Modelling HR Analytics' Impact on Employee Performance: A PLS-SEM Approach

Abstract

The purpose of this article is to investigate the link between Human Resources analytics (HRA) and workplace productivity through two mediating attributes, namely employee engagement (EE) and job satisfaction (JS). The effect of the relationship between HRA and performance outcomes was analysed by “Partial Least Squares Structural Equation Modelling"(PLS-SEM). The field of HRA is gaining steam, in part because it may help people make better decisions and manage their workforces more effectively by using data to help workers succeed. To conduct this research, the author has utilized a quantitative survey method, convenience sampling and online questionnaires were used to collect data from 376 Information technology (IT) industry employees in Delhi (NCR) of India. The findings show that HRA significantly enhances employee performance (EP) and productivity through the mediating role of EE and JS, with moderate- to- high R^2 value shows a reliable and acceptable level of model fit and explanatory power. As per this study, we found that the 67.02% respondents agree that HRA is very useful for EP. Also, this study makes some good comments concerning the convenience of the collection of data and sampling at a particular time point, which prevents extrapolation to other populations. In this field, future research and longitudinal studies should focus on management as well as company culture as significant mediating factors. An extensive examination of the ethical issues surrounding the use of worker information for HRA is essential to ensuring fair and private data-driven choices. To provide equitable and confidential data-driven decision-making, the ethical issues surrounding the use of employee data for HR analytics must be thoroughly examined.