Identifying potential managerial personnel using pagerank and social network analysis: The case study of a European IT company

Jan Y.K. Chan, Zhihao Wang, Yunbo Xie, Carlos A. Meisel, Jose D. Meisel, Paula Solano, Heidy Murillo

Research output: Contribution to journalArticlepeer-review

Abstract

Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team.

Original languageEnglish
Article number6985
JournalApplied Sciences (Switzerland)
Volume11
Issue number15
DOIs
StatePublished - 1 Aug 2021

Keywords

  • E-HRM
  • Graph convolutional network
  • PageRank
  • Performance appraisal
  • Social network analysis

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