Talent Flow Analytics in Online Professional Network
Abstract 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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-08-01
|
| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41019-018-0070-8 |
| _version_ | 1819179594695049216 |
|---|---|
| author | Richard J. Oentaryo Ee-Peng Lim Xavier Jayaraj Siddarth Ashok Philips Kokoh Prasetyo Koon Han Ong Zi Quan Lau |
| author_facet | Richard J. Oentaryo Ee-Peng Lim Xavier Jayaraj Siddarth Ashok Philips Kokoh Prasetyo Koon Han Ong Zi Quan Lau |
| author_sort | Richard J. Oentaryo |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-22T22:00:56Z |
| format | Article |
| id | doaj.art-2a9970cb8dff44108f90f4d9139e5efc |
| institution | Directory Open Access Journal |
| issn | 2364-1185 2364-1541 |
| language | English |
| last_indexed | 2024-12-22T22:00:56Z |
| publishDate | 2018-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj.art-2a9970cb8dff44108f90f4d9139e5efc2022-12-21T18:11:07ZengSpringerOpenData Science and Engineering2364-11852364-15412018-08-013319922010.1007/s41019-018-0070-8Talent Flow Analytics in Online Professional NetworkRichard J. Oentaryo0Ee-Peng Lim1Xavier Jayaraj Siddarth Ashok2Philips Kokoh Prasetyo3Koon Han Ong4Zi Quan Lau5McLaren Applied TechnologiesLiving Analytics Research Centre, Singapore Management UniversityLiving Analytics Research Centre, Singapore Management UniversityLiving Analytics Research Centre, Singapore Management UniversitySingapore Management UniversitySingapore Management UniversityAbstract 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.http://link.springer.com/article/10.1007/s41019-018-0070-8Talent flowJob hopNetwork analysisCentrality |
| spellingShingle | Richard J. Oentaryo Ee-Peng Lim Xavier Jayaraj Siddarth Ashok Philips Kokoh Prasetyo Koon Han Ong Zi Quan Lau Talent Flow Analytics in Online Professional Network Data Science and Engineering Talent flow Job hop Network analysis Centrality |
| title | 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_short | Talent Flow Analytics in Online Professional Network |
| title_sort | talent flow analytics in online professional network |
| topic | Talent flow Job hop Network analysis Centrality |
| url | http://link.springer.com/article/10.1007/s41019-018-0070-8 |
| work_keys_str_mv | AT richardjoentaryo talentflowanalyticsinonlineprofessionalnetwork AT eepenglim talentflowanalyticsinonlineprofessionalnetwork AT xavierjayarajsiddarthashok talentflowanalyticsinonlineprofessionalnetwork AT philipskokohprasetyo talentflowanalyticsinonlineprofessionalnetwork AT koonhanong talentflowanalyticsinonlineprofessionalnetwork AT ziquanlau talentflowanalyticsinonlineprofessionalnetwork |