Start Date
10-12-2017 12:00 AM
Description
HR analytics is an important area for the application of big data analysis techniques, and the organizational insight that it provides enables effective management of employees. In this paper, we analyze employee review data posted on a representative third-party employee review website. We identify the relative importance of factors affecting job satisfaction and then extract topic differences after classifying employees according to retention and turnover. First, LDA Topic Modeling by adopting n-grams is performed on unstructured text data to analyze employee review data. Second, a dominance analysis is conducted to examine the relative importance of job factors. We found that the “Culture and Values” and “Senior Management” factors have the highest influence on both retention and turnover. Our model follows a novel approach in applying the analysis of reviews and text mining to the HR domain and will be of practical relevance for enhancing employee retention.
Recommended Citation
Lee, Jongseo and Kang, Juyoung, "A Study on Job Satisfaction Factors in Retention and Turnover Groups using Dominance Analysis and LDA Topic Modeling with Employee Reviews on Glassdoor.com" (2017). ICIS 2017 Proceedings. 26.
https://aisel.aisnet.org/icis2017/DataScience/Presentations/26
A Study on Job Satisfaction Factors in Retention and Turnover Groups using Dominance Analysis and LDA Topic Modeling with Employee Reviews on Glassdoor.com
HR analytics is an important area for the application of big data analysis techniques, and the organizational insight that it provides enables effective management of employees. In this paper, we analyze employee review data posted on a representative third-party employee review website. We identify the relative importance of factors affecting job satisfaction and then extract topic differences after classifying employees according to retention and turnover. First, LDA Topic Modeling by adopting n-grams is performed on unstructured text data to analyze employee review data. Second, a dominance analysis is conducted to examine the relative importance of job factors. We found that the “Culture and Values” and “Senior Management” factors have the highest influence on both retention and turnover. Our model follows a novel approach in applying the analysis of reviews and text mining to the HR domain and will be of practical relevance for enhancing employee retention.