This document summarizes a study on social network analysis in organizations. The study analyzed work networks at a large IT service organization using data from 108,050 support tickets over 10 months. Four features were considered for each of the 315 sysadmins: work shift, department, customer, and problem severity. Networks were built connecting sysadmins based on similarity across these features. The results showed distinct networks emerged for each feature combination, such as separate networks for sysadmins based on similarity in service lines vs shift or severity. The document concludes that network analysis is a promising approach to uncover relationships in complex work environments and more research is needed on methods for very large multi-edge networks.
sysadmin that work in same work shift tend to be more similar than the ones that work in different work shift connection among sysadmins during the work is higher to sysadmins that work in same department in same work shift, no matter the costumer or the tickets’ severity
sysadmin that solves same type of ticket tend to be similar to each other
most of tickets with severity one occurs in work shift 3 - night most of the tickets with severity three (Figure 3 (c) red) occurs in work shift 1 – morning