This research aims to examine the influence of IHRMP, recruitment and selection, training, compensation, and performance appraisal on the productivity of Faculty Members (FM) productivity working in private universities in the UAE. The study also examines the mediating role of Organizational Commitment (OC) and the moderating role of the Entrepreneurial Mind-set (EM). The research adopted the social exchange theory. A survey was conducted comprising 160 FM. The data was analyzed using Structural Equation Modelling, Smart-PLS. The findings indicate a positive relationship between IHRMP and the productivity of the FM. The findings also show that OC mediates the relationship between IHRMP and the productivity of FM. Finally, an EM was found to moderate the relationship between IHRMP and the productivity of FM.

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.

Employees’ loyalty is essential for improving the organization’s performance, thus aiding sustainable economic growth. The study examines the relationship between employee loyalty, organizational performance, and economic sustainability in Malaysian organizations. The results indicate a robust positive correlation between organizational performance and employee loyalty, suggesting loyalty drives productivity, profitability, and operational efficiency. Additionally, the study highlights organizational performance as a mediator that connects loyalty to aggregate-level economic consequences, such as resilience and adaptability under volatile market conditions. The research emphasizes the role of leadership, company culture, and work environments that support cultivating loyalty. It also highlights how loyal employees can be a cornerstone of innovation and corporate social responsibility, which aligns with Malaysia’s sustainable development agenda. By addressing this, organizations are encouraged to adopt measures that can foster loyalty and ensure long-term economic sustainability, including employee engagement initiatives, talent management, and recognition systems. Research to come should investigate longitudinal dynamics, cross-cultural comparisons, and sector-specific factors to cement a better base of understanding about the impact of employee loyalty on organizational and economic outcomes.

The purpose of this research is to investigate the relationship between transformational leadership variables and organizational citizenship behavior (OCB) variables, investigate the relationship between job satisfaction variables and organizational citizenship behavior (OCB), and investigate the relationship between organizational commitment variables and organizational citizenship behavior (OCB). This research method uses quantitative methods. In this study, the researchers used a simple random sampling technique with a sample size of 368 SMEs employee. The data collection method for this research is by distributing an online questionnaire designed using a Likert scale of 1 to 7. The data analysis technique uses Partial Least Square—Structural Equation Modeling (PLS-SEM) and data analysis tools use SmartPLS software version 3.0. The stages of data analysis are validity testing, reliability testing and hypothesis testing. The independent variables in this research are transformational leadership, job satisfaction and organizational commitment, while the dependent variable is organizational citizenship behavior (OCB). The results of this research are that transformational leadership has a positive influence on organizational citizenship behavior (OCB), Job Satisfaction has a positive influence on organizational citizenship behavior (OCB) and organizational commitment has a positive influence on organizational citizenship behavior (OCB). The theoretical implications of this research support the results of previous research that transformational leadership, job satisfaction, and organizational commitment make a positive contribution to increasing organizational citizenship behavior in SME employees. The practical implication of this research is that SME owners apply transformational leadership, create work breadth and create organizational commitment within the SME organization to support increasing employee organizational citizenship behavior so that it can encourage increased performance and competitiveness of SMEs.

Managing the spread of “disinformation” is becoming an increasingly difficult task of our time, with an emphasis on digital marketing and its influence on organizational reputation. This paper aims to analyze the phenomenon of disinformation, with emphasis on the role of digital marketing and the consequent effect on organizational image. Thus, using the systematic literature review methodology, the study defines and categorizes different types of disinformation, namely fake news, misinformation, and propaganda, and how they are spread across different channels. Using the research, it is possible to conclude that digital marketing is more effective in spreading disinformation than traditional media and word-of-mouth; social media management and content marketing are the most effective. The work also evaluates the catastrophic impact of disinformation on an organization’s image, fiscal health, and the trust of its stakeholders. Using the Chi-Square Test for Independence and Logistic Regression, the study determines the factors likely to lead to severe consequences of disinformation campaigns. Last but not least, the paper also suggests ways of preventing the spread of disinformation, which include improved education on the use of digital platforms, better fact-checking systems, and an improved code of ethics in digital marketing.