MARKETING research in statistical physics, likely to enrich the






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The purpose
of this research proposal is to show that social network analytics are able to
shed new light on the issues raised in the marketing arena. Indeed, while this
type of analysis already has a long history, recent progress in this area is
leading to new ways of responding in many disciplines, including marketing. First
light is shed on the notion of network in the particular context of marketing
and then some results, resulting from research in statistical physics, likely
to enrich the analysis of phenomena particularly important in marketing. The
notion of social networks is thoroughly revised in its fundamental and
relational contexts in order to conceptualize implemented mechanisms.

Keywords: Social networks, Networks,
Statistical Marketing, Marketing



The origins
of the analysis of social networks go back to the works of Georg Simmel
(1858-1918). According to him, in order to study society, one must first of all
observe the links that exist between individuals. A little later, Jacob Levy
Moreno (1889-1974) created “sociometry”, a quantitative method of studying
networks. Then, with the introduction of the theory of graphs and linear
algebra in the study of social networks, is born the analysis of social
networks (SNA, Social Network Analysis). Today, this are works from physics
(Watts and Strogatz, 1998, Newman et al., 2001, Barabasi and Albert, 1999) that
bring new perspectives.

The analysis
of social networks is based on a conception of social relations in terms of
networks, which is to say in terms of links and nodes. A network can be
represented by a graph. It is a diagram consisting of a set of points
(representing the actors of the network) and a set of segments connecting two
of these points, thus indicating the links existing between the members of the
network. A property of many real networks is the so-called “little
world”. In a network, the distances between elements are measured by the
length of the paths that exist between them, a path being composed of a
succession of links. By definition, the geodesic distance between two vertices
is the shortest distance between them. For example, networks with low geodetic
distances globally verify the property of the “small world”. The
experience of Milgram (1967) is famous for highlighting the phenomenon and the
so-called “6 degrees of separation”, indicating that on average, it
takes no more than six intermediaries for two people on the earth are put in
relation. The world is so small! Another important feature of a network is its
clustering coefficient. This indicator is based on an easily observable tendency
in society: homophilia, a natural tendency of men to regroup, to bond with
like-minded people, the idea finally that “who looks alike”. Already
Granovetter (1973) notes that the society is organized into subgroups of people
with special ties. This results in a high clustering coefficient.

As it is
easy to imagine, the actors and relationships that can be studied are very
varied. Actors can be individuals, organizations or even countries (Iacobucci,
2007). Relationships are affiliation, knowledge, friendship, power,
cooperation, etc. The modeling possibilities are extremely broad, allowing you
to see almost any situation from the perspective of an underlying network. So,
for Barabasi (2003), “A cocktail. A terrorist cell. An old bacterium. An
international conglomerate – All are networks, and all are part of a surprising
scientific revolution”.


Literature review

In the same
way, in the field of marketing, the possibilities of study in terms of networks
are infinite (Iacobucci, 2007). Stakeholders and relationships of interest are
as varied (relationships between organizations: the company with its suppliers,
distributors, competitors, relations between the company and consumers:
potential consumers, influencers, innovators, etc. and interactions between
consumers, who share more and more their consumption experiences).

networks are not new to the marketing literature. Word of mouth and diffusion
phenomena have been studied for many years. Already in 1957, Brooks notes the
existence of “powerful networks of interpersonal relations” and
explains the famous S curve characterizing the phenomena of diffusion of
innovations. Since then, the literature has been greatly enriched, particularly
in the field of dissemination, in which there have been more than 4,000
publications since 1940. This is one of the most important lines of research in
the social sciences (Goldenberg et al., 2000). The importance of interpersonal
communication is emphasized in many articles. Some model cognitive networks.
For example, Bagozzi et al. (1996) use network analysis to study medium-end
chains in order to understand consumer attitudes towards recycling. Iacobucci
et al. (1996) deal with a contingency table for brand change data in the
automotive industry with network analysis techniques.

technology offers new possibilities of analysis of these previously observed
phenomena, phenomena sometimes also brought up to date. Thus, a new term “buzz”
has spread in recent years in place of the traditional “word of
mouth”. According to Rosen (2000), who himself contributed to the success
of the phenomenon by his bestseller book “The Anatomy of Buzz”, the buzz is
simply: the whole word of mouth of a brand. It is the sum of all interpersonal
communications “about the same object (brand, brand, product, etc.)”
at a given moment “. Networks have always been important, but they are
becoming more and more important. The influence of the Internet is in this area
undeniable. So much so that we speak today of “internet wom”,
referring to “word of mouse” in place of the traditional “word
of mouth” that managers try to exploit through campaigns of “viral
marketing” (Goldenberg et al., 2001). As far as researchers are concerned,
the Internet also allows better access to data. But how, in particular, study
this word of mouth whereby the conversations exchanged online between
individuals are private? With the Internet, for example, it is now possible to
process online chat data as conceptualized by Godes and Mayzlin (2004). The
social structure of Internet discussion groups has also recently been modeled
by Steyer et al. (2007) which “seem likely to considerably change the
understanding of the phenomena of diffusion of innovations and preferences via
this medium”, thus joining the opinion of Barabasi (2003) which we
mentioned above that he described these advances as “revolution”.

Conceptual Model

From the
theoretical point of view, random networks have been widely studied. Their
properties are now well known. Unfortunately, they are far removed from real
networks, especially in terms of clustering. Regular networks whose properties
have also been analyzed are closer to real networks because they also have high


“Small World” networks

Watts and Strogatz (1998) propose a new type of network that presents the two
desired characteristics (strong clustering and low geodetic distance); these
are the so-called “small world” networks. The small world graph is
located between the two extremes that are the regular graph and the random graph.
It is, indeed, a regular graph to which it is enough to add a dose of
randomness. This dose of randomness concerns the existence of
“shortcuts”, random links that make the world small. However, this
thesis is not entirely satisfactory. The presence of central elements observed
in real networks is not taken into account in these models. In fact, some
elements are particularly active in networks. These elements are called hubs.
They are distinguished because they communicate more than the average of others.
In management, they are generally seen as opinion leaders and are the focus of
all marketers’ attention. They also help to make the world small, to their
measure. Let’s take an extreme case and imagine that there is a dominant player
to whom most of the others are related. Just go through it to get in touch
quickly with any other individual. The distances between elements are then
necessarily very small. Some tools from the analysis of social networks exist
that identify these particularly influential actors. These are so-called
centrality measures (Freeman, 1979). The challenge is to identify these central
elements or leaders and then communicate optimally with them (Vernette and
Flores, 2004).




Laws of power

A number of
studies, carried out in very different domains, show that the number of links
of an actor in networks, follow a law of power. The laws of power are, in fact,
very widespread in reality. We find many of these laws in marketing. A
particular example is Pareto’s Law, which is undoubtedly also the best known of
the laws of power. Pareto observes, in fact, that 80% of wealth is held by 20%
of households. This is where comes what will first be released under the name
of “Pareto Principle” and formalized by the Pareto distribution. In
management, we can translate this law by saying that 20% of the means make it
possible to reach 80% of the objectives. In other words, 20% of customers can
achieve 80% of sales.

examples of power laws exist in marketing. Kohli and Sah (2006) show that market
shares follow a power law. In another study, Kalyanaram et al. (1995) show that
the relationship between markets share and order of market entry can be
described by a power law.

The term
“networks without scale” is introduced by Barabasi and Albert (1999)
in the qualification of these many real networks whose degree distribution has
been shown to be a law of power. In these networks without scale, some nodes
have many connections while others have few. A calculation of average size then
has little sense. This is why they are called “without scale”.


H1: Social Networks are
increasingly, with the development of technology, an imminent aspect of modern

H2: Most conceptual models
for the measuring of social network characteristics are not yet up to the
standard of certainty



Research Questions

elaboration of the argumentative or problematic discourse for identifying the
issue requires bringing together elements of different types and origins and
assembling them in such a way as to build a reasoning that bears witness to
their interest and relevance. Research questions can come directly from the
situation. They can come from other sources reporting similar observations

The problem
must progress in order to circumscribe and describe in a relevant way: the
object of research; the objective (s) of research; the problem (s) or issue (s)
to be studied.

What is the importance of social networks in

Is there a model that can outline social networks for
marketing purposes?



Research design

This thesis
is put through by analyzing diverse social disciplinal dimensions in the frame
of domestic, regional and international in the context of the rise of populism.
The implicated rhetoric is put in a dichotomy with historic and contemporary
economic, asocial and political ground realities. The work of various scholars
in different fields help to conceptualize the meaning and limitations of the
referent phenomenon, while analysis of the European Council on Foreign
Relations underpins the hypothesis formulated in this research proposal.
Moreover, the assessment and culmination of the qualitative analysis are in
reflection a mere subjective deduction.


Measuring Instruments

qualitative research focuses on collecting primarily verbal data rather than
data that can be measured. This is manifested in the usage of diverse
scholastic research papers and academic articles. The information collected is
then analyzed in an interpretative, subjective, impressionistic or even
diagnostic manner. The main purpose of this research is to provide a complete
and detailed description of the research topic. A model is implemented where
the focus is on the processes that develops within or around the hypothesis. As
a result, it seeks to understand, seeks to describe, explore a rising domain,
evaluate the performances of the referent elements, go to the discovery of the
others, and evaluate their implications.

Data collection Method

The access
of various academic documents enabled to get an important background to the
problem including the conceptual model. It made able to study relevant
documentation of past studies and empirical findings. This greatly increased
the understanding of the difficulties related to the phenomenon.

Analysis Method

gathered from scholars is analyzed using a qualitative method of data analysis.
First, similar themes, patterns and relationships between reflections of the
questions, are grouped and analyzed. This is done by scanning primary data for
words and phrases most commonly used by the respondents, comparing subjective
understandings with findings from literature review and analyzing primary
research findings to the phenomena and locate similarities and differences.
Second, research findings is linked to the hypotheses.



These reflections and new theories add to research but also to
managerial practices. In the research aspect, the above studied models are
regularly revisited. They are the best known of diffusion models, albeit in the
field of their publications are very numerous. The new advances in network
analysis have therefore not escaped the attention of researchers and it is
first in this area that we find recent developments taking into account the
latest results in statistical physics. The importance of the field probably
explains that the new developments have first of all around this subject of
diffusion but everything suggests that other themes can be re-discussed in the
light of these new models and theories. There is therefore a growing interest
in marketing for the concept of the network from both an academic and consumer
point of view. Networks of collaboration between researchers have attracted the
interest of many academics, in various disciplines (Newman, 2001). On the
consumer side, another proof of growing interest in networks is the global
success of sites like, Wikipedia or YouTube. These sites are
so-called service communities or collaborative networks representative of the
Web 2.0 approach. The latter is based on interactions between users and
therefore on the notion of network itself.


As seen so far, the models exposed do not allow the presence
of hubs and in this lose their ability to explain the real situations. The
“small world” networks, which for a moment were thought to be an
“elegant compromise” between random networks and regular networks,
are finally quite close to random networks in the sense that they are based on
“a profound vision of society in which links are established by a dice-roll
“(Barabasi, 2003).



Noting that marketing is, by definition, the discipline of
exchange and that, consequently, the notion of relationship is fundamental. The
analysis of social networks is therefore naturally indicated. But beyond the
evidence of these findings, the recent discoveries (that many researchers in
various fields agree that they are, if not revolutionary, signs of a profound
and important change) make it possible to hope new results and new approaches
in marketing.










David Meerman Scott., “The New Rules of Marketing and PR:
How to Use News Releases, Blogs, Podcasting, Viral Marketing and Online Media
to Reach Buyers Directly”, Published by Wiley; First Edition , 2008.

Lon Safko & David K. Brake ., “The Social Media Bible:
Tactics, Tools, and Strategies for Business Success”, Wiley, 1 edition, 2009

Olson, T. (2009). 
Build Business Using Social Networks. r/article/159309/build_business_using_social_networks.html

Pettey, C. (2008). 
Gartner Says Social Networks Are Attracting Too Much Traffic for
Retailers to Ignore.  Gartner

Mary Madden (Pew Internet), “Older Adults and Social Media.
Social networking use among those ages 50 and older nearly doubled over the
past year,” August 27, 2010.

Adrian Palmer, Nicole Koenig?Lewis, (2009) “An experiential, social
network?based approach to direct marketing”, Direct Marketing: An
International Journal, Vol. 3 Issue: 3, pp.162-176,