Talent flow analytics in online professional network

Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are condu...

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Main Authors: OENTARYO, Richard J., LIM, Ee-peng, ASHOK, Xavier Jayaraj Siddarth, PRASETYO, Philips Kokoh
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4384
https://ink.library.smu.edu.sg/context/sis_research/article/5387/viewcontent/Oentaryo2018_Article_TalentFlowAnalyticsInOnlinePro.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-53872021-03-26T05:07:57Z Talent flow analytics in online professional network OENTARYO, Richard J. LIM, Ee-peng ASHOK, Xavier Jayaraj Siddarth PRASETYO, Philips Kokoh Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data-driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework repurposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that the duration of a job’s existence (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform connectivity analysis at job and organization levels to derive insights on talent flow as well as job and organizational competitiveness. 2018-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4384 info:doi/10.1007/s41019-018-0070-8 https://ink.library.smu.edu.sg/context/sis_research/article/5387/viewcontent/Oentaryo2018_Article_TalentFlowAnalyticsInOnlinePro.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Centrality Job hop Network analysis Talent flow Databases and Information Systems Human Resources Management
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Centrality
Job hop
Network analysis
Talent flow
Databases and Information Systems
Human Resources Management
spellingShingle Centrality
Job hop
Network analysis
Talent flow
Databases and Information Systems
Human Resources Management
OENTARYO, Richard J.
LIM, Ee-peng
ASHOK, Xavier Jayaraj Siddarth
PRASETYO, Philips Kokoh
Talent flow analytics in online professional network
description Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data-driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this paper, we present a new data analytics framework to analyze the talent flow patterns of close to 1 million working professionals from three different countries/regions using their publicly accessible profiles in an established OPN. As OPN data are originally generated for professional networking applications, our proposed framework repurposes the same data for a different analytics task. Prior to performing job hop analysis, we devise a job title normalization procedure to mitigate the amount of noise in the OPN data. We then devise several metrics to measure the amount of work experience required to take up a job, to determine that the duration of a job’s existence (also known as the job age), and the correlation between the above metric and propensity of hopping. We also study how job hop behavior is related to job promotion/demotion. Lastly, we perform connectivity analysis at job and organization levels to derive insights on talent flow as well as job and organizational competitiveness.
format text
author OENTARYO, Richard J.
LIM, Ee-peng
ASHOK, Xavier Jayaraj Siddarth
PRASETYO, Philips Kokoh
author_facet OENTARYO, Richard J.
LIM, Ee-peng
ASHOK, Xavier Jayaraj Siddarth
PRASETYO, Philips Kokoh
author_sort OENTARYO, Richard J.
title Talent flow analytics in online professional network
title_short Talent flow analytics in online professional network
title_full Talent flow analytics in online professional network
title_fullStr Talent flow analytics in online professional network
title_full_unstemmed Talent flow analytics in online professional network
title_sort talent flow analytics in online professional network
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4384
https://ink.library.smu.edu.sg/context/sis_research/article/5387/viewcontent/Oentaryo2018_Article_TalentFlowAnalyticsInOnlinePro.pdf
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