College of Business Education

Ineffective governance structures and regulatory practices have continued to affect transparency, accounts, and efficiency in Tanzania’s upstream petroleum sector, which makes the sector prone to risks associated with compliance and challenges in governance. The study aims to evaluate the impact of governance structures and best practices to the oversight of the country’s upstream petroleum sector. This study was guided by Agency Theory, which focuses on monitoring and accountabilities between regulators and operators, and Institutional Theory, which focuses on Legitimacy and Best Practices based on global standards. A cross-sectional research design and approach based on a quantitative strategy was used employed. 328-sample size were sought from various organizations in Tanzania. Semi-structured questionnaire instrument was used as data collection methods. GLM to control for regression was adopted for data analysis. The results show highly positive relationship between governance structures and oversight effectiveness in Tanzania’s upstream petroleum sector with ρ of 0.747, p < 0.01 and a highly positive relationship between Best Practices and oversight effectiveness in Tanzania’s upstream petroleum sector with ρ of 0.686, p < 0.01. GLM tests showed both governance structures (β = 0.512, p < 0.001) and Best Practices (β = 0.427, p < 0.001) have positive influences on oversight effectiveness in Tanzania’s upstream petroleum sector and explained a total joint percentage of 62%. Study concluded that effective governance structures and Best Practices have played a crucial role in oversight effectiveness in Tanzania’s upstream petroleum sector. It recommends the improvement of the regulatory clarity, coordination of institutions, engagement of stakeholders, capacity enhancement, and technology-enabled monitoring of sustainable upstream petroleum governance in Tanzania.

Abstract Route optimization is regarded as a strategic emission reduction practice in freight transport, but its contribution to sustainable environmental performance in developing economies and the accelerating impact of digital technology are understudied. This study fills that gap empirically with Tanzanian truck operators based on the Natural Resource-Based View (NRBV) theory. Across 170 trucking operating firms, a cross-sectional survey was carried out in SmartPLS 4 with covariance-based structural equation modelling (CB-SEM). In this way, it tested the direct effect of route optimization on sustainable environmental performance as well as the moderating effect of technology adoption (GPS, IoT). Statistically, the results show that route optimization improves sustainable environmental performance (β = 0.229, p = 0.007). In addition to this effect (β = 0.621, p = 0.017), technology adoption confirms that the fuel savings as well as emission reductions of optimized routing are augmented by digital tools. Theoretically, the study builds on NRBV theory and green logistics practices, as it shows how pollution prevention capability via route optimization in combination with digital resources leads to sustainability outcomes. In practice, it shapes environmental policy and sustainable transport in Tanzania by pushing policymakers & industry to adopt technology for cleaner and more efficient freight operations.

The aim of this study was to examine the moderating effect of political interference in the relationship between ethical practices and internal auditing in predicting procurement performance in Tanzania's parastatal entities. The structured questionnaire was administered to 256 randomly selected department heads out of 712 found in 93 state-owned organizations in the Dar es Salaam Region. Out of the 256 questionnaires distributed to respondents, 241 were returned fully completed and hence suitable for analysis (response rate: 94.1%). Descriptively, the data were summarized using frequency distribution tables, while relationships between variables were analyzed using the Covariance-Based Structural Equation Model (CB-SEM). The CB-SEM findings revealed that political interference had a negative effect on procurement performance. Moreover, political interference had a statistically significant positive moderating effect on internal auditing and ethical practices in predicting procurement performance. For instance, respondents in the high political interference group had a 0.708 higher procurement performance for each unit increase in internal auditing as compared to 0.272 in the low political interference group, even though both were statistically significant at p<0.001. The study recommended that the management of the parastatal organizations should institute effective control mechanisms by ensuring that politicians do not get room to interfere with the procurement process. This study adds to a new body of knowledge, as there are few studies that have studied the determinant of procurement performance, accounting for the moderating effect of political interference in parastatal organizations in sub-Saharan Africa.

Globally, procurement is currently viewed as a tactical function for the effective and efficient operations of both private and governmental organisations. The implementation of internal control is essential to all organisations and more so to the public sector organisations, particularly government agencies, to manage public funds in a responsible and transparent manner. The study focused on three components of internal controls, namely, control environment, risk assessment and monitoring. This study applied correlational research design and followed quantitative approach. The study carried out in 30 government organizations from which 270 respondents were randomly selected to represent a study sample. Data were collected using self administered questionnaire, analysed by descriptive and inferential statistics. The descriptive results revealed that, to a greater degree government agencies have established internal control systems, and effectively implement them. In one hand, the findings of the first regression model indicate that two components of internal control; control environment and monitoring have negative and highly significant association with lead time as proxy of procurement efficiency, while risk assessment has negative but moderate significant relationship with procurement efficiency. On the other hand, the findings of the second regression model show that all three variables; control environment, risk assessment and monitoring have positive and highly significant relationship with financial compliance in procurement process in the government agencies. Based on the study findings, it implies that control environment, risk assessment and monitoring are important elements which have potential to contribute to the procurement efficiency and compliance with rules and regulation.

This study explores the opportunities and challenges of integrating robotics technologies into science-hub libraries in Zanzibar, focusing on three hubs located on Unguja Island. The study population included staff working in the hubs and stakeholders dealing with robotics technologies in the country. A case study research design with a qualitative research approach was employed to collect, analyze, and interpret the data. Qualitative data was gathered through focus group discussions to gauge the respondents’ perceptions and experiences regarding educational robotics technologies in the hub settings. The findings highlight promising opportunities, including government support through infrastructure investments; grants for science, technology, engineering, and mathematics education; a competence-based curriculum; and access to professional development such as online courses. However, key challenges persist, including limited robotics infrastructure, insufficient librarian training, and financial constraints. To address these issues, the study recommends securing long-term funding, advocating for the inclusion of robotics in Zanzibar's education curriculum, and introducing affordable mobile robotics kits.

This study intended to investigate the possibility of using online learning as the solution for improving students’ academic performance at Makurumla Secondary School. The Unified Theory of Acceptance and Use of Technology (UTAUT) are used to lead this study. A mixed methods approach is employed because it allowed for a comprehensive understanding of the role of online learning in improving students’ academic performance. The quantitative data provided measurable evidence on patterns, trends, and the effectiveness of online learning tools, while the qualitative data offered deeper insights into teachers’ and parents’ experiences, perceptions, and challenges. Therefore, using both methods together ensured that the research captured not only statistical relationships but also the contextual realities behind them. The stratified random sampling technique was used. The study was conducted at Makurumla Secondary School in Ubungo Municipality. The target population for this study included teachers, students, and parents, from which a sample of 321 respondents was selected to provide data for the study. Data collection methods include questionnaires, interviews, and focus group discussions. Quantitative data are processed by using SPSS and analyzed using descriptive data analysis and multiple regression analysis. While in Qualitative data from interviews and focus group discussions are analyzed through content analysis and MAXQDA software, which is a qualitative and mixed-methods data analysis software used by the researchers to organize, code, analyze, and visualize data. The findings reveal that the majority of the students at Makurumla Secondary School are interested in and have access to an online learning system at their home, but the school infrastructure does not support implementing the online learning system. The study concluded that for the implementation of online learning in secondary schools, there should be sufficient and supportive infrastructure, such as computers, computer laboratories, stable electricity, and strong internet connectivity. The study recommended that the school should ensure that students and teachers have access to digital devices and a strong and stable internet connection, and that teachers receive proper training to use the online learning system effectively.

This study intended to assess the impact of employee engagement on organisational effectiveness, using the Muhimbili Orthopaedic Institute in Tanzania as a case study. Specifically, it investigated the effect of employee engagement on organisational performance. Guided by Human Capital Theory, the study adopted a case study design within a quantitative research framework. Data were collected through structured questionnaires administered to 222 respondents. Analysis was conducted using SPSS version 21, employing correlation techniques, with results presented in statistical tables. The findings indicated a significant positive correlation between employee engagement and organisational performance, underscoring their critical role in enhancing institutional effectiveness. Based on these results, the study recommends that Muhimbili Orthopaedic Institute establish clear key performance indicators (KPIs). These KPIs should link training outcomes with patient satisfaction, staff productivity, and surgical success rates. The study also recommends connecting engagement initiatives directly with organisational performance. Furthermore, it advocates for a National Health Workforce Engagement Policy. This policy should promote employee involvement, recognition, and well-being across public health institutions. The Muhimbili Orthopaedic Institute can serve as a model for its implementation.

Aflatoxin is a common contaminant in grains and cereals, creating serious health hazards for consumers and economic challenges for producers. Reliable prediction of whether its levels surpass safe thresholds using weather data, soil properties, and farming practices is essential for guiding informed decisions. In this study, four machine learning algorithms: Gaussian Process Classification (GPC), Support Vector Machine (SVM), Random Forest Classifier (RFC), and K-Nearest Neighbours (KNN) were applied to predict aflatoxin contamination in maize and groundnuts. GPC outperformed other models by predicting correctly 92 out 100 groundnuts samples (92%) and 93 out of 100 maize samples (93%). The findings indicate that humidity and rainfall are stronger predictors of aflatoxin contamination compared to temperature or soil $\mathbf{p H}$. This work represents an important step toward applying machine learning techniques for aflatoxin prediction in crops. Nevertheless, the study is primarily simulation-based, serving to highlight the potential of machine learning models when applied to available datasets.

Grounded in the Relationship Marketing Theory, the study examines the role of product quality, customer service, and brand image in fostering long- term customer loyalty. The study utilized a simple random sampling technique to collect quantitative data from 285 customers. Correlational research design and multiple linear regression were employed to examine relationships among key variables. The findings indicate that strong brand image (β3 = 0.617, P < 0.001), perceived superior product quality (β1 = 0.252, P = 0.001) and effective customer service (β2 = 0.173, P = 0.012), all have a strong significant positive influence on customer loyalty respectively. The findings show that a strong brand image, perceived superior product quality, and effective customer service are decisive factors in fostering customer loyalty in the iron roofing industry, with brand image exerting the most substantial influence. The implication is that businesses should prioritize strategic branding, high-quality products, and exceptional service to secure long-term consumer commitment and competitive advantage. It is recommended that iron sheets manufacturing firms formulate, plan and invest in marketing strategies which substantially capitalize in producing superior quality roofing iron sheets, enhance exceptional reputable brand image and responsive customer service. This study introduces a novel framework linking brand reputation factors and customer loyalty.

Background: Prior research had predominantly emphasized traditional Structural Equation Modeling (SEM), with limited exploration of dynamic SEM. Yet, dynamic SEM was essential, as it enhanced the precision of real-time consumer behavior prediction. Objectives: This study critically examined recent advances in dynamic SEM, focusing on their effectiveness in improving predictive accuracy, model efficiency, and adaptive decision-making in response to temporal variations in consumer behavior.Design/methodology/approach: A review of peer-reviewed empirical studies published between 2010 and 2025 was conducted. Using predefined inclusion and exclusion criteria, relevant works were retrieved from Scopus and Web of Science. Comparative synthesis highlighted differences between traditional and dynamic SEM applications.Findings/Results: The results demonstrate that dynamic SEM substantially outperforms traditional SEM by incorporating temporal dynamics, capturing interindividual variations, and effectively managing intensive longitudinal data. Its strength lies in analyzing large-scale, high-frequency datasets from digital platforms such as Google Analytics, enabling accurate prediction and monitoring of consumer behavior over time. The study further contributes original constructs - including behaviorally relevant triggers, sentiment indicators, personalization measures, and engagement metrics - thus extending the scope of consumer analytics.Conclusion: Dynamic SEM was shown to exert a transformative impact on consumer behavior research and marketing management by supporting real-time behavioral adjustments and agile decision-making. However, challenges remained regarding its computational capacity with large and complex datasets, underscoring the need for advanced data governance and sophisticated analytical tools.Originality/value: The study evaluated the methodological innovations in a unique and systematic way and gave an advice on how to improve the SEM applications and theory when handling large and complex datasets when dealing with the temporal changes in consumer behavior. Researchers, policy-makers and practitioners were given the actionable recommendations on how to improve and utilize the dynamic SEM as a future-proof marketing analytics approach. Keywords: big data analytics, dynamic structural equation modelling, latent growth modeling, marketing decision-making, temporal dynamics