The synergy of social network analysis and knowledge mapping: a case study

The synergy of social network analysis and

knowledge mapping: a case study

Kelvin Chan

Department of Information Technology,

Graduate Division of Business and Management, Johns Hopkins University, 9601 Medical Center Drive, Rockville, MD 20850, USA

E-mail: [email protected]

Jay Liebowitz*

Department of Information Technology,

School of Professional Studies in Business and Education, Johns Hopkins University, 9601 Medical Center Drive, Rockville, Maryland 20850-3332, USA

Fax: 301-315-2892 E-mail: [email protected] *Corresponding author

Abstract: Formal structures underpinning organisational charts may not really reflect the actual knowledge flows. It is the informal networks that have played a critical role in getting important work done in organisations. In order to better understand the knowledge flow through these informal networks, knowledge maps can be developed to illustrate the actual knowledge flows. Social network analysis is a technique that can be applied in building knowledge maps and can help analyse the strengths and weaknesses of the networks effectively. This paper provides a case study to illustrate the application of social network analysis to develop knowledge maps for a leading organisation. Borrowing and adapting techniques from other disciplines, such as social network analysis, needs to be done to push the new frontiers of knowledge management.

Keywords: social network analysis; knowledge mapping; knowledge management.

Reference to this paper should be made as follows: Chan, K. and Liebowitz, J. (2006) ‘The synergy of social network analysis and knowledge mapping: a case study’, Int. J. Management and Decision Making, Vol. 7, No. 1, pp.19–35. Biographical notes: Kelvin Chan completed his Master of Science in Information and Telecommunications Systems for Business program at Johns Hopkins University. He received his Bachelor of Science in Computer Studies at City University of Hong Kong. Prior to studying at Hopkins, he was a Project Manager at ADP Wilco, which is a subsidiary of Automatic Data Processing, Inc. He has nine years of experience in IT development and implementation with global financial institutions. He has also worked at the Hong Kong & Shanghai Banking Corporation in Hong Kong

Dr. Jay Liebowitz is a Full Professor in the Graduate Division of Business and Management at Johns Hopkins University. Prior to joining Hopkins, he

Flight Center. Before NASA, Dr. Liebowitz was the Robert W. Deutsch Distinguished Professor of Information Systems at the University of Maryland-Baltimore County, Professor of Management Science at George Washington University, and Chair of Artificial Intelligence at the US Army War College. He is the Founder and Editor-in-Chief of Expert Systems With Applications: An International Journal, published by Elsevier, and the Founder/Chair of The World Congress on Expert Systems. He has published 30 books and a multitude of papers dealing with expert/ intelligent systems, knowledge management, and information technology management. His newest book is titled Addressing the Human Capital Crisis in the Federal Government: A Knowledge Management Perspective, published by Butterworth-Heinemann/Elsevier (2004). He also recently completed a study on ‘Bridging the Knowledge and Skills Gaps: Tapping Federal Retirees’ for the IBM Center for the Business of Government. He is a Fulbright Scholar, IEEE-USA Federal Communications Commission Executive Fellow, and Computer Educator of the Year (International Association for Computer Information Systems). He has consulted and lectured worldwide for numerous organisations.

1 Introduction

Informal networks within organisations have attracted much attention from senior management in recent years. Organisations are aware that important work is increasingly accomplished collaboratively through these networks. The formal structures underpinning organisational charts do not really reflect the actual knowledge flows within the organisations. Most corporations, however, do not know how to manage these informal networks as they find them unobservable and ungovernable (Cross and Prusak, 2002).

In order to better understand the knowledge flow within the organisations, knowledge maps can be built to illustrate the knowledge sources, sinks, and constraints. Once the knowledge maps are created, the managers can then review the flows and determine if there is any potential inefficiency in knowledge exchange and implement changes to improve the overall networks. However, without a systematic way of analysing the networks, one may find it difficult to determine the networks’ strengths or weaknesses. It is therefore beneficial to apply social network analysis (SNA) in analysing the knowledge flows depicted by the knowledge maps. SNA, originating from social science research, is a set of analytical tools that can be used to map networks of relationships and provides an important means of assessing and promoting collaboration in strategically important groups (Cross et al., 2003). A number of software tools, such as NetMiner, UCINET, and NetDraw, have been developed to aid the analysis and provide the visualisation of the networks.

This paper aims at introducing the importance of the application of SNA to knowledge mapping through a real-life example. It first provides a literature review on the recent research and application of knowledge mapping and SNA. It is followed by a discussion of the concepts of SNA. A case study based on a leading organisation is then used to illustrate how SNA is applied to develop knowledge maps for the organisation. Future directions in applying SNA in knowledge management are then presented.

2 Literature review

According to Grey (1999), a knowledge map is a navigation aid to explicit and tacit knowledge, illustrating how knowledge flows throughout an organisation. The knowledge map portrays the sources, flows, constraints and sinks of knowledge within an organisation (Liebowitz, 1999). Knowledge mapping provides a basic tool for managers and employees to retrieve necessary knowledge and to analyse the relationships between knowledge sources. Knowledge maps point to knowledge but do not contain it. Knowledge maps are guides, not repositories (Davenport and Prusak, 1998).

Eppler (2001) categorised fivse types of knowledge maps that can be used in managing organisational knowledge: knowledge-source, -asset, -structure, -application, and -development maps. Each of these categories provides a unique view of the knowledge within the company to fulfill different requirements. A knowledge source map structures a population of experts along their domains of expertise. It answers questions such as ‘who has experience in managing a large global project?’. A knowledge asset map qualifies the existing stock of knowledge of an individual, a department, or a whole organisation. Questions such as ‘how many of our software developers can do Java programming?’ can be answered by this type of map. A knowledge structure map outlines the global architecture of a knowledge domain and how its parts relate to one another. Questions that it handles are ‘which are the skills needed to evaluate a company, and how do they relate to one another?’. A knowledge application map illustrates the types of knowledge that have to be applied at a certain process stage or in a specific business situation, and locates pointers to find such knowledge. It answers questions such as ‘what are our experiences in moving from in-house development to outsourcing?’. A knowledge development map can be used to depict the necessary stages to develop a certain competence for individuals or an organisation. It answers questions such as ‘how do we achieve business excellence for our team?’. In developing the knowledge maps, different methodologies are proposed by various researchers (Kim et al., 2003; Eppler, 2001). For instance, Kim et al. (2003) proposed a six-step procedure: defining organisational knowledge, process map analysis, knowledge extraction, knowledge profiling, knowledge linking, and knowledge map validation.

One of the key advantages of knowledge maps is to increase the visibility of knowledge sources and thus facilitate and accelerate the process of locating relevant expertise or experience in an organisation. They also provide a systematic context for the retrieval of reference information (Eppler, 2001). However, knowledge maps do not provide a systematic way to access the efficiency of the knowledge flows. SNA complements such weakness by providing an important means of analysing knowledge flows systematically. SNA has made significant contributions to a variety of fields including sociology, social psychology, anthropology, epidemiology, management studies (Cross, 2004), and terrorist network studies (Kleiner, 2002). Application of the SNA technique to knowledge mapping is relatively new. SNA focuses on analysing the relationships (ties) among the employees (actors) in terms of knowledge acquisition. For example, questions such as ‘who do you ask for technical advice?’, are used to determine the relationships among actors. SNA is helpful in identifying the strengths and inefficiencies in knowledge flows. It makes the invisible network of relationships between people seem more visible and thus gives valuable inputs to the managers to make decisions for improving the performance of their organisations (Krackhardt and

experiences, the need, to be cautious about overcorrecting the networks. In-depth analysis is required first to determine whether the ‘problems’ revealed are real and then to develop effective ‘cures’. Cross et al. (2002) introduced a set of procedures of performing SNA in organisations: picking the right group; asking the right question; designing the survey; collecting the data; analysing the data; and sharing the results.

Besides improving knowledge exchange, companies apply SNA to assist in workforce diversity analysis, post-merger integration, process improvement, and organisational reengineering. SNA has also been applied to understanding the emergent dynamics in the network of alliances between firms in the internet industry (Krebs, 1998; Preece, 2000). Examples of companies which have applied SNA include Rubbermaid, TRW, IBM, Lucent Technologies (Krebs, 1998), J.P. Morgan Chase, Steelcase Inc, and Hewlett-Packard (Kleiner, 2002).

Studies performed by Krackhardt and Hanson (1993) showed that understanding informal networks could increase the influence of managers. If the managers learned who possessed power in networks and how various coalitions functioned, they could work with the informal organisation to get work done more efficiently.

By analysing informal networks using SNA at more than fifty large organisations, Cross and Prusak (2002) identified four common role-players: central connectors, boundary spanners, information brokers, and peripheral specialists. Each of these four role-players has a significant role in the network and is critical to the productivity of the organisation. Central connectors are the ones who link most people in an informal network with one another. They are usually not the formal leaders in the organisation but they know who can provide the expertise to the organisation to get work done. Boundary spanners connect an informal network with other parts of the organisation or with similar networks in other organisations. They frequently consult with individuals from many different departments and external information areas (Cross and Prusak, 2002; Tushman, 1977). Information brokers are the ones who keep the different subgroups in an informal network together. If they did not communicate across the subgroups, the network as a whole would disintegrate into smaller, less effective segments. Peripheral specialists are the ones who provide expertise to anyone in an informal network.

In any organisation, according to social network theorist Karen Stephenson (Kleiner, 2002), there are at least six core layers of knowledge; each comprises its own informal network of people. These six layers are: work network, social network, innovation network, expert knowledge network, career guidance/strategic network, and learning network. Everybody exists in all the networks, but different people play different roles in each; a central connector in one may be a boundary spanner in another (Kleiner, 2002). Research has shown that employees who are central connectors learn faster, perform better, and are more committed to the organisation. These employees are also less likely to leave the company. On the other hand, employees with low centrality, those who are on the periphery, have much higher turnover rates (Krebs, 1998). It is vital for managers to recognise these different role-players to maximise the productivity. SNA is a powerful tool to help analyse and identify such role-players for the managers.

While sharing of explicit knowledge, which can be easily codified, can be transferred indirectly through various technologies (e.g., e-mail), sharing of complex tacit knowledge through the informal networks requires direct interaction between two or more individuals. A direct tie with the knowledge source(s) must be established and trust must be built (Krebs, 2003; Ford, 2003). Trust plays an important role in knowledge sharing, which is the most commonly discussed knowledge management process with respect to

trust (Ford, 2003). It is frequently commented that in order for people to be willing to share their knowledge, they must have trust (e.g., Davenport and Prusak, 1998; Politis, 2003). The study performed by Politis (2003) suggested that trust is essential in the process of strengthening collaboration and knowledge sharing between members of self-managing teams in particular. On the other hand, it is also said that the sharing of information also increases the level of trust (Wang and Rubenstein-Montano, 2003; Ford, 2003). In other words, as one shares information and knowledge with another individual, the perceived trust increases between these individuals. Managers can create the environments in which trust can be increased by encouraging employees to share knowledge. They cannot, however, force trust to occur (Ford, 2003). One easy way to improve the level of trust, anytime and anywhere, is simply to increase the speed with which people respond to communication.

3 Social Network Analysis (SNA)

As mentioned earlier, SNA focuses on analysing the relationships among actors within a network. A common framework for SNA is the mathematical approach of graph theory (Scott, 1991). The analyses involved in SNA require intensive computations which can be handled by SNA tools. So in order to take advantage of the techniques of SNA, one may not need to understand the underlying calculations but he/she has to grasp the concepts behind the methodology. This section gives an overview of the basic SNA concepts.

The basic idea of a social network is very simple. A social network is a set of actors (or nodes) that may have relationships (or ties) with one another (Hanneman, 2001). SNA represents networks as graphs. There are two types of graphs used in SNA: simple, and directed. It is best to use an example to illustrate their differences. A network which depicts the relationships of people seeking adviceis a directed graph as each relation represents an actor (A) seeking advice from another actor (B). Actor B does not seek advice from actor A. Thus the graph is directed. Arrows are used to indicate the direction of the relations. Relations, such as conversation, are simple as a conversation only occurs when two actors talk to each other.

There are two major concepts of describing network structures. These are centrality and substructures. Centrality is important to understanding power, stratification, ranking, and inequality in social structures; whereas the idea of substructures (or subgroups) within a network is a powerful tool for understanding social structure and the embeddedness of individuals (Hanneman, 2001).

3.1 Centrality

The idea of the centrality of individuals and organisations in their social networks was one of the earliest to be pursued by social network analysts (Scott, 1991). Centrality is the measure of how close an individual is to the centre of the action in a network. There are three main approaches to measure centrality: degree, closeness, and betweenness.

3.1.1 Degree

Measuring the degrees of the actors in the network is the simplest and most straight-forward way to determine centrality. The degree is the number of direct ties an actor has. An actor is regarded as central if it has a high degree. The higher degree an actor has, the more powerful it (may) be in the network. Actors who have high degrees have greater opportunities because they have alternatives and they place less reliance on any specific actors. Thus they are more powerful. With directed data, centrality of an actor is segregated into that based on in-degree and that based on out-degree. An actor with a high in-degree is regarded to be prominent or to have high prestige, as many other actors seek to directly connect to them. An actor is often said to be influential if it has a high out-degree as it is able to make others aware of its views.

The limitation of degree centrality is that it only takes into account the immediate ties that an actor has, rather than indirect ties to all others. One actor might have high degree centrality, but those to which it connects might be rather disconnected from the network as a whole. In this case, although the actor is central, it is only central in a local neighbourhood (Hanneman, 2001).

3.1.2 Closeness

Closeness focuses on how close an actor is to all the other actors in the network. The idea is that an actor is central if it can access all others more quickly than anyone else. Power can come from being a center of attention whose views are heard by larger numbers of actors. Actors occupying central locations with respect to closenesscan be very productive in communicating information to the other actors (Wasserman and Faust, 1997). Closeness centrality approaches emphasise the distance of an actor from all others in the network by focusing on the geodesic distance from each actor to all others. Geodesic distance is the number of relations in the shortest possible path from one actor to another. One could consider either directed or undirected geodesic distances among actors (Hanneman, 2001).

3.1.3 Betweenness

Betweenness is the extent to which a particular actor lies between the various other actors in the network. The actor with high betweenness plays an important ‘broker’ or ‘gatekeeper’ role with a potential for control over others. It may extract ‘service charges’ and isolate actors or prevent contacts. Such an actor thus has great influence over what flows in the network (Hanneman, 2001).

3.2 Substructures

Another way to analyse network structures is to discover the substructures that may be present in a network. There are basically two approaches: bottom-up and top-down. 3.2.1 Bottom-up approach

The bottom-up approach is to think of networks as building up out of the combining of the simplest relations (i.e., dyads and triads) into larger, but still closely connected structures or cliques. Dyad is the relation between two actors whereas triad is the relation

among three actors. A clique is a subset of actors in which every possible pair of actors is directly connected by a relation and the clique is not contained within any other clique (Scott, 1991). Cliques are used to identify how larger structures are compounded from smaller ones. Variations of cliques are n-cliques, n-clans, k-plexes, and k-cores, which have more relaxed definitions than cliques.

Analyses are generally performed to examine the sizes of the various cliques or their variations, and noting their size and overlaps. Researchers are interested in the extent to which these sub-structures overlap, and which actors are most central and most isolated from the cliques.

3.2.2 Top-down approach

Another approach to analyse substructures is from top-down. This approach is to start from the whole network, instead of the dyads, and identify substructures as parts that are locally denser than the others as a whole. In a sense, this approach is looking for holes or vulnerabilities in the overall network. The top-down approach includes components, blocks and cutpoints, and Lambda sets.

Components are subsets of a graph that are connected within, but disconnected between sub-graphs (Hanneman, 2001). Within a component, all actors are connected through paths, but no paths run to points outside the component (Scott, 1991). Isolates within graphs are also regarded as components. The pattern of components of a graph – their number and size – is taken as an indication of the opportunities and obstacles to communication or transfer of resources in the associated network (Scott, 1991).

Another approach to analyse a network is to determine whether the structure of a graph will be divided into disconnected groups if one of the actors is removed from the network. If so, the network is said to have cutpoint – the actor being removed. The divisions are called blocks. Cutpoints are typically important actors as they may act as brokers among otherwise disconnected groups (Hanneman, 2001; Wasserman and Faust, 1997).

Alternately, similar to the blocks and cutpoints approach, the relations (instead of actors) in a graph can be examined to see whether, if any one of the relations is removed, it would result in a disconnected structure or not. This is to determine if there is any key relation in the network. This is called Lambda sets approach (Wasserman and Faust, 1997).

3.3 Data collection

Before analysis can be performed, network data will need to be collected. There are several approaches.

3.3.1 Full network method

The full network method requires collecting data about each actor’s ties with all other actors in the organisation. This yields maximum information, but can be costly and the response rate may not be as high as expected (Hanneman, 2001).

3.3.2 Snowball method

The snowball method begins with a focal actor (or set of actors). The focal actors are asked to provide all their ties to other actors. Each of these actors is then recursively asked to also provide all their ties until no new actors are identified, or until sufficient sample size is reached. This is particularly helpful for tracking down special populations (often small subsets of actors mixed in with large numbers of others). Limitations of this method are that it is not possible to locate isolated actors, and it is difficult to determine the right focal actor who can lead up to the desired sample population (Hanneman, 2001). 3.3.3 Ego-centric networks

The ego-centric networks approach is to begin with a list of focal actors. Similar to the snowball method, each of these focal actors (ego) is asked to name all the ties to other actors (alter). Then the ties of the alters may or may not be identified, depending on the sampling requirements. This approach provides a local view of the network instead of the overall picture (Hanneman, 2001).

3.4 SNA Tools

SNA will not be performed efficiently without the use of an appropriate tool. SNA tools provide functionalities for network data analysis, relieving the burden of intensive computation for the researchers. SNA tools also provide 2-D and 3-D visualisation of the network data so that the researchers can have a better understanding of the network structure by viewing it. Currently, there are a number of SNA tools available in the market. Some of the common ones are NetMiner (Cyram, 2003), UCINET (Borgatti et al., 2002), NetDraw (Borgatti, 2002), MAGE (Richardson, 2000) and Pajek (Batagelj and Mrvar, 2004). The website of the International Network for Social Network Analysis (INSNA) (http://www.sfu.ca/~insna/) is an excellent source for accessing these tools.

4 Application of SNA to knowledge mapping: case study

4.1 Background

In order to apply the social network analysis and knowledge mapping techniques, a knowledge audit was conducted at one of the most endowed Foundations in Washington DC. The Foundation believed in the strategic theme, based on their annual report, that sharing knowledge is power. The Foundation had about 85 staff members, and consisted of six major divisions. They were very interested in initiating a knowledge management strategy to better leverage their knowledge internally and externally.

To help establish a knowledge management strategy, a knowledge audit was first conducted in order to identify the sources and sinks of knowledge, and determine the knowledge flows in the organisation (Liebowitz, 2004). A knowledge audit survey was sent to each member in the organisation, with follow-up interviews as appropriate. Part of the knowledge audit survey asked questions that helped to build knowledge maps for people-to-people connections in the Foundation.

The following sample question was asked in the survey:

“Who are the top three people you would ask for each of the following areas: general advice, management and leadership advice, institutional knowledge, subject matter expertise, and technical/procedural knowledge?”

4.2 Analysis

The data gathered from the survey was analysed to determine the knowledge flow within the organisation. The analysis considered two levels of detail: knowledge flow among the departments, and that among the individual employees. The SNA tool, NetMiner, was used to assist in the analysis (Cyram, 2003).

Degree centrality was first analysed. In performing the analysis, a weight which ranges from one to three was assigned to each of the relations to indicate the priority of the person being asked (three has the highest priority). The values of the degree centrality thus range from zero (minimum, i.e., not being asked by anyone) to three (maximum, i.e., being the first person asked by everyone). An analysis report, based on the survey results, is generated by NetMiner as shown in Figure 1.

Figure 1 Analysis report of degree centrality for general advice

The in-degree of the ‘General Management’ department was the highest among the groups for general advice. This indicates that ‘General Management’ was a major knowledge source of general advice. In contrast, ‘Knowledge Access and Technology Strategy’ department had a zero in-degree, meaning it was not approached by any department in this aspect. This is paradoxical as this department was in charge of knowledge management yet they did not practice knowledge management. The organisation should establish some kind of mechanism to encourage other departments to directly connect with this department.

Table 1 summarises the in-degrees for each department with regard to each area of the survey. It is shown that ‘General Management’ was an important source for seeking management and leadership advice, institutional knowledge, as well as general advice. As for subject matter expertise, both ‘Finance and Administration’ and ‘Knowledge Access and Technology Strategy’ departments were important sources. ‘Finance and Administration’ was also a popular place to look for technical or procedural knowledge. It is worth noting, however, that most people sought management and leadership advice from ‘outsiders’ rather than other departments when they could not find it within their own departments. This may be an area the organisation should investigate to see if people could not find anyone in the organisation that they trusted for management and leadership advice.

Table 1 Degree centrality (In-degree) by department

General advice

Management and leadership

advice Institutional knowledge

Subject matter expertise Technical/ procedural knowledge Department In-degree In-degree In-degree In-degree In-degree

Communications 0.333 0.333 0.667 0.143 0.333 General management 1 1.5 1 0.571 0.333 Finance and administration 0.333 0.5 0.667 1.143 0.889 Housing and community initiatives 0.833 0.167 0.667 0.571 0 Knowledge access and technology strategy 0 0.333 0.333 1 0.333 Financial innovation

planning and research 0.5 0.5 0.167 0.571 0.222

Outsiders 0.167 1.667 0.167 0.571 0.111

When the individuals are taken into account, it is found that Rob F. was a key player for advice as indicated by the highest in-degree on general advice and management and leadership advice, though the values are quite low compared with the group due to the large number of nodes for individuals (Table 2). In other words, Rob is the most popular person in the organisation, for being asked for advice. Analysis on the corresponding network diagrams reveals that most people who sought advice from him were within his department (‘Housing and Community Initiatives’) (in Figure 2, Rob is on the left towards the middle). Rob is known as a central connector, who links most people in an informal network with one another, according to Cross and Prusak (2002). Note that Rob might provide the advice his colleagues want, but on the other hand, he might also be a bottleneck if he could not spare time to answer his colleagues’ questions.

Table 2 Individuals with high degree centrality General advice Management and leadership advice Institutional knowledge Subject matter expertise Technical/ procedural knowledge

Name In-deg Name In-deg Name In-deg Name In-deg Name In-deg

Rob F. 0.149 Rob F. 0.205 Kevin S. 0.2 Sheila M. 0.156 Jana F. 0.196 Kevin S. 0.149 Peter B. 0.182 Michelle G. 0.2 Laurie S. 0.114 Brendt H. 0.152 Greg T. 0.128 Beverly B. 0.136 Peter B. 0.175 Linda B. 0.114 Michelle G. 0.152 Beverly B. 0.085 Marc S. 0.136 Rob F. 0.125 Stephanie J. 0.114 Jamie H. 0.152 Michelle G. 0.085

Figure 2 Network diagram: general advice (Individual)

Kevin S. was another key player for advisory and non-technical knowledge. He also had the highest in-degree on general advice, as well as on institutional knowledge. In contrast with Rob F., most people who normally asked Kevin for advice were from other departments as indicated in the network diagrams. Although Kevin was the head of ‘Finance and Administration’ (G1 on the diagram), members of his department did not normally ask him questions or seek advice (in Figure 2, Kevin is at the top right hand corner). They tended to ask people in other departments even for management and leadership advice. For example, for management and leadership advice, Susan P. (Finance and Administration) went to her line manager, Michelle G., who in turn went to her peers, Linda B. (in the same department) or Rob F. (in a different department), instead of seeking advice from her boss, Kevin. Alternately, Susan might ask Andrew P., who was the head of ‘Housing and Community Initiatives’, but not Kevin. It is likewise

for Mark J., who would ask the head of ‘Communications’, Beverly B., instead of Kevin. This may indicate a potential communication issue with Kevin and his group.

Table 3 Individuals with high betweenness centrality

General advice Management and leadership advice Institutional knowledge Subject matter expertise Technical/ procedural knowledge

Name Score Name Score Name Score Name Score Name Score

Cathy G. 0.011 Greg T. 0.013 Glen H. 0.014 Greg T. 0.010 Cathy G. 0.005 Michelle G. 0.009 Jeff S. 0.010 Cathy G. 0.010 Michelle G. 0.008 Michelle G. 0.004 Greg T. 0.008 Michelle G. 0.007 Michelle G. 0.007 Jeff S. 0.007 Randy F. 0.004 Glen H. 0.008 Laurie S. 0.004 Randy F. 0.005 Linda B. 0.007 Glen H. 0.003

Analysis on the betweenness centrality shows that Glen H. has a relatively high betweenness (Table 3). The institutional knowledge network diagram (Figure 3) shows that Glen gathered institutional knowledge from Peter B. and Kevin S., while at the same time he was also the preferred person for the same information by Susan P., Colette T., Lewis C., and Cathy G. Glen thus acted as a broker in providing the institutional knowledge from the head of ‘Knowledge Access and Technology Strategy’ (Peter) and the head of ‘Finance and Administration’ (Kevin) to Susan and other members. Glen was also known as a cutpoint and if he left the organisation, the knowledge flow from Peter and Kevin to Susan and others might be affected.

Analysis on the closeness centrality for subject matter expertise shows that the ‘Communications’ group had the lowest closeness among other departments (Table 4). That means this group is the most difficult one to be accessed by the others. Ironically, the ‘Communications’ group is responsible for internal and external communications, consumer outreach, events, and publications. Figure 4 illustrates that there is only one tie (in both directions) linking ‘Communications’ and ‘Housing and Community Initiatives’, indicating that ‘Housing and Community Initiatives’ is the only group who would ask ‘Communications’ for subject matter expertise. In other words, the other groups did not regard ‘Communications’ as a source of knowledge on subject matters. This may indicate a potential issue within the organisation that the knowledge was not shared effectively. Figure 4 Network diagram: subject matter expertise (department)

Table 4 Closeness centrality (in-closeness) by department

Subject matter expertise

Department In-closeness Communications 0.284 Financial innovation planning and research 0.391

Housing and community initiatives 0.446

Finance and administration 0.446

Knowledge access and technology strategy 0.446

By using the top-down approach to determine the number of components in the network, it is found that there are five components for the technical/procedural knowledge network (Figure 5). Besides the main component which contains most of the people, there are four components which have only three people or fewer. That means that for those people in the four components, they were disconnected with the rest of the organisation and thus the knowledge they received was only from one or two people within their components. This is probably an issue which needs to be addressed to make sure that everyone in the organisation is connected with each other.

Figure 5 Network diagram: technical/procedural knowledge (components)

4.3 Discussion

This case study aims to illustrate the application of the SNA tool to knowledge mapping. Through the use of visualisation and analysis reports of the SNA tool, potential issues not easily discovered by other means were identified. It is important to note, however, that further investigation into these issues should be performed before any changes to the network are done in order to confirm whether those are real issues. For instance, it was indicated in the analysis that the knowledge of the ‘Communications’ group was not shared effectively as there was only one other group who went to ‘Communications’ for subject matter expertise. A further analysis might find out that all subject matter knowledge of ‘Communications’ was documented as explicit knowledge for reference from other groups and that the knowledge sharing was effective.

There were a couple of limitations to this case study.

• The response rate for the survey was not as high as preferred. Among the 85 staff members, only around 25 responded. The response rate was around 30%. Ideally the response rate should be at least 80% (Kleiner, 2002; Lesser and Prusak, 2004). As a result of the low response rate, our network is similar to an ego-centric network (ego only). An ego-centric network is one which has information on ego

(i.e., the actor) connections to alters (i.e., the one whom the actor relates to), but not information on the connections among those alters. Such data does not give a complete picture of the network as a whole, but it still gives a pretty good picture of the local networks or neighbourhoods of individuals (Hanneman, 2001).

• The source of information of each tie was given by the actor himself/herself without any confirmation of whom the actor related to. However, the information can be obtained based on each actor’s perceptions about the whole network. Each person provides his or her own estimate of who was related to whom in the organisation. This gives a higher accuracy to the network information and is preferred by some researchers (Krackhardt, 1992).

5 Future directions in applying SNA to knowledge management

The importance of informal networks in organisations cannot be ignored. In order to capture the tacit knowledge flowing through these informal networks, knowledge maps will need to be developed. Through our case study, it is shown that SNA is a very powerful tool in developing knowledge maps and in analysing knowledge flows for an organisation. Though there is an increasing trend of recognising the informal networks in business, it is still far from common as a business practice. Going forward, it is important to further increase business executives’ awareness of the significance of informal networks and how to manage these networks through knowledge mapping.

SNA tools have played an important role in analysing social networks. They will continue to do so as new computation intensive statistical models evolve. The current SNA tools have made network analysis easier by providing interactive visualisations and highly portable analysis reports. Enhancements on such tools should continue to support any analysis techniques that are not currently supported, and also to provide even better visualisations. For example, NetMiner is able to set node colours based on the attributes of the nodes. However, it is still not easy to determine the groups to which a node has ties, especially in a large network. It will be very helpful if different colours can be defined for ties linking different groups.

In our case study, we have assigned weights to represent the strength of the ties between actors. We used a value of three to indicate the strongest tie between any two actors. However, in reality, the strength of the strongest tie of one actor is very likely to be different from that of another one. Currently, more research is needed on how to accurately determine the strength of ties. Specifically, developing methods to create interval/ratio scales for measuring the strength of ties between actors is warranted.

In reaching the new frontiers of knowledge management, more techniques from other disciplines will need to be applied and adapted for knowledge management application. In keeping with the spirit of knowledge sharing, it should be natural to borrow

methods to the knowledge management discipline. Without adapting and creating new techniques and tools for it, knowledge management will remain more of an art than a science. In the years to come, the hope is to provide more rigor and science behind the knowledge management field so that knowledge management does not become simply a passing fad. Towards improving the science behind knowledge management, this paper demonstrated how social network analysis can be applied to knowledge mapping in the knowledge audit process for organisations.

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