Analysing Secondary Data to Understand the Socio-Technical Complexities of Construction-Design Decision-Making


Researchers in construction have identified considerable benefits of integrating construction expertise and knowledge into early project decision-making (Lingard et al., 2014; Song et al., 2009). Improved constructability and health and safety (H&S) have been highlighted frequently among other benefits. Early-stage collaboration and effective interaction within and between design and construction participants are vital to making construction process knowledge accessible to design decision-makers. Nevertheless, collaboration and effective interaction still seem to be a problem in practice and, in many cases, efforts to promote collaborative interactions fail to address the complex and dynamic nature of the design process.

Exploring the interactions between project participants while making decisions helps to understand the way in which construction knowledge is used during the design process. This understanding reveals the particular features of the interactions which support collaborative decision-making, leading to improved constructability and H&.S outcomes. The patterns of interactions and information exchanges between project participants can be conceptualised as social networks. According to Pryke and Smyth (2006), through these networks, individuals involved in different project functions (e.g. planning, design, construction) exchange information, co-ordinate their tasks and establish a sense of mutual understanding about terminology, values, and priorities. Austin et al. (2007) suggested that collaborative design needs an easy flow of information between participants outside the rigid structures imposed by contractual arrangements. Participants’ engagement in informal interactions creates the flexibility needed to adjust knowledge transactions to the particular needs of specific design decisions. Consequently, better informed decisions can be made.

For the study provided in this chapter, a social network perspective on design decision-making was adopted. The study drew upon data from a previous research project reported by Lingard et al. (2014). A comprehensive dataset collected during the previous research project was re-analysed. The dataset included 23 case studies, each focused on the design process of a construction project element, e.g. steel structure. The original research explored the overall interactions and information exchanges during the design of these elements. The aim was to

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understand what characteristics of the interactions were linked to positive H&S outcomes. However, the nature of this link was not explored in detail, because the analysis took a static view of interactions by aggregating the interactions over the whole design process and using descriptive network measures to understand patterns of interaction.

In contrast, the new study built on this previous research and extended it by providing detailed evidence, linking specific interaction features to decision outcomes. The aim of the new study was to unpack the mechanisms by which positive influence on decision outcomes occurs through intra-team communication in construction projects. To achieve this, it was necessary to examine systematically each decision circumstance, the knowledge inputs, and the way in which solutions were devised, to understand the effects on constructability and H&S outcomes.

Consequently, a new approach was used to enable a more refined analysis of the data. For the new study the qualitative and quantitative data were combined and a multi-level perspective was adopted to investigate the interdependence between social interactions and decision outcomes at different levels. The multi-level conceptualisation made it possible to capture and recognise three types of interdependence in the design process as follows (illustrated in Figure 16.1):

  • • At the macro-level, design decisions and the interdependence between them form a technical network.
  • • At the micro-level, design participants and the information exchanges between them create a social network.
  • • At the meso-level, participants’ involvement in (and influence on) design decisions forms a two-mode, socio-technical network.

Furthermore, interaction patterns were studied at each decision point to identify changes during the decision-making process. Thus, by combining multi-level and longitudinal perspectives during the new study, it was possible to capture and understand the complexities and dynamics of building-design decision-making

and the social interactions underpinning it. During the secondary analysis of the data, a statistical network technique was used in combination with qualitative analytical techniques. This made it possible to investigate the “building blocks” of social interaction networks. This understanding helped to explain how and why social interactions influence decision outcomes.

When using secondary data, it is important to ensure that the existing data is suitable for answering the new research questions. In the new study, this was achieved by: (1) ensuring an alignment between the objectives of the new study and the original study, for which the dataset had been collected; (2) using a case study approach in the new study similar to the original study; (3) applying case selection criteria in the new study that were similar to the original study; (4) considering the data types and data collection methods in the original study when developing the research design for the new study; and (5) ensuring high familiarity of the researchers with the original study and dataset as a result of their involvement in the original study. These considerations have been explained further in the section about research approach.

Secondary analysis can be conducted using a sub-set of the original dataset. This requires access to large and rich datasets as well as a clear set of selection criteria. In the new study, two additional case selection criteria were established (see the section on research approach) and applied to select a sub-set of six cases from the original dataset of 23 cases. The selected six cases were deemed to be most applicable to answering the research question in the new study. These criteria were used further to ensure the relevance and richness of data for each case and the context variability of the cases in the new study. This, in turn, contributed to the external validity and generalisability of the study. Figure 16.2 illustrates the case selection process and secondary analysis in the new study.

Case selection process and secondary analysis in the new study

Figure 16.2 Case selection process and secondary analysis in the new study.

In the following sections, the complexities of construction-design decision-making are explained, followed by a description of the existing data and the approach used for its re-analysis. Then, a case study is presented illustrating the application of the analysis together with a discussion of the results and their implications.

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