bram koster - the definition of a social media strategy

8/8/2019 Bram Koster - The Definition of a Social Media Strategy

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The Definition of a

Corporate Social Media Strategy

Bram Koster


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The Definition of a Corporate Social Media Strategy

Bram Koster

[email protected]

A thesis submitted to obtain the degree of

Master of Marketing Management

at

Erasmus University Rotterdam / SRM

January 2008

Reviewers:

Koen Hazewinkel

Drs. Okke Postmus


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The Definition of a Corporate Social Media Strategy 3

1 Abstract

Social media have given voice to internet users through a number of technological, social,

economic and legal developments. The users are taking that opportunity to make themselves

heard: it is expected that in three years time 70% of the online content will be user-

generated. Some of that content is targeted at companies and their products & services. This

behavior of users, either favorable or not, is making it harder for these companies to control

their brands, as their own messaging is mixed with the users messaging about these brands.

To understand how this brand messaging works, this thesis will draw on complexity theory,

social network theory and social capital theory to investigate communications in complex

systems in general, to explain the role of social media in those communications and theimpact of social media on brands and corporate brands in particular.

Finally, it will describe how companies can organize their corporate branding to cope with

social media and external complexity.


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2 Table of contents

1 Abstract ..........................................................................................................................................3

2 Table of contents..........................................................................................................................4

3 Acknowledgements.......................................................................................................................6

4 Introduction...................................................................................................................................7

5 Social media...................................................................................................................................8

5.1 Definition of social media................................................................................................8

5.2 Types of social media........................................................................................................9

5.2.1 Weblogs................................................................................................................9

5.2.2 Wikis.....................................................................................................................9

5.2.3 Social networks .................................................................................................10

5.2.4 Sharing services.................................................................................................10

5.2.5 Podcasts..............................................................................................................10

5.2.6 Virtual worlds....................................................................................................10

5.3 Social media visitors........................................................................................................11

5.4 Social media contributors ...............................................................................................11

5.5 Properties of social media..............................................................................................12

5.6 Impact of social media on corporate brands...............................................................15

5.6.1 Collaborative behavior.....................................................................................155.6.2 Sociallyconstructive behavior ........................................................................16

5.6.3 Aggressive, destructive behavior ....................................................................16

6 Complexity and network theory an introduction...............................................................17

6.1 Complexity theory ...........................................................................................................17

6.2 Network theory................................................................................................................18

6.2.1 Small-world effect.............................................................................................18

6.2.2 Clustering...........................................................................................................19

6.2.3 Scale-free networks...........................................................................................19

6.2.4 Assortative degree correlations.......................................................................20

6.3 The internet as complex adaptive network..................................................................20

7 Communications in complex systems .....................................................................................24

7.1 Opinion leaders vs. brokers ...........................................................................................25

7.2 Influentials and opinion formation...............................................................................27

7.2.1 Cascades of influence.......................................................................................28


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7.3 Conclusion ........................................................................................................................29

8 Social media in complex networks...........................................................................................31

8.1 The role of the internet in complex networks ............................................................31

8.2 Social media as weak tie hubs........................................................................................32

8.3 Information push and pull in social media..................................................................33

8.3.1 Information push: social media as facilitators of information cascades..33

8.3.2 Information pull: social media as information archive...............................34

9 Branding in complex networks.................................................................................................35

9.1 Branding as a complex concept.....................................................................................35

9.2 Social aspect of branding................................................................................................36

9.3 A branding strategy for complex networks .................................................................37

9.3.1 Messaging...........................................................................................................37

9.3.2 Monitoring.........................................................................................................38

9.3.3 Alternative messaging......................................................................................38

9.3.4 Consistency........................................................................................................38

9.3.5 Organization......................................................................................................39

9.4 Corporate branding in complex networks...................................................................39

9.4.1 Corporate reputation management................................................................40

9.4.2 Corporate brand PR.........................................................................................41

9.4.3 Corporate brand architecture..........................................................................42

10 References...........................................................................................................................43


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3 Acknowledgements

Writing a thesis is a complex process, requiring the input and support of many. My thanks go

out to:

Koen for his help in defining the subject and scope of this thesis, his guidance and his

support,

Arno for reading along and giving feedback,

Thomas and Guy-Manuel for keeping me alive throughout most of the writing,

Susan for her support and for letting me neglect her for too long without complaining,

and Kiki for her smiles.


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4 Introduction

Social media have empowered normal people by enabling them to easily share information

and opinions. Some of this information will be about companies, their products and services,

and their brands. This information dilutes the information that the company provides itself,

influencing the brand image.

Therefore, it is important for any company to understand the impact social media can have

on its branding efforts. This thesis will try to answer questions such as How do opinions

from users influence other users brand perceptions?, What is the role of social media in

this?, Can social media be used to spread the brand identity?, How should companies

organize to cope with social media?, etc.

The answers to these questions will help answer the central question of this paper:

What corporate social media strategy should a companypursue

to strengthen its corporate rebranding online?


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5 Social media

In recent years a number of technological, social, economic and legal developments have

enabled users to create and distribute content themselves (OECD, 2007, p. 30). And that is

exactly what these users did and are doing: it is expected that in three years time 70% of the

online content will be user-generated1.

One of the most important developments that caused this tidal wave ofbottom-up contentwas

the availability of websites, web services and software for easy creation, editing, distributing

and sharing of content. These tools are often referred to as social software and the websites

that are created through this software are referred to as social media.

5.1 Definition of social media

Wikipedia defines social media as anything that uses the internet to facilitate conversations.

Its expanded definition is: Social media is the democratization of information, transforming

people from content readers into content publishers. It is the shift from a broadcast

mechanism to a many-to-many model, rooted in conversations between authors, people, and

peers.2

This expanded variation describes the effects of social media rather than the media

themselves. Wikispaces Social Media wiki offers a more specific description of the

phenomenon for social media. It defines social media as the collection of online tools and

platforms that people use to share opinions, insights, experiences, and perspectives with each

other3.4 It is this definition that will be used in this thesis.

1 According to Datamonitor: http:/ / www.immediatefuture.co.uk/ the-top-100-brands-in-social-media-/.2 http:/ / en.wikipedia.org/ wiki/ Social_media3 Slight adaptation from: http:/ / socialmedia.wikispaces.com/ What+ is4 Interesting about these definitions is of course that they are self-referential: they were drafted by internet userscollaborating through wikis, one of the very media the definitions describe.


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5.2.3 Social networks

Social networks are web-based services that allow individuals to (1) construct a public or

semi-public profile within a bounded system, (2) articulate a list of other users with whom

they share a connection, and (3) view and traverse their list of connections and those made

by others within the system (boyd, 2007). Some examples of these networks are MySpace,

Facebook, LinkedIn and Xing, of which the first two are primarily aimed at networks of

friends and the latter two on professional networks.

5.2.4 Sharing services

Sharing services are websites through which users can share multimedia assets, such as

photos, videos, documents, presentations or bookmarks. The users can make these assets

available to specific others or to the public. Sharing services can offer functionality for

visitors to react to assets, to rate them or to send them on to other users. Examples of these

websites are Flickr (photo sharing), YouTube (videos), Google Docs (documents),

SlideShare (presentations) and del.icio.us (bookmarks).

5.2.5 Podcasts

Podcasts are multimedia files which are distributed over the internet, often using syndication

techniques, for playback on portable media players and personal computers.9 Podcasts can

be considered the poor mans radio station or television station. They enable people to

produce their own audio or video show with relatively simple means and send them out to

their audience through the internet.

5.2.6 Virtual worlds

Virtual worlds are computer-based simulated environments intended for its users to inhabit

and interact via avatars10. Mostly, these worlds consist of two-dimensional or three-

dimensional environments in which the users are represented as humanoids. These worlds

allow multiple users to occupy the same virtual room and interact with each other, e.g. in

games. The best-known examples are Second Life, EverQuest, World of Warcraft and

RuneScape.

9 Based on the definition on Wikipedia: http:/ / en.wikipedia.org/ wiki/ Podcasting10 Source: http:/ / en.wikipedia.org/ wiki/ Virtual_world


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5.3 Social media visitors

Social media have found their way into the general publics media diet. A recent study

amongst German heavy internet users found that 87.3% of them read blogs and 34.6% write

on one or more blogs (Zerfass and Bogosyan, 2007). 55% of American teens have online

profiles (Lenhart and Madden, 2007), while at least 57% create content for the internet

(Lenhart and Madden, 2005).

MySpace, the largest social network community in the world, has 289 million profiles, while

Facebook, the number two, has 73.5 million11. Monthly, respectively 68 million and 26

million people from all ages, educational and income brackets visit these sites (Atal, 2007).

Blogs are also hugely popular. Concrete numbers are hard to find. Some sources simply state

that there are over 70 million blogs worldwide and the number is growing at a rate of some

120.000 blogs per day and these blogs are filled with over 1,5 million postings per day.12 But

the China Internet Network Information Center claims that in China alone there are some

73 million weblogs, run by approximately 47 million users13. Even if we cant be sure which

numbers are correct, they are impressive enough to understand that blogs in particular and

social media in general are popular destinations on the internet. This statement is further

supported by the fact that out of the global top 10 most visited websites 6 are social media

websites14.

5.4 Social media contributors

Now that weve established that social media are highly visited, it is interesting to find out

who is responsible for the creation of the content in social media. A popular belief on the

internet is the 90-9-1 rule: out of every 100 visitors, only one person will actively contribute to

a social media website, 9 will react to it by commenting or editing, and 90 will only read or

view it (Arthur, 2006). Thisparticipation inequality (Nielsen, 2006) is supported by findings

from social media such as YouTube, which has 100 million downloads vs. 65.000 uploads

per day, resulting in a creator-to-consumer ratio of 0,5%, and Wikipedia, for which more

than 70% of the content was written by its top 1,8% editors (Arthur, 2006).

11 http:/ / en.wikipedia.org/ wiki/ List_of_social_networking_websites12 http:/ / www.sifry.com/alerts/ archives/ 000493.html13 http:/ / www.cnnic.cn/ html/ Dir/ 2007/ 12/ 27/ 4954.htm14 http:/ / www.alexa.com/ site/ ds/ top_sites?ts_mode=global&lang=none


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The level of participation can be influenced by the specific functionality of a website. By

making it easy to initiate a conversation or to participate in them, the participation can be

stimulated. On the other hand, the easier it is to react to content, the less connected people

will feel to the site or to the discussion or to each other (Xia et al., 2007).

5.5 Properties of social media

boyd (in press: 9) defines four properties of mediating technologies that distinguish normal,

face-to-face conversations from those between networked people, namely:

Persistence: networked communications are recorded indefinitely, enabling

asynchronous communications and extending the period of existence.

Searchability: online conversations can easily be indexed and found through search

engines, especially conversations in text format.

R eplicability: networked communications can be replicated verbatim so there is no

difference between the original and the copied content.

Invisible audiences: it is impossible to determine who will take note of any networked

communications.

While boyd15 argues that these properties affect both the potential audience and the context

in which the expression is received (in press: 9), one can argue that they also affect the

subjects being discussed, e.g. companies and its brands. Opinions being discussed between

the networked people will remain available for a long, indefinite time, are highly findable

through search engines and can easily be forwarded. This sums up nicely why it is important

for companies to keep track of these online conversations.

The only aspect that is of less importance to the subject is the invisibility of the audiences.

Even if the identities of the entire audience of an online conversation were known

- something of which one cannot be sure on the internet most of the time -, it would be very

hard to determine their relationship to the company and its brand. A member of the

audience could be a direct customer, an end user, a supplier, a potential employee or be part

of the general public.

15 danah boyd prefers her name to be written without capitals: http:/ / www.danah.org/ name.html.


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Therefore, it would be more interesting to know more about the (potential) size of the audience.

On the generic level of social media types, this property is determined by the penetration of

the social media type.

The four properties dont apply equally to each type of social media. The persistence of

conversations on weblogs is most likely higher than of those in virtual worlds such as Second

Life, simply because the weblog discussions are stored and the Second Life conversations by

default arent. Also, written articles are better searchable than video messages on YouTube,

primarily because most search engines focus on text search.

Therefore, the level to which these properties apply to social media will determine their

potential impact on companies and brands. In the table on the next page an overviewis

provided of the level to which the properties apply to types of social media.


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Type of social

media

Persistence Searchability Replicability Audience size

Blogs High. All blog postings

and comments are

stored on the website.

High. High number of

links between weblogs,

frequent updating and

text-based content

results in high search

results in major search

engines.

High. Content can easily

be copied.

High. 80% + of internet

users visit blogs (Zerfass

& Bogosyan, 2007).

Wikis High. All versions of a

wiki entry are stored,

even after being

changed.

High. Text-based

content and high

number of incoming

links make wiki entries

popular search results.


be copied.

Medium, as most wikis

serve niches. Exception

is Wikipedia, which has

a wide audience.

Social

networks

High. Conversations

taking place through the

social network are

stored indefinitely.

High. Text-based

content, high linkability,

so easily searched and

high in search results.


be copied.

Medium. Top social

networks have some

300 to 500 million

profiles combined.

However, most users

dont use social

networks for messaging.

Sharing

services

High. Assets, tags and

comments are stored.

Medium high. Tags and

comments are text-

based, less focus on

discussion, high

linkability.


be copied.

Medium to high,

depending on sort of

asset.

Podcasts Medium high. Most

podcasts are available

indefinitely, but some

are only available for a

limited time period.

Medium high. Tags and

comments are text-

based, linkability limited

because hard to

deeplink (link to specific

moment in audio or

video message).

Medium. Entire audio

or video message can be

copied, but threshold

for sharing of specific

pieces of messages

because of tools and

knowledge required.

Low. Technically

challenging to listen on

MP3 player and often

niche programs.

Virtual worlds Low. Conversations in

online worlds are stored

only sporadically.

Low. Conversations

mostly not stored, so

not searchable.

Low. Proprietary nature

of virtual worlds make

sharing assets and

actions through other

platforms hard.

Low (e.g. Second Life

only had 44.000 users in

the Netherlands at its

peak, early 200716).

Table 1 Properties of types of social media

16 http:/ / www.nu.nl/ news.jsp?n=1047920&c=55


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From the overview above it appears that blogs, wikis and sharing services are the most likely

types of social media to potentially enable online discussions with a sufficiently large

audience to influence a companys corporate or product brands. That being said, it can be

hard to discern these specific types as many websites combine several types of social media.

One example is that many of the social network services enable their users to maintain blogs

and offer services to share photos, videos, etc.

5.6 Impact of social media on corporate brands

From earlier paragraphs it has become clear that social media enable people to create and

publish their own content. In doing so, some are also addressing issues concerning

companies, influencing these companies brands. They do this for different reasons and with

different attitudes, ranging from positive to negative from the companys perspective.

The publics behavior in social media can be categorized into three categories: collaborative

behavior, socially constructive behavior and aggressive, destructive behavior (Gasser and

Palfrey, 2007). These will be clarified below.

5.6.1 Collaborative behavior

Collaborative behavior is the interaction between a consumer and a company where the user

seeks a positive result for the company and himself by providing relevant feedback on thecompanys products and services. Even though the feedback itself can be negative, the users

attitude is positive towards the company and its products and services.

An example of this collective behavior can be found in any beta-testing of software.

Although the comments, often provided publicly, can be harsh, direct and apparently

negative, the goal of the users is to help the company provide better software for the

customers and thereby to the users themselves.

Another, more specific, example is Jeff Jarvis Dell Hell affair. In June 2005, Jeff, a

communications professional and blogger at weblog BuzzMachine.com, started posting

complaints about Dells service on his new pc. Soon he received hundreds, even thousands

of similar complaints form other aggrieved customers. His story was picked up by other large

blogs, and also by the traditional media. From an analysis of the affair it appeared that Jarvis

post and the subsequent comments and discussions had a larger impact on the publics


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6 Complexity and network theory an introduction

6.1 Complexity theory

In 1948, Warren Weaver recognized the difference in advancements made until 1900 in

physical sciences compared to those in life sciences. According to Weaver, the difference lay

in the fact that physical sciences concerned two-variableproblems of simplicity, whereas the life

sciences had hardly begun explaining the mechanisms making up biological events because

of their complexity (Weaver 1948).

He distinguished disorganized and organized complexity. D isorganiz ed complexityresults from a

system with a large number of variables with individually erratic behavior. The system as a

whole however, has average properties that can be analyzed through statistical methodology.

The only problem was that the means to calculate the large number of variables were not

available yet. Organiz ed complex ity on the other hand, contains a sizeable number of variables

which are interrelated and influence each other through their behavior to form an organic

whole. Weaver pleaded that science had to learn how to deal with this organized complexity

(ibidem.).

This plea meant that a paradigm shift was needed from science in the reductionist traditionof Newton, which models reality to its simplest components and then uses these to describe

them in a complete, objective and deterministic manner (Heylighen 2008). Furthermore,

the study of complexity had to break with the classical division of sciences as complexity is

not limited to life sciences as described by Weaver, but it is apparent in a large number of

other fields, such as mathematics, social sciences and physics, if not in all sciences. This also

means that the study of complexity itself is hard to define because complexity research is

trying to grapple with questions that defy all the conventional categories (Waldrop 1992,

p. 9).

But even though complexity can be recognized in issues in a wide variety of scientific fields,

these issues have a lot in common as they all refer to complex adaptive networks that share

certain characteristics. The systems undergo spontaneous self-organiz ation, are adaptive, and are

situated at the edge between order and chaos. The elements in the system continuously organize

themselves to better handle internal and external disturbances, constantly reacting to each


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others actions, thereby adapting for constant change. In doing this, they balance order and

chaos, and show behaviors in which the components of the system never quite lock into

place, yet never quite dissolve into turbulence, either (Waldrop 1992, p. 293). This way, the

system finds itself in equilibrium, a steady state of continuous change, without any

centralized control.

6.2 N etwork theory

The result of the self-organization can be represented as a so-called complex network. In the

initial state of an emerging complex network the agents, also referred to as elements or

actors, interact randomly with any agent in their direct vicinity. However, agents then start to

develop preferences for retaining some interactions, turning them into permanent links, and

discarding others. These permanent links tie the agents together into a network, in which the

agents themselves can be seen as the nodes.

An agents self-organization is aimed, sometimes consciously and explicitly, at constantly

improving its position and fitness within the network. The agent seeks to generate, sustain

or improve ties with other agents and shows specific behavior in order to survive or even

obtain a better position within the network. These local actions at the level of the agents lead

to changing patterns on the global network level (Ethier, unknown). We therefore refer to

these specific complex networks as complex adaptive network s (Monge, 2003).

The fact that local actions lead to patterns on a network level means that even though

complex adaptive networks may be intrinsically unpredictable and uncontrollable

(Heylighen 2008, p. 2) because of their nature, we can analyze its qualitative behavior

through network analysis. Through this analysis, researchers have found that complex

adaptive networks show specific characteristics which distinguish them from normal

networks: the small-worldeffect, clustering, and scale-free networks.

6.2.1 Small-world effect

The small-world effect can be observed in networks in which the diameter of the network,

the maximum length of the shortest path connecting two nodes, is relatively small compared

to the number of nodes. This phenomenon is more popularly recognized from the six

degrees of separation that Stanley Milgram found in an experiment in the sixties.


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In this experiment, Milgram asked people to send an acquaintance of his a letter. However,

he did not provide an address but asked the participants to hand the letter to someone they

suspected would be socially closer to the addressee and ask them to do the same. Most of

the letters arrived, on average within six steps.

After this experiment Milgram claimed that despite the six billion inhabitants of our planet,

any person in the world can be traced to any other by a chain of five or six acquaintances,

rendering the world small. In analyzing the small-world effect on the worldwide web,

Barabsi found that any two online documents were separated by approximately 19 clicks on

an estimated total of one billion documents (Barabsi, 2001).

In a regular network, links exist only according to strict rules. The small-world effect can be

easily introduced in such regular networks by adding a small number of random links

(Buchanan, 2002; as cited by Scharnhorst, 2003). These links, connecting nodes that are

otherwise not directly linked, drastically reduce the diameter of the network.

6.2.2 Clustering

Another characteristic of complex networks is clustering, which means that there is a high

probability that friends of friends are also directly linked. This means that if agent A is linked

to agent B, and agent B is linked to agent C, the chance that A and C are directly linked as

well is higher than in normal probability. Through clustering, groups or communities start to

form within a larger network.

Other than the small-world effect, clustering will increase the distance between two random

nodes in a network as clusters have many links inside the cluster itself, but only a few outside

it.

6.2.3 Scale-free networks

In a regular network all agents have approximately the same number of links to other agents.

However, in complex adaptive networks the numbers of links that agents have to each other

are not evenly distributed. In fact, this distribution tends to follow apower law: some agents

have very many links, while many agents only have a small number (Barabsi, 2001). The

highly linked agents in a network are the hubs that connect many different agents and clusters

of agents, thereby decreasing the distance in a network.


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The most likely explanation for the power law distribution is preferential attachment: new agents

in a network prefer to be linked to agents that already have a large number of links (Albert &

Barabsi, 2002). They pursue these links to improve their personal network fitness,

meanwhile further improving the fitness of the already heavily linked agent. This effect is

more popularly referred to as the rich get richer.

6.2.4 Assortative degree correlations

Complex networks, and especially social networks, are also characterized by assortative

degree correlations (Nekovee et al., 2007). This is a specific case of the above described

preferential attachment meaning that highly linked agents in a network preferably link to

other agents with many ties.

6.3 The internet as complex adaptive network

The internet as referred to in general is in fact a multi-layered concept that consists of at least

three levels of networks. The technological network is the infrastructure of the internet, built up

from computers, routers and connections (wired and wireless). The information network is the

level at which information is being transferred. Examples are e-mail, Usenet and the World

Wide Web in which documents are the nodes, linked together through hyperlinks. The social

network is made up by the people interacting with each other through the internet, either by e-mail, chat or through social media such as discussed in this thesis.

One can claim that all systems are complex adaptive systems (Harris & Zeisler, 2002),

which would automatically lead to the conclusion that at least the social network layer of the

internet is just that. And there are several clear signals to support that conclusion.

In an analysis of the economy as a complex adaptive system, John Holland, a leading

researcher in complexity, compares the characteristics of such a system to the global

economy (Waldrop, 1992, p. 144-147). To a large extent, this comparison applies to the

internet seamlessly.

The first characteristic is that the system consists ofagents acting in parallel, defining their own

situations through their interactions with other agents and constantly acting and reacting to

each other. This is true for the people using the internet to communicate, but also for the


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information on the web. This is most clearly illustrated by the fact that a documents search

ranking in the major search engines such as Google and Yahoo! heavily depends on the

number of links to that document. Therefore, the status of information is determined by

people creating new information that links to it. Furthermore, in social media it is quite

common that the information that is being linked to acknowledges that link with a backtrack

link (see chapter 5.2.1 on page 9), thus influencing the status of that information. This

interaction is a good example of information defining its own situation through interaction

with other agents.

Also, complex adaptive systems have a highly dispersed control, or, as some might say, no control

at all. Any coherent behavior will only arise from competition and coordination between the

agents in the network. Qvortup (2006) illustrates this point by referring to the international

commotion about the Mohammed cartoons in Denmark and the fact that no formal control,

e.g. from governments, sorted any effect in stopping the commotion. Another interesting

example is the student protests in the Netherlands at the end of November 2007. These

protests, aimed at reducing the number of school hours to improve educational quality, were

massive, spontaneous and without any form of organization (Versluis, 2007). Students

were incited to take parts in the protest primarily through messages on the internet (and via

their mobile phones). Students received several messages, all different and through different

ties, but there was no way to find out where the messages had originated. More than 300

websites concerning the actions were created on the countrys most popular social network

site, Hyves, and there are more than 700 movies of the actions on YouTube. Through the

teenagers constant interactions, to a large extent online, there is no way to determine who

initiates actions, theres no hierarchy at all (ibidem.).19

The second characteristic is that a complex adaptive system has many levels of organiz ation, in

which agents on one level serve as building blocks for agents at a higher level. This holds

true for the internet as well. As pointed out earlier, the internet consists of several levels. The

agents on the technological level, pcs, connections, routers, servers, etc., are needed to

enable the network of information. And the information is needed in order to connect the

people, agents in the social level. Even within these network levels there are a number of

levels of organization distinguishable. On the information level, several documents can

constitute a web page, several web pages together form a website, etc.

19 This example not only illustrates lack of central control on the internet, but also the effects in other, offline,parts of society, and thereby the fact that the internet is an integral part of that social network.


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An important aspect is that the building blocks are constantly being revised and rearrangedwithin

complex adaptive networks as the agents and the networks gain experience. This

evolutionary process, also being referred to as self-reflection, means that successful links will

be repeated whereas less successful ones will not, and that reorganization will take place to

improve the success rate. This behavior can be witnessed in the preferential attachment as

mentioned above, where links to already highly linked documents or people are most popular

because they provide status, but also in the creation of new websites, new online networks,

etc. aimed at providing new possibilities for the agents for building up a position in the

network.

The third characteristic is anticipation and prediction of the future. Every complex adaptive system is

constantly predicting the future by looking at its own internal models. The system and its

agents have learned how to react to certain circumstances in the world around it and are

anticipating these circumstances. This holds true on all levels of the internet, even on the

most fundamental of them, the technological level. The technology stems from military data

systems that were built in such a way that they would automatically reroute data streams if

parts of the network would collapse, e.g. from demolition in a war. Information is split into

numerous small data packets that each find their own way through the network, only to be

joined again into their original state at their destionation. The system learns from these

interruptions and anticipates them.

Finally, complex adaptive systems are characterized by niches. These niches can be exploited

by agents if they adapt to that niche properly. The abundance of niches can be found in all

aspects of the internet, e.g. in the rise of a wide variety of agencies and professionals offering

their specific expertise in internet itself, such as website development and design, online

advertising, search engine optimization and marketing, online public relations, etc. Another

indication is the introduction of many generic services through the internet, from weather

services to online dating, from product comparison websites to online shops, and from

education to job sites.

From the discussion above it is clear that the internet itself is a complex adaptive system with

multiple levels and effects that spread beyond its own boundaries. Or as Bauer (2005: 3)

describes it: The Internet is a large, adaptive socio-technical system. It is, furthermore, a


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multi-layer system in that it is embedded in and has effects on local, sub-national regional,

national, supra-national, regional, and global levels.


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7 Communications in complex systems

Now that we have established that the internet is a complex adaptive network, it is

interesting to investigate how information is spread over these types of networks.

As a study of dynamics on networks rather than of networks, the communications on

networks resemble similar dynamics on other networks, such as the spread of diseases in

social networks, chemical reactions in a physical space, or the spread of viruses on computer

networks. Additionally, also a lot of research in this area was performed on the dissemination

of innovations and of rumors through networks.

When talking about the spread of diseases in networks, in the most basic form this research

looks at whether a network node or agent is infected or not, the number or percentage of

infected agents within the network and the rate at which the epidemic spreads. From this

research it appears that there is a characteristic threshold for epidemics on exponential

networks (Scharnhorst, 2003). This threshold is the result of two processes. First of all there

is the infection process, determining the rate at which nodes are infected. Secondly, there is

the healing process, determining the rate at which infected nodes become healthy again. If

the healing process is stronger than the infection process, no or hardly any infection will take

place. Above a critical point, the infection process will gain momentum, infecting evergrowing parts of the network at an accelerating speed.

Studies have shown that in scale-free networks with a specific rate of link distribution, the

epidemic threshold does not exist and any infection can always infect the entire network

(ibidem.). When looking at real-world scale-free networks, it becomes clear the agents with

the highest number of links are very susceptible to infection themselves, but also make the

entire network susceptible because of the high connectivity the have with other agents.

Lowering of the vulnerability of these networks can therefore be only achieved by focusing

on immunization of the agents with the highest connectivity as they are the most likely

spreaders of the disease.

A study of the spreading of computer viruses through the internet confirmed this tactic.

However, it needs a global immunization organization that secures a small set of selected

high-traffic routers or Internet domains. And the downside is that the self-organized


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nature of the Internet does not allow to easily figure out how such an organization should

operate (Pastor-Satorras & Vespignani, 2001, p.8; as quoted by Scharnhorst, 2003).

A slightly more sophisticated version of this model includes the state recovered, meaning that

after a certain period an infected agent will no longer infect other agents, either because it

has been removed from the network or because it recovered from the disease (Jackson &

Yariv, 2005). This version of the model is very similar to the basic DK rumor model, named

after the authors first describing it in 1965, Daley and Kendal. This model describes the

dissemination of rumors through networks, where each of the agents is either ignorant,

spreader or stifler. Ignorant people have not heard the rumor but are susceptible to it,

spreaders actively spread the rumor and stiflers are agents who know the rumor but have

stopped spreading it. (Boccaletti et al., 2005). In the DK model, a key predictor of whether

an agent knows a rumor is the number of neighboring agents in his network that know and

spread it.

Although this model adds a level of complexity, it still falls short of explaining the spread of

information in complex networks. One of the reasons for that is that the model does not

take into account the underlying social networks along which a rumour spreads. In other

words, it assumes that the network is completely homogeneous (Nekovee, 2007b). It thereby

ignores the different characteristics and roles that agents have in complex adaptive networks,

and also the contents of the message itself.

7.1 Opinion leaders vs. brokers

In the 1940s and 50s, two researchers studied the way in which mass media influenced the

public opinion. These researchers, Paul Lazarsfeld and Elihu Katz, found that in several

decision-making processes, people were much more influenced by other people than by the

media. A small number of so-called opinion leaders acted as the intermediaries between media

and the general public. Based on these findings, they introduced the concept of the two-step

flow of communications, in which information flows from the mass media to the opinion leaders,

who then pass on the information to the people. Until then, the general idea about the role

of the mass media had been that they directly impregnated individuals with ideas and

opinions, a theory referred to as the hypodermic needle (Watts & Dodds, 2007).


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(Granovetter, 1973, p. 1361). His hypothesis is that if two people have a strong tie they will

share a lot of ties with other people20. This means that good friends share other friends, to a

large extent because of the time commitment involved in stronger ties. In other words, if A

has a strong tie to B and a strong tie to C, B and C will most likely also have a tie, either

strong or weak.

Using the admittedly exaggerated supposition that in such cases B and C will always have a

tie, it then follows that no strong tie can be a bridge. For information flow, this means that

weak ties that create local bridges provide more and shorter paths within a network.

Information can reach a larger number of people, and traverse greater social difference, such

as path length, when passed through weak ties rather than strong.

Summarizing, opinion leaders are not leaders so much as brokers, connecting several groups

of agents in a network with each other through most likely weak ties. These brokers are

essential in the diffusion of information over a network. And since these brokers are

relatively sparse and are potentially findable through network analysis, efforts can be made to

contact and influence these brokers directly in order to make them pass the information on

with as little distortion and noise as a result of the broker adapting the message to suit his

own needs as possible.

7.2 Influentials and opinion formation

And that is precisely what companies and other organizations, such as political parties, have

tried over the past few years. With the rise of online social networks the possibilities of

tracking and influencing information brokers have increased, triggering more interest in these

networks, but also the establishment of companies to cater for that need (LaPlante, 2007).

But to think that a relatively small group of influentials can drive trends in complex adaptive

systems seems contradictory. After all, complex adaptive systems have highly dispersed

control and any behavior is the result of competition and coordination between all agents. It

therefore seems unlikely that only a limited number of agents could indeed effect the entire

network. And indeed, the concept of highly influential information brokers appears to be a

simplification that does not do justice to the complex reality.

20 A phenomenon described earlier in this thesis as clustering(see chapter 6.2.2 on page 19).


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7.2.1 Cascades of influence

In recent research into so-called cascades of influence, it was found that large-scale changes inpublic opinion were not the result of highly influential people influencing everyone else, but

rather of easily influenced people influencing other easily influenced people (Watts & Dodds,

2007).

Cascades are sequences of activations through interpersonal influencing, i.e. a network agent

influencing his network neighbors, who then influence their own neighbors, and so on21. The

scale of cascades can differ with the highest scale being global cascades that affect many

individuals, propagate for many steps, and are ultimately constrained only by the size of the

population through which they pass (Watts & Dodds, 2007: 14). These global cascades

require a critical mass consisting of sufficiently many early adopters that are connected to

each other that the network percolates throughout the entire influence network. Without a

critical mass, only local cascades will occur.

The research focused on the role of influentials the minority of agents who influence an

exceptional number of their peers on cascades. This role appeared to be far less than

suggested in earlier theories. The hypothesis that influentials are in some way essential to

diffusion was clearly not supported, as influentials were neither necessary nor sufficient to

trigger large cascades.

The study was aimed at interpersonal influence. This means that media personalities, etc.,

who may have far greater visibility than ordinary individuals, and whose influence is

transmitted indirectly via media of various forms, were initially not considered. Related to

this distinction between personal and media influence is web-mediated influence, such as

that exerted via blogs, social sharing sites, and online forums. Although individuals can

indeed gain considerable exposure for their views by expressing them online, the influence of

the blogger seems closer to that of a traditional newspaper columnist or professional critic

than that of a trusted confidant, or even a casual acquaintance.

21 Note that such a cascade can involve more than the two steps of the two-step flow of communications. It istherefore called a multi-step flow.


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In fact, under certain circumstances it appeared that in real social networks influentials

actually triggered smaller cascades than average agents. This surprising effect can be

attributed to assortativity, the tendency of influentials to interact with other influentials. As

described earlier (internal cross-reference) assortativity is a characteristic of complex adaptive

systems.

Based on the research, Watts & Dodds conclude that cascades are not the result of a few

highly influential individuals influencing everyone else, but rather of a critical mass of easily

influenced individuals influencing other easy-to-influence people. Influentials only have a

modestly greater chance than average of triggering this critical mass, and only if it this critical

mass already exists.

Although in retrospect it is always possible to reconstruct what seems to be the cause of a

notable social change, there is no way to predict them. Watts & Dodds use the following

analogy: Major forest fires require a conspiracy of wind, temperature, low humidity, and

combustible fuel that extends over large tracts of land. Just as for large cascades in social

influence networks, when the right global combination of conditions exists, any spark will

do; and when it does not, none will suffice (2007, p. 34).

7.3 Conclusion

The bottom line then is that so-called influentials are actually not disproportionately

influencing networks and that there really is no way of predicting if and how information will

cascade through a complex network. Is this an unexpected result? Hardly, if one considers

that a model needs to be sufficiently complex in order to explain the complex reality.

Assuming that a limited number of agents have such a large influence on the general opinion

within a network is simply too oversimplified. Or as Luhmann puts it: One cannot address

external complexity by absolute closure. Only complexity can reduce complexity (1995: 26;

as quoted by Qvortup, 2006: 352).

Although we may as yet have not come up with a way to predict how communications work

in complex adaptive systems, some of the findings as mentioned before remain valid. For

instance, weak ties still play a vital role in diffusing information through groups within a

network.


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Another interesting finding is that once a critical mass exists, the creation of a cascade is

relatively easy. This means that recognition of critical masses could proof to be interesting to

make predictions about which messages will be spread through networks and which will not.


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8 Social media in complex networks

Having discussed the way in which information spreads through complex networks, we will

now investigate the role of the internet in general in these communications, and the role of

social media specifically.

8.1 The role of the internet in complex networks

The internet has a specific role in communications within complex networks. As explained

earlier, the internet is a complex network in itself. And as a medium for communications it

helps to manage the complexity of the society in which it is used. As stated before: only

complexity can manage complexity.

In comparison, societies based on oral communications have only little capability for

managing complexity, primarily geographically and historically, limited mainly by its physical

reach and agents ability to remember. Thus, only societies with a relatively low level of

complexity can manage themselves through oral media.

During the writing era, the size of a communications community was restricted by access to

the limited number of copies available, the ability to write and read and the preservation of

hand-written documents. The printing era expanded the geographical reach to a global level,

but it was limited by delays in physical transportation, limited reading abilities and the

challenge to organize the growing amount of information (Qvortup, 2006).

The internet has kicked off the digital era. The size of the communications community is

now determined by the number of people with access to the internet, which is limited

compared to during the printed era (despite its apparent rapid growth). On the other hand,

information can be exchanged almost immediately, so delays are minimal, and stored

indefinitely. And even more importantly, the internet integrates all known media into oneconverged multi-semantic system. These features increase the internets internal complexity

enabling it to cope with the ever increasing social complexity, but at the same time create

complexity management problems, such as an abundance of information (ibidem.).


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8.2 Social media as weak tie hubs

The internet has enlarged the communications community by connecting smaller

communities previously unconnected because the communications they used before were

not able to handle the additional complexity. Social media have provided people with a low-

threshold access to this platform by enabling them to contribute to it. The result is that

people previously unrelated are now exchanging information and experiences.

Social media are then the only way in which these people are tied. From this singular

medium being used by the agents to communicate with each other, their mutual tie strength

can be deduced. As the tie between two agents strengthens, they will use more media to

communicate with each other. Therefore, for agents otherwise unconnected, we can

conclude that social media connect them through weak ties. As agents with a high numbers

of weak ties, or weak tie hubs, social media are highly capable of introducing new information

to a large number of groups within a network (Haythorntwaite, 2005).

Social media do not only establish new weak-tied relationships. For agents previously

connected, the internet and social media are another medium through which they can

communicate, enabling them to sustain and reinforce their strong-tie relationship. The

primary effect of social media on these relationships is that these relationships themselves

and parts of their mutual communications become public. The clearest example for this is

the social network sites. To a large extent people use these websites to connect to existing

relations (Pew Internet, 2007), thereby making their strong ties explicit, and to communicate

with these relations, making the communications explicit. Since most of these websites are

publicly accessible, the result is that existing private relationships become at least partly

public (Haythorntwaite, 2005).

The public nature of communications through social media, whether between weakly or

strongly tied agents, makes these communications searchable. In theory, information will be

available online until eternity, accessible through search engines. And because social mediaare weak tied-agents linking many groups, they have a bigger change of being hyperlinked to

by their visitors, e.g. by people passing on the information within their groups through their

strong ties. Since search engines such as Google use criteria such as proximity, link structure,

link text and number of links to determine their search results (Brin, 1998), information in

social media often ends up between the highest and most used search result. This may lead


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to additional previously unconnected agents getting connected, thereby switching latent ties

into weak ones (Haythorntwaite, 2005). In this way, social media reinforce their position as

weak tie hubs.

8.3 Information push and pull in social media

As just described, the public, explicit nature of information in highly-linked social media

makes the information highly searchable and findable. Whereas the information cascades

described earlier are relatively short-lived andpushedby momentum, online information thus

also enables long-termpull of information. We will describe social medias capabilities to

facilitate both information flows.

8.3.1 Information push: social media as facilitators of information cascades

As discussed, even agents with high numbers of weak ties, such as social media, can not be

regarded as influential (see chapter 7.3 on page 29). However, there is a role for social media

in information cascades.

Information cascades require large numbers of neighboring agents to turn into global

cascades. Rather than using social media to influence their large number of neighboring

agents, social media should be used to helping other agents influence each other. They can

do this by using social media as a platform through which agents can contact each other andshare ideas, information and opinions (Thompson, 2008).

One example of how this may work is a campaign performed by Disneyland Paris in 2007. It

created an interactive poster that was put up in Paris. The poster allowed children to post

their picture and a one-sentence description of their dream, which would then be shown on

the poster. Disneyland Paris then approached fifteen highly visited blogs and rather than

trying to make these blogs mention the campaign favorably, they gave the blogs the

opportunity to each have children post messages for the poster through their websites for

one day. These children could than forward these messages to their friends and family. The

campaign resulted in thousands of messages being sent, not by having the blogs influence the

children, but rather by having the blogs facilitate the children to cascade the information..

The influencing was done by the children, not by the blogs22.

22 Case presented during the conference Maximising Website and Online Communications Summit on January30, 2007 in Amsterdam. Numbers and figures undisclosed.


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8.3.2 Information pull: social media as information archive

Even if information does not lead to a cascade, e.g. because of a lack of momentum, or

critical mass, the highly searchable character of social media and the persistence of online

information can help spread the information over a prolonged period of time. In these cases,

the timing depends on the explicit information need of the receiving agent, expressed in an

information pull by using a search engine.

Therefore, some companies perceive social media as a good opportunity to obtain favorable

search results. Other companies, being confronted with unfavorable brand messaging

through social media, make a lot of effort to ensure that the search results for this messaging

are pushed into the lower regions by promoting alternative information. Both approaches

emphasize the interconnectedness of social media with search engines and their mutual

importance and clarify the rise ofsocial media optimization, the generation of publicity through

social media, as an important branding tool.

Summarizing, social media are weak tie hubs within social networks. This status pushes their

importance in search engine results, enabling continuous creation of additional weak ties. In

this position, social media can be considered important information or opinion brokers and

search engines as the agent paving the way for new ties to spread the information or

opinions.


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9 Branding in complex networks

As discussed earlier, the way in which information is spread through complex adaptive

networks can not really be predicted. It is interesting to apply this knowledge to theories

about branding and, more specifically, corporate branding.

9.1 Branding as a complex concept

Brands in themselves are a concept resulting from increased complexity. With the rise of

mass production and advances in distribution and communication techniques, successful

economic communication became a challenge at the turn of the 20th century. Customers

were confronted with a large variety of largely similar products, intransparent prices, insecure

availability, various suppliers and middlemen. This over-complexity of consumption

threatened to slow down economic dynamics.

To increase the likelihood of successful economic communication, the economic system had

already introduced money, prices, interest, and payments, all of which reduce complexity

through abstraction. With the growing complexity of a depersonalized economy of mass

production, however, there was a need for a way to provide more meaning to products.

This was when brands were introduced to the economic system. Branding allowed for

additional layers of information, including social value, to be added to products, helping

customers to make a choice. Brands enabled the abstraction from both suppliers and

customers underlying intentions by referring to shared cultural meaning. Advertising was

introduced for suggesting favorable interpretations for certain products, making it possible

for brand owners to signal social status and prestige to strangers through their brand choices

(Ldicke, 2007).

This social function of brands was possible because of other people noticing the sameadvertising at the same time, thereby developing a common set of cultural perceptions. The

basic assumption is that people need to exchange information about brands in order to

develop these perceptions, which means that they will add their own meaning to the meaning

as intended by the brand owner.


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Obviously, this makes sense from the perspective of complex systems. As described before,

these systems, such as a society, have a highly decentralized control system. There is no

mastermind, or master plan, or clear hierarchical structure. Thus, no one system can

coordinate or control all others. This will then also apply to branding: there is no central

system that controls the way in which a brand is perceived by all agents within the network.

Instead, the meaning of a product as perceived by agents is the result of the interactions

between these agents and the information they receive about the product through those

interactions. The maker of the product and the product itself can be agents in this network,

but they are only partly responsible for a particular agents perception of that product and its

brand.

9.2 Social aspect of branding

Although this is hardly a new insight, marketing theories have up until now not, or not

sufficiently, included this social aspect of branding. By their focus on the process of

exchange these theories limit their capacity to embrace emerging practices and to explain the

process of brand building outside the direct influence of the brand owner. In order to do

this, the building of preference towards a brand should be the primary focus of any

marketing theory, with the exchange or transaction as a secondary code (Ldicke, 2007).

It should then be taken into account that the building of preference to a large extent happensamongst observers communicating abouta brand (e.g. talking about the new Apple MacBook

Air) and communicating through a brand (e.g. by buying the Apple MacBook Air and showing

it or telling about it to friends). The goal of branding would then be to enable and encourage

reproduction of strong and broadly accepted distinctions and ongoingcommunications,

consequently creating social noise. The more interested the observers are in the message

itself or in the addressed brand, the more likely it is that social connectivity and ongoing

communication will occur.

The paradox, however, is that the more successful brand owners are at having their brand

messages proliferated by society, the more limited the power of the brand owner over these

messages becomes. The ways in which society picks up the messages and extents them into

social life is largely out of the reach of the brand owners. The observers will use and

reproduce the brand messages extensively based on their own agendas. They accept, reject,


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or ignore the messages and alter them to fit to their needs. This implies that the brand

owners can find that their brand ends up having unintended meaning (ibidem.).23

In the next chapter we will discuss how organizations can cope with these specific

characteristics of complex networks.

9.3 A branding strategy for complex networks

The fact that a company does not have full control over its own brands does not mean that

there is no control at all. There are ways to stimulate a brands success even in complex

environments. This chapter will discuss these ways by defining a strategy for branding in

complex systems.

9.3.1 Messaging

In the preceding chapter, we have implicitly identified some of the conditions for successful

brand messages. The first one of these is that a message needs to be interesting enough for

people to pass them on. The best way to achieve this is by making sure the message appeals

to an agents line of perception or personal situation. Depending on the target audience, this

means that a message needs to be concrete and to the point to an extent that the agents can relate

to the brand message, e.g. through quantification of a message.

The second condition is that the message is broadly accepted. This means that the message

needs to be credible from the point of view of the receiver of the message. The implicit

prerequisite is that the initiator of the message has an understanding of the observers

perception of a product and its brand. The way in which this understanding can be obtained

is through market research and monitoring of the way in which messages are being received

and reproduced. This will be elaborated on in one of the next paragraphs.

However, one important consequence that is good to note is that the brand identity can not be

too far detached from the brand image or the identity will simply not be accepted. In passing

on brand messages, agents will primarily exchange their views on the brand and its message

in order to make them fit their own needs. This means that the brand image has a bigger

23 One example of this is the Lonsdale clothing company, whose clothing, originally aimed at boxing, washijacked by skinheads in the Netherlands, Belgium and Germany during the 1980s and 1990s. Ever since, thebrand is associated with right wing extremism in those countries. Source:http:/ / en.wikipedia.org/ wiki/ Lonsdale_%28clothing%29.


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change of being reproduced rather than the brand identity. To ensure that the identity is

being reproduced, it needs to be credible. Consequently, for a change in brand identity to be

reproduced within a network, agents first have to experience a change in the brand image.

The final condition for brand messages is that they need to be easy to reproduce to enable

receivers to pass the message on to others. One of the ways in which to do that is through

storytelling. By introducing a narrative a brand message can be made to appeal to the receivers

imagination, thereby strongly enhancing the change that the agent will remember and

reproduce the narrative.

9.3.2 Monitoring

As mentioned earlier, a message needs to be credible to an audience for it to be passed on. A

solid understanding of the audiences perception of the brand and product are required for

that. This understanding can be gained by setting up a monitoring system which tracks the

way all (types of) agents receive and reproduce brand messaging, such as mass media,

customers, and competitors. In that process, an organization should utilize its own social

structure such as its sales force, customer support, customer relationship management and

market research. The monitoring should also serve to obtain information on changes in the

dynamics in society that can help spread the brand messaging, such as current events.

9.3.3 Alternative messaging

A brand owner should be prepared for situations in which a brand message is incorrectly

reproduced at a large scale, as well as for the above described changes in dynamics in society.

Alternative versions of the brand message need to be available to oppose unwanted brand

associations and to leverage opportunities to diffuse the original brand message.

9.3.4 Consistency

In situations where audiences will adapt a senders brand messages to fit their own needs

before reproducing them, there is all the more reason for the sender to ensure that the initial

messages are consistent. This consistency should apply to all brand messaging, not just the

messaging aboutthe brand, such as advertising, but also messaging through the brand, captured

in the products distribution, price, design, service, etc. Consistency in all these facets of the

brand will drive acceptance of the brand message with its observers and consequently

encourage reproduction.


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9.3.5 Organization

Although this last insight may not seem ground-breaking at first sight, it does require a

paradigm shift for some organizations and brand owners. As discussed earlier, it means that

the creation of meaning in order to induce a brand preference is the primary goal of

marketing, only to be followed by the exchange. As a consequence, marketing departments

need to reorganize internally to reflect the bigger influence of branding on other aspects of

marketing, such as product design, pricing, etc., and to allow for a stronger focus on

continuous monitoring of, reacting to and interacting with the social network in which it

operates.

Furthermore, they will need to claim a more prominent role within the organization as such

and redefine their relation with other departments, such as finance, human resources, etc. in

order to align the entire organizations activities to the corporate brand.

9.4 Corporate branding in complex networks

The strategy for branding in complex systems as defined above also applies to corporate

branding. However, there are a few specific areas that need specific attention.

As described, the brand messages need to be accepted by the audiences in order to be spreadand therefore have to match their perceptions of the brand. Corporate brand associations are

even more strongly tied to reality as they have to be credible and attractive to a large number

of specific audiences or stakeholders. Employees and potential employees, business

customers, suppliers, financial markets, governments, and journalists will all monitor the

corporate brand messages seriously and critically to evaluate a companys economic and

social efforts (Kralingen, 2003).

This means that rebranding on a corporate level will require a rebranding of the entire

internal organization in order to achieve the required consistency, i.e. to make sure that the

newly introduced brand identity (the branding aboutthe brand) matches the customers

experience with the organization, e.g. through price setting, service, account management,

logistics, etc. (the branding through the brand). This process of internal branding should

precede the external branding to allow for the organizational change to occur. Otherwise

external audiences will reproduce the new brand identity messaging while including their old


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personal brand image and specifically describing the discrepancies, thus harming the new

brand.

The advantage, on the other hand, is that corporate brand associations, once achieved, are

harder to imitate by competitors and can therefore provide a sustainable competitive

advantage.

9.4.1 Corporate reputation management

The above described holistic approach to branding as a company compass for its external

and internal activities makes it very similar to corporate reputation management. Where

traditionally the distinction could be made that corporate brand management was about

communicating the corporate identity and reputation management consisted of this

communication as well as making sure that the companys activities were aligned with it (Van

der Jagt, 2004), the distinction between the two becomes blurry in light of the subject of this

thesis.

A corporate reputation can be measured by its performance in six categories, or pillars

(ibidem.), being emotional appeal, product & service, vision & leadership, workplace

environment, financial environment and social responsibility. To obtain a solid reputation, a

company should score well in all these categories, and preferably outperform its competitors

in them.

The way in which a company is perceived can be defined as the reflection of three types of

expression of the companys identity, behaviour, communications and symbolism, of which a

companys behaviour is most important: it will be the companys actions on which it will be

judged.

Corporate reputations are the result of information processing and perception on three

levels. The first level is first-hand ex perience, being the individual perception of somebody

dealing with a company, either as a customer, employee, investor or external observer. The

second level is word-of-mouth, where peoples perceptions are formed based on other people

relating their first-hand experiences to them. The third level is from the (mass) media, where

perceptions are based on what media tell about a company. In this set-up the higher level will

have the most impact on ones perception of a company (Van der Jagt, 2004).


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It can then be deduced that a companys behaviour in any category or pillar will always lead

tofirst-hand experience and may lead to word-of-mouth. This makes the behaviour part of the

branding territory, leading to a complete overlap between corporate brand management and

corporate reputation management. Therefore it is this thesis contention that no distinction

should be made between those disciplines and that corporate reputation management should

be part of the corporate brand managers responsibilities. Also, an integrated approach is

needed between corporate brand and reputation management and brand management on a

business or product level.

One example that supports this contention is the wide-spread outrage that broke out when

Anders Moberg was appointed as the new CEO of Ahold, based in the Netherlands, in 2003.

Moberg succeeded Cees van der Hoeven, who was forced to resign after creating a financial

chaos, requiring the company to lay off people throughout the entire organization. The

outrage started when Mobergs remuneration was made public: his base salary was said to be

approximately 10 million a year, independent of the results he would achieve. The media

had a field day with knowledge, but the general public felt offended as well. Chain e-mails

were sent around and websites were quickly set up to initiate a boycott of Albert Heijn,

Aholds flagship supermarket chain in the Netherlands. These actions were sufficiently

successful to convince Moberg, who at first reacted indifferently to the outburst of public

anger, to eventually accept a significantly lower -though still generous- remuneration.

This example shows that small actions on an agents level can cause effects on a network

level. It also demonstrates that actions on a corporate and organizational level can influence

both the corporate brand, e.g. the employer and investor brand, and underlying business

brands.

9.4.2 Corporate brand PR

As described above, branding in complex environments requires continuous monitoring of

the brand messaging by agents in the network, as well as the availability of alternative

messaging to correct incorrect or inconsistent messages. These processes are actually quite

well known for public relations professionals. It therefore makes sense to stimulate close

collaboration between brand managers and PR professionals, especially if they are

represented on the same organizational level. In large companies, this is often the case on the


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corporate level and less so on lower levels, where PR will be a role on an organizational level,

whereas brands are often product-related.

Brand managers will need to learn to assume an observing and responsive role in order to

constantly be aware of the audiences brand perceptions and be able to react to those. By

extending the existing corporate monitoring service by including other channels (e.g. social

media) and the organizations own social structure (e.g. its account management), and by

sharing their expertise in story telling, PR professionals can help brand managers cope better

with their complex environments.

9.4.3 Corporate brand architecture

As described earlier in this thesis, there are three categories of online public behavior:

collaborative behavior, socially constructive behavior and aggressive, destructive behavior.

The latter two categories are aimed at the company as such, which means that corporate

branding is susceptible to this kind of behavior.

The first category, collaborative behavior, takes place at the level of a companys brands and

products. Whether this kind of behavior influences the corporate branding, directly or

indirectly, depends on a companys brand architecture.

A companys brand strategy can more or less be classified in any of four categories: house of

brands, endorsed brands, subbrands and branded house (Aaker and Joachimsthaler, 2000).

The first category has the strongest link between the corporate brand and the product

brands, the last one the loosest. This implies that the positive collaborative behavior

regarding a companys products and services will have the strongest effect on a corporate

brand in the situation of a branded house and hardly any effect in case of a house of brands.

It also means that corporate branding professionals in organizations with a house of brands

branding strategy can expect mostly negative, or at least seemingly negative, online behavior.

The only way to profit from positive collaborative behavior is by defining a stronger

relationship between the corporate and product brands, in other words by introducing a

subbrand or endorsed brand strategy.


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10 References

Aaker, D.A. and Joachimsthaler, E. (2000). The Brand Relationship Spectrum, The Key to

the Brand Architecture Challenge, in: California M anagement Review, vol. 42, no. 4, p. 1-

17.

Albert, R. and Albert-Lszl Barabsi, A.-:L. (2002). Statistical mechanics of complex

networks, in:R eviews of Modern Physics, vol. 74, January 2002.

Arthur, C. (2006) W hat is the 1% rule?, in: The Guardian, July 20, 2006. Accessed through

http:/ / www.guardian.co.uk/ technology/ 2006/ jul/ 20/ guardianweeklytechnologysection

2 on January 27, 2008.

Atal, M. (2007), MySpace, Facebook: A Tale of Two Cultures, in: Busi

bram koster - the definition of a social media strategy

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