, the authors have tested a five-stage methodology for determining the support-generated parameters of output, efficiency, impact, revenue, and profitability. This methodology takes into account both natural and economic factors that contribute to the competitive advantages of each region. The study aims to identify the parts of the performance indicators, such as gross agricultural output and revenue, that are influenced by the amount of subsidies in five different types of territories, which are categorized by the cadastral value of their farmland. It has been found that the allocation of subsidies is not entirely based on the return on the funds allocated. There is a discrepancy between the competitive advantages of these territories in agricultural production and the amount of funds they receive through government support programs. The efficiency of government support differs significantly depending on the type of agricultural product produced in each territory. The approach developed by the authors provides a tool that policy makers can use when tuning the allocation of subsidies based on the differences in the agricultural potential of each territory.

This financial modelling case study describes the development of the 3-statement financial model for a large-scale transportation infrastructure business dealing with truck (and some rail) modalities. The financial modelling challenges in this area, especially for large-scale transport infrastructure operators, lie in automatically linking the operating activity volumes with the investment volumes. The aim of the paper is to address these challenges: The proposed model has an innovative retirement/reinvestment schedule that automates the estimation of the investment needs for the Business based on the designated age-cohort matrix analysis and controlling for the maximum service ceiling for trucks as well as the possibility of truck retirements due to the reduced scope of tracking operations in the future. The investment schedule thus automated has a few calibrating parameters that help match it to the current stock of trucks/rolling stock in the fleet, making it to be a flexible tool in financial modelling for diverse transport infrastructure enterprises employing truck, bus and/or rail fleets for the carriage of bulk cargo quantifiable by weight (or fare-paying passengers) on a network of set, but modifiable, routes.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.

Purpose: The level of the environment is gradually declining, especially with regard to the serious problem of solid waste. Solid waste segregation-at-source is seen as the most essential approach to helping the natural environment minimize the amount of waste generated before being transferred to waste disposal sites and landfills in many rapidly growing towns and cities in developing countries. However, a number of previous environmental-based research have focused only on the general scope of recycling, sustainable development, and the purchase intention for sustainable food products. This situation has led to useful and relevant information on the research scope of households’ intention to segregate solid waste at source, which remains largely unanswered. The aim of this paper is, therefore, to provide a literature review to develop a novel theoretical framework in understanding the determinants of households’ intention to practise solid waste segregation-at-source. Theoretical framework: The study provides a detailed explanation of the application of the Theory of Reasoned Action, the Fietkau-Kessel Model, the Focus Theory of Normative Conduct, and the Value-Basis Theory to predict the relationship between attitude, subjective norms, environmental concerns, and environmental knowledge of households on intention to practise solid waste segregation-at-source. Design/methodology/approach: This research is descriptive in nature. Findings: A better understanding of the potential mediator and moderator is needed to contribute to the body of knowledge on the causal relationship between the studied variables. In conclusion, the researchers discuss how the framework can be used to address future research implications as more evidence emerges. Research, practical and social implications: The current study is expected to broaden previous research in order to improve general understanding of attitudes and subjective norms towards the specific research scope of solid waste segregation-at-source.