MARKETING research in statistical physics, likely to enrich the

MARKETING THEORY (MAN 525)    AbstractThe purposeof this research proposal is to show that social network analytics are able toshed new light on the issues raised in the marketing arena. Indeed, while thistype of analysis already has a long history, recent progress in this area isleading to new ways of responding in many disciplines, including marketing. Firstlight is shed on the notion of network in the particular context of marketingand then some results, resulting from research in statistical physics, likelyto enrich the analysis of phenomena particularly important in marketing. Thenotion of social networks is thoroughly revised in its fundamental andrelational contexts in order to conceptualize implemented mechanisms.Keywords: Social networks, Networks,Statistical Marketing, Marketing IntroductionThe originsof 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 allobserve the links that exist between individuals. A little later, Jacob LevyMoreno (1889-1974) created “sociometry”, a quantitative method of studyingnetworks. Then, with the introduction of the theory of graphs and linearalgebra in the study of social networks, is born the analysis of socialnetworks (SNA, Social Network Analysis).

Today, this are works from physics(Watts and Strogatz, 1998, Newman et al., 2001, Barabasi and Albert, 1999) thatbring new perspectives.The analysisof social networks is based on a conception of social relations in terms ofnetworks, which is to say in terms of links and nodes. A network can berepresented 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 twoof these points, thus indicating the links existing between the members of thenetwork. A property of many real networks is the so-called “littleworld”. In a network, the distances between elements are measured by thelength of the paths that exist between them, a path being composed of asuccession of links.

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By definition, the geodesic distance between two verticesis the shortest distance between them. For example, networks with low geodeticdistances globally verify the property of the “small world”. Theexperience of Milgram (1967) is famous for highlighting the phenomenon and theso-called “6 degrees of separation”, indicating that on average, ittakes no more than six intermediaries for two people on the earth are put inrelation. The world is so small! Another important feature of a network is itsclustering coefficient. This indicator is based on an easily observable tendencyin society: homophilia, a natural tendency of men to regroup, to bond withlike-minded people, the idea finally that “who looks alike”. AlreadyGranovetter (1973) notes that the society is organized into subgroups of peoplewith special ties. This results in a high clustering coefficient.As it iseasy to imagine, the actors and relationships that can be studied are veryvaried.

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 youto see almost any situation from the perspective of an underlying network. So,for Barabasi (2003), “A cocktail. A terrorist cell.

An old bacterium. Aninternational conglomerate – All are networks, and all are part of a surprisingscientific revolution”. Literature reviewIn the sameway, in the field of marketing, the possibilities of study in terms of networksare infinite (Iacobucci, 2007).

Stakeholders and relationships of interest areas varied (relationships between organizations: the company with its suppliers,distributors, competitors, relations between the company and consumers:potential consumers, influencers, innovators, etc. and interactions betweenconsumers, who share more and more their consumption experiences).However,networks are not new to the marketing literature.

Word of mouth and diffusionphenomena have been studied for many years. Already in 1957, Brooks notes theexistence of “powerful networks of interpersonal relations” andexplains the famous S curve characterizing the phenomena of diffusion ofinnovations. Since then, the literature has been greatly enriched, particularlyin the field of dissemination, in which there have been more than 4,000publications since 1940. This is one of the most important lines of research inthe social sciences (Goldenberg et al.

, 2000). The importance of interpersonalcommunication is emphasized in many articles. Some model cognitive networks.For example, Bagozzi et al. (1996) use network analysis to study medium-endchains in order to understand consumer attitudes towards recycling.

Iacobucciet al. (1996) deal with a contingency table for brand change data in theautomotive industry with network analysis techniques. Today,technology offers new possibilities of analysis of these previously observedphenomena, phenomena sometimes also brought up to date.

Thus, a new term “buzz”has spread in recent years in place of the traditional “word ofmouth”. According to Rosen (2000), who himself contributed to the successof the phenomenon by his bestseller book “The Anatomy of Buzz”, the buzz issimply: the whole word of mouth of a brand. It is the sum of all interpersonalcommunications “about the same object (brand, brand, product, etc.)”at a given moment “. Networks have always been important, but they arebecoming more and more important. The influence of the Internet is in this areaundeniable. So much so that we speak today of “internet wom”,referring to “word of mouse” in place of the traditional “wordof mouth” that managers try to exploit through campaigns of “viralmarketing” (Goldenberg et al.

, 2001). As far as researchers are concerned,the Internet also allows better access to data. But how, in particular, studythis word of mouth whereby the conversations exchanged online betweenindividuals are private? With the Internet, for example, it is now possible toprocess online chat data as conceptualized by Godes and Mayzlin (2004).

Thesocial structure of Internet discussion groups has also recently been modeledby Steyer et al. (2007) which “seem likely to considerably change theunderstanding of the phenomena of diffusion of innovations and preferences viathis medium”, thus joining the opinion of Barabasi (2003) which wementioned above that he described these advances as “revolution”.Conceptual ModelFrom thetheoretical point of view, random networks have been widely studied. Theirproperties are now well known. Unfortunately, they are far removed from realnetworks, especially in terms of clustering. Regular networks whose propertieshave also been analyzed are closer to real networks because they also have highclustering.  “Small World” networksFinally,Watts and Strogatz (1998) propose a new type of network that presents the twodesired characteristics (strong clustering and low geodetic distance); theseare the so-called “small world” networks. The small world graph islocated 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 ofrandomness. This dose of randomness concerns the existence of”shortcuts”, random links that make the world small. However, thisthesis is not entirely satisfactory. The presence of central elements observedin real networks is not taken into account in these models. In fact, someelements 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 ofall marketers’ attention. They also help to make the world small, to theirmeasure. Let’s take an extreme case and imagine that there is a dominant playerto whom most of the others are related. Just go through it to get in touchquickly with any other individual. The distances between elements are thennecessarily very small. Some tools from the analysis of social networks existthat identify these particularly influential actors. These are so-calledcentrality measures (Freeman, 1979).

The challenge is to identify these centralelements or leaders and then communicate optimally with them (Vernette andFlores, 2004).   Laws of powerA number ofstudies, carried out in very different domains, show that the number of linksof 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. Aparticular example is Pareto’s Law, which is undoubtedly also the best known ofthe 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 nameof “Pareto Principle” and formalized by the Pareto distribution. Inmanagement, we can translate this law by saying that 20% of the means make itpossible to reach 80% of the objectives. In other words, 20% of customers canachieve 80% of sales.

Otherexamples of power laws exist in marketing. Kohli and Sah (2006) show that marketshares follow a power law. In another study, Kalyanaram et al. (1995) show thatthe relationship between markets share and order of market entry can bedescribed 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 hasbeen shown to be a law of power. In these networks without scale, some nodeshave many connections while others have few.

A calculation of average size thenhas little sense. This is why they are called “without scale”. Hypotheses·        H1: Social Networks areincreasingly, with the development of technology, an imminent aspect of modernmarketing.

·        H2: Most conceptual modelsfor the measuring of social network characteristics are not yet up to thestandard of certainty  Research QuestionsTheelaboration of the argumentative or problematic discourse for identifying theissue requires bringing together elements of different types and origins andassembling them in such a way as to build a reasoning that bears witness totheir interest and relevance. Research questions can come directly from thesituation. They can come from other sources reporting similar observationselsewhere.The problemmust progress in order to circumscribe and describe in a relevant way: theobject of research; the objective (s) of research; the problem (s) or issue (s)to be studied.·        What is the importance of social networks inmarketing?·        Is there a model that can outline social networks formarketing purposes? MethodologyResearch designThis thesisis put through by analyzing diverse social disciplinal dimensions in the frameof domestic, regional and international in the context of the rise of populism.The implicated rhetoric is put in a dichotomy with historic and contemporaryeconomic, asocial and political ground realities.

The work of various scholarsin different fields help to conceptualize the meaning and limitations of thereferent phenomenon, while analysis of the European Council on ForeignRelations underpins the hypothesis formulated in this research proposal.Moreover, the assessment and culmination of the qualitative analysis are inreflection a mere subjective deduction. Measuring InstrumentsThisqualitative research focuses on collecting primarily verbal data rather thandata that can be measured. This is manifested in the usage of diversescholastic research papers and academic articles. The information collected isthen analyzed in an interpretative, subjective, impressionistic or evendiagnostic manner.

The main purpose of this research is to provide a completeand detailed description of the research topic. A model is implemented wherethe focus is on the processes that develops within or around the hypothesis. Asa result, it seeks to understand, seeks to describe, explore a rising domain,evaluate the performances of the referent elements, go to the discovery of theothers, and evaluate their implications.

Data collection MethodThe accessof various academic documents enabled to get an important background to theproblem including the conceptual model. It made able to study relevantdocumentation of past studies and empirical findings. This greatly increasedthe understanding of the difficulties related to the phenomenon.

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

 FindingsThese reflections and new theories add to research but also tomanagerial practices. In the research aspect, the above studied models areregularly revisited. They are the best known of diffusion models, albeit in thefield of their publications are very numerous. The new advances in networkanalysis have therefore not escaped the attention of researchers and it isfirst in this area that we find recent developments taking into account thelatest results in statistical physics. The importance of the field probablyexplains that the new developments have first of all around this subject ofdiffusion but everything suggests that other themes can be re-discussed in thelight of these new models and theories. There is therefore a growing interestin marketing for the concept of the network from both an academic and consumerpoint of view. Networks of collaboration between researchers have attracted theinterest of many academics, in various disciplines (Newman, 2001).

On theconsumer side, another proof of growing interest in networks is the globalsuccess of sites like Facebook.com, Wikipedia or YouTube. These sites areso-called service communities or collaborative networks representative of theWeb 2.

0 approach. The latter is based on interactions between users andtherefore on the notion of network itself.LimitationsAs seen so far, the models exposed do not allow the presenceof 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).  RecommendationsNoting that marketing is, by definition, the discipline ofexchange and that, consequently, the notion of relationship is fundamental. Theanalysis of social networks is therefore naturally indicated. But beyond theevidence of these findings, the recent discoveries (that many researchers invarious fields agree that they are, if not revolutionary, signs of a profoundand important change) make it possible to hope new results and new approachesin marketing.

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