HR 애널리틱스 연구 및 실무에서의 베이지안 통계 활용: 퇴임 임원의 데이터를 중심으로

In the era of digital transformation, which is represented by technologies such as machine learning and artificial intelligence, HR is also rapidly adopting new technologies. In particular, there have been increased interests in HR analytics which utilizes the technologies to process various data collected in organizations. Among recent studies related to HR analytics, the number of quantitative research is less than the number of qualitative research. Many methods in machine learning and artificial intelligence are based on Bayesian statistics, but researchers and practitioners in HR Analytics, who are more experienced in social science and frequentist statistics, have relatively less experiences in the Bayesian methods. Despite the use of various methods in predictive business analytics such as Bayesian network, naive Bayes and topic modeling, Bayesian statistics has not been thoroughly introduced and has been rarely used in the HR fields. In this study, we provide an introduction to Bayesian statistics and examples of Bayesian data analysis for the wide use of Bayesian methods in HR analytics. In addition, we analyzed data of executives in an organization and explained which capability is highly associated with the probability of retirement. This is in line with HR analytics studies that characterizes successful and unsuccessful leaders, which is of great interest, and this research has contributions to both academia and practice as there has been little progress in Korea