On analyzing job hop behavior and talent flow networks

• Main Authors: OENTARYO, Richard J., ASHOK, Xavier Jayaraj Siddarth, LIM, Ee-peng, PRASETYO, Philips Kokoh
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Career progression; Flow network; Gain insight; Job seekers; Job-hopping; Population levels; Work experience; Working professionals; Data mining; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/3972; https://ink.library.smu.edu.sg/context/sis_research/article/4974/viewcontent/Jobhop_Behav_2017_afv.pdf
• Institution: Singapore Management University

Analyzing job hopping behavior is important for theunderstanding of job preference and career progression of working individuals.When analyzed at the workforce population level, job hop analysis helps to gaininsights of talent flow and organization competition. Traditionally, surveysare conducted on job seekers and employers to study job behavior. While surveysare good at getting direct user input to specially designed questions, they areoften not scalable and timely enough to cope with fast-changing job landscape.In this paper, we present a data science approach to analyze job hops performedby about 490,000 working professionals located in a city using their publiclyshared profiles. We develop several metrics to measure how much work experienceis needed to take up a job and how recent/established the job is, and thenexamine how these metrics correlate with the propensity of hopping. We alsostudy how job hop behavior is related to job promotion/demotion. Finally, weperform network analyses at the job and organization levels in order to deriveinsights on talent flow as well as job and organizational competitiveness.