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The wider accessibility of rainfall data and data-driven analytical approaches has boosted climate regionalization research worldwide, particularly in Tanzania. However, despite substantial research on rainfall trends and machine learning applications in Tanzania, we did not find a study that systematically analyzed the possibility of reclassifying the country’s traditional rainfall climatic zones using data-driven approaches. As a result, this study fills that gap by investigating long-term rainfall variability and determining whether the current climatic zoning remains valid under observed rainfall patterns. The study used a quantitative, comparative research design with 40 years of monthly rainfall data (1980–2020) from the GPCC and CHIRPS satellite datasets. Machine learning techniques were used to reclassify zones using the k-means, hierarchical clustering, and Partitioning Around Medoids (PAM) algorithms. The study also performed cluster validation and zone agreement analysis (data-driven vs. traditional zones) using silhouette scores, chi-square tests, Cramér’s V, Cohen’s Kappa, and the Adjusted Rand Index (ARI). The results show significant interannual rainfall variability among zones, with no statistically significant long-term trends. Agreement analysis, on the other hand, shows that traditional zoning is robust, despite small divergences in some transitional zones. The divergence indicates that 10 regions out of 31(32.25
This study explores the embeddedness of Corporate Social Responsibility (CSR) practices among listed Tanzanian firms from 2020 to 2024, focusing on both communication and organisational dimensions within a theoretically grounded framework. The study is guided by Institutional and Stakeholder theories, analysing how CSR is integrated into corporate strategies and operational structures, with particular emphasis on variation across banking and finance, telecommunications, manufacturing, extractive, agriculture, and transport industries. Using a mixed-methods approach combining qualitative content analysis of annual reports with organisational indicators, the study draws on 20 firms (95 firms annual reports) to identify significant industry-specific differences in CSR embeddedness, with the banking industry demonstrating more advanced structural integration through dedicated departments, budgets, and employee engagement. In contrast, industries such as agriculture and transport exhibit lower levels of CSR institutionalisation, with practices largely remaining symbolic and weakly embedded. The study highlights the need for stronger regulatory frameworks, organisational capacity, and stakeholder engagement to enhance CSR integration across Tanzanian industries. The findings provide novel longitudinal and cross-industry evidence by distinguishing symbolic CSR disclosure from substantive organisational embeddedness in an emerging market setting.
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.
The increasing integration of social media into organizational settings has transformed how employees communicate and engage with stakeholders. In the public sector, this shift is particularly complex due to heightened expectations regarding visibility, accountability, and professional conduct. While prior research has applied technology adoption models to explain such behavior, these approaches offer limited insight into underlying mechanisms. This study proposes and tests a mechanism-based model in which perceived self-image mediates the effects of social influence, hedonic motivation, and perceived risk on behavioral intention. Data were collected from 600 employees in Tanzania’s Government Ministries, Departments, and Agencies and analyzed using Partial Least Squares Structural Equation Modelling. The results show that perceived self-image significantly predicts behavioural intention and partially mediates all relationships, highlighting the role of professional image evaluation in shaping behavior.
The transition to smart learning is accelerating globally, including in Tanzania, where higher learning institutions are gradually shifting from traditional to technology-enhanced learning. This study examined the readiness of higher learning institutions in Tanzania for smart learning transformation. A cross-sectional design with a quantitative approach was adopted. Using stratified sampling, 375 students were selected, of whom 349 completed structured online questionnaires distributed via Google Forms. Data were analyzed using IBM SPSS version 20. The findings indicate that while internet connectivity is generally available, its performance remains unsatisfactory. A positive relationship (r = 0.308) was found between internet reliability and the frequency of using digital academic resources. The study also identified the presence of smart classrooms, although some are non-functional. Findings further indicate that most students demonstrated high proficiency in using smart learning tools, with a significant relationship between gender and tool usage (r = 0.163, p < 0.01). However, the integration of digital library services into smart learning platforms is still incomplete. Overall, the study concludes that higher learning institutions in Tanzania are ready for smart learning adoption, though key improvements are required. It is therefore recommended that institutions strengthen technological infrastructure, particularly by ensuring stable and high-performance internet connectivity. Additionally, institutions should increase the number of fully functional smart classrooms and prioritize the comprehensive integration of digital library services into smart learning platforms to enhance flexible and efficient access to academic resources.
