This study explores the relationship between leadership style and organizational citizenship behavior (OCB), focusing on the moderating role of leaders' emotional intelligence (EI). Organizational citizenship behavior, which includes discretionary actions that go beyond formal job requirements, plays a critical role in enhancing workplace effectiveness and fostering a positive organizational culture. Leadership style, which significantly influences employee behavior, is theorized to impact the likelihood of employees exhibiting OCB. However, this relationship is often contingent on the emotional intelligence of the leader. Emotional intelligence, defined as the ability to perceive, understand, and manage emotions, may influence how leaders motivate and guide employees to engage in OCB. This research employs a quantitative approach, using surveys distributed to employees and their direct supervisors across various industries. The results suggest that transformational leadership style positively affects organizational citizenship behavior, while transactional leadership shows a weaker effect. More importantly, emotional intelligence was found to moderate this relationship, with high EI enhancing the positive impact of transformational leadership on OCB. These findings emphasize the importance of leadership development programs that focus on emotional intelligence as a means to foster a more engaged and productive workforce. The study contributes to a deeper understanding of how leadership styles and emotional intelligence together influence employee behavior and organizational performance.

This study explores the application of advanced machine learning models for market trend forecasting, incorporating external factors such as economic indicators and sentiment analysis to enhance prediction accuracy. Comparative analysis of models including Random Forest, Support Vector Machines, Gradient Boosting, and Neural Networks revealed distinct performance differences, with Gradient Boosting achieving the highest accuracy of 92.7% and the lowest mean squared error of 0.014. External factors contributed significantly to improving model precision, as evidenced by a 7.5% increase in overall forecasting accuracy. The study emphasizes the efficacy of integrating diverse machine learning algorithms and external variables in creating robust forecasting systems. These findings highlight the potential for machine learning to revolutionize market analysis and decision-making, offering practical implications for industries seeking data-driven strategies to optimize performance in dynamic economic environments.