This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Project success requires team commitment, which is a product of an encouraging culture of cooperation and teamwork among project team members. The research work aims to ascertain which components of team commitment affect the performance of construction projects in Nigeria. The research adopted a quantitative design where questionnaires were used for data collection. Out of 1233 questionnaires distributed, 975 were received with valid responses and used for data analysis. Data were analysed descriptively using percentage, mean score, and relative agreement index. The study showed the factors of team commitment having an effect on project performance, as rated by the respondents, to be: Normative component: “Project team members owe a great deal to this organisation”; “Members of the project team do not feel it is right to quit the project before completion”; “This organisation has a great deal of personal meaning for project team members”. Affective component: “This organisation deserves the loyalty of project team members”; “The project team considers the team’s problems as their own. Then, “One of the few negative consequences of leaving this organisation will be the scarcity of available alternatives” is for continuance. In conclusion, the emotional attachment of the team members and sense of obligation to the project team and construction organisation are the driving forces behind pushing for the successful outcome of projects within the Nigerian construction industry.

This research investigates the dynamic landscape of succession planning (SP) strategies in higher education, with a focus on synthesizing existing literature to guide improvements in presidential succession practices. The intense global competition in higher education has led to imbalances in the quantity and composition of potential successors, hindering institutions’ rapid advancement and affecting their competitiveness on the global stage. The study addresses critical challenges such as attracting, retaining, and nurturing successors in key positions beyond material incentives. Employing a literature analysis methodology, the research comprehensively examines the existing body of literature related to succession planning, offering recommendations to promote stability in leadership, foster continuous talent development, and mitigate talent crises. The study evaluates the current state of succession planning in higher education, identifying issues and their root causes. It provides a summary and analysis of ongoing research efforts related to successor quality, team formation, and cultivation models. Despite advancements through national talent cultivation policies, persistent challenges like talent scarcity, the absence of gender-inclusive succession plans, a lack of originality, and inconsistent staff flow hinder progress. The research attributes these challenges to traditional personnel systems and university administrators. Proactive measures are proposed, including creating awareness of succession planning, advocating for personnel mechanism reform, establishing a comprehensive training system, and developing a scientifically-grounded succession plan. Though the study aims to contribute to leadership development and address pressing issues faced by higher education institutions, with only a limited number utilizing mixed techniques, it restricted the comprehensive inclusion of social context knowledge and evidence regarding the motivations, beliefs, and experiences of individuals in this investigation.

This study examines conditions that impact PPP delivery success or failure in the roadways sector in India using Qualitative Comparative Analysis. QCA is well-suited for problems where multiple factors combine to create pathways leading to an outcome. Past investigations have compared PPP and non-PPP project delivery performance, but this study examines performance within PPPs by uncovering a set of conditions that combine to influence the success or failure road PPP project delivery in India. Based on data from 21 cases, pathways explaining project delivery success or failure were identified. Specifically, PPPs with high concessionaire equity investment and low regional industrial activity led to project delivery success. Projects with lower concessionaire equity investment and low reliance on toll revenue and with either: (a) high project technical complexity or (b) high regional industrial activity, led to project delivery failure. The pathways identified did not have coverage values that they were extremely strong. Coverage strength was hindered by lack of access to information on additional conditions that could be configurationally important. Further, certain characteristics of the Indian market limit generalization. Identification of combinations of conditions leading to PPP project delivery success or failure improves knowledge of the impacts of structure and characteristics of these complex arrangements. This study is one of the first to use fuzzy QCA to understand project delivery success/failure in road PPP projects. Moreover, this study takes into account factors specific to a sector and delivery mode to explain project delivery performance.