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Work Engagement as A Mediator Between Autonomy Support and Psychological Well-Being
Get PDFThis study explored the role of supervisor and colleague autonomy support in relation to psychological, social, and emotional well-being, using the components of work engagement—vigor, devotion, and absorption—as mediators. Grounded in the Job-Demands Resources (JD-R) Model, the research highlights the importance of job resources, such as peer-support, in managing job demands and enhancing employee well-being, particularly within the context of education during the pandemic. A total of 315 participants, including teachers, staff members, and administrators, completed scales assessing work self-efficacy, job engagement, and mental health. The study utilized a cross-sectional and predictive research design, conducting the survey at a single point in time to determine the direct relationships and mediating roles of autonomy support and work engagement in relation to well-being. Data was analyzed using mediation analysis and confirmatory factor analysis, with model fit indices indicating an excellent fit. The findings revealed that supervisor and colleague autonomy support significantly predicted well-being, with work engagement fully mediating these relationships. Consequently, the direct effects of autonomy support on well-being became non-significant when work engagement was accounted for, indicating full mediation. Based on these results, it is recommended that educational institutions promote a supportive social environment, provide growth opportunities, and respect employee autonomy. This study contributes to the literature by elucidating pathways through which supervisor and peer autonomy support influence psychological, emotional, and social well-being through work vigor, dedication, and commitmen
Patriotic Leadership and National Development: A Comparative Analysis of The United States and Nigeria - Insights and Lessons
Get PDFPatriotic leadership is integral to a country’s development and is desirous for effective state-society interactions. Such leadership is often characterized by selflessness, legitimacy, dedication to service, and efficient administration of resources, both human and natural. This paper examines the import of patriotic leadership to national development in the America and Nigeria’s context. The paper employs a historical and descriptive investigative approach with an element of comparative design. It utilizes relevant data collected from documentary sources, which were analysed qualitatively within the framework of Lucian Pye’s modernization theory. From the analysis, it became obvious that America’s history abounds with leaders who, in their private and public lives, have worked assiduously to attain national unity, liberty, equality, and civic rights, hence facilitating national development. In contrast, Nigeria lacks patriotic leaders instilled with vision and commitment to genuine national development. Besides, patriotism and commitment to leadership are ingrained in American culture, which are conducive to national development. In contrast, Nigeria has yet to reach such heights in its political history, with leaders often prioritizing ethnic and regional interests over national interests antagonistic to national development. Consequently, the paper recommends, among others, that for Nigeria to match up with its America counterpart, its political leadership class must undergo a total attitudinal reorientation to embrace national interest and commitment to patriotism and national consciousness. The study offers valuable insights for improving political leadership in Nigeria, emphasizing the need for a nationalistic leadership approach to foster developmen
PO23_Colonial Legacies, Clan Politics and Contested Federalism: Analysis of Governance Issues in Somalia
No full textSomalia’s governance structure results from three fundamental factors that include historical legacies combined with tribal clan systems and state building initiatives in the present day. Somalia gained independent in 1960 after colonial years yet civil war started in 1991. This study analyses Somalia’s governance issues as it looks a current limitation while exploring challenges of federalism from the source of the literature review. The findings emphasize the enduring weaknesses that originate from deep corruption, political instability, week rule of law, poor government effectiveness, and limited voice and accountability stemming from clan conflicts, federal disputes, and continuing influence from vested groups. The decentralized system of power through federalism struggles to achieve inclusive governance mainly due to competing ideological views, and constitutional ambiguous of power distribution along with dispute of resource sharing. The study suggest enhancing institutional reform and fiscal transparency measures and advocating political procedures that secure all participants. This study underscores multifaceted reforms to advance Somalia’s path toward sustainable governance and legitimacy
Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
Get PDFMalware represents a serious and continuously evolving threat in the modern digital environment. Detecting malware is essential to safeguard devices and systems from risks such as data corruption, data theft, account compromises, and unauthorized access that could result in total system takeover. As malware has progressed from its simpler, monomorphic variants to more sophisticated forms like oligomorphic, polymorphic, and metamorphic, a machine learning-based detection system is now required, surpassing the limitations of traditional signature-based methods. Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. However, they continue to struggle with false positives and false negatives, striving to reach zero tolerance in malware detection. This study introduces the IGCS method, a combined feature selection approach that integrates InformationGain with Chi-Square (X²) to enhance both the effectiveness and efficiency of machine learning classifiers. Using IGCS, six classifiers—Random Forest, XGBoost, kNN, Decision Tree, Logistic Regression, and Naïve Bayes—achieved higher performance scores compared to other scenarios, such as when classifiers were combined with Information Gain, Chi-Square, PCA, or even without any feature selection. As a result, Random Forest with 30 features selected by IGCS proved superior to any combination of classifiers and feature selection methods in malware detection, achieving 99.0% accuracy, recall, precision, and F1-Score. This combination also demonstrated efficiency with a 52.5% decrease in training time and a 56.9% decrease in testing tim
