This study explores the key factors influencing online shopping behavior, including convenience, price comparison; trust in online platforms, product variety, and digital marketing strategies. It also examines the role of demographic variables such as age, income, and education level in shaping consumer preferences. The findings highlight that consumers prioritize ease of use, secure payment systems, and positive user experiences when engaging in online purchases. The study underscores the importance for businesses to adapt their online strategies to meet evolving consumer expectations and enhance customer satisfaction, ultimately driving higher conversion rates and loyalty in the digital marketplace.

This paper explores the development and application of AI models designed to monitor, predict, and support mental health within corporate environments. These models leverage data from various sources, including employee surveys, digital communications, wearables, and performance metrics, to identify early signs of stress, burnout, or emotional distress. Machine learning and natural language processing algorithms enable real-time sentiment analysis, behavioral pattern recognition, and personalized mental health interventions. The abstract discusses the potential benefits, such as scalable and proactive mental health support, reduced stigma, and data-driven decision-making for HR and management teams. It also addresses critical ethical considerations, including data privacy, consent, bias mitigation, and the importance of maintaining a human-centered approach. The paper concludes by emphasizing the need for interdisciplinary collaboration and robust governance frameworks to ensure that AI tools serve as effective, ethical, and empathetic complements to traditional mental health resources in the workplace.