LECTURERS

This study examined the relationship between organizational silence and organizational citizenship behaviour (OCB) among lecturers in the University of Benin. The study sought to determine how the dimensions of organizational silence, acquiescent silence, defensive silence, prosocial silence, and supervisor silence climate influence the display of OCB among academic staff. The research was driven by concerns that silence in academic institutions may limit participation, reduce innovation, and hinder voluntary behaviours that promote institutional performance. A descriptive survey research design was adopted, and data were collected from a sample of 100 lecturers across various faculties using a structured questionnaire. Descriptive statistics (mean and standard deviation) were used to summarize responses, while Pearson correlation and multiple regression analyses were employed to test the hypotheses at a 0.05 level of significance. The results revealed that prosocial silence recorded the highest mean (M = 4.04, SD = 0.90), indicating that lecturers often withhold information for altruistic or constructive reasons, such as maintaining team harmony or protecting colleagues. Conversely, acquiescent silence (M = 2.73, SD = 1.39) and defensive silence (M = 2.88, SD = 1.10) were relatively low, suggesting that most lecturers do not remain silent out of fear or a belief that their opinions will not matter. The regression model yielded R = 0.304, R² = 0.093, F(4,91) = 2.325, p = 0.062, indicating that the combined effect of the four silence dimensions on OCB was not statistically significant. Further analysis showed that none of the individual silence dimensions significantly predicted OCB (p > 0.05), though prosocial silence exhibited a weak positive relationship (β = 0.185, p = 0.082). The correlation analysis confirmed these findings, revealing weak and statistically insignificant relationships between organizational silence dimensions and OCB. The study concludes that while organizational silence exists within the University of Benin, it does not significantly influence lecturers’ willingness to engage in citizenship behaviours such as altruism, conscientiousness, courtesy, sportsmanship, and civic virtue. The findings suggest that lecturers’ engagement in OCB is primarily driven by intrinsic motivation and professional commitment rather than silence dynamics. The study recommends that the university should continue to foster open communication channels, participative decision-making, and supportive leadership practices to sustain a positive organizational culture that encourages voluntary, extra-role behaviour among academic staff.

This study investigates the effect of open access resources on research productivity among lecturers in the Department of Educational Management, University of Benin, Benin City, Edo State, Nigeria. The research explores how freely available scholarly materials impact the research output, accessibility, and efficiency of academic work. A descriptive survey research design was employed, with data collected through questionnaires distributed to lecturers. The study analyzed the extent of awareness, utilization, and challenges associated with open access resources. Findings reveal that while open access resources significantly enhance research productivity by increasing access to high-quality materials, challenges such as inadequate ICT skills and unreliable internet connectivity limit their full potential. Based on the findings, recommendations were made to improve awareness, infrastructure, and training on the effective use of open access platforms. Keywords: Open Access Resources, Research Productivity, Academic Research, University of Benin, Lecturers.

This project focused on the design and implementation of SmartLMS, an Artificial Intelligence assisted Learning Management System (LMS) developed to enhance digital teaching and learning in the University of Benin. The aim of the study was to create a flexible and user friendly platform that integrates AI features to support both lecturers and students in managing learning materials, assessments and performance analytics. The system was designed using Next.Js for the Frontend, Node.Js with the Express.Js framework for the Backend and MongoDB as the database, with JWT authentication to regulate signup and secured login to the platform, Cloudinary to handle and store uploaded materials and OpenAI and Gemini AI for AI-driven functionalities. The AI modules were implemented to help students with course materials summarization, self-practice test generation and keep track of students’ analytics. The system followed a modular design and was implemented to be responsive, reliable and accessible on both mobile and desktop devices. Results showed that SmartLMS improved teaching efficiency, student engagement and learning personalization while maintaining lecturer control over assessments, grading and students’ academic performance. The project concluded that SmartLMS provides a practical and scalable solution for integrating AI into education, offering a more interactive, intelligent and accessible learning experience that can be integrated into
different learning environments