This study investigates the impact of perceived innovative leadership on team innovation performance, with innovation climate acting as a mediating variable. A quantitative research approach, including a survey of team members across various industries, was used to collect data. Analysis through Structural Equation Modeling (SEM) reveals that perceived innovative leadership significantly positively influences team innovation performance, with innovation climate partially mediating this relationship. The findings emphasize the critical role of innovative leadership and a positive innovation climate in fostering organizational innovation, offering valuable insights for management practices. This paper also discusses the study’s limitations and provides directions for future research.

This research aims to develop a Synergy Learning Model in the context of science learning.  This research was conducted at Islamic Junior High School, Madrasah Tsanawiyah Negeri 2 Medan, involving 64 students of Grade 7 as the research subject. The method used in this research refers to the development research approach (R&D). In collecting the data, the research employed test and non-test techniques. The results prove that the Synergy learning model developed is effective in improving student learning outcomes. This is evident through the t-test statistical test where the t-count of 4.26 is higher than the t-table of 1.99. In addition, the level of practicality with a score of 3.39 is categorized as practical. This learning model emphasizes the learning process that supports the development of science skills and develops students' competencies in planning, collaborating, and critically reflecting. The findings of this study contribute to pedagogical practices and literature in the field of science learning.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

In the context of a globalized economic environment, businesses are facing an increasing number of environmental challenges, prompting them not only to pursue economic benefits but also to focus on environmental protection and social responsibility. Green supply chain management (GSCM) and green innovation have become key strategies for enterprises aiming for sustainable development. This study explores the impact of green supply chain practices on green innovation performance, with a focus on how knowledge management and organizational integration serve as mediating variables in this relationship. Grounded in the resource-based view (RBV) and knowledge-based view (KBV) theories, this research employs surveys and in-depth interviews with companies across various industries, combined with the analysis of structural equation modeling, to reveal the complex relationship between GSCM practices, knowledge management capabilities, levels of organizational integration, and green innovation performance. The results show that GSCM practices significantly enhance corporate green innovation performance through effective knowledge management and organizational integration. These findings enrich the theories of GSCM and green innovation, providing practical guidance for enterprises on how to enhance green innovation performance through strengthening knowledge management and organizational integration. Finally, this study discusses its limitations and suggests possible directions for future research, such as exploring the differences in findings across different industry backgrounds and examining other potential mediating or moderating variables.

Sustainability has become a generalized concern for society, specifically businesses, governments, and academia. In the specific case of universities, sustainability has been approached from different perspectives, some viewing it from environmental practices, management initiatives, operational criteria, green buildings, and even education for sustainable development. This research focuses on sustainability as a managerial practice and investigates how it affects the performance of five private universities in Medellin, Colombia. For this purpose, a literature review using a mixed sequential approach, including bibliometric and content analysis, was initially conducted. In the s second phase, more than 5000 responses from students, professors, and employees of the five mentioned private universities were collected. A previously validated instrument for both sustainability and performance was applied in the quantitative phase, and a novel dimensionality of the constructs was proposed by conducting an exploratory factor analysis using the SPSS software. Results were then processed through a structural equation analysis with the Smart PLS software. The impact of sustainability on university performance is verified, making some managerial recommendations.

 Beach protection is vital to reduce the damage to shorelines and coastal areas; one of the artificial protections that can be utilized is the tetrapod. However, much damage occurred when using a traditional tetrapod due to the lack of stability coefficient (K_D). Therefore, this research aims to increase the stability coefficient by providing minor modifications to the cape of the tetrapod, such as round-caped or cube-caped. The modification seeks to hold the drag force from the wave and offer a good interlocking in between the tetrapod. This research applied physical model test research using a breakwater model made from the proposed innovative tetrapod with numerous variations in dimensions and layers simulated with several scenarios. The analysis was carried out by graphing the relationship between the parameters of the measurement results and the relationship between dimensionless parameters, such as wave steepness H/gT2, and other essential parameters, such as the K_D stability number and the level of damage in %. The result shows that the modified and innovative tetrapod has a more excellent K_D value than the conventional tetrapod. In addition, the innovative tetrapod with the cube-shaped has a recommended K_D value greater than the round shape. This means that for the modified tetrapod structure and the same level of security, the required weight of the tetrapod with the cube cap will be lighter than the tetrapod with the round cap. These findings have significant practical implications for coastal protection and engineering, potentially leading to more efficient and cost-effective solutions.