This study examines the impact of digital consumer engagement, human resource innovation and sustainable marketing practices on the performance of the organization and customer loyalty in emerging business markets. In today's fierce business world, companies are becoming more reliant on digital platforms, creative employee practices and marketing centred around sustainability if they want to boost performance and foster customer relations with end users. The study is quantitative in nature, and a structured questionnaire is utilized to gather responses from individuals who are associated with the SMEs, Startup, Entrepreneurial ventures and the service-based companies. The data obtained were analyzed using descriptive analysis, reliability analysis, correlation analysis and regression analysis with the total of 250 respondents. Results indicate that digital engagement with consumers, innovation in HR practices and sustainable marketing approaches have positive and significant impacts on both organizational performance and customer loyalty. Human resource innovation was identified as the best predictor of organizational performance, and digital consumer engagement was the best predictor of customer loyalty. Based on the results, other companies in emerging markets can reap better business results by integrating three elements: digital interaction with customers, innovative HR practices, and responsible marketing. The study presents a framework that integrates digital engagement, HR innovation, sustainable marketing, performance of organizations, and customers' loyalty to contribute to the digital engagement literature, HR innovation literature, sustainable marketing literature, organization performance literature, and customer loyalty literature.

Employee turnover presents a persistent challenge for healthcare organizations because workforce instability can affect operational continuity, staffing capacity, and human resource planning. This study examined the potential of AI-driven workforce analytics for predicting employee turnover and supporting strategic retention decisions. A healthcare workforce dataset comprising 1,676 employees was analyzed using demographic, occupational, compensation, satisfaction, and career-related characteristics. Employee attrition was treated as the prediction outcome, and Logistic Regression, Random Forest, and Gradient Boosting models were developed using a stratified training-test approach. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. Overall attrition was 11.87%, with employees experiencing turnover characterized by younger age, lower monthly income, shorter organizational tenure, lower job satisfaction, and poorer work-life balance. Overtime showed a marked association with turnover, with attrition rates of 29.2% among employees working overtime and 5.0% among those without overtime. Gradient Boosting achieved the highest accuracy (0.909) and ROC-AUC (0.911), while Logistic Regression achieved the highest recall (0.760). The findings demonstrate that predictive workforce analytics can identify meaningful turnover patterns while supporting risk-informed retention planning. Integrating predictive evidence with workforce characteristics may help healthcare organizations prioritize workload management, employee engagement, compensation, career development, and work-life balance initiatives.