This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.

There has been a growing interest in studying dysfunctional personality traits in the workplace. In line with this trend, this study examines how the Dark Triad personalities (Machiavellianism, narcissism, and psychopathy) can predict innovative work behavior. Additionally, the study builds on Trait Activation Theory and proposes a moderating effect of training satisfaction on this relationship. The purpose of this study is to understand if the Dark Triad traits predict innovative behavior while simultaneously examining the role of training satisfaction in channeling these traits toward innovative behavior. A questionnaire-based survey was conducted on the five largest telecommunication companies in Pakistan. The data gathered was analyzed using structural equation modeling. Results established a positive relationship between each trait of the Dark Triad and innovative work behavior. Moreover, training satisfaction was found to moderate the relationship between the psychopathy trait and innovative work behavior. In light of these findings, the study contributes to personality-behavior research in organizations by demonstrating that the Dark Triad predicts innovative work behavior in managers and that the innovative behaviors associated with the psychopathy trait can be enhanced in the presence of training satisfaction.

Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.

Purpose: This study aims to identify the primary determinants of consumer behavior influencing customer satisfaction in the context of online mobile application (App) purchases of perishable products. Utilizing the well-established SERVQUAL (Service Quality) model, which has been extensively studied in various service-oriented settings, the research seeks to determine the factors with the greatest impact on customer satisfaction during online transactions of perishable products. Design: The investigation focuses on analyzing the five core dimensions of the SERVQUAL model: tangibles, reliability, responsiveness, assurance, and empathy. The study employs a survey methodology administered through Google Forms, targeting the population residing in the Klang Valley of Malaysia. A total of 400 samples were successfully collected using a snowball sampling technique. Methodology: The study employs the SERVQUAL model as the theoretical framework to examine the dimensions of tangibles, reliability, responsiveness, assurance, and empathy. The survey, conducted through Google Forms, targeted the population in the Klang Valley of Malaysia, with a sample size of 400 collected through snowball sampling. Findings: The study’s outcomes reveal the robust predictive capability of the overarching SERVQUAL model in the realm of online perishable product procurement. Notably, the assurance dimension emerges as the most influential factor, emphasizing its pivotal role in shaping and defining customer satisfaction for online retailers of perishable goods in the Malaysian market. Novelty: This research contributes to the understanding of consumer behavior in online perishable product purchases, by identifying determinants of consumer behavior; the study promotes sustainable production and responsible consumption within the perishable products category, offering insights beneficial for online retailers in the Malaysian market. This study aligns with United Nations sustainable development goals especially industry innovation, food security and responsible consumption.

This study aimed to examine the impact of digital leadership among school principals and evaluate the mediating effect of Professional Learning Communities (PLCs) on enhancing teachers’ innovation skills for sustainable technology integration, both in traditional classroom settings and e-learning environments. Employing a quantitative approach with a regression design model, Structural Equation Modelling (SEM) and Partial Least Squares (PLS-SEM) were utilized in this research. A total of 257 teachers from 7 excellent senior high schools in Makassar city participated in the study, responding to the questionnaires administered. The study findings indicate that while principal digital leadership does not directly influence teachers’ innovation skills in technology integration, it directly impacts Professional Learning Communities (PLCs). Moreover, PLCs themselves have a significant influence on teachers’ innovation skills in technology integration. The structural model presented in this study illustrates a noteworthy impact of principal digital leadership on teachers’ innovation skills for technology integration through Professional Learning Communities (PLCs), with a coefficient value of 47.4%. Principal digital leadership is crucial in enhancing teachers’ innovation skills for sustainable technology integration, primarily by leveraging Professional Learning Communities (PLCs). As a result, principals must prioritize the creation of supportive learning environments and implement programs to foster teachers’ proficiency for sustainable technology integration. Additionally, teachers are encouraged to concentrate on communication, collaboration, and relationship-building with colleagues to exchange insights, address challenges, and devise solutions for integrating technology, thereby contributing to sustained school improvement efforts. Finally, this research provides insights for school leaders, policymakers, and educators, emphasizing the need to leverage PLCs to enhance teaching practices and student outcomes, particularly in sustainable technology integration.

 In the face of growing disruptions within the unconventional business environment, this study focuses on enhancing supply chain resilience through strategically reforming resources. It highlights the importance of understanding the dynamics and interactions of resources to tackle supply chain vulnerability (SCV) in the manufacturing sector. Employing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology alongside an adapted Analytic Network Process (ANP), the research investigates supply chain vulnerabilities in Pakistan’s large-scale manufacturing (LSM) public sector firms. The DANP method, through expert questionnaires, helps validate a theoretical framework by assessing the interconnectedness of supply chain readiness dimensions and criteria. Findings underscore Resource Reformation (RR) as a critical dimension, with the positive restructuring of resources identified as pivotal for public sector firms to align their operations with disruption magnitudes, advocating for a detailed analysis of resource utilization.