A SOCIAL NETWORK ANALYSIS ON THE SHARING OF ACCIDENT REPORTS IN A CONSTRUCTION SITE

Abstract

Social Network Analysis (SNA) has been a popular tool to understand how people communicate in their network, and the effectiveness of such network. This study employs SNA to find out the relationship between workers’ social network and their ability to remember accident bulletin. The specific objectives of this dissertation are: (1) to identify the effectiveness of different communication channels and media used by the construction industry to share reports of accidents; (2) find out the relationship of workers’ memory to their social network and; (3) identify the communication-related roles of employees. It is hypothesized that (a) the longer workers work in the construction site, the better their memory of accident reports and; (b) the more workers interact with one another via informal discussion, the more they will remember accidents reports.

An egocentric data collection is adopted where each actor in a 36 people work crew was asked to record with whom they communicated reports of accidents and complete a memory test. Through qualitative data analysis, it was found that the safety officer was the star of the network, as he has knowledge, while the safety supervisor is the “gatekeeper” of the network as he controls what information should be shared.

It was also found through quantitative data analysis that SNA that there is a relationship in the cluster workers belongs to, their memory test score; and a positive relationship between the experiences of the worker to the memory test score. Moreover, it was found that the more connected a worker is to their co-workers, the higher their memory test score.

However, this study only looked at social network and experiences as factors that affected workers memory. There could be other factors not looked into in this study, like language ability and cultural diversity. Furthermore, work crews could only be selected if everyone participated; if not the work crews cannot be studied. From the data analysis, recommendations were given to improve the social network in construction site, and improve the way accident cases are shared.