21COE, University of Tokyo

MMRC Discussion Paper No. 79

MMRC
DISCUSSION PAPER SERIES

MMRC-F-79

The word-of-mouth effect on the web
A case of the network of massage board in site A

Graduate School of Economics, Faculty of Economics,
The University of Tokyo
Yufu Kuwashima

21COE, University of Tokyo

MMRC Discussion Paper No. 79

The word-of-mouth effect on the web
A case of the network of massage board in site A
Graduate School of Economics, Faculty of Economics,
The University of Tokyo
Yufu Kuwashima
April 2006

1 Introduction
People are affected by others in buying behavior because they do not live alone. Since the 1950’s,
marketing studies have demonstrated that the word of mouth effect exists. Today, there are large
numbers of on-line communities, and they have much more opportunities to give the effect to eachother. The purpose of this study is to do a research on how buying behavior transmits among
consumers by seeing the relationship between the network structure and the buying behavior. We
studied the network in the cosmetic word of mouth site. The variables are users’ comments about
items (regarded as proxy variable of buying behavior) and their talking there. Two classes of networkmodels are used for the analysis, cohesion and structural equivalence model. Two fact findings are
drawn: (a) In cosmetics, which is not conspicuous, cohesion is not the dominant factor in taking thesame buying behavior. (b)Where there is an evidence of cohesion and structural equivalence, many
people take the same buying behavior. From these fact findings, cohesion and structural equivalence
may be the dominant factor that drives contagion of buying behavior.

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Yufu Kuwashima

2 Research backgroundIn this section, we review studies on information transmission: word of mouth communication,
studies about network, studies about on-line communities.

2-1 Word of mouth communication:
Classification of goods
In the field of word of mouth communication research, we looked at which goods are easily
affected by otheres.
Whyte(1954) compared the private goods of which consumption is invisible and the public goodsof which consumption is visible, and showed that reference group has much more effect on public
goods. Furthermore, Bearden & Etzel(1982) showed differences in reference group influence
between publicly and privately consumed products and between luxuries and necessities.
To sum up, people’s buying behavior is affected by others in different ways depending on the
publicly or private goods. In this study we focus on cosmetics which is private necessarily goods.

2-2 Network:

Adaptation of innovation in the network
People are not always affected by those who have direct relations. So, we take into account thecase in which they do not have direct relations. We looked up the adaptation of innovation in the
network.
Coleman(1957) investigated the diffusion of a new drug among physicians. At first the influence of
these social networks operated only among doctors who were integrated into the community of theircolleagues through ties of a professional nature. Then it spread through the friendship networks,
finally there came a phase during which most of the remaining doctors. In short, the social linkage
affected the adoption of the innovation in the early stage. This is because, according toColeman(1990), ‘for purchase for which the cost of a bad disicion is high, a rational actor will
engage in a search for information before deciding. But along with his information search, he willplace trust in the judgment of others.’ Since effectiveness and safety of the new drug are not clear, thejudgment of others is taken seriously for the time being. Burt(1987) also investigated the adoption of the
new drug and the network structure, and indicated that social contagion occurred through structural equivalence,2

The word-of-mouth effect on the web
but not through cohesion. Cohesion is that Ａ and Ｂ is a direct acquaintance. Structural equivalence is that A
and B have the same relationship to other people in the network. (Taqble 1)

2-3 Studies about on-line communities

The internet user keeps steadily increasing. According to internet white paper of 2004 by thefoundation internet society, the internet population of Japan is the 63 million people in the end in
February, 2004, and 48.1% of the internet utilization household is connected with the broadband inJapan. Many on-line communities exist, and it is possible that the consumer hears users’ opinion on
product and service.
There are many papers which studied the on-line community. For example, Miyata(1997)
examinedtheinternet's

interactive-ness,andclassified

theinteractions

amongusers.

Mathewick(2002) classified the users in the on-line communities. And, the research on on-line
community and sales and marketing result of the brand force recently increases. Most studies in theon-line community have used the questionnaire. Especially, the research of the on-line shopping site
has analyzed visit frequency and purchasing probability using the purchasing data on the site.
There are studies about the structure between blog sites, there are few studies that conducted thesocial network analysis of the structure of consumers in on-line community in one specific site.
In this study, we will analyze the relationship between the between the buying behavior and the
network structure of the consumer in site A.

3 The network in the site A
3-1 Hypothesis

Site A is a word of mouth site about cosmetics. The number of users is over 0.5 million, and theircomments are over 2.5 million. The displayed information is user’s handle name, skin type, age, thedate of her comment, and an assessment of a certain item (by the number of stars). We take thesecomments as proxy variable of buying behavior.
Comments about items and the message board are on different pages in the same site. That is, they
form two distinct networks. In the message board, users have talks with others who have the sameinterests. Some of these topics have continued for a long time, so the members of the topics comprise3

Yufu Kuwashima

networks. In this site, we can see the relationship between word of mouth communication (as proxyvariable of buying behavior) and the network of users (as consumers). In this paper, ‘comment’ refers
to the word of mouth communication in this site, and ‘talk’ refers to the writing in the massage board(because when they write in the massage board, they recognize who they are talking to).
We regard the fact that the users have talks as their networks, but it is difficult to investigate thewhole topics, because there are over 4000 topics in the last three month.
As the first step in our analysis, we picked up 5 topics to build a hypothesis. Some users have talks
in more than one topics. Figure1 is the network drawn by Netdraw.
To capture the structure of this network, we use the block model by CONCOR in Ucinet 1 . Foursubgroups are found, and the members of each group are structurally equivalent. The users who
wrote comments

on one certain item are found to be structurally equivalent（see Figure2: the date is

the day when the user wrote the comment）. Therefore, following hypothesis can be deduced.
Hypothesis：Some consumers take the same buying behavior not only because they are cohesion(they have a talk) but also because they are structural equivalence in the network.

3-2 Method

To verify the hypothesis, it is necessary to investigate the central structure, because part of the
network is not enough to consider the whole network. Generally in marketing, 20% of consumers
are said to take over 80% of sales, and 35% of users who have talks in massage board take over
70% of talks. That is, we can see the rough structure of their communication by checking frequent
users.
We took out two networks: network of buying behavior (network A), network of talks (network B).
We picked up top 250 users who have many talks on the massage board in terms of top 100 items(Network A), and 100 topics (Network B). To put it more concretely, Network A is made by matrix inwhich row is user id and column is item id. Network B is made by matrix in which row is user id andcolumn is topic id（Network A：Figure3, Network B：Figure 4）.
QAP correlation of these matrix is 0.014. When there is a tie between two users in Network A, itmeans they bought the same item. And when there is a tie in Network B, it means they have a talk inthe same topic. Low correlation means that they bought rarely the same item if they have talks.
1

Borqutt, S. P., M. G. Everett, and L. C. Freeman. 2002. Ucinet for Windows: Software for Social Network
Analysis Harvard: Analytic Technologies
4The word-of-mouth effect on the web

Therefore, following fact finding can be drawn.
Fact finding 1：Users don’t take the same buying behavior when there is a tie between two users.
What type of network structure derives the same buying behavior?
We repeated the same procedure with which we built the hypothesis, and we found that Network B
consists of 9 subgroups. We examined the features of every subgroup in network B. For each item,
we calculated a percentage of users who commented on the item (i.e., all comments divided by allusers). We also calculated the percentages for each subgroup. There are some popular items whose
percentages in a certain subgroup are higher than that in the whole network. We counted the numberof these items. For example, we count one when the proportion that has commented on item A in thewhole is 10% , and 20% user has commented in group 1.
The correlation between the density of each subgroup and the number of popular items was 0.9. It
means that the higher the network density, the higher the proportion they write comment on the same
items.
Being the member of the same subgroup means that they are structurally equivalent. And the highdensity means most of them are directly connected (cohesion). Therefore, following fact finding canbe drawn.
Fact finding2：Many users take the same buying behavior when they are structurally equivalent andcohesion.
This finding may not be surprising if the comment and talks are not separated. However, since hecomment for item and conversation in message board is originally carried out and they makedifferent networks, this fact finding is fascinating.

4 Conclusion and discussion
Two-person relation of conversation in the message board directly cannot explain comment to thesame goods (fact finding 1). However, structural equivalence (i.e., users have conversation withothers of the same interest in the massage board), leads to the increase of the comments on the same
goods (fact finding 2). Therefore, the following conclusion can be drawn from these fact findings.
Conclusion：In the goods that is not conspicuous, consumers who are not cohesion but structural
equivalence, or not structural equivalence but cohesion do not take the same buying
behavior. Many consumers take the same buying behavior only when they are both
structural equivalence and cohesion.
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Yufu Kuwashima

Word of mouth research so far have focused only on the case there is cohesion (Table 2). Little is
known about the relationship between the network structure

and the buying behaviror. This study

reveals that as for the goods which are not conspicuous, the word of mouth effect works when people
are both cohesion and structural equivalence. The effect is weak when they are cohesion without
structural equivalence.
With the network analysis, Kuwashima(2004) demonstrated that the contagion of the buying
behavior is happening when consumers are structurally equivalent without cohesion (Table 3). That is
to say, word of mouth is related to not only cohesion but also the consumer’s position in the network.
Why will such things do happen ?
According to Fiske (1989), people establish their identity in imitation and discrimination.
Baudrillard (1981) claimed that the function of goods not only satisfies the individual need, but also
connects the individual to the social system. Thus, the consumers receive pressure towards
homogeneity and discrimination in the network. Goods they possess represents the identity in the
network to some extent. This indicates that in analyzing the consumer network, it is important toconsider not only cohesion but also the whole network structure. Not only cohesion but also
structural equivalence produces the pressure towards homogeneity.

In the previous research, in

explaining the contagion of buying behavior, they have taken into account only the existence ofcohesion, so the pressure towards homogeneity from structural equivaqlence could not be explained.
The conclusion of this study emphasizes the importance of consumer networks for the analysis of thebuying behavior.
This study analyzed the buying behavior by the concept of cohesion and structural equivalence inthe network on Web. It was indicated that different kinds of buying behavior could be explained bystructural equivalence and cohesion as a tool of the analysis. It may be possible that this finding canbe generalized to actual networks. In the future research we will apply the current model to actual
consumer network and construct the new framework in explaining the consumer buying behavior.

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The word-of-mouth effect on the web
Table１Cohesion and Structural Equivalence

Structural Equivalence ○Cohesion○

C

Structural Equivlalence ×
B
C
C

C

A

A

B

D
Cohesion×

C
B

A

A

B

B

A

D

D

D

C

C

C

A

B

A

D

B

D

B

A

B

D
D

Figure 1

A

Figure 2

The network and the buying behavior

2005.01.07
2005.01.28
2005.03.19

2005.01.08
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Yufu Kuwashima
Figure 3 Network A：network of buying bebavior

Figure 4 Network B：network of talks

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The word-of-mouth effect on the web

Table 2

Characteristics of subgroups

group

density

item/user

1

0.238

1.40

2

0.385

2.15

3

0.051

0.37

4

0.364

3.00

5

0.359

2.00

6

0.333

1.75

7

0.221

1.18

8

0.279

2.60

9

0.009

0.00

Table 3 Previous research

SE

○

SE

×

Cohesion○

Word of mouth studies

Cohesion×

not considered9Yufu Kuwashima

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[１１]

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