Mining organizational networks for layoff prediction model construction

Huo-Tsan Chang, Hui Ju Wu, I. Hsien Ting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
Pages411-416
Number of pages6
DOIs
Publication statusPublished - 2009 Oct 15
Event2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 - Athens, Greece
Duration: 2009 Jul 202009 Jul 22

Publication series

NameProceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009

Other

Other2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
CountryGreece
CityAthens
Period09-07-2009-07-22

Fingerprint

employee
Personnel
social data
layoffs
recession
Data mining
Taiwan
social network
Managers
manager
firm
Economics
industry
present
management
resources
economics
Industry

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Social Sciences(all)

Cite this

Chang, H-T., Wu, H. J., & Ting, I. H. (2009). Mining organizational networks for layoff prediction model construction. In Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 (pp. 411-416). (Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009). https://doi.org/10.1109/ASONAM.2009.52
Chang, Huo-Tsan ; Wu, Hui Ju ; Ting, I. Hsien. / Mining organizational networks for layoff prediction model construction. Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009. 2009. pp. 411-416 (Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009).
@inproceedings{70da3a114ae5487bb90ddb540cd32f8e,
title = "Mining organizational networks for layoff prediction model construction",
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.",
author = "Huo-Tsan Chang and Wu, {Hui Ju} and Ting, {I. Hsien}",
year = "2009",
month = "10",
day = "15",
doi = "10.1109/ASONAM.2009.52",
language = "English",
isbn = "9780769536897",
series = "Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009",
pages = "411--416",
booktitle = "Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009",

}

Chang, H-T, Wu, HJ & Ting, IH 2009, Mining organizational networks for layoff prediction model construction. in Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009. Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009, pp. 411-416, 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009, Athens, Greece, 09-07-20. https://doi.org/10.1109/ASONAM.2009.52

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 proceedingConference 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 -

Chang H-T, Wu HJ, Ting IH. Mining organizational networks for layoff prediction model construction. In 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). https://doi.org/10.1109/ASONAM.2009.52