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.
Highlights
In behavioral theories of leadership theory [1], research findings [2] show that leadership characteristics are translated into behavioral patterns within social networks
Apart from those temporal and textual node features, we propose to derive new features by: (1) PageRank to assess the importance of an employee within the corporate social network; (2) word2vec to generate word embeddings to assess the underlying meanings in phrasal values; (3) graph convolutional network to aggregate information from the adjacency nodes in the corporate social network
We propose a method that combines social network analysis and machine learning techniques to help decision makers estimate the managerial potential of the members in an organization
Summary
In behavioral theories of leadership theory [1], research findings [2] show that leadership characteristics are translated into behavioral patterns within social networks. Use social network analysis to identify instructional teacher leaders. In [12], Hollenbeck and Jamieson explain phenomena and outcomes related to human capital, such as recruiting and onboarding, teamwork and communication, knowledge management, and employee satisfaction, which are all dependent on social capital and the relational networks that exist among employees They present how SNA can be applied to both research and practice, to develop new ways of thinking about human capital, social capital, and the important interaction between the two. Understand the social perspective of organizational sustainability and the roles of electronic performance appraisal and transformational leadership in shaping it Social network analysis: a simple but powerful tool for identifying teacher leaders [5].
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