Abstract
Global economic recession has been causing the unpaid leave and massive layoffs in major high-tech firms of Taiwan, both factors present great potential hardship to many employees according to the reports from industry. Therefore, layoff prediction and management have become great concerns of employees and managers. Employees wish to retain their jobs and keep their work for a long time. Hence, they need to predict the possible layoff and then utilize their resources to retain their job. In response to the difficulty of layoff prediction, this study applies social networks and data mining techniques to build a model for layoff prediction. This study compares various techniques to propose a better approach to generate a possible layoff list for employees. Through an empirical study, the results indicate that the proposed approach has pretty good prediction accuracy by using organizational networks, employee databases and layoff records to build the layoff prediction model.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 |
| Pages | 411-416 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2009 Oct 15 |
| Event | 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 - Athens, Greece Duration: 2009 Jul 20 → 2009 Jul 22 |
Publication series
| Name | Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 |
|---|
Other
| Other | 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 09-07-20 → 09-07-22 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications
- Software
- Social Sciences(all)
Cite this
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Mining organizational networks for layoff prediction model construction. / Chang, Huo-Tsan; Wu, Hui Ju; Ting, I. Hsien.
Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009. 2009. p. 411-416 (Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Mining organizational networks for layoff prediction model construction
AU - Chang, Huo-Tsan
AU - Wu, Hui Ju
AU - Ting, I. Hsien
PY - 2009/10/15
Y1 - 2009/10/15
N2 - Global economic recession has been causing the unpaid leave and massive layoffs in major high-tech firms of Taiwan, both factors present great potential hardship to many employees according to the reports from industry. Therefore, layoff prediction and management have become great concerns of employees and managers. Employees wish to retain their jobs and keep their work for a long time. Hence, they need to predict the possible layoff and then utilize their resources to retain their job. In response to the difficulty of layoff prediction, this study applies social networks and data mining techniques to build a model for layoff prediction. This study compares various techniques to propose a better approach to generate a possible layoff list for employees. Through an empirical study, the results indicate that the proposed approach has pretty good prediction accuracy by using organizational networks, employee databases and layoff records to build the layoff prediction model.
AB - Global economic recession has been causing the unpaid leave and massive layoffs in major high-tech firms of Taiwan, both factors present great potential hardship to many employees according to the reports from industry. Therefore, layoff prediction and management have become great concerns of employees and managers. Employees wish to retain their jobs and keep their work for a long time. Hence, they need to predict the possible layoff and then utilize their resources to retain their job. In response to the difficulty of layoff prediction, this study applies social networks and data mining techniques to build a model for layoff prediction. This study compares various techniques to propose a better approach to generate a possible layoff list for employees. Through an empirical study, the results indicate that the proposed approach has pretty good prediction accuracy by using organizational networks, employee databases and layoff records to build the layoff prediction model.
UR - http://www.scopus.com/inward/record.url?scp=70349808042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349808042&partnerID=8YFLogxK
U2 - 10.1109/ASONAM.2009.52
DO - 10.1109/ASONAM.2009.52
M3 - Conference contribution
AN - SCOPUS:70349808042
SN - 9780769536897
T3 - Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
SP - 411
EP - 416
BT - Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
ER -