Impact of HR practices on employee performance and job satisfaction using statistical analysis methods

Abstract

Training, performance evaluation, and remuneration are three critical human resource (HR) practices that this study examines in relation to employee performance and work satisfaction in different organizational sectors. First, we wanted to see how HR practices relate to productivity in the workplace. Second, we wanted to see how these practices affected workers' happiness on the job, and third, we wanted to find out what factors were most important in predicting productivity and happiness on the job. Lastly, we wanted to build a model that took these practices into account. The study used a cross-sectional survey approach, indicative of a quantitative research design. Three hundred workers from the healthcare, manufacturing, and information technology industries filled out standardized questionnaires. Multiple regression, ANOVA, Pearson's correlation, and stepwise regression were among the statistical methods utilized. The results showed that training had the greatest predictive value among HR practices and employee performance, which was strongly positive. Job satisfaction varied significantly across industries, with IT workers reporting the highest levels of contentment, according to an analysis of variance. The two most important factors in determining whether an employee is happy in their job, according to stepwise regression, are pay and performance reviews. Thanks to regression analysis, a predictive model was formed. The results show how important strategic HR practices are for better workforce outcomes and give businesses a data-driven plan to boost morale and productivity in any workplace.