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This study introduces a novel network analysis method to understand organizational dynamics. It reveals how employee relationships and motivations shape organizational roles and identifies key leaders through multilayer network patterns.

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Area of Science:

  • Organizational behavior
  • Network science
  • Sociology

Background:

  • Understanding organizational structures is crucial for effective management.
  • Traditional methods often overlook the complex interplay of relationships and communication.
  • Network analysis offers a powerful lens to examine these dynamics.

Purpose of the Study:

  • To develop a multidimensional organizational network model.
  • To analyze relationships, motivations, and perceptions within organizations.
  • To identify typical employee roles and key personnel.

Main Methods:

  • Designed a multidimensional organizational network.
  • Applied association rule mining to edge labels.
  • Utilized frequent itemset-based similarity analysis for node characterization.
  • Conducted a survey across three companies to define 15 network layers.

Main Results:

  • Revealed how relationships, motivations, and perceptions interdetermine across various organizational scopes.
  • Characterized typical organizational roles and co-worker clusters.
  • Demonstrated the method's applicability in real-world company settings.
  • Identified overlapping edges in the multilayer network to highlight leader capabilities and key persons.

Conclusions:

  • Organizational members can be evaluated as frequent multidimensional patterns within multilayer networks.
  • The proposed network analysis effectively highlights leader motivations and managerial skills.
  • This approach aids in identifying key individuals based on perceived similarities.