Data Modelling in Human Resource Management: Influencing Factors of Employees' Job Satisfaction

Abstract

In the digital era, data mining and statistical analysis have been widely used to solve problems, especially in the field of management and engineering. Therefore, we aim to make a new insight of human resource management based on multiple regression modelling and quantile regression modelling. Specifically, the systematic framework of job satisfaction in this research is constructed by three dimensions from the perspective of psychology, namely, the perception of interpersonal relationship, financial compensation, and work conditions. Each dimension consists of two measures which reflect the employees' view towards them. The empirical estimation results show the following. (1) Perceived relationship with managers, perceived rationality of compensation, perceived match degree of job, and perceived autonomy degree of work are all significantly positively correlated with job satisfaction. (2) The effect of perceived rationality of compensation is significantly different between the high quantile and the low quantile. For those with lower perceived rationality of compensation, their job satisfaction is more likely to be affected due to the perceived compensation than those with higher perception. This research enriches the existing theory by constructing a comprehensive framework of the influencing factors of job satisfaction, which provides useful implications of human resource management optimization for enterprises.