A190013010926

This research explores the socio-economic background and quality-of-life issues among transgender individuals in Kolkata and Lucknow. Based on survey data gathered from 300 participants (150 from each city). To compare demographic, educational, and occupational profiles; income; housing and homelessness experiences; workplace satisfaction; and satisfaction with living conditions, we used statistical techniques such as the chi-square test, Mann-Whitney U test, Spearman’s rank correlation, and binary logistic regression. The two samples selected from the cities have similar demographics (age, education, and income) but different occupation profiles. Housing and homelessness problems were more prevalent among people in Kolkata. Still, a considerable portion of respondents from Lucknow opted not to answer this question, which may indicate underreporting. Employment satisfaction did not vary between the two cities, but the response distribution differed. Living condition satisfaction differed only slightly between the two cities. Employment satisfaction and living condition satisfaction are not related. This implies that employment satisfaction and living condition satisfaction represent different aspects of quality of life. Logistic regression revealed that occupation and city both mattered in housing problems. People in informal occupations face the highest risk of these problems. The study highlights the continuing socio-economic inequalities experienced by transgender individuals and shows that employment security and housing remain important areas for policy attention.

A189913010926

This investigation examines the integration of Data Science (DS) into Total Quality Management (TQM) processes within enterprises, focusing on DS’s impact on quality and operational efficiency. Jordanian enterprises often face considerable challenges in maintaining high standards of quality and efficiency due to limited resources. This study examines how DS affects key Total Quality Management (TQM) metrics, including defect rates, operational efficiency, customer satisfaction, inventory management, and downtime, across varying degrees of DS adoption. This study used a quantitative, correlational research approach, drawing on data from structured surveys and operational records from businesses classified as having low, moderate, or high DS integration. The ANOVA, t tests, and correlation analyses indicated significant statistical improvements across all metrics associated with higher levels of Data Science (DS) integration. Specifically, DS-driven quality control was correlated with lower defect rates, improved production efficiency, and greater customer satisfaction. Additionally, using DS in predictive maintenance and inventory management reduced waste and downtime. These results imply that incorporating DS into Total Quality Management (TQM) offers businesses strategic advantages, such as improved quality, operational resilience, and enhanced competitiveness. The study concludes that integrating DS into TQM processes is a promising approach for companies aiming for sustainable growth in a technology-focused market.

L189512120826

Diverse representation in the nursing workforce does not automatically align with equity in role utilization, advancement, compensation, or leadership. This study examines the alignment between human capital investment and role utilization among black women nurses. Along with the Nursing Human Capital Value Model, advanced degree attainment was selected as the measure of human capital investment and advanced nursing role utilization as the measure of occupational alignment. A quantitative, non-experimental secondary data design was employed to analyze a sample of 769 women nurses from the publicly available Survey of Income and Program Participation. Based on the sample, higher degree attainment was associated with advanced nursing role utilization at a notably high rate. However, race-specific results showed an alignment gap. Among White women nurses, 137 held a master’s degree or PhD, and 77 worked in advanced nursing roles. Among Black women nurses, 12 held a master’s degree or a PhD, but none were represented in advanced nursing roles. Advanced degree attainment alone may not ensure advanced role utilization across racial groups. This study highlights the necessity for health care organizations to examine internal mobility, promotion pathways, credential review, and talent management practices to ensure that advanced-educated black female nurses are equitably positioned in roles aligned with their preparation.

L189312120826

Predicting customer churn is essential for improving retention and supporting long-term business growth. In this study, we compared explainable machine learning models for predicting customer churn using the IBM Telco Customer Churn dataset in R. Our approach included data preprocessing, exploratory analysis, model development, performance evaluation, and further analysis. We developed and evaluated four classification algorithms: Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, using an 80:20 train-test split. We assessed each model’s accuracy, precision, recall, and F1-score. Logistic Regression achieved the best performance, with an accuracy of 82.30% on this dataset. Feature importance analysis indicated that contract type, customer tenure, monthly charges, total charges, and internet service were the key factors influencing churn. These results suggest that explainable machine learning offers both strong predictive performance and greater transparency. The R-based framework we present provides a practical, reproducible approach to support customer retention strategies and help managers make evidence-based decisions in customer relationship management.

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