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.

State-owned enterprises (SOEs) manage significant portion of world economy, including in the developing countries. SOEs are expected to be active and play significant role in improving the country’s economic performance and welfare through enhancing innovation performance. However, closed innovation process and lack of collaboration hinders SOEs to reach satisfying innovation performance level. This paper explores the construction and role of innovation ecosystem in the strategic entrepreneurship process of SOEs, of which is represented by dynamic capability framework, business model innovation, and collaborative advantage. Based on the analysis, this paper concluded that the collaboration between actors in the Innovation Ecosystem (IE) has positive effect to strengthening SOE’s Sensing Capabilities (SC) related to the process of exploring and identifying innovation opportunities. The increase of Sensing Capabilities (SC) will play significant role as input or antecedent on formulating proactive Innovation Strategy (IS) in orchestrating SOE’s innovation process. SOEs which has implementing proactive Innovation Strategy (IS) will be able to build collaboration and finding right Business Model Innovation (BMI). Finally, by building collaboration with other actors through the innovative business model has significant role to increase SOE’s Collaborative Advantage (CA), which considered as a proxy for competitiveness of SOEs.

Quality human resources will be formed if education focuses on improving students’ skills. Of course, the foundation of education must be quality. Qualified human resources will later be responsible for making Indonesia a good country in all fields. This study aims to examine the effect of applying the REACT learning model (Relating, Experiencing, Applying, Cooperating, Transferring) on learning outcomes and critical thinking skills of students of SMAN 9 KENDARI. Quantitative research method with experimental research type. The research design used was post experimental control design. The research location was at SMAN 9 KENDARI. The instruments used include learning outcomes test and critical thinking skills test. The data obtained were explained using statistical tests to see the differences between the experimental group and the control group in chemistry subjects. The results showed that the application of REACT model significantly improved students’ learning outcomes and critical thinking skills compared to conventional learning methods in chemistry subjects. The findings indicated that the REACT model was effective in improving the quality of learning and developing critical thinking skills of students of SMAN 9 KENDARI, especially in chemistry learning.

As the aging trend intensifies, the Chinese government prioritizes technological innovation in smart elderly care services to enhance quality and efficiency, catering to the diverse needs of the elderly. This study examines the acceptance and usage behavior of smart elderly care services among elderly individuals in Xi’an, using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model that includes digital literacy as a moderating variable. Data were collected via a survey of 299 elderly individuals aged 60 and above in Xi’an. The study aims to identify factors influencing the acceptance and usage behavior of smart elderly care services and to understand how digital literacy moderates the relationship between these factors and usage behavior. Regression analysis assessed the direct effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on usage behavior. These dimensions were then integrated into a comprehensive index Service Acceptance to evaluate their overall impact on usage behavior, with behavioral intention examined as a potential mediating variable. Results indicate that EE and SI significantly impact the adoption of smart elderly care services, whereas PE and FC do not. Behavioral intention mediates the relationship between these variables and usage behavior. Additionally, gender, age, and digital literacy significantly moderate the impact of service acceptance on usage behavior. This study provides valuable theoretical and practical insights for designing and promoting smart elderly care services, emphasizing the importance of usability and social promotion to enhance the quality of life for the elderly.

Consumers waste significant amounts of food. Food waste presents a substantial problem for the environment, society and economy. Addressing the food waste challenge is crucial for fostering sustainable behavior and achieving the Sustainability Development Goal 12.3 agenda. Norms are a significant determinant in motivating consumers to prevent food waste and could be activated by other factors. Religiosity has the potential to influence norms related to food waste behavior. This study investigated how religiosity affects the intentions of consumers to minimize food waste. The interplay of religiosity, personal norms, subjective norms, and intention to avoid food waste was examined by the extended norm activation model. Data were obtained from Muslim consumers in Indonesia. Structural equation modeling evaluation showed that religiosity positively affects the intention to prevent food waste. The intention to avoid food waste is more closely associated with personal norms compared to subjective norms. Personal norms mediate the religiosity and food waste reduction intention relationship. Consumer awareness activates personal norms by making them feel accountable for food waste’s negative impact. These findings provide insights to stakeholders in developing policies to mitigate the food waste issue.

We present an interdisciplinary exploration of technostress in knowledge-intensive organizations, including both business and healthcare settings, and its impact on a healthy working life. Technostress, a contemporary form of stress induced by information and communication technology, is associated with reduced job satisfaction, diminished organizational commitment, and adverse patient care outcomes. This article aims to construct an innovative framework, called The Integrated Technostress Resilience Framework, designed to mitigate technostress and promote continuous learning within dynamic organizational contexts. In this perspective article we incorporate a socio-technical systems approach to emphasize the complex interplay between technological and social factors in organizational settings. The proposed framework is expected to provide valuable insights into the role of transparency in digital technology utilization, with the aim of mitigating technostress. Furthermore, it seeks to extend information systems theory, particularly the Technology Acceptance Model, by offering a more nuanced understanding of technology adoption and use. Our conclusion includes considerations for the design and implementation of information systems aimed at fostering resilience and adaptability in organizations undergoing rapid technological change.