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Construction & Study of Consumer Behavior in a

Virtual Social Space

ABSTRACT

The growth of YouTube.com, MySpace.com and SecondLife, are part of a trend that consumers seek to

partake in communities with increasingly real virtual simulations of actual social environments. Within this

context, consumption takes on social meaning and is mostly wrapped in the excuse of self-expression within

these virtual social spaces. What constitutes marketing relevant behavior in these social spaces is a dominant

issue for consumer behavior in the future, as this is where consumers will increasingly act out their lives.�Based

on cultural composition of virtual communities, ethnographic-based approaches are warranted to better yield

understanding of the meanings that are common to a particular community. Nevertheless, there is no existing

theory that adopts this way to addresses the consumer behavior in a virtual space of real simulation.

This research focuses on the creation of a live video virtual social space where users can freely enter and

utilize the space, and application of grounded theory and NVIVO software to uncover marketing relevant

behavior. As a result, thirty-four types of consumer behavior are constructed and divided into four categories:

egocasting, non-verbal behavior, relational pattern, and participation behavior. Next, the technique of social

network analysis and the UCINET software package help define groups of consumers and understand their

behavioral differences within this virtual space, resulting in a bridge group and core group that exhibit high

levels of various types of behaviors than the peripheral or isolated groups. Results are significant for consumer

behavior theory development within the context of the emerging online virtual citizen.

Keywords: virtual social space, consumer behavior, grounded theory, social network analysis, NVIVO

software, UCINET software package

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Construction & Study of Consumer Behavior in a

Virtual Social Space

І. Research Background With the popularity of the Internet use, numerous types of virtual social spaces have emerged as popular

meeting venues online. Nowadays, the growth of YouTube.com, MySpace.com and Second Life, reveal a trend

that people seek to partake in communities with increasingly real virtual simulations of actual social

environments, and feel comfortable showing themselves and sharing their lives online. These virtual social

spaces are often heavily immersed in consumption (Flanagin & Metzger, 2001; Kozinets, 1999), and products

take on social meaning within this context (Solomon, 1983). Some scholars notice that consumers express a

dislike of all things commercial and tend to wrap consumption in the excuse of self-expression (Kozinets, 2002;

Kozinets & Handleman, 2004).

The virtual meeting places are commonly referred to as �virtual communities� (Rheingold, 1993). Due to a

social nature, many researchers demonstrate the interpersonal influence of virtual communities from

perspectives of computer-mediated-communication, social network, small group, as well as social psychology.

These concerns also draw a lot of interests from both academic and commercial marketing researchers because

of potential consequential effects on consumer behavior existing within virtual communities have been long

recognized (Britt, 1950; Granitz & Ward, 1996; Kozinets, 1998; Levy, 1959; Muniz & O' Guinn, 2001). Such

an interest stems not only from their ability to influence members’ choices, and to rapidly disseminate

knowledge and perceptions regarding new products (e.g.,U. M. Dholakia, & Bagozzi, R. P. , 2001), but also

from the numerous opportunities to engage, collaborate with, and advance customer relationships actively in

such forums. While there is no existing theory addressing consumer behavior in a real virtual space.

Based on cultural composition of virtual communities, ethnographic-based approaches are warranted to

better yield understanding of the meanings that are common to a particular community (Kozinets, 1999), such

as participant observation within a predominantly inductivist framework (Gill & Johnson, 1997). Nevertheless,

marketing researchers have not begun to more fully explore the role of culture in buying behavior and apply

associated ethnographic research methods until the past two decades (Maclaren & Catterall, 2002).

From the above, the current research has three objects���irst of all, the creation of a live video virtual social

space where users can freely enter and take advantage of the space will be centered to understand the

willingness of consumers joining such a space. Secondly, through the use of the qualitative method of grounded

theory, this research will be able to uncover marketing related consumer behavior within a virtual social space.

Finally, social network analysis method will be applied to further understand the behavioral differences of

consumers in terms of the social structure.

�. Literature Review In order to explore fully consumer behavior within a virtual community, this chapter will focus on the four

facets: virtual community, marketing & virtual community, technology aspects of virtual community, and social

aspects of virtual community.

1. Virtual Community

Many definitions of virtual communities exist, while there is no single definition accepted. The term "

virtual community"(VC) was first cited as commonplace by Howard Rheingold (1993a), he has defined VC as

"social aggregations that emerge from the Net when enough people carry on those public discussions long

enough, with sufficient human feeling, to form webs of personal relationships in cyberspace." It is explicit from

the definition that the “technological” and “social” aspects are two pillars of VC. However, researchers suggest

not all conversations on the Internet constitute VC (Erickson, 1997; Fernback, 1999). Key attributes are

required to form it, such as: Erickson (1997) lists the following attributes that the term community suggests:1)

notion of membership; 2) relationships with other people; 3) Commitment and generalized reciprocity; 4)

Presence of shared values and practices; 5) production and distribution of collective goods; 6) existence for

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some duration; DeSouza and Preece (2004) also represent key components of an online community as: people

purposes, policies and software. In addition, there are many disputes over whether virtual communities are real

communities (Foster, 1997; Galston, 2000; Postman, 1993; Sardar, 1996; Snyder, 1996). Scholars point out that

the "virtual" might misleadingly imply that these communities are less "real" than physical communities (Jones,

1995), but their “real” existence for participation may have consequential effects on many aspects of behavior,

including consumer behavior (Kozinets, 1998).

2. Marketing &. Virtual Community

Marketing scholars have suggested the exchange model as a conceptual foundation for the discipline.

(Richard P. Bagozzi, 1975; Grönroos, 1999; Hirschman, 1987). While the �economic model� which assumes

that things are exchanged for their economic or utilitarian value had evolved in social exchange perspective,

which has been described marketing as the process of creating, resolving and maintaining exchange

relationships (Richard P. Bagozzi, 1974). While VC has been seen as a new social constructs created by the

Internet, and characterized by groups of people with common value systems, norms, rules and a sense of

identity and association (Fernback, 1999). This means that each VC is likely to have its own cultural

composition, a unique collective sense that members share (Maclaren & Catterall, 2002). The influence of

culture on consumer behavior has long been recognized by both academic and commercial marketing

researchers (Britt. 1950; Levy 1959). Nevertheless, marketing researchers have not begun to more fully explore

the role of culture in buying behavior and apply associated ethnographic research methods until the past two

decades (Maclaren & Catterall, 2002). Kozinet (1999) suggests that the nature of cultural composition warrants

“ethnographic-based approaches” to better yield understand- ing of the meanings that are common to a

particular community, such as participant observation within a predominantly inductivist framework (Gill &

Johnson, 1997). �Netnography�, or ethnography on the Internet, is suggested to be particularly useful for

revealing the rich symbolic online world that underlies needs, desires, meanings, and choice (see, eg., Levy

1959). Undoubtedly, such an approach can provide current research with a window into naturally occurring

behavior within a VC.

3. Technological Aspect of Virtual Community

The development of new electronic technology lets numerous types of virtual communities bloom and has

been affecting the way participants interact. Haythornthwaite, Wellman et al. (1998) notice six types of VC,

including text-based email, bulletin boards and newsgroups, text-based synchronous chat (IRC chat lines) and

role-playing games (e.g., MUDs, MUSHs and MOOs), voice-based teleconferencing and voice-mail systems,

desktop video -conferencing and video mail, and hypertext and multimedia systems. Catterall and Maclaran

(2001) also present seven types as email lists, Website bulletin boards, Usenet Newsgroups, Real-time

online-chat systems, Web-based chat rooms, multiplayer virtual games, Multi-user domains (MUDs). With the

advancement of technology, the types of virtual communities revolutionized from asynchronous, time-delayed

communication to synchronous, real-time communication.

4. Social Aspect of Virtual Community

(1) Computer-Mediated Communication (CMC)

When the primary interaction is electronic or enabled by technology, the community is virtual. This type of

communication is called CMC. When it comes to the implication of CMC in the context of interpersonal

interaction, there are different perspectives among researchers, including cues filtered out theory, social

identity/ deindividuation theory, social information processing theory, and hyper-social interaction theory. From

these viewpoints, it is apparent that the positive implication of CMC to interpersonal relations online is

increasingly noticed by researchers. Meanwhile, there are still much evidences demonstrating the arising effects

of CMC on online relations in past literature, such as Schlosser (2002) found consumers in the CMC were more

likely to convey their pre-discussion attitudes, and exhibited less choice shifts and acceptance of the groups first

attitude than those in the face to face environment.

(2) Social Network and Small Group

Virtual communities can be studied as either small groups or social networks (Wellman, 1997). Social

network theory suggests that Internet social communication supplements and extends traditional social

behaviors. The more individuals in organizations are connected, communicate face-to-face, and the more

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intimate their relationships, the more frequently and intimately they use email and a variety of media to

communicate (Haythornthwaite, Wellman, & Garton, 1998). Specifically, specific interaction in social

networks has significant social influence on specific attitudes and behaviors (Rice, 1993). One the other hand,

much concerns of small group reveal group influences on attitudes and behavior (Merton & Rossi, 1949).

Findings suggest that individuals behave in a similar fashion to the groups in which they belong. Obviously, no

matter the perspective of social network or small group indicates the significant role of social influence within

virtual communities. Especially, group membership has long since been recognized as a factor that influences

consumption.

(3) Causes of Social Interaction

What draws participants to virtual communities, what they are used for, and how they influence the

subsequent knowledge, opinion, and behavior of participants have been concerned in previous literature. What

underlies theses investigations is a common theme that explores the nature and role of the social influence

exerted by the community on its members (Alon, Brunel, & Schneier Siegal, 2003). While the social influence

does not constitute all causes of social interaction within virtual communities, the antecedents of social

influence should be considered as well (Dholakia, Bagozzi, & Pearo, 2004). The causes of social interaction in

terms of individual-level and group-level have been postulated to influence the participation behavior in virtual

communities separately (Richard P. Bagozzi, 2000), including the causes shown in Table 1.

Table 1 Conceptual Framework for Causes of Social Interaction in VC

Level of Influence Determinants Scholars

Individual Level Cognitive needs

Affective needs

Personal integrative needs

Social integrative needs

Tension release needs

Katz, Haas et al. (1973); Stafford, Stafford et al. (2004);

(Sunanda, 2005).

Group Level

Internalization

Social identity

Presentation of public self

Social anxiety

Sociability

Loneliness

Postmes, Haslam et al. (2005); Walrond-Skinner (1986).

Tajfel,H. and Turner, J. (1979); Festinger (1954).

Goffman (1959); (Ajzen, 1977; Thibaut & Kelley, 1959).

Clark and Wells (1995); Kiesler, Siegel et al. (1984).

Cheek and Buss (1981); Asendorpf and Wilpers (1998).

Peplau and Heim (1979);(Hojat, 1982);Burger (1997).

�. Methodology

This research mainly comprises three stages (see Figure 1): The first stage is mainly technical, centering

on the creation and testing of a virtual social space with video and audio. A preliminary trial is undertaken to

prove the viability of this research. The second stage focuses on a grounded theory qualitative research

approach to develop an understanding of the behaviors exhibited in the virtual social space. Digital recording is

adopted to capture all activities in this space. All records can then be transcribed, and analyzed by NVIVO

software to construct the theory afterwards. The final stage also involves in a chain of technical software

application to group together participants and depict the behaviors that make them similar and different. The

software of NVIVO, UCINET, and NETDRAW are all heavily used.

1. The First Stage: Virtual Social Space Design and Implementation

The emphasis of this research is to explore virtual social communities that employ real-time video and

audio interaction. Accordingly, a Flash Communication Server (developed by MacroMedia Company and now

marketed by Adobe) appears to be suitable in current research that it allows instantaneous use by any Web user

with an existing install of the ubiquitous Flash player. Stage one of this research emphasizes on creation and

implementation of the virtual social space, including database design, interface programming, video server

programming, and user testing.The preliminary design of virtual social space includes five main elements: open

login, user identification, individual meeting window, audio level, and visual meeting space, which is shown in

Figure 2. Any user was allowed to enter and login using any identifier. Upon login, any user can move anyone’s

video box and the movement will be updated on all users’ screens. All activities are socially shared

synchronously.

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Figure 1 Research Stage

Figure 2 Preliminary Virtual Social Space Design Figure 3 Research Spiral

2. The Second Stage: Grounded Theory Construction

The second stage of this research builds on the first’s hardware and software design and implementation.

Drawing more participants should not prove an obstacle in terms of the existing groups of users observed in

first stage. This stage will adopt digital recording to capture all videos and audio, which can then be transcribed

and analyzed by the software of NVIVO. During this process, the use of motion analysis will be centered

because users show a tendency to re-enact social personal space behaviors in the meeting area where windows

could be collectively shared and moved about. A grounded theory qualitative research approach will be adopted

in this stage to get an understanding of the behaviors exhibited in the virtual social space and how they

represent their self through consumption, due to the exploratory nature of the current research, and the heavy

use of behavioral observation, rather than surveys. The research spiral involves in several important steps

during three coding procedures (see Figure 3). After theory construction has begun, developments are

constantly checked against existing theory and observation in what Strauss & Corbin (1998) call the constant

comparative method. Validity is increased as the theory aligns well with existing theory, while expanding it,

and while real-world evidence of the theory can be collaborated.

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3. The Third Stage: Social Network Analysis

Every kind of social aggregation can be represented in terms of units composing this aggregation and

relations between these units. This kind of representation of a social structure is called “Social Network”

(Martino & Spoto, 2006).Through network analysis techniques, it then becomes possible to study the impact of

structure on the functioning of the group and/ or the influence of structure on individuals within the group

(Wasserman & Faust, 1994). Accordingly, this research wants to look for �sub-structures�, or groupings of

actors that are closer to on another that they are to other groups through social network analysis, to understand

similarities and differences of behavior among groups in this social virtual space. The third stage is threefold:

data collection, data analysis, and group behavior construction and validity test. The first part mainly includes

the establishment of relationship matrix, which is based on observation data in the second stage and is a

requirement to run the following analysis. Secondly, Ucinet 6.0 is a software package used to perform the

analysis and is one of the most popular, comprehensive and user-friendly social network analysis tools.

Through it, subgroups within this space can be presented and then further visualized by Netdraw software

(contained in Ucinet software package). Finally, contrast to the observing data in NVIVO, the behavior of

subgroups can be constructed and compared with each other thorough the quantitative method of One-way

ANOVA afterwards.

IV. RESULTS

The results of this research include three parts: pre-test results, categories of consumer behavior, and

results of social network analysis. The each will be explored in detail in the following sections. The statistics of

observing and participants data are shown in Table 2, Table 3.

Table 2 The Statistics of Observing Data

The Time of

Observation

Total Numbers of

Observing Days (Sources) Observing Length

(hour-minutes-seconds)

Total Numbers of

Analysis Units (References)

December, 2006 9 17-15-31 57

January, 2007 17 27-35-55 240

February, 2007 13 34-54-41 290

Total 39 79-46-7 587

Table 3. The Data of Participants

Gender Number ID

Male 24 Nerix Hazard Tit-Lou Senna Allam nora Eusebus Yohan Trillium kiwi Moogle Slider bineto

Benzer Angeblanc Embry deglingos LoiC zeplaY Javelle Alex Apollyon Jeanine Julien

Female 19 Aelita Ang Ioo Personne Nerak zaza Luna Maellys Melilou Elnaie estia Isouille Mclaggan

Chestouille Gwen Welhemina cloux ppetitefee claggy d'ed'e89 AAE Xav42

Unknown 47

NicePowa Meily Cocyte GrEaT Pom lk Onlyhope Nutella Snake :o Mcice Kiwi-FaB srf bidule

rubio Shmolt YourName SaphYr AquaLiSs GNI fete Kakamoolu Moiom Mikey erbina NiX

shry FuraxzZ Elw Sakoo mumi crepe bisoire mdr your valopal lulu charly chelSy Bibi-cricrou

Warang _Zoz BugMaster Ahmet

Total 90

1. Pre-test Results

� During the three month testing period, with no advertising or messages about the open video space being

sent out, a number of international users were visiting the virtual space regularly, which was well shown by server

log. It seemed that these unsolicited visitors were using the space during the night in Taiwan, and spread this

space via word-of-mouth. The visitors were indicated to be from France, Belgium, and other East European

locations through the languages in use and server records.They were found to utilize all functions very well, and

use commercial products to represent or supplement the self commonly in this space. However,when lots of users

were online at once, the audio communication was difficult for users. Thus, participants tend to communication

with each other through other ways, such as the use of ICR (Internet Chat Relay), or MSN messenger, or text

written on paper and held up to the camera etc. In conclusion, the preliminary trial yielded a rich data for

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facilitating the following research.

2. Categories of Consumer Behavior

After analyzing and classifying the consumer behavior occurred within the videoconferencing, there are

four main categories under the observation, including: egocasting, nonverbal behavior, relational patterns, and

participation behavior (see Figure 4). The statistics of observing data is shown in Table 4.

Table 4 The Coding Statistics of Categories

Figure 4 The Categories of Videoconferencing

(1) Egocasting

With the personalization communication offered in this virtual social space, participants have the

Categories Sub-categories Phenomenon Sources References Categories Sub-categories Phenomenon Sources References

Smoke 25 59

Drink 23 52 Silence 25 103

Listen to Music 23 44

Eat 7 8

Departure

Chain Reaction 31 66

Type 3 4

Consumptive

Activities

Cellphone 3 4 Stationary 22 64

Room Layout 22 52 Environment

Outdoor View 2 4 Exclusion 18 44

Face 38 246 Real Self

Partial Body 5 8

Regressive

Spirals

Rule Breaking 2 5

Toy 7 12

Monitor 7 12

Movement of

Window 18 40

Logo 8 11

Poster 5 5 Show 11 23

Keyboard 2 4

Relational

Patterns

Progressive

Spirals

Additional

Channel 4 6

Egocasting

Representation

of Goods

Paper 2 3 Visible 38 282

Sociable 31 113 Facial Expression 14 21

Group Unity

Persistent 26 87 Body Language

Gesture 5 5 Hidden 35 182

Lining up 34 95 Non-sociable 27 158

Nonverbal

Behavior

Proximity Distance 25 67

Participatio

n Behavior Individual

Expression Impatient 11 17

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capability to create personal bubble, inside which they as �egocasters� are the sole masters of what they see and

hear, such as consumptive products, surroundings, even their real face and body are contents they present. As

Rosen (2004) defined, "Egocasting is the thoroughly personalized and extremely narrow pursuit of one’s

personal taste, where we exercise an unparalleled degree of control over what we watch, and what we hear." In

current research, we find the concept of egocasting not only involves in the consumption of on-demand music,

monitor, beverage, food, and other products that cater to individual tastes, but also their real self. Participants

consciously or unconsciously control images by �different plots.� Sometimes, they �focus� their self or stuff to

be representations. However, sometimes they may become as �performers� on the platform, such as engaging in

some activities. As Rosen (2004) mentioned, "media audiences are seen as frequently selecting material that

confirms their beliefs, values, and attitudes, while rejecting media content that conflicts with these cognitions."

Every egocaster produces and displays their content in their own way. No matter �focus� or �action�, it is

obvious from the observation that the concept of egocasting extensively contains real self, every day life, and

consumptive goods, which can be well shown in sub-categories of egocasting. There are four dimensions

classified, including real self, environment, representation of goods, as well as consumptive activities.

(2) Relational Patterns

As relationships progress, patterns of interactions take shapes, such as rigid role relations,disconfirmations,

spirals, as well as dependencies and counter dependencies (Borchers, 1999). Among these patterns, spiral

patterns are conspicuous in current research. In a spiral, one partner's behavior intensifies that of the other

(Trenholm, 1995). The observing data shows spirals can be progressive or regressive. When in the progressive

spiral, one partner's behavior leads to increasing levels of satisfaction for the other. Such as window movements,

additional channels, as well as shows are behaviors that result in constant conversation afterwards. The

progressive spiral can be deeply experienced by cordial atmosphere of their interaction, including the behaviors

of imitation among participants. On the other hand, spirals can also be regressive, where one partner's

communication leads to increasing dissatisfaction, which is well evidenced by behaviors of stationary,

exclusion, silence, rule breaking, and departure reaction. Compared with progressive spirals, it is cooler when

participants interact in regressive spirals. Therefore, relational patterns can be divided into progressive spirals

and regressive spirals, which contain numerous related behaviors separately.

(3) Non-verbal Behavior

Numerous types of non-verbal communications are found in current research— messages and meanings

are exchanged by facial expression, posture, or physical movement. For participants, body language can

increase personal believability, even if they do not use an audible spoken language. They adopt and respond to

gestures and facial expression to present how they feel. Gestures are used in greeting and pointing, and

sometimes as a supplement of verbal communication. Cheerful conversation is much experienced by

participants’ facial expression, especially in the situation of sharing. From their smiles, it can be surmised that

there is a good relationship between them. In addition, the observing data also shows that participants are

inclined to line up to get closer or keep a distance from each other by window movement, which is what

Giddens terms �physical movement� in real environment. It can be inferred that if one person likes another, his

or her relationship with him or her is good, resulting in a close proximity. However, if not, when the proximity

becomes too small, the one backs away. Obviously, those who emphasize personal space attempt to keep social

distance as possible as they can, even if the capacity of this space may result in intimacy. Accordingly, the

subcategories of non-verbal behavior can be classified as body language and proximity, which comprises all

types of non-verbal communication observed in current research.

(4) Participation Behavior

There are numerous participation behaviors found in observing data, which can be individual expression or

group unity. The individual expression signifies the behavior that mainly center on self-reliance, liberty, and

privacy. Participants sometimes are shy, cool, and reluctant to reveal themselves. Most of time, only some

ambiguous stuff showed. It seems that they think highly of the importance of privacy. Besides, they seldom

engage in social activities, such as greet others actively, join the discussion, or even ask for another channel of

contact. They mostly keep silent and are impatient to take part in social interaction so that duration of their stay

does not last for a long time. Contrast to the individualists, being visible, sociable, and persistent are features of

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the group unity. Participants have tendency to show their real self, share their stuff with others. Every time

when newcomers join this space, the participants are enthusiastic greeting them actively, no matter

acquaintances or strangers. Such sociability also reflects on �persistent behavior�, including dependence of

people and space; they not only involve in persistent social discussion, but also see this space their virtual home.

Even if they are alone in this space, they feel free to show their self, real life, and stay for a long time.

3. Results of Social Network Analysis

The subgroups existing in this social virtual space can be effectively analyzed through UCINET and

NETDRAW software. Furthermore, the observing data in NVIVO facilitate tracing the behavior of each

subgroup, which can be then statistically compared with each other via One-way ANOVA and Turkey test.

(1) Subgroups Analysis

Analyzing subgroups in this network can adopt either top-down approaches or bottom-up approaches. In

order to define subgroups which the participants are more closely tied to one another than they are to other

members of the networks, cliques analysis of UCINET software is adopted in current research, resulting in 74

cliques and 74.19% overlap (see Table 5). Obviously, the network cannot be subdivided into exclusive cohesive

subgroups or factions, although some actors may be much better connected than others. Therefore, it is required

to adopt more abstract ways of making sense of the patterns of relations among social actors. The meanings of

�core�, �periphery�, �bridge�, and �isolation� are seen as ways of thinking about and describing how the actors

in a network may be divided into subgroups on the basis of their patterns of relations with one another.

The concept of a core/periphery structure suggested as a common but informal and intuitive notion in

social network analysis and other fields (Borgatti & Everett 1999) is based on the physical center and periphery

of a cloud of points in Euclidean space. Through two-mode core-periphery analysis by UCINET software, a fit

(correlation) of 0.334 is yielded. Besides, the result of density matrix shows core-to-core is 0.218,

eriphery-to-periphery is 0.020 (see Table 6). While far from perfect1, the model here is moderately good to be

taken. Core and peripheral memberships are shown in Table 8. In addition, the top-down approach like

cutpoints analysis, which is suggested to be able to find particularly important actors—who may act as brokers

among otherwise disconnected groups (Hanneman & Riddle, 2005), is also employed in current research. The

divisions into which cutpoints divide a graph are called blocks. Six cutpoints are found via blocks and cutpoints

analysis of UCINET software (see Table 7) and termed as �bridges group� in current research. Finally, the

disconnected members are also found in the block-by-actor indicator matrix (see Table 7). These people are

termed as �isolate group�. The four sub-groups are well shown in Table 8 and visualized in Figure 5, Figure 6,

Figure 7, Figure 8.

Table 5 Cliques Analysis

Strength of the relation Cliques Overlaps (%)

All ties 74 74.19

Table 6 Two-Mode Core-Periphery Analysis

Fitness (Correlation Criterion) 0.334

Density matrix

Core Periphery

Core 0.218 0.032

Periphery 0.031 0.020

Table 7 Blocks and Cutpoints Analysis

Blocks (12 blocks found)

Block 1: valopal charly

Block 2: Chestouille NiX

Block 3: Hazard shry FuraxzZ

Block 4: Personne Isouille

Block 5: Apollyon Nutella

Block 6: Isouille Kiwi-FaB

Block 7: Isouille d'ed'e89

Block8: Welhemina moiom Elw

Block9: nerix Hazard Tit-Lou Senna Allam nora Eusebus Trillium kiwi Moogle Slider

bineto Benzer Angeblanc deglingos LoiC zeplaY Aelita Ioo Nerak zaza Luna

Melilou Elnaie estia Isouille Mclaggan Chestouille Gwen Welhemina cloux

claggy NicePowa Julien Meily Cocyte GrEaT Pom Onlyhope Nutella :o bidule

rubio Your Name SaphYr AquaLiSs GNI fete Kakamoolu Mikey erbina Sakoo

mumi crepe bisoire mdr your charly Jeanine Warang _Zoz BugMaster Ahmet

AAE Xav42

Block 10: Yohan lk

Block 11:Javelle Ang

Block 12: Alex

Maellys

1 Borgatti & Everett (1999) suggests that a "fitness" score 0 means bad fit, 1 means excellent fit. Besides, the ideal structure matrix

consists of one in the core block and zeros in the peripheral block.

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Block-by-actor indicator matrix (*Cutpoints)

1 1 1

1 2 3 4 5 6 7 8 9 0 1 2

1 1 1

1 2 3 4 5 6 7 8 9 0 1 2

1 1 1

1 2 3 4 5 6 7 8 9 0 1 2

1 1 1

1 2 3 4 5 6 7 8 9 0 1 2

1 nerix

2* Hazard

3 Tit-Lou

4 Senna

5 Allam

6 nora

7 Eusebus

8 Yohan

9 Trillium

10 kiwi

11 Moogle

12 Slider

13 bineto

14 Benzer

15 Angeblanc

16 Embry

17 deglingos

18 LoiC

19 zeplaY

20 Javelle

21 Alex

22 Apollyon

23 Aelita

24 Ang

25 Ioo

26 Personne

27 Nerak

28 zaza

29 Luna

30 Maellys

0 0 0 0 0 0 0 0 1 0 0 0

0 0 1 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 1

0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1

31 Melilou

32 Elnaie

33 estia

34* Isouille

35 Mclaggan

36* Chestouille

37 Gwen

38* Welhemina

39 cloux

40 ppetitefee

41 claggy

42 NicePowa

43 Julien

44 Meily

45 Cocyte

46 GrEaT

47 Pom

48 lk

49 Onlyhope

50* Nutella

51 Snake

52 :o

53 Mcice

54 Kiwi-FaB

55 srf

56 bidule

57 rubio

58 Shmolt

59 Your Name

60 SaphYr

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 1 0 1 1 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 1 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 1 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 1 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

61 AquaLiSs

62 GNI

63 fete

64 Kakamoolu

65 d'ed'e89

66 moiom

67 Mikey

68 erbina

69 NiX

70 shry

71 FuraxzZ

72 Elw

73 Sakoo

74 mumi

75 crepe

76 bisoire

77 mdr

78 your

79 valopal

80 lulu

81* charly

82 chelSy

83 Bibi-cricrou

84 Jeanine

85 Warang

86 _Zoz

87 BugMaster

88 Ahmet

89 AAE

90 Xav42

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 1 0 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0

0 0 1 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 1 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

0 0 0 0 0 0 0 0 1 0 0 0

Table 8 Subgroup Analysis

Subgroups Memberships Number of subset

Core

nerix Tit-Lou Senna Allam nora Eusebus Trillium kiwi Moogle Slider bineto Benzer

Angeblanc LoiC zeplaY Aelita Nerak zaza Luna _Zoz Melilou Elnaie Mclaggan Gwen cloux

claggy NicePowa Onlyhope :o bidule YourName Mikey Sakoo mumi 34

Periphery

Yohan Embry deglingos Javelle Alex Apollyon Ang Ioo Personne Maellys estia ppetitefee

Julien Meily Cocyte GrEaT Pom lk Snake Mcice Xav42 Kiwi-FaB srf rubio Shmolt SaphYr

AquaLiSs GNI fete Kakamoolu d'ed'e89 moiom erbina NiX shry FuraxzZ Elw crepe bisoire

mdr your valopal lulu chelSy Bibi-cricrou Jeanine Warang BugMaster Ahmet AAE

41

Bridge Hazard charly Welhemina Nutella Isouille Chestouille 6

Isolation Embry chelSy Bibi-cricrou Ppetitefee Snake Mcice srf lulu Shmolt 9

Figure 5 NETDRAW Visualization of Core Group

Figure 6 NETDRAW Visualization of Bridge Group

2008ING

10

Figure 7 NETDRAW Visualization of Peripheral Group

Figure 8 NETDRAW Visualization of Isolated Group

(2) Behavioral Difference Analysis of the Subgroups

After grouping the participants, One-way ANOVA and the Turkey HSD method are performed to examine

if there are significant differences in thirty-four behaviors among four subgroups, and where these differences

can be found. Of the 90 observing samples, 9 came from Isolate Group, 6 from Bridge Group, 41 from

Peripheral Group, and 34 from Core Group. Results from a statistical analysis of the samples are summarized in

to Table 9, which presents the analysis for average daily behavior. In Table 9, the one-way ANOVA result

indicates significant differences in a half of all behaviors (P<.05) based on type of group when exploring 34

behaviors, with multiple comparisons of Turkey test revealing that most of the differences separate bridge

group from other groups, and core group is segmented by the rest. The values of the means reflect that most

differences place bridge group with the highest behaviors of being impatient, visible, non-sociable, sociable,

exclusion, keeping a distance, listening to music, drinking, smoking, and showing logo and partial body. As for

the core group, rating highest in behaviors of being sociable, departure chain reaction, keeping a distance, lining

up, facial expression, showing a partial body, and face. On the other hand, when considering four main

behaviors, significant differences are found in self-disclosure and nonverbal behaviors, which separates core

group from other groups with highest values of means. While isolate group rates lowest.

The ANOVA result supports the idea that the people who have more social contacts, are surmised to be

more extrovert, gregarious, and willing to reveal their behavior, than those who have less social intercourse

(Cody et al., 1997; Joe, 1997). Apparently, the core group and bridge group, have larger social networks as a

result of social positions, are found to reveal more behaviors with higher level than the other two groups.

Specifically, as stated in Krackhardt (1999), an individual who is a member of two separates cliques is

advantaged by acting differently in different groups in private scenario where only the particular clique and ego

know about the behavior. Thus, s/he becomes motivated to control social situations such that two cliques cannot

converge. This reveals the important role of the bridge group, and may explain why the bridge group presents

more various behaviors with the highest level than other groups in 34 behaviors.

V. Conclusion Numerous consumer behaviors found in this virtual social space warn marketers to better realize the

consumers’ needs of seeking out such a simulation of real environment. Besides, the results of social network

analysis manifest the key role of the bridge group, which leaves opportunities for word-of-mouth marketing.

Four marketing relevant implications are suggested below.

1. Significance of egocasting

In current research, egocasting plays an important role that what it contains not only numerous consumer

behaviors but also a progressive spiral effect on interpersonal relationship. Such a phenomenon corresponds to

the social response theory that people tend to react to computer technology as though it is a social entity (Moon,

2000). When people are confronted with a computer or software program, they have a tendency to engage in a

�social response to communication technologies�� (Morkes, Kernal, & Nass, 1999). While this virtual social

space having a high degree of social presence allows people to reveal their real self and social responses.

Thus,we can imagine that an Internet venue which can offer �para-social interactions� undoubtedly will be the

2008ING

11

following popular meeting place, and afford marketers an opportunity to realize preferences and needs of

consumers via the behavior of egocasting, and engage, collaborate with, and advance customer relationships

actively. Such as knowledge and concepts regarding new products can be rapidly promoted. Nevertheless,

marketers have to pay attention to the privacy concerns of consumers, which may reduce the motivation of self

presentation. Marketers can use a number of approaches to alter their assessments of the costs and benefits, and

encourage consumers to present their self, such as offering rewards or posting extensive privacy policies that

claim to protect consumer privacy, especially when business messages are embedded in this venue.

2. Avoidance of regressive spiral effect

Negative performance spirals are induced in groups via strongly negative performance feedback before a

group has established trust (Peterson & Behfar, 2003), which can be observed in current research. Past research

shows that groups are particularly sensitive to negative rather than positive feedback information (Guzzo,

Wagner, MacGuire, Herr, & Hawley, 1986). Unambiguous negative performance feedback can have serious

repercussions for future group process and performance, and may send the group into a downward spiral of

relationship conflict and poor performance (Peterson & Behfar, 2003). Therefore, regressive spiral effect will

be a crucial issue for marketers when they attempt to promote the activity level of consumers online and

consumer relationships. Marketers need to understand how to avoid the occurrence of regressive spirals, such as

stationary, silence, rule breaking, exclusion, and departure chain reaction are behaviors that they have to keep

an eye on.

3. Consideration to different attributes of participants

The participation behavior found in current research implicates two attributes of participants in this space,

namely individualist and collectivist. This finding also indicates the possibility of small group that participants’

participation behaviors may be different when fronting out-group or in-group memberships. It cannot be

excluded that individuals who are sociable in interaction with acquaintances are more likely become

individualist as they encounter strangers. Accordingly, based on the importance of self disclosure, marketers

have to consider about how to encourage the individualists who tend to be hidden, shy, impatient to increase the

activity level and disclose their self more. They can enhance the personalized communication like sub-meeting

room to satisfy the private interaction needs for individualists or small groups.

4. Word–of–mouth recommendations in social network

Through social network analysis, marketers can know what common or different behaviors are likely

revealed by consumers of different groups and realize how to spread word–of–mouth recommendations of

products and services via the key consumers like bridges in consumers’ social network. According to the results,

the bridge group connecting the core group and peripheral group has the larger social contacts, and reveals the

numerous social behaviors. The person who creates a bridge between otherwise disconnected people are

strongly proposed to benefit from this position. In an ideal case, this actor is called tertius gaudens, or �the third

one who benefits� (Kemppainen, Timonen, & Yrjönen, 2003). Not only does the individual gain from having

access to a different set of information, s/he has the power to control what aspects of this information can be

shared with the different social clusters to which s/he belongs (Boyd, Potter, & Viegas, 2007). Therefore, the

behaviors of bridge group are potential to influence the demands and ideas of both parties. How to take

advantage of the bridge group to make the peripheral group, even the isolated group become a member of core

group would be a crucial issue for marketers.

There are three suggestions concluded for future research. Firstly, due to the diversified backgrounds of

sample, specific levels of samples cannot be explored in detail. Thus, through the promotion of the open video

space on purpose, future research could draw particular backgrounds to get understandings of behavioral

characteristics. Secondly, this research is mainly based on the motion analysis to understand the consumer

behavior. The future study could adopt semantic analysis that would shed some more light on the meaning

between the lines and psychological state of consumers so that their behavior would be further comprehended.

Finally, the future research can also consider including increasingly intrusive levels of commercial marketing

messages to construct a theory of consumer behavior within a commercialized virtual social space, which can

then be contrasted with the theory from the current research. Certain behaviors may differ sharply between the

commercial and non-commercial space, warning marketers what will be left behind when commercializing a

virtual social space. To date, these issues have been completely unexplored.

2008ING

1

2

Tab

le 9

. O

ne-

way A

NO

VA

An

alysi

s fo

r A

ver

age

Dai

ly B

ehav

iors

Dim

ensi

ons

Tota

l

Mea

n

(n=

90)

Isola

te

Gro

up

(n=

9)

Bri

dge

Gro

up

(n=

6)

Per

ipher

al

Gro

up

(n=

41)

Core

Gro

up

(n=

34)

F V

alue

Sig

. M

ult

iple

Co

mp

aris

on

s D

imen

sions

Tota

l

Mea

n

(n=

90)

Isola

te

Gro

up

(n=

9)

Bri

dge

Gro

up

(n=

6)

Per

iph

eral

Gro

up

(n=

41)

Core

Gro

up

(n=

34)

F V

alue

Sig

. M

ult

iple

Co

mp

aris

on

s

Impat

ient

0.1

389

0.2

222

0.5

0.0

732

0.1

324

2.7

97*

2

B>

P3

Non-s

oci

able

0.7

844

1

1.3

333

0.3

659

1.1

353

2.9

67*

B>

P

Hid

den

1.6

687

0.8

333

1.3

333

1.5

2.1

525

1.8

9

--

Per

sist

ent

0.8

613

0.2

222

1.6

0.5

61

1.2

623

3.4

83*

--

Vis

ible

1.4

724

0.7

778

1.8

639

0.4

024

2.8

775

8.1

43*

B>

P

Soci

able

0.5

926

0

1.3

413

0.4

39

0.8

025

8.0

2*

B.C

>I

B>

P

Par

tici

pat

io

n

Beh

avio

r

2.0

4

1.8

1

1.8

8

1.8

9

2.3

1

.497

--

Dep

artu

re

Chai

n

Rea

ctio

n

0.5

519

0.2

222

0.8

889

0.3

659

0.8

039

5.6

06*

C>

I,P

Ex

clusi

on

0.3

87

0

1.0

833

0.3

415

0.4

216

5.2

*

B>

I,P

,C

Sta

tionar

y

1.0

754

0.2

222

1.4

722

0.8

78

1.4

691

2.4

8

--

Rule

Bre

akin

g

0.0

667

0

0

0.1

463

0

0.8

--

Sil

ence

1.3

115

0.5

556

1.4

762

1.1

707

1.6

522

1.4

12

--

Addit

ional

Chan

nel

0.1

222

0

0.1

667

0.0

732

0.2

059

1.0

51

--

Win

dow

Movem

ent

0.7

115

0

0.7

0.5

976

1.0

392

2.0

59

--

Show

0.1

583

0.1

111

0.3

333

0.0

488

0.2

721

1.9

24

--

Rel

atio

nal

Pat

tern

s

1.3

9

.56

1.3

5

1.4

6

1.5

3

2.3

87

--

Kee

pin

g a

dis

tance

0.8

339

0.2

222

1.4

444

0.5

935

1.1

779

5.4

37*

B,C

>I

C>

P

Lin

ing u

p

1.1

109

0.1

111

1.5

735

0.9

634

1.4

718

4.3

13*

C>

I

Fac

ial

0.1

532

0

0.2

381

0.0

244

0.3

342

5.4

99*

C>

P

Non-v

erb

al

Beh

avio

r

1.2

4

.33

1.5

5

1.2

2

1.4

5

4.6

91*

B

,P,C

> I

2 *P

<.0

5

3 B

=B

rid

ge

Gro

up, C

= C

ore

Gro

up,

I= I

sola

te G

roup, P

= P

erip

her

al G

rou

p

2008ING

1

3

Ex

pre

ssio

n

Ges

ture

0.0

667

0.1

111

0

0.0

244

0.1

176

1.0

95

--

Outs

ide

0.0

222

0

0

0.0

244

0.0

294

0.1

38

--

Insi

de

0.2

819

0.2

222

0.3

667

0.0

488

0.5

637

1.4

64

--

Par

tial

Bod

y

0.0

778

0

0.8

333

0.0

244

0.0

294

7.1

3*

B,C

,P>

I

Fac

e 1.3

078

0.6

667

1.9

356

0.4

268

2.4

29

6.4

26*

C>

P

Musi

c 0.2

583

0.4

444

0.8

194

0.0

244

0.3

922

4.1

42*

B>

P

Food

0.0

556

0

0.1

667

0

0.1

176

1.6

2

--

Bev

erag

e 0.1

31

0.1

111

0.5

482

0.0

244

0.1

912

3.0

24*

B>

P

Tel

ephone

0.0

556

0

0

0

0.1

471

1.5

87

--

Cig

aret

te

0.1

304

0.3

333

0.9

563

0.0

244

0.0

588

8.5

85*

B>

C,P

,I

Typew

riti

ng

0.0

444

0

0

0

0.1

176

1.5

18

--

Post

er

0.0

111

0

0

0

0.0

294

0.5

41

--

Pap

er

0.0

167

0

0

0

0.0

441

0.5

41

--

Monit

or

0.0

889

0.1

111

0

0.0

976

0.0

882

0.0

82

--

Keyboar

d

0.0

667

0

0

0

0.1

765

1.4

59

--

Lo

go

0.0

139

0

0.2

083

0

0

5.3

51*

B>

C,P

,I

To

y

0.0

389

0

0

0

0.1

029

1.0

91

--

Sel

f-

dis

closu

re

1.1

2

.41

1.2

9

.52

1.9

8

8.1

05*

CG

> I

G

CG

> P

G

2008ING

14

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