The nodes in the network are employees and the links between them operationalize the transfer of knowledge. The latter is defined by »advice seeking« and »learning from«. Empirical studies show that the relationships in advice (Carley & Krackhardt, 1996) and learning (Škerlavaj, Dimovski, & Desouza, 2010) networks tend to be asymmetric or non-reciprocated.
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“Advice seeking” and “learning from” are the most often used operationalizations of the flow of knowledge in empirical research, yet measuring different kinds of knowledge (like meta- knowledge, problem reformulation, validation and legitimation) increases the validity of the measurement since different kinds of knowledge can lead to very different global network structures (Cross, Borgatti, & Parker, 2001). The distinction must also be made between advice- giving and advice-receiving because they might differently affect the construct of interest. For example, both advice-giving and advice-receiving are related to job involvement, while only advice-receiving is positively related to commitment to the working group (Zagenczyk & Murrell, 2009).
Many studies have looked at how centrality in the network affects knowledge transfer and performance. In general, a more central individual has a relatively large number of links compared to others in the network, giving them an opportunity to obtain resources from many others. This makes them less dependent on any particular individual (Cook & Emerson, 1978). Sparrowe et al. (2001) showed that those leaders with a higher level of in-degree centrality (i.e. are more popular) estimate their performance higher than leaders with a lower level of in-degree centrality. On the other hand, leaders from groups with higher group centralization (degree centrality was used (Freeman, 1978; Wasserman & Faust, 1994)) estimate the performance of the group as lower. Wong (2008) thought this might relate to the variety of knowledge, which is lower in more centralized groups. She justified the hypothesis by stating:
Internal advice network centralization, in particular, can foster inequality of member influence when there are increasingly a few group members who are the objects of advice-seeking from other members. /…/ Thus, in a highly centralized internal advice network, there are a few individuals who are most central in providing task knowledge. As such, we can expect their knowledge to become increasingly valued relative to others and they become increasingly influential in decision making (e.g., Bottger (1984), Wittenbaum (1998)). This inequality in influence can lead to increasing deference to the knowledge of more central members (Kirchler & Davis (1986)). When this happens, the opportunity to create new understandings through integrating different viewpoints can be reduced as members become less likely to contradict the perspectives of more central members. In addition, as the knowledge of more central members becomes more valued relative to others, there is the risk of convergence on these ‘valued’ knowledge domains and less emphasis on developing other knowledge domains, thus decreasing knowledge variety over time.
A more limited variety of knowledge is not the only long-term negative outcome of highly centralized groups. Although being central in the advice-giving network can provide a central individual with greater prestige, they can become overloaded by requests for advice from others.
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In order to avoid such an overload, the most central individual starts referring the advice-seekers to others in the network. This requires fresh coordination between the most popular ones in order to avoid status competition or conflicts (Lazega, Lemercier, & Mounier, 2006). In addition, maintaining a high number of social ties can lead to lower level of well-being (Rook, 1984), and can become so demanding that work performance is lowered (Burt & Ronchi, 1990; Mehra et al., 2001).
A global network structure can arise as a result of local network processes which may include policies of the company. Studies show that global network structures, when not influenced by company policies, depend on the difficulty of the tasks at hand. Brown and Miller (2000) observed that groups working on more complex problems tend to develop less central communication patterns, while groups working on less complex tasks tend to develop more centralized communication patterns. This may be related to the more efficient knowledge transfer that occurs in less centralized networks.
Sharing knowledge outside the group is especially important when groups are structurally more diverse since members can benefit from different, unique sources of knowledge outside their group (Cummings, 2004). However, group heterogeneity can also bring certain cost. For example, members of different business units can find it difficult to transfer knowledge (Szulanski, 1996). Yet, Cummings (2004) found no difference in group performance between a structurally homogenous and a structurally heterogenous group. A factor more significantly affecting group performance was the extent to which the individuals within a group shared their knowledge. Establishing links between different groups is associated with structural holes or bridging nodes or groups (Burt, 2009). They are important when it is assumed that different internally highly linked groups of nodes possess different types of knowledge. It is expected that bridging nodes or groups of nodes enable knowledge to be transferred among different groups of nodes. Studies show this depends largely on the knowledge complexity. When knowledge is simple, the presence of a bridge is a necessary and sufficient condition for knowledge transfer, yet more complex knowledge is more likely to transfer (across bridging nodes or groups) when the individuals who bridge either have a strong tie across to both groups or have a diverse network (Reagans & McEvily, 2003). In short, more complex knowledge is more likely to stay embedded in local communities of practice (Reagans & McEvily, 2003). In terms of group productivity – the most productive teams are
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internally well connected and have external networks full of structural holes, which connect these teams with external groups (Reagans, Zuckerman, & McEvily, 2004). It is hypothesized that the bridging nodes or groups have a greater absorptive capacity29 than the others (Reagans & McEvily,
2003).