INFLUENCES OF SOCIAL CAPITAL ON KNOWLEDGE CREATION

Transcription

1 Working Papers in Real Estate and INFLUENCES OF SOCIAL CAPITAL ON KNOWLEDGE CREATION Dr Esra Kurul Department of Real Estate &, Oxford Brookes University Dr Andrea Colantonio Department of Real Estate &, Oxford Brookes University Dr Noriko Otsuka Department of Real Estate &, Oxford Brookes University REF: 2006/No2 OXFORD INSTITUTE FOR SUSTAINABLE DEVELOPMENT INTERNATIONAL LAND MARKETS RESEARCH GROUP (OISD-ILM)

2 Abstract This paper is a contribution towards closing the gap in the literature on the influence of social capital on project-level knowledge processes (e.g. knowledge creation and sharing). Its primary aim is to identify how the structural dimension of social capital, i.e. social networks, influences knowledge creation capability of integrated project teams in the construction industry. First, social networks of teams are analysed. Then, knowledge creation capacity inherent in these networks is evaluated. This evaluation is based on knowledge/information flows within teams as an important condition of knowledge creation. The paper concludes with the identification and verification of potential improvement areas in terms of the teams knowledge creation capability and absorptive capacity; and their utilisation of social networks

3 1. Introduction Knowledge creation is viewed as an important source of competitive advantage. Even so, much of the existing research on knowledge management (KM) and its application by different industrial sectors focuses on effectively capitalizing on an organisation s knowledge assets. Approaching knowledge and its management from an organisational asset capitalisation perspective poses four main problems: 1) It overlooks the dynamic knowledge processes that include, among others, knowledge creation and sharing. This suggests that there is a gap in the deployment of KM that is potentially problematic; 2) The asset capitalization approach focuses on the organisation as its unit of analysis, and thus inevitably presumes that knowledge processes that take place at the individual and project levels lead to, for example, knowledge creation at the organisational level. This position neglects the processes that are necessary to integrate, for example, knowledge creation at the project level into organisational learning; 3) The particular complexities of managing knowledge at the project level are not attended to; 4) Last, but not least, while the asset capitalisation approach pays due cognisance to the possession and re-use of knowledge, it does not properly recognise the capability of continuously sourcing, combining, developing and applying knowledge, which is the main source of competitive advantage (Roth, 2003; Lang, 2001; Lank, 1997). This paper is based on the findings of a research project which aspires to address these problems by exploring the influence of social capital on project-level knowledge processes (e.g. knowledge creation and sharing). Its primary aim is to identify how the structural dimension of social capital, i.e. social networks, influences knowledge creation capability of integrated project teams in the construction industry. Creation of new knowledge is defined as a series of transformations, by which standard resources, which are available in open markets [or contained within the project teams], are used and combined within the organisational context in order to produce [competences and] capabilities (Ciborra and Andreu, 2001: 74). Competences and capabilities (unlike resources) are unique to each organisation, and so are the sources of competitive advantage (Grant, 2000). Nahapiet and Ghoshal (1998) argue that social capital facilitates the development of [knowledge] by affecting the conditions necessary for [knowledge creation] to occur, including opportunity to exchange and combine existing knowledge, recognition of value creation opportunities by individuals, and motivation (Ghoshal and Moran, 1996). Social capital is defined as the set of resources that accrue to an individual or group by virtue of their social connections (Edelman et al., 2004: 59). Hence, this paper adopts not only Bourdieu s (1986) conceptualisation of social capital in that it includes both the social structure itself and the networks and assets that people can access through that structure, but also the resource-based view of social capital in that reference to resources, such as knowledge and information, is made (Grootaert et al., 2004). Nahapiet and Ghoshal (1998) identify three dimensions of social capital: structural, relational and cognitive. Structural dimension is concerned with the network of relations that are embedded in the structure of any social group. It purely relates to the impersonal configuration of linkages between people or units. We term this as the formal structure of project teams. Such structure of project teams in the construction industry is mainly determined by the contractual arrangements that underlie the project practices. In this context, contracts determine the extent of knowledge creation and sharing that takes place within each team and that of knowledge sharing between teams. We argue that informal structures of project teams, as identified in social networks, are important in creating and sharing knowledge. In this context, social aspects of knowledge processes should be emphasised (Fernie et al., 2003; Bresnen et al., 2003; Roth, 2003). These networks transcend the organisational, contractual and project boundaries. Hence, as Bresnen et al. (2003) argue, such boundaries could become barriers to creating informal networks, and limit absorptive capacity by mitigating the development of prior related knowledge. The relational dimension of social capital describes the personal relationships people have developed through social interactions. Relational dimension may or may not relate closely to formal structural dimension because it is influenced by, among others, the strength of connections between people

4 The cognitive dimension refers to those resources providing shared representations, interpretations, and systems of meaning among parties (Nahapiet and Ghoshal, 1998). Hence, the cognitive dimension of social capital provides people with a common language and understanding, in other words some prior related knowledge, which underlies transfer of knowledge from one project to another, and learning from projects (Cohen and Levinthal, 1990) as the basis of knowledge creation. 2. Methodology Among the conditions for knowledge creation (discussed above), we focus in this paper on the structural dimension of social capital, which facilitates knowledge development in organisations as social communities (Nahapiet and Ghoshal, 1998). We use Social Network Analysis (SNA) which was firmly established as a method of structural analysis by the work of White and his associates at Harvard in the early 1970s (Scott, 2000). Our case studies are two intra-organisational project teams which are undertaking projects over 3m value. We chose intra-organisational project teams as our unit of analysis for two main reasons. First, they are the most common units of execution in the construction industry. Second, they have long been regarded as the settings of complex processes of new value creation and innovation (Sayles and Chandler, 1971;Winch, 1998). One is the construction of an office block in the City of London for one of the UK s largest development companies. Two is the refurbishment of a laboratory/office facility for a multi-national pharmaceutical company. In both cases, the client and the main contactor had established working relationships for more than a decade. Interviewees were identified in collaboration with Directors (or his equivalent) in each case. Due consideration was given to ensuring that members of the project team who had the highest potential to bring about innovation were interviewed and that a cross-section of members who were representative of the project team was selected. Twenty and eighteen interviews were conducted for 1 and 2 respectively. In all but one case the interviews were recorded. On completion of the interviews, data was logged onto relational matrices which became the basis of SNA, using InFlow 3.1. Given that the special facility organisations have for the creation and transfer of tacit knowledge drives their knowledge creation capability (Nahapiet and Ghoshal, 1998), we use SNA metrics which enables us to evaluate knowledge and information flows within these networks. Hence, we utilise the following SNA concepts: density, network reach, and centrality (degree, betweeness and closeness). 3. Knowledge Creation Capacity & Social Network Analysis Metrics 3.1. Density Density measures the connectivity of nodes (interviewees) to others in the network. It is a representation of the number of existing connections as a percentage of all theoretically possible connections. A density of 1.0 indicates that all nodes in the network are directly connected to others. However, such completion is very rare (Scott, 2000), neither is it desirable. Krebs and Holley (no date) argue that above the 0.50 (or 50%) density threshold there is risk of communication and/or information overload in the network. Table 1 shows the network densities for each project. 2 has a higher network density than 1. Also, its density, at 0.46, is very close to the 0.50 threshold. Therefore, there is the risk of communication and/or information overload in 2. This condition could have well emanated from the hierarchical decision-making procedure imposed upon the team by the client which requires extensive consultation with all stakeholders involved in the project. The relatively low density of Network 1 could be an indication of the project-role-bound information flows between the main contractor and its consultants, which reduces the need to establish extensive links, for example, between consultants themselves

5 Table 1. Network densities 3.2. Network Reach 1 2 Tied Nodes Potential Ties Actual Ties Density Network reach is a measure of the length of information paths between nodes. Research has shown that shorter paths are more important in terms of knowledge creation, and has proposed that the key paths in networks are 1 and 2 steps, and on rare occasions 3 steps (Burt, 1997, Friedkin, 1983). Horizon of observability indicates the maximum path length, beyond which people are unlikely to be aware of other people s roles and work (Friedkin, 1983). Such awareness is instrumental in knowing who knows what within the team, and thus establishing prior related knowledge which is the basis for knowledge transfer. Friedkin (1983) states that persons who are more than three steps removed are unlikely to be aware of each other s current work. Thus, we argue that the average path length should not exceed 2 for a context to be conducive to knowledge transfer, and that length 1 paths are favourable to length 2 paths in knowledge flows. Table 2. Path Lengths 1 2 Number of % of all Number of % of all paths paths paths paths Path Length % % Path Length % % Path Length % 0 0 Path Length % 0 0 Path Length Table 2 shows the lengths of paths that are present in each network. Network 2 is conducive to knowledge transfer in that the maximum path length is 2. However, 75% of the paths in this network are length 2 paths, leaving room for improving its knowledge transfer capacity by increasing the proportion of length 1 paths. A higher number of length 1 paths means that centrality of certain individual nodes will be increased. Higher centrality would in turn reduce the likelihood of such nodes leaving their organisations (Krebs, 1998). The absence of paths with lengths higher than 2 suggests that Network 2 is a closely-knit network where each node has the opportunity to be aware of every other node s role and work in the network. Length 1 and 2 paths constitute 63% of all the paths in Network 1. Hence, the team members who are connected via these paths are likely to be aware of each other s work, while those connected via the remaining 37% of the paths are unlikely to be aware of each other s work because these paths are beyond the horizon of observability. If the nodes in this latter group are pivotal to the knowledge transfer capability of Network 1, then our suggestion to the team would be to explore ways of reducing the number of such length 3 and 4 paths Centrality The concept of centrality embraces indices which attempt to quantify the prominence of an individual [node] embedded in a network (Wasserman and Faust, 1994:169). It had been much confused until Freeman (1979) elucidated the concept by differentiating between degree, betweenness and closeness centralities (Loosemore, 1998; Mizruchi and Potts, 1998). This differentiation (see Table 3) was instrumental in clarifying the confusion not only between point centrality and the overall centralisation of a network (Scott, 2000) but also on the relationship between centrality and power (Mizruchi and Potts, 1998)

6 Degree centrality is a measure of the direct links that a node has, and thus an indication of the extent to which a node is connected to its immediate environment. According to Freeman (1979), degree centrality denotes a node s ability to communicate directly with others in the network, in other words their communication activity. Loosemore (1998) furthers this theoretical construct by arguing that a node s in-degree centrality indicates his/her popularity in a network and his/her ability to access information, while its out-degree centrality indicates his/her control over a network and of the dependence of the network upon him/her in terms of supply of information and/or knowledge. Tsai (2000) states high [degree] centrality may represent unique advantages in getting access to certain resources or nodes, and facilitates a node s external resource acquisition (Tsai, 1998). Table 3. Differentiating between three centrality metrics Reflects a node s: Degree Centrality ability to communicate directly with others in the network. Some authors suggest that degree centrality is directly proportional to power. Betweenness Centrality potential to control information flow between two other nodes Closeness Centrality Level of independence in terms of accessing knowledge/information sources Table 4 shows the degree centrality results for each node in the two networks that we analysed, as well as their roles and those of their affiliated companies. In both cases, the project managers (PMs) have the highest centrality, which illustrates that they are well-connected to their teams, have high levels of communication activity and they can directly communicate with other nodes in the network. This advantage could become a disadvantage if PMs become the communication hubs in their respective networks, and thus get heavily involved in non-value adding communication. As we will discuss below, betweeness centrality values could be interpreted as a reflection of the proportion of the PMs communication activity that is dedicated to facilitating information/knowledge flow between other nodes in their networks. Top ranking of the PMs centrality scores also indicates that they have a unique advantage in accessing resources or nodes and have the potential to acquire external knowledge. However, the PMs do not make adequate use of this advantage. The PM in 1 has a low level of external knowledge use, which is sourced from web-sites, journals and personal contacts. His need for information triggers his search for external knowledge. The PM in 2 has an average level of external knowledge use, which is sourced from colleagues in his office and web-sites. We can thus argue that the PMs could spread the net more wider than they currently do in terms of accessing external knowledge and thus make more effective use of their advantageous centrality position in the network. There is a clear distinction between the positioning of trade contractors and; the construction management company and their consultants in the centrality ranking of 1. All the trade contractors rank below the average centrality score (0.32), hence they have a limited number of direct links mainly with the construction management company. This reflects a traditional project structure where the contractors opportunity to have an input into strategic project decisions, such as design, is limited due to the absence of their direct links with the consultants. Another interesting aspect of 1 centrality scores is the location of the Document Controller who is the gate-keeper of the document management system (DMS). This position indicates that s/he is not well connected to the rest of the team and provides further evidence on the low-level of uptake of the DMS among project team members who find the system difficult to use. If we follow the construct that there is an association between power and centrality of node, the ranking in 2 suggest delegation of power from the Sponsor 1 to the project team. This situation contrasts with 1 where the himself still enjoys a prominent location in the centrality ranking. 1 We use the terms that the host company uses in its Standard. The Sponsor in this Standard is equivalent to the in a traditional project set up

7 Table 4. Degree Centrality Results 1 2 Degree Company Interviewee s Degree centrality Role Role centrality Company Role Cradle to grave Developer/ Cost Consultant Commercial Director Director Commercial Assistant Associate Cost Consultant Cost Consultant Concrete Works M&E Cost Consultant Surveyor Document Controller Associate Surveyor Logistics Site Structural Steelwork Façade Package Contractors Structural Design (Qual. Cont.) Building Systems Site Co- Technical ordinator M&E Senior Resident Engineer 0.32 Average 0.46 Average Interviewee s Role representative Cradle to grave Assistant Partner/Principal representative M&E Engineering Senior Engineer Environmental H&S Cradle to grave Cost representative Sponsor Steering Group Member European Operations Capital PM Cradle to grave Supervisor Building Services Engineer Maintenance representative Cradle to grave Team Leader H&S Consultants Planning Supervisor Safety Advisor & - 6 -

8 Table 5. 1 in-degree & out degree centrality Company Role Interviewee s Role Company Role Interviewee s Role In-degree Centrality Outdegree Centrality Commercial Director Commercial Document Controller / Developer Director / Commercial Developer Cost Consultants Director Surveyor Assistant Document Controller Cost Consultants Associate Cost Director Commercial Assistant Consultant Cost Associate Cost Technical Coordinator Consultants Consultant Structural Steelwork Site Concrete Works M&E Cost Associate Cost Consultants Surveyor Consultants M&E Surveyor Concrete Works Logistics Site Façade Structural Site Package Steelwork Contractors Structural Senior M&E Cost Associate M&E Design (Qual.Cont.) Resident Engineer Consultants Surveyor Logistics Site Façade Package Contractors Technical Coordinator (Qual. Cont.) Engineer Structural Design Senior Resident Building Building Systems Systems Average: 0.44 Average: 0.44 Table 5 illustrates in rank order the in-degree and out-degree centrality results for all the nodes in 1. The core team members of the construction management company occupy very prominent positions in the ranking, followed by the Developer/. The third ranking of the in out-degree centrality ranking is an exception which indicates the high dependence of the network on the as an information supplier. The rise in the prominence of the Document Controller from tenth on the centrality ranking to the fifth in the out-degree centrality can be interpreted as another indication of the high levels of information dependence in the network. Within this context, resolving the problems around the document management system gains prominence for this project team because it can become a barrier to transferring information and reduce the opportunities to exchange and combine knowledge and information. Both these conditions would negatively influence the team s knowledge creation capability

9 The difference between the Director s (PD) out-degree centrality and his/her in-degree centrality is worthy of noting. It suggests that the PD s ability to access knowledge is high, while the network s dependence on him/her is low. The network is dependent on the Commercial and the who could control the network. This could be interpreted as an indication of the delegation of control within the project team, from the Director to nodes with operational responsibility. Again, there is a clear distinction between the positioning of the trade contractors and; the construction management company and their consultants on Table 5. The network is controlled by and is dependent on consultants, while the contractors simply perform the tasks that are allocated to them. Their low ranking in Table 5 also suggests that their ability to access information is limited, reducing their knowledge creation capability as part of this team. Table 6 shows the in-degree and out-degree results for 2. The PM ranks the highest in both lists which indicates that the network is dependent on him and he can easily access information in the network. In this context, 2 differs from 1 where different nodes ranked high in terms of their dependability and ability to access knowledge and information. The difference between the Assistant PM s out-degree and in-degree centrality results shows that the network is highly dependent on him, but his ability to access information is limited. We therefore suggest that the PM and the Team Leader explore ways of improving the Assistant PM s ability to access information. There are two other issues worthy of discussion in Table 6. First, is the relatively low out-degree centrality of the Sponsor which suggests that s/he does not have a strong control of the network and that the network does not depend on her/him. Ranking on this table suggests that such powers have been delegated to the PM and the consultants in the project team, all of whom occupy prominent locations on this list. The second point to note is the locations of the four Representatives and the European Operations Capital PM, who follow the PM at the top of the in-degree centrality ranking. This illustrates that these nodes can easily access knowledge. When we interpret these scores alongside scores for out-degree centrality, we can argue that the consultants need to access information/knowledge either through the PM, Assistant PM or Representative 1, who have the top three out-degree centrality scores. If such heavy reliance on these three nodes was undesirable, the project structure could be altered such that the consultants can directly access information without relying on brokers

10 Table 6. 2 in-degree & out-degree centrality Company Role Interviewee s role Outdegree centrality Cradle to grave Cradle to grave Assistant Company Role Interviewee s role In-degree centrality Representative Representative 1 Representative 3 Partner/Principal European Operations Capital PM M&E Engineering Senior Engineer Representative 4 Environmental H&S Representative 2 Cradle to grave Cost M&E Engineering Senior Engineer Representative Cradle to grave Assistant Environmental Representative 3 H&S European Operations Partner/Principal Capital s Steering Group Sponsor Member Cradle to grave Team Leader Cradle to grave Cost Sponsor Steering Group Member Cradle to grave Cradle to grave Supervisor Supervisor Building Services Engineer Building Services Engineer Maintenance Maintenance Cradle to grave Team Leader Representative 4 H&S Consultants Planning H&S Consultants Planning Supervisor & Supervisor & Safety Advisor Safety Advisor Laboratory Laboratory Design Specialist Design Specialist adding value to the projects. Table 7 illustrates the betweeness centrality scores for each node in each project team. These scores quantify the extent to which [a node] falls between pairs of other actors on the shortest paths connecting them (Freeman, 1979 quoted in Nohria and Eccles, 1992). They reflect a node s potential to control other nodes by controlling and filtering the information flow between them (Loosemore, 1998). Hence, nodes with high betweenness centralities could play brokerage or gatekeeper roles in a network with a potential for control over the others (Scott, 2000) or restricting the communication of others (Freeman, 1979). Once again, the PMs have the highest betweeness centrality scores. Hence, they can filter and control information flow between other nodes in the network, becoming information/knowledge brokers. The betweeness centrality scores and the significant drop in the scores following the PMs in the rank suggest that the PMs are distinguishable as knowledge brokers from the rest of the team. 29% and 36% of all their communication activity is dedicated to controlling and filtering information/knowledge between other nodes in their networks. Therefore, the nature of - 9 -

11 this brokerage role should be further explored in order to identify whether it is all necessary and/or desired in terms of adding value to the projects. Table 7. Betweenness Centrality Results 1 2 Betweeness Centrality Company Role Interviewee s Position Betweeness Centrality Company Role Interviewee s Position Cradle to grave representative Cradle to grave Cost Director Document Controller representative Commercial Assistant Partner/Principal Commercial M&E Engineering Senior Engineer Developer/ Director Cradle to grave Assistant representative Concrete Steering Group Works Member Cost Environmental Consultants Surveyor H&S Logistics Site Sponsor M&E Cost Associate European Consultants M&E Operations Surveyor Capital s Structural Cradle to grave Senior Design Supervisor Resident (Quality C&B Engineer Control) Façade Package Contractors Structural Steelwork Building Systems Technical Co-ordinator Site Team Leader H&S Consultants Planning Supervisor & Safety Advisor Representative Building Services Maintenance Engineer Closeness centrality scores are given in Table 8 They represent the extent to which an actor can reach a large number of other actors in a small number of steps (Mizruchi and Potts, 1998). Freeman (1979) argues that nodes with high closeness centrality are independent, i.e. they have the ability to reach large numbers of nodes while they rely on a minimum number of intermediaries, can avoid the control of others, and are efficient, i.e. they can reach all other actors in the shortest number of steps (Nohria and Eccles, 1992). The average for scores in Table 8 suggest that the nodes in 1 can, on average, potentially access 55% of the remaining nodes in their network through two or less steps, while the figure for those in 2 is 66%. If we follow the argument that shorter path lengths are conducive to knowledge creation, then both networks have potentially higher knowledge

12 creation capabilities. The distribution of the majority of the scores in Network 2 around the average, i.e , suggests that members of this project team have similar levels of efficiencies and independence from each other. The scores are spread across a much wider spectrum in 1, suggesting a wider range of node efficiency and independence. Among the individual node scores, the score for Assistant PM in 2 is worthy of noting. It indicates that s/he can potentially access almost 70% of the network via short path-length. When we consider this score together with his in-degree centrality, it becomes obvious that his/her structural positioning offers the Assistant PM the opportunity to improve his ability to access information/knowledge. Table 8. Closeness Centrality Results 1 2 Closeness Centrality Company Role Interviewee s Position Closeness Centrality Company Role Interviewee s Position Cradle to grave Commercial representative Director Cradle to grave Assistant Developer/ Director Partner/Principal representative Commercial M&E Engineering Senior Engineer Assistant Cost Consultants Assoc.Cost Consultant Environmental H&S Cradle to grave Cost Concrete Works representative Cost Consultants Sponsor Surveyor Document Steering Group Controller Member Structural Steelwork Site European Operations Capital PM Logistics Site Cradle to grave Supervisor Technical Building Services Engineer Co-ordinator Maintenance M&E Cost Associate Consultants M&E Surv. representative Façade Package Cradle to grave Team Leader Contractors Structural Design (Qual. Cont.) Building Systems Senior Resident Engineer Average Average H&S Consultants Planning Supervisor & Safety Advisor

13 4. Summary This paper demonstrated that SNA is a robust methodology to explore knowledge flows within project teams. SNA metrics, i.e. density, network reach and centrality, have been used to identify the significant features of these flows within the networks studied. Hence, this paper identified the methodological potential of SNA in understanding knowledge flows within project contexts. It thus paved the way to developing a rigorous methodological approach that would help intra-organisational project teams enhance their knowledge creation capability. The paper achieved this by providing the basis of an analytical tool to understand the dynamics of knowledge/information flows within project networks. The very small number of case studies is the main limitation of this research. It significantly restrains the possibility of making broad generalisations. Another limitation is the lack of SNA metric benchmarks in the context of knowledge flows. The benchmarks used in this paper are mainly drawn from studies on communication within networks. Therefore, results should be treated cautiously. This latter limitation provides an opportunity for the research team to apply this methodological approach on a statistically significant sample, and to study the correlations between knowledge creation capacity and different SNA metrics in order to identify the features of a social network which is conducive to knowledge creation. 5. Acknowledgements This paper is based on the findings of a research project which is funded by the Engineering and Physical Sciences Research Council (EPSRC, project reference: EP/C530160/1). References Bourdieu, P. (1986) The Forms of Capital, in: R. G. Richardson(Ed.Handbook of Theory and Research for the Sociology of Education. New York: Greenwood. Bresnen, M., et al. (2003) Social practices and the management of knowledge in project environments., International Journal of, 21(3), pp Burt, R. S. (1997) A note on social capital and network content, Social Networks 19(4), pp Ciborra, C. U. and Andreu, R. (2001) Sharing Knowledge Across Boundaries, Journal of Information Technology, 16, pp Cohen, W. M. and Levinthal, D. A. (1990) Absorptive capacity: a new perspective on learning and innovation, Administrative Science Quarterly, 35, pp Edelman, L. F., et al. (2004). The Benefits and Pitfalls of Social Capital: Empirical Evidence from Two Organizations in the United Kingdom. British Journal of, Blackwell Publishing Limited. 15: 59-S69. Fernie, S., et al. (2003) Knowledge sharing: context, confusion and controversy, International Journal of, 21(3), pp Freeman, L. C. (1979 ) Centrality in Social Concepts, Social Networks, 1, pp Friedkin, N. E. (1983) Horizons of Observability and Limits of Informal Control in Organisations, Social Forces, 62(1), pp Ghoshal, S. and Moran, P. (1996) Bad for Practice: a critique of the transaction cost theory, Academy of Review, 21, pp Grant, R. M. (2000) Shifts in the World Economy: the Drivers of Knowledge, in: C. Despres and D. Chauvel(Eds)Knowledge Horizons, pp Grootaert, C., et al. (2004) Measuring Social Capital Retrieved in 20th June 2006 from the World Wide Web: Krebs, V. and Holley, J. (no date). Building Smart Communities through Network Weaving. Krebs, V. o. c. (1998) Knowledge Networks- mapping and measuring knowledge creation & reuse, in: httpp:// Lang, J. C. (2001) ial Concerns in Knowledge, Journal of Knowledge, 5(1), pp Lank, E. (1997) Building Structural Capital: a new key to generating business value, Knowledge and Process, 4(2), pp

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