Detecting Informal Organization Through Data Mining Techniques

TL;DR

This paper presents a data mining-based method to identify informal organizations within companies by analyzing employee data and social characteristics, aiding management in organizational development.

Contribution

It introduces a novel approach combining factor analysis, clustering, decision trees, and association rules to recognize informal groups in organizations.

Findings

Effective identification of informal organizations using data mining techniques.

A comprehensive model for recognizing employee social and work-related characteristics.

Potential for improved organizational management and development strategies.

Abstract

One of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.