A Study and Social Network Analysis on the World Business
Council for Sustainable Development
Theresa Schaffner, Bucknell ‘15
Advisor: Peter Wilshusen
Environmental Studies Department
Table of Contents
Introduction ................................................................................................................................................... 2
History and Importance of the WBCSD ....................................................................................................... 4
Social Network Analysis............................................................................................................................... 7
History ...................................................................................................................................................... 7
Terminology.............................................................................................................................................. 8
Social Network Analysis Software ......................................................................................................... 12
Network Behavior Theories .................................................................................................................... 12
Modes of Analysis .................................................................................................................................. 15
Methods ...................................................................................................................................................... 19
Defining the network .............................................................................................................................. 19
Preliminary networks .............................................................................................................................. 19
Making Ties ............................................................................................................................................ 22
Data Collection ....................................................................................................................................... 23
Future Analysis ........................................................................................................................................... 25
Conclusion and Future Plans ...................................................................................................................... 27
Appendices.................................................................................................................................................. 28
Appendix A: Member Organizations divided by Region ....................................................................... 28
Appendix B: Membership Diagrams ...................................................................................................... 33
Appendix C: Extended Terminology List ............................................................................................... 34
Appendix D: Data Sheet ......................................................................................................................... 35
1
Introduction
In the past few decades, there has been an increase in transnational capitalism, businesses,
and organizations. Many economists and policy theorists have specifically discussed the rise of a
transnational capitalist class; a class that is changing the face and structure of the global
economy. The transnational capitalist class (TCC) is commonly thought of as the social network
of individuals who leverage tools of the world economy such as transnational organizations and
political bodies. The TCC is interested in the world economy as a system of international private
commerce to allow free movement of capital across countries (Carroll 2003). However, trade and
investment are not sufficient structural conditions for long term sustainable development, nor is
neoliberal capitalism a suitable long term vision for a Green Economy.
A level of collaboration beyond businesses, region, and sector is necessary so that
business leaders may discuss issues of “shared concern, to find common ground, and devise
strategies for action” in shaping a global economy (Carroll 2003). The significance of
collaborative work areas is made greater by globalization and the search for new types of global
economic policies that can support an increasing population and decreasing natural resources.
With transnational organizations (TNOs) gaining more influence over global economic, social,
and environmental policy there is an increasing interest in the social networks formed between
and amongst transnational organizations. This paper will focus specifically on a social network
analysis of the World Business Council for Sustainability Development.
The World Business Council for Sustainable Development (WBCSD), formed by the
United Nations in 1995, is a transnational organization whose goal is to encourage the “global
business community to create a sustainable future for business, society and the environment”
(WBCSD 2104). The WBCSD as well as other international policy groups such as the World
2
Economic Forum and the Trilateral Commission have been integral to forming the transnational
economy by synthesizing public and private elements of capitalism. However, the WEF is
focused around a “highly elite core of transnational capitalists” and champions paradigmatic
neoliberal structuralism (Carroll 2003). The TC favors regulatory practices to promote capitalism
amongst northern Europe, North America, and Japan. The World Business Council for
Sustainable Development is the only leading transnational organization committed to creating a
sustainable global economy by promoting corporate social responsibility and environmentally
sound changes across industries and regions.
Conducting a social network analysis on the WBCSD aims to determine whether the
structure of the organization is allowing collaboration amongst business leaders as intended.
Social network analysis (SNA) can also reveal which actors in the organization are the most
influential and how that affects what the organization accomplishes. This study will focus on a
social network analysis of the WBCSD to determine which actors, based on centrality measures
outlined in diffusion of innovation theory, have the greatest ability to influence the decisions of
others and therefore affect the direction and success of WBCSD initiatives.
3
History and Importance of the WBCSD
The World Business Council for Sustainable Development started its life as the Business
Council for Sustainable Development in 1990. It consisted of forty-eight business leaders who
banded together to represent the corporate world at the 1992 Rio Earth Summit. The first BCSD
meeting took place in 1991 with Stephan Schmidheiny as president. By 1995 the BCSD merged
with the World Industry Council for the Environment to create the WBCSD; organization
headquarters were setup in Geneva and Björn Stigson was named the first president. Nearly
twenty years later, the WBCSD is made up of 176 member organizations across five continents
(WBCSD 2014). See Appendix A for member list and sector details.
Companies that are a part of WBCSD are represented in the organization by their CEO or
an appointed substitute. The organization is governed by an Executive Committee made up of
elected member CEOs who serve on the Committee for two years. The Executive Committee
meets three times a year to maintain daily management of the WBCSD, determine priorities of
the organization, and discuss issues related to sustainable development (WBCSD 2014). The
WBSCD meetings provide a medium for leading business minds to analyze and deliberate
aspects of sustainable business management.
WBSCD makes advancements in sustainable development by dividing its attention into
focus areas separated by business sector. The varying challenges presented by each sector are
tackled by projects, systems solutions, and capacity building initiatives. Sector projects are
practical initiatives in independent research that investigate how critical industries can meet
sustainability challenges. Systems solutions provided an approach that deals with the overlapping
challenges presented by sustainable development. Capacity building activities support increased
integration of sustainable development into business practices. The overarching goal is to find
4
comprehensive solutions to creating sustainable business practices that can be applied across
entire industries.
The WBCSD has already contributed to many successes in increasing sustainable
practices among member businesses and respective sectors. Since its founding after the 1992 Rio
Earth Summit, WBCSD has been promoting the importance business expertise in achieving
environmental protection as well as economic growth. Several milestones in sustainable
development have since been reached: in 1997 the Rio+5 Convention was held and the WBCSD
published Signals of Change, a report describing the progress of business since the 1992 Earth
Summit. In 1999 the United Nations established the Global Compact which “asks companies to
embrace universal principles and to partner with the United Nations. The Global Compact has
grown to become a critical platform for the UN to engage effectively with enlightened global
business”(Ki-moon 1999). The most recent achievements include the greenhouse-gas-reducing
Kyoto Protocol and the 2015 Millennium Development Goals. Finally, the collective efforts of
these initiatives have been defined by the United Nations as working towards a Green Economy.
The Green Economy is defined as an economy “that results in improved human well-being and
social equity, while significantly reducing environmental risks and ecological scarcities. In its
simplest expression, a green economy can be thought of as one which is low carbon, resource
efficient and socially inclusive” (United Nations 2014).
The WBCSD is continuing to make advancements toward a Green Economy with an
increasing number of members and initiatives working around the world. However, an increasing
number of companies involved in the council also leads to an increase in personal interests and
agendas. As with any organization, there will be some members who are in greater positions of
influence than other members. The people in positions of influence will naturally affect the type
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of initiatives and programs being implemented and completed. Furthermore, the members
elected to the Executive Committee may not necessarily have the most influence in the
organization. Social network analysis provides an insight to how the structure of an organization,
such as WBCSD, influences which members gain the ability to influence others.
6
Social Network Analysis
Social network analysis is a means to analyze connections amongst objects. There are two
basic concepts that validate social network analysis: homophily and influence. Homophily is the
idea that people or things with like characteristics are usually connected. Influence is the idea
that those connected people or objects have an effect on one another and the effect is greater with
stronger connections (Kadushin 2012). Social network analysis (SNA) can be applied anywhere
from small groups to entire global social systems. A classic example uses data pulled from
Amazon marketing techniques. Valdis Krebs, a consultant who specializes in SNA, made a
network consisting of books related to the 2008 presidential election. He connected books bought
by the same person. The results were a connected group of democratic leaning books, a
connected group of books pertaining to republican ideas, and a third group related entirely to
then presidential candidate Barack Obama (Kadushin 2012). This network showed two things: 1)
people who read politically focused books rarely or never read books that are in opposition to
their own political leanings and 2) Amazon’s marketing strategy to convince people to buy
multiple books on the same subject is highly successful . The point of this example is to show
that social network analysis can reveal larger concepts from data that may seem mundane or
simple.
History
Social network analysis has seen a recent resurgence in academic literature but has been
developing over several decades. SNA was first promoted by sociologist Georg Simmel in his
late 19th century publications about the importance of studying patterns of relationships. In 1954,
sociologist J.A. Barnes started using the term “social network analysis” to exclusively and
specifically relate to patterns of ties amongst people or objects. One of the most well-known
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results from a SNA experiment is the concept of six degrees of separation. Stanley Millgram, yet
another sociologist, conducted an experiment in the 1960s known as the small-world problem.
He randomly selected people from small towns in Kansas and Nebraska to attempt to get a letter
to a specific person in upper-class Boston. The participants were instructed to send the letter to
someone they knew personally who would have a better chance of getting the letter to the
intended person. The results showed that on average it took six people to get the letter from the
Midwest to Boston. Millgram never used the phrase “six degrees of separation” – his experiment
was simply to show that American people are more connected to each other than they realize- but
his experiment promoted the concept nonetheless (Famiglietti 2014).
Ten years later in 1971, the International Network for Social Network Analysis (INSNA) was
founded as the professional association for researchers interested in social network analysis.
SNA has gained enough interest amongst sociologists and other researchers to warrant a
professional organization because technological developments have made it possible to use
advanced software to analysis larger networks at a faster, more detailed pace (Famiglietti 2014).
Terminology
In order to fully understand the potential of social network analysis, we have to understand
the terminology. A network is defined as a group of objects and a description of the relationships
between those objects. The objects, which can range from people to books to anything in
between, are called nodes. The relationships between the nodes are called ties or edges. There are
four types of relationships between nodes that constitute a tie:
1. Similarities are attributes shared by nodes; for example, holding membership in the
same group or committee
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2. Social relationships are based on feelings or emotions towards another node; for
example, feelings of friendship or trust
3. Interactions are ties based on behaviors; for example, voting for a particular
candidate
4. Flows are ties that show a flow of information or objects through a network; for
example, the way money flows through a retail organization
The ties can be either directed or undirected. Directed ties show and imbalance relationship
between the nodes, while undirected show a balanced relationship. For example, a directed tie
would show that Person A sent a letter to Person B. The direction of the tie would be from
person A to person B. An undirected tie would merely show that the two persons communicated
with each other (Famiglietti 2014).
Directed Tie
A
B
Person A sent a letter to person B
Undirected Tie
B
A
Person A and Person B communicated with each other
Image 1: directed vs. undirected ties
Ties can be binary or weighted. Binary ties merely show the existence of a relationship.
Weighted ties show the strength of a relationship based on a quantitative variable. Finally, the
number of ties attached to the node is the degree. Nodes in directed networks have in-degrees
9
and out-degrees, which are the number of ties directed towards them or away from them
respectively (Prell 2012).
Binary Tie
A
B
Person A and Person B are connected
Weighted Tie
B
A
C
Person A and Person C are more strongly
connected than Person A and Person B
Image 2: binary vs. weighted ties
Directed Degrees of Node B
In-degree = 2
Out-degree = 5
Undirected Degree of Node A
n=4
A
B
Image 3: undirected and directed degrees
10
The shape of a network created by the relationships amongst nodes can range from dyads
to large encompassing networks. A dyad is two nodes and the ties between them: the simplest
form of a network. A triad consists of three nodes with ties only to each other or no ties at all.
Larger networks can consist of hundreds of thousands of nodes as long as they are in the range
defined by the researcher (Famiglietti 2014).
Simple Dyad
Triad Variation
Image 4: dyad and triad
Networks with only one type of node are called single mode; however, it is possible to
have multiple types of nodes within a network. A network with two types of nodes is called bimodal, and a network with three or more types of nodes is called k-modal (k=number of different
node types). An example of a single mode network is the Amazon example used before: the only
type of node in the network is books. An example of a bi-modal network is connecting students
(the first type of node) to each other based on if they lived in the same dormitory, as well as
connecting them to their respective major departments (the second type of mode). A k-modal
network would entail adding another category, such as connecting students to their minor
department. Having multiple types of nodes allows for a more advanced analysis but also a more
11
difficult design phase (Famiglietti 2014). Appendix C has a list of more advanced terminology
used in social network analysis, but the ones discussed above are sufficient for this study.
Social Network Analysis Software
After defining the range and type of nodes as well as what ties them together, it is time to
enter the data into a database. The database can be an Excel spreadsheet, matrices, or other tables.
However, most SNA software programs are easiest to use with Excel spreadsheets. The database
needs to be carefully designed before any data is entered. Depending on the analysis, there can
be upwards of thousands of nodes and ties. The database must be simple and organized, with
clear labels and consistent formatting, in order for the software to process the information.
Popular software programs include Cytoscape, UCINET, and Gephi, the latter being used for this
study. The software is able to perform a wide range of analyses using advanced algorithms that
most people, myself included, would not be able use on their own. These algorithms have helped
sociologists develop several theories in explaining network behavior.
Network Behavior Theories
The purpose of social network analysis is to reveal information about the relationships
between nodes that would not be seen by just looking at the nodes as groups or at their intrinsic
characteristics. Therefore, all modes of analysis related to SNA focus on the relationships
between nodes. The analyses can find patterns, irregularities, strengths and weaknesses, as well
as help create theories and “rules” governing networks. General consensus among SNA experts
is that some rules apply to all networks (Famiglietti 2014). The first of these is the Power Law.
In social network analysis, Power Law states that most nodes have few ties and few nodes have
many ties. When put in a two-dimensional graph, the power law curve looks as follows:
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Undirected Degree of Nodes in Network A
F(x)=x^n, n=2
Degree
Node
Image 5: Power Law Curve
The power law tells us that the frequency of nodes with a high degree is very low compared to
the frequency of nodes with a low degree. Because of this, if a network were to lose a few nodes
chances are that the lost nodes will only have a few ties and the connectivity of the network will
not be greatly affected. However, losing a highly connected node can change the entire network
structure. If the loss of nodes is a random occurrence, then there is a low probability of losing a
node with a high degree and the network is strong. If the loss of nodes is not random, and high
degree nodes leave the network more frequently, then the network is more susceptible to change.
Depending on the real-world situation, those maintaining a network may wish to flatten the
Power Curve (Famiglietti 2014).
The second theory I will discuss is known as the small world theory. Small world theory
discusses how nodes can be connected to many other nodes in a network despite their number of
direct ties. There is a path between nodes via ties across the network. In a small world network,
most nodes are not connected to each other, yet can easily reach other nodes in the network via a
few hops along ties between other nodes. But what if a network is not a small world?
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Researchers have defined a “regular” world and a “random” world as well. A regular world
network has a high number of ties, like the small world, but the path between nodes is longer.
Read: it takes more hops for one node to reach another, but there is a high probability all nodes
connect eventually. In a random world network, there are a low number of connections, yet the
nodes that are connected have a short path between them (Kadushin 2012).
Regular world
Small world
Random world
Increasing Randomness
Image 6: (Watts and Strogatz 1998)
One of the most important concepts to understand in SNA is the theory of preferential
attachment. Preferential attachment is the idea that nodes “prefer” to connect to nodes that
already have many connections; that is, nodes are more likely to form a tie with nodes that have
a high degree than nodes that have a low degree. This concept supports the Power Law in that,
rather than increasing ties on low-degree nodes, new nodes increase the number of ties on a high
degree node. One example of this theory in actuality is the fact that highly cited academic papers
are more likely to be cited again than a low cited paper (Famiglietti 2014). A more contemporary
example is the growing divide between upper and lower economic class and the decrease of a
middle class in the United States (Lynch 2011). The “rich” gain more connections and access to
14
capital, while the poor remain the same and become relatively “poorer”. Preferential attachment
theory is a focal point in explaining the behavior of networks as they change over time.
Modes of Analysis
There is a plethora of analyses that can be used to investigate a network, but the analysis
you choose is dependent on the type of network and what information you seek. The most
common types of analyses are outlined below:
Degree: as outlined before, degree is the number of ties connected to a node. Most
software will run an average degree as well as the range.
Weighted degree: weighted degree is the total weight of all ties connected to a node.
Average weighted degree is the average weight per node in the network.
Density: the number of ties in a network divided by the total number of possible ties.
Neither extremely dense nor sparse networks are useful for analysis.
Average path length: the number of ties, on average, it takes to connect each node in the
network. A path is defined as a group of connecting nodes where no node is
visited more than once.
Network Diameter: the longest shortest path through the network, also known as the
geodesic distance. This metric is important for optimization analyses.
Clustering: how connected a node is to all other nodes in the network. If every node is
connected, then there is 100% clustering for that network. The more ties a node
has, the higher its chance of gaining a connection to a new node.
Average clustering coefficient: The average clustering coefficient (Ci) for a node is the
proportion of links between the nodes within the network divided by the number
of links that could possibly exist between them (Kadushin 2012).
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Centrality: the measure of analysis that identifies the most important nodes in the
network. Sociologists have defined importance to mean many different things, so
there are multiple centrality measures to portray the different meanings of the
word. The most basic centrality measure – degree centrality- identifies the most
influential nodes in a network by number of ties. Centrality can also measure
importance as cohesiveness in the network (Scott 2011). The following three
measures are types of centrality measures, as shown in the image below.
Image 7: The image shows colors ranging from dark blue to red, with red being most central,
illustrating the different types of centrality measures applied to the same network. (Rocchini 2012)
A) degree centrality
B) closeness centrality
C) betweeness centrality
D) Eigenvector centrality
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1) Closeness centrality: measures how long it will take one node to reach all other nodes.
In this study, closeness centrality will measure how long it will take information to
spread from one WBCSD member to all other members. The “closer” a node is, the
faster it is capable of spreading information (Kadushin 2012).
Image 8: closeness centrality (Activate Networks, Inc.
2014)
2) Betweeness centrality: measures the “frequency with which a node falls between
pairs of other nodes on their shortest paths.” Points central in this respect have
potential for control because they control flow of information to many other nodes. In
social theory, this would allow nodes with a high betweeness centrality to control
what information does and does not pass through the network and manipulate the
information to their advantage (Kadushin 2012).
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Image 9: betweeness centrality (Activate Networks, Inc. 2014)
3) Eigenvector Centrality: shows which nodes are well-connected to the most influential
nodes in the network. For example, a person may not have many connections to the
political world but he is long-time friends with the vice-president (Famiglietti 2014).
Conversely, a CEO may not directly work with many people in her company, but she
is in direct contact with the head of each department. When a node has connections to
other nodes with high degrees of other centrality measures, it becomes an eigenvector
node (Kadushin 2012).
Image 10: Eigenvector centrality (Activate Networks, Inc. 2014)
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Methods
The World Business Council for Sustainable Development is an organization made up of
global business leaders with excellent communication skills, persuasive abilities, and charismatic
personalities that have enabled them to reach the position of influence they currently hold. It only
makes sense to use social network analysis to determine which people in this network of
corporate magnates are the most influential within WBCSD. This social network analysis will
give a sense of how WBCSD, as a policy group, integrates businesses across sector and region
allowing the opportunity for actors to be more influential than others based on their positions
within the group. The analysis will use the four previously mentioned centrality measures to
identify the central actors in the network.
Defining the network
Let us first review the structure of WBCSD. Every member company is represented in
the Council by its CEO or an elected replacement. The company representatives are the set of
nodes in the network. Each company is represented once by one person. The nodes will be
labeled by company name because the company name has more meaning to a person looking at
the network.
Preliminary networks
Before creating a network of all the nodes and their ties to each other, I created a set of
preliminary networks to achieve a more basic view of the organization. The preliminary
networks are k-modal with three types of nodes: the company representatives, the region or
sector they belong to, and the executive council. The ties between nodes are undirected and
binary, and were justified by group membership in either the region, sector, or council. All of the
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companies based in Europe are connected to the Europe region node. All the companies with
seats on the executive council are connected to the council node, etc.
Europe
Latin America
Executive
Committee
North America
Image 11: Network A-company, region,Asia
council
Africa
Image 11: WBCSD companies connected to region where headquarters are located and executive committee
20
Image 12: WBCSD companies connected to industry
21
Making Ties
After making the preliminary networks, I moved onto the setup for the network that
would encompass all of the WBCSD members and the ties between them. Before I could start
data collection, I had to decide what constituted a tie between members. Since the analysis is
based on who in the network has influence over others by the ability to spread or manipulate
information, a tie should connect people if there is information being exchanged between them.
Diffusion of Innovation Theory states that information and new ideas are best spread through
interpersonal communication (Valente and Davis 1999). This theory has been applied to many
SNA studies in the past, specifically on the concept of board interlock. Board interlock is based
on the fact that the board members interact and influence each other to make decisions to benefit
the companies they champion (Carroll 2004). Sitting on a board is similar to serving on the same
committee or council; it creates a space for communication and discussion. Ideally, I would be
able to use personal communication between WBCSD members as justification for a tie between
them. However, I am not able to observe every interaction between them or access the archives
folder in their email accounts. A “proxy” for observing communication must be used. By looking
at the structure of WBCSD as a collection of boards, committees, and work groups we can
assume some level of personal communication happened between members.
The WBCSD divides their initiatives into several categories and subcategories in order to
accomplish the wide range of goals the organization is working towards. The members of
WBCSD work specifically on one or multiple projects at a time with other members;
approximately five to twelve people make up a working group. The categories and subcategories
of initiatives are as follows (WBCSD 2014):

Natural Capital
o Ecosystems solutions
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



o Forest solutions
o Water solutions
o Energy and climate
o Electric utilities
o GHG management
Business Applications
o Cement sustainability initiative
o Chemicals
o Tire industry project
o Energy efficiency in Buildings 2.0
o Sustainable Mobility 2.0
o Urban infrastructure initiative
Social Capital
o Inclusive Growths
o Performance & Valuation
Capacity Building
o Consumption & value chain
o Education initiatives
o People matter
o Future leaders team
Financial Capital
o Reporting and investing
In addition to being a part of the committees on the above categories, members are also involved
with creating publications, reports, and case studies that are made available to member
companies as well as whole industries and the general public. If WBCSD members are in the
same committee, have worked on the same publication, or there is evidence of any other sort of
collaboration, it is assumed that they would have to directly communicate with each other at
multiple points throughout the project. This justifies a tie between two people. The ties will be
undirected because the communication between the two people is assumed to be mutual. Ties
will be weighted based on the number of times two people have collaborated on an initiative.
Data Collection
I have begun to collect data by systematically going through the WBCSD website,
category by category, recording which company representatives are currently serving on the
same work group or have published a document currently available on the WBCSD website. I
23
am using Microsoft Excel to record data because it easily converts into a file that the software
will accept. The information in the data collection sheet includes: node identification number,
company name, representative name, and type of node. The members are categorized by the
committee in which they participate. For an example spreadsheet, see Appendix D. The
spreadsheets will be converted to CSV files and imported to Gephi, the open-source software I
am using to conduct the analysis.
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Future Analysis
After completing data collection and transferring the data to Gephi, I will use the
centrality measures previously discussed to analyze the data. Although I cannot discuss the
quantitative results just yet, it is important to understand why the centrality measures work as
analysis tool and what they are going to show about the network.
The most crucial concept in the analysis is that power and influence are a function of
position within the network. More central nodes are more influential, more peripheral nodes are
less influential (Cook 1983). Centrality identifies the most “important” nodes that allow
information to flow through the network. Degree centrality, the first measure, is the most basic
way of measuring the number of nodes one person in WBCSD reaches. However, it gives only
one possible perspective on the network. There are more ways of having influence than being in
communication with a large number of people. Most people are likely have other sources of
information that may conflict with information from the central node. Closeness centrality shows
how capable a node is of avoiding control. Having many short connections allows the node to
gain information from multiple sources, and therefore be less likely to receive limited or
manipulated information (Cook 1983). Nodes with high closeness centrality are less likely to be
influenced by other central nodes.
Betweeness centrality, a concept introduced by sociologist Linton Freeman, is the
opposite of closeness. Introduced as a measure for quantifying the ability of a person to control
the communication between other people in a social network, it measures a nodes ability to
control the flow of information between different areas of a network (Famiglietti 2014). For
example, if one company member is part of both the executive committee and a water solutions
publication he or she in theory would be able to control some information that flows between the
25
two groups. The water solutions publication may receive more attention or funding because of
the connection to the executive committee. It is easy to see how a node with a high level of
betweeness may impact decisions on what and how the WBCSD works on.
Eigenvector centrality is the most difficult measure to clearly explain; it is not based on
number or length of connections. An eigenvector central node is a node that has a connection to
other nodes that are high in other measures of centrality. Eigenvector nodes do not necessarily
have influence over a large number of other nodes and usually only have very limited connection
to the periphery of the network. However, they are also usually in formal positions of power or
authority within the network. Any eigenvector node in the WBCSD network may have a
disproportionate amount of influence over the function of the organization (Kadushin 2012).
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Conclusion and Future Plans
All of the analyses discussed tell us about one thing: the ability to control information.
Since mass diffusion of innovation leads to change in a network (Valente and Davis 1999), the
way information spreads throughout WBCSD and who controls it has a massive impact on the
policy tools and mission of the organization. WBCSD is a transnational powerhouse when it
comes to shaping the future of the global economy, making it vital that its actions are in the best
interest of a sustainable future. The intentions, whether good or bad, of a few individuals should
not dictate the changes WBCSD is striving to make. Understanding how the organization works
as network via social network analysis will allow global leaders to evaluate the effectiveness of
the organization. They can then discuss whether the structure needs to be adapted to better suit a
more effective and communicative space in effort to lead the business world into a green
economy. I will discuss the specific implications for the WBCSD network once the analysis is
done and the central nodes have been identified. I will then begin to look at the specific
initiatives WBCSD is producing to see if there is any correlation between the central nodes and
the projects that seem most prominent and successful. Additionally, I will discuss any
commonalities among the central nodes such as sector or region and then look at the projects
specific to any category that stands out in the analysis. I do not have predictions for what I will
discover, but I hope to find that the World Business Council for Sustainable Development is a
network that allows communication and collaboration amongst world leaders to create a
sustainable future for business, society and the environment.
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Appendices
Appendix A: Member Organizations divided by Region
Africa
South Africa
ESKOM Holdings Ltd.
Mondi
Sasol
Europe
Austria
Andritz AG
Borealis
Belgium
Solvay
KBC
Denmark
Novozymes
Finland
KONE Oyj
Metsä Group
Metso Corp
Pöyry Oyj
UPM-Kymmene Corp
France
EDF Group
GDFSuez
Havas Group
L'Oréal
Lafarge
Michelin
Kering
Schneider Electric
Suez Environnement
Veolia Environnement
Germany
BASF
Bayer
BMW Group
Continental AG
Daimler AG
Deutsche Bahn AG
Deutsche Bank
Deutsche Post DHL
E.ON
Evonik Industries
HeidelbergCement
Henkel
RWE AG
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Siemens
Volkswagen
Greece
Public Power Corp. (PPC)
Titan Cement
Ireland
CRH
Italy
Eni S.p.A.
Italcementi Group
Pirelli & Co., S.p.A.
Kazakhstan
ENRC Group
Luxemburg
ArcelorMittal
The Netherlands
Akzo Nobel
Arcadis
DSM N.V.
KPMG
Royal Dutch Shell plc.
Royal Philips Electronics
TNT N.V.
Unilever
Norway
Det Norske Veritas
Norsk Hydro
Statkraft AS
Statoil
Portugal
ALTRI Group
Brisa
EDP - Electricidade de Portugal, S.A.
Portucel Soporcel Group
Sonae SGPS
Spain
ACCIONA
Sweden
Skanska
Stora Enso
Svenska Cellulosa AB (SCA)
Sweco
Vattenfall AB
Switzerland
ABB
F. Hoffmann-La Roche
Firmenich
Holcim
Nestlé S.A.
Novartis
Sika
29
SGS Société Générale de Surveillance Holding
Syngenta International
Turkey
Borusan Holdings
Eczacibasi Holding
United Kingdom
Anglo American
BG Group
BP
BT plc
Environmental Resources Management Group-ERM
SABMiller
Vodafone Group
Asia
China
Baosteel Group Corporation
China Ocean Shipping (Group) Company, (COSCO)
China Petrochemical Corporation (SINOPEC)
CLP Holdings Ltd.
State Grid Corporation of China (SGCC)
India
Aditya Birla Group
Infosys Technologies
Reliance Industries
Vedanta Resources
ITC Limited
Indonesia
Asia Pacific Resources International Holdings Ltd
(APRIL)
Japan
AGC Group
Bridgestone Corporation
Canon
Denso Corporation
Fujitsu
Hitachi Ltd.
Honda Motor Co.
Komatsu
Mitsubishi Chemical Holding Corporation
Mitsubishi Corporation
Nissan Motor
Osaka Gas
Sompo Japan Insurance
Sumitomo Chemical Company Ltd.
Sumitomo Rubber Industries, Ltd.
Taiheiyo Cement Corp.
Toyo Tire & Rubber Co., Ltd
Toshiba Corp.
Toyota Motor Corp.
The Yokohama Rubber Company
30
Korea
GS Caltex Corporation
Hankook Tire Co., Ltd
KUMHO Tire Co., Inc.
Samsung Electronics
Malaysia
Sime Darby Berhad
Saudi Arabia
SABIC - Saudi Basic Industries Corp.
Taiwan
Acer Group
CPC Corporation, Taiwan
Chunghwa Telecom
Thailand
PTT Public Company Limited
Siam Cement Group (SCG)
Latin America
Brazil
Abril Group
Cimentos Liz
Fibria
InterCement
Natura Cosméticos S.A.
Petrobras
Suzano Papel e Celulose
Vale
Votorantim
Chile
Masisa
Empresas CMPC
Colombia
Empresas Públicas de Medellín (EPM Group)
Grupo Argos
Mexico
CEMEX
North America
Canada
Suncor Energy
U.S.A
3M
Accenture
Alcoa
Bank of America
CH2M HILL
Chevron Corporation
Cooper Tire & Rubber Company
Deloitte Touche Tohmatsu
Dow Chemical
DuPont
31
Eastman Chemical
Eaton Corp.
Ernst & Young LLP
Fluor Corporation
Ford Motor Company
General Electric
Greif
Harley Davidson
International Paper
Kimberly-Clark Corporation
MWV
Monsanto Company
PepsiCo
PricewaterhouseCoopers
Procter & Gamble
S.C. Johnson & Son
The Coca-Cola Company
The Goodyear Tire & Rubber Company
The New York Times
United Technologies Corporation-UTC
UPS
Weyerhaeuser
32
Appendix B: Membership Diagrams
33
Appendix C: Extended Terminology List
State relation: has a degree of permanency or durability that makes it easier for the researcher to
see ex. Family, religion
Event relation: more temporary and may or may not imply a more durable relation, more like
individual occurrences ex. Attending same conference, sending an email
Digraph: graph with directed relations, called arcs
Boundary: defining which actors are included in the network
Isolate: no ties to other nodes in the network
Path: walk where no node occurs more than once
Semipath: ignores direction of arcs
Symmetric matrix: contains data for undirected network
Asymmetric matrix: contains data for directed network, records direction of ties
Valued matrix: shows weighted values
(Prell 2012)
34
Appendix D: Data Sheet Example
35