Detecting the Employee Satisfaction in Retail: A Latent Dirichlet Allocation and Machine Learning approach

Abstract

Human resource management is one of the most critical functions but is often overlooked, which can impact the organisation in a negative manner. Proper understanding and implementation of satisfaction factors in the workplace can enable human resource managers or decision-makers to make effective decisions regarding retention strategies, recruitment and selection techniques, employer branding and budget allocation. Traditionally, surveys, exit interviews, annual evaluations, and informal interactions were the primary source of data collection to identify what employees feel about the organisation. With the increase in the usage of platforms for posting online reviews, researchers and decision-makers are making appropriate use of text structured and unstructured responses to identify and resolve issues. In this paper, we have explored the electronic word of mouth in form of employee reviews of five companies in the retail sector using Latent Dirichlet Allocation in the first phase. In the second phase, we collected the data from employees for predicting employee satisfaction using IBM SPSS Modeler 18.2.1. The top twelve topics are identified using Latent Dirichlet Allocation and a word cloud is also formed. We have also made the efficient use of predictive analytics to predict the satisfaction among employees that can contribute to the organisations in various forms. The methodology followed in this paper addresses the gap of using employee reviews and responses to evaluate the employee satisfaction as researchers have focused on using LDA for customer reviews and predictive analytics is mostly used for employee churn. LDA technique is often used in hospitality; however, no relevant literature is available for retail employees.