This article is part of an ongoing series looking at knowledge management (KM) in the building and construction industries.
New research1 from Western Sydney University looks at current trends and future research directions related to the use of social network analysis (SNA) for knowledge management (KM) in the construction industry.
Social network analysis (SNA) is a tool for rigorously analysing the relationships between actors in a network.
A systematic review and thematic analysis were used to critically review existing studies. The study findings include the identification of various SNA-related concepts and methodologies that can be used by practitioners to improve KM in the construction industry, under the themes below. Knowledge gaps and future research directions were also identified.
1. Knowledge brokering theme
Knowledge brokering is an emerging concept in KM research using SNA. A knowledge broker is an intermediary (organisation or person) that links knowledge or knowledge sources to others within a network.
The Knowledge Network Analysis (KNA)3 technique facilitates the identification of knowledge sharing barriers in knowledge networks. KNA is an extension to generic SNA to incorporate properties such as knowledge velocity (speed of movement of knowledge) and viscosity (richness of the knowledge transferred).
Knowledge brokering is further highlighted in the Bosua–Scheepers Model (BSM)5, a knowledge sharing model that embodies similar ideas to the KSEM. The BSM integrates formal and informal social networks for knowledge sharing. Research in the development of the BSM revealed that a lack of facilitating mechanisms could lead to a multitude of knowledge-sharing problems, and highlighted the importance of knowledge brokers to avoid delays in passing appropriate information from knowledge experts to knowledge seekers.
2. Knowledge mapping theme
Some studies have looked at possibilities of introducing knowledge mapping. The K-Mapping Model6 includes criteria to identify a suitable knowledge map considering various characteristics and conditions related to personnel, processes, and knowledge transfer technologies used by organisations. Four types of K-Mapping Model have been developed, based on the characteristics and conditions of construction personnel, construction processes, and knowledge transfer technologies.
SNA has been integrated with Analytic Hierarchy Process (AHP)7 to develop interval measures for knowledge mapping purposes and determine the strength of relationships between actors. However, scalability might be an issue when using this approach for large social networks.
3. Social sustainability theme
Some research has examined sustainability concepts. The Social Sustainability Health Check (SSHC) model8 is a dynamic model that considers sustainability and equity theories to evaluate the contribution of construction projects in a social context. It checks how a project performs and satisfies the needs of the stakeholders. SNA is used in this model to understand and map the complex patterns of stakeholder positions and their relationships with each other.
A social sustainability conceptual framework9 analyses the working relationships of stakeholders, facilitating the better embedding of social sustainability aspects. The framework has been developed based on project-based organisations, but could be generalised for a broader context related to construction management.
4. General theme
A social network model for construction10 emphasises team development and knowledge exchange to produce construction projects with high performance. This model includes both mechanics (knowledge exchange) and dynamics (social collaboration within the project team to motivate exchange).
Similarly, SNA integrated with Building Information Modelling (BIM) and big data14 can improve project management through social data integration.
5. Knowledge gaps and future research directions
Gaps and future research directions in regard to social network analysis (SNA) for knowledge management (KM) in the construction industry are shown in Figure 1.
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