Following are examples of actor attributes found in social network and knowledge transfer combined literature. The general actor attributes identified and described in section 3.4.2 seem to also apply to the social network perspective of KT and were thus confirmed: nationality, gender, age and hierarchy. Moreover the importance of access to the knowledge source and the transactive memory, in terms of knowing who knows what were also confirmed through SN literature.
The cooperative norms also play an important role in knowledge transfer, as the sender of knowledge assumes that, if they share knowledge with somebody now, someone else will be willing to do the same for them in the future (Reagans, McEvily, 2003). For that reason the sender’s reputation within the network seems to be vital. Otherwise news of uncooperative behaviour will spread through the network rapidly and limit their ability to interact with others in the future (Reagans, McEvily, 2003). This approach is similar to the one argued by Wilkesmann et al. (2009), presented in section 3.4.1.3
The competitive advantage is no longer based on how much you own, it is about how much you know and how you use it (Ruddy, 2000). Nahapiet and Ghoshal (1998) state that team work and cooperation play a significant role, rather than competition. The group identification may not only increase the perceived
opportunities for exchange, but also enhance the actual frequency of cooperation (Nahapiet, Ghoshal, 1998). A competitor on one project may become a partner on another (Seufert et al., 1999). Nonetheless, if strong ties are absent, particularly in alliances between competitors, partners may not develop the necessary relationships for deliberate KT (Inkpen, Tsang, 2005).
The actor centrality also seems to influence KT. Wasserman and Faust (2009) describe central actors as the most active ones in the network, as they have the most ties to other actors. Hence these central actors should be recognized by other actors as a major channel for information (ibid). There are four different centrality values to consider. Degree centrality shows the average degree of which relations are focused around one or a few central network members. The degree centrality measures are divided into in-degree and out-degree. The in-degree of an actor is the total number of other nodes which have ties towards it, while the out-degree is the total number of other nodes to which it directs ties (Scott, 2003). Actors with more ties are in advantage positions, as they have alternative ways to access resources (Hanneman, Riddle, 2005). Closeness centrality describes the integration or isolation of network members. Hence it is based on the sum of the distances from each actor to all the others. Hanneman and Riddle (2005) state that closeness centrality can be misleading in larger and more complex networks. Therefore they recommend the Eigenvector centrality to identify the most central actors in terms of the overall network structure. In addition betweenness centrality identifies the so-called broker or gatekeeper. This concept was defined by Freeman (1979; in Scott, 2000). The overall betweenness of an actor measures the extent to which an actor lies between other actors in the social network. Scott (2000) put forward that the betweenness centrality is probably the most complex calculation for actor centrality. The concept bases on dependency, as other actors depend on the so-called broker or gatekeeper to transfer knowledge. Thus the concept of betweenness centrality is similar to the one of structural holes by Burt (1992; in Scott, 2000), as the actors on opposite sides of a structural hole could also be called gatekeepers.
Furthermore Müller-Prothmann (2007) defines four different actor roles as crucial for KT, when analysing SNA data. Hence it is important to identify these actors within each network:
• Experts, possessing specific knowledge and experience on the subject area with a central position and a high number of external links.
• Gatekeepers know ‘who knows what’ and build bridges between different subgroups and additionally transfer requested expert knowledge. They are identified through the betweenness centrality as mentioned above.
• Knowledge consumers ask for knowledge and have a rather peripheral network position.
• Contact persons, who provide contacts with experts without actively transferring the knowledge themselves. They have an intermediate position between the experts and the knowledge consumers. These are difficult to detect in this research project due to the nature of questions in the survey. Respondents did not indicate whether someone provided them with a contact to a knowledge source, or the actual required knowledge.
4.5. Conclusion
This chapter has shown how to approach KT from a social network perspective. This was done by reviewing and discussing literature on social networks combined with KT. Social capital theory was debated as an alternative approach towards networks. However, in the end SN theory was chosen for this study, due to the more appropriate methods and the wider perspective of networks as a resource for more than economic benefit. As a result it can be assumed that social networks offer a possibility to enhance KT on how to build sustainably. Moreover they provide the means to map the knowledge flow inside a construction project team for a better analysis of the current KT practices. This then allows making recommendations on how to enhance the KT on building sustainably.
Various social network models and concepts combined with KT were presented and discussed in terms of their applicability to the problem statement. The concepts drew attention to numerous factors which influence KT. The chapter concluded by identifying these influencing social network characteristics, in line with the second research objective presented in Chapter 1. These characteristics were categorised into four groups. This eased the development of the conceptual framework of this study, in line with the third research objective. The conceptual framework will be presented in the following chapter.
The four categories of KT influencing social network characteristics were determined as:
• Network Structure (e.g. Density, Connectivity, Hierarchy, Structural Holes) • Tie characteristics (e.g. Strength, Weakness)
• Actor Attributes (e.g. Centrality) • Tie content