Vishal Singh

Integrated construction supply chain design and delivery solutions

This study describes the development of a decision framework to support multi-disciplinary information and knowledge management model which focuses on integrated design and delivery solutions for all construction supply chain actors. The framework was developed within the context of two national information technology research projects in Australia. The first study used diffusion theory to explain the barriers and enablers to future adoption of advanced information technology solutions such as building information modelling (BIM). A grounded theory methodology was deployed and a pathways model for innovative information technology diffusion accommodating diverse patterns of adoption and different levels of expertize was developed. The second study built on the findings of the first study but specifically focussed on innovators, early and late adopters of BIM and the development of a decision framework towards advanced collaborative platform solutions. This study summarizes the empirical results of the previous studies. The core of the decision framework is the creation, use and ownership of building information sub-models and integrated models. The decision framework relies on holistic collaborative design management. Design expertise is diffused and can be found in various locations along the construction supply chain within project teams. A wide definition of design is considered from conceptual to developed to detailed design. The recent development to the decision model offers much potential as the early upstream decisions are often made in a creative, collaborative and uncertain environment. However, decision making needs to balance both a reductionist and exploratory creative empowerment approach. Shared team expertise and competency and team mental models are explored as a fundamental requirement to collaborative BIM. New skills in interdisciplinarity are discussed as an implication of future construction industry collaborative platforms.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.