Moshi Co-operative University

This article reframes Principle 6 as an inter-cooperative learning capability and synthesizes how cooperation architectures convert solidarity into measurable economic and social value. Using secondary data, the study integrates a structured review of cooperative identity and policy guidance with cross-case synthesis of six widely documented cooperation models; evidence was triangulated from ICA, ILO, UN, the World Cooperative Monitor 2025, and publicly available organizational reports. Five recurring learning pathways emerge: purchasing and shared services; benchmarking and peer advisory systems; federation-enabled standard setting; joint ventures and platform cooperation; and capability building through training and mentorship. Pathways perform best when governance preserves member autonomy, uses transparent redistribution, and institutionalizes learning roles. Cooperation among cooperatives operates as a learn-and-scale system that strengthens governance, innovation, resilience, and SDG delivery. Limitation: reliance on secondary, publicly available evidence. Subsequent research should test the Inter-Cooperative Learning Capability framework with primary and longitudinal data. Keywords: Capability Building; Cooperative Networks; Inter-Cooperation; Organizational Learning; Resilience

This study presents a data-driven machine learning framework for cybersecurity risk prediction using behavioral and temporal features extracted from email server logs. The dataset consists of 955 authentication records, including protocol types, login outcomes, error classifications, timestamps, and spam scores. Initial statistical analysis, comprising descriptive statistics, correlation analysis, and chi-square tests, was conducted to examine relationships among variables and feature relevance. Subsequently, supervised machine learning models, logistic regression, decision trees, and random forests, were implemented to classify cybersecurity risk events. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Model performance was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results indicate that the Random Forest model outperformed other models, achieving the highest AUC of 0.84, compared to 0.723 for logistic regression. The findings demonstrate that integrating behavioral and temporal features significantly enhances the detection of cybersecurity threats. This study highlights the effectiveness of ensemble learning methods in capturing complex patterns within log data and provides a robust framework for developing intelligent intrusion detection systems. The proposed approach offers practical implications for improving cybersecurity monitoring and risk prediction in real-world email-based communication environments.

This study quantified post-harvest losses (PHLs) along the rice supply chain in Kilombero and Mbarali districts, Tanzania, to generate evidence for loss reduction strategies. Assessments were conducted at harvesting, threshing, winnowing, storage and milling, identifying four technical strands at the farm level and twelve at the processing level. At the farm level, PHLs varied: sickle harvesting with heap beating, wind winnowing and home storage (20%); sickle harvesting with bambam threshing, wind winnowing and home storage (23%); sickle harvesting with machine threshing and home storage (13%) and combine harvesting with home storage (16%). At the processing level, losses ranged from 16% to 31%. The lowest occurred with sickle harvesting, machine threshing, storage and multi-stage milling (16%), while the highest involved sickle harvesting, bambam threshing, wind winnowing, storage and Engelberg milling (31%). These findings suggest that PHLs are largely technology-dependent, with improved machinery and multi-stage milling reducing losses and supporting food security.

Purpose: This paper aims to examine the green management practices adopted by small and medium enterprises (SMEs) in The Gambia and to analyze how these practices contribute to competitive advantage within a resource-constrained economic context. Method: The study employs a qualitative documentary approach based on the analysis of secondary data drawn from government policies, reports issued by international agencies, and SME-related publications produced between 2018 and 2025. The data were analyzed using thematic analysis, guided by the Resource-Based View and Institutional Theory. Result: The findings reveal several common, although largely informal, green management practices among SMEs, primarily focused on energy conservation, basic waste management, and resource protection. These practices are predominantly cost-driven and reactive in nature. Nevertheless, they generate competitive advantages by lowering operational costs, strengthening organizational legitimacy, and enhancing business resilience. Their wider adoption, however, is constrained by limited financial capacity, weak regulatory enforcement, and insufficient strategic managerial awareness. Practical Implications for Economic Growth and Development: The findings indicate that integrating sustainability into SME capacity-building programs is essential for strengthening long-term competitiveness and promoting employment generation. Policymakers and development partners should therefore reposition green practices as strategic investments in resilience by supporting them through financial incentives, practical implementation tools, and targeted training initiatives. Originality/Value: This study provides one of the earliest firm-level qualitative examinations of the strategic adoption of green management practices by SMEs in The Gambia. In doing so, it addresses an important gap in the sustainability literature concerning small African economies.

The adoption of ChatGPT in reference services delivery among academic libraries is perceived as an innovation aimed at replacing traditional reference services. This study examines the challenges and opportunities of implementing ChatGPT in reference service delivery within academic libraries. A systematic review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) framework. Various databases were consulted, including DOAJ, EBSCOhost, Google Scholar, and Emerald. A total of 123 articles were retrieved, of which 47 (38.2%) met the selection criteria. The study is guided by the theory of diffusion of innovation. The theory provides effective frameworks for explaining the adoption and use of technology inorganisations. The findings revealed that ChatGPT offers several benefits when integrated into reference service delivery.These benefits include its ability to provide prompt responses to users, 24/7 accessibility, research assistance, support for information literacy, and information retrieval. The study established that, despite its potential for libraries, ChatGPT has several drawbacks, including a lack of privacy and security, the potential to provide incorrect answers to users, and inherent bias. The study revealed that the integration of ChatGPT in reference service delivery across academic libraries will not completely replace the role of reference librarians, as they will be required to intervene and respond to users' queries should ChatGPT fail. The study recommends that librarians acquire the necessary skills to use ChatGPT for providing reference services. They should also train users to become skilful information consumers with the ability to evaluate content generated by ChatGPT.